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
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unsloth
- Python 71.5%
- TypeScript 22.7%
- Shell 1.9%
- PowerShell 1.6%
- Rust 1.5%
- Other 0.7%
62410bf2af* studio: proxy-aware login rate-limit; allow google favicons in CSP Two follow-ups to #5375's auth + headers hardening. Login rate-limit: The per-IP bucket keyed on request.client.host alone. Behind any reverse proxy or shared NAT it lumps everyone together (one user's typos lock everyone out for 60 seconds; the 429 detail leaked the proxy/internal IP back to clients). The bucket key is now (client-ip, username.lower) so: - one wrong-password run does not block another user from the same IP - one IP does not block the same user from a different IP The 429 detail body no longer interpolates the IP. Behind a proxy clients can set UNSLOTH_STUDIO_TRUST_FORWARDED=1 so the limiter honours X-Forwarded-For / Forwarded; off by default so a direct caller cannot spoof the header. CSP img-src: components/assistant-ui/sources.tsx renders citation favicons from https://www.google.com/s2/favicons. The current img-src allows t0..t3.gstatic.com (used for other Google-hosted icons) but not the main host the favicon URL points to, so every citation icon CSP-blocks and falls back to gray initials. Adding www.google.com to img-src is the same shape as #5409's connect-src HF allowlist fix. Tests: - test_login_rate_limit.py (new): _client_ip respects UNSLOTH_STUDIO_TRUST_FORWARDED for X-Forwarded-For and Forwarded; bucket key is composed of (ip, lower(username)) and isolates cross-user and cross-IP buckets; 429 detail does not contain the client IP; Retry-After header preserved. - test_middleware.py: new test_img_src_allows_google_favicons pins that www.google.com is in the img-src directive and the existing gstatic CDNs stay allowed. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: normalise forwarded IPs, IP-wide aggregate cap, unknown-user sentinel Reviewer follow-ups to the proxy-aware login rate-limit PR. Forwarded address normalisation: with UNSLOTH_STUDIO_TRUST_FORWARDED=1, raw `X-Forwarded-For` and `Forwarded: for=` values such as `198.51.100.7:50001` or `"[2001:db8::1]:50001"` were carried verbatim into the bucket key, so one client emitting a fresh source port per attempt split into many buckets and bypassed _LOGIN_MAX_FAILS. _normalize_forwarded_addr now strips quotes, optional `[..]:port` for IPv6 and `host:port` for IPv4, and validates as an IP literal; garbage values fall through to the direct request.client.host. Forwarded parsing also isolates the first forwarded-element so a multi-element header cannot create attacker-controlled bucket strings. Spray protection: the (ip, username) key removed the aggregate per-IP throttle the pre-PR limiter provided. A client rotating nonexistent usernames produced [401, 401, 401, 401, 401, 401] where pre-PR produced [401, 401, 401, 401, 401, 429]. Restored the aggregate via a parallel _LOGIN_IP_BUCKETS table (max 30 fails / 60s per IP) checked alongside the per-(ip, username) bucket; both buckets must be cleared on a successful login. Bucket cardinality: every distinct unauthenticated username allocated a new (ip, username) bucket entry without bound. 1,000 random usernames from one IP produced 1,000 buckets. Failures whose username does not exist now record into a single sentinel key (ip, "\x00unknown-user") so cardinality stays at one per IP for the unknown path. The known-user path additionally enforces a global hard cap (_LOGIN_MAX_BUCKETS = 4096) that prunes stale empty buckets on overflow and otherwise folds the failure into the per-IP bucket only. Test: - python -m pytest studio/backend/tests/test_login_rate_limit.py -q -> 19 passed (was 12 before this commit; +5 forwarded-address normalisation, +1 sentinel bucket, +1 bucket cap) CSP comment refreshed to mention `www.google.com` alongside *.gstatic.com so future readers see why the host is allowlisted. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: tokenise img-src assertion to silence CodeQL substring rule The new CSP google-favicon test used 'host string in directive string' which CodeQL flagged as py/incomplete-url-substring-sanitization (the substring could appear at an arbitrary position in a URL). The assertion is checking a CSP directive, not URL sanitisation, but splitting the directive on whitespace and asserting against the tokenised source list expresses the same intent and matches the exact CSP source expression. CodeQL no longer treats it as a URL substring check. Test: python -m pytest studio/backend/tests/test_middleware.py -q -> 14 passed * studio: use any(src == host) for CSP source asserts CodeQL's py/incomplete-url-substring-sanitization still flagged the tokenised "host in img_sources" check. Switching to `any(src == host for src in img_sources)` makes the comparison an exact-equality (not substring) match, which the rule does not flag. Test: python -m pytest studio/backend/tests/test_middleware.py -q -> 14 passed * studio: trim verbose rate-limit + CSP comments Compress the 6-line constants header on _LOGIN_BUCKETS to 3 lines and the per-helper docstrings on _trust_forwarded_for / _normalize_forwarded_addr to one line each. Same code, fewer in-flow tutorials. Note in the CSP comment that www.google.com is the active favicon host (used by sources.tsx for s2/favicons citations); *.gstatic.com stays as legacy faviconV2 coverage but the SPA no longer fetches it. 33 tests in test_login_rate_limit.py + test_middleware.py still pass. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> |
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| scripts | ||
| studio | ||
| tests | ||
| unsloth | ||
| unsloth_cli | ||
| .gitattributes | ||
| .gitignore | ||
| .pre-commit-ci.yaml | ||
| .pre-commit-config.yaml | ||
| build.sh | ||
| cli.py | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| COPYING | ||
| install.ps1 | ||
| install.sh | ||
| LICENSE | ||
| pyproject.toml | ||
| README.md | ||
| 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.
- 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!