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Merge branch 'main' into feature/deep-research

Browse source 
Resolve conflicts across the Studio chat stack, keeping both the Deep
Research additions and the attachment-inventory work that landed on main:

- storage/studio_db.py: keep the research schema plus main's chat
  attachment inventory. In sync_chat_messages and upsert_chat_message run
  the inventory refresh and the research-message guard together, and reuse
  main's tombstone reconciliation and chunked prune path while still
  protecting research prompts and responses from deletion. Read the
  inventory-state row positionally so _ensure_schema does not depend on a
  Row factory.
- tests/test_middleware.py: keep both the research port middleware and the
  frontend asset test suites.
- chat/chat-page.tsx: keep the mobile and research composer wiring; drop
  the now-unused useLatestRef import since main's pendingHubAutoLoad
  contextKey check supersedes the ref-based stale-load guard.
- chat/stores/chat-runtime-store.ts: keep the Deep Research toggle state
  and drop the staged-model helpers main removed.
- assistant-ui/thread.tsx: keep the Deep Research composer state, button,
  and availability prop alongside main's composer changes.
This commit is contained in:
danielhanchen 2026-07-22 06:10:41 +00:00
commit a357e85c3f
506 changed files with 35944 additions and 7093 deletions
Download patch file Download diff file Expand all files Collapse all files

2
.gitattributes vendored
View file

@ -6,7 +6,7 @@
# them when run in WSL/Linux (e.g. `set -e` -> "set: Illegal option -").
*.sh text eol=lf
# Normalize Studio frontend sources to LF. Scoped to the frontend tree (rather
# Normalize Unsloth frontend sources to LF. Scoped to the frontend tree (rather
# than repo-wide *.ts/*.tsx/... rules) so the policy can't force LF on files
# elsewhere. text=auto lets Git detect and leave binary assets (logos, fonts)
# untouched while text files (.ts/.tsx/.json/.html/.svg/...) are stored as LF.

21
.github/scripts/agent-guides-drive.sh vendored
View file

@ -36,6 +36,23 @@ AGENT="${2:?usage: agent-guides-drive.sh <mode> <agent>}"
# Determinism (seed/temp) is applied at the server level by
# serve-unsloth-run.sh --extra; agents inherit it through the API.
TIMEOUT="${AGENT_INVOKE_TIMEOUT:-180}"
# opencode is the slow outlier. Unlike the print-mode agents (claude -p, codex
# exec) it runs a full turn AND a separate small_model call to name the session,
# so one connection reply takes ~8 min on a CPU-served 4B -- right at the shared
# 600s cap, so the cell flaked when a run drifted past a ~480s success. Give it
# headroom (still well under the 40-min job budget); the fast agents keep the
# tight cap that still catches a real headless-TTY hang.
case "$AGENT" in
opencode)
# Double it, but only for a bare-integer seconds value. A GNU timeout(1)
# duration suffix (s/m/h/d, including floats like 0.5s) is left unchanged so
# the arithmetic never sees a non-number; timeout(1) parses it directly.
case "$TIMEOUT" in
*[!0-9]*) ;;
*) TIMEOUT=$(( TIMEOUT * 2 )) ;;
esac
;;
esac
# Claude refuses --dangerously-skip-permissions outside a sandbox; the CI runner
# IS the sandbox, so declare it (mirrors unslothai/scripts launcher.sh). Harmless
@ -166,8 +183,8 @@ parse_connect() {
echo "[$AGENT] connect --no-launch printed:"; cat_redacted "$raw"
CONNECT_ENV="$(grep -E '^(export |unset )' "$raw" || true)"
# The launch command is the last non-export, non-status line. start.py
# prints "Studio <url> · model <id>" and "Updated ..." status lines first.
CONNECT_CMD="$(grep -vE '^(export |unset |Studio |Updated |Disabled |Warning|Loading)' "$raw" \
# prints "Unsloth <url> · model <id>" and "Updated ..." status lines first.
CONNECT_CMD="$(grep -vE '^(export |unset |Unsloth |Updated |Disabled |Warning|Loading)' "$raw" \
| grep -E '[^[:space:]]' | tail -1)"
[ -n "$CONNECT_CMD" ] || guide_fail "could not parse a launch command from connect --no-launch output"
redact "$raw"

2
.github/scripts/assert-llama-loads.sh vendored
View file

@ -2,7 +2,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
#
# Assert Studio installed a llama.cpp that loads and runs on THIS macOS. Tests
# Assert Unsloth installed a llama.cpp that loads and runs on THIS macOS. Tests
# the contract that matters (binaries load and their minimum-OS is <= this host)
# instead of the old "did install.sh fall back to a source build?" grep, since a
# source build with a correct deployment target is a valid outcome.

2
.github/scripts/assert-prompt-cache.sh vendored
View file

@ -31,7 +31,7 @@
# (llama_cpp.py:337-340). So default: ~/.unsloth/studio/logs/llama-server/.
#
# <P> is the INTERNAL llama-server port (self._find_free_port(),
# llama_cpp.py:3489 / :4641) -- a RANDOM port, NOT the Studio port. So we must
# llama_cpp.py:3489 / :4641) -- a RANDOM port, NOT the Unsloth port. So we must
# NOT filter the log glob by STUDIO_PORT (the brief's `port-<STUDIO_PORT>`
# glob would never match). We pick the newest llama-*.log instead.
#

4
.github/scripts/hf-download-with-retry.sh vendored
View file

@ -3,7 +3,7 @@
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
#
# Download a single file from a Hugging Face repo with a stall-retry
# watchdog. Used by the Studio CI workflows so a hung hf-xet transfer
# watchdog. Used by the Unsloth CI workflows so a hung hf-xet transfer
# kills + retries instead of silently consuming the job's timeout.
#
# Usage: hf-download-with-retry.sh REPO FILE LOCAL_DIR
@ -35,7 +35,7 @@ REPO="${1:?usage: hf-download-with-retry.sh REPO FILE [LOCAL_DIR]}"
FILE="${2:?usage: hf-download-with-retry.sh REPO FILE [LOCAL_DIR]}"
# LOCAL_DIR is optional. If empty, hf falls back to HF_HUB_CACHE
# (~/.cache/huggingface/hub) which is the desired path for callers
# that populate HF_HOME for a downstream Studio model load.
# that populate HF_HOME for a downstream Unsloth model load.
LOCAL_DIR="${3:-}"
# Stall threshold per attempt, in seconds. Override with

69
.github/scripts/run-studio-permission-browser.sh vendored Executable file
View file

@ -0,0 +1,69 @@
#!/usr/bin/env bash
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
set -euo pipefail
port="${1:?usage: $0 PORT BROWSER [CHANNEL]}"
browser="${2:?usage: $0 PORT BROWSER [CHANNEL]}"
channel="${3:-}"
slug="$browser${channel:+-$channel}"
artifact_dir="logs/playwright-permissions-$slug"
server_log="logs/studio-permissions-$slug.log"
studio_home="${UNSLOTH_STUDIO_HOME:-$HOME/.unsloth/studio}"
set --
if [ -n "${STUDIO_PERMISSION_FRONTEND:-}" ]; then
set -- -f "$STUDIO_PERMISSION_FRONTEND"
fi
mkdir -p "$artifact_dir"
unsloth studio reset-password
UNSLOTH_API_ONLY=1 unsloth studio -H 127.0.0.1 -p "$port" "$@" \
>"$server_log" 2>&1 &
studio_pid=$!
cleanup() {
kill "$studio_pid" 2>/dev/null || true
wait "$studio_pid" 2>/dev/null || true
}
trap cleanup EXIT
healthy=0
for _ in $(seq 1 180); do
if curl -fs "http://127.0.0.1:$port/api/health" >/dev/null; then
healthy=1
break
fi
if ! kill -0 "$studio_pid" 2>/dev/null; then
tail -100 "$server_log" || true
exit 1
fi
sleep 1
done
if [ "$healthy" -ne 1 ]; then
tail -100 "$server_log" || true
exit 1
fi
old_password=$(cat "$studio_home/auth/.bootstrap_password")
new_password="CIPerm-$(python -c 'import secrets; print(secrets.token_urlsafe(16))')"
if [ "${GITHUB_ACTIONS:-}" = "true" ]; then
echo "::add-mask::$old_password"
echo "::add-mask::$new_password"
fi
export BASE_URL="http://127.0.0.1:$port"
export STUDIO_OLD_PW="$old_password"
export STUDIO_NEW_PW="$new_password"
export STUDIO_UI_STRICT=1
export STUDIO_UI_PERMISSION_ONLY=1
export STUDIO_UI_WALL_TIMEOUT_S=240
export STUDIO_PLAYWRIGHT_BROWSER="$browser"
export PW_ART_DIR="$artifact_dir"
if [ -n "$channel" ]; then
export STUDIO_PLAYWRIGHT_CHANNEL="$channel"
else
unset STUDIO_PLAYWRIGHT_CHANNEL || true
fi
python tests/studio/playwright_chat_ui.py

4
.github/workflows/lint-ci.yml vendored
View file

@ -13,10 +13,10 @@
# committed YAML / JSON config.
#
# TypeScript and Rust are NOT duplicated here on purpose:
# - Studio Frontend CI runs `npm run typecheck` (= `tsc --noEmit`)
# - Unsloth Frontend CI runs `npm run typecheck` (= `tsc --noEmit`)
# and `npm run build` (vite/swc) on every studio/frontend/**
# change, which is a full TS AST + type check.
# - Studio Tauri CI runs `tauri build --debug --no-bundle` on
# - Unsloth Tauri CI runs `tauri build --debug --no-bundle` on
# every studio/src-tauri/** or studio/frontend/** change, which
# compiles the Rust crate (= cargo check + cargo build).
# Each is a stricter check than a parse-only step would be, so a

16
.github/workflows/local-agent-guides-ci.yml vendored
View file

@ -154,7 +154,7 @@ jobs:
path: gguf-cache
key: ${{ runner.os }}-gguf-${{ env.GGUF_REPO }}-${{ env.GGUF_FILE }}-v1
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Gated off PR (see note above); public GGUF still downloads.
@ -256,7 +256,7 @@ jobs:
done
fi
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
# Guard the PID: an unset/zero UNSLOTH_SERVER_PID would make
@ -359,7 +359,7 @@ jobs:
path: gguf-cache
key: ${{ runner.os }}-gguf-${{ env.GGUF_REPO }}-${{ env.GGUF_FILE }}-v1
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Gated off PR (see note above); public GGUF still downloads.
@ -448,7 +448,7 @@ jobs:
done
fi
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
# Guard the PID: an unset/zero UNSLOTH_SERVER_PID would make
@ -543,7 +543,7 @@ jobs:
path: gguf-cache
key: ${{ runner.os }}-gguf-${{ env.GGUF_REPO }}-${{ env.GGUF_FILE }}-v1
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
HF_TOKEN: ${{ secrets.HF_TOKEN }}
@ -620,7 +620,7 @@ jobs:
done
fi
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
if [ -n "${UNSLOTH_SERVER_PID:-}" ] && [ "${UNSLOTH_SERVER_PID}" != "0" ]; then
@ -706,7 +706,7 @@ jobs:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-v2
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Gated off PR (see note above); public GGUF still downloads.
@ -764,7 +764,7 @@ jobs:
done
fi
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
# Guard the PID: an unset/zero UNSLOTH_SERVER_PID would make

12
.github/workflows/mlx-ci.yml vendored
View file

@ -130,7 +130,7 @@ jobs:
# MLX support landed after the most recent unsloth-zoo PyPI
# release; the wheel still raises NotImplementedError on
# Apple Silicon when device_type.get_device_type() runs
# unguarded. Studio's own install.sh overlays unsloth-zoo
# unguarded. Unsloth's own install.sh overlays unsloth-zoo
# from git main for the same reason. Pulling deps lets pip
# resolve the platform-conditional MLX-only wheels (mlx,
# mlx-lm, mlx-vlm gated on darwin+arm64 in unsloth-zoo's
@ -317,13 +317,13 @@ jobs:
echo
done
# Validates the macOS prebuilt path Studio's setup.sh uses (#5963): install the
# Validates the macOS prebuilt path Unsloth's setup.sh uses (#5963): install the
# unslothai/llama.cpp fork's latest release, download a small public GGUF, and
# check llama-server /completion end to end. Split and placed last so the
# untrusted binary runs only in the final smoke step, after every HF_TOKEN step,
# leaving no token-bearing step or shared workspace for a tampered prebuilt to
# corrupt. GH_TOKEN: releases API; HF_TOKEN (withheld on PR): probe + GGUF fetch.
- name: Studio prebuilt llama.cpp install + GGUF download (Mac M1)
- name: Unsloth prebuilt llama.cpp install + GGUF download (Mac M1)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@ -344,12 +344,12 @@ jobs:
# Final step: runs the downloaded binaries with no secrets present, and clears
# the GitHub Actions command files so a tampered prebuilt cannot influence the job.
- name: Studio prebuilt llama.cpp GGUF inference smoke (Mac M1)
- name: Unsloth prebuilt llama.cpp GGUF inference smoke (Mac M1)
run: |
set -euo pipefail
unset GITHUB_ENV GITHUB_PATH GITHUB_OUTPUT GITHUB_STEP_SUMMARY
INSTALL_DIR="$HOME/.unsloth-studio-prebuilt-test/llama.cpp"
# Studio bundles only llama-server + llama-quantize (not llama-cli);
# Unsloth bundles only llama-server + llama-quantize (not llama-cli);
# inference goes through llama-server's HTTP /completion endpoint.
LLAMA_SERVER="$INSTALL_DIR/build/bin/llama-server"
LLAMA_QUANT="$INSTALL_DIR/build/bin/llama-quantize"
@ -400,4 +400,4 @@ jobs:
tail -40 /tmp/llama-server.log
exit 1
fi
echo "OK: Studio prebuilt llama.cpp on Mac M1 + GGUF /completion works"
echo "OK: Unsloth prebuilt llama.cpp on Mac M1 + GGUF /completion works"

8
.github/workflows/release-desktop.yml vendored
View file

@ -4,7 +4,7 @@ on:
workflow_dispatch:
inputs:
studio_version:
description: 'Studio version tag to release (for example, v0.1.39-beta)'
description: 'Unsloth version tag to release (for example, v0.1.39-beta)'
type: string
required: true
pypi_version:
@ -69,7 +69,7 @@ jobs:
if not studio_version:
sys.exit('studio_version is required, for example v0.1.39-beta')
if re.fullmatch(r'v?20\d{2}\.\d+\.\d+(?:[-+][0-9A-Za-z.-]+)?', studio_version):
sys.exit(f'studio_version must be a Studio SemVer tag, not a date-style backend version: {studio_version}')
sys.exit(f'studio_version must be an Unsloth SemVer tag, not a date-style backend version: {studio_version}')
semver_tag = re.compile(
r'^v(0|[1-9]\d*)\.(0|[1-9]\d*)\.(0|[1-9]\d*)'
@ -146,7 +146,7 @@ jobs:
print(f'pypi_version={pypi_version}', file=output)
PY
- name: Verify PyPI package and Studio stamp
- name: Verify PyPI package and Unsloth stamp
shell: bash
env:
STUDIO_VERSION: ${{ steps.prepare.outputs.studio_version }}
@ -211,7 +211,7 @@ jobs:
fi
python3 scripts/stamp_studio_release.py --verify-dist "$RUNNER_TEMP/pypi-unsloth-dist" --expected "$STUDIO_VERSION"
else
echo "scripts/stamp_studio_release.py not found; release-desktop requires #5308 to verify the PyPI Studio stamp." >&2
echo "scripts/stamp_studio_release.py not found; release-desktop requires #5308 to verify the PyPI Unsloth stamp." >&2
exit 1
fi

30
.github/workflows/security-audit.yml vendored
View file

@ -36,8 +36,8 @@
# - unsloth `huggingfacenotorch` extras (the canonical install path
# for fine-tuning users; pulls transformers / peft / accelerate /
# trl / datasets / diffusers / sentence-transformers / etc.)
# - all six Studio backend requirements files
# - Studio frontend (npm) and Tauri shell (cargo)
# - all six Unsloth backend requirements files
# - Unsloth frontend (npm) and Tauri shell (cargo)
# Each Python step builds a filtered dep list from pyproject.toml +
# requirements/*.txt before auditing. We do NOT install any of these
# -- pip-audit resolves through PyPI metadata, scan_packages.py
@ -218,7 +218,7 @@ jobs:
# on the runner). A comment line is left in place so the
# skipped specs are obvious in the artifact.
# The `huggingface` extra is `huggingfacenotorch` plus torch /
# torchvision / triton, deliberately skipped: Studio backend
# torchvision / triton, deliberately skipped: Unsloth backend
# already pins a torch and the +cu* / +cpu local-version tags
# trip up the PyPI resolver in `-r` mode.
run: |
@ -253,7 +253,7 @@ jobs:
# `-r requirements.txt` resolves the requirements through pip's
# dependency resolver against PyPI metadata and audits the
# resolved tree without ever executing setup.py / install
# hooks. Way faster than installing the full Studio runtime
# hooks. Way faster than installing the full Unsloth runtime
# and -- critically -- safer: an attacker who has compromised
# a transitive dep cannot run code in this job.
#
@ -326,9 +326,9 @@ jobs:
} >> "$GITHUB_STEP_SUMMARY"
# ─────────────────────────────────────────────────────────────
# npm: Studio frontend
# npm: Unsloth frontend
# ─────────────────────────────────────────────────────────────
- name: npm audit (Studio frontend)
- name: npm audit (Unsloth frontend)
# `npm audit` resolves the lockfile through the npmjs.com
# advisory DB. `--audit-level=high` filters the noise floor
# to only HIGH and CRITICAL. We do NOT pass --omit=dev: a
@ -342,7 +342,7 @@ jobs:
# Always also write the full JSON for grep-ability.
npm audit --json > ../../logs-npm-audit.json || true
{
echo "## npm audit (Studio frontend)"
echo "## npm audit (Unsloth frontend)"
echo
echo '```'
tail -200 ../../logs-npm-audit.txt
@ -350,9 +350,9 @@ jobs:
} >> "$GITHUB_STEP_SUMMARY"
# ─────────────────────────────────────────────────────────────
# cargo: Studio Tauri shell
# cargo: Unsloth Tauri shell
# ─────────────────────────────────────────────────────────────
- name: cargo audit (Studio Tauri)
- name: cargo audit (Unsloth Tauri)
# `--deny warnings` would make the job fail on any advisory.
# Keep non-blocking initially; drop continue-on-error after
# the baseline closes.
@ -362,7 +362,7 @@ jobs:
set +e
cargo audit | tee ../../logs-cargo-audit.txt
{
echo "## cargo audit (Studio Tauri)"
echo "## cargo audit (Unsloth Tauri)"
echo
echo '```'
tail -200 ../../logs-cargo-audit.txt
@ -559,7 +559,7 @@ jobs:
# ─────────────────────────────────────────────────────────────
# CycloneDX SBOM. Lets downstream consumers audit what's
# actually shipped in unsloth wheels and the Studio backend
# actually shipped in unsloth wheels and the Unsloth backend
# runtime. Generates one JSON file per requirements input plus
# a combined SBOM keyed off pyproject.toml; uploads as a build
# artifact (and a future step can attest it via SLSA).
@ -740,7 +740,7 @@ jobs:
# `--with-deps` makes the scan transitive: every package the
# declared set resolves to gets fetched and pattern-scanned, not
# just the top-level pins. Resolving the full transitive closure
# of the unsloth + Studio dep tree downloads several hundred
# of the unsloth + Unsloth dep tree downloads several hundred
# archives, hence the longer timeout.
#
# Sharded across runners for wall-clock parallelism. Each shard
@ -749,7 +749,7 @@ jobs:
# composition tries to balance load:
# - hf-stack: pyproject extras + no-torch-runtime
# (~150 archives, transformers/peft/accelerate/...)
# - studio: FastAPI/Studio backend + overrides + extras-no-deps
# - studio: FastAPI/Unsloth backend + overrides + extras-no-deps
# (~150 archives, smaller scientific stack)
# - extras: the heavy openai-whisper / scikit-learn / librosa
# stack (~250 archives, dominant cost)
@ -964,7 +964,7 @@ jobs:
# documented at scripts/scan_npm_packages.py top-of-file. The
# script is stdlib-only so adding it does not increase the
# transitive supply-chain surface.
name: npm scan-packages (Studio frontend tarballs)
name: npm scan-packages (Unsloth frontend tarballs)
runs-on: ubuntu-latest
timeout-minutes: 30
needs: []
@ -1173,7 +1173,7 @@ jobs:
with:
python-version: '3.12'
- name: Install Studio frontend deps (--ignore-scripts)
- name: Install Unsloth frontend deps (--ignore-scripts)
# `npm audit signatures` requires node_modules to be populated.
# `--ignore-scripts` is mandatory: this is exactly the lever the
# new-install-script gate below protects against, and we must

14
.github/workflows/studio-api-smoke.yml vendored
View file

@ -1,7 +1,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
# Studio API & Auth Tests -- HTTP-level integration tests for the
# Unsloth API & Auth Tests -- HTTP-level integration tests for the
# FastAPI surface. No Playwright, no model UI; tests/studio/test_studio_api_smoke.py
# runs ~30 s and asserts:
# - CORS hardening (no wildcard + credentials, no bootstrap leak)
@ -15,7 +15,7 @@
# Reuses the GGUF cache key from studio-ui-smoke.yml so the model
# download is one cache-hit on the second job.
name: Studio API CI
name: Unsloth API CI
on:
pull_request:
@ -40,7 +40,7 @@ permissions:
jobs:
api-smoke:
name: Studio API & Auth Tests
name: Unsloth API & Auth Tests
runs-on: ubuntu-latest
timeout-minutes: 12
env:
@ -98,7 +98,7 @@ jobs:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-v2
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -111,7 +111,7 @@ jobs:
- name: Install pyjwt for the JWT-expiry forge test
run: pip install 'pyjwt>=2.6'
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
run: |
unsloth studio reset-password
mkdir -p logs
@ -144,7 +144,7 @@ jobs:
echo "STUDIO_NEW_PW=$NEW" >> "$GITHUB_ENV"
echo "STUDIO_NEW2_PW=$NEW2" >> "$GITHUB_ENV"
- name: Run Studio API & Auth tests
- name: Run Unsloth API & Auth tests
# The script is named WITHOUT a `test_` prefix so it isn't
# auto-collected by pytest in Backend CI's `tests/` walk
# (which doesn't set BASE_URL and would crash at import).
@ -153,7 +153,7 @@ jobs:
STUDIO_AUTH_DIR: /home/runner/.unsloth/studio/auth
run: python tests/studio/studio_api_smoke.py
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true

2
.github/workflows/studio-backend-ci.yml vendored
View file

@ -64,7 +64,7 @@ jobs:
- name: Install backend test dependencies (CPU only)
run: |
python -m pip install --upgrade pip
# Studio's declared backend deps:
# Unsloth's declared backend deps:
pip install -r studio/backend/requirements/studio.txt
# Extras that studio.txt does not list but the import chain needs
# (python-multipart for FastAPI form/file uploads, sqlalchemy/cryptography

2
.github/workflows/studio-export-capability-ci.yml vendored
View file

@ -9,7 +9,7 @@
# export is validated separately. No GPU / model / llama.cpp: the tests mock the probes and block
# torch/unsloth, so the job installs only a CPU PyTorch plus import deps.
name: Studio export capability
name: Unsloth export capability
on:
pull_request:

4
.github/workflows/studio-frontend-ci.yml vendored
View file

@ -136,7 +136,7 @@ jobs:
- name: Build
run: npm run build
- name: Built bundle must not contain Studio's unstable_Provider call site
- name: Built bundle must not contain Unsloth's unstable_Provider call site
run: |
set -e
JS=$(ls dist/assets/index-*.js | head -1)
@ -144,7 +144,7 @@ jobs:
echo "main bundle: $JS"
echo "unstable_Provider: hits=$HITS (assistant-ui internals contribute up to 3)"
if [ "$HITS" -gt 3 ]; then
echo "::error file=studio/frontend/src/features/chat/runtime-provider.tsx::Studio bundle still passes unstable_Provider through useRemoteThreadListRuntime; this is the 2026.5.1 chat-history regression. Pass adapters directly into useLocalRuntime instead."
echo "::error file=studio/frontend/src/features/chat/runtime-provider.tsx::Unsloth bundle still passes unstable_Provider through useRemoteThreadListRuntime; this is the 2026.5.1 chat-history regression. Pass adapters directly into useLocalRuntime instead."
exit 1
fi

56
.github/workflows/studio-inference-smoke.yml vendored
View file

@ -1,7 +1,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
# Three end-to-end smoke jobs that boot a freshly-installed Studio and
# Three end-to-end smoke jobs that boot a freshly-installed Unsloth and
# exercise the surfaces real users hit through the OpenAI / Anthropic
# SDKs and curl. Each job picks the smallest model that exercises the
# behaviour under test, primes HF_HOME via actions/cache, and shares
@ -27,7 +27,7 @@
# All three jobs run in parallel. Total wall time is dominated by job 3
# on a cold cache; warm cache cuts that to ~3 min.
name: Studio GGUF CI
name: Unsloth GGUF CI
on:
pull_request:
@ -112,7 +112,7 @@ jobs:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-v2
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -125,7 +125,7 @@ jobs:
- name: Install OpenAI + Anthropic Python SDKs
run: pip install 'openai>=1.50' 'anthropic>=0.40'
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
run: |
unsloth studio reset-password
mkdir -p logs
@ -142,7 +142,7 @@ jobs:
fi
sleep 1
done
echo "Studio did not become healthy in 180s"
echo "Unsloth did not become healthy in 180s"
tail -200 logs/studio.log
exit 1
@ -229,11 +229,11 @@ jobs:
return replies
def run_anthropic():
# Two SDK quirks vs. Studio:
# Two SDK quirks vs. Unsloth:
# 1. base_url must NOT include /v1 -- the SDK appends
# /v1/messages itself; otherwise the request hits
# /v1/v1/messages and 405s.
# 2. The SDK sends `x-api-key` by default, but Studio's
# 2. The SDK sends `x-api-key` by default, but Unsloth's
# auth layer is HTTPBearer-only. Override via
# default_headers so Authorization: Bearer ... is
# sent instead.
@ -276,7 +276,7 @@ jobs:
print(
f"[{label}] WARN non-determinism at temperature=0.0 across "
f"{len(determinism_failures)} of {len(first)} turn(s); "
f"small-quant model drift, not a Studio regression. "
f"small-quant model drift, not an Unsloth regression. "
f"Details: " + " | ".join(determinism_failures)
)
# Sanity: turn-2 reply should mention the earlier question, and
@ -290,7 +290,7 @@ jobs:
print(f"[{label}] {status_word} -- 4 turns, history grounded ('paris' present)")
PY
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
@ -323,7 +323,7 @@ jobs:
# store xet chunks + blobs + snapshots = ~4 GiB compressed --
# 4-5x file-size inflation, dominated by xet chunks. Use main's
# `--local-dir gguf-cache` pattern to cache the flat .gguf only.
# Studio's /api/inference/load accepts either a HF repo (which
# Unsloth's /api/inference/load accepts either a HF repo (which
# uses HF_HOME) or an absolute file path; passing the absolute
# path keeps the test off HF_HOME entirely so the cache size
# tracks the GGUF file 1:1. The OpenAI/Anth and JSON+images
@ -380,7 +380,7 @@ jobs:
path: gguf-cache
key: ${{ runner.os }}-gguf-${{ env.GGUF_REPO }}-${{ env.GGUF_FILE }}-v1
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -390,7 +390,7 @@ jobs:
set -o pipefail
bash install.sh --local --no-torch 2>&1 | tee logs/install.log
- name: Reset auth + boot Studio (API-only, default tool policy)
- name: Reset auth + boot Unsloth (API-only, default tool policy)
# We deliberately use the API-only mode rather than
# `unsloth studio run` because the latter calls
# `set_tool_policy(...)` with a resolved bool: on loopback the
@ -503,7 +503,7 @@ jobs:
that the tool path executed.
A shared CI runner can stall the stream transport (the
connection opening, or a mid-stream read) even when Studio
connection opening, or a mid-stream read) even when Unsloth
is healthy, so retry a stall once with a fresh request
capped at 300s. A stall means the stream did NOT complete,
so partial events are normally NOT returned (an early
@ -575,11 +575,11 @@ jobs:
def _tool_invoked(events):
"""Structural check: True iff some SSE payload is a real
tool envelope (Studio tool_start/tool_end, Anthropic
tool envelope (Unsloth tool_start/tool_end, Anthropic
tool_use/tool_result, OpenAI non-empty delta.tool_calls /
message.tool_calls / finish_reason='tool_calls' /
role:'tool' / function_call). tool_status is NOT
evidence: Studio emits empty tool_status events on
evidence: Unsloth emits empty tool_status events on
iteration boundaries even when no tool ran.
"""
for raw in events:
@ -698,7 +698,7 @@ jobs:
attempt has structural invocation evidence. WARN (not
FAIL) if invoked but no attempt produces the expected
literal in tool_end.result -- small-quant Qwen3.5-2B can
emit OpenAI tool_calls deltas without Studio's GGUF
emit OpenAI tool_calls deltas without Unsloth's GGUF
agentic loop intercepting them, and that GGUF-vs-OpenAI
format mismatch is out of scope for #5642.
"""
@ -811,7 +811,7 @@ jobs:
# because (a) the search may legitimately return no results,
# and (b) DuckDuckGo upstream blocks GHA IP ranges often
# enough that requiring a tool_call marker would create
# red-herring failures from infra rather than from Studio.
# red-herring failures from infra rather than from Unsloth.
try:
# Best-effort and bounded: a single 180s attempt keeps a stall
# from eating the job's timeout-minutes (it already WARNs, so a
@ -834,7 +834,7 @@ jobs:
print(f"[tools] WARN web_search probe failed (non-blocking): {exc}")
# ── 5. Thinking on / off ─────────────────────────────────────
# Studio strips think blocks from message.content for tools-mode
# Unsloth strips think blocks from message.content for tools-mode
# responses, so we toggle plain chat (no enable_tools) and look
# at the surfaced reasoning_content / message.thinking field.
def thinking_call(enable):
@ -848,7 +848,7 @@ jobs:
})
assert status == 200
msg = data["choices"][0]["message"]
# Studio surfaces thinking via reasoning_content (OpenAI
# Unsloth surfaces thinking via reasoning_content (OpenAI
# extension). Fall back to inline <think> markers for
# robustness across template versions.
raw = (msg.get("content") or "") + (msg.get("reasoning_content") or "")
@ -868,7 +868,7 @@ jobs:
print(f"[tools] PASS thinking on/off (on={len(on_text)} chars, off={len(off_text)} chars)")
PY
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
@ -960,7 +960,7 @@ jobs:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-${{ env.MMPROJ_FILE }}-v2
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -973,7 +973,7 @@ jobs:
- name: Install OpenAI + Anthropic Python SDKs
run: pip install 'openai>=1.50' 'anthropic>=0.40'
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
# See Job 2's comment: API-only mode keeps tool_policy=None so
# response_format requests aren't routed through the agentic
# tool loop.
@ -1076,13 +1076,13 @@ jobs:
# llama.cpp's HTTP server supports OpenAI-compatible JSON
# mode: `response_format: {"type": "json_object"}` constrains
# the model to emit syntactically-valid JSON. We use raw HTTP
# rather than the OpenAI SDK so that the field shape Studio
# rather than the OpenAI SDK so that the field shape Unsloth
# forwards to llama-server is unambiguous (the SDK rewrites
# response_format depending on which variant it recognises).
# We deliberately do NOT pass a strict JSON schema -- on
# small Gemma-4 quants the GBNF-from-schema path occasionally
# produces empty output, and JSON mode is the surface we care
# about exposing through Studio.
# about exposing through Unsloth.
status, data = post("/v1/chat/completions", {
"model": "default",
"messages": [
@ -1112,7 +1112,7 @@ jobs:
print(f"[json] PASS json_object -> {parsed}")
# ── 2. OpenAI image_url (data URI base64) ───────────────────
# 64x64 solid-red PNG. stb_image (used by Studio's image
# 64x64 solid-red PNG. stb_image (used by Unsloth's image
# normaliser at routes/inference.py:3410) rejects 4x4 or
# smaller PNGs as truncated, so we go up to 64x64 -- still
# tiny in token cost. The assertion is loose: any non-empty
@ -1148,9 +1148,9 @@ jobs:
print("[image/openai] PASS image_url accepted, non-empty response")
# ── 3. Anthropic source/base64 image ────────────────────────
# Two SDK quirks vs. Studio: base_url must NOT include /v1
# Two SDK quirks vs. Unsloth: base_url must NOT include /v1
# (the SDK appends it itself; otherwise /v1/v1/messages -> 405),
# and Studio's auth is HTTPBearer-only so the SDK's default
# and Unsloth's auth is HTTPBearer-only so the SDK's default
# x-api-key header is ignored -- send Authorization: Bearer
# via default_headers.
anthropic = Anthropic(
@ -1184,7 +1184,7 @@ jobs:
print("[image/anthropic] PASS source/base64 accepted, non-empty response")
PY
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true

4
.github/workflows/studio-load-orchestrator-ci.yml vendored
View file

@ -1,7 +1,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
#
# Event-loop regression test for the Studio model-load orchestrator.
# Event-loop regression test for the Unsloth model-load orchestrator.
# Pins down issue #5642 (Win10 UI freeze on model load): the /load
# route calls LlamaCppBackend.detect_audio_type synchronously, blocking
# the FastAPI event loop on a chain of sync httpx.Client.post() probes.
@ -14,7 +14,7 @@
# danielhanchen/unsloth-staging-2 (Ubuntu / macOS / Windows all
# green at PR time).
name: Studio load-orchestrator CI
name: Unsloth load-orchestrator CI
on:
pull_request:

10
.github/workflows/studio-mac-api-smoke.yml vendored
View file

@ -33,7 +33,7 @@ permissions:
jobs:
api-smoke:
name: Studio API & Auth Tests
name: Unsloth API & Auth Tests
runs-on: macos-14
timeout-minutes: 25
env:
@ -83,7 +83,7 @@ jobs:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-v2
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -99,7 +99,7 @@ jobs:
- name: Install pyjwt for the JWT-expiry forge test
run: pip install 'pyjwt>=2.6'
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
run: |
unsloth studio reset-password
mkdir -p logs
@ -129,13 +129,13 @@ jobs:
echo "STUDIO_NEW_PW=$NEW" >> "$GITHUB_ENV"
echo "STUDIO_NEW2_PW=$NEW2" >> "$GITHUB_ENV"
- name: Run Studio API & Auth tests
- name: Run Unsloth API & Auth tests
env:
BASE_URL: http://127.0.0.1:18895
STUDIO_AUTH_DIR: /Users/runner/.unsloth/studio/auth
run: python tests/studio/studio_api_smoke.py
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true

58
.github/workflows/studio-mac-inference-smoke.yml vendored
View file

@ -1,7 +1,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
# Three end-to-end smoke jobs that boot a freshly-installed Studio and
# Three end-to-end smoke jobs that boot a freshly-installed Unsloth and
# exercise the surfaces real users hit through the OpenAI / Anthropic
# SDKs and curl. Each job picks the smallest model that exercises the
# behaviour under test, primes a model cache via actions/cache, and
@ -108,7 +108,7 @@ jobs:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-v2
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -124,7 +124,7 @@ jobs:
- name: Install OpenAI + Anthropic Python SDKs
run: pip install 'openai>=1.50' 'anthropic>=0.40'
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
run: |
unsloth studio reset-password
mkdir -p logs
@ -141,7 +141,7 @@ jobs:
fi
sleep 1
done
echo "Studio did not become healthy in 180s"
echo "Unsloth did not become healthy in 180s"
tail -200 logs/studio.log
exit 1
@ -228,11 +228,11 @@ jobs:
return replies
def run_anthropic():
# Two SDK quirks vs. Studio:
# Two SDK quirks vs. Unsloth:
# 1. base_url must NOT include /v1 -- the SDK appends
# /v1/messages itself; otherwise the request hits
# /v1/v1/messages and 405s.
# 2. The SDK sends `x-api-key` by default, but Studio's
# 2. The SDK sends `x-api-key` by default, but Unsloth's
# auth layer is HTTPBearer-only. Override via
# default_headers so Authorization: Bearer ... is
# sent instead.
@ -283,7 +283,7 @@ jobs:
print(f"[{label}] OK -- 4 turns, run1 == run2, history grounded")
PY
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
@ -363,7 +363,7 @@ jobs:
path: gguf-cache
key: ${{ runner.os }}-gguf-${{ env.GGUF_REPO }}-${{ env.GGUF_FILE }}-v1
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -376,7 +376,7 @@ jobs:
- name: Assert llama.cpp loads on this macOS
run: bash .github/scripts/assert-llama-loads.sh
- name: Reset auth + boot Studio (API-only, default tool policy)
- name: Reset auth + boot Unsloth (API-only, default tool policy)
# We deliberately use the API-only mode rather than
# `unsloth studio run` because the latter calls
# `set_tool_policy(...)` with a resolved bool: on loopback the
@ -478,7 +478,7 @@ jobs:
call with enable_tools=true must use this helper.
A shared CI runner can stall the stream transport (the
connection opening, or a mid-stream read) even when Studio
connection opening, or a mid-stream read) even when Unsloth
is healthy, so harden the read three ways: retry a stall
once with a fresh request capped at 300s; return any text
already streamed before a stall (a stall on the trailing
@ -574,11 +574,11 @@ jobs:
assert status == 200, f"tool call status {status}: {data}"
choice = data["choices"][0]
tool_calls = (choice.get("message") or {}).get("tool_calls") or []
# Studio's contract: when tool_choice='required', llama.cpp's
# Unsloth's contract: when tool_choice='required', llama.cpp's
# grammar should force a tool_calls payload. On Mac that
# contract is sometimes broken by the underlying quant; the
# PASS path is "tool_calls present + correct schema", the
# WARN path documents Studio still returned 200 with a
# WARN path documents Unsloth still returned 200 with a
# well-formed choices[] envelope.
if tool_calls:
tc = tool_calls[0]
@ -660,7 +660,7 @@ jobs:
print(f"[tools] WARN web_search probe failed (non-blocking): {exc}")
# ── 4. Thinking on / off ─────────────────────────────────────
# Studio strips think blocks from message.content for tools-mode
# Unsloth strips think blocks from message.content for tools-mode
# responses, so we toggle plain chat (no enable_tools) and look
# at the surfaced reasoning_content / message.thinking field.
def thinking_call(enable):
@ -678,7 +678,7 @@ jobs:
}, timeout = 180)
assert status == 200
msg = data["choices"][0]["message"]
# Studio surfaces thinking via reasoning_content (OpenAI
# Unsloth surfaces thinking via reasoning_content (OpenAI
# extension). Fall back to inline <think> markers for
# robustness across template versions.
raw = (msg.get("content") or "") + (msg.get("reasoning_content") or "")
@ -704,7 +704,7 @@ jobs:
print(f"[tools] PASS thinking on/off (on={len(on_text)} chars, off={len(off_text)} chars)")
PY
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
@ -810,7 +810,7 @@ jobs:
path: gguf-cache
key: ${{ runner.os }}-gguf-${{ env.GGUF_REPO }}-${{ env.GGUF_FILE }}-${{ env.MMPROJ_FILE }}-v2
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -826,7 +826,7 @@ jobs:
- name: Install OpenAI + Anthropic Python SDKs
run: pip install 'openai>=1.50' 'anthropic>=0.40'
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
# See Job 2's comment: API-only mode keeps tool_policy=None so
# response_format requests aren't routed through the agentic
# tool loop.
@ -929,13 +929,13 @@ jobs:
# llama.cpp's HTTP server supports OpenAI-compatible JSON
# mode: `response_format: {"type": "json_object"}` constrains
# the model to emit syntactically-valid JSON. We use raw HTTP
# rather than the OpenAI SDK so that the field shape Studio
# rather than the OpenAI SDK so that the field shape Unsloth
# forwards to llama-server is unambiguous (the SDK rewrites
# response_format depending on which variant it recognises).
# We deliberately do NOT pass a strict JSON schema -- on
# small Gemma-4 quants the GBNF-from-schema path occasionally
# produces empty output, and JSON mode is the surface we care
# about exposing through Studio.
# about exposing through Unsloth.
status, data = post("/v1/chat/completions", {
"model": "default",
"messages": [
@ -1007,7 +1007,7 @@ jobs:
)
# ── 2. OpenAI image_url (data URI base64) ───────────────────
# 64x64 solid-red PNG. stb_image (used by Studio's image
# 64x64 solid-red PNG. stb_image (used by Unsloth's image
# normaliser at routes/inference.py:3410) rejects 4x4 or
# smaller PNGs as truncated, so we go up to 64x64 -- still
# tiny in token cost. The assertion is loose: any non-empty
@ -1023,11 +1023,11 @@ jobs:
# The Mac prebuilt llama.cpp server has a known crash when
# processing image inputs alongside the gemma-4-E2B mmproj
# (server disconnects mid-completion). This is upstream
# llama.cpp behaviour, not Studio. Wrap both SDK calls in
# llama.cpp behaviour, not Unsloth. Wrap both SDK calls in
# try/except so an upstream crash registers as a WARN rather
# than failing the whole job. Studio's contract (OpenAI/
# than failing the whole job. Unsloth's contract (OpenAI/
# Anthropic image fields are accepted and forwarded) is
# validated by the request body Studio constructs, not by
# validated by the request body Unsloth constructs, not by
# whether llama.cpp can decode it on Mac Metal.
client = OpenAI(base_url = f"{BASE}/v1", api_key = KEY)
try:
@ -1053,14 +1053,14 @@ jobs:
except Exception as exc:
print(
f"[image/openai] WARN image_url SDK call raised: {type(exc).__name__}: "
f"{exc}. Likely upstream llama.cpp Mac+vision crash, NOT a Studio "
f"regression. Studio successfully forwarded the request."
f"{exc}. Likely upstream llama.cpp Mac+vision crash, NOT an Unsloth "
f"regression. Unsloth successfully forwarded the request."
)
# ── 3. Anthropic source/base64 image ────────────────────────
# Two SDK quirks vs. Studio: base_url must NOT include /v1
# Two SDK quirks vs. Unsloth: base_url must NOT include /v1
# (the SDK appends it itself; otherwise /v1/v1/messages -> 405),
# and Studio's auth is HTTPBearer-only so the SDK's default
# and Unsloth's auth is HTTPBearer-only so the SDK's default
# x-api-key header is ignored -- send Authorization: Bearer
# via default_headers.
anthropic = Anthropic(
@ -1099,11 +1099,11 @@ jobs:
print(
f"[image/anthropic] WARN anthropic image SDK call raised: "
f"{type(exc).__name__}: {exc}. Likely upstream llama.cpp Mac+vision "
f"crash, NOT a Studio regression."
f"crash, NOT an Unsloth regression."
)
PY
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true

4
.github/workflows/studio-mac-install-matrix.yml vendored
View file

@ -1,7 +1,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
# Proves Studio's llama.cpp install loads on every supported macOS. The heavy
# Proves Unsloth's llama.cpp install loads on every supported macOS. The heavy
# app smokes stay single-OS; this matrix covers the OS-version dimension cheaply
# (install.sh + binary-load assert). Regression guard for the macOS-version
# selection in studio/install_llama_prebuilt.py.
@ -60,7 +60,7 @@ jobs:
with:
python-version: '3.12'
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.

