Merge remote-tracking branch 'origin/main' into studio/api-monitor-and-per-model-settings

# Conflicts:
#	studio/frontend/src/features/model-picker/components/sidebar-model-config.tsx

.github/workflows/consolidated-tests-ci.yml

@@ -373,11 +373,10 @@ jobs:
             tests/test_bad_mappings_redirect.py \
             tests/test_prefetch_snapshot_scope.py \
             tests/test_gemma_2b_mapper_key.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
-          # requires CUDA + dev toolchain, which the CPU-only ubuntu-latest
-          # runner does not have. The other Bucket-A tests pass cleanly.
+            tests/test_raw_text_json_loading.py
+          # test_run_attention_flash_varlen_receives_window_and_softcap was deselected
+          # until attention_dispatch.py predefined flash_attn_varlen_func as None; it
+          # monkeypatches that name, so it no longer needs flash_attn on this runner.
 
       - name: unsloth_zoo @ ${{ env.UNSLOTH_ZOO_REF }} — full pytest (CPU)
         # 106 of 111 test_* in unsloth_zoo are CPU-only. The two CUDA-skip

.github/workflows/studio-tauri-smoke.yml

@@ -91,6 +91,16 @@ jobs:
           npm run build
           test -f dist/index.html
 
+      # The crate carries ~100 unit tests (native_file_dialogs, preflight,
+      # install, desktop_auth, ...) that nothing ran until now: this workflow
+      # only ever built. Run them here, where the toolchain and the WebKit dev
+      # packages are already installed, so a broken assertion fails the PR
+      # instead of sitting unnoticed. `--no-fail-fast` reports every failing
+      # test in one run rather than stopping at the first.
+      - name: Rust unit tests (studio/src-tauri)
+        working-directory: studio/src-tauri
+        run: cargo test --no-fail-fast
+
       - name: Tauri debug build (Linux, no bundle, no codesign)
         # `--debug` + `--no-bundle` keeps this lean: compiles the Rust crate,
         # confirms the frontend dist is wired into Tauri, but skips the AppImage

install.sh

@@ -321,10 +321,25 @@ _gfx906_bnb_prune() {
         || "$_VENV_PY" -m pip uninstall -y bitsandbytes >/dev/null 2>&1 || true
 }
 
-# Install bitsandbytes on AMD ROCm hosts. Uses the continuous-release_main
-# wheel for the ROCm 4-bit GEMV fix (bnb PR #1887, post-0.49.2); bnb <= 0.49.2
-# NaNs at decode shape on every AMD GPU. Falls back to PyPI >=0.49.1 if the
-# pre-release URL is unreachable. Drop the pin once bnb 0.50+ ships on PyPI.
+# Install bitsandbytes on AMD ROCm hosts. bnb <= 0.49.2 NaNs at 4-bit decode
+# shape on every AMD GPU; the fix (bnb #1887) ships in continuous-release_main
+# and, on PyPI, first in 0.50.0. Keep this floor in step with the amd extra in
+# pyproject.toml and studio/install_python_stack.py.
+_BNB_ROCM_PYPI_FALLBACK="bitsandbytes>=0.50.0"
+# bitsandbytes ships no ROCm binary in its aarch64 wheel at any version: the PyPI
+# 0.50.0 and continuous-release_main aarch64 wheels both carry only
+# libbitsandbytes_cpu.so plus CUDA variants. So neither install path below gives
+# aarch64 a 4-bit backend, and the messages must not claim one. Cf. gfx906.
+_bnb_rocm_arch_has_binary() {
+    case "$_ARCH" in
+        aarch64|arm64) return 1 ;;
+        *) return 0 ;;
+    esac
+}
+_warn_bnb_no_rocm_binary() {
+    _bnb_rocm_arch_has_binary && return 0
+    substep "[WARN] aarch64: bitsandbytes ships no ROCm kernels on this arch; 4-bit QLoRA needs a source build -- https://docs.unsloth.ai/get-started/install-and-update/amd" "$C_WARN"
+}
 _install_bnb_rocm() {
     _label="$1"
     _venv_py="$2"
@@ -339,9 +354,8 @@ _install_bnb_rocm() {
             _bnb_whl_url=""
             ;;
     esac
-    # uv rejects the continuous-release_main bitsandbytes wheel because the
-    # filename version (1.33.7rc0) does not match the embedded metadata version
-    # (0.50.0.dev0). pip accepts the mismatch, so bootstrap pip and use it.
+    # uv rejects the pre-release wheel: filename version (1.33.7rc0) does not
+    # match metadata (0.50.x.dev0). pip accepts it, so bootstrap pip and use it.
     if ! "$_venv_py" -m pip --version >/dev/null 2>&1; then
         if ! run_maybe_quiet "$_venv_py" -m ensurepip --upgrade; then
             run_maybe_quiet uv pip install --python "$_venv_py" pip || \
@@ -357,6 +371,7 @@ _install_bnb_rocm() {
             --retries 8 --timeout 90 \
             "$_bnb_whl_url" >"$_bnb_log" 2>&1; then
             rm -f "$_bnb_log"
+            _warn_bnb_no_rocm_binary
             return 0
         fi
         _bnb_rc=$?
@@ -365,10 +380,17 @@ _install_bnb_rocm() {
         fi
         rm -f "$_bnb_log"
         step "warning" "$_label (pre-release) failed (exit code $_bnb_rc)" "$C_WARN" >&2
-        substep "[WARN] bnb pre-release install failed; falling back to PyPI (4-bit decode broken on ROCm)" "$C_WARN"
+        if _bnb_rocm_arch_has_binary; then
+            substep "[WARN] bnb pre-release install failed; falling back to PyPI $_BNB_ROCM_PYPI_FALLBACK, which carries the ROCm 4-bit fix" "$C_WARN"
+        else
+            substep "[WARN] bnb pre-release install failed; falling back to PyPI $_BNB_ROCM_PYPI_FALLBACK" "$C_WARN"
+        fi
     fi
     run_install_cmd "$_label (pypi fallback)" "$_venv_py" -m pip install \
-        --force-reinstall --no-cache-dir --no-deps "bitsandbytes>=0.49.1"
+        --force-reinstall --no-cache-dir --no-deps "$_BNB_ROCM_PYPI_FALLBACK"
+    _bnb_pypi_rc=$?
+    _warn_bnb_no_rocm_binary
+    return $_bnb_pypi_rc
 }
 
 if [ "$_next_is_package" = true ]; then

pyproject.toml

@@ -1257,8 +1257,11 @@ intel = [
 ]
 amd = [
     "unsloth[huggingfacenotorch]",
-    "bitsandbytes>=0.49.1 ; ('linux' in sys_platform) and (platform_machine == 'AMD64' or platform_machine == 'x86_64' or platform_machine == 'aarch64')",
-    "bitsandbytes>=0.49.1 ; (sys_platform == 'win32') and (platform_machine == 'AMD64' or platform_machine == 'x86_64')",
+    # 4-bit decode is unreliable on ROCm before 0.50.0, the first PyPI release
+    # carrying the full path: blocksize/warp decoupling (bnb #1887), fused SIMT
+    # GEMM on RDNA (#1979), RDNA3/4 workgroup fix (#2012).
+    "bitsandbytes>=0.50.0 ; ('linux' in sys_platform) and (platform_machine == 'AMD64' or platform_machine == 'x86_64' or platform_machine == 'aarch64')",
+    "bitsandbytes>=0.50.0 ; (sys_platform == 'win32') and (platform_machine == 'AMD64' or platform_machine == 'x86_64')",
 ]
 rocm702-torch280 = [
     "unsloth[amd]",

studio/backend/core/inference/llama_admission.py

@@ -58,6 +58,80 @@ DEFAULT_ADMISSION_QUEUE_PER_SLOT = 16
 DEFAULT_ADMISSION_MIN_QUEUE = 64
 
 
+def _executor_workers() -> int:
+    """Threads asyncio's default executor runs to_thread work on.
+
+    Mirrors ThreadPoolExecutor's own default sizing, which is what
+    ``run_in_executor(None, ...)`` builds. 3.13 sizes it from
+    ``process_cpu_count()``, which honours CPU affinity and cgroup quotas;
+    ``cpu_count()`` would budget from the whole host inside a one-core container.
+    """
+    cpus = getattr(os, "process_cpu_count", os.cpu_count)() or 1
+    return min(32, cpus + 4)
+
+
+def _executor_reserve(workers: int) -> int:
+    """Threads kept clear of parked approvals, for generation steps, stream
+    teardown and unrelated to_thread work. Scaled rather than flat: a flat count
+    would leave a 5-worker executor (one usable CPU) no budget at all.
+    """
+    return max(2, workers // 8)
+
+
+def _max_parked(capacity: int) -> int:
+    """How many holders may sit on an approval prompt with their slot given back.
+
+    A pending prompt parks an executor thread (the loop blocks inside
+    to_thread(next, gen)) whether or not it parked its slot, the pool already
+    permits `capacity` of those, and every park admits one more, so budget only
+    what the executor has left over. Zero on a backend whose --parallel alone
+    fills it: the prompt then holds its slot, as it did before parking existed.
+    """
+    workers = _executor_workers()
+    spare = workers - _executor_reserve(workers) - max(0, capacity)
+    # A quarter of the executor, floored at two while `spare` allows: a quarter of
+    # five is one, and one park cannot cover the two simultaneous prompts #7455
+    # exists for.
+    return max(0, min(max(2, workers // 4), spare))
+
+
+# Process-wide, not per queue: there is one executor, and base_url takes a fresh
+# port on every load, so a per-queue budget would hand the same allowance to each
+# backend and to every reload, blind to the approvals parked on the old queue.
+_PARK_LOCK = threading.Lock()
+_parked_total = 0
+
+
+def _claim_park(limit: int) -> bool:
+    global _parked_total
+    with _PARK_LOCK:
+        if _parked_total >= limit:
+            return False
+        _parked_total += 1
+        return True
+
+
+def _drop_park() -> None:
+    global _parked_total
+    with _PARK_LOCK:
+        _parked_total = max(0, _parked_total - 1)
+
+
+def _live_capacity(current: "LlamaAdmissionQueue") -> int:
+    """Slots across every backend still serving requests.
+
+    One queue's capacity is the wrong denominator for a budget sized against the
+    one executor: a reload drains the old queue alongside the new one, and
+    prompts on both park threads. Idle queues hold nothing and are about to be
+    evicted.
+    """
+    with _QUEUES_LOCK:
+        queues = list(_QUEUES.values())
+    # is_idle takes each queue's own lock, so never while holding _QUEUES_LOCK.
+    total = sum(queue._capacity for queue in queues if queue is current or not queue.is_idle())
+    return total if any(queue is current for queue in queues) else total + current._capacity
+
+
 @dataclass(frozen = True, **_SLOTS)
 class LlamaAdmissionConfig:
     enabled: bool = DEFAULT_ADMISSION_ENABLED
@@ -214,7 +288,7 @@ class _Waiter:
 
 
 class LlamaAdmissionLease:
-    __slots__ = ("_queue", "_slot", "_released", "_release_lock", "_parked")
+    __slots__ = ("_queue", "_slot", "_released", "_release_lock", "_parked", "_budgeted")
 
     def __init__(
         self,
@@ -226,27 +300,52 @@ class LlamaAdmissionLease:
         self._released = False
         self._release_lock = threading.Lock()
         self._parked = False
+        self._budgeted = False
 
     @property
     def slot(self) -> Optional[int]:
         """Pool slot this lease holds, or None when admission is disabled."""
         return self._slot
 
-    def park(self) -> None:
+    def park(self) -> bool:
         """Hand the slot back while this holder waits on something off the GPU.
 
         A run stopped on a tool approval prompt is not decoding, so holding its
         slot would let unanswered prompts fill the pool while llama-server idles.
         The lease itself stays valid: releasing it after a park is still correct.
+
+        False when the park budget is spent and nothing was given back: the
+        caller keeps its slot across the prompt, as it did before parking
+        existed. Slower for whoever is behind it, but each freed slot admits
+        another run that can park too, on the executor the generators run on.
         """
         queue = self._queue
-        slot = None
         with self._release_lock:
             if queue is None or self._released or self._parked:
-                return
+                return False
+            # Under the lease lock so the decision and the handover cannot split.
+            # Nothing takes the queue lock then a lease lock, so this order is
+            # the only one in play.
+            if not queue.try_park(self._slot):
+                return False
             self._parked = True
-            slot, self._slot = self._slot, None
-        queue.park(slot)
+            self._budgeted = True
+            self._slot = None
+        return True
+
+    def _drop_budget(self) -> None:
+        """Give the executor budget back now the prompt wait is over.
+
+        Separate from the queue's parked count, which lasts until the slot is
+        back: the executor thread is free the moment the answer arrives. Holding
+        the budget until the resume lands would refuse someone else's park for a
+        finished wait, and that someone holds the slot the resumer wants.
+        """
+        with self._release_lock:
+            if not self._budgeted:
+                return
+            self._budgeted = False
+        _drop_park()
 
     def unpark(self) -> None:
         """Drop the parked state without reclaiming a slot.
@@ -259,6 +358,7 @@ class LlamaAdmissionLease:
             if not self._parked:
                 return
             self._parked = False
+        self._drop_budget()
         if self._queue is not None:
             self._queue.unpark()
 
@@ -278,6 +378,9 @@ class LlamaAdmissionLease:
         queue = self._queue
         if queue is None or not self._parked:
             return
+        # Before the wait, not after: the prompt is answered, so this holder is
+        # already off the executor and must not keep anyone else off it.
+        self._drop_budget()
         slot = await queue.acquire_parked_slot(cancel_event = cancel_event, poll_s = poll_s)
         stranded = None
         with self._release_lock:
@@ -304,6 +407,7 @@ class LlamaAdmissionLease:
             self._released = True
             queue = self._queue
             parked, self._parked = self._parked, False
+        self._drop_budget()
         if queue is not None:
             if parked:
                 queue.unpark()
@@ -513,12 +617,20 @@ class LlamaAdmissionQueue:
             self._release_slot_locked(slot)
             self._grant_waiters_locked()
 
-    def park(self, slot: Optional[int]) -> None:
-        """Return a parked holder's slot to the pool. See ``LlamaAdmissionLease.park``."""
+    def try_park(self, slot: Optional[int]) -> bool:
+        """Return a parked holder's slot to the pool. See ``LlamaAdmissionLease.park``.
+
+        False leaves the slot with its holder, so a refused park costs nothing to
+        undo. The per-queue count is only what ``is_idle`` reads; the budget and
+        the capacity it is sized from are both process-wide.
+        """
+        if not _claim_park(_max_parked(_live_capacity(self))):
+            return False
         with self._lock:
             self._parked += 1
             self._release_slot_locked(slot)
             self._grant_waiters_locked()
+        return True
 
     def unpark(self) -> None:
         with self._lock:
@@ -684,5 +796,10 @@ def get_llama_admission_queue(key: str) -> LlamaAdmissionQueue:
 
 
 def reset_llama_admission_queues() -> None:
+    global _parked_total
     with _QUEUES_LOCK:
         _QUEUES.clear()
+    # The budget outlives the queues it was claimed against, so dropping them
+    # without it leaks the count and shrinks the budget for good.
+    with _PARK_LOCK:
+        _parked_total = 0

studio/backend/core/inference/llama_cpp.py

@@ -92,6 +92,7 @@ from utils.subprocess_compat import (
 from utils.process_lifetime import child_popen_kwargs as _child_popen_kwargs
 from core.inference.tool_call_parser import (
     MAX_ACT_REPROMPTS as _MAX_REPROMPTS,
+    NUDGE_TOOL_CALLS_STATUS as _NUDGE_TOOL_CALLS_STATUS,
     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,
@@ -309,6 +310,15 @@ def _native_linux_system_rocm_lib_dirs(binary_dir: str = "") -> "list[str]":
                 os.path.join(d, "libhsa-runtime64.so.1")
             ):
                 out.append(d)
+                # ROCm keeps LLVM's versioned runtime under <root>/lib/llvm, so a
+                # lib64 host still finds it under lib. Probe both and keep them
+                # ahead of the bundle, else system libamd_comgr binds to the
+                # bundle's incompatible libLLVM.so.*.
+                for _sub in (lib_sub, "lib"):
+                    llvm_lib = os.path.join(base, _sub, "llvm", "lib")
+                    if llvm_lib not in seen and os.path.isdir(llvm_lib):
+                        seen.add(llvm_lib)
+                        out.append(llvm_lib)
     return out
 
 
@@ -1688,9 +1698,20 @@ def _kv_unified_from_args(
     return enabled
 
 
-def _flash_attn_enabled_from_args(args: Optional[Iterable[str]], default: bool = True) -> bool:
-    """Resolve llama.cpp's last-wins flash-attention CLI setting."""
+def _flash_attn_enabled_from_args(
+    args: Optional[Iterable[str]],
+    default: bool = True,
+    env: Optional[Mapping[str, str]] = None,
+) -> bool:
+    """Resolve llama.cpp's environment and last-wins flash-attention settings."""
     enabled = default
+    # llama.cpp applies LLAMA_ARG_FLASH_ATTN before parsing argv (arg.cpp set_env),
+    # so the CLI still wins. --flash-attn has no args_neg, so no LLAMA_ARG_NO_ twin.
+    value = (os.environ if env is None else env).get("LLAMA_ARG_FLASH_ATTN")
+    if value in _LLAMA_ARG_FALSE_VALUES:
+        enabled = False
+    elif value in _LLAMA_ARG_TRUE_OR_AUTO_VALUES:
+        enabled = True
     values = [str(arg) for arg in args] if args else []
     for i, raw in enumerate(values):
         if _flag_name(raw) not in {"-fa", "--flash-attn"}:
@@ -2172,6 +2193,8 @@ class LlamaCppBackend:
         self._effective_context_length: Optional[int] = None
         self._max_context_length: Optional[int] = None
         self._effective_parallel_slots: int = 1
+        # --parallel the last load asked for, before any fit-time reduction.
+        self._requested_n_parallel: int = 1
         self._chat_template: Optional[str] = None
         self._chat_template_override: Optional[str] = None
         self._supports_reasoning: bool = False
@@ -2408,6 +2431,17 @@ class LlamaCppBackend:
             slots = 1
         return max(1, slots)
 
+    @property
+    def requested_parallel_slots(self) -> int:
+        """--parallel the last load asked for, before any fit-time reduction.
+        The reload dedupe compares requested-vs-requested (like requested_n_ctx);
+        the effective count would reload forever after a fitter reduction."""
+        try:
+            slots = int(getattr(self, "_requested_n_parallel", 1))
+        except (TypeError, ValueError):
+            slots = 1
+        return max(1, slots)
+
     @property
     def max_context_length(self) -> Optional[int]:
         """Return the largest context that fits on this hardware at load time.
@@ -2433,6 +2467,8 @@ class LlamaCppBackend:
 
     def _reset_effective_parallel_slots(self) -> None:
         self._effective_parallel_slots = 1
+        # Cleared with the effective count so a stale value can't skew the dedupe.
+        self._requested_n_parallel = 1
 
     @staticmethod
     def _read_rss_bytes(pid: int) -> Optional[int]:
@@ -6778,6 +6814,7 @@ class LlamaCppBackend:
                 chat_template_override = chat_template_override,
                 extra_args = extra_args,
                 is_vision = is_vision,
+                n_parallel = n_parallel,
                 preserve_multi_gpu_on_layer = preserve_multi_gpu_on_layer,
             ):
                 logger.info(
@@ -9029,7 +9066,8 @@ class LlamaCppBackend:
                     int(self._DEFAULT_N_UBATCH if _effective_ubatch is None else _effective_ubatch),
                 )
                 self._flash_attn_enabled = (
-                    _flash_attn_enabled_from_args(_last_spawn_cmd) and self._architecture != "grok"
+                    _flash_attn_enabled_from_args(_last_spawn_cmd, env = env)
+                    and self._architecture != "grok"
                 )
                 self._effective_cache_types = _effective_main_cache_types(
                     _last_spawn_cmd,
@@ -9057,6 +9095,8 @@ class LlamaCppBackend:
                     self._extra_args = list(extra_args)
                     self._extra_args_source = (model_identifier, hf_variant)
                 self._requested_n_ctx = int(n_ctx)
+                # Local n_parallel may have been reduced above; the snapshot has the ask.
+                self._requested_n_parallel = max(1, int(_pending_load_kwargs["n_parallel"]))
                 # Commit the known-good snapshot + whether MTP+tensor is live, then
                 # watch this load for a mid-generation crash.
                 self._last_load_kwargs = _pending_load_kwargs
@@ -9469,6 +9509,7 @@ class LlamaCppBackend:
         tensor_split: Optional[List[float]] = None,
         gpu_ids: Optional[List[int]] = None,
         mtp_draft_path: Optional[str] = None,
+        n_parallel: int = 1,
         preserve_multi_gpu_on_layer: bool = False,
     ) -> bool:
         """True iff the live server already satisfies these load kwargs.
@@ -9533,6 +9574,10 @@ class LlamaCppBackend:
             # A GPU-memory-mode flip (Unsloth / manual) must always reload.
             if self._gpu_memory_mode != gpu_memory_mode:
                 return False
+            # Requested-vs-requested (like n_ctx): comparing the effective count
+            # would reload forever whenever the fitter launched fewer slots.
+            if self._requested_n_parallel != max(1, int(n_parallel)):
+                return False
             # Manual: a layer-count change always reloads (covers Auto(-1) <-> a
             # pinned count); MoE/split only matter with an explicit offload.
             if gpu_memory_mode == "manual" and (
@@ -12375,7 +12420,10 @@ class LlamaCppBackend:
                             _it_r = _iter_timings or {}
                             _accumulated_predicted_ms += _it_r.get("predicted_ms", 0)
                             _accumulated_predicted_n += _it_r.get("predicted_n", 0)
+                            # Blank first (the route resets its text cursor only on an
+                            # empty status), then the badge so the retry is not a hang.
                             yield {"type": "status", "text": ""}
+                            yield {"type": "status", "text": _NUDGE_TOOL_CALLS_STATUS}
                             continue
 
                         if _forced_tool_call_pending:

studio/backend/core/inference/llama_server_args.py

@@ -16,11 +16,18 @@ from __future__ import annotations
 import os
 from typing import Iterable, Mapping, Optional
 
+# Valid llama-server --parallel range, shared with LoadRequest.n_parallel.
+# Mirrored by callers that cannot import this: run.py and unsloth_cli/commands/
+# studio.py (_PARALLEL_MIN/MAX), per-model-config.ts (N_PARALLEL_MIN/MAX);
+# test_parallel_slots_per_load.py pins them together.
+PARALLEL_MIN = 1
+PARALLEL_MAX = 64
+
 # Each group = every alias (short + long) of one hard-denied flag.
 # Extend the matching group when llama.cpp adds a new alias.
 _DENYLIST_GROUPS: tuple[frozenset[str], ...] = (
-    # Parallel slots: owned by typer --parallel; a pass-through would desync
-    # app.state.llama_parallel_slots from llama-server.
+    # Parallel slots: owned by typer --parallel and LoadRequest.n_parallel; a
+    # pass-through would desync the slot bookkeeping from llama-server.
     frozenset({"-np", "--parallel", "--n-parallel"}),
     # Model identity: Unsloth resolves it from LoadRequest; a second -m would
     # load a different model than Unsloth thinks it loaded.

studio/backend/core/inference/mcp_client.py

@@ -971,7 +971,12 @@ def _call_stdio_tool(
                     raise RuntimeError("MCP server connection is not available")
             else:
                 rem = _remaining()
-                coro = _race_tool_call(session.client.call_tool(name, args), rem, cancel_event)
+                # raise_on_error=False for the same reason as the one-shot path.
+                coro = _race_tool_call(
+                    session.client.call_tool(name, args, raise_on_error = False),
+                    rem,
+                    cancel_event,
+                )
                 return session.run(coro, rem)
         except (_MCPCancelled, asyncio.TimeoutError):
             # _race_tool_call cancels the pending call but cancellation is

studio/backend/core/inference/safetensors_agentic.py

@@ -35,6 +35,7 @@ from core.inference.tool_call_parser import (
     _strip_mistral_reasoning,
     BUDGET_EXHAUSTED_NUDGE,
     MAX_ACT_REPROMPTS,
+    NUDGE_TOOL_CALLS_STATUS,
     RAG_MAX_SEARCHES_PER_TURN,
     RAG_SEARCH_CAP_NUDGE,
     TOOL_XML_SIGNALS,
@@ -1032,9 +1033,10 @@ def run_safetensors_tool_loop(
                             "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.
+                    # Blank first: it clears the badge and resets the route's per-turn
+                    # text cursor. The badge then shows the pause is a re-prompt, not a stall.
                     yield {"type": "status", "text": ""}
+                    yield {"type": "status", "text": NUDGE_TOOL_CALLS_STATUS}
                     continue
 
                 # Final answer. If a literal tool marker in prose was buffered but

studio/backend/core/inference/tool_call_parser.py

@@ -183,6 +183,9 @@ INTENT_SIGNAL = re.compile(
 # times since #5620); safetensors and MLX inherit the same cap from here.
 MAX_ACT_REPROMPTS = 3
 REPROMPT_MAX_CHARS = 2000
+# Composer badge while a hidden re-prompted turn regenerates, else the UI looks
+# hung. Matched exactly by the frontend (utils/tool-status.ts); keep in sync.
+NUDGE_TOOL_CALLS_STATUS = "Nudging tool calls"
 
 
 def is_short_intent_without_action(text: str) -> bool:

studio/backend/models/inference.py

@@ -18,6 +18,7 @@ from pydantic import (
     model_validator,
 )
 
+from core.inference.llama_server_args import PARALLEL_MAX, PARALLEL_MIN
 from picker.schemas import MAX_CHAT_TEMPLATE_BYTES
 
 
@@ -113,6 +114,18 @@ class LoadRequest(BaseModel):
             "'mtp' or 'mtp+ngram'."
         ),
     )
+    n_parallel: Optional[int] = Field(
+        None,
+        ge = PARALLEL_MIN,
+        le = PARALLEL_MAX,
+        description = (
+            "Parallel decode slots for llama-server (--parallel) for this "
+            f"load ({PARALLEL_MIN}..{PARALLEL_MAX}). Omit for the server-wide "
+            "default set at launch (the --parallel CLI flag). The VRAM fitter "
+            "may launch fewer slots to keep the model fully on GPU. Ignored "
+            "for non-GGUF models."
+        ),
+    )
     tensor_parallel: bool = Field(
         False,
         description = (
@@ -265,6 +278,16 @@ class ValidateModelRequest(BaseModel):
             "delegate fitting to llama.cpp, while explicit layers are user-owned."
         ),
     )
+    n_parallel: Optional[int] = Field(
+        None,
+        ge = PARALLEL_MIN,
+        le = PARALLEL_MAX,
+        description = (
+            "Parallel decode slots intended for the follow-up load, so the "
+            "coexistence estimate sizes the KV cache like /load. Omit for the "
+            "server-wide --parallel default."
+        ),
+    )
     include_context_length: bool = Field(
         False,
         description = "Also read the native context length from the local GGUF header. "
@@ -533,6 +556,23 @@ class LoadResponse(BaseModel):
             "or None for automatic selection."
         ),
     )
+    requested_parallel_slots: Optional[int] = Field(
+        None,
+        description = (
+            "Parallel decode slots the load was invoked with (per-load "
+            "n_parallel, else the server-wide --parallel default). None for "
+            "non-GGUF loads and for the diffusion runner, which ignores "
+            "--parallel."
+        ),
+    )
+    parallel_slots: Optional[int] = Field(
+        None,
+        description = (
+            "Serving slots the active llama-server actually runs (--parallel "
+            "after any fit-time slot reduction). None for non-GGUF loads and "
+            "for the diffusion runner, which ignores --parallel."
+        ),
+    )
 
 
 class UnloadResponse(BaseModel):
@@ -708,6 +748,23 @@ class InferenceStatusResponse(BaseModel):
             "or None for automatic selection."
         ),
     )
+    requested_parallel_slots: Optional[int] = Field(
+        None,
+        description = (
+            "Parallel decode slots the active load was invoked with (per-load "
+            "n_parallel, else the server-wide --parallel default). None when "
+            "no GGUF model is loaded and for the diffusion runner, which "
+            "ignores --parallel."
+        ),
+    )
+    parallel_slots: Optional[int] = Field(
+        None,
+        description = (
+            "Serving slots the active llama-server actually runs (--parallel "
+            "after any fit-time slot reduction). None when no GGUF model is "
+            "loaded and for the diffusion runner, which ignores --parallel."
+        ),
+    )
     llama_cpp_supports_mtp: bool = Field(
         True,
         description = (

studio/backend/routes/chat_history.py

@@ -11,6 +11,7 @@ from fastapi import APIRouter, Depends, HTTPException, Query, Request
 from pydantic import BaseModel, ConfigDict, Field, ValidationError
 
 from auth.authentication import get_current_subject
+from core.inference.llama_server_args import PARALLEL_MAX, PARALLEL_MIN
 from loggers import get_logger
 from utils.utils import safe_curated_detail, log_and_http_error
 from storage.studio_db import (
@@ -169,6 +170,7 @@ class ChatPresetLoadConfig(BaseModel):
     kvCacheDtype: Optional[str] = None
     speculativeType: Optional[str] = None
     specDraftNMax: Optional[int] = Field(default = None, ge = 1, le = 16)
+    nParallel: Optional[int] = Field(default = None, ge = PARALLEL_MIN, le = PARALLEL_MAX)
     tensorParallel: Optional[bool] = None
     gpuMemoryMode: Optional[Literal["manual"]] = None
     gpuLayers: Optional[int] = None

studio/backend/routes/inference.py

@@ -3311,10 +3311,25 @@ def _is_explicit_tensor_drop(request: LoadRequest) -> bool:
     return override is not None and override.strip().lower() != "tensor"
 
 
+def _parallel_slot_echo(llama_backend: LlamaCppBackend) -> dict:
+    """requested/effective parallel-slot fields for /load and /status echoes.
+
+    The diffusion runner ignores ``--parallel`` and never commits a count, so it
+    reports None like the non-GGUF paths; echoing the reset placeholder 1 would
+    fabricate an "invoked with 1 slot"."""
+    if llama_backend.is_diffusion:
+        return {"requested_parallel_slots": None, "parallel_slots": None}
+    return {
+        "requested_parallel_slots": llama_backend.requested_parallel_slots,
+        "parallel_slots": llama_backend.effective_parallel_slots,
+    }
+
+
 def _request_matches_loaded_settings(
     request: LoadRequest,
     llama_backend: LlamaCppBackend,
     effective_chat_template_override: Optional[str] = None,
+    requested_parallel_slots: Optional[int] = None,
 ) -> bool:
     """True iff every runtime setting on the request matches the loaded server.
     Caller has already checked model+variant+is_loaded. See #5401.
@@ -3323,11 +3338,22 @@ def _request_matches_loaded_settings(
     launched (user override, else a bundled family template such as the
     gemma-4 override), so the dedup compares against what the backend actually
     holds rather than the raw request field. Defaults to the request field for
-    callers that do not resolve a bundled override."""
+    callers that do not resolve a bundled override.
+
+    ``requested_parallel_slots`` is the resolved count the load would use
+    (per-load ``n_parallel``, else the server-wide default); None skips it."""
     # Compare requested n_ctx (not effective) so VRAM-cap doesn't mask an
     # Auto-vs-explicit slider flip.
     if request.max_seq_length != llama_backend.requested_n_ctx:
         return False
+    # Requested-vs-requested for the same reason: the fitter may launch fewer
+    # slots. Diffusion ignores --parallel, so a change there must not reload.
+    if (
+        requested_parallel_slots is not None
+        and not llama_backend.is_diffusion
+        and int(requested_parallel_slots) != llama_backend.requested_parallel_slots
+    ):
+        return False
     if _normalise_settings_str(request.cache_type_kv) != _normalise_settings_str(
         llama_backend.cache_type_kv
     ):
@@ -4865,6 +4891,20 @@ def _guard_chat_load_against_training(
             cpu_only = LlamaCppBackend._effective_gpu_count() == 0,
         )
 
+    # Size with the count that will actually launch, or a load that fits gets a
+    # 409: diffusion never receives --parallel, and load_model clamps to 1 on an
+    # llama-server without --kv-unified. An unclassified GGUF keeps the ask.
+    if is_gguf and n_parallel > 1:
+        if diffusion_kind is True:
+            n_parallel = 1
+        else:
+            try:
+                caps = LlamaCppBackend.probe_server_capabilities()
+                if caps.get("found") and not caps.get("supports_kv_unified"):
+                    n_parallel = 1
+            except Exception as e:
+                logger.warning("Could not probe llama-server slots for chat-load guard: %s", e)
+
     required_override_gb = (
         _estimate_gguf_required_gb(
             config,
@@ -5413,6 +5453,17 @@ async def _load_model_impl(
         backend = get_inference_backend()
         llama_backend = get_llama_cpp_backend()
 
