Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 6 — Pruning and Knowledge Distillation
Sixth post of the LLM Primer VI walkthrough. Structured vs unstructured pruning, 2:4 sparsity on Hopper, and the distillation lineage from soft probabilities to Patient Knowledge Distillation and MiniLLM.
2026-04-28
Chapter 9 — Managing the Attention Budget
Ninth post of the LLM Primer IV walkthrough. Context rot, the lost-in-the-middle cliff, tool-loadout rot, and the three architectural answers — MCP, RAG, fine-tuning — to the question of where a model's missing knowledge actually belongs.
2026-04-07
Chapter 14 — Practical Knowledge for Engineers
Twelfth post — the closing chapter of the LLM Primer II walkthrough. How to keep deepening your understanding after the book ends, the tools and libraries that turn the math into shipping work, and the bridge to the other books in the LLM Primer series.
2026-03-16
7.2 Resource-Efficient Training
A preview from Chapter 7.2: Learn how techniques like distillation, quantization, distributed training, and data efficiency make LLMs faster, cheaper, and greener.
2024-10-08