Название: Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs, Second Edition (Early Release)
Автор: Sinan Ozdemir
Издательство: Addison-Wesley Professional/Pearson Education
Год: 2024
Страниц: 139
Язык: английский
Формат: epub (true)
Размер: 14.9 MB

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, 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, parameters, and performance.

In the second edition, readers will find comprehensive updates and new chapters that reflect the latest advancements in the field. In addition to updating existing code to meet current versions and expectations, this edition significantly expands content on Retrieval-Augmented Generation and AI Agents and introduces new chapters dedicated to manual and automated methods for evaluating LLMs, as well as alignment principles, highlighting the differences and implications of instructional versus value alignment. Additionally, more examples of fine-tuning larger models are included, and all code and model references have been updated to include the latest package versions and AI models like Llama 3 and Mistral v0.2 ensuring the new edition remains at the cutting edge of LLM technology.

Large language models (LLMs) are AI models that are usually (but not necessarily) derived from the Transformer architecture and are designed to understand and generate human language, code, and much more. These models are trained on vast amounts of text data, allowing them to capture the complexities and nuances of human language. LLMs can perform a wide range of language-related tasks, from simple text classification to text generation, with high accuracy, fluency, and style.

My goal is to guide you on how to use, train, and optimize all kinds of LLMs for practical applications while giving you just enough insight into the inner workings of the model to know how to make optimal decisions about model choice, data format, fine-tuning parameters, and so much more. My aim is to make use of Transformers accessible for software developers, data scientists, analysts, and hobbyists alike. To do that, we should start on a level playing field and learn a bit more about LLMs.

More content on RAG and AI Agents

A new chapter on evaluating LLMs both manually and automatically

A new chapter on alignment principles (instructional versus value alignment, etc.)

General updates so all code is more current (using the latest package versions + AI models, like Llama 3, etc.)

Includes more content on fine-tuning principles








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