GitHub - shadyuwugurl/unsloth: Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.

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

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, 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`
Hermes Agent	`unsloth start hermes`
OpenClaw	`unsloth start openclaw`
OpenCode	`unsloth start opencode`
DeepSeek Harness	`unsloth start dsh`

📥 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 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

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

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. 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. Blog
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
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, 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 Bypass
.\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

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:

You can force the backend during installation:

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

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!

🔬 Unsloth Studio Fork - Research & Development Roadmap

This fork explores advanced model optimization, compression, and training techniques. Below is the implementation roadmap for integrating cutting-edge research into Unsloth Studio.

📚 Research Papers & Implementations

Technique	Paper	Repository	Status
LoPT (Low-Rank Parameter Tuning)	arXiv:2605.04913	HumyuShi/LoPT	📋 Planned
QuEPT (Quantization-Aware Parameter-Efficient Tuning)	arXiv:2602.12609	xuke225/QuEPT	📋 Planned
OneComp (One-Shot Compression)	arXiv:2603.28845	FujitsuResearch/OneCompression	📋 Planned
E-PMQ (Efficient Post-Training Mixed Quantization)	arXiv:2605.16882	wwjzhy/E-PMQ	📋 Planned
Blockwise SFT	arXiv:2508.19529	Bowen-Sun-0728/Blockwise-SFT	📋 Planned
CT-SFT (Curriculum Training for SFT)	arXiv:2601.08146	-	📋 Planned

🧬 Model Merging & Architecture Fusion

Feature	Description	Status
Darwin Merge	Evolutionary model merging with fitness-based selection	📋 Planned
Franken Merge (Burger Style)	Layer-wise merging with "burger" architecture (bottom-mid-top)	📋 Planned
Active Aware Quant Training/Tuning/Merging	Quantization-aware training with active layer selection	📋 Planned
Cross-Architecture Layer Swapping	Swap layers between architectures (e.g., Mamba2 → Mamba3)	📋 Planned
Fusion Layers & Weights	Fuse layers/weights from different models into target model	📋 Planned
Multi-Model & Multi-Adapter Merging	Merge multiple models, adapters, and architectures	📋 Planned

🏗️ Training from Scratch & Extraction

Feature	Description	Status
Train from Scratch Guide	Complete walkthrough for pretraining models with Unsloth	📋 Planned
Training/Tuning Extraction	Extract LoRAs, deltas, datasets, extra weights/layers from trained models	📋 Planned
Custom Training Reverse Engineering	Recover training configuration from final model weights	📋 Planned

📁 Implementation Structure

unsloth/
├── unsloth/
│   ├── compression/          # QuEPT, E-PMQ, OneComp implementations
│   ├── merging/              # Darwin, Franken, cross-arch merging
│   ├── extraction/           # Training extraction utilities
│   ├── quantization/         # Active aware quant training
│   ├── architectures/        # Layer swapping, fusion layers
│   └── training/
│       ├── from_scratch/     # Pretraining guides & scripts
│       ├── blockwise/        # Blockwise SFT
│       ├── curriculum/       # CT-SFT
│       └── lora_extraction/  # LoPT, LoRA recovery
├── studio/
│   ├── components/           # UI for merging, extraction, quantization
│   └── pages/                # New Studio pages for advanced features
└── docs/
    ├── research/             # Paper implementations documentation
    ├── guides/               # Train from scratch, merging guides
    └── api/                  # API documentation for new features

🚀 Getting Started with This Fork

# Clone the fork
git clone https://github.com/shadyuwugurl/unsloth.git
cd unsloth

# Install with local development setup
./install.sh --local

# Launch Unsloth Studio with experimental features
unsloth studio -p 8888 --experimental

📖 Documentation

All implementation details, guides, and API references are documented in:

GitHub Wiki: Project Wiki
Supermemory: Persistent knowledge base for implementation decisions
docs/ folder: Technical documentation and guides

🤝 Contributing

This is a research fork. For contributing to upstream Unsloth, please see CONTRIBUTING.md and submit PRs to unslothai/unsloth.

📄 License

This fork maintains the same dual-licensing model as upstream Unsloth (Apache 2.0 / AGPL-3.0).

Name		Name	Last commit message	Last commit date
.github		.github
images		images
scripts		scripts
studio		studio
tests		tests
unsloth		unsloth
unsloth_cli		unsloth_cli
.gitattributes		.gitattributes
.gitignore		.gitignore
.pre-commit-ci.yaml		.pre-commit-ci.yaml
.pre-commit-config.yaml		.pre-commit-config.yaml
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
COPYING		COPYING
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
build.sh		build.sh
cli.py		cli.py
install.ps1		install.ps1
install.sh		install.sh
pyproject.toml		pyproject.toml
unsloth-cli.py		unsloth-cli.py

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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

HTTP Secure Deployment

Docker

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

🦥 Unsloth News

📥 Advanced Installation

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

Developer / Nightly / Experimental installs: Windows PowerShell:

Advanced launch options

Vulkan, custom llama.cpp backends:

Uninstall

Deleting model files

💚 Community and Links

Citation

License

Thank You to

🔬 Unsloth Studio Fork - Research & Development Roadmap

📚 Research Papers & Implementations

🧬 Model Merging & Architecture Fusion

🏗️ Training from Scratch & Extraction

📁 Implementation Structure

🚀 Getting Started with This Fork

📖 Documentation

🤝 Contributing

📄 License

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