29
.github/workflows/studio-mac-ui-smoke.yml vendored
View file

@ -19,6 +19,7 @@ on:
- 'install.sh'
- 'pyproject.toml'
- 'tests/studio/**'
- '.github/scripts/run-studio-permission-browser.sh'
- '.github/workflows/studio-mac-ui-smoke.yml'
push:
branches: [main, pip]
@ -83,7 +84,7 @@ jobs:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-v2
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -96,7 +97,7 @@ jobs:
- name: Assert llama.cpp loads on this macOS
run: bash .github/scripts/assert-llama-loads.sh
- name: Install Playwright + Chromium
- name: Install Playwright browsers
# No --with-deps on Mac: that flag installs Linux apt packages.
# GitHub-hosted macos-14 ships the system frameworks Chromium
# needs already.
@ -112,7 +113,7 @@ jobs:
# in-script retry recover from any residual flakes.
run: |
pip install 'playwright>=1.55,<1.58'
python -m playwright install chromium
python -m playwright install chromium webkit
- name: Patch Playwright pipeTransport.js to tolerate malformed JSON
# In Playwright 1.55-1.58, pipeTransport.js does
@ -143,7 +144,7 @@ jobs:
print(f"pipeTransport.js: patched JSON.parse calls in {path}")
PY
- name: Reset auth + boot Studio
- name: Reset auth + boot Unsloth
run: |
unsloth studio reset-password
mkdir -p logs
@ -188,7 +189,7 @@ jobs:
# dies mid-test, (2) Chromium net::ERR_NO_BUFFER_SPACE when the
# runner's kernel briefly runs out of socket buffers, and (3) a
# goto 'interrupted by another navigation' when the SPA auth
# guard redirects mid-navigation. The retry FULLY resets Studio
# guard redirects mid-navigation. The retry FULLY resets Unsloth
# (kill, reset-password, reboot, wait /api/health, re-export
# bootstrap pw) before re-running the script. A real test failure
# (assertion / timeout) does NOT match any pattern so it bypasses
@ -209,7 +210,7 @@ jobs:
|| grep -q "ERR_NO_BUFFER_SPACE" logs/playwright_attempt_${attempt}.log \
|| grep -q "interrupted by another navigation" logs/playwright_attempt_${attempt}.log; } \
&& [ "$attempt" -lt "$max_attempts" ]; then
echo "::warning::Playwright flake on attempt ${attempt}; resetting Studio and retrying..."
echo "::warning::Playwright flake on attempt ${attempt}; resetting Unsloth and retrying..."
kill "${STUDIO_PID}" 2>/dev/null || true
sleep 2
unsloth studio reset-password
@ -238,13 +239,17 @@ jobs:
exit "$rc"
done
- name: Stop Studio (chat-ui ends with Shutdown click; this is belt-and-suspenders)
- name: Stop Unsloth (chat-ui ends with Shutdown click; this is belt-and-suspenders)
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
sleep 2
- name: Reset auth + boot Studio for extra UI tests (port 18897)
- name: Cross-browser permission controls
run: |
bash .github/scripts/run-studio-permission-browser.sh 18895 webkit
- name: Reset auth + boot Unsloth for extra UI tests (port 18897)
run: |
unsloth studio reset-password
mkdir -p logs
@ -271,7 +276,7 @@ jobs:
echo "STUDIO_EXTRA_OLD_PW=$OLD" >> "$GITHUB_ENV"
echo "STUDIO_EXTRA_NEW_PW=$NEW" >> "$GITHUB_ENV"
- name: Drive Compare/Recipes/Export/Studio/Settings with Playwright
- name: Drive Compare/Recipes/Export/Unsloth/Settings with Playwright
env:
BASE_URL: http://127.0.0.1:18897
STUDIO_OLD_PW: ${{ env.STUDIO_EXTRA_OLD_PW }}
@ -300,7 +305,7 @@ jobs:
|| grep -q "ERR_NO_BUFFER_SPACE" logs/playwright_extra_attempt_${attempt}.log \
|| grep -q "interrupted by another navigation" logs/playwright_extra_attempt_${attempt}.log; } \
&& [ "$attempt" -lt "$max_attempts" ]; then
echo "::warning::Playwright flake on attempt ${attempt}; resetting Studio and retrying..."
echo "::warning::Playwright flake on attempt ${attempt}; resetting Unsloth and retrying..."
kill "${STUDIO_EXTRA_PID}" 2>/dev/null || true
sleep 2
unsloth studio reset-password
@ -327,7 +332,7 @@ jobs:
exit "$rc"
done
- name: Stop second Studio
- name: Stop second Unsloth
if: always()
run: |
kill "${STUDIO_EXTRA_PID}" 2>/dev/null || true
@ -343,5 +348,7 @@ jobs:
logs/studio_extra.log
logs/install.log
logs/playwright
logs/playwright-permissions-*
logs/playwright_extra
logs/studio-permissions-*.log
retention-days: 7

16
.github/workflows/studio-mac-update-smoke.yml vendored
View file

@ -4,15 +4,15 @@
# Mac counterpart to studio-update-smoke.yml. Verifies that on a real
# Apple Silicon (macos-14, M1) runner:
#
# 1. install.sh --local --no-torch installs Studio AND auto-fetches
# 1. install.sh --local --no-torch installs Unsloth AND auto-fetches
# the prebuilt llama.cpp Mac binary (llama-bNNNN-bin-macos-arm64
# from ggml-org/llama.cpp). Hitting the source-build fallback is
# treated as an Unsloth bug -- Studio must always pick the
# treated as an Unsloth bug -- Unsloth must always pick the
# prebuilt on Mac.
# 2. unsloth studio update --local is idempotent. Two consecutive
# runs both report "prebuilt up to date and validated", no
# source-build fallback.
# 3. The installed Studio still boots and /api/health returns
# 3. The installed Unsloth still boots and /api/health returns
# healthy after the update path.
name: Mac Studio Update CI
@ -42,7 +42,7 @@ permissions:
jobs:
update-idempotency:
name: Studio Updating Tests
name: Unsloth Updating Tests
runs-on: macos-14
timeout-minutes: 30
steps:
@ -59,7 +59,7 @@ jobs:
python-version: '3.12'
cache: 'pip'
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -106,7 +106,7 @@ jobs:
grep -qE "prebuilt up to date and validated|prebuilt installed and validated" logs/update2.log
echo "second update was clean"
- name: Boot Studio briefly to confirm the install is still usable
- name: Boot Unsloth briefly to confirm the install is still usable
run: |
mkdir -p logs
UNSLOTH_API_ONLY=1 unsloth studio -H 127.0.0.1 -p 18891 \
@ -123,13 +123,13 @@ jobs:
sleep 1
done
if [ -z "$HEALTHY" ]; then
echo "Studio failed to come up after \`update\`"
echo "Unsloth failed to come up after \`update\`"
tail -200 logs/studio.log
kill "$PID" 2>/dev/null || true
exit 1
fi
kill "$PID" 2>/dev/null || true
echo "post-update Studio /api/health OK"
echo "post-update Unsloth /api/health OK"
- name: Uninstall and verify clean
# Round-trip through scripts/uninstall.sh on real macOS. As a side

2
.github/workflows/studio-tauri-smoke.yml vendored
View file

@ -12,7 +12,7 @@
# stay in release-desktop.yml (manual `workflow_dispatch`) because they need
# code-signing secrets and ~30 min of runner time each.
name: Studio Tauri CI
name: Unsloth Tauri CI
on:
pull_request:

100
.github/workflows/studio-ui-smoke.yml vendored
View file

@ -1,8 +1,8 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
# End-to-end Studio chat UI smoke via Playwright + Chromium against a
# headless Linux runner. Boots Studio with the smallest GGUF
# End-to-end Unsloth chat UI smoke via Playwright + Chromium against a
# headless Linux runner. Boots Unsloth with the smallest GGUF
# (gemma-3-270m-it UD-Q4_K_XL, ~254 MiB), drives the actual frontend
# bundle, and asserts the full bootstrap-password / change-password /
# send-message / persist-on-reload journey works end to end.
@ -14,7 +14,7 @@
# frontend-only CI happily pass while the actual user-visible UI is
# broken (cf. the 2026.5.1 chat-history release).
name: Studio UI CI
name: Unsloth UI CI
on:
pull_request:
@ -27,6 +27,7 @@ on:
# The Playwright test files themselves -- a PR that ONLY edits
# the test must still trigger UI CI.
- 'tests/studio/**'
- '.github/scripts/run-studio-permission-browser.sh'
- '.github/workflows/studio-ui-smoke.yml'
push:
branches: [main, pip]
@ -97,7 +98,7 @@ jobs:
path: hf-cache
key: ${{ runner.os }}-hf-${{ env.GGUF_REPO }}-${{ env.GGUF_VARIANT }}-v2
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Withheld on PR: this step runs checked-out PR code; public GGUF still downloads.
@ -107,15 +108,12 @@ jobs:
set -o pipefail
bash install.sh --local --no-torch 2>&1 | tee logs/install.log
- name: Install Playwright + Chromium
- name: Install Playwright browsers
run: |
pip install 'playwright>=1.45'
# --with-deps installs the OS-level runtime libs Chromium
# needs (libnss3, libxkbcommon, etc.). About 30 s on a
# warm runner.
python -m playwright install --with-deps chromium
python -m playwright install --with-deps chromium firefox webkit
- name: Reset auth + boot Studio
- name: Reset auth + boot Unsloth
run: |
unsloth studio reset-password
mkdir -p logs
@ -147,7 +145,7 @@ jobs:
# NEW + NEW2 are generated freshly per CI run via secrets.token_urlsafe
# rather than hardcoded. If a workflow gets compromised, the
# attacker can't replay a known-good rotated password against
# any future / parallel Studio install -- the rotated value
# any future / parallel Unsloth install -- the rotated value
# only ever exists for the lifetime of this single job, masked
# in the log via ::add-mask::.
run: |
@ -165,29 +163,35 @@ jobs:
env:
BASE_URL: http://127.0.0.1:18892
# The test file lives in the repo so it can be run locally
# against a freshly-installed Studio (BASE_URL=...; STUDIO_OLD_PW=
# against a freshly-installed Unsloth (BASE_URL=...; STUDIO_OLD_PW=
# $(cat ~/.unsloth/studio/auth/.bootstrap_password); python ...).
PW_ART_DIR: logs/playwright
# Strict mode: in CI a missing button / nav / dialog must
# FAIL the test. Locally the test still runs against partial
# Studio installs without STUDIO_UI_STRICT.
# Unsloth installs without STUDIO_UI_STRICT.
STUDIO_UI_STRICT: '1'
run: |
mkdir -p logs/playwright
python tests/studio/playwright_chat_ui.py
- name: Stop Studio (chat-ui ends with Shutdown click; this is belt-and-suspenders)
- name: Stop Unsloth (chat-ui ends with Shutdown click; this is belt-and-suspenders)
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
sleep 2
- name: Cross-browser permission controls
run: |
bash .github/scripts/run-studio-permission-browser.sh 18893 firefox
bash .github/scripts/run-studio-permission-browser.sh 18893 webkit
bash .github/scripts/run-studio-permission-browser.sh 18893 chromium chrome
# The chat UI test ends by clicking the Shutdown menuitem, which
# leaves the server dead. The extra UI test (Compare / Recipes /
# Export / Studio / Settings) needs a fresh Studio, so we boot a
# Export / Unsloth / Settings) needs a fresh Unsloth, so we boot a
# second one on a different port. Boot is fast (~3-5s on the
# warm install we already did) so this adds little wall time.
- name: Reset auth + boot Studio for extra UI tests (port 18894)
- name: Reset auth + boot Unsloth for extra UI tests (port 18894)
run: |
unsloth studio reset-password
mkdir -p logs
@ -214,7 +218,7 @@ jobs:
echo "STUDIO_EXTRA_OLD_PW=$OLD" >> "$GITHUB_ENV"
echo "STUDIO_EXTRA_NEW_PW=$NEW" >> "$GITHUB_ENV"
- name: Drive Compare/Recipes/Export/Studio/Settings with Playwright
- name: Drive Compare/Recipes/Export/Unsloth/Settings with Playwright
env:
BASE_URL: http://127.0.0.1:18894
STUDIO_OLD_PW: ${{ env.STUDIO_EXTRA_OLD_PW }}
@ -227,16 +231,64 @@ jobs:
mkdir -p logs/playwright_extra
python tests/studio/playwright_extra_ui.py
- name: Stop second Studio
- name: Stop second Unsloth
if: always()
run: |
kill "${STUDIO_EXTRA_PID}" 2>/dev/null || true
sleep 2
# Model-picker per-model-config regression (PR #7207 re-land of #6647).
# Fourth Unsloth on its own port; loads the tiny GGUF and drives the
# picker's run-settings surface: Context Length persists across a reload,
# Reset clears the stored override (never pins it), and the infra models
# (RAG embedder + llama.cpp probe) stay hidden from the picker.
- name: Reset auth + boot Unsloth for model-config tests (port 18898)
run: |
unsloth studio reset-password
mkdir -p logs
UNSLOTH_API_ONLY=1 unsloth studio -H 127.0.0.1 -p 18898 \
> logs/studio_modelcfg.log 2>&1 &
echo "STUDIO_MODELCFG_PID=$!" >> "$GITHUB_ENV"
- name: Wait for /api/health on 18898
run: |
for i in $(seq 1 180); do
if curl -fs "http://127.0.0.1:18898/api/health" > /tmp/health4.json; then
jq -e '.status == "healthy"' /tmp/health4.json && break
fi
sleep 1
done
jq -e '.status == "healthy"' /tmp/health4.json
- name: Pass bootstrap pw for model-config test
run: |
NEW="CIModelCfg-$(python -c 'import secrets; print(secrets.token_urlsafe(16))')"
echo "::add-mask::$NEW"
echo "STUDIO_MODELCFG_NEW_PW=$NEW" >> "$GITHUB_ENV"
- name: Drive model-picker per-model-config with Playwright
env:
BASE_URL: http://127.0.0.1:18898
STUDIO_NEW_PW: ${{ env.STUDIO_MODELCFG_NEW_PW }}
PW_ART_DIR: logs/playwright_modelcfg
STUDIO_UI_STRICT: '1'
GGUF_REPO: ${{ env.GGUF_REPO }}
GGUF_VARIANT: ${{ env.GGUF_VARIANT }}
STUDIO_MODEL_HINT: gemma-3-270m
run: |
mkdir -p logs/playwright_modelcfg
python tests/studio/playwright_model_config.py
- name: Stop fourth Unsloth
if: always()
run: |
kill "${STUDIO_MODELCFG_PID}" 2>/dev/null || true
sleep 2
# IME + multilingual paste regression (issue #5318 / PR #5327).
# Third Studio on its own port so a hang here cannot poison the
# Third Unsloth on its own port so a hang here cannot poison the
# earlier UI tests. No GGUF -- the bug surface is the composer.
- name: Reset auth + boot Studio for IME / i18n tests (port 18896)
- name: Reset auth + boot Unsloth for IME / i18n tests (port 18896)
run: |
unsloth studio reset-password
mkdir -p logs
@ -256,7 +308,7 @@ jobs:
- name: Pass bootstrap pw for IME / i18n test
# IME smoke does the change-password against the bootstrap that
# Studio's frontend injects into the page, so it only needs the
# Unsloth's frontend injects into the page, so it only needs the
# NEW password.
run: |
NEW="CIIme-$(python -c 'import secrets; print(secrets.token_urlsafe(16))')"
@ -273,7 +325,7 @@ jobs:
mkdir -p logs/playwright_ime
python tests/studio/playwright_chat_ime_i18n.py
- name: Stop third Studio
- name: Stop third Unsloth
if: always()
run: |
kill "${STUDIO_IME_PID}" 2>/dev/null || true
@ -293,10 +345,14 @@ jobs:
path: |
logs/studio.log
logs/studio_extra.log
logs/studio_modelcfg.log
logs/studio_ime.log
logs/install.log
logs/server-logs/
logs/playwright
logs/playwright-permissions-*
logs/playwright_extra
logs/playwright_modelcfg
logs/playwright_ime
logs/studio-permissions-*.log
retention-days: 7

12
.github/workflows/studio-update-smoke.yml vendored
View file

@ -9,7 +9,7 @@
# This catches regressions in setup.sh's update path that the existing
# GGUF / wheel jobs would miss because they only invoke install.sh once.
name: Studio Update CI
name: Unsloth Update CI
on:
pull_request:
@ -36,7 +36,7 @@ permissions:
jobs:
update-idempotency:
name: Studio Updating Tests
name: Unsloth Updating Tests
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
@ -63,7 +63,7 @@ jobs:
# post-step then fatal-errors with "Cache folder path is
# retrieved for pip but doesn't exist on disk".
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
# Pass the workflow token so the llama.cpp prebuilt installer's
# GitHub-API call to list releases isn't rate-limited (60/hr
# unauthenticated). Without this, three consecutive install +
@ -122,7 +122,7 @@ jobs:
grep -qE "prebuilt up to date and validated|prebuilt installed and validated" logs/update2.log
echo "second update was clean"
- name: Boot Studio briefly to confirm the install is still usable
- name: Boot Unsloth briefly to confirm the install is still usable
# If `update --local` accidentally broke the venv or wiped the
# llama-server binary, the server would fail to start here.
run: |
@ -138,13 +138,13 @@ jobs:
sleep 1
done
if ! jq -e '.status == "healthy"' /tmp/health.json 2>/dev/null; then
echo "Studio failed to come up after `update`"
echo "Unsloth failed to come up after `update`"
tail -200 logs/studio.log
kill "$PID" 2>/dev/null || true
exit 1
fi
kill "$PID" 2>/dev/null || true
echo "post-update Studio /api/health OK"
echo "post-update Unsloth /api/health OK"
- name: Uninstall and verify clean
# Round-trip the installer through scripts/uninstall.sh: confirms the

16
.github/workflows/studio-windows-api-smoke.yml vendored
View file

@ -9,7 +9,7 @@
# (Section 6) is Linux-only and short-circuits on non-POSIX; the rest
# is platform-portable.
name: Windows Studio API CI
name: Windows Unsloth API CI
on:
pull_request:
@ -34,7 +34,7 @@ permissions:
jobs:
api-smoke:
name: Studio API & Auth Tests
name: Unsloth API & Auth Tests
runs-on: windows-latest
timeout-minutes: 30
defaults:
@ -105,7 +105,7 @@ jobs:
# studio-windows-update-smoke.yml for the full rationale --
# creating an empty studio/frontend/dist trips setup.ps1's
# mtime-based staleness check into "frontend up to date, skip
# rebuild" and Studio boots with an empty dist directory.
# rebuild" and Unsloth boots with an empty dist directory.
# Add-MpPreference accepts paths that do not yet exist.
foreach ($p in @(
"$env:USERPROFILE\.unsloth",
@ -121,7 +121,7 @@ jobs:
}
}
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
shell: pwsh
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@ -161,7 +161,7 @@ jobs:
echo "install.ps1 installed the Windows prebuilt llama.cpp:"
cat "$INFO"
- name: Add Studio shim to GITHUB_PATH
- name: Add Unsloth shim to GITHUB_PATH
# install.ps1's User-PATH update doesn't propagate to a
# running Git Bash session; export the shim dir so the
# next `unsloth ...` invocation finds it.
@ -177,7 +177,7 @@ jobs:
- name: Install pyjwt for the JWT-expiry forge test
run: python -m pip install 'pyjwt>=2.6'
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
run: |
unsloth studio reset-password
mkdir -p logs
@ -207,7 +207,7 @@ jobs:
echo "STUDIO_NEW_PW=$NEW" >> "$GITHUB_ENV"
echo "STUDIO_NEW2_PW=$NEW2" >> "$GITHUB_ENV"
- name: Run Studio API & Auth tests
- name: Run Unsloth API & Auth tests
# Do NOT pin STUDIO_AUTH_DIR here. The Mac/Linux mirrors
# hardcode runner-specific paths (/Users/runner/...,
# /home/runner/...), but on Windows the path is
@ -219,7 +219,7 @@ jobs:
BASE_URL: http://127.0.0.1:18895
run: python tests/studio/studio_api_smoke.py
- name: Stop Studio
- name: Stop Unsloth
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true

90
.github/workflows/studio-windows-inference-smoke.yml vendored
View file

@ -1,7 +1,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
# Three end-to-end smoke jobs that boot a freshly-installed Studio and
# Three end-to-end smoke jobs that boot a freshly-installed Unsloth and
# exercise the surfaces real users hit through the OpenAI / Anthropic
# SDKs and curl, on the FREE windows-latest runner. Each job picks the
# smallest model that exercises the behaviour under test, primes
@ -16,7 +16,7 @@
# Qwen3-VL-2B-Instruct UD-IQ2_XXS + mmproj-F16 (~1.4 GiB total).
# Within the 14 GB windows-latest SSD budget.
name: Windows Studio GGUF CI
name: Windows Unsloth GGUF CI
on:
pull_request:
@ -57,7 +57,7 @@ jobs:
STUDIO_PORT: '18888'
HF_HOME: ${{ github.workspace }}/hf-cache
# Force UTF-8 for stdio (Windows defaults to cp1252; hf
# download / Studio CLI print "✓" checkmarks and crash
# download / Unsloth CLI print "✓" checkmarks and crash
# otherwise).
PYTHONIOENCODING: utf-8
PYTHONUTF8: '1'
@ -160,7 +160,7 @@ jobs:
# studio-windows-update-smoke.yml for the full rationale --
# creating an empty studio/frontend/dist trips setup.ps1's
# mtime-based staleness check into "frontend up to date, skip
# rebuild" and Studio boots with an empty dist directory.
# rebuild" and Unsloth boots with an empty dist directory.
# Add-MpPreference accepts paths that do not yet exist.
foreach ($p in @(
"$env:USERPROFILE\.unsloth",
@ -176,7 +176,7 @@ jobs:
}
}
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
shell: pwsh
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@ -214,7 +214,7 @@ jobs:
echo "install.ps1 installed the Windows prebuilt llama.cpp:"
cat "$INFO"
- name: Add Studio shim to GITHUB_PATH
- name: Add Unsloth shim to GITHUB_PATH
run: |
SHIM_DIR=~/.unsloth/studio/bin
if [ ! -f "$SHIM_DIR/unsloth.exe" ]; then
@ -227,7 +227,7 @@ jobs:
- name: Install OpenAI + Anthropic Python SDKs
run: python -m pip install 'openai>=1.50' 'anthropic>=0.40'
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
run: |
unsloth studio reset-password
mkdir -p logs
@ -244,7 +244,7 @@ jobs:
fi
sleep 1
done
echo "Studio did not become healthy in 180s"
echo "Unsloth did not become healthy in 180s"
tail -200 logs/studio.log
exit 1
@ -281,7 +281,7 @@ jobs:
# Retry the load step a few times so a transient TCP RST during
# llama-server warm-up (Windows runner image churn,
# windows-latest -> windows-2025-vs2026 rollout) doesn't fail
# the whole job. The Studio backend's _wait_for_health now
# the whole job. The Unsloth backend's _wait_for_health now
# catches httpx.ReadError too; this retry layer covers the
# cases the backend can't recover from on its own.
LOAD_OK=0
@ -382,15 +382,15 @@ jobs:
print(f"[{label}] OK -- 4 turns, run1 == run2, history grounded")
PY
- name: Stop Studio
- name: Stop Unsloth
if: always()
# Run as cmd so we are not running through the Git Bash shell;
# Git Bash on windows-latest has been observed to exit 143
# (SIGTERM) from any inline kill/sleep block, masking a green
# test run. The runner reclaims the Studio child process at
# test run. The runner reclaims the Unsloth child process at
# job end either way, so just emit a marker and exit 0.
shell: cmd
run: echo Stop Studio (no-op; runner reclaims STUDIO_PID=%STUDIO_PID% at job end)
run: echo Stop Unsloth (no-op; runner reclaims STUDIO_PID=%STUDIO_PID% at job end)
- name: Collect llama-server logs
if: always()
@ -398,10 +398,10 @@ jobs:
# copy must not fail an otherwise-green job.
continue-on-error: true
shell: bash
# Copy llama-server's own stdout/stderr (teed by Studio under
# Copy llama-server's own stdout/stderr (teed by Unsloth under
# ~/.unsloth/studio/logs/llama-server/) into the workspace so
# upload-artifact can pick it up. Crucial for diagnosing a
# subprocess crash where Studio's traceback only shows the
# subprocess crash where Unsloth's traceback only shows the
# symptom (httpx ReadError) but not the cause.
run: |
mkdir -p logs/llama-server
@ -439,14 +439,14 @@ jobs:
# (211 s on first run; subsequent runs hit the cache, but the
# one-time cost recurs every time the cache key bumps). Use
# main's `--local-dir gguf-cache` pattern: cache the flat .gguf
# only, pass an absolute path to Studio's /api/inference/load.
# only, pass an absolute path to Unsloth's /api/inference/load.
# The OpenAI/Anth and JSON+images jobs still cover the
# gguf_variant resolution path.
GGUF_REPO: unsloth/Qwen3.5-2B-GGUF
GGUF_FILE: Qwen3.5-2B-UD-Q4_K_XL.gguf
STUDIO_PORT: '18898'
# Force UTF-8 for stdio (Windows defaults to cp1252; hf
# download / Studio CLI print "✓" checkmarks and crash
# download / Unsloth CLI print "✓" checkmarks and crash
# otherwise).
PYTHONIOENCODING: utf-8
PYTHONUTF8: '1'
@ -507,7 +507,7 @@ jobs:
# studio-windows-update-smoke.yml for the full rationale --
# creating an empty studio/frontend/dist trips setup.ps1's
# mtime-based staleness check into "frontend up to date, skip
# rebuild" and Studio boots with an empty dist directory.
# rebuild" and Unsloth boots with an empty dist directory.
# Add-MpPreference accepts paths that do not yet exist.
foreach ($p in @(
"$env:USERPROFILE\.unsloth",
@ -523,7 +523,7 @@ jobs:
}
}
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
shell: pwsh
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@ -561,7 +561,7 @@ jobs:
echo "install.ps1 installed the Windows prebuilt llama.cpp:"
cat "$INFO"
- name: Add Studio shim to GITHUB_PATH
- name: Add Unsloth shim to GITHUB_PATH
run: |
SHIM_DIR=~/.unsloth/studio/bin
if [ ! -f "$SHIM_DIR/unsloth.exe" ]; then
@ -571,7 +571,7 @@ jobs:
fi
cygpath -w "$SHIM_DIR" >> "$GITHUB_PATH"
- name: Reset auth + boot Studio (API-only, default tool policy)
- name: Reset auth + boot Unsloth (API-only, default tool policy)
run: |
unsloth studio reset-password
mkdir -p logs
@ -607,7 +607,7 @@ jobs:
# raw string, but we cannot embed `\a` etc. in JSON without
# JSON-string-escaping every backslash. Replace `\` with `/`
# via bash parameter expansion -- pathlib.Path on Windows
# accepts forward slashes natively, so Studio's loader sees
# accepts forward slashes natively, so Unsloth's loader sees
# a normal path.
GGUF_PATH="${GITHUB_WORKSPACE//\\//}/gguf-cache/${GGUF_FILE}"
ls -lh "$GGUF_PATH"
@ -680,7 +680,7 @@ jobs:
def post_sse(path, body, *, timeout = 600, retries = 1, soft = False):
# The server-side agentic loop always answers over SSE. A
# shared CI runner can stall the stream transport (the
# connection opening, or a mid-stream read) even when Studio
# connection opening, or a mid-stream read) even when Unsloth
# is healthy, so harden the read three ways:
# * retry a transport stall once with a fresh request,
# capped at 300s (a healthy server answers a retry
@ -882,15 +882,15 @@ jobs:
print(f"[tools] PASS thinking on/off (on={len(on_text)} chars, off={len(off_text)} chars)")
PY
- name: Stop Studio
- name: Stop Unsloth
if: always()
# Run as cmd so we are not running through the Git Bash shell;
# Git Bash on windows-latest has been observed to exit 143
# (SIGTERM) from any inline kill/sleep block, masking a green
# test run. The runner reclaims the Studio child process at
# test run. The runner reclaims the Unsloth child process at
# job end either way, so just emit a marker and exit 0.
shell: cmd
run: echo Stop Studio (no-op; runner reclaims STUDIO_PID=%STUDIO_PID% at job end)
run: echo Stop Unsloth (no-op; runner reclaims STUDIO_PID=%STUDIO_PID% at job end)
- name: Collect llama-server logs
if: always()
@ -898,10 +898,10 @@ jobs:
# copy must not fail an otherwise-green job.
continue-on-error: true
shell: bash
# Copy llama-server's own stdout/stderr (teed by Studio under
# Copy llama-server's own stdout/stderr (teed by Unsloth under
# ~/.unsloth/studio/logs/llama-server/) into the workspace so
# upload-artifact can pick it up. Crucial for diagnosing a
# subprocess crash where Studio's traceback only shows the
# subprocess crash where Unsloth's traceback only shows the
# symptom (httpx ReadError) but not the cause.
run: |
mkdir -p logs/llama-server
@ -939,7 +939,7 @@ jobs:
STUDIO_PORT: '18899'
HF_HOME: ${{ github.workspace }}/hf-cache
# Force UTF-8 for stdio (Windows defaults to cp1252; hf
# download / Studio CLI print "✓" checkmarks and crash
# download / Unsloth CLI print "✓" checkmarks and crash
# otherwise).
PYTHONIOENCODING: utf-8
PYTHONUTF8: '1'
@ -1005,7 +1005,7 @@ jobs:
# studio-windows-update-smoke.yml for the full rationale --
# creating an empty studio/frontend/dist trips setup.ps1's
# mtime-based staleness check into "frontend up to date, skip
# rebuild" and Studio boots with an empty dist directory.
# rebuild" and Unsloth boots with an empty dist directory.
# Add-MpPreference accepts paths that do not yet exist.
foreach ($p in @(
"$env:USERPROFILE\.unsloth",
@ -1021,7 +1021,7 @@ jobs:
}
}
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
shell: pwsh
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@ -1059,7 +1059,7 @@ jobs:
echo "install.ps1 installed the Windows prebuilt llama.cpp:"
cat "$INFO"
- name: Add Studio shim to GITHUB_PATH
- name: Add Unsloth shim to GITHUB_PATH
run: |
SHIM_DIR=~/.unsloth/studio/bin
if [ ! -f "$SHIM_DIR/unsloth.exe" ]; then
@ -1072,7 +1072,7 @@ jobs:
- name: Install OpenAI + Anthropic Python SDKs
run: python -m pip install 'openai>=1.50' 'anthropic>=0.40'
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
run: |
unsloth studio reset-password
mkdir -p logs
@ -1262,7 +1262,7 @@ jobs:
except Exception as exc:
print(
f"[image/openai] WARN image_url SDK call raised: {type(exc).__name__}: "
f"{exc}. Studio successfully forwarded the request; failure here is "
f"{exc}. Unsloth successfully forwarded the request; failure here is "
f"upstream llama.cpp vision behaviour."
)
@ -1303,19 +1303,19 @@ jobs:
print(
f"[image/anthropic] WARN anthropic image SDK call raised: "
f"{type(exc).__name__}: {exc}. Likely upstream llama.cpp vision "
f"behaviour, NOT a Studio regression."
f"behaviour, NOT an Unsloth regression."
)
PY
- name: Stop Studio
- name: Stop Unsloth
if: always()
# Run as cmd so we are not running through the Git Bash shell;
# Git Bash on windows-latest has been observed to exit 143
# (SIGTERM) from any inline kill/sleep block, masking a green
# test run. The runner reclaims the Studio child process at
# test run. The runner reclaims the Unsloth child process at
# job end either way, so just emit a marker and exit 0.
shell: cmd
run: echo Stop Studio (no-op; runner reclaims STUDIO_PID=%STUDIO_PID% at job end)
run: echo Stop Unsloth (no-op; runner reclaims STUDIO_PID=%STUDIO_PID% at job end)
- name: Collect llama-server logs
if: always()
@ -1323,10 +1323,10 @@ jobs:
# copy must not fail an otherwise-green job.
continue-on-error: true
shell: bash
# Copy llama-server's own stdout/stderr (teed by Studio under
# Copy llama-server's own stdout/stderr (teed by Unsloth under
# ~/.unsloth/studio/logs/llama-server/) into the workspace so
# upload-artifact can pick it up. Crucial for diagnosing a
# subprocess crash where Studio's traceback only shows the
# subprocess crash where Unsloth's traceback only shows the
# symptom (httpx ReadError) but not the cause.
run: |
mkdir -p logs/llama-server
@ -1348,7 +1348,7 @@ jobs:
# ── folded from studio-windows-no-vs-smoke.yml: install + run with no Visual Studio ──
no-vs-cpu:
name: Studio install + inference without Visual Studio
name: Unsloth install + inference without Visual Studio
runs-on: windows-latest
timeout-minutes: 35
defaults:
@ -1502,7 +1502,7 @@ jobs:
python -m pip install torch --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple
python -c "import torch; print('torch', torch.__version__, 'cuda?', torch.cuda.is_available())"
- name: Install Studio (--local, --no-torch) with no build tools present
- name: Install Unsloth (--local, --no-torch) with no build tools present
shell: pwsh
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@ -1538,13 +1538,13 @@ jobs:
echo "Prebuilt installed with no build tools:"
cat "$INFO"
- name: Add Studio shim to GITHUB_PATH
- name: Add Unsloth shim to GITHUB_PATH
run: |
SHIM_DIR=~/.unsloth/studio/bin
[ -f "$SHIM_DIR/unsloth.exe" ] || { echo "::error::unsloth.exe shim not found"; ls -la ~/.unsloth/studio/ || true; exit 1; }
cygpath -w "$SHIM_DIR" >> "$GITHUB_PATH"
- name: Reset auth + boot Studio (API-only)
- name: Reset auth + boot Unsloth (API-only)
run: |
unsloth studio reset-password
mkdir -p logs
@ -1613,10 +1613,10 @@ jobs:
}
Remove-Item -LiteralPath $root -Recurse -Force -ErrorAction SilentlyContinue
- name: Stop Studio
- name: Stop Unsloth
if: always()
shell: cmd
run: echo Stop Studio (no-op; runner reclaims STUDIO_PID=%STUDIO_PID% at job end)
run: echo Stop Unsloth (no-op; runner reclaims STUDIO_PID=%STUDIO_PID% at job end)
- name: Collect llama-server logs
if: always()

39
.github/workflows/studio-windows-ui-smoke.yml vendored
View file

@ -4,11 +4,11 @@
# Windows counterpart to studio-ui-smoke.yml / studio-mac-ui-smoke.yml.
# Same Playwright + Chromium end-to-end chat UI flow + extra UI flow,
# but on the FREE windows-latest runner so we catch Windows-specific
# regressions in the install path (install.ps1), the Studio CLI's
# regressions in the install path (install.ps1), the Unsloth CLI's
# Windows process-management branches, and the llama.cpp prebuilt's
# Windows HTTP layer.
name: Windows Studio UI CI
name: Windows Unsloth UI CI
on:
pull_request:
@ -19,6 +19,7 @@ on:
- 'install.ps1'
- 'pyproject.toml'
- 'tests/studio/**'
- '.github/scripts/run-studio-permission-browser.sh'
- '.github/workflows/studio-windows-ui-smoke.yml'
push:
branches: [main, pip]
@ -49,7 +50,7 @@ jobs:
GGUF_FILE: gemma-3-270m-it-UD-Q4_K_XL.gguf
STUDIO_PORT: '18896'
HF_HOME: ${{ github.workspace }}/hf-cache
# Force UTF-8 for stdio so Python tools (hf download, Studio
# Force UTF-8 for stdio so Python tools (hf download, Unsloth
# CLI, etc.) can print Unicode characters like the success
# checkmark "✓". Windows defaults to cp1252 / charmap and
# any tool that prints "OK ✓" hits a UnicodeEncodeError.
@ -121,7 +122,7 @@ jobs:
# studio-windows-update-smoke.yml for the full rationale --
# creating an empty studio/frontend/dist trips setup.ps1's
# mtime-based staleness check into "frontend up to date, skip
# rebuild" and Studio boots with an empty dist directory.
# rebuild" and Unsloth boots with an empty dist directory.
# Add-MpPreference accepts paths that do not yet exist.
foreach ($p in @(
"$env:USERPROFILE\.unsloth",
@ -148,7 +149,7 @@ jobs:
Set-Content -LiteralPath (Join-Path $appDir 'launch-studio.vbs') -Value 'WScript.Echo "legacy"' -Encoding Unicode
Write-Host "seeded legacy launch-studio.vbs at $appDir"
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
# install.ps1 is the supported Windows installer. install.sh
# has no Windows branch (apt-get / brew calls). The PS1
# script's `Install-UnslothStudio @args` line at the bottom
@ -205,7 +206,7 @@ jobs:
echo "install.ps1 installed the Windows prebuilt llama.cpp:"
cat "$INFO"
- name: Assert Studio launcher chain (no VBS, hidden PowerShell shortcut)
- name: Assert Unsloth launcher chain (no VBS, hidden PowerShell shortcut)
# The shortcut launch path is otherwise untested here (the steps below
# boot `unsloth studio` directly). Guard against re-introducing the VBS
# that tripped Kaspersky HEUR:Trojan.VBS.Agent.gen and against the .lnk
@ -234,7 +235,7 @@ jobs:
}
Write-Host "launcher chain OK (no VBS; hidden powershell over launch-studio.ps1)"
- name: Launch Studio via the shortcut and assert health
- name: Launch Unsloth via the shortcut and assert health
# Run the exact command the .lnk stores (hidden PowerShell over
# launch-studio.ps1) and confirm it brings the backend up. This is the
# only step that proves the shortcut launch is not silently broken.
@ -265,10 +266,10 @@ jobs:
$owner = (Get-NetTCPConnection -LocalPort $foundPort -State Listen -ErrorAction Stop | Select-Object -First 1).OwningProcess
if ($owner) { taskkill /PID $owner /T /F 2>$null | Out-Null }
} catch {}
if (-not $foundPort) { throw "Studio did not become healthy when launched via the shortcut" }
Write-Host "Studio healthy on port $foundPort (launched via the shortcut)"
if (-not $foundPort) { throw "Unsloth did not become healthy when launched via the shortcut" }
Write-Host "Unsloth healthy on port $foundPort (launched via the shortcut)"
- name: Add Studio shim to GITHUB_PATH
- name: Add Unsloth shim to GITHUB_PATH
# install.ps1 puts unsloth.exe at $StudioHome\bin\unsloth.exe
# and adds that dir to the User PATH via the Windows registry.
# Registry-level PATH updates don't propagate to a running
@ -284,7 +285,7 @@ jobs:
fi
# GITHUB_PATH wants Windows-style paths; convert via cygpath.
cygpath -w "$SHIM_DIR" >> "$GITHUB_PATH"
echo "Added Studio shim dir to PATH: $(cygpath -w "$SHIM_DIR")"
echo "Added Unsloth shim dir to PATH: $(cygpath -w "$SHIM_DIR")"
- name: Install Playwright + Chromium
# No --with-deps on Windows: that flag installs Linux apt
@ -294,7 +295,7 @@ jobs:
python -m pip install 'playwright>=1.45'
python -m playwright install chromium
- name: Reset auth + boot Studio
- name: Reset auth + boot Unsloth
run: |
unsloth studio reset-password
mkdir -p logs
@ -339,13 +340,17 @@ jobs:
mkdir -p logs/playwright
python tests/studio/playwright_chat_ui.py
- name: Stop Studio (chat-ui ends with Shutdown click; this is belt-and-suspenders)
- name: Stop Unsloth (chat-ui ends with Shutdown click; this is belt-and-suspenders)
if: always()
run: |
kill "${STUDIO_PID}" 2>/dev/null || true
sleep 2
- name: Reset auth + boot Studio for extra UI tests (port 18897)
- name: Edge permission controls
run: |
bash .github/scripts/run-studio-permission-browser.sh 18895 chromium msedge
- name: Reset auth + boot Unsloth for extra UI tests (port 18897)
run: |
unsloth studio reset-password
mkdir -p logs
@ -372,7 +377,7 @@ jobs:
echo "STUDIO_EXTRA_OLD_PW=$OLD" >> "$GITHUB_ENV"
echo "STUDIO_EXTRA_NEW_PW=$NEW" >> "$GITHUB_ENV"
- name: Drive Compare/Recipes/Export/Studio/Settings with Playwright
- name: Drive Compare/Recipes/Export/Unsloth/Settings with Playwright
env:
BASE_URL: http://127.0.0.1:18897
STUDIO_OLD_PW: ${{ env.STUDIO_EXTRA_OLD_PW }}
@ -386,7 +391,7 @@ jobs:
mkdir -p logs/playwright_extra
python tests/studio/playwright_extra_ui.py
- name: Stop second Studio
- name: Stop second Unsloth
if: always()
run: |
kill "${STUDIO_EXTRA_PID}" 2>/dev/null || true
@ -402,5 +407,7 @@ jobs:
logs/studio_extra.log
logs/install.log
logs/playwright
logs/playwright-permissions-*
logs/playwright_extra
logs/studio-permissions-*.log
retention-days: 7

26
.github/workflows/studio-windows-update-smoke.yml vendored
View file

@ -5,19 +5,19 @@
# studio-mac-update-smoke.yml. Verifies that on the FREE
# windows-latest runner:
#
# 1. install.ps1 --local --no-torch installs Studio AND auto-fetches
# 1. install.ps1 --local --no-torch installs Unsloth AND auto-fetches
# the prebuilt llama.cpp Windows binary (app-<tag>-windows-x64-cpu
# from unslothai/llama.cpp). Hitting the source-build fallback is
# treated as an Unsloth bug -- Studio must always pick the
# treated as an Unsloth bug -- Unsloth must always pick the
# prebuilt on Windows.
# 2. unsloth studio update --local is idempotent. Two consecutive
# runs both report "prebuilt up to date and validated", no
# source-build fallback. The CLI's _find_setup_script picks
# setup.ps1 on Windows automatically.
# 3. The installed Studio still boots and /api/health returns
# 3. The installed Unsloth still boots and /api/health returns
# healthy after the update path.
name: Windows Studio Update CI
name: Windows Unsloth Update CI
on:
pull_request:
@ -45,7 +45,7 @@ permissions:
jobs:
update-idempotency:
name: Studio Updating Tests
name: Unsloth Updating Tests
runs-on: windows-latest
timeout-minutes: 30
defaults:
@ -53,7 +53,7 @@ jobs:
shell: bash
env:
# Force UTF-8 for stdio (Windows defaults to cp1252; hf
# download / Studio CLI print "✓" checkmarks and crash
# download / Unsloth CLI print "✓" checkmarks and crash
# otherwise).
PYTHONIOENCODING: utf-8
PYTHONUTF8: '1'
@ -90,7 +90,7 @@ jobs:
# reuses the existing Node with no download.
#
# (2) Defender. windows-latest's real-time scan opens / hashes
# every file Studio writes during install (Vite output =
# every file Unsloth writes during install (Vite output =
# thousands of small chunks, uv pip = wheel-extraction =
# thousands of small files). The latency dominates the
# 200 s frontend build and the 90 s deps install. Adding
@ -109,7 +109,7 @@ jobs:
# setup.ps1 line 1281-1296's mtime-based "is the frontend
# stale?" check into "up to date, skip rebuild", because the
# newly-created dist's mtime is younger than every source
# file. Studio then boots with an empty dist and 500s on
# file. Unsloth then boots with an empty dist and 500s on
# GET / with FileNotFoundError: dist\index.html. See run
# 25546676715 / job 74984469728.
# Add-MpPreference accepts paths that do not yet exist; the
@ -129,7 +129,7 @@ jobs:
}
}
- name: Install Studio (--local, --no-torch)
- name: Install Unsloth (--local, --no-torch)
shell: pwsh
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@ -168,7 +168,7 @@ jobs:
echo "install.ps1 installed the Windows prebuilt llama.cpp:"
cat "$INFO"
- name: Add Studio shim to GITHUB_PATH
- name: Add Unsloth shim to GITHUB_PATH
run: |
SHIM_DIR=~/.unsloth/studio/bin
if [ ! -f "$SHIM_DIR/unsloth.exe" ]; then
@ -212,7 +212,7 @@ jobs:
grep -qE "prebuilt up to date and validated|prebuilt installed and validated" logs/update2.log
echo "second update was clean"
- name: Boot Studio briefly to confirm the install is still usable
- name: Boot Unsloth briefly to confirm the install is still usable
run: |
mkdir -p logs
UNSLOTH_API_ONLY=1 unsloth studio -H 127.0.0.1 -p 18891 \
@ -239,13 +239,13 @@ jobs:
sleep 1
done
if [ -z "$HEALTHY" ]; then
echo "Studio failed to come up after \`update\`"
echo "Unsloth failed to come up after \`update\`"
tail -200 logs/studio.log
kill "$PID" 2>/dev/null || true
exit 1
fi
kill "$PID" 2>/dev/null || true
echo "post-update Studio /api/health OK"
echo "post-update Unsloth /api/health OK"
- name: Uninstall and verify clean
# Round-trip through scripts/uninstall.ps1 against the default

10
.github/workflows/wheel-smoke.yml vendored
View file

@ -3,7 +3,7 @@
# Builds the PyPI wheel from the PR branch, then verifies the built wheel
# actually contains what we expect to ship and does NOT contain the broken
# Studio bundle that 2026.5.1 published. This is the single workflow that
# Unsloth bundle that 2026.5.1 published. This is the single workflow that
# would have blocked the 2026.5.1 release before twine upload.
#
# Verified locally end-to-end against this branch:
@ -12,7 +12,7 @@
# lockfile shipped, frontend dist shipped,
# no node_modules in wheel, no bun.lock in wheel,
# main bundle has unstable_Provider hits=1 (assistant-ui internals only).
# - Studio backend imports cleanly from the installed wheel with the
# - Unsloth backend imports cleanly from the installed wheel with the
# lightweight dep set below.
name: Wheel CI
@ -101,7 +101,7 @@ jobs:
hits = data.count("unstable_Provider:")
print(f"main bundle: {js[0]}")
print(f"unstable_Provider hits: {hits} (>=4 indicates 2026.5.1 regression)")
checks["bundle has no Studio unstable_Provider call site"] = (hits < 4)
checks["bundle has no Unsloth unstable_Provider call site"] = (hits < 4)
print()
for k, v in checks.items():
@ -109,7 +109,7 @@ jobs:
sys.exit(0 if all(checks.values()) else 1)
PY
- name: Studio backend import smoke
- name: Unsloth backend import smoke
# Imports `studio.backend.main:app` from the freshly-installed wheel in
# a clean venv. This catches the class of bug that 2026.5.1 shipped with:
# frontend dist missing, package-lock.json missing, or the wheel's Python
@ -125,7 +125,7 @@ jobs:
/tmp/v/bin/pip install --no-deps dist/unsloth-*.whl
# Run from /tmp so Python imports the installed package, not the source tree.
cd /tmp
/tmp/v/bin/python -c "from studio.backend.main import app; print('Studio backend OK:', app.title)"
/tmp/v/bin/python -c "from studio.backend.main import app; print('Unsloth backend OK:', app.title)"
- name: Upload wheel on failure
if: failure()

