Overview

The remarkable progress of artificial intelligence in recent years has been driven in large part by the development of large language models, with Generative Pre-trained Transformers (GPT) standing at the forefront of this transformation. GPT models have reshaped how machines understand, generate, and interact with human language, enabling applications that span education, software development, scientific research, and intelligent systems. Despite their widespread adoption, many practitioners still interact with these models as end users, without fully understanding how they are designed, trained, and deployed. This book, Step-by-Step Instructions for Building and Training a GPT Model, was written to demystify GPT technology and to provide readers with a clear, practical pathway from foundational concepts to real-world implementation. The goal is not only to explain what GPT models do, but also how and why they work. By combining theoretical insight with hands-on guidance, this book empowers readers to move from using pre-trained models to building, training, refining, and deploying their own GPT-based systems. The chapters are organized to follow the natural lifecycle of a GPT model. The book begins by exploring the architectural foundations of GPT, including attention mechanisms, transformers, and autoregressive modeling. It then guides readers through essential data preparation and tokenization techniques, which form the backbone of effective language modeling. Subsequent chapters focus on constructing a GPT model from scratch, pre-training it on large-scale data, and fine-tuning it for specialized tasks. Later chapters address the critical aspects of evaluation, testing, and optimization, helping readers understand how to measure model performance, improve efficiency, and ensure robustness. The final chapter brings the journey full circle by demonstrating how to deploy and integrate GPT models into real applications, bridging the gap between research and practical use. This book is intended for students, researchers, engineers, and professionals who seek a structured, step-by-step approach to mastering GPT models. While a basic background in programming and machine learning is helpful, the material is presented progressively to support learners at different levels. Throughout the book, emphasis is placed on clarity, reproducibility, and best practices, enabling readers to build confidence as they advance. Ultimately, this book aims to foster deeper understanding, creativity, and responsible innovation in the field of generative artificial intelligence. By equipping readers with both conceptual knowledge and practical skills, it aspires to support the next generation of AI practitioners in shaping intelligent systems that are powerful, transparent, and impactful.

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