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How to compare how current LLM architectures are built with LLM Architecture From Scratch

Use the Implemented table to contrast normalization, positional encoding, and attention across models.

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

Who it is forAn ML engineer or student who has read about transformers and wants to see concrete design differences between recent open models before choosing or explaining one.

The problemArchitecture descriptions in papers and blog posts are scattered, and it is hard to line up choices like RMSNorm versus LayerNorm or RoPE versus learned absolute positions.

The outcomeA short side-by-side comparison of at least two models, with each design choice tied to a folder of code in the repo.

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 https://github.com/YeonwooSung/llm-architecture-from-scratch and find the Implemented table.
  2. Note the Normalization, Positional Encoding, Attention, and Mixture of Experts columns for each row.
  3. Pick two rows with different attention types, for example Llama3-8B and DeepSeek v3.
  4. Open the linked folders for both models and read the model code side by side.
  5. Write a one-page comparison that cites the table cell and the code location for each design choice.

Prompt for your agent

Using the repo YeonwooSung/llm-architecture-from-scratch, compare the rows for <model A> and <model B> in the Implemented table. Output a table of normalization, positional encoding, attention type, and Mixture of Experts use. Verify each cell against the README row before reporting.

Caveat. The README is a table of design choices and does not describe training results, so the comparison covers architecture only.

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