+        # Resolve the slot count once (per-load field, else the server-wide
+        # --parallel default) so the dedupe, the training guard and the load
+        # kwargs all size against what launches. app.state stays the launch
+        # intent / admission fallback; getattr because direct callers have no app.
+        _app_state = getattr(getattr(fastapi_request, "app", None), "state", None)
+        _n_parallel = (
+            request.n_parallel
+            if request.n_parallel is not None
+            else getattr(_app_state, "llama_parallel_slots", 1)
+        )
+
         is_direct_gguf_request = model_identifier.lower().endswith(".gguf")
         if request.gguf_variant or is_direct_gguf_request:
             gguf_variant_matches = is_direct_gguf_request or bool(
@@ -5430,6 +5481,7 @@ async def _load_model_impl(
                     request,
                     llama_backend,
                     effective_chat_template_override,
+                    requested_parallel_slots = _n_parallel,
                 )
                 # Skip if a prior audio probe failed -- let load_model retry.
                 and getattr(llama_backend, "_audio_probed", True)
@@ -5484,6 +5536,7 @@ async def _load_model_impl(
                     n_moe_layers = llama_backend.n_moe_layers,
                     gpu_ids = llama_backend.gpu_ids,
                     requested_gpu_ids = llama_backend.requested_gpu_ids,
+                    **_parallel_slot_echo(llama_backend),
                 )
         else:
             if (
@@ -5622,7 +5675,7 @@ async def _load_model_impl(
             max_seq_length = request.max_seq_length,
             requested_gpu_ids = effective_gpu_ids,
             llama_extra_args = extra_llama_args,
-            n_parallel = getattr(fastapi_request.app.state, "llama_parallel_slots", 1),
+            n_parallel = _n_parallel,
             cache_type_kv = request.cache_type_kv,
             tensor_parallel = bool(request.tensor_parallel),
             gpu_memory_mode = request.gpu_memory_mode,
@@ -5699,7 +5752,6 @@ async def _load_model_impl(
             # Route to HF or local mode based on config. Run in a thread so the
             # event loop stays free for progress polling and other requests
             # during the (potentially long) GGUF download + llama-server start.
-            _n_parallel = getattr(fastapi_request.app.state, "llama_parallel_slots", 1)
 
             # Load kwargs common to HF and local modes; the two differ only by
             # the model-source args (hf_repo/-token vs gguf_path/mmproj).
@@ -5897,6 +5949,7 @@ async def _load_model_impl(
                 n_moe_layers = llama_backend.n_moe_layers,
                 gpu_ids = llama_backend.gpu_ids,
                 requested_gpu_ids = llama_backend.requested_gpu_ids,
+                **_parallel_slot_echo(llama_backend),
             )
 
         # ── Standard path: load via Unsloth/transformers ──────────
@@ -6297,9 +6350,14 @@ async def validate_model(
                 requested_gpu_ids = effective_gpu_ids,
                 llama_extra_args = effective_extra_args,
                 n_parallel = (
-                    getattr(fastapi_request.app.state, "llama_parallel_slots", 1)
-                    if fastapi_request is not None
-                    else 1
+                    request.n_parallel
+                    if request.n_parallel is not None
+                    # Same getattr chain as the load path: preflight must size like the load.
+                    else getattr(
+                        getattr(getattr(fastapi_request, "app", None), "state", None),
+                        "llama_parallel_slots",
+                        1,
+                    )
                 ),
                 cache_type_kv = request.cache_type_kv,
                 tensor_parallel = request.tensor_parallel,
@@ -7144,6 +7202,7 @@ async def get_status(current_subject: str = Depends(get_current_subject)):
                 n_moe_layers = llama_backend.n_moe_layers,
                 gpu_ids = llama_backend.gpu_ids,
                 requested_gpu_ids = llama_backend.requested_gpu_ids,
+                **_parallel_slot_echo(llama_backend),
                 llama_cpp_supports_mtp = _supports_mtp,
                 spec_fallback_reason = llama_backend.spec_fallback_reason,
                 llama_cpp_prebuilt_stale = _stale,
@@ -9574,7 +9633,10 @@ async def openai_chat_completions(
                     if lease is None:
                         return
                     if on:
-                        lease.park()
+                        # Refused when the budget is spent: the slot stays here,
+                        # so there is nothing to take back afterwards.
+                        if not lease.park():
+                            return
                     elif wait:
                         # Resuming: park() may have handed our slot to a waiter, so wait for room instead
                         # of putting two holders on one slot.

studio/backend/run.py

@@ -1920,7 +1920,8 @@ def _build_arg_parser():
         default = _PARALLEL_DEFAULT_PLAIN,
         help = (
             f"llama-server parallel decode slots ({_PARALLEL_MIN}..{_PARALLEL_MAX}). "
-            f"Default {_PARALLEL_DEFAULT_PLAIN}."
+            f"Default {_PARALLEL_DEFAULT_PLAIN}. The Studio run settings "
+            "(Parallel Slots) override it per load."
         ),
     )
     return parser

studio/backend/tests/test_llama_admission.py

@@ -1066,3 +1066,229 @@ def test_an_immediate_arrival_cannot_take_an_approved_chats_slot():
         assert queue.snapshot().active <= 1
 
     asyncio.run(scenario())
+
+
+def test_parking_is_bounded_so_the_thread_pool_cannot_be_drained(monkeypatch):
+    # A pending prompt parks an executor thread (the loop blocks inside
+    # to_thread(next, gen)) and frees a slot that admits another run which can
+    # park too, so unbounded parking drains the pool the generators run on.
+    # Pinned because the real budget follows the runner's usable CPUs.
+    monkeypatch.setattr(llama_admission, "_executor_workers", lambda: 32)
+
+    async def scenario():
+        queue = get_llama_admission_queue("http://llama.test")
+        config = LlamaAdmissionConfig()
+        limit = llama_admission._max_parked(1)
+        assert limit >= 1
+
+        leases = []
+        for _ in range(limit):
+            lease = queue.reserve(capacity = 1, config = config).lease_nowait()
+            assert lease is not None and lease.park()
+            leases.append(lease)
+
+        refused = queue.reserve(capacity = 1, config = config).lease_nowait()
+        assert refused is not None
+        assert not refused.park(), "parking is unbounded"
+        # Refusing means keeping the slot, the old behaviour, not an error.
+        assert refused.slot is not None
+        assert queue.snapshot().active == 1
+
+        leases[0].unpark()
+        assert refused.park(), "budget was not returned"
+        for lease in leases[1:] + [refused]:
+            lease.release()
+        leases[0].release()
+
+    asyncio.run(scenario())
+
+
+def test_the_park_budget_is_shared_by_every_queue(monkeypatch):
+    # One executor, so a per-queue budget would be handed out again to every
+    # backend and to every reload onto a fresh ephemeral port.
+    monkeypatch.setattr(llama_admission, "_executor_workers", lambda: 32)
+
+    async def scenario():
+        config = LlamaAdmissionConfig()
+        first = get_llama_admission_queue("http://llama.test:1")
+        second = get_llama_admission_queue("http://llama.test:2")
+        limit = llama_admission._max_parked(1)
+
+        for index in range(limit):
+            queue = first if index % 2 == 0 else second
+            lease = queue.reserve(capacity = 1, config = config).lease_nowait()
+            assert lease.park()
+
+        spare = second.reserve(capacity = 1, config = config).lease_nowait()
+        assert not spare.park(), "each queue got its own budget"
+
+        # A reset drops the queues the count was claimed against, so it must drop
+        # the count too or the leak shrinks the budget process-wide.
+        reset_llama_admission_queues()
+        revived = get_llama_admission_queue("http://llama.test:1")
+        fresh = revived.reserve(capacity = 1, config = config).lease_nowait()
+        assert fresh.park(), "reset leaked the park count"
+        fresh.release()
+
+    asyncio.run(scenario())
+
+
+def test_the_park_budget_leaves_the_executor_room_to_work(monkeypatch):
+    # The pool already permits `capacity` pending prompts and every park admits
+    # one more, so the budget must account for both. Swept across executor sizes
+    # rather than read off this host, since a container gets a small one.
+    for cpus in (1, 2, 4, 8, 16, 28, 64):
+        workers = min(32, cpus + 4)
+        monkeypatch.setattr(llama_admission, "_executor_workers", lambda w = workers: w)
+        reserve = llama_admission._executor_reserve(workers)
+        assert reserve >= 2, f"{workers} workers left no reserve"
+
+        # Even the smallest executor fits the two simultaneous prompts #7455 needs.
+        assert llama_admission._max_parked(1) >= 2, f"no room for two on {workers} workers"
+        assert llama_admission._max_parked(1) <= workers // 2
+        # A backend whose --parallel alone fills the executor gets no parks.
+        assert llama_admission._max_parked(workers) == 0
+        for capacity in range(0, workers + 8):
+            budget = llama_admission._max_parked(capacity)
+            assert budget >= 0, f"negative budget at capacity {capacity}"
+            assert (
+                budget == 0 or capacity + budget <= workers - reserve
+            ), f"{workers} workers: capacity {capacity} plus {budget} parks leaves no room"
+
+
+def test_the_park_budget_follows_the_executors_own_cpu_count(monkeypatch):
+    # 3.13 sizes ThreadPoolExecutor from process_cpu_count(), which honours CPU
+    # affinity and cgroup quotas; cpu_count() would budget from the whole host
+    # inside a one-core container. Pulled apart here, since they usually match.
+    import concurrent.futures
+
+    monkeypatch.setattr(os, "cpu_count", lambda: 64)
+    if hasattr(os, "process_cpu_count"):
+        monkeypatch.setattr(os, "process_cpu_count", lambda: 1)
+    # Against the real thing rather than the formula: the default executor is a
+    # plain ThreadPoolExecutor(), so its own sizing is the answer on any version.
+    with concurrent.futures.ThreadPoolExecutor() as pool:
+        assert llama_admission._executor_workers() == pool._max_workers
+
+
+def test_the_stream_retries_a_park_that_was_refused():
+    # _park_admission short-circuits on `on == _parked`, so recording a refused
+    # park as parked would skip every later approval in the run even once the
+    # budget frees up. Structural because that only shows on a second approval.
+    import ast
+
+    # Read rather than import: routes.inference pulls in the whole app.
+    route = os.path.join(_backend, "routes", "inference.py")
+    with open(route, encoding = "utf-8") as handle:
+        tree = ast.parse(handle.read())
+    helpers = [
+        node
+        for node in ast.walk(tree)
+        if isinstance(node, ast.AsyncFunctionDef) and node.name == "_park_admission"
+    ]
+    assert len(helpers) == 1, f"expected one _park_admission, found {len(helpers)}"
+
+    guards = [
+        node
+        for node in ast.walk(helpers[0])
+        if isinstance(node, ast.If)
+        and isinstance(node.test, ast.UnaryOp)
+        and isinstance(node.test.op, ast.Not)
+        and isinstance(node.test.operand, ast.Call)
+        and getattr(node.test.operand.func, "attr", None) == "park"
+        and getattr(node.test.operand.func.value, "id", None) == "lease"
+    ]
+    assert len(guards) == 1, "lease.park()'s answer is ignored"
+    assert all(
+        isinstance(stmt, ast.Return) for stmt in guards[0].body
+    ), "a refused park must leave _parked alone, so a later approval retries it"
+
+
+def test_the_park_budget_counts_every_live_backend(monkeypatch):
+    # base_url takes a fresh port on every load, so a reload mints a queue while
+    # the old one drains. Prompts on both park threads of the one executor, so a
+    # budget sized from either backend alone lets them add up past the reserve.
+    monkeypatch.setattr(llama_admission, "_executor_workers", lambda: 32)
+
+    async def scenario():
+        config = LlamaAdmissionConfig()
+        old = get_llama_admission_queue("http://llama.test:1")
+        draining = old.reserve(capacity = 16, config = config).lease_nowait()
+        assert draining is not None  # in flight, so the registry keeps this queue
+
+        new = get_llama_admission_queue("http://llama.test:2")
+        lease = new.reserve(capacity = 16, config = config).lease_nowait()
+        assert lease is not None
+
+        # 16 slots each against 32 workers: their prompts alone can fill it.
+        assert llama_admission._max_parked(16) > 0, "this test needs a budget to remove"
+        assert not lease.park(), "budget sized from one backend of two"
+
+        draining.release()  # the old backend drains and is up for eviction
+        assert lease.park(), "an idle backend still counted against the budget"
+        lease.release()
+
+    asyncio.run(scenario())
+
+
+def test_the_park_budget_is_freed_when_the_prompt_is_answered(monkeypatch):
+    # The executor thread comes back the moment the answer arrives, before the
+    # resume queues for a slot. Holding the budget until the slot lands refuses
+    # someone else's park, and that someone holds the slot the resumer wants.
+    monkeypatch.setattr(llama_admission, "_executor_workers", lambda: 32)
+
+    async def scenario():
+        config = LlamaAdmissionConfig()
+        queue = get_llama_admission_queue("http://llama.test")
+
+        parked = []
+        for _ in range(llama_admission._max_parked(1)):
+            lease = queue.reserve(capacity = 1, config = config).lease_nowait()
+            assert lease is not None and lease.park()
+            parked.append(lease)
+
+        blocked = queue.reserve(capacity = 1, config = config).lease_nowait()
+        assert blocked is not None
+        assert not blocked.park(), "the budget was not full to begin with"
+
+        # One prompt is answered. Its slot is taken, so the resume queues for one.
+        resumed = asyncio.ensure_future(parked[0].unpark_async(poll_s = 0.01))
+        await asyncio.sleep(0.05)
+        assert not resumed.done(), "the resume needs to still be waiting for its slot"
+
+        assert blocked.park(), "budget held for a prompt wait that is over"
+        # Which is what frees the slot the resumer was waiting for.
+        await asyncio.wait_for(resumed, timeout = 2)
+        for lease in parked[1:] + [blocked]:
+            lease.release()
+        parked[0].release()
+
+    asyncio.run(scenario())
+
+
+def test_releasing_a_parked_holder_returns_its_budget(monkeypatch):
+    # A client that disconnects on the prompt releases straight out of parked,
+    # never unparking. Its executor thread went with it, so keeping the budget
+    # would lose one for the life of the process.
+    monkeypatch.setattr(llama_admission, "_executor_workers", lambda: 32)
+
+    async def scenario():
+        config = LlamaAdmissionConfig()
+        queue = get_llama_admission_queue("http://llama.test")
+
+        parked = []
+        for _ in range(llama_admission._max_parked(1)):
+            lease = queue.reserve(capacity = 1, config = config).lease_nowait()
+            assert lease is not None and lease.park()
+            parked.append(lease)
+
+        blocked = queue.reserve(capacity = 1, config = config).lease_nowait()
+        assert blocked is not None
+        assert not blocked.park(), "the budget was not full to begin with"
+
+        parked[0].release()
+        assert blocked.park(), "a released park never gave its budget back"
+        for lease in parked[1:] + [blocked]:
+            lease.release()
+
+    asyncio.run(scenario())

studio/backend/tests/test_llama_cpp_tool_loop.py

@@ -26,6 +26,7 @@ from core.inference.llama_cpp import (
     _PROVISIONAL_ARGS_MIN_CHARS,
     LlamaCppBackend,
 )
+from core.inference.tool_call_parser import NUDGE_TOOL_CALLS_STATUS
 from state import tool_approvals
 from state.tool_approvals import TOOL_REJECTED_MESSAGE, resolve_tool_decision
 
@@ -1841,6 +1842,140 @@ def test_reprompted_tool_call_still_streams_final_answer(monkeypatch):
     assert len(payloads) == 3
 
 
+def _status_texts(events: list[dict]) -> list[str]:
+    return [event["text"] for event in events if event.get("type") == "status"]
+
+
+_WEB_SEARCH_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "web_search",
+        "description": "Search the web.",
+        "parameters": {
+            "type": "object",
+            "properties": {"query": {"type": "string"}},
+            "required": ["query"],
+        },
+    },
+}
+
+
+def _nudge_then_search_streams() -> list[list[str]]:
+    """Stall, then a re-prompted turn that finally searches, then the answer."""
+
+    return [
+        [_sse({"content": "I will search the web now."}), _done()],
+        [
+            _sse(
+                {
+                    "tool_calls": [
+                        {
+                            "index": 0,
+                            "id": "call_search",
+                            "type": "function",
+                            "function": {
+                                "name": "web_search",
+                                "arguments": json.dumps({"query": "red square"}),
+                            },
+                        }
+                    ]
+                }
+            ),
+            _done(),
+        ],
+        [_sse({"content": "Final answer: the square is red."}), _done()],
+    ]
+
+
+def test_plan_without_action_nudge_is_announced_on_the_status_channel(monkeypatch):
+    """The re-prompted turn is hidden, so without a badge the UI looks frozen."""
+
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, _nudge_then_search_streams(), payloads)
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda *_a, **_k: "Search results: red is #f00.",
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "What colour is the square?"}],
+            tools = [_WEB_SEARCH_TOOL],
+            max_tool_iterations = 2,
+        )
+    )
+
+    statuses = _status_texts(events)
+    assert NUDGE_TOOL_CALLS_STATUS in statuses
+    index = statuses.index(NUDGE_TOOL_CALLS_STATUS)
+    # Blank first: the route resets its text cursor only on an empty status.
+    # index > 0 matters: at 0, statuses[-1] wraps to the terminal clear.
+    assert index > 0 and statuses[index - 1] == ""
+    assert statuses[index + 1].startswith("Searching:")
+    assert statuses[-1] == ""
+
+
+def test_plan_without_action_nudge_status_clears_when_the_retry_just_answers(monkeypatch):
+    streams = [
+        [_sse({"content": "I will search the web now."}), _done()],
+        [_sse({"content": "No search needed. Final answer: the square is red."}), _done()],
+    ]
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, streams, payloads)
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "What colour is the square?"}],
+            tools = [_WEB_SEARCH_TOOL],
+            max_tool_iterations = 2,
+        )
+    )
+
+    statuses = _status_texts(events)
+    assert NUDGE_TOOL_CALLS_STATUS in statuses
+    assert statuses[-1] == ""
+
+
+def test_direct_answer_never_shows_the_nudge_status(monkeypatch):
+    payloads: list[dict] = []
+    backend = _make_backend(
+        monkeypatch,
+        [[_sse({"content": "The square is red."}), _done()]],
+        payloads,
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "What colour is the square?"}],
+            tools = [_WEB_SEARCH_TOOL],
+            max_tool_iterations = 2,
+        )
+    )
+
+    assert NUDGE_TOOL_CALLS_STATUS not in _status_texts(events)
+
+
+def test_nudge_status_absent_when_nudging_is_disabled(monkeypatch):
+    payloads: list[dict] = []
+    backend = _make_backend(monkeypatch, _nudge_then_search_streams(), payloads)
+    monkeypatch.setattr(
+        "core.inference.tools.execute_tool",
+        lambda *_a, **_k: "Search results: red is #f00.",
+    )
+
+    events = list(
+        backend.generate_chat_completion_with_tools(
+            messages = [{"role": "user", "content": "What colour is the square?"}],
+            tools = [_WEB_SEARCH_TOOL],
+            max_tool_iterations = 2,
+            nudge_tool_calls = False,
+        )
+    )
+
+    assert NUDGE_TOOL_CALLS_STATUS not in _status_texts(events)
+    assert len(payloads) == 1
+
+
 def test_confirm_tool_calls_allow_executes_gguf_tool(monkeypatch):
     streams = [
         _structured_tool_call("python", {"code": "print(1)"}, "call_py"),

studio/backend/tests/test_llama_server_args.py

@@ -77,8 +77,7 @@ validate_extra_args = _lsa.validate_extra_args
         ["--reasoning-format", "deepseek"],
         ["-rea", "auto"],
         # Soft-managed: user flags last-wins over Unsloth's auto-set version.
-        # --parallel / -np / --n-parallel are hard-denied (KV-cache + slot
-        # count would desync); use `unsloth studio run --parallel N` instead.
+        # --parallel / -np / --n-parallel are hard-denied; use Parallel Slots.
         ["-c", "131072"],
         ["--ctx-size", "8192"],
         ["--flash-attn", "off"],
@@ -128,7 +127,7 @@ def test_non_flag_token_passes_through():
 @pytest.mark.parametrize(
     "denied",
     [
-        # Parallel slots -- owned by the typer --parallel flag.
+        # Parallel slots -- owned by typer --parallel and LoadRequest.n_parallel.
         "-np",
         "--parallel",
         "--n-parallel",
@@ -201,9 +200,8 @@ def test_denylist_rejects_all_aliases(denied):
 @pytest.mark.parametrize(
     "args,offending",
     [
-        # Pass-through --parallel would last-wins-override the real slot
-        # count while Unsloth's KV-cache fit + llama_parallel_slots stay at
-        # the typer value -- plan vs. process disagree.
+        # Pass-through --parallel would last-wins-override the real slot count
+        # while the KV-cache fit and slot bookkeeping stay at the resolved value.
         (["--parallel", "8"], "--parallel"),
         (["--parallel=8"], "--parallel"),
         (["--n-parallel", "16"], "--n-parallel"),
@@ -213,7 +211,7 @@ def test_denylist_rejects_all_aliases(denied):
         # `["-np8"]` must still resolve to managed.
         (["-np8"], "-np"),
         (["-np64"], "-np"),
-        # Out-of-range values that would bypass the typer 1..64 guard.
+        # Out-of-range values that would bypass the PARALLEL_MIN/MAX bounds.
         (["--parallel", "999"], "--parallel"),
         (["-np", "0"], "-np"),
         (["-np999"], "-np"),
@@ -300,7 +298,7 @@ def test_is_managed_flag_true_for_denied():
     assert is_managed_flag("--api-key") is True
     assert is_managed_flag("-m") is True
     assert is_managed_flag("--model") is True
-    # Parallel slots owned by the typer --parallel flag.
+    # Parallel slots owned by typer --parallel and LoadRequest.n_parallel.
     assert is_managed_flag("--parallel") is True
     assert is_managed_flag("--n-parallel") is True
     assert is_managed_flag("-np") is True

studio/backend/tests/test_mcp_flatten_result.py

@@ -175,3 +175,46 @@ def test_call_tool_sync_passes_raise_on_error_false_and_keeps_error_images(monke
     assert out.startswith("Error: boom")
     assert MCP_IMAGES_SENTINEL in out
     assert is_tool_error(out)
+
+
+def test_stdio_session_call_also_passes_raise_on_error_false(monkeypatch):
+    seen = {}
+
+    class _FakeStdioClient:
+        def __init__(self):
+            self.connected = False
+            self.transport = SimpleNamespace(_is_session_dead = lambda: False)
+
+        async def __aenter__(self):
+            self.connected = True
+            return self
+
+        async def __aexit__(self, *exc):
+            self.connected = False
+
+        def is_connected(self):
+            return self.connected
+
+        async def call_tool(
+            self,
+            name,
+            args,
+            raise_on_error = True,
+        ):
+            seen["raise_on_error"] = raise_on_error
+            return _result(_text("boom"), _image(), is_error = True)
+
+    monkeypatch.setattr(
+        mcp_client, "_client", lambda url, headers, use_oauth = False: _FakeStdioClient()
+    )
+    try:
+        out = call_tool_sync(
+            "npx fake-stdio-server", None, "take_screenshot", {}, scope = "s=p:t=thread1"
+        )
+    finally:
+        mcp_client.close_stdio_sessions()
+
+    assert seen["raise_on_error"] is False
+    assert out.startswith("Error: boom")
+    assert MCP_IMAGES_SENTINEL in out
+    assert is_tool_error(out)

studio/backend/tests/test_mcp_stdio_sessions.py

@@ -60,7 +60,12 @@ class FakeClient:
     def is_connected(self) -> bool:
         return self.connected
 
-    async def call_tool(self, name: str, args: dict):
+    async def call_tool(
+        self,
+        name: str,
+        args: dict,
+        raise_on_error: bool = True,
+    ):
         if self.call_delay:
             await asyncio.sleep(self.call_delay)
         if self.fail_next:
@@ -120,10 +125,15 @@ def test_tool_error_does_not_recycle_session(fake_clients, monkeypatch):
     from fastmcp.exceptions import ToolError
 
     class ToolFailure(FakeClient):
-        async def call_tool(self, name, args):
+        async def call_tool(
+            self,
+            name,
+            args,
+            raise_on_error = True,
+        ):
             if name == "boom":
                 raise ToolError("tool exploded")  # tool-level: session stays connected
-            return await super().call_tool(name, args)
+            return await super().call_tool(name, args, raise_on_error)
 
     monkeypatch.setattr(
         mcp_client, "_client", lambda url, headers, use_oauth = False: ToolFailure(url)
@@ -441,12 +451,17 @@ def test_overlapping_calls_serialize_on_shared_session(fake_clients, monkeypatch
         active = 0
         max_active = 0
 
-        async def call_tool(self, name, args):
+        async def call_tool(
+            self,
+            name,
+            args,
+            raise_on_error = True,
+        ):
             OverlapDetect.active += 1
             OverlapDetect.max_active = max(OverlapDetect.max_active, OverlapDetect.active)
             try:
                 await asyncio.sleep(0.2)
-                return await super().call_tool(name, args)
+                return await super().call_tool(name, args, raise_on_error)
             finally:
                 OverlapDetect.active -= 1
 
@@ -473,9 +488,14 @@ def test_timeout_budget_spans_connect_and_call(fake_clients, monkeypatch):
             await asyncio.sleep(0.4)
             return await super().__aenter__()
 
-        async def call_tool(self, name, args):
+        async def call_tool(
+            self,
+            name,
+            args,
+            raise_on_error = True,
+        ):
             await asyncio.sleep(0.5)
-            return await super().call_tool(name, args)
+            return await super().call_tool(name, args, raise_on_error)
 
     monkeypatch.setattr(mcp_client, "_client", lambda url, headers, use_oauth = False: SlowBoth(url))
     start = time.monotonic()
@@ -565,7 +585,11 @@ def test_execute_tool_config_check_tracks_row(tmp_path, monkeypatch):
 
 
 def test_multi_block_result_flattens_through_session(fake_clients):
-    async def _rich_call(name, args):
+    async def _rich_call(
+        name,
+        args,
+        raise_on_error = True,
+    ):
         return SimpleNamespace(
             content = [
                 SimpleNamespace(type = "text", text = "### Page"),

studio/backend/tests/test_mtp_vram_budget.py

@@ -817,7 +817,26 @@ class TestExtraArgsMtpDetection:
         ],
     )
     def test_flash_attn_last_value_wins(self, args, expected):
-        assert _flash_attn_enabled_from_args(args) is expected
+        assert _flash_attn_enabled_from_args(args, env = {}) is expected
+
+    @pytest.mark.parametrize(
+        "value,expected",
+        [
+            ("off", False),
+            ("disabled", False),
+            ("false", False),
+            ("0", False),
+            ("on", True),
+            ("auto", True),
+            ("garbage", True),  # llama.cpp refuses to start, so the default is moot
+        ],
+    )
+    def test_flash_attn_env_applies(self, value, expected):
+        env = {"LLAMA_ARG_FLASH_ATTN": value}
+        assert _flash_attn_enabled_from_args([], env = env) is expected
+        # llama.cpp parses the environment first, so an explicit flag still wins.
+        assert _flash_attn_enabled_from_args(["-fa", "on"], env = env) is True
+        assert _flash_attn_enabled_from_args(["-fa", "off"], env = env) is False
 
