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unslothai/unsloth

Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.

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README.md

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<h1 align="center" style="margin:0;">
  <a href="https://unsloth.ai/docs"><picture>
    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png">
    <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png">
    <img alt="Unsloth logo" src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png" height="80" style="max-width:100%;">
  </picture></a>
</h1>
<h3 align="center" style="margin: 0; margin-top: 0;">
Unsloth is the first desktop app to run and train models.
</h3>

<p align="center">
  <a href="#-features">Features</a> •
  <a href="#-get-started">Quickstart</a> •
  <a href="#-free-notebooks">Notebooks</a> •
  <a href="https://unsloth.ai/docs">Documentation</a>
</p>

## ⚡ Get started
Download the native Unsloth Desktop app for your operating system:
<table>
  <tr>
    <td><b>Platform</b></td>
    <td><b>Link</b></td>
  </tr>
  <tr>
    <td><b>Windows</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Windows.exe'>Download</a></td>
  </tr>
  <tr>
    <td><b>macOS</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-MacOS.dmg'>Download</a></td>
  </tr>
  <tr>
    <td><b>Linux / Ubuntu (deb)</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Ubuntu.deb'>Download</a></td>
  </tr>
  <tr>
    <td><b>Linux / Ubuntu (ARM64) (deb)</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Ubuntu-ARM64.deb'>Download</a></td>
  </tr>
  <tr>
    <td><b>Linux (AppImage)</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Linux.AppImage'>Download</a></td>
  </tr>
  <tr>
    <td><b>Windows (ARM64)</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Windows-ARM64.exe'>Download</a></td>
  </tr>
</table>

Download from [Unsloth](https://unsloth.ai/download) or [GitHub Releases](https://github.com/unslothai/unsloth/releases).

Or if you prefer to install manually:

#### macOS, Linux, WSL:
```bash
curl -fsSL https://unsloth.ai/install.sh | sh
```

#### Windows:
```powershell
irm https://unsloth.ai/install.ps1 | iex
```
#### Docker:

The [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) `unsloth/unsloth` is available on Docker. [Read guide.](https://unsloth.ai/docs/get-started/install/docker)

#### Homebrew:
```bash
brew install --cask unsloth
```

#### Winget (Windows):
```bash
winget install --id UnslothAI.Unsloth -e
```

#### Community:

- [Discord](https://discord.gg/unsloth)
- [𝕏 (Twitter)](https://x.com/UnslothAI)
- [Reddit](https://reddit.com/r/unsloth)

## ⭐ Features
Unsloth works on **Windows, Linux, WSL** and **macOS**. We support **Multi GPU setups, NVIDIA, AMD, Intel GPUs, CPUs** and the **Vulkan** backend.

### Run & Build with AI
* Run and train LLMs, MLX, GGUF, diffusion, decision, embedding, audio models: [Qwen3.8](https://unsloth.ai/docs/models/qwen3.8), [GLM-5.3-Flash](https://unsloth.ai/docs/models/glm-5.3-flash), [Kimi K3](https://unsloth.ai/docs/models/kimi-k3), [Qwen-Image-2.1](https://unsloth.ai/docs/models/qwen-image-2.1), MiniMax-H3, [DeepSeek-V4](https://unsloth.ai/docs/models/deepseek-v4), [Gemma 4](https://unsloth.ai/docs/models/gemma-4).
* **Agents & Tools:** Use local models with [Claude Code](https://unsloth.ai/docs/basics/claude-code), [Codex](https://unsloth.ai/docs/basics/codex), and [MCP](https://unsloth.ai/docs/basics/mcp), including tool calling and code execution.
* **Search & RAG:** Use private and unlimited web search, deep research, auto-compaction (rolling context window) and RAG.
* **Image and video:** Run and train [image](https://unsloth.ai/docs/basics/diffusion-image) and video diffusion or multimodal models
* **Remote & LAN:** Access your local models from any device on [LAN](https://unsloth.ai/docs/basics/lan) or remotely through secure [Cloudflare](https://unsloth.ai/docs/basics/how-to-serve-local-llms-anywhere-secure-remote-access-with-cloudflare-and-unsloth) HTTPS.
* **Connect:** Serve models through an [OpenAI compatible API](https://unsloth.ai/docs/basics/api). Also connect your ChatGPT/Codex subscription and [cloud providers](https://unsloth.ai/docs/integrations/connections)

