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

by Sinan Ozdemir

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 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 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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Genres: Nonfiction, Artificial Intelligence

288 pages, Kindle Edition

Published September 20, 2023

Reviews

[Walter Ullon · Rating 2 out of 5]

Not the most didactic book I've ever read, which is disappointing considering how much I enjoyed the author's previous books.

The beginning chapters do a great job explaining the basics of LLMs, but the rest of the book jumps around too much to offer a cohesive exposition of the concepts, to my liking, that is.

I deduct one star cause the repo is not structured in a way I would ever want to share with the public.

I will not be recommending this one.

[Paul Macovei · Rating 2 out of 5]

For somebody already working in/involved in this space, this book doesn't add much novel insights. It's overly focused on implementation details and model creation steps, topics which are already covered many times over by actual providers/platforms (eg HuggingFace) or web tutorials. I was expecting more analysis of using LLM's in production, but I guess that's still a nascent area.

[Paweł Rusin · Rating 4 out of 5]

Very good introduction to using LLM's, explaining both theory and practical aspects of the Large Language Models.

[Lucille Nguyen · Rating 4 out of 5]

Provides a basic overview of implementing LLMs, not terribly complex but a good introduction. Accomplishes what it seeks out to do.