unsloth

unslothaiApache-2.0活跃维护

本地跑和训练大模型、扩散模型的图形界面，支持 GGUF、MLX 及 Qwen、DeepSeek、Gemma、FLUX 等主流模型，省去命令行配置的麻烦。

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.

v0.1.916-beta发布于 2026-11-06 14:43 UTC79,296star +161 / 7 天7,385fork同步于 2026-11-10 23:19 UTC

本地推理模型微调扩散模型低显存训练图形界面

对比 GitHub 官网 agent ai chatgpt deepseek fine-tuning

怎么上手

由 README 提炼，执行前请确认

原文

curl -fsSL https://unsloth.ai/install.sh | sh
unsloth studio

硬件要求按 README 说明

支持 Windows、Linux、WSL、macOS，可多 GPU，兼容 NVIDIA、AMD、Intel GPU、CPU 及 Vulkan 后端；README 未给出具体显存数值。

支持 ROCm / AMD有 Docker 镜像

下载：源码 zip·GitHub Releases（安装包）本站不镜像安装包

项目说明

由 README 自动翻译提炼

没人工点评？写下你的经验

本地桌面应用，一键运行和微调 LLM 与扩散模型，省显存省时间

本地跑和训练大模型、扩散模型的图形界面，支持 GGUF、MLX 及 Qwen、DeepSeek、Gemma、FLUX 等主流模型，省去命令行配置的麻烦。

核心能力

用 Unsloth 微调，速度提升 2 倍、显存占用减少 70%
支持 LoRA、QLoRA、全参微调、RL、GRPO、DPO 和 FP8
可运行并训练 GGUF、MLX、扩散、嵌入、音频等多类模型
内置 OpenAI 兼容 API，可接入 Claude Code、Codex 和 MCP
支持导出 GGUF、NVFP4、FP8 等格式并部署

容易踩的坑

Desktop 默认端口 8888，可用 UNSLOTH_DESKTOP_PORT 修改，改后需完全退出并重启应用；暴露到网络（--secure 或非回环 -H）会要求设置管理员密码，服务端工具默认开启，需注意安全；Windows 上用 uv 安装 Core 需先单独装 torchao，否则会静默装到旧版 Unsloth。

内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README

最近发版

5 个版本v0.1.916-beta

全部 release

v0.1.916-betaLarge Performance Fixes最新正式版2026-11-06 · 2026-11-06 14:43 UTC
This release brings large performance fixes across Unsloth: faster typing and search in long chats, faster image/video loads, two-GPU image/video gen. It also adds separate browsers for each chat, floating browser window.