     def test_effective_main_cache_types_follow_env_then_cli(self):
         env = {

studio/backend/tests/test_parallel_slots_per_load.py

@@ -0,0 +1,517 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Backend contract for the per-load parallel-slots knob.
+
+An optional ``n_parallel`` (llama-server ``--parallel``) rides on LoadRequest;
+omitted, the server-wide launch default (``run.py --parallel``) applies. These
+tests pin the pydantic contract and the shared PARALLEL_MIN/MAX mirrors, the
+``requested_parallel_slots`` lifecycle, the ``_already_in_target_state``
+requested-vs-requested reload branch with its diffusion skip, and the route
+wiring behind the /load, /validate and /status echoes.
+"""
+
+from __future__ import annotations
+
+import inspect
+import re
+import struct
+import sys
+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)
+
+# Same external-dep stubs as the other llama_cpp unit tests.
+_loggers_stub = _types.ModuleType("loggers")
+_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
+sys.modules.setdefault("loggers", _loggers_stub)
+
+_structlog_stub = _types.ModuleType("structlog")
+_structlog_stub.get_logger = lambda *a, **k: __import__("logging").getLogger("stub")
+sys.modules.setdefault("structlog", _structlog_stub)
+
+# Real httpx: a stub would poison a combined run (routes/inference reads its
+# attrs at def time).
+import httpx  # noqa: F401
+
+from core.inference import llama_cpp as llama_cpp_module
+from core.inference.llama_server_args import PARALLEL_MAX, PARALLEL_MIN
+from core.inference.llama_cpp import LlamaCppBackend
+from models.inference import (
+    InferenceStatusResponse,
+    LoadRequest,
+    LoadResponse,
+    ValidateModelRequest,
+)
+
+
+class _FakeProcess:
+    def terminate(self):
+        pass
+
+    def wait(self, timeout = None):
+        return 0
+
+    def kill(self):
+        pass
+
+    def poll(self):
+        return 0
+
+
+# ── Pydantic contract ────────────────────────────────────────────────
+
+
+def test_load_request_defaults_n_parallel_none():
+    assert LoadRequest(model_path = "owner/repo").n_parallel is None
+
+
+@pytest.mark.parametrize("value", [PARALLEL_MIN, 4, PARALLEL_MAX])
+def test_load_request_accepts_in_range_n_parallel(value):
+    assert LoadRequest(model_path = "owner/repo", n_parallel = value).n_parallel == value
+
+
+@pytest.mark.parametrize("value", [0, -1, PARALLEL_MAX + 1])
+def test_load_request_rejects_out_of_range_n_parallel(value):
+    with pytest.raises(ValueError):
+        LoadRequest(model_path = "owner/repo", n_parallel = value)
+
+
+def test_load_request_round_trips_json_key():
+    req = LoadRequest.model_validate({"model_path": "owner/repo", "n_parallel": 8})
+    assert req.n_parallel == 8
+    assert req.model_dump()["n_parallel"] == 8
+
+
+def test_validate_request_n_parallel_contract():
+    # /validate sizes like /load, so it carries the same field and bounds.
+    assert ValidateModelRequest(model_path = "owner/repo").n_parallel is None
+    assert (
+        ValidateModelRequest(model_path = "owner/repo", n_parallel = PARALLEL_MAX).n_parallel
+        == PARALLEL_MAX
+    )
+    with pytest.raises(ValueError):
+        ValidateModelRequest(model_path = "owner/repo", n_parallel = PARALLEL_MAX + 1)
+
+
+@pytest.mark.parametrize("model_cls", [LoadResponse, InferenceStatusResponse])
+def test_response_models_emit_parallel_slot_fields(model_cls):
+    kwargs = (
+        dict(status = "loaded", model = "owner/repo", display_name = "repo", inference = {})
+        if model_cls is LoadResponse
+        else {}
+    )
+    empty = model_cls(**kwargs).model_dump()
+    assert empty["requested_parallel_slots"] is None
+    assert empty["parallel_slots"] is None
+    dumped = model_cls(**kwargs, requested_parallel_slots = 8, parallel_slots = 4).model_dump()
+    assert dumped["requested_parallel_slots"] == 8
+    assert dumped["parallel_slots"] == 4
+
+
+# ── Shared bounds and their deliberate mirrors ───────────────────────
+
+
+def _mirrored_bounds(source_path: Path) -> tuple[int, int]:
+    src = source_path.read_text(encoding = "utf-8")
+    low = re.search(r"^_PARALLEL_MIN\s*=\s*(\d+)$", src, re.MULTILINE)
+    high = re.search(r"^_PARALLEL_MAX\s*=\s*(\d+)$", src, re.MULTILINE)
+    assert low and high, f"{source_path} must define _PARALLEL_MIN/_PARALLEL_MAX"
+    return int(low.group(1)), int(high.group(1))
+
+
+def test_run_py_mirror_matches_shared_bounds():
+    assert _mirrored_bounds(Path(_BACKEND_DIR) / "run.py") == (PARALLEL_MIN, PARALLEL_MAX)
+
+
+def test_cli_mirror_matches_shared_bounds():
+    cli = Path(_BACKEND_DIR).parent.parent / "unsloth_cli" / "commands" / "studio.py"
+    assert _mirrored_bounds(cli) == (PARALLEL_MIN, PARALLEL_MAX)
+
+
+def test_frontend_mirror_matches_shared_bounds():
+    # The UI clamps with its own copy; a bumped PARALLEL_MAX that skips it would
+    # leave the UI silently capping lower.
+    src = (
+        Path(_BACKEND_DIR).parent
+        / "frontend"
+        / "src"
+        / "features"
+        / "model-picker"
+        / "model-config"
+        / "per-model-config.ts"
+    ).read_text(encoding = "utf-8")
+    low = re.search(r"^export const N_PARALLEL_MIN = (\d+);$", src, re.MULTILINE)
+    high = re.search(r"^export const N_PARALLEL_MAX = (\d+);$", src, re.MULTILINE)
+    assert low and high, "per-model-config.ts must export N_PARALLEL_MIN/MAX"
+    assert (int(low.group(1)), int(high.group(1))) == (PARALLEL_MIN, PARALLEL_MAX)
+
+
+def test_preset_model_reuses_shared_bounds():
+    # Bounds drifting from PARALLEL_MIN/MAX would 422 valid presets on every sync.
+    from routes.chat_history import ChatPresetLoadConfig
+
+    field = ChatPresetLoadConfig.model_fields["nParallel"]
+    bounds = {type(m).__name__: getattr(m, "ge", getattr(m, "le", None)) for m in field.metadata}
+    assert bounds.get("Ge") == PARALLEL_MIN
+    assert bounds.get("Le") == PARALLEL_MAX
+
+
+# ── requested_parallel_slots lifecycle ───────────────────────────────
+
+
+@pytest.fixture
+def backend(monkeypatch):
+    monkeypatch.setattr(LlamaCppBackend, "_kill_orphaned_servers", lambda self: 0)
+    monkeypatch.setattr(llama_cpp_module.atexit, "register", lambda *_args, **_kwargs: None)
+    return LlamaCppBackend()
+
+
+def test_requested_parallel_slots_initial_value_is_one(backend):
+    assert backend.requested_parallel_slots == 1
+
+
+def test_requested_parallel_slots_reflects_field(backend):
+    backend._requested_n_parallel = 8
+    assert backend.requested_parallel_slots == 8
+
+
+@pytest.mark.parametrize("value", [None, 0, -2, "not-an-int"])
+def test_requested_parallel_slots_invalid_value_falls_back_to_one(backend, value):
+    backend._requested_n_parallel = value
+    assert backend.requested_parallel_slots == 1
+
+
+def test_reset_effective_parallel_slots_also_resets_requested(backend):
+    backend._requested_n_parallel = 8
+    backend._commit_effective_parallel_slots(4)
+
+    backend._reset_effective_parallel_slots()
+
+    assert backend.requested_parallel_slots == 1
+    assert backend.effective_parallel_slots == 1
+
+
+def test_unload_resets_requested_parallel_slots(backend):
+    backend._process = _FakeProcess()
+    backend._requested_n_parallel = 8
+
+    backend.unload_model()
+
+    assert backend.requested_parallel_slots == 1
+
+
+def test_load_model_commits_requested_from_pending_kwargs():
+    # n_parallel may be reduced before the commit, so the requested value must
+    # come from the pre-reduction pending snapshot.
+    src = inspect.getsource(LlamaCppBackend.load_model)
+    commit = src.find(
+        'self._requested_n_parallel = max(1, int(_pending_load_kwargs["n_parallel"]))'
+    )
+    healthy = src.find("self._healthy = True\n", 0, commit if commit != -1 else None)
+    snapshot = src.find("self._last_load_kwargs = _pending_load_kwargs")
+    assert commit != -1, "load_model must commit the requested slot count"
+    assert healthy != -1 and healthy < commit < snapshot
+
+
+# ── _already_in_target_state requested-vs-requested branch ───────────
+
+
+def _loaded_backend() -> LlamaCppBackend:
+    backend = LlamaCppBackend()
+    backend._process = _FakeProcess()  # is_loaded only checks "is not None"
+    backend._healthy = True
+    backend._model_identifier = "owner/repo"
+    backend._hf_variant = "Q4_K_M"
+    backend._requested_n_ctx = 8192
+    backend._cache_type_kv = None
+    backend._requested_spec_mode = "auto"
+    backend._chat_template_override = None
+    backend._is_vision = False
+    backend._extra_args = None
+    backend._gguf_path = None
+    return backend
+
+
+def _target_state(backend: LlamaCppBackend, n_parallel: int) -> bool:
+    return backend._already_in_target_state(
+        gguf_path = None,
+        model_identifier = "owner/repo",
+        hf_variant = "Q4_K_M",
+        n_ctx = 8192,
+        cache_type_kv = None,
+        speculative_type = "auto",
+        chat_template_override = None,
+        extra_args = None,
+        is_vision = False,
+        n_parallel = n_parallel,
+    )
+
+
+def test_already_in_target_state_matches_same_slots():
+    backend = _loaded_backend()
+    backend._requested_n_parallel = 4
+    assert _target_state(backend, 4) is True
+
+
+def test_already_in_target_state_reloads_on_slots_change():
+    backend = _loaded_backend()
+    backend._requested_n_parallel = 4
+    assert _target_state(backend, 8) is False
+
+
+def test_already_in_target_state_compares_requested_not_effective():
+    # An identical re-Apply must dedupe even after the fitter reduced the slots.
+    backend = _loaded_backend()
+    backend._requested_n_parallel = 8
+    backend._commit_effective_parallel_slots(4)
+    assert _target_state(backend, 8) is True
+
+
+def test_already_in_target_state_ignores_slots_for_diffusion():
+    # The diffusion runner ignores --parallel, so a slots change must not reload.
+    backend = _loaded_backend()
+    backend._is_diffusion = True
+    backend._requested_n_parallel = 1
+    assert _target_state(backend, 8) is True
+
+
+# ── Route wiring (source contract, mirroring test_gpu_memory_mode) ───
+
+
+def _route_source() -> str:
+    return (Path(_BACKEND_DIR) / "routes" / "inference.py").read_text(encoding = "utf-8")
+
+
+def _load_impl_source() -> str:
+    """Body of _load_model_impl only, so positional assertions can't be
+    satisfied by a later function in the module."""
+    src = _route_source()
+    body = src[src.index("async def _load_model_impl") :]
+    return body[: body.index("\n@router.")]
+
+
+def test_route_resolves_slots_once_before_dedupe_guard_and_load():
+    load_impl = _load_impl_source()
+    resolve = load_impl.index("request.n_parallel")
+    fallback = load_impl.index('getattr(_app_state, "llama_parallel_slots", 1)')
+    dedupe = load_impl.index("requested_parallel_slots = _n_parallel")
+    guard = load_impl.index("_guard_chat_load_against_training")
+    # The GGUF launch kwargs, not the guard's own kwarg (which shares the spelling).
+    load_kwargs = load_impl.index("_common_load_kwargs = dict(")
+    assert resolve < dedupe, "resolution must precede the reload dedupe"
+    assert fallback < dedupe
+    assert resolve < guard < load_kwargs
+    # Guard and load kwargs share the resolved value; app.state is read once.
+    assert load_impl.count("n_parallel = _n_parallel") == 2
+    assert "n_parallel = _n_parallel" in load_impl[load_kwargs : load_kwargs + 800]
+    assert load_impl.count('getattr(_app_state, "llama_parallel_slots", 1)') == 1
+    # getattr, so a direct caller without an app cannot raise, and no re-read.
+    assert "fastapi_request.app.state" not in load_impl
+
+
+def test_route_dedupe_compares_requested_slots_and_skips_diffusion():
+    match_impl = _route_source()[_route_source().index("def _request_matches_loaded_settings") :]
+    match_impl = match_impl[: match_impl.index("\ndef ")]
+    assert "requested_parallel_slots is not None" in match_impl
+    assert "not llama_backend.is_diffusion" in match_impl
+    assert "llama_backend.requested_parallel_slots" in match_impl
+
+
+def test_route_echoes_requested_and_effective_slots():
+    route_src = _route_source()
+    # Both /load returns plus the /status GGUF branch, via the shared helper.
+    assert route_src.count("**_parallel_slot_echo(llama_backend)") == 3
+
+
+def test_parallel_slot_echo_reports_none_for_diffusion():
+    # Diffusion never commits a count, so echoing the reset placeholder 1 would lie.
+    from routes.inference import _parallel_slot_echo
+
+    backend = _loaded_backend()
+    backend._requested_n_parallel = 8
+    backend._commit_effective_parallel_slots(4)
+    assert _parallel_slot_echo(backend) == {"requested_parallel_slots": 8, "parallel_slots": 4}
+    backend._is_diffusion = True
+    assert _parallel_slot_echo(backend) == {
+        "requested_parallel_slots": None,
+        "parallel_slots": None,
+    }
+
+
+def test_validate_route_prefers_request_n_parallel():
+    validate_impl = _route_source()[_route_source().index("async def validate_model") :]
+    resolve = validate_impl.index("request.n_parallel")
+    fallback = validate_impl.index('"llama_parallel_slots",')
+    guard = validate_impl.index("_guard_chat_load_against_training")
+    assert guard < resolve and guard < fallback, "the guard call resolves the slots inline"
+
+
+def _load_model_source() -> str:
+    return inspect.getsource(LlamaCppBackend.load_model)
+
+
+def test_slots_fall_back_to_one_without_kv_unified():
+    # Without --kv-unified llama-server gives each slot -c/N, so an explicit
+    # --parallel N shrinks every context window.
+    src = _load_model_source()
+    clamp = src.find("supports_kv_unified")
+    assert clamp != -1, "load_model must check for --kv-unified before honouring the slots"
+    block = src[clamp : clamp + 700]
+    assert (
+        "n_parallel > 1" in src[clamp - 300 : clamp]
+    ), "only an explicit multi-slot load is clamped"
+    assert "n_parallel = 1" in block
+
+
+def test_clamp_sits_between_the_echo_and_the_fit():
+    # The echo reports the ask and the fit uses what launches, so the clamp
+    # belongs between the two.
+    src = _load_model_source()
+    pending = src.index("_pending_load_kwargs")
+    clamp = src.index("supports_kv_unified")
+    estimate = src.index("_estimate")
+    commit = src.index("_commit_effective_parallel_slots")
+    assert pending < clamp, "the requested count is captured before the clamp"
+    assert clamp < estimate, "the fit must be estimated from the effective slot count"
+    assert clamp < commit, "the committed effective count is the clamped one"
+
+
+# ── Training-guard sizing ────────────────────────────────────────────
+
+
+def _write_swa_gguf(path: Path) -> str:
+    """Smallest DiffusionGemma-shaped header the KV estimator can size: the
+    canvas marker routing it to the diffusion runner, plus the sliding-window
+    dims that make llama.cpp's SWA cache slot-scaled."""
+
+    def _kv_str(key: str, value: str) -> bytes:
+        kb, vb = key.encode(), value.encode()
+        return (
+            struct.pack("<Q", len(kb)) + kb + struct.pack("<I", 8) + struct.pack("<Q", len(vb)) + vb
+        )
+
+    def _kv_u32(key: str, value: int) -> bytes:
+        kb = key.encode()
+        return struct.pack("<Q", len(kb)) + kb + struct.pack("<I", 4) + struct.pack("<I", value)
+
+    arch = "diffusion-gemma"
+    kvs = [
+        _kv_str("general.architecture", arch),
+        _kv_u32("diffusion.canvas_length", 256),
+        _kv_u32(f"{arch}.context_length", 32768),
+        _kv_u32(f"{arch}.block_count", 30),
+        _kv_u32(f"{arch}.attention.head_count", 16),
+        _kv_u32(f"{arch}.attention.head_count_kv", 8),
+        _kv_u32(f"{arch}.attention.key_length", 512),
+        _kv_u32(f"{arch}.attention.value_length", 512),
+        _kv_u32(f"{arch}.attention.sliding_window", 1024),
+        _kv_u32(f"{arch}.attention.key_length_swa", 256),
+        _kv_u32(f"{arch}.attention.value_length_swa", 256),
+    ]
+    path.write_bytes(struct.pack("<IIQQ", 0x46554747, 3, 0, len(kvs)) + b"".join(kvs))
+    return str(path)
+
+
+def _guard_required_gb(
+    monkeypatch,
+    gguf_path: str,
+    *,
+    n_parallel: int,
+    diffusion,
+    caps = None,
+) -> float:
+    """Run the training guard over a local GGUF and return the size it budgeted."""
+    import routes.inference as inf
+
+    seen = {}
+
+    core_training = _types.ModuleType("core.training")
+    core_training.get_training_backend = lambda: _types.SimpleNamespace(
+        is_training_active = lambda: True
+    )
+
+    def _can_load(**kwargs):
+        seen.update(kwargs)
+        return True, {"mode": "single_device"}
+
+    training_vram = _types.ModuleType("routes.training_vram")
+    training_vram.can_load_chat_during_training = _can_load
+    monkeypatch.setitem(sys.modules, "core.training", core_training)
+    monkeypatch.setitem(sys.modules, "routes.training_vram", training_vram)
+
+    monkeypatch.setattr(inf, "_classify_diffusion_gguf", lambda _config: diffusion)
+    monkeypatch.setattr(LlamaCppBackend, "_is_vulkan_backend", staticmethod(lambda *a, **k: False))
+    monkeypatch.setattr(LlamaCppBackend, "_effective_gpu_count", staticmethod(lambda *a, **k: 1))
+    monkeypatch.setattr(LlamaCppBackend, "_diffusion_gpu_arg", staticmethod(lambda *a, **k: "0"))
+    # Pin the --kv-unified probe so the estimate cannot depend on a locally
+    # installed llama-server. Default "no binary found" leaves the count alone.
+    monkeypatch.setattr(
+        LlamaCppBackend,
+        "probe_server_capabilities",
+        classmethod(lambda cls, binary = None: dict(caps or {})),
+    )
+
+    inf._guard_chat_load_against_training(
+        _types.SimpleNamespace(is_gguf = True, gguf_file = gguf_path, identifier = "local/model"),
+        model_identifier = "local/model",
+        hf_token = None,
+        load_in_4bit = False,
+        max_seq_length = 8192,
+        requested_gpu_ids = None,
+        n_parallel = n_parallel,
+        gpu_memory_mode = "auto",
+    )
+    return seen["required_override_gb"]
+
+
+def test_training_guard_sizes_a_diffusion_gguf_at_one_slot(monkeypatch, tmp_path):
+    # Diffusion ignores --parallel, so slots must not inflate the estimate and 409
+    # a load that would have fitted beside training.
+    gguf = _write_swa_gguf(tmp_path / "diffusion.gguf")
+    one = _guard_required_gb(monkeypatch, gguf, n_parallel = 1, diffusion = True)
+    many = _guard_required_gb(monkeypatch, gguf, n_parallel = 8, diffusion = True)
+    assert one == many
+
+
+def test_training_guard_still_sizes_slots_for_an_ordinary_gguf(monkeypatch, tmp_path):
+    # llama-server does allocate per-slot SWA cells, so the reduction above must
+    # be scoped to diffusion and not flatten every GGUF to one slot.
+    gguf = _write_swa_gguf(tmp_path / "chat.gguf")
+    one = _guard_required_gb(monkeypatch, gguf, n_parallel = 1, diffusion = False)
+    many = _guard_required_gb(monkeypatch, gguf, n_parallel = 8, diffusion = False)
+    assert many > one
+
+
+def test_training_guard_sizes_one_slot_when_the_binary_has_no_kv_unified(monkeypatch, tmp_path):
+    # load_model clamps a multi-slot request to 1 on such a build, where each slot
+    # carries its own SWA stream, so sizing the asked count would 409 a load that fits.
+    gguf = _write_swa_gguf(tmp_path / "chat.gguf")
+    old = {"found": True, "supports_kv_unified": False}
+    one = _guard_required_gb(monkeypatch, gguf, n_parallel = 1, diffusion = False, caps = old)
+    many = _guard_required_gb(monkeypatch, gguf, n_parallel = 8, diffusion = False, caps = old)
+    assert one == many
+
+
+def test_training_guard_sizes_every_slot_when_kv_unified_exists(monkeypatch, tmp_path):
+    # The clamp is scoped to binaries that cannot serve the slots; a capable one
+    # really does allocate the SWA window per slot.
+    gguf = _write_swa_gguf(tmp_path / "chat.gguf")
+    new = {"found": True, "supports_kv_unified": True}
+    one = _guard_required_gb(monkeypatch, gguf, n_parallel = 1, diffusion = False, caps = new)
+    many = _guard_required_gb(monkeypatch, gguf, n_parallel = 8, diffusion = False, caps = new)
+    assert many > one
+
+
+def test_training_guard_keeps_slots_for_an_unclassified_gguf(monkeypatch, tmp_path):
+    # None = inconclusive header, so keep the larger estimate rather than
+    # under-size against training.
+    gguf = _write_swa_gguf(tmp_path / "unknown.gguf")
+    one = _guard_required_gb(monkeypatch, gguf, n_parallel = 1, diffusion = None)
+    many = _guard_required_gb(monkeypatch, gguf, n_parallel = 8, diffusion = None)
+    assert many > one

studio/backend/tests/test_safetensors_tool_loop.py

@@ -24,6 +24,7 @@ from core.inference.safetensors_agentic import (
     strip_tool_markup_streaming,
 )
 from core.inference.tool_call_parser import (
+    NUDGE_TOOL_CALLS_STATUS,
     RAG_MAX_SEARCHES_PER_TURN,
     has_tool_signal,
     parse_tool_calls_from_text,
@@ -2231,6 +2232,24 @@ def test_reprompt_names_only_active_tools_not_hardcoded():
     assert "python" not in reprompt["content"]
 
 
+def test_reprompt_is_announced_on_the_status_channel():
+    # The re-prompted turn is hidden, so the badge is the only sign of life.
+    # Blank still comes first: the route resets its text cursor only on that.
+    _captured, events = _reprompt_loop(auto_heal_tool_calls = True)
+    statuses = [e["text"] for e in events if e["type"] == "status"]
+    assert NUDGE_TOOL_CALLS_STATUS in statuses
+    index = statuses.index(NUDGE_TOOL_CALLS_STATUS)
+    # index > 0 matters: at 0, statuses[-1] wraps to the terminal clear.
+    assert index > 0 and statuses[index - 1] == ""
+    assert statuses[-1] == ""
+
+
+def test_reprompt_status_absent_without_a_nudge():
+    _captured, events = _reprompt_loop(auto_heal_tool_calls = False)
+    statuses = [e["text"] for e in events if e["type"] == "status"]
+    assert NUDGE_TOOL_CALLS_STATUS not in statuses
+
+
 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.

studio/frontend/package-lock.json

@@ -34,6 +34,7 @@
         "@tanstack/react-virtual": "3.13.25",
         "@tauri-apps/api": "^2.10.1",
         "@tauri-apps/plugin-clipboard-manager": "^2.3.2",
+        "@tauri-apps/plugin-deep-link": "2.4.9",
         "@tauri-apps/plugin-notification": "^2.3.3",
         "@tauri-apps/plugin-opener": "^2.5.3",
         "@tauri-apps/plugin-process": "^2.3.1",
@@ -6451,6 +6452,15 @@
         "@tauri-apps/api": "^2.8.0"
       }
     },
+    "node_modules/@tauri-apps/plugin-deep-link": {
+      "version": "2.4.9",
+      "resolved": "https://registry.npmjs.org/@tauri-apps/plugin-deep-link/-/plugin-deep-link-2.4.9.tgz",
+      "integrity": "sha512-u0SKOUHnJ1wqeqXsDFq2+kASCBj9xxbG0g9XZWPy9SOmU4wXtp6b/wiYpm6oH6/5fBTQsLqnLhIvqLBRpgHJlA==",
+      "license": "MIT OR Apache-2.0",
+      "dependencies": {
+        "@tauri-apps/api": "^2.11.0"
+      }
+    },
     "node_modules/@tauri-apps/plugin-notification": {
       "version": "2.3.3",
       "resolved": "https://registry.npmjs.org/@tauri-apps/plugin-notification/-/plugin-notification-2.3.3.tgz",

studio/frontend/package.json

@@ -44,6 +44,7 @@
     "@tanstack/react-virtual": "3.13.25",
     "@tauri-apps/api": "^2.10.1",
     "@tauri-apps/plugin-clipboard-manager": "^2.3.2",
+    "@tauri-apps/plugin-deep-link": "2.4.9",
     "@tauri-apps/plugin-notification": "^2.3.3",
     "@tauri-apps/plugin-opener": "^2.5.3",
     "@tauri-apps/plugin-process": "^2.3.1",

studio/frontend/src/app/provider.tsx

@@ -15,6 +15,7 @@ import { TooltipProvider } from "@/components/ui/tooltip";
 import { WebUpdateBanner } from "@/components/web/update-banner";
 import { fetchDeviceType } from "@/config/env";
 import { getTauriAuthFailure, tauriAutoAuth } from "@/features/auth";
+import { DeepLinkHandler } from "@/features/deep-links";
 import { DownloadManagerPanel } from "@/features/hub/download-manager";
 import { NativeIntentDrain } from "@/features/native-intents/native-intent-drain";
 import {
@@ -255,7 +256,7 @@ const MAC_NATIVE_CHROME_STYLE = {
   "--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-header-padding-top": "7px",
   "--studio-chat-control-height": "33px",
   "--studio-chat-header-right-inset": "0px",
 } as CSSProperties;
@@ -500,6 +501,7 @@ export function AppProvider({ children }: AppProviderProps) {
     <MotionConfig reducedMotion={REDUCED_MOTION_MAP[reduceMotion]}>
       <TooltipProvider>
         <AppearanceCustomizationEffect />
+        <DeepLinkHandler />
         <TauriWrapper>{children}</TauriWrapper>
         <Toaster
           position="top-right"

studio/frontend/src/app/routes/hub.tsx

@@ -13,6 +13,9 @@ const ModelsPage = lazyRouteComponent(
 export interface ModelsSearch {
   tab?: "discover" | "downloaded";
   model?: string;
+  file?: string;
+
+  intent?: number;
   section?: "trending" | "latest" | "finetune";
   kind?: "models" | "datasets";
 }
@@ -28,6 +31,18 @@ export const Route = createRoute({
     if (raw === "discover" || raw === "downloaded") next.tab = raw;
     const model = search.model;
     if (typeof model === "string" && model.length > 0) next.model = model;
+    const file = search.file;
+    if (next.model && typeof file === "string" && file.length > 0)
+      next.file = file;
+
+    const intent = search.intent;
+    if (
+      next.file &&
+      typeof intent === "number" &&
+      Number.isSafeInteger(intent)
+    ) {
+      next.intent = intent;
+    }
     const section = search.section;
     if (
       section === "trending" ||

studio/frontend/src/components/app-sidebar.tsx

@@ -1183,7 +1183,11 @@ export function AppSidebar() {
     <Sidebar
       collapsible="icon"
       variant="sidebar"
-      className="font-heading group-data-[collapsible=icon]:[&_[data-sidebar=sidebar]]:bg-white dark:group-data-[collapsible=icon]:[&_[data-sidebar=sidebar]]:bg-background"
+      className={cn(
+        "font-heading group-data-[collapsible=icon]:[&_[data-sidebar=sidebar]]:bg-white dark:group-data-[collapsible=icon]:[&_[data-sidebar=sidebar]]:bg-background",
+        usesNativeMacTitlebar &&
+          "group-data-[collapsible=icon]:[&_[data-sidebar=sidebar]]:border-r-0",
+      )}
     >
       <SidebarHeader
         className={cn(
@@ -1205,6 +1209,7 @@ export function AppSidebar() {
               />
             )}
             <div
+              data-tauri-drag-region={usesNativeMacTitlebar || undefined}
               className={cn(
                 "relative z-10 flex items-center gap-[8.5px] group-data-[collapsible=icon]:hidden",
                 showCompactMacBrand &&

studio/frontend/src/components/assistant-ui/code-plugin.ts

@@ -47,20 +47,151 @@ const normalizeLanguage = (language: string): BundledLanguage => {
   return (override ?? (key as BundledLanguage));
 };
 
+// A streaming fence re-enters highlight() every frame with the whole block, so
+// Shiki re-tokenizes it in full ~60x/sec. Past MIN_INCREMENTAL_CHARS, reuse the
+// cached tokens with an unstyled tail, re-tokenizing at most every REFRESH_MS.
+const MIN_INCREMENTAL_CHARS = 2000;
+const REFRESH_MS = 250;
+// Wall-clock Date.now() can step backwards (NTP, sleep resume) and make
+// `elapsed` negative; the throttle only needs elapsed time, so stay monotonic.
+const monotonicNow = (): number =>
+  typeof performance !== "undefined" && typeof performance.now === "function"
+    ? performance.now()
+    : Date.now();
+
+// One slot per fence: a message can hold several large fences, and Streamdown
+// revisits all of them on every render.
+const MAX_SLOTS_PER_KEY = 8;
+
+type TokenLine = HighlightResult["tokens"][number];
+type Dispatch = {
+  opts: HighlightOptions;
+  language: BundledLanguage;
+  callback?: (result: HighlightResult) => void;
+};
+type Slot = {
+  /** Code that produced `result`. Only ever set together with it. */
+  code: string;
+  result: HighlightResult | null;
+  /** Code of the dispatch awaiting a callback. */
+  inFlight: string | null;
+  lastDispatchAt: number;
+  trailing: ReturnType<typeof setTimeout> | null;
+  pending: Dispatch | null;
+};
+
+// No colour fields, so it renders in the default foreground instead of
+// inheriting a neighbouring token's colour.
+const plainLine = (text: string): TokenLine =>
+  [{ content: text, offset: 0 }] as unknown as TokenLine;
+
 export function createCodePlugin(
   options: CodePluginOptions = {},
 ): CodeHighlighterPlugin {
   const inner = createShikiCodePlugin(options);
+  const slotsByKey = new Map<string, Slot[]>();
+
+  const clearTrailing = (slot: Slot) => {
+    if (slot.trailing !== null) clearTimeout(slot.trailing);
+    slot.trailing = null;
+    slot.pending = null;
+  };
+
+  const adopt = (slot: Slot, code: string, result: HighlightResult) => {
+    // Write code and result together so a reuse cannot slice one against the other.
+    slot.code = code;
+    slot.result = result;
+    slot.inFlight = null;
+  };
+
+  const dispatch = (slot: Slot, d: Dispatch) => {
+    slot.inFlight = d.opts.code;
+    slot.lastDispatchAt = monotonicNow();
+    const immediate = inner.highlight({ ...d.opts, language: d.language }, (result) => {
+      if (slot.inFlight === d.opts.code) {
+        adopt(slot, d.opts.code, result);
+      }
+      d.callback?.(result);
+    });
+    // @streamdown/code answers out of its own cache synchronously and never
+    // invokes the callback, so adopt here too or the slot keeps older tokens.
+    if (immediate) {
+      adopt(slot, d.opts.code, immediate);
+    }
+    return immediate;
+  };
+
   return {
     ...inner,
-    supportsLanguage: (language) => inner.supportsLanguage(normalizeLanguage(language)),
+    supportsLanguage: (language) =>
+      inner.supportsLanguage(normalizeLanguage(language)),
     highlight: (
       opts: HighlightOptions,
       callback?: (result: HighlightResult) => void,
-    ) =>
-      inner.highlight(
-        { ...opts, language: normalizeLanguage(opts.language) },
-        callback,
-      ),
+    ) => {
+      const language = normalizeLanguage(opts.language);
+      if (opts.code.length < MIN_INCREMENTAL_CHARS) {
+        return inner.highlight({ ...opts, language }, callback);
+      }
+
+      const key = `${language} ${JSON.stringify(opts.themes)}`;
+      let slots = slotsByKey.get(key);
+      if (!slots) {
+        slots = [];
+        slotsByKey.set(key, slots);
+      }
+
+      // Longest-prefix match, so sibling fences do not evict each other.
+      let slot: Slot | null = null;
+      let bestLength = -1;
+      for (const candidate of slots) {
+        const anchor = candidate.code || candidate.inFlight || "";
+        if (!anchor || !opts.code.startsWith(anchor)) continue;
+        if (anchor.length > bestLength) {
+          slot = candidate;
+          bestLength = anchor.length;
+        }
+      }
+      if (!slot) {
+        slot = { code: "", result: null, inFlight: null, lastDispatchAt: 0, trailing: null, pending: null };
+        slots.unshift(slot);
+        for (const dropped of slots.splice(MAX_SLOTS_PER_KEY)) clearTrailing(dropped);
+      }
+
+      // Finished fence re-rendered unchanged: serve it, never re-tokenize.
+      if (slot.result && slot.code === opts.code) return slot.result;
+
+      const elapsed = monotonicNow() - slot.lastDispatchAt;
+      const grew = slot.result !== null && opts.code.length > slot.code.length;
+      if (!grew || elapsed >= REFRESH_MS) {
+        clearTrailing(slot);
+        return dispatch(slot, { opts, language, callback });
+      }
+
+      // Close out a reused run, so a final render is never left unstyled.
+      slot.pending = { opts, language, callback };
+      if (slot.trailing === null) {
+        const target = slot;
+        target.trailing = setTimeout(() => {
+          target.trailing = null;
+          const next = target.pending;
+          target.pending = null;
+          if (!next) return;
+          const immediate = dispatch(target, next);
+          // Nothing consumes this return value, so hand a synchronous cache
+          // hit to the callback or the fence keeps its unstyled tail.
+          if (immediate) next.callback?.(immediate);
+        }, Math.max(0, REFRESH_MS - elapsed));
+      }
+
+      const previous = slot.result as HighlightResult;
+      // Drop the cached final line: it may have been cut mid-token.
+      const keptLines = previous.tokens.slice(
+        0,
+        Math.max(0, slot.code.split("\n").length - 1),
+      );
+      const tail = opts.code.split("\n").slice(keptLines.length);
+      return { ...previous, tokens: [...keptLines, ...tail.map(plainLine)] };
+    },
   };
 }

studio/frontend/src/components/assistant-ui/thread.tsx

@@ -91,6 +91,7 @@ import {
   useResearchRunStore,
 } from "@/features/chat/stores/research-run-store";
 import { parseExternalModelId } from "@/features/chat/external-providers";
+import { toolStatusKind } from "@/features/chat/utils/tool-status";
 import { McpComposerButton } from "@/features/chat/mcp-composer-button";
 import { getExternalReasoningCapabilities } from "@/features/chat/provider-capabilities";
 import { useRagToolDisabled } from "@/features/chat/hooks/use-rag-tool-disabled";
@@ -2847,15 +2848,28 @@ const ToolStatusDisplay: FC = () => {
   }
   // From the store's start time, so returning to the conversation resumes rather than restarting.
   const elapsed = Math.max(0, Math.floor((now - startedAt) / 1000));
-  const isRunning = toolStatus.startsWith("Running");
-  const StatusIcon = isRunning ? TerminalIcon : GlobeIcon;
+  const kind = toolStatusKind(toolStatus);
+  const isNudging = kind === "nudge";
+  const StatusIcon = kind === "terminal" ? TerminalIcon : GlobeIcon;
   return (
     <div
       data-testid="composer-tool-status"
       className="mb-2 flex w-full flex-row items-center gap-2 px-1.5 pt-0.5 pb-1"
     >
-      <div className="flex animate-pulse items-center gap-2 rounded-full border border-primary/20 bg-primary/5 px-3 py-1.5 text-xs text-primary">
-        <StatusIcon className="size-3.5" />
+      <div
+        className={cn(
+          "flex items-center gap-2 rounded-full border border-primary/20 bg-primary/5 px-3 py-1.5 text-xs text-primary",
+          // The spinner is its own motion cue; pulsing too just fades it mid-spin.
+          !isNudging && "animate-pulse",
+        )}
+      >
+        {isNudging ? (
+          // label, not the default "Loading": the spinner is the badge's only
+          // role="status" region, so its name is what gets announced.
+          <Spinner className="size-3.5" label={toolStatus} />
+        ) : (
+          <StatusIcon className="size-3.5" />
+        )}
         <span>{toolStatus}</span>
         <span className="tabular-nums opacity-60">{elapsed}s</span>
       </div>

studio/frontend/src/features/chat/api/chat-adapter.ts

@@ -1604,6 +1604,7 @@ async function autoLoadSmallestModel(): Promise<{
           ? {
               gpu_ids: effectiveGpuIds ?? undefined,
               gpu_memory_mode: effectiveGpuMemoryMode,
+              n_parallel: config.nParallel ?? null,
             }
           : {}),
       }))
@@ -1637,6 +1638,8 @@ async function autoLoadSmallestModel(): Promise<{
             gpu_layers: effectiveGpuLayers,
             n_cpu_moe: effectiveNCpuMoe,
             gpu_ids: effectiveGpuIds ?? undefined,
+            // Per-model too, or the auto-load reverts a remembered override.
+            n_parallel: config.nParallel ?? null,
           }
         : {}),
     });
@@ -1689,6 +1692,11 @@ async function autoLoadSmallestModel(): Promise<{
         effectiveGpuLayers,
         config.customContextLength ?? null,
       );
+      // Slots this auto-load committed. Diffusion ignores --parallel, so a count
+      // there would mint a phantom override a saved preset carries onto a GGUF.
+      const committedSlots = (loadResp.is_diffusion ?? false)
+        ? null
+        : (config.nParallel ?? null);
       useChatRuntimeStore.setState({
         ggufContextLength: loadResp.context_length ?? 131072,
         ggufMaxContextLength:
@@ -1703,6 +1711,9 @@ async function autoLoadSmallestModel(): Promise<{
         ...resolveToolsEnabledOnLoad(loadResp.supports_tools ?? false),
         kvCacheDtype: loadResp.cache_type_kv ?? null,
         loadedKvCacheDtype: loadResp.cache_type_kv ?? null,
+        // Click-time value, not the resolved backend echo (see performLoad).
+        nParallel: committedSlots,
+        loadedNParallel: committedSlots,
         tensorParallel: loadResp.tensor_parallel ?? false,
         loadedTensorParallel: loadResp.tensor_parallel ?? false,
         ...loadedGpuMemoryFields(loadResp),
@@ -1728,6 +1739,10 @@ async function autoLoadSmallestModel(): Promise<{
         ...resolveToolsEnabledOnLoad(loadResp.supports_tools ?? false),
         kvCacheDtype: loadResp.cache_type_kv ?? null,
         loadedKvCacheDtype: loadResp.cache_type_kv ?? null,
+        // GGUF-only and never sent here: a staged override would be saved for
+        // a model that cannot use it.
+        nParallel: null,
+        loadedNParallel: null,
         tensorParallel: loadResp.tensor_parallel ?? false,
         loadedTensorParallel: loadResp.tensor_parallel ?? false,
         // Non-GGUF response: clears any stale GPU baseline a prior manual-GPU
@@ -2001,6 +2016,10 @@ async function autoLoadSmallestModel(): Promise<{
         ...resolveToolsEnabledOnLoad(loadResp.supports_tools ?? false),
         kvCacheDtype: loadResp.cache_type_kv ?? null,
         loadedKvCacheDtype: loadResp.cache_type_kv ?? null,
+        // The request above omits n_parallel: a staged override left from a
+        // preset would read as applied and be re-sent by the next Apply.
+        nParallel: null,
+        loadedNParallel: null,
         tensorParallel: loadResp.tensor_parallel ?? false,
         loadedTensorParallel: loadResp.tensor_parallel ?? false,
         ...loadedGpuMemoryFields(loadResp),

studio/frontend/src/features/chat/api/chat-api.ts

@@ -199,6 +199,8 @@ export async function validateModel(
       // --fit, while a pinned layer count is owned by the user. Tell validate
       // so it applies the same training-guard policy as /load.
       gpu_memory_mode: payload.gpu_memory_mode,
+      // Slots scale the KV estimate; keep validate sized like the load.
+      n_parallel: payload.n_parallel,
     }),
   });
   return parseJsonOrThrow<ValidateModelResponse>(response);

studio/frontend/src/features/chat/artifacts/artifact-surface.tsx

@@ -30,7 +30,7 @@ import { Streamdown } from "streamdown";
 import { ArtifactHtmlFrame, type ArtifactViewMode } from "./html-frame";
 import { useChatArtifactsStore } from "./store";
 import type { ChatArtifact } from "./types";
-import { getArtifactFilename } from "./types";
+import { buildArtifactSourceKey, getArtifactFilename } from "./types";
 
 const COPY_RESET_MS = 2000;
 const artifactSourceCodePlugin = createCodePlugin({
@@ -338,6 +338,8 @@ export function ArtifactSurface({
         ) : (
           <div className="h-full overflow-auto text-xs leading-relaxed [&_[data-streamdown=code-block]]:!my-0 [&_[data-streamdown=code-block]]:!gap-0 [&_[data-streamdown=code-block]]:!rounded-none [&_[data-streamdown=code-block]]:!border-0 [&_[data-streamdown=code-block]]:!bg-transparent [&_[data-streamdown=code-block]]:!p-0 [&_[data-streamdown=code-block-body]]:!border-0 [&_[data-streamdown=code-block-body]]:!bg-transparent [&_[data-streamdown=code-block-body]]:!p-0 [&_pre]:!m-0 [&_pre]:!bg-transparent [&_pre]:!p-0 [&_pre]:text-xs [&_pre]:leading-relaxed [&_code]:text-xs">
             <Streamdown
+              // Only computed when the source view is actually on screen.
+              key={buildArtifactSourceKey(artifact)}
               mode="streaming"
               plugins={{ code: artifactSourceCodePlugin }}
               controls={{ code: false }}

studio/frontend/src/features/chat/artifacts/types.ts

@@ -41,6 +41,15 @@ export function hashArtifactCode(code: string): string {
   return (hash >>> 0).toString(36);
 }
 
