Bharath Kumar Bolla, Kalpa Subbaiah, Sashi Kiran Kaata | Large Language Model Recipes. A Hands-On Guide to Fine-Tuning, Optimization, Deployment, and Real-World Applications (2026) [PDF, EPUB]
Автор: Bharath Kumar Bolla, Kalpa Subbaiah, Sashi Kiran Kaata
Издательство: Apress
ISBN: 979-8-8688-2607-8, 979-8868826061
Жанр: Cloud Computing, Python Programming, Statistics
Язык: Английский
Формат: PDF, EPUB
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание:The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.
Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.
The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.
Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today.
What you will learn:
Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework
Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation
Build and optimize RAG pipelines with effective retrieval strategies and vector databases
Deploy optimized LLMs using quantization techniques and scalable inference frameworks
Develop multimodal and agentic AI applications with vision-language models and autonomous agents
Who this book is for:
This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start
About the Authors xiii
About the Technical Reviewer xv
Acknowledgments xvii
Introduction xix
Part I: Foundations and Environment Setup 1
Chapter 1: The LLM Landscape and Core Concepts 3
Chapter 2: Configuring Your Development Environment 11
Part II: Models and Data Preparation 19
Chapter 3: LLM Architectures and Foundational Models 21
Chapter 4: Data Preparation and Tokenization 43
Part III: Core Techniques for Prompting and Fine-Tuning 67
Chapter 5: Prompt Engineering 69
Chapter 6: Full-Parameter Fine-Tuning 95
Chapter 7: Instruction Fine-Tuning 137
Chapter 8: Parameter-Efficient Fine-Tuning (PEFT) 175
Chapter 9: Augmenting with Synthetic Data 209
Part IV: Optimization, Serving, and Evaluation 233
Chapter 10: Model Quantization 235
Chapter 11: Inference and Deployment Strategies 267
Chapter 12: Metrics and Benchmarks 297
Part V: Advanced Applications and Future Directions 315
Chapter 13: Retrieval-Augmented Generation (RAG) 317
Chapter 14: Exploring Vision-Language Models 341
Chapter 15: Future Trends and Responsible AI 357
Appendix A: Glossary of LLM Terminology 379
Appendix B: Tooling Cheat Sheets 383
Appendix C: Curated Resources and Further Reading 389
Index 395
Скриншоты:
Время раздачи: с 10 до 20 (минимум до появления первых 3-5 скачавших) 
