🦥 Unsloth Studio
A modern, full-stack web interface for fine-tuning, managing, and chatting with large language models — locally or in the cloud.
Features •
Quick Start •
API Reference •
Project Structure
---
## Features
| Area | Capabilities |
|---|---|
| **Training** | Configure and launch LoRA / QLoRA fine-tuning jobs with real-time SSE progress streaming, live loss charts, and one-click stop / resume |
| **Model Management** | Browse, load, and manage Hugging Face hub models and local checkpoints |
| **Inference** | Interactive chat playground for testing fine-tuned models |
| **Dataset Tools** | Upload, preview, and prepare datasets (JSON, CSV, Parquet, PDF, DOCX) |
| **Export** | Export & push trained adapters to the Hugging Face Hub |
| **Auth** | Token-based authentication with JWT access / refresh flow and first-time setup token |
## Quick Start
### Prerequisites
| Requirement | Linux / WSL | Windows |
|---|---|---|
| **GPU** | NVIDIA GPU with working driver | NVIDIA GPU with working driver |
| **Python** | 3.11 – 3.13 | 3.11 – 3.13 |
| **Git** | Pre-installed on most distros | Auto-installed by setup script (via `winget`) |
| **CMake** | Pre-installed or `sudo apt install cmake` | Auto-installed by setup script (via `winget`) |
| **C++ compiler** | `build-essential` (auto-detected) | Visual Studio Build Tools 2022 (auto-installed by setup script) |
| **CUDA Toolkit** | Optional — setup auto-detects `nvcc` | Auto-installed by setup script (version matched to driver) |
> [!NOTE]
> On **WSL**, the setup script will also run `sudo apt-get install build-essential cmake curl git libcurl4-openssl-dev` so that GGUF export works in non-interactive subprocesses. You may be prompted for your password during setup.
---
### Linux / Windows WSL
```bash
# 1. Clone the repo
git clone https://github.com/unslothai/unsloth-studio.git
cd unsloth-studio
# 2. Run setup (installs Node, builds frontend, creates .venv, builds llama.cpp)
bash setup.sh
# 3. Open a new terminal (or source your shell rc), then launch:
unsloth-studio -H 0.0.0.0 -p 8000
```
What does setup.sh do?
1. Installs **Node.js ≥ 20** via nvm (if needed)
2. Runs `npm install && npm run build` for the React frontend
3. Detects the best **Python 3.11 – 3.13** on your system and creates a `.venv`
4. Installs all Python dependencies (unsloth, PyTorch with CUDA, triton kernels, etc.)
5. On **WSL**: pre-installs build dependencies via `apt-get`
6. Clones and builds **llama.cpp** at `~/.unsloth/llama.cpp` (GPU-accelerated if CUDA is found)
7. Registers `unsloth-studio` and `unsloth-ui` shell aliases in your shell rc (bash, zsh, fish, or ksh)
---
### Windows (Native)
> [!IMPORTANT]
> Requires an **NVIDIA GPU** — CPU-only machines are not supported on Windows.
```powershell
# 1. Clone the repo
git clone https://github.com/unslothai/unsloth-studio.git
cd unsloth-studio
# 2. Run setup (Right-click → "Run with PowerShell", or from a terminal):
.\setup.bat
# Or directly:
powershell -ExecutionPolicy Bypass -File setup.ps1
```
After setup completes, **open a new terminal** and run:
```powershell
# PowerShell
unsloth-studio -H 0.0.0.0 -p 8000
# Or cmd.exe
unsloth-studio -H 0.0.0.0 -p 8000
```
What does setup.ps1 do?
1. Enables **Windows Long Paths** (required for deep dependency trees — prompts for UAC)
2. Auto-installs missing system tools via `winget`: **Git**, **CMake**, **Visual Studio Build Tools 2022**, **CUDA Toolkit** (version-matched to your driver), **Node.js LTS**, **Python 3.12**, **OpenSSL dev**
3. Builds the React frontend (`npm install && npm run build`)
4. Creates a `.venv` and installs all Python dependencies (including CUDA-enabled PyTorch from the official index)
5. Sets `TORCHINDUCTOR_CACHE_DIR=C:\tc` to avoid Windows MAX_PATH issues with Triton
6. Clones and builds **llama.cpp** at `%USERPROFILE%\.unsloth\llama.cpp` with CUDA + Visual Studio
7. Registers `unsloth-studio` and `unsloth-ui` commands in both PowerShell profile and `cmd.exe` (via batch files on PATH)
---
### Google Colab
The setup script auto-detects Colab and installs everything into the existing system Python (no venv):
```python
!bash setup.sh
```
---
### Launching the Studio
After setup on any platform, the command is the same:
```bash
unsloth-studio -H 0.0.0.0 -p 8000
```
| Flag | Description |
|---|---|
| `-H` / `--host` | Bind address (`0.0.0.0` for all interfaces, `127.0.0.1` for local only) |
| `-p` / `--port` | Port number (default: `8000`) |
On **first launch**, a one-time setup token is printed to the console. Open the URL shown in your browser and use this token to create your admin account.
