| # 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. |
|
|
