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
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Usage
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