SPECIAL OFFERS
Keep up with new releases and promotions. Sign up to hear from us.
Register your product to gain access to bonus material or receive a coupon.
This eBook includes the following formats, accessible from your Account page after purchase:
EPUB
The open industry format known for its reflowable content and usability on supported mobile devices.
PDF
The popular standard, used most often with the free Acrobat® Reader® software.
This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.
This eBook includes the following formats, accessible from your Account page after purchase:
EPUB
The open industry format known for its reflowable content and usability on supported mobile devices.
PDF
The popular standard, used most often with the free Acrobat® Reader® software.
This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products
Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, 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, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up to date code for working with open and closed source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), Meta (BART and the LLaMA family), and more.
"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
Foreword to the First Edition
Preface
Acknowledgments
About the Author
Part I: Introduction to Large Language Models
Chapter 1: Overview of Large Language Models
Chapter 2: Semantic Search with LLMs
Chapter 3: First Steps with Prompt Engineering
Chapter 4: The AI Ecosystem--Putting the Pieces Together
Part II: Getting the Most Out of LLMs
Chapter 5: Optimizing LLMs with Customized Fine-Tuning
Chapter 6: Advanced Prompt Engineering
Chapter 7: Customizing Embeddings and Model Architectures
Chapter 8: AI Alignment: First Principles
Part III: Advanced LLM Usage
Chapter 9: Moving Beyond Foundation Models
Chapter 10: Advanced Open-Source LLM Fine-Tuning
Chapter 11: Moving LLMs into Production
Chapter 12: Evaluating LLMs
Part IV: Appendixes
Appendix A: LLM FAQs
Appendix B: LLM Glossary
Appendix C: LLM Application Archetypes
Index