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axolotl

Free

Go ahead and axolotl questions

Model APIsFreeFree tier
Inputs: text, imageOutputs: text
Type
Open Source
Visit Website GitHub (11.8k stars)

About axolotl

Axolotl is a free and open-source framework designed for fine-tuning large language models (LLMs) and multimodal models. It provides a comprehensive toolkit for researchers and developers to adapt pre-trained models to specific tasks, supporting a wide range of model architectures including recent releases from Mistral, Qwen, Gemma, Llama, and others. The framework emphasizes efficiency and scalability, offering features such as LoRA, QLoRA, quantization-aware training, and various parallelism strategies (FSDP, DeepSpeed, tensor parallelism, context parallelism) to enable training on single or multiple GPUs. Axolotl is actively maintained with frequent updates that add support for new models, optimization techniques, and training methods like text diffusion and entropy-aware focal training.

Key Features

Supports fine-tuning of a wide range of LLMs and multimodal models (e.g., Mistral, Qwen, Gemma, Llama, InternVL)
Includes LoRA, QLoRA, and fused LoRA (SonicMoE, ScatterMoE) for memory-efficient fine-tuning
Offers quantization-aware training (QAT) with NVFP4 support
Provides multiple parallelism strategies: FSDP, DeepSpeed, tensor parallelism, context parallelism, sequence parallelism
Supports advanced training techniques like text diffusion, GDPO, and entropy-aware focal training
Integrates with tools like SageAttention and supports distributed optimizers (Muon)
Open-source and free to use, with active community updates

Pros & Cons

Pros
  • Free and open-source, with no licensing costs
  • Actively maintained with frequent updates and new model support
  • Supports a broad range of model architectures and training techniques
  • Designed for scalability from single GPU to multi-node clusters
  • Includes memory optimization features like LoRA and quantization
Cons
  • Requires technical expertise in machine learning and command-line usage
  • Documentation and setup may be complex for beginners
  • Hardware requirements (e.g., GPU VRAM) can be high for larger models
  • Some advanced features (e.g., specific parallelism modes) may require additional configuration

Best For

Fine-tuning large language models for domain-specific tasks (e.g., legal, medical, customer support)Adapting multimodal models for vision-language tasksResearch and experimentation with new fine-tuning techniques and model architecturesEfficient training of MoE (Mixture of Experts) models with reduced VRAM usageLong-context fine-tuning using sequence parallelism and scalable softmax

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FAQ

Is Axolotl completely free to use?
Based on available information, Axolotl is free and open-source. Users should verify the license terms on the official GitHub repository for any usage restrictions.
What models can I fine-tune with Axolotl?
Axolotl supports a wide range of models, including recent releases from Mistral, Qwen, Gemma, Llama, and others. The full list of supported models is maintained in the documentation and is updated regularly.
Does Axolotl support multimodal models?
Yes, Axolotl supports fine-tuning of multimodal models, as indicated in its update logs (e.g., InternVL 3.5, Qwen3-vl). Specific capabilities should be checked in the documentation.
What hardware do I need to use Axolotl?
Hardware requirements vary depending on the model size and training configuration. Axolotl supports single GPU, multi-GPU, and multi-node setups, but larger models may require significant VRAM. Users should consult the documentation for specific recommendations.
How do I install Axolotl?
Installation instructions are provided in the official documentation. Axolotl is now uv-first, suggesting a modern Python packaging approach. Users should follow the setup guide for their environment.
Can I use Axolotl for commercial projects?
As an open-source framework, Axolotl is likely free for commercial use, but users should verify the license (e.g., Apache 2.0 or similar) on the GitHub repository to confirm any conditions.