datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
chess-sft-corpus-4x
Chess SFT Training Data
A supervised fine-tuning dataset for teaching language models to reason about
chess. It covers board perception, legal move generation, tactics, evaluation,
openings, endgames, and planning.
Total examples
15,100,000
Total size
18902.7 MB
Format
JSONL chat rows with messages
Eval companion
Chess-Nut-Engine/chess-sft-eval
Tier Overview
Tier
Category
Tasks
Examples
Size
0
Unknown
1
300,000
556.2 MB
1… See the full description on the dataset page: https://huggingface.co/datasets/Chess-Nut-Engine/chess-sft-corpus-4x.chess-traces
Chess Reasoning Traces (Streaming)
This dataset is uploaded incrementally while it is being generated.
Columns
fen: FEN string
valid_moves: list of legal moves in UCI
reasoning_trace: natural-language trace text
chosen_move: Stockfish best move in UCI
Usage
from datasets import load_dataset
ds = load_dataset("amazingvince/chess-traces", split="train")
print(ds[0])
# Or stream while it's growing:
stream = load_dataset("amazingvince/chess-traces"… See the full description on the dataset page: https://huggingface.co/datasets/Chess-Nut-Engine/chess-traces.chess-sft-eval
Chess SFT Eval & Benchmark
Held-out evaluation splits and a frozen benchmark for the
Chess SFT training pipeline.
Every FEN in these files is excluded from training data via a blocklist to guarantee
zero contamination.
Eval examples
13,000
Benchmark examples
13,000
Splits
9 (perception, rules, tactics, evaluation, openings, endgames, planning, chess960, mate)
Format
JSONL
Training companion
Chess-Nut-Engine/chess-sft-data
How eval and benchmark differ… See the full description on the dataset page: https://huggingface.co/datasets/Chess-Nut-Engine/chess-sft-eval.chess-sft-data
Chess SFT Training Data
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.chess-sft-corpus-4x-eval
Chess SFT Eval and Benchmark
Held-out evaluation splits and a frozen benchmark for
Chess-Nut-Engine/chess-sft-corpus-4x.
Every FEN in these files is excluded from generated training data (the
blocklist is game-scoped: sibling positions of eval games are excluded too).
Frozen from the 4x corpus generation run of 2026-07-06 (generator revision 3cd161b1078cdfa6598fba939f40250072adb524)
Benchmark: 13,000 frozen examples across 9 splits; eval splits share the game-scoped blocklist… See the full description on the dataset page: https://huggingface.co/datasets/Chess-Nut-Engine/chess-sft-corpus-4x-eval.