ChessFormer

ChessFormer is a chess transformer engine developed by @ncylich that leverages the power of transformer models trained on lichess datasets. Designed to simulate a player with an approximate Elo rating of ~1800, ChessFormer provides a sophisticated approach to chess move prediction and game analysis.

Project Overview

ChessFormer uses extensive datasets from lichess, a popular online chess platform, to train the transformer model. The project includes several scripts that handle different aspects of chess data processing and model interaction, providing a comprehensive toolkit for chess enthusiasts and researchers.

Repository Structure

• transformer.py: Implements the chess transformer model.
• chess_loader.py: Module for loading and processing chess game datasets.
• uci_to_pos.py: Script for converting Universal Chess Interface (UCI) notation to board positions.
• inference.py: Contains functions for model inference, including preprocessing and postprocessing.
• file_filter.cpp: C++ script to filter chess games from PGN files based on Elo ratings.
• write_positions.py: Python script to extract and record specific chess positions from games.
• models.zip: The best model weights I could produce with my limited m1 pro setup.

Setup and Requirements

Before running the scripts, ensure you have Python 3.x and a C++ compiler installed. Follow these steps:

1. Clone the ChessFormer repository:

   git clone https://github.com/ncylich/chessformer
   cd chessformer

2. Install the required Python dependencies:

   pip install -r requirements.txt

3. Compile the C++ script for filtering PGN files:

   g++ -o file_filter file_filter.cpp

Usage

• Chess Transformer Model: The chessformer.py script is the core of ChessFormer, encapsulating the transformer model for move prediction.

• Data Loading and Processing: Use chess_loader.py for loading and processing chess game data.

• Notation Conversion: Convert UCI notations to board positions using chess_moves_to_input_data .py.

• Running the Engine: Perform model inference with inference_test.py, which includes both preprocessing of chess positions and postprocessing of the model's output.

• Playing Against Engine: Run play_against.py. Must unzip models.zip first though.

• Filtering PGN Files: Utilize the compiled file_filter program to filter games from PGN files based on specific Elo ratings.

Refer to each script's documentation for more detailed instructions.

Contributing

Feel free to contribute to ChessFormer. You can contribute by:

1. Forking the repository.
2. Creating a new branch for your feature or bug fix.
3. Committing your changes.
4. Pushing your branch and submitting a pull request.

License

This project is licensed under the MIT License.

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