Highlights
- Large performance fixes with more Glass theme optimizations coming next week
- Glass theme fixes no more blur, better rendering, more options
- Unified memory offloading now properly works for big models
- Separate browsers for each chat - Multiple chats can now run their own browser individually
- Floating browser you can move and resize over the chat & let the model do it's thing
- Dynamic V3.1 Qwen3.8-Flash - 50% less token use, 15% more accurate
- Two-GPU image and video up to 2x faster on matching NVIDIA cards
- Faster chat search and deleting - search and delete chats much faster
- Stable diffusion image/video models loads up to 40% faster
- LTX-2.5 video much faster for new prompts on 16 to 24 GB GPUs
Chat performance
- Typing in a long chat no longer lags when a Glass theme or chat background is on.
- Chat search stays fast on very long histories instead of failing to load.
- Clearing and deleting chats is much faster on large histories.
- The first use of voice or audio features after launch no longer freezes a streaming reply for seconds.
- Read aloud with the Unsloth model engine no longer waits seconds on a model lookup before speaking.
- Glass themes over a background image respond up to 2x faster without GPU rendering, as on some Linux desktops.
Images and video
- With two matching, idle NVIDIA GPUs, models such as SDXL, Qwen-Image and Wan2.2 5B run half of each step on the second card when they fit on one, about 1.5 to 1.8x faster.
- On the same setup, FLUX and Lumina 2 split an even-sized batch of images across both cards.
- SDXL loads up to 40% faster, so the first image arrives sooner.
- On 16 to 24 GB GPUs, LTX-2.5 encodes new prompts on the GPU instead of the CPU, so a clip with a new prompt renders much faster.
- LTX-2.5 GGUF Q8_0 video renders about 10% faster on NVIDIA GPUs, with identical frames.
- Repeat renders with the same LoRA are faster when the model is not quantized, GGUF or offloaded.
- Some image models, such as FLUX and Z-Image, render faster on Linux with AMD RDNA3 and RDNA4 GPUs.
New in Unsloth
- Float the built-in browser over the chat as a small window you can move and resize; each chat remembers whether it floats.
- Load Anima, Chroma, MOSS-Image and Krea 2 image models from single-file checkpoints, with LoRA and LoKR files for all but Chroma.
- Glass themes dim the background image instead of blurring it, and controls on other pages match the chat's glass.
- Owners can rename a managed account without losing its chats, files or settings.
- Owners can cap model download speed in Settings > General > Downloads.
- OpenAI-compatible connections can send custom headers.
Fixes
- On chat-only installs with a non-GGUF model already downloaded, the first chat fetches the default model instead of failing.
- GGUF models on CPU-only machines no longer run out of memory with Auto context.
- On Windows, HTTPS downloads resume after a pause or interruption instead of starting over.
- Updating Unsloth on Windows no longer sits silent for half a minute at the end.
- Connecting Blender through MCP works again.
- Compare mode no longer leaves one pane empty on the first message.
Training and library
- In Python, model.generate() for Llama, Qwen2 and Qwen3 models is faster by default on NVIDIA GPUs under Linux, up to several times for small models.
- Training on a Hub dataset split into hundreds of files starts up to a minute sooner.
- With Assistant completions only, answers that start with a newline are no longer left out of training.
Download Unsloth Desktop

Unsloth Desktop is free and open source. Download it for:
v0.1.915-betaImproved Customization + Glass + Browser Use2026-11-04 · 2026-11-04 17:54 UTC
查看更新说明
This release introduces Qwen3.8-Flash-Next Dynamic V3.1, polishes the Glass themes (now in Beta), adds a Composer position slider and lets models interact with files in the built-in browser. It also brings lower idle memory usage, preserved chat context when switching GGUF models, faster MLX loading and FP8 LoRA training.

Highlights
- Qwen3.8-Flash-Next Dynamic V3.1 - 10-15% extra accuracy Guide
- Glass theme improvements across pages, menus, the sidebar and browser
- Browser file interaction lets models click, read and edit HTML, files and pages
- Lower idle memory with faster MLX loading and reloading
- Preserved chat context when switching between GGUF models
- FP8 LoRA training with lower GPU memory usage
- Composer position slider to place the chat box anywhere on new chats
Glass and appearance
- Set the chat box position, from top to bottom, in Settings > Appearance > Composer.
- Glass themes now have softer highlights, lighter shadows and improved frosted effects across menus, panels and the browser.
- Pages like Library, Images and Train no longer heavily blur background images.
- Open conversations now slightly dim the background and use denser glass for the composer.
- Windows and Linux title bars better match Glass themes and background images.
- Settings changes made in one browser tab now sync across other open tabs.
Built-in browser
- With Browser use enabled, models can click and type in HTML and React pages created in chat.
- Models can read and edit HTML, React, Markdown, code and text files directly in the browser. Back restores the previous version.
- Models can also read PDFs and other documents open in the browser.
- Download prompts can remember permissions per site or across all sites, including for program files.
Chat
- Chats now show activity between tool calls, including context compaction on GGUF models.
- Attach saved Library files using + > More > Attach from Library.
- Switching away from a GGUF model and back now preserves chat context, avoiding the need to re-read long histories.
- GGUF models can read up to four web pages at once when tools run without asking.
Connections and API
- OpenRouter models now load live, with working Reload models support.
- Improved compatibility with newer MCP servers.
- The Decision API adds LiquidAI's d1 models and Decider 2B (MLX only).
- Settings > API > Quickstart now includes curl, Python and JavaScript examples.
Performance and media
- Unsloth no longer loads PyTorch and other ML libraries at startup, significantly reducing idle memory usage. Configure startup features in Settings > General > Startup performance.
- MLX models load and reload much faster, and reducing a loaded model's context now takes seconds.
- Long prompts on supported MLX models use less memory.
- SDXL renders faster with very limited VRAM, including 4 GB setups.
- Image LoRAs in your model folders now appear on the Images page.
Training
- Faster, lower-memory LoRA training on 16-bit bases with recent NVIDIA GPUs using float8_training = "fp8_lora".
- DPO, ORPO, KTO, GRPO and CPO training now run on MLX.
- Fixed crashes when full fine-tuning Gemma 4 in float16.
Trim overlapping highlight bulletsCombine related Glass bullets

