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Check out the documentation for more information.
T2 Residue Classifier
This is a Tier 2 experiment using output_base: "p". The model emits a single
base-p digit, so the task is a learned residue classification problem rather
than fixed-width decimal digit generation.
Inference-time preprocessing reduces each operand separately modulo p, matching
the representation normalization used by the reference neural baselines. The
model then uses learned p/residue embeddings, an MLP scorer, and a learned
low-rank bilinear residue-product head to emit logits over residues 0..255.
It does not compute (a*b) mod p at inference time in Python or tensor code.
Train locally:
.\.venv\Scripts\python.exe .\my-t2-model\train.py --minutes 10
GPU full-table continuation:
.\.venv\Scripts\python.exe .\my-t2-model\train.py --minutes 8 --resume --full-table --batch 8192 --bilinear-dim 128
Evaluate locally:
.\.venv\Scripts\modchallenge.exe check .\my-t2-model
.\.venv\Scripts\modchallenge.exe evaluate .\my-t2-model --total 110
.\.venv\Scripts\modchallenge.exe evaluate .\my-t2-model --total 1100
For a minimal HuggingFace submission, keep manifest.json, model.py,
weights.pt, and this README. train.py is development-only.