Company
Date Published
Author
Waleed Kadous, Jun Gong, Antoni Baum, Richard Liaw
Word count
2055
Language
English
Hacker News points
None

Summary

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.