Fine-Tuning LLMs: Best Practices and When to Go Small - Lecture 124
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore best practices for fine-tuning Large Language Models (LLMs) and learn when to opt for smaller models in this 54-minute talk by Mark Kim-Huang, Co-Founder and Head of AI at Preemo Inc. Dive into challenges and state-of-the-art techniques for fine-tuning LLMs, including strategies to improve performance, efficient data parallelism, and parameter-efficient fine-tuning with LoRa. Discover the benefits of custom LLMs over closed-source models, understand various types of fine-tuning, and learn how to define tasks and construct effective prompts. The talk also covers data synthesis, cloud platform costs, vector databases, and the trade-offs between fine-tuning and prompting with context windows. Gain insights into Mark's journey to LLMs and participate in a Q&A session addressing topics such as task clustering, LangChain Auto Evaluator, and finding reasoning paths in models.