Axolotl

GitHub License tests codecov Releases
contributors GitHub Repo stars
discord twitter
tests-nightly multigpu-semi-weekly tests

## πŸŽ‰ Latest Updates - 2025/07: Voxtral with mistral-common tokenizer support has been integrated in Axolotl. Read the [docs](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/voxtral)! - 2025/07: 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! - 2025/06: 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! - 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the [docs](https://docs.axolotl.ai/docs/qat.html) to learn more! - 2025/04: 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: 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. - 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 tool designed to streamline post-training for various AI models. Features: - **Multiple Model Support**: Train various models like LLaMA, Mistral, Mixtral, Pythia, and more. We are compatible with HuggingFace transformers causal language models. - **Training Methods**: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), Multimodal, and Reward Modelling (RM) / Process Reward Modelling (PRM). - **Easy Configuration**: Re-use a single YAML file between dataset preprocess, 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. ## πŸš€ Quick Start **Requirements**: - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU - Python 3.11 - PyTorch β‰₯2.6.0 ### 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. ## πŸ“š 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 [βœ‰οΈ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) ## πŸ“œ License This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.