423 lines
30 KiB
Markdown
423 lines
30 KiB
Markdown
<div align="center">
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<a href="https://unsloth.ai/docs"><picture>
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<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png">
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<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png">
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<img alt="unsloth logo" src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png" height="110" style="max-width: 100%;">
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</picture></a>
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<a href="https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/start free finetune button.png" width="154"></a>
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<a href="https://discord.com/invite/unsloth"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord button.png" width="165"></a>
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<a href="https://unsloth.ai/docs"><img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/Documentation%20Button.png" width="137"></a>
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### Train gpt-oss, DeepSeek, Gemma, Qwen & Llama 2x faster with 70% less VRAM!
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</div>
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## ✨ Train for Free
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Notebooks are beginner friendly. Read our [guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). Add dataset, run, then deploy your trained model.
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| Model | Free Notebooks | Performance | Memory use |
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|-----------|---------|--------|----------|
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| **gpt-oss (20B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb) | 1.5x faster | 70% less |
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| **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 |
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| **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 |
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| **Qwen3: Advanced GRPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb) | 2x faster | 50% less |
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| **Qwen3-VL (8B): GSPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision-GRPO.ipynb) | 1.5x faster | 80% less |
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| **Gemma 3 (270M)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(270M).ipynb) | 1.7x faster | 60% less |
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| **Gemma 3n (4B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_(4B)-Conversational.ipynb) | 1.5x faster | 50% less |
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| **DeepSeek-OCR (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B).ipynb) | 1.5x faster | 30% less |
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| **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 |
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| **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 |
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| **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 |
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- 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) & [Vision](https://unsloth.ai/docs/get-started/unsloth-notebooks#vision-multimodal-notebooks)
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- 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)
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- See detailed documentation for Unsloth [here](https://unsloth.ai/docs)
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## ⚡ Quickstart
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### Linux or WSL
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```bash
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pip install unsloth
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```
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### Windows
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For Windows, `pip install unsloth` works only if you have Pytorch installed. Read our [Windows Guide](https://unsloth.ai/docs/get-started/install-and-update/windows-installation).
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### Docker
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Use our official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Read our [Docker Guide](https://unsloth.ai/docs/get-started/install-and-update/docker).
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### Blackwell & DGX Spark
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For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide](https://unsloth.ai/docs/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark Guide](https://unsloth.ai/docs/basics/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth) for more details.
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## 🦥 Unsloth News
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- New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://unsloth.ai/docs/new/3x-faster-training-packing)
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- **New Mistral**: Run Ministral 3 or Devstral 2 and fine-tune with vision/RL sodoku notebooks. [Guide](https://unsloth.ai/docs/models/ministral-3) • [Notebooks](https://unsloth.ai/docs/models/ministral-3#fine-tuning-ministral-3)
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- **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU. [Blog](https://unsloth.ai/docs/new/500k-context-length-fine-tuning)
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- **FP8 Reinforcement Learning**: You can now do FP8 GRPO on consumer GPUs. [Blog](https://unsloth.ai/docs/new/fp8-reinforcement-learning) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb)
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- **DeepSeek-OCR**: Fine-tune to improve language understanding by 89%. [Guide](https://unsloth.ai/docs/models/deepseek-ocr-how-to-run-and-fine-tune) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B).ipynb)
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- **Docker**: Use Unsloth with no setup & environment issues with our new image. [Guide](https://unsloth.ai/docs/new/how-to-fine-tune-llms-with-unsloth-and-docker) • [Docker image](https://hub.docker.com/r/unsloth/unsloth)
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- **gpt-oss RL**: Introducing the fastest possible inference for gpt-oss RL! [Read blog](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/gpt-oss-reinforcement-learning)
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- **Vision RL**: You can now train VLMs with GRPO or GSPO in Unsloth! [Read guide](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl)
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- **gpt-oss** by OpenAI: Read our [Unsloth Flex Attention](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training) blog and [gpt-oss Guide](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune). 20B works on 14GB VRAM. 120B on 65GB.
