Shypd.aiThe largest open index of AI toolsStanford CME 295 Transformers & Large Language Models is a research & education tool that provides a VIP cheatsheet for the course. It summarizes important notions on transformers, self-attention, architecture, and various LLM concepts.
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Stanford CME 295 Transformers & Large Language Models offers a comprehensive VIP cheatsheet for the Stanford CME 295 course. This resource condenses key concepts related to Transformers and Large Language Models, including self-attention mechanisms, architectural variants, and optimization techniques like sparse attention and flash attention. It also covers LLM-specific topics such as prompting, fine-tuning (SFT, LoRA), preference tuning, and optimization methods like mixture of experts, distillation, and quantization. The cheatsheet extends to practical applications like LLM-as-a-judge, RAG, agents, and reasoning models, making it an invaluable study aid for students and researchers.
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