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| Parameters | 35.8M |
| Architecture | Decoder-only transformer (RMSNorm, SwiGLU, RoPE) |
| d_model | 512 |
| Layers | 8 |
| Heads | 8 |
| Vocabulary | 4,278 tokens (4,096 grid + 176 promotions + 5 outcomes + 1 PAD) |
| Sequence length | 256 |
| Training steps | 56.5K/100K |
| Best val loss | 3.183 (step 56,535) |
| Best val accuracy | 7.5% |
1import torch
2from pawn.config import CLMConfig
3from pawn.model import PAWNCLM
4
5cfg = CLMConfig.base()
6model = PAWNCLM(cfg)
7
8ckpt = torch.load("model.pt", map_location="cpu", weights_only=False)
9model.load_state_dict(ckpt["model_state_dict"])
10model.eval()