Features • Quickstart • Notebooks • Documentation

Download the native Unsloth Desktop app for your operating system:

Download from Unsloth or GitHub Releases.

Or if you prefer to install manually:

curl -fsSL https://unsloth.ai/install.sh | sh

On macOS, you can also install Unsloth Desktop with Homebrew:

brew install --cask unsloth

irm https://unsloth.ai/install.ps1 | iex

The Unsloth Docker image unsloth/unsloth is available on Docker. Read guide.

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Unsloth works on Windows, Linux, WSL and macOS. We support Multi GPU setups, NVIDIA, AMD, Intel GPUs, CPUs and the Vulkan backend.

• Run and train LLMs, MLX, GGUF, diffusion, embedding, audio models: Qwen3.8, GLM-5.3-Flash, Kimi K3, Qwen-Image-2.1, MiniMax-H3, DeepSeek-V4, Gemma 4.
• Agents & Tools: Use local models with Claude Code, Codex, and MCP, including tool calling and code execution.
• Search & RAG: Use private and unlimited web search, deep research, auto-compaction (rolling context window) and RAG.
• Image and video: Run and train image and video diffusion or multimodal models
• Remote & LAN: Access your local models from any device on LAN or remotely through secure Cloudflare HTTPS.
• Connect: Serve models through an OpenAI compatible API. Also connect your ChatGPT/Codex subscription and cloud providers

• Fine-tuning: Train LLMs, diffusion, TTS, and embedding models 2× faster with 70% less VRAM with no accuracy loss
• Complete support: Supports reinforcement learning, LoRA, QLoRA, full fine tuning, pretraining, RL, GRPO, DPO, and FP8.
• Export & Deploy: Export or Deploy models including GGUF, NVFP4, FP8 and more formats.
• Datasets: Build datasets from PDFs, CSVs, DOCX files, and more with Data Recipes.

Unsloth Start connects Claude Code, Codex and other agents to local models with one command.

unsloth start claude --model unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL

Unsloth can be used in three ways: Unsloth Desktop, the desktop app; Unsloth Studio, the web UI; or Unsloth Core, the code based version.

curl -fsSL https://unsloth.ai/install.sh | sh

On macOS, you can also install Unsloth Desktop with Homebrew:

brew install --cask unsloth

irm https://unsloth.ai/install.ps1 | iex

unsloth studio

unsloth studio --secure

Use our Docker image unsloth/unsloth. On Linux, set up GPU access once with curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/docker/install_nvidia_toolkit.sh -o install_nvidia_toolkit.sh && sudo -E bash install_nvidia_toolkit.sh (Windows: Docker Desktop with WSL 2).

Linux / WSL (Bash):

# use  -e UNSLOTH_STUDIO_SECURE=1  instead of -p 8000:8000 for a public Cloudflare HTTPS link
docker run -d --name unsloth --gpus all --ipc=host \
  -p 8000:8000 -p 8888:8888 \
  -v "$PWD":/workspace/host \
  -v "$HOME/.cache/huggingface":/workspace/.cache/huggingface \
  -v unsloth-studio:/opt/unsloth-studio \
  unsloth/unsloth && docker logs -f unsloth

See Docker docs for more information. For cloud hosting / global serving, add -e UNSLOTH_STUDIO_SECURE=1, drop -p 8000:8000 and bind JupyterLab to -p 127.0.0.1:8888:8888, or bind both to 127.0.0.1 and use an SSH tunnel. Tags (unsloth/unsloth:core for notebooks only), GPU support and options: Docker Hub.

On AMD there is a separate image, unsloth/unsloth-rocm, with the run command and the supported cards on its Docker Hub page.

Server-side tools are on by default - so be careful! Keep your password safe, or use --disable-tools when exposing Unsloth.

Global HTTPS Access: Creates a free Cloudflare link that serves Unsloth - you can access the link globally (even on your phone!)

unsloth studio --secure

-H 0.0.0.0 and different ports also work:

unsloth studio -H 0.0.0.0 -p 8888

LAN Access (home network): Settings > API keys > LAN access

Exposing Unsloth (--secure, --cloudflare, or a non-loopback -H) asks once at the terminal for a new admin password. Ctrl+C there aborts the launch rather than exposing the auto-generated one; set a password non-interactively instead, or use -H 127.0.0.1 to stay off the network.

Headless starts:

UNSLOTH_STUDIO_PASSWORD='your-strong-password' unsloth studio --secure   # via env var

Reset your password:

unsloth studio reset-password

To see developer, nightly and uninstallation etc. instructions, see advanced installation.

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

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

See our Blackwell guide and DGX Spark guide.
To install Unsloth on AMD and Intel GPUs, follow our AMD Guide and Intel Guide.

Train for free with our notebooks. Read our guide. Add dataset, run, then deploy your trained model.

