Developing LLMs: Building, Training, Fine-tuning

In this video, Sebastian Raschka provides a comprehensive overview of the process of developing a Large Language Model (LLM), focusing on the stages of building, training, and fine-tuning. The video begins by discussing the various ways LLMs are used today, including through public APIs, running custom models locally, and deploying custom models. Raschka then delves into the specifics of building an LLM, starting with data preparation and sampling, and explaining how LLMs predict the next word in a text. He covers tokenization, the architecture of LLMs, and the pre-training process, highlighting the importance of using large datasets and the challenges associated with them. The video also explores fine-tuning LLMs for specific tasks, such as text classification and creating personal assistants, and discusses the concept of preference tuning to refine model responses. Raschka emphasizes the importance of evaluating LLMs using various benchmarks and metrics, such as MMLU scores and pairwise comparisons. He concludes with practical advice on when to use different training methods and the benefits of using pre-trained models for specific applications.