Unsloth is a local UI for training and running Gemma 4, Qwen3.6, DeepSeek, Kimi, GLM and other models.
https://unsloth.ai/docs
agent
deepseek
fine-tuning
gemma
gemma3
gpt-oss
llama
llama3
llm
llms
mistral
openai
qwen
reinforcement-learning
self-hosted
text-to-speech
tts
ui
unsloth
- Python 71.5%
- TypeScript 22.7%
- Shell 1.9%
- PowerShell 1.6%
- Rust 1.5%
- Other 0.7%
37166efcfc* Fix Gemma 4 GGUF OpenAI API streams * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Avoid duplicate Responses stream disconnect watcher * Keep reasoning-only Responses output hidden * Address Gemma stream review comments * Avoid Responses stream task-group cleanup * Harden OpenAI chat completion streams * Address OpenAI stream review issues * Clean up Studio OpenAI stream helpers * Fix Studio passthrough cold stream timeout * Fix tool parser compatibility exports lint * Preserve audio stream disconnect cancellation * Avoid synthetic finish after passthrough errors * Address stream cleanup and Gemma parser reviews * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Gemma 4: parse bare-string tool args and keep safetensors tools for native <|tool_call> - Quote bare unquoted string values in Gemma native tool-call args (e.g. {location:Tokyo,unit:celsius}) so they parse; JSON scalars stay typed. - Stop _detect_safetensors_features from suppressing supports_tools for templates that emit Gemma native <|tool_call>, which the shared parser now reads. - Add tests for both. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Harden Gemma tool-call parsing and stream-error detection Address three issues in the Gemma-native tool-call path: - _quote_gemma_object_keys stopped a bare (unquoted) string value at the first comma, so an argument like `location:New York, NY` was split mid-value and the synthesized JSON failed to parse, dropping the whole tool call. A bare value now ends only at `}` or a comma that begins the next `key:` pair. - parse_tool_calls_from_text scanned the entire response for Gemma markers even inside a tool call already parsed from a `<tool_call>{...}` JSON block, so a marker-like string inside an argument (data) was promoted to a second, unintended tool call. Matches inside an already-consumed call span are now skipped. - _openai_passthrough_stream relied on _monitor_openai_sse_line to flag a stream error, which returns early when monitor_id is None (skip_api_monitor), so an upstream error chunk left saw_stream_error unset and the synthetic-finish guard emitted a successful finish_reason after a failed stream. Error chunks are now detected independently of API monitoring. Adds tests/test_gemma_tool_parse_edge_cases.py covering the comma and marker-injection cases. * Emit the terminal finish_reason chunk in GGUF streams The OpenAI chat-completions GGUF tool stream and plain stream both built a final ChatCompletionChunk carrying finish_reason but never yielded it, so clients received the optional usage chunk and [DONE] with no chunk carrying finish_reason. OpenAI-compatible consumers rely on that terminal choice to distinguish stop/length/tool_calls. Yield it before the usage chunk and [DONE], matching the other streaming paths. * Parse tool calls in document order and skip nested markers both ways Unify the JSON- and Gemma-format tool-call passes into a single position-ordered scan: - Calls are now emitted in byte order across both formats, so a mixed output like `<|tool_call>call:create{...}<tool_call|> ... <tool_call> {"name":"read",...}</tool_call>` executes create before read, matching the order they appear in (tools run in returned order). - A candidate that starts inside an already-accepted call's span is skipped, in both directions: a JSON marker inside a Gemma argument and a Gemma marker inside a JSON argument are treated as data, not promoted to a second executable tool call. Extends tests/test_gemma_tool_parse_edge_cases.py with the ordering and JSON-in-Gemma nesting cases. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Quote bare Gemma array elements; order finish before trailing usage - _quote_gemma_object_keys skipped array values, so a Gemma call with a bare-string array argument like labels:[bug,ui] produced invalid JSON and the whole tool call was dropped. Array values are now scanned and bare string elements quoted, while numbers, quoted strings, and JSON literals are preserved. - In the OpenAI passthrough stream, a trailing usage-only chunk (stream_options.include_usage) that arrived before any finish chunk was relayed before the synthetic finish, producing usage -> finish -> [DONE]. Emit the synthetic finish before that usage chunk so the order matches the other streams (finish -> usage -> [DONE]). Extends tests/test_gemma_tool_parse_edge_cases.py with the bare-array cases. * Harden Gemma array parsing, XML-parameter guard, and stream teardown Address five review findings on the Gemma tool-call and OpenAI passthrough streaming paths: - parse_tool_calls_from_text collected JSON and Gemma markers without the _inside_open_parameter guard, so a marker embedded in an existing <function=...><parameter=...