Unsloth Studio Installation

Learn how to install Unsloth Studio on your local device.

Unsloth Studio works on Windows, Linux, WSL and MacOS. You should use the same installation process on every device, although the system requirements may differ by device.

WindowsMacOSLinux & WSLDockerDeveloper Install

• Mac: Like CPU - Chat + Data Recipes works for now. MLX training coming very soon.

• CPU: Unsloth still works without a GPU, but for Chat + Data Recipes.

• Training: Works on NVIDIA: RTX 30, 40, 50, Blackwell, DGX Spark/Station etc. + Intel GPUs

• Coming soon: Support for Apple MLX and AMD.

Install Instructions

Remember install instructions are the same across every device:

1

Install Unsloth

MacOS, Linux, WSL:

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

Windows PowerShell:

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

First install should now be 6x faster and with 50% reduced size due to precompiled llama.cpp binaries.

WSL users: you will be prompted for your sudo password to install build dependencies (cmake, git, libcurl4-openssl-dev).

2

Launch Unsloth Studio

unsloth studio -H 0.0.0.0 -p 8888

Then open http://localhost:8888 in your browser.

3

Onboarding

On first launch you will need to create a password to secure your account and sign in again later. You’ll then see a brief onboarding wizard to choose a model, dataset, and basic settings. You can skip it at any time.

4

Start training and running

Start fine-tuning and building datasets immediately after launching. See our step-by-step guide to get started with Unsloth Studio:

Get Started

Update Unsloth Studio:

To update Unsloth Studio use:

unsloth studio update 

If that does not work, you can use the below:

MacOS, Linux, WSL:

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

Windows PowerShell:

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

System Requirements

Windows

Unsloth Studio works directly on Windows without WSL. To train models, make sure your system satisfies these requirements:

Requirements

• Windows 10 or Windows 11 (64-bit)

• NVIDIA GPU with drivers installed

• App Installer (includes winget): here

• Git: winget install --id Git.Git -e --source winget

• Python: version 3.11 up to, but not including, 3.14

• Work inside a Python environment such as uv, venv, or conda/mamba

MacOS

Unsloth Studio works on Mac devices for Chat for GGUF models and Data Recipes (Export coming very soon). MLX training coming soon!

• macOS 12 Monterey or newer (Intel or Apple Silicon)

• Install Homebrew: here

• Git: brew install git

• cmake: brew install cmake

• openssl: brew install openssl

• Python: version 3.11 up to, but not including, 3.14

• Work inside a Python environment such as uv, venv, or conda/mamba

Linux & WSL

• Ubuntu 20.04+ or similar distro (64-bit)

• NVIDIA GPU with drivers installed

• CUDA toolkit (12.4+ recommended, 12.8+ for blackwell)

• Git: sudo apt install git

• Python: version 3.11 up to, but not including, 3.14

• Work inside a Python environment such as uv, venv, or conda/mamba

Docker

Our Docker image now works for Studio! We're working on Mac compatibility.

• Pull our latest Unsloth container image: docker pull unsloth/unsloth

• Run the container via:

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

For more information, see here.

• Access your studio instance at http://localhost:8000 or external ip address http://external_ip_address:8000/

CPU only

Unsloth Studio supports CPU devices for Chat for GGUF models and Data Recipes (Export coming very soon)

• Same as the ones mentioned above for Linux (except for NVIDIA GPU drivers) and MacOS.

Developer Installation (Advanced)

Install from Main Repo

macOS, Linux, WSL developer installs:

git clone https://github.com/unslothai/unsloth
cd unsloth
./install.sh --local
unsloth studio -H 0.0.0.0 -p 8888

Windows PowerShell developer installs:

winget install -e --id Python.Python.3.13 --source winget
winget install --id=astral-sh.uv  -e --source winget
winget install --id Git.Git -e --source winget
git clone https://github.com/unslothai/unsloth
cd unsloth
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -H 0.0.0.0 -p 8888

Nightly Install

Nightly - MacOS, Linux, WSL:

git clone https://github.com/unslothai/unsloth
cd unsloth
git checkout nightly
./install.sh --local

Then to launch every time:

unsloth studio -H 0.0.0.0 -p 8888

Nightly - Windows:

Run in Windows Powershell:

winget install -e --id Python.Python.3.13 --source winget
winget install --id=astral-sh.uv  -e --source winget
winget install --id Git.Git -e --source winget
git clone https://github.com/unslothai/unsloth
cd unsloth
git checkout nightly
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local

Then to launch every time:

unsloth studio -H 0.0.0.0 -p 8888

Uninstall

To uninstall Unsloth Studio, follow these 4 steps:

1. Remove the application

• MacOS, WSL, Linux: rm -rf ~/.unsloth/studio/unsloth ~/.unsloth/studio/studio

• Windows (PowerShell): Remove-Item -Recurse -Force "$HOME\.unsloth\studio\unsloth", "$HOME\.unsloth\studio\studio"

This removes the application but keeps your model checkpoints, exports, history, cache, and chats intact.

