What is Axolotl AI?

Axolotl AI offers a comprehensive open-source solution for fine-tuning Large Language Models (LLMs). It simplifies the process, making it accessible, scalable, and efficient for users ranging from individual developers to large enterprises. The platform supports a wide array of popular LLMs, including Llama, Mistral, Falcon, and others, leveraging HuggingFace transformers. Axolotl emphasizes speed and performance, integrating cutting-edge techniques like FSDP+qLoRA, LoRA+, and Multipack to accelerate the fine-tuning process, sometimes achieving speeds 3-5 times faster than alternative methods.

Built with flexibility in mind, Axolotl allows users to run it in various environments, including their own cloud or Kubernetes setups, ensuring full control and compliance through a 'Bring Your Own Data' (BYOD) approach. This means sensitive data doesn't need to be uploaded to external services. Supported by a vibrant open-source community, Axolotl provides pre-existing configurations and extensive support, enabling users to start training models quickly without needing specialized hardware or deep configuration knowledge. It's trusted by research companies, AI platforms, and practitioners for large-scale fine-tuning projects.

Features

Wide LLM Support: Fine-tune models like Llama, Mistral, Falcon, Gemma, Phi, Qwen, Cerebras, XGen, RWKV, Eleuther AI, MPT via HuggingFace transformers.
High-Performance Techniques: Integrates FSDP+qLoRA, LoRA+, Multipack, PEFT/LoRA for accelerated fine-tuning.
Flexible Deployment: Run anywhere, including private cloud, Docker, and Kubernetes setups.
Bring Your Own Data (BYOD): Maintain data control and compliance by using your own datasets without uploading them externally.
Multi-GPU Support: Integrates with tools like xformer and Deepspeed for efficient large-scale training.
Open Source & Community Driven: Access pre-existing configurations, extensive documentation ('cookbooks'), and support from a large community.
Dataset Flexibility: Supports various dataset formats for the fine-tuning process.

Use Cases

Customizing LLMs for specific tasks or domains.
Improving the performance of existing AI models.
Conducting research on LLM fine-tuning techniques.
Building proprietary AI models while maintaining data privacy.
Scaling AI model development within enterprises.
Enabling developers to experiment with LLM fine-tuning without hardware constraints.

FAQs

What models does Axolotl AI support for fine-tuning?

Axolotl AI supports a variety of LLMs including LLaMA, Falcon, and Mistral. It also allows users to fine-tune other popular models like Gemma, Phi, Qwen, Cerebras, XGen, RWKV, Eleuther AI, and MPT with customizable configurations via HuggingFace transformers.
Can Axolotl AI be used for multi-GPU fine-tuning?

Yes, Axolotl AI integrates with tools like xformer and Deepspeed, which facilitate efficient multi-GPU usage for large-scale model fine-tuning.
What kind of datasets can be used with Axolotl AI?

Axolotl AI supports various dataset formats, making it flexible for users to utilize different types of data in the fine-tuning process.
Do I need specific hardware to use Axolotl?

No, you can start training models right away with pre-existing configurations without needing to tweak settings or necessarily own specific hardware, utilizing integrations with platforms like Runpod, Latitude, Modal, and Jarvislabs.
Is my data secure when using Axolotl?

Yes, Axolotl supports a 'Bring Your Own Data' approach, meaning your data doesn't have to be uploaded to external services, allowing for robust compliance and data governance.