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<details>
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<summary>Click for more news</summary>
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- **Quantization-Aware Training**: We collabed with Pytorch, recovering ~70% accuracy. [Read blog](https://unsloth.ai/docs/basics/quantization-aware-training-qat)
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- **Memory-efficient RL**: We're introducing even better RL. Our new kernels & algos allows faster RL with 50% less VRAM & 10× more context. [Read blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/memory-efficient-rl)
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- **Gemma 3n** by Google: [Read Blog](https://unsloth.ai/docs/models/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/gemma-3n-685d3874830e49e1c93f9339).
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- **[Text-to-Speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning)** is now supported, including `sesame/csm-1b` and STT `openai/whisper-large-v3`.
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- **[Qwen3](https://unsloth.ai/docs/models/qwen3-how-to-run-and-fine-tune)** is now supported. Qwen3-30B-A3B fits on 17.5GB VRAM.
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- Introducing **[Dynamic 2.0](https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs)** quants that set new benchmarks on 5-shot MMLU & Aider Polyglot.
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- [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) - all models (TTS, BERT, Mamba), FFT, etc. [MultiGPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) coming soon. Enable FFT with `full_finetuning = True`, 8-bit with `load_in_8bit = True`.
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- 📣 [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - run or fine-tune them [with our guide](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5).
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- 📣 Introducing Long-context [Reasoning (GRPO)](https://unsloth.ai/blog/grpo) in Unsloth. Train your own reasoning model with just 5GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs!
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- 📣 Introducing Unsloth [Dynamic 4-bit Quantization](https://unsloth.ai/blog/dynamic-4bit)! We dynamically opt not to quantize certain parameters and this greatly increases accuracy while only using <10% more VRAM than BnB 4-bit. See our collection on [Hugging Face here.](https://huggingface.co/collections/unsloth/unsloth-4-bit-dynamic-quants-67503bb873f89e15276c44e7)
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- 📣 **[Llama 4](https://unsloth.ai/blog/llama4)** by Meta, including Scout & Maverick are now supported.
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- 📣 [Phi-4](https://unsloth.ai/blog/phi4) by Microsoft: We also [fixed bugs](https://unsloth.ai/blog/phi4) in Phi-4 and [uploaded GGUFs, 4-bit](https://huggingface.co/collections/unsloth/phi-4-all-versions-677eecf93784e61afe762afa).
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- 📣 [Vision models](https://unsloth.ai/blog/vision) now supported! [Llama 3.2 Vision (11B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb), [Qwen 2.5 VL (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) and [Pixtral (12B) 2409](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_(12B)-Vision.ipynb)
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- 📣 [Llama 3.3 (70B)](https://huggingface.co/collections/unsloth/llama-33-all-versions-67535d7d994794b9d7cf5e9f), Meta's latest model is supported.
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- 📣 We worked with Apple to add [Cut Cross Entropy](https://arxiv.org/abs/2411.09009). Unsloth now supports 89K context for Meta's Llama 3.3 (70B) on a 80GB GPU - 13x longer than HF+FA2. For Llama 3.1 (8B), Unsloth enables 342K context, surpassing its native 128K support.
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- 📣 We found and helped fix a [gradient accumulation bug](https://unsloth.ai/blog/gradient)! Please update Unsloth and transformers.
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- 📣 We cut memory usage by a [further 30%](https://unsloth.ai/blog/long-context) and now support [4x longer context windows](https://unsloth.ai/blog/long-context)!
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</details>
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## 🔗 Links and Resources
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| Type | Links |
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| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
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| <img width="15" src="https://redditinc.com/hs-fs/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png" /> **r/unsloth Reddit** | [Join Reddit community](https://reddit.com/r/unsloth) |
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| 📚 **Documentation & Wiki** | [Read Our Docs](https://unsloth.ai/docs) |
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| <img width="13" src="https://upload.wikimedia.org/wikipedia/commons/0/09/X_(formerly_Twitter)_logo_late_2025.svg" /> **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai) |
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| 💾 **Installation** | [Pip & Docker Install](https://unsloth.ai/docs/get-started/install-and-update) |
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| 🔮 **Our Models** | [Unsloth Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog) |
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| ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog) |
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## ⭐ Key Features
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* Supports **full-finetuning**, pretraining, 4b-bit, 16-bit and **FP8** training
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* Supports **all models** including [TTS](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), multimodal, [BERT](https://unsloth.ai/docs/get-started/unsloth-notebooks#other-important-notebooks) and more! Any model that works in transformers, works in Unsloth.
