Transformers in Action

Nicole Koenigstein

• Publisher: Manning
• Year: 2025
• Topic: Computer Science
• eBook ISBN:
  9781638358015
• Subtopic: Data Processing
• Index: Computer Science

Understand the architecture that underpins today’s most powerful AI models.

Transformers are the superpower behind large language models (LLMs) like ChatGPT, Gemini, and Claude. Transformers in Action gives you the insights, practical techniques, and extensive code samples you need to adapt pretrained transformer models to new and exciting tasks.

Inside Transformers in Action you’ll learn:

• How transformers and LLMs work
• Modeling families and architecture variants
• Efficient and specialized large language models
• Adapt HuggingFace models to new tasks
• Automate hyperparameter search with Ray Tune and Optuna
• Optimize LLM model performance
• Advanced prompting and zero/few-shot learning
• Text generation with reinforcement learning
• Responsible LLMs

Transformers in Action takes you from the origins of transformers all the way to fine-tuning an LLM for your own projects. Author Nicole Koenigstein demonstrates the vital mathematical and theoretical background of the transformer architecture practically through executable Jupyter notebooks. You’ll discover advice on prompt engineering, as well as proven-and-tested methods for optimizing and tuning large language models. Plus, you’ll find unique coverage of AI ethics, specialized smaller models, and the decoder encoder architecture.

Foreword by Luis Serrano.

About the technology

Transformers are the beating heart of large language models (LLMs) and other generative AI tools. These powerful neural networks use a mechanism called self-attention, which enables them to dynamically evaluate the relevance of each input element in context. Transformer-based models can understand and generate natural language, translate between languages, summarize text, and even write code—all with impressive fluency and coherence.

About the book

Transformers in Action introduces you to transformers and large language models with careful attention to their design and mathematical underpinnings. You’ll learn why architecture matters for speed, scale, and retrieval as you explore applications including RAG and multi-modal models. Along the way, you’ll discover how to optimize training and performance using advanced sampling and decoding techniques, use reinforcement learning to align models with human preferences, and more. The hands-on Jupyter notebooks and real-world examples ensure you’ll see transformers in action as you go.

What's inside

• Optimizing LLM model performance
• Adapting HuggingFace models to new tasks
• How transformers and LLMs work under the hood
• Mitigating bias and responsible ethics in LLMs

About the reader

For data scientists and machine learning engineers.

About the author

Nicole Koenigstein is the Co-Founder and Chief AI Officer at the fintech company Quantmate.

Table of Contents

Part 1
1 The need for transformers
2 A deeper look into transformers
Part 2
3 Model families and architecture variants
4 Text generation strategies and prompting techniques
5 Preference alignment and retrieval-augmented generation
Part 3
6 Multimodal models
7 Efficient and specialized small language models
8 Training and evaluating large language models
9 Optimizing and scaling large language models
10 Ethical and responsible large language models

Table of contents

• Transformers in Action
• copyright
• contents
• foreword
• preface
• acknowledgments
• about this book
• about the author
• about the cover illustration
• Part 1 Foundations of modern transformer models
• 1 The need for transformers
• 2 A deeper look into transformers
• Part 2 Generative transformers
• 3 Model families and architecture variants
• 4 Text generation strategies and prompting techniques
• 5 Preference alignment and retrieval-augmented generation
• Part 3 Specialized models
• 6 Multimodal models
• 7 Efficient and specialized small language models
• 8 Training and evaluating large language models
• 9 Optimizing and scaling large language models
• 10 Ethical and responsible large language models
• references