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
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Meredith Birchfield –
“Quick Start Guide to Large Language Models” by Sinan is a transformative read for anyone interested in the rapidly evolving field of AI and LLM. This book serves as an exceptional guide, making complex concepts of Large Language Models (LLM) accessible to readers from all backgrounds, offering clarity and in-depth understanding.Additionally, Sinan takes a practical approach to his book. He provides comprehensive insights and examples into how to interact and work with these models effectively. This aspect was incredibly helpful, and I gained a much deeper and more confident understanding of LLMs and their applications.Rating: 10/10. A highly recommended read that offers valuable knowledge and confidence in the world of Large Language Models and AI.
Murray Fife –
I have to admit that I am not a fast reader, and I usually don’t read technical books; I just keep them for reference, but I devoured Sinan Ozdemir’s book on LLMs. It has the right mix of theory, examples, and code to keep me interested.Now I’ve got more tools to start looking at, for sure.
E –
Good theoretical overview but appreciated the practical examples for actual uses
anzelix –
The book is quite comprehensive but the code base needs to be updated. I don’t see complete relevant codes on the repo. Will update to 5* once repo is good. Thanks
Raul Garcia –
It has been long since I found a truly excellent tech book and for such a groundbreaking field like LLMs. The author is very experienced and does share his insight and tech knowledge with us. It felt like having a mentor next to you. It is best if you have some background in NLP and ML. Every line in the book counts. The books just need a better-quality printing and some layout/graphics improvements – maybe ChatGPT can help there?
Ganesh Prasad –
The first two chapters introduce the reader to the world of LLMs and are worth the price of the book on their own. The book covers a broad overview of LLMs, their application in semantic search, effective prompts, fine-tuning LLMs, prompt engineering deep dive, more technical aspects of LLMs such as modifying model architectures and embeddings, next-generation models and architectures, combining multiple LLMs, hands-on guidelines and examples, reinforcement learning, and more.
Tommy –
As a full-stack engineer interested in building applications with LLMs, this book was truly a godsend. The author has a knack for explaining complex concepts in an approachable, simple manner. This book starts by covering the fundamentals and lay of the land of LLMs. Later chapters are packed with practical advice and examples for prompt engineering, fine tuning, and running LLMs in production.Highly recommended!
Jerry –
This book is just awesome had my whole team get it. It’s the perfect blend of explaining the inner working of llms and connecting it with real world problems!Def recommend!
Alex Mathews –
As an engineer looking to get started with LLMs, this book was a great introduction to some of the core concepts. The author does a fantastic job of introducing LLM specific topics in an approachable way and builds on top of them with coding examples.
Publius –
I write software every day, and have worked with some earlier machine learning algorithms, so I thought this would be an appropriate book to start getting familiar with LLMs. So far, I’d call it half successful.The introduction is useful mostly as an outline of relevant concepts, as it rarely gets into enough detail to actually explain how the components of LLM work. The diagrams / infographics included on most pages are of low quality and often confuse more than they explain. The code samples are not consistently well formatted.There is some useful information in the book. If I can’t find anything better I will revisit, but for now the book’s issues are too much.