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* The most efficient library for [Reinforcement Learning (RL)](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide), using 80% less VRAM. Supports GRPO, GSPO, DrGRPO, DAPO etc.
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* **0% loss in accuracy** - no approximation methods - all exact.
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* Export and [deploy your model](https://unsloth.ai/docs/basics/inference-and-deployment) to GGUF, llama.cpp, vLLM, SGLang and Hugging Face.
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* Supports NVIDIA (since 2018), [AMD](https://unsloth.ai/docs/get-started/install-and-update/amd) and Intel GPUs. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc)
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* Works on **Linux**, WSL and **Windows**
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* All kernels written in OpenAI's Triton language. Manual backprop engine.
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* If you trained a model with 🦥Unsloth, you can use this cool sticker! <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" />
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## 💾 Install Unsloth
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You can also see our docs for more detailed installation and updating instructions [here](https://unsloth.ai/docs/get-started/install-and-update).
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Unsloth supports Python 3.13 or lower.
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### Pip Installation
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**Install with pip (recommended) for Linux devices:**
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```
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pip install unsloth
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```
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**To update Unsloth:**
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```
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pip install --upgrade --force-reinstall --no-cache-dir unsloth unsloth_zoo
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```
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See [here](#advanced-pip-installation) for advanced pip install instructions.
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### Windows Installation
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1. **Install NVIDIA Video Driver:**
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You should install the latest driver for your GPU. Download drivers here: [NVIDIA GPU Driver](https://www.nvidia.com/Download/index.aspx).
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3. **Install Visual Studio C++:**
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You will need Visual Studio, with C++ installed. By default, C++ is not installed with [Visual Studio](https://visualstudio.microsoft.com/vs/community/), so make sure you select all of the C++ options. Also select options for Windows 10/11 SDK. For detailed instructions with options, see [here](https://unsloth.ai/docs/get-started/install-and-update/windows-installation#method-3-windows-directly).
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5. **Install CUDA Toolkit:**
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Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).
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6. **Install PyTorch:**
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You will need the correct version of PyTorch that is compatible with your CUDA drivers, so make sure to select them carefully.
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[Install PyTorch](https://pytorch.org/get-started/locally/).
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7. **Install Unsloth:**
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```python
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pip install unsloth
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```
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#### Advanced/Troubleshooting
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For **advanced installation instructions** or if you see weird errors during installations:
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First try using an isolated environment via then `pip install unsloth`
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```bash
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python -m venv unsloth
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source unsloth/bin/activate
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pip install unsloth
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```
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1. Install `torch` and `triton`. Go to https://pytorch.org to install it. For example `pip install torch torchvision torchaudio triton`
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2. Confirm if CUDA is installed correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers.
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3. Install `xformers` manually via:
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```bash
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pip install ninja
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pip install -v --no-build-isolation -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
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```
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Check if `xformers` succeeded with `python -m xformers.info` Go to https://github.com/facebookresearch/xformers. Another option is to install `flash-attn` for Ampere GPUs and ignore `xformers`
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5. For GRPO runs, you can try installing `vllm` and seeing if `pip install vllm` succeeds.
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6. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix) may be useful.
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5. Finally, install `bitsandbytes` and check it with `python -m bitsandbytes`
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### Conda Installation (Optional)
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`⚠️Only use Conda if you have it. If not, use Pip`. Select either `pytorch-cuda=11.8,12.1` for CUDA 11.8 or CUDA 12.1. We support `python=3.10,3.11,3.12`.
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```bash
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conda create --name unsloth_env \
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python=3.11 \
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pytorch-cuda=12.1 \
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pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \
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-y
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conda activate unsloth_env
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pip install unsloth
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```
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<details>
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<summary>If you're looking to install Conda in a Linux environment, <a href="https://docs.anaconda.com/miniconda/">read here</a>, or run the below 🔽</summary>
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```bash
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mkdir -p ~/miniconda3
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
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bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
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rm -rf ~/miniconda3/miniconda.sh
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~/miniconda3/bin/conda init bash
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~/miniconda3/bin/conda init zsh
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```
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</details>
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### Advanced Pip Installation
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`⚠️Do **NOT** use this if you have Conda.` Pip is a bit more complex since there are dependency issues. The pip command is different for `torch 2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9` and CUDA versions.