What's Changed
- Bump install.sh / install.ps1 pins to unsloth>=2026.11.1 by @danielhanchen in https://github.com/unslothai/unsloth/pull/16554
- Sync the pad id to FastModel's fallback tokenizer by @danielhanchen in https://github.com/unslothai/unsloth/pull/16557
- Studio: faster MLX load and reload, ready to serve when loaded by @Lyxot in https://github.com/unslothai/unsloth/pull/16536
- Parallel env tests: pin UNSLOTH_RETURN_LOGITS unset for the packing checks by @danielhanchen in https://github.com/unslothai/unsloth/pull/16594
- Tray status test: end the liste
v0.1.914-betaGlass Theme, Personalize & Faster Generation2026-11-03 · 2026-11-03 14:19 UTC
查看更新说明
Unsloth now has Glass themes and more customization options for backgrounds, greetings and chats. This release also brings faster generation, more multi-GPU training options and CPU training in Unsloth.

Highlights
- New Glass themes with 5 themes, transparency + native macOS glass
- Customization with custom greetings, background controls and code fonts
- DeepSeek-V4.1 512K context benchmarks show Unsloth's GGUFs rank strongest across Terminal Bench, DeepSWE and AutomationBench. Analysis
- Chat navigation to preview, bookmark and jump between earlier turns
- CPU text training directly from the Train page
Make it yours
- Choose from five Glass styles with adjustable strength and optional glass menus, with or without a background image.
- Unsloth Desktop on Mac supports native glass, letting your desktop show through the window.
- Adjust background size and position, and choose where it appears across your chats.
- Add your own greeting with text, an icon or an image, or hide it entirely.
- Customize code fonts, line numbers, word wrapping and ligatures with a live preview.
- Use the Trick or Treat theme year-round without the seasonal extras.
Chat
- Preview earlier messages, bookmark turns and jump through long conversations with the new chat navigator.
- Paste formatted content as Markdown, including links, lists and spreadsheet tables.
- Arabic, Persian, Hebrew and Urdu messages display right to left while code keeps its original direction.
- Long chats stay more responsive while replies stream and you scroll.
- MCP connections recover after a server restart.
- Coding agents can follow instructions from files in your project's working folder.
Performance and media
- Supported single-GPU Llama and Qwen3 models generate faster in Unsloth's Transformers backend on NVIDIA GPUs with Linux or WSL. In our A100 test, 4-bit Qwen3-8B reached 99.1 tokens/s, up from 25.7, a 3.9x speedup.
- SDXL now renders under a 4 GiB VRAM budget, tested on T4 and L4 GPUs.
- Search large document collections faster with a new index for RAG.
- Save generated images as PNG faster without changing the output.
- Video models reuse cached INT8 weights for faster repeat loads.
- Wan2.2 A14B uses less system memory when building INT8 weights for offloaded generation.
- Adjust image LoRA strengths without reloading adapter files.
- Transcribe audio up to four hours long with audio.cpp, subject to upload and storage limits.
Mac
- Try experimental Fast Verify for faster generation with supported MLX speculative decoding models. Replies may differ slightly.
- MLX streaming spends less time processing text as replies grow.
- Unsloth now uses the Mac system font by default.
Training
- Fine-tune text models on CPU from the Train page when no GPU is available.
- Chat-only installs can add CPU training through Install Training.
- Fine-tune audio vision models such as Gemma 3n and Gemma 4 using audio and images together in the same dataset.
- Load supported non-quantized models larger than a single GPU by loading only each GPU's assigned pipeline layers.
- Configure supported combinations of data, tensor, pipeline and context parallelism with UNSLOTH_PARALLEL.
- Reduce MLP activation memory during training with unsloth.tile_mlp(model).
- Send images to vision models through CLI chat and inference commands.
What's Changed
- Bump install.sh / install.ps1 pins to unsloth>=2026.10.11 by @danielhanchen in https://github.com/unslothai/unsloth/pull/16131
- Don't warn about loss_type = "nll" on a TRL whose SFTConfig predates it by @debojyoti-dev in https://github.com/unslothai/unsloth/pull/16042
- Studio MLX estimate: ask the quantized-attention route about a cache lo
v0.1.913-betaCustomize + Performance + UI/UX improvements2026-10-30 · 2026-10-30 14:46 UTC
查看更新说明
Make Unsloth your own with custom backgrounds and a improved UI/UX. This release also cuts app update times and speeds up Linux app and Qwen-Image gen when VRAM is limited. Also new: DeepSeek-V4.1-Flash-1017 in Unsloth Dynamic v3.1 GGUFs (preview), FLUX.2-klein-9B on low-RAM machines, smoother chats and batched MoE fine-tuning.

