Unsloth is the first desktop app to run and train models.

Features • Quickstart • Notebooks • Documentation

⚡ Get started

Download the native Unsloth Desktop app for your operating system:

Platform	Link
Windows	Download
macOS	Download
Linux / Ubuntu (deb)	Download
Linux (AppImage)	Download

Download from Unsloth or GitHub Releases.

Or if you prefer to install manually:

macOS, Linux, WSL:

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

Windows:

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

Community:

• Discord
• 𝕏 (Twitter)
• Reddit

⭐ Features

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

Run & Build with AI

• Run and train LLMs, MLX, GGUF, diffusion, embedding, audio models: Qwen3.8, GLM-5.3-Flash, Kimi K3, 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

Train & Deploy

• 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 with including GGUF, NVFP4, FP8 and more formats.
• Datasets: Build datasets from PDFs, CSVs, DOCX files, and more with Data Recipes.

🚀 Unsloth Start

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

| Agent | Command | | --- | --- | | Claude Code | unsloth start claude | | OpenAI Codex | unsloth start codex | | DeepSeek Harness | unsloth start dsh | | Hermes Agent | unsloth start hermes | | OpenCode | unsloth start opencode | | OpenClaw | unsloth start openclaw |

📥 Install

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

Unsloth Desktop (recommended)

Platform	Link
Windows	Download
macOS	Download
Linux / Ubuntu (deb)	Download
Linux (AppImage)	Download

Unsloth Studio (web UI)

macOS, Linux, WSL:

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

Windows:

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

Launch

unsloth studio

HTTP Secure Deployment

unsloth studio --secure

Docker

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). Run:

docker run -d --gpus all --ipc=host \
  -p 8000:8000 -p 8888:8888 \
  -e UNSLOTH_STUDIO_PASSWORD="mypassword" -e JUPYTER_PASSWORD="mypassword" \
  -v "$PWD":/workspace/host \
  -v unsloth-studio:/opt/unsloth-studio \
  unsloth/unsloth

Follow startup with docker logs -f. Studio is at http://localhost:8000 (user unsloth), JupyterLab at http://localhost:8888. The unsloth-studio volume keeps your accounts, chats and trained models across docker rm; each image brings its own Studio code, and a volume from an older image is migrated on the first start (its old code is kept under .unsloth-studio-legacy/). Tags (unsloth/unsloth:core for notebooks only), GPU support and options: Docker Hub.

Remote HTTPS & LAN Access

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

Password management & headless starts

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. On a non-loopback -H bind a terminal nobody answers is not a refusal: after ~30s Unsloth starts anyway and shuts down on the bootstrap deadline, so detached launches (docker run -dt, tmux new -d) are unaffected. Setting UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT=0 disables that shutdown, so give those launches a password instead. A tunnel gets no such timeout and waits indefinitely rather than publish a public URL unasked, so give a detached --secure / --cloudflare launch its password non-interactively.

Headless starts:

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

Reset your password:

unsloth studio reset-password

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

AMD, Intel, DGX Spark, Blackwell:

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

📒 Free Notebooks

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

| Model | Free Notebooks | Performance | Memory use | |-----------|---------|--------|----------| | Unsloth Studio | ▶️ Start for free | | | | Gemma 4 (E2B) | ▶️ Start for free-Vision.ipynb) | 1.5x faster | 50% less | | Qwen3.5 (4B) | ▶️ Start for free_Vision.ipynb) | 1.5x faster | 60% less | | gpt-oss (20B) | ▶️ Start for free-Fine-tuning.ipynb) | 2x faster | 70% less | | Qwen3.5 GSPO | ▶️ Start for free_Vision_GRPO.ipynb) | 2x faster | 70% less | | gpt-oss (20B): GRPO | ▶️ Start for free-GRPO.ipynb) | 2x faster | 80% less | | Qwen3: Advanced GRPO | ▶️ Start for free-GRPO.ipynb) | 2x faster | 70% less | | embeddinggemma (300M) | ▶️ Start for free.ipynb) | 2x faster | 20% less | | Llama 3.1 (8B) Alpaca | ▶️ Start for free-Alpaca.ipynb) | 2x faster | 70% less | | Llama 3.2 Conversational | ▶️ Start for free-Conversational.ipynb) | 2x faster | 70% less | | Orpheus-TTS (3B) | ▶️ Start for free-TTS.ipynb) | 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

• 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

More News

• 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

📥 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, 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

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, 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

Advanced launch options

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 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

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.

Vulkan, custom llama.cpp backends:

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

Uninstall

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.

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 | |   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](https://github.com/unslothai/unsloth/p