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For other torch versions, we support `torch211`, `torch212`, `torch220`, `torch230`, `torch240`, `torch250`, `torch260`, `torch270`, `torch280`, `torch290` and for CUDA versions, we support `cu118` and `cu121` and `cu124`. For Ampere devices (A100, H100, RTX3090) and above, use `cu118-ampere` or `cu121-ampere` or `cu124-ampere`.
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For example, if you have `torch 2.4` and `CUDA 12.1`, use:
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```bash
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pip install --upgrade pip
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pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"
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```
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Another example, if you have `torch 2.9` and `CUDA 13.0`, use:
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```bash
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pip install --upgrade pip
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pip install "unsloth[cu130-torch290] @ git+https://github.com/unslothai/unsloth.git"
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```
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And other examples:
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```bash
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pip install "unsloth[cu121-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git"
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pip install "unsloth[cu118-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git"
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pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"
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pip install "unsloth[cu118-torch240] @ git+https://github.com/unslothai/unsloth.git"
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pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git"
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pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"
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pip install "unsloth[cu121-torch250] @ git+https://github.com/unslothai/unsloth.git"
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pip install "unsloth[cu124-ampere-torch250] @ git+https://github.com/unslothai/unsloth.git"
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```
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Or, run the below in a terminal to get the **optimal** pip installation command:
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```bash
|
||
wget -qO- https://raw.githubusercontent.com/unslothai/unsloth/main/unsloth/_auto_install.py | python -
|
||
```
|
||
|
||
Or, run the below manually in a Python REPL:
|
||
```python
|
||
try: import torch
|
||
except: raise ImportError('Install torch via `pip install torch`')
|
||
from packaging.version import Version as V
|
||
import re
|
||
v = V(re.match(r"[0-9\.]{3,}", torch.__version__).group(0))
|
||
cuda = str(torch.version.cuda)
|
||
is_ampere = torch.cuda.get_device_capability()[0] >= 8
|
||
USE_ABI = torch._C._GLIBCXX_USE_CXX11_ABI
|
||
if cuda not in ("11.8", "12.1", "12.4", "12.6", "12.8", "13.0"): raise RuntimeError(f"CUDA = {cuda} not supported!")
|
||
if v <= V('2.1.0'): raise RuntimeError(f"Torch = {v} too old!")
|
||
elif v <= V('2.1.1'): x = 'cu{}{}-torch211'
|
||
elif v <= V('2.1.2'): x = 'cu{}{}-torch212'
|
||
elif v < V('2.3.0'): x = 'cu{}{}-torch220'
|
||
elif v < V('2.4.0'): x = 'cu{}{}-torch230'
|
||
elif v < V('2.5.0'): x = 'cu{}{}-torch240'
|
||
elif v < V('2.5.1'): x = 'cu{}{}-torch250'
|
||
elif v <= V('2.5.1'): x = 'cu{}{}-torch251'
|
||
elif v < V('2.7.0'): x = 'cu{}{}-torch260'
|
||
elif v < V('2.7.9'): x = 'cu{}{}-torch270'
|
||
elif v < V('2.8.0'): x = 'cu{}{}-torch271'
|
||
elif v < V('2.8.9'): x = 'cu{}{}-torch280'
|
||
elif v < V('2.9.1'): x = 'cu{}{}-torch290'
|
||
elif v < V('2.9.2'): x = 'cu{}{}-torch291'
|
||
else: raise RuntimeError(f"Torch = {v} too new!")
|
||
if v > V('2.6.9') and cuda not in ("11.8", "12.6", "12.8", "13.0"): raise RuntimeError(f"CUDA = {cuda} not supported!")
|
||
x = x.format(cuda.replace(".", ""), "-ampere" if False else "") # is_ampere is broken due to flash-attn
|
||
print(f'pip install --upgrade pip && pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git && pip install "unsloth[{x}] @ git+https://github.com/unslothai/unsloth.git" --no-build-isolation')
|
||
```
|
||
### Docker Installation
|
||
You can use our pre-built Docker container with all dependencies to use Unsloth instantly with no setup required.
|
||
[Read our guide](https://unsloth.ai/docs/get-started/install-and-update/docker).
