Full Description
Large Language Models Recipes is a comprehensive guide designed to help developers, and AI practitioners navigate the complexities of working with LLMs. It explains fine-tuning open-source models and deploying scalable AI solutions, providing practical insights and hands-on examples in a recipe-style format for easy understanding and application.
The book begins with a step-by-step guide to setting up an efficient development environment, covering hardware considerations, cloud services, and essential tools like PyTorch and TensorFlow. It then introduces readers to open-source language models, offering guidance on selecting and loading models such as GPT-J, LLaMA, and Falcon. It has dedicated chapters exploring fine-tuning, transfer learning, and quantization techniques to optimize performance. Readers will also discover advanced topics, including model distillation, deployment strategies on cloud platforms like AWS and GCP, and efficient data handling methods. Additionally, it covers scaling down large models for limited-resource environments, monitoring and debugging techniques, and integrating external tools such as vector databases for Retrieval-Augmented Generation (RAG).
By the end of this book, readers will have a solid foundation in working with LLMs—from setting up their environment to deploying efficient, scalable AI solutions. With practical recipes, real-world applications, and cutting-edge techniques, Large Language Models Recipes is an essential resource for anyone looking to harness the full potential of LLMs in modern AI workflows.
What you will learn:
How to configure hardware, install essential tools, and optimize workflows for working with LLMs.
Explore techniques like fine-tuning, quantization, and model distillation for efficient performance.
Explore deployment strategies, cloud platforms, and edge computing for real-world applications.
Understand multimodal LLMs, Retrieval-Augmented Generation (RAG), and external tool integrations.
Who this book is for:
This book is ideal for data scientists, machine learning engineers, and AI enthusiasts looking to understand and develop Large Language Models and their applications.
Contents
Part I: Setting Up Your AI Culinary Station.- Chapter 1: An Introduction.- Chapter 2: Environment Setup.- Part II: Sourcing & Preparing Ingredients: Models & Data.- Chapter 3: Open Source vs. Closed Source.- Chapter 4: Data Handling & Tokenization.- Part III: Mastering Core Techniques: Prompting & Fine-Tuning.- Chapter 5: Prompt Engineering Mastery.- Chapter 6: LLM Full Fine-Tuning.- Chapter 7: Precision Seasoning: Instruction Fine-Tuning.- Chapter 8: Parameter-Efficient Fine-Tuning (PEFT).- Chapter 9: Augmenting with Synthetic Data.- Part IV: Optimization, Serving & Evaluation.- Chapter 10: Making Models Leaner: Quantization Techniques.- Chapter 11: LLM Deployment Strategies.- Chapter 12: Evaluation Metrics & Benchmarks.- Part V: Advanced Recipes & Future Flavors.- Chapter 13: Retrieval-Augmented Generation (RAG).- Chapter 14: Exploring Multimodal Models.- Chapter 15: Future Trends & Responsible AI.- Appendix A: Glossary of LLM Terminology.- Appendix B: Tooling Cheat Sheets (Hugging Face CLI & Libraries, PyTorch Essentials, LangChain Basics).- Appendix C: Curated List of Datasets, Model Hubs, and Further Reading.