Highlights
- Custom backgrounds to personalize your chats (+ move composer to bottom - more customization coming next week)
- DeepSeek-V4.1-Flash-1017 new Unsloth Dynamic v3.1 GGUFs (preview), the most accurate for their size on nearly all benchmarks Blog
- 14 new languages for Unsloth
- App updates finish twice as fast
- Linux much less lag and improved performance
- Qwen-Image renders up to 2x faster under a 16 GB VRAM limit
- Updated interface with consistency and cleaner UI
- MoE fine-tuning runs compatible API jobs together
Customization and interface
- Move the chat box to the bottom of new chats with Composer at the bottom in Settings > Chat.
- Hide the greeting entirely for a simpler new-chat screen.
- The Library gets shorter chat cards, clearer tooltips and consistent sort and filter icons.
- Copy images between input slots by dragging a filled slot onto an empty one.
- Pinned models stay saved across Unsloth Desktop restarts.
- Each browser tab keeps its own unsent message.
- Scrolling long chats is smoother on Linux with NVIDIA, X11 and driver 610+. Our high-resolution test improved from about 17 to 60 fps.
Images and video
- FLUX.2-klein-9B now loads and renders on Linux with 12.7 GB of system RAM and a supported GPU.
- Qwen-Image-2512 renders up to 2x faster under a 16 GB VRAM limit.
- Qwen-Image-Edit-2511 renders about 1.3x faster under the same limit.
- MiniMax-H3 clips finish about 1.7x faster in our memory-limited NVIDIA test.
Performance
- App updates take about half as long by skipping Python precompilation. The tradeoff is a longer first launch afterwards, as before v0.1.912.
- Long replies stream more smoothly, even with unfinished math formatting. Parsing an 83 KB reply with a stray $$ took 0.7 seconds instead of 71 seconds.
- Previewing a 1 GB local Parquet file uses about 300 MB of peak memory, down from 1.5 GB.
- Unsloth stays responsive as Transformers and MLX models finish loading.
Chat and API
- GGUF models reload in the background after a llama-server crash.
- Failed or cancelled GGUF downloads leave your current model loaded.
- Very long GGUF chats can compact and keep going.
- Claude Code connected directly to a local GGUF reuses cached prompts across new sessions.
- Rejected GGUF requests show llama-server’s explanation instead of a generic error.
Mac
- Claude Code, Codex and OpenCode requests can share an MLX batch. In our test, four simultaneous requests finished up to 2.3x faster.
- Experimental Neural Engine Prefill processes prompts up to 31% faster in our Qwen3.8-27B-4bit test on M3 Max. It is opt-in and can slightly change replies.
- Auto context starts GGUF models at no more than 65,536 tokens, reducing upfront memory use. You can still set a larger context manually.
- Send LFM3-Audio or LFM3-Omni a voice clip on MLX and hear its answer alongside the text.
Training
- Compatible LoRA and QLoRA jobs on MoE models such as Qwen3.6-35B-A3B train together through the fine-tuning API, each with its own adapter.
- Use via API on the Train page opens Python, JavaScript and curl examples for your setup.
- Single-column text datasets are recognised automatically, with no manual column mapping.
What's Changed
- Bump install.sh / install.ps1 pins to unsloth>=2026.10.10, unsloth-zoo>=2026.10.10 by @danielh
v0.1.912-betaTrick or Treat + Optimizations2026-10-28 · 2026-10-28 14:09 UTC
查看更新说明
Halloween 👻 is here. Through October, a Trick or treat row in the sidebar opens eleven Halloween themes, spooky greetings and a few spiders and bats.

