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).

Why You Should Read This Book

This book accommodates varying levels of experience in machine learning, making it accessible to both those with prior knowledge and beginners who can code in Python. It offers flexibility in how deeply you engage with its content, allowing you to focus on practical aspects, experiment with code, or delve into the theoretical aspects without coding. The chapters in this book build upon each other, with knowledge and skills from previous sections aiding in later ones. Expect challenges along the way, as overcoming obstacles is part of the learning process. Just like the author's experience developing a visual question-answering system, you may face frustrations and setbacks but will eventually achieve breakthroughs. Embrace these challenges, for they lead to moments of triumph in your learning journey.

Table of Contents

Part I: Introduction to Large Language Models

1 Overview of Large Language Models

2 Semantic Search with LLMs

3 First Steps with Prompt Engineering

Part II: Getting the Most Out of LLMs

4 Optimizing LLMs with Customized Fine-Tuning

5 Advanced Prompt Engineering

6 Customizing Embeddings and Model Architectures

Part III: Advanced LLM Usage

7 Moving Beyond Foundation Models

8 Advanced Open-Source LLM Fine-Tuning

9 Moving LLMs into Production

Part IV: Appendices

A. LLM FAQS

B. LLM Glossary

C. LLM Application Archetypes

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

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.