### Train & Deploy
* **Fine-tuning:** Train LLMs, diffusion, decision, TTS, and embedding models 2× faster with 70% less VRAM with [no accuracy loss](https://unsloth.ai/blog#training)
* **Complete support:** Supports [reinforcement learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide), LoRA, QLoRA, full fine tuning, pretraining, RL, GRPO, DPO, and FP8.
* **Export & Deploy:** [Export](https://unsloth.ai/docs/new/studio/export) or Deploy models including [GGUF](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf), NVFP4, FP8 and more formats.
* **Datasets:** Build datasets from PDFs, CSVs, DOCX files, and more with [Data Recipes](https://unsloth.ai/docs/new/studio/data-recipe).
  
## 🚀 Unsloth Start

[Unsloth Start](https://unsloth.ai/docs/integrations/unsloth-start) connects [Claude Code](https://unsloth.ai/docs/basics/claude-code), [Codex](https://unsloth.ai/docs/basics/codex) and other agents to local models with one command.

```bash
unsloth start claude --model unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL
```

| Agent | Command |
| --- | --- |
| Claude Code | `unsloth start claude` |
| OpenAI Codex | `unsloth start codex` |
| DeepSeek Harness | `unsloth start dsh` |
| OpenCode | `unsloth start opencode` |
| Hermes Agent | `unsloth start hermes` |
| Pi | `unsloth start pi` |
| OpenClaw | `unsloth start openclaw` |

## 📥 Install
Unsloth can be used in three ways: **[Unsloth Desktop](https://unsloth.ai/download)**, the desktop app; **[Unsloth Studio](https://unsloth.ai/docs/new/studio/)**, the web UI; or **Unsloth Core**, the code based version.

### Unsloth Desktop (recommended)

<table>
  <tr>
    <td><b>Platform</b></td>
    <td><b>Link</b></td>
  </tr>
  <tr>
    <td><b>Windows</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Windows.exe'>Download</a></td>
  </tr>
  <tr>
    <td><b>macOS</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-MacOS.dmg'>Download</a></td>
  </tr>
  <tr>
    <td><b>Linux / Ubuntu (deb)</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Ubuntu.deb'>Download</a></td>
  </tr>
  <tr>
    <td><b>Linux / Ubuntu (ARM64) (deb)</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Ubuntu-ARM64.deb'>Download</a></td>
  </tr>
  <tr>
    <td><b>Linux (AppImage)</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Linux.AppImage'>Download</a></td>
  </tr>
  <tr>
    <td><b>Windows (ARM64)</b></td>
    <td><a href='https://github.com/unslothai/unsloth/releases/latest/download/Unsloth-Desktop-Windows-ARM64.exe'>Download</a></td>
  </tr>
</table>

### Unsloth Studio (web UI)

#### macOS, Linux, WSL:
```bash
curl -fsSL https://unsloth.ai/install.sh | sh
```

#### Windows:
```powershell
irm https://unsloth.ai/install.ps1 | iex
```

#### Homebrew:
```bash
brew install --cask unsloth
```

#### Docker
Use our [Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth```. On Linux and Mac, set up GPU access once with
```bash
curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/docker/install_nvidia_toolkit.sh -o install_nvidia_toolkit.sh && sudo -E bash install_nvidia_toolkit.sh
```
For Windows instructions and more information, read our [Unsloth Docker Guide](https://unsloth.ai/docs/get-started/install/docker).

