| Metric | Value |
|---|---|
| Parameters | 47,450,753 |
| Best Val Loss | 0.030174 |
| Architecture | SE-ResNet-20 |
| Input | 18x8x8 board tensor |
| Value Output | [-1, 1] (tanh) |
| Policy Output | 20,480 move logits (from-square x to-square x underpromotion) |
| Source | Size |
|---|---|
| Kaggle Chess Evaluations | 12.9M positions (Stockfish evals) |
| Lichess 2023-10 | 2M moves from ELO 2000+ games |
pip install torch python-chess huggingface_hub numpy1from inference import load_model, get_best_move
2import chess
3
4model = load_model()
5value, moves = get_best_move(model, chess.STARTING_FEN, top_k=3)
6print(value, moves)| File | Purpose |
|---|---|
| model_weights.pt (190MB) | Inference weights: use this to load the model |
| checkpoint.pt (569MB) | Full training checkpoint (model + optimizer + scheduler state): only needed to resume training |
| config.json | Architecture config |
| inference.py | Minimal working inference example |
| training_curves.png | Training/validation loss curves |