Building Large Language Models: Step-by-Step Guide to Modern Architectures, Optimization, and Deployment

Master the science - and engineering - behind the world's most powerful AI systems. In Building Large Language Models, you'll learn how today's most advanced AIs - from GPT to LLaMA and Mistral - are trained, optimized, and deployed. This isn't another surface-level overview. It's a hands-on, code-driven roadmap that takes you from the raw foundations of language modeling to the complete design and scaling of production-grade LLMs. Written by an expert engineer and author in practical AI systems, this guide combines clarity, technical precision, and real-world experience. Each chapter builds on the last - teaching you how to go from understanding transformers to constructing and deploying intelligent models that perform reliably at scale. Whether you're a developer, ML engineer, researcher, or AI enthusiast, this book will show you not just how large language models work, but how to build and optimize them yourself. Inside You'll Learn: Core Foundations: Tokenization, embeddings, attention mechanisms, and transformer architectures explained from the ground up - in plain English. Modern Architectures: Dive deep into encoder-decoder models, decoder-only transformers, state space models, and Mixture-of-Experts systems. Training and Optimization: Learn distributed training (DDP, FSDP, DeepSpeed), mixed precision, checkpointing, gradient accumulation, and FlashAttention. Fine-Tuning Techniques: Apply LoRA, adapters, and prefix/prompt tuning for domain-specific optimization. Evaluation and Scaling: Benchmark with MMLU, HellaSwag, BLEU, and ROUGE; plan data scaling and cost-efficient pipelines. Deployment at Scale: Convert models to ONNX and TensorRT, serve APIs with FastAPI + LangServe, and scale with Docker and Kubernetes. Advanced Topics: Retrieval-Augmented Generation (RAG), vector databases (FAISS, Chroma, Milvus), multimodal LLMs, and agentic AI workflows. Responsible AI: Implement ethical alignment, bias control, data governance, and sustainable model development. Each chapter includes detailed PyTorch examples, Hugging Face integrations, and mini-projects that move from concept to working code. You'll Build Hands-On Projects Including: A next-word prediction model from scratch A 100M-parameter transformer trained with modern optimization techniques A domain-specific LLM fine-tuned using LoRA A fully functional RAG assistant with embeddings and vector search A production-ready inference API powered by FastAPI and Docker Every project mirrors real-world workflows used in research labs and enterprise AI engineering teams - ensuring what you learn is directly transferable to your professional or academic work. Who This Book Is For This book is for software engineers, AI developers, ML practitioners, data scientists, and researchers who want a complete, practical understanding of LLMs - from architecture to deployment. If you've used ChatGPT or Hugging Face models but want to learn how they're actually built, this book is your gateway. Basic Python and PyTorch knowledge is helpful, but everything is explained step by step, with runnable code and clear guidance for every stage. Why Readers Love This Book Deep yet approachable: Explains complex architectures in plain language without skipping the math or mechanics. Truly hands-on: Every concept comes with code and real datasets - not just theory. Up to date: Covers the latest trends in LLM design - from FlashAttention to MoE and multimodal transformers. Future-ready: Includes advanced insights on agents, long-context memory, sustainability, and LLM governance. Empower yourself to move beyond using AI - to building it.