Run and train AI models with a unified local interface.

Features • Quickstart • Notebooks • Documentation • Discord

Unsloth Studio lets you run and train models for text, audio, embedding, vision and more. Available 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 run code, data and verify results so answers are more accurate.
• 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 works on Windows, Linux, WSL and macOS.

• CPU: Supported for chat inference only
• NVIDIA: Training works on RTX 30/40/50, Blackwell, DGX Spark, Station and more
• macOS: Currently supports chat only; 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 or WSL Setup (One time):

pip install -U pip uv
uv venv unsloth_studio
source unsloth_studio/bin/activate
uv pip install unsloth --torch-backend=auto
unsloth studio setup
unsloth studio -H 0.0.0.0 -p 8888

Then to launch every time:

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

Windows PowerShell (One time):

pip install -U pip uv
uv venv unsloth_studio
.\unsloth_studio\Scripts\activate
uv pip install unsloth --torch-backend=auto
unsloth studio setup
unsloth studio -H 0.0.0.0 -p 8888

Then to launch every time:

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

Use our Docker image unsloth/unsloth container. Read our Docker Guide.

Nightly Installation - MacOS, Linux or WSL Setup (One time):

pip install -U pip uv
git clone --filter=blob:none https://github.com/unslothai/unsloth.git unsloth_studio
cd unsloth_studio
uv venv
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 Installation - Windows Powershell (One time):

pip install -U pip uv
git clone --filter=blob:none https://github.com/unslothai/unsloth.git unsloth_studio
cd unsloth_studio
uv venv
.\.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

pip install -U pip uv
uv venv unsloth_env
source unsloth_env/bin/activate
uv pip install unsloth --torch-backend=auto

Windows Powershell

pip install -U pip uv
uv venv unsloth_env
.\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.

Model	Free Notebooks	Performance	Memory use
Qwen3.5 (4B)	▶️ Start for free	1.5x faster	60% less
gpt-oss (20B)	▶️ 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	50% less
Gemma 3 (4B) Vision	▶️ Start for free	1.7x faster	60% 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

• 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

Type	Links
r/unsloth Reddit	Join Reddit community
📚 Documentation & Wiki	Read Our Docs
Twitter (aka X)	Follow us on X
💾 Installation	Pip & Docker Install
🔮 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
• And of course for every single person who has contributed or has used Unsloth!