This model is a GPT-2-based language model trained on chess game sequences using our unique
xLAN notation, focusing on predicting legal chess moves and understanding game dynamics.
For more information see
GitHubThe model is intended for predicting chess moves, analyzing game positions, and studying chess strategies.
This model is not designed for general language understanding or tasks unrelated to chess.
The model reflects the strategies and styles present in the training dataset and may not encompass all possible chess scenarios.
To use the model use the Notebooks on
GitHubThe model was trained on the "Leon-LLM/Leon-Chess-Dataset-71k" dataset, sourced from Lichess database (September 2023).
Chess games from Lichess were converted from PGN into
xLAN as part of the preprocessing.
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Average Number of Correct Plies: Measures the model's ability to simulate chess games, evaluating the average number of correctly generated plies in 100 games.
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Hard Position Accuracy: Assesses the model's handling of 67 challenging chess positions, including unusual castling, pawn promotions, and checkmate scenarios. Success is measured by the model's ability to generate legal moves in these complex positions.
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Legal Piece Moves Accuracy: Examines the model's proficiency in keeping the board state various situations, including checks, pinned pieces, and pawn promotions. The metric focuses on the model's understanding of the board state.
