{"repo":"unslothai/unsloth","free":true,"listed":false,"github":"https://github.com/unslothai/unsloth","clone":"git clone https://github.com/unslothai/unsloth.git","description":"Local UI to run and train LLMs and diffusion models, including Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4, FLUX and more.","language":"Python","stars":72449,"topics":["agent","ai","chatgpt","deepseek","fine-tuning","gemma","image-generation","llama","llm","llms","openai","python","qwen","reinforcement-learning","self-hosted","stable-diffusion","text-to-speech","tts","ui","unsloth"],"license":"Apache-2.0","category":"dev_tool","readme_excerpt":"

\n \n \n \n \"Unsloth\n \n

\n

\nUnsloth is the first desktop app to run and train models.\n

\n\n

\n Features •\n Quickstart •\n Notebooks •\n Documentation\n

\n\n

\n \n \"unsloth\n \n

\n\n## ⚡ Get started\nDownload the native Unsloth Desktop app for your operating system:\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PlatformLink
WindowsDownload
macOSDownload
Linux / Ubuntu (deb)Download
Linux (AppImage)Download
Linux (Arm64)Download
\n\nDownload from [Unsloth](https://unsloth.ai/download) or [GitHub Releases](https://github.com/unslothai/unsloth/releases).\n\nOr if you prefer to install manually:\n\n#### macOS, Linux, WSL:\n```bash\ncurl -fsSL https://unsloth.ai/install.sh | sh\n```\n#### Windows:\n```powershell\nirm https://unsloth.ai/install.ps1 | iex\n```\n#### Community:\n\n- [Discord](https://discord.gg/unsloth)\n- [𝕏 (Twitter)](https://x.com/UnslothAI)\n- [Reddit](https://reddit.com/r/unsloth)\n\n## ⭐ Features\nUnsloth lets you run, train, and deploy AI models locally, with support for all types of models.\n\n### Run & Build with AI\n* Run and train LLMs, diffusion, embedding, audio models: [Kimi K3](https://unsloth.ai/docs/models/kimi-k3), MiniMax-H3, Qwen3.8, [Muse Glimmer](https://unsloth.ai/docs/models/muse-glimmer), [DeepSeek-V4](https://unsloth.ai/docs/models/deepseek-v4), [Gemma 4](https://unsloth.ai/docs/models/gemma-4).\n* **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.\n* **Search & RAG:** Use private and unlimited web search, deep research, and RAG.\n* **Image and video:** Run and train [image](https://unsloth.ai/docs/basics/diffusion-image) and video diffusion or multimodal models\n* **Audio:** Use private and unlimited web search, deep research, and RAG.\n* **Hardware:** Supports CPU, NVIDIA, AMD, Intel, macOS, and multi GPU setups.\n* **Remote Access:** Access your local models remotely through secure [Cloudflare](https://unsloth.ai/docs/basics/how-to-serve-local-llms-anywhere-secure-remote-access-with-cloudflare-and-unsloth) HTTPS.\n\n\n### Train & Deploy\n* **Fine-tuning:** Train LLMs, diffusion, TTS, and embedding models 2× faster with 70% less VRAM\n* **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.\n* **Export & Deploy:** [Export](https://unsloth.ai/docs/new/studio/export) or Deploy models with including [GGUF](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf), NVFP4, FP8 and more formats.\n* **Datasets:** Build datasets from PDFs, CSVs, DOCX files, and more with [Data Recipes](https://unsloth.ai/docs/new/studio/data-recipe).\n* **OpenAI Compatible API:** Serve models through an [OpenAI compatible API](https://unsloth.ai/docs/basics/api) and also connect to [cloud providers](https://unsloth.ai/docs/integrations/connections)\n\n## 🚀 Unsloth Start\n\n[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.