unsloth

Sun, 16 Aug 2026 15:48:12 +0800

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
Linux (Arm64)	Download

Download from Unsloth or GitHub Releases.

Or if you prefer to install manually:

macOS, Linux, WSL:

`1`	`curl -fsSL https://unsloth.ai/install.sh \| sh`

Windows:

`1`	`irm https://unsloth.ai/install.ps1 \| iex`

Community:

⭐ Features

Unsloth lets you run, train, and deploy AI models locally, with support for all types of models.

Run & Build with AI

Run and train LLMs, diffusion, embedding, audio models: Kimi K3, MiniMax-H3, Qwen3.8, Muse Glimmer, 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, and RAG.
Image and video: Run and train image and video diffusion or multimodal models
Audio: Use private and unlimited web search, deep research, and RAG.
Hardware: Supports CPU, NVIDIA, AMD, Intel, macOS, and multi GPU setups.
Remote Access: Access your local models remotely through secure Cloudflare HTTPS.

Train & Deploy

Fine-tuning: Train LLMs, diffusion, TTS, and embedding models 2× faster with 70% less VRAM
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.
OpenAI Compatible API: Serve models through an OpenAI compatible API and also connect to cloud providers

🚀 Unsloth Start

Unsloth Start connects Claude Code, Codex and other agents to local models with one command.

Start Unsloth, load a model, open your project folder, then run:

`1`	`unsloth start claude`

Replace claude with any supported agent:

Agent	Command
Claude Code	`unsloth start claude`
OpenAI Codex	`unsloth start codex`
Hermes Agent	`unsloth start hermes`
OpenClaw	`unsloth start openclaw`
OpenCode	`unsloth start opencode`

Claude Code, Codex and OpenCode can keep their current model and use Unsloth as a local subagent:

`1`	`unsloth start claude --as-subagent --model unsloth/model-GGUF:quant`

📥 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)

The Tauri based desktop app is the easiest way to use Unsloth and needs no setup, so start here.

Platform	Link
Windows	Download
macOS	Download
Linux / Ubuntu (deb)	Download
Linux (AppImage)	Download
Linux (Arm64)	Download

Unsloth Studio (web UI)

Unsloth Studio (Beta) works on Windows, Linux, WSL and macOS.

CPU: Supported for Chat and Data Recipes currently
NVIDIA: Training works on RTX 30/40/50, Blackwell, DGX Spark, Station and more
macOS: Training, MLX and GGUF inference are ALL supported.
AMD: Training, RL, chat and deployment work on Windows, WSL and Linux. Read the AMD guide.
Vulkan: GGUF inference is supported on compatible GPUs, including Intel GPUs. Vulkan accelerates GGUF inference only; training still requires a supported PyTorch or MLX backend.
Multi-GPU: Available now, with a major upgrade on the way

macOS, Linux, WSL:

`1`	`curl -fsSL https://unsloth.ai/install.sh \| sh`

Use the same command to update.

The 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.

To 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:

1
2

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

On 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.

macOS 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.

Windows:

`1`	`irm https://unsloth.ai/install.ps1 \| iex`

Use the same command to update.

To 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):

1
2

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

Re-running the current installer replaces a previously selected bundle when the backend differs. A separate Vulkan SDK is not required; the GPU driver must provide a working Vulkan runtime.

Launch

`1`	`unsloth studio -p 8888`

For LAN or cloud access, add -H 0.0.0.0 (raw port only; add --cloudflare for a public URL). By default, Unsloth is accessible only locally.

To reach Unsloth over HTTPS, use unsloth studio --secure. Unsloth stays bound to localhost and is reached only through a free Cloudflare tunnel, which publishes it at a public https://*.trycloudflare.com URL (it fails closed if the tunnel can’t start, so the raw port is never exposed). This makes Unsloth reachable from the internet, so anyone with the link and API key can use it and run code: keep your API key private (see Remote access below).

Docker

Use our Docker image unsloth/unsloth container. Run:

docker run -d -e JUPYTER_PASSWORD="mypassword" \
  -p 8888:8888 -p 8000:8000 -p 2222:22 \
  -v $(pwd)/work:/workspace/work \
  --gpus all \
  unsloth/unsloth

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

For Windows, pip install unsloth works only if you have PyTorch installed. Read our Windows Guide. You can use the same Docker image as Unsloth Studio.