> value was promoted to a separate tool call. Candidates that start inside an open XML parameter are now skipped, matching the guard the XML-style parser already applies. - _quote_gemma_array_elements preserved array elements starting with { or [ verbatim, so an array of objects (items:[{path:a}]) or a nested array failed json.loads and the whole call was dropped. Object and nested-array elements are now normalised recursively. - _openai_passthrough_stream synthesized a finish chunk before a trailing usage-only chunk and set saw_finish_reason, which made the EOF guard skip the [DONE] sentinel. The EOF path now emits [DONE] whenever the upstream omitted it, even after a finish chunk was already synthesized. - /generate/stream drove generation through asyncio.to_thread with no disconnect watcher, so a client disconnect during a long generation went unnoticed until the next send. It now runs _await_disconnect_then_cancel against the request, matching the other local streaming endpoints. - _SameTaskStreamingResponse closed the body iterator with aclose() on a send-side disconnect, raising GeneratorExit so the generators' cancellation handlers (which finish the api_monitor entry) never ran. It now throws CancelledError, falling back to aclose() when athrow is unavailable. Extends tests/test_gemma_tool_parse_edge_cases.py with array-of-objects, nested-array, and marker-inside-XML-parameter cases. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Watch disconnects on Anthropic streams; keep timestamps in Gemma values Two follow-ups on the streaming and tool-parse paths: - _anthropic_tool_stream and _anthropic_plain_stream drove generation through asyncio.to_thread(next, gen, ...) and only polled is_disconnected() between events, so a client disconnect during prefill or a long generation/tool step held the decode slot until the next event or a failed send. Both now run the _await_disconnect_then_cancel watcher used by the other local streams, stop it in finally, and break promptly when cancel_event is set. - _GEMMA_NEXT_KEY_RE treated any comma followed by word-chars-then-colon as the next key, so a bare value such as "meet at 10:00, 11:00 tomorrow" was split into bogus keys. The next-key token must now be identifier-shaped (start with a letter or underscore), so a comma before a timestamp, ratio, or other numeric-then-colon text stays part of the value. Adds a timestamp-in-bare-value regression test. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Guard nested markers, reset on disconnect, clean unstarted streams Three follow-ups on the tool-parse and streaming paths: - parse_tool_calls_from_text only skipped markers that fell inside a span it had already parsed successfully, so when an unquoted Gemma argument contained a literal marker (code:<|tool_call>call:terminal{...}<tool_call|>) the outer object failed to normalize, its span was never recorded, and the inner marker was promoted to a standalone terminal call. Candidates nested inside any other candidate's brace span are now skipped regardless of whether the enclosing candidate parsed, so a marker in malformed outer data is never executed. - /generate/stream skipped backend.reset_generation_state() when the disconnect watcher set cancel_event between chunks: the loop broke and the finally's reset is guarded on cancel_event being unset. A subprocess backend kept decoding after the client left. The cancel-break path now resets the backend. - _SameTaskStreamingResponse threw CancelledError / called aclose() on the body iterator on a send-side disconnect, but neither runs the try/finally of a generator that never started (early disconnect on http.response.start), so the passthrough's eagerly-opened upstream httpx stream and cancel-registry entry leaked. It now tracks whether the body started and, when it did not, runs an optional unstarted_cleanup hook; the OpenAI passthrough wires it to close the upstream resp/client and exit the cancel tracker. Adds a nested-unquoted-marker regression test. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> |
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| unsloth-cli.py | ||
Unsloth Studio lets you run and train models locally.
Features • Quickstart • Notebooks • Documentation
⚡ Get started
macOS, Linux, WSL:
curl -fsSL https://unsloth.ai/install.sh | sh
Windows:
irm https://unsloth.ai/install.ps1 | iex
Community:
⭐ Features
Unsloth Studio (Beta) lets you run and train text, audio, embedding, vision models on Windows, Linux and macOS.
Inference
- Search + download + run models including GGUF, LoRA adapters, safetensors
- Export models: Save or export models to GGUF, 16-bit safetensors and other formats.
- Tool calling: Support for self-healing tool calling and web search
- Code execution: lets LLMs test code in Claude artifacts and sandbox environments
- API inference endpoint: Deploy and run local LLMs in Claude Code, Codex tools with Unsloth
- Auto set inference settings and customize chat templates.
- We work directly with teams behind gpt-oss, Qwen3, Llama 4, Mistral, Gemma 1-3, and Phi-4, where we’ve fixed bugs that improve model accuracy.
- Chat with images, audio, PDFs, code, DOCX and more. Connect API providers (OpenAI, Anthropic) or servers (vLLM, Ollama).
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 and Hugging Face.
