Overview

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, and hands-on exercises. 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), 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 building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks ""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 Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

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9780135346563

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Reviews

""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 Hugging Face ""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 stepping stones 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 ""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

"""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 Hugging Face ""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 stepping stones 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 ""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"

Author Information

Sinan Ozdemir is currently the founder and CTO of LoopGenius and an advisor to several AI companies. 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.

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