# Axolotl: Streamlining LLM Fine-tuning with a Powerful Open-Source Framework This repository profile is provided by osrepos.com, an open source repository discovery platform. Source: osrepos.com Repository profile: https://osrepos.com/repo/axolotl-ai-cloud-axolotl Generated for open source discovery and AI-assisted research. Axolotl is a comprehensive, free, and open-source framework designed to simplify the post-training and fine-tuning processes for large language models (LLMs). It offers extensive model support, diverse training methods, and robust performance optimizations, making it an invaluable tool for researchers and developers. With easy configuration and cloud-ready deployment, Axolotl empowers users to efficiently customize and enhance LLMs. GitHub: https://github.com/axolotl-ai-cloud/axolotl OSRepos URL: https://osrepos.com/repo/axolotl-ai-cloud-axolotl ## Summary Axolotl is a comprehensive, free, and open-source framework designed to simplify the post-training and fine-tuning processes for large language models (LLMs). It offers extensive model support, diverse training methods, and robust performance optimizations, making it an invaluable tool for researchers and developers. With easy configuration and cloud-ready deployment, Axolotl empowers users to efficiently customize and enhance LLMs. ## Topics - fine-tuning - llm - python - machine-learning - deep-learning - ai - nlp - open-source ## Repository Information Last analyzed by OSRepos: Tue Jul 07 2026 16:15:22 GMT+0100 (Western European Summer Time) Detail views: 2 GitHub clicks: 0 ## Safety Notice OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything. ## Content ## Introduction Axolotl is a powerful, open-source framework dedicated to simplifying the post-training and fine-tuning of cutting-edge large language models (LLMs). Developed by axolotl-ai-cloud, this Python-based tool provides a streamlined approach to customizing models, supporting a wide array of architectures and advanced training techniques. With over 12,000 stars on GitHub, Axolotl has become a popular choice for those looking to efficiently adapt LLMs for specific tasks and datasets. Key features include support for numerous models from the Hugging Face Hub, multimodal training capabilities for vision-language and audio models, and a variety of training methods such as full fine-tuning, LoRA, QLoRA, and advanced preference tuning techniques like DPO and GRPO. Its emphasis on easy configuration via YAML files and extensive performance optimizations ensures efficient and scalable training across different hardware setups. ## Installation Getting started with Axolotl is straightforward. The project recommends using `uv` for package management and provides clear steps for setting up your environment. **Requirements**: * NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU * Python >=3.11 (3.12 recommended) * PyTorch ?2.11.0 **Steps**: 1. **Install `uv` (if not already installed)**: bash curl -LsSf https://astral.sh/uv/install.sh | sh # Restart your shell after installation 2. **Set `UV_TORCH_BACKEND` (change depending on your system)**: bash export UV_TORCH_BACKEND=cu130 3. **Create a new virtual environment and activate it**: bash uv venv --python 3.12 source .venv/bin/activate 4. **Install PyTorch and Axolotl**: bash uv pip install torch==2.12.0 torchvision uv pip install --no-build-isolation axolotl[deepspeed] 5. **Download example configurations (optional)**: bash axolotl fetch examples axolotl fetch deepspeed_configs # OPTIONAL **Using Docker**: For an even simpler setup, you can use the official Docker image: bash docker run --gpus '"all"' --ipc=host --rm -it axolotlai/axolotl:main-latest More detailed installation approaches are available in the [official documentation](https://docs.axolotl.ai/docs/installation.html). ## Examples Once Axolotl is installed, you can quickly fine-tune a model using the provided examples. 1. **Fetch Axolotl examples**: bash axolotl fetch examples # Or, specify a custom path axolotl fetch examples --dest path/to/folder 2. **Train a model using LoRA**: bash axolotl train examples/llama-3/lora-1b.yml For a more comprehensive guide, refer to the [Getting Started Guide](https://docs.axolotl.ai/docs/getting-started.html). ## Why Use Axolotl? Axolotl stands out as a premier tool for LLM fine-tuning due to its extensive feature set and focus on performance and usability: * **Broad Model Compatibility**: Supports a vast range of models, including GPT-OSS, LLaMA, Mistral, Mixtral, Pythia, and many others available on the Hugging Face Hub. * **Advanced Training Methods**: Offers a comprehensive suite of training techniques, from full fine-tuning and LoRA/QLoRA to GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO, GDPO), and Reward Modelling. * **Multimodal Capabilities**: Enables fine-tuning of vision-language models (VLMs) like LLaMA-Vision, Qwen2-VL, LLaVA, and audio models such as Voxtral, with support for image, video, and audio data. * **Performance Optimizations**: Integrates cutting-edge optimizations like Multipacking, Flash Attention (2/3/4), Xformers, Sequence Parallelism (SP), LoRA optimizations, and multi-GPU/multi-node training (FSDP1, FSDP2, DeepSpeed, Torchrun, Ray) to maximize training efficiency. * **Simplified Workflow**: Utilizes a single YAML configuration file for the entire fine-tuning pipeline, covering dataset preprocessing, training, evaluation, quantization, and inference. * **Cloud Ready**: Provides Docker images and PyPI packages for seamless deployment on various cloud platforms and local hardware. * **AI Agent Support**: Includes built-in documentation optimized for AI coding agents, making it easier to integrate with tools like Claude Code, Cursor, and Copilot. ## Links * **GitHub Repository**: [https://github.com/axolotl-ai-cloud/axolotl](https://github.com/axolotl-ai-cloud/axolotl "Axolotl GitHub Repository" target="_blank") * **Official Documentation**: [https://docs.axolotl.ai/](https://docs.axolotl.ai/ "Axolotl Documentation" target="_blank") * **Discord Community**: [https://discord.com/invite/HhrNrHJPRb](https://discord.com/invite/HhrNrHJPRb "Axolotl Discord" target="_blank") * **PyPI Package**: [https://pypi.org/project/axolotl/](https://pypi.org/project/axolotl/ "Axolotl PyPI Package" target="_blank")