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Axolotl
Fine‑tune LLMs fast with flexible, scalable open‑source framework
Axolotl streamlines LLM and multimodal model fine‑tuning, offering LoRA, QLoRA, QAT, DPO, and multi‑GPU/Node support via simple YAML configs and Docker/PyPI deployment.
Overview
Overview
Axolotl is a free, community‑driven framework that simplifies post‑training and fine‑tuning of the latest large language and multimodal models. It targets researchers, ML engineers, and teams who need to adapt models such as GPT‑OSS, LLaMA, Mistral, Mixtral, Pixtral, or Voxtral to custom data, while offering a unified YAML‑based workflow that covers dataset preprocessing, training, evaluation, quantization, and inference.
The toolkit supports a broad spectrum of training methods—including full‑parameter fine‑tuning, LoRA, QLoRA, GPTQ, QAT, preference tuning (DPO, IPO, KTO, ORPO), GRPO, and reward modelling—paired with performance optimizations like Flash Attention, Sequence Parallelism, and multi‑GPU/‑node strategies (FSDP, DeepSpeed, Torchrun, Ray). Flexible dataset loading from local storage, Hugging Face Hub, or cloud buckets makes it easy to integrate diverse data sources.
Deployment is straightforward: install via pip with optional extras, pull the official Docker image, or use the provided PyPI packages on cloud platforms such as RunPod, Vast.ai, or Modal. With comprehensive documentation and an active Discord community, Axolotl enables rapid experimentation and production‑grade scaling.
Highlights
Supports a wide range of LLMs and multimodal models
Multiple fine‑tuning methods including LoRA, QLoRA, QAT, DPO, GRPO, and reward modelling
Scalable training with Flash Attention, Sequence Parallelism, FSDP, DeepSpeed, and multi‑node Torchrun
Simple YAML‑driven workflow plus Docker and PyPI packages for cloud or local use
Pros
- Extensive model and method coverage
- Optimized for modern GPU hardware
- Flexible dataset sources (local, HF, cloud)
- Easy to start via Docker or pip
Considerations
- Advanced parallelism options need careful tuning
- Best performance requires Ampere‑or‑newer GPUs
- Documentation spread across multiple sections
- Primary focus on Linux; Windows support may be limited
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Fit guide
Great for
- Researchers fine‑tuning cutting‑edge LLMs
- Teams needing multi‑GPU or multi‑node scaling
- Developers building multimodal models (vision, audio)
- Organizations deploying on cloud platforms with Docker
Not ideal when
- Users with only CPU resources
- Beginners needing a single‑click GUI
- Projects requiring strict Windows compatibility
- Small models where framework overhead outweighs benefits
How teams use it
Domain‑specific LLM fine‑tuning
Achieve higher accuracy on specialized corpora using LoRA or full‑parameter training.
Multimodal vision‑language model adaptation
Fine‑tune LLaVA or Pixtral on custom image‑text datasets with GPU acceleration.
Quantization‑aware training for edge deployment
Train models with QAT to produce 8‑bit models ready for low‑latency inference.
Large‑scale instruction tuning across multiple nodes
Leverage FSDP and Torchrun to train instruction‑tuned models on hundreds of GPUs.
Tech snapshot
Python96%
Jinja3%
Shell1%
CSS1%
Dockerfile1%
Frequently asked questions
What hardware is required for optimal performance?
An NVIDIA Ampere‑class GPU (or newer) with BF16 support is recommended; multi‑GPU setups benefit from NVLink or PCIe.
How do I install Axolotl?
Use pip with optional extras (e.g., `pip install axolotl[flash-attn,deepspeed]`) or run the official Docker image `axolotlai/axolotl:main-latest`.
Can I fine‑tune multimodal models?
Yes, Axolotl supports vision‑language and audio models such as LLaVA, Pixtral, and Voxtral with image, video, and audio inputs.
Is there support for quantization?
Axolotl includes QAT, GPTQ, and 8‑bit finetuning via torchao, enabling efficient inference on limited hardware.
Where can I get help?
Join the Discord community, consult the documentation, or email wing@axolotl.ai for dedicated support.
Project at a glance
Active- Stars
- 11,128
- Watchers
- 11,128
- Forks
- 1,241
LicenseApache-2.0
Repo age2 years old
Last commit3 hours ago
Primary languagePython
Last synced 3 hours ago