This release also speeds up the first launch, Linux tool calls and model loading, and trains compatible fine-tuning jobs together with multi-LoRA.

Highlights
- Trick or treat: 🎃 new Halloween themes - until Nov 1
- Faster Unsloth: 2x faster first launch, plus speedups for tool calls, model lists, dataset prep and MiniMax-H3 loads
- Multi-LoRA batching: up to 4 compatible queued fine-tuning jobs train together
- SGLang thinking + auto-compact: Resume and JSON output now work on vLLM and SGLang too
- Browser use switch: let models control the built-in browser from its new tab page
Optimizations
- Setup now precompiles Python files, so the first launch is about 2x faster.
- On Linux, tool calls start in 1 to 2 ms instead of 84 to 174 ms with a 2 GB server process.
- With 497 cached models, the model list uses about a third of the CPU time it did.
- Preparing a 1,000-row chat dataset for Gemma 3 4B took 1.75 s instead of 6.6 s in our test.
- MiniMax-H3 loaded in 49 s instead of 61 s on a Linux A100.
Multi-LoRA + API

When several compatible jobs wait in the fine-tuning queue, Unsloth now trains them together, each with its own LoRA adapter on a shared base model.
- Jobs sent while the GPU is busy queue up in order, and each can be cancelled on its own.
- Load a job's fine_tuned_model name to chat with its trained adapter.
- /v1/videos requests now queue while a clip renders, and matching /v1/images/generations requests are batched.
How it works
- New Triton kernels run the base layer once per batch, while each adapter learns only from its own tokens with its own loss and learning rate.
- With 4 adapters, a 4096x4096 layer's forward and backward pass ran 1.52x faster than one adapter at a time (A100, bf16, rank 16).
Chat + tools
- Local models skip tool calls that hit the output limit, so a half-written cat > app.py can't overwrite a file.
- Before, cut-off rewrites shrank a 175-line test file to as few as 6 lines on an RTX 3090.
- Thinking models such as Qwen3 and DeepSeek R1 now show their reasoning and the Think toggle on SGLang.
- With Auto-compact on, long SGLang chats drop their oldest turns and keep answering.
- On vLLM and SGLang, you can resume a reply cut off at Max Tokens and request JSON with response_format.
Browser + Desktop
- The browser's new tab page has a Browser use switch that lets models control the browser.
- On Desktop, a browser agent working for a temporary chat no longer adds pages to your history.
- On macOS with Safari 18.4 or earlier, Desktop browser tabs and their close buttons work again.
- Log out is now in the Unsloth Desktop user menu.
Trick or treat

The Trick or treat… row sits above More in the sidebar and opens a Halloween menu.
- Pick one of eleven themes, such as Pumpkin, Coven and Ghost Ship, each with a light and dark mode.
- Turn on Spooky greetings and new chats open with a Halloween line and sloth.
- With Spiders and bats on, Halloween themes get the odd spider and bats crossing the window.
- Not sure which theme? Trick or treat! picks one at random.
Unsloth returns to normal at midnight on November 1, or right away with Remove now.

