| Dataset descriptions: | |
| - lichess_6gb: 6GB of 16 million games from lichess's database. No elo filtering performed. Comprised of games from lichess 2016-06 and 2017-05. | |
| - lichess_9gb: 9GB of games from lichess's database. No elo filtering performed. Comprised of gameds from lichess 2017-07 and 2017-08. | |
| - 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. | |
| - lichess_200k_elo_bins: We include a maximum of 200k games from every 100 Elo bucket, so the model trains on a more even distribution of Elos. | |
| 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 |