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README.md

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+---
+tags:
+- chess
+- reinforcement-learning
+- mcts
+- game-playing
+license: mit
+---
+
+# Checkmate Chess Engine
+
+A neural network trained to play chess using MCTS (Monte Carlo Tree Search) guidance.
+
+## Model Description
+
+This model evaluates chess positions and suggests moves. It outputs:
+- **Policy (P)**: Probability distribution over legal moves
+- **Value (V)**: Position evaluation from -1 (losing) to +1 (winning)
+
+## Architecture
+
+- Input: 773-dimensional board encoding (pieces, turn, castling rights)
+- Hidden layers: 3x512 with ReLU + BatchNorm + Dropout
+- Output heads:
+  - Policy head: 4672-dim output (all possible moves)
+  - Value head: Single scalar (-1 to +1)
+
+## Training Data Format
+
+The model is trained on game positions with format:
+```json
+{"fen": "...", "move": "e2e4", "value": -1}
+```
+
+## Usage
+
+```python
+from inference import ChessModelInference
+import chess
+
+# Load model
+model = ChessModelInference("checkmate_model.pt")
+
+# Get predictions
+board = chess.Board()
+P, V = model.predict(board.fen(), board)
+
+print(f"Position value: {V}")
+print(f"Best moves: {sorted(P.items(), key=lambda x: x[1], reverse=True)[:3]}")
+```
+
+## Integration with MCTS
+
+This model is designed to work with MCTS for move selection. The policy priors guide
+the search, while value estimates help evaluate unvisited positions.
+
+## License
+
+MIT License