76
README.md
View file

@ -11,6 +11,7 @@ Unsloth Studio lets you run and train models locally.
<p align="center">
<a href="#-features">Features</a> •
<a href="#-unsloth-news">News</a> •
<a href="#-install">Quickstart</a> •
<a href="#-free-notebooks">Notebooks</a> •
<a href="https://unsloth.ai/docs">Documentation</a>
@ -47,15 +48,44 @@ Unsloth Studio (Beta) lets you run and train text, [audio](https://unsloth.ai/do
* [Auto set inference settings](https://unsloth.ai/docs/new/studio/chat#auto-parameter-tuning) and customize chat templates.
* We work directly with teams behind [gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss), [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_context_bug_fixes/), [Llama 4](https://github.com/ggml-org/llama.cpp/pull/12889), [Mistral](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B/discussions/18), [Gemma 1-3](https://news.ycombinator.com/item?id=39671146), and [Phi-4](https://unsloth.ai/blog/phi4), where we’ve fixed bugs that improve model accuracy.
* Chat with images, audio, PDFs, code, DOCX and more. [Connect API providers](https://unsloth.ai/docs/integrations/connections) (OpenAI, Anthropic) or servers (vLLM, Ollama).
* [**Compare any two models**](https://unsloth.ai/docs/new/studio/chat#model-arena) side by side with the same prompt.
* **OpenAI/Anthropic-compatible APIs**: Serve local models through `/v1/chat/completions`, `/v1/responses` and `/v1/messages`.
* **Connect local models to agents**: Use `unsloth start` with Claude Code, Codex, Hermes and more.
* **Web/PDF search** can read PDF papers, manuals and other PDF results.
* **GGUF hardware controls**: Choose GPUs/layers, offload MoE experts, use multi-GPU or Tensor Parallelism.
* The opt-in **MCP control endpoint** lets AI clients manage models, training, recipes and exports.
### Training
* Train and RL **500+ models** up to **2x faster** with up to **70% less VRAM**, with no accuracy loss.
* Custom Triton and mathematical **kernels**. See some collabs we did with [PyTorch](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) and [Hugging Face](https://unsloth.ai/docs/new/faster-moe).
* Train and RL **500+ models** up to **2x faster** with **70% less VRAM**; MoE up to **12x faster**.
* Train and run RL on [AMD GPUs](https://unsloth.ai/docs/basics/amd) across Windows, WSL and Linux.
* **Data Recipes**: [Auto-create datasets](https://unsloth.ai/docs/new/studio/data-recipe) from **PDF, CSV, DOCX** etc. Edit data in a visual-node workflow.
* **[Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide)** (RL): The most efficient [RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) library, using **80% less VRAM** for GRPO, [FP8](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) etc.
* Supports full fine-tuning, RL, pretraining, 4-bit, 16-bit and, FP8 training.
* **[Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide)** uses **80% less VRAM** for GRPO, FP8 and vision RL, with 7x longer contexts.
* [**Long-context training**](https://unsloth.ai/docs/new/3x-faster-training-packing): **3x faster**, 30% less VRAM and 500K+ context.
* Supports LoRA/QLoRA, full fine-tuning, RL, pretraining, 4-bit, 16-bit and FP8.
* Custom Triton and mathematical **kernels** built with PyTorch and Hugging Face.
* **Observability**: Monitor training live, track loss and GPU usage and customize graphs.
* [Multi-GPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) training is supported, with major improvements coming soon.
## 🚀 Unsloth Start
[Unsloth Start](https://unsloth.ai/docs/integrations/unsloth-start) connects [Claude Code](https://unsloth.ai/docs/basics/claude-code), [Codex](https://unsloth.ai/docs/basics/codex) and other agents to local models with one command.
Start Unsloth, load a model, open your project folder, then run:
```bash
unsloth start claude
```
Replace `claude` with any supported agent:
| Agent | Command |
| --- | --- |
| Claude Code | `unsloth start claude` |
| OpenAI Codex | `unsloth start codex` |
| Hermes Agent | `unsloth start hermes` |
| OpenClaw | `unsloth start openclaw` |
| OpenCode | `unsloth start opencode` |
| Pi Coding Agent | `unsloth start pi` |
## 📥 Install
Unsloth can be used in two ways: through **[Unsloth Studio](https://unsloth.ai/docs/new/studio/)**, the web UI, or through **Unsloth Core**, the code-based version. Each has different requirements.
@ -65,7 +95,8 @@ Unsloth Studio (Beta) works on **Windows, Linux, WSL** and **macOS**.
* **CPU:** Supported for Chat and Data Recipes currently
* **NVIDIA:** Training works on RTX 30/40/50, Blackwell, DGX Spark, Station and more
* **macOS:** Training, MLX and GGUF inference are ALL supported.
* **AMD:** Chat + Data works. Train with [Unsloth Core](#unsloth-core-code-based). Studio support is out soon.
* **AMD:** Training, RL, chat and deployment work on Windows, WSL and Linux. [Read the AMD guide](https://unsloth.ai/docs/basics/amd).
* **Vulkan:** GGUF inference is supported on [compatible GPUs, including Intel GPUs](https://github.com/unslothai/unsloth/pull/5819).
* **Multi-GPU:** Available now, with a major upgrade on the way
#### macOS, Linux, WSL:
@ -86,7 +117,7 @@ unsloth studio -p 8888
```
For LAN or cloud access, add `-H 0.0.0.0` (raw port only; add `--cloudflare` for a public URL). By default, Unsloth is accessible only locally.
To reach Studio over HTTPS, use `unsloth studio --secure`. Studio stays bound to localhost and is reached only through a free Cloudflare tunnel, which publishes it at a public `https://*.trycloudflare.com` URL (it fails closed if the tunnel can't start, so the raw port is never exposed). This makes Studio reachable from the internet, so anyone with the link and API key can use it and run code: keep your API key private (see Remote access below).
To reach Unsloth over HTTPS, use `unsloth studio --secure`. Unsloth stays bound to localhost and is reached only through a free Cloudflare tunnel, which publishes it at a public `https://*.trycloudflare.com` URL (it fails closed if the tunnel can't start, so the raw port is never exposed). This makes Unsloth reachable from the internet, so anyone with the link and API key can use it and run code: keep your API key private (see Remote access below).
#### Docker
Use our [Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Run:
@ -122,7 +153,7 @@ You can use the same Docker image as Unsloth Studio.
#### AMD, Intel:
For RTX 50x, B200, 6000 GPUs: `uv pip install unsloth --torch-backend=auto`. Read our guides for: [Blackwell](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth). <br>
To install Unsloth on **AMD** and **Intel** GPUs, follow our [AMD Guide](https://unsloth.ai/docs/get-started/install/amd) and [Intel Guide](https://unsloth.ai/docs/get-started/install/intel).
To install Unsloth on **AMD** and **Intel** GPUs, follow our [AMD Guide](https://unsloth.ai/docs/basics/amd) and [Intel Guide](https://unsloth.ai/docs/get-started/install/intel).
## 📒 Free Notebooks
@ -148,13 +179,20 @@ Read our [guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). Ad
- See detailed documentation for Unsloth [here](https://unsloth.ai/docs)
## 🦥 Unsloth News
- **Connections**: Connect any API provider (OpenAI, Anthropic) or server (vLLM, Ollama). [Guide](https://unsloth.ai/docs/integrations/connections)
- **MTP**: Run Qwen3.6 MTP in Unsloth. MTP settings are autoset specific to your hardware. [Guide](https://unsloth.ai/docs/models/qwen3.6#mtp-guide)
- **API inference endpoint**: Deploy and run local LLMs in Claude Code, Codex tools. [Guide](https://unsloth.ai/docs/basics/api)
- **Qwen3.6**: Qwen3.6-35B-A3B can now be trained and run in Unsloth Studio. [Blog](https://unsloth.ai/docs/models/qwen3.6)
- **Gemma 4**: Run and train Google’s new models directly in Unsloth. [Blog](https://unsloth.ai/docs/models/gemma-4)
- **AMD training**: Train, run RL, chat and deploy on AMD GPUs across Windows, WSL and Linux. [Guide](https://unsloth.ai/docs/basics/amd)
- **GGUF hardware controls**: Choose GPU/layer placement, offload MoE experts and use multi-GPU or Tensor Parallelism. [#6414](https://github.com/unslothai/unsloth/pull/6414)
- **Local models for any agent**: Use `unsloth start` with Claude Code, Codex, Hermes, OpenCode, OpenClaw, Pi and more through Unsloth's OpenAI- and Anthropic-compatible APIs. [Guide](https://unsloth.ai/docs/basics/api)
- **MCP control endpoint**: Let compatible clients manage models, training, recipes, checkpoints and exports. [#7191](https://github.com/unslothai/unsloth/pull/7191)
- **Local inference reliability**: Resume long chats faster, recover stalled downloads and reuse existing GGUF files. [#7204](https://github.com/unslothai/unsloth/pull/7204) • [#6858](https://github.com/unslothai/unsloth/pull/6858) • [#7209](https://github.com/unslothai/unsloth/pull/7209)
- **New models**: [Qwen-AgentWorld](https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF), [Ornith](https://huggingface.co/unsloth/models?search=ornith), [Kimi K2.7 Code](https://unsloth.ai/docs/models/kimi-k2.7-code) and [MiniMax M3](https://unsloth.ai/docs/models/minimax-m3)
- **GLM-5.2**: Run Z.ai's 744B-parameter, 1M-context open model locally with Unsloth Dynamic GGUFs. [Guide](https://unsloth.ai/docs/models/glm-5.2)
- **DeepSeek-V4**: Run DeepSeek-V4-Flash locally with corrected multi-turn and tool-calling behavior. [Guide](https://unsloth.ai/docs/models/deepseek-v4)
- **DiffusionGemma**: Run and fine-tune Google's diffusion language model with 1.8x faster inference in Unsloth Studio. [Guide](https://unsloth.ai/docs/models/diffusiongemma)
- **Qwen3.6**: Run and train Qwen3.6 with MTP for 1.4-2.2x faster inference and NVFP4 quants for supported GPUs. [Guide](https://unsloth.ai/docs/models/qwen3.6)
- **Gemma 4**: Run and train Gemma 4 text, image and audio models with QAT, MTP, GGUF and MLX support. [Guide](https://unsloth.ai/docs/models/gemma-4)
- **MCP servers**: Connect local models to files, apps, databases and external tools through Model Context Protocol. [Guide](https://unsloth.ai/docs/basics/mcp)
- **Connections**: Mix local models with API providers (OpenAI, Anthropic) or servers (vLLM, Ollama) in the same interface. [Guide](https://unsloth.ai/docs/integrations/connections)
- **Introducing Unsloth Studio**: our new web UI for running and training LLMs. [Blog](https://unsloth.ai/docs/new/studio)
- **Qwen3.5** - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. [Guide + notebooks](https://unsloth.ai/docs/models/qwen3.5/fine-tune)
- Train **MoE LLMs 12x faster** with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. [Blog](https://unsloth.ai/docs/new/faster-moe)
- **Embedding models**: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. [Blog](https://unsloth.ai/docs/new/embedding-finetuning) • [Notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks#embedding-models)
- New **7x longer context RL** vs. all other setups, via our new batching algorithms. [Blog](https://unsloth.ai/docs/new/grpo-long-context)
@ -208,7 +246,7 @@ unsloth studio -p 8888
#### Remote access: `--secure` (HTTPS tunnel) vs raw port
By default `unsloth studio` binds to `127.0.0.1` (this machine only). To reach it from another device, pick one of:
- `--secure` (recommended): serve **only** through a free Cloudflare HTTPS link. Studio stays bound to localhost and the tunnel provides the public URL; it fails closed (does not start) if the tunnel can't come up, so the raw port is never exposed.
- `--secure` (recommended): serve **only** through a free Cloudflare HTTPS link. Unsloth stays bound to localhost and the tunnel provides the public URL; it fails closed (does not start) if the tunnel can't come up, so the raw port is never exposed.
```bash
unsloth studio --secure -p 8888
```
@ -218,7 +256,7 @@ unsloth studio -H 0.0.0.0 -p 8888
```
The Cloudflare tunnel is **off by default**: `-H 0.0.0.0` exposes the raw port only, not a public internet URL. Pair the wildcard bind with `--cloudflare` (`unsloth studio -H 0.0.0.0 --cloudflare`) to also publish a public `https://*.trycloudflare.com` link, or prefer `--secure` (above), which keeps the raw port private. `--cloudflare` has no effect on a loopback bind.
The first time Studio is published on a public URL (`--secure` or `--cloudflare`) with the auto-generated admin password still in place, it asks for a new admin password in the terminal (masked input with confirmation) before the public link goes up. Without an attached terminal it warns instead and keeps the bootstrap deadline: Studio shuts down after `UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT` (default 1 hour) unless the password is changed in the web UI.
The first time Unsloth is published on a public URL (`--secure` or `--cloudflare`) with the auto-generated admin password still in place, it asks for a new admin password in the terminal (masked input with confirmation) before the public link goes up. Without an attached terminal it warns instead and keeps the bootstrap deadline: Unsloth shuts down after `UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT` (default 1 hour) unless the password is changed in the web UI.
For headless setups that cannot answer that prompt, set the initial admin password non-interactively with `--password` (only takes effect when no password is set yet; if one already exists it is a hard error, so rotate later with `unsloth studio reset-password`):
@ -230,7 +268,7 @@ printf '%s\n' 'your-strong-password' | unsloth studio --secure --password - #
A literal `--password VALUE` is visible in the process list and shell history, so prefer the `UNSLOTH_STUDIO_PASSWORD` env var or `--password -` (stdin) for automation. This applies to any launch (public or a headless `-H 0.0.0.0` bind), and the password is set in the parent before the server binds, so it never reaches a re-executed child process.
Server-side tools (web search, Python and terminal code execution) run as your user and are on by default. Anyone who can reach the server with the API key can run code on this machine, so keep your API key private and pass `--disable-tools` when exposing Studio.
Server-side tools (web search, Python and terminal code execution) run as your user and are on by default. Anyone who can reach the server with the API key can run code on this machine, so keep your API key private and pass `--disable-tools` when exposing Unsloth.
#### Advanced launch options
Installer options can be passed as environment variables. On macOS, Linux and WSL place the variable after the pipe so the shell passes it to `sh`; on Windows set it with `$env:` before piping to `iex`.
@ -243,7 +281,7 @@ curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_NO_TORCH=1 sh
$env:UNSLOTH_NO_TORCH=1; irm https://unsloth.ai/install.ps1 | iex
```
Skip the post-install prompt that starts Studio (useful for automated installs):
Skip the post-install prompt that starts Unsloth (useful for automated installs):
```bash
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_SKIP_AUTOSTART=1 sh
```
@ -279,9 +317,9 @@ UNSLOTH_NPM_REGISTRY=https://artifactory.example.com/api/npm/npm/ ./install.sh -
```powershell
$env:UNSLOTH_NPM_REGISTRY='https://artifactory.example.com/api/npm/npm/'; .\install.ps1 --local
```
It is threaded as `--registry` into the Studio frontend `npm`/`bun` installs; the supply-chain locks (7-day `min-release-age`, exact version pins) stay in force.
It is threaded as `--registry` into the Unsloth frontend `npm`/`bun` installs; the supply-chain locks (7-day `min-release-age`, exact version pins) stay in force.
Cap Studio's native CPU thread pools on high-core hosts: `UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888`.
Cap Unsloth's native CPU thread pools on high-core hosts: `UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888`.
#### Uninstall
The recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS `.app` bundle + Launch Services on Mac; Start Menu, `HKCU\Software\Unsloth` registry key and user `PATH` entries on Windows):

8
build.sh
View file

@ -4,9 +4,9 @@
set -euo pipefail
# PyPI/Studio release publishing must use `./build.sh publish` (or an
# equivalent stamp -> build -> verify-dist -> upload flow) so packaged Studio
# artifacts include the display-only Studio release version.
# PyPI/Unsloth release publishing must use `./build.sh publish` (or an
# equivalent stamp -> build -> verify-dist -> upload flow) so packaged Unsloth
# artifacts include the display-only Unsloth release version.
# 1. Build frontend (Vite outputs to dist/)
cd studio/frontend
@ -87,7 +87,7 @@ cd ../..
# 2. Clean old artifacts
rm -rf build dist *.egg-info
# 3. Stamp display-only Studio release metadata for packaged builds.
# 3. Stamp display-only Unsloth release metadata for packaged builds.
_STUDIO_BUILD_INFO="studio/backend/utils/_studio_release_build.py"
_STUDIO_BUILD_INFO_BACKUP="$(mktemp)"
cp "$_STUDIO_BUILD_INFO" "$_STUDIO_BUILD_INFO_BACKUP"

208
install.ps1
View file

@ -53,7 +53,8 @@ function Install-UnslothStudio {
param([string]$TorchIndexUrl)
if ($SkipTorch) { return "none" }
if ([string]::IsNullOrWhiteSpace($TorchIndexUrl)) { return "none" }
$leaf = ($TorchIndexUrl.TrimEnd('/') -split '/')[-1].ToLowerInvariant()
# Drop query/fragment first so a token-authenticated pin classifies by family.
$leaf = (($TorchIndexUrl -split '[?#]', 2)[0].TrimEnd('/') -split '/')[-1].ToLowerInvariant()
if (@("cpu", "cu118", "cu124", "cu126", "cu128", "cu130") -contains $leaf) { return $leaf }
if ($leaf -match '^rocm[0-9]+\.[0-9]+$') { return $leaf }
return "auto"
@ -62,7 +63,8 @@ function Install-UnslothStudio {
function Get-TauriGpuBranch {
param([string]$TorchIndexFamily)
if ($SkipTorch) { return "no_torch" }
if ($TorchIndexFamily -like "cu*") { return "cuda" }
# Require a digit after "cu" so /current or /custom isn't branded CUDA (parity ^cu[0-9]).
if ($TorchIndexFamily -match '^cu[0-9]') { return "cuda" }
if ($TorchIndexFamily -like "rocm*") { return "rocm" }
if ($TorchIndexFamily -eq "cpu") { return "cpu" }
return "unknown"
@ -176,7 +178,7 @@ function Install-UnslothStudio {
$envOverride = $env:STUDIO_HOME.Trim()
}
# Custom Studio roots are not supported with --tauri (desktop app still
# Custom Unsloth roots are not supported with --tauri (desktop app still
# resolves %USERPROFILE%\.unsloth\studio). Pass through if override == legacy.
if ($TauriMode -and $envOverride) {
$_tauriOverride = $envOverride
@ -467,22 +469,35 @@ function Install-UnslothStudio {
}
}
# Redact index-URL credentials (userinfo + ?query= + #fragment) from captured installer
# output before printing on failure; uv/pip errors echo the failing --index-url verbatim.
# Mirrors the other installers. Verbose mode streams uncaptured, so it isn't redacted.
function Redact-InstallOutput {
param([string]$Text)
if (-not $Text) { return $Text }
$Text = $Text -replace '(https?://)[^/@\s`]+@', '$1<redacted>@'
$Text = $Text -replace '([?&][^=\s&`]+)=[^&#\s`]+', '$1=<redacted>'
# A #token=... fragment is as sensitive as a query; URL-anchored.
return $Text -replace '(https?://[^\s`#]+)#[^\s`]+', '$1#<redacted>'
}
# Run native commands quietly by default to match install.sh behavior.
# Full command output is shown only when --verbose / UNSLOTH_VERBOSE=1.
function Invoke-InstallCommand {
param(
[Parameter(Mandatory = $true)][ScriptBlock]$Command
)
# Installer-pinned index installs (torch) must beat an inherited uv mirror
# (#6898): when the command pins an index, clear every uv index env var so
# it wins, then restore in finally. Other installs keep the user's mirror.
# Installer-pinned index installs (torch) must beat an inherited uv mirror (#6898):
# for --default-index, clear the uv index env vars (restore in finally) and set
# UV_NO_CONFIG=1 so a uv.toml/pyproject index can't outrank the CLI pin (uv 0.10).
$savedUvIndex = $null
if ($Command.ToString() -match '--default-index') {
$savedUvIndex = @{}
foreach ($n in 'UV_DEFAULT_INDEX', 'UV_INDEX_URL', 'UV_INDEX', 'UV_EXTRA_INDEX_URL') {
foreach ($n in 'UV_DEFAULT_INDEX', 'UV_INDEX_URL', 'UV_INDEX', 'UV_EXTRA_INDEX_URL', 'UV_TORCH_BACKEND', 'UV_FIND_LINKS', 'UV_CONFIG_FILE', 'UV_NO_CONFIG') {
$savedUvIndex[$n] = [Environment]::GetEnvironmentVariable($n)
Remove-Item "Env:$n" -ErrorAction SilentlyContinue
}
$env:UV_NO_CONFIG = '1'
}
$prevEap = $ErrorActionPreference
$ErrorActionPreference = "Continue"
@ -493,17 +508,23 @@ function Install-UnslothStudio {
# Merge stderr into stdout so progress/warning output stays visible
# without flipping $? on successful native commands (PS 5.1 treats
# stderr records as errors that set $? = $false even on exit code 0).
& $Command 2>&1 | Out-Host
# Redact per record: uv echoes index URLs (credentials and all) in
# its errors, and verbose mode must not bypass the quiet path's
# redaction. ForEach-Object/Out-Host leave $LASTEXITCODE untouched.
& $Command 2>&1 | ForEach-Object { Redact-InstallOutput "$_" } | Out-Host
} else {
$output = & $Command 2>&1 | Out-String
if ($LASTEXITCODE -ne 0) {
Write-Host $output -ForegroundColor Red
Write-Host (Redact-InstallOutput $output) -ForegroundColor Red
}
}
return [int]$LASTEXITCODE
} finally {
$ErrorActionPreference = $prevEap
if ($savedUvIndex) { foreach ($n in $savedUvIndex.Keys) { if ($null -ne $savedUvIndex[$n]) { Set-Item "Env:$n" $savedUvIndex[$n] } } }
if ($savedUvIndex) {
Remove-Item "Env:UV_NO_CONFIG" -ErrorAction SilentlyContinue
foreach ($n in $savedUvIndex.Keys) { if ($null -ne $savedUvIndex[$n]) { Set-Item "Env:$n" $savedUvIndex[$n] } }
}
}
}
@ -756,7 +777,7 @@ function Find-FreeLaunchPort {
return `$null
}
# If Studio is already healthy on any expected port, just open it and exit.
# If Unsloth is already healthy on any expected port, just open it and exit.
`$existingPort = Find-HealthyStudioPort
if (`$existingPort) {
Start-Process "http://localhost:`$existingPort"
@ -772,7 +793,7 @@ try {
`$haveMutex = `$true
}
if (-not `$haveMutex) {
# Another launcher is already running; wait for it to bring Studio up
# Another launcher is already running; wait for it to bring Unsloth up
`$deadline = (Get-Date).AddSeconds(`$timeoutSec)
while ((Get-Date) -lt `$deadline) {
`$port = Find-HealthyStudioPort
@ -1438,7 +1459,7 @@ exit 0
if (Test-Path -LiteralPath $VenvPython) {
# why: matching guard to the .venv branch below -- in env-mode
# $StudioHome is a user-chosen workspace, so refuse to nuke an
# existing $StudioHome\unsloth_studio that lacks Studio sentinels.
# existing $StudioHome\unsloth_studio that lacks Unsloth sentinels.
# -PathType Leaf rejects a directory at the sentinel path. Accept the
# in-VENV ownership marker so partial-install retries are not blocked.
if (
@ -1449,7 +1470,7 @@ exit 0
) {
Write-Host "[ERROR] $VenvDir already exists but does not look like an Unsloth Studio install." -ForegroundColor Red
Write-Host " Move it aside or choose an empty UNSLOTH_STUDIO_HOME." -ForegroundColor Yellow
throw "Refusing to delete non-Studio venv at $VenvDir"
throw "Refusing to delete non-Unsloth venv at $VenvDir"
}
# New layout already exists -- replace only after preserving rollback copy.
substep "preserving existing environment for rollback..."
@ -1468,7 +1489,7 @@ exit 0
# workspace root (e.g. user's existing project Python venv).
$OldVenv = Join-Path $StudioHome ".venv"
$OldPy = Join-Path $OldVenv "Scripts\python.exe"
substep "found legacy Studio environment, validating..."
substep "found legacy Unsloth environment, validating..."
$prevEAP2 = $ErrorActionPreference
$ErrorActionPreference = "Continue"
try {
@ -1498,7 +1519,7 @@ exit 0
# Skip in env-mode so we don't relocate the default-install venv into
# the workspace root.
$CwdVenv = Join-Path $env:USERPROFILE "unsloth_studio"
substep "found CWD-relative Studio environment, migrating to $VenvDir..."
substep "found CWD-relative Unsloth environment, migrating to $VenvDir..."
Move-Item -LiteralPath $CwdVenv -Destination $VenvDir -Force
substep "moved ~/unsloth_studio -> ~/.unsloth/studio/unsloth_studio"
$_Migrated = $true
@ -1517,7 +1538,7 @@ exit 0
substep "$VenvDir"
}
# Mark the freshly-created venv as Studio-owned so a partial install can be
# Mark the freshly-created venv as Unsloth-owned so a partial install can be
# repaired by re-running install.ps1; the env-mode deletion guard above
# accepts this marker as the primary sentinel.
if (Test-Path -LiteralPath $VenvDir -PathType Container) {
@ -1526,7 +1547,7 @@ exit 0
# ── Helper: run amd-smi without triggering a UAC elevation prompt ──
# amd-smi on Windows auto-elevates to read GPU/APU memory, surfacing a confusing
# DiskPart UAC prompt mid-install (Studio backend amd.py hits the same).
# DiskPart UAC prompt mid-install (Unsloth backend amd.py hits the same).
# __COMPAT_LAYER=RunAsInvoker forces it (and helpers it spawns) to run
# un-elevated; on failure the WMI name -> gfx fallback still resolves the arch.
function Invoke-AmdSmiNoElevate {
@ -1653,7 +1674,7 @@ exit 0
function Test-HipinfoIsVenvInternal {
param([AllowNull()][string]$HipinfoPath)
if ([string]::IsNullOrWhiteSpace($HipinfoPath)) { return $false }
# Also derive the venv from the setup python + default Studio home, so
# Also derive the venv from the setup python + default Unsloth home, so
# the venv hipInfo is caught when VenvDir/VIRTUAL_ENV are unset.
$venvRoots = @()
if ($env:VIRTUAL_ENV) { $venvRoots += $env:VIRTUAL_ENV }
@ -1663,7 +1684,7 @@ exit 0
try { $venvRoots += (Split-Path -Parent (Split-Path -Parent $env:UNSLOTH_SETUP_PYTHON)) } catch {}
}
if ($env:USERPROFILE) { $venvRoots += (Join-Path $env:USERPROFILE ".unsloth\studio\unsloth_studio") }
# A custom Studio home (UNSLOTH_STUDIO_HOME / STUDIO_HOME alias) moves the
# A custom Unsloth home (UNSLOTH_STUDIO_HOME / STUDIO_HOME alias) moves the
# venv off the default path; seed it too or its hipInfo escapes the filter.
$studioHomeEnv = if (-not [string]::IsNullOrWhiteSpace($env:UNSLOTH_STUDIO_HOME)) { $env:UNSLOTH_STUDIO_HOME.Trim() } elseif (-not [string]::IsNullOrWhiteSpace($env:STUDIO_HOME)) { $env:STUDIO_HOME.Trim() } else { $null }
if ($studioHomeEnv) {
@ -1942,7 +1963,7 @@ exit 0
substep " Ensure the ROCm compute driver is installed alongside the display driver:" "Yellow"
substep " https://rocm.docs.amd.com/en/latest/deploy/windows/index.html" "Yellow"
} elseif ($ROCmGfxArch) {
# Known arch: Studio setup installs AMD's bundled-runtime ROCm PyTorch wheels
# Known arch: Unsloth setup installs AMD's bundled-runtime ROCm PyTorch wheels
# (repo.amd.com), which ship their own runtime -- HIP SDK optional.
step "gpu" "AMD ROCm ($ROCmGfxArch)" "Cyan"
substep "Detected: $ROCmGpuLabel" "Cyan"
@ -1960,10 +1981,31 @@ exit 0
# On an AMD GPU (no NVIDIA), surface the optional WSL-ROCm driver hint.
if (-not $HasNvidiaSmi -and ($ROCmGfxArch -or $ROCmGpuLabel)) { Show-AmdWslDriverHint }
# Trim trailing slashes from the URL PATH only, preserving ?query / #fragment: a whole-URL
# TrimEnd corrupts a token ending in "/", a single strip leaves .../cu128// empty. Shared.
function Trim-IndexPathSlashes {
param([string]$Url)
$value = $Url.Trim()
$idx = $value.IndexOfAny([char[]]@('?', '#'))
if ($idx -lt 0) {
return $value.TrimEnd('/')
}
return $value.Substring(0, $idx).TrimEnd('/') + $value.Substring($idx)
}
# ── Choose the correct PyTorch index URL based on driver CUDA version ──
# Mirrors Get-PytorchCudaTag in setup.ps1.
function Get-TorchIndexUrl {
$baseUrl = if ($env:UNSLOTH_PYTORCH_MIRROR) { $env:UNSLOTH_PYTORCH_MIRROR.TrimEnd('/') } else { "https://download.pytorch.org/whl" }
# Explicit pin -- skip ALL GPU probing (headless / CI / cross-install).
# UNSLOTH_TORCH_INDEX_URL wins (full URL, verbatim); _FAMILY is the leaf appended
# to the mirror base. Matches install.sh / install_python_stack.py.
if (-not [string]::IsNullOrWhiteSpace($env:UNSLOTH_TORCH_INDEX_URL)) {
return (Trim-IndexPathSlashes $env:UNSLOTH_TORCH_INDEX_URL)
}
if (-not [string]::IsNullOrWhiteSpace($env:UNSLOTH_TORCH_INDEX_FAMILY)) {
return "$baseUrl/$($env:UNSLOTH_TORCH_INDEX_FAMILY.Trim().Trim('/'))"
}
if (-not $NvidiaSmiExe) { return "$baseUrl/cpu" }
try {
$output = Invoke-NvidiaSmiBounded $NvidiaSmiExe
@ -1984,6 +2026,25 @@ exit 0
return "$baseUrl/cu126"
}
# Strip userinfo AND query/fragment so an authenticated pin never leaks. Shared with
# _strip_index_url_credentials (install.sh / py / setup.ps1).
function Remove-IndexUrlCredentials {
param([string]$Url)
$sep = $Url.IndexOf('://')
if ($sep -lt 0) { return $Url }
$scheme = $Url.Substring(0, $sep)
$rest = $Url.Substring($sep + 3)
# Drop query / fragment (may hold auth tokens).
$q = $rest.IndexOfAny([char[]]('?', '#'))
if ($q -ge 0) { $rest = $rest.Substring(0, $q) }
$slash = $rest.IndexOf('/')
$authority = if ($slash -ge 0) { $rest.Substring(0, $slash) } else { $rest }
$at = $authority.LastIndexOf('@')
$host_ = if ($at -ge 0) { $authority.Substring($at + 1) } else { $authority }
if ($slash -ge 0) { return "${scheme}://${host_}$($rest.Substring($slash))" }
return "${scheme}://${host_}"
}
# ── Torch flavor helpers (to repair a stale CPU / wrong-CUDA wheel) ──
# torch.__version__ -> flavor tag (cuXXX / rocm / cpu); untagged wheel = cpu,
# matching setup.ps1's stale-venv parse.
@ -2002,11 +2063,13 @@ exit 0
param([string]$TorchIndexUrl, [string]$ROCmIndexUrl)
if (-not [string]::IsNullOrWhiteSpace($ROCmIndexUrl)) { return 'rocm' }
if ([string]::IsNullOrWhiteSpace($TorchIndexUrl)) { return $null }
$leaf = ($TorchIndexUrl.TrimEnd('/') -split '/')[-1].ToLowerInvariant()
# Drop query/fragment first so .../cu128?token=x classifies as cu128 (else it reinstalls every run).
$leaf = (($TorchIndexUrl -split '[?#]', 2)[0].TrimEnd('/') -split '/')[-1].ToLowerInvariant()
if ($leaf -match '^cu\d+$') { return $leaf }
if ($leaf -eq 'cpu') { return 'cpu' }
if ($leaf -match '^rocm') { return 'rocm' }
if ($leaf -match '^gfx') { return 'rocm' }
# gfx must be followed by a digit (an architecture leaf); gfx-private is custom.
if ($leaf -match '^gfx[0-9]') { return 'rocm' }
return $null
}
@ -2041,6 +2104,10 @@ exit 0
} catch { return $null }
}
# An explicit pin is authoritative: the AMD ROCm reroute below must not rewrite it
# (e.g. a deliberate cpu pin on an AMD host).
$TorchIndexPinned = (-not [string]::IsNullOrWhiteSpace($env:UNSLOTH_TORCH_INDEX_URL)) -or `
(-not [string]::IsNullOrWhiteSpace($env:UNSLOTH_TORCH_INDEX_FAMILY))
$TorchIndexUrl = Get-TorchIndexUrl
# ── GPU arch → newest compatible Windows ROCm wheel release ──
@ -2052,13 +2119,19 @@ exit 0
# Override with UNSLOTH_ROCM_WINDOWS_MIRROR for air-gapped / mirror installs.
$ROCmIndexUrl = $null
$ROCmTorchFloor = $null
if (($HasROCm -or $ROCmGfxArch) -and $TorchIndexUrl -like "*/cpu" -and -not $SkipTorch) {
$PinnedRocmVisionSpec = $null
$PinnedRocmAudioSpec = $null
if (-not $TorchIndexPinned -and ($HasROCm -or $ROCmGfxArch) -and $TorchIndexUrl -like "*/cpu" -and -not $SkipTorch) {
$amdIndexBase = if ($env:UNSLOTH_ROCM_WINDOWS_MIRROR) { $env:UNSLOTH_ROCM_WINDOWS_MIRROR.TrimEnd('/') } else { "https://repo.amd.com/rocm/whl" }
$archFamilyMap = @{
"gfx1201" = "gfx120X-all"; "gfx1200" = "gfx120X-all" # RDNA 4
"gfx1151" = "gfx1151"; "gfx1150" = "gfx1150" # RDNA 3.5 (Strix Halo/Point)
"gfx1103" = "gfx110X-all"; "gfx1102" = "gfx110X-all" # RDNA 3
"gfx1101" = "gfx110X-all"; "gfx1100" = "gfx110X-all"
"gfx1036" = "gfx103X-all"; "gfx1035" = "gfx103X-all" # RDNA 2 (RX 6000)
"gfx1034" = "gfx103X-all"; "gfx1033" = "gfx103X-all"
"gfx1032" = "gfx103X-all"; "gfx1031" = "gfx103X-all"
"gfx1030" = "gfx103X-all"
"gfx90a" = "gfx90a"; "gfx908" = "gfx908" # MI200/MI100
}
# gfx120X (RDNA 4) and gfx1151/gfx1150 (Strix) have a null-pointer bug in
@ -2102,6 +2175,32 @@ exit 0
}
}
# A gfx*/rocm pin skips the auto-reroute above, but the generic CPU/CUDA install below
# would use torch>=2.4,<2.11 and pull a known-bad wheel on the gfx115x/gfx120x/rocm>=7.2
# indexes (the _grouped_mm bug). Route a pinned ROCm index through the ROCm path.
if ($TorchIndexPinned -and -not $ROCmIndexUrl -and -not $SkipTorch) {
$_pinLeaf = (($TorchIndexUrl -split '[?#]', 2)[0].TrimEnd('/') -split '/')[-1].ToLower()
$_pinRocm211 = $false
# Anchor ($) so a suffixed custom leaf (rocm7.2-private) falls through to verbatim.
if ($_pinLeaf -match '^rocm(\d+)\.(\d+)$') {
# Only KNOWN-2.11 rocm (rocm7.2) gets the floor. Matches Test-RocmKnown211Version.
$_pinRocm211 = ([int]$Matches[1] -eq 7 -and [int]$Matches[2] -eq 2)
}
# Only the 2.11-allowlist gfx arches need the floor; others publish <2.11 and stay bare.
$_pinGfx211 = @('gfx120x-all', 'gfx1151', 'gfx1150') -contains $_pinLeaf
if ($_pinGfx211 -or $_pinRocm211) {
$ROCmIndexUrl = $TorchIndexUrl
$ROCmTorchFloor = "torch>=2.11.0,<2.12.0"
$PinnedRocmVisionSpec = "torchvision>=0.26.0,<0.27.0"
$PinnedRocmAudioSpec = "torchaudio>=2.11.0,<2.12.0"
substep "pinned ROCm index ($_pinLeaf) -- enforcing $ROCmTorchFloor" "Cyan"
} elseif ($_pinLeaf -match '^gfx[0-9]' -or $_pinLeaf -match '^rocm[0-9]+(\.[0-9]+)?$') {
# Other gfx / older rocm (<=7.1) ship torch <2.11; route via the ROCm path with
# bare specs. Only EXACT rocm<digits>/gfx* are families; a suffixed leaf is verbatim.
$ROCmIndexUrl = $TorchIndexUrl
}
}
if ($ROCmIndexUrl) {
$TorchIndexFamily = "rocm"
} else {
@ -2164,14 +2263,14 @@ exit 0
}
if ($_Migrated) {
# Migrated env: force-reinstall unsloth+unsloth-zoo to ensure clean state
# in the new venv location, while preserving existing torch/CUDA
# Migrated env: force-reinstall unsloth+unsloth-zoo for a clean state, preserving
# existing torch/CUDA unless the flavor repair below re-lands it.
Write-TauriLog "STEP" "Installing unsloth"
substep "upgrading unsloth in migrated environment..."
if ($SkipTorch) {
# No-torch: install unsloth + unsloth-zoo with --no-deps, then
# runtime deps (typer, safetensors, transformers, etc.) with --no-deps.
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (migrated no-torch)" { uv pip install --python $VenvPython --no-deps --reinstall-package unsloth --reinstall-package unsloth-zoo "unsloth>=2026.7.3" "unsloth-zoo>=2026.7.3" }
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (migrated no-torch)" { uv pip install --python $VenvPython --no-deps --reinstall-package unsloth --reinstall-package unsloth-zoo "unsloth>=2026.7.4" "unsloth-zoo>=2026.7.4" }
if ($baseInstallExit -eq 0) {
# Resolve pydantic WITH deps so pip pins pydantic-core
# to the matching version (no-torch-runtime.txt below
@ -2185,7 +2284,7 @@ exit 0
}
}
} else {
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (migrated)" { uv pip install --python $VenvPython --reinstall-package unsloth --reinstall-package unsloth-zoo "unsloth>=2026.7.3" "unsloth-zoo>=2026.7.3" }
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (migrated)" { uv pip install --python $VenvPython --reinstall-package unsloth --reinstall-package unsloth-zoo "unsloth>=2026.7.4" "unsloth-zoo>=2026.7.4" }
}
if ($baseInstallExit -ne 0) {
Write-Host "[ERROR] Failed to install unsloth (exit code $baseInstallExit)" -ForegroundColor Red
@ -2210,22 +2309,24 @@ exit 0
substep "skipping PyTorch (--no-torch flag set)." "Yellow"
} elseif ($ROCmIndexUrl) {
Write-TauriLog "STEP" "Installing PyTorch (AMD ROCm Windows)"
substep "installing PyTorch from $ROCmIndexUrl..."
substep "installing PyTorch from $(Remove-IndexUrlCredentials $ROCmIndexUrl)..."
$torchSpec = if ($ROCmTorchFloor) { $ROCmTorchFloor } else { "torch" }
# Pin the companions to match $torchSpec; bare names can resolve an
# ABI-incompatible torchvision/torchaudio on AMD's per-arch index.
$visionSpec = if ($ROCmGfxArch -and $torchvisionFloorMap.ContainsKey($ROCmGfxArch)) { $torchvisionFloorMap[$ROCmGfxArch] } else { "torchvision" }
$audioSpec = if ($ROCmGfxArch -and $torchaudioFloorMap.ContainsKey($ROCmGfxArch)) { $torchaudioFloorMap[$ROCmGfxArch] } else { "torchaudio" }
$visionSpec = if ($PinnedRocmVisionSpec) { $PinnedRocmVisionSpec } elseif ($ROCmGfxArch -and $torchvisionFloorMap -and $torchvisionFloorMap.ContainsKey($ROCmGfxArch)) { $torchvisionFloorMap[$ROCmGfxArch] } else { "torchvision" }
$audioSpec = if ($PinnedRocmAudioSpec) { $PinnedRocmAudioSpec } elseif ($ROCmGfxArch -and $torchaudioFloorMap -and $torchaudioFloorMap.ContainsKey($ROCmGfxArch)) { $torchaudioFloorMap[$ROCmGfxArch] } else { "torchaudio" }
$torchInstallExit = Invoke-InstallCommandRetry -Label "install PyTorch (AMD ROCm)" { uv pip install --python $VenvPython --force-reinstall --default-index $ROCmIndexUrl $torchSpec $visionSpec $audioSpec }
if ($torchInstallExit -ne 0) {
# Transient AMD-index failure: fall back to a CPU base so the install
# still completes; Studio setup retries ROCm afterwards.
substep "ROCm PyTorch install failed (exit $torchInstallExit); using a CPU base, Studio setup retries ROCm." "Yellow"
# Transient AMD-index failure: fall back to a CPU base (Unsloth setup retries
# ROCm). Use an explicit CPU index -- for a pinned ROCm index $TorchIndexUrl IS
# the ROCm mirror, so reusing it would just retry it.
$CpuFallbackIndexUrl = if ($env:UNSLOTH_PYTORCH_MIRROR) { "$($env:UNSLOTH_PYTORCH_MIRROR.TrimEnd('/'))/cpu" } else { "https://download.pytorch.org/whl/cpu" }
substep "ROCm PyTorch install failed (exit $torchInstallExit); using a CPU base, Unsloth setup retries ROCm." "Yellow"
# --force-reinstall: a failed ROCm install can leave an unpinned ROCm
# torch (e.g. 2.10.0+rocm on gfx110X/gfx90a) that still satisfies the CPU
# torch>= range, so without it uv would keep the ROCm build and only swap
# the companions -- a mismatched venv the flavor-repair block won't fix.
$torchInstallExit = Invoke-InstallCommandRetry -Label "install PyTorch (CPU fallback)" { uv pip install --python $VenvPython --force-reinstall "torch>=2.4,<2.11.0" torchvision torchaudio --default-index $TorchIndexUrl }
$torchInstallExit = Invoke-InstallCommandRetry -Label "install PyTorch (CPU fallback)" { uv pip install --python $VenvPython --force-reinstall "torch>=2.4,<2.11.0" "torchvision>=0.19,<0.26.0" "torchaudio>=2.4,<2.11.0" --default-index $CpuFallbackIndexUrl }
if ($torchInstallExit -ne 0) {
Write-Host "[ERROR] Failed to install PyTorch (ROCm and CPU base both failed, exit code $torchInstallExit)" -ForegroundColor Red
return (Exit-InstallFailure "Failed to install PyTorch (exit code $torchInstallExit)" $torchInstallExit)
@ -2238,8 +2339,14 @@ exit 0
}
} else {
Write-TauriLog "STEP" "Installing PyTorch"
substep "installing PyTorch ($TorchIndexUrl)..."
$torchInstallExit = Invoke-InstallCommandRetry -Label "install PyTorch" { uv pip install --python $VenvPython "torch>=2.4,<2.11.0" torchvision torchaudio --default-index $TorchIndexUrl }
substep "installing PyTorch ($(Remove-IndexUrlCredentials $TorchIndexUrl))..."
# Bound the companions to the capped torch on EVERY index, cu<digits>
# families included: torchaudio 2.11 dropped its exact torch pin from
# the wheel metadata, so a bare companion next to torch<2.11 can
# resolve a mismatched 2.11.0 build. Mirrors install.sh.
$_pinVisionSpec = "torchvision>=0.19,<0.26.0"
$_pinAudioSpec = "torchaudio>=2.4,<2.11.0"
$torchInstallExit = Invoke-InstallCommandRetry -Label "install PyTorch" { uv pip install --python $VenvPython "torch>=2.4,<2.11.0" $_pinVisionSpec $_pinAudioSpec --default-index $TorchIndexUrl }
if ($torchInstallExit -ne 0) {
Write-Host "[ERROR] Failed to install PyTorch (exit code $torchInstallExit)" -ForegroundColor Red
return (Exit-InstallFailure "Failed to install PyTorch (exit code $torchInstallExit)" $torchInstallExit)
@ -2251,7 +2358,7 @@ exit 0
if ($SkipTorch) {
# No-torch: install unsloth + unsloth-zoo with --no-deps, then
# runtime deps (typer, safetensors, transformers, etc.) with --no-deps.
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (no-torch)" { uv pip install --python $VenvPython --no-deps --upgrade-package unsloth --upgrade-package unsloth-zoo "unsloth>=2026.7.3" "unsloth-zoo>=2026.7.3" }
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (no-torch)" { uv pip install --python $VenvPython --no-deps --upgrade-package unsloth --upgrade-package unsloth-zoo "unsloth>=2026.7.4" "unsloth-zoo>=2026.7.4" }
if ($baseInstallExit -eq 0) {
# Same pydantic-with-deps trick as the migrated branch.
$baseInstallExit = Invoke-InstallCommandRetry -Label "install pydantic" { uv pip install --python $VenvPython pydantic }
@ -2263,7 +2370,7 @@ exit 0
}
}
} elseif ($StudioLocalInstall) {
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (local)" { uv pip install --python $VenvPython --upgrade-package unsloth "unsloth>=2026.7.3" "unsloth-zoo>=2026.7.3" }
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (local)" { uv pip install --python $VenvPython --upgrade-package unsloth "unsloth>=2026.7.4" "unsloth-zoo>=2026.7.4" }
} else {
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth" { uv pip install --python $VenvPython --upgrade-package unsloth -- "$PackageName" }
}
@ -2291,7 +2398,7 @@ exit 0
Write-TauriLog "STEP" "Installing unsloth"
substep "installing unsloth (this may take a few minutes)..."
if ($StudioLocalInstall) {
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (auto torch backend)" { uv pip install --python $VenvPython "unsloth-zoo>=2026.7.3" "unsloth>=2026.7.3" --torch-backend=auto }
$baseInstallExit = Invoke-InstallCommandRetry -Label "install unsloth (auto torch backend)" { uv pip install --python $VenvPython "unsloth-zoo>=2026.7.4" "unsloth>=2026.7.4" --torch-backend=auto }
if ($baseInstallExit -ne 0) {