AMD, Intel:

For RTX 50x, B200, 6000 GPUs: uv pip install unsloth --torch-backend=auto. Read our guides for: Blackwell and DGX Spark.
To install Unsloth on AMD and Intel GPUs, follow our AMD Guide and Intel Guide.

📒 Free Notebooks

Train for free with our notebooks. You can use our new free Unsloth Studio notebook to run and train models for free in a web UI. Read our guide. Add dataset, run, then deploy your trained model.

Model	Free Notebooks	Performance	Memory use
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
Mistral Ministral 3 (3B)	▶️ Start for free	1.5x faster	60% 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
GGUF hardware controls: Choose GPU/layer placement, offload MoE experts and use multi-GPU or Tensor Parallelism. #6414
Local models for any agent: Use unsloth start with Claude Code, Codex, Hermes, OpenCode, OpenClaw and more through Unsloth’s OpenAI- and Anthropic-compatible APIs. Guide
MCP control endpoint: Let compatible clients manage models, training, recipes, checkpoints and exports. #7191
Local inference reliability: Resume long chats faster, recover stalled downloads and reuse existing GGUF files. #7204 • #6858 • #7209
New models: Qwen-AgentWorld, Ornith, Kimi K2.7 Code and MiniMax M3
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
DiffusionGemma: Run and fine-tune Google’s diffusion language model with 1.8x faster inference in Unsloth Studio. Guide
Qwen3.6: Run and train Qwen3.6 with MTP for 1.4-2.2x faster inference and NVFP4 quants for supported GPUs. 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
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. Blog
Train MoE LLMs 12x faster with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. Blog
Embedding models: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. Blog • Notebooks
New 7x longer context RL vs. all other setups, via our new batching algorithms. Blog
New RoPE & MLP Triton Kernels & Padding Free + Packing: 3x faster training & 30% less VRAM. Blog
500K Context: Training a 20B model with >500K context is now possible on an 80GB GPU. Blog
FP8 & Vision RL: You can now do FP8 & VLM GRPO on consumer GPUs. FP8 Blog • Vision RL

📥 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 (its own virtual env, auth/, studio.db, cache and llama.cpp build), set UNSLOTH_STUDIO_HOME and pass it again at launch:

1
2

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

Then to update :

1
2
3

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 Bypass
.\install.ps1 --local
unsloth studio -p 8888

To install into an isolated location (its own virtual env, auth/, studio.db, cache and llama.cpp build), set UNSLOTH_STUDIO_HOME and pass it again at launch:

1
2

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

Then to update :

1
2
3

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

Remote access: `--secure` (HTTPS tunnel) vs raw port

By default unsloth studio binds to 127.0.0.1 (this machine only). To reach it from another device, pick one of:

--secure (recommended): serve only through a free Cloudflare HTTPS link. Unsloth stays bound to localhost and the tunnel provides the public URL; it fails closed (does not start) if the tunnel can’t come up, so the raw port is never exposed.

`1`	`unsloth studio --secure -p 8888`

-H 0.0.0.0: bind the raw port on all network interfaces, reachable from anywhere on the network (subject to your firewall). It does not create a public internet URL; add --cloudflare to also publish an internet-reachable https://*.trycloudflare.com link even behind a firewall. Only use this on a network you trust.

`1`	`unsloth studio -H 0.0.0.0 -p 8888`

The Cloudflare tunnel is off by default: -H 0.0.0.0 exposes the raw port only, not a public internet URL. Pair the wildcard bind with --cloudflare (unsloth studio -H 0.0.0.0 --cloudflare) to also publish a public https://*.trycloudflare.com link, or prefer --secure (above), which keeps the raw port private. --cloudflare has no effect on a loopback bind.

On a wildcard bind Unsloth works out the address to share by asking ifconfig.me for the public IP, then asks check-host.net whether that port is reachable so it can tell you if a firewall is in the way. Both contact a third party. Set UNSLOTH_STUDIO_DISABLE_PUBLIC_CHECK=1 to skip them; the banner then shows the LAN address and no reachability line.

