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 levelan 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.

Covers from setup to deployment, making it a one-stop guide for building LLMs Focuses on efficiency, covering GPU memory management, debugging, and performance tuning Equips readers with expertise in fine-tuning, transfer learning, and prompt engineering for AI careers

Autorentext

Dilyan Grigorov is a software developer with a passion for Python software development, generative deep learning & machine learning, data structures, and algorithms. He is an advocate for open source and the Python language itself. He has 16 years of industry experience programming in Python and has spent 5 of those years researching and testing Generative AI solutions. His passion for them stems from his background as an SEO specialist dealing with search engine algorithms daily. He enjoys engaging with the software community, often giving talks at local meetups and larger conferences. In his spare time, he enjoys reading books, hiking in the mountains, taking long walks, playing with his son, and playing the piano.

Inhalt

Chapter 1: What is a Large Language Model? Getting Started with Libraries and Environment Setup For Building an LLM from Scratch.- Chapter 2: Foundational Concepts in LLM Development.- Chapter 3: Building a Tokenizer For Transformers Architecture Model.- Chapter 4: RMS Normalization and Model Configuration.- Chapter 5: Rotary Positional Embeddings: Integrating NTK and YaRN Scaling.- Chapter 6: Scaled Dot-Product Attention Core - Sliding Window and Grouped Query Attention - he Core Behind All Transformer Models.- Chapter 7: AttentionBlock with Rotary Embedding & GQA & Sliding Window & Sink Tokens.- Chapter 8: MultiLayer Perceptron Block with Mixture of Experts (MoE) and SwiGLU.- Chapter 9: Transformer Block & Full Transformer Model - It's Time To Put The Puzzle Together.- Chapter 10: Dataset Preparation, Model Training, TokenGenerator for Inference & Prompting - The BIG Moment.- Chapter 11: Advanced Training and CUDA Kernels.