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--- |
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license: mit |
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tags: |
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- chess |
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- game-ai |
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- pytorch |
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- safetensors |
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library_name: transformers |
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datasets: |
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- Maxlegrec/ChessFENS |
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--- |
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# ChessBot Chess Model |
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This is a ChessBot model for chess move prediction and position evaluation. This model is way worse than stockfish. It is better than most humans however. |
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For stronger play, reducing temperature T (lower is stronger) is suggested. |
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## Model Description |
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The ChessBot model is a transformer-based architecture designed for chess gameplay. It can: |
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- Predict the next best move given a chess position (FEN) |
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- Evaluate chess positions |
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- Generate move probabilities |
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## Please Like if this model is useful to you :) |
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A like goes a long way ! |
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## Usage |
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```python |
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import torch |
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from transformers import AutoModel |
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model = AutoModel.from_pretrained("Maxlegrec/ChessBot", trust_remote_code=True) |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model = model.to(device) |
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# Example usage |
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fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1" |
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# Sample move from policy |
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move = model.get_move_from_fen_no_thinking(fen, T=0.1, device=device) |
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print(f"Policy-based move: {move}") |
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#e2e4 |
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# Get the best move using value analysis |
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value_move = model.get_best_move_value(fen, T=0, device=device) |
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print(f"Value-based move: {value_move}") |
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#e2e4 |
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# Get position evaluation |
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position_value = model.get_position_value(fen, device=device) |
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print(f"Position value [black_win, draw, white_win]: {position_value}") |
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#[0.2318, 0.4618, 0.3064] |
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# Get move probabilities |
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probs = model.get_move_from_fen_no_thinking(fen, T=1, device=device, return_probs=True) |
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top_moves = sorted(probs.items(), key=lambda x: x[1], reverse=True)[:5] |
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print("Top 5 moves:") |
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for move, prob in top_moves: |
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print(f" {move}: {prob:.4f}") |
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#Top 5 moves: |
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# e2e4: 0.9285 |
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# d2d4: 0.0712 |
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# g1f3: 0.0001 |
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# e2e3: 0.0000 |
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# c2c3: 0.0000 |
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``` |
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## Requirements |
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- torch>=2.0.0 |
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- transformers>=4.48.1 |
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- python-chess>=1.10.0 |
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- numpy>=1.21.0 |
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## Model Architecture |
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The architecture is strongly inspired from the LCzero project. Although written in pytorch. |
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- **Transformer layers**: 10 |
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- **Hidden size**: 512 |
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- **Feed-forward size**: 736 |
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- **Attention heads**: 8 |
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- **Vocabulary size**: 1929 (chess moves) |
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## Training Data |
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This model was trained on training data from the LCzero project. It consists of around 750M chess positions. I will publish the training dataset very soon. |
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## Limitations |
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- The model works best with standard chess positions |
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- Performance may vary with unusual or rare positions |
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- Requires GPU for optimal inference speed |
