Language Models In-Depth Bundle

Modern large language models perform astonishing computations -- writing code, solving math, translating between languages -- yet their internal mechanisms remain largely opaque. This book takes you inside the black box. From the mathematical foundations of neural computation to the cutting edge of mechanistic interpretability research, you will learn how to reverse-engineer the algorithms transformers have learned during training. You will understand how attention heads discover structural patterns, how features emerge and compete in the residual stream, how sparse autoencoders decompose polysemantic neurons into human-readable concepts and how causal intervention methods trace information through computational circuits. Along the way, you will write real code to analyze actual models, reproduce landmark results and develop the skills to conduct independent interpretability research. This is not about what models can do. It is about how they do it.

Mixture-of-Experts models have emerged as the dominant architecture for scaling language models to hundreds of billions and even trillions of parameters while maintaining tractable inference costs. Yet their massive parameter counts create significant memory and deployment challenges. Expert pruning: the systematic removal of redundant or low-contribution experts: offers a direct path to reducing model size without requiring complete retraining. This book provides a rigorous, implementation-focused treatment of expert pruning in MoE models: the mathematical foundations, the complete landscape of scoring and selection methods, the architectural subtleties that determine success or failure, verified open-source tooling and complete working code for loading, analyzing, pruning and evaluating MoE models. By the end, you will be able to implement expert-pruning systems independently, diagnose failures, adapt methods to unfamiliar architectures and make principled decisions about when and how to prune experts in production.

Large language models have become too big to run on a single GPU. Quantization is the technology that makes them deployable, and it has evolved from an academic curiosity into an engineering discipline with its own algorithms, toolchains, and best practices. This book explains how LLM quantization works, from first principles through production deployment. You will learn the internals of GPTQ, AWQ, GGUF, NF4, and other methods; understand their trade-offs in quality, speed, and memory; and find clear guidance for choosing the right approach for your hardware and use case. Along the way you will encounter real benchmarks, reproducible code examples, and a decision framework for navigating one of the most rapidly evolving areas of applied machine learning.

This book teaches engineers how to build reliable evaluation systems for small and locally deployed language models. You will learn to measure performance accurately, compare models across hardware and runtimes, interpret results without being misled by benchmarks, and make evidence-based deployment decisions for real-world workloads. The guide combines rigorous experimental methodology with example code so you can independently establish an evaluation laboratory and produce trustworthy, reproducible results.