Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs (Addison-Wesley Data & Analytics Series)

by: Sinan Ozdemir (Author)

Publisher: Addison-Wesley Professional

Edition: 1st

Publication Date: 2023/9/21

Language: English

Print Length: 288 pages

ISBN-10: 0138199191

ISBN-13: 9780138199197

Book Description

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and ProductsLarge 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).Lea key concepts: pre-training, transfer leaing, fine-tuning, attention, embeddings, tokenization, and moreUse APIs and Python to fine-tune and customize LLMs for your requirementsBuild a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generationMaster advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot promptingCustomize LLM embeddings to build a complete recommendation engine from scratch with user dataConstruct and fine-tune multimodal Transformer architectures using opensource LLMsAlign LLMs using Reinforcement Leaing 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 NeuronRegister your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

About the Author

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and ProductsLarge 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).Lea key concepts: pre-training, transfer leaing, fine-tuning, attention, embeddings, tokenization, and moreUse APIs and Python to fine-tune and customize LLMs for your requirementsBuild a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generationMaster advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot promptingCustomize LLM embeddings to build a complete recommendation engine from scratch with user dataConstruct and fine-tune multimodal Transformer architectures using opensource LLMsAlign LLMs using Reinforcement Leaing 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 NeuronRegister your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.