**Linux / WSL (Bash):**
```bash
# for a public Cloudflare HTTPS link: use  -e UNSLOTH_STUDIO_SECURE=1  instead of -p 8000:8000, and  -p 127.0.0.1:8888:8888  instead of -p 8888:8888
docker run -d --name unsloth --gpus all --ipc=host \
  -p 8000:8000 -p 8888:8888 \
  -v "$PWD":/workspace/host \
  -v "$HOME/.cache/huggingface":/workspace/.cache/huggingface \
  -v unsloth-studio:/opt/unsloth-studio \
  unsloth/unsloth && docker logs -f unsloth
```
See [Docker docs](https://unsloth.ai/docs/get-started/install/docker) for more information. For notebooks only, use the `unsloth/unsloth:core` tag; all tags and options are on [Docker Hub](https://hub.docker.com/r/unsloth/unsloth).<br>For **AMD** there is a separate image: [`unsloth/unsloth-rocm`](https://hub.docker.com/r/unsloth/unsloth-rocm).

---

#### Launch
```bash
unsloth studio
```

#### HTTP Secure Deployment
```bash
unsloth studio --secure
```

#### Remote HTTPS & LAN Access
Server-side tools are on by default - so **be careful**! Keep your password safe, or use `--disable-tools` when exposing Unsloth.

**Global HTTPS Access**:
Creates a free Cloudflare link that serves Unsloth - you can access the link globally (even on your phone!)
```bash
unsloth studio --secure
```
`-H 0.0.0.0` and different ports also work:
```bash
unsloth studio -H 0.0.0.0 -p 8888
```
**LAN Access (home network)**: `Settings > API keys > LAN access`

#### Password management & headless starts
Exposing Unsloth (`--secure`, `--cloudflare`, or a non-loopback `-H`) asks once at the terminal for a new admin password. Ctrl+C there aborts the launch rather than exposing the auto-generated one; set a password non-interactively instead, or use `-H 127.0.0.1` to stay off the network.

Headless starts:
```bash
UNSLOTH_STUDIO_PASSWORD='your-strong-password' unsloth studio --secure   # via env var
```
Reset your password:
```bash
unsloth studio reset-password
```

#### Developer, Nightly, Uninstall
To see developer, nightly and uninstallation etc. instructions, see [advanced installation](#-advanced-installation).

### Unsloth Core (code-based)
#### Linux, WSL:
```bash
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 torchao
uv pip install unsloth --torch-backend=auto
```
Install torchao first on Windows: `--torch-backend` looks for it only on the PyTorch index, which has no recent Windows torchao, so a one-step install silently resolves an old Unsloth.

#### AMD, Intel, DGX Spark, Blackwell:
See our [Blackwell guide](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark guide](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth). <br>
To install Unsloth on **AMD** and **Intel** GPUs, follow our [AMD Guide](https://unsloth.ai/docs/basics/amd) and [Intel Guide](https://unsloth.ai/docs/get-started/install/intel).

## 📒 Free Notebooks

Train for free with our notebooks.
Read our [guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). Add dataset, run, then deploy your trained model.

| Model | Free Notebooks | Performance | Memory use |
|-----------|---------|--------|----------|
| **Unsloth Studio**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb)               |  |  |
| **Gemma 4 (E2B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_(E2B)-Vision.ipynb)               | 1.5x faster | 50% less |
| **Qwen3.5 (4B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision.ipynb)               | 1.5x faster | 60% less |
| **gpt-oss (20B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb)               | 2x faster | 70% less |
| **Qwen3.5 GSPO**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision_GRPO.ipynb)               | 2x faster | 70% less |
| **gpt-oss (20B): GRPO**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb)               | 2x faster | 80% less |
| **Qwen3: Advanced GRPO**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)               | 2x faster | 70% less |
| **embeddinggemma (300M)**    | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_(300M).ipynb)               | 2x faster | 20% less |
| **Llama 3.1 (8B) Alpaca**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb)               | 2x faster | 70% less |
| **Llama 3.2 Conversational**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)               | 2x faster | 70% less |
| **Orpheus-TTS (3B)**     | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb)               | 1.5x faster | 50% less |

