Dataset descriptions:
- lichess: 7GB of 16 million games from lichess's database. No elo filtering performed.
- Lichess_gt_18k: ~4GB of games from lichess. Per OpenAI's weak to strong generalization paper, filtered to only include games where white is > 1800 ELO.
- Stockfish: 4.5GB of games generated by White playing as Stockfish ELO 3200 against a range of Stockfish ELO 1300-3200 as black.
- Lichess-stockfish mix: a 50 / 50 mix of > 1800 ELO lichess games and stockfish generated games
- Lichess results: lichess, but we include the result before every game. Hopefully, we can then prompt the model with ";1-0#1.", indicating to the model that it's supposed to win this game.
Blocks dataset include only one column and are used for training. Every cell is a batch I created that is 1024 characters long. Datasets without "blocks" in the name contain metadata like player skill, result, etc.
This script is used to create the batches of 1024 characters from a file with a bunch of PGNs: https://github.com/adamkarvonen/chess_gpt_eval/blob/dataset_generation/logs/batching.ipynb