Upload chess policy network model

README.md

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 ---
+tags:
+- chess
+- reinforcement-learning
+- pytorch
+- neural-network
+language:
+- en
 license: mit
 ---
+
+# Chess Policy Network
+
+A neural network-based chess engine that uses a policy network to predict the best moves.
+
+## Model Details
+
+- **Architecture**: Convolutional ResNet with policy head
+- **Input**: 12-channel board representation (piece positions for each color)
+- **Output**: 4672 move logits
+- **Total Parameters**: 5,827,456
+
+## Architecture Configuration
+
+```python
+input_channels: 12
+num_res_blocks: 8
+filters: 128
+policy_channels: 32
+num_move_classes: 4672
+dropout: 0.1
+activation: relu
+```
+
+## Training Data
+
+- Trained on high-ELO chess games (minimum rating: 2200+)
+- Supervised learning on master games
+- Cross-entropy loss with AdamW optimizer
+- Cosine annealing learning rate schedule
+
+## Usage
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from training.models import PolicyNetwork
+from training.utils import encode_board
+
+# Load model
+model_path = hf_hub_download("rzhang-7/chesshacks-model", "pytorch_model.bin")
+model = PolicyNetwork.from_config(config)
+model.load(model_path)
+model.eval()
+
+# Get move predictions for a position
+board_tensor = encode_board(board)
+with torch.no_grad():
+    logits = model(board_tensor.unsqueeze(0))
+
+# Convert to legal moves
+move_probs = filter_policy_to_legal(logits[0].numpy(), board)
+```
+
+## Performance
+
+Evaluate with:
+```bash
+python training/scripts/evaluate.py --model-path path/to/model.pt --data-dir training/data
+```
+
+## License
+
+MIT
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@model{chess_policy_net,
+  title={Chess Policy Network},
+  author={Chess Hacks},
+  year={2024},
+  url={https://huggingface.co/rzhang-7/chesshacks-model}
+}
+```
+
+## Disclaimers
+
+- This model is trained on historical chess games and may reflect biases in those games
+- The model is provided as-is without guarantees
+- For competitive chess, consider using dedicated engines like Stockfish or Leela Chess Zero

config.json

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+{
+  "input_channels": 12,
+  "num_res_blocks": 8,
+  "filters": 128,
+  "policy_channels": 32,
+  "num_move_classes": 4672,
+  "dropout": 0.1,
+  "activation": "relu"
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b583fa9cb5da9ac848f545e54c08962515e2d24856a07ff49d91a92abbab106a
+size 23365825