- See all our notebooks for: [Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks), [GRPO](https://unsloth.ai/docs/get-started/unsloth-notebooks#grpo-reasoning-rl-notebooks), [TTS](https://unsloth.ai/docs/get-started/unsloth-notebooks#text-to-speech-tts-notebooks), [embedding](https://unsloth.ai/docs/new/embedding-finetuning) & [Vision](https://unsloth.ai/docs/get-started/unsloth-notebooks#vision-multimodal-notebooks)
- See [all our models](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [all our notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks)
- See detailed documentation for Unsloth [here](https://unsloth.ai/docs)

## 🦥 Unsloth News
- **AMD training**: Train, run RL, chat and deploy on AMD GPUs across Windows, WSL and Linux. [Guide](https://unsloth.ai/docs/basics/amd)
- **Local models for any agent**: Use `unsloth start` with Claude Code, Codex, Hermes, OpenCode, OpenClaw, DeepSeek Harness and more through Unsloth's OpenAI- and Anthropic-compatible APIs. [Guide](https://unsloth.ai/docs/basics/api)
- **GLM-5.2**: Run Z.ai's 744B-parameter, 1M-context open model locally with Unsloth Dynamic GGUFs. [Guide](https://unsloth.ai/docs/models/glm-5.2)
- **DeepSeek-V4**: Run DeepSeek-V4-Flash locally with corrected multi-turn and tool-calling behavior. [Guide](https://unsloth.ai/docs/models/deepseek-v4)
- **Gemma 4**: Run and train Gemma 4 text, image and audio models with QAT, MTP, GGUF and MLX support. [Guide](https://unsloth.ai/docs/models/gemma-4)
- **MCP servers**: Connect local models to files, apps, databases and external tools through Model Context Protocol. [Guide](https://unsloth.ai/docs/basics/mcp)
- **New models**: [Qwen-AgentWorld](https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF), [Ornith](https://huggingface.co/unsloth/models?search=ornith), [Kimi K2.7 Code](https://unsloth.ai/docs/models/kimi-k2.7-code) and [MiniMax M3](https://unsloth.ai/docs/models/minimax-m3)

- **Connections**: Mix local models with API providers (OpenAI, Anthropic) or servers (vLLM, Ollama) in the same interface. [Guide](https://unsloth.ai/docs/integrations/connections)
  - **Introducing Unsloth Studio**: our new web UI for running and training LLMs. [Blog](https://unsloth.ai/docs/new/studio)
  - **DiffusionGemma**: Run and fine-tune Google's diffusion language model with 1.8x faster inference in Unsloth Studio. [Guide](https://unsloth.ai/docs/models/diffusiongemma)
  - **Qwen3.6**: Run and train Qwen3.6 with MTP for 1.4-2.2x faster inference and NVFP4 quants for supported GPUs. [Guide](https://unsloth.ai/docs/models/qwen3.6)
  - Train **MoE LLMs 12x faster** with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. [Blog](https://unsloth.ai/docs/new/faster-moe)
  - **Embedding models**: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. [Blog](https://unsloth.ai/docs/new/embedding-finetuning) • [Notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks#embedding-models)
  - New **7x longer context RL** vs. all other setups, via our new batching algorithms. [Blog](https://unsloth.ai/docs/new/grpo-long-context)
  - New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://unsloth.ai/docs/new/3x-faster-training-packing)
  - **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU. [Blog](https://unsloth.ai/docs/blog/500k-context-length-fine-tuning)
  - **FP8 & Vision RL**: You can now do FP8 & VLM GRPO on consumer GPUs. [FP8 Blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl)

</details>

## 📥 Advanced Installation
The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, [view our docs](https://unsloth.ai/docs/get-started/install/pip-install#advanced-pip-installation).

