A Free and Open Source LLM Fine-tuning Framework

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✨ 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, GLM-4.6V, InternVL 3.5, Gemma 3n, PaddleOCR-VL, Muse Glimmer, and audio models like Voxtral with image, video, and audio support.
• Training Methods: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT (int8/int4/FP8/NVFP4/MXFP4), FP8 mixed-precision training, NVFP4/MXFP4 MoE LoRA, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO, GDPO), 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, Flash Attention 2/3/4, Xformers, Flex Attention, SageAttention, Liger Kernel, Cut Cross Entropy, ScatterMoE, Sequence Parallelism (SP), LoRA optimizations, Multi-GPU training (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 - LLM Fine-tuning in Minutes

Requirements:

• NVIDIA GPU (Ampere or newer for bf16 and Flash Attention) or AMD GPU
• Python >=3.12 (3.12 recommended)
• PyTorch ≥2.13.0 (2.13.0 recommended)

Google Colab

# install uv if you don't already have it installed (restart shell after)
curl -LsSf https://astral.sh/uv/install.sh | sh
change depending on system
export UV_TORCH_BACKEND=cu130
create a new virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv pip install torch==2.14.0 torchvision
uv pip install axolotl[deepspeed]
Download example axolotl configs, deepspeed configs
axolotl fetch examples
axolotl fetch deepspeed_configs  # OPTIONAL

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

Other installation approaches are described here.

Cloud Providers

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
• Support Matrix - Feature support, compatibility, and known gaps
• 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

AI Agent Support

Axolotl ships with built-in documentation optimized for AI coding agents (Claude Code, Cursor, Copilot, etc.). These docs are bundled with the pip package, no repo clone needed.

# Show overview and available training methods
axolotl agent-docs
Topic-specific references
axolotl agent-docs sft                 # supervised fine-tuning
axolotl agent-docs grpo                # GRPO online RL
axolotl agent-docs preference_tuning   # DPO, KTO, ORPO, SimPO
axolotl agent-docs reward_modelling    # outcome and process reward models
axolotl agent-docs pretraining         # continual pretraining
axolotl agent-docs --list              # list all topics
Dump config schema for programmatic use
axolotl config-schema
axolotl config-schema --field adapter

If you're working with the source repo, agent docs are also available at docs/agents/ and the project overview is in AGENTS.md.

🤝 Getting Help

• Join our Discord community for support
• Check out our Examples directory
• Read our Debugging Guide
• Need dedicated support? Please contact ✉️[email protected] for options

🌟 Contributing

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

📈 Telemetry

Axolotl has opt-out telemetry that helps us understand how the project is being used and prioritize improvements. We collect basic system information, model types, and error rates, never personal data or file paths. Telemetry is enabled by default. To disable it, set AXOLOTL_DO_NOT_TRACK=1. For more details, see our telemetry documentation.

❤️ Sponsors

Interested in sponsoring? Contact us at [email protected]

📝 Citing Axolotl

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

@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 file for details.