
axolotl
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Axolotl is a lightweight and efficient tool for managing and analyzing large datasets. It provides a user-friendly interface for data manipulation, visualization, and statistical analysis. With Axolotl, users can easily import, clean, and explore data to gain valuable insights and make informed decisions. The tool supports various data formats and offers a wide range of functions for data processing and modeling. Whether you are a data scientist, researcher, or business analyst, Axolotl can help streamline your data workflows and enhance your data analysis capabilities.
README:
A Free and Open Source LLM Fine-tuning Framework
- 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 for more info.
- Axolotl adds more models: GPT-OSS, Gemma 3n, Liquid Foundation Model 2 (LFM2), and Arcee Foundation Models (AFM).
- FP8 finetuning with fp8 gather op is now possible in Axolotl via
torchao
. Get started here! - Voxtral, Magistral 1.1, and 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 for using ALST with Axolotl!
- 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!
- 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.
Expand older 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!
- 2025/04: 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: (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.
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, 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.
Requirements:
- NVIDIA GPU (Ampere or newer for
bf16
and Flash Attention) or AMD GPU - Python 3.11
- PyTorch โฅ2.6.0
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
Installing with Docker can be less error prone than installing in your own environment.
docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
Other installation approaches are described here.
# 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.
- 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
- Join our Discord community for support
- Check out our Examples directory
- Read our Debugging Guide
- Need dedicated support? Please contact โ๏ธ[email protected] for options
Contributions are welcome! Please see our Contributing Guide for details.
Interested in sponsoring? Contact us at [email protected]
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}
}
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
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