\n\nStart Unsloth, load a model, open your project folder, then run:\n\n```bash\nunsloth start claude\n```\n\nReplace `claude` with any supported agent:\n\n| Agent | Command |\n| --- | --- |\n| Claude Code | `unsloth start claude` |\n| OpenAI Codex | `unsloth start codex` |\n| Hermes Agent | `unsloth start hermes` |\n| OpenClaw | `unsloth start openclaw` |\n| OpenCode | `unsloth start opencode` |\n\nClaude Code, Codex and OpenCode can keep their current model and use Unsloth as a local\nsubagent:\n\n```bash\nunsloth start claude --as-subagent --model unsloth/model-GGUF:quant\n```\n\n## 📥 Install\nUnsloth 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.\n\n### Unsloth Desktop (recommended)\n\nThe Tauri based desktop app is the easiest way to use Unsloth and needs no setup, so start here.\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
PlatformLink
WindowsDownload
macOSDownload
Linux / Ubuntu (deb)Download
Linux (AppImage)Download
Linux (Arm64)Download
\n\n### Unsloth Studio (web UI)\nUnsloth Studio (Beta) works on **Windows, Linux, WSL** and **macOS**.\n\n* **CPU:** Supported for Chat and Data Recipes currently\n* **NVIDIA:** Training works on RTX 30/40/50, Blackwell, DGX Spark, Station and more\n* **macOS:** Training, MLX and GGUF inference are ALL supported.\n* **AMD:** Training, RL, chat and deployment work on Windows, WSL and Linux. [Read the AMD guide](https://unsloth.ai/docs/basics/amd).\n* **Vulkan:** GGUF inference is supported on [compatible GPUs, including Intel GPUs](https://github.com/unslothai/unsloth/pull/5819). Vulkan accelerates GGUF inference only; training still requires a supported PyTorch or MLX backend.\n* **Multi-GPU:** Available now, with a major upgrade on the way\n\n#### macOS, Linux, WSL:\n```bash\ncurl -fsSL https://unsloth.ai/install.sh | sh\n```\nUse the same command to update.\n\nThe GGUF inference backend can be changed from **Settings > System > GGUF inference engine** once Studio is running: pick CPU, CUDA, ROCm or Vulkan (only the ones with a build for your machine are listed) and Apply. The choice is recorded with the install, so updates keep it, and Automatic returns to hardware detection.\n\nTo pick it before the first launch instead, set `UNSLOTH_LLAMA_CPP_BACKEND` **before installing or updating**. It selects the llama.cpp binary bundle, so setting it only when launching Studio cannot replace an existing one, and it overrides whatever was chosen in Settings:\n\n```bash\nexport UNSLOTH_LLAMA_CPP_BACKEND=vulkan # or cpu, cuda, rocm, auto\ncurl -fsSL https://unsloth.ai/install.sh | sh\n```\n\nOn Linux and WSL this is the path for the AMD GPUs Unsloth has no ROCm PyTorch wheels for: Polaris (RX 470/480/570/580/590) and RDNA 1 (RX 5500/5600/5700). torch stays on CPU there, so training and GPU inference are unavailable, but GGUF chat runs on the GPU through Vulkan. Not every pre-RDNA 2 card is in this group: Vega 20 (Radeon VII, MI50, `gfx906`) keeps a ROCm PyTorch path and the installer routes it there. The older `UNSLOTH_FORCE_VULKAN=1` still works and is read when `UNSLOTH_LLAMA_CPP_BACKEND` is unset.\n\nmacOS has no Vulkan llama.cpp bundle and does not need one: the installer always uses the Metal build, which covers Apple Silicon and the AMD GPUs in Intel Macs, and it says so and carries on if the variable is set.\n\n#### Windows:\n```powershell\nirm https://unsloth.ai/install.ps1 | iex\n```\nUse the same command to update.\n\nTo pick the GGUF inference backend before the first launch, set the environment variable before running the installer or updater (or change it later in **Settings > System > GGUF inference engine**):\n\n```powershell\n$env:UNSLOTH_LLAMA_CPP_BACKEND=\"vulkan\" # or cpu, cuda, rocm, auto\nirm https://unsloth.ai/install.ps1 | iex\n```\n\nRe-running the current installer replaces a previously selected bundle when the backend differs. A separate Vulkan SDK is not required; the GPU 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