This repository provides a condensed VIP cheatsheet for Stanford’s CME 295 Transformers and Large Language Models course. It covers key concepts including self-attention mechanisms, transformer architecture variants, and optimization techniques such as sparse attention and low-rank attention. The materials are available in 15 languages to serve a global audience of learners.