weiruihhh/cs336_note_and_hw

• highabout#1

  Update the repository's short description

  原因：

  当前

  记录我在cs336学习时的笔记和作业

  复制粘贴的修复

  复制

  Stanford CS336: Building Large Language Models course notes and completed assignments, covering LLM architecture, optimization (Flash Attention), scaling laws, data cleaning, and RLHF (DPO, GRPO).

  待办完成忽略
• mediumhomepage#2

  Add the course homepage URL to the repository metadata

  原因：

  复制粘贴的修复

  复制

  https://stanford-cs336.github.io/spring2024/

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Resources for learning to build large language models from scratch, including practical assignments?

  你：未被推荐
  AI 推荐顺序：

  1. Natural Language Processing with Deep Learning (CS224N)
  2. Deep Learning Specialization by Andrew Ng (Coursera)
  3. Hugging Face Transformers Course
  4. The Annotated Transformer by Alexander Rush
  5. Let's build GPT: from scratch, in code, spelled out. by Andrej Karpathy
  6. Deep Learning for Coders with fastai & PyTorch
  7. fastai library

  AI 推荐了 7 个替代方案，却始终没点名 weiruihhh/cs336_note_and_hw。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Seeking practical examples for implementing advanced LLM techniques like Flash Attention and DPO?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Transformers Library (huggingface/transformers)
  2. `flash-attn` package (Dao-AILab/flash-attention)
  3. `trl` (Transformer Reinforcement Learning) library (huggingface/trl)
  4. `flash-attn` GitHub Repository (Dao-AILab/flash-attention)
  5. OpenAI Cookbook (openai/openai-cookbook)
  6. `trl` library (huggingface/trl)
  7. Lit-GPT (Lightning-AI/lit-gpt)
  8. DeepSpeed (microsoft/DeepSpeed)
  9. Megatron-LM (NVIDIA/Megatron-LM)

  AI 推荐了 9 个替代方案，却始终没点名 weiruihhh/cs336_note_and_hw。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of weiruihhh/cs336_note_and_hw?

  pass
  ⚠AI 未点名 weiruihhh/cs336_note_and_hw —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts weiruihhh/cs336_note_and_hw in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 weiruihhh/cs336_note_and_hw

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo weiruihhh/cs336_note_and_hw solve, and who is the primary audience?

  pass
  ⚠AI 未点名 weiruihhh/cs336_note_and_hw —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？