README

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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.
• Upload images, audio, PDFs, code, DOCX and more file types to chat with.

Training

• Train 500+ models up to 2x faster with up to 70% less VRAM, with no accuracy loss.
• Supports full fine-tuning, pretraining, 4-bit, 16-bit and, FP8 training.
• Observability: Monitor training live, track loss and GPU usage and customize graphs.
• Data Recipes: Auto-create datasets from PDF, CSV, DOCX etc. Edit data in a visual-node workflow.
• Reinforcement Learning: The most efficient RL library, using 80% less VRAM for GRPO, FP8 etc.
• 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 works. Train with Unsloth Core. Studio support is coming 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://raw.githubusercontent.com/unslothai/unsloth/main/install.sh | sh

If you don't have curl, use wget. Launch after setup via:

source unsloth_studio/bin/activate
unsloth studio -H 0.0.0.0 -p 8888

Windows:

irm https://raw.githubusercontent.com/unslothai/unsloth/main/install.ps1 | iex

Launch after setup via:

& .\unsloth_studio\Scripts\unsloth.exe studio -H 0.0.0.0 -p 8888

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

macOS, Linux, WSL developer installs:

curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv unsloth_studio --python 3.13
source unsloth_studio/bin/activate
uv pip install unsloth --torch-backend=auto
unsloth studio setup
unsloth studio -H 0.0.0.0 -p 8888

Windows PowerShell developer installs:

winget install -e --id Python.Python.3.13
winget install --id=astral-sh.uv  -e
uv venv unsloth_studio --python 3.13
.\unsloth_studio\Scripts\activate
uv pip install unsloth --torch-backend=auto
unsloth studio setup
unsloth studio -H 0.0.0.0 -p 8888

Nightly - MacOS, Linux, WSL:

curl -LsSf https://astral.sh/uv/install.sh | sh
git clone --filter=blob:none https://github.com/unslothai/unsloth.git unsloth_studio
cd unsloth_studio
uv venv --python 3.13
source .venv/bin/activate
uv pip install -e . --torch-backend=auto
unsloth studio setup
unsloth studio -H 0.0.0.0 -p 8888

Then to launch every time:

cd unsloth_studio
source .venv/bin/activate
unsloth studio -H 0.0.0.0 -p 8888

Nightly - Windows:

Run in Windows Powershell:

winget install -e --id Python.Python.3.13
winget install --id=astral-sh.uv  -e
git clone --filter=blob:none https://github.com/unslothai/unsloth.git unsloth_studio
cd unsloth_studio
uv venv --python 3.13
.\.venv\Scripts\activate
uv pip install -e . --torch-backend=auto
unsloth studio setup
unsloth studio -H 0.0.0.0 -p 8888

Then to launch every time:

cd unsloth_studio
.\.venv\Scripts\activate
unsloth studio -H 0.0.0.0 -p 8888

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. 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

🦥 Unsloth News

• 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.

🔗 Links and Resources

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!