2. Remove shortcuts and symlinks

macOS:

rm -rf ~/Applications/Unsloth\ Studio.app ~/Desktop/Unsloth\ Studio

Linux:

rm -f ~/.local/share/applications/unsloth-studio.desktop ~/Desktop/unsloth-studio.desktop

WSL / Windows (PowerShell):

Remove-Item -Force "$HOME\Desktop\Unsloth Studio.lnk"
Remove-Item -Force "$env:APPDATA\Microsoft\Windows\Start Menu\Programs\Unsloth Studio.lnk"

3. Remove the CLI command

macOS, Linux, WSL:

rm -f ~/.local/bin/unsloth

Windows (PowerShell): The installer added the venv's Scripts directory to your User PATH. To remove it, open Settings → System → About → Advanced system settings → Environment Variables, find Path under User variables, and remove the entry pointing to .unsloth\studio\...\Scripts.

4. Remove everything (optional)

To also delete history, cache, chats, model checkpoints, and model exports, delete the entire Unsloth folder:

• MacOS, WSL, Linux: rm -rf ~/.unsloth

• Windows (PowerShell): Remove-Item -Recurse -Force "$HOME\.unsloth"

Note that downloaded HF model files are stored separately in the Hugging Face cache — none of the steps above will remove them. See Deleting model files below if you want to reclaim that disk space.

Note: Using the rm -rf commands will delete everything, including your history, cache, chats etc.

Deleting cached HF 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, Hugging Face uses ~/.cache/huggingface/hub/ on macOS/Linux/WSL and C:\Users\<username>\.cache\huggingface\hub\ on Windows.

• MacOS, Linux, WSL: ~/.cache/huggingface/hub/

• Windows: %USERPROFILE%\.cache\huggingface\hub\

If HF_HUB_CACHE or HF_HOME is set, use that location instead. On Linux and WSL, XDG_CACHE_HOME can also change the default cache root.

Using old / existing GGUF models

Apr 1 update: You can now select an existing folder for Unsloth to detect from.

Mar 27 update: Unsloth Studio now automatically detects older / pre-existing models downloaded from Hugging Face, LM Studio etc.

Manual instructions: Unsloth Studio detects models downloaded to your Hugging Face Hub cache (C:\Users{your_username}.cache\huggingface\hub). If you have GGUF models downloaded through LM Studio, note that these are stored in C:\Users{your_username}.cache\lm-studio\models OR C:\Users{your_username}\lm-studio\models . Sometimes when they are not visible, you will need to move or copy those .gguf files into your Hugging Face Hub cache directory (or another path accessible to llama.cpp) for Unsloth Studio to load them.

After fine-tuning a model or adapter in Studio, you can export it to GGUF and run local inference with llama.cpp directly in Studio Chat. Unsloth Studio is powered by llama.cpp and Hugging Face.

Google Colab notebook

We’ve created a free Google Colab notebook so you can explore all of Unsloth’s features on Colab’s T4 GPUs. You can train and run most models up to 22B parameters, and switch to a larger GPU for bigger models. Just Click 'Run all' and the UI should pop up after installation.

Google Colabcolab.research.google.com

Once installation is complete, scroll to Start Unsloth Studio and click Open Unsloth Studio in the white box shown on the left:

Scroll further down, to see the actual UI.

Sometimes the Studio link may return an error. This happens because you might have disabled cookies or you're using an adblocker or Mozilla. You can still access the UI by scrolling below the button.

Google Colab also expects you to stay on the Colab page; if it detects inactivity, it may shut down the GPU session.

Troubleshooting

Problem	Fix
Python version error	sudo apt install python3.12 python3.12-venv version 3.11 up to, but not including, 3.14
nvidia-smi not found	Install NVIDIA drivers from https://www.nvidia.com/Download/index.aspx
nvcc not found (CUDA)	sudo apt install nvidia-cuda-toolkit or add /usr/local/cuda/bin to PATH
llama-server build failed	Non-fatal, Studio still works, GGUF inference won't be available. Install cmake and re-run setup to fix.
cmake not found	sudo apt install cmake
git not found	sudo apt install git
Build failed	Delete ~/.unsloth/llama.cpp and re-run setup