https://github.com/unslothai/unsloth
Finetune Qwen3, Llama 4, TTS, DeepSeek-R1 & Gemma 3 LLMs 2x faster with 70% less memory! 🦥
https://github.com/unslothai/unsloth
deepseek deepseek-r1 fine-tuning finetuning gemma gemma3 llama llama-4 llama3 llama4 llm llms lora mistral qlora qwen qwen3 text-to-speech tts unsloth
Last synced: 14 days ago
JSON representation
Finetune Qwen3, Llama 4, TTS, DeepSeek-R1 & Gemma 3 LLMs 2x faster with 70% less memory! 🦥
- Host: GitHub
- URL: https://github.com/unslothai/unsloth
- Owner: unslothai
- License: apache-2.0
- Created: 2023-11-29T16:50:09.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-05-05T01:06:41.000Z (12 months ago)
- Last Synced: 2025-05-05T14:09:19.637Z (12 months ago)
- Topics: deepseek, deepseek-r1, fine-tuning, finetuning, gemma, gemma3, llama, llama-4, llama3, llama4, llm, llms, lora, mistral, qlora, qwen, qwen3, text-to-speech, tts, unsloth
- Language: Python
- Homepage: https://unsloth.ai
- Size: 6.46 MB
- Stars: 38,080
- Watchers: 224
- Forks: 2,982
- Open Issues: 1,045
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
Run and train AI models with a unified local interface.
Features •
Quickstart •
Notebooks •
Documentation •
Reddit
Unsloth Studio (Beta) lets you run and train text, [audio](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [embedding](https://unsloth.ai/docs/new/embedding-finetuning), [vision](https://unsloth.ai/docs/basics/vision-fine-tuning) models on Windows, Linux and macOS.
## ⭐ Features
Unsloth provides several key features for both inference and training:
### Inference
* **Search + download + run models** including GGUF, LoRA adapters, safetensors
* **Export models**: [Save or export](https://unsloth.ai/docs/new/studio/export) models to GGUF, 16-bit safetensors and other formats.
* **Tool calling**: Support for [self-healing tool calling](https://unsloth.ai/docs/new/studio/chat#auto-healing-tool-calling) and web search
* **[Code execution](https://unsloth.ai/docs/new/studio/chat#code-execution)**: lets LLMs test code in Claude artifacts and sandbox environments
* [Auto-tune inference parameters](https://unsloth.ai/docs/new/studio/chat#auto-parameter-tuning) and customize chat templates.
* We work directly with teams behind [gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss), [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_context_bug_fixes/), [Llama 4](https://github.com/ggml-org/llama.cpp/pull/12889), [Mistral](models/tutorials/devstral-how-to-run-and-fine-tune.md), [Gemma 1-3](https://news.ycombinator.com/item?id=39671146), and [Phi-4](https://unsloth.ai/blog/phi4), where we’ve fixed bugs that improve model accuracy.
* Upload images, audio, PDFs, code, DOCX and more file types to chat with.
### Training
* Train and RL **500+ models** up to **2x faster** with up to **70% less VRAM**, with no accuracy loss.
* Custom Triton and mathematical **kernels**. See some collabs we did with [PyTorch](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) and [Hugging Face](https://unsloth.ai/docs/new/faster-moe).
* **Data Recipes**: [Auto-create datasets](https://unsloth.ai/docs/new/studio/data-recipe) from **PDF, CSV, DOCX** etc. Edit data in a visual-node workflow.
* **[Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide)** (RL): The most efficient [RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) library, using **80% less VRAM** for GRPO, [FP8](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) etc.
* Supports full fine-tuning, RL, pretraining, 4-bit, 16-bit and, FP8 training.
* **Observability**: Monitor training live, track loss and GPU usage and customize graphs.
* [Multi-GPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) training is supported, with major improvements coming soon.
## ⚡ Quickstart
Unsloth can be used in two ways: through **[Unsloth Studio](https://unsloth.ai/docs/new/studio/)**, the web UI, or through **Unsloth Core**, the code-based version. Each has different requirements.
### 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:** Currently supports chat and Data Recipes. **MLX training** is coming very soon
* **AMD:** Chat + Data works. Train with [Unsloth Core](#unsloth-core-code-based). Studio support is out soon.
* **Coming soon:** Training support for Apple MLX, AMD, and Intel.
* **Multi-GPU:** Available now, with a major upgrade on the way
#### macOS, Linux, WSL:
```bash
curl -fsSL https://unsloth.ai/install.sh | sh
```
#### Windows:
```powershell
irm https://unsloth.ai/install.ps1 | iex
```
#### Launch
```bash
unsloth studio -H 0.0.0.0 -p 8888
```
#### Update
To update, use the same install commands as above. Or run (does not work on Windows):
```bash
unsloth studio update
```
#### Docker
Use our [Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Run:
```bash
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](#-advanced-installation).
### Unsloth Core (code-based)
#### Linux, WSL:
```bash
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:
```powershell
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](https://unsloth.ai/docs/get-started/install/windows-installation).
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](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth).
To install Unsloth on **AMD** and **Intel** GPUs, follow our [AMD Guide](https://unsloth.ai/docs/get-started/install/amd) and [Intel Guide](https://unsloth.ai/docs/get-started/install/intel).
