---
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

```python
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