+// The canvas source view keys its Streamdown on this. Streamdown memoizes a code
+// fence on its node's line/column span, ignoring the text, so equal-line-count
+// canvases keep the old source. Tool artifact IDs omit the code, so hash it in.
+export function buildArtifactSourceKey(
+  artifact: Pick<ChatArtifact, "id" | "code">,
+): string {
+  return `${artifact.id}:${hashArtifactCode(artifact.code)}`;
+}
+
 export function createArtifactId(input: ChatArtifactInput): string {
   const threadSegment = input.threadId || "no-thread";
   const messageSegment = input.sourceMessageId || "transient";

studio/frontend/src/features/chat/chat-page.tsx

@@ -781,6 +781,7 @@ 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
@@ -3192,7 +3193,7 @@ export function ChatPage({
     // Provides `active` to ChatRuntimeProvider (drops the message views/composers
     // while off-route, keeping the runtime alive) and to the compare chrome.
     <ChatActiveContext.Provider value={active}>
-    <div className="flex min-h-0 min-w-0 flex-1 basis-0 bg-background overflow-hidden">
+    <div className="flex min-h-0 min-w-0 flex-1 basis-0 overflow-hidden bg-background">
       {/* Portaled surfaces render to document.body, escaping the parent's hidden
           wrapper, so gate them on `active` to keep them off other tabs. */}
       {active && <GuidedTour {...tour.tourProps} />}

studio/frontend/src/features/chat/chat-settings-sheet.tsx

@@ -397,6 +397,7 @@ export function ChatSettingsPanel({
   const nCpuMoe = useChatRuntimeStore((s) => s.nCpuMoe);
   const tensorParallel = useChatRuntimeStore((s) => s.tensorParallel);
   const specDraftNMax = useChatRuntimeStore((s) => s.specDraftNMax);
+  const nParallel = useChatRuntimeStore((s) => s.nParallel);
   const speculativeType = useChatRuntimeStore((s) => s.speculativeType);
   const specFallbackReason = useChatRuntimeStore((s) => s.specFallbackReason);
   const mtpUpdatable =
@@ -504,6 +505,7 @@ export function ChatSettingsPanel({
     tensorParallel,
     speculativeType,
     specDraftNMax,
+    nParallel,
     params.maxSeqLength,
   ]);
   const activePresetLoadSummary = useMemo(
@@ -522,6 +524,7 @@ export function ChatSettingsPanel({
       tensorParallel,
       speculativeType,
       specDraftNMax,
+      nParallel,
       params.maxSeqLength,
     ],
   );

studio/frontend/src/features/chat/hooks/use-chat-model-runtime.ts

@@ -567,6 +567,8 @@ export function useChatModelRuntime() {
           applyActiveModelStatusToStore(residentStatus, {
             previousCheckpoint: selectedCheckpoint,
             previousGgufVariant,
+            // Id and variant matched above: same model, only the tab moved.
+            readoptingSameModel: true,
           });
           syncModelCapabilities(modelId, residentStatus);
           return;
@@ -669,6 +671,14 @@ export function useChatModelRuntime() {
           let previousWasUnloaded = false;
           const pendingLoadConfig =
             typeof selection !== "string" ? selection.config : undefined;
+          // The outgoing model's slot INTENT (blank = follow the server
+          // default), which the resolved baseline cannot express. previousConfig
+          // is the snapshot the picker took before pre-applying the target's
+          // config, so the live control is only the outgoing one without it.
+          const previousNParallel =
+            typeof selection !== "string" && selection.previousConfig
+              ? (selection.previousConfig.nParallel ?? null)
+              : useChatRuntimeStore.getState().nParallel;
           if (pendingLoadConfig) {
             applyPerModelConfigToRuntime(pendingLoadConfig);
           }
@@ -761,6 +771,8 @@ export function useChatModelRuntime() {
               : stateBeforeUnload.speculativeType;
           let loadSpecDraftNMax =
             pendingLoadConfig?.specDraftNMax ?? stateBeforeUnload.specDraftNMax;
+          let loadNParallel =
+            pendingLoadConfig?.nParallel ?? stateBeforeUnload.nParallel;
           try {
             // Lightweight pre-flight validation: avoid unloading a working model
             // if the new identifier is clearly invalid (e.g. bad HF id / path).
@@ -792,6 +804,10 @@ export function useChatModelRuntime() {
             const validateGpuLayers = resetsPerModelSettings
               ? GPU_LAYERS_AUTO
               : loadGpuLayers;
+            // Per-model: the reset re-baselines to the staged config, like the load.
+            const validateNParallel = resetsPerModelSettings
+              ? (pendingLoadConfig?.nParallel ?? null)
+              : loadNParallel;
             const validateMaxSeqLength = resolveFitMaxSeqLength(
               isGguf,
               loadGpuMemoryMode,
@@ -820,7 +836,12 @@ export function useChatModelRuntime() {
               cache_type_kv: loadKvCacheDtype,
               tensor_parallel: loadTensorParallel,
               gpu_ids: validateGpuIds ?? undefined,
-              ...(isGguf ? { gpu_memory_mode: loadGpuMemoryMode } : {}),
+              ...(isGguf
+                ? {
+                    gpu_memory_mode: loadGpuMemoryMode,
+                    n_parallel: validateNParallel,
+                  }
+                : {}),
             });
             // Upgrade consent runs before the security dialogs; Accept installs and the load continues.
             if (validation.requires_transformers_upgrade) {
@@ -903,6 +924,10 @@ export function useChatModelRuntime() {
                 loadedSpeculativeType: persistedSpeculativeType,
                 specDraftNMax: null,
                 loadedSpecDraftNMax: null,
+                // Per-model too: a different model follows the server default
+                // unless its staged config overrides it.
+                nParallel: null,
+                loadedNParallel: null,
                 // Per-model GPU knobs must not follow onto a different model
                 // (gpuMemoryMode is a standing preference and is kept).
                 selectedGpuIds: null,
@@ -918,6 +943,7 @@ export function useChatModelRuntime() {
                   ? normalizeSpeculativeType(pendingLoadConfig.speculativeType)
                   : persistedSpeculativeType;
               loadSpecDraftNMax = pendingLoadConfig?.specDraftNMax ?? null;
+              loadNParallel = pendingLoadConfig?.nParallel ?? null;
               // Keep the click-time snapshot in lock-step with the store reset so
               // the load below sizes against the cleared per-model knobs, not the
               // previous model's (gpuMemoryMode is standing, so left as captured).
@@ -984,6 +1010,8 @@ export function useChatModelRuntime() {
               cache_type_kv: loadKvCacheDtype,
               speculative_type: loadSpeculativeType,
               spec_draft_n_max: loadSpecDraftNMax,
+              // GGUF-only: slots mean nothing for a transformers load.
+              n_parallel: isGguf ? loadNParallel : null,
               tensor_parallel: loadTensorParallel,
               gpu_memory_mode: loadGpuMemoryMode,
               gpu_layers: loadGpuLayers,
@@ -1034,6 +1062,14 @@ export function useChatModelRuntime() {
             const loadedSpec = normalizeSpeculativeType(
               loadResponse.speculative_type,
             );
+            // Slots the load actually committed. Non-GGUF never sends them and
+            // diffusion ignores --parallel, so a click-time count on either
+            // would mint a phantom override a saved preset carries onto a GGUF.
+            const committedSlots =
+              (loadResponse.is_gguf ?? false) &&
+              !(loadResponse.is_diffusion ?? false)
+                ? (loadNParallel ?? null)
+                : null;
             const nativeCtx = loadResponse.is_gguf
               ? (loadResponse.context_length ?? 131072)
               : null;
@@ -1109,6 +1145,10 @@ export function useChatModelRuntime() {
               loadedSpeculativeType: loadedSpec,
               specDraftNMax: loadResponse.spec_draft_n_max ?? null,
               loadedSpecDraftNMax: loadResponse.spec_draft_n_max ?? null,
+              // Keep the click-time value: the echo is the resolved count, and
+              // adopting it would pin a blank "server default" control.
+              nParallel: committedSlots,
+              loadedNParallel: committedSlots,
               customContextLength: keepCustomCtx,
               loadedCustomContextLength: keepCustomCtx,
               defaultChatTemplate: loadResponse.chat_template ?? null,
@@ -1211,6 +1251,7 @@ export function useChatModelRuntime() {
                     stateBeforeUnload.loadedSpeculativeType,
                   spec_draft_n_max:
                     stateBeforeUnload.loadedSpecDraftNMax,
+                  n_parallel: stateBeforeUnload.loadedNParallel,
                   // Restore the previous model in the split mode it was running,
                   // not the default layer split.
                   tensor_parallel: stateBeforeUnload.loadedTensorParallel ?? false,
@@ -1237,6 +1278,9 @@ export function useChatModelRuntime() {
                   // model's; the loaded baselines below come from its reload echo.
                   speculativeType: stateBeforeUnload.loadedSpeculativeType ?? null,
                   specDraftNMax: stateBeforeUnload.loadedSpecDraftNMax ?? null,
+                  // Control keeps its intent; only the baseline takes the echo.
+                  nParallel: previousNParallel,
+                  loadedNParallel: stateBeforeUnload.loadedNParallel ?? null,
                   loadedSpeculativeType: rollbackSpeculativeType,
                   loadedSpecDraftNMax:
                     rollbackResponse.spec_draft_n_max ?? null,

studio/frontend/src/features/chat/lib/apply-inference-status-to-store.ts

@@ -1,6 +1,9 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
 
+// Barrel import (lint rule); the model-picker cycle is fine because the call
+// happens at runtime, not module eval.
+import { resolveInitialConfig } from "@/features/model-picker";
 import { getInferenceStatus } from "../api/chat-api";
 import {
   mergeBackendRecommendedInference,
@@ -131,6 +134,9 @@ export type ApplyInferenceStatusOptions = {
    * status -- without it a variant-only switch underneath the tab reads as
    * steady state and the hydration reseed keeps the old quant's baselines. */
   previousGgufVariant?: string | null;
+  /** The caller verified the status is the model this tab just picked, so the
+   * slot control it holds belongs to that model and must survive. */
+  readoptingSameModel?: boolean;
 };
 
 /** Mirror refresh() hydration so adopted CLI models get reasoning/tools flags. */
@@ -201,6 +207,22 @@ export function applyActiveModelStatusToStore(
   // While a load is in flight, performLoad owns the load params. Seeding them
   // from a stale poll here would clobber the values the load dialog just set.
   const seedLoadParams = !prevState.modelLoading;
+  // A model/variant change underneath this tab, as opposed to re-adopting the
+  // model the tab just picked, where hydratingExistingModel fires on the stale
+  // checkpoint. The echo cannot stand in: a new model can report the old count.
+  const slotsModelChanged =
+    hydratingExistingModel && !options.readoptingSameModel;
+  // This model's remembered override, read only on a fresh store or a model
+  // change, so a steady poll cannot re-pin a control the user just blanked.
+  const slotsUnseeded =
+    prevState.loadedNParallel === null && prevState.nParallel === null;
+  const remembered =
+    status.is_gguf && (slotsUnseeded || slotsModelChanged)
+      ? resolveInitialConfig(checkpointId, status.gguf_variant ?? null)
+      : null;
+  const rememberedNParallel = remembered?.remembered
+    ? (remembered.config.nParallel ?? null)
+    : null;
   // A Manual + Auto-layers load sent its positive context pin as max_seq_length,
   // and status only exposes the RESOLVED context; re-seed the pin from the
   // requested value (parity with the load paths' keepCustomCtx). Baselines
@@ -322,6 +344,35 @@ export function applyActiveModelStatusToStore(
         tensorParallel: status.tensor_parallel,
         loadedTensorParallel: status.tensor_parallel,
       }),
+    // Baseline only, never the control: the echo is the RESOLVED count and would
+    // pin a blank "server default" control. The rollback re-sends the baseline,
+    // so without this a rollback after a tab reload loses the override.
+    ...(seedLoadParams &&
+      status.requested_parallel_slots != null &&
+      (prevState.loadedNParallel === null || hydratingExistingModel) && {
+        loadedNParallel: status.requested_parallel_slots,
+      }),
+    // A slotless model must not keep the previous GGUF's baseline: the rollback
+    // re-sends it. /status omits the echo for non-GGUF and sends an explicit
+    // null for diffusion, so an absent field on a GGUF is an older backend.
+    ...(seedLoadParams &&
+      (status.is_gguf === false || status.requested_parallel_slots === null) && {
+        loadedNParallel: null,
+      }),
+    // Per-model: a change underneath this tab blanks the control like
+    // performLoad's cross-model reset, or the old count follows onto the new
+    // model. The baseline above still carries the rollback.
+    ...(seedLoadParams && slotsModelChanged && { nParallel: null }),
+    // AFTER that clear, which both a first hydration and a model change trip:
+    // either would leave the control blank while the model runs on a remembered
+    // override, so the next Apply would save the blank over it. Adopted only
+    // when the running count matches, proving it is this model's own.
+    ...(seedLoadParams &&
+      (slotsUnseeded || slotsModelChanged) &&
+      rememberedNParallel != null &&
+      rememberedNParallel === status.requested_parallel_slots && {
+        nParallel: rememberedNParallel,
+      }),
     // Re-seed on first hydration, model/variant changes, or a same-model backend
     // placement change. gpuStatusFields preserves dirty local edits in the last
     // case while advancing their loaded baselines.

studio/frontend/src/features/chat/presets/preset-load-config.ts

@@ -12,6 +12,8 @@ import {
   DEFAULT_MAX_SEQ_LENGTH,
   KV_CACHE_DTYPES,
   MTP_SPECULATIVE_TYPES,
+  N_PARALLEL_MAX,
+  N_PARALLEL_MIN,
   SPECULATIVE_TYPES,
   normalizeMaxSeqLength,
   type PerModelConfig,
@@ -30,6 +32,7 @@ export type PresetLoadConfig = Pick<
   | "kvCacheDtype"
   | "speculativeType"
   | "specDraftNMax"
+  | "nParallel"
   | "tensorParallel"
   | "gpuMemoryMode"
   | "gpuLayers"
@@ -45,6 +48,7 @@ export const EMPTY_PRESET_LOAD_CONFIG: PresetLoadConfig = {
   kvCacheDtype: null,
   speculativeType: null,
   specDraftNMax: null,
+  nParallel: null,
   tensorParallel: false,
 };
 
@@ -107,6 +111,14 @@ export function normalizePresetLoadConfig(
         ? speculativeType
         : null,
     specDraftNMax,
+    nParallel:
+      typeof partial.nParallel === "number" &&
+      Number.isFinite(partial.nParallel)
+        ? Math.max(
+            N_PARALLEL_MIN,
+            Math.min(N_PARALLEL_MAX, Math.round(partial.nParallel)),
+          )
+        : null,
     tensorParallel:
       typeof partial.tensorParallel === "boolean"
         ? partial.tensorParallel
@@ -151,6 +163,7 @@ export function capturePresetLoadConfig(): PresetLoadConfig | undefined {
     kvCacheDtype: snapshot.kvCacheDtype ?? null,
     speculativeType: normalizeSpeculativeType(snapshot.speculativeType),
     specDraftNMax: snapshot.specDraftNMax ?? null,
+    nParallel: snapshot.nParallel ?? null,
     tensorParallel: snapshot.tensorParallel ?? false,
     ...(snapshot.gpuMemoryMode === "manual"
       ? { gpuMemoryMode: "manual" as const }
@@ -206,6 +219,7 @@ export function applyPresetLoadConfig(
     kvCacheDtype: config.kvCacheDtype ?? null,
     speculativeType: config.speculativeType ?? null,
     specDraftNMax: config.specDraftNMax ?? null,
+    nParallel: config.nParallel ?? null,
     tensorParallel: config.tensorParallel ?? false,
     chatTemplateOverride: null,
     gpuMemoryMode: config.gpuMemoryMode,
@@ -231,6 +245,9 @@ export function formatPresetLoadConfigSummary(
   if (config.speculativeType && config.speculativeType !== "auto") {
     parts.push(`Spec ${config.speculativeType}`);
   }
+  if (config.nParallel != null) {
+    parts.push(`${config.nParallel} slots`);
+  }
   if (config.gpuMemoryMode === "manual") {
     parts.push("GPU manual");
   }

studio/frontend/src/features/chat/shared-composer.tsx

@@ -1130,6 +1130,8 @@ export function SharedComposer({
             ? {
                 gpu_ids: effectiveSelectedGpuIds ?? undefined,
                 gpu_memory_mode: effectiveGpuMemoryMode,
+                // Slots scale the KV estimate; keep validate sized like the load.
+                n_parallel: ownConfig.nParallel ?? null,
               }
             : {}),
         });
@@ -1198,6 +1200,7 @@ export function SharedComposer({
                 n_cpu_moe: effectiveNCpuMoe,
                 tensor_split: compareLoadKnobs.splitRatio ?? undefined,
                 gpu_ids: effectiveSelectedGpuIds ?? undefined,
+                n_parallel: ownConfig.nParallel ?? null,
               }
             : {}),
         });
@@ -1229,6 +1232,12 @@ export function SharedComposer({
               effectiveCustomContextLength,
             )
           : null;
+        // Slots this compare load committed. Diffusion ignores --parallel, so a
+        // count there would mint a phantom override a preset carries onto a GGUF.
+        const committedSlots =
+          targetIsGguf && !(resp.is_diffusion ?? false)
+            ? (ownConfig.nParallel ?? null)
+            : null;
         useChatRuntimeStore.setState({
           supportsReasoning: resp.supports_reasoning ?? false,
           reasoningAlwaysOn: resp.reasoning_always_on ?? false,
@@ -1237,6 +1246,9 @@ export function SharedComposer({
           supportsTools: resp.supports_tools ?? false,
           kvCacheDtype: resp.cache_type_kv ?? null,
           loadedKvCacheDtype: resp.cache_type_kv ?? null,
+          // Click-time value, not the resolved echo (see the single-model load).
+          nParallel: committedSlots,
+          loadedNParallel: committedSlots,
           tensorParallel: resp.tensor_parallel ?? false,
           loadedTensorParallel: resp.tensor_parallel ?? false,
           defaultChatTemplate: resp.chat_template ?? null,

studio/frontend/src/features/chat/stores/chat-runtime-store.ts

@@ -968,6 +968,12 @@ type ChatRuntimeStore = {
   /** User --spec-draft-n-max override (null = platform default). */
   specDraftNMax: number | null;
   loadedSpecDraftNMax: number | null;
+  /** User --parallel slots override for GGUF loads (null = server default).
+   *  Never re-seeded from an echo: the resolved count would pin a blank control. */
+  nParallel: number | null;
+  /** Slots the last successful load sent (null = default); the rollback
+   *  re-sends it so a failed switch can't lose the override. */
+  loadedNParallel: number | null;
   /** Tensor-parallel split (--split-mode tensor) toggle, GGUF multi-GPU only. */
   tensorParallel: boolean;
   /** Backend-reported tensor-parallel state; null until first hydrated. */
@@ -1491,6 +1497,8 @@ export const useChatRuntimeStore = create<ChatRuntimeStore>((set, get) => ({
   specFallbackReason: null,
   specDraftNMax: null,
   loadedSpecDraftNMax: null,
+  nParallel: null,
+  loadedNParallel: null,
   tensorParallel: false,
   loadedTensorParallel: null,
   gpuMemoryMode: readPersistedGpuMemoryMode(),
@@ -1874,6 +1882,8 @@ export const useChatRuntimeStore = create<ChatRuntimeStore>((set, get) => ({
       specFallbackReason: null,
       specDraftNMax: null,
       loadedSpecDraftNMax: null,
+      nParallel: null,
+      loadedNParallel: null,
       tensorParallel: false,
       loadedTensorParallel: null,
       // Standing preference: survives unload, unlike the per-model knobs above.

studio/frontend/src/features/chat/types/api.ts

@@ -65,6 +65,11 @@ export interface LoadModelRequest {
    * when speculative_type resolves to "mtp" or "mtp+ngram".
    */
   spec_draft_n_max?: number | null;
+  /**
+   * Parallel decode slots for llama-server (--parallel), 1..64. Omit/null =
+   * the launch default. The VRAM fitter may launch fewer to stay on GPU.
+   */
+  n_parallel?: number | null;
   /**
    * Split the model across GPUs by tensor (--split-mode tensor) instead
    * of by layer for GGUF models. Multi-GPU only; no effect on a single GPU.
@@ -202,6 +207,12 @@ export interface LoadModelResponse {
   gpu_ids?: number[] | null;
   /** User-requested GPU placement pool before fit-time narrowing. */
   requested_gpu_ids?: number[] | null;
+  /** Slots the load was invoked with (else the --parallel default). Null for
+   * non-GGUF loads. */
+  requested_parallel_slots?: number | null;
+  /** Slots llama-server actually runs, after any fit-time reduction. Null for
+   * non-GGUF loads. */
+  parallel_slots?: number | null;
 }
 
 export interface UnloadModelRequest {
@@ -263,6 +274,12 @@ export interface InferenceStatusResponse {
   gpu_ids?: number[] | null;
   /** User-requested GPU placement pool before fit-time narrowing. */
   requested_gpu_ids?: number[] | null;
+  /** Slots the active load was invoked with (else the --parallel default).
+   * Null when no GGUF model is loaded. */
+  requested_parallel_slots?: number | null;
+  /** Slots llama-server actually runs, after any fit-time reduction. Null when
+   * no GGUF model is loaded. */
+  parallel_slots?: number | null;
   n_layers?: number | null;
   /** Model's MoE expert-layer count (the n_cpu_moe ceiling); 0 if not MoE. */
   n_moe_layers?: number;

studio/frontend/src/features/chat/utils/tool-status.ts

@@ -0,0 +1,15 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+/** Mirrors NUDGE_TOOL_CALLS_STATUS in backend core/inference/tool_call_parser.py; keep in sync. */
+export const NUDGE_TOOL_CALLS_STATUS = "Nudging tool calls";
+
+export type ToolStatusKind = "nudge" | "terminal" | "web";
+
+/** Which glyph the badge shows: exact match for the nudge, "Running" prefix for sandbox tools, globe otherwise. */
+export function toolStatusKind(status: string): ToolStatusKind {
+  if (status === NUDGE_TOOL_CALLS_STATUS) {
+    return "nudge";
+  }
+  return status.startsWith("Running") ? "terminal" : "web";
+}

studio/frontend/src/features/deep-links/deep-link-handler.tsx

@@ -0,0 +1,92 @@
+// 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 { isTauri } from "@/lib/api-base";
+import { useNavigate } from "@tanstack/react-router";
+import { useEffect } from "react";
+
+import { createDeepLinkIntentGate } from "./deep-link-intent";
+import { parseUnslothDeepLink } from "./parse-deep-link";
+
+const acceptIntent = createDeepLinkIntentGate(2_000);
+
+async function restoreMainWindow(): Promise<void> {
+  const { getCurrentWindow } = await import("@tauri-apps/api/window");
+  const window = getCurrentWindow();
+  await window.show();
+  await window.unminimize();
+  await window.setFocus();
+}
+
+export function DeepLinkHandler() {
+  const navigate = useNavigate();
+
+  useEffect(() => {
+    if (!isTauri) return;
+
+    let disposed = false;
+    let receivedLiveIntent = false;
+    let unlisten: (() => void) | undefined;
+
+    const handleUrls = (urls: string[]): boolean => {
+      if (disposed) return false;
+
+      let hasValidIntent = false;
+      let intent: ReturnType<typeof parseUnslothDeepLink> = null;
+
+      let intentSequence: number | null = null;
+      for (const rawUrl of urls) {
+        const parsed = parseUnslothDeepLink(rawUrl);
+        if (!parsed) continue;
+        hasValidIntent = true;
+        const sequence = acceptIntent(parsed.model, parsed.file);
+        if (sequence !== null) {
+          intent = parsed;
+          intentSequence = sequence;
+        }
+      }
+      if (!intent || intentSequence === null) return hasValidIntent;
+
+      void restoreMainWindow().catch(() => undefined);
+      void navigate({
+        to: "/hub",
+        search: {
+          tab: "discover",
+          kind: "models",
+          model: intent.model,
+          file: intent.file,
+
+          intent: intentSequence,
+        },
+      });
+      return true;
+    };
+
+    async function subscribe() {
+      const { getCurrent, onOpenUrl } =
+        await import("@tauri-apps/plugin-deep-link");
+      if (disposed) return;
+
+      const cleanup = await onOpenUrl((urls) => {
+        if (handleUrls(urls)) receivedLiveIntent = true;
+      });
+      if (disposed) {
+        cleanup();
+        return;
+      }
+      unlisten = cleanup;
+
+      const currentUrls = await getCurrent();
+      if (currentUrls && !receivedLiveIntent) handleUrls(currentUrls);
+    }
+
+    void subscribe().catch(() => undefined);
+
+    return () => {
+      disposed = true;
+      unlisten?.();
+    };
+  }, [navigate]);
+
+  return null;
+}

studio/frontend/src/features/deep-links/deep-link-intent.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
+
+export function createDeepLinkIntentGate(
+  deduplicationWindowMs: number,
+  now: () => number = Date.now,
+) {
+  let lastIntent: { key: string; handledAt: number } | null = null;
+  let sequence = 0;
+
+  return (model: string, file?: string): number | null => {
+    const handledAt = now();
+    const key = `${model}\0${file ?? ""}`;
+    if (
+      lastIntent?.key === key &&
+      handledAt - lastIntent.handledAt < deduplicationWindowMs
+    ) {
+      return null;
+    }
+    lastIntent = { key, handledAt };
+    sequence += 1;
+    return sequence;
+  };
+}

studio/frontend/src/features/deep-links/index.ts

@@ -0,0 +1,4 @@
+// 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 { DeepLinkHandler } from "./deep-link-handler";

studio/frontend/src/features/deep-links/parse-deep-link.ts

@@ -0,0 +1,101 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+const MAX_REPO_ID_SEGMENT_LENGTH = 96;
+const MAX_GGUF_FILE_LENGTH = 512;
+const REPO_SEGMENT = /^[A-Za-z0-9](?:[A-Za-z0-9._-]*[A-Za-z0-9])?$/;
+function hasControlCharacters(value: string): boolean {
+  return [...value].some((character) => {
+    const codePoint = character.codePointAt(0) ?? 0;
+    return codePoint <= 0x1f || codePoint === 0x7f;
+  });
+}
+
+export interface UnslothDeepLinkIntent {
+  model: string;
+  file?: string;
+}
+
+function isValidRepoSegment(segment: string): boolean {
+  return (
+    segment.length <= MAX_REPO_ID_SEGMENT_LENGTH &&
+    REPO_SEGMENT.test(segment) &&
+    !segment.includes("--") &&
+    !segment.includes("..")
+  );
+}
+
+function isValidGgufFile(file: string): boolean {
+  if (
+    file.length === 0 ||
+    file.length > MAX_GGUF_FILE_LENGTH ||
+    file !== file.trim() ||
+    hasControlCharacters(file) ||
+    file.includes("\\") ||
+    file.startsWith("/") ||
+    !file.toLowerCase().endsWith(".gguf")
+  ) {
+    return false;
+  }
+  return file
+    .split("/")
+    .every((segment) => segment !== "" && segment !== "." && segment !== "..");
+}
+
+export function parseUnslothDeepLink(
+  rawUrl: string,
+): UnslothDeepLinkIntent | null {
+  const queryIndex = rawUrl.indexOf("?");
+  const target = queryIndex === -1 ? rawUrl : rawUrl.slice(0, queryIndex);
+  if (
+    target !== "unsloth://open_from_hf" &&
+    target !== "unsloth://open_from_hf/"
+  ) {
+    return null;
+  }
+
+  let url: URL;
+  try {
+    url = new URL(rawUrl);
+  } catch {
+    return null;
+  }
+
+  if (
+    url.protocol !== "unsloth:" ||
+    url.hostname !== "open_from_hf" ||
+    (url.pathname !== "" && url.pathname !== "/") ||
+    url.username !== "" ||
+    url.password !== "" ||
+    url.port !== "" ||
+    url.hash !== ""
+  ) {
+    return null;
+  }
+
+  const keys = [...url.searchParams.keys()];
+  if (
+    keys.length < 1 ||
+    keys.length > 2 ||
+    !keys.includes("model") ||
+    new Set(keys).size !== keys.length ||
+    keys.some((key) => key !== "model" && key !== "file")
+  ) {
+    return null;
+  }
+
+  const model = url.searchParams.get("model") ?? "";
+  const segments = model.split("/");
+  if (
+    model.endsWith(".git") ||
+    segments.length !== 2 ||
+    !segments.every(isValidRepoSegment)
+  ) {
+    return null;
+  }
+
+  const file = url.searchParams.get("file");
+  if (file !== null && !isValidGgufFile(file)) return null;
+
+  return file === null ? { model } : { model, file };
+}

studio/frontend/src/features/hub/catalog/download-section.tsx

@@ -15,6 +15,9 @@ export function DownloadSection({
   canRun = true,
   isActive,
   activeQuant,
+  preferredGgufFile = null,
+
+  preferredGgufFileIntent = 0,
   isLoadingThisModel,
   gpuGb,
   systemRamGb,
@@ -35,6 +38,9 @@ export function DownloadSection({
   canRun?: boolean;
   isActive: boolean;
   activeQuant: string | null;
+  preferredGgufFile?: string | null;
+
+  preferredGgufFileIntent?: number;
   isLoadingThisModel: boolean;
   gpuGb?: number;
   systemRamGb?: number;
@@ -46,12 +52,15 @@ export function DownloadSection({
   onTrain?: () => void;
   onChange?: () => void;
 }) {
-  if (isGguf) {
+  if (isGguf || preferredGgufFile) {
     return (
       <GgufDownloadCard
         repoId={repoId}
         isActive={isActive}
         activeQuant={activeQuant}
+        preferredFile={preferredGgufFile}
+
+        preferredFileIntent={preferredGgufFileIntent}
         isLoadingThisModel={isLoadingThisModel}
         gpuGb={gpuGb}
         systemRamGb={systemRamGb}

studio/frontend/src/features/hub/catalog/gguf-download-card.tsx

@@ -57,6 +57,10 @@ import { useOnlineStatus } from "../hooks/use-online-status";
 import { type GgufVariantDetail, deleteCachedModel } from "../inventory";
 import { formatBytes } from "../lib/format";
 import { type GgufFitClass, classifyGgufFit } from "../lib/gguf-fit";
+import {
+  ggufFilenamesMatch,
+  ggufSelectionOverrideMatchesIntent,
+} from "../lib/gguf-filename";
 import {
   ggufVariantDisplayLabel,
   ggufVariantDownloadSizeBytes,
@@ -540,6 +544,9 @@ export function GgufDownloadCard({
   repoId,
   isActive,
   activeQuant,
+  preferredFile = null,
+
+  preferredFileIntent = 0,
   isLoadingThisModel,
   gpuGb,
   systemRamGb,
@@ -553,6 +560,9 @@ export function GgufDownloadCard({
   repoId: string;
   isActive: boolean;
   activeQuant: string | null;
+  preferredFile?: string | null;
+
+  preferredFileIntent?: number;
   isLoadingThisModel: boolean;
   gpuGb?: number;
   systemRamGb?: number;
@@ -579,9 +589,25 @@ export function GgufDownloadCard({
     repoId: string;
     quant: string | null;
     userPicked?: boolean;
+    preferredFile?: string | null;
+
+    preferredFileIntent?: number;
   }>(() => ({ repoId, quant: null }));
+  const preferredQuant = preferredFile
+    ? (variants?.find((variant) =>
+        ggufFilenamesMatch(variant.filename, preferredFile),
+      )?.quant ?? null)
+    : null;
   const selectedQuantOverride =
-    selectedQuantState.repoId === repoId ? selectedQuantState.quant : null;
+    selectedQuantState.repoId === repoId &&
+    ggufSelectionOverrideMatchesIntent(
+      preferredFile,
+      preferredFileIntent,
+      selectedQuantState.preferredFile,
+      selectedQuantState.preferredFileIntent,
+    )
+      ? selectedQuantState.quant
+      : preferredQuant;
   const [open, setOpen] = useState(false);
   const [deleteTarget, setDeleteTarget] = useState<string | null>(null);
   const [updateTarget, setUpdateTarget] = useState<string | null>(null);
@@ -732,10 +758,13 @@ export function GgufDownloadCard({
         repoId,
         quant,
         userPicked: true,
+        preferredFile,
+
+        preferredFileIntent,
       });
       setOpen(false);
     },
-    [repoId],
+    [preferredFile, preferredFileIntent, repoId],
   );
   const handleDeleteVariant = useCallback((quant: string) => {
     setDeleteTarget(quant);

studio/frontend/src/features/hub/catalog/local-on-device-card.tsx

@@ -38,6 +38,11 @@ import {
   deleteCachedModel,
 } from "../inventory";
 import { formatBytes } from "../lib/format";
+
+import {
+  ggufFilenamesMatch,
+  ggufSelectionOverrideMatchesIntent,
+} from "../lib/gguf-filename";
 import {
   ggufVariantDisplayLabel,
   sortLocalGgufVariants,
@@ -88,6 +93,9 @@ interface LocalOnDeviceCardProps {
   activeGgufVariant?: string | null;
   isLoading: boolean;
   loadingPhase?: "downloading" | "starting";
+  preferredFile?: string | null;
+  preferredFileIntent?: number;
+
   gpuGb?: number;
   systemRamGb?: number;
   unsupportedReason?: string | null;
@@ -210,6 +218,9 @@ export function LocalOnDeviceCard({
   activeGgufVariant = null,
   isLoading,
   loadingPhase,
+  preferredFile = null,
+  preferredFileIntent = 0,
+
   gpuGb,
   systemRamGb,
   unsupportedReason,
@@ -285,6 +296,8 @@ export function LocalOnDeviceCard({
   const [selectedVariantState, setSelectedVariantState] = useState<{
     key: string;
     quant: string | null;
+    preferredFile?: string | null;
+    preferredFileIntent?: number;
   }>(() => ({
     key: variantKey,
     quant: null,
@@ -328,8 +341,21 @@ export function LocalOnDeviceCard({
       systemRamGb,
     ],
   );
+  const preferredQuant = preferredFile
+    ? (variants?.find((variant) =>
+        ggufFilenamesMatch(variant.filename, preferredFile),
+      )?.quant ?? null)
+    : null;
   const selectedVariantOverride =
-    selectedVariantState.key === variantKey ? selectedVariantState.quant : null;
+    selectedVariantState.key === variantKey &&
+    ggufSelectionOverrideMatchesIntent(
+      preferredFile,
+      preferredFileIntent,
+      selectedVariantState.preferredFile,
+      selectedVariantState.preferredFileIntent,
+    )
+      ? selectedVariantState.quant
+      : preferredQuant;
   const selectedQuant =
     selectedVariantOverride &&
     sortedVariants?.some((variant) =>
@@ -506,6 +532,9 @@ export function LocalOnDeviceCard({
                               setSelectedVariantState({
                                 key: variantKey,
                                 quant: variant.quant,
+
+                                preferredFile,
+                                preferredFileIntent,
                               });
                               setVariantOpen(false);
                             }}

studio/frontend/src/features/hub/catalog/model-inspector.tsx

@@ -411,6 +411,9 @@ export const ModelInspector = memo(function ModelInspector({
   model,
   runtime,
   actions,
+  preferredGgufFile = null,
+
+  preferredGgufFileIntent = 0,
   isDataset = false,
   metadataUnavailable = false,
   selectionHiddenByFilters = false,
@@ -419,6 +422,9 @@ export const ModelInspector = memo(function ModelInspector({
   isDataset?: boolean;
   metadataUnavailable?: boolean;
   selectionHiddenByFilters?: boolean;
+  preferredGgufFile?: string | null;
+
+  preferredGgufFileIntent?: number;
   runtime: ModelInspectorRuntime;
   actions: ModelInspectorActions;
 }) {
@@ -696,6 +702,9 @@ export const ModelInspector = memo(function ModelInspector({
               loadingPhase={loadingPhase}
               gpuGb={gpuGb}
               systemRamGb={systemRamGb}
+
+              preferredFile={preferredGgufFile}
+              preferredFileIntent={preferredGgufFileIntent}
               unsupportedReason={
                 unslothSupport.status === "unsupported"
                   ? (unslothSupport.reason ?? "Unsupported format")
@@ -721,6 +730,9 @@ export const ModelInspector = memo(function ModelInspector({
               canRun={canRunModel}
               isActive={isActive}
               activeQuant={isActive ? (activeGgufVariant ?? null) : null}
+              preferredGgufFile={preferredGgufFile}
+
+              preferredGgufFileIntent={preferredGgufFileIntent}
               isLoadingThisModel={isLoadingThisModel}
               gpuGb={gpuGb}
               systemRamGb={systemRamGb}

studio/frontend/src/features/hub/hub-page.tsx

@@ -365,7 +365,9 @@ export function ModelsPage() {
   const deviceType = usePlatformStore((s) => s.deviceType);
   const hubSearch = useSearch({ from: "/hub" });
   const urlModel = hubSearch.model ?? null;
+  const preferredGgufFile = hubSearch.file ?? null;
 
