Unsloth
Unified web UI for training and running open models like Qwen, DeepSeek, gpt-oss and Gemma locally.
Install / Use
/learn @unslothai/UnslothREADME
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 Setup:
curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/install.sh | sh
If you don't have curl, use wget. Then to launch after setup:
source unsloth_studio/bin/activate
unsloth studio -H 0.0.0.0 -p 8888
Windows PowerShell Setup:
irm https://raw.githubusercontent.com/unslothai/unsloth/main/install.ps1 | iex
Then to launch after setup:
& .\unsloth_studio\Scripts\unsloth.exe studio -H 0.0.0.0 -p 8888
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
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
Nightly Install - 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 Install - 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 Powershell
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. <br>
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 |
