In this session, we’ll explore the technical aspects of fine-tuning large language models (LLMs). We’ll focus on understanding the fine-tuning process, its underlying concepts, when and why it’s needed, and best practices for optimizing model performance. The session will also cover frameworks and techniques for implementing fine-tuning effectively, along with real-world use cases to illustrate its applications.

Prerequisites: Participants should have a basic understanding of machine learning concepts like supervised and unsupervised learning, as well as a general understanding of neural networks. Familiarity with foundational NLP concepts such as tokenization and embeddings, along with a basic awareness of popular pretrained models like GPT and LLaMA, will also be helpful for this session, though not strictly necessary.