| | --- |
| | license: mit |
| | tags: |
| | - chess |
| | - game-ai |
| | - pytorch |
| | - safetensors |
| | library_name: transformers |
| | datasets: |
| | - Maxlegrec/ChessFENS |
| | --- |
| | |
| | # ChessBot Chess Model |
| |
|
| | 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. |
| | For stronger play, reducing temperature T (lower is stronger) is suggested. |
| |
|
| | ## Model Description |
| |
|
| | The ChessBot model is a transformer-based architecture designed for chess gameplay. It can: |
| | - Predict the next best move given a chess position (FEN) |
| | - Evaluate chess positions |
| | - Generate move probabilities |
| |
|
| | ## Please Like if this model is useful to you :) |
| |
|
| | A like goes a long way ! |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | import torch |
| | from transformers import AutoModel |
| | |
| | model = AutoModel.from_pretrained("Maxlegrec/ChessBot", trust_remote_code=True) |
| | device = "cuda" if torch.cuda.is_available() else "cpu" |
| | model = model.to(device) |
| | |
| | # Example usage |
| | fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1" |
| | |
| | # Sample move from policy |
| | move = model.get_move_from_fen_no_thinking(fen, T=0.1, device=device) |
| | print(f"Policy-based move: {move}") |
| | #e2e4 |
| | |
| | # Get the best move using value analysis |
| | value_move = model.get_best_move_value(fen, T=0, device=device) |
| | print(f"Value-based move: {value_move}") |
| | #e2e4 |
| | |
| | # Get position evaluation |
| | position_value = model.get_position_value(fen, device=device) |
| | print(f"Position value [black_win, draw, white_win]: {position_value}") |
| | #[0.2318, 0.4618, 0.3064] |
| | |
| | # Get move probabilities |
| | probs = model.get_move_from_fen_no_thinking(fen, T=1, device=device, return_probs=True) |
| | top_moves = sorted(probs.items(), key=lambda x: x[1], reverse=True)[:5] |
| | print("Top 5 moves:") |
| | for move, prob in top_moves: |
| | print(f" {move}: {prob:.4f}") |
| | #Top 5 moves: |
| | # e2e4: 0.9285 |
| | # d2d4: 0.0712 |
| | # g1f3: 0.0001 |
| | # e2e3: 0.0000 |
| | # c2c3: 0.0000 |
| | ``` |
| |
|
| | ## Requirements |
| |
|
| | python-version <=3.11 |
| | cuda-version < 13.0 |
| |
|
| | - torch>=2.0.0 |
| | - transformers>=4.48.1 |
| | - python-chess>=1.10.0 |
| | - numpy>=1.21.0 |
| |
|
| | ## Model Architecture |
| |
|
| | The architecture is strongly inspired from the LCzero project. Although written in pytorch. |
| |
|
| | - **Transformer layers**: 10 |
| | - **Hidden size**: 512 |
| | - **Feed-forward size**: 736 |
| | - **Attention heads**: 8 |
| | - **Vocabulary size**: 1929 (chess moves) |
| |
|
| | ## Training Data |
| |
|
| | 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. |
| |
|
| | ## Limitations |
| |
|
| | - The model works best with standard chess positions |
| | - Performance may vary with unusual or rare positions |
| | - Requires GPU for optimal inference speed |
