visual-generation ·

unsloth

Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4.1, MiniMax-H3, Gemma 4, FLUX and more.

Unsloth is the first desktop app to run and train models.

Features • Quickstart • Notebooks • Documentation

## ⚡ Get started Download the native Unsloth Desktop app for your operating system:

Platform	Link
Windows	Download
macOS	Download
Linux / Ubuntu (deb)	Download
Linux / Ubuntu (ARM64) (deb)	Download
Linux (AppImage)	Download
Windows (ARM64)	Download

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): ```powershell 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` | | Visual Studio Code | `unsloth start vscode` | | DeepSeek Harness | `unsloth start dsh` | | OpenCode | `unsloth start opencode` | | Hermes Agent | `unsloth start hermes` | | Pi | `unsloth start pi` | | OpenClaw | `unsloth start openclaw` | | Mistral Vibe | `unsloth start vibe` | ## 📥 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)

Platform	Link
Windows	Download
macOS	Download
Linux / Ubuntu (deb)	Download
Linux / Ubuntu (ARM64) (deb)	Download
Linux (AppImage)	Download
Windows (ARM64)	Download

### 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 "$PWD":/workspace/host \ -v unsloth-workspace:/workspace \ -v "$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).
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).
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) | | | | **Decision model (Qwen3.5-4B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)-Decision.ipynb) | 1.5x faster | 60% less | | **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) ## 📥 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

原始来源：https://github.com/unslothai/unsloth#readme