|
||
|
||
This container requires installing [NVIDIA's Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
|
||
|
||
```bash
|
||
docker run -d -e JUPYTER_PASSWORD="mypassword" \
|
||
-p 8888:8888 -p 2222:22 \
|
||
-v $(pwd)/work:/workspace/work \
|
||
--gpus all \
|
||
unsloth/unsloth
|
||
```
|
||
|
||
Access Jupyter Lab at `http://localhost:8888` and start fine-tuning!
|
||
|
||
## 📜 Documentation
|
||
* Go to our official [Documentation](https://unsloth.ai/docs) for [running models](https://unsloth.ai/docs/basics/inference-and-deployment), [saving to GGUF](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf), [checkpointing](https://unsloth.ai/docs/basics/finetuning-from-last-checkpoint), [evaluation](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide#evaluation) and more!
|
||
* Read our Guides for: [Fine-tuning](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide), [Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide), [Text-to-Speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [Vision](https://unsloth.ai/docs/basics/vision-fine-tuning) and [any model](https://unsloth.ai/docs/models/tutorials-how-to-fine-tune-and-run-llms).
|
||
* We support Huggingface's transformers, TRL, Trainer, Seq2SeqTrainer and Pytorch code.
|
||
|
||
Unsloth example code to fine-tune gpt-oss-20b:
|
||
|
||
```python
|
||
from unsloth import FastLanguageModel, FastModel
|
||
import torch
|
||
from trl import SFTTrainer, SFTConfig
|
||
from datasets import load_dataset
|
||
max_seq_length = 2048 # Supports RoPE Scaling internally, so choose any!
|
||
# Get LAION dataset
|
||
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
|
||
dataset = load_dataset("json", data_files = {"train" : url}, split = "train")
|
||
|
||
# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
|
||
fourbit_models = [
|
||
"unsloth/gpt-oss-20b-unsloth-bnb-4bit", #or choose any model
|
||
|
||
] # More models at https://huggingface.co/unsloth
|
||
|
||
model, tokenizer = FastModel.from_pretrained(
|
||
model_name = "unsloth/gpt-oss-20b",
|
||
max_seq_length = 2048, # Choose any for long context!
|
||
load_in_4bit = True, # 4-bit quantization. False = 16-bit LoRA.
|
||
load_in_8bit = False, # 8-bit quantization
|
||
load_in_16bit = False, # 16-bit LoRA
|
||
full_finetuning = False, # Use for full fine-tuning.
|
||
trust_remote_code = False, # Enable to support new models
|
||
# token = "hf_...", # use one if using gated models
|
||
)
|
||
|
||
# Do model patching and add fast LoRA weights
|
||
model = FastLanguageModel.get_peft_model(
|
||
model,
|
||
r = 16,
|
||
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
|
||
"gate_proj", "up_proj", "down_proj",],
|
||
lora_alpha = 16,
|
||
lora_dropout = 0, # Supports any, but = 0 is optimized
|
||
bias = "none", # Supports any, but = "none" is optimized
|
||
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
|
||
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
|
||
random_state = 3407,
|
||
max_seq_length = max_seq_length,
|
||
use_rslora = False, # We support rank stabilized LoRA
|
||
loftq_config = None, # And LoftQ
|
||
)
|
||
|
||
trainer = SFTTrainer(
|
||
model = model,
|
||
train_dataset = dataset,
|
||
tokenizer = tokenizer,
|
||
args = SFTConfig(
|
||
max_seq_length = max_seq_length,
|
||
per_device_train_batch_size = 2,
|
||
gradient_accumulation_steps = 4,
|
||
warmup_steps = 10,
|
||
max_steps = 60,
|
||
logging_steps = 1,
|
||
output_dir = "outputs",
|
||
optim = "adamw_8bit",
|
||
seed = 3407,
|
||
),
|
||
)
|
||
trainer.train()
|
||
|
||
# Go to https://unsloth.ai/docs for advanced tips like
|
||
# (1) Saving to GGUF / merging to 16bit for vLLM or SGLang
|
||
# (2) Continued training from a saved LoRA adapter
|
||
# (3) Adding an evaluation loop / OOMs
|
||
# (4) Customized chat templates
|
||
```
|
||
|
||
<a name="RL"></a>
|
||
## 💡 Reinforcement Learning
|
||
[RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) including [GRPO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide#training-with-grpo), [GSPO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning), [**FP8** training](https://unsloth.ai/docs/new/fp8-reinforcement-learning), DrGRPO, DAPO, PPO, Reward Modelling, Online DPO all work with Unsloth.
