Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 3 - Tranformers & Large Language Models

JMM·2026년 1월 9일

Chain-of-thought, self-consistency Dense & Sparse MoE Greedy decoding & beam search Guided decoding Impact of temperature on predictions Inference optimizations with KV cache LLM definition Mixture of Experts MoE in LLMs PagedAttention, MLA Prompting strategie Response generation Sampling-based methods in-context learning

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Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 3 - Tranformers & Large Language Models
를 보고 공부한 후 노션에 정리한 내용 :

https://www.notion.so/Tranformers-Large-Language-Models-2e1f9d3ab5ce80ccb8cbd20be0d2dd88?source=copy_link

이전 포스트

Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 2 - Transformer-Based Models & Tricks

다음 포스트

Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 4 - LLM Training

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