Transformers in Action (Final Release)

Generative AI has set up shop in almost every aspect of business and society. Transformers and Large Language Models (LLMs) now power everything from code creation tools like Copilot and Cursor to AI agents, live language translators, smart chatbots, text generators, and much more.

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.

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