Unsloth is a local UI for training and running Gemma 4, Qwen3.6, DeepSeek, Kimi, GLM and other models.
https://unsloth.ai/docs
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unsloth
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d79fd92798* studio: scope cancel-cleanup to in-flight tmp dirs; walk back tool_call_id Two follow-ups to #5375's training and chat hardening. _cleanup_cancelled_checkpoints used to rmtree every checkpoint-N directory on Cancel. That is the opposite of what the user expects. A user cancelling an 8h run with save_steps=2000 loses every completed checkpoint they could have resumed from. The 67 MB residue the audit memo flagged is the HF Trainer atomic-rename partial (tmp-checkpoint-N), not the completed ones. The cleanup now targets only tmp-checkpoint subdirs; completed checkpoint-N directories are user-owned and stay. Symlinked output_dir and symlinked children are skipped so the realpath containment cannot be levered into deleting arbitrary content via a symlink trick. ChatMessage._validate_role_shape stamped a random secrets.token_hex id on tool messages with no tool_call_id. That id is uncorrelated with the prior assistant tool_calls id, so strict passthrough backends (OpenAI, Anthropic) reject the request as orphaned and llama.cpp treats the tool result as "no preceding call" and hallucinates. The synthesis moves up to ChatCompletionRequest, where the whole conversation is visible: for each tool message missing an id we walk back to the most recent assistant turn with tool_calls (stopping at user turns), prefer a function.name match, otherwise take the first unconsumed tool_call. Synthesis is the fallback when no candidate assistant turn exists, preserving the prior round-trip guarantee for orphaned tool messages. Tests: - test_cleanup_cancelled_checkpoints.py (new): pins that completed checkpoint subdirs survive, tmp-checkpoint partials are removed, non-int suffixes (checkpoint-final, checkpoint-best) are left alone, output_dir outside outputs_root is refused, symlinked output_dir and symlinked child are both skipped, missing dir is a no-op. - test_inference_model_validation.py: 6 new walkback cases covering name-match preference, first-unconsumed fallback, explicit-id passthrough, multi-tool-result pairing, synth-on-no-parent, and no-cross-user-turn invariant. - test_openai_tool_passthrough.py: the two ChatMessage-level synth-on-missing tests are rewritten to assert that the per- message validator now leaves tool_call_id untouched; resolution coverage lives in the request-level tests above. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: explicit tool_call_id reserve, numeric tmp-checkpoint suffix only Reviewer follow-ups to the training-cleanup + tool_call_id walkback PR. tool_call_id walkback: a mixed assistant turn with [call_a, call_b] followed by a tool result that carried tool_call_id="call_a" and a sibling tool result with no id resolved to ['call_a', 'call_a'] because the explicit id never reserved call_a in the consumed set. Added a pre-pass over the message list that walks back from every role="tool" message carrying an explicit id and marks the matching (asst_idx, tc_idx) consumed, then the missing-id walkback runs against that pre-populated set. The second result now resolves to call_b. While here, also harden the function-shape check: if a provider ships a malformed tool_call where `function` is a string rather than a dict, the old `(tc.get("function") or {}).get("name")` raised AttributeError on the string's .get; now isinstance-gated so the walkback falls through to the fallback id without raising. Cancel cleanup: `tmp-checkpoint-*` is too broad. HF Trainer's in-flight partials are always `tmp-checkpoint-<integer-step>`, so constrain the cleanup regex to `^tmp-checkpoint-\d+$`. A user folder named `tmp-checkpoint-final`, `tmp-checkpoint-backup`, or `tmp-checkpoint-user-notes` is now preserved. ChatMessage docstring still pointed at the pre-PR contract that required `tool_call_id` on every role="tool" message. Updated to say missing ids are accepted at message scope and resolved at ChatCompletionRequest scope. Inline comment above the cancel-cleanup call now describes the actual behaviour (in-flight tmp partials, completed checkpoints preserved). Test: - python -m pytest studio/backend/tests/test_inference_model_validation.py studio/backend/tests/test_cleanup_cancelled_checkpoints.py studio/backend/tests/test_openai_tool_passthrough.py -q -> 76 passed (was 67 before this commit; +2 walkback regression tests, +1 numeric-suffix preservation test) * studio: trim verbose comments in cleanup + tool_call_id walkback Move the HF tmp-checkpoint regex to module scope as a named constant. Drop the multi-paragraph docstring on _cleanup_cancelled_checkpoints and the inline call-site rationale; the function name + the test class already cover the why. Compress _resolve_missing_tool_call_ids docstring from a six-line explanation to two. Same logic, fewer in-flow tutorials. 76 tests in cleanup + inference-model-validation + tool-passthrough pass. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> |
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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.
- Upload images, audio, PDFs, code, DOCX and more file types to chat with.
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: Currently supports chat and Data Recipes. MLX training is coming very soon
- AMD: Chat + Data works. Train with Unsloth Core. Studio support is out soon.
- Coming soon: Training support for Apple MLX, AMD, and Intel.
- Multi-GPU: Available now, with a major upgrade on the way
macOS, Linux, WSL:
curl -fsSL https://unsloth.ai/install.sh | sh
Windows:
irm https://unsloth.ai/install.ps1 | iex
Launch
unsloth studio -p 8888
For cloud VMs or LAN access, add
-H 0.0.0.0to bind on all interfaces.
Update
To update, use the same install commands as above. Or run (does not work on Windows):
unsloth studio update
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
- 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 installs: macOS, Linux, WSL:
git clone https://github.com/unslothai/unsloth
cd unsloth
./install.sh --local
unsloth studio -p 8888
Then to update :
unsloth studio update
Developer installs: Windows PowerShell:
git clone https://github.com/unslothai/unsloth.git
cd unsloth
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -p 8888
Then to update :
unsloth studio update
Nightly: MacOS, Linux, WSL:
git clone https://github.com/unslothai/unsloth
cd unsloth
git checkout nightly
./install.sh --local
unsloth studio -p 8888
Then to launch every time:
unsloth studio -p 8888
Nightly: Windows:
Run in Windows Powershell:
git clone https://github.com/unslothai/unsloth.git
cd unsloth
git checkout nightly
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -p 8888
Then to launch every time:
unsloth studio -p 8888
Uninstall
You can uninstall Unsloth Studio by deleting its install folder usually located under $HOME/.unsloth/studio on Mac/Linux/WSL and %USERPROFILE%\.unsloth\studio on Windows. Using the rm -rf commands will delete everything, including your history, cache:
- MacOS, WSL, Linux:
rm -rf ~/.unsloth/studio - Windows (PowerShell):
Remove-Item -Recurse -Force "$HOME\.unsloth\studio"
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!