metadata
license: mit
tags:
- chess
- reinforcement-learning
- game-ai
- pytorch
library_name: transformers
ChessBot Chess Model
This is a ChessBot model for chess move prediction and position evaluation.
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
Usage
import torch
from huggingface_hub import snapshot_download
# Download the model files
model_path = snapshot_download(repo_id="Maxlegrec/ChessBot")
# Add to path and import
import sys
sys.path.append(model_path)
from modeling_chessbot import ChessBotModel, ChessBotConfig
# Load the model
config = ChessBotConfig()
model = ChessBotModel.from_pretrained(model_path)
# Example usage
fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# Get the best move
move = model.get_move_from_fen_no_thinking(fen, T=0.1, device=device)
print(f"Predicted move: {move}")
Requirements
- torch>=2.0.0
- transformers>=4.30.0
- python-chess>=1.10.0
- numpy>=1.21.0
Model Architecture
- 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 chess game data to learn optimal move selection and position evaluation.
Limitations
- The model works best with standard chess positions
- Performance may vary with unusual or rare positions
- Requires GPU for optimal inference speed