+  const preferredGgufFileIntent = hubSearch.intent ?? 0;
   const { selectModel, loadingModel, loadProgress, ejectModel } =
     useChatModelRuntime();
   const checkpoint = useChatRuntimeStore((s) => s.params.checkpoint);
@@ -1081,7 +1083,7 @@ export function ModelsPage() {
       setSelected(id);
       void navigate({
         to: "/hub",
-        search: (prev) => ({ ...prev, model: id }),
+        search: (prev) => ({ ...prev, model: id, file: undefined }),
       });
     },
     [setSelected, navigate],
@@ -1167,7 +1169,7 @@ export function ModelsPage() {
     setSelected(firstId);
     void navigate({
       to: "/hub",
-      search: (prev) => ({ ...prev, model: firstId }),
+      search: (prev) => ({ ...prev, model: firstId, file: undefined }),
       replace: true,
     });
   }, [
@@ -1868,6 +1870,9 @@ export function ModelsPage() {
             >
               <HubDetailView
                 model={selectedModel}
+                preferredGgufFile={preferredGgufFile}
+
+                preferredGgufFileIntent={preferredGgufFileIntent}
                 isDataset={isDatasetMode}
                 metadataUnavailable={metadataUnavailable}
                 selectionHiddenByFilters={selectionHiddenByFilters}
@@ -1890,6 +1895,9 @@ export function ModelsPage() {
             >
               <HubDetailView
                 model={selectedModel}
+                preferredGgufFile={preferredGgufFile}
+
+                preferredGgufFileIntent={preferredGgufFileIntent}
                 isDataset={isDatasetMode}
                 metadataUnavailable={metadataUnavailable}
                 selectionHiddenByFilters={selectionHiddenByFilters}

studio/frontend/src/features/hub/lib/gguf-filename.ts

@@ -0,0 +1,33 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+const GGUF_SPLIT_SUFFIX = /-\d{3,}-of-\d{3,}(?=\.gguf$)/i;
+
+function normalizeGgufFilename(filename: string): string {
+  return filename
+    .trim()
+    .replace(/\\/g, "/")
+    .replace(GGUF_SPLIT_SUFFIX, "")
+    .toLowerCase();
+}
+
+export function ggufFilenamesMatch(
+  left: string | null | undefined,
+  right: string | null | undefined,
+): boolean {
+  if (!(left && right)) return false;
+  return normalizeGgufFilename(left) === normalizeGgufFilename(right);
+}
+
+export function ggufSelectionOverrideMatchesIntent(
+  preferredFile: string | null | undefined,
+  preferredFileIntent: number,
+  selectedPreferredFile: string | null | undefined,
+  selectedPreferredFileIntent: number | undefined,
+): boolean {
+  return (
+    !preferredFile ||
+    (selectedPreferredFile === preferredFile &&
+      selectedPreferredFileIntent === preferredFileIntent)
+  );
+}

studio/frontend/src/features/model-picker/components/model-config-page.tsx

@@ -47,6 +47,8 @@ import {
   MAX_SEQ_LENGTH_MIN,
   MAX_SEQ_LENGTH_STEP,
   MTP_SPECULATIVE_TYPES,
+  N_PARALLEL_MAX,
+  N_PARALLEL_MIN,
   type PerModelConfig,
   SPECULATIVE_TYPES,
   deletePerModelConfig,
@@ -91,6 +93,7 @@ function hasNonDefaultAdvanced(config: PerModelConfig): boolean {
     config.kvCacheDtype != null ||
     (config.speculativeType ?? "auto") !== "auto" ||
     config.specDraftNMax != null ||
+    config.nParallel != null ||
     config.tensorParallel ||
     config.chatTemplateOverride != null ||
     (config.gpuMemoryMode ?? "auto") !== "auto" ||
@@ -545,6 +548,44 @@ function GgufAdvancedSettings({
         </div>
       )}
 
+      <div className={ROW_CLASS}>
+        <div className="flex min-w-0 items-center gap-1.5">
+          <span className={LABEL_CLASS}>Parallel Slots</span>
+          <InfoHint>
+            llama-server decode slots (--parallel) for concurrent requests.
+            Leave blank for the server default. More slots share the context
+            pool and use more VRAM; if they don't fit on GPU, fewer slots are
+            launched.
+          </InfoHint>
+        </div>
+        <input
+          type="number"
+          min={N_PARALLEL_MIN}
+          max={N_PARALLEL_MAX}
+          step={1}
+          value={config.nParallel ?? ""}
+          placeholder="auto"
+          onChange={(event) => {
+            const raw = event.target.value;
+            if (raw === "") {
+              update({ nParallel: null });
+              return;
+            }
+            const parsed = Number.parseInt(raw, 10);
+            if (Number.isFinite(parsed)) {
+              update({
+                nParallel: Math.max(
+                  N_PARALLEL_MIN,
+                  Math.min(N_PARALLEL_MAX, parsed),
+                ),
+              });
+            }
+          }}
+          aria-label="Parallel decode slots"
+          className={NUMBER_INPUT_CLASS}
+        />
+      </div>
+
       <div className={ROW_CLASS}>
         <div className="flex min-w-0 items-center gap-1.5">
           <span className={LABEL_CLASS}>Tensor Parallelism</span>

studio/frontend/src/features/model-picker/hooks/use-active-model-config.ts

@@ -20,6 +20,7 @@ export function useActiveModelConfig(): ActiveModelConfigState {
   const kvCacheDtype = useChatRuntimeStore((s) => s.kvCacheDtype);
   const speculativeType = useChatRuntimeStore((s) => s.speculativeType);
   const specDraftNMax = useChatRuntimeStore((s) => s.specDraftNMax);
+  const nParallel = useChatRuntimeStore((s) => s.nParallel);
   const tensorParallel = useChatRuntimeStore((s) => s.tensorParallel);
   const chatTemplateOverride = useChatRuntimeStore(
     (s) => s.chatTemplateOverride,
@@ -44,6 +45,7 @@ export function useActiveModelConfig(): ActiveModelConfigState {
       kvCacheDtype: kvCacheDtype ?? null,
       speculativeType: speculativeType ?? "auto",
       specDraftNMax: specDraftNMax ?? null,
+      nParallel: nParallel ?? null,
       tensorParallel: tensorParallel ?? false,
       chatTemplateOverride: chatTemplateOverride ?? null,
     };
@@ -65,6 +67,7 @@ export function useActiveModelConfig(): ActiveModelConfigState {
     kvCacheDtype,
     speculativeType,
     specDraftNMax,
+    nParallel,
     tensorParallel,
     chatTemplateOverride,
     gpuMemoryMode,

studio/frontend/src/features/model-picker/model-config/apply-per-model-config.ts

@@ -44,6 +44,7 @@ export function applyPerModelConfigToRuntime(config: PerModelConfig): void {
       normalizeSpeculativeType(config.speculativeType) ??
       readPersistedSpeculativeType(),
     specDraftNMax: config.specDraftNMax ?? null,
+    nParallel: config.nParallel ?? null,
     tensorParallel: config.tensorParallel ?? false,
     chatTemplateOverride: cleanTemplate(config.chatTemplateOverride),
     // GPU Memory knobs are per-model (GGUF-only). Absent = defaults; the mode is
@@ -82,6 +83,7 @@ export function currentRuntimePerModelConfig(
     kvCacheDtype: s.kvCacheDtype ?? null,
     speculativeType: normalizeSpeculativeType(s.speculativeType),
     specDraftNMax: s.specDraftNMax ?? null,
+    nParallel: s.nParallel ?? null,
     tensorParallel: s.tensorParallel ?? false,
     chatTemplateOverride: cleanTemplate(s.chatTemplateOverride),
     // Snapshot the live GPU knobs too so a failed switch rolls the previous
@@ -106,6 +108,7 @@ export function perModelConfigsEqual(
     normalizeSpeculativeType(a.speculativeType) ===
       normalizeSpeculativeType(b.speculativeType) &&
     (a.specDraftNMax ?? null) === (b.specDraftNMax ?? null) &&
+    (a.nParallel ?? null) === (b.nParallel ?? null) &&
     Boolean(a.tensorParallel) === Boolean(b.tensorParallel) &&
     cleanTemplate(a.chatTemplateOverride) ===
       cleanTemplate(b.chatTemplateOverride) &&

studio/frontend/src/features/model-picker/model-config/config-signature.ts

@@ -54,6 +54,7 @@ export function loadedConfigSignature(
     config.kvCacheDtype ?? "",
     config.speculativeType ?? "",
     config.specDraftNMax ?? "",
+    config.nParallel ?? "",
     config.tensorParallel ? "1" : "0",
     config.chatTemplateOverride == null
       ? ""

studio/frontend/src/features/model-picker/model-config/per-model-config.ts

@@ -15,6 +15,7 @@ export interface PerModelConfig {
   kvCacheDtype: string | null;
   speculativeType: string | null;
   specDraftNMax: number | null;
+  nParallel: number | null;
   tensorParallel: boolean;
   chatTemplateOverride: string | null;
   // GPU Memory controls (per-model, GGUF-only), optional so older blobs still
@@ -33,10 +34,16 @@ export const DEFAULT_PER_MODEL_CONFIG: PerModelConfig = {
   kvCacheDtype: null,
   speculativeType: null,
   specDraftNMax: null,
+  nParallel: null,
   tensorParallel: false,
   chatTemplateOverride: null,
 };
 
+// Mirrors llama_server_args.py PARALLEL_MIN/MAX (LoadRequest.n_parallel
+// bounds). null = follow the server-wide default.
+export const N_PARALLEL_MIN = 1;
+export const N_PARALLEL_MAX = 64;
+
 export const MAX_SEQ_LENGTH_MIN = 128;
 export const MAX_SEQ_LENGTH_MAX = 1048576;
 export const MAX_SEQ_LENGTH_STEP = 128;
@@ -92,6 +99,7 @@ const STORED_CONFIG_FIELDS = new Set([
   "kvCacheDtype",
   "speculativeType",
   "specDraftNMax",
+  "nParallel",
   "tensorParallel",
   "chatTemplateOverride",
   "gpuMemoryMode",
@@ -295,6 +303,8 @@ function legacyEntryToConfig(raw: Record<string, unknown>): PerModelConfig {
       typeof raw.speculativeType === "string" ? raw.speculativeType : null,
     specDraftNMax:
       typeof raw.specDraftNMax === "number" ? raw.specDraftNMax : null,
+    // Legacy blobs predate the parallel-slots knob.
+    nParallel: null,
     tensorParallel:
       typeof raw.tensorParallel === "boolean" ? raw.tensorParallel : false,
     chatTemplateOverride: null,
@@ -466,6 +476,10 @@ function normalizeV1(partial: RawConfig): PerModelConfig {
         : null,
     speculativeType,
     specDraftNMax,
+    nParallel:
+      typeof partial.nParallel === "number" && Number.isFinite(partial.nParallel)
+        ? Math.max(N_PARALLEL_MIN, Math.min(N_PARALLEL_MAX, Math.round(partial.nParallel)))
+        : null,
     tensorParallel:
       typeof partial.tensorParallel === "boolean"
         ? partial.tensorParallel
@@ -613,6 +627,7 @@ export function isDefaultConfig(config: PerModelConfig): boolean {
     (config.kvCacheDtype ?? null) === DEFAULT_PER_MODEL_CONFIG.kvCacheDtype &&
     config.speculativeType === DEFAULT_PER_MODEL_CONFIG.speculativeType &&
     config.specDraftNMax == null &&
+    config.nParallel == null &&
     Boolean(config.tensorParallel) ===
       Boolean(DEFAULT_PER_MODEL_CONFIG.tensorParallel) &&
     (config.chatTemplateOverride ?? null) === null &&

studio/frontend/tests/artifact-source-key.test.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 assert from "node:assert/strict";
+import { readFileSync } from "node:fs";
+import test from "node:test";
+import { fileURLToPath } from "node:url";
+
+import ts from "typescript";
+
+import {
+  buildArtifactSourceKey,
+  createArtifactId,
+  createChatArtifact,
+  hashArtifactCode,
+} from "../src/features/chat/artifacts/types.ts";
+
+// The shipped helper the component keys on, not a copy of it.
+const sourceKey = buildArtifactSourceKey;
+
+const toolInput = (code: string) => ({
+  code,
+  source: "tool" as const,
+  threadId: "thread-1",
+  sourceMessageId: "msg-1",
+  sourceToolCallId: "call_0",
+});
+
+const fenceInput = (code: string) => ({
+  code,
+  source: "fence" as const,
+  threadId: "thread-1",
+  sourceMessageId: "msg-1",
+});
+
+test("tool artifact IDs are stable across code changes, so the ID alone is not enough", () => {
+  const first = createArtifactId(toolInput("<p>first</p>"));
+  const second = createArtifactId(toolInput("<p>second</p>"));
+  assert.equal(first, second);
+});
+
+test("the source key changes when a tool artifact's code changes", () => {
+  const first = createChatArtifact(toolInput("<p>first</p>"));
+  const second = createChatArtifact(toolInput("<p>second</p>"));
+  assert.notEqual(sourceKey(first), sourceKey(second));
+});
+
+test("the source key changes when switching between fence artifacts", () => {
+  const first = createChatArtifact(fenceInput("<p>alpha</p>"));
+  const second = createChatArtifact(fenceInput("<p>bravo</p>"));
+  assert.notEqual(sourceKey(first), sourceKey(second));
+});
+
+test("the source key is stable for an unchanged artifact, so no needless remount", () => {
+  const code = "<p>same</p>";
+  assert.equal(
+    sourceKey(createChatArtifact(toolInput(code))),
+    sourceKey(createChatArtifact(toolInput(code))),
+  );
+});
+
+// Equal line count, the shape where Streamdown's comparator sees no change.
+test("the source key changes for two canvases with the same shape", () => {
+  const first = createChatArtifact(
+    toolInput("<html>\n<body>\n<h1>Alpha</h1>\n</body>\n</html>"),
+  );
+  const second = createChatArtifact(
+    toolInput("<html>\n<body>\n<h1>Bravo</h1>\n</body>\n</html>"),
+  );
+  assert.equal(first.code.length, second.code.length);
+  assert.equal(first.code.split("\n").length, second.code.split("\n").length);
+  assert.notEqual(sourceKey(first), sourceKey(second));
+});
+
+test("hashArtifactCode separates same-length codes and empty from whitespace", () => {
+  assert.notEqual(hashArtifactCode("<p>ab</p>"), hashArtifactCode("<p>ba</p>"));
+  assert.notEqual(hashArtifactCode(""), hashArtifactCode(" "));
+});
+
+const KEYED_BY_HELPER = /^\{buildArtifactSourceKey\(\s*artifact\s*\)\}$/;
+
+const SURFACE_PATH = fileURLToPath(
+  new URL(
+    "../src/features/chat/artifacts/artifact-surface.tsx",
+    import.meta.url,
+  ),
+);
+
+/** The opening tag of `node`, for both `<x>` and `<x />`. */
+const openingTag = (node: ts.Node): ts.JsxOpeningLikeElement | null => {
+  if (ts.isJsxSelfClosingElement(node)) return node;
+  if (ts.isJsxElement(node)) return node.openingElement;
+  return null;
+};
+
+/** The `key` expression on the source view's Streamdown, or null if unkeyed. */
+function readStreamdownKey(): string | null {
+  const source = ts.createSourceFile(
+    SURFACE_PATH,
+    readFileSync(SURFACE_PATH, "utf8"),
+    ts.ScriptTarget.ESNext,
+    true,
+    ts.ScriptKind.TSX,
+  );
+  let key: string | null = null;
+  const visit = (node: ts.Node): void => {
+    const opening = openingTag(node);
+    if (opening?.tagName.getText() === "Streamdown") {
+      for (const attribute of opening.attributes.properties) {
+        if (
+          ts.isJsxAttribute(attribute) &&
+          attribute.name.getText() === "key"
+        ) {
+          key = attribute.initializer?.getText() ?? "";
+        }
+      }
+    }
+    node.forEachChild(visit);
+  };
+  source.forEachChild(visit);
+  return key;
+}
+
+// Without this the suite passes with the key deleted, which is the regression.
+// No DOM renderer is available here, so assert the wiring in the source.
+test("the source view's Streamdown is keyed by the shipped helper", () => {
+  const key = readStreamdownKey();
+  assert.ok(key, "source view <Streamdown> has no key prop");
+  assert.match(key, KEYED_BY_HELPER);
+});

studio/frontend/tests/model-config-instance-key.test.ts

@@ -17,6 +17,7 @@ const LIVE: PerModelConfig = {
   kvCacheDtype: "q8_0",
   speculativeType: "ngram",
   specDraftNMax: 6,
+  nParallel: 4,
   tensorParallel: true,
   chatTemplateOverride: null,
   gpuMemoryMode: "manual",
@@ -32,6 +33,7 @@ const SAVED: PerModelConfig = {
   kvCacheDtype: null,
   speculativeType: "auto",
   specDraftNMax: null,
+  nParallel: null,
   tensorParallel: false,
   chatTemplateOverride: null,
   gpuMemoryMode: "auto",
@@ -100,6 +102,7 @@ test("every mirrored setting moves the instance key", () => {
     { ...LIVE, kvCacheDtype: "f16" },
     { ...LIVE, speculativeType: "off" },
     { ...LIVE, specDraftNMax: 4 },
+    { ...LIVE, nParallel: 1 },
     { ...LIVE, tensorParallel: false },
     { ...LIVE, chatTemplateOverride: "{{ bos_token }}" },
     { ...LIVE, gpuMemoryMode: "auto" },

studio/frontend/tests/per-model-config-storage-identity.test.ts

@@ -45,6 +45,7 @@ function config(maxSeqLength: number, kvCacheDtype: string | null = null) {
     kvCacheDtype,
     speculativeType: null,
     specDraftNMax: null,
+    nParallel: null,
     tensorParallel: false,
     chatTemplateOverride: null,
   };

studio/frontend/tests/tool-status.test.ts

@@ -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
+
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import {
+  NUDGE_TOOL_CALLS_STATUS,
+  toolStatusKind,
+} from "../src/features/chat/utils/tool-status.ts";
+
+test("the nudge status is the exact string the backend sends", () => {
+  // Mirrors tool_call_parser.py, so a reword on either side must break here.
+  assert.equal(NUDGE_TOOL_CALLS_STATUS, "Nudging tool calls");
+  assert.equal(toolStatusKind(NUDGE_TOOL_CALLS_STATUS), "nudge");
+});
+
+test("sandbox tools keep the terminal glyph", () => {
+  for (const status of [
+    "Running Python: print(1)",
+    "Running Python...",
+    "Running: ls -la",
+    "Running command...",
+  ]) {
+    assert.equal(toolStatusKind(status), "terminal", status);
+  }
+});
+
+test("every other status keeps the globe", () => {
+  for (const status of [
+    "Searching: red square",
+    "Reading: unsloth.ai",
+    "Reading page...",
+    "Searching documents: quarterly report",
+    "Calling: get_weather",
+  ]) {
+    assert.equal(toolStatusKind(status), "web", status);
+  }
+});
+
+test("a status that merely mentions nudging is not the nudge itself", () => {
+  // Exact match only: a tool named after the phrase must not steal the spinner.
+  assert.equal(toolStatusKind("Calling: Nudging tool calls"), "web");
+  assert.equal(toolStatusKind("Nudging tool calls again"), "web");
+});

studio/install_llama_prebuilt.py

@@ -4807,6 +4807,15 @@ def _native_linux_system_rocm_lib_dirs(binary_dir: str = "") -> list[str]:
                 os.path.join(d, "libhsa-runtime64.so.1")
             ):
                 out.append(d)
+                # ROCm keeps LLVM's versioned runtime under <root>/lib/llvm, so a
+                # lib64 host still finds it under lib. Probe both and keep them
+                # ahead of the bundle, else system libamd_comgr binds to the
+                # bundle's incompatible libLLVM.so.*.
+                for _sub in (lib_sub, "lib"):
+                    llvm_lib = os.path.join(base, _sub, "llvm", "lib")
+                    if llvm_lib not in seen and os.path.isdir(llvm_lib):
+                        seen.add(llvm_lib)
+                        out.append(llvm_lib)
     return out
 
 

studio/install_python_stack.py

@@ -426,8 +426,8 @@ _GFX_TO_AMD_INDEX_ARCH: dict[str, str] = {
 }
 
 # bitsandbytes continuous-release_main wheels with the ROCm 4-bit GEMV fix
-# (bnb PR #1887, post-0.49.2). bnb <= 0.49.2 NaNs at decode shape on every
-# AMD GPU. Drop the pin once bnb 0.50+ ships on PyPI.
+# (bnb #1887, post-0.49.2). bnb <= 0.49.2 NaNs at decode shape on every AMD GPU;
+# PyPI 0.50.0 is the first release with the fix, so the fallback below is safe.
 _BNB_ROCM_PRERELEASE_URLS: dict[str, str] = {
     "x86_64": (
         "https://github.com/bitsandbytes-foundation/bitsandbytes/releases/"
@@ -448,7 +448,8 @@ _BNB_ROCM_PRERELEASE_URLS: dict[str, str] = {
         "bitsandbytes-1.33.7.preview-py3-none-win_amd64.whl"
     ),
 }
-_BNB_ROCM_PYPI_FALLBACK = "bitsandbytes>=0.49.1"
+# Keep in step with the amd extra in pyproject.toml and the install.sh fallback.
+_BNB_ROCM_PYPI_FALLBACK = "bitsandbytes>=0.50.0"
 
 
 def _bnb_rocm_prerelease_url() -> str | None:
@@ -460,6 +461,16 @@ def _bnb_rocm_prerelease_url() -> str | None:
     return _BNB_ROCM_PRERELEASE_URLS.get(arch)
 
 
+def _bnb_rocm_arch_has_binary() -> bool:
+    """False on aarch64: bitsandbytes ships no ROCm kernels there at any version.
+    The PyPI 0.50.0 and continuous-release_main aarch64 wheels both carry only
+    libbitsandbytes_cpu.so plus CUDA variants, so neither install path gives
+    aarch64 a 4-bit backend and neither message may claim one.
+    """
+    arch = platform.machine().lower()
+    return {"amd64": "x86_64", "arm64": "aarch64"}.get(arch, arch) != "aarch64"
+
+
 def _amd_smi_env() -> dict[str, str] | None:
     """On Windows, env with __COMPAT_LAYER=RunAsInvoker; None elsewhere.
     NB: RunAsInvoker doesn't stop amd-smi's runtime elevation (its manifest is
@@ -1243,29 +1254,46 @@ _rocm_windows_torch_installed: bool = False
 
 
 def _install_bnb_windows_rocm() -> bool:
-    """Install the AMD Windows BNB prerelease wheel. Returns True on success.
+    """Install AMD Windows BNB, pre-release wheel first. Returns True on success.
 
-    The continuous-release wheel is intentionally mismatched: the filename
-    encodes 1.33.7.preview (parsed as 1.33.7rc0 by PEP 440) while the wheel
-    metadata reports 0.50.0.dev0. uv rejects this filename/metadata mismatch,
-    and bypassing it with UV_SKIP_WHEEL_FILENAME_CHECK still leaves uv mangling
-    the bitsandbytes install. Per the AMD install guide
-    (https://unsloth.ai/docs/get-started/install/amd/amd-hackathon) the wheel
-    must be installed with plain pip, not uv, so we force pip (force_pip=True);
-    plain pip performs no wheel filename/metadata check.
+    The wheel's filename version (1.33.7.preview, PEP 440 1.33.7rc0) does not
+    match its metadata (0.50.x.dev0). uv rejects the mismatch and still mangles
+    the install under UV_SKIP_WHEEL_FILENAME_CHECK, so force plain pip, which
+    performs no such check. Per the AMD install guide
+    (https://unsloth.ai/docs/get-started/install/amd/amd-hackathon).
+
+    When that URL is blocked, fall back to PyPI. Its win_amd64 wheel ships
+    libbitsandbytes_rocm{714,72}.dll from 0.50.0 on, so the fallback is a real
+    ROCm build; before 0.50.0 it was CUDA-only, which is why there was none.
     """
     _bnb_win_url = _BNB_ROCM_PRERELEASE_URLS.get("win_amd64")
-    if _bnb_win_url is None:
-        return False
-    _ok = pip_install_try(
-        "bitsandbytes (AMD Windows, pre-release main)",
-        "--force-reinstall",
-        "--no-cache-dir",
-        "--no-deps",
-        _bnb_win_url,
-        constrain = False,
-        force_pip = True,
-    )
+    _ok = False
+    if _bnb_win_url is not None:
+        _ok = pip_install_try(
+            "bitsandbytes (AMD Windows, pre-release main)",
+            "--force-reinstall",
+            "--no-cache-dir",
+            "--no-deps",
+            _bnb_win_url,
+            constrain = False,
+            force_pip = True,
+        )
+        if not _ok:
+            print(
+                _red(
+                    "   bnb pre-release install failed; falling back to PyPI "
+                    f"{_BNB_ROCM_PYPI_FALLBACK}, which carries the ROCm 4-bit fix"
+                )
+            )
+    if not _ok:
+        _ok = pip_install_try(
+            "bitsandbytes (AMD Windows)",
+            "--force-reinstall",
+            "--no-cache-dir",
+            "--no-deps",
+            _BNB_ROCM_PYPI_FALLBACK,
+            constrain = False,
+        )
     if not _ok:
         return False
     # Detect the actual ROCm DLL suffix in the wheel and set BNB_ROCM_VERSION so bnb
@@ -1755,8 +1783,8 @@ def _ensure_rocm_torch() -> None:
             pass
         if _torch_ok:
             _rocm_windows_torch_installed = True
-            # ROCm torch is already installed, but the AMD Windows BNB wheel is still
-            # needed (the PyPI bitsandbytes ships only CUDA DLLs, fails on ROCm).
+            # ROCm torch is already installed, but bnb still needs the ROCm build
+            # (pre-release wheel, else PyPI >=0.50.0).
             _install_bnb_windows_rocm()
             return
         # torch was wiped between runs; fall through to the full install path
@@ -1834,12 +1862,12 @@ def _ensure_rocm_torch() -> None:
         # separate dependency -- a BNB install failure must NOT roll back the
         # torch ROCm install.
         _rocm_windows_torch_installed = True
-        # Always install AMD Windows bitsandbytes -- the PyPI wheel ships only
-        # CUDA DLLs and fails on ROCm. Install even when torch was already a
-        # ROCm build so `studio update` repairs a broken bnb.
+        # Always install AMD Windows bitsandbytes, even when torch was already a
+        # ROCm build, so `studio update` repairs a broken bnb.
         if not _install_bnb_windows_rocm():
             print(
-                "   Warning: AMD Windows bitsandbytes install failed; "
+                "   Warning: AMD Windows bitsandbytes install failed "
+                "(pre-release and PyPI); "
                 "ROCm torch is installed but bitsandbytes may need manual install"
             )
         return
@@ -2170,10 +2198,13 @@ def _ensure_rocm_torch() -> None:
                 force_pip = True,
             )
             if not _bnb_installed:
+                _fallback_note = (
+                    ", which carries the ROCm 4-bit fix" if _bnb_rocm_arch_has_binary() else ""
+                )
                 print(
                     _red(
                         "   bnb pre-release install failed; falling back to PyPI "
-                        "(4-bit decode will be broken on ROCm)"
+                        f"{_BNB_ROCM_PYPI_FALLBACK}{_fallback_note}"
                     )
                 )
         if not _bnb_installed:
@@ -2185,6 +2216,14 @@ def _ensure_rocm_torch() -> None:
                 _BNB_ROCM_PYPI_FALLBACK,
                 constrain = False,
             )
+        if not _bnb_rocm_arch_has_binary():
+            print(
+                _red(
+                    "   aarch64: bitsandbytes ships no ROCm kernels on this arch; "
+                    "4-bit QLoRA needs a source build -- "
+                    "https://docs.unsloth.ai/get-started/install-and-update/amd"
+                )
+            )
 
 
 # _uv_safe_path is imported from backend.utils.uv_path_safety (shared with mlx_repair).

studio/setup.ps1

@@ -3122,7 +3122,7 @@ sys.exit(0 if install_manifest.remove_manifest() else 1)
 if (-not $_ManifestDropped) {
     Write-Host "[ERROR] Could not remove the stale unsloth_install_manifest.json." -ForegroundColor Red
     Write-Host "        Refusing to install behind a marker that still reports this venv as complete." -ForegroundColor Red
-    exit 1
+    Exit-SetupFailure "Could not remove the stale unsloth_install_manifest.json"
 }
 
 if ($script:UnslothVerbose) {

studio/src-tauri/Cargo.lock

@@ -558,6 +558,26 @@ version = "0.10.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "a6ef517f0926dd24a1582492c791b6a4818a4d94e789a334894aa15b0d12f55c"
 
+[[package]]
+name = "const-random"
+version = "0.1.18"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "87e00182fe74b066627d63b85fd550ac2998d4b0bd86bfed477a0ae4c7c71359"
+dependencies = [
+ "const-random-macro",
+]
+
+[[package]]
+name = "const-random-macro"
+version = "0.1.16"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f9d839f2a20b0aee515dc581a6172f2321f96cab76c1a38a4c584a194955390e"
+dependencies = [
+ "getrandom 0.2.17",
+ "once_cell",
+ "tiny-keccak",
+]
+
 [[package]]
 name = "convert_case"
 version = "0.4.0"
@@ -896,7 +916,7 @@ dependencies = [
  "libc",
  "option-ext",
  "redox_users",
- "windows-sys 0.59.0",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -945,6 +965,15 @@ dependencies = [
  "syn 2.0.117",
 ]
 