Training
- Gemma 4 E2B and E4B now keep a dataset's thinking text in training instead of dropping it.
- New chat training setups turn on sequence packing and Assistant completions where the model and data support them.
- Local JSON and JSONL datasets load even when later rows add new keys.
Download Unsloth Desktop

Unsloth Desktop is free and open source. Download it for:

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README 原文

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在 GitHub 打开

⚡ Get started

Download the native Unsloth Desktop app for your operating system:

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:

⭐ 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.
Browser use: Navigate sites, fill out forms, read PDFs, and edit files beside your chat with a local model.
Sandboxing: Restrict which files your model can access when it runs commands in a sandbox.
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)

To choose Desktop's local HTTP port, set UNSLOTH_DESKTOP_PORT=9000 in the environment that launches the app, then fully quit and reopen Desktop. Unset or empty keeps the default port 8888. If the selected port is busy, Desktop tries the next 20 ports. Choose a port between 1024 and 65515; Desktop rejects ranges containing a browser-restricted port. This setting applies to Desktop, including backend restarts after updates; it does not change the web UI's unsloth studio -p option or the separate LAN access port.

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 model.

Model	Free Notebooks	Performance	Memory use
Unsloth Studio	▶️ Start for free
Decision model (Qwen3.5-4B)	▶️ Start for free	1.5x faster	60% less
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

📥 Advanced Installation

The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, view our docs.

Developer / Nightly / Experimental installs: macOS, Linux, WSL:

The developer install builds from the main branch, which is the latest (nightly) source.

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:

UNSLOTH_STUDIO_HOME="$PWD/.studio" ./install.sh --local
UNSLOTH_STUDIO_HOME="$PWD/.studio" unsloth studio -p 8888

Then to update:

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.

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:

$env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; .\install.ps1 --local
$env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; unsloth studio -p 8888

Then to update:

cd unsloth; git pull
.\install.ps1 --local
unsloth studio -p 8888

Advanced launch options

Skip PyTorch (GGUF-only mode):

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_NO_TORCH=1 sh

$env:UNSLOTH_NO_TORCH=1; irm https://unsloth.ai/install.ps1 | iex

Skip the post-install prompt that starts Unsloth (useful for automated installs):

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_SKIP_AUTOSTART=1 sh

$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:

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_ISOLATE_UV_CACHE=1 sh

$env:UNSLOTH_ISOLATE_UV_CACHE=1; irm https://unsloth.ai/install.ps1 | iex

For a local run the flag is --isolated-uv-cache:

./install.sh --local --isolated-uv-cache

.\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:

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_INSTALL_NO_ROLLBACK=1 sh

$env:UNSLOTH_INSTALL_NO_ROLLBACK=1; irm https://unsloth.ai/install.ps1 | iex

For a local run the flag is --no-rollback:

./install.sh --local --no-rollback

.\install.ps1 --local --no-rollback

Pinning the Python version:

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_PYTHON=3.12 sh

$env:UNSLOTH_PYTHON='3.12'; irm https://unsloth.ai/install.ps1 | iex

Install to a custom location with UNSLOTH_STUDIO_HOME:

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_STUDIO_HOME=/abs/path sh

$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:

UNSLOTH_NPM_REGISTRY=https://artifactory.example.com/api/npm/npm/ ./install.sh --local

$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:

Pick the llama.cpp build GGUF chat runs on during installation. PyTorch is picked separately from your GPU, so this does not change it:

export UNSLOTH_LLAMA_CPP_BACKEND=vulkan   # or cpu, cuda, rocm, auto
curl -fsSL https://unsloth.ai/install.sh | sh

$env:UNSLOTH_LLAMA_CPP_BACKEND="vulkan"   # or cpu, cuda, rocm, auto
irm https://unsloth.ai/install.ps1 | iex

To choose PyTorch yourself, pin its index:

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_TORCH_INDEX_FAMILY=cpu sh   # or a family like rocm7.2

