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This repository aims at summing up in the same place all the important notions that are covered in Stanford's CME 295 Transformers & Large Language Models course. It includes:
- Transformers: self-attention, architecture, variants, optimization techniques (sparse attention, low-rank attention, flash attention)
- LLMs: prompting, finetuning (SFT, LoRA), preference tuning, optimization techniques (mixture of experts, distillation, quantization)
- Applications: LLM-as-a-judge, RAG, agents, reasoning models (train-time and test-time scaling from DeepSeek-R1)
This VIP cheatsheet gives an overview of what is in the "Super Study Guide: Transformers & Large Language Models" book, which contains ~600 illustrations over 250 pages and goes into the following concepts in depth. You can find more details at https://superstudy.guide.
Afshine Amidi (Ecole Centrale Paris, MIT) and Shervine Amidi (Ecole Centrale Paris, Stanford University)
