unslothai/unsloth

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 x64 / Ubuntu (deb) Download Linux ARM64 / Ubuntu 24.04+ (deb) Download Linux x64 (AppImage) 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 On macOS, you can also install Unsloth Desktop with Homebrew: brew install --cask unsloth Windows: irm https: // unsloth.ai / install.ps1 | iex Docker The Unsloth Docker image unsloth/unsloth is available on Docker. Read guide. 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 DeepSeek Harness unsloth start dsh Hermes Agent unsloth start hermes Mistral Vibe unsloth start vibe OpenCode unsloth start opencode OpenClaw unsloth start openclaw 📥 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 x64 / Ubuntu (deb) Download Linux ARM64 / Ubuntu 24.04+ (deb) Download Linux x64 (AppImage) Download Windows ARM64 Download Unsloth Studio (web UI) macOS, Linux, WSL: curl -fsSL https://unsloth.ai/install.sh | sh On macOS, you can also install Unsloth Desktop with Homebrew: brew install --cask unsloth Windows: irm https: // unsloth.ai / install.ps1 | iex Launch unsloth studio HTTP Secure Deployment unsloth studio --secure Docker Use our Docker image unsloth/unsloth . On Linux, 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 (Windows: Docker Desktop with WSL 2 ). Linux / WSL (Bash): # use -e UNSLOTH_STUDIO_SECURE=1 instead of -p 8000:8000 for a public Cloudflare HTTPS link 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 for more information. For cloud hosting / global serving, add -e UNSLOTH_STUDIO_SECURE=1 , drop -p 8000:8000 and bind JupyterLab to -p 127.0.0.1:8888:8888 , or bind both to 127.0.0.1 and use an SSH tunnel. Tags ( unsloth/unsloth:core for notebooks only), GPU support and options: Docker Hub . On AMD there is a separate image, unsloth/unsloth-rocm , with the run command and the supported cards on its Docker Hub page . 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 model. Model Free Notebooks Performance Memory use Unsloth Studio ▶️ Start for free Gemma 4 (E2B) ▶️ Start for free 1.5x faster 50% less Qwen3.5 (4B) ▶️ Start for free 1.5x faster 60% less gpt-oss (20B) ▶️ Start for free 2x faster 70% less Qwen3.5 GSPO ▶️ Start for free 2x faster 70% less gpt-oss (20B): GRPO ▶️ Start for free 2x faster 80% less Qwen3: Advanced GRPO ▶️ Start for free 2x faster 70% less embeddinggemma (300M) ▶️ Start for free 2x faster 20% less Llama 3.1 (8B) Alpaca ▶️ Start for free 2x faster 70% less Llama 3.2 Conversational ▶️ Start for free 2x faster 70% less Orpheus-TTS (3B) ▶️ Start for free 1.5x faster 50% less See all our notebooks for: Kaggle , GRPO , TTS , embedding & Vision See all our models and all our notebooks See detailed documentation for Unsloth here 🦥 Unsloth News AMD training : Train, run RL, chat and deploy on AMD GPUs across Windows, WSL and Linux. Guide 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 GLM-5.2 : Run Z.ai's 744B-parameter, 1M-context open model locally with Unsloth Dynamic GGUFs. Guide DeepSeek-V4 : Run DeepSeek-V4-Flash locally with corrected multi-turn and tool-calling behavior. Guide Gemma 4 : Run and train Gemma 4 text, image and audio models with QAT, MTP, GGUF and MLX support. Guide MCP servers : Connect local models to files, apps, databases and external tools through Model Context Protocol. Guide New models : Qwen-AgentWorld , Ornith , Kimi K2.7 Code and MiniMax M3 More News Connections : Mix local models with API providers (OpenAI, Anthropic) or servers (vLLM, Ollama) in the same interface. Guide Introducing Unsloth Studio : our new web UI for running and training LLMs.