 Название: Designing Large Language Model Applications: A Holistic Approach to LLMs
Автор: Suhas Pai
Издательство: O’Reilly Media, Inc.
Год: 2025
Страниц: 431
Язык: английский
Формат: epub
Размер: 10.1 MB
Large language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note. But transitioning from demos and prototypes to full-fledged applications can be difficult. This book helps close that gap, providing the tools, techniques, and playbooks that practitioners need to build useful products that incorporate the power of language models.
Experienced ML researcher Suhas Pai offers practical advice on harnessing LLMs for your use cases and dealing with commonly observed failure modes. You’ll take a comprehensive deep dive into the ingredients that make up a language model, explore various techniques for customizing them such as fine-tuning, learn about application paradigms like RAG (retrieval-augmented generation) and agents, and more.
Plenty of software frameworks have emerged that enable rapid prototype development of LLM applications. However, advancing from prototypes to production-grade applications is a road much less traveled, and is still a very challenging task. This is where this book comes in—a holistic overview of the LLM landscape that provides you with the intuition and tools to build complex LLM applications.
With this book, my goal is to provide you with an intuitive understanding of how LLMs work, the tools you have at your disposal to harness them, and the various application paradigms they can be built with. Unique to this book are the exercises; more than 80 exercises are sprinkled throughout to help you solidify your intuitions and sharpen your understanding of what is happening underneath the hood. While preparing the content of the book, I read over 800 research papers, with many of them referenced and linked at appropriate locations in the book, providing you with a jumping off point for further exploration. All in all, I am confident that you will come out of the book an LLM expert if you read the book in its entirety, complete all the exercises, and explore the recommended references.
Understand how to prepare datasets for training and fine-tuning
Develop an intuition about the Transformer architecture and its variants
Adapt pretrained language models to your own domain and use cases
Learn effective techniques for fine-tuning, domain adaptation, and inference optimization
Interface language models with external tools and data and integrate them into an existing software ecosystem
Who This Book Is For:
This book is intended for a broad audience, including software engineers transitioning to AI application development, Machine Learning practitioners and scientists, and product managers. Much of the content in this book is borne from my own experiments with LLMs, so even if you are an experienced scientist, I expect you will find value in it. Similarly, even if you have very limited exposure to the world of AI, I expect you will still find the book useful for understanding the fundamentals of this technology.
The only prerequisites for this book are knowledge of Python coding and an understanding of basic Machine Learning and Deep Learning principles. Where required, I provide links to external resources that you can use to sharpen or develop your prerequisites.
Contents:
Preface
I. LLM Ingredients
1. Introduction
2. Pre-Training Data
3. Vocabulary and Tokenization
4. Architectures and Learning Objectives
II. Utilizing LLMs
5. Adapting LLMs to Your Use Case
6. Fine-Tuning
7. Advanced Fine-Tuning Techniques
8. Alignment Training and Reasoning
9. Inference Optimization
III. LLM Application Paradigms
10. Interfacing LLMs with External Tools
11. Representation Learning and Embeddings
12. Retrieval-Augmented Generation
13. Design Patterns and System Architecture
Index
Скачать Designing Large Language Model Applications: A Holistic Approach to LLMs
|
Навигация
Вход на сайт
Реклама
Designing Large Language Model Applications: A Holistic Approach to LLMs 