#### Developer / Nightly / Experimental installs: macOS, Linux, WSL:
The developer install builds from the `main` branch, which is the latest (nightly) source.
```bash
git clone https://github.com/unslothai/unsloth
cd unsloth
./install.sh --local
unsloth studio -p 8888
```
To install into an isolated location, set `UNSLOTH_STUDIO_HOME`:
```bash
UNSLOTH_STUDIO_HOME="$PWD/.studio" ./install.sh --local
UNSLOTH_STUDIO_HOME="$PWD/.studio" unsloth studio -p 8888
```
Then to update:
```bash
cd unsloth && git pull
./install.sh --local
unsloth studio -p 8888
```

#### Developer / Nightly / Experimental installs: Windows PowerShell:
The developer install builds from the `main` branch, which is the latest (nightly) source.
```powershell
git clone https://github.com/unslothai/unsloth.git
cd unsloth
Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned
.\install.ps1 --local
unsloth studio -p 8888
```
To install into an isolated location, set `UNSLOTH_STUDIO_HOME`:
```powershell
$env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; .\install.ps1 --local
$env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; unsloth studio -p 8888
```
Then to update:
```powershell
cd unsloth; git pull
.\install.ps1 --local
unsloth studio -p 8888
```

#### Advanced launch options

Skip PyTorch (GGUF-only mode):
```bash
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_NO_TORCH=1 sh
```
```powershell
$env:UNSLOTH_NO_TORCH=1; irm https://unsloth.ai/install.ps1 | iex
```

Skip the post-install prompt that starts Unsloth (useful for automated installs):
```bash
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_SKIP_AUTOSTART=1 sh
```
```powershell
$env:UNSLOTH_SKIP_AUTOSTART=1; irm https://unsloth.ai/install.ps1 | iex
```

Keep the install-time package cache under the Unsloth Studio directory instead of reusing an existing uv cache. Downloads are slower the first time, and an explicit `UV_CACHE_DIR` still wins over this:
```bash
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_ISOLATE_UV_CACHE=1 sh
```
```powershell
$env:UNSLOTH_ISOLATE_UV_CACHE=1; irm https://unsloth.ai/install.ps1 | iex
```
For a local run the flag is `--isolated-uv-cache`:
```bash
./install.sh --local --isolated-uv-cache
```
```powershell
.\install.ps1 --local --isolated-uv-cache
```

Discard the previous environment immediately when reinstalling, instead of keeping a copy until the new one works. A reinstall normally holds both at once, so it needs room for two; this needs room for one, at the cost of not being able to undo a failed install:
```bash
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_INSTALL_NO_ROLLBACK=1 sh
```
```powershell
$env:UNSLOTH_INSTALL_NO_ROLLBACK=1; irm https://unsloth.ai/install.ps1 | iex
```
For a local run the flag is `--no-rollback`:
```bash
./install.sh --local --no-rollback
```
```powershell
.\install.ps1 --local --no-rollback
```

Pinning the Python version:
```bash
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_PYTHON=3.12 sh
```
```powershell
$env:UNSLOTH_PYTHON='3.12'; irm https://unsloth.ai/install.ps1 | iex
```

Install to a custom location with `UNSLOTH_STUDIO_HOME`:
```bash
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_STUDIO_HOME=/abs/path sh
```
```powershell
$env:UNSLOTH_STUDIO_HOME='C:\path'; irm https://unsloth.ai/install.ps1 | iex
```

Point the frontend build at a corporate npm mirror/proxy with `UNSLOTH_NPM_REGISTRY`:
```bash
UNSLOTH_NPM_REGISTRY=https://artifactory.example.com/api/npm/npm/ ./install.sh --local
```
```powershell
$env:UNSLOTH_NPM_REGISTRY='https://artifactory.example.com/api/npm/npm/'; .\install.ps1 --local
```

Cap Unsloth's native CPU thread pools on high-core hosts: `UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888`.

