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
- Python 71.5%
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8e977445d4* feat: inject local model provider into recipe jobs via JWT * feat: auto-generate JWT for local model providers in recipes * feat: add is_local flag to model provider config types and utils * fix(studio): skip endpoint validation for local providers * feat(studio): add local/external model source toggle to provider dialog * feat(studio): thread localProviderNames through model config dialog chain * feat(studio): show 'Local model (Chat)' label for local model_provider configs * fix: hardcode loopback for local endpoint, clear stale creds on toggle * fix: document TOCTOU/JWT rotation, add deferred import comments, fix is_local serialization * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix(studio): clear stale local model state on provider toggle and validation * fix(studio): override empty local endpoint in validation and skip model gate for unused providers * fix(studio): resolve loopback port from app.state, clear stale local provider fields, sync model id on toggle Address review feedback on the local-model-provider flow: - Backend (jobs.py): _resolve_local_v1_endpoint now reads the actual bound port from app.state.server_port (set in run.py after binding) instead of parsing it out of request.base_url, which is wrong behind any reverse proxy or non-default port. The two duplicated urlparse blocks are gone. - Backend (jobs.py): defensively pop api_key_env, extra_headers, extra_body from local providers so a previously external provider that flipped to local cannot leak invalid JSON or rogue auth headers into the local /v1 call. Also dedupe the post-loop assignment and tighten the local-name intersection so empty names cannot match. - Backend (jobs.py): hoist datetime and urllib.parse imports to the top import block for consistency with the rest of the file. - Backend (run.py): expose the bound port on app.state.server_port after the uvicorn server is constructed. - Frontend (model-provider-dialog.tsx): clear extra_headers and extra_body when toggling to local mode. Hidden inputs would otherwise keep stale JSON blocking validate/run. - Frontend (model-config-dialog.tsx): factor the local-aware provider selection logic into applyProviderChange and call it from both onValueChange and onBlur, so manually typing a provider name and tabbing away keeps the model field consistent. - Frontend (recipe-studio.ts store): handle both directions of the is_local toggle in the cascade. external -> local now backfills model: "local" on already-linked model_configs so they pass validation immediately, mirroring the existing local -> external clear path. - Frontend (validate.ts + build-payload.ts): thread localProviderNames into validateModelConfigProviders and skip the "model is required" check for local-linked configs. Local providers do not need a real model id since the inference endpoint uses the loaded Chat model. * fix(studio): narrow store cascade types, sync model placeholder on graph relink and node removal, harden ephemeral port path Loop 2 review fixes: - recipe-studio.ts: type-narrow next.is_local by also checking next.kind === "model_provider". TS otherwise raised TS2339 because next was typed as the union NodeConfig after the spread. The behavior is unchanged but the code now compiles cleanly. - model-config-dialog.tsx: convert the lastProviderRef / providerInputRef ref-during-render pattern (pre-existing react-hooks/refs lint error) to a useEffect that syncs providerInputRef from config.provider. The combobox blur path still uses applyProviderChange and remains stable. - recipe-graph-connection.ts: when a graph drag links a model_provider to a model_config, mirror the dialog applyProviderChange behavior: fill model: "local" if the new provider is local and the model field is blank, clear model when relinking from a local placeholder to an external provider, otherwise leave the model alone. - reference-sync.ts: when a referenced provider node is removed, clear the synthetic model: "local" placeholder along with the provider field, so a future relink to an external provider does not pass validation with a stale value that fails at runtime. - run.py: only publish app.state.server_port when the bound port is a real positive integer; for ephemeral binds (port==0) leave it unset and let request handlers fall back to request.base_url. - jobs.py: _resolve_local_v1_endpoint also falls back when app.state.server_port is non-positive, and uses `is None` instead of the truthy fallback so a literal 0 is handled correctly. * fix(studio): strict is_local check, narrow loaded-model gate to LLM-reachable configs, add scope-server port fallback Loop 3 review fixes: - jobs.py, validate.py: require `is_local is True` instead of truthy check. Malformed payloads such as is_local: "false" or is_local: 1 would otherwise be treated as local and silently rewritten to the loopback endpoint. - jobs.py: _resolve_local_v1_endpoint now tries request.scope["server"] (the actual uvicorn-assigned (host, port) tuple) as a second resolution step before falling back to parsing request.base_url. This covers direct-uvicorn startup paths and ephemeral binds that never publish app.state.server_port. - jobs.py: new _used_llm_model_aliases helper collects the set of model_aliases that an LLM column actually references, and the "Chat model loaded" gate is now only triggered when a local provider is reachable from that set. Orphan model_config nodes on the canvas no longer block unrelated recipe runs. * fix(studio): force skip_health_check on local-linked configs, skip JSON parsing for local providers, local-aware inline editor Loop 4 review fixes: - jobs.py: after rewriting local providers, also force skip_health_check: true on any model_config linked to a local provider. The /v1/models endpoint only advertises the real loaded model id, so data_designer's default model-availability health check would otherwise fail against the placeholder "local" id before the first chat completion call. The inference route already ignores the model id in chat completions, so skipping the check is safe. - builders-model.ts: buildModelProvider now short-circuits for local providers and emits only { name, endpoint: "", provider_type, is_local } without running parseJsonObject on the hidden extra_headers/extra_body inputs. Imported or hydrated recipes with stale invalid JSON in those fields no longer block client-side validate/run. - inline-model.tsx: the model_config branch now accepts an optional localProviderNames prop and mirrors the dialog applyProviderChange behavior. Changing provider to/from a local one auto-fills or clears the "local" placeholder consistently with the other edit paths. - recipe-graph-node.tsx: derive localProviderNames from the store via useMemo (stable identity) and pass it through renderNodeBody to <InlineModel>. Hooks order is preserved by declaring them above the early return for markdown_note nodes. - run.py: minor comment tweak - loop 3 already added the scope-server fallback path, note that in the comment. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: danielhanchen <info@unsloth.ai> |
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Run and train AI models with a unified local interface.
Features • Quickstart • Notebooks • Documentation • Reddit
Unsloth Studio (Beta) lets you run and train text, audio, embedding, vision models on Windows, Linux and macOS.
⭐ Features
Unsloth provides several key features for both inference and training:
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
- Auto-tune inference parameters 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.
⚡ Quickstart
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 -H 0.0.0.0 -p 8888
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
- Gemma 4: Run and train Google’s new models directly in Unsloth Studio! 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
- gpt-oss by OpenAI: Read our RL blog, Flex Attention blog and Guide.
📥 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 -H 0.0.0.0 -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 -H 0.0.0.0 -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 -H 0.0.0.0 -p 8888
Then to launch every time:
unsloth studio -H 0.0.0.0 -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 -H 0.0.0.0 -p 8888
Then to launch every time:
unsloth studio -H 0.0.0.0 -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!