Technology and Innovation Community

Stanford's CME 295 Transformers & Large Language Models cheatsheet 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)