#### Vulkan, custom llama.cpp backends:

You can force the backend during installation: 
```bash
export UNSLOTH_LLAMA_CPP_BACKEND=vulkan   # or cpu, cuda, rocm, auto
curl -fsSL https://unsloth.ai/install.sh | sh
```
```powershell
$env:UNSLOTH_LLAMA_CPP_BACKEND="vulkan"   # or cpu, cuda, rocm, auto
irm https://unsloth.ai/install.ps1 | iex
```

#### audio.cpp audio engine:

Setup also installs [audio.cpp](https://github.com/0xShug0/audio.cpp) (prebuilt by [unslothai/audio.cpp](https://github.com/unslothai/audio.cpp)), which runs the audio.cpp speech, music and speech-to-text models on the Audio page, in Voice settings and behind `/v1/audio/*`. It is optional and never blocks setup:
- `UNSLOTH_SKIP_AUDIO_CPP_INSTALL=1` skips it.
- `UNSLOTH_AUDIO_CPP_ACCELERATOR=cpu` (or `cuda`, `vulkan`, `metal`) picks the bundle instead of auto-detecting.
- `AUDIOCPP_SERVER_PATH=/path/to/audiocpp_server` uses your own build.

#### Uninstall

**MacOS, WSL, Linux:** `curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh`

**Windows (PowerShell):** `irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex`

For more info, [see our docs](https://unsloth.ai/docs/new/studio/install#uninstall).

#### Deleting 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, HF uses:

**MacOS, Linux, WSL:** `~/.cache/huggingface/hub/`

**Windows:** `%USERPROFILE%\.cache\huggingface\hub\`

## 💚 Community and Links
| Type                                                                                                                                      | Links                                                                          |
| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
| <img width="16" src="https://cdn.prod.website-files.com/6257adef93867e50d84d30e2/66e3d80db9971f10a9757c99_Symbol.svg" />  **Discord**                       | [Join Discord server](https://discord.com/invite/unsloth)                          |
| <img width="15" src="https://redditinc.com/hs-fs/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png" />  **r/unsloth Reddit**                       | [Join Reddit community](https://reddit.com/r/unsloth)                          |
| 📚 **Documentation & Wiki**                                                                                                               | [Read Our Docs](https://unsloth.ai/docs)                                       |
| <img width="13" src="https://upload.wikimedia.org/wikipedia/commons/0/09/X_(formerly_Twitter)_logo_late_2025.svg" />  **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai)                                |
| 🔮 **Our Models**                                                                                                                         | [Unsloth Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog)   |
| ✍️ **Blog**                                                                                                                               | [Read our Blogs](https://unsloth.ai/blog)                                      |

### Citation

You can cite the Unsloth repo as follows:
```bibtex
@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!   <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made%20with%20unsloth.png" width="200" align="center" />

### 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](https://github.com/unslothai/unsloth?tab=Apache-2.0-1-ov-file)**, while certain optional components, such as the Unsloth Studio UI are licensed under the open-source license **[AGPL-3.0](https://github.com/unslothai/unsloth?tab=AGPL-3.0-2-ov-file)**.

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](https://github.com/ggml-org/llama.cpp) that lets users run and save models with Unsloth
- The Hugging Face team and their libraries: [transformers](https://github.com/huggingface/transformers) and [TRL](https://github.com/huggingface/trl)
- The Pytorch and [Torch AO](https://github.com/unslothai/unsloth/pull/3391) team for their contributions
- NVIDIA for their [NeMo DataDesigner](https://github.com/NVIDIA-NeMo/DataDesigner) library and their contributions
- And of course for every single person who has contributed or has used Unsloth!