Quick Start Guide to Large Language Models

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.

"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

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