How to fine tune and serve LLMs simply, quickly and cost effectively using Ray + DeepSpeed + HuggingFace

This blog post discusses the use of Ray, HuggingFace, DeepSpeed, and PyTorch to build a system for fine-tuning and serving Large Language Models (LLMs) in a cost-effective and efficient manner. It highlights the benefits of using this tech stack, including its simplicity, speed, and scalability. The authors demonstrate how to fine-tune a 6 billion parameter GPT-J model on Shakespeare's works and serve it as a web service using Ray and HuggingFace. They also discuss the importance of cost-effectiveness in LLM applications, particularly when dealing with large models and high-performance computing requirements. By leveraging Ray's distributed capabilities, the authors show that running multiple machines can be both cheaper and faster than using a single large machine.

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