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bc52904 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | # 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:
```powershell
.\.venv\Scripts\python.exe .\my-t2-model\train.py --minutes 10
```
GPU full-table continuation:
```powershell
.\.venv\Scripts\python.exe .\my-t2-model\train.py --minutes 8 --resume --full-table --batch 8192 --bilinear-dim 128
```
Evaluate locally:
```powershell
.\.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.
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