Unsloth：本地运行模型并接入编程智能体

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 or GitHub Releases. Or if you prefer to install manually: macOS, Linux, WSL: curl -fsSL https://unsloth.ai/install.sh | sh Windows: irm https://unsloth.ai/install.ps1 | iex Docker: The Unsloth Docker image unsloth/unsloth is available on Docker. Read guide. Homebrew: brew install --cask unsloth Winget (Windows): winget install --id UnslothAI.Unsloth -e Community: Discord 𝕏 (Twitter) Reddit ⭐ 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, GLM-5.3-Flash, Kimi K3, Qwen-Image-2.1, MiniMax-H3, DeepSeek-V4, Gemma 4. Agents & Tools: Use local models with Claude Code, Codex, and 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 and video diffusion or multimodal models Remote & LAN: Access your local models from any device on LAN or remotely through secure Cloudflare HTTPS. Connect: Serve models through an OpenAI compatible API. Also connect your ChatGPT/Codex subscription and cloud providers Train & Deploy Fine-tuning: Train LLMs, diffusion, decision, TTS, and embedding models 2× faster with 70% less VRAM with no accuracy loss Complete support: Supports reinforcement learning, LoRA, QLoRA, full fine tuning, pretraining, RL, GRPO, DPO, and FP8. Export & Deploy: Export or Deploy models including GGUF, NVFP4, FP8 and more formats. Datasets: Build datasets from PDFs, CSVs, DOCX files, and more with Data Recipes. 🚀 Unsloth Start Unsloth Start connects Claude Code, Codex and other agents to local models with one command. 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, the desktop app; Unsloth 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: curl -fsSL https://unsloth.ai/install.sh | sh Windows: irm https://unsloth.ai/install.ps1 | iex Homebrew: brew install --cask unsloth Docker Use our Docker image unsloth/unsloth. On Linux and Mac, set up GPU access once with 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. Linux / WSL (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 for more information. For notebooks only, use the unsloth/unsloth:core tag; all tags and options are on Docker Hub. For AMD there is a separate image: unsloth/unsloth-rocm. Launch unsloth studio HTTP Secure Deployment 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!) unsloth studio --secure -H 0.0.0.0 and different ports also work: 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: UNSLOTH_STUDIO_PASSWORD='your-strong-password' unsloth studio --secure # via env var Reset your password: unsloth studio reset-password Developer, Nightly, Uninstall To see developer, nightly and uninstallation etc. instructions, see advanced installation. 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: 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 and DGX Spark guide. 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 mod