|
||
|
||
Read our [Reinforcement Learning Guide](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) or our [advanced RL docs](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) for batching, generation & training parameters.
|
||
|
||
List of RL notebooks:
|
||
- gpt-oss GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb)
|
||
- - ***FP8*** Qwen3-8B GRPO notebook (L4): [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb)
|
||
- Qwen2.3-VL GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision-GRPO.ipynb)
|
||
- Advanced Qwen3 GRPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)
|
||
- ORPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-ORPO.ipynb)
|
||
- DPO Zephyr notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_(7B)-DPO.ipynb)
|
||
- KTO notebook: [Link](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing)
|
||
- SimPO notebook: [Link](https://colab.research.google.com/drive/1Hs5oQDovOay4mFA6Y9lQhVJ8TnbFLFh2?usp=sharing)
|
||
|
||
## 🥇 Performance Benchmarking
|
||
- For our most detailed benchmarks, read our [Llama 3.3 Blog](https://unsloth.ai/blog/llama3-3).
|
||
- Benchmarking of Unsloth was also conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl).
|
||
|
||
We tested using the Alpaca Dataset, a batch size of 2, gradient accumulation steps of 4, rank = 32, and applied QLoRA on all linear layers (q, k, v, o, gate, up, down):
|
||
|
||
| Model | VRAM | 🦥 Unsloth speed | 🦥 VRAM reduction | 🦥 Longer context | 😊 Hugging Face + FA2 |
|
||
|----------------|-------|-----------------|----------------|----------------|--------------------|
|
||
| Llama 3.3 (70B)| 80GB | 2x | >75% | 13x longer | 1x |
|
||
| Llama 3.1 (8B) | 80GB | 2x | >70% | 12x longer | 1x |
|
||
|
||
### Context length benchmarks
|
||
|
||
#### Llama 3.1 (8B) max. context length
|
||
We tested Llama 3.1 (8B) Instruct and did 4bit QLoRA on all linear layers (Q, K, V, O, gate, up and down) with rank = 32 with a batch size of 1. We padded all sequences to a certain maximum sequence length to mimic long context finetuning workloads.
|
||
| GPU VRAM | 🦥Unsloth context length | Hugging Face + FA2 |
|
||
|----------|-----------------------|-----------------|
|
||
| 8 GB | 2,972 | OOM |
|
||
| 12 GB | 21,848 | 932 |
|
||
| 16 GB | 40,724 | 2,551 |
|
||
| 24 GB | 78,475 | 5,789 |
|
||
| 40 GB | 153,977 | 12,264 |
|
||
| 48 GB | 191,728 | 15,502 |
|
||
| 80 GB | 342,733 | 28,454 |
|
||
|
||
#### Llama 3.3 (70B) max. context length
|
||
We tested Llama 3.3 (70B) Instruct on a 80GB A100 and did 4bit QLoRA on all linear layers (Q, K, V, O, gate, up and down) with rank = 32 with a batch size of 1. We padded all sequences to a certain maximum sequence length to mimic long context finetuning workloads.
|
||
|
||
| GPU VRAM | 🦥Unsloth context length | Hugging Face + FA2 |
|
||
|----------|------------------------|------------------|
|
||
| 48 GB | 12,106 | OOM |
|
||
| 80 GB | 89,389 | 6,916 |
|
||
|
||
<br>
|
||
|
||
|
||
<br>
|
||
|
||
### Citation
|
||
|
||
You can cite the Unsloth repo as follows:
|
||
```bibtex
|
||
@software{unsloth,
|
||
author = {Daniel Han, Michael Han and Unsloth team},
|
||
title = {Unsloth},
|
||
url = {http://github.com/unslothai/unsloth},
|
||
year = {2023}
|
||
}
|
||
```
|
||
|
||
### Thank You to
|
||
- The [llama.cpp library](https://github.com/ggml-org/llama.cpp) that lets users 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
|
||
- And of course for every single person who has contributed or has used Unsloth!
|