- Data Recipes: Auto-create datasets from PDF, CSV, DOCX etc. Edit data in a visual-node workflow.
- Reinforcement Learning (RL): The most efficient RL library, using 80% less VRAM for GRPO, FP8 etc.
- Supports full fine-tuning, RL, pretraining, 4-bit, 16-bit and, FP8 training.
- Observability: Monitor training live, track loss and GPU usage and customize graphs.
- Multi-GPU training is supported, with major improvements coming soon.
📥 Install
Unsloth can be used in two ways: through Unsloth Studio, the web UI, or through Unsloth Core, the code-based version. Each has different requirements.
Unsloth Studio (web UI)
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. Studio support is out soon.
- Multi-GPU: Available now, with a major upgrade on the way
macOS, Linux, WSL:
curl -fsSL https://unsloth.ai/install.sh | sh
Use the same command to update.
Windows:
irm https://unsloth.ai/install.ps1 | iex
Use the same command to update.
Launch
unsloth studio -p 8888
For cloud or global access, add -H 0.0.0.0. By default, Unsloth is accessible only locally.
For a secure HTTPS link instead of a raw network port, use unsloth studio --secure. Studio stays bound to localhost and is served only through a free Cloudflare HTTPS tunnel (it fails closed if the tunnel can't start, so the raw port is never exposed).
Docker
Use our Docker image unsloth/unsloth container. Run:
docker run -d -e JUPYTER_PASSWORD="mypassword" \
-p 8888:8888 -p 8000:8000 -p 2222:22 \
-v $(pwd)/work:/workspace/work \
--gpus all \
unsloth/unsloth
Developer, Nightly, Uninstall
To see developer, nightly and uninstallation etc. instructions, see advanced installation.
Unsloth Core (code-based)
Linux, WSL:
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv unsloth_env --python 3.13
source unsloth_env/bin/activate
uv pip install unsloth --torch-backend=auto
Windows:
winget install -e --id Python.Python.3.13
winget install --id=astral-sh.uv -e
uv venv unsloth_env --python 3.13
.\unsloth_env\Scripts\activate
uv pip install unsloth --torch-backend=auto
For Windows, pip install unsloth works only if you have PyTorch installed. Read our Windows Guide.
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 and DGX Spark.
To install Unsloth on AMD and Intel GPUs, follow our AMD Guide and Intel Guide.
📒 Free Notebooks
Train for free with our notebooks. You can use our new free Unsloth Studio notebook to run and train models for free in a web UI. Read our guide. Add dataset, run, then deploy your trained model.
| Model | Free Notebooks | Performance | Memory use |
|---|---|---|---|
| Gemma 4 (E2B) | ▶️ Start for free | 1.5x faster | 50% less |
| Qwen3.5 (4B) | ▶️ Start for free | 1.5x faster | 60% less |
| gpt-oss (20B) | ▶️ Start for free | 2x faster | 70% less |
| Qwen3.5 GSPO | ▶️ Start for free | 2x faster | 70% less |
| gpt-oss (20B): GRPO | ▶️ Start for free | 2x faster | 80% less |
| Qwen3: Advanced GRPO | ▶️ Start for free | 2x faster | 70% less |
| embeddinggemma (300M) | ▶️ Start for free | 2x faster | 20% less |
| Mistral Ministral 3 (3B) | ▶️ Start for free | 1.5x faster | 60% less |
| Llama 3.1 (8B) Alpaca | ▶️ Start for free | 2x faster | 70% less |
| Llama 3.2 Conversational | ▶️ Start for free | 2x faster | 70% less |
| Orpheus-TTS (3B) | ▶️ Start for free | 1.5x faster | 50% less |
- See all our notebooks for: Kaggle, GRPO, TTS, embedding & Vision
- See all our models and all our notebooks
- See detailed documentation for Unsloth here
🦥 Unsloth News
- Connections: Connect any API provider (OpenAI, Anthropic) or server (vLLM, Ollama). Guide
- MTP: Run Qwen3.6 MTP in Unsloth. MTP settings are autoset specific to your hardware. Guide
- API inference endpoint: Deploy and run local LLMs in Claude Code, Codex tools. Guide
- Qwen3.6: Qwen3.6-35B-A3B can now be trained and run in Unsloth Studio. Blog
- Gemma 4: Run and train Google’s new models directly in Unsloth. Blog
- Introducing Unsloth Studio: our new web UI for running and training LLMs. Blog
- Qwen3.5 - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. Guide + notebooks
- Train MoE LLMs 12x faster with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. Blog
- Embedding models: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. Blog • Notebooks
- New 7x longer context RL vs. all other setups, via our new batching algorithms. Blog
- New RoPE & MLP Triton Kernels & Padding Free + Packing: 3x faster training & 30% less VRAM. Blog
- 500K Context: Training a 20B model with >500K context is now possible on an 80GB GPU. Blog
- FP8 & Vision RL: You can now do FP8 & VLM GRPO on consumer GPUs. FP8 Blog • Vision RL
📥 Advanced Installation
The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, view our docs.
