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https://github.com/axolotl-ai-cloud/axolotl

Go ahead and axolotl questions
https://github.com/axolotl-ai-cloud/axolotl

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Go ahead and axolotl questions

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        Axolotl

    



  


      A Free and Open Source LLM Fine-tuning Framework


  





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## 🎉 Latest Updates



- 2025/07:

  - ND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the [blog post](https://huggingface.co/blog/accelerate-nd-parallel) for more info.

  - Axolotl adds more models: [GPT-OSS](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gpt-oss), [Gemma 3n](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/gemma3n), [Liquid Foundation Model 2 (LFM2)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/lfm2), and [Arcee Foundation Models (AFM)](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/afm).

  - FP8 finetuning with fp8 gather op is now possible in Axolotl via `torchao`. Get started [here](https://docs.axolotl.ai/docs/mixed_precision.html#sec-fp8)!

  - [Voxtral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/voxtral), [Magistral 1.1](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral), and [Devstral](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/devstral) with mistral-common tokenizer support has been integrated in Axolotl!

  - TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst) for using ALST with Axolotl!

- 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more!

- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the [blog](https://huggingface.co/blog/axolotl-ai-co/long-context-with-sequence-parallelism-in-axolotl) and [docs](https://docs.axolotl.ai/docs/sequence_parallelism.html) to learn how to scale your context length when fine-tuning.



Expand older updates



- 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/magistral) to start training your own Magistral models with Axolotl!

- 2025/04: Llama 4 support has been added in Axolotl. See [examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-4) to start training your own Llama 4 models with Axolotl's linearized version!

- 2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the [docs](https://docs.axolotl.ai/docs/multimodal.html) to fine-tune your own!

- 2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the [docs](https://docs.axolotl.ai/docs/lora_optims.html) to give it a try.

- 2025/02: Axolotl has added GRPO support. Dive into our [blog](https://huggingface.co/blog/axolotl-ai-co/training-llms-w-interpreter-feedback-wasm) and [GRPO example](https://github.com/axolotl-ai-cloud/grpo_code) and have some fun!

- 2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See [docs](https://docs.axolotl.ai/docs/reward_modelling.html).



## ✨ Overview



Axolotl is a free and open-source tool designed to streamline post-training and fine-tuning for the latest large language models (LLMs).



Features:



- **Multiple Model Support**: Train various models like GPT-OSS, LLaMA, Mistral, Mixtral, Pythia, and many more models available on the Hugging Face Hub.

- **Multimodal Training**: Fine-tune vision-language models (VLMs) including LLaMA-Vision, Qwen2-VL, Pixtral, LLaVA, SmolVLM2, and audio models like Voxtral with image, video, and audio support.

- **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), and Reward Modelling (RM) / Process Reward Modelling (PRM).

- **Easy Configuration**: Re-use a single YAML configuration file across the full fine-tuning pipeline: dataset preprocessing, training, evaluation, quantization, and inference.

- **Performance Optimizations**: [Multipacking](https://docs.axolotl.ai/docs/multipack.html), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Xformers](https://github.com/facebookresearch/xformers), [Flex Attention](https://pytorch.org/blog/flexattention/), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), [Cut Cross Entropy](https://github.com/apple/ml-cross-entropy/tree/main), [Sequence Parallelism (SP)](https://docs.axolotl.ai/docs/sequence_parallelism.html), [LoRA optimizations](https://docs.axolotl.ai/docs/lora_optims.html), [Multi-GPU training (FSDP1, FSDP2, DeepSpeed)](https://docs.axolotl.ai/docs/multi-gpu.html), [Multi-node training (Torchrun, Ray)](https://docs.axolotl.ai/docs/multi-node.html), and many more!

- **Flexible Dataset Handling**: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.

- **Cloud Ready**: We ship [Docker images](https://hub.docker.com/u/axolotlai) and also [PyPI packages](https://pypi.org/project/axolotl/) for use on cloud platforms and local hardware.



