斯坦福从零手搓大模型课程
为什么值得看
CS336是斯坦福2024年新课，带学生完整实现一个GPT级别的语言模型，涵盖预训练、SFT、RLHF全流程。适合想深入理解Transformer底层而非只调API的工程师，课程材料和代码已开源。
编辑判断
这门课的价值在于填补了'懂理论'和'能训练'之间的断层。市面上大多数教程停在调用HuggingFace接口，而CS336直接让你手写CUDA kernel和分布式训练逻辑。
对比Andrej Karpathy的nanoGPT，这门课更系统化：nanoGPT是100行教学代码，CS336是工业级完整pipeline。如果你团队正在考虑自研基座模型而非直接微调开源模型，这门课的训练细节（数据配比、学习率调度、checkpoint策略）能帮你避开很多踩坑成本。
课程作业设计很硬核，据说完成全部作业相当于独立复现了一个7B模型的训练过程。建议优先看Lecture 12之后的RLHF部分，这块公开的高质量中文资料极少。
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核心争论：课程质量获认可，但外部自学者能否负担GPU算力成争议焦点
Thanks for releasing this again! What are this year's changes to prior offerings?
TA here. Biggest changes are in the second assignment (distributed) where we added a bunch of memory, profiling and distributed tasks, as well as in the fifth assignment (alignment), where most of the RL tasks are fresh this year. Assignment 3 (scaling laws) was also completely updated, but in a way
I have fond memories of cs224d [1] taught by richardsocher. It’s a bit dated at this point as it was created in the pre-transformer era, but it was a very cool introduction to applying deep learning to nlp at the time. [1] https://cs224d.stanford.edu