Developer / Nightly / Experimental installs: macOS, Linux, WSL:
The developer install builds from the main branch, which is the latest (nightly) source.
git clone https://github.com/unslothai/unsloth
cd unsloth
./install.sh --local
unsloth studio -p 8888
To install into an isolated location (its own virtual env, auth/, studio.db, cache and llama.cpp build), set UNSLOTH_STUDIO_HOME and pass it again at launch:
UNSLOTH_STUDIO_HOME="$PWD/.studio" ./install.sh --local
UNSLOTH_STUDIO_HOME="$PWD/.studio" unsloth studio -p 8888
Then to update :
cd unsloth && git pull
./install.sh --local
unsloth studio -p 8888
Developer / Nightly / Experimental installs: Windows PowerShell:
The developer install builds from the main branch, which is the latest (nightly) source.
git clone https://github.com/unslothai/unsloth.git
cd unsloth
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -p 8888
To install into an isolated location (its own virtual env, auth/, studio.db, cache and llama.cpp build), set UNSLOTH_STUDIO_HOME and pass it again at launch:
$env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; .\install.ps1 --local
$env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; unsloth studio -p 8888
Then to update :
cd unsloth; git pull
.\install.ps1 --local
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.
unsloth studio --secure -p 8888
-H 0.0.0.0: bind the raw port on all network interfaces, reachable from anywhere on the network. Only use this on a trusted network.
unsloth studio -H 0.0.0.0 -p 8888
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.
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.
Skip PyTorch (GGUF-only mode):
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_NO_TORCH=1 sh
$env:UNSLOTH_NO_TORCH=1; irm https://unsloth.ai/install.ps1 | iex
Pin the Python version:
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_PYTHON=3.12 sh
$env:UNSLOTH_PYTHON='3.12'; irm https://unsloth.ai/install.ps1 | iex
Install to a custom location with UNSLOTH_STUDIO_HOME:
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_STUDIO_HOME=/abs/path sh
$env:UNSLOTH_STUDIO_HOME='C:\path'; irm https://unsloth.ai/install.ps1 | iex
Cap Studio's native CPU thread pools on high-core hosts: UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888.
Uninstall
The recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS .app bundle + Launch Services on Mac; Start Menu, HKCU\Software\Unsloth registry key and user PATH entries on Windows):
- MacOS, WSL, Linux:
curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh - Windows (PowerShell):
irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex
If you only want to drop the install dir and keep the launcher/shortcut for a later reinstall, you can instead run rm -rf ~/.unsloth/studio (Mac/Linux/WSL) or Remove-Item -Recurse -Force "$HOME\.unsloth\studio" (Windows). The model cache at ~/.cache/huggingface is not touched by any of these.
For more info, see our docs.
Deleting model files
You can delete old model files either from the bin icon in model search or by removing the relevant cached model folder from the default Hugging Face cache directory. By default, HF uses:
- MacOS, Linux, WSL:
~/.cache/huggingface/hub/ - Windows:
%USERPROFILE%\.cache\huggingface\hub\
💚 Community and Links
| Type | Links |
|---|---|
 Discord |
Join Discord server |
 r/unsloth Reddit |
Join Reddit community |
| 📚 Documentation & Wiki | Read Our Docs |
_logo_late_2025.svg){kind=logo} Twitter (aka X) |
Follow us on X |
| 🔮 Our Models | Unsloth Catalog |
| ✍️ Blog | Read our Blogs |
Citation
You can cite the Unsloth repo as follows:
@software{unsloth,
author = {Daniel Han, Michael Han and Unsloth team},
title = {Unsloth},
url = {https://github.com/unslothai/unsloth},
year = {2023}
}
If you trained a model with 🦥Unsloth, you can use this cool sticker!
License
Unsloth uses a dual-licensing model of Apache 2.0 and AGPL-3.0. The core Unsloth package remains licensed under Apache 2.0, while certain optional components, such as the Unsloth Studio UI are licensed under the open-source license AGPL-3.0.
This structure helps support ongoing Unsloth development while keeping the project open source and enabling the broader ecosystem to continue growing.
Thank You to
- The llama.cpp library that lets users run and save models with Unsloth
- The Hugging Face team and their libraries: transformers and TRL
- The Pytorch and Torch AO team for their contributions
- NVIDIA for their NeMo DataDesigner library and their contributions
- And of course for every single person who has contributed or has used Unsloth!