+[[package]]
+name = "dlv-list"
+version = "0.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "442039f5147480ba31067cb00ada1adae6892028e40e45fc5de7b7df6dcc1b5f"
+dependencies = [
+ "const-random",
+]
+
 [[package]]
 name = "dom_query"
 version = "0.27.0"
@@ -1111,7 +1140,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "39cab71617ae0d63f51a36d69f866391735b51691dbda63cf6f96d042b63efeb"
 dependencies = [
  "libc",
- "windows-sys 0.59.0",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -1738,6 +1767,12 @@ version = "0.12.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "8a9ee70c43aaf417c914396645a0fa852624801b24ebb7ae78fe8272889ac888"
 
+[[package]]
+name = "hashbrown"
+version = "0.14.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e5274423e17b7c9fc20b6e7e208532f9b19825d82dfd615708b70edd83df41f1"
+
 [[package]]
 name = "hashbrown"
 version = "0.15.5"
@@ -1941,7 +1976,7 @@ dependencies = [
  "tokio",
  "tower-service",
  "tracing",
- "windows-registry",
+ "windows-registry 0.6.1",
 ]
 
 [[package]]
@@ -2531,7 +2566,7 @@ dependencies = [
  "png 0.18.1",
  "serde",
  "thiserror 2.0.18",
- "windows-sys 0.60.2",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -2944,6 +2979,16 @@ version = "0.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "04744f49eae99ab78e0d5c0b603ab218f515ea8cfe5a456d7629ad883a3b6e7d"
 
+[[package]]
+name = "ordered-multimap"
+version = "0.7.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "49203cdcae0030493bad186b28da2fa25645fa276a51b6fec8010d281e02ef79"
+dependencies = [
+ "dlv-list",
+ "hashbrown 0.14.5",
+]
+
 [[package]]
 name = "ordered-stream"
 version = "0.2.0"
@@ -2961,7 +3006,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7d8fae84b431384b68627d0f9b3b1245fcf9f46f6c0e3dc902e9dce64edd1967"
 dependencies = [
  "libc",
- "windows-sys 0.45.0",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -3824,6 +3869,16 @@ dependencies = [
  "windows-sys 0.52.0",
 ]
 
+[[package]]
+name = "rust-ini"
+version = "0.21.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "796e8d2b6696392a43bea58116b667fb4c29727dc5abd27d6acf338bb4f688c7"
+dependencies = [
+ "cfg-if",
+ "ordered-multimap",
+]
+
 [[package]]
 name = "rustc-hash"
 version = "2.1.1"
@@ -3849,7 +3904,7 @@ dependencies = [
  "errno",
  "libc",
  "linux-raw-sys",
- "windows-sys 0.59.0",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -3905,7 +3960,7 @@ dependencies = [
  "security-framework",
  "security-framework-sys",
  "webpki-root-certs",
- "windows-sys 0.59.0",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -4347,7 +4402,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "3a766e1110788c36f4fa1c2b71b387a7815aa65f88ce0229841826633d93723e"
 dependencies = [
  "libc",
- "windows-sys 0.60.2",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -4774,6 +4829,27 @@ dependencies = [
  "thiserror 2.0.18",
 ]
 
+[[package]]
+name = "tauri-plugin-deep-link"
+version = "2.4.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "70ee75bc5627f77bfdf40c913255ebc258117b10ebe2b2239a1a1cf40b0b58aa"
+dependencies = [
+ "dunce",
+ "plist",
+ "rust-ini",
+ "serde",
+ "serde_json",
+ "tauri",
+ "tauri-plugin",
+ "tauri-utils",
+ "thiserror 2.0.18",
+ "tracing",
+ "url",
+ "windows-registry 0.5.3",
+ "windows-result 0.3.4",
+]
+
 [[package]]
 name = "tauri-plugin-dialog"
 version = "2.7.1"
@@ -4876,6 +4952,7 @@ dependencies = [
  "serde",
  "serde_json",
  "tauri",
+ "tauri-plugin-deep-link",
  "thiserror 2.0.18",
  "tracing",
  "windows-sys 0.60.2",
@@ -5014,7 +5091,7 @@ dependencies = [
  "serde_with",
  "swift-rs",
  "thiserror 2.0.18",
- "toml 0.9.12+spec-1.1.0",
+ "toml 1.1.2+spec-1.1.0",
  "url",
  "urlpattern",
  "uuid",
@@ -5051,10 +5128,10 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "32497e9a4c7b38532efcdebeef879707aa9f794296a4f0244f6f69e9bc8574bd"
 dependencies = [
  "fastrand",
- "getrandom 0.3.4",
+ "getrandom 0.4.2",
  "once_cell",
  "rustix",
- "windows-sys 0.59.0",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -5174,6 +5251,15 @@ dependencies = [
  "time-core",
 ]
 
+[[package]]
+name = "tiny-keccak"
+version = "2.0.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2c9d3793400a45f954c52e73d068316d76b6f4e36977e3fcebb13a2721e80237"
+dependencies = [
+ "crunchy",
+]
+
 [[package]]
 name = "tinystr"
 version = "0.8.2"
@@ -5460,7 +5546,7 @@ dependencies = [
  "png 0.18.1",
  "serde",
  "thiserror 2.0.18",
- "windows-sys 0.60.2",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -5500,7 +5586,7 @@ checksum = "f2f6fb2847f6742cd76af783a2a2c49e9375d0a111c7bef6f71cd9e738c72d6e"
 dependencies = [
  "memoffset",
  "tempfile",
- "windows-sys 0.60.2",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -5590,6 +5676,7 @@ dependencies = [
  "tauri",
  "tauri-build",
  "tauri-plugin-clipboard-manager",
+ "tauri-plugin-deep-link",
  "tauri-plugin-dialog",
  "tauri-plugin-notification",
  "tauri-plugin-opener",
@@ -6069,7 +6156,7 @@ version = "0.1.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c2a7b1c03c876122aa43f3020e6c3c3ee5c05081c9a00739faf7503aeba10d22"
 dependencies = [
- "windows-sys 0.59.0",
+ "windows-sys 0.61.2",
 ]
 
 [[package]]
@@ -6257,6 +6344,17 @@ dependencies = [
  "windows-link 0.2.1",
 ]
 
+[[package]]
+name = "windows-registry"
+version = "0.5.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5b8a9ed28765efc97bbc954883f4e6796c33a06546ebafacbabee9696967499e"
+dependencies = [
+ "windows-link 0.1.3",
+ "windows-result 0.3.4",
+ "windows-strings 0.4.2",
+]
+
 [[package]]
 name = "windows-registry"
 version = "0.6.1"

studio/src-tauri/Cargo.toml

@@ -7,7 +7,8 @@ edition = "2021"
 
 [dependencies]
 tauri = { version = "2", features = ["tray-icon"] }
-tauri-plugin-single-instance = "2"
+tauri-plugin-single-instance = { version = "2", features = ["deep-link"] }
+tauri-plugin-deep-link = "2"
 tauri-plugin-process = "2"
 serde = { version = "1", features = ["derive"] }
 serde_json = "1"

studio/src-tauri/capabilities/default.json

@@ -16,10 +16,12 @@
     "core:window:allow-start-dragging",
     "core:window:allow-start-resize-dragging",
     "core:window:allow-minimize",
+    "core:window:allow-unminimize",
     "core:window:allow-toggle-maximize",
     "core:window:allow-close",
     "core:tray:default",
     "process:default",
+    "deep-link:default",
     "notification:allow-is-permission-granted",
     "notification:allow-request-permission",
     "notification:allow-notify",

studio/src-tauri/linux/unsloth.desktop

@@ -0,0 +1,12 @@
+[Desktop Entry]
+Categories={{categories}}
+{{#if comment}}
+Comment={{comment}}
+{{/if}}
+Exec={{exec}} %u
+StartupWMClass={{exec}}
+Icon={{icon}}
+Name={{name}}
+Terminal=false
+Type=Application
+MimeType=x-scheme-handler/unsloth;

studio/src-tauri/src/main.rs

@@ -17,7 +17,7 @@ mod process;
 mod update;
 mod windows_job;
 
-use log::info;
+use log::{info, warn};
 use process::new_backend_state;
 use simplelog::{
     CombinedLogger, Config, LevelFilter, SharedLogger, TermLogger, TerminalMode, WriteLogger,
@@ -176,9 +176,11 @@ fn main() {
         .plugin(tauri_plugin_single_instance::init(|app, _args, _cwd| {
             if let Some(window) = app.get_webview_window("main") {
                 let _ = window.show();
+                let _ = window.unminimize();
                 let _ = window.set_focus();
             }
         }))
+        .plugin(tauri_plugin_deep_link::init())
         .plugin(tauri_plugin_process::init())
         .plugin(tauri_plugin_opener::init())
         .plugin(tauri_plugin_notification::init())
@@ -234,6 +236,13 @@ fn main() {
             has_saved_window_state,
         ])
         .setup(|app| {
+            #[cfg(any(target_os = "linux", all(debug_assertions, windows)))]
+            {
+                use tauri_plugin_deep_link::DeepLinkExt;
+                if let Err(error) = app.deep_link().register_all() {
+                    warn!("Failed to register deep-link handlers: {error}");
+                }
+            }
             #[cfg(any(target_os = "windows", target_os = "linux"))]
             setup_custom_titlebar(app)?;
             setup_tray(app)?;

studio/src-tauri/src/native_file_dialogs.rs

@@ -335,7 +335,13 @@ mod tests {
         let path = std::env::temp_dir().join(OsString::from_vec(vec![
             b'u', b'n', b's', b'l', b'o', b't', b'h', 0xff, b'.', b'c', b's', b'v',
         ]));
-        fs::write(&path, "role,content\nuser,hello\n").unwrap();
+        // Linux happily stores arbitrary bytes in a filename, but macOS enforces
+        // UTF-8 on APFS/HFS+ and rejects this name outright. The name-recovery
+        // path being asserted here is only reachable where such a file can
+        // exist, so skip rather than fail on filesystems that forbid it.
+        if fs::write(&path, "role,content\nuser,hello\n").is_err() {
+            return;
+        }
         let imported = read_selected_import(Some(path.clone())).unwrap().unwrap();
         assert_eq!(imported.name, "chat-import.csv");
         let _ = fs::remove_file(path);

studio/src-tauri/src/preflight/managed.rs

@@ -704,7 +704,15 @@ mod tests {
         fs::write(venv.join("pyvenv.cfg"), "home = /usr/bin\n").unwrap();
         fs::write(venv.join("unsloth_install_manifest.json"), "{}").unwrap();
 
-        let site_packages = venv.join("lib").join("python3.11").join("site-packages");
+        // site_packages_dirs() only walks lib/<pyver>/site-packages on unix; on
+        // Windows it looks at Lib/site-packages. Building the posix layout
+        // everywhere left the dist-info invisible to the fingerprint on Windows,
+        // so removing it changed nothing and the assert_ne below could not hold.
+        let site_packages = if cfg!(windows) {
+            venv.join("Lib").join("site-packages")
+        } else {
+            venv.join("lib").join("python3.11").join("site-packages")
+        };
         fs::create_dir_all(site_packages.join("unsloth_cli").join("commands")).unwrap();
         fs::write(
             site_packages

studio/src-tauri/tauri.conf.json

@@ -35,6 +35,11 @@
     ]
   },
   "plugins": {
+    "deep-link": {
+      "desktop": {
+        "schemes": ["unsloth"]
+      }
+    },
     "updater": {
       "pubkey": "dW50cnVzdGVkIGNvbW1lbnQ6IG1pbmlzaWduIHB1YmxpYyBrZXk6IDFBQzA4RjczODM0RjE1QjcKUldTM0ZVK0RjNC9BR2t4R0RVaFR5cTkyUlRVQ1FwaGV0Nk04eWNwWXBhZnlzalJydllmZm1QTS8K",
       "endpoints": [
@@ -80,6 +85,7 @@
         "bundleMediaFramework": false
       },
       "deb": {
+        "desktopTemplate": "./linux/unsloth.desktop",
         "postRemoveScript": "./linux/postremove.sh"
       }
     }

tests/python/test_cross_platform_parity.py

@@ -862,3 +862,76 @@ class TestNoTorchPersistenceParity:
         manifest = (REPO_ROOT / "studio" / "install_manifest.py").read_text(encoding = "utf-8")
         assert 'NO_TORCH_TRUTHY: Tuple[str, ...] = ("1", "true", "yes", "on")' in manifest
         assert "install_manifest.NO_TORCH_TRUTHY" in STACK_PY.read_text(encoding = "utf-8")
+
+
+class TestAmdBnbFloorParity:
+    """bitsandbytes <= 0.49.2 NaNs at 4-bit decode shape on every AMD GPU; the ROCm
+    4-bit GEMV fix (bnb #1887) first ships on PyPI in 0.50.0. The `amd` extra,
+    install.sh and the Studio stack resolve bitsandbytes independently, so all three
+    must carry the same floor or an unreachable pre-release wheel silently reinstates
+    the broken range."""
+
+    FLOOR = "0.50.0"
+    PYPROJECT = REPO_ROOT / "pyproject.toml"
+
+    def test_amd_extra_floor(self):
+        text = self.PYPROJECT.read_text(encoding = "utf-8")
+        amd = re.search(r"^amd = \[(.*?)^\]", text, re.S | re.M)
+        assert amd, "pyproject.toml must define an `amd` extra"
+        specs = re.findall(r'"(bitsandbytes[^"]*)"', amd.group(1))
+        assert specs, "the amd extra must pin bitsandbytes"
+        for spec in specs:
+            assert spec.startswith(
+                f"bitsandbytes>={self.FLOOR}"
+            ), f"amd extra bitsandbytes floor must be >={self.FLOOR}, got {spec!r}"
+
+    def test_install_sh_pypi_fallback_floor(self):
+        text = INSTALL_SH.read_text(encoding = "utf-8")
+        assert (
+            f'_BNB_ROCM_PYPI_FALLBACK="bitsandbytes>={self.FLOOR}"' in text
+        ), f"install.sh _install_bnb_rocm PyPI fallback must floor at {self.FLOOR}"
+
+    def test_stack_py_pypi_fallback_floor(self):
+        text = STACK_PY.read_text(encoding = "utf-8")
+        assert (
+            f'_BNB_ROCM_PYPI_FALLBACK = "bitsandbytes>={self.FLOOR}"' in text
+        ), f"install_python_stack.py PyPI fallback must floor at {self.FLOOR}"
+
+    def test_no_installer_still_allows_the_broken_range(self):
+        for path in (INSTALL_SH, INSTALL_PS1, SETUP_PS1, STACK_PY, self.PYPROJECT):
+            text = path.read_text(encoding = "utf-8")
+            for line in text.splitlines():
+                if "bitsandbytes>=0.49" in line and not line.lstrip().startswith(("#", "//")):
+                    raise AssertionError(
+                        f"{path.name} still floors bitsandbytes in the broken ROCm range: {line.strip()!r}"
+                    )
+
+    def test_fallback_is_not_reported_as_broken(self):
+        """The fallback now installs the first fixed release, so neither installer
+        may still call 4-bit decode broken on ROCm."""
+        for path in (INSTALL_SH, STACK_PY):
+            text = path.read_text(encoding = "utf-8")
+            assert (
+                "4-bit decode broken on ROCm" not in text
+            ), f"{path.name} still reports the repaired PyPI fallback as broken"
+            assert (
+                "4-bit decode will be broken on ROCm" not in text
+            ), f"{path.name} still reports the repaired PyPI fallback as broken"
+
+    def test_aarch64_is_not_told_it_has_a_rocm_backend(self):
+        """bitsandbytes ships no ROCm kernels in its aarch64 wheel at any version, so
+        neither installer may hand aarch64 the x86_64 "carries the ROCm 4-bit fix"
+        message, and both must warn that 4-bit needs a source build there."""
+        sh = INSTALL_SH.read_text(encoding = "utf-8")
+        assert "_bnb_rocm_arch_has_binary()" in sh
+        assert "_warn_bnb_no_rocm_binary()" in sh
+        assert (
+            sh.count("_warn_bnb_no_rocm_binary\n") >= 2
+        ), "install.sh must warn on aarch64 after both the pre-release and the fallback install"
+        py = STACK_PY.read_text(encoding = "utf-8")
+        assert "def _bnb_rocm_arch_has_binary(" in py
+        assert "_bnb_rocm_arch_has_binary()" in py
+        for text, name in ((sh, "install.sh"), (py, "install_python_stack.py")):
+            assert (
+                "4-bit QLoRA needs a source build" in text
+            ), f"{name} must tell aarch64 users 4-bit needs a source build"

tests/python/test_import_without_bitsandbytes.py

@@ -0,0 +1,296 @@
+"""`import unsloth` must survive a missing bitsandbytes.
+
+device_type.py already tells the user "bitsandbytes is not installed - 4bit QLoRA
+unallowed, but 16bit and full finetuning works", and the gfx906 install path
+(#7354) deliberately removes the generic wheel because it carries no gfx906
+kernels. Any module-level `import bitsandbytes` on the import chain turns that
+into an unimportable package instead.
+
+peft's 4bit LoRA layer is exported only when bnb is importable, so
+`from peft.tuners.lora import Linear4bit` fails on the same hosts and is checked
+here too.
+"""
+
+# Path | None below is a PEP 604 union; the project still supports Python 3.9.
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+ROOT_MODULE = "unsloth"
+
+
+def _module_path(name: str) -> Path | None:
+    base = REPO_ROOT / Path(*name.split("."))
+    for candidate in (base.with_suffix(".py"), base / "__init__.py"):
+        if candidate.is_file():
+            return candidate
+    return None
+
+
+def _bnb_dependent(node: ast.stmt) -> bool:
+    """True for an import that raises when bitsandbytes is absent."""
+    if isinstance(node, ast.Import):
+        return any(a.name.split(".")[0] == "bitsandbytes" for a in node.names)
+    if isinstance(node, ast.ImportFrom) and node.level == 0:
+        module = node.module or ""
+        if module.split(".")[0] == "bitsandbytes":
+            return True
+        # peft re-exports Linear4bit only when bnb imported cleanly.
+        if module.startswith("peft.tuners.lora"):
+            return any(a.name == "Linear4bit" for a in node.names)
+    return False
+
+
+def _allow_bitsandbytes_gated(test: ast.expr) -> bool:
+    """device_type.py sets ALLOW_BITSANDBYTES=False exactly when the import failed,
+    so a branch keyed on it cannot run without bnb."""
+    return any(isinstance(n, ast.Name) and n.id == "ALLOW_BITSANDBYTES" for n in ast.walk(test))
+
+
+def _scan(path: Path, module: str):
+    """Yield (lineno, source) for unguarded top-level imports.
+
+    Imports inside a `try`, or under an ALLOW_BITSANDBYTES branch, are guarded.
+    Other `if` bodies are not: the condition may well be true on a host without bnb.
+    """
+    is_package = path.name == "__init__.py"
+    package = module if is_package else module.rpartition(".")[0]
+    tree = ast.parse(path.read_text(encoding = "utf-8"))
+    risky, edges = [], []
+
+    def walk(body, guarded):
+        for node in body:
+            if isinstance(node, (ast.Import, ast.ImportFrom)):
+                if not guarded and _bnb_dependent(node):
+                    risky.append((node.lineno, ast.unparse(node)))
+                if isinstance(node, ast.Import):
+                    edges.extend(a.name for a in node.names)
+                elif node.level:
+                    parts = package.split(".")
+                    base = ".".join(parts[: len(parts) - (node.level - 1)])
+                    edges.append(f"{base}.{node.module}" if node.module else base)
+                else:
+                    edges.append(node.module or "")
+            elif isinstance(node, ast.Try):
+                walk(node.body, True)
+                for handler in node.handlers:
+                    walk(handler.body, True)
+                walk(node.orelse, True)
+                walk(node.finalbody, guarded)
+            elif isinstance(node, ast.If):
+                walk(node.body, guarded or _allow_bitsandbytes_gated(node.test))
+                walk(node.orelse, guarded)
+
+    walk(tree.body, False)
+    return risky, edges
+
+
+def test_no_unguarded_bitsandbytes_import_on_the_unsloth_import_chain():
+    seen, pending, offenders = set(), [(ROOT_MODULE, [])], []
+    while pending:
+        module, chain = pending.pop()
+        if module in seen:
+            continue
+        seen.add(module)
+        path = _module_path(module)
+        if path is None:
+            continue
+        risky, edges = _scan(path, module)
+        for lineno, source in risky:
+            rel = path.relative_to(REPO_ROOT).as_posix()
+            offenders.append(f"{rel}:{lineno}  {source}\n    via {' -> '.join(chain + [module])}")
+        pending.extend(
+            (edge, chain + [module]) for edge in edges if edge.split(".")[0] == ROOT_MODULE
+        )
+
+    assert len(seen) > 20, f"import chain walk collapsed, only reached {seen}"
+    assert not offenders, (
+        "`import unsloth` must not hard-require bitsandbytes. Wrap these in "
+        "try/except and fall back to a placeholder:\n  " + "\n  ".join(offenders)
+    )
+
+
+def test_missing_bnb_leaves_a_callable_that_reports_the_real_cause():
+    """The 4bit ctypes handles degrade to a stub, not a NameError later on."""
+    src = (REPO_ROOT / "unsloth" / "kernels" / "utils.py").read_text(encoding = "utf-8")
+    assert "def _bnb_required(" in src
+    assert "get_ptr = _bnb_required" in src
+    for name in (
+        "cdequantize_blockwise_fp32",
+        "cdequantize_blockwise_fp16_nf4",
+        "cdequantize_blockwise_bf16_nf4",
+        "cgemm_4bit_inference_naive_fp16",
+        "cgemm_4bit_inference_naive_bf16",
+    ):
+        assert f"{name} = _bnb_required" in src, f"{name} has no bnb-less fallback"
+
+
+def test_capability_flags_come_from_a_guarded_import_not_find_spec():
+    """kernels/utils.py and _gpu_init.py treat any import failure as unavailable.
+    device_type.py must agree, or an installed-but-unusable wheel leaves
+    ALLOW_BITSANDBYTES true while the kernels fall back to the stub."""
+    src = (REPO_ROOT / "unsloth" / "device_type.py").read_text(encoding = "utf-8")
+    head = src.split('if DEVICE_TYPE == "hip":')[0]
+    assert "import bitsandbytes as _bnb_probe" in head
+    assert 'find_spec("bitsandbytes")' not in head, "find_spec cannot see a broken wheel"
+    assert head.count("ALLOW_BITSANDBYTES = False") >= 1
+
+
+def _bnb_guards():
+    src = (REPO_ROOT / "unsloth" / "models" / "loader.py").read_text(encoding = "utf-8")
+    tree = ast.parse(src)
+    return src, [
+        node
+        for node in ast.walk(tree)
+        if isinstance(node, ast.If)
+        and any(
+            isinstance(n, ast.Name) and n.id == "ALLOW_BITSANDBYTES" for n in ast.walk(node.test)
+        )
+    ]
+
+
+def test_bitsandbytes_guard_is_not_gated_on_use_exact_model_name():
+    """use_exact_model_name suppresses repo-name remapping; it cannot make bnb
+    available. Gating on it left the default load_in_4bit=True set on a host
+    without bitsandbytes."""
+    _, guards = _bnb_guards()
+    assert len(guards) == 2, f"expected both loader guards, found {len(guards)}"
+    for guard in guards:
+        names = {n.id for n in ast.walk(guard.test) if isinstance(n, ast.Name)}
+        assert (
+            "use_exact_model_name" not in names
+        ), f"guard at line {guard.lineno} still gates the capability check on naming"
+
+
+def test_bitsandbytes_guard_drops_a_bnb_quantization_config():
+    """A BitsAndBytesConfig in kwargs re-sets the flags downstream, so clearing
+    load_in_4bit/8bit alone still builds the bnb quantizer in Transformers. A
+    non-bnb config (GPTQ/AWQ/fp8) must not be touched."""
+    _, guards = _bnb_guards()
+    for guard in guards:
+        # ast.unparse normalises quotes, so match on the call shape instead.
+        def _is_pop(node):
+            return (
+                isinstance(node, ast.Call)
+                and isinstance(node.func, ast.Attribute)
+                and node.func.attr == "pop"
+                and isinstance(node.func.value, ast.Name)
+                and node.func.value.id == "kwargs"
+                and node.args
+                and isinstance(node.args[0], ast.Constant)
+                and node.args[0].value == "quantization_config"
+            )
+
+        assert any(
+            _is_pop(n) for n in ast.walk(guard)
+        ), f"guard at line {guard.lineno} leaves the bnb config in kwargs"
+        # the pop must be conditional on the config actually asking for bnb
+        pops = [
+            node
+            for node in ast.walk(guard)
+            if isinstance(node, ast.If) and any(_is_pop(n) for n in ast.walk(node))
+        ]
+        assert pops, f"guard at line {guard.lineno} pops unconditionally"
+        assert any(
+            isinstance(n, ast.Name) and n.id == "_wants_bnb"
+            for node in pops
+            for n in ast.walk(node.test)
+        ), f"guard at line {guard.lineno} does not gate the pop on a bnb request"
+
+
+def test_bitsandbytes_guard_clears_8bit_as_well_as_4bit():
+    """8bit is bitsandbytes too: leaving load_in_8bit set sends the request to
+    Transformers, which builds the bnb quantizer and fails there instead."""
+    src = (REPO_ROOT / "unsloth" / "models" / "loader.py").read_text(encoding = "utf-8")
+    tree = ast.parse(src)
+    guards = [
+        node
+        for node in ast.walk(tree)
+        if isinstance(node, ast.If)
+        and any(
+            isinstance(n, ast.Name) and n.id == "ALLOW_BITSANDBYTES" for n in ast.walk(node.test)
+        )
+    ]
+    assert len(guards) == 2, f"expected both loader guards, found {len(guards)}"
+    for guard in guards:
+        cleared = {
+            target.id
+            for stmt in guard.body
+            if isinstance(stmt, ast.Assign)
+            for target in stmt.targets
+            if isinstance(target, ast.Name)
+            and isinstance(stmt.value, ast.Constant)
+            and stmt.value.value is False
+        }
+        assert {
+            "load_in_4bit",
+            "load_in_8bit",
+        } <= cleared, f"guard at line {guard.lineno} clears only {sorted(cleared)}"
+
+
+def test_capability_fallback_precedes_the_mutually_exclusive_mode_check():
+    """load_in_4bit defaults to True, so load_in_16bit=True trips the
+    "can only load in 4bit or 8bit or 16bit" RuntimeError unless the unavailable
+    4bit request is cleared first. That check must come after the fallback."""
+    src, _ = _bnb_guards()
+    tree = ast.parse(src)
+    checked = 0
+    # Scope to the enclosing function: the other loader's guard sits earlier in the
+    # file and would otherwise satisfy a plain line-number comparison.
+    for func in ast.walk(tree):
+        if not isinstance(func, ast.FunctionDef):
+            continue
+        raises = [
+            node.lineno
+            for node in ast.walk(func)
+            if isinstance(node, ast.Raise)
+            and "Can only load in 4bit or 8bit or 16bit" in ast.unparse(node)
+        ]
+        if not raises:
+            continue
+        guards = [
+            node.lineno
+            for node in ast.walk(func)
+            if isinstance(node, ast.If)
+            and any(
+                isinstance(n, ast.Name) and n.id == "ALLOW_BITSANDBYTES"
+                for n in ast.walk(node.test)
+            )
+        ]
+        for lineno in raises:
+            checked += 1
+            assert any(g < lineno for g in guards), (
+                f"{func.name}: the mode check at line {lineno} runs before this "
+                "function's ALLOW_BITSANDBYTES fallback, so load_in_16bit=True on a "
+                "bnb-less host raises instead of taking the 16bit path"
+            )
+    assert checked, "mode-exclusivity check not found"
+
+
+def test_bitsandbytes_compile_patch_is_never_called_unguarded():
+    """unsloth_zoo's patch_compiling_bitsandbytes imports bitsandbytes
+    unconditionally, so an unwrapped call raises on a bnb-less host before any
+    fallback can run."""
+    src = (REPO_ROOT / "unsloth" / "models" / "loader.py").read_text(encoding = "utf-8")
+    tree = ast.parse(src)
+    calls = [
+        node
+        for node in ast.walk(tree)
+        if isinstance(node, ast.Call)
+        and isinstance(node.func, ast.Name)
+        and node.func.id == "patch_compiling_bitsandbytes"
+    ]
+    assert calls, "call sites not found"
+    guarded = {
+        call.lineno
+        for node in ast.walk(tree)
+        if isinstance(node, ast.Try)
+        for call in ast.walk(node)
+        if isinstance(call, ast.Call)
+        and isinstance(call.func, ast.Name)
+        and call.func.id == "patch_compiling_bitsandbytes"
+    }
+    unguarded = sorted({c.lineno for c in calls} - guarded)
+    assert not unguarded, f"patch_compiling_bitsandbytes called unguarded at {unguarded}"

tests/saving/language_models/test_merge_model_perplexity_llama-3.2.py

@@ -96,12 +96,11 @@ def load_and_compute_8bit_ppl(
 if __name__ == "__main__":
     mp.set_start_method("spawn", force = True)
 
-    if torch.cuda.is_bf16_supported():
-        compute_dtype = torch.bfloat16
-        attn_implementation = "flash_attention_2"
-    else:
-        compute_dtype = torch.float16
-        attn_implementation = "sdpa"
+    from unsloth import is_bfloat16_supported
+    from unsloth.models._utils import HAS_FLASH_ATTENTION
+
+    compute_dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16
+    attn_implementation = "flash_attention_2" if HAS_FLASH_ATTENTION else "sdpa"
 
     model, tokenizer = FastLanguageModel.from_pretrained(
         model_name = "unsloth/Llama-3.2-3B-Instruct",

tests/saving/language_models/test_merge_model_perplexity_mistral.py

@@ -121,12 +121,11 @@ def load_and_compute_8bit_ppl(
 if __name__ == "__main__":
     mp.set_start_method("spawn", force = True)
 
-    if torch.cuda.is_bf16_supported():
-        compute_dtype = torch.bfloat16
-        attn_implementation = "flash_attention_2"
-    else:
-        compute_dtype = torch.float16
-        attn_implementation = "sdpa"
+    from unsloth import is_bfloat16_supported
+    from unsloth.models._utils import HAS_FLASH_ATTENTION
+
+    compute_dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16
+    attn_implementation = "flash_attention_2" if HAS_FLASH_ATTENTION else "sdpa"
 
     model, tokenizer = FastLanguageModel.from_pretrained(
         model_name = "unsloth/mistral-7b-v0.3",

tests/saving/language_models/test_merge_model_perplexity_phi_4.py

@@ -98,12 +98,11 @@ def load_and_compute_8bit_ppl(
 if __name__ == "__main__":
     mp.set_start_method("spawn", force = True)
 
-    if torch.cuda.is_bf16_supported():
-        compute_dtype = torch.bfloat16
-        attn_implementation = "flash_attention_2"
-    else:
-        compute_dtype = torch.float16
-        attn_implementation = "sdpa"
+    from unsloth import is_bfloat16_supported
+    from unsloth.models._utils import HAS_FLASH_ATTENTION
+
+    compute_dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16
+    attn_implementation = "flash_attention_2" if HAS_FLASH_ATTENTION else "sdpa"
 
     model, tokenizer = FastLanguageModel.from_pretrained(
         model_name = "unsloth/Phi-4",

tests/saving/language_models/test_merged_model_perplexity_llama-3.1-8b.py

@@ -95,12 +95,11 @@ def load_and_compute_8bit_ppl(
 if __name__ == "__main__":
     mp.set_start_method("spawn", force = True)
 
-    if torch.cuda.is_bf16_supported():
-        compute_dtype = torch.bfloat16
-        attn_implementation = "flash_attention_2"
-    else:
-        compute_dtype = torch.float16
-        attn_implementation = "sdpa"
+    from unsloth import is_bfloat16_supported
+    from unsloth.models._utils import HAS_FLASH_ATTENTION
+
+    compute_dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16
+    attn_implementation = "flash_attention_2" if HAS_FLASH_ATTENTION else "sdpa"
 
     model, tokenizer = FastLanguageModel.from_pretrained(
         model_name = "unsloth/Llama-3.1-8B-Instruct",

tests/saving/language_models/test_merged_model_perplexity_qwen_2.5.py

@@ -164,12 +164,11 @@ def load_and_compute_8bit_ppl(
 if __name__ == "__main__":
     mp.set_start_method("spawn", force = True)
 
-    if torch.cuda.is_bf16_supported():
-        compute_dtype = torch.bfloat16
-        attn_implementation = "flash_attention_2"
-    else:
-        compute_dtype = torch.float16
-        attn_implementation = "sdpa"
+    from unsloth import is_bfloat16_supported
+    from unsloth.models._utils import HAS_FLASH_ATTENTION
+
+    compute_dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16
+    attn_implementation = "flash_attention_2" if HAS_FLASH_ATTENTION else "sdpa"
 
     model, tokenizer = FastLanguageModel.from_pretrained(
         model_name = "unsloth/Qwen2.5-7B-Instruct",
@@ -210,8 +209,6 @@ if __name__ == "__main__":
         loftq_config = None,
     )
 
-    from unsloth import is_bfloat16_supported
-
     trainer = SFTTrainer(
         model = model,
         tokenizer = tokenizer,

tests/studio/install/test_rocm_native_linux_lib_dirs.py

@@ -124,7 +124,12 @@ def _call(
     tmp_path factory branches on it, so a session-wide patch breaks the fixture on
     a Windows test host."""
     with patch.object(sys, "platform", platform):
-        with patch("os.path.exists", _fake_exists(present)):
+        # isdir too: the llvm probe requires a directory, so a fake host that only
+        # answers exists() would report every nested llvm dir as missing.
+        with (
+            patch("os.path.exists", _fake_exists(present)),
+            patch("os.path.isdir", _fake_exists(present)),
+        ):
             return _norm(impl(str(bundle)))
 
