A supervised fine-tuning dataset for teaching language models to reason about chess.
It covers 28 tasks across 7 tiers of increasing difficulty, from basic board
perception through tactical analysis to endgame play and strategic planning.
Every example uses standard chess conventions: positions are encoded in
FEN, moves in
UCI notation (e.g. e2e4,
g1f3, a7a8q for promotion), and board diagrams use a consistent rank-file layout.
Approximately 10-20% of… See the full description on the dataset page:
https://huggingface.co/datasets/Chess-Nut-Engine/chess-sft-data.
