Dilyan Grigorov | Building Large Language Models from Scratch. Design, Train, and Deploy LLMs with PyTorch (2026) [PDF, EPUB]
Автор: Dilyan Grigorov
Издательство: Apress
ISBN: 979-8-8688-2297-1, 979-8868822964
Жанр: Statistics, Artificial Intelligence, Python Programming
Язык: Английский
Формат: PDF, EPUB
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание:This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)—from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.
Starting from the essentials, you’ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You’ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level—an essential skill for scaling real-world LLMs. You’ll also gain mastery over the phases of training that define today’s leading models:
Pretraining - Building general linguistic and semantic understanding.
Midtraining - Expanding domain-specific capabilities and adaptability.
Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.
Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.
The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.
By the end of this book, you’ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.
What You’ll Learn
How to configure and optimize your development environment using PyTorch
The mechanics of tokenization, embeddings, normalization, and attention mechanisms.
How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.
How to integrate custom CUDA kernels to accelerate transformer computations.
The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.
Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.
How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.
Who this book is for:
Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch.
About the Author xxi
About the Technical Reviewer xxiii
Introduction xxv
Chapter 1: What Is a Large Language Model? Getting Started with Libraries and Environment Setup for Building an LLM from Scratch 1
Chapter 2: Foundational Concepts in LLM Development 47
Chapter 3: Building a Tokenizer for the Transformers Architecture Model 75
Chapter 4: RMS Normalization and Model Configuration 133
Chapter 5: Rotary Positional Embeddings: Integrating NTK and YaRN Scaling 161
Chapter 6: Scaled Dot-Product Attention Core—Sliding Window and Grouped Query Attention—The Core Behind All Transformer Models 207
Chapter 7: AttentionBlock with Rotary Embedding, GQA, Sliding Window, and Sink Tokens 241
Chapter 8: Multilayer Perceptron Block with Mixture of Experts (MoE) and SwiGLU 277
Chapter 9: Transformer Block and Full Transformer Model—It’s Time to Put the Puzzle Together 325
Chapter 10: Dataset Preparation, Model Training, Token Generator for Inference and Prompting—The BIG Moment 367
Chapter 11: Advanced Training and CUDA Kernels 453
Index 513
Скриншоты:
Время раздачи: с 10 до 20 (минимум до появления первых 3-5 скачавших) 