 
@@ -260,6 +265,78 @@ class TestNativeLinuxRootResolution:
         for where, impl in _impls().items():
             assert self._run(impl, bundle_dir, present) == ["/opt/rocm/lib64"], where
 
+    def test_nested_llvm_runtime_follows_system_rocm_lib(self, bundle_dir):
+        """#7446: libamd_comgr depends on ROCm's versioned LLVM runtime, which is
+        installed below lib/llvm/lib rather than directly in lib."""
+        present = {
+            "/dev/kfd",
+            "/opt/rocm/lib/libhsa-runtime64.so",
+            "/opt/rocm/lib/llvm/lib",
+        }
+        for where, impl in _impls().items():
+            assert self._run(impl, bundle_dir, present) == [
+                "/opt/rocm/lib",
+                "/opt/rocm/lib/llvm/lib",
+            ], where
+
+    def test_lib64_host_still_finds_llvm_under_lib(self, bundle_dir):
+        """ROCm puts LLVM under <root>/lib/llvm even where HSA lives in lib64, so
+        deriving the llvm dir from the HSA dir alone would miss it and leave
+        libamd_comgr binding to the bundle's libLLVM."""
+        present = {
+            "/dev/kfd",
+            "/opt/rocm/lib64/libhsa-runtime64.so",
+            "/opt/rocm/lib/llvm/lib",
+        }
+        for where, impl in _impls().items():
+            assert self._run(impl, bundle_dir, present) == [
+                "/opt/rocm/lib64",
+                "/opt/rocm/lib/llvm/lib",
+            ], where
+
+    def test_lib64_host_prefers_its_own_llvm_dir_when_both_exist(self, bundle_dir):
+        present = {
+            "/dev/kfd",
+            "/opt/rocm/lib64/libhsa-runtime64.so",
+            "/opt/rocm/lib64/llvm/lib",
+            "/opt/rocm/lib/llvm/lib",
+        }
+        for where, impl in _impls().items():
+            assert self._run(impl, bundle_dir, present) == [
+                "/opt/rocm/lib64",
+                "/opt/rocm/lib64/llvm/lib",
+                "/opt/rocm/lib/llvm/lib",
+            ], where
+
+    def test_llvm_path_that_is_a_file_is_not_prepended(self, tmp_path, bundle_dir):
+        """Real filesystem: the serve-time caller joins these straight into
+        LD_LIBRARY_PATH without an is-dir filter, so a non-directory must not
+        reach it."""
+        root = tmp_path / "rocm"
+        (root / "lib").mkdir(parents = True)
+        (root / "lib" / "libhsa-runtime64.so").write_text("")
+        (root / "lib" / "llvm").mkdir()
+        (root / "lib" / "llvm" / "lib").write_text("not a directory")
+        real_exists = os.path.exists
+        # Pin both device nodes: a WSL test host really has /dev/dxg, which would
+        # take the WSL early-return and make this pass for the wrong reason.
+        pinned = {"/dev/kfd": True, "/dev/dxg": False}
+
+        def _exists(p):
+            return pinned.get(str(p), None) if str(p) in pinned else real_exists(p)
+
+        # A test host may itself have a real /opt/rocm (the default candidate), so
+        # assert on the bogus entry rather than on the whole list.
+        for where, impl in _impls().items():
+            with (
+                patch.object(sys, "platform", "linux"),
+                patch.dict(os.environ, {"ROCM_PATH": str(root)}, clear = True),
+                patch("os.path.exists", _exists),
+            ):
+                out = impl(str(bundle_dir))
+            assert str(root / "lib") in out, where
+            assert str(root / "lib" / "llvm" / "lib") not in out, where
+
     def test_lib_precedes_lib64_when_both_exist(self, bundle_dir):
         present = {
             "/dev/kfd",

tests/studio/install/test_rocm_support.py

@@ -3157,12 +3157,35 @@ class TestInstallBnbWindowsRocm:
             assert result is False
             assert "BNB_ROCM_VERSION" not in os.environ
 
-    def test_no_op_when_win_amd64_url_missing(self):
-        """Should be silent no-op if win_amd64 key absent from _BNB_ROCM_PRERELEASE_URLS."""
+    def test_falls_back_to_pypi_when_win_amd64_url_missing(self):
+        """No win_amd64 pre-release wheel must not mean no bitsandbytes: PyPI
+        >=0.50.0 ships libbitsandbytes_rocm{714,72}.dll, so it is a real ROCm build."""
         with patch.object(stack_mod, "_BNB_ROCM_PRERELEASE_URLS", {}):
-            with patch.object(stack_mod, "pip_install_try") as mock_pip:
+            with patch.object(stack_mod, "pip_install_try", return_value = True) as mock_pip:
                 stack_mod._install_bnb_windows_rocm()
-        mock_pip.assert_not_called()
+        assert mock_pip.call_count == 1
+        assert stack_mod._BNB_ROCM_PYPI_FALLBACK in mock_pip.call_args.args
+
+    def test_falls_back_to_pypi_when_prerelease_install_fails(self):
+        """A blocked GitHub pre-release URL must fall through to the PyPI floor rather
+        than leaving Windows ROCm with no working bitsandbytes."""
+        with patch.object(stack_mod, "pip_install_try", side_effect = [False, True]) as mock_pip:
+            with patch.object(stack_mod, "_detect_bnb_rocm_dll_ver", return_value = "72"):
+                result = stack_mod._install_bnb_windows_rocm()
+        assert result is True
+        assert mock_pip.call_count == 2
+        assert "win_amd64" in str(mock_pip.call_args_list[0])
+        assert stack_mod._BNB_ROCM_PYPI_FALLBACK in mock_pip.call_args_list[1].args
+
+    def test_returns_false_only_when_both_paths_fail(self):
+        """Both the pre-release wheel and the PyPI fallback must fail before the
+        helper reports failure."""
+        with patch.dict(os.environ, {}, clear = False):
+            os.environ.pop("BNB_ROCM_VERSION", None)
+            with patch.object(stack_mod, "pip_install_try", return_value = False) as mock_pip:
+                result = stack_mod._install_bnb_windows_rocm()
+        assert result is False
+        assert mock_pip.call_count == 2
 
     def test_sets_bnb_rocm_version_from_detected_dll(self):
         """BNB_ROCM_VERSION is set from the DLL detected after install."""

tests/studio/test_chat_preset_load_config.py

@@ -68,3 +68,18 @@ def test_backend_chat_preset_accepts_load_config():
     routes = _read("studio/backend/routes/chat_history.py")
     assert "class ChatPresetLoadConfig" in routes
     assert "loadConfig: Optional[ChatPresetLoadConfig]" in routes
+
+
+def test_preset_load_config_carries_parallel_slots():
+    # Captured, clamped on read, applied, and accepted by the extra="forbid"
+    # backend model (a missing backend field would 422 every settings sync).
+    source = _read("studio/frontend/src/features/chat/presets/preset-load-config.ts")
+    assert '| "nParallel"' in source
+    assert "nParallel: snapshot.nParallel ?? null" in source
+    assert "nParallel: config.nParallel ?? null" in source
+    assert "N_PARALLEL_MAX, Math.round(partial.nParallel)" in source
+    routes = _read("studio/backend/routes/chat_history.py")
+    assert (
+        "nParallel: Optional[int] = Field(default = None, ge = PARALLEL_MIN, le = PARALLEL_MAX)"
+        in routes
+    )

tests/studio/test_desktop_reliability_frontend_contract.py

@@ -28,6 +28,7 @@ APP_PROVIDER = FRONTEND / "app/provider.tsx"
 
 CLIPBOARD_FILES = FRONTEND / "features/chat/utils/clipboard-files.ts"
 TAURI_CAPABILITIES = REPO / "studio/src-tauri/capabilities/default.json"
+CHAT_PAGE = FRONTEND / "features/chat/chat-page.tsx"
 
 
 def test_file_actions_route_through_native_commands_only_in_tauri():
@@ -216,6 +217,34 @@ def test_expanded_titlebar_button_and_corner_match_sidebar_edge():
     )
 
 
+def test_visible_mac_sidebar_header_is_a_drag_region():
+    source = APP_SIDEBAR.read_text(encoding = "utf-8")
+    header = source.split("<SidebarHeader", 1)[1].split("</SidebarHeader>", 1)[0]
+    drag_region = "data-tauri-drag-region={usesNativeMacTitlebar || undefined}"
+
+    assert drag_region in header
+    assert header.index(drag_region) < header.index('"relative z-10 flex items-center')
+
+
+def test_mac_chat_header_controls_share_the_titlebar_row():
+    source = CHAT_PAGE.read_text(encoding = "utf-8")
+    provider = APP_PROVIDER.read_text(encoding = "utf-8")
+
+    assert "shouldUseNativeMacWindowTitlebar" not in source
+    assert "[--studio-content-top-inset:var(--studio-mac-titlebar-height" not in source
+    assert source.count("var(--studio-mac-traffic-light-inset") == 2
+    assert '"--studio-chat-header-padding-top": "7px"' in provider
+    assert "pt-[var(--studio-content-top-inset,0px)] md:flex-row" in source
+    assert "absolute top-[var(--studio-content-top-inset,0px)]" in source
+
+
+def test_collapsed_mac_sidebar_hides_divider():
+    source = APP_SIDEBAR.read_text(encoding = "utf-8")
+
+    assert "group-data-[collapsible=icon]:[&_[data-sidebar=sidebar]]:border-r-0" in source
+    assert "top-[var(--studio-mac-titlebar-height,34px)]" not in source
+
+
 def test_chat_sidebar_row_actions_visible_on_coarse_pointers():
     """unslothai/unsloth#7276: Recents chat kebab must be tappable on iPad."""
     sidebar_source = APP_SIDEBAR.read_text(encoding = "utf-8")

tests/studio/test_model_picker_contracts.py

@@ -642,6 +642,253 @@ def test_legacy_migration_is_idempotent_and_non_destructive():
     assert "if (isDefaultConfig(migrated) || Object.hasOwn(map, key)) {" in src
 
 
+def test_parallel_slots_setting_wired_end_to_end():
+    """The per-load Parallel Slots knob (llama-server --parallel) must flow from
+    the run-settings form through persistence, every /load builder, the validate
+    preflight and the cross-model reset; a lost hop silently reverts the model to
+    the server-wide slot default."""
+    config = _read("features/model-picker/model-config/per-model-config.ts")
+    # Persisted per model, clamped on every read/write, and null (= server
+    # default) counts as default so blank configs are not stored.
+    assert '"nParallel",' in config
+    assert "N_PARALLEL_MAX, Math.round(partial.nParallel)" in config
+    assert "config.nParallel == null &&" in config
+    page = _read("features/model-picker/components/model-config-page.tsx")
+    # Rendered in the GGUF advanced section, which a remembered override reopens.
+    assert "Parallel Slots" in page
+    assert "config.nParallel != null ||" in page
+    assert 'aria-label="Parallel decode slots"' in page
+    api_types = _read("features/chat/types/api.ts")
+    assert "n_parallel?: number | null;" in api_types
+    runtime = _read("features/chat/hooks/use-chat-model-runtime.ts")
+    # Click-time snapshot, /load body, validate preflight, cross-model reset and
+    # failed-switch rollback all carry the value.
+    assert "pendingLoadConfig?.nParallel" in runtime
+    # GGUF-gated, like the compare pane: a transformers load has no slots.
+    assert "n_parallel: isGguf ? loadNParallel : null," in runtime
+    assert "n_parallel: validateNParallel," in runtime
+    assert "loadNParallel = pendingLoadConfig?.nParallel ?? null;" in runtime
+    assert "n_parallel: stateBeforeUnload.loadedNParallel," in runtime
+    chat_api = _read("features/chat/api/chat-api.ts")
+    assert "n_parallel: payload.n_parallel," in chat_api
+    composer = _read("features/chat/shared-composer.tsx")
+    # The compare pane is a second /load builder; its preflight sizes like its load.
+    assert composer.count("n_parallel: ownConfig.nParallel ?? null,") == 2
+    adapter = _read("features/chat/api/chat-adapter.ts")
+    # The startup auto-load is a third builder reading the remembered config.
+    assert adapter.count("n_parallel: config.nParallel ?? null,") == 2
+    # ... and records it as loaded through the diffusion-gated local below.
+    assert "loadedNParallel: committedSlots," in adapter
+    status = _read("features/chat/lib/apply-inference-status-to-store.ts")
+    # Hydration seeds the rollback BASELINE only; adopting the resolved echo into
+    # the control would pin a blank "server default" to a number.
+    assert "loadedNParallel: status.requested_parallel_slots," in status
+    assert "nParallel: status.requested_parallel_slots," not in status
+    sidebar = _read("features/model-picker/components/sidebar-model-config.tsx")
+    # The sidebar form remounts when an external change lands.
+    assert 'config.nParallel ?? "",' in sidebar
+
+
+def test_parallel_slots_control_cleared_when_the_load_never_sent_them():
+    """`nParallel` is the editable control ("blank = follow the server default")
+    and `loadedNParallel` the rollback baseline. A success path that sends no
+    slot count must blank the control, or a value staged for another model shows
+    as applied, is persisted into this model's config (`isDefaultConfig` keys on
+    nParallel) and is re-sent by the next Apply. Each assertion below is the only
+    thing pinning one such path."""
+    status = " ".join(_read("features/chat/lib/apply-inference-status-to-store.ts").split())
+    # A model/variant swap underneath this tab must reset the control like
+    # performLoad's cross-model reset, or model A's count follows onto model B.
+    # Narrowly gated -- see test_hydration_keeps_the_slot_control_when_readopting_the_running_model.
+    assert "...(seedLoadParams && slotsModelChanged && { nParallel: null })," in status
+    # ... while still never adopting the RESOLVED echo into the control.
+    assert "nParallel: status.requested_parallel_slots," not in status
+
+    adapter = _read("features/chat/api/chat-adapter.ts")
+    # Slice the two success branches apart, bounding the second at the shared tail
+    # so it cannot swallow the fresh-default path below and stay green.
+    candidate = adapter.split("async function loadAutoLoadCandidate", 1)[1]
+    gguf_branch, non_gguf_rest = candidate.split('if (candidate.kind === "gguf") {', 1)[1].split(
+        "\n    } else {\n", 1
+    )
+    non_gguf_branch = non_gguf_rest.split("if (!(loadResp.is_lora ?? false)) {", 1)[0]
+    # The cached-GGUF branch keeps the remembered override via the gated local...
+    assert "nParallel: committedSlots," in gguf_branch
+    assert "nParallel: null," not in gguf_branch
+    # ... the safetensors fallback sends no slots, so it clears both, or the count
+    # survives on a model whose form does not even render the field.
+    assert "nParallel: null," in non_gguf_branch
+    assert "loadedNParallel: null," in non_gguf_branch
+
+    fresh_default = adapter.split("No downloaded models found. Fetching", 1)[1].split(
+        'showAutoLoadSuccess("Loaded Qwen', 1
+    )[0]
+    # The fresh-default download omits the slots, so its success state clears both,
+    # or the control reads as an unapplied edit against the seeded baseline.
+    assert "n_parallel" not in fresh_default.split("saveSpeculativeType", 1)[0]
+    assert "nParallel: null," in fresh_default
+    assert "loadedNParallel: null," in fresh_default
+
+
+def test_hydration_clears_the_slot_baseline_for_a_slotless_model():
+    """The baseline is what a rollback re-sends and what preset capture reads, so
+    a model that cannot have slots must not inherit the previous GGUF's count.
+    /status omits the echo for non-GGUF and sends an explicit null for diffusion;
+    an absent field on a GGUF is an older backend and must NOT wipe it."""
+    src = _read("features/chat/lib/apply-inference-status-to-store.ts")
+    assert (
+        "(status.is_gguf === false || status.requested_parallel_slots === null) && {" in src
+    ), "the slotless clear must key on is_gguf or an explicit null echo"
+    clear = src.index("status.is_gguf === false || status.requested_parallel_slots === null")
+    assert "loadedNParallel: null," in src[clear : clear + 200]
+    # Never `!= null`: that also matches the absent field an older backend sends.
+    assert "status.requested_parallel_slots !== null && {" not in src
+
+
+def test_hydration_keeps_the_slot_control_when_readopting_the_running_model():
+    """`hydratingExistingModel` is true whenever the incoming status disagrees
+    with what this tab last recorded, which includes RE-ADOPTING a model the tab
+    never lost: the resident-adopt branch restores the model's own per-model
+    config and only then hydrates, passing the EXTERNAL id as
+    `previousCheckpoint`. An ungated clear there wipes the slot count that branch
+    just restored, and the blank persists into `savePerModelConfig`, so a Save
+    the user reads as a no-op erases their remembered override.
+
+    Only that branch knows the model is unchanged, so it says so explicitly.
+    Slot counts cannot stand in: the echo falls back to the server-wide default,
+    so a genuine A->B swap can echo exactly A's explicit count."""
+    status = " ".join(_read("features/chat/lib/apply-inference-status-to-store.ts").split())
+    assert (
+        "const slotsModelChanged = hydratingExistingModel && !options.readoptingSameModel;"
+        in status
+    )
+    assert "...(seedLoadParams && slotsModelChanged && { nParallel: null })," in status
+    # Never a slot-count proxy for "same model".
+    assert "prevState.loadedNParallel === (status.requested_parallel_slots" not in status
+    # The baseline seed stays ungated, or a rollback after a tab reload restores
+    # the model at the server default slots.
+    assert "loadedNParallel: status.requested_parallel_slots," in status
+
+    runtime = " ".join(_read("features/chat/hooks/use-chat-model-runtime.ts").split())
+    resident = runtime.split("if (!forceReload && isExternalModelId(selectedCheckpoint)) {", 1)[
+        1
+    ].split("const stopDecision", 1)[0]
+    # What makes the scenario reachable: the branch restores the model's own
+    # config, then hydrates against the external id.
+    assert "applyPerModelConfigToRuntime(selection.previousConfig);" in resident
+    assert "previousCheckpoint: selectedCheckpoint," in resident
+    # Only reachable because the branch matched the id AND the variant first.
+    assert "resolveInferenceCheckpointId(residentStatus) === modelId" in resident
+    assert "readoptingSameModel: true," in resident
+    # The refresh() hydrate must NOT claim it: there the model really can change.
+    poll = runtime.split("setModels(listRes.models.map(toChatModelSummary));", 1)[1].split(
+        "} else if (!statusRes.active_model", 1
+    )[0]
+    assert "applyActiveModelStatusToStore(statusRes, {" in poll
+    assert "readoptingSameModel" not in poll
+
+
+def test_parallel_slots_are_never_recorded_for_a_diffusion_load():
+    """A DiffusionGemma GGUF answers ``is_gguf: true``, but its runner ignores
+    ``--parallel``, so ``_parallel_slot_echo`` reports null slots for it. The
+    three load success paths must gate on ``is_diffusion`` too, or they record a
+    click-time count the load never committed.
+
+    That phantom does not stay put: ``capturePresetLoadConfig`` snapshots
+    ``nParallel`` with no model gate and a preset carries no model identity, so
+    applying it over a TEXT GGUF sends the count as a real ``n_parallel``.
+    """
+    runtime = " ".join(_read("features/chat/hooks/use-chat-model-runtime.ts").split())
+    # One gated local feeds the control and the baseline, so they cannot drift.
+    assert "(loadResponse.is_gguf ?? false) && !(loadResponse.is_diffusion ?? false)" in runtime
+    assert "nParallel: committedSlots," in runtime
+    assert "loadedNParallel: committedSlots," in runtime
+
+    adapter = " ".join(_read("features/chat/api/chat-adapter.ts").split())
+    assert (
+        "const committedSlots = (loadResp.is_diffusion ?? false) ? null "
+        ": (config.nParallel ?? null);" in adapter
+    )
+    assert "nParallel: committedSlots," in adapter
+    assert "loadedNParallel: committedSlots," in adapter
+
+    composer = " ".join(_read("features/chat/shared-composer.tsx").split())
+    assert "targetIsGguf && !(resp.is_diffusion ?? false)" in composer
+    assert "nParallel: committedSlots," in composer
+    assert "loadedNParallel: committedSlots," in composer
+
+
+def test_hydration_restores_a_remembered_slot_override():
+    """The control is never seeded from the status echo, so a model running on a
+    remembered override shows a BLANK slot control after a browser reload or a
+    tab move to another GGUF. `ModelConfigPage.resolveInitial` prefers the live
+    store for the active model, so that blank is what the form edits: the next
+    Apply reloads at the server default and a Save writes the blank over the
+    remembered count.
+
+    The seed is deliberately narrow: storage is read only on a fresh store or a
+    model change, never on a steady poll, and the value is adopted only when the
+    server already runs that exact count, which proves it is this model's own.
+    """
+    src = _read("features/chat/lib/apply-inference-status-to-store.ts")
+    status = " ".join(src.split())
+    assert (
+        "resolveInitialConfig(checkpointId, status.gguf_variant ?? null)" in status
+    ), "the remembered override comes from per-model storage, not the echo"
+    assert (
+        "const slotsUnseeded = prevState.loadedNParallel === null && "
+        "prevState.nParallel === null;" in status
+    )
+    assert (
+        "status.is_gguf && (slotsUnseeded || slotsModelChanged)" in status
+    ), "storage is read on a fresh store or a model change, never on a steady poll"
+    assert (
+        "...(seedLoadParams && (slotsUnseeded || slotsModelChanged) &&" in status
+    ), "the seed fires in both cases the clear leaves the control blank"
+    assert (
+        "rememberedNParallel != null && rememberedNParallel === "
+        "status.requested_parallel_slots && { nParallel: rememberedNParallel, }" in status
+    )
+    # Both cases trip the model-change clear, so the seed only survives by
+    # being spread after it.
+    assert src.index("slotsModelChanged && { nParallel: null }") < src.index(
+        "nParallel: rememberedNParallel,"
+    )
+
+
+def test_failed_switch_rollback_restores_the_slot_intent_not_the_resolved_count():
+    """`loadedNParallel` holds a RESOLVED count even for a load that sent no
+    slots (the echo falls back to the server-wide default), so it is the right
+    value to re-send when recreating the previous server and the wrong one to put
+    back in the control: it turns "follow the server default" into an explicit
+    override that a later Save or preset capture pins. The outer catch only
+    repairs that for a staged config, so a plain string pick keeps the phantom.
+
+    The intent comes from the picker's own pre-switch snapshot when there is one:
+    chat-page pre-applies the TARGET's config before calling selectModel, so the
+    live control describes the outgoing model only for a bare pick."""
+    runtime = " ".join(_read("features/chat/hooks/use-chat-model-runtime.ts").split())
+    assert (
+        'const previousNParallel = typeof selection !== "string" && '
+        "selection.previousConfig ? (selection.previousConfig.nParallel ?? null) "
+        ": useChatRuntimeStore.getState().nParallel;" in runtime
+    )
+    assert runtime.index("const previousNParallel") < runtime.index(
+        "applyPerModelConfigToRuntime(pendingLoadConfig);"
+    ), "a config staged on the selection must not replace it either"
+    picker = " ".join(_read("features/chat/chat-page.tsx").split())
+    assert (
+        "const previousConfig = currentRuntimePerModelConfig({ includeMaxSeqLength: true, }); "
+        "const hasAppliedConfig = applyModelLoadConfigToRuntime(" in picker
+    ), "the snapshot must be taken before the target's config is applied"
+    rollback = runtime.split("const rollbackSpeculativeType", 1)[1]
+    assert "nParallel: previousNParallel," in rollback
+    # Baseline and reload payload keep the resolved count, or the rollback
+    # recreates the previous model at a different slot count.
+    assert "loadedNParallel: stateBeforeUnload.loadedNParallel ?? null," in rollback
+    assert "n_parallel: stateBeforeUnload.loadedNParallel," in runtime
+
+
 def test_vulkan_inference_devices_are_the_pickable_set():
     """GGUF loads run through llama-server, so on a Vulkan build the picker must
     offer the inference inventory (ggml ordinals, the space `--device Vulkan<i>`

tests/studio/test_tauri_deep_link_contract.py

@@ -0,0 +1,188 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Desktop deep-link configuration, routing, and validation contracts."""
+
+import json
+from pathlib import Path
+import shutil
+import subprocess
+import textwrap
+
+import pytest
+
+try:
+    import tomllib
+except ModuleNotFoundError:
+    tomllib = pytest.importorskip("tomli")
+
+
+REPO = Path(__file__).resolve().parents[2]
+FRONTEND = REPO / "studio/frontend"
+TAURI = REPO / "studio/src-tauri"
+PARSER = FRONTEND / "src/features/deep-links/parse-deep-link.ts"
+INTENT_GATE = FRONTEND / "src/features/deep-links/deep-link-intent.ts"
+GGUF_FILENAME = FRONTEND / "src/features/hub/lib/gguf-filename.ts"
+
+
+def test_unsloth_deep_link_parser_guardrails(tmp_path: Path) -> None:
+    if shutil.which("node") is None:
+        pytest.skip("node not available")
+    probe = subprocess.run(
+        ["node", "--experimental-strip-types", "--version"],
+        capture_output = True,
+        text = True,
+        timeout = 5,
+    )
+    if probe.returncode != 0:
+        pytest.skip("node --experimental-strip-types not available")
+
+    (tmp_path / "parse-deep-link.ts").write_text(
+        PARSER.read_text(encoding = "utf-8"), encoding = "utf-8"
+    )
+
+    (tmp_path / "gguf-filename.ts").write_text(
+        GGUF_FILENAME.read_text(encoding = "utf-8"), encoding = "utf-8"
+    )
+
+    (tmp_path / "deep-link-intent.ts").write_text(
+        INTENT_GATE.read_text(encoding = "utf-8"), encoding = "utf-8"
+    )
+    script = textwrap.dedent("""
+        import assert from "node:assert/strict";
+        import { parseUnslothDeepLink } from "./parse-deep-link.ts";
+
+        import { createDeepLinkIntentGate } from "./deep-link-intent.ts";
+        import {
+          ggufFilenamesMatch,
+          ggufSelectionOverrideMatchesIntent,
+        } from "./gguf-filename.ts";
+
+        const valid = new Map([
+          [
+            "unsloth://open_from_hf?model=unsloth/Laguna-S-2.1-GGUF",
+            { model: "unsloth/Laguna-S-2.1-GGUF" },
+          ],
+          [
+            "unsloth://open_from_hf/?model=org/repo_name",
+            { model: "org/repo_name" },
+          ],
+          [
+            "unsloth://open_from_hf?model=org%2Frepo",
+            { model: "org/repo" },
+          ],
+          [
+            "unsloth://open_from_hf?model=unsloth/Laguna-S-2.1-GGUF&file=Laguna-S-2.1-UD-IQ3_XXS.gguf",
+            {
+              model: "unsloth/Laguna-S-2.1-GGUF",
+              file: "Laguna-S-2.1-UD-IQ3_XXS.gguf",
+            },
+          ],
+          [
+            "unsloth://open_from_hf?file=weights%2Fmodel-Q4_K_M.gguf&model=org/repo",
+            { model: "org/repo", file: "weights/model-Q4_K_M.gguf" },
+          ],
+          [
+            `unsloth://open_from_hf?model=${"a".repeat(96)}/${"b".repeat(96)}`,
+            { model: `${"a".repeat(96)}/${"b".repeat(96)}` },
+          ],
+        ]);
+        for (const [url, intent] of valid) {
+          assert.deepEqual(parseUnslothDeepLink(url), intent, url);
+        }
+
+        assert.equal(
+          ggufFilenamesMatch(
+            "weights/model-Q4_K_M-00002-of-00002.gguf",
+            "weights/model-Q4_K_M-00001-of-00002.gguf",
+          ),
+          true,
+        );
+        assert.equal(
+          ggufFilenamesMatch("model-Q4_K_M.GGUF", "model-q4_k_m.gguf"),
+          true,
+        );
+        assert.equal(ggufFilenamesMatch("mmproj-F16.gguf", "model-F16.gguf"), false);
+
+        assert.equal(ggufSelectionOverrideMatchesIntent("a.gguf", 2, "a.gguf", 2), true);
+        assert.equal(ggufSelectionOverrideMatchesIntent("a.gguf", 2, "a.gguf", 1), false);
+
+        let now = 1_000;
+        const acceptIntent = createDeepLinkIntentGate(2_000, () => now);
+        assert.equal(acceptIntent("org/repo", "a.gguf"), 1);
+        assert.equal(acceptIntent("org/repo", "a.gguf"), null);
+        assert.equal(acceptIntent("org/repo", "b.gguf"), 2);
+        now = 3_000;
+        assert.equal(acceptIntent("org/repo", "b.gguf"), 3);
+
+
+        const invalid = [
+          "",
+          "https://open_from_hf?model=org/repo",
+          "UNSLOTH://open_from_hf?model=org/repo",
+          "unsloth://OPEN_FROM_HF?model=org/repo",
+          "unsloth://open_from_hf/path?model=org/repo",
+          "unsloth://open_from_hf/%2e%2e?model=org/repo",
+          "unsloth://user@open_from_hf?model=org/repo",
+          "unsloth://open_from_hf:42?model=org/repo",
+          "unsloth://open_from_hf?model=org/repo#fragment",
+          "unsloth://open_from_hf?model=org/repo&download=true",
+
+          "unsloth://open_from_hf?model=org/repo&file=model.gguf&file=other.gguf",
+          "unsloth://open_from_hf?model=org/repo&file=",
+          "unsloth://open_from_hf?model=org/repo&file=../model.gguf",
+          "unsloth://open_from_hf?model=org/repo&file=%2Fmodel.gguf",
+          "unsloth://open_from_hf?model=org/repo&file=model.safetensors",
+          "unsloth://open_from_hf?model=org/repo&model=other/repo",
+          "unsloth://open_from_hf?model=repo",
+          "unsloth://open_from_hf?model=org/repo/extra",
+          "unsloth://open_from_hf?model=-org/repo",
+          "unsloth://open_from_hf?model=org/repo.",
+
+          "unsloth://open_from_hf?model=org/repo.git",
+          "unsloth://open_from_hf?model=org/repo--name",
+          "unsloth://open_from_hf?model=org/repo..name",
+        ];
+        for (const url of invalid) {
+          assert.equal(parseUnslothDeepLink(url), null, url);
+        }
+    """)
+    result = subprocess.run(
+        ["node", "--experimental-strip-types", "--no-warnings", "--input-type=module"],
+        input = script,
+        cwd = tmp_path,
+        capture_output = True,
+        text = True,
+        timeout = 30,
+    )
+    assert result.returncode == 0, f"stderr: {result.stderr}\nstdout: {result.stdout}"
+
+
+def test_tauri_registers_only_the_unsloth_scheme() -> None:
+    cargo = tomllib.loads((TAURI / "Cargo.toml").read_text(encoding = "utf-8"))
+    dependencies = cargo["dependencies"]
+    assert "tauri-plugin-deep-link" in dependencies
+    single_instance = dependencies["tauri-plugin-single-instance"]
+    assert isinstance(single_instance, dict)
+    assert "deep-link" in single_instance.get("features", [])
+
+    config = json.loads((TAURI / "tauri.conf.json").read_text(encoding = "utf-8"))
+    assert config["plugins"]["deep-link"]["desktop"]["schemes"] == ["unsloth"]
+
+    capabilities = json.loads((TAURI / "capabilities/default.json").read_text(encoding = "utf-8"))
+    assert "deep-link:default" in capabilities["permissions"]
+    assert "core:window:allow-unminimize" in capabilities["permissions"]
+
+    main = (TAURI / "src/main.rs").read_text(encoding = "utf-8")
+    assert main.index("tauri_plugin_single_instance::init") < main.index(
+        "tauri_plugin_deep_link::init()"
+    )
+    assert "DeepLinkExt" in main
+    assert "if let Err(error) = app.deep_link().register_all()" in main
+    assert 'warn!("Failed to register deep-link handlers: {error}")' in main
+    assert 'target_os = "linux"' in main
+    desktop_template = TAURI / "linux/unsloth.desktop"
+    assert config["bundle"]["linux"]["deb"]["desktopTemplate"] == "./linux/unsloth.desktop"
+    desktop = desktop_template.read_text(encoding = "utf-8")
+    assert "Exec={{exec}} %u" in desktop
+    assert "MimeType=x-scheme-handler/unsloth;" in desktop

tests/test_fp8_tiny_e8m0.py

@@ -11,7 +11,11 @@ dequant reference.
 import pytest
 import torch
 
-pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason = "needs CUDA")
+cuda_available = torch.cuda.is_available()
+xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
+dev = "cuda" if cuda_available else "xpu" if xpu_available else "cpu"
+
+pytestmark = pytest.mark.skipif(not (cuda_available or xpu_available), reason = "needs CUDA or XPU")
 
 
 def _reference(X, weight, scale, block):
@@ -27,7 +31,6 @@ 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)
@@ -50,7 +53,6 @@ def test_e8m0_scale_is_upcast_and_runs():
     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)
@@ -70,7 +72,6 @@ def test_rectangular_block_dequant_matches_reference():
     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)
@@ -94,7 +95,6 @@ def test_e8m0_scale_preserves_non_default_block_size_attr():
         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

tests/test_new_mapper_fetched_fp8.py

@@ -14,6 +14,11 @@ Two gaps it misses:
    future rename): reading them with ``[]`` raises ``KeyError`` into the bare ``except``,
    taking the 4bit half, the probe's whole purpose, down with it.
 