Or skip PyTorch for GGUF chat only:

curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_NO_TORCH=1 sh

audio.cpp audio engine:

Setup also installs audio.cpp (prebuilt by unslothai/audio.cpp), which runs the audio.cpp speech, music and speech-to-text models on the Audio page, in Voice settings and behind /v1/audio/*. It is optional and never blocks setup:

UNSLOTH_SKIP_AUDIO_CPP_INSTALL=1 skips it.
UNSLOTH_AUDIO_CPP_ACCELERATOR=cpu (or cuda, vulkan, metal) picks the bundle instead of auto-detecting.
AUDIOCPP_SERVER_PATH=/path/to/audiocpp_server uses your own build.

Uninstall

MacOS, WSL, Linux: curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh

Windows (PowerShell): irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex

For more info, see our docs.

Deleting model files

You can delete old model files either from the bin icon in model search or by removing the relevant cached model folder from the default Hugging Face cache directory. By default, HF uses:

MacOS, Linux, WSL: ~/.cache/huggingface/hub/

Windows: %USERPROFILE%\.cache\huggingface\hub\

💚 Community and Links

Type	Links
Discord	Join Discord server
r/unsloth Reddit	Join Reddit community
📚 Documentation & Wiki	Read Our Docs
Twitter (aka X)	Follow us on X
🔮 Our Models	Unsloth Catalog
✍️ Blog	Read our Blogs

Citation

You can cite the Unsloth repo as follows:

@software{unsloth,
  author = {Daniel Han, Michael Han and Unsloth team},
  title = {Unsloth},
  url = {https://github.com/unslothai/unsloth},
  year = {2023}
}

If you trained a model with 🦥Unsloth, you can use this cool sticker!

License

Unsloth uses a dual-licensing model of Apache 2.0 and AGPL-3.0. The core Unsloth package remains licensed under Apache 2.0, while certain optional components, such as the Unsloth Studio UI are licensed under the open-source license AGPL-3.0.

This structure helps support ongoing Unsloth development while keeping the project open source and enabling the broader ecosystem to continue growing.

Thank You to

The llama.cpp library that lets users run and save models with Unsloth
The Hugging Face team and their libraries: transformers and TRL
The Pytorch and Torch AO team for their contributions
NVIDIA for their NeMo DataDesigner library and their contributions
And of course for every single person who has contributed or has used Unsloth!

怎么上手

项目说明

核心能力

容易踩的坑

最近发版

Highlights

Chat performance

Images and video

New in Unsloth

Fixes

Training and library

Download Unsloth Desktop

Highlights

Glass and appearance

Built-in browser

Chat

Connections and API

Performance and media

Training

What's Changed

Highlights

Make it yours

Chat

Performance and media

Mac

Training

What's Changed

Highlights

Customization and interface

Images and video

Performance

Chat and API

Mac

Training

What's Changed

Highlights

Optimizations

Multi-LoRA + API

Chat + tools

Browser + Desktop

Trick or treat

Training

Download Unsloth Desktop

用户点评

README 原文

⚡ Get started

macOS, Linux, WSL:

Windows:

Docker:

Homebrew:

Winget (Windows):

Community:

⭐ Features

Run & Build with AI

Train & Deploy

🚀 Unsloth Start

📥 Install

Unsloth Desktop (recommended)

Unsloth Studio (web UI)

macOS, Linux, WSL:

Windows:

Homebrew:

Docker

Launch

HTTP Secure Deployment

Remote HTTPS & LAN Access

Password management & headless starts

Developer, Nightly, Uninstall

Unsloth Core (code-based)

Linux, WSL:

Windows:

AMD, Intel, DGX Spark, Blackwell:

📒 Free Notebooks

📥 Advanced Installation

Developer / Nightly / Experimental installs: macOS, Linux, WSL:

Developer / Nightly / Experimental installs: Windows PowerShell:

Advanced launch options

Vulkan, custom llama.cpp backends:

audio.cpp audio engine:

Uninstall