mcmcmcmc/maia3-training-data

Maia3 Training Data

Preprocessed chess position data for training Maia3 human move-prediction models. Positions are stored as precomputed features, not raw FENs, so training reads and expands them without re-tokenizing every epoch.

Contents

path	rows	size
lichess_parquet/train_YYYY-MM_precomputed.parquet (31 files, 2023-01 … 2025-07)	334,438,119	~33 GB
allie_data/test_precomputed.parquet	884,049	69 MB
allie_data/2022-test-annotated.jsonl	—	45 MB
valid_2019-01_history_7_precomputed.parquet	1,000,000	104 MB

Schema

All parquet files share one schema, with maia3_history: "7" in the file metadata (boards stored per position; a model may use fewer).

column	type
piece_ids	list<list<uint8>>	per-board piece ids, 64 squares per board, oldest board first
castling	list<uint8>	castling rights per board, bit-packed
ep_square	list<int8>	en-passant target square per board, -1 if none
move_idx	int16	index of the played move in the fixed 4352-move vocabulary
legal_idx	list<int16>	indices of all legal moves in that vocabulary
self_elo	int32	Elo of the side to move (900–2600)
oppo_elo	int32	Elo of the opponent (900–2600)
value_target	int8	game result from the mover's view — 0 loss, 1 draw, 2 win

Boards are stored from the mover's perspective. move_idx is always present in legal_idx.

How it was produced

Training shards — monthly Lichess standard rated dumps, one parquet per month, via maia3-preprocess with --history 7 --balance. Game and position filtering happens at preprocess time; see maