The Practical Guide to Large Language Models
Год издания: 2025
Автор: Gridin I.
Издательство: Apress
ISBN: 979-8-8688-2216-2
Язык: Русский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 351
Описание: This book is a practical guide to harnessing Hugging Face's powerful transformers library, unlocking access to the largest open-source LLMs. By simplifying complex NLP concepts and emphasizing practical application, it empowers data scientists, machine learning engineers, and NLP practitioners to build robust solutions without delving into theoretical complexities.
The book is structured into three parts to facilitate a step-by-step learning journey. Part One covers building production-ready LLM solutions introduces the Hugging Face library and equips readers to solve most of the common NLP challenges without requiring deep knowledge of transformer internals. Part Two focuses on empowering LLMs with RAG and intelligent agents exploring Retrieval-Augmented Generation (RAG) models, demonstrating how to enhance answer quality and develop intelligent agents. Part Three covers LLM advances focusing on expert topics such as model training, principles of transformer architecture and other cutting-edge techniques related to the practical application of language models.
Each chapter includes practical examples, code snippets, and hands-on projects to ensure applicability to real-world scenarios. This book bridges the gap between theory and practice, providing professionals with the tools and insights to develop practical and efficient LLM solutions.
What you will learn:
- What are the different types of tasks modern LLMs can solve
- How to select the most suitable pre-trained LLM for specific tasks
- How to enrich LLM with a custom knowledge base and build intelligent systems
- What are the core principles of Language Models, and how to tune them
- How to build robust LLM-based AI Applications
Примеры страниц (скриншоты)
Оглавление
About the Author xi
About the Technical Reviewer xiii
Introduction xv
Part I: LLM Basics 1
Chapter 1: Discovering Transformers 3
Chapter 2: LLM Internals and Evaluation 57
Chapter 3: Improving Chat Model Responses 93
Part II: Empowering LLM Applications with RAG and Agents 131
Chapter 4: Enriching the Model’s Knowledge with Retrieval-Augmented Generation 133
Chapter 5: Building Agent Systems 175
Part III: LLM Advances 225
Chapter 6: Mastering Model Training 227
Chapter 7: Unpacking the Transformer Architecture 295
Index 349
