🎉 Latest Updates

• 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See examples to start training your own Magistral models with Axolotl!
• 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!
• 2025/04: Llama 4 support has been added in Axolotl. See examples 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 and docs 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 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 to give it a try.
• 2025/02: Axolotl has added GRPO support. Dive into our blog and GRPO example and have some fun!
• 2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See docs.

✨ 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, Flash Attention, Xformers, Flex Attention, Liger Kernel, Cut Cross Entropy, Sequence Parallelism (SP), LoRA optimizations, Multi-GPU training (FSDP1, FSDP2, DeepSpeed), Multi-node training (Torchrun, Ray), and many more!
• Flexible Dataset Handling: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.
• Cloud Ready: We ship Docker images and also PyPI packages for use on cloud platforms and local hardware.

🚀 Quick Start

Requirements:

• NVIDIA GPU (Ampere or newer for bf16 and Flash Attention) or AMD GPU
• Python 3.11
• PyTorch ≥2.5.1

Installation

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

Other installation approaches are described here.

Your First Fine-tune

# 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 for a more detailed walkthrough.

📚 Documentation

• Installation Options - Detailed setup instructions for different environments
• Configuration Guide - Full configuration options and examples
• Dataset Loading - Loading datasets from various sources
• Dataset Guide - Supported formats and how to use them
• Multi-GPU Training
• Multi-Node Training
• Multipacking
• API Reference - Auto-generated code documentation
• FAQ - Frequently asked questions

🤝 Getting Help

• Join our Discord community for support
• Check out our Examples directory
• Read our Debugging Guide
• Need dedicated support? Please contact ✉️wing@axolotl.ai for options

🌟 Contributing

Contributions are welcome! Please see our Contributing Guide for details.

❤️ Sponsors

Thank you to our sponsors who help make Axolotl possible:

• Modal - Modal lets you run jobs in the cloud, by just writing a few lines of Python. Customers use Modal to deploy Gen AI models at large scale, fine-tune large language models, run protein folding simulations, and much more.

Interested in sponsoring? Contact us at wing@axolotl.ai

📜 License

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