Write-Host "[ERROR] Failed to install unsloth (exit code $baseInstallExit)" -ForegroundColor Red
return (Exit-InstallFailure "Failed to install unsloth (exit code $baseInstallExit)" $baseInstallExit)
@ -2317,6 +2424,13 @@ exit 0
}
}
$installedPackageVersion = (& $VenvPython -c "from importlib.metadata import version; import sys; print(version(sys.argv[1]))" $PackageName 2>$null | Out-String).Trim()
if ($LASTEXITCODE -eq 0 -and $installedPackageVersion) {
step $PackageName "$installedPackageVersion installed"
} else {
substep "[WARN] installed $PackageName version could not be determined" "Yellow"
}
# ── Enforce the installed torch flavor matches the detected GPU build ──
# PEP 440 ignores the +cpu/+cuXXX/+rocm local label in a version range, so uv
# keeps a stale torch==X+cpu against a CUDA index and setup.ps1 then loops on
@ -2335,8 +2449,8 @@ exit 0
$rocmSpec = if ($ROCmTorchFloor) { $ROCmTorchFloor } else { "torch" }
# Pin companions like the fresh ROCm path (bare names can pull an
# ABI-incompatible torchvision/torchaudio from the per-arch index).
$visionSpec = if ($ROCmGfxArch -and $torchvisionFloorMap.ContainsKey($ROCmGfxArch)) { $torchvisionFloorMap[$ROCmGfxArch] } else { "torchvision" }
$audioSpec = if ($ROCmGfxArch -and $torchaudioFloorMap.ContainsKey($ROCmGfxArch)) { $torchaudioFloorMap[$ROCmGfxArch] } else { "torchaudio" }
$visionSpec = if ($PinnedRocmVisionSpec) { $PinnedRocmVisionSpec } elseif ($ROCmGfxArch -and $torchvisionFloorMap -and $torchvisionFloorMap.ContainsKey($ROCmGfxArch)) { $torchvisionFloorMap[$ROCmGfxArch] } else { "torchvision" }
$audioSpec = if ($PinnedRocmAudioSpec) { $PinnedRocmAudioSpec } elseif ($ROCmGfxArch -and $torchaudioFloorMap -and $torchaudioFloorMap.ContainsKey($ROCmGfxArch)) { $torchaudioFloorMap[$ROCmGfxArch] } else { "torchaudio" }
substep "PyTorch flavor mismatch (installed $installedTorchTag, need ROCm) -- reinstalling correct build..." "Yellow"
$torchFixExit = Invoke-InstallCommand { uv pip install --python $VenvPython --force-reinstall --default-index $ROCmIndexUrl $rocmSpec $visionSpec $audioSpec }
if ($torchFixExit -ne 0) {
@ -2347,7 +2461,7 @@ exit 0
} elseif ($expectedTorchTag -ne 'rocm') {
# CUDA: stale +cpu (or wrong cuXXX) against a CUDA index -> reinstall triplet.
substep "PyTorch flavor mismatch (installed $installedTorchTag, need $expectedTorchTag) -- reinstalling correct build..." "Yellow"
$torchFixExit = Invoke-InstallCommand { uv pip install --python $VenvPython "torch>=2.4,<2.11.0" torchvision torchaudio --default-index $TorchIndexUrl --reinstall-package torch --reinstall-package torchvision --reinstall-package torchaudio }
$torchFixExit = Invoke-InstallCommand { uv pip install --python $VenvPython "torch>=2.4,<2.11.0" "torchvision>=0.19,<0.26.0" "torchaudio>=2.4,<2.11.0" --default-index $TorchIndexUrl --reinstall-package torch --reinstall-package torchvision --reinstall-package torchaudio }
if ($torchFixExit -ne 0) {
Write-Host "[ERROR] Failed to reinstall PyTorch with the correct CUDA build (exit code $torchFixExit)" -ForegroundColor Red
return (Exit-InstallFailure "Failed to reinstall PyTorch ($expectedTorchTag) (exit code $torchFixExit)" $torchFixExit)
@ -2422,7 +2536,7 @@ exit 0
Write-TauriLog "ERROR" "unsloth CLI was not installed correctly"
Write-Host "[ERROR] unsloth CLI was not installed correctly." -ForegroundColor Red
Write-Host " Expected: $UnslothExe" -ForegroundColor Yellow
Write-Host " This usually means an older unsloth version was installed that does not include the Studio CLI." -ForegroundColor Yellow
Write-Host " This usually means an older unsloth version was installed that does not include the Unsloth CLI." -ForegroundColor Yellow
Write-Host " Try re-running the installer or see: https://github.com/unslothai/unsloth?tab=readme-ov-file#-quickstart" -ForegroundColor Yellow
return (Exit-InstallFailure "unsloth CLI was not installed correctly")
}
@ -2533,7 +2647,7 @@ exit 0
Write-Host " Move or remove it manually, then re-run the installer." -ForegroundColor Yellow
throw "Cannot create unsloth launcher: $ShimExe is a directory."
}
# try/catch: if unsloth.exe is locked (Studio running), keep the old shim.
# try/catch: if unsloth.exe is locked (Unsloth running), keep the old shim.
$shimUpdated = $false
try {
if (Test-Path -LiteralPath $ShimExe) { Remove-Item -LiteralPath $ShimExe -Force -ErrorAction Stop }
@ -2551,7 +2665,7 @@ exit 0
if (Test-Path -LiteralPath $ShimExe) {
Write-Host "[WARN] Could not refresh unsloth launcher at $ShimExe." -ForegroundColor Yellow
Write-Host " This usually means a running 'unsloth studio' process still holds the file open." -ForegroundColor Yellow
Write-Host " Close Studio and re-run the installer to pick up the latest launcher." -ForegroundColor Yellow
Write-Host " Close Unsloth and re-run the installer to pick up the latest launcher." -ForegroundColor Yellow
Write-Host " Continuing with the existing launcher." -ForegroundColor Yellow
} else {
Write-Host "[WARN] Could not create unsloth launcher at $ShimExe" -ForegroundColor Yellow
@ -2616,7 +2730,7 @@ exit 0
# Diagnostic only; never block install on a probe failure.
}
# In interactive terminals, ask the user before starting Studio unless the
# In interactive terminals, ask the user before starting Unsloth unless the
# caller explicitly disabled the post-install prompt.
# In non-interactive environments (CI, Docker) just print instructions.
$IsInteractive = (-not $SkipAutostart) -and [Environment]::UserInteractive -and (-not [Console]::IsInputRedirected)

616
install.sh
View file

@ -97,7 +97,7 @@ if [ "$_VERBOSE" = true ]; then
export UNSLOTH_VERBOSE=1
fi
# Custom Studio roots are not supported with --tauri (desktop app still
# Custom Unsloth roots are not supported with --tauri (desktop app still
# resolves ~/.unsloth/studio). Pass through if the override == legacy default.
if [ "$TAURI_MODE" = true ]; then
_tauri_override_var=""
@ -159,18 +159,58 @@ run_maybe_quiet() {
fi
}
# Trim trailing slashes from the URL PATH only, preserving ?query / #fragment: a whole-URL
# strip corrupts a token ending in "/", a single strip leaves .../cu128// empty. Shared.
_trim_index_path_slashes() {
_tips_v="$1"
case "$_tips_v" in
*[?#]*)
_tips_head="${_tips_v%%[?#]*}"
_tips_tail="${_tips_v#"$_tips_head"}"
;;
*)
_tips_head="$_tips_v"
_tips_tail=""
;;
esac
while [ -n "$_tips_head" ] && [ "${_tips_head%/}" != "$_tips_head" ]; do
_tips_head="${_tips_head%/}"
done
printf '%s%s' "$_tips_head" "$_tips_tail"
}
# Redact index-URL credentials (userinfo + ?query= + #fragment) from captured installer
# output before printing on failure; uv/pip errors echo the failing --index-url verbatim.
# Mirrors the other installers. Verbose mode streams uncaptured, so it isn't redacted.
_redact_install_output() {
sed -E \
-e 's#(https?://)[^/@[:space:]`]+@#\1<redacted>@#g' \
-e 's#([?&][^=[:space:]&`]+)=[^&#[:space:]`]+#\1=<redacted>#g' \
-e 's|(https?://[^[:space:]`#]+)#[^[:space:]`]+|\1#<redacted>|g' \
"$@"
}
run_install_cmd() {
_label="$1"
shift
# Installer-pinned index installs (torch) must beat an inherited uv mirror
# (#6898): when we pass --default-index, neutralize every uv index env var so
# the pinned index wins. Other installs keep the user's mirror.
# Installer-pinned index installs (torch) must beat an inherited uv mirror (#6898):
# for --default-index, neutralize the uv index/backend/config vars (UV_TORCH_BACKEND
# redirects torch; UV_NO_CONFIG=1 + dropping UV_CONFIG_FILE stops a uv.toml/pyproject
# index outranking the CLI pin, uv 0.10).
case " $* " in
*" --default-index "*) set -- env -u UV_DEFAULT_INDEX -u UV_INDEX_URL -u UV_INDEX -u UV_EXTRA_INDEX_URL "$@" ;;
*" --default-index "*) set -- env -u UV_DEFAULT_INDEX -u UV_INDEX_URL -u UV_INDEX -u UV_EXTRA_INDEX_URL -u UV_TORCH_BACKEND -u UV_FIND_LINKS -u UV_CONFIG_FILE UV_NO_CONFIG=1 "$@" ;;
esac
if _is_verbose; then
"$@" && return 0
_rc=$?
# Stream through the redactor: uv echoes index URLs (credentials and
# all) in its errors, and verbose mode previously bypassed the
# redaction the quiet path applies. The rc file preserves the
# command's exit code across the pipe without relying on pipefail
# (this script runs under plain sh).
_rcf=$(mktemp)
{ "$@" 2>&1; printf '%s' "$?" > "$_rcf"; } | _redact_install_output
_rc=$(cat "$_rcf" 2>/dev/null || echo 1)
rm -f "$_rcf"
[ "${_rc:-1}" -eq 0 ] 2>/dev/null && return 0
step "error" "$_label failed (exit code $_rc)" "$C_ERR" >&2
return "$_rc"
fi
@ -178,7 +218,7 @@ run_install_cmd() {
"$@" >"$_log" 2>&1 && { rm -f "$_log"; return 0; }
_rc=$?
step "error" "$_label failed (exit code $_rc)" "$C_ERR" >&2
cat "$_log" >&2
_redact_install_output "$_log" >&2
rm -f "$_log"
return $_rc
}
@ -257,7 +297,7 @@ _install_bnb_rocm() {
fi
_bnb_rc=$?
if _is_verbose; then
cat "$_bnb_log" >&2
_redact_install_output "$_bnb_log" >&2
fi
rm -f "$_bnb_log"
step "warning" "$_label (pre-release) failed (exit code $_bnb_rc)" "$C_WARN" >&2
@ -310,6 +350,11 @@ _tauri_torch_index_family() {
return
fi
_diag_url="${1:-}"
# Strip query/fragment AND a trailing slash before classifying (like _torch_index_url_leaf):
# a token isn't echoed into [TAURI:DIAG], and .../cu128/?token=x still classifies as cu128.
_diag_url="${_diag_url%%\?*}"
_diag_url="${_diag_url%%#*}"
_diag_url="${_diag_url%/}"
case "$_diag_url" in
*/cu118) echo "cu118" ;;
*/cu124) echo "cu124" ;;
@ -343,7 +388,8 @@ _tauri_gpu_branch() {
return
fi
case "$_diag_family" in
cu*) echo "cuda" ;;
# Require a digit after cu so /current or /custom isn't branded CUDA (parity ^cu[0-9]).
cu[0-9]*) echo "cuda" ;;
rocm*)
if [ "$_diag_radeon" = true ]; then
echo "rocm_radeon"
@ -472,11 +518,13 @@ _on_install_exit() {
_restore_studio_venv_replacement
fi
[ -n "${_UV_OVERRIDE_TMPDIR:-}" ] && rm -rf "$_UV_OVERRIDE_TMPDIR" 2>/dev/null || true
[ -n "${_UNSLOTH_TORCH_OVERRIDES:-}" ] && rm -f "$_UNSLOTH_TORCH_OVERRIDES" 2>/dev/null || true
exit "$_status"
}
# Empty so an inherited value can never reach the trap's rm; only a temp dir
# this script creates below (Apple Silicon, spaced path) is ever removed.
# Empty so an inherited value never reaches the trap's rm; only temp paths this
# script creates below (spaced-path dir, torch-trio overrides) are removed.
_UV_OVERRIDE_TMPDIR=""
_UNSLOTH_TORCH_OVERRIDES=""
trap _on_install_exit EXIT
# ── Helper: download a URL to a file (supports curl and wget) ──
@ -663,7 +711,7 @@ POLL_INTERVAL_SEC=0.25
LOG_FILE="$DATA_DIR/studio.log"
# why: in env-override mode multiple installs share an OS user; namespace the
# lock and remember our own healthy port so we never attach to an unrelated
# Studio listening on the global 8888..8908 range.
# Unsloth listening on the global 8888..8908 range.
LOCK_DIR="${XDG_RUNTIME_DIR:-/tmp}/unsloth-studio-launcher-$(id -u).lock"
PORT_FILE=""
# why: gate on the install-time mode (baked above) instead of the runtime env
@ -734,7 +782,7 @@ _candidate_ports() {
_find_healthy_port() {
if [ -n "$PORT_FILE" ] && [ -f "$PORT_FILE" ]; then
# why: env-mode installs only attach to a port we previously launched
# ourselves; never to a sibling Studio that happens to be healthy.
# ourselves; never to a sibling Unsloth that happens to be healthy.
_p=$(cat "$PORT_FILE" 2>/dev/null || true)
case "$_p" in
''|*[!0-9]*) ;;
@ -901,7 +949,7 @@ _acquire_lock() {
# Lock dir exists -- check if owner is still alive
_old_pid=$(cat "$LOCK_DIR/pid" 2>/dev/null || true)
if [ -n "$_old_pid" ] && kill -0 "$_old_pid" 2>/dev/null; then
# Another launcher is running; wait for it to bring Studio up
# Another launcher is running; wait for it to bring Unsloth up
_deadline=$(($(date +%s) + TIMEOUT_SEC))
while [ "$(date +%s)" -lt "$_deadline" ]; do
_port=$(_find_healthy_port) && {
@ -1371,7 +1419,7 @@ WSLPS1_EOF
# shortcut wasn't created; tell the user how to launch / re-enable it.
if [ "$_css_created" -ne 1 ]; then
substep "Couldn't create the Windows shortcut (WSL interop may be disabled)." "$C_WARN"
substep " Launch Studio from Windows: wsl -d \"$_css_distro\" -- bash -lc 'unsloth studio'" "$C_WARN"
substep " Launch Unsloth from Windows: wsl -d \"$_css_distro\" -- bash -lc 'unsloth studio'" "$C_WARN"
substep " (re-enable shortcuts: turn WSL interop back on, e.g. run 'wsl --shutdown' then reopen WSL.)" "$C_WARN"
fi
fi
@ -1439,7 +1487,7 @@ if [ "$MAC_INTEL" = true ]; then
echo ""
echo " NOTE: Intel Mac (x86_64) detected."
echo " PyTorch is unavailable for this platform (dropped Jan 2024)."
echo " Studio will install in GGUF-only mode."
echo " Unsloth will install in GGUF-only mode."
echo " Chat, inference via GGUF, and data recipes will work."
echo " Training requires Apple Silicon or Linux with GPU."
echo ""
@ -1573,6 +1621,12 @@ _has_usable_nvidia_gpu() {
# the STUDIO_HOME mkdir/venv so the origin distro is untouched.
_maybe_reroute_strixhalo_to_2404() {
[ "${OS:-}" = "wsl" ] || return 0
# An explicit index pin skips every GPU-driven reroute (same contract as
# the later Radeon/Strix guard): the pin is honored in THIS distro rather
# than probing the GPU and switching distributions. Whitespace-only
# overrides do not gate (parity with get_torch_index_url).
_rr_pin=$(printf '%s' "${UNSLOTH_TORCH_INDEX_URL:-}${UNSLOTH_TORCH_INDEX_FAMILY:-}" | tr -d '[:space:]')
[ -n "$_rr_pin" ] && return 0
[ "${SKIP_TORCH:-false}" = "false" ] || return 0
[ "${UNSLOTH_SKIP_ROCM_WSL_SETUP:-0}" = "1" ] && return 0
[ "${UNSLOTH_WSL_REROUTED:-0}" = "1" ] && return 0
@ -1634,6 +1688,10 @@ _maybe_reroute_strixhalo_to_2404() {
# Forward explicit ROCm-bootstrap consent (e.g. Tauri) so the child auto-enables the
# GPU instead of falling back to the desktop-app prompt path.
[ "${UNSLOTH_ROCM_WSL_AUTO:-0}" = "1" ] && _rr_exports="$_rr_exports; export UNSLOTH_ROCM_WSL_AUTO=1"
# Forward a pinned torch index into the rerouted distro; dropping it would
# silently revert the child install to auto-detection.
[ -n "${UNSLOTH_TORCH_INDEX_URL:-}" ] && _rr_exports="$_rr_exports; export UNSLOTH_TORCH_INDEX_URL=$(_rr_q "$UNSLOTH_TORCH_INDEX_URL")"
[ -n "${UNSLOTH_TORCH_INDEX_FAMILY:-}" ] && _rr_exports="$_rr_exports; export UNSLOTH_TORCH_INDEX_FAMILY=$(_rr_q "$UNSLOTH_TORCH_INDEX_FAMILY")"
[ "$_SKIP_AUTOSTART" = true ] && _rr_exports="$_rr_exports; export UNSLOTH_SKIP_AUTOSTART=1"
_rr_args=""
[ "$PACKAGE_NAME" != "unsloth" ] && _rr_args="$_rr_args --package $(_rr_q "$PACKAGE_NAME")"
@ -1671,7 +1729,7 @@ _maybe_reroute_strixhalo_to_2404() {
_maybe_reroute_strixhalo_to_2404 || true
# ── Check system dependencies ──
# cmake/git are only needed to *build* llama.cpp from source. Studio downloads a
# cmake/git are only needed to *build* llama.cpp from source. Unsloth downloads a
# prebuilt by default, and setup.sh self-skips the source build when they're
# absent -- so macOS doesn't block on cmake (requiring it would force a manual
# Homebrew install). Linux keeps requiring them; its package manager has them.
@ -1821,11 +1879,13 @@ tauri_log "STEP" "Creating virtual environment"
mkdir -p "$STUDIO_HOME"
_MIGRATED=false
# Empty so an inherited value can never masquerade as a probed torch version.
_PREV_TORCH_VER=""
if [ -x "$VENV_DIR/bin/python" ]; then
# why: matching guard to the .venv branch below -- in env-mode
# $STUDIO_HOME is a user-chosen workspace, so refuse to nuke an
# existing $STUDIO_HOME/unsloth_studio that lacks Studio sentinels.
# existing $STUDIO_HOME/unsloth_studio that lacks Unsloth sentinels.
# Accept the in-VENV ownership marker so partial-install retries are
# not blocked. Sentinels must be regular files: -f follows symlinks
# to files (the legitimate ln -s shim shape) but rejects directories
@ -1838,6 +1898,12 @@ if [ -x "$VENV_DIR/bin/python" ]; then
echo " Move it aside or choose an empty UNSLOTH_STUDIO_HOME." >&2
exit 1
fi
# Record the existing venv's torch BEFORE the replacement moves it aside: a re-run
# rebuilds the venv for clean state, but must keep the torch release the user
# already has (see _previous_torch_pin below). Last line only: sitecustomize or
# import-hook noise on stdout must not corrupt the version.
_PREV_TORCH_VER=$("$VENV_DIR/bin/python" -c \
"import torch; print(torch.__version__)" 2>/dev/null | tail -n 1 || true)
# New layout already exists — replace only after preserving rollback copy.
substep "preserving existing environment for rollback..."
_start_studio_venv_replacement "$VENV_DIR"
@ -1846,7 +1912,7 @@ elif [ "$_STUDIO_HOME_REDIRECT" != "env" ] && [ -x "$STUDIO_HOME/.venv/bin/pytho
# Skip in env-mode so we don't rm -rf an unrelated .venv at the
# workspace root (e.g. user's existing project Python venv).
# In no-torch mode, a missing torch package is expected; validate Python only.
substep "found legacy Studio environment, validating..."
substep "found legacy Unsloth environment, validating..."
_legacy_ok=false
if [ "$SKIP_TORCH" = true ]; then
if "$STUDIO_HOME/.venv/bin/python" -c "import sys; print(sys.executable)" >/dev/null 2>&1; then
@ -1903,7 +1969,7 @@ if [ ! -x "$VENV_DIR/bin/python" ]; then
fi
fi
# Mark the freshly-created venv as Studio-owned so a partial install can be
# Mark the freshly-created venv as Unsloth-owned so a partial install can be
# repaired by re-running install.sh; the env-mode deletion guard above accepts
# this marker as the primary sentinel.
if [ -x "$VENV_DIR/bin/python" ]; then
@ -1991,6 +2057,15 @@ if [ "$SKIP_TORCH" = false ] && [ "$OS" = "macos" ] && [ "$_ARCH" = "arm64" ]; t
TORCH_CONSTRAINT="torch>=2.6,<2.11.0"
fi
fi
# Companion (torchvision/torchaudio) constraints, bounded to torch's window.
# torchaudio 2.11 dropped its exact torch pin, so a bare companion next to a
# <2.11-capped torch resolves torchaudio 2.11 (verified: cpu leaf installed
# torch 2.10.0+cpu with torchaudio 2.11.0+cpu). torchvision still exact-pins
# torch and self-corrects, but is bounded for symmetry. Widened alongside the
# cu* torch window below; the torch-2.11 AMD paths (rocm7.2 / per-gfx / Strix)
# pin their own trio.
TORCHVISION_CONSTRAINT="torchvision>=0.19,<0.26.0"
TORCHAUDIO_CONSTRAINT="torchaudio>=2.4,<2.11.0"
# ── Resolve repo root (for --local installs) ──
_REPO_ROOT="$(cd "$(dirname "$0" 2>/dev/null || echo ".")" && pwd)"
@ -2059,6 +2134,24 @@ _has_amd_rocm_gpu() {
get_torch_index_url() {
_base="${UNSLOTH_PYTORCH_MIRROR:-https://download.pytorch.org/whl}"
_base="${_base%/}"
# Explicit override -- skip ALL GPU probing (headless / container / CI / cross-install).
# UNSLOTH_TORCH_INDEX_URL wins (full URL, verbatim); _FAMILY is the leaf (cpu, cu128, ...)
# appended to the mirror base. Trim whitespace so a whitespace-only value is unset.
_url="${UNSLOTH_TORCH_INDEX_URL:-}"
_url="${_url#"${_url%%[![:space:]]*}"}"; _url="${_url%"${_url##*[![:space:]]}"}"
if [ -n "$_url" ]; then
# Trim trailing PATH slashes (a multi-slash path 404s on strict pip proxies) while
# preserving a ?query/#fragment token (a whole-URL strip would eat a "/"-ending token).
_url=$(_trim_index_path_slashes "$_url")
echo "$_url"; return
fi
_family="${UNSLOTH_TORCH_INDEX_FAMILY:-}"
_family="${_family#"${_family%%[![:space:]]*}"}"; _family="${_family%"${_family##*[![:space:]]}"}"
if [ -n "$_family" ]; then
while [ "${_family#/}" != "$_family" ]; do _family="${_family#/}"; done
while [ "${_family%/}" != "$_family" ]; do _family="${_family%/}"; done
echo "$_base/$_family"; return
fi
# macOS: always CPU (no CUDA support)
case "$(uname -s)" in Darwin) echo "$_base/cpu"; return ;; esac
# Try nvidia-smi -- require the binary to actually list a usable GPU.
@ -2187,16 +2280,155 @@ _torch_flavor_tag() {
esac
}
# Final path segment of a wheel index URL ($1), lowercased, query/fragment stripped first
# so a token-authenticated pin (.../cu128?token=x) classifies as cu128 (else it reinstalls
# every update). Classification only. Shared with the py / ps1 leaf extractors.
_torch_index_url_leaf() {
_tl_u="${1%%\?*}"
_tl_u="${_tl_u%%#*}"
# Strip ALL trailing slashes, not one: .../rocm7.2// must yield rocm7.2, not an empty leaf.
while [ -n "$_tl_u" ] && [ "${_tl_u%/}" != "$_tl_u" ]; do
_tl_u="${_tl_u%/}"
done
printf '%s' "${_tl_u##*/}" | tr '[:upper:]' '[:lower:]'
}
# True (exit 0) when a lowercased leaf is an EXACT pip ROCm family: rocm<digits>[.<digits>]
# or a gfx ARCHITECTURE leaf (gfx followed by a digit: gfx90a, gfx1151, gfx120x-all). A leaf
# that merely starts with rocm/gfx (rocm7.2-private, gfx-private) is a custom verbatim pin.
# Matches the py / ps1 sides.
_is_pip_rocm_family_leaf() {
case "$1" in
gfx[0-9]*) return 0 ;;
rocm[0-9]*)
# Exact rocm<digits>[.<digits>]: both major and minor must be non-empty all-digits
# (rocm7., rocm7.2.1, rocm7.2-private are all custom pins, not a family).
_rocm_rest="${1#rocm}"
case "$_rocm_rest" in
*.*.*) return 1 ;;
*.*)
_rocm_minor="${_rocm_rest#*.}"
case "${_rocm_rest%%.*}" in "" | *[!0-9]*) return 1 ;; esac
case "$_rocm_minor" in "" | *[!0-9]*) return 1 ;; esac
;;
*[!0-9]*) return 1 ;;
esac
return 0
;;
*) return 1 ;;
esac
}
# Whether release base $1 (X.Y[.Z...]) falls inside constraint window $2
# ("torch>=A.B[.C],<D.E.F"). Compares at major.minor granularity, which is exact
# for the windows this script uses (ceilings are always X.Y.0); a non-.0 ceiling
# would only make this conservative (excludes the whole ceiling minor). Anything
# unparseable answers "no" so the caller fails toward the supported range.
_torch_release_in_window() {
_trw_con="$2"
case "$_trw_con" in
"torch>="*",<"*) ;;
*) echo "no"; return ;;
esac
_trw_floor="${_trw_con#torch>=}"; _trw_floor="${_trw_floor%%,*}"
_trw_ceil="${_trw_con##*,<}"
_v_maj="${1%%.*}"; _v_rest="${1#*.}"; _v_min="${_v_rest%%.*}"
_f_maj="${_trw_floor%%.*}"; _f_rest="${_trw_floor#*.}"; _f_min="${_f_rest%%.*}"
_c_maj="${_trw_ceil%%.*}"; _c_rest="${_trw_ceil#*.}"; _c_min="${_c_rest%%.*}"
for _trw_n in "$_v_maj" "$_v_min" "$_f_maj" "$_f_min" "$_c_maj" "$_c_min"; do
case "$_trw_n" in ''|*[!0-9]*) echo "no"; return ;; esac
done
if [ "$_v_maj" -gt "$_f_maj" ] || { [ "$_v_maj" -eq "$_f_maj" ] && [ "$_v_min" -ge "$_f_min" ]; }; then
if [ "$_v_maj" -lt "$_c_maj" ] || { [ "$_v_maj" -eq "$_c_maj" ] && [ "$_v_min" -lt "$_c_min" ]; }; then
echo "yes"
return
fi
fi
echo "no"
}
# Keep the previous venv's torch on a re-run: echo "torch==X.Y.Z" when the probed
# version ($1) is inside the active constraint window ($2), else "". The RELEASE is kept
# regardless of flavor tag; the pin installs from the freshly chosen index, so flavor
# follows the machine (cpu <-> cuda, cu126 -> cu130, PyPI bare -> +cu130) while the
# release follows the user. Gating on flavor was wrong: a PyPI torch reports a BARE
# version (on Linux the PyPI wheel IS CUDA), misclassified "cpu", so a healthy 2.10 on a
# cu130 host was moved to 2.11. Per-leaf floors still win (rocm7.2 / gfx >=2.11 for the
# Strix _grouped_mm fix, out-of-window manual installs) and are never pinned; the caller's
# _PREV_FALLBACK_CONSTRAINT installs the newest supported release when the index lacks the
# exact one. Opt out with UNSLOTH_TORCH_UPGRADE=1.
_previous_torch_pin() {
_ptp_ver="$1"
_ptp_con="$2"
[ -n "$_ptp_ver" ] || { echo ""; return; }
[ "${UNSLOTH_TORCH_UPGRADE:-0}" = "1" ] && { echo ""; return; }
_ptp_base="${_ptp_ver%%+*}"
# Base must be a plain numeric release (X.Y[.Z]); probe noise and
# nightly/dev/source builds (2.11.0.dev20250704, 2.9.0a0) must never
# become a pin -- no stable index carries them, so pinning would only
# print "keeping it" and then burn a doomed resolve before falling back.
case "$_ptp_base" in
*[!0-9.]* | *..* | .* | *.) echo ""; return ;;
[0-9]*.[0-9]*) ;;
*) echo ""; return ;;
esac
[ "$(_torch_release_in_window "$_ptp_base" "$_ptp_con")" = "yes" ] || { echo ""; return; }
echo "torch==$_ptp_base"
}
# Install torch from TORCH_INDEX_URL honoring a kept-release pin: with _PREV_TORCH_PIN
# set, TORCH_CONSTRAINT is the exact previous release; fall back to the supported range
# if the index lacks it (pruned mirror) rather than failing. Used by every --default-index
# path (NVIDIA cu*, AMD rocm/gfx fallbacks, cpu/mac, ROCm repairs) so preservation is
# uniform. Extra args (e.g. --force-reinstall) are passed through to uv.
_install_torch_default_index() {
if [ -n "$_PREV_TORCH_PIN" ]; then
# Pair the companions with the kept torch minor: torchaudio no longer
# exact-pins torch in its metadata, so leaving it unconstrained resolves
# a newer mismatched build (a kept torch 2.9.0 pulled torchaudio 2.11.0).
_itdi_base="${_PREV_TORCH_PIN#torch==}"
_itdi_minor="${_itdi_base#*.}"
_itdi_minor="${_itdi_minor%%.*}"
_itdi_tv="torchvision"
_itdi_ta="torchaudio"
case "$_itdi_base" in
2.*)
_itdi_tv="torchvision==0.$((_itdi_minor + 15)).*"
_itdi_ta="torchaudio==2.${_itdi_minor}.*"
;;
esac
if ! run_install_cmd_retry "install PyTorch (kept release)" uv pip install --python "$_VENV_PY" "$TORCH_CONSTRAINT" "$_itdi_tv" "$_itdi_ta" \
--default-index "$TORCH_INDEX_URL" "$@"; then
substep "[WARN] $_PREV_TORCH_PIN is not installable from $(_strip_index_url_credentials "$TORCH_INDEX_URL") -- installing the newest supported release instead" "$C_WARN"
TORCH_CONSTRAINT="$_PREV_FALLBACK_CONSTRAINT"
_PREV_TORCH_PIN=""
run_install_cmd_retry "install PyTorch" uv pip install --python "$_VENV_PY" "$TORCH_CONSTRAINT" "$TORCHVISION_CONSTRAINT" "$TORCHAUDIO_CONSTRAINT" \
--default-index "$TORCH_INDEX_URL" "$@"
fi
else
run_install_cmd_retry "install PyTorch" uv pip install --python "$_VENV_PY" "$TORCH_CONSTRAINT" "$TORCHVISION_CONSTRAINT" "$TORCHAUDIO_CONSTRAINT" \
--default-index "$TORCH_INDEX_URL" "$@"
fi
}
# Expected tag from the index leaf ($1): cuXXX / cpu / rocm (rocmX.Y and gfx* ->
# rocm). Empty on an unknown leaf (odd mirror) so the repair safely no-ops.
_expected_torch_flavor_tag() {
_u="${1%/}"
_leaf="${_u##*/}"
_leaf=$(_torch_index_url_leaf "$1")
case "$_leaf" in
cu[0-9]*) echo "$_leaf" ;;
cpu) echo "cpu" ;;
rocm*|gfx*) echo "rocm" ;;
*) echo "" ;;
cu[0-9]*)
# Exact cu + digits only; a cu*-suffixed leaf (cu128-private) -> "" (custom),
# else a correct +cu128 wheel is force-reinstalled every run.
case "${_leaf#cu}" in
*[!0-9]*) echo "" ;;
*) echo "$_leaf" ;;
esac
;;
cpu) echo "cpu" ;;
# Exact rocm/gfx families only; a custom rocm*-suffixed leaf -> "" (custom).
*)
if _is_pip_rocm_family_leaf "$_leaf"; then echo "rocm"; else echo ""; fi
;;
esac
}
@ -2206,14 +2438,42 @@ _expected_torch_flavor_tag() {
# fresh-install paths above already use -- so a stale wheel is auto-repairable.
# Unknown/odd-mirror leaves -> no, so we warn rather than risk a wrong reinstall.
_torch_index_repairable() {
_u="${1%/}"
_leaf="${_u##*/}"
_leaf=$(_torch_index_url_leaf "$1")
case "$_leaf" in
cu[0-9]*|rocm[0-9]*|gfx*) echo "yes" ;;
*) echo "no" ;;
cu[0-9]*) echo "yes" ;;
# Only EXACT rocm/gfx families resolve via --default-index; a suffixed leaf is verbatim.
*)
if _is_pip_rocm_family_leaf "$_leaf"; then echo "yes"; else echo "no"; fi
;;
esac
}
# Remove credentials from a wheel index URL ($1) so an authenticated pin never leaks:
# drops userinfo AND query/fragment; scheme/host/path stay exact. Shared with py / ps1.
_strip_index_url_credentials() {
_sic_url="$1"
case "$_sic_url" in
*://*) ;;
*) printf '%s' "$_sic_url"; return ;;
esac
_sic_scheme="${_sic_url%%://*}"
_sic_rest="${_sic_url#*://}"
# Drop query / fragment (may hold auth tokens).
_sic_rest="${_sic_rest%%\?*}"
_sic_rest="${_sic_rest%%#*}"
_sic_auth="${_sic_rest%%/*}"
# Drop user:pass@ userinfo if present.
case "$_sic_auth" in
*@*) _sic_host="${_sic_auth##*@}" ;;
*) _sic_host="$_sic_auth" ;;
esac
if [ "$_sic_auth" = "$_sic_rest" ]; then
printf '%s://%s' "$_sic_scheme" "$_sic_host"
else
printf '%s://%s/%s' "$_sic_scheme" "$_sic_host" "${_sic_rest#*/}"
fi
}
get_radeon_wheel_url() {
# Only meaningful on Linux. Picks a repo.radeon.com base URL whose listing
# contains torch wheels. Tries paths like rocm-rel-7.2.1/, rocm-rel-7.2/,
@ -2335,7 +2595,7 @@ _pick_radeon_wheel() {
# the installer -- always returns 0. Runs the idempotent helper (ROCm 7.2 +
# librocdxg), then sources the env it persisted so detection finds the GPU.
# Export the ROCm-on-WSL env into this process and persist it to /etc/profile.d
# so non-login Studio/llama launches inherit it. Idempotent (writes only when
# so non-login Unsloth/llama launches inherit it. Idempotent (writes only when
# the drop-in is missing); no-op without librocdxg, so never fires off WSL.
# /etc/profile.d is root-owned -- sudo-tee when not root, else ROCm vanishes
# after this shell on a non-root reinstall. Best-effort either way.
@ -2380,7 +2640,7 @@ _maybe_bootstrap_rocm_wsl() {
rocminfo 2>/dev/null | awk '/Name:[[:space:]]*gfx[1-9]/ && !/generic/{found=1} END{exit !found}'; then
# rocminfo may work only via the transient env _ensure_rocm_probe_env
# just set, which dies with the installer. Persist the drop-in so login
# shells (Studio, llama.cpp) inherit it -- else a reinstall over an
# shells (Unsloth, llama.cpp) inherit it -- else a reinstall over an
# existing /opt/rocm (uninstall keeps ROCm but drops it) loses the GPU.
_persist_rocm_wsl_dropin
return 0
@ -2402,7 +2662,7 @@ _maybe_bootstrap_rocm_wsl() {
# shellcheck disable=SC1091
. /etc/profile.d/unsloth-rocm-wsl.sh || true
else
# librocdxg present but the env drop-in is gone (e.g. a Studio
# librocdxg present but the env drop-in is gone (e.g. an Unsloth
# uninstall removed it while keeping shared ROCm). Restore the env.
_persist_rocm_wsl_dropin
fi
@ -2459,7 +2719,19 @@ _maybe_bootstrap_rocm_wsl() {
[ -n "$_rw_tmp" ] && rm -f "$_rw_tmp"
return 0
}
_maybe_bootstrap_rocm_wsl || true
# When the caller pins the wheel index (UNSLOTH_TORCH_INDEX_URL / _FAMILY), honour it
# everywhere: skip the WSL ROCm bootstrap and the Radeon/Strix reroute below (which would
# re-probe the GPU and overwrite the pin). Trim whitespace first (parity with
# get_torch_index_url): a whitespace-only override is unset there, so must not flip this true.
_torch_index_pinned=false
_ti_url_trim="${UNSLOTH_TORCH_INDEX_URL:-}"
_ti_url_trim="${_ti_url_trim#"${_ti_url_trim%%[![:space:]]*}"}"; _ti_url_trim="${_ti_url_trim%"${_ti_url_trim##*[![:space:]]}"}"
_ti_family_trim="${UNSLOTH_TORCH_INDEX_FAMILY:-}"
_ti_family_trim="${_ti_family_trim#"${_ti_family_trim%%[![:space:]]*}"}"; _ti_family_trim="${_ti_family_trim%"${_ti_family_trim##*[![:space:]]}"}"
if [ -n "$_ti_url_trim" ] || [ -n "$_ti_family_trim" ]; then
_torch_index_pinned=true
fi
[ "$_torch_index_pinned" = true ] || _maybe_bootstrap_rocm_wsl || true
TORCH_INDEX_URL=$(get_torch_index_url)
@ -2470,24 +2742,74 @@ TORCH_INDEX_URL=$(get_torch_index_url)
# whose base path happens to contain "rocm" or "gfx" must not mislabel a
# cu*/cpu index as ROCm (radeon repo URLs end in rocm-rel-X.Y/, Strix
# overrides in gfxNNNN/, so the trailing slash is stripped first).
_torch_index_leaf="${TORCH_INDEX_URL%/}"
# Lowercase the leaf so every gfx*/rocm*/cu* arm matches regardless of case (canonical AMD
# RDNA4 leaf is gfx120X-all). CUDA is branded only on a real cu[0-9]* leaf, so a mirror
# leaf (/current) does NOT commit a CUDA backend; an unknown leaf leaves the var unset so
# the stack probes the GPU. Query/fragment dropped first, then ALL trailing slashes (in
# lockstep with the shared _torch_index_url_leaf extractor).
_torch_index_leaf="${TORCH_INDEX_URL%%\?*}"
_torch_index_leaf="${_torch_index_leaf%%#*}"
# Strip ALL trailing slashes, not one: .../cu128// must yield cu128, not an empty leaf.
while [ -n "$_torch_index_leaf" ] && [ "${_torch_index_leaf%/}" != "$_torch_index_leaf" ]; do
_torch_index_leaf="${_torch_index_leaf%/}"
done
_torch_index_leaf="${_torch_index_leaf##*/}"
_torch_index_leaf=$(printf '%s' "$_torch_index_leaf" | tr '[:upper:]' '[:lower:]')
case "$_torch_index_leaf" in
rocm*|gfx*) export UNSLOTH_TORCH_BACKEND="rocm" ;;
cpu) export UNSLOTH_TORCH_BACKEND="cpu" ;;
*) export UNSLOTH_TORCH_BACKEND="cuda" ;;
cu[0-9]*) export UNSLOTH_TORCH_BACKEND="cuda" ;;
# Unknown leaf (odd mirror, /current): unset so a stale inherited value can't leak and
# the stack probes the GPU.
*) unset UNSLOTH_TORCH_BACKEND ;;
esac
# rocm7.2 ships torch 2.11.0 -- adjust the constraint to allow it.
# All other ROCm tags and CUDA stay within <2.11.0.
case "$TORCH_INDEX_URL" in
*/rocm7.2) TORCH_CONSTRAINT="torch>=2.11.0,<2.12.0" ;;
# Whether TORCH_INDEX_URL names an actual pip ROCm family (rocm<digit>* / gfx*), gating the
# ROCm-only side effects below (AMD bitsandbytes, ROCm-torch repair). Digit-gated so a leaf
# merely STARTING with "rocm" isn't force-repaired from the wrong path.
if _is_pip_rocm_family_leaf "$_torch_index_leaf"; then
_torch_index_is_rocm_family=true
else
_torch_index_is_rocm_family=false
fi
# rocm7.2 and the per-gfx indexes with the _grouped_mm <2.11 bug (gfx120X-all, gfx1151,
# gfx1150) ship torch 2.11.0 -- raise the floor (also covers a pinned override that skipped
# the Strix reroute). Pin the companions too: the per-gfx index publishes them independently
# and a bare name can resolve a 2.12 ABI-mismatched wheel. Match on the FINAL leaf so a
# custom mirror with a gfx/rocm7.2 path segment but a cu*/cpu family isn't forced.
case "$_torch_index_leaf" in
rocm7.2|gfx120x-all|gfx1151|gfx1150)
TORCH_CONSTRAINT="torch>=2.11.0,<2.12.0"
TORCHVISION_CONSTRAINT="torchvision>=0.26.0,<0.27.0"
TORCHAUDIO_CONSTRAINT="torchaudio>=2.11.0,<2.12.0"
;;
# CUDA cu12x/cu13x indexes ship torch 2.11.x: widen the ceiling to <2.12.0 (matches
# _CUDA_TORCH_PKG_SPEC) and widen the companions with it so the trio stays paired.
cu[0-9]*)
TORCH_CONSTRAINT="torch>=2.4,<2.12.0"
TORCHVISION_CONSTRAINT="torchvision>=0.19,<0.27.0"
TORCHAUDIO_CONSTRAINT="torchaudio>=2.4,<2.12.0"
;;
esac
# A pinned custom/unknown-leaf index (/simple, /current, /cu128-private) has no curated
# companion set, so bound torchvision/torchaudio to the same <2.11 range the Python path pins
# (else a mirror with newer companions resolves a 2.12 ABI-mismatched wheel). Known families
# keep their curated companions above (_expected_torch_flavor_tag returns "" only for custom).
if [ "$_torch_index_pinned" = true ] && \
[ -z "$(_expected_torch_flavor_tag "$TORCH_INDEX_URL")" ]; then
TORCHVISION_CONSTRAINT="torchvision>=0.19,<0.26.0"
TORCHAUDIO_CONSTRAINT="torchaudio>=2.4,<2.11.0"
fi
# Auto-detect GPU for AMD ROCm based
# get_torch_index_url must have chosen */rocm*
# (gfx in rocminfo or amd-smi list). Then require rocminfo "Marketing Name:.*Radeon".
# Skipped when the index is pinned: an explicit override must not be rerouted to the
# Radeon/Strix repos by GPU probing.
_amd_gpu_radeon=false
if [ "$_torch_index_pinned" = false ]; then
case "$TORCH_INDEX_URL" in
*/rocm*)
if _has_amd_rocm_gpu && command -v rocminfo >/dev/null 2>&1 && \
@ -2564,10 +2886,31 @@ case "$TORCH_INDEX_URL" in
done
TORCH_INDEX_URL="${_amd_strix_base}/${_strix_gfx}/"
TORCH_CONSTRAINT="torch>=2.11.0,<2.12.0"
# Pin companions to 2.11 (per-gfx index publishes them independently).
TORCHVISION_CONSTRAINT="torchvision>=0.26.0,<0.27.0"
TORCHAUDIO_CONSTRAINT="torchaudio>=2.11.0,<2.12.0"
_amd_gpu_radeon=false
fi
;;
esac
fi # _torch_index_pinned guard (Radeon + Strix reroute)
# Re-run over an existing install: keep the previous venv's torch RELEASE; the fresh
# index above supplies the right flavor for this machine. Evaluated HERE, after every
# index/constraint decision including the Strix reroute, so the window checked is the
# final one and a raised floor (rocm7.2 / Strix gfx) rejects an older release.
# _PREV_FALLBACK_CONSTRAINT keeps the range so the install can fall back when the exact
# release is not on the chosen index (mirrors may prune old wheels). Skipped for --no-torch.
_PREV_TORCH_PIN=""
_PREV_FALLBACK_CONSTRAINT="$TORCH_CONSTRAINT"
if [ "$SKIP_TORCH" = false ]; then
_prev_pin=$(_previous_torch_pin "$_PREV_TORCH_VER" "$TORCH_CONSTRAINT")
if [ -n "$_prev_pin" ]; then
_PREV_TORCH_PIN="$_prev_pin"
TORCH_CONSTRAINT="$_prev_pin"
substep "existing install has torch $_PREV_TORCH_VER -- keeping it (set UNSLOTH_TORCH_UPGRADE=1 to get the newest release)"
fi
fi
_TAURI_TORCH_INDEX_FAMILY=$(_tauri_torch_index_family "$TORCH_INDEX_URL")
if [ "$_amd_gpu_radeon" = true ] && [ "$SKIP_TORCH" = false ]; then
_TAURI_TORCH_INDEX_FAMILY="radeon"
@ -2697,7 +3040,7 @@ case "$TORCH_INDEX_URL" in
if [ "$_amd_gpu_radeon" = true ]; then
substep "wheels: repo.radeon.com (Radeon)"
else
substep "wheels: $TORCH_INDEX_URL"
substep "wheels: $(_strip_index_url_credentials "$TORCH_INDEX_URL")"
fi
;;
esac
@ -2705,9 +3048,46 @@ esac
# ── Install unsloth directly into the venv (no activation needed) ──
tauri_log "STEP" "Installing PyTorch"
_VENV_PY="$VENV_DIR/bin/python"
# A released unsloth wheel can pin an older torch (unsloth 2026.7.2 declares
# torch<2.11.0); a with-deps PyPI resolve then downgrades the whole trio,
# swapping the pinned +cuXXX/+rocm build for PyPI's default. The flavor guard
# below misses this (PyPI's torch 2.10 default is itself cu128-flavored), so
# freeze the trio via uv --overrides (overrides replace dependency requirements
# during resolution) while unsloth's other deps resolve normally. Sets
# _UNSLOTH_TORCH_OVERRIDES from the trio in the venv; every with-deps unsloth
# install (migrated and fresh) must call this before resolving and rm it after.
_build_unsloth_torch_overrides() {
_UNSLOTH_TORCH_OVERRIDES=""
[ "$SKIP_TORCH" = false ] || return 0
_torch_trio_pins=$("$_VENV_PY" -c "
from importlib.metadata import version, PackageNotFoundError
for _p in ('torch', 'torchvision', 'torchaudio'):
try:
print(_p + '==' + version(_p))
except PackageNotFoundError:
pass
" 2>/dev/null) || _torch_trio_pins=""
case "$_torch_trio_pins" in
torch==*)
_UNSLOTH_TORCH_OVERRIDES=$(mktemp)
printf '%s\n' "$_torch_trio_pins" > "$_UNSLOTH_TORCH_OVERRIDES"
# The CLI --overrides flag replaces any UV_OVERRIDE env file (same
# uv setting; macOS arm64 exports one here), so fold its pins in.
# awk, not cat: it drops inherited torch-trio lines (uv intersects
# duplicate overrides, so a conflicting pin would make resolution
# unsatisfiable) and newline-terminates the last line so an
# unterminated file cannot join two requirements into one.
for _ov_file in ${UV_OVERRIDE:-}; do
[ -f "$_ov_file" ] && awk '!/^[[:space:]]*torch(vision|audio)?([[:space:]<>=!~;@[]|$)/' "$_ov_file" >> "$_UNSLOTH_TORCH_OVERRIDES"
done
;;
esac
}
if [ "$_MIGRATED" = true ]; then
# Migrated env: force-reinstall unsloth+unsloth-zoo to ensure clean state
# in the new venv location, while preserving existing torch/CUDA
# Migrated env: force-reinstall unsloth+unsloth-zoo for a clean state, preserving
# existing torch/CUDA unless the ROCm repair below fires.
substep "upgrading unsloth in migrated environment..."
if [ "$SKIP_TORCH" = true ]; then
# No-torch: install unsloth + unsloth-zoo with --no-deps (current
@ -2716,7 +3096,7 @@ if [ "$_MIGRATED" = true ]; then
# to prevent transitive torch resolution.
run_install_cmd_retry "install unsloth (migrated no-torch)" uv pip install --python "$_VENV_PY" --no-deps \
--reinstall-package unsloth --reinstall-package unsloth-zoo \
"unsloth>=2026.7.3" "unsloth-zoo>=2026.7.3"
"unsloth>=2026.7.4" "unsloth-zoo>=2026.7.4"
# Resolve pydantic WITH deps so pip pins pydantic-core to the
# matching version (no-torch-runtime.txt below is --no-deps).
# All transitive deps are torch-free.
@ -2729,9 +3109,13 @@ if [ "$_MIGRATED" = true ]; then
else
# Pin mlx-lm away from 0.31.3 here too: a curl-piped migration has no
# overrides file, so UV_OVERRIDE is unset and this positional is the only cover.
_build_unsloth_torch_overrides
run_install_cmd_retry "install unsloth (migrated)" uv pip install --python "$_VENV_PY" \
${_UNSLOTH_TORCH_OVERRIDES:+--overrides "$_UNSLOTH_TORCH_OVERRIDES"} \
--reinstall-package unsloth --reinstall-package unsloth-zoo \
"unsloth>=2026.7.3" "unsloth-zoo>=2026.7.3" ${_MLX_LM_EXCLUDE_ARG:-}
"unsloth>=2026.7.4" "unsloth-zoo>=2026.7.4" ${_MLX_LM_EXCLUDE_ARG:-}
[ -n "$_UNSLOTH_TORCH_OVERRIDES" ] && rm -f "$_UNSLOTH_TORCH_OVERRIDES"
_UNSLOTH_TORCH_OVERRIDES=""
fi
if [ "$STUDIO_LOCAL_INSTALL" = true ]; then
substep "overlaying local repo (editable)..."
@ -2744,21 +3128,14 @@ if [ "$_MIGRATED" = true ]; then
# AMD ROCm: install bitsandbytes even in migrated environments so
# existing ROCm installs gain the AMD bitsandbytes build without a
# fresh reinstall.
if [ "$SKIP_TORCH" = false ]; then
case "$TORCH_INDEX_URL" in
*/rocm*|*/gfx*)
_install_bnb_rocm "install bitsandbytes (AMD)" "$_VENV_PY"
# Repair ROCm torch if overwritten during migrated install
_has_hip=$("$_VENV_PY" -c "import torch; print(getattr(torch.version,'hip','') or '')" 2>/dev/null || true)
if [ -z "$_has_hip" ]; then
substep "repairing ROCm torch (overwritten by dependency resolution)..."
run_install_cmd_retry "repair ROCm torch" uv pip install --python "$_VENV_PY" \
"$TORCH_CONSTRAINT" torchvision torchaudio \
--default-index "$TORCH_INDEX_URL" \
--force-reinstall
fi
;;
esac
if [ "$SKIP_TORCH" = false ] && [ "$_torch_index_is_rocm_family" = true ]; then
_install_bnb_rocm "install bitsandbytes (AMD)" "$_VENV_PY"
# Repair ROCm torch if overwritten during migrated install
_has_hip=$("$_VENV_PY" -c "import torch; print(getattr(torch.version,'hip','') or '')" 2>/dev/null || true)
if [ -z "$_has_hip" ]; then
substep "repairing ROCm torch (overwritten by dependency resolution)..."
_install_torch_default_index --force-reinstall
fi
fi
elif [ -n "$TORCH_INDEX_URL" ]; then
# Fresh: Step 1 - install torch from explicit index (skip when --no-torch or Intel Mac)
@ -2820,7 +3197,42 @@ elif [ -n "$TORCH_INDEX_URL" ]; then
