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How to trace attention variants from code to table rows with LLM Architecture From Scratch

Map the attention types in the table, such as GQA, sliding window, and MLA, to their implementations.

  • Research
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Who it is forA developer preparing a talk or internal review on efficient attention who needs working code examples for each variant.

The problemTerms like Grouped-Query Attention, sliding window, and Multi-head Latent Attention are easy to mix up without seeing how each one is written.

The outcomeA map from each attention label in the table to the folder that implements it, with notes on the main code differences.

What you need first

  • Python, as listed for the repo
  • PyTorch, as stated in the repo description
  • Basic familiarity with transformer attention and normalization layers

Steps

  1. Open the Attention column of the Implemented table in the repo README.
  2. Group the rows by attention label: Multi-Head Self-Attention, Grouped-Query Attention, Sliding window + GQA, Multi-head Latent Attention, mLSTM, and GQA.
  3. Open one folder per label, starting with the gpt2-xl and llama3-8b folders.
  4. Compare how query, key, and value heads are shaped in each folder.
  5. Record the differences in a short table for the talk or review.

Prompt for your agent

In the repo YeonwooSung/llm-architecture-from-scratch, find the folder for <attention type> from the Attention column and summarize how its heads are built. Output a bullet list of code-level differences from Grouped-Query Attention. Verify the folder name matches the README link before reporting.

Caveat. The repo does not document setup or run commands, so check each folder for its own instructions before running any code.

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