weiruihhh/cs336_note_and_hw resource

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What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of personal course notes and completed homework solutions for Stanford CS336: Building Large Language Models from scratch. It covers Transformer fundamentals, training optimization with Triton and Flash Attention, scaling laws, data cleaning pipelines, and RL-based alignment methods like GRPO and DPO.

Use cases

• study notes for stanford cs336 building large language models
• learn how to build an llm from scratch
• reference solutions for cs336 assignments
• understand flash attention and triton kernel implementation
• learn scaling laws for large language models
• implement GRPO and DPO for LLM alignment
• learn the LLM training data cleaning pipeline

When to choose

• you are taking or self-studying Stanford CS336 and want notes and worked assignments
• you want a structured path to building LLM components from scratch in PyTorch
• you want reference implementations of GRPO, DPO, or Flash Attention exercises

When to avoid

• you need a production-ready LLM training framework
• you want fully polished, complete notes - the author states several chapters are unfinished
• you need commercially licensed material - it is CC BY-NC-SA 4.0, non-commercial only

Facets

learning-resource · maturity active

llm-training deep-learning machine-learning prompt-engineering large-language-models deep-learning tutorials machine-learning python cs336 stanford course-notes homework-solutions transformers pytorch triton flash-attention scaling-laws grpo dpo reinforcement-learning data-cleaning tokenizer

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