RockyGPT

A nanoGPT-style character-level model trained on Project Hail Mary Rocky/Grace dialogue. Not a product. A forcing function to understand a forward pass, weight memory layout, softmax numerics, and quantization cost — concretely, with real numbers, not just assumed.

Status: not started. Side project — runs alongside seatorch once C fundamentals are done, not blocked on seatorch shipping.

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Goal

Generate coherent-ish Rocky/Grace-style dialogue, and walk away with a real, measured INT8 quantization accuracy-drop number — not a guess.

Milestones

1. Data pipeline — extract Rocky/Grace dialogue from novel + transcript, careful speaker attribution, clean corpus.
2. Model — adapt nanoGPT, character-level, small config appropriate to corpus size.
3. Training — train to convergence on the small corpus, log loss curves.
4. Quantization experiment — run torch.quantization.quantize_dynamic, measure and record the actual accuracy drop.

Ship checklist

• Repo with data pipeline + training script
• Writeup including the actual quantization number (before/after accuracy) — this is the point of the project, don't skip it
• Optional: r/MachineLearning if the writeup has a sharp angle

What this is explicitly not

A product, a polished chatbot, or something expected to generalize past the small corpus. Overfitting is expected, not a failure to fix.

Decision log

• Fill in as you go: character-level vs token-level, why, config chosen.