## 🚀 Quick Start - LLM Fine-tuning in Minutes



**Requirements**:



- NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU

- Python 3.11

- PyTorch ≥2.6.0



### Google Colab



[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/axolotl-ai-cloud/axolotl/blob/main/examples/colab-notebooks/colab-axolotl-example.ipynb#scrollTo=msOCO4NRmRLa)



### Installation



#### Using pip



```bash

pip3 install -U packaging==23.2 setuptools==75.8.0 wheel ninja

pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]



# Download example axolotl configs, deepspeed configs

axolotl fetch examples

axolotl fetch deepspeed_configs  # OPTIONAL

```



#### Using Docker



Installing with Docker can be less error prone than installing in your own environment.

```bash

docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest

```



Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).



#### Cloud Providers



- [RunPod](https://runpod.io/gsc?template=v2ickqhz9s&ref=6i7fkpdz)

- [Vast.ai](https://cloud.vast.ai?ref_id=62897&template_id=bdd4a49fa8bce926defc99471864cace&utm_source=github&utm_medium=developer_community&utm_campaign=template_launch_axolotl&utm_content=readme)

- [PRIME Intellect](https://app.primeintellect.ai/dashboard/create-cluster?image=axolotl&location=Cheapest&security=Cheapest&show_spot=true)

- [Modal](https://www.modal.com?utm_source=github&utm_medium=github&utm_campaign=axolotl)

- [Novita](https://novita.ai/gpus-console?templateId=311)

- [JarvisLabs.ai](https://jarvislabs.ai/templates/axolotl)

- [Latitude.sh](https://latitude.sh/blueprint/989e0e79-3bf6-41ea-a46b-1f246e309d5c)



### Your First Fine-tune



```bash

# Fetch axolotl examples

axolotl fetch examples



# Or, specify a custom path

axolotl fetch examples --dest path/to/folder



# Train a model using LoRA

axolotl train examples/llama-3/lora-1b.yml

```



That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/getting-started.html) for a more detailed walkthrough.



## 📚 Documentation



- [Installation Options](https://docs.axolotl.ai/docs/installation.html) - Detailed setup instructions for different environments

- [Configuration Guide](https://docs.axolotl.ai/docs/config-reference.html) - Full configuration options and examples

- [Dataset Loading](https://docs.axolotl.ai/docs/dataset_loading.html) - Loading datasets from various sources

- [Dataset Guide](https://docs.axolotl.ai/docs/dataset-formats/) - Supported formats and how to use them

- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)

- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)

- [Multipacking](https://docs.axolotl.ai/docs/multipack.html)

- [API Reference](https://docs.axolotl.ai/docs/api/) - Auto-generated code documentation

- [FAQ](https://docs.axolotl.ai/docs/faq.html) - Frequently asked questions



## 🤝 Getting Help



- Join our [Discord community](https://discord.gg/HhrNrHJPRb) for support

- Check out our [Examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/) directory

- Read our [Debugging Guide](https://docs.axolotl.ai/docs/debugging.html)

- Need dedicated support? Please contact [✉️wing@axolotl.ai](mailto:wing@axolotl.ai) for options



## 🌟 Contributing



Contributions are welcome! Please see our [Contributing Guide](https://github.com/axolotl-ai-cloud/axolotl/blob/main/.github/CONTRIBUTING.md) for details.



## ❤️ Sponsors



Interested in sponsoring? Contact us at [wing@axolotl.ai](mailto:wing@axolotl.ai)



## 📝 Citing Axolotl



If you use Axolotl in your research or projects, please cite it as follows:



```bibtex

@software{axolotl,

  title = {Axolotl: Open Source LLM Post-Training},

  author = {{Axolotl maintainers and contributors}},

  url = {https://github.com/axolotl-ai-cloud/axolotl},

  license = {Apache-2.0},

  year = {2023}

}

```



## 📜 License



This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.