Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generation Master advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data Construct and fine-tune multimodal Transformer architectures using opensource LLMs Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind "By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application." --Giada Pistilli, Principal Ethicist at HuggingFace "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Table of Contents:
Foreword xv Preface xvii Acknowledgments xxi About the Author xxiii Part I: Introduction to Large Language Models 1 Chapter 1: Overview of Large Language Models 3 What Are Large Language Models? 4 Popular Modern LLMs 20 Domain-Specific LLMs 22 Applications of LLMs 23 Summary 29 Chapter 2: Semantic Search with LLMs 31 Introduction 31 The Task 32 Solution Overview 34 The Components 35 Putting It All Together 51 The Cost of Closed-Source Components 54 Summary 55 Chapter 3: First Steps with Prompt Engineering 57 Introduction 57 Prompt Engineering 57 Working with Prompts Across Models 65 Building a Q/A Bot with ChatGPT 69 Summary 74 Part II: Getting the Most Out of LLMs 75 Chapter 4: Optimizing LLMs with Customized Fine-Tuning 77 Introduction 77 Transfer Learning and Fine-Tuning: A Primer 78 A Look at the OpenAI Fine-Tuning API 82 Preparing Custom Examples with the OpenAI CLI 84 Setting Up the OpenAI CLI 87 Our First Fine-Tuned LLM 88 Case Study: Amazon Review Category Classification 93 Summary 95 Chapter 5: Advanced Prompt Engineering 97 Introduction 97 Prompt Injection Attacks 97 Input/Output Validation 99 Batch Prompting 103 Prompt Chaining 104 Chain-of-Thought Prompting 111 Revisiting Few-Shot Learning 113 Testing and Iterative Prompt Development 123 Summary 124 Chapter 6: Customizing Embeddings and Model Architectures 125 Introduction 125 Case Study: Building a Recommendation System 126 Summary 144 Part III: Advanced LLM Usage 145 Chapter 7: Moving Beyond Foundation Models 147 Introduction 147 Case Study: Visual Q/A 147 Case Study: Reinforcement Learning from Feedback 163 Summary 173 Chapter 8: Advanced Open-Source LLM Fine-Tuning 175 Introduction 175 Example: Anime Genre Multilabel Classification with BERT 176 Example: LaTeX Generation with GPT2 189 Sinan's Attempt at Wise Yet Engaging Responses: SAWYER 193 The Ever-Changing World of Fine-Tuning 206 Summary 207 Chapter 9: Moving LLMs into Production 209 Introduction 209 Deploying Closed-Source LLMs to Production 209 Deploying Open-Source LLMs to Production 210 Summary 225 Part IV: Appendices 227 Appendix A: LLM FAQs 229 Appendix B: LLM Glossary 233 Appendix C: LLM Application Archetypes 239 Index 243

About the Author :
Sinan Ozdemir is currently the founder and CTO of Shiba Technologies. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA.

Review :
"Ozdemir's book cuts through the noise to help readers understand where the LLM revolution has come from--and where it is going. Ozdemir breaks down complex topics into practical explanations and easy to follow code examples." --Shelia Gulati, former GM at Microsoft and current Managing Director of Tola Capital "When it comes to building Large Language Models (LLMs), it can be a daunting task to find comprehensive resources that cover all the essential aspects. However, my search for such a resource recently came to an end when I discovered this book. "One of the stand-out features of Sinan is his ability to present complex concepts in a straightforward manner. The author has done an outstanding job of breaking down intricate ideas and algorithms, ensuring that readers can grasp them without feeling overwhelmed. Each topic is carefully explained, building upon examples that serve as steppingstones for better understanding. This approach greatly enhances the learning experience, making even the most intricate aspects of LLM development accessible to readers of varying skill levels. "Another strength of this book is the abundance of code resources. The inclusion of practical examples and code snippets is a game-changer for anyone who wants to experiment and apply the concepts they learn. These code resources provide readers with hands-on experience, allowing them to test and refine their understanding. This is an invaluable asset, as it fosters a deeper comprehension of the material and enables readers to truly engage with the content. "In conclusion, this book is a rare find for anyone interested in building LLMs. Its exceptional quality of explanation, clear and concise writing style, abundant code resources, and comprehensive coverage of all essential aspects make it an indispensable resource. Whether you are a beginner or an experienced practitioner, this book will undoubtedly elevate your understanding and practical skills in LLM development. I highly recommend Quick Start Guide to Large Language Models to anyone looking to embark on the exciting journey of building LLM applications." --Pedro Marcelino, Machine Learning Engineer, Co-Founder and CEO @overfit.study