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This book provides a foundational overview of Large Language Models (LLMs), structured across six core chapters covering pre-training, generative models, prompting, alignment, inference, and reasoning. It aims to equip students, professionals, and practitioners with a deep understanding of LLM fundamentals, eschewing exhaustive coverage of the latest advancements for a focus on enduring principles. The work serves as a comprehensive reference for anyone seeking to grasp the underlying concepts that drive modern LLM capabilities.
Uncover the core principles of LLMs, from pre-training to reasoning, in a structured, foundational guide.
This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into six main chapters, each exploring a key area: pre-training, generative models, prompting, alignment, inference, and reasoning. It is intended for college students, professionals, and practitioners in natural language processing and related fields, and can serve as a reference for anyone interested in large language models.