+Both of the above only reach the block table. The last two tests take the row branch, which
+``load_in_fp8 = True`` plus ``UNSLOTH_HAS_FBGEMM`` selects ahead of block: deleting that branch,
+or dropping ``_resolve_with_mappers``' ``fp8_row`` argument so it falls back to the installed
+table, both leave every other test here green.
+
 ``loader_utils`` imports torch, so ast-extract the resolvers and run them against a stubbed
 ``requests``, as in ``tests/test_bad_mappings_redirect.py``.
 """
@@ -33,6 +38,8 @@ _NEW_OFFICIAL = "zeta-org/Zeta-9B-Only-On-Main-FP8"
 _NEW_BLOCK = "unsloth/Zeta-9B-Only-On-Main-FP8-Block"
 _NEW_ROW = "unsloth/Zeta-9B-Only-On-Main-FP8-Row"
 _ANCHOR = '    "unsloth/Kimi-K2-Instruct-BF16" : ('
+# Row table only, so the block branch cannot answer for it and mask a row-path regression.
+_ROW_ONLY = "zeta-org/Zeta-9B-Row-Only-FP8"
 
 
 def _mapper_source():
@@ -51,6 +58,11 @@ def _with_extra_fp8_model(source):
     return source.replace(_ANCHOR, entry + _ANCHOR, 1)
 
 
+def _with_row_only_fp8_model(source):
+    """Fetched row table only. Block must not know it, or the block branch answers instead."""
+    return source + f'\nFLOAT_TO_FP8_ROW_MAPPER["{_ROW_ONLY.lower()}"] = "{_NEW_ROW}"\n'
+
+
 def _without_fp8_tables(source):
     """A mapper.py from before the fp8 tables existed."""
     return source.replace("FLOAT_TO_FP8_BLOCK_MAPPER", "SOME_OTHER_BLOCK_TABLE").replace(
@@ -153,3 +165,44 @@ def test_probe_survives_a_fetched_mapper_without_the_fp8_tables(monkeypatch):
     assert (
         int_to_float and float_to_int and map_to_16bit
     ), "a fetched mapper.py without the fp8 tables must not take the 4bit upgrade check down"
+
+
+def test_fbgemm_prefers_the_row_table_over_the_block_one(monkeypatch):
+    """With FBGEMM, `load_in_fp8 = True` must resolve row-scaled, not blockwise."""
+    monkeypatch.setenv("UNSLOTH_HAS_FBGEMM", "1")
+    namespace = _load_resolver(_mapper_source())
+    row = namespace["FLOAT_TO_FP8_ROW_MAPPER"]
+    block = namespace["FLOAT_TO_FP8_BLOCK_MAPPER"]
+
+    key = next(k for k in row if k in block and row[k] != block[k])
+    resolved = namespace["get_model_name"](key, load_in_4bit = False, load_in_fp8 = True)
+
+    assert resolved == row[key], (
+        f"FBGEMM must take the row branch for {key!r}, got {resolved!r} "
+        f"(the blockwise answer is {block[key]!r})"
+    )
+
+
+def test_probe_answers_for_a_row_only_repo_the_fetched_mapper_knows(monkeypatch):
+    """The row half of the probe needs the FETCHED row table, same as the block half."""
+    monkeypatch.setenv("UNSLOTH_HAS_FBGEMM", "1")
+    installed = _mapper_source()
+    namespace = _load_resolver(installed)
+    installed_row = namespace["FLOAT_TO_FP8_ROW_MAPPER"]
+    key = _ROW_ONLY.lower()
+    assert key not in installed_row, "the installed row table must not know it"
+    assert key not in namespace["FLOAT_TO_FP8_BLOCK_MAPPER"], "no block entry, or block answers"
+
+    _install_fake_requests(monkeypatch, _with_row_only_fp8_model(installed))
+    _install_fake_vllm_absent(monkeypatch, namespace)
+
+    try:
+        resolved = namespace["get_model_name"](_ROW_ONLY, load_in_4bit = False, load_in_fp8 = True)
+    except NotImplementedError as error:
+        assert "not supported in your current Unsloth version" in str(error)
+    else:
+        raise AssertionError(
+            f"a fetched-only row-scaled repo must raise the upgrade error, got {resolved!r}"
+        )
+
+    assert namespace["FLOAT_TO_FP8_ROW_MAPPER"] is installed_row

tests/test_raw_text_json_loading.py

@@ -0,0 +1,128 @@
+"""Regression test for .json parsing in unsloth/dataprep/raw_text.py.
+
+Both .json and .jsonl map to the "json_lines" handler, which used to parse the
+file one line at a time. A real .json file is a single JSON document (commonly
+a top-level list of records), so every line failed json.loads, the whole
+document was dropped, and the handler returned "" (load_from_file then rejected
+the valid file as "empty"). The handler now parses the file as one JSON value
+first and falls back to line-by-line for true .jsonl.
+
+raw_text.py's only third-party import is `datasets`, so we stub it and exec the
+module directly, with no `import unsloth` (which needs a GPU / unsloth_zoo).
+"""
+
+import json
+import sys
+import types
+from pathlib import Path
+
+RAW_TEXT_PATH = Path(__file__).parents[1] / "unsloth" / "dataprep" / "raw_text.py"
+
+
+def _load_raw_text():
+    sys.modules.setdefault("datasets", types.SimpleNamespace(Dataset = object))
+    module = types.ModuleType("unsloth_raw_text_under_test")
+    exec(
+        compile(RAW_TEXT_PATH.read_text(encoding = "utf-8"), str(RAW_TEXT_PATH), "exec"),
+        module.__dict__,
+    )
+    return module
+
+
+def test_json_document_is_parsed_whole(tmp_path):
+    loader = _load_raw_text().RawTextDataLoader(tokenizer = object())
+    path = tmp_path / "data.json"
+    path.write_text(
+        json.dumps([{"text": "hello world"}, {"text": "second sample"}], indent = 2), encoding = "utf-8"
+    )
+    assert loader._read_file_by_format(str(path), "json_lines") == "hello world\n\nsecond sample"
+
+
+def test_jsonl_is_still_parsed_line_by_line(tmp_path):
+    loader = _load_raw_text().RawTextDataLoader(tokenizer = object())
+    path = tmp_path / "data.jsonl"
+    path.write_text('{"text": "a"}\n{"text": "b"}\n', encoding = "utf-8")
+    assert loader._read_file_by_format(str(path), "json_lines") == "a\n\nb"
+
+
+def test_jsonl_is_never_materialized(tmp_path):
+    """A .jsonl file must keep streaming, whole-document parsing is only for .json."""
+    real_open = open
+
+    class _StreamOnlyFile:
+        """File wrapper that fails the test if the whole file is pulled into memory."""
+
+        def __init__(self, handle):
+            self.handle = handle
+
+        def __enter__(self):
+            return self
+
+        def __exit__(self, *exc_info):
+            self.handle.close()
+            return False
+
+        def __iter__(self):
+            return iter(self.handle)
+
+        def read(self, *args, **kwargs):
+            raise AssertionError(".jsonl was read whole instead of streamed line by line")
+
+        def seek(self, *args, **kwargs):
+            raise AssertionError(".jsonl was re-read instead of streamed line by line")
+
+    module = _load_raw_text()
+    module.open = lambda *args, **kwargs: _StreamOnlyFile(real_open(*args, **kwargs))
+
+    path = tmp_path / "big.jsonl"
+    path.write_text('{"text": "a"}\n\n{"text": "b"}\nnot json at all\n', encoding = "utf-8")
+    loader = module.RawTextDataLoader(tokenizer = object())
+    assert loader._read_file_by_format(str(path), "json_lines") == "a\n\nb"
+
+
+def test_json_holding_json_lines_still_falls_back(tmp_path):
+    """A .json file that actually holds JSON Lines still parses, via the per-line fallback."""
+    loader = _load_raw_text().RawTextDataLoader(tokenizer = object())
+    path = tmp_path / "mislabelled.json"
+    path.write_text('{"text": "a"}\n{"text": "b"}\n', encoding = "utf-8")
+    assert loader._read_file_by_format(str(path), "json_lines") == "a\n\nb"
+
+
+def test_utf8_bom_json_document_is_parsed(tmp_path):
+    """Windows tooling prefixes a UTF-8 BOM; it must not sink the whole document."""
+    loader = _load_raw_text().RawTextDataLoader(tokenizer = object())
+    path = tmp_path / "bom.json"
+    path.write_text(
+        json.dumps([{"text": "hello world"}, {"text": "second sample"}], indent = 2),
+        encoding = "utf-8-sig",
+    )
+    assert path.read_bytes().startswith(b"\xef\xbb\xbf")
+    assert loader._read_file_by_format(str(path), "json_lines") == "hello world\n\nsecond sample"
+
+
+def test_utf8_bom_jsonl_keeps_first_record(tmp_path):
+    """A BOM must not silently drop the first .jsonl record."""
+    loader = _load_raw_text().RawTextDataLoader(tokenizer = object())
+    path = tmp_path / "bom.jsonl"
+    path.write_text('{"text": "a"}\n{"text": "b"}\n', encoding = "utf-8-sig")
+    assert loader._read_file_by_format(str(path), "json_lines") == "a\n\nb"
+
+
+def test_utf8_bom_json_holding_json_lines_falls_back(tmp_path):
+    """The per-line fallback re-reads from byte 0, so the BOM must be stripped again."""
+    loader = _load_raw_text().RawTextDataLoader(tokenizer = object())
+    path = tmp_path / "bom_mislabelled.json"
+    path.write_text('{"text": "a"}\n{"text": "b"}\n', encoding = "utf-8-sig")
+    assert loader._read_file_by_format(str(path), "json_lines") == "a\n\nb"
+
+
+def test_utf8_bom_plain_text_and_csv(tmp_path):
+    """The BOM also leaks into .txt training text and the first .csv column name."""
+    loader = _load_raw_text().RawTextDataLoader(tokenizer = object())
+    txt = tmp_path / "bom.txt"
+    txt.write_text("hello", encoding = "utf-8-sig")
+    assert loader._read_file_by_format(str(txt), "plain_text") == "hello"
+
+    csv_path = tmp_path / "bom.csv"
+    csv_path.write_text("text,other\nhello,x\n", encoding = "utf-8-sig")
+    assert loader._read_file_by_format(str(csv_path), "csv_text_column") == "hello"

tests/utils/perplexity_eval.py

@@ -2,6 +2,9 @@ from tqdm import tqdm
 import torch
 import pandas as pd
 
+# DEVICE_TYPE_TORCH, not DEVICE_TYPE: the latter can be "hip"/"mlx", which .to() rejects.
+from unsloth.device_type import DEVICE_TYPE_TORCH
+
 model_comparison_results = {}
 
 
@@ -17,7 +20,7 @@ def ppl_model(model, tokenizer, dataset):
         for begin_loc in range(0, seq_len, stride):
             end_loc = min(begin_loc + max_length, seq_len)
             trg_len = end_loc - prev_end_loc
-            input_ids = encodings.input_ids[:, begin_loc:end_loc].to("cuda")
+            input_ids = encodings.input_ids[:, begin_loc:end_loc].to(DEVICE_TYPE_TORCH)
             target_ids = input_ids.clone()
             target_ids[:, :-trg_len] = -100
             pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0

tests/utils/test_batched_leftpad_generation_gpu.py

@@ -4,7 +4,7 @@ Greedy generation in a left-padded batch must match solo batch-size-1
 generation for the first PREFIX_TOKENS tokens (the bug makes padded rows
 diverge into garbage immediately; a full-length match would be flaky due to
 benign batch-numerics tie-flips deep in the sequence) and must not be
-gibberish. Skipped without CUDA. Run: `python -m pytest
+gibberish. Skipped without a GPU. Run: `python -m pytest
 tests/utils/test_batched_leftpad_generation_gpu.py -v`.
 """
 
@@ -12,8 +12,19 @@ import pytest
 import torch
 
 cuda_available = torch.cuda.is_available()
+xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
+device = "cuda" if cuda_available else "xpu" if xpu_available else "cpu"
 
-pytestmark = pytest.mark.skipif(not cuda_available, reason = "requires a CUDA GPU")
+# Non-strict rather than CUDA-only: keeps the XPU divergence visible, and goes
+# green by itself once XPU generation is fixed.
+pytestmark = [
+    pytest.mark.skipif(not (cuda_available or xpu_available), reason = "requires a CUDA or XPU GPU"),
+    pytest.mark.xfail(
+        xpu_available and not cuda_available,
+        reason = "batched left-padded generation diverges on XPU",
+        strict = False,
+    ),
+]
 
 MODEL_NAME = "unsloth/Qwen2.5-0.5B-Instruct"
 MAX_NEW_TOKENS = 32
@@ -53,7 +64,7 @@ def _chat(tokenizer, prompt):
 
 def _generate(model, tokenizer, texts):
     inputs = tokenizer(texts, return_tensors = "pt", padding = True, add_special_tokens = False).to(
-        "cuda"
+        device
     )
     with torch.inference_mode():
         out = model.generate(

tests/utils/test_packing.py

@@ -44,6 +44,8 @@ def _build_packed_training_setup(tmp_path, device):
             dtype = torch.bfloat16
         else:
             dtype = torch.float16
+    elif device.type == "xpu":
+        dtype = torch.bfloat16
 
     try:
         model, tokenizer = FastLanguageModel.from_pretrained(
@@ -76,8 +78,8 @@ def _build_packed_training_setup(tmp_path, device):
         max_length = 64,
         logging_steps = 1,
         max_steps = 1,
-        fp16 = device.type == "cuda" and not torch.cuda.is_bf16_supported(),
-        bf16 = device.type == "cuda" and torch.cuda.is_bf16_supported(),
+        fp16 = dtype == torch.float16,
+        bf16 = dtype == torch.bfloat16,
         dataset_num_proc = 1,
         output_dir = str(tmp_path),
         packing = True,
@@ -974,7 +976,12 @@ def test_enable_sample_packing():
 
 
 def test_enable_sample_packing_trl_collator(tmp_path):
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    elif torch.xpu.is_available():
+        device = torch.device("xpu")
+    else:
+        device = torch.device("cpu")
     model, _, trainer, _ = _build_packed_training_setup(tmp_path, device)
 
     enable_sample_packing(model, trainer)
@@ -1030,7 +1037,12 @@ def test_enable_padding_free_metadata():
 
 
 def test_packing_sdpa(tmp_path):
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    elif torch.xpu.is_available():
+        device = torch.device("xpu")
+    else:
+        device = torch.device("cpu")
     model, batch, trainer, llama_mod = _build_packed_training_setup(tmp_path, device)
 
     assert "packed_seq_lengths" in batch

tests/utils/test_qat.py

@@ -130,8 +130,14 @@ def _test_fake_quantizers_are_called(
                     # Weight fake quantizers must always be called.
                     assert child.weight_fake_quantizer.count == 1
 
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    elif torch.xpu.is_available():
+        device = torch.device("xpu")
+    else:
+        pytest.skip("No GPU available")
     for k, v in example_inputs.items():
-        example_inputs[k] = v.cuda()
+        example_inputs[k] = v.to(device)
     model.apply(_swap_fake_quantizers)
     model(**example_inputs)
     model.apply(_assert_fake_quantizers_are_called)

tests/utils/test_rope_scaling_drift.py

@@ -15,18 +15,20 @@ import pytest
 import torch
 
 
-def _has_real_cuda():
-    try:
-        torch.zeros(1).to("cuda")
-        return True
-    except Exception:
-        return False
+def _has_real_gpu():
+    for backend in ("cuda", "xpu"):
+        try:
+            torch.zeros(1).to(backend)
+            return True
+        except Exception:
+            pass
+    return False
 
 
-HAS_REAL_CUDA = _has_real_cuda()
-requires_cuda = pytest.mark.skipif(
-    not HAS_REAL_CUDA,
-    reason = "LlamaRotaryEmbedding builds per-device CUDA caches in __init__",
+HAS_REAL_GPU = _has_real_gpu()
+requires_gpu = pytest.mark.skipif(
+    not HAS_REAL_GPU,
+    reason = "LlamaRotaryEmbedding builds per-device caches in __init__ (needs CUDA or XPU)",
 )
 
 REPO_ROOT = Path(__file__).resolve().parents[2]
@@ -360,7 +362,7 @@ def _cos_at_position(rot, position):
 # --- Layer 3: CUDA behavioral guard (real instantiation needs a device) ---
 
 
-@requires_cuda
+@requires_gpu
 def test_constructor_applies_llama3_scaling():
     config = _make_config(LLAMA3_ROPE_SCALING)
     rot = _unsloth_rotary(config)
@@ -371,7 +373,7 @@ def test_constructor_applies_llama3_scaling():
     ), "LlamaRotaryEmbedding built from a llama3 config produced unscaled inv_freq (issue #2405)."
 
 
-@requires_cuda
+@requires_gpu
 def test_constructor_unscaled_config_uses_vanilla_inv_freq():
     rot = _unsloth_rotary(_make_config(None))
     got = rot.inv_freq.float().cpu()
@@ -381,7 +383,7 @@ def test_constructor_unscaled_config_uses_vanilla_inv_freq():
     ), "LlamaRotaryEmbedding with no rope_scaling must use the vanilla inv_freq"
 
 
-@requires_cuda
+@requires_gpu
 def test_cos_cache_differs_between_scaled_and_unscaled_at_long_position():
     scaled = _unsloth_rotary(_make_config(LLAMA3_ROPE_SCALING))
     unscaled = _unsloth_rotary(_make_config(None))
@@ -397,7 +399,7 @@ def test_cos_cache_differs_between_scaled_and_unscaled_at_long_position():
     )
 
 
-@requires_cuda
+@requires_gpu
 def test_extended_cache_keeps_scaling_after_growth():
     scaled = _unsloth_rotary(_make_config(LLAMA3_ROPE_SCALING))
     # Grow past the initial cache size (mirrors long-context decode).
@@ -456,7 +458,7 @@ def _build_longrope_rotary():
     return rot, config
 
 
-@requires_cuda
+@requires_gpu
 @pytest.mark.parametrize(
     "build", [_build_llama3_rotary, _build_longrope_rotary], ids = ["llama3", "longrope"]
 )

unsloth/_gpu_init.py

@@ -374,7 +374,15 @@ elif DEVICE_TYPE == "hip":
     # NO-OP for rocm device
     pass
 elif DEVICE_TYPE == "xpu":
-    import bitsandbytes as bnb
+    # Same degradation as the cuda branch above: no bnb means no 4bit, not a
+    # failed `import unsloth`.
+    try:
+        import bitsandbytes as bnb
+    except Exception:
+        print(
+            "Unsloth: `bitsandbytes` is not installed - 4bit QLoRA unallowed, but 16bit and full finetuning works!"
+        )
+        bnb = None
 
     # TODO: check triton for intel installed properly.
     pass

unsloth/dataprep/raw_text.py

@@ -216,19 +216,32 @@ class RawTextDataLoader:
 
     def _read_file_by_format(self, file_path, file_format):
         """Read file content based on detected format."""
-        with open(file_path, "r", encoding = "utf-8") as f:
+        # utf-8-sig: Windows tooling (PowerShell's Out-File, Excel's "CSV UTF-8") prepends
+        # a BOM that plain utf-8 keeps as a leading character. Without a BOM it decodes
+        # exactly like utf-8.
+        with open(file_path, "r", encoding = "utf-8-sig") as f:
             if file_format == "plain_text" or file_format == "markdown":
                 return f.read()
             elif file_format == "json_lines":
-                lines = []
-                for line in f:
+                if Path(file_path).suffix.lower() == ".json":
+                    # A .json file is a single JSON document (commonly a list
+                    # of records), so parsing it per line drops the whole file.
                     try:
-                        data = json.loads(line.strip())
-                        text = self._extract_text_from_json(data)
-                        if text:
-                            lines.append(text)
+                        parsed = json.load(f)
+                        records = parsed if isinstance(parsed, list) else [parsed]
                     except json.JSONDecodeError:
-                        continue
+                        # Some files carry JSON Lines under a .json name.
+                        f.seek(0)
+                        records = self._iter_json_lines(f)
+                else:
+                    # A .jsonl file is one JSON value per line: stay streaming so
+                    # a large file is never held in memory all at once.
+                    records = self._iter_json_lines(f)
+                lines = []
+                for data in records:
+                    text = self._extract_text_from_json(data)
+                    if text:
+                        lines.append(text)
                 return "\n\n".join(lines)
             elif file_format == "csv_text_column":
                 reader = csv.DictReader(f)
@@ -244,6 +257,17 @@ class RawTextDataLoader:
     _TEXT_FIELDS = ("text", "content", "message", "body", "description", "prompt")
     _TEXT_COLUMNS = _TEXT_FIELDS
 
+    def _iter_json_lines(self, handle):
+        """Yield one parsed JSON value per line, skipping blank and malformed lines."""
+        for line in handle:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                yield json.loads(line)
+            except json.JSONDecodeError:
+                continue
+
     def _extract_text_from_json(self, data):
         """Extract text from JSON object using common field names."""
         # Skip non-object lines (str/list/number): `field in data` would be a

unsloth/device_type.py

@@ -117,6 +117,17 @@ DEVICE_COUNT: int = get_device_count()
 ALLOW_PREQUANTIZED_MODELS: bool = True
 # HSA_STATUS_ERROR_EXCEPTION checks - sometimes AMD fails for BnB
 ALLOW_BITSANDBYTES: bool = True
+# Unusable bitsandbytes on any backend, not just hip: clear the flags the loader
+# reads before it selects a 4bit checkpoint. Same guarded import the fallbacks in
+# _gpu_init.py and kernels/utils.py use rather than a find_spec probe, so an
+# installed-but-broken wheel (missing .so, wrong ROCm/CUDA build) is treated as
+# unavailable by all three, not only by the ones that import it.
+try:
+    import bitsandbytes as _bnb_probe
+    del _bnb_probe
+except Exception:
+    ALLOW_PREQUANTIZED_MODELS = False
+    ALLOW_BITSANDBYTES = False
 # gfx906 (MI50 / Radeon VII / Vega 20): Dynamo/Inductor codegen is broken on this
 # legacy GCN arch (ROCm dropped it after 6.3) - compiled graphs crash or miscompile
 # while the eager path trains fine. Default compile off; setdefault so a user

unsloth/kernels/utils.py

@@ -133,11 +133,28 @@ def calculate_settings(
 
 
 HAS_CUDA_STREAM = False
-import bitsandbytes as bnb
+try:
+    import bitsandbytes as bnb
+except Exception:
+    # device_type.py already degrades to 16bit/full finetuning when bnb is missing
+    # (e.g. gfx906, whose generic wheel has no kernels). Keep the import working and
+    # fail only if a 4bit path is actually entered.
+    bnb = None
 
-# https://github.com/bitsandbytes-foundation/bitsandbytes/pull/1330/files
-HAS_CUDA_STREAM = Version(bnb.__version__) > Version("0.43.3")
-get_ptr = bnb.functional.get_ptr
+
+def _bnb_required(*args, **kwargs):
+    raise RuntimeError(
+        "Unsloth: 4bit QLoRA needs `bitsandbytes`, which is not installed. "
+        "16bit LoRA and full finetuning work without it."
+    )
+
+
+if bnb is not None:
+    # https://github.com/bitsandbytes-foundation/bitsandbytes/pull/1330/files
+    HAS_CUDA_STREAM = Version(bnb.__version__) > Version("0.43.3")
+    get_ptr = bnb.functional.get_ptr
+else:
+    get_ptr = _bnb_required
 
 if DEVICE_TYPE == "xpu":
     HAS_XPU_STREAM = True
@@ -235,18 +252,25 @@ else:
 # Bitsandbytes operations
 ctypes_c_int = ctypes.c_int
 ctypes_c_int32 = ctypes.c_int32
-cdequantize_blockwise_fp32 = bnb.functional.lib.cdequantize_blockwise_fp32
-cdequantize_blockwise_fp16_nf4 = bnb.functional.lib.cdequantize_blockwise_fp16_nf4
-cdequantize_blockwise_bf16_nf4 = bnb.functional.lib.cdequantize_blockwise_bf16_nf4
-
-if DEVICE_TYPE == "xpu":
-    # https://github.com/bitsandbytes-foundation/bitsandbytes/blob/c3b8de268fdb55a88f92feada23fc811a1e6877a/bitsandbytes/backends/xpu/ops.py#L115
-    # for xpu, inference gemv using above link
-    cgemm_4bit_inference_naive_fp16 = bnb.functional.lib.cgemv_4bit_inference_fp16
-    cgemm_4bit_inference_naive_bf16 = bnb.functional.lib.cgemv_4bit_inference_bf16
+if bnb is None:
+    cdequantize_blockwise_fp32 = _bnb_required
+    cdequantize_blockwise_fp16_nf4 = _bnb_required
+    cdequantize_blockwise_bf16_nf4 = _bnb_required
+    cgemm_4bit_inference_naive_fp16 = _bnb_required
+    cgemm_4bit_inference_naive_bf16 = _bnb_required
 else:
-    cgemm_4bit_inference_naive_fp16 = bnb.functional.lib.cgemm_4bit_inference_naive_fp16
-    cgemm_4bit_inference_naive_bf16 = bnb.functional.lib.cgemm_4bit_inference_naive_bf16
+    cdequantize_blockwise_fp32 = bnb.functional.lib.cdequantize_blockwise_fp32
+    cdequantize_blockwise_fp16_nf4 = bnb.functional.lib.cdequantize_blockwise_fp16_nf4
+    cdequantize_blockwise_bf16_nf4 = bnb.functional.lib.cdequantize_blockwise_bf16_nf4
+
+    if DEVICE_TYPE == "xpu":
+        # https://github.com/bitsandbytes-foundation/bitsandbytes/blob/c3b8de268fdb55a88f92feada23fc811a1e6877a/bitsandbytes/backends/xpu/ops.py#L115
+        # for xpu, inference gemv using above link
+        cgemm_4bit_inference_naive_fp16 = bnb.functional.lib.cgemv_4bit_inference_fp16
+        cgemm_4bit_inference_naive_bf16 = bnb.functional.lib.cgemv_4bit_inference_bf16
+    else:
+        cgemm_4bit_inference_naive_fp16 = bnb.functional.lib.cgemm_4bit_inference_naive_fp16
+        cgemm_4bit_inference_naive_bf16 = bnb.functional.lib.cgemm_4bit_inference_naive_bf16
 
 
 torch_device_stream = (

unsloth/models/granite.py

@@ -31,8 +31,20 @@ from .llama import (
     LlamaLinearScalingRotaryEmbedding,
 )
 from .mistral import *
-from bitsandbytes.nn import Linear4bit as Bnb_Linear4bit
-from peft.tuners.lora import Linear4bit as Peft_Linear4bit
+
+# Without bnb, peft stops exporting its 4bit LoRA layer too. Both names only feed
+# isinstance checks, so placeholders nothing can match are exact stand-ins.
+try:
+    from bitsandbytes.nn import Linear4bit as Bnb_Linear4bit
+    from peft.tuners.lora import Linear4bit as Peft_Linear4bit
+except Exception:
+
+    class Bnb_Linear4bit:
+        pass
+
+    class Peft_Linear4bit:
+        pass
+
 
 try:
     from transformers.models.granite.modeling_granite import (

unsloth/models/loader.py

@@ -472,13 +472,42 @@ class FastLanguageModel(FastLlamaModel):
                         fast_inference = False
                         break
 
-        # Check if 4bit is allowed specifically for AMD
-        if not ALLOW_BITSANDBYTES and not use_exact_model_name:
-            if load_in_4bit or load_in_8bit or model_name.lower().endswith("-bnb-4bit"):
-                print(
-                    "Unsloth: AMD currently is not stable with 4bit bitsandbytes. Disabling for now."
+        # bitsandbytes unusable (absent, or unstable as on some AMD stacks). This is
+        # a capability check, so it is not gated on use_exact_model_name: that only
+        # suppresses repo-name remapping and cannot make bitsandbytes available.
+        if not ALLOW_BITSANDBYTES:
+            # A user-supplied config sets load_in_4bit/8bit above and is forwarded
+            # in kwargs, so clearing the flags alone still rebuilds the bnb
+            # quantizer downstream. Only drop it when it asks for bnb: a GPTQ /
+            # AWQ / fp8 / torchao config must pass through untouched.
+            _quant_cfg = kwargs.get("quantization_config", None)
+            if isinstance(_quant_cfg, dict):
+                _wants_bnb = bool(
+                    _quant_cfg.get("load_in_4bit", False) or _quant_cfg.get("load_in_8bit", False)
                 )
+            elif _quant_cfg is not None:
+                _wants_bnb = bool(
+                    getattr(_quant_cfg, "load_in_4bit", False)
+                    or getattr(_quant_cfg, "load_in_8bit", False)
+                )
+            else:
+                _wants_bnb = False
+            if (
+                load_in_4bit
+                or load_in_8bit
+                or _wants_bnb
+                or model_name.lower().endswith("-bnb-4bit")
+            ):
+                print(
+                    "Unsloth: `bitsandbytes` is unavailable here - disabling 4bit/8bit. "
+                    "16bit LoRA and full finetuning still work."
+                )
+            # 8bit is bitsandbytes too: leaving either set sends the request on to
+            # Transformers, which builds the bnb quantizer and fails there.
             load_in_4bit = False
+            load_in_8bit = False
+            if _wants_bnb:
+                kwargs.pop("quantization_config", None)
 
         # Find FP8, BnB 4bit, other mapped names
         old_model_name = model_name
@@ -1102,7 +1131,13 @@ class FastModel(FastBaseModel):
         assert load_in_fp8 in (True, False, "block")
 
         patch_compiled_autograd()
-        patch_compiling_bitsandbytes()
+        # Same best-effort wrapper as the FastLanguageModel path: unsloth_zoo's
+        # patch imports bitsandbytes unconditionally, so on a host without it this
+        # raised before the capability fallback below could take the 16bit path.
+        try:
+            patch_compiling_bitsandbytes()
+        except Exception as e:
+            print(f"Unsloth: Could not patch bitsandbytes for torch.compile - {e}")
 
         if full_finetuning and (load_in_4bit or load_in_8bit):
             print(
@@ -1113,6 +1148,43 @@ class FastModel(FastBaseModel):
             load_in_fp8 = False
             load_in_16bit = False
 
+        # bitsandbytes unusable (absent, or unstable as on some AMD stacks). This is
+        # a capability check, so it is not gated on use_exact_model_name: that only
+        # suppresses repo-name remapping and cannot make bitsandbytes available.
+        if not ALLOW_BITSANDBYTES:
+            # A user-supplied config sets load_in_4bit/8bit above and is forwarded
+            # in kwargs, so clearing the flags alone still rebuilds the bnb
+            # quantizer downstream. Only drop it when it asks for bnb: a GPTQ /
+            # AWQ / fp8 / torchao config must pass through untouched.
+            _quant_cfg = kwargs.get("quantization_config", None)
+            if isinstance(_quant_cfg, dict):
+                _wants_bnb = bool(
+                    _quant_cfg.get("load_in_4bit", False) or _quant_cfg.get("load_in_8bit", False)
+                )
+            elif _quant_cfg is not None:
+                _wants_bnb = bool(
+                    getattr(_quant_cfg, "load_in_4bit", False)
+                    or getattr(_quant_cfg, "load_in_8bit", False)
+                )
+            else:
+                _wants_bnb = False
+            if (
+                load_in_4bit
+                or load_in_8bit
+                or _wants_bnb
+                or model_name.lower().endswith("-bnb-4bit")
+            ):
+                print(
+                    "Unsloth: `bitsandbytes` is unavailable here - disabling 4bit/8bit. "
+                    "16bit LoRA and full finetuning still work."
+                )
+            # 8bit is bitsandbytes too: leaving either set sends the request on to
+            # Transformers, which builds the bnb quantizer and fails there.
+            load_in_4bit = False
+            load_in_8bit = False
+            if _wants_bnb:
+                kwargs.pop("quantization_config", None)
+
         if (
             int(load_in_4bit) + int(load_in_8bit) + int(load_in_16bit) + int(load_in_fp8 != False)
             >= 2
@@ -1142,14 +1214,6 @@ class FastModel(FastBaseModel):
             if is_dist:
                 device_map = distributed_device_map
 
-        # Check if 4bit is allowed specifically for AMD
-        if not ALLOW_BITSANDBYTES and not use_exact_model_name:
-            if load_in_4bit or load_in_8bit or model_name.lower().endswith("-bnb-4bit"):
-                print(
-                    "Unsloth: AMD currently is not stable with 4bit bitsandbytes. Disabling for now."
-                )
-            load_in_4bit = False
-
         if fast_inference:
             if importlib.util.find_spec("vllm") is None:
                 raise ImportError(

unsloth/save.py

@@ -32,8 +32,20 @@ except ImportError:
     import sys
     IS_WINDOWS = sys.platform == "win32"
     LLAMA_CPP_DEFAULT_DIR = "llama.cpp"
-from bitsandbytes.nn import Linear4bit as Bnb_Linear4bit
-from peft.tuners.lora import Linear4bit as Peft_Linear4bit
+# Without bnb, peft stops exporting its 4bit LoRA layer too. Both names only feed
+# isinstance checks, so placeholders nothing can match are exact stand-ins.
+try:
+    from bitsandbytes.nn import Linear4bit as Bnb_Linear4bit
+    from peft.tuners.lora import Linear4bit as Peft_Linear4bit
+except Exception:
+
+    class Bnb_Linear4bit:
+        pass
+
+    class Peft_Linear4bit:
+        pass
+
+
 from peft.tuners.lora import Linear as Peft_Linear
 from typing import Optional, Callable, Union, List
 import sys
@@ -3843,10 +3855,10 @@ from .models.loader_utils import (
     _tokenizer_cache_dir,
     _tokenizer_wants_local_only,
 )
-from unsloth_zoo.saving_utils import (
-    merge_and_overwrite_lora,
-    prepare_saving,
-)
+
+# Imported lazily at the two call sites below: a zoo older than the one that made
+# its own bitsandbytes import optional would otherwise break `import unsloth` on a
+# host without bnb, which is the whole point of the guards above.
 from unsloth_zoo.llama_cpp import (
     install_llama_cpp,
     convert_to_gguf as _convert_to_gguf,
@@ -4094,6 +4106,8 @@ def save_to_gguf_generic(
             quantization_type = quantization_type,
         )
         if repo_id is not None:
+            from unsloth_zoo.saving_utils import prepare_saving
+
             prepare_saving(
                 model,
                 repo_id,
@@ -4225,6 +4239,7 @@ 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)
+        from unsloth_zoo.saving_utils import merge_and_overwrite_lora
         merge_and_overwrite_lora(
             get_model_name,
             model = model,