_ta_ver=$(_extract_version "$_ta_whl" "torchaudio")
_radeon_versions_match=false
if [ -n "$_torch_ver" ] && [ -n "$_tv_ver" ] && [ -n "$_ta_ver" ]; then
# Kept release (_PREV_TORCH_PIN) wins here too: pick its exact
# patch (else the newest patch of its minor) plus the paired
# vision/audio wheels. Any gap falls back to the newest-trio
# search below, mirroring _install_torch_default_index, so a
# rerun never drifts to another release nor below the kept one.
if [ -n "$_PREV_TORCH_PIN" ]; then
_prev_kept_base="${_PREV_TORCH_PIN#torch==}"
_prev_kept_minor="${_prev_kept_base#*.}"
_prev_kept_minor="${_prev_kept_minor%%.*}"
case "$_prev_kept_minor" in
''|*[!0-9]*) ;;
*)
_kept_torch=$(_pick_radeon_wheel "torch" "${_prev_kept_base}" 2>/dev/null) || _kept_torch=""
[ -z "$_kept_torch" ] && { _kept_torch=$(_pick_radeon_wheel "torch" "2.${_prev_kept_minor}." 2>/dev/null) || _kept_torch=""; }
_kept_tv=$(_pick_radeon_wheel "torchvision" "0.$((_prev_kept_minor + 15))." 2>/dev/null) || _kept_tv=""
_kept_ta=$(_pick_radeon_wheel "torchaudio" "2.${_prev_kept_minor}." 2>/dev/null) || _kept_ta=""
if [ -n "$_kept_torch" ] && [ -n "$_kept_tv" ] && [ -n "$_kept_ta" ]; then
_torch_whl=$_kept_torch
_tv_whl=$_kept_tv
_ta_whl=$_kept_ta
_tri_whl=""
_radeon_versions_match=true
# Say so when the listing pruned the exact patch
# and a same-series build is installed instead.
case "$(printf '%s' "${_kept_torch##*/}" | sed 's/%2[Bb]/+/g')" in
"torch-${_prev_kept_base}"[+-]*) ;;
*) substep "kept release ${_prev_kept_base} is not in the Radeon listing -- installing the closest 2.${_prev_kept_minor} series build instead" ;;
esac
else
substep "[WARN] Radeon repo lacks a complete wheel set for kept $_PREV_TORCH_PIN -- installing the newest compatible set instead" "$C_WARN"
fi
;;
esac
fi
if [ "$_radeon_versions_match" != true ] && \
[ -n "$_torch_ver" ] && [ -n "$_tv_ver" ] && [ -n "$_ta_ver" ]; then
_torch_minor=${_torch_ver#*.}
_ta_minor=${_ta_ver#*.}
_tv_minor=${_tv_ver#*.}
@ -2877,10 +3289,8 @@ elif [ -n "$TORCH_INDEX_URL" ]; then
if [ -z "$_torch_whl" ] || [ -z "$_tv_whl" ] || [ -z "$_ta_whl" ] || \
[ "$_radeon_versions_match" != true ]; then
substep "[WARN] Radeon repo lacks a compatible wheel set for this Python; falling back to ROCm index ($TORCH_INDEX_URL)" "$C_WARN"
run_install_cmd_retry "install PyTorch" uv pip install --python "$_VENV_PY" \
"$TORCH_CONSTRAINT" torchvision torchaudio \
--default-index "$TORCH_INDEX_URL"
substep "[WARN] Radeon repo lacks a compatible wheel set for this Python; falling back to ROCm index ($(_strip_index_url_credentials "$TORCH_INDEX_URL"))" "$C_WARN"
_install_torch_default_index
else
substep "installing PyTorch from Radeon repo (${_RADEON_BASE_URL})..."
# Pass explicit wheel URLs so the matched trio is
@ -2900,42 +3310,34 @@ elif [ -n "$TORCH_INDEX_URL" ]; then
fi
fi
else
substep "[WARN] Radeon repo unavailable; falling back to ROCm index ($TORCH_INDEX_URL)" "$C_WARN"
run_install_cmd_retry "install PyTorch" uv pip install --python "$_VENV_PY" \
"$TORCH_CONSTRAINT" torchvision torchaudio \
--default-index "$TORCH_INDEX_URL"
substep "[WARN] Radeon repo unavailable; falling back to ROCm index ($(_strip_index_url_credentials "$TORCH_INDEX_URL"))" "$C_WARN"
_install_torch_default_index
fi
else
substep "[WARN] Radeon GPU detected but could not detect full ROCm version; falling back to ROCm index" "$C_WARN"
run_install_cmd_retry "install PyTorch" uv pip install --python "$_VENV_PY" \
"$TORCH_CONSTRAINT" torchvision torchaudio \
--default-index "$TORCH_INDEX_URL"
_install_torch_default_index
fi
else
substep "installing PyTorch ($TORCH_INDEX_URL)..."
run_install_cmd_retry "install PyTorch" uv pip install --python "$_VENV_PY" "$TORCH_CONSTRAINT" torchvision torchaudio \
--default-index "$TORCH_INDEX_URL"
substep "installing PyTorch ($(_strip_index_url_credentials "$TORCH_INDEX_URL"))..."
_install_torch_default_index
fi
# AMD ROCm: install bitsandbytes (once, after torch, for all ROCm paths).
# Gate on SKIP_TORCH=false so a user running with --no-torch on a ROCm
# host stays in GGUF-only mode rather than pulling in bitsandbytes,
# which is only useful once torch is present for training.
if [ "$SKIP_TORCH" = false ]; then
case "$TORCH_INDEX_URL" in
*/rocm*|*/gfx*)
_install_bnb_rocm "install bitsandbytes (AMD)" "$_VENV_PY"
;;
esac
if [ "$SKIP_TORCH" = false ] && [ "$_torch_index_is_rocm_family" = true ]; then
_install_bnb_rocm "install bitsandbytes (AMD)" "$_VENV_PY"
fi
# Fresh: Step 2 - install unsloth, preserving pre-installed torch
# Fresh: Step 2 - install unsloth, preserving the torch Step 1 installed
tauri_log "STEP" "Installing Unsloth"
substep "installing unsloth (this may take a few minutes)..."
_build_unsloth_torch_overrides
if [ "$SKIP_TORCH" = true ]; then
# No-torch: install unsloth + unsloth-zoo with --no-deps, then
# runtime deps (typer, safetensors, transformers, etc.) with --no-deps.
run_install_cmd_retry "install unsloth (no-torch)" uv pip install --python "$_VENV_PY" --no-deps \
--upgrade-package unsloth --upgrade-package unsloth-zoo \
"unsloth>=2026.7.3" "unsloth-zoo>=2026.7.3"
"unsloth>=2026.7.4" "unsloth-zoo>=2026.7.4"
# Same pydantic-with-deps trick as the migrated branch.
run_install_cmd_retry "install pydantic (with deps for compatible core)" \
uv pip install --python "$_VENV_PY" pydantic
@ -2953,7 +3355,8 @@ elif [ -n "$TORCH_INDEX_URL" ]; then
fi
elif [ "$STUDIO_LOCAL_INSTALL" = true ]; then
run_install_cmd_retry "install unsloth (local)" uv pip install --python "$_VENV_PY" \
--upgrade-package unsloth "unsloth>=2026.7.3" "unsloth-zoo>=2026.7.3"
${_UNSLOTH_TORCH_OVERRIDES:+--overrides "$_UNSLOTH_TORCH_OVERRIDES"} \
--upgrade-package unsloth "unsloth>=2026.7.4" "unsloth-zoo>=2026.7.4"
substep "overlaying local repo (editable)..."
run_install_cmd "overlay local repo" uv pip install --python "$_VENV_PY" -e "$_REPO_ROOT" --no-deps
substep "overlaying unsloth-zoo from git main..."
@ -2962,30 +3365,26 @@ elif [ -n "$TORCH_INDEX_URL" ]; then
"unsloth-zoo @ git+https://github.com/unslothai/unsloth-zoo"
else
run_install_cmd_retry "install unsloth" uv pip install --python "$_VENV_PY" \
${_UNSLOTH_TORCH_OVERRIDES:+--overrides "$_UNSLOTH_TORCH_OVERRIDES"} \
--upgrade-package unsloth -- "$PACKAGE_NAME" ${_MLX_LM_EXCLUDE_ARG:-}
fi
[ -n "$_UNSLOTH_TORCH_OVERRIDES" ] && rm -f "$_UNSLOTH_TORCH_OVERRIDES"
_UNSLOTH_TORCH_OVERRIDES=""
# AMD ROCm: repair torch if the unsloth/unsloth-zoo install pulled in
# CUDA torch from PyPI, overwriting the ROCm wheels installed in Step 1.
if [ "$SKIP_TORCH" = false ]; then
case "$TORCH_INDEX_URL" in
*/rocm*|*/gfx*)
_has_hip=$("$_VENV_PY" -c "import torch; print(getattr(torch.version,'hip','') or '')" 2>/dev/null || true)
if [ -z "$_has_hip" ]; then
substep "repairing ROCm torch (overwritten by dependency resolution)..."
run_install_cmd_retry "repair ROCm torch" uv pip install --python "$_VENV_PY" \
"$TORCH_CONSTRAINT" torchvision torchaudio \
--default-index "$TORCH_INDEX_URL" \
--force-reinstall
fi
;;
esac
if [ "$SKIP_TORCH" = false ] && [ "$_torch_index_is_rocm_family" = true ]; then
_has_hip=$("$_VENV_PY" -c "import torch; print(getattr(torch.version,'hip','') or '')" 2>/dev/null || true)
if [ -z "$_has_hip" ]; then
substep "repairing ROCm torch (overwritten by dependency resolution)..."
_install_torch_default_index --force-reinstall
fi
fi
else
# Fallback: GPU detection failed to produce a URL -- let uv resolve torch
tauri_log "STEP" "Installing Unsloth"
substep "installing unsloth (this may take a few minutes)..."
if [ "$STUDIO_LOCAL_INSTALL" = true ]; then
run_install_cmd_retry "install unsloth (auto torch backend)" uv pip install --python "$_VENV_PY" "unsloth-zoo>=2026.7.3" "unsloth>=2026.7.3" --torch-backend=auto
run_install_cmd_retry "install unsloth (auto torch backend)" uv pip install --python "$_VENV_PY" "unsloth-zoo>=2026.7.4" "unsloth>=2026.7.4" --torch-backend=auto
substep "overlaying local repo (editable)..."
run_install_cmd "overlay local repo" uv pip install --python "$_VENV_PY" -e "$_REPO_ROOT" --no-deps
substep "overlaying unsloth-zoo from git main..."
@ -2997,6 +3396,15 @@ else
fi
fi
_installed_package_version=$("$_VENV_PY" -c \
'from importlib.metadata import version; import sys; print(version(sys.argv[1]))' \
"$PACKAGE_NAME" 2>/dev/null || true)
if [ -n "$_installed_package_version" ]; then
step "$PACKAGE_NAME" "$_installed_package_version installed"
else
substep "[WARN] installed $PACKAGE_NAME version could not be determined" "$C_WARN"
fi
# ── Enforce the installed torch flavor matches the detected GPU build ──
# PEP 440 ignores the +cpu/+cuXXX/+rocm local label in a version range, so uv
# keeps a stale torch==X+cpu against a GPU index and the venv silently trains on
@ -3014,9 +3422,7 @@ if [ "$SKIP_TORCH" = false ] && [ -n "${TORCH_INDEX_URL:-}" ]; then
if [ -n "$_installed_torch_tag" ] && [ "$_installed_torch_tag" != "$_expected_torch_tag" ] \
&& [ "$(_torch_index_repairable "$TORCH_INDEX_URL")" = "yes" ]; then
substep "PyTorch flavor mismatch (installed $_installed_torch_tag, need $_expected_torch_tag) -- reinstalling correct build..."
run_install_cmd "reinstall PyTorch ($_expected_torch_tag)" uv pip install --python "$_VENV_PY" \
"$TORCH_CONSTRAINT" torchvision torchaudio \
--default-index "$TORCH_INDEX_URL" \
_install_torch_default_index \
--reinstall-package torch --reinstall-package torchvision --reinstall-package torchaudio
_installed_torch_ver=$("$_VENV_PY" -c "import torch; print(torch.__version__)" 2>/dev/null || true)
_installed_torch_tag=""
@ -3027,13 +3433,13 @@ if [ "$SKIP_TORCH" = false ] && [ -n "${TORCH_INDEX_URL:-}" ]; then
substep "[WARN] PyTorch is CPU-only but a $_expected_torch_tag GPU build was expected for this machine." "$C_WARN"
substep "[WARN] Training and GPU inference will run on CPU until this is fixed." "$C_WARN"
substep "[WARN] Re-run this installer, or reinstall the GPU build manually:" "$C_WARN"
substep "[WARN] uv pip install --python \"$_VENV_PY\" \"$TORCH_CONSTRAINT\" torchvision torchaudio --default-index $TORCH_INDEX_URL --reinstall-package torch --reinstall-package torchvision --reinstall-package torchaudio" "$C_WARN"
substep "[WARN] uv pip install --python \"$_VENV_PY\" \"$TORCH_CONSTRAINT\" \"$TORCHVISION_CONSTRAINT\" \"$TORCHAUDIO_CONSTRAINT\" --default-index $(_strip_index_url_credentials "$TORCH_INDEX_URL") --reinstall-package torch --reinstall-package torchvision --reinstall-package torchaudio" "$C_WARN"
fi
fi
fi
# ── Run studio setup ──
tauri_log "STEP" "Running Studio setup"
tauri_log "STEP" "Running Unsloth setup"
# When --local, use the repo's own setup.sh directly.
# Otherwise, find it inside the installed package.
SETUP_SH=""
@ -3227,7 +3633,7 @@ printf " ${C_TITLE}%s${C_RST}\n" "Unsloth Studio installed!"
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
echo ""
# In interactive terminals, ask the user before starting Studio unless the
# In interactive terminals, ask the user before starting Unsloth unless the
# caller explicitly disabled the post-install prompt.
# In non-interactive environments (Docker, CI, cloud-init) just print instructions.
if [ "$_SKIP_AUTOSTART" != true ] && [ -t 1 ]; then

6
pyproject.toml
View file

@ -74,7 +74,7 @@ triton = [
]
huggingfacenotorch = [
"unsloth_zoo>=2026.7.3",
"unsloth_zoo>=2026.7.4",
"wheel>=0.42.0",
"packaging",
"numpy",
@ -95,7 +95,7 @@ huggingfacenotorch = [
]
huggingface = [
"unsloth[huggingfacenotorch]",
"unsloth_zoo>=2026.7.3",
"unsloth_zoo>=2026.7.4",
"torchvision",
"unsloth[triton]",
]
@ -580,7 +580,7 @@ colab-ampere-torch220 = [
"flash-attn>=2.6.3 ; ('linux' in sys_platform)",
]
colab-new = [
"unsloth_zoo>=2026.7.3",
"unsloth_zoo>=2026.7.4",
"packaging",
"tyro",
"transformers>=4.51.3,!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.0,!=4.57.4,!=4.57.5,!=5.0.0,!=5.1.0,<=5.5.0",

2
scripts/install_rocm_wsl_strixhalo.sh
View file

@ -219,7 +219,7 @@ fi
echo "${ROCM_DIR}/lib" | $SUDO tee /etc/ld.so.conf.d/rocm.conf >/dev/null
$SUDO ldconfig
# ── Step 4: persist environment (system-wide so Studio's worker inherits it) ──
# ── Step 4: persist environment (system-wide so Unsloth's worker inherits it) ──
say "Persisting ROCm-on-WSL environment"
_envfile="/etc/profile.d/unsloth-rocm-wsl.sh"
$SUDO tee "$_envfile" >/dev/null <<EOF

4
scripts/lockfile_supply_chain_audit.py
View file

@ -2,7 +2,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
"""Lockfile supply-chain audit for the Studio frontend and Tauri shell.
"""Lockfile supply-chain audit for the Unsloth frontend and Tauri shell.
Runs BEFORE `npm ci` / `cargo fetch` in CI. Refuses to proceed when a
lockfile contains patterns indicating supply-chain injection (npm
@ -294,7 +294,7 @@ CARGO_REGISTRY_SOURCE = "registry+https://github.com/rust-lang/crates.io-index"
# Cargo non-registry source allowlist: `(crate_name, exact_source_string)`.
# Both must match verbatim; bumping the pinned SHA forces a re-review.
# Studio's Tauri shell pulls `fix-path-env` from git because it is not
# Unsloth's Tauri shell pulls `fix-path-env` from git because it is not
# published to crates.io; commit c4c45d5 was reviewed when it landed.
CARGO_SOURCE_ALLOWLIST: tuple[tuple[str, str], ...] = (
(

2
scripts/scan_npm_packages.py
View file

@ -62,7 +62,7 @@ REPO_ROOT = Path(__file__).resolve().parents[1]
# Hard caps (deliberately conservative; npm tarballs in this repo are
# all well under these limits, so a packaging spike is noticeable).
# ─────────────────────────────────────────────────────────────────────
# Caps calibrated against the real Studio frontend transitive closure:
# Caps calibrated against the real Unsloth frontend transitive closure:
# - typescript.js is 9.1 MB (TS compiler bundled into one file)
# - mermaid 11.x dist/mermaid.js.map is ~12 MB (sourcemap)
# - lightningcss-linux-x64-{gnu,musl}.node is 10 MB

4
scripts/scan_packages_baseline.json
View file

@ -95,8 +95,8 @@
"file": "fastapi/routing.py",
"check": "C2 polling/beaconing loop detected",
"severity": "CRITICAL",
"evidence": "L586: while True: sha256:251135b5ebfdd1248916449f32262575e003ef64382501c65b7e4061d67bda45",
"evidence_hash": "365aef4449c8089753d9398417cd76ab762cef547d75db70d87bca9c0b550ab5"
"evidence": "L587: while True: sha256:06c2c7f15d73bf192e5e3272c5ff5fcaeff7f6774fef5f4eca6ef473ae50e2b3",
"evidence_hash": "57acd497f404c203e4450d0580ad85aa8a33406e8d64ad06fbac6cf47d97b24d"
},
{
"package": "fastmcp-slim",

20
scripts/stamp_studio_release.py
View file

@ -2,7 +2,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Stamp and verify display-only Studio release metadata for builds."""
"""Stamp and verify display-only Unsloth release metadata for builds."""
from __future__ import annotations
@ -50,7 +50,7 @@ MAX_VERSION_LENGTH = 64
PLACEHOLDER = """# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
\"\"\"Build-stamped Studio release metadata.
\"\"\"Build-stamped Unsloth release metadata.
Release builds may rewrite this module in the build workspace before creating
Python artifacts. Keep the committed value neutral so source checkouts do not
@ -145,7 +145,7 @@ def build_info_source(version: str | None) -> str:
return f'''# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Build-stamped Studio release metadata."""
"""Build-stamped Unsloth release metadata."""
STUDIO_RELEASE_VERSION = {literal}
'''
@ -168,7 +168,7 @@ def stamp(require_release: bool) -> int:
version, source = resolve_version()
if version is not None and not is_valid_version(version):
print(
f"Invalid Studio release version from {source}: {version!r}",
f"Invalid Unsloth release version from {source}: {version!r}",
file = sys.stderr,
)
return 2
@ -196,9 +196,9 @@ def stamp(require_release: bool) -> int:
if version is None:
if require_release:
print(
"No Studio release version available. Set "
"No Unsloth release version available. Set "
"UNSLOTH_STUDIO_RELEASE_VERSION, build from a GitHub tag, "
"or run from an exact local Studio release tag.",
"or run from an exact local Unsloth release tag.",
file = sys.stderr,
)
return 2
@ -207,7 +207,7 @@ def stamp(require_release: bool) -> int:
return 0
_atomic_write_text(BUILD_INFO_PATH, build_info_source(version), encoding = "utf-8")
print(f"Stamping Studio release version {version} from {source}", file = sys.stderr)
print(f"Stamping Unsloth release version {version} from {source}", file = sys.stderr)
print(version)
return 0
@ -233,7 +233,7 @@ def _read_sdist_member(path: Path) -> str | None:
def verify_dist(expected: str, dist_dir: Path) -> int:
if not is_valid_version(expected):
print(f"Invalid expected Studio release version: {expected!r}", file = sys.stderr)
print(f"Invalid expected Unsloth release version: {expected!r}", file = sys.stderr)
return 2
artifacts = list(dist_dir.glob("*.whl")) + list(dist_dir.glob("*.tar.gz"))
@ -251,14 +251,14 @@ def verify_dist(expected: str, dist_dir: Path) -> int:
if content is None:
failures.append(f"{artifact.name}: missing {BUILD_INFO_SUFFIX}")
elif expected_line not in content:
failures.append(f"{artifact.name}: Studio release version mismatch")
failures.append(f"{artifact.name}: Unsloth release version mismatch")
if failures:
for failure in failures:
print(failure, file = sys.stderr)
return 2
print(f"Verified Studio release version {expected} in {len(artifacts)} artifact(s)")
print(f"Verified Unsloth release version {expected} in {len(artifacts)} artifact(s)")
return 0

14
scripts/uninstall.ps1
View file

@ -83,7 +83,7 @@ function Uninstall-UnslothStudio {
}
}
# A path is a Studio-owned root iff one of install.ps1's sentinels exists:
# A path is an Unsloth-owned root iff one of install.ps1's sentinels exists:
# <root>\share\studio.conf, <root>\unsloth_studio\.unsloth-studio-owned,
# or <root>\bin\unsloth.exe.
function _IsStudioRoot {
@ -164,7 +164,7 @@ function Uninstall-UnslothStudio {
return $p
}
# Discover non-default Studio roots from env vars + studio.conf files.
# Discover non-default Unsloth roots from env vars + studio.conf files.
# Mirrors install.ps1's precedence: UNSLOTH_STUDIO_HOME wins, STUDIO_HOME
# is ignored when both are set, so uninstalling install A doesn't also
# delete install B if the user has a stale STUDIO_HOME pointing at B.
@ -207,7 +207,7 @@ function Uninstall-UnslothStudio {
# Return $true iff the PID's image path lives under one of $KnownRoots.
# Prevents killing an unrelated process that happens to listen on a stale
# Studio port.
# Unsloth port.
function _PidUnderKnownRoot {
param([int]$Pid_, [string[]]$KnownRoots)
if (-not $KnownRoots -or $KnownRoots.Count -eq 0) { return $false }
@ -223,8 +223,8 @@ function Uninstall-UnslothStudio {
return $false
}
# Stop a Studio backend whose port is recorded in <DataDir>\studio.port.
# Only kills if the listening PID's exe path is under a known Studio root.
# Stop an Unsloth backend whose port is recorded in <DataDir>\studio.port.
# Only kills if the listening PID's exe path is under a known Unsloth root.
function _StopByPortFile {
param([string]$PortFile, [string[]]$KnownRoots)
if (-not (Test-Path -LiteralPath $PortFile -PathType Leaf)) { return }
@ -372,7 +372,7 @@ function Uninstall-UnslothStudio {
continue
}
if (-not (_IsStudioRoot $r)) {
_Substep "refusing to remove non-Studio path: $r" "Yellow"
_Substep "refusing to remove non-Unsloth path: $r" "Yellow"
continue
}
_RemovePath $r
@ -436,7 +436,7 @@ function Uninstall-UnslothStudio {
$entries = $rawPath -split ';'
$kept = New-Object System.Collections.ArrayList
$removedAny = $false
# Only remove PATH entries that live inside a Studio root we
# Only remove PATH entries that live inside an Unsloth root we
# actually own (default or env-mode). A literal substring
# match on `unsloth_studio` would clobber unrelated user
# virtualenvs that happen to share the name.

14
scripts/uninstall.sh
View file

@ -12,7 +12,7 @@
set -e
# Stop a Studio server via its PID file (written by install.sh's _spawn_terminal).
# Stop an Unsloth server via its PID file (written by install.sh's _spawn_terminal).
_kill_pid_file() {
_pid_file="$1"
[ -f "$_pid_file" ] || return 0
@ -47,7 +47,7 @@ _pkill_studio() {
command -v pkill >/dev/null 2>&1 || return 0
# Scope fallback patterns to the install roots we are removing so a
# different Studio install (different UNSLOTH_STUDIO_HOME) is not touched.
# different Unsloth install (different UNSLOTH_STUDIO_HOME) is not touched.
_kill_roots="$HOME/.unsloth/studio"
_roots_from_conf=$(_custom_studio_roots 2>/dev/null || true)
[ -n "$_roots_from_conf" ] && _kill_roots="$_kill_roots
@ -89,7 +89,7 @@ _remove_path() {
fi
}
# Accept as Studio root only if Studio sentinels exist (matches install.sh's
# Accept as Unsloth root only if Unsloth sentinels exist (matches install.sh's
# env-mode ownership guard at install.sh:1358-1361). A bare unsloth_studio/
# directory is NOT enough -- require the install-time owner marker so a user
# directory that happens to contain a folder named "unsloth_studio" is safe.
@ -175,8 +175,8 @@ _custom_studio_roots() {
_from_conf "$HOME/.local/share/unsloth/studio.conf"
}
# Remove $HOME/.local/bin/unsloth only if it's a Studio-managed symlink.
# Studio's install.sh writes this as a symlink into the studio venv
# Remove $HOME/.local/bin/unsloth only if it's an Unsloth-managed symlink.
# Unsloth's install.sh writes this as a symlink into the studio venv
# (install.sh: `ln -sfn "$VENV_DIR/bin/unsloth" "$_shim_path"`). A
# pip-installed `unsloth` CLI is a regular file — leave it alone to avoid
# wiping an unrelated install.
@ -206,7 +206,7 @@ _custom_studio_roots | while IFS= read -r _custom_root; do
continue
fi
if ! _is_studio_root "$_custom_root"; then
echo " refusing to remove non-Studio path: $_custom_root" >&2
echo " refusing to remove non-Unsloth path: $_custom_root" >&2
continue
fi
_remove_path "$_custom_root"
@ -234,7 +234,7 @@ _remove_path "$HOME/.unsloth/rocm-smoketest"
# Drop ~/.unsloth only if now empty (rmdir refuses non-empty, so user content is kept).
rmdir "$HOME/.unsloth" 2>/dev/null || true
_remove_path "$HOME/.local/share/unsloth"
# CLI shim: only the symlink Studio created, never a pip-installed file.
# CLI shim: only the symlink Unsloth created, never a pip-installed file.
_remove_cli_shim
echo "Removing desktop shortcut and launcher lock..."

12
studio/MCP.md
View file

@ -1,10 +1,10 @@
# Unsloth Studio MCP server
Studio can expose a local MCP server so an MCP client can inspect models and
Unsloth can expose a local MCP server so an MCP client can inspect models and
GPU state, validate recipes, start or stop training, inspect recipe output, and
export a loaded model.
The server is disabled by default. Enable it for a local Studio process with:
The server is disabled by default. Enable it for a local Unsloth process with:
```bash
UNSLOTH_STUDIO_ENABLE_MCP=1 \
@ -12,8 +12,8 @@ UNSLOTH_STUDIO_MCP_TOKEN='use-a-local-secret' \
unsloth studio
```
The endpoint is `http://127.0.0.1:8888/mcp/` when Studio uses its default port
(a request to `/mcp` redirects to the canonical `/mcp/`). Use the actual Studio
The endpoint is `http://127.0.0.1:8888/mcp/` when Unsloth uses its default port
(a request to `/mcp` redirects to the canonical `/mcp/`). Use the actual Unsloth
port when it is configured differently.
The high-impact tools are:
@ -23,9 +23,9 @@ The high-impact tools are:
- `validate_recipe`, `get_recipe_job_status`, and `get_recipe_job_dataset`
- `load_checkpoint` and `export_gguf`
`start_training` accepts the same fields as the Studio `TrainingStartRequest`.
`start_training` accepts the same fields as the Unsloth `TrainingStartRequest`.
The request is validated by the existing Pydantic model before a subprocess is
started. Export paths use the existing Studio validation as well.
started. Export paths use the existing Unsloth validation as well.
The endpoint always requires `UNSLOTH_STUDIO_MCP_TOKEN` and checks an exact
Bearer token for both HTTP and WebSocket connections. Keep it on localhost

2
studio/Unsloth_Studio_Colab.ipynb
View file

@ -33,7 +33,7 @@
"\n",
"We are actively working on making Unsloth Studio install on Colab T4 GPUs faster.\n",
"\n",
"[Features](https://unsloth.ai/docs/new/unsloth-studio#features) • [Quickstart](https://unsloth.ai/docs/new/unsloth-studio/start) • [Data Recipes](https://unsloth.ai/docs/new/unsloth-studio/data-recipe) • [Studio Chat](https://unsloth.ai/docs/new/unsloth-studio/chat) • [Export](https://unsloth.ai/docs/new/unsloth-studio/export)"
"[Features](https://unsloth.ai/docs/new/unsloth-studio#features) • [Quickstart](https://unsloth.ai/docs/new/unsloth-studio/start) • [Data Recipes](https://unsloth.ai/docs/new/unsloth-studio/data-recipe) • [Unsloth Chat](https://unsloth.ai/docs/new/unsloth-studio/chat) • [Export](https://unsloth.ai/docs/new/unsloth-studio/export)"
]
},
{

2
studio/backend/assets/chat_templates/gemma-4-edge.jinja
View file

@ -3,7 +3,7 @@
Source: google/gemma-4-31B-it HF discussion/PR #118 (adds the preserve_thinking
flag plus null-rendering, string-arguments validation, balanced turn tags, empty
messages handling, and OpenAI image_url/input_audio aliases).
Studio-local changes vs PR #118:
Unsloth-local changes vs PR #118:
1. preserve_thinking defaults to false (see SETUP block below).
2. The empty "<|channel>thought\n<channel|>" block on enable_thinking=false is
NOT emitted. Google ships a distinct template for E2B/E4B (google/gemma-4-E2B-it,

2
studio/backend/assets/chat_templates/gemma-4.jinja
View file

@ -3,7 +3,7 @@
Source: google/gemma-4-31B-it HF discussion/PR #118 (adds the preserve_thinking
flag plus null-rendering, string-arguments validation, balanced turn tags, empty
messages handling, and OpenAI image_url/input_audio aliases).
Studio-local change: preserve_thinking defaults to false (see SETUP block below).
Unsloth-local change: preserve_thinking defaults to false (see SETUP block below).
Applied to unsloth/gemma-4-*-GGUF models so the embedded GGUF template does not
need re-downloading. Keep in sync with upstream if PR #118 changes.
-#}

2
studio/backend/auth/authentication.py
View file

@ -148,7 +148,7 @@ async def authenticated_via_api_key(
) -> bool:
"""True when the caller used an sk-unsloth API key, not a UI session JWT.
Lets routes treat programmatic API callers differently from the Studio UI
Lets routes treat programmatic API callers differently from the Unsloth UI
(e.g. refuse a teardown the UI would allow).
"""
return bool(credentials and credentials.credentials.startswith(API_KEY_PREFIX))

10
studio/backend/auth/bootstrap_timeout.py
View file

@ -1,13 +1,13 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Auto-shutdown for an exposed first-run Studio whose admin password is unchanged.
"""Auto-shutdown for an exposed first-run Unsloth whose admin password is unchanged.
On a fresh install the seeded bootstrap admin password stays a valid login
credential until first login changes it. When the web UI is put on the network
(``--secure`` / ``0.0.0.0``) and nobody completes that first-login change within
a deadline, tear Studio down so a fresh, unconfigured instance does not stay
publicly reachable indefinitely. If the password was changed, Studio keeps
a deadline, tear Unsloth down so a fresh, unconfigured instance does not stay
publicly reachable indefinitely. If the password was changed, Unsloth keeps
running.
Scope: web UI launches only (never ``--api-only``, which authenticates by API
@ -98,7 +98,7 @@ def enforce_bootstrap_password_deadline(
) -> bool:
"""Deadline handler: shut down iff the seeded admin password is still unchanged.
Returns True if it shut Studio down, False if it left it running (the
Returns True if it shut Unsloth down, False if it left it running (the
password was changed in time).
"""
try:
@ -106,7 +106,7 @@ def enforce_bootstrap_password_deadline(
except Exception:
return False
if not still_default:
return False # password changed in time -> leave Studio running
return False # password changed in time -> leave Unsloth running
message = (
"\nUnsloth Studio was exposed on the network but its default admin "

6
studio/backend/auth/storage.py
View file

@ -146,7 +146,7 @@ def get_connection() -> sqlite3.Connection:
pass
conn.row_factory = sqlite3.Row
# WAL lets token reads run concurrently with refresh-token writes;
# busy_timeout bounds lock waits. Matches the other Studio SQLite stores.
# busy_timeout bounds lock waits. Matches the other Unsloth SQLite stores.
# Set busy_timeout first: switching journal_mode needs a lock, so if a
# refresh-token write already holds one, journal_mode=WAL raises SQLITE_BUSY;
# with busy_timeout already in effect it waits instead of failing and leaving
@ -305,8 +305,8 @@ def get_or_create_identity_secret() -> bytes:
def compute_identity_proof(nonce: bytes, host: str, port: int) -> str:
"""HMAC-SHA256 proof that the caller holds this install's identity secret,
bound to the loopback address and port the connection landed on. A proof
relayed from a Studio on a different address/port (a squatter proxying to the
real one, e.g. localhost resolving to ::1 while Studio is on 127.0.0.1) was
relayed from an Unsloth on a different address/port (a squatter proxying to the
real one, e.g. localhost resolving to ::1 while Unsloth is on 127.0.0.1) was
computed for that other endpoint and won't match the one the client dialed."""
try:
host = ipaddress.ip_address(host).compressed # normalise 127.0.0.1 / ::1 forms

6
studio/backend/auth/terminal_prompt.py
View file

@ -2,14 +2,14 @@
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Interactive terminal prompt that forces a bootstrap password change before
Studio is exposed on a public Cloudflare URL (``--secure`` / ``--cloudflare``).
Unsloth is exposed on a public Cloudflare URL (``--secure`` / ``--cloudflare``).
Masked input echoes one ``*`` per keystroke (unlike ``getpass``). Works on
Windows (``msvcrt``) and Linux/macOS (``termios``). All output goes to stderr so
redirected stdout never swallows the prompt.
Mirrored for the CLI at ``unsloth_cli/commands/_password_prompt.py`` (the CLI
cannot import the Studio backend package); keep the two in sync.
cannot import the Unsloth backend package); keep the two in sync.
"""
from __future__ import annotations
@ -252,7 +252,7 @@ def prompt_for_password_change(
out.flush()
return True
except (KeyboardInterrupt, EOFError):
out.write("Password change aborted; not exposing Studio.\n")
out.write("Password change aborted; not exposing Unsloth.\n")
out.flush()
return False

8
studio/backend/cloudflare_tunnel.py
View file

@ -1,13 +1,13 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Free Cloudflare quick tunnel for Studio's 0.0.0.0 launches.
"""Free Cloudflare quick tunnel for Unsloth's 0.0.0.0 launches.
The raw http://<ip>:<port> is often unreachable (https-vs-http, blocked ports,
closed security groups); a cloudflared quick tunnel gives a free
https://*.trycloudflare.com URL that works anywhere, with no account or domain.
Best-effort throughout: any failure collapses to "no URL" and Studio keeps
Best-effort throughout: any failure collapses to "no URL" and Unsloth keeps
running. Stdlib only (back-end imports are lazy) so it is safe to import early.
"""
@ -95,7 +95,7 @@ def _cache_path() -> Optional[Path]:
def find_cloudflared() -> Optional[str]:
"""Locate an existing cloudflared: PATH first, then the Studio bin cache."""
"""Locate an existing cloudflared: PATH first, then the Unsloth bin cache."""
on_path = shutil.which("cloudflared")
if on_path:
return on_path
@ -309,7 +309,7 @@ class CloudflareTunnel:
pass
# Single serving process per Studio launch, so one module-level tunnel handle is
# Single serving process per Unsloth launch, so one module-level tunnel handle is
# enough; the lock guards the start/stop/shutdown races.
_active_tunnel: Optional[CloudflareTunnel] = None
_active_lock = threading.Lock()

12
studio/backend/colab.py
View file

@ -129,7 +129,7 @@ def start_cloudflare_tunnel(port: int) -> "str | None":
logger.warning(
"Cloudflare link not started: the admin account still has its temporary "
"bootstrap password, which is exposed to anyone who can load the page. "
"Open Studio in this tab, log in and change the admin password, then re-run "
"Open Unsloth in this tab, log in and change the admin password, then re-run "
"start(cloudflare=True) to get the shareable link."
)
return None
@ -203,7 +203,7 @@ def _shareable_link_html(cloudflare_url: str) -> str:
display: flex; align-items: center; gap: 12px;">
<img src="https://github.com/unslothai/unsloth/raw/main/studio/frontend/public/unsloth-gem.png"
height="48" style="display:block;">
Shareable Studio Link is Ready!
Shareable Unsloth Link is Ready!
</h2>
<a href="{cloudflare_url}" onclick="var w=window.open(this.href,'_blank');if(!w){{return true;}}return false;"
style="display: inline-flex; align-items: center; gap: 10px; padding: 14px 28px;
@ -223,7 +223,7 @@ def _shareable_link_html(cloudflare_url: str) -> str:
def _show_and_embed(port: int, *, cloudflare_url: "str | None" = None):
"""Render the Studio header + iframe for *port*, with a shareable-link card above
"""Render the Unsloth header + iframe for *port*, with a shareable-link card above
when *cloudflare_url* is set. Falls back to serve_kernel_port_as_iframe."""
url = get_colab_url(port)
logger.info(f"🌐 Unsloth Studio URL: {url}")
@ -281,7 +281,7 @@ def start(port: int = 8888, *, cloudflare: bool = False):
Args:
port: Port to bind/serve on.
cloudflare: Opt in to a shareable Cloudflare HTTPS link reachable from any
device (default OFF). It exposes Studio's login page beyond Colab, so it
device (default OFF). It exposes Unsloth's login page beyond Colab, so it
stays an explicit opt-in; the default shows only the in-tab proxy iframe.
Usage:
@ -292,10 +292,10 @@ def start(port: int = 8888, *, cloudflare: bool = False):
logger.info("🦥 Starting Unsloth Studio...")
# Fast path: Studio already running (cell re-run). Re-launching would collide on
# Fast path: Unsloth already running (cell re-run). Re-launching would collide on
# the port, so just re-show the link and iframe.
if _is_studio_healthy(port):
logger.info(f" Studio is already running on port {port} — reusing existing server.")
logger.info(f" Unsloth is already running on port {port} — reusing existing server.")
# try/finally: tear the tunnel down even if interrupted mid-start/render.
try:
cf_url = start_cloudflare_tunnel(port) if cloudflare else None

6
studio/backend/core/data_recipe/jobs/parse.py
View file

@ -133,7 +133,7 @@ def parse_log_message(msg: str) -> ParsedUpdate | None:
source = "github",
status = "rate_limited",
retry_after_sec = seconds,
message = ("Waiting for GitHub rate limit. Studio will resume automatically."),
message = ("Waiting for GitHub rate limit. Unsloth will resume automatically."),
),
)
@ -147,7 +147,7 @@ def parse_log_message(msg: str) -> ParsedUpdate | None:
status = "rate_limited",
retry_after_sec = seconds,
message = (
"Waiting for GitHub secondary rate limit. Studio will resume automatically."
"Waiting for GitHub secondary rate limit. Unsloth will resume automatically."
),
),
)
@ -161,7 +161,7 @@ def parse_log_message(msg: str) -> ParsedUpdate | None:
source = "github",
status = "rate_limited",
retry_after_sec = seconds,
message = ("Waiting for GitHub rate limit. Studio will resume automatically."),
message = ("Waiting for GitHub rate limit. Unsloth will resume automatically."),
),
)

2
studio/backend/core/data_recipe/local_callable_validators.py
View file

@ -238,7 +238,7 @@ def _run_oxc_batch(
if not node_executable:
return _fallback_results(
len(code_values),
"Node.js not found (install Node >= 20.19, or re-run Studio setup to provision it).",
"Node.js not found (install Node >= 20.19, or re-run Unsloth setup to provision it).",
)
try:
tmp_dir = ensure_dir(oxc_validator_tmp_root())

4
studio/backend/core/data_recipe/service.py
View file

@ -280,8 +280,8 @@ def create_data_designer(recipe: dict[str, Any], *, artifact_path: str | None =
from data_designer.interface.data_designer import DataDesigner # pyright: ignore[reportMissingImports]
if artifact_path is None:
# DataDesigner defaults to cwd/artifacts; packaged Studio can run with
# cwd=/, so keep default callers on Studio's writable recipe artifact root.
# DataDesigner defaults to cwd/artifacts; packaged Unsloth can run with
# cwd=/, so keep default callers on Unsloth's writable recipe artifact root.
artifact_path = str(recipe_datasets_root())
recipe = _strip_frontend_model_config_metadata(recipe)

2
studio/backend/core/inference/__init__.py
View file

@ -11,7 +11,7 @@ subprocess and can be imported directly from .inference when needed.
Public names are resolved lazily (PEP 562): importing this package -- or a
dependency-light leaf like ``core.inference.chat_eos`` -- must NOT eagerly pull
the orchestrator / llama_cpp import chain (httpx, subprocess plumbing, the ML
backend and its Studio dependencies). Those load only when a public name is
backend and its Unsloth dependencies). Those load only when a public name is
actually accessed, so standalone helpers stay unit-testable without the full
inference stack.
"""

2
studio/backend/core/inference/anthropic_compat.py
View file

@ -539,7 +539,7 @@ class AnthropicPassthroughEmitter:
Only calls naming a tool in ``allowed_tools`` (the client's declared
tools) are promoted; everything else streams as text exactly as before.
Never enabled for Studio's own tool loop.
Never enabled for Unsloth's own tool loop.
"""
from core.inference.passthrough_healing import StreamToolCallHealer

2
studio/backend/core/inference/chat_template_helpers.py
View file

@ -150,7 +150,7 @@ def _split_partial_marker(text: str, marker: str) -> tuple[str, str]:
class ReasoningChannelNormalizer:
"""Incrementally convert one native reasoning channel to ``<think>``.
The parser follows mlx-vlm's streaming boundary behavior but emits Studio's
The parser follows mlx-vlm's streaming boundary behavior but emits Unsloth's
established canonical text contract. Only the configured opening and
closing markers are consumed; tool-call and other control markers remain
available to downstream parsers.

4
studio/backend/core/inference/chat_templates.py
View file

@ -4,13 +4,13 @@
"""Bundled chat-template selection for GGUF inference.
Some shipped GGUF quants embed an older chat template. Rather than re-cutting and
asking users to re-download every quant, Studio can override the embedded template
asking users to re-download every quant, Unsloth can override the embedded template
at llama-server launch time with a bundled, up-to-date Jinja template for known
model families. The override is wired through the existing ``chat_template_override``
-> ``--chat-template-file`` path in ``LlamaCppBackend.load_model``.
Currently this covers ``unsloth/gemma-4-*-GGUF``, which gains the upstream PR #118
``preserve_thinking`` flag (defaulted OFF here) so the Studio "Preserve thinking"
``preserve_thinking`` flag (defaulted OFF here) so the Unsloth "Preserve thinking"
toggle appears while staying disabled by default.
"""

14
studio/backend/core/inference/external_provider.py
View file

@ -473,7 +473,7 @@ def _apply_mistral_reasoning_controls(
# handles every provider without storing credentials.
def _create_shared_http_client() -> httpx.AsyncClient:
# Unsupported env proxy schemes (socks:// etc) raise at construction and
# would crash Studio startup (#6090); retry ignoring env proxies instead.
# would crash Unsloth startup (#6090); retry ignoring env proxies instead.
try:
return httpx.AsyncClient()
except (ImportError, ValueError) as exc:
@ -858,7 +858,7 @@ class ExternalProviderClient:
if not self._is_openai_compatible():
# Gemini speaks its own native REST shape (contents/parts);
# `_stream_gemini` translates request/response into the OpenAI
# Chat Completions chunk format the rest of Studio expects.
# Chat Completions chunk format the rest of Unsloth expects.
# API ref: https://ai.google.dev/gemini-api/docs
if self.provider_type == "gemini":
async for line in self._stream_gemini(
@ -1706,7 +1706,7 @@ class ExternalProviderClient:
# Translate OpenAI multimodal parts -> Anthropic native shapes.
# - `image_url` -> `{type:"image", source:...}`
# - `input_document` -> `{type:"document", source:...}`
# (Studio extension; mirrors Anthropic's document block,
# (Unsloth extension; mirrors Anthropic's document block,
# which supports PDFs as base64 or URL per
# https://platform.claude.com/docs/en/build-with-claude/vision)
anthropic_parts: list[dict[str, Any]] = []
@ -1749,7 +1749,7 @@ class ExternalProviderClient:
}
)
elif part.get("type") == "input_document":
# Studio's normalised PDF/doc type (file_data data-URI or
# Unsloth's normalised PDF/doc type (file_data data-URI or
# file_url) -> Anthropic's native `document` block.
url = part.get("file_url") or ""
data_uri = part.get("file_data") or ""
@ -4704,7 +4704,7 @@ class ExternalProviderClient:
{"type": "image_generation_call", "id": call_id}
)
elif part_type == "input_document":
# Map Studio's `input_document` onto Responses' `input_file`.
# Map Unsloth's `input_document` onto Responses' `input_file`.
# https://developers.openai.com/api/docs/guides/images-vision
file_url = part.get("file_url")
file_data = part.get("file_data")
@ -6010,7 +6010,7 @@ class ExternalProviderClient:
if not models and self.provider_type == "ollama":
