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 / Ubuntu (deb) Download Linux (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 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, embedding, audio models: Qwen3.8 , GLM-5.3-Flash , Kimi K3 , 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, 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 with 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 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 / Ubuntu (deb) Download Linux (AppImage) Download Unsloth Studio (web UI) macOS, Linux, WSL: curl -fsSL https://unsloth.ai/install.sh | sh 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 The log ends with your links and a generated JupyterLab password, plus a generated Unsloth Studio password on the first run against a new unsloth-studio volume; change that one on first sign-in or Unsloth Studio stops after an hour. A reused volume keeps the password already stored on it, and docker exec unsloth unsloth studio reset-password --username unsloth mints a new one and prints it. Ctrl-C stops following the log, not the container; docker rm -f unsloth deletes it. The Hugging Face cache keeps your models and the unsloth-studio volume keeps your accounts, chats and trained models, both across docker rm ; a volume from an older image is migrated on first start, its old code kept under .unsloth-studio-legacy/ . Those ports publish on every interface: on a cloud host 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 . It carries the training stack only, so there is no Unsloth Studio or JupyterLab in it, and it needs native Linux: WSL exposes /dev/dxg rather than the /dev/kfd that ROCm needs. 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. On a non-loopback -H bind a terminal nobody answers is not a refusal: after ~30s Unsloth starts anyway and shuts down on the bootstrap deadline, so detached launches ( docker run -dt , tmux new -d ) are unaffected. Setting UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT=0 disables that shutdown, so give those launches a password instead. A tunnel gets no such timeout and waits indefinitely rather than publish a public URL unasked, so give a detached --secure / --cloudflare launch its password non-interactively. 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 unsloth -- torch - backend = auto 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.