Axolotl: Fine-tune LLMs with Unprecedented Ease & Power! 🚀

Your ultimate toolkit for efficient, scalable, and versatile large language model fine-tuning.

🎉 Latest Innovations & Updates!

• 2025/06: Magistral with mistral-common tokenizer support! Dive into examples to train your own Magistral models.
• 2025/05: Quantization Aware Training (QAT) support! Explore the docs to learn more.
• 2025/04: Llama 4 support! See examples to train Llama 4 with Axolotl's linearized version!
• 2025/03: Sequence Parallelism (SP) support! Scale your context length. Read the blog and docs.
• 2025/03: (Beta) Fine-tuning Multimodal models! Check out the docs.
• 2025/02: LoRA optimizations! Reduce memory and improve speed. Jump into the docs.
• 2025/02: GRPO support! Dive into our blog and GRPO example.
• 2025/01: Reward Modelling / Process Reward Modelling fine-tuning! See docs.

Axolotl is a powerful, flexible, and user-friendly tool designed to supercharge your post-training workflows for a wide range of cutting-edge AI models.

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.

Dive deep into Axolotl's capabilities with our extensive 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

❤️ Our Esteemed Sponsors

A huge thank you to our visionary sponsors who provide the essential resources to keep Axolotl at the forefront of LLM fine-tuning:

Modal: Revolutionizing cloud computing for Gen AI. Run jobs, deploy models, and fine-tune LLMs at scale with ease.

Interested in powering the future of Axolotl? Become a sponsor! Contact us at wing@axolotl.ai