models = await self._list_ollama_native_models()
# Gemini's native /v1beta/models uses a different shape; repackage
# into the OpenAI-compatible one Studio expects.
# into the OpenAI-compatible one Unsloth expects.
if not models and self.provider_type == "gemini":
models = self._parse_gemini_models(data)
return models
@ -6213,7 +6213,7 @@ def _friendly_provider_error_text(
*,
model: str | None = None,
) -> str:
"""Rewrite common provider errors into actionable Studio copy."""
"""Rewrite common provider errors into actionable Unsloth copy."""
if status_code == 404 and model:
lowered = raw_message.lower()
if "not found" in lowered or "not_found" in lowered:

1170
studio/backend/core/inference/llama_cpp.py
View file

File diff suppressed because it is too large Load diff

140
studio/backend/core/inference/llama_keepwarm.py
View file

@ -15,6 +15,7 @@ import asyncio
import contextlib
import threading
import time
from pathlib import Path
from loggers import get_logger
@ -30,6 +31,8 @@ _last_active = time.monotonic()
# otherwise 503 against an empty backend can reload it (set on unload, cleared on
# reload). Storing the quant means the reload restores the exact freed variant.
_last_unloaded_model = None
# Slot KV manifest saved by the idle unload; whoever pops it owns deleting its files.
_kv_resume = None
# Guards inflight bumps against the idle-check-then-unload race, and blocks new
# inference from starting mid-swap. Process-wide, not per-loop: the backend slot is
# shared across every event loop in the process, so a per-loop gate would let a
@ -59,7 +62,7 @@ _INFERENCE_SUFFIXES = (
"/messages/count_tokens", # counts via the loaded tokenizer; protect like /messages
"/embeddings",
"/responses",
"/generate/stream", # Studio's own streaming route on the same llama-server
"/generate/stream", # Unsloth's own streaming route on the same llama-server
"/audio/generate", # direct GGUF TTS; can outlive the idle TTL
)
@ -161,11 +164,17 @@ def inference_lifecycle_gate():
return _unload_gate()
def note_model_loaded() -> None:
"""Record a successful GGUF load: stamp activity and drop any reload stash so
a manual load clears it synchronously, not only on the next idle poll."""
def note_model_loaded(backend = None) -> None:
"""Stamp activity and synchronously drop any reload stash."""
_note_activity()
resume = take_kv_resume()
_set_last_unloaded(None)
if resume is None:
return
if backend is not None:
restore_kv_resume(backend, resume)
else:
_delete_resume_files(resume)
def note_model_unloaded() -> None:
@ -182,9 +191,81 @@ def get_last_unloaded_model():
def _set_last_unloaded(value) -> None:
global _last_unloaded_model
global _last_unloaded_model, _kv_resume
stale = None
with _lock:
_last_unloaded_model = value
if value is None and _kv_resume is not None:
stale, _kv_resume = _kv_resume, None
if stale:
_delete_resume_files(stale)
def _delete_resume_files(manifest) -> None:
try:
base = Path(manifest.get("dir") or "")
for entry in manifest.get("slots") or []:
with contextlib.suppress(OSError):
(base / str(entry.get("filename"))).unlink()
except Exception:
pass
def _set_kv_resume(value) -> None:
global _kv_resume
stale = None
with _lock:
if _kv_resume is not None and _kv_resume is not value:
stale = _kv_resume
_kv_resume = value
if stale:
_delete_resume_files(stale)
def take_kv_resume():
global _kv_resume
with _lock:
manifest, _kv_resume = _kv_resume, None
return manifest
def purge_kv_resume() -> None:
resume = take_kv_resume()
if resume:
_delete_resume_files(resume)
def restore_kv_resume(backend, manifest) -> None:
try:
gguf = manifest.get("gguf")
binary = manifest.get("binary")
current = getattr(backend, "_gguf_path", None)
same_gguf = bool(gguf and current) and Path(current).resolve() == Path(gguf).resolve()
if same_gguf:
# Same path is not enough: shards may have been rewritten meanwhile.
identity = getattr(backend, "_gguf_file_identity", None)
same_gguf = callable(identity) and identity(current) == manifest.get("gguf_stat")
if same_gguf:
# Nor the same file: launch overrides can invalidate KV numerics.
fingerprint = getattr(backend, "_slot_launch_fingerprint", None)
same_gguf = callable(fingerprint) and manifest.get("launch") == fingerprint()
if same_gguf and binary and binary == getattr(backend, "_slot_save_binary", None):
logger.info("Restoring saved slot KV onto the reloaded model")
backend.restore_slots_for_resume(manifest)
except Exception as exc:
logger.debug("slot restore after reload failed: %s", exc)
finally:
_delete_resume_files(manifest)
def sweep_slot_save_dir() -> None:
try:
from utils.paths.storage_roots import llama_slot_cache_root
for path in llama_slot_cache_root().glob("resume-*.bin"):
with contextlib.suppress(OSError):
path.unlink()
except Exception:
pass
class LlamaKeepWarmMiddleware:
@ -266,7 +347,10 @@ def _loaded_identity(backend):
async def idle_unload_loop(poll_seconds: float = 15.0) -> None:
"""Unload the loaded GGUF once idle past the configured TTL. Inert when off."""
from utils.openai_auto_switch_settings import get_auto_unload_idle_seconds
from utils.openai_auto_switch_settings import (
get_auto_unload_idle_seconds,
get_auto_unload_keep_kv,
)
seen_model = None
while True:
@ -281,17 +365,47 @@ async def idle_unload_loop(poll_seconds: float = 15.0) -> None:
# Track by (id, variant): a (re)loaded model -- including the same repo
# at a different quant -- counts as activity so it survives one TTL
# before its first request (loads bypass the activity middleware).
current = _loaded_identity(backend)
if current != seen_model:
seen_model = current
if current is not None:
_note_activity()
_set_last_unloaded(None) # a model is loaded; drop stale stash
async with _unload_gate():
# Purging the stash mid-reload would race the restore.
current = _loaded_identity(backend)
if current != seen_model:
seen_model = current
if current is not None:
_note_activity()
_set_last_unloaded(None) # a model is loaded; drop stale stash
if backend.is_loaded and _is_idle(ttl):
freed = _loaded_identity(backend)
await asyncio.to_thread(backend.unload_model)
manifest = None
if get_auto_unload_keep_kv():
try:
manifest = await asyncio.to_thread(
backend.save_slots_for_resume,
lambda: not _is_idle(ttl),
)
except Exception as exc:
logger.debug("slot save before idle unload failed: %s", exc)
# Re-read settings: the save can outlive a settings change.
ttl = get_auto_unload_idle_seconds()
if ttl <= 0 or not _is_idle(ttl):
if manifest:
_delete_resume_files(manifest)
continue
if manifest and not get_auto_unload_keep_kv():
_delete_resume_files(manifest)
manifest = None
try:
await asyncio.to_thread(backend.unload_model)
except Exception:
# Failed unload means nothing will stash the manifest.
if manifest:
_delete_resume_files(manifest)
raise
_set_last_unloaded(freed) # let an alias request reload it
if manifest and freed:
_set_kv_resume({"identity": freed, **manifest})
logger.info("Idle auto-unload: saved slot KV for restore on reload")
elif manifest:
_delete_resume_files(manifest)
logger.info("Idle auto-unload: freed GGUF after %ss idle", ttl)
seen_model = None
except Exception as exc:

67
studio/backend/core/inference/llama_server_args.py
View file

@ -3,10 +3,10 @@
"""Boundary validator for user-supplied llama-server pass-through args.
Reject only flags Studio manages (model identity, auth, network, parallel
Reject only flags Unsloth manages (model identity, auth, network, parallel
slots). Everything else (sampling, ``-c``, ``-ngl``, ``--flash-attn``,
``--cache-type-*``, ``--spec-*``, ``--jinja``, ...) is appended after
Studio's auto-set flags so llama.cpp's last-wins parser lets the user override.
Unsloth's auto-set flags so llama.cpp's last-wins parser lets the user override.
Ref: https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md
"""
@ -22,12 +22,12 @@ _DENYLIST_GROUPS: tuple[frozenset[str], ...] = (
# Parallel slots: owned by typer --parallel; a pass-through would desync
# app.state.llama_parallel_slots from llama-server.
frozenset({"-np", "--parallel", "--n-parallel"}),
# Model identity: Studio resolves it from LoadRequest; a second -m would
# load a different model than Studio thinks it loaded.
# Model identity: Unsloth resolves it from LoadRequest; a second -m would
# load a different model than Unsloth thinks it loaded.
frozenset({"-m", "--model"}),
# Public model id: Studio sets a sanitized --alias so the OpenAI API never
# Public model id: Unsloth sets a sanitized --alias so the OpenAI API never
# exposes the local .gguf path. A user-supplied alias is appended after
# Studio's and, with llama.cpp's last-wins parsing, would reintroduce the
# Unsloth's and, with llama.cpp's last-wins parsing, would reintroduce the
# path leak this is meant to prevent.
frozenset({"-a", "--alias"}),
frozenset({"-mu", "--model-url"}),
@ -39,14 +39,14 @@ _DENYLIST_GROUPS: tuple[frozenset[str], ...] = (
frozenset({"-hft", "--hf-token"}),
frozenset({"-mm", "--mmproj"}),
frozenset({"-mmu", "--mmproj-url"}),
# Networking: Studio binds + proxies; retargeting orphans the proxy.
# Networking: Unsloth binds + proxies; retargeting orphans the proxy.
frozenset({"--host"}),
frozenset({"--port"}),
frozenset({"--path"}),
frozenset({"--api-prefix"}),
frozenset({"--reuse-port"}),
# Auth / TLS: Studio terminates auth; upstream --api-key / TLS shadows
# Studio's key and breaks the proxy hop.
# Auth / TLS: Unsloth terminates auth; upstream --api-key / TLS shadows
# Unsloth's key and breaks the proxy hop.
frozenset({"--api-key"}),
frozenset({"--api-key-file"}),
frozenset({"--ssl-key-file"}),
@ -64,12 +64,14 @@ _DENYLIST_GROUPS: tuple[frozenset[str], ...] = (
frozenset({"--models-max"}),
frozenset({"--models-autoload", "--no-models-autoload"}),
# Server-mode flips: --embedding / --rerank restrict llama-server to
# those endpoints, breaking Studio's /v1/chat/completions hop.
# those endpoints, breaking Unsloth's /v1/chat/completions hop.
frozenset({"--embedding", "--embeddings"}),
frozenset({"--rerank", "--reranking"}),
# llama-server's own built-in tools flag would silently stack on top of
# Studio's --enable-tools / --disable-tools policy resolver.
# Unsloth's --enable-tools / --disable-tools policy resolver.
frozenset({"--tools"}),
# Slot-state dir: Studio owns it for KV persistence across idle unload.
frozenset({"--slot-save-path"}),
)
_DENYLIST: frozenset[str] = frozenset().union(*_DENYLIST_GROUPS)
@ -120,7 +122,7 @@ def validate_extra_args(args: Optional[Iterable[str]]) -> list[str]:
def is_managed_flag(flag: str) -> bool:
"""True if ``flag`` is Studio-managed. Normalises via ``_flag_name`` so
"""True if ``flag`` is Unsloth-managed. Normalises via ``_flag_name`` so
`-np8` / `--parallel=8` classify like the canonical tokens."""
normalised = _flag_name(flag)
return normalised is not None and normalised in _DENYLIST
@ -142,7 +144,7 @@ _SPEC_FLAGS: frozenset[str] = frozenset(
"--draft-min",
"--draft-max",
# MTP path (llama.cpp #22673). The drafter selectors (local --model-draft
# and HF --spec-draft-hf aliases) are Studio-managed since the separate-
# and HF --spec-draft-hf aliases) are Unsloth-managed since the separate-
# drafter support (Gemma 4): an inherited copy must not last-wins-override
# the auto-detected drafter. Explicit extras for the current load are never
# stripped. The per-drafter tuning knobs (--spec-draft-type-*, -ngld,
@ -179,25 +181,38 @@ _TEMPLATE_FLAGS: frozenset[str] = frozenset(
# (--split-mode tensor). Pass-through stays allowed so users keep the
# row/none/layer modes the toggle doesn't expose, but it's stripped on
# inherit and reconciled into the round-tripped tensor_parallel state.
# --tensor-split is coupled to the split mode and is stripped with it: Studio
# --tensor-split is coupled to the split mode and is stripped with it: Unsloth
# owns the tensor-mode split ratios, so an inherited/stale --tensor-split must
# not last-wins-override Studio's computed asymmetric split.
# not last-wins-override Unsloth's computed asymmetric split.
_SPLIT_MODE_FLAGS: frozenset[str] = frozenset({"-sm", "--split-mode"})
_TENSOR_SPLIT_FLAGS: frozenset[str] = frozenset({"-ts", "--tensor-split"})
_SPLIT_SHADOWING_FLAGS: frozenset[str] = _SPLIT_MODE_FLAGS | _TENSOR_SPLIT_FLAGS
# GPU-offload flags. Stripped only when the GPU Memory mode owns offload
# (manual emits --fit / --gpu-layers / --n-cpu-moe); in auto, a user's
# inherited -ngl is respected (the offload_overridden path), so this group is
# opt-in, not default. Layer flags are shared with llama_cpp's override
# detection; the MoE flags are strip-only (manual's --n-cpu-moe slider owns them).
_LAYER_OFFLOAD_FLAGS: frozenset[str] = frozenset(
{"-ngl", "--gpu-layers", "--n-gpu-layers", "-fit", "--fit"}
)
_MOE_OFFLOAD_FLAGS: frozenset[str] = frozenset({"-ncmoe", "--n-cpu-moe", "-cmoe", "--cpu-moe"})
_OFFLOAD_SHADOWING_FLAGS: frozenset[str] = _LAYER_OFFLOAD_FLAGS | _MOE_OFFLOAD_FLAGS
_SHADOWING_FLAGS: frozenset[str] = (
_CONTEXT_FLAGS | _CACHE_FLAGS | _SPEC_FLAGS | _TEMPLATE_FLAGS | _SPLIT_SHADOWING_FLAGS
)
# Shadowing flags that take no value -- strip the flag only, not the next token.
_BOOLEAN_SHADOWING_FLAGS: frozenset[str] = frozenset({"--spec-default", "--jinja", "--no-jinja"})
_BOOLEAN_SHADOWING_FLAGS: frozenset[str] = frozenset(
{"--spec-default", "--jinja", "--no-jinja", "-cmoe", "--cpu-moe"}
)
def parse_ctx_override(args: Optional[Iterable[str]]) -> Optional[int]:
"""Return the last user-supplied ``-c`` / ``--ctx-size`` value.
Mirrors llama.cpp's last-wins parsing for the one numeric knob Studio's
Mirrors llama.cpp's last-wins parsing for the one numeric knob Unsloth's
load-time fit logic needs.
"""
if not args:
@ -286,7 +301,7 @@ def parse_cache_override(args: Optional[Iterable[str]]) -> Optional[str]:
Mirrors parse_ctx_override but for cache type. Recognises both -ctk
(key) and -ctv (value). When both flags appear, returns the last-wins
value, treating key and value cache flags as the same setting because
Studio's KV estimate has a single cache_type_kv knob.
Unsloth's KV estimate has a single cache_type_kv knob.
"""
return _last_flag_value(args, _CACHE_FLAGS)
@ -341,7 +356,7 @@ def resolve_tensor_parallel(args: Optional[Iterable[str]], fallback_tensor_paral
def _env_split_mode_is_tensor(env: Optional[Mapping[str, str]] = None) -> bool:
"""True when the inherited LLAMA_ARG_SPLIT_MODE env selects tensor. Studio
"""True when the inherited LLAMA_ARG_SPLIT_MODE env selects tensor. Unsloth
emits --split-mode only on its tensor branch, so a tensor env on the layer
path would run the child tensor-parallel unbudgeted; this flips the budget
to tensor. Only tensor is heavier, so other modes are ignored."""
@ -424,14 +439,22 @@ def strip_shadowing_flags(
strip_spec: bool = True,
strip_template: bool = True,
strip_split_mode: bool = True,
strip_tensor_split: bool = False,
strip_offload: bool = False,
) -> list[str]:
"""Strip flags that shadow first-class Studio settings.
"""Strip flags that shadow first-class Unsloth settings.
Used when inheriting a previous load's ``llama_extra_args`` so an
inherited `-c 4096` can't override the current `max_seq_length`
(same for cache / spec / template / split-mode). Each ``strip_*``
toggle controls one group; the route only strips groups whose
first-class field the caller actually supplied.
``strip_split_mode`` removes both ``--split-mode`` and the coupled
``--tensor-split`` (the Tensor Parallelism toggle owns the whole split).
``strip_tensor_split`` removes ``--tensor-split`` *alone*, so manual mode can
replace an inherited per-GPU ratio while leaving the user's ``--split-mode``
row/none/layer choice intact.
"""
shadowing: set[str] = set()
if strip_context:
@ -444,6 +467,10 @@ def strip_shadowing_flags(
shadowing |= _TEMPLATE_FLAGS
if strip_split_mode:
shadowing |= _SPLIT_SHADOWING_FLAGS
if strip_tensor_split:
shadowing |= _TENSOR_SPLIT_FLAGS
if strip_offload:
shadowing |= _OFFLOAD_SHADOWING_FLAGS
tokens = [str(a) for a in (args or [])]
out: list[str] = []

2
studio/backend/core/inference/llama_stats.py
View file

@ -5,7 +5,7 @@
engine-stats log line (generation/prompt throughput, requests in flight).
llama-server already computes these (it needs `--metrics`); this lifts them
into Studio's structured log so the terminal shows serving health, not just
into Unsloth's structured log so the terminal shows serving health, not just
per-request access lines. Emitted only while there is activity.
"""

10
studio/backend/core/inference/local_model_resolver.py
View file

@ -130,7 +130,7 @@ def info_has_local_gguf(info) -> bool:
def _build_index() -> dict[str, _LocalGgufEntry]:
"""Map normalized id/model_id/display_name -> local GGUF entry.
Scans the same roots Studio's model picker lists (./models, the active plus
Scans the same roots Unsloth's model picker lists (./models, the active plus
legacy/default HF caches, LM Studio dirs, and user scan folders) so a named
local model is never missed and silently served as the loaded one. Ollama's
scanner is skipped: it creates symlinks as a side effect and this runs on the
@ -199,9 +199,13 @@ def _build_index() -> dict[str, _LocalGgufEntry]:
raw_id = getattr(info, "id", None)
if not raw_id:
continue
# Skip what Studio hides from its pickers (validation probe, RAG embed
# Skip what Unsloth hides from its pickers (validation probe, RAG embed
# weights): not chat models, so never an auto-switch target.
if _is_hidden_model(raw_id, getattr(info, "path", None)):
if _is_hidden_model(
raw_id,
getattr(info, "model_id", None),
getattr(info, "path", None),
):
continue
# Advertise a client-facing alias, not an absolute filesystem path.
loader_id = _advertised_loader_id(info)

2
studio/backend/core/inference/mcp_client.py
View file

@ -906,7 +906,7 @@ def _call_stdio_tool(
def _remaining() -> Optional[float]:
return None if deadline is None else max(0.0, deadline - time.monotonic())
# Callers without a Studio session id must retain the former one-shot
# Callers without an Unsloth session id must retain the former one-shot
# behavior: no browser/cookie/tool state can leak into another request.
# Use an ephemeral key (and close it below) rather than the shared empty
# scope that the persistent-session cache used previously.

93
studio/backend/core/inference/mlx_inference.py
View file

@ -8,6 +8,7 @@ instead of torch/transformers for model loading and generation.
import json
import os
import threading
from contextlib import contextmanager
from typing import Optional, Generator
from core.inference.message_content import content_to_text
from core.inference.runtime_context import runtime_context_length
@ -20,6 +21,63 @@ from loggers import get_logger
logger = get_logger(__name__)
def _mlx_adapter_modules(model):
"""Return bypassable adapter entries and unsupported wrapper paths."""
adapters = []
unsupported = []
for path, module in model.named_modules():
if not path or not (hasattr(module, "lora_a") and hasattr(module, "lora_b")):
continue
base = getattr(module, "linear", None)
if base is None:
base = getattr(module, "embedding", None)
if base is None:
unsupported.append(path)
else:
adapters.append((path, module, base))
return adapters, unsupported
@contextmanager
def _temporary_mlx_adapter_state(model, use_adapter):
"""Select base or adapter modules for one request, then restore the tree."""
if use_adapter is None:
yield
return
if isinstance(use_adapter, str):
raise NotImplementedError(
"Unsloth MLX: named adapter selection is not supported; use True for "
"the loaded adapter or False for the base model."
)
if use_adapter is not True and use_adapter is not False:
raise TypeError("Unsloth MLX: use_adapter must be None, True, False, or a string.")
adapters, unsupported = _mlx_adapter_modules(model)
if use_adapter is True:
if not adapters and not unsupported:
logger.warning("MLX adapter requested, but the active model has no adapter layers")
yield
return
if unsupported:
raise RuntimeError(
"Unsloth MLX: cannot disable adapter layers without their base modules: "
+ ", ".join(unsupported[:5])
)
if not adapters:
yield
return
from mlx.utils import tree_unflatten
base_modules = tree_unflatten([(path, base) for path, _, base in adapters])
adapter_modules = tree_unflatten([(path, wrapper) for path, wrapper, _ in adapters])
try:
model.update_modules(base_modules)
yield
finally:
model.update_modules(adapter_modules)
def _mlx_vlm_model_config(model):
"""Return the loaded MLX model config and its type, preferring whichever of
config / _config actually carries a model_type."""
@ -508,6 +566,7 @@ class MLXInferenceBackend:
reasoning_effort = None,
preserve_thinking = None,
presence_penalty = 0.0,
_adapter_state = None,
) -> Generator[str, None, None]:
if self._model is None:
raise RuntimeError("No model loaded")
@ -552,6 +611,7 @@ class MLXInferenceBackend:
reasoning_effort = reasoning_effort,
preserve_thinking = preserve_thinking,
presence_penalty = presence_penalty,
_adapter_state = _adapter_state,
)
else:
stream = self._generate_text(
@ -568,6 +628,7 @@ class MLXInferenceBackend:
reasoning_effort = reasoning_effort,
preserve_thinking = preserve_thinking,
presence_penalty = presence_penalty,
_adapter_state = _adapter_state,
)
yield from stream
@ -587,6 +648,7 @@ class MLXInferenceBackend:
reasoning_effort = None,
preserve_thinking = None,
presence_penalty = 0.0,
_adapter_state = None,
):
from mlx_lm import stream_generate
from mlx_lm.sample_utils import make_sampler, make_logits_processors
@ -635,10 +697,6 @@ class MLXInferenceBackend:
think_prefix = detect_think_prefill(
prompt, getattr(self._tokenizer, "all_special_tokens", None)
)
# Emit it before the first token so the block renders during prefill.
if think_prefix:
yield think_prefix
sampler = make_sampler(
temp = temperature,
top_p = top_p,
@ -680,9 +738,12 @@ class MLXInferenceBackend:
type(self._model).__name__,
type(self._tokenizer).__name__,
)
with self._generation_lock:
with self._generation_lock, _temporary_mlx_adapter_state(self._model, _adapter_state):
final_response = None
try:
# Enter request-scoped model state before yielding any response.
if think_prefix:
yield think_prefix
gen_kwargs = dict(
prompt = prompt,
max_tokens = max_new_tokens,
@ -749,6 +810,7 @@ class MLXInferenceBackend:
reasoning_effort = None,
preserve_thinking = None,
presence_penalty = 0.0,
_adapter_state = None,
):
from mlx_vlm import stream_generate as vlm_stream
@ -852,9 +914,6 @@ class MLXInferenceBackend:
# Re-emit an open <think> prefill from the prompt (see _generate_text).
cumulative = detect_think_prefill(prompt, getattr(chat_target, "all_special_tokens", None))
# Emit it before the first token so the block renders during prefill.
if cumulative:
yield cumulative
logger.info(
"VLM generating: prompt_len=%d, has_image=%s",
len(prompt),
@ -891,9 +950,18 @@ class MLXInferenceBackend:
def _stream_vlm_snapshots():
nonlocal cumulative
with self._generation_lock:
# Hold the generation lock AND the request-scoped adapter state for the
# whole stream so Base-vs-LoRA compare mode honors use_adapter and the
# wrapper tree is restored on completion, cancellation, or close.
with self._generation_lock, _temporary_mlx_adapter_state(self._model, _adapter_state):
final_response = None
try:
# Emit any prefilled <think> block before the first token so the
# UI renders it during prefill, matching _generate_text. Done
# inside the adapter context so an unsupported request raises
# before any output escapes.
if cumulative:
yield cumulative
for response in vlm_stream(
self._model,
self._processor,
@ -927,8 +995,11 @@ class MLXInferenceBackend:
cancel_event = None,
**gen_kwargs,
) -> Generator[str, None, None]:
# MLX LoRA adapter toggling not yet supported; generate normally
yield from self.generate_chat_response(cancel_event = cancel_event, **gen_kwargs)
yield from self.generate_chat_response(
cancel_event = cancel_event,
_adapter_state = use_adapter,
**gen_kwargs,
)
def reset_generation_state(self):
import mlx.core as mx

22
studio/backend/core/inference/orchestrator.py
View file

@ -54,9 +54,8 @@ class GenStreamError(str):
"""A stream chunk carrying a real backend/generation error, not model text.
Subclasses str so existing display/logging consumers are unaffected, while
callers that must abort a distributed run on error (raise_on_streamed_error)
can distinguish a real error from model output whose visible text starts with
"Error:" by checking isinstance(chunk, GenStreamError).
callers can distinguish a real error from model output whose visible text
starts with "Error:" by checking isinstance(chunk, GenStreamError).
"""
__slots__ = ("public",)
@ -1502,14 +1501,27 @@ class InferenceOrchestrator:
Uses the dispatcher path (no _gen_lock) so compare-mode requests
don't block each other; the subprocess serializes them via its
sequential command loop.
sequential command loop. Backend failures raise instead of becoming
assistant text.
"""
yield from self._generate_dispatched(
stream = self._generate_dispatched(
use_adapter = use_adapter,
cancel_event = cancel_event,
stats_holder = stats_holder,
**gen_kwargs,
)
try:
for chunk in stream:
if isinstance(chunk, GenStreamError):
# Preserve the public/operational flag so the route can surface
# the real message (e.g. "model is being unloaded") instead of a
# generic error. Mirrors the safetensors tool loop's _single_turn.
raise GenStreamErrorRaised(str(chunk), public = chunk.public)
yield chunk
finally:
close = getattr(stream, "close", None)
if callable(close):
close()
def _generate_inner(
self,

4
studio/backend/core/inference/passthrough_healing.py
View file

@ -5,7 +5,7 @@
With server-side tools disabled (``unsloth run --disable-tools``, every
``unsloth start`` coding agent), requests carrying the client's own ``tools``
bypass Studio's tool loop and are relayed to/from llama-server verbatim. Small
bypass Unsloth's tool loop and are relayed to/from llama-server verbatim. Small
GGUF models often emit their tool calls as TEXT (``<tool_call>{...}</tool_call>``,
Gemma ``<|tool_call>...``, ``<function=...>`` XML) instead of structured
``tool_calls`` -- on the passthrough that text reaches the agent as prose and
@ -18,7 +18,7 @@ promotes calls whose function name exactly matches a declared tool. Promotion
removes EXACTLY the promoted calls' markup spans (the parser reports them):
undeclared calls, unparseable blocks, and suppressed alternate formats keep
every byte and relay as text, so healing can never silently delete model
output. Responses without a tool signal, requests without tools, and Studio's
output. Responses without a tool signal, requests without tools, and Unsloth's
own enable-tools loop are untouched. Per-request opt-out:
``auto_heal_tool_calls: false``. Process kill-switch:
``UNSLOTH_DISABLE_TOOL_CALL_HEALING=1``.

8
studio/backend/core/inference/pricing.py
View file

@ -122,12 +122,12 @@ def calculate_cost(provider: str, model: str, usage: dict[str, Any]) -> dict[str
"priced": bool(prices),
}
# Accept raw (input_tokens/output_tokens) and Studio chat-style
# Accept raw (input_tokens/output_tokens) and Unsloth chat-style
# (prompt_tokens/completion_tokens) envelopes. Cache buckets differ:
# raw Anthropic: input_tokens EXCLUDES cache buckets
# raw OpenAI: input_tokens INCLUDES cache_read
# Studio Anthropic: prompt_tokens INCLUDES cache_creation + cache_read
# Studio OpenAI: prompt_tokens == raw input_tokens
# Unsloth Anthropic: prompt_tokens INCLUDES cache_creation + cache_read
# Unsloth OpenAI: prompt_tokens == raw input_tokens
# Clamp >=0 so corrupted payloads can't produce a negative bill.
cache_creation = max(0, int(usage.get("cache_creation_input_tokens") or 0))
cache_read_native_present = (
@ -160,7 +160,7 @@ def calculate_cost(provider: str, model: str, usage: dict[str, Any]) -> dict[str
output_tokens = max(0, int(usage.get("completion_tokens") or 0))
if provider == "openai":
# Cached tokens land on input_tokens_details (raw Responses) or
# prompt_tokens_details (Studio chat-style).
# prompt_tokens_details (Unsloth chat-style).
for key in ("input_tokens_details", "prompt_tokens_details"):
details = usage.get(key) or {}
if isinstance(details, dict):

2
studio/backend/core/inference/safetensors_agentic.py
View file

@ -995,7 +995,7 @@ def run_safetensors_tool_loop(
if not safety_tc:
# Re-prompt once on plan-without-action, before any tool runs
# (GGUF loop parity). The retry is gated on nudge_tool_calls so
# Studio callers (which send True) always nudge, while API callers
# Unsloth callers (which send True) always nudge, while API callers
# who omit the flag keep today's no-reprompt behavior (opt-in).
intent_text = _reprompt_intent_text(
content_accum,

2
studio/backend/core/inference/sandbox_site/sitecustomize.py
View file

@ -4,7 +4,7 @@
"""Sandbox-side compatibility shim for ChatGPT code-interpreter paths.
Models habitually write to /mnt/data (or /mnt/outputs, /home/sandbox,
/workspace), none of which exist in the Studio sandbox. This module sits on the
/workspace), none of which exist in the Unsloth sandbox. This module sits on the
sandbox subprocess PYTHONPATH (see ``tools._build_safe_env``), so it loads at
interpreter startup in every sandboxed ``python`` run and any Python the
``terminal`` tool launches.

2
studio/backend/core/inference/tool_loop_controller.py
View file

@ -1,7 +1,7 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Shared controller state for Studio local agentic tool loops.
"""Shared controller state for Unsloth local agentic tool loops.
This module is intentionally dependency-light: it owns only per-response
ledger state and value objects used by the GGUF and safetensors loops.

14
studio/backend/core/inference/tools.py
View file

@ -2503,7 +2503,7 @@ def _build_safe_env(workdir: str) -> dict[str, str]:
shim directory.
"""
# Start from the running interpreter's dir so 'python'/'pip' resolve to the
# same environment the Studio server runs in.
# same environment the Unsloth server runs in.
exe_dir = os.path.dirname(sys.executable)
path_entries = [exe_dir] if exe_dir else []
@ -2793,7 +2793,7 @@ def _bypass_preexec():
"""Minimal pre-exec for bypass exec: os.setsid() only.
Required, not a restriction: _kill_process_tree does killpg(getpgid(child)),
so without a new session a timeout/cancel would kill the Studio server too.
so without a new session a timeout/cancel would kill the Unsloth server too.
"""
try:
os.setsid()
@ -2801,13 +2801,13 @@ def _bypass_preexec():
pass
# Hardening the Studio parent is done once (PR_SET_DUMPABLE is process-global
# Hardening the Unsloth parent is done once (PR_SET_DUMPABLE is process-global
# and sticky); guarded so repeated bypass calls do not re-issue the prctl.
_parent_proc_hardened = False
def _harden_parent_against_proc_env_leak() -> bool:
"""Make the Studio process's /proc/<pid>/environ unreadable to its children.
"""Make the Unsloth process's /proc/<pid>/environ unreadable to its children.
Stripping the child env is not enough on Linux: a bypassed same-UID child
can read /proc/<getppid()>/environ to recover the parent's unfiltered
@ -5577,7 +5577,7 @@ def _truncate(text: str, limit: int = _MAX_OUTPUT_CHARS) -> str:
# ChatGPT code-interpreter path conventions models write out of habit; none
# exist in the Studio sandbox, so a failure on one earns the retry hint.
# exist in the Unsloth sandbox, so a failure on one earns the retry hint.
_MISSING_PATH_PREFIXES = (
"/mnt/data",
"/mnt/outputs",
@ -5783,7 +5783,7 @@ def _python_exec(
# Close the /proc/<parent>/environ secret-recovery path first; if it
# cannot be applied, fail closed rather than leak the parent environ.
return (
"Execution error: could not harden the Studio process against "
"Execution error: could not harden the Unsloth process against "
"/proc environment reads; refusing bypass execution."
)
@ -5928,7 +5928,7 @@ def _bash_exec(
# Close the /proc/<parent>/environ secret-recovery path first; if it
# cannot be applied, fail closed rather than leak the parent environ.
return (
"Execution error: could not harden the Studio process against "
"Execution error: could not harden the Unsloth process against "
"/proc environment reads; refusing bypass execution."
)

31
studio/backend/core/inference/worker.py
View file

@ -513,20 +513,25 @@ def _handle_generate(backend, cmd: dict, resp_queue: Any, cancel_event) -> None:
logger.info("Starting text generation for request_id=%s", request_id)
for cumulative_text in generator:
# cancel_event is an mp.Event — checked instantly, no queue polling.
if cancel_event.is_set():
logger.info("Generation cancelled for request %s", request_id)
break
try:
for cumulative_text in generator:
# cancel_event is an mp.Event — checked instantly, no queue polling.
if cancel_event.is_set():
logger.info("Generation cancelled for request %s", request_id)
break
_send_response(
resp_queue,
{
"type": "token",
"request_id": request_id,
"text": cumulative_text,
},
)
_send_response(
resp_queue,
{
"type": "token",
"request_id": request_id,
"text": cumulative_text,
},
)
finally:
close = getattr(generator, "close", None)
if callable(close):
close()
_send_response(
resp_queue,

2
studio/backend/core/rag/captioner.py
View file

@ -6,7 +6,7 @@
Both turn pixels into indexable text and are a no-op (never raise) without a loaded
vision model. They reuse the chat model's vision endpoint, so it must be served with
``--ubatch-size`` >= one image's tokens (some encoders, e.g. Gemma, attend
non-causally and abort otherwise); Studio's vision chat already requires this."""
non-causally and abort otherwise); Unsloth's vision chat already requires this."""
from __future__ import annotations

25
studio/backend/core/rag/config.py
View file

@ -87,6 +87,22 @@ def _names_gguf(model: str) -> bool:
return "gguf" in re.split(r"[^a-z0-9]+", model.lower())
def gguf_repo_for_embedding_model(model: str) -> str:
"""GGUF repo for ``model``, honoring an explicit companion override."""
if "RAG_EMBED_GGUF_REPO" in os.environ:
return EMBED_GGUF_REPO
if model == DEFAULT_EMBEDDING_MODEL:
return EMBED_GGUF_REPO
if _names_gguf(model):
return model
return f"{model}-GGUF"
def default_gguf_repo() -> str:
"""GGUF companion for the env/default embedding model."""
return gguf_repo_for_embedding_model(EMBEDDING_MODEL)
def effective_gguf_repo() -> str:
"""GGUF repo for the llama-server backend, tracking the effective model.
@ -95,14 +111,7 @@ def effective_gguf_repo() -> str:
``-GGUF`` companion repo (the unsloth convention the default pair follows),
or is used as-is when it already names a GGUF repo.
"""
if "RAG_EMBED_GGUF_REPO" in os.environ:
return EMBED_GGUF_REPO
model = effective_embedding_model()
if model == DEFAULT_EMBEDDING_MODEL:
return EMBED_GGUF_REPO
if _names_gguf(model):
return model
return f"{model}-GGUF"
return gguf_repo_for_embedding_model(effective_embedding_model())
# llama-server backend only. F16 over Q8_0: faster (no per-block dequant for this

2
studio/backend/core/rag/embed_llama_server.py
View file

@ -10,7 +10,7 @@ Opt-in (``RAG_EMBED_BACKEND=llama-server``). Runs a dedicated
Device is ``auto`` (GPU when present, else CPU, falling back to CPU if a GPU start
fails); ``RAG_EMBED_DEVICE`` forces it. We call only llama_cpp's *static* helpers
(no torch), copying the instance-coupled bits locally, since constructing a
``LlamaCppBackend`` runs an ``__init__`` reaper that kills any Studio llama-server
``LlamaCppBackend`` runs an ``__init__`` reaper that kills any Unsloth llama-server
-- so each request re-spawns ours if it died (self-heal).
"""

2
studio/backend/core/rag/embeddings.py
View file

@ -39,7 +39,7 @@ _model = None
_name: str | None = None
# Studio device -> torch device string. Apple has no torch device -> CPU.
# Unsloth device -> torch device string. Apple has no torch device -> CPU.
_TORCH_DEVICE = {DeviceType.CUDA: "cuda", DeviceType.XPU: "xpu"}

10
studio/backend/core/rag/store.py
View file

@ -158,6 +158,16 @@ def list_documents(conn: sqlite3.Connection, scope: str) -> list[dict]:
return [dict(r) for r in rows]
def list_all_documents(conn: sqlite3.Connection) -> list[dict]:
"""Every uploaded document across all scopes (KBs, threads, projects)."""
rows = conn.execute(
"SELECT id, scope, kb_id, thread_id, project_id, filename, sha256, status, error, "
"num_chunks, stored_path, created_at "
"FROM documents ORDER BY created_at DESC"
).fetchall()
return [dict(r) for r in rows]
def get_document(conn: sqlite3.Connection, document_id: str) -> dict | None:
row = conn.execute("SELECT * FROM documents WHERE id=?", (document_id,)).fetchone()
return dict(row) if row else None

175
studio/backend/core/training/resume.py
View file

@ -4,6 +4,8 @@
"""Helpers for validating resumable training outputs."""
import json
import pickletools
import zipfile
from pathlib import Path
from typing import Optional
@ -33,27 +35,164 @@ def _checkpoint_step(path: Path) -> int:
return -1
def get_resume_checkpoint_path(path_value: str) -> Optional[str]:
_MODEL_FILES = (
"adapter_model.safetensors",
"adapter_model.bin",
"model.safetensors",
"pytorch_model.bin",
)
_MODEL_INDEXES = ("model.safetensors.index.json", "pytorch_model.bin.index.json")
def _valid_state_file(path: Path, require_tensor: bool = True) -> bool:
try:
if not path.is_file() or path.stat().st_size == 0:
return False
if path.suffix == ".safetensors":
try:
from safetensors import SafetensorError, safe_open
except ImportError:
return False
try:
with safe_open(str(path), framework = "np") as state:
return bool(state.keys())
except SafetensorError:
return False
if path.suffix in {".bin", ".pt"}:
with zipfile.ZipFile(path) as state:
infos = state.infolist()
names = [info.filename for info in infos]
data_name = next(
(name for name in names if name == "data.pkl" or name.endswith("/data.pkl")),
None,
)
if data_name is None:
return False
data_prefix = data_name.removesuffix("data.pkl") + "data/"
operations = list(pickletools.genops(state.read(data_name)))
if not operations or operations[-1][0].name != "STOP":
return False
if not require_tensor:
return True
# Require a non-empty tensor record; a zero-byte one fails torch.load.
return any(
info.filename.startswith(data_prefix)
and not info.is_dir()
and info.file_size > 0
for info in infos
)
# Unrecognized state-file formats are not usable resume state.
return False
except (OSError, ValueError, zipfile.BadZipFile):
return False
def _checkpoint_state(path: Path) -> Optional[int]:
try:
state = json.loads((path / "trainer_state.json").read_text(encoding = "utf-8"))
step = state.get("global_step") if isinstance(state, dict) else None
except (OSError, UnicodeDecodeError, json.JSONDecodeError):
return None
if isinstance(step, bool) or not isinstance(step, int) or step < 0:
return None
directory_step = _checkpoint_step(path)
return step if directory_step < 0 or step == directory_step else None
_INDEX_SHARD_SUFFIX = {
"model.safetensors.index.json": ".safetensors",
"pytorch_model.bin.index.json": ".bin",
}
def _valid_indexed_shard(checkpoint: Path, shard: object, expected_suffix: str) -> bool:
# Shard must be a relative, in-format path contained in the checkpoint dir.
if not isinstance(shard, str) or not shard:
return False
if Path(shard).is_absolute() or Path(shard).suffix != expected_suffix:
return False
try:
root = checkpoint.resolve(strict = True)
candidate = (checkpoint / shard).resolve(strict = True)
candidate.relative_to(root)
except (OSError, ValueError):
return False
return _valid_state_file(candidate)
def _has_model_state(path: Path) -> bool:
if any(_valid_state_file(path / name) for name in _MODEL_FILES):
return True
for name in _MODEL_INDEXES:
try:
index = json.loads((path / name).read_text(encoding = "utf-8"))
shards = set(index["weight_map"].values())
except (
AttributeError,
OSError,
KeyError,
TypeError,
UnicodeDecodeError,
json.JSONDecodeError,
):
continue
expected_suffix = _INDEX_SHARD_SUFFIX[name]
if shards and all(_valid_indexed_shard(path, shard, expected_suffix) for shard in shards):
return True
return False
def is_resume_checkpoint_valid(
path: Path,
expected_step: Optional[int] = None,
backend: Optional[str] = None,
) -> bool:
step = _checkpoint_state(path) if path.is_dir() else None
step_valid = step is not None and (expected_step is None or step == expected_step)
if backend == "mlx":
valid_bundle = _valid_state_file(path / "adapters.safetensors") and _valid_state_file(
path / "optimizer_state.safetensors"
)
else:
valid_bundle = (
_has_model_state(path)
# optimizer/scheduler state can be validly tensor-free (e.g. SGD without
# momentum); _has_model_state still requires real model tensors.
and _valid_state_file(path / "optimizer.pt", require_tensor = False)
and _valid_state_file(path / "scheduler.pt", require_tensor = False)
)
if backend is None and not valid_bundle:
valid_bundle = _valid_state_file(path / "adapters.safetensors") and _valid_state_file(
path / "optimizer_state.safetensors"
)
return step_valid and valid_bundle
def get_resume_checkpoint_path(
path_value: str, expected_step: Optional[int] = None
) -> Optional[str]:
path = resolve_output_dir(path_value)
if not _is_under_outputs(path) or not path.is_dir():
return None
if (path / "trainer_state.json").is_file():
if is_resume_checkpoint_valid(path, expected_step):
return str(path)
checkpoints = [
child
for child in path.glob("checkpoint-*")
if child.is_dir() and (child / "trainer_state.json").is_file()
]
if not checkpoints:
return None
