Explore advanced techniques for adapting large language models through parameter-efficient fine-tuning methods in this comprehensive lecture from MIT's Hands-On Deep Learning course. Delve into the intricacies of Generative Pretrained Transformers (GPTs), examining version differences and the nuances of training data that impact model performance. Learn about instruction tuning methodologies and discover how to effectively adapt base language learning models without requiring extensive computational resources. Understand the theoretical foundations and practical applications of parameter-efficient approaches that allow for model customization while maintaining the core capabilities of pre-trained LLMs. Gain insights into the latest developments in generative AI and how these techniques are revolutionizing the field of natural language processing and machine learning applications.