Understanding Transformers and GPTs
Goals: Equip students with foundational theoretical knowledge of transformers and GPTs, ensuring a robust understanding beneficial for effective LLM training and utilization.
This section comprehensively examines the inner workings and components surrounding Transformers and GPT. Participants begin with a detailed study of the Transformer architecture, understanding its foundational concepts and essential parts. The progression covers evaluations of LLMs, their control mechanisms, and the nuances of prompting, pretraining, and finetuning. Each lesson is designed to impart intricate details, practical knowledge, and a tiered understanding of these technologies.
- Understanding Transformers: This lesson provides an in-depth look at Transformers, breaking down their complex components and the network's essential mechanics. We begin by examining the paper "Attention is all you need.” We conclude by highlighting the use of these components in Hugging Face's transformers library.
- Transformers Architectures: This chapter is a concise guide to Transformer architectures. We will first dissect the encoder-decoder framework, which is pivotal for sequence-to-sequence tasks. Next, we provide a high-level overview of the GPT model, known for its language generation capabilities. We also spotlight BERT, emphasizing its significance in understanding the context within textual data.
- Deep Dive on the GPT architecture: This section explores the GPT architecture. We shed light on the structural specifics, the objective function, and the principles of causal modeling. This technical session is designed for individuals seeking an in-depth understanding of the intricate details and mathematical foundations of GPT.
- Evaluating LLM Performance: This lesson explores the nuances of evaluating Large Language Model performance. We differentiate between objective functions and metrics and transition into perplexity, BLEU, and ROUGE metrics. We also provide an overview of popular benchmarks in the domain.
- Controlling LLM Outputs: This lesson delves into decoding techniques like Greedy and Beam Search, followed by concepts such as Temperature and the use of stop sequences. We will also discuss the importance of these methods, with references to frameworks like ReAct. It also presents concepts like Frequency and Presence Penalties.
- Prompting and few-shot prompting: This lesson provides an overview of how carefully crafted prompts can guide LLMs in tasks like answering questions and generating text. We will progress from zero-shot prompting, where LLMs operate without specific examples, to in-context and few-shot prompting, teaching the model to manage intricate tasks with sparse training data.
- Pretraining and Finetuning: This module examines the foundational concepts of pretraining and finetuning in the context of Large Language Models. In subsequent chapters, we will discern the differences between pretraining, finetuning, and instruction tuning, setting the stage for deeper dives. While the lesson touches upon various types of instruction tuning, detailed exploration of specific methods like SFT and RLHF will be reserved for later sessions, ensuring a progressive understanding of the topic.
After navigating the diverse terrain of Transformers and LLMs, participants now deeply understand significant architectures like GPT and BERT. The sessions shed light on model evaluation metrics, advanced control techniques for optimal outputs, and the roles of pretraining and finetuning. The upcoming module dives into the complexities of deciding when to train an LLM from scratch, the operational necessities of LLMs, and the sequential steps crucial for the training process.