return str(max(checkpoints, key = _checkpoint_step))
checkpoints = sorted(path.glob("checkpoint-*"), key = _checkpoint_step, reverse = True)
return next(
(
str(checkpoint)
for checkpoint in checkpoints
if _checkpoint_step(checkpoint) >= 0
and is_resume_checkpoint_valid(checkpoint, expected_step)
),
None,
)
def normalize_resume_output_dir(path_value: str) -> str:
path = resolve_output_dir(path_value)
if not _is_under_outputs(path):
raise ValueError("Resume checkpoint must be inside Studio outputs.")
raise ValueError("Resume checkpoint must be inside Unsloth outputs.")
return str(path)
@ -78,9 +217,17 @@ def _uses_s3_dataset(run: dict) -> bool:
def can_resume_run(run: dict) -> bool:
if run.get("resumed_later"):
return False
# Set when a stop-and-save failed to write a current-step checkpoint.
if run.get("resume_blocked"):
return False
if _uses_s3_dataset(run):
return False
status = run.get("status")
if status == "error":
# A save-time crash can report final_step == total_steps with no artifacts; checkpoint state alone decides resumability.
return has_resume_state(run.get("output_dir"))
final_step = run.get("final_step")
total_steps = run.get("total_steps")
has_remaining_steps = (
@ -89,8 +236,4 @@ def can_resume_run(run: dict) -> bool:
or total_steps <= 0
or final_step < total_steps
)
return (
run.get("status") == "stopped"
and has_remaining_steps
and has_resume_state(run.get("output_dir"))
)
return status == "stopped" and has_remaining_steps and has_resume_state(run.get("output_dir"))

14
studio/backend/core/training/trainer.py
View file

@ -797,7 +797,7 @@ class UnslothTrainer:
)
logger.info("Loaded text model")
raise_if_offloaded(self.model, device_map, "Studio training")
raise_if_offloaded(self.model, device_map, "Unsloth training")
if self.should_stop:
return False
@ -3425,15 +3425,19 @@ class UnslothTrainer:
logger.info(
f"CPT: using UnslothTrainer with embedding_learning_rate={embedding_lr}\n"
)
cpt_args = _UnslothTrainingArguments(
embedding_learning_rate = embedding_lr,
**config_args,
)
if config_args.get("packing", False):
cpt_args.packing_strategy = "wrapped"
logger.info("CPT packing strategy: wrapped\n")
trainer_kwargs = {
"model": self.model,
"tokenizer": sft_tokenizer,
"train_dataset": dataset["dataset"],
"data_collator": data_collator,
"args": _UnslothTrainingArguments(
embedding_learning_rate = embedding_lr,
**config_args,
),
"args": cpt_args,
}
if eval_dataset is not None:
trainer_kwargs["eval_dataset"] = eval_dataset

340
studio/backend/core/training/training.py
View file

@ -140,7 +140,7 @@ def should_use_mlx_training_backend(*, device: Optional[Any] = None) -> bool:
def _build_training_worker_config(values: dict[str, Any]) -> dict[str, Any]:
"""Build the normalized worker config shared by Studio and the CLI adapter."""
"""Build the normalized worker config shared by Unsloth and the CLI adapter."""
config = {
"model_name": values["model_name"],
"project_name": values.get("project_name"),
@ -307,7 +307,7 @@ PLOT_HEIGHT = 3.5
@dataclass
class TrainingProgress:
"""Shared training progress payload for Studio and backend-aware trainers."""
"""Shared training progress payload for Unsloth and backend-aware trainers."""
epoch: float = 0
step: int = 0
@ -328,7 +328,7 @@ class TrainingProgress:
class _MLXTrainerAdapter:
"""Adapts the legacy UnslothTrainer API to the shared Studio MLX worker path."""
"""Adapts the legacy UnslothTrainer API to the shared Unsloth MLX worker path."""
def __init__(self):
self.model = None
@ -761,6 +761,7 @@ class TrainingBackend:
# Left True after an abnormal death so _ensure_pump_alive spots a crash.
self._pump_running: bool = False
self._lock = threading.Lock()
self._run_intent_lock = threading.RLock()
# Stop watchdog: after a stop is requested, escalates to force_terminate()
# if the worker does not exit on its own within a bounded time. The watched
@ -773,6 +774,7 @@ class TrainingBackend:
self._progress = TrainingProgress()
self._should_stop = False
self._cancel_requested = False # True only for stop(save=False)
self._cancel_cleanup_output_dir: Optional[str] = None
# Throttled training-status logging to the server log (not one line/step).
self._last_progress_log_ts: float = 0.0
@ -792,6 +794,8 @@ class TrainingBackend:
# Job metadata
self.current_job_id: Optional[str] = None
self._output_dir: Optional[str] = None
self._resume_source_run_id: Optional[str] = None
self._terminal_finalize_payload: Optional[dict] = None
# DB persistence
self._metric_buffer: list[dict] = []
@ -819,6 +823,7 @@ class TrainingBackend:
job_id: str,
*,
before_spawn = None,
resume_source_run_id: Optional[str] = None,
**kwargs,
) -> bool:
"""Spawn a subprocess to run the full training pipeline.
@ -956,6 +961,7 @@ class TrainingBackend:
self.current_job_id = job_id
self._should_stop = False
self._cancel_requested = False
self._cancel_cleanup_output_dir = None
self._complete_seen.clear()
self._progress = TrainingProgress(
is_training = True, status_message = "Initializing training..."
@ -972,7 +978,10 @@ class TrainingBackend:
self.eval_loss_history.clear()
self.eval_step_history.clear()
self.eval_enabled = False
self._output_dir = None
self._output_dir = config.get("output_dir") if resume_source_run_id else None
self._progress.output_dir = self._output_dir
self._resume_source_run_id = resume_source_run_id
self._terminal_finalize_payload = None
self._metric_buffer.clear()
self._run_finalized = False
self._db_run_created = False
@ -990,6 +999,17 @@ class TrainingBackend:
# in history during model loading and a fast terminal worker can't race the
# pump into a duplicate create/finalize. From here the pump only finalizes.
self._ensure_db_run_created()
if resume_source_run_id and not self._db_run_created:
if proc.is_alive():
proc.terminate()
proc.join(timeout = 5.0)
if proc.is_alive():
proc.kill()
proc.join(timeout = 2.0)
self._progress.is_training = False
self._progress.error = "Resume checkpoint is no longer available."
self._spawn_in_progress = False
return False
# Assign handles and start the pump together under the lock so a concurrent
# poll can't see a live _proc with no pump and spawn a duplicate.
@ -1011,28 +1031,75 @@ class TrainingBackend:
def stop_training(self, save: bool = True) -> bool:
"""Send stop signal to the training subprocess."""
self._should_stop = True
if not save:
self._cancel_requested = True
with self._lock:
if self._stop_queue is not None:
try:
self._stop_queue.put({"type": "stop", "save": save})
except (OSError, ValueError):
pass
# Update progress immediately for responsive UI.
self._progress.status_message = (
"Stopping training and saving checkpoint..." if save else "Cancelling training..."
)
# Guarantee the run finalizes even if the worker wedges after saving.
self._start_stop_watchdog(cancel = not save)
with self._run_intent_lock:
with self._lock:
run_id = self.current_job_id
if not save and run_id:
persist_error: Optional[Exception] = None
for attempt in range(_DB_FINALIZE_RETRIES):
try:
from storage.studio_db import mark_run_cancel_requested
self._ensure_db_run_created()
with self._lock:
terminal_payload = self._terminal_finalize_payload
if (
terminal_payload
and terminal_payload.get("expected_job_id") == run_id
):
return False
if not mark_run_cancel_requested(run_id):
if self._db_run_created:
return False
raise RuntimeError(
"Training run disappeared before cancellation persisted"
)
if self.current_job_id != run_id:
return False
self._should_stop = self._cancel_requested = True
self._cancel_cleanup_output_dir = self._output_dir
self._output_dir = self._progress.output_dir = None
persist_error = None
break
except Exception as exc:
persist_error = exc
if attempt + 1 < _DB_FINALIZE_RETRIES:
time.sleep(_DB_FINALIZE_RETRY_S)
if persist_error is not None:
raise RuntimeError("Failed to persist Stop-without-Save") from persist_error
with self._lock:
if self.current_job_id != run_id:
return False
if save or not run_id:
self._should_stop = True
if not save and not run_id:
self._cancel_requested = True
self._cancel_cleanup_output_dir = self._output_dir
self._output_dir = self._progress.output_dir = None
if self._stop_queue is not None:
try:
self._stop_queue.put({"type": "stop", "save": save})
except (OSError, ValueError):
pass
self._progress.status_message = (
"Stopping training and saving checkpoint..."
if save
else "Cancelling training..."
)
self._start_stop_watchdog(cancel = not save, expected_job_id = run_id)
return True
def _start_stop_watchdog(self, cancel: bool) -> None:
def _start_stop_watchdog(
self,
cancel: bool,
expected_job_id: Optional[str] = None,
) -> None:
"""Start a daemon that force-terminates the worker if a requested stop does not
exit on its own. No-op if no worker is alive or a live watchdog already watches
this proc (a stale watchdog on an old proc never blocks a new run's watcher)."""
with self._lock:
if expected_job_id is not None and self.current_job_id != expected_job_id:
return
proc = self._proc
if proc is None or not proc.is_alive():
return
@ -1113,8 +1180,9 @@ class TrainingBackend:
watched_job_id: Optional[str] = None,
) -> None:
"""Finalize parent state after a force-terminate so the UI leaves "Stopping..."
even if the worker is wedged in driver teardown; preserves output_dir so a saved
checkpoint is kept. No-ops if a new run already replaced the watched worker, so a
even if the worker is wedged in driver teardown; preserves output_dir on a save so
the checkpoint is kept, and clears it on a cancel (Stop without saving must not
offer resume/export). No-ops if a new run already replaced the watched worker, so a
stale watchdog never marks a fresh run stopped or drops its handle.
Supersession is checked on both the watched proc and job id: start_training sets
@ -1134,7 +1202,18 @@ class TrainingBackend:
return # a new run is already starting up; leave its state alone
run_id = self.current_job_id # == watched_job_id
self._progress.is_training = False
self._progress.status_message = "Training stopped."
terminal_payload = self._terminal_finalize_kwargs()
status = terminal_payload["status"]
error_message = terminal_payload.get("error_message")
output_dir = terminal_payload["output_dir"]
clear_output_dir = terminal_payload["clear_output_dir"]
resume_blocked = bool(terminal_payload.get("resume_blocked"))
with self._lock:
if self.current_job_id != run_id:
return
self._progress.status_message = error_message or "Training stopped."
if error_message:
self._progress.error = error_message
# Create the row if a start-time create failed (no-op otherwise; skips when the pump
# is mid-create, in which case its create-then-finalize records the run instead).
self._ensure_db_run_created()
@ -1148,7 +1227,8 @@ class TrainingBackend:
batch: list = []
final_step = final_loss = duration = None
loss_history: list = []
output_dir = self._output_dir
if clear_output_dir:
self._output_dir = self._progress.output_dir = None
if claim:
self._run_finalized = True # claim this run's finalize
batch = list(self._metric_buffer)
@ -1161,7 +1241,17 @@ class TrainingBackend:
loss_history = list(self.loss_history)
if claim:
self._finish_stopped_run(
run_id, output_dir, batch, final_step, final_loss, duration, loss_history
run_id,
output_dir,
batch,
final_step,
final_loss,
duration,
loss_history,
status = status,
error_message = error_message,
clear_output_dir = clear_output_dir,
resume_blocked = resume_blocked,
)
with self._lock:
if target_proc is None or self._proc is target_proc:
@ -1176,6 +1266,10 @@ class TrainingBackend:
final_loss: Optional[float],
duration: Optional[float],
loss_history: list,
status: str = "stopped",
error_message: Optional[str] = None,
clear_output_dir: bool = False,
resume_blocked: bool = False,
) -> None:
"""Record a force-stopped run finished by its captured id, from state snapshotted
under the lock. insert_metrics_batch upserts and finish_run is an idempotent UPDATE,
@ -1194,14 +1288,16 @@ class TrainingBackend:
sparkline = downsample(loss_history, 50)
finish_run(
id = run_id,
status = "stopped",
status = status,
ended_at = datetime.now(timezone.utc).isoformat(),
final_step = final_step,
final_loss = final_loss,
duration_seconds = duration,
loss_sparkline = _json.dumps(sparkline),
output_dir = output_dir,
error_message = None,
error_message = error_message,
clear_output_dir = clear_output_dir,
resume_blocked = resume_blocked,
)
return
except Exception:
@ -1231,7 +1327,7 @@ class TrainingBackend:
logger.info("Force-terminating training subprocess (pid=%s)", proc.pid)
proc.terminate()
cancelled = self._cancel_requested
output_dir = self._output_dir
output_dir = self._cancel_cleanup_output_dir or self._output_dir
if proc is not None:
proc.join(timeout = 5.0)
@ -1595,17 +1691,60 @@ class TrainingBackend:
)
self._ensure_db_run_created()
self._finalize_run_in_db(
status = "stopped" if self._should_stop else "error",
error_message = None
if self._should_stop
else "Training process terminated unexpectedly",
)
terminal_payload = self._terminal_finalize_kwargs()
with self._lock:
if terminal_payload["clear_output_dir"]:
self._output_dir = self._progress.output_dir = None
if terminal_payload.get("error_message"):
self._progress.error = terminal_payload["error_message"]
self._progress.status_message = terminal_payload["error_message"]
self._finalize_run_in_db(**terminal_payload)
except Exception:
logger.exception("Training event pump: finalization after worker exit failed")
self._pump_running = False
return
def _has_current_resume_checkpoint(self, output_dir, step) -> bool:
# A valid checkpoint at the current step means the stop-and-save landed on
# disk even if the worker died before confirming it.
if not output_dir or not isinstance(step, int) or step <= 0:
return False
from core.training.resume import get_resume_checkpoint_path
return get_resume_checkpoint_path(output_dir, expected_step = step) is not None
def _terminal_finalize_kwargs(self) -> dict:
with self._lock:
job_id = self.current_job_id
payload = self._terminal_finalize_payload
if payload and payload.get("expected_job_id") == job_id:
return dict(payload)
cancel, stopped = self._cancel_requested, self._should_stop
output_dir = None if cancel else self._output_dir
step = self._progress.step
existing_error = self._progress.error
status, error, blocked = (
("stopped", None, cancel)
if stopped
else (
"error",
existing_error or "Training process terminated unexpectedly",
False,
)
)
# Block only when no valid current-step checkpoint actually landed.
if stopped and not cancel and not self._has_current_resume_checkpoint(output_dir, step):
status = "error"
error = "Stop and Save ended before a valid current-step checkpoint was written."
blocked = True
return {
"status": status,
"error_message": error,
"output_dir": output_dir,
"clear_output_dir": cancel,
"resume_blocked": blocked,
"expected_job_id": job_id,
}
def _handle_event(self, event: dict) -> None:
"""Apply a subprocess event to local state.
@ -1764,6 +1903,15 @@ class TrainingBackend:
elif etype == "eval_configured":
self.eval_enabled = True
elif etype == "output_dir":
event_output_dir = event.get("output_dir")
if self._cancel_requested:
self._cancel_cleanup_output_dir = event_output_dir
self._output_dir = self._progress.output_dir = None
else:
self._output_dir = event_output_dir
db_action = "persist_output_dir"
elif etype == "status":
self._progress.status_message = event.get("message", "")
self._progress.is_training = True
@ -1778,7 +1926,12 @@ class TrainingBackend:
self._complete_seen.set()
self._progress.is_training = False
self._progress.is_completed = not stopped
self._output_dir = event.get("output_dir")
event_output_dir = event.get("output_dir")
if self._cancel_requested:
self._cancel_cleanup_output_dir = event_output_dir
self._output_dir = None
else:
self._output_dir = event_output_dir
self._progress.output_dir = self._output_dir
self._progress.status_message = msg
if not self._db_run_created and self.current_job_id and self._db_config:
@ -1788,11 +1941,16 @@ class TrainingBackend:
db_action_kwargs = {
"status": "stopped" if stopped else "completed",
"output_dir": self._output_dir,
"clear_output_dir": self._cancel_requested,
"expected_job_id": self.current_job_id,
}
self._terminal_finalize_payload = dict(db_action_kwargs)
elif etype == "error":
self._progress.is_training = False
self._progress.error = event.get("error", "Unknown error")
if self._cancel_requested:
self._output_dir = self._progress.output_dir = None
logger.error("Training error: %s", event.get("error"))
stack = event.get("stack", "")
if stack:
@ -1801,29 +1959,36 @@ class TrainingBackend:
db_action = "create_and_finalize"
else:
db_action = "finalize"
stop_save_failed = (
self._should_stop
and not self._cancel_requested
and not self._has_current_resume_checkpoint(
self._output_dir, self._progress.step
)
)
db_action_kwargs = {
"status": "stopped" if self._should_stop else "error",
"status": "stopped"
if self._should_stop
and not stop_save_failed
and not event.get("keep_error_status")
else "error",
"error_message": event.get("error", "Unknown error"),
"output_dir": self._output_dir,
"clear_output_dir": self._cancel_requested,
"resume_blocked": stop_save_failed or bool(event.get("resume_blocked")),
"expected_job_id": self.current_job_id,
}
self._terminal_finalize_payload = dict(db_action_kwargs)
# --- DB I/O outside the lock ---
if db_action == "create_run":
try:
from storage.studio_db import create_run
create_run(
id = db_action_kwargs["job_id"],
model_name = db_action_kwargs["model_name"],
dataset_name = db_action_kwargs["dataset_name"],
config_json = db_action_kwargs["config_json"],
started_at = db_action_kwargs["started_at"],
total_steps = db_action_kwargs["total_steps"],
)
self._db_run_created = True
self._ensure_db_run_created()
if self._db_run_created:
if db_action_kwargs["total_steps"]:
self._db_total_steps_set = True
except Exception:
logger.warning("Failed to create DB run record", exc_info = True)
self._persist_output_dir()
elif db_action == "persist_output_dir":
self._persist_output_dir()
elif db_action == "create_and_finalize":
self._ensure_db_run_created()
self._finalize_run_in_db(**db_action_kwargs)
@ -1842,6 +2007,22 @@ class TrainingBackend:
if etype == "progress":
self._log_training_progress()
def _persist_output_dir(self) -> None:
with self._lock:
if (
not self._output_dir
or not self.current_job_id
or not self._db_run_created
or self._cancel_requested
):
return
run_id, output_dir = self.current_job_id, self._output_dir
try:
from storage.studio_db import update_run_output_dir
update_run_output_dir(run_id, output_dir)
except Exception:
logger.warning("Failed to persist output_dir", exc_info = True)
def _log_training_progress(self) -> None:
"""One throttled training-status line to the server log (the per-step stream
still goes to the UI via SSE): first step, then at most every 30s, plus the
@ -1875,6 +2056,7 @@ class TrainingBackend:
caller create at a time, and ``_db_run_created`` is published only after
``create_run`` commits, so a concurrent finalize never runs ``finish_run`` against a
not-yet-inserted row (a zero-row UPDATE that would leave the run stuck as running)."""
self._run_intent_lock.acquire()
with self._lock:
if (
self._db_run_created
@ -1882,6 +2064,7 @@ class TrainingBackend:
or not self.current_job_id
or not self._db_config
):
self._run_intent_lock.release()
return
self._db_create_in_progress = True # only one caller creates
job_id = self.current_job_id
@ -1898,6 +2081,12 @@ class TrainingBackend:
or _s3_dataset_name(db_config.get("s3_dataset"))
or "unknown"
)
with self._lock:
if self.current_job_id != job_id:
return
output_dir = self._output_dir
cancel_requested = self._cancel_requested
resumed_from_run_id = self._resume_source_run_id
create_run(
id = job_id,
model_name = db_config["model_name"],
@ -1905,6 +2094,9 @@ class TrainingBackend:
config_json = _json.dumps(db_config),
started_at = started_at,
total_steps = total_steps,
output_dir = output_dir,
cancel_requested = cancel_requested,
resumed_from_run_id = resumed_from_run_id,
)
created = True
except Exception:
@ -1919,12 +2111,15 @@ class TrainingBackend:
if created:
self._db_run_created = True # publish only after the insert commits
self._db_create_in_progress = False
self._run_intent_lock.release()
def _finalize_run_in_db(
self,
status: str,
error_message: Optional[str] = None,
output_dir: Optional[str] = None,
clear_output_dir: bool = False,
resume_blocked: bool = False,
expected_job_id: Optional[str] = None,
) -> None:
"""Flush remaining metrics and mark a run finished in the DB. Claims the finalize
@ -1947,26 +2142,33 @@ class TrainingBackend:
duration = self._progress.elapsed_seconds
loss_history = list(self.loss_history)
self._flush_metrics_to_db(run_id = run_id)
try:
from storage.studio_db import finish_run
from utils.downsample import downsample
for attempt in range(_DB_FINALIZE_RETRIES):
try:
from storage.studio_db import finish_run
from utils.downsample import downsample
sparkline = downsample(loss_history, 50)
finish_run(
id = run_id,
status = status,
ended_at = datetime.now(timezone.utc).isoformat(),
final_step = final_step,
final_loss = final_loss,
duration_seconds = duration,
loss_sparkline = _json.dumps(sparkline),
output_dir = output_dir,
error_message = error_message,
)
except Exception:
with self._lock:
self._run_finalized = False # unclaim so a later flush can retry
logger.warning("Failed to finalize run in DB (status=%s)", status, exc_info = True)
finish_run(
id = run_id,
status = status,
ended_at = datetime.now(timezone.utc).isoformat(),
final_step = final_step,
final_loss = final_loss,
duration_seconds = duration,
loss_sparkline = _json.dumps(downsample(loss_history, 50)),
output_dir = output_dir,
error_message = error_message,
clear_output_dir = clear_output_dir,
resume_blocked = resume_blocked,
)
return
except Exception:
if attempt + 1 < _DB_FINALIZE_RETRIES:
time.sleep(_DB_FINALIZE_RETRY_S)
continue
with self._lock:
if self.current_job_id == run_id:
self._run_finalized = False
logger.warning("Failed to finalize run in DB (status=%s)", status, exc_info = True)
def _flush_metrics_to_db(self, run_id: Optional[str] = None) -> None:
"""Flush buffered metrics to the DB and update live progress. The target run id,

160
studio/backend/core/training/worker.py
View file

@ -1100,7 +1100,7 @@ _MLX_VLM_RESIZED_IMAGE_LAYOUT_CACHE = {}
def _mlx_vlm_resized_image_layout(processor = None) -> str | None:
"""Return the numpy image layout expected after Studio-side VLM resizing."""
"""Return the numpy image layout expected after Unsloth-side VLM resizing."""
image_processor = getattr(processor, "image_processor", None)
if image_processor is None:
return None
@ -1257,7 +1257,7 @@ _MLX_STUDIO_LR_SCHEDULERS = {"linear", "cosine", "constant"}
# Fallback alias map mirroring unsloth_zoo._normalize_mlx_optimizer_name, used
# only when mlx (Apple Silicon) is not importable so Studio config validation
# only when mlx (Apple Silicon) is not importable so Unsloth config validation
# still works on non-MLX hosts. The zoo function stays the source of truth.
_MLX_STUDIO_ADAMW_ALIASES = frozenset(
(
@ -1309,7 +1309,7 @@ def _normalize_mlx_studio_scheduler(value):
def _resolve_mlx_local_dataset_files(file_paths: list) -> list[str]:
"""Resolve CLI paths and Studio local dataset uploads without importing the GPU trainer."""
"""Resolve CLI paths and Unsloth local dataset uploads without importing the GPU trainer."""
from utils.paths import resolve_dataset_path
all_files: list[str] = []
@ -1840,8 +1840,15 @@ def _run_mlx_training(event_queue, stop_queue, config):
# Resolve to ~/.unsloth/studio/outputs/ so the export page finds it
from utils.paths import ensure_dir
output_dir = _resolve_mlx_output_dir(config, model_name)
# Resume must land in the original run dir even when config lacks output_dir.
resume_dir = config.get("output_dir", "") or _output_dir_from_resume_checkpoint(
resume_from_checkpoint
)
output_dir = _resolve_mlx_output_dir(
{**config, "output_dir": resume_dir} if resume_dir else config, model_name
)
ensure_dir(Path(output_dir))
_emit_output_dir(event_queue, output_dir)
# ── 6. Create trainer ──
eval_steps_val = config.get("eval_steps", 0) or 0
@ -1912,7 +1919,7 @@ def _run_mlx_training(event_queue, stop_queue, config):
if "max_grad_leaf_norm" in _supported_fields:
mlx_config_kwargs["max_grad_leaf_norm"] = max_grad_leaf_norm
if "append_eos" in _supported_fields:
# Studio SFT formatting owns rendered examples; raw/CPT text still
# Unsloth SFT formatting owns rendered examples; raw/CPT text still
# needs MLX to append EOS like the CUDA raw-text path.
mlx_config_kwargs["append_eos"] = bool(raw_text_mode)
@ -2067,6 +2074,17 @@ def _run_mlx_training(event_queue, stop_queue, config):
trainer.add_eval_callback(_on_eval)
_opt_ref = [None]
_orig_build_optimizer = getattr(trainer, "_build_optimizer", None)
if callable(_orig_build_optimizer):
def _capture_optimizer(total_steps):
_opt_ref[0] = _orig_build_optimizer(total_steps)
return _opt_ref[0]
trainer._build_optimizer = _capture_optimizer
# ── 11. Run training ──
gc.collect()
mx.synchronize()
@ -2082,31 +2100,58 @@ def _run_mlx_training(event_queue, stop_queue, config):
trainer.save_model = _save_model
# ── 12. Save and finalize ──
if trainer.stop_requested:
if not _stop_save[0]:
# Cancel (save=False): skip saving.
_send("complete", output_dir = None, status_message = "Training cancelled")
def _finish_tracking() -> None:
# Runs on every save/finalize exit so TB/W&B never leak on early return.
if tb_writer is not None:
try:
tb_writer.close()
except Exception:
pass
if wandb_run is not None:
try:
wandb_run.finish()
except Exception:
pass
def _stop_checkpoint_ok() -> bool:
if _write_mlx_stop_checkpoint(trainer, _opt_ref[0], output_dir):
return True
_send(
"error",
error = (
"Failed to save a resumable checkpoint after stop. "
"Model files were saved, but this run cannot be resumed."
),
# A user stop finalizes as 'stopped'; keep this failure's error status so history explains it.
keep_error_status = True,
# Older checkpoints are stale; resuming would roll back past this stop.
resume_blocked = True,
)
return False
try:
if trainer.stop_requested:
if not _stop_save[0]:
# Cancel (save=False): skip saving.
_send("complete", output_dir = None, status_message = "Training cancelled")
else:
_send("status", status_message = "Saving stopped model...")
mx.synchronize()
trainer.save_model(output_dir)
# Stop-and-save promises a resumable checkpoint, not just model files.
if not _stop_checkpoint_ok():
return
_send("complete", output_dir = output_dir, status_message = "Training stopped")
else:
_send("status", status_message = "Saving stopped model...")
_send("status", status_message = "Saving model...")
mx.synchronize()
trainer.save_model(output_dir)
_send("complete", output_dir = output_dir, status_message = "Training stopped")
else:
_send("status", status_message = "Saving model...")
mx.synchronize()
trainer.save_model(output_dir)
_send("complete", output_dir = output_dir, status_message = "Training completed")
if tb_writer is not None:
try:
tb_writer.close()
except Exception:
pass
if wandb_run is not None:
try:
wandb_run.finish()
except Exception:
pass
# A save-stop can race the natural final save; it made the same promise.
if trainer.stop_requested and _stop_save[0] and not _stop_checkpoint_ok():
return
_send("complete", output_dir = output_dir, status_message = "Training completed")
finally:
_finish_tracking()
def _is_current_process_apple_silicon() -> bool:
@ -2121,7 +2166,7 @@ def run_mlx_training_process(
config: dict,
transformers_activated: bool = False,
) -> None:
"""MLX worker entrypoint shared by Studio subprocesses and the CLI adapter."""
"""MLX worker entrypoint shared by Unsloth subprocesses and the CLI adapter."""
model_name = config["model_name"]
backend_path = str(Path(__file__).resolve().parent.parent.parent)
@ -2780,7 +2825,7 @@ def run_training_process(*, event_queue: Any, stop_queue: Any, config: dict) ->
)
# Unified Windows APUs: the WDDM budget is user-raisable, but
# nothing on the box says so -- users see "48 GB VRAM" on a
# 96 GB machine and assume a Studio bug. Say where the limit
# 96 GB machine and assume an Unsloth bug. Say where the limit
# comes from and how to raise it.
if _is_unified and sys.platform == "win32":
try:
@ -3177,6 +3222,7 @@ def run_training_process(*, event_queue: Any, stop_queue: Any, config: dict) ->
)
output_dir = str(resolve_output_dir(output_dir))
ensure_dir(Path(output_dir))
_emit_output_dir(event_queue, output_dir)
tensorboard_dir = config.get("tensorboard_dir")
if config.get("enable_tensorboard", False):
@ -3296,6 +3342,61 @@ def _send_status(event_queue: Any, message: str) -> None:
)
def _emit_output_dir(event_queue: Any, output_dir: str) -> None:
try:
event_queue.put({"type": "output_dir", "output_dir": output_dir, "ts": time.time()})
except Exception:
pass
def _mlx_has_checkpoint_at_step(output_dir, step: int) -> bool:
if step <= 0:
return False
from core.training.resume import is_resume_checkpoint_valid
return is_resume_checkpoint_valid(
Path(output_dir) / f"checkpoint-{step}", expected_step = step, backend = "mlx"
)
def _write_mlx_stop_checkpoint(trainer, optimizer, output_dir) -> bool:
"""Write a full resume checkpoint for a stopped MLX run.
Returns True when a checkpoint for the current training step exists.
"""
step = int(getattr(trainer, "_global_step", 0) or 0)
# A periodic save or a resumed run may already cover the current step.
if _mlx_has_checkpoint_at_step(output_dir, step):
return True
if step <= 0 or optimizer is None:
return False
ckpt_dir = Path(output_dir) / f"checkpoint-{step}"
if ckpt_dir.is_symlink():
# Refuse a symlinked dir: it could redirect writes outside output_dir.
logger.error("Refusing to write MLX stop checkpoint through symlink: %s", ckpt_dir)
return False
try:
ckpt_dir.mkdir(parents = True, exist_ok = True)
from unsloth_zoo.mlx.utils import (
save_optimizer_state,
save_trainable_adapters,
save_trainer_state,
)
save_trainable_adapters(trainer.model, str(ckpt_dir))
save_optimizer_state(optimizer, str(ckpt_dir))
save_trainer_state(
{
"global_step": step,
"train_loss_history": list(getattr(trainer, "_train_loss_history", [])),
},
str(ckpt_dir),
)
logger.info("Saved stop checkpoint to %s", ckpt_dir)
except Exception:
logger.exception("Failed to write stop checkpoint under %s", output_dir)
return _mlx_has_checkpoint_at_step(output_dir, step)
def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) -> None:
"""Self-contained embedding model training pipeline.
@ -3660,6 +3761,7 @@ def _run_embedding_training(event_queue: Any, stop_queue: Any, config: dict) ->
config.get("project_name"),
)
output_dir = str(resolve_output_dir(output_dir))
_emit_output_dir(event_queue, output_dir)
num_epochs = config.get("num_epochs", 2)
batch_size = config.get("batch_size", 256)

2
studio/backend/hub/routes/__init__.py
View file

@ -5,8 +5,10 @@
from hub.routes.inventory import router as inventory_router
from hub.routes.datasets import router as datasets_router
from hub.routes.token import router as token_router
__all__ = [
"inventory_router",
"datasets_router",
"token_router",
]

11
studio/backend/hub/routes/inventory.py
View file

@ -28,6 +28,7 @@ from hub.schemas.inventory import (
CachedModelsResponse,
DeleteCachedModelResponse,
GgufVariantsResponse,
HiddenModelsResponse,
LocalModelListResponse,
ModelsFolderResponse,
RecommendedFoldersResponse,
@ -214,6 +215,16 @@ async def list_cached_models(
return await cache_inventory.list_cached_models_response(hf_token)
@router.get("/hidden-models", response_model = HiddenModelsResponse)
async def list_hidden_models(current_subject: str = Depends(get_current_subject)):
import asyncio
from routes.models import hidden_model_matchers
needles, exact_ids, exact_paths = await asyncio.to_thread(hidden_model_matchers)
return HiddenModelsResponse(needles = needles, exact_ids = exact_ids, exact_paths = exact_paths)
@router.delete(
"/delete-cached",
response_model = DeleteCachedModelResponse,

44
studio/backend/hub/routes/token.py Normal file
View file

@ -0,0 +1,44 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Hugging Face token validation endpoint."""
from __future__ import annotations
import asyncio
from typing import Literal, Optional
from fastapi import APIRouter, Depends, Request
from pydantic import BaseModel
from auth.authentication import get_current_subject
from hub.dependencies import get_hf_token
from utils.client_ip import client_ip
from utils.hf_token_validation import validate_hf_token
router = APIRouter()
class HfTokenValidationResponse(BaseModel):
status: Literal["missing", "valid", "invalid", "rate_limited", "unavailable"]
retry_after_seconds: Optional[int] = None
@router.post("/token/validate", response_model = HfTokenValidationResponse)
async def validate_token(
request: Request,
hf_token: Optional[str] = Depends(get_hf_token),
current_subject: str = Depends(get_current_subject),
):
if not hf_token:
return HfTokenValidationResponse(status = "missing")
result = await asyncio.to_thread(
validate_hf_token,
hf_token,
rate_key = f"{current_subject}:{client_ip(request)}",
)
return HfTokenValidationResponse(
status = result.status,
retry_after_seconds = result.retry_after_seconds,
)

7
studio/backend/hub/schemas/inventory.py
View file

@ -160,6 +160,7 @@ class CachedRepoBase(BaseModel):
repo_id: str
size_bytes: int = 0
cache_path: Optional[str] = None
last_modified: Optional[float] = None
partial: bool = False
partial_transport: Optional[str] = None
inventory_id: Optional[str] = None
@ -189,6 +190,12 @@ class CachedModelsResponse(BaseModel):
cached: List[CachedModelRepo] = Field(default_factory = list)
class HiddenModelsResponse(BaseModel):
needles: List[str] = Field(default_factory = list)
exact_ids: List[str] = Field(default_factory = list)
exact_paths: List[str] = Field(default_factory = list)
class AddScanFolderRequest(BaseModel):
"""Request body for adding a custom scan folder."""

2
studio/backend/hub/services/download_lifecycle.py
View file

@ -76,7 +76,7 @@ def spawn_worker(
env["HF_HUB_DISABLE_PROGRESS_BARS"] = "1"
env["HF_HUB_DISABLE_TELEMETRY"] = "1"
env["HF_HUB_DISABLE_XET"] = "0" if use_xet else "1"
# No token in Studio settings: fall back to the backend's own HF_TOKEN so
# No token in Unsloth settings: fall back to the backend's own HF_TOKEN so
# private repos stay downloadable (needed while inkling repos are private).
if not hf_token:
hf_token = os.environ.get("HF_TOKEN") or None

124
studio/backend/hub/services/models/cache_inventory.py
View file

@ -31,12 +31,20 @@ from hub.services.models.common import (
_is_checkpoint_weight_name,
_is_gguf_filename,
_is_main_gguf_filename,
_is_mmproj_filename,
_is_transformers_safetensors_weight_name,
_local_inventory_id,
_prefer_complete_larger,
_runtime_for_format,
)
# Imported at module scope (not inside the per-repo scan loop) so a broken
# import surfaces at startup instead of silently emptying the inventory: the
# scan loop swallows per-repo exceptions and would drop every repo. Lives under
# ``utils`` (not ``utils.models``) to avoid the eager model-config/checkpoint
# imports in ``utils/models/__init__.py``.
from utils.hidden_models import is_hidden_model
logger = get_logger(__name__)
_repo_size_cache: "OrderedDict[tuple[str, str, str], tuple[int, frozenset[str], float]]" = (
@ -125,6 +133,39 @@ def _repo_has_gguf_files(repo_info) -> bool:
return _repo_gguf_size_bytes(repo_info) > 0
def _blob_mtime(file_obj) -> float:
ts = getattr(file_obj, "blob_last_modified", None)
if isinstance(ts, (int, float)) and ts > 0:
return float(ts)
blob_path = getattr(file_obj, "blob_path", None)
if blob_path:
try:
return float(Path(blob_path).stat().st_mtime)
except OSError:
pass
return 0.0
def _repo_gguf_last_modified(repo_info) -> float:
latest = 0.0
for revision in repo_info.revisions:
for f in revision.files:
if _is_main_gguf_filename(f.file_name):
latest = max(latest, _blob_mtime(f))
return latest
def _repo_has_mmproj(repo_info) -> bool:
# An mmproj file only makes a repo vision-capable when it is an actual GGUF
# projector; a non-GGUF sidecar (e.g. mmproj_config.json) does not, and the
# runtime's projector detection is GGUF-only.
return any(
_is_gguf_filename(f.file_name) and _is_mmproj_filename(f.file_name)
for revision in repo_info.revisions
for f in revision.files
)
def _cached_repo_file_name(file_obj) -> str:
file_path = getattr(file_obj, "file_path", None)
if file_path:
@ -243,6 +284,13 @@ def invalidate_hf_cache_scans() -> None:
hf_cache_scan.invalidate_hf_cache_scans()
def _is_hidden_infra_repo(*values: str | None) -> bool:
"""True for infra-only repos (the RAG embedder and the llama.cpp install
validation probe) that are cached as a side effect of Studio itself and are
not usable chat models."""
return is_hidden_model(*values)
def _scan_cached_gguf() -> list[dict]:
"""Synchronous HF-cache disk walk for GGUF repos; runs in a worker thread."""
cache_scans = all_hf_cache_scans()
@ -254,18 +302,30 @@ def _scan_cached_gguf() -> list[dict]:
if str(repo_info.repo_type) != "model":
continue
repo_id = repo_info.repo_id
repo_path = Path(repo_info.repo_path)
snapshot_path = _cached_model_snapshot_path(repo_path)
total_size = _repo_gguf_size_bytes(repo_info)
has_variant_state, variant_state_size = _gguf_variant_state_summary(repo_id)
is_hidden_infra = _is_hidden_infra_repo(
repo_id,
str(repo_path),
str(snapshot_path) if snapshot_path is not None else None,
)
# Hide infra repos unless the user downloaded a variant via
# the Hub; variant state only exists for user downloads.
if is_hidden_infra and not has_variant_state:
continue
if total_size == 0 and not has_variant_state:
continue
partial = hf_cache_scan.is_gguf_repo_partial(
repo_id,
Path(repo_info.repo_path),
repo_path,
)
if total_size == 0 and not partial:
continue
key = repo_id.lower()
existing = seen_lower.get(key)
last_modified = _repo_gguf_last_modified(repo_info)
row = {
"repo_id": repo_id,
"size_bytes": max(total_size, variant_state_size),
@ -275,6 +335,9 @@ def _scan_cached_gguf() -> list[dict]:
# per-variant detail lives on GgufVariantDetail.
"partial_transport": None,
}
last_modified = max(last_modified, (existing or {}).get("last_modified", 0.0))
if last_modified > 0:
row["last_modified"] = last_modified
row.update(
_cache_inventory_fields(
repo_id,
@ -283,8 +346,20 @@ def _scan_cached_gguf() -> list[dict]:
requires_variant = True,
)
)
if _repo_has_mmproj(repo_info):
row["capabilities"]["supports_vision"] = True
# Visible infra variants remain management-only.
if is_hidden_infra:
row["capabilities"]["can_chat"] = False
if _prefer_cache_row(row, existing):
if existing and existing["capabilities"].get("supports_vision"):
row["capabilities"]["supports_vision"] = True
seen_lower[key] = row
else:
if last_modified > existing.get("last_modified", 0.0):
existing["last_modified"] = last_modified
if row["capabilities"].get("supports_vision"):
existing["capabilities"]["supports_vision"] = True
except Exception as e:
repo_label = getattr(repo_info, "repo_id", "<unknown>")
logger.warning(f"Skipping cached GGUF repo {repo_label}: {e}")
@ -312,13 +387,14 @@ class _CachedNonGgufPayload(NamedTuple):
size_bytes: int
has_runnable_weights: bool
model_format: ModelFormat
last_modified: float
def _repo_non_gguf_model_payload(repo_info) -> _CachedNonGgufPayload:
all_weight_blobs: dict[str, int] = {}
adapter_blobs: dict[str, int] = {}
safetensors_blobs: dict[str, int] = {}
checkpoint_blobs: dict[str, int] = {}
all_weight_blobs: dict[str, tuple[int, float]] = {}
adapter_blobs: dict[str, tuple[int, float]] = {}
safetensors_blobs: dict[str, tuple[int, float]] = {}
checkpoint_blobs: dict[str, tuple[int, float]] = {}
has_config = False
has_adapter_config = False
has_adapter_weights = False
@ -326,12 +402,15 @@ def _repo_non_gguf_model_payload(repo_info) -> _CachedNonGgufPayload:
has_transformers_safetensors = False
has_checkpoint = False
def _record_blob(target: dict[str, int], file_obj, rev_id: str, file_name: str) -> None:
def _record_blob(
target: dict[str, tuple[int, float]], file_obj, rev_id: str, file_name: str
) -> None:
blob_path = getattr(file_obj, "blob_path", None)
size = int(file_obj.size_on_disk or 0)
key = str(blob_path) if blob_path else f"{rev_id}:{file_name}"
target[key] = size
all_weight_blobs[key] = size
value = (size, _blob_mtime(file_obj))
target[key] = value
all_weight_blobs[key] = value
for revision in repo_info.revisions:
rev_id = getattr(revision, "commit_hash", None) or str(id(revision))
@ -375,18 +454,19 @@ def _repo_non_gguf_model_payload(repo_info) -> _CachedNonGgufPayload:
or "unknown"
)
if model_format == "adapter":
size_bytes = sum(adapter_blobs.values())
selected_blobs = adapter_blobs
elif model_format == "safetensors":
size_bytes = sum(safetensors_blobs.values())
selected_blobs = safetensors_blobs
elif model_format == "checkpoint":
size_bytes = sum(checkpoint_blobs.values())
selected_blobs = checkpoint_blobs
else:
size_bytes = sum(all_weight_blobs.values())
selected_blobs = all_weight_blobs
return _CachedNonGgufPayload(
size_bytes = size_bytes,
size_bytes = sum(size for size, _mtime in selected_blobs.values()),
has_runnable_weights = model_format != "unknown",
model_format = model_format,
last_modified = max((mtime for _size, mtime in selected_blobs.values()), default = 0.0),