## ✨ Free Notebooks
Train for free with our notebooks. Read our [guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). Add dataset, run, then deploy your trained model.
| Model | Free Notebooks | Performance | Memory use |
|-----------|---------|--------|----------|
| **Qwen3.5 (4B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision.ipynb) | 1.5x faster | 60% less |
| **gpt-oss (20B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb) | 2x faster | 70% less |
| **Qwen3.5 GSPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision_GRPO.ipynb) | 2x faster | 70% less |
| **gpt-oss (20B): GRPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) | 2x faster | 80% less |
| **Qwen3: Advanced GRPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb) | 2x faster | 70% less |
| **Gemma 3 (4B) Vision** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B)-Vision.ipynb) | 1.7x faster | 60% less |
| **embeddinggemma (300M)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_(300M).ipynb) | 2x faster | 20% less |
| **Mistral Ministral 3 (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_VL_(3B)_Vision.ipynb) | 1.5x faster | 60% less |
| **Llama 3.1 (8B) Alpaca** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) | 2x faster | 70% less |
| **Llama 3.2 Conversational** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2x faster | 70% less |
| **Orpheus-TTS (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb) | 1.5x faster | 50% less |
- See all our notebooks for: [Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks), [GRPO](https://unsloth.ai/docs/get-started/unsloth-notebooks#grpo-reasoning-rl-notebooks), [TTS](https://unsloth.ai/docs/get-started/unsloth-notebooks#text-to-speech-tts-notebooks), [embedding](https://unsloth.ai/docs/new/embedding-finetuning) & [Vision](https://unsloth.ai/docs/get-started/unsloth-notebooks#vision-multimodal-notebooks)
- See [all our models](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [all our notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks)
- See detailed documentation for Unsloth [here](https://unsloth.ai/docs)
## 🦥 Unsloth News
- **Introducing Unsloth Studio**: our new web UI for running and training LLMs. [Blog](https://unsloth.ai/docs/new/studio)
- **Qwen3.5** - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. [Guide + notebooks](https://unsloth.ai/docs/models/qwen3.5/fine-tune)
- Train **MoE LLMs 12x faster** with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. [Blog](https://unsloth.ai/docs/new/faster-moe)
- **Embedding models**: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. [Blog](https://unsloth.ai/docs/new/embedding-finetuning) • [Notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks#embedding-models)
- New **7x longer context RL** vs. all other setups, via our new batching algorithms. [Blog](https://unsloth.ai/docs/new/grpo-long-context)
- New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://unsloth.ai/docs/new/3x-faster-training-packing)
- **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU. [Blog](https://unsloth.ai/docs/blog/500k-context-length-fine-tuning)
- **FP8 & Vision RL**: You can now do FP8 & VLM GRPO on consumer GPUs. [FP8 Blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl)
- **gpt-oss** by OpenAI: Read our [RL blog](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/gpt-oss-reinforcement-learning), [Flex Attention](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training) blog and [Guide](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune).
## 📥 Advanced Installation
The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, [view our docs](https://unsloth.ai/docs/get-started/install/pip-install#advanced-pip-installation).
#### Developer installs: macOS, Linux, WSL:
```bash
git clone https://github.com/unslothai/unsloth
cd unsloth
./install.sh --local
unsloth studio -H 0.0.0.0 -p 8888
```
Then to update :
```bash
unsloth studio update
```
#### Developer installs: Windows PowerShell:
```powershell
git clone https://github.com/unslothai/unsloth.git
cd unsloth
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -H 0.0.0.0 -p 8888
```
Then to update :
```bash
unsloth studio update
```
#### Nightly: MacOS, Linux, WSL:
```bash
git clone https://github.com/unslothai/unsloth
cd unsloth
git checkout nightly
./install.sh --local
unsloth studio -H 0.0.0.0 -p 8888
```
Then to launch every time:
```bash
unsloth studio -H 0.0.0.0 -p 8888
```
#### Nightly: Windows:
Run in Windows Powershell:
```bash
git clone https://github.com/unslothai/unsloth.git
cd unsloth
git checkout nightly
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -H 0.0.0.0 -p 8888
```
Then to launch every time:
```bash
unsloth studio -H 0.0.0.0 -p 8888
```
#### Uninstall
You can uninstall Unsloth Studio by deleting its install folder usually located under `$HOME/.unsloth/studio` on Mac/Linux/WSL and `%USERPROFILE%\.unsloth\studio` on Windows. Using the `rm -rf` commands will **delete everything**, including your history, cache:
* **MacOS, WSL, Linux:** `rm -rf ~/.unsloth/studio`
* **Windows (PowerShell):** `Remove-Item -Recurse -Force "$HOME\.unsloth\studio"`
For more info, [see our docs](https://unsloth.ai/docs/new/studio/install#uninstall).
#### 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](https://discord.com/invite/unsloth) |
| 
**r/unsloth Reddit** | [Join Reddit community](https://reddit.com/r/unsloth) |
| 📚 **Documentation & Wiki** | [Read Our Docs](https://unsloth.ai/docs) |
| _logo_late_2025.svg)
**Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai) |
| 🔮 **Our Models** | [Unsloth Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog) |
| ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog) |
### Citation
You can cite the Unsloth repo as follows:
```bibtex
@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](https://github.com/unslothai/unsloth?tab=Apache-2.0-1-ov-file)**, while certain optional components, such as the Unsloth Studio UI are licensed under the open-source license **[AGPL-3.0](https://github.com/unslothai/unsloth?tab=AGPL-3.0-2-ov-file)**.
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](https://github.com/ggml-org/llama.cpp) that lets users run and save models with Unsloth
- The Hugging Face team and their libraries: [transformers](https://github.com/huggingface/transformers) and [TRL](https://github.com/huggingface/trl)
- The Pytorch and [Torch AO](https://github.com/unslothai/unsloth/pull/3391) team for their contributions
- NVIDIA for their [NeMo DataDesigner](https://github.com/NVIDIA-NeMo/DataDesigner) library and their contributions
- And of course for every single person who has contributed or has used Unsloth!