| --- |
| 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 |