> [!TIP]
> This repo is in active development. After pulling new changes, **always re-run the setup script** (`bash setup.sh` or `.\setup.bat`) to pick up dependency and build updates.
## API Reference
All endpoints require a valid JWT `Authorization: Bearer ` header (except `/api/auth/*` and `/api/health`).
| Method | Endpoint | Description |
|---|---|---|
| `GET` | `/api/health` | Health check |
| `GET` | `/api/system` | System info (GPU, CPU, memory) |
| `POST` | `/api/auth/signup` | Create account (requires setup token on first run) |
| `POST` | `/api/auth/login` | Login and receive JWT tokens |
| `POST` | `/api/auth/refresh` | Refresh an expired access token |
| `GET` | `/api/auth/status` | Check if auth is initialized |
| `POST` | `/api/train/start` | Start a training job |
| `POST` | `/api/train/stop` | Stop a running training job |
| `POST` | `/api/train/reset` | Reset training state |
| `GET` | `/api/train/status` | Get current training status |
| `GET` | `/api/train/metrics` | Get training metrics (loss, LR, steps) |
| `GET` | `/api/train/stream` | SSE stream of real-time training progress |
| `GET` | `/api/models/` | List available models |
| `POST` | `/api/inference/chat` | Send a chat message for inference |
| `GET` | `/api/datasets/` | List / manage datasets |
> Full interactive docs are available at `/docs` (Swagger UI) and `/redoc` when the server is running.
## CLI Commands
The Unsloth CLI (`cli.py`) provides the following commands:
```
Usage: cli.py [COMMAND]
Commands:
train Fine-tune a model
inference Run inference on a trained model
export Export a trained adapter
list-checkpoints List saved checkpoints
ui Launch the Unsloth Studio web UI
studio Launch the studio (alias)
```
## Project Structure
```
new-ui-prototype/
├── cli.py # CLI entry point
├── cli/ # Typer CLI commands
│ └── commands/
│ ├── train.py
│ ├── inference.py
│ ├── export.py
│ ├── ui.py
│ └── studio.py
├── setup.sh # Bootstrap script (Linux / WSL / Colab)
├── setup.ps1 # Bootstrap script (Windows native)
├── setup.bat # Wrapper to launch setup.ps1 via double-click
├── install_python_stack.py # Cross-platform Python dependency installer
└── studio/
├── backend/
│ ├── main.py # FastAPI app & middleware
│ ├── run.py # Server launcher (uvicorn)
│ ├── auth/ # Auth storage & JWT logic
│ ├── routes/ # API route handlers
│ │ ├── training.py
│ │ ├── models.py
│ │ ├── inference.py
│ │ ├── datasets.py
│ │ └── auth.py
│ ├── models/ # Pydantic request/response schemas
│ ├── core/ # Training engine & config
│ ├── utils/ # Hardware detection, helpers
│ └── requirements.txt
├── frontend/
│ ├── src/
│ │ ├── features/ # Feature modules
│ │ │ ├── auth/ # Login / signup flow
│ │ │ ├── training/ # Training config & monitoring
│ │ │ ├── studio/ # Main studio workspace
│ │ │ ├── chat/ # Inference chat UI
│ │ │ ├── export/ # Model export flow
│ │ │ └── onboarding/# Onboarding wizard
│ │ ├── components/ # Shared UI components (shadcn)
│ │ ├── hooks/ # Custom React hooks
│ │ ├── stores/ # Zustand state stores
│ │ └── types/ # TypeScript type definitions
│ ├── package.json
│ └── vite.config.ts
└── tests/ # Backend test suite
```
## License
This project is licensed under the [GNU Affero General Public License v3.0 (AGPL-3.0)](https://www.gnu.org/licenses/agpl-3.0.html).
Copyright © 2026 Unsloth AI.