The first time Unsloth is published on a public URL (--secure or --cloudflare) with the auto-generated admin password still in place, it asks for a new admin password in the terminal (masked input with confirmation) before the public link goes up. Without an attached terminal it warns instead and keeps the bootstrap deadline: Unsloth shuts down after UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT (default 1 hour) unless the password is changed in the web UI.

For headless setups that cannot answer that prompt, set the initial admin password non-interactively with --password (only takes effect when no password is set yet; if one already exists it is a hard error, so rotate later with unsloth studio reset-password):

1
2
3

unsloth studio --secure --password 'your-strong-password'        # visible in `ps`/history
UNSLOTH_STUDIO_PASSWORD='your-strong-password' unsloth studio --secure   # via env var
printf '%s\n' 'your-strong-password' | unsloth studio --secure --password -   # via stdin

A literal --password VALUE is visible in the process list and shell history, so prefer the UNSLOTH_STUDIO_PASSWORD env var or --password - (stdin) for automation. This applies to any launch (public or a headless -H 0.0.0.0 bind), and the password is set in the parent before the server binds, so it never reaches a re-executed child process.

Server-side tools (web search, Python and terminal code execution) run as your user and are on by default. Anyone who can reach the server with the API key can run code on this machine, so keep your API key private and pass --disable-tools when exposing Unsloth.

Advanced launch options

Installer options can be passed as environment variables. On macOS, Linux and WSL place the variable after the pipe so the shell passes it to sh; on Windows set it with $env: before piping to iex.

Skip PyTorch (GGUF-only mode):

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

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

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

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

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

Pin the Python version:

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

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

Install to a custom location with UNSLOTH_STUDIO_HOME:

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

`1`	`$env:UNSLOTH_STUDIO_HOME='C:\path'; irm https://unsloth.ai/install.ps1 \| iex`

On macOS, the installer defaults to the system certificate store (UV_SYSTEM_CERTS=1) so uv trusts the CAs in your Keychain, needed behind TLS-inspecting proxies (Cisco Umbrella, Zscaler, etc.). Opt out with:

`1`	`curl -fsSL https://unsloth.ai/install.sh \| UV_SYSTEM_CERTS=0 sh`

Point the frontend build at a corporate npm mirror/proxy with UNSLOTH_NPM_REGISTRY (for the developer install behind a firewall that blocks registry.npmjs.org):

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

`1`	`$env:UNSLOTH_NPM_REGISTRY='https://artifactory.example.com/api/npm/npm/'; .\install.ps1 --local`

It is threaded as --registry into the Unsloth frontend npm/bun installs; the supply-chain locks (7-day min-release-age, exact version pins) stay in force.

Cap Unsloth’s native CPU thread pools on high-core hosts: UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888.

Uninstall

The recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS .app bundle + Launch Services on Mac; Start Menu, HKCU\Software\Unsloth registry key and user PATH entries on Windows):

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

If you only want to drop the install dir and keep the launcher/shortcut for a later reinstall, you can instead run rm -rf ~/.unsloth/studio (Mac/Linux/WSL) or Remove-Item -Recurse -Force "$HOME\.unsloth\studio" (Windows). The model cache at ~/.cache/huggingface is not touched by any of these.

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!

Local Training on Producthunt daily

unsloth

unslothai/unsloth

Unsloth is the first desktop app to run and train models.

⚡ Get started

macOS, Linux, WSL:

Windows:

Community:

⭐ Features

Run & Build with AI

Train & Deploy

🚀 Unsloth Start

📥 Install

Unsloth Desktop (recommended)

Unsloth Studio (web UI)

macOS, Linux, WSL:

Windows:

Launch

Docker

Developer, Nightly, Uninstall

Unsloth Core (code-based)

Linux, WSL:

Windows:

AMD, Intel:

📒 Free Notebooks

🦥 Unsloth News

📥 Advanced Installation

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

Developer / Nightly / Experimental installs: Windows PowerShell:

Remote access: --secure (HTTPS tunnel) vs raw port

Advanced launch options

Uninstall

Deleting model files

💚 Community and Links

Citation

License

Thank You to

Remote access: `--secure` (HTTPS tunnel) vs raw port