• See all our notebooks for: Kaggle, GRPO, TTS, embedding & Vision
• See all our models and all our notebooks
• See detailed documentation for Unsloth here

• AMD training: Train, run RL, chat and deploy on AMD GPUs across Windows, WSL and Linux. Guide
• Local models for any agent: Use unsloth start with Claude Code, Codex, Hermes, OpenCode, OpenClaw, DeepSeek Harness and more through Unsloth's OpenAI- and Anthropic-compatible APIs. Guide
• GLM-5.2: Run Z.ai's 744B-parameter, 1M-context open model locally with Unsloth Dynamic GGUFs. Guide
• DeepSeek-V4: Run DeepSeek-V4-Flash locally with corrected multi-turn and tool-calling behavior. Guide
• Gemma 4: Run and train Gemma 4 text, image and audio models with QAT, MTP, GGUF and MLX support. Guide
• MCP servers: Connect local models to files, apps, databases and external tools through Model Context Protocol. Guide
• New models: Qwen-AgentWorld, Ornith, Kimi K2.7 Code and MiniMax M3

• Connections: Mix local models with API providers (OpenAI, Anthropic) or servers (vLLM, Ollama) in the same interface. Guide
• Introducing Unsloth Studio: our new web UI for running and training LLMs. Blog
• DiffusionGemma: Run and fine-tune Google's diffusion language model with 1.8x faster inference in Unsloth Studio. Guide
• Qwen3.6: Run and train Qwen3.6 with MTP for 1.4-2.2x faster inference and NVFP4 quants for supported GPUs. Guide
• 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

The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, view our docs.

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, set UNSLOTH_STUDIO_HOME:

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

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 RemoteSigned
.\install.ps1 --local
unsloth studio -p 8888

To install into an isolated location, set UNSLOTH_STUDIO_HOME:

$env:UNSLOTH_STUDIO_HOME="$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

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

Skip the post-install prompt that starts Unsloth (useful for automated installs):

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_SKIP_AUTOSTART=1 sh

$env:UNSLOTH_SKIP_AUTOSTART=1; irm https://unsloth.ai/install.ps1 | iex

Keep the install-time package cache under the Unsloth Studio directory instead of reusing an existing uv cache. Downloads are slower the first time, and an explicit UV_CACHE_DIR still wins over this:

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_ISOLATE_UV_CACHE=1 sh

$env:UNSLOTH_ISOLATE_UV_CACHE=1; irm https://unsloth.ai/install.ps1 | iex

For a local run the flag is --isolated-uv-cache:

./install.sh --local --isolated-uv-cache

.\install.ps1 --local --isolated-uv-cache

Discard the previous environment immediately when reinstalling, instead of keeping a copy until the new one works. A reinstall normally holds both at once, so it needs room for two; this needs room for one, at the cost of not being able to undo a failed install:

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_INSTALL_NO_ROLLBACK=1 sh

$env:UNSLOTH_INSTALL_NO_ROLLBACK=1; irm https://unsloth.ai/install.ps1 | iex

For a local run the flag is --no-rollback:

./install.sh --local --no-rollback

.\install.ps1 --local --no-rollback

Pinning 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

Point the frontend build at a corporate npm mirror/proxy with UNSLOTH_NPM_REGISTRY:

UNSLOTH_NPM_REGISTRY=https://artifactory.example.com/api/npm/npm/ ./install.sh --local

$env:UNSLOTH_NPM_REGISTRY='https://artifactory.example.com/api/npm/npm/'; .\install.ps1 --local

Cap Unsloth's native CPU thread pools on high-core hosts: UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888.

You can force the backend during installation:

export UNSLOTH_LLAMA_CPP_BACKEND=vulkan   # or cpu, cuda, rocm, auto
curl -fsSL https://unsloth.ai/install.sh | sh

$env:UNSLOTH_LLAMA_CPP_BACKEND="vulkan"   # or cpu, cuda, rocm, auto
irm https://unsloth.ai/install.ps1 | iex

Setup also installs audio.cpp (prebuilt by unslothai/audio.cpp), which runs the audio.cpp speech, music and speech-to-text models on the Audio page, in Voice settings and behind /v1/audio/*. It is optional and never blocks setup:

• UNSLOTH_SKIP_AUDIO_CPP_INSTALL=1 skips it.
• UNSLOTH_AUDIO_CPP_ACCELERATOR=cpu (or cuda, vulkan, metal) picks the bundle instead of auto-detecting.
• AUDIOCPP_SERVER_PATH=/path/to/audiocpp_server uses your own build.

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

For more info, see our docs.

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\

Type	Links
Discord	Join Discord server
r/unsloth Reddit	Join Reddit community
📚 Documentation & Wiki	Read Our Docs
Twitter (aka X)	Follow us on X
🔮 Our Models	Unsloth Catalog
✍️ Blog	Read our Blogs

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!  

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.

• 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!