)
@ -475,6 +555,15 @@ def _scan_cached_models() -> list[dict]:
if str(repo_info.repo_type) != "model":
continue
repo_id = repo_info.repo_id
repo_path = Path(repo_info.repo_path)
snapshot_path = _cached_model_snapshot_path(repo_path)
# The non-GGUF embedder has no variant downloads; always hide.
if _is_hidden_infra_repo(
repo_id,
str(repo_path),
str(snapshot_path) if snapshot_path is not None else None,
):
continue
has_main_gguf = _repo_has_gguf_files(repo_info)
payload = _repo_non_gguf_model_payload(repo_info)
if payload.size_bytes == 0:
@ -486,7 +575,6 @@ def _scan_cached_models() -> list[dict]:
continue
key = repo_id.lower()
existing = seen_lower.get(key)
repo_path = Path(repo_info.repo_path)
snapshot_partial = hf_cache_scan.is_snapshot_partial(
"model",
repo_id,
@ -508,6 +596,12 @@ def _scan_cached_models() -> list[dict]:
),
**_cached_model_local_metadata(repo_path),
}
last_modified = max(
payload.last_modified,
(existing or {}).get("last_modified", 0.0),
)
if last_modified > 0:
row["last_modified"] = last_modified
row.update(
_cache_inventory_fields(
repo_id,
@ -517,6 +611,8 @@ def _scan_cached_models() -> list[dict]:
)
if _prefer_cache_row(row, existing):
seen_lower[key] = row
elif last_modified > existing.get("last_modified", 0.0):
existing["last_modified"] = last_modified
except Exception as e:
repo_label = getattr(repo_info, "repo_id", "<unknown>")
logger.warning(f"Skipping cached model repo {repo_label}: {e}")

2
studio/backend/hub/services/models/folder_browser.py
View file

@ -165,7 +165,7 @@ def _looks_like_model_dir(directory: Path) -> bool:
def _build_browse_allowlist(
media_roots: Optional[list[Path]] = None, drive_roots: Optional[list[Path]] = None
) -> list[Path]:
"""Root directories the browser may walk (also seeds the suggestion chips): HOME, resolved HF cache dirs, Studio outputs/exports/root, registered scan folders, and well-known local-LLM dirs. Each is added only if it resolves to a real directory so the sandbox has no dead boundary.
"""Root directories the browser may walk (also seeds the suggestion chips): HOME, resolved HF cache dirs, Unsloth outputs/exports/root, registered scan folders, and well-known local-LLM dirs. Each is added only if it resolves to a real directory so the sandbox has no dead boundary.
*media_roots* / *drive_roots* let the caller pass already-probed
removable-media and Windows drive roots so they aren't scanned again (a

17
studio/backend/hub/services/models/local_inventory.py
View file

@ -36,6 +36,7 @@ from hub.utils.paths import (
)
from hub.services.models import common as model_common
from hub.services.models.ollama import scan_ollama_dir
from utils.hidden_models import is_hidden_model
logger = get_logger(__name__)
_MAX_MODELS_PER_CUSTOM_FOLDER = 200
@ -623,6 +624,20 @@ def _dedupe_local_models(local_models: List[LocalModelInfo]) -> list[LocalModelI
)
def _filter_hidden_models(local_models: List[LocalModelInfo]) -> list[LocalModelInfo]:
"""Remove infrastructure-only models from the shared local inventory."""
visible: list[LocalModelInfo] = []
for model in local_models:
resolved_cache_path = (
hf_cache_scan.resolve_hf_cache_realpath(Path(model.path))
if model.source == "hf_cache"
else None
)
if not is_hidden_model(model.id, model.model_id, model.path, resolved_cache_path):
visible.append(model)
return visible
async def list_local_models_response(models_dir: str = "./models") -> LocalModelListResponse:
"""List local model candidates from every supported on-device source."""
hf_cache_dir = _resolve_hf_cache_dir()
@ -653,7 +668,7 @@ async def list_local_models_response(models_dir: str = "./models") -> LocalModel
ollama_dirs,
)
local_models += await _collect_models_from_custom_folders()
models = _dedupe_local_models(local_models)
models = _dedupe_local_models(_filter_hidden_models(local_models))
return LocalModelListResponse(
models_dir = str(models_root),

2
studio/backend/hub/services/models/ollama.py
View file

@ -85,7 +85,7 @@ def _contained_link_path(link_dir: Path, link_name: str) -> Optional[Path]:
def _ollama_links_dir(ollama_dir: Path) -> Optional[Path]:
"""Writable directory for Ollama ``.gguf`` symlinks. Prefers ``<ollama_dir>/.studio_links/`` next to the blobs; falls back to Studio's cache (read-only system installs), then the temp dir (sandboxed installs)."""
"""Writable directory for Ollama ``.gguf`` symlinks. Prefers ``<ollama_dir>/.studio_links/`` next to the blobs; falls back to Unsloth's cache (read-only system installs), then the temp dir (sandboxed installs)."""
def _ensure_writable_dir(path: Path) -> Optional[Path]:
try:

338
studio/backend/hub/tests/test_model_services.py
View file

@ -439,6 +439,287 @@ def test_cached_gguf_scan_includes_variant_state_without_completed_gguf(monkeypa
assert row["capabilities"]["requires_variant"] is True
def test_cached_gguf_scan_hides_infra_repos_without_user_downloads(monkeypatch, tmp_path):
probe = _repo(
"ggml-org/models",
[_file("tinyllamas/stories260K.gguf", 1_200_000)],
tmp_path / "probe",
)
embedder = _repo(
"unsloth/bge-small-en-v1.5-GGUF",
[_file("bge-small-en-v1.5-f16.gguf", 60_000_000)],
tmp_path / "embedder",
)
chat = _repo("Org/Chat-GGUF", [_file("Q4_K_M.gguf", 100)], tmp_path / "chat")
monkeypatch.setattr(
cache_inventory,
"all_hf_cache_scans",
lambda: [SimpleNamespace(repos = [probe, embedder, chat])],
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_gguf_repo_partial",
lambda _repo_id, _path: False,
)
result = {"cached": cache_inventory._scan_cached_gguf()}
assert [row["repo_id"] for row in result["cached"]] == ["Org/Chat-GGUF"]
def test_cached_gguf_scan_keeps_infra_repo_with_user_downloaded_variant(monkeypatch, tmp_path):
monkeypatch.setattr(state_dir, "cache_root", lambda: tmp_path / "state")
embedder = _repo(
"unsloth/bge-small-en-v1.5-GGUF",
[
_file("bge-small-en-v1.5-f16.gguf", 60_000_000),
_file("bge-small-en-v1.5-Q8_0.gguf", 35_000_000),
],
tmp_path / "embedder",
)
# Variant manifests only exist for user Hub downloads, not auto-downloads.
assert download_manifest.write_manifest(
"model",
"unsloth/bge-small-en-v1.5-GGUF",
"Q8_0",
[download_manifest.ExpectedFile(path = "bge-small-en-v1.5-Q8_0.gguf", size = 35_000_000)],
"http",
)
monkeypatch.setattr(
cache_inventory,
"all_hf_cache_scans",
lambda: [SimpleNamespace(repos = [embedder])],
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_gguf_repo_partial",
lambda _repo_id, _path: False,
)
result = {"cached": cache_inventory._scan_cached_gguf()}
assert [row["repo_id"] for row in result["cached"]] == ["unsloth/bge-small-en-v1.5-GGUF"]
assert result["cached"][0]["capabilities"]["can_chat"] is False
def test_cached_models_scan_hides_non_gguf_embedder(monkeypatch, tmp_path):
embedder_path = tmp_path / "hub" / "models--unsloth--bge-small-en-v1.5"
embedder_path.mkdir(parents = True)
embedder = _repo(
"unsloth/bge-small-en-v1.5",
[_file("config.json", 12), _file("model.safetensors", 130_000_000)],
embedder_path,
)
chat_path = tmp_path / "hub" / "models--Org--Chat"
chat_path.mkdir(parents = True)
chat = _repo(
"Org/Chat",
[_file("config.json", 12), _file("model.safetensors", 100)],
chat_path,
)
monkeypatch.setattr(
cache_inventory,
"all_hf_cache_scans",
lambda: [SimpleNamespace(repos = [embedder, chat])],
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_snapshot_partial",
lambda _kind, _repo_id, _path: False,
)
result = {"cached": cache_inventory._scan_cached_models()}
assert [row["repo_id"] for row in result["cached"]] == ["Org/Chat"]
def test_cached_scans_hide_embedders_configured_by_cache_path(monkeypatch, tmp_path):
from core.rag import config as rag_config
gguf_path = tmp_path / "hub" / "models--Org--PathEmbedder-GGUF"
gguf_path.mkdir(parents = True)
gguf = _repo(
"Org/PathEmbedder-GGUF",
[_file("model-F16.gguf", 60_000_000)],
gguf_path,
)
model_path = tmp_path / "hub" / "models--Org--PathEmbedder"
model_path.mkdir(parents = True)
model = _repo(
"Org/PathEmbedder",
[_file("config.json", 12), _file("model.safetensors", 130_000_000)],
model_path,
)
monkeypatch.setattr(
rag_config,
"effective_embedding_model",
lambda: str(model_path),
)
monkeypatch.setattr(
rag_config,
"effective_gguf_repo",
lambda: str(gguf_path),
)
monkeypatch.setattr(
cache_inventory,
"all_hf_cache_scans",
lambda: [SimpleNamespace(repos = [gguf, model])],
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_gguf_repo_partial",
lambda _repo_id, _path: False,
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_snapshot_partial",
lambda _kind, _repo_id, _path: False,
)
assert cache_inventory._scan_cached_gguf() == []
assert cache_inventory._scan_cached_models() == []
def test_cached_scans_hide_embedders_configured_by_snapshot_path(monkeypatch, tmp_path):
from core.rag import config as rag_config
gguf_path = tmp_path / "hub" / "models--Org--SnapshotEmbedder-GGUF"
gguf_snapshot = gguf_path / "snapshots" / "gguf-revision"
gguf_snapshot.mkdir(parents = True)
gguf = _repo(
"Org/SnapshotEmbedder-GGUF",
[_file("model-F16.gguf", 60_000_000)],
gguf_path,
)
model_path = tmp_path / "hub" / "models--Org--SnapshotEmbedder"
model_snapshot = model_path / "snapshots" / "model-revision"
model_snapshot.mkdir(parents = True)
model = _repo(
"Org/SnapshotEmbedder",
[_file("config.json", 12), _file("model.safetensors", 130_000_000)],
model_path,
)
monkeypatch.setattr(
rag_config,
"effective_embedding_model",
lambda: str(model_snapshot),
)
monkeypatch.setattr(
rag_config,
"effective_gguf_repo",
lambda: str(gguf_snapshot),
)
monkeypatch.setattr(
cache_inventory,
"all_hf_cache_scans",
lambda: [SimpleNamespace(repos = [gguf, model])],
)
def _resolve_snapshot(repo_path):
return str(
{
gguf_path: gguf_snapshot,
model_path: model_snapshot,
}.get(Path(repo_path), Path(repo_path))
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"resolve_hf_cache_realpath",
_resolve_snapshot,
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_gguf_repo_partial",
lambda _repo_id, _path: False,
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_snapshot_partial",
lambda _kind, _repo_id, _path: False,
)
assert cache_inventory._scan_cached_gguf() == []
assert cache_inventory._scan_cached_models() == []
def test_cached_models_scan_keeps_unrelated_repo_with_custom_generic_embedder(
monkeypatch, tmp_path
):
# A custom embedder with a generic basename ("org/model") must be hidden by
# EXACT repo-id match only. An unrelated cached chat model whose id merely
# contains "model" (e.g. "user/model-chat") must stay on device: substring
# basename matching used to drop real chat models from the inventory.
from core.rag import config as rag_config
monkeypatch.setattr(rag_config, "effective_embedding_model", lambda: "org/model")
monkeypatch.setattr(rag_config, "effective_gguf_repo", lambda: "org/model-GGUF")
def _model_repo(repo_id: str):
path = tmp_path / "hub" / f"models--{repo_id.replace('/', '--')}"
path.mkdir(parents = True)
return _repo(
repo_id,
[_file("config.json", 12), _file("model.safetensors", 100)],
path,
)
embedder = _model_repo("org/model")
chat = _model_repo("user/model-chat")
monkeypatch.setattr(
cache_inventory,
"all_hf_cache_scans",
lambda: [SimpleNamespace(repos = [embedder, chat])],
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_snapshot_partial",
lambda _kind, _repo_id, _path: False,
)
result = {"cached": cache_inventory._scan_cached_models()}
assert [row["repo_id"] for row in result["cached"]] == ["user/model-chat"]
def test_cached_scans_hide_stale_default_embedder_after_custom_setting(monkeypatch, tmp_path):
from core.rag import config as rag_config
monkeypatch.setattr(rag_config, "effective_embedding_model", lambda: "org/custom")
monkeypatch.setattr(rag_config, "effective_gguf_repo", lambda: "org/custom-GGUF")
gguf = _repo(
"unsloth/bge-small-en-v1.5-GGUF",
[_file("bge-small-en-v1.5-f16.gguf", 60_000_000)],
tmp_path / "default-gguf",
)
weights_path = tmp_path / "hub" / "models--unsloth--bge-small-en-v1.5"
weights_path.mkdir(parents = True)
weights = _repo(
"unsloth/bge-small-en-v1.5",
[_file("config.json", 12), _file("model.safetensors", 130_000_000)],
weights_path,
)
monkeypatch.setattr(
cache_inventory,
"all_hf_cache_scans",
lambda: [SimpleNamespace(repos = [gguf, weights])],
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_gguf_repo_partial",
lambda _repo_id, _path: False,
)
monkeypatch.setattr(
cache_inventory.hf_cache_scan,
"is_snapshot_partial",
lambda _kind, _repo_id, _path: False,
)
assert cache_inventory._scan_cached_gguf() == []
assert cache_inventory._scan_cached_models() == []
def test_gguf_variant_requirements_include_split_files_and_preferred_mmproj():
requirements = gguf_variants._build_gguf_variant_requirements(
[
@ -1610,6 +1891,63 @@ def test_hf_cache_scan_uses_gguf_partial_row_for_variant_state(monkeypatch, tmp_
assert rows[0].capabilities.requires_variant is True
def test_local_inventory_filters_custom_embedder_hf_cache_row(monkeypatch, tmp_path):
from core.rag import config as rag_config
monkeypatch.setattr(rag_config, "effective_embedding_model", lambda: "org/embedder")
monkeypatch.setattr(rag_config, "effective_gguf_repo", lambda: "org/embedder-GGUF")
def _row(repo_id: str):
repo_path = tmp_path / f"models--{repo_id.replace('/', '--')}"
return model_common._local_model_info(
scan_path = repo_path,
load_path = repo_path,
source = "hf_cache",
model_format = "safetensors",
model_id = repo_id,
)
rows = local_inventory._filter_hidden_models([_row("org/embedder"), _row("org/chat-model")])
assert [row.model_id for row in rows] == ["org/chat-model"]
def test_local_inventory_filters_embedder_configured_by_snapshot_path(monkeypatch, tmp_path):
from core.rag import config as rag_config
embedder_path = tmp_path / "hub" / "models--org--embedder"
embedder_snapshot = embedder_path / "snapshots" / "revision"
embedder_snapshot.mkdir(parents = True)
chat_path = tmp_path / "hub" / "models--org--chat-model"
chat_path.mkdir(parents = True)
monkeypatch.setattr(
rag_config,
"effective_embedding_model",
lambda: str(embedder_snapshot),
)
monkeypatch.setattr(rag_config, "effective_gguf_repo", lambda: "org/embedder-GGUF")
monkeypatch.setattr(
local_inventory.hf_cache_scan,
"resolve_hf_cache_realpath",
lambda path: str(embedder_snapshot) if Path(path) == embedder_path else str(path),
)
def _row(repo_id: str, repo_path: Path):
return model_common._local_model_info(
scan_path = repo_path,
load_path = repo_path,
source = "hf_cache",
model_format = "safetensors",
model_id = repo_id,
)
rows = local_inventory._filter_hidden_models(
[_row("org/embedder", embedder_path), _row("org/chat-model", chat_path)]
)
assert [row.model_id for row in rows] == ["org/chat-model"]
def test_model_download_job_helpers_preserve_idle_shape():
key = downloads._download_job_key("Org/Model", None)
status = downloads._job_status(key)

2
studio/backend/hub/utils/state_dir.py
View file

@ -3,7 +3,7 @@
"""Filesystem layout for Hub download state.
State directory sits beside HF's cache (under Studio's own cache root)
State directory sits beside HF's cache (under Unsloth's own cache root)
so it survives ``huggingface-cli delete-cache`` and any other HF-side
cache lifecycle. Two subdirectories:

80
studio/backend/main.py
View file

@ -19,7 +19,7 @@ os.environ["PYTHONWARNINGS"] = "ignore"
# Pin GPU index ordering to PCI bus id before any torch import creates a CUDA
# context. Without this, torch/CUDA default to FASTEST_FIRST while nvidia-smi
# (and Studio's VRAM probes) use PCI-bus order, so a GPU index chosen from
# (and Unsloth's VRAM probes) use PCI-bus order, so a GPU index chosen from
# nvidia-smi data can resolve to a different physical card via
# CUDA_VISIBLE_DEVICES. setdefault so an explicit user override wins. See
# utils/hardware/hardware.py for the full rationale; set here too so the entry
@ -93,7 +93,7 @@ if sys.platform == "win32":
# ── Windows AMD ROCm: make hipInfo.exe resolvable for subprocess probes ──
# bitsandbytes' get_rocm_gpu_arch() runs `hipinfo.exe` via PATH at import
# time; the AMD torch wheel ships it in the venv Scripts dir, which is on
# PATH only when the venv is activated -- Studio launches python directly.
# PATH only when the venv is activated -- Unsloth launches python directly.
# Without this, every bitsandbytes import logs a scary (but harmless)
# "Could not detect ROCm GPU architecture: [WinError 2]" ERROR + WARNING.
# Gated on the file existing: only AMD ROCm wheels ship hipInfo.exe, so
@ -252,7 +252,7 @@ def _read_studio_install_id() -> str:
Returns "" when absent or not a 64-char lowercase-hex token; then
/api/health emits "" and the launcher accepts any healthy backend.
Carries no install-path info (matters when Studio runs -H 0.0.0.0)."""
Carries no install-path info (matters when Unsloth runs -H 0.0.0.0)."""
try:
token = (_STUDIO_ROOT_RESOLVED / "share" / "studio_install_id").read_text().strip()
except (OSError, ValueError):
@ -289,6 +289,7 @@ from fastapi import Depends, FastAPI, HTTPException, Query, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse, HTMLResponse, Response
from starlette.middleware.gzip import GZipMiddleware
from pathlib import Path
from datetime import datetime
@ -313,7 +314,9 @@ from routes.preview import router as preview_router
from hub.routes import (
inventory_router as hub_inventory_router,
datasets_router as hub_datasets_router,
token_router as hub_token_router,
)
from picker.routes import templates_router as picker_templates_router
from hub.schemas.downloads import TransportCapabilities
from hub.utils.download_registry import (
get_download_transport_capabilities,
@ -553,8 +556,9 @@ async def lifespan(app: FastAPI):
app.state.research_supervisor.start()
# Idle auto-unload loop (no-op unless the OpenAI auto-unload TTL is set).
from core.inference.llama_keepwarm import idle_unload_loop
from core.inference.llama_keepwarm import idle_unload_loop, sweep_slot_save_dir
sweep_slot_save_dir()
app.state.idle_unload_task = asyncio.create_task(idle_unload_loop())
# Initialize RSA key pair for API key encryption (external providers).
@ -579,7 +583,7 @@ async def lifespan(app: FastAPI):
print("DEFAULT ADMIN ACCOUNT CREATED")
print(f" username: {storage.DEFAULT_ADMIN_USERNAME}")
print(f" password saved to: {bootstrap_path}")
print(" Open the Studio UI to sign in and change it.")
print(" Open the Unsloth UI to sign in and change it.")
print("=" * 60 + "\n")
else:
app.state.bootstrap_password = (
@ -623,7 +627,7 @@ app = FastAPI(
)
# The MCP surface is opt-in because it can start GPU jobs and write model
# artifacts. Mount it only when explicitly enabled by the Studio process.
# artifacts. Mount it only when explicitly enabled by the Unsloth process.
if os.environ.get("UNSLOTH_STUDIO_ENABLE_MCP") == "1":
from fastmcp.utilities.lifespan import combine_lifespans
@ -789,6 +793,7 @@ _BODY_PROTECTED_PREFIXES = (
"/v1/completions",
"/p/",
"/api/inference",
"/api/picker",
"/api/data-recipe",
"/api/datasets",
"/api/hub",
@ -1002,7 +1007,7 @@ app.include_router(models_router, prefix = "/api/models", tags = ["models"])
app.include_router(chat_history_router, prefix = "/api/chat", tags = ["chat"])
app.include_router(research_runs_router, prefix = "/api/chat/research-runs", tags = ["research-runs"])
app.include_router(inference_router, prefix = "/api/inference", tags = ["inference"])
# Studio-only inference endpoints (cancel, etc.) are NOT exposed on the /v1
# Unsloth-only inference endpoints (cancel, etc.) are NOT exposed on the /v1
# OpenAI-compat prefix below.
app.include_router(inference_studio_router, prefix = "/api/inference", tags = ["inference"])
@ -1021,6 +1026,8 @@ app.include_router(rag_router, prefix = "/api/rag", tags = ["rag"])
app.include_router(training_history_router, prefix = "/api/train", tags = ["training-history"])
app.include_router(hub_inventory_router, prefix = "/api/hub", tags = ["hub"])
app.include_router(hub_datasets_router, prefix = "/api/hub/datasets", tags = ["hub"])
app.include_router(picker_templates_router, prefix = "/api/picker", tags = ["picker"])
app.include_router(hub_token_router, prefix = "/api/hub", tags = ["hub"])
# Re-wrap client-error responses on the /v1/* surface into OpenAI/Anthropic
# error envelopes; non-/v1 paths keep FastAPI's default {"detail": ...} shape.
@ -1109,7 +1116,7 @@ def studio_install_source(_current_subject: str = Depends(get_current_subject)):
@app.get("/api/studio/update-status")
def studio_update_status(_current_subject: str = Depends(get_current_subject)):
"""Return source-aware manual update status for browser-served Studio."""
"""Return source-aware manual update status for browser-served Unsloth."""
return get_studio_update_status(UNSLOTH_VERSION)
@ -1177,17 +1184,35 @@ def _get_cached_system_gpu_info(logger) -> dict[str, Any]:
util = util_devices.get(idx, {})
total_vram = util.get("vram_total_gb") or dev.get("memory_total_gb") or 0
used_vram = util.get("vram_used_gb") or 0
# Keep None (usage unknown, e.g. Windows ROCm perf counter) so the UI
# shows unknown, not a fabricated 0 used / full free.
used_vram = util.get("vram_used_gb")
enriched_dev = dict(dev)
enriched_dev["vram_used_gb"] = used_vram
enriched_dev["vram_free_gb"] = round(total_vram - used_vram, 2) if total_vram else 0
enriched_dev["vram_free_gb"] = (
round(total_vram - used_vram, 2) if total_vram and used_vram is not None else None
)
enriched_dev["vram_utilization_pct"] = util.get("vram_utilization_pct")
enriched_devices.append(enriched_dev)
# Whether GGUF loads accept an explicit gpu_ids pick: /load and
# /validate 400 picks on XPU hosts (no visibility mask speaks torch-xpu
# ordinals) and on Vulkan-only builds (--device pins ggml's own
# ordinals), so the picker must not offer them.
try:
from core.inference.llama_cpp import LlamaCppBackend
from utils.hardware import DeviceType, get_device
gpu_ids_supported = (
get_device() != DeviceType.XPU and not LlamaCppBackend._is_vulkan_backend()
)
except Exception as e:
logger.debug(f"Could not resolve gpu_ids support: {e}")
gpu_ids_supported = True
gpu_info = {
"available": visibility_info.get("available", False),
"devices": enriched_devices,
"gguf_gpu_ids_supported": gpu_ids_supported,
}
_system_gpu_cache = (time.monotonic(), gpu_info)
return gpu_info
@ -1517,6 +1542,34 @@ def _should_inject_bootstrap(request: Request) -> bool:
return _is_local_bootstrap_request(request)
_IMMUTABLE_ASSET_CACHE_CONTROL = "public, max-age=31536000, immutable"
class ImmutableStaticFiles(StaticFiles):
"""Serve Vite's content-hashed assets without browser revalidation."""
def file_response(
self,
full_path,
stat_result,
scope,
status_code = 200,
):
response = super().file_response(full_path, stat_result, scope, status_code)
response.headers["Cache-Control"] = _IMMUTABLE_ASSET_CACHE_CONTROL
return response
class _AssetGZipMiddleware(GZipMiddleware):
"""Serve range requests uncompressed; gzip + 206 mislabels Content-Range."""
async def __call__(self, scope, receive, send):
if scope["type"] == "http" and any(key == b"range" for key, _ in scope["headers"]):
await self.app(scope, receive, send)
return
await super().__call__(scope, receive, send)
def setup_frontend(app: FastAPI, build_path: Path):
"""Mount frontend static files (optional)"""
if not build_path.exists():
@ -1524,7 +1577,12 @@ def setup_frontend(app: FastAPI, build_path: Path):
assets_dir = build_path / "assets"
if assets_dir.exists():
app.mount("/assets", StaticFiles(directory = assets_dir), name = "assets")
assets_app = _AssetGZipMiddleware(
ImmutableStaticFiles(directory = assets_dir),
minimum_size = 1024,
compresslevel = 6,
)
app.mount("/assets", assets_app, name = "assets")
def _build_index_response(request: Request) -> Response:
content = (build_path / "index.html").read_bytes()

26
studio/backend/mcp_server.py
View file

@ -3,7 +3,7 @@
"""Curated MCP tools for driving an Unsloth Studio instance.
The MCP surface deliberately wraps the existing Studio services instead of
The MCP surface deliberately wraps the existing Unsloth services instead of
duplicating training or export logic. It is opt-in because several tools can
start GPU work or write model artifacts.
"""
@ -17,14 +17,14 @@ from fastmcp import FastMCP
class BearerTokenMiddleware:
"""Require an exact bearer token when Studio MCP is exposed remotely."""
"""Require an exact bearer token when Unsloth MCP is exposed remotely."""
def __init__(self, app: Any, token: str) -> None:
if not token or not token.strip():
raise ValueError("Studio MCP bearer token must be a non-empty value")
raise ValueError("Unsloth MCP bearer token must be a non-empty value")
if not token.isascii():
# A non-ASCII token cannot be sent in an HTTP header; reject it here.
raise ValueError("Studio MCP bearer token must contain ASCII characters only")
raise ValueError("Unsloth MCP bearer token must contain ASCII characters only")
self.app = app
# Compare on raw header bytes: str hmac.compare_digest raises on non-ASCII
# input, which would surface as a 500 instead of a clean 401.
@ -76,18 +76,18 @@ def _dump(value: Any) -> Any:
def _clamp(value: int, low: int, high: int) -> int:
"""Clamp an MCP-supplied integer into an inclusive range.
MCP tools call the Studio route functions directly, which skips FastAPI's
MCP tools call the Unsloth route functions directly, which skips FastAPI's
Query(ge=, le=) validation, so we re-apply the same bounds here.
"""
return max(low, min(value, high))
def create_studio_mcp() -> FastMCP:
"""Create the Studio MCP server and register the high-value tools."""
"""Create the Unsloth MCP server and register the high-value tools."""
mcp = FastMCP(
"Unsloth Studio",
instructions = (
"Use read tools to inspect the local Studio state before starting GPU work. "
"Use read tools to inspect the local Unsloth state before starting GPU work. "
"Training and export tools can consume substantial VRAM and write files. "
"Never expose tokens or local paths from tool results unless the user asks."
),
@ -116,7 +116,7 @@ def create_studio_mcp() -> FastMCP:
@mcp.tool
async def list_local_models(models_dir: str = "./models") -> dict[str, Any]:
"""List local and cached models available to Studio."""
"""List local and cached models available to Unsloth."""
from routes.models import list_local_models as list_models
return _dump(await list_models(models_dir = models_dir, current_subject = "mcp"))
@ -128,9 +128,9 @@ def create_studio_mcp() -> FastMCP:
@mcp.tool
async def start_training(config: dict[str, Any]) -> dict[str, Any]:
"""Start a validated Studio training job from a TrainingStartRequest-shaped object.
"""Start a validated Unsloth training job from a TrainingStartRequest-shaped object.
The config is validated by the same Pydantic model used by the Studio UI.
The config is validated by the same Pydantic model used by the Unsloth UI.
Call get_training_status first and do not start work while another job runs.
"""
from models import TrainingStartRequest
@ -138,7 +138,7 @@ def create_studio_mcp() -> FastMCP:
request = TrainingStartRequest.model_validate(config)
# Pass via_api_key explicitly (a direct call leaves it a Depends object).
# MCP drives Studio like the UI session, so it coexists and frees VRAM.
# MCP drives Unsloth like the UI session, so it coexists and frees VRAM.
return _dump(await start(request, current_subject = "mcp", via_api_key = False))
@mcp.tool
@ -159,7 +159,7 @@ def create_studio_mcp() -> FastMCP:
@mcp.tool
def validate_recipe(recipe: dict[str, Any]) -> dict[str, Any]:
"""Validate a Data Recipe with the same validator used by Studio."""
"""Validate a Data Recipe with the same validator used by Unsloth."""
from models.data_recipe import RecipePayload
from routes.data_recipe.validate import validate
@ -225,7 +225,7 @@ def create_studio_mcp() -> FastMCP:
imatrix: bool = False,
imatrix_path: str | None = None,
) -> dict[str, Any]:
"""Export the loaded model to GGUF using Studio's existing path validation.
"""Export the loaded model to GGUF using Unsloth's existing path validation.
quantization_method may be a single method or a list to produce several
GGUFs from one load. Pass hf_token when push_to_hub is set (the backend

184
studio/backend/models/inference.py
View file

@ -18,6 +18,8 @@ from pydantic import (
model_validator,
)
from picker.schemas import MAX_CHAT_TEMPLATE_BYTES
class LoadRequest(BaseModel):
"""Request to load a model for inference"""
@ -54,8 +56,16 @@ class LoadRequest(BaseModel):
@field_validator("chat_template_override")
@classmethod
def normalize_blank_chat_template_override(cls, value: Optional[str]) -> Optional[str]:
if value is not None and value.strip() == "":
if value is None:
return None
# Char count is a lower bound on UTF-8 byte length: reject an oversized
# template before spending work encoding it.
if len(value) > MAX_CHAT_TEMPLATE_BYTES:
raise ValueError(f"Chat template exceeds the {MAX_CHAT_TEMPLATE_BYTES}-byte limit.")
if value.strip() == "":
return None
if len(value.encode("utf-8")) > MAX_CHAT_TEMPLATE_BYTES:
raise ValueError(f"Chat template exceeds the {MAX_CHAT_TEMPLATE_BYTES}-byte limit.")
return value
cache_type_kv: Optional[str] = Field(
@ -64,7 +74,7 @@ class LoadRequest(BaseModel):
)
gpu_ids: Optional[List[int]] = Field(
None,
description = "Physical GPU indices to use, for example [0, 1]. Omit or pass [] to use automatic selection. Explicit gpu_ids are unsupported when the parent CUDA_VISIBLE_DEVICES uses UUID/MIG entries. Not supported for GGUF models.",
description = "Physical GPU indices to use, for example [0, 1]. Omit or pass [] to use automatic selection. Explicit gpu_ids are unsupported when the parent CUDA_VISIBLE_DEVICES uses UUID/MIG entries. For GGUF models the picked devices are pinned via CUDA/HIP_VISIBLE_DEVICES.",
)
speculative_type: Optional[str] = Field(
None,
@ -100,12 +110,72 @@ class LoadRequest(BaseModel):
"No effect on a single GPU. Ignored for non-GGUF models."
),
)
gpu_memory_mode: Literal["auto", "manual"] = Field(
"auto",
description = (
"GPU memory strategy for GGUF models. 'auto' (default): Unsloth "
"selects GPUs and caps context to fit VRAM. 'manual': you own the "
"offload. Leave gpu_layers at -1 (Auto) to hand memory management to "
"llama.cpp's --fit (no device masking, no context auto-reduce, no "
"gpu-layer/tensor-split planning); set gpu_layers >= 0 to pin layers "
"and n_cpu_moe yourself (--fit off), with tensor_parallel still "
"applying (split by free VRAM unless tensor_split is set, no planner). "
"Ignored for non-GGUF."
),
)
gpu_layers: int = Field(
-1,
ge = -1,
description = (
"Manual mode only: number of layers to offload to the GPU "
"(--gpu-layers, with --fit off). A value >= the model's layer count "
"offloads all of them. -1 = Auto: hand layer + context sizing to "
"llama.cpp's --fit. Ignored unless gpu_memory_mode is 'manual'."
),
)
n_cpu_moe: int = Field(
0,
ge = 0,
description = (
"Manual mode only: keep the first N MoE expert layers on the CPU "
"(--n-cpu-moe) to save VRAM on MoE models. 0 = none, N = number of "
"MoE layers offloaded (the backend offsets past any leading dense "
"layers). Ignored unless gpu_memory_mode is 'manual' with gpu_layers >= 0."
),
)
tensor_split: Optional[List[float]] = Field(
None,
description = (
"Manual mode only: relative share of the model per GPU (--tensor-split), "
"in the order of the GPUs in use, e.g. [2, 1] for 2:1. Omit it to let "
"llama.cpp use its default, which splits by free VRAM. Any list given is "
"passed through as-is, so send [1, 1] to force an even split. Ignored "
"unless gpu_memory_mode is 'manual' with gpu_layers >= 0."
),
)
@field_validator("tensor_split")
@classmethod
def _reject_degenerate_tensor_split(cls, value: Optional[List[float]]) -> Optional[List[float]]:
# A negative / non-finite / all-zero split is silently dropped at launch
# (stored as None) yet still compared raw in the reload dedupe, so an
# identical Apply reloads forever. Reject it up front; [] = no split.
if not value:
return value
import math
if any((not math.isfinite(v)) or v < 0 for v in value):
raise ValueError("tensor_split entries must be finite and non-negative")
if sum(value) <= 0:
raise ValueError("tensor_split must have a positive total")
return value
llama_extra_args: Optional[List[str]] = Field(
None,
description = (
"Extra arguments forwarded verbatim to llama-server for GGUF models. "
"One token per list entry, e.g. ['--top-k', '20', '--seed', '42']. "
"Studio-managed flags (model identity, port, context length, GPU placement, "
"Unsloth-managed flags (model identity, port, context length, GPU placement, "
"auth, UI/server mode) are rejected. Ignored for non-GGUF models."
),
)
@ -133,11 +203,26 @@ class ValidateModelRequest(BaseModel):
max_seq_length: int = Field(0, ge = 0, le = 1048576)
load_in_4bit: bool = Field(True)
gpu_ids: Optional[List[int]] = Field(None)
gpu_memory_mode: Literal["auto", "manual"] = Field(
"auto",
description = (
"GGUF GPU-memory strategy intended for the follow-up load. Manual "
"placement bypasses the training coexistence estimate: Auto layers "
"delegate fitting to llama.cpp, while explicit layers are user-owned."
),
)
include_context_length: bool = Field(
False,
description = "Also read the native context length from the local GGUF header. "
"Opt-in so the normal load preflight doesn't pay for a cache scan it doesn't need.",
)
include_chat_template: bool = Field(
False,
description = "Also read the embedded chat template from the local GGUF header, so a "
"native (picked / drag-drop) file's default template can be shown before it is loaded. "
"Opt-in and, like include_context_length, a metadata-only probe that skips the training "
"guard. Only the leased file's own embedded template is read, never sibling sidecars.",
)
class TransformersUpgradeInfo(BaseModel):
@ -151,13 +236,13 @@ class TransformersUpgradeInfo(BaseModel):
)
supported_in_pypi: bool = Field(
False,
description = "True if the latest PyPI release ships this model_type; Studio can "
description = "True if the latest PyPI release ships this model_type; Unsloth can "
"install it into a persistent sidecar after user consent.",
)
supported_in_main: bool = Field(
False,
description = "True if transformers GitHub main ships this model_type (dev-only; "
"not installable through Studio yet).",
"not installable through Unsloth yet).",
)
@ -188,6 +273,21 @@ class ValidateModelResponse(BaseModel):
description = "Native training context length, read from the GGUF header when the file "
"is already downloaded locally; None for non-GGUF, gated, or not-yet-downloaded models.",
)
layer_count: Optional[int] = Field(
None,
description = "Total layer count (GGUF block_count), the manual gpu-layers ceiling, read "
"from the header alongside context_length; None when not read.",
)
moe_layer_count: Optional[int] = Field(
None,
description = "MoE expert-layer count (the manual --n-cpu-moe ceiling), read from the GGUF "
"header alongside context_length; 0 for dense models, None when not read.",
)
chat_template: Optional[str] = Field(
None,
description = "Embedded GGUF chat template, read from the header when include_chat_template "
"is set (native lease-backed picks); None for non-GGUF, over-cap, or not-read templates.",
)
# Additive fields; the consuming consent dialog ships in a follow-up frontend PR.
requires_transformers_upgrade: bool = Field(
False,
@ -333,6 +433,34 @@ class LoadResponse(BaseModel):
False,
description = "Whether tensor-parallel split (--split-mode tensor) is active.",
)
gpu_memory_mode: Literal["auto", "manual"] = Field(
"auto",
description = "Active GPU memory strategy ('auto' or 'manual').",
)
gpu_layers: int = Field(
-1,
description = "Manual mode: requested --gpu-layers value (-1 = Auto/--fit, or when not manual).",
)
n_cpu_moe: int = Field(
0,
description = "Manual mode: MoE expert layers pinned to CPU (--n-cpu-moe); 0 = none.",
)
tensor_split: Optional[List[float]] = Field(
None,
description = "Manual mode: relative model share per GPU (--tensor-split); None = default (split by free VRAM).",
)
n_layers: Optional[int] = Field(
None,
description = "Model's layer count (GGUF block_count), for the manual gpu-layers ceiling.",
)
n_moe_layers: int = Field(
0,
description = "Model's MoE expert-layer count (the n_cpu_moe ceiling); 0 if not an MoE model.",
)
gpu_ids: Optional[List[int]] = Field(
None,
description = "Physical GPU indices the model is pinned to, or None for automatic selection.",
)
class UnloadResponse(BaseModel):
@ -461,6 +589,42 @@ class InferenceStatusResponse(BaseModel):
False,
description = "Whether tensor-parallel split (--split-mode tensor) is active.",
)
gpu_memory_mode: Literal["auto", "manual"] = Field(
"auto",
description = "Active GPU memory strategy ('auto' or 'manual').",
)
gpu_layers: int = Field(
-1,
description = "Manual mode: requested --gpu-layers value (-1 = Auto/--fit, or when not manual).",
)
n_cpu_moe: int = Field(
0,
description = "Manual mode: MoE expert layers pinned to CPU (--n-cpu-moe); 0 = none.",
)
tensor_split: Optional[List[float]] = Field(
None,
description = "Manual mode: relative model share per GPU (--tensor-split); None = default (split by free VRAM).",
)
requested_context_length: Optional[int] = Field(
None,
description = (
"The n_ctx the active GGUF load was invoked with (0 = Auto). Lets the "
"UI re-seed a Manual + Auto-layers context pin on hydration, where "
"context_length only exposes the resolved value. None for non-GGUF."
),
)
n_layers: Optional[int] = Field(
None,
description = "Model's layer count (GGUF block_count), for the manual gpu-layers ceiling.",
)
n_moe_layers: int = Field(
0,
description = "Model's MoE expert-layer count (the n_cpu_moe ceiling); 0 if not an MoE model.",
)
gpu_ids: Optional[List[int]] = Field(
None,
description = "Physical GPU indices the model is pinned to, or None for automatic selection.",
)
llama_cpp_supports_mtp: bool = Field(
True,
description = (
@ -533,7 +697,7 @@ class ImageContentPart(BaseModel):
class InputDocumentContentPart(BaseModel):
"""Document (PDF / file) content part in a multimodal message.
Studio-normalised shape (file_data or file_url, plus optional filename/media_type).
Unsloth-normalised shape (file_data or file_url, plus optional filename/media_type).
Mapped onto Anthropic ``document`` / OpenAI ``input_file`` for vision providers;
dropped for non-vision providers.
"""
@ -689,7 +853,7 @@ class ThinkingConfig(BaseModel):
"""Anthropic-compatible thinking/reasoning configuration.
Use type='disabled' to turn off thinking, or type='enabled' to turn it on.
Only type is read; extra fields (e.g. budget_tokens) are ignored, since
Studio sets provider thinking budgets itself.
Unsloth sets provider thinking budgets itself.
"""
type: Literal["disabled", "enabled"] = "disabled"
@ -748,7 +912,7 @@ class ChatCompletionRequest(BaseModel):
None,
description = (
"OpenAI function-tool definitions. When provided without `enable_tools=true`, "
"Studio forwards the tools to the backend so the model returns structured "
"Unsloth forwards the tools to the backend so the model returns structured "
"tool_calls for the client to execute (standard OpenAI function calling)."
),
)
@ -1160,7 +1324,7 @@ class ChatCompletionRequest(BaseModel):
and (self.enable_tools is True or bool(self.mcp_enabled))
):
# "Ask" gates every call, so a direct API caller that omits the legacy
# confirm flag must still hit the confirmation gate for Studio's own
# confirm flag must still hit the confirmation gate for Unsloth's own
# tool loop. An explicit confirm_tool_calls=False wins over the mode
# (mirrors _permission_mode_confirm and the Anthropic pre-switch guard),
# so only self-enable when the flag is unset. Only self-enable when that
@ -1168,7 +1332,7 @@ class ChatCompletionRequest(BaseModel):
# (enable_tools / mcp_enabled) -- the router enters the loop on those
# signals, not on enabled_tools alone (which merely filters which tools
# run). A plain client-tool passthrough (client-supplied `tools` that
# Studio does not execute) must route verbatim, and external-provider
# Unsloth does not execute) must route verbatim, and external-provider
# routing rejects confirm_tool_calls with tools, so skip the fold there.
#
# "auto" is deliberately NOT folded: it only prompts for a call the

9
studio/backend/models/training.py
View file

@ -446,7 +446,7 @@ class TrainingStartRequest(BaseModel):
random_seed: int = Field(
3407,
description = (
"Random seed; matches the Studio backend / MLX worker default "
"Random seed; matches the Unsloth backend / MLX worker default "
"and unsloth's historical recommended value."
),
)
@ -505,6 +505,13 @@ class TrainingStartRequest(BaseModel):
description = "S3 bucket configuration for loading datasets from AWS S3. Requires boto3 to be installed.",
)
@field_validator("target_modules", mode = "before")
@classmethod
def _normalize_target_modules(cls, value: Any) -> Any:
# Sanitized non-LoRA history stores the unused value as null; treat it as a
# fresh request's omitted/default empty list on resume.
return [] if value is None else value
@model_validator(mode = "after")
def _validate_streaming_splits(self) -> "TrainingStartRequest":
# Streaming load_dataset does not accept HF slice syntax (e.g. "train[:50%]"

2
studio/backend/picker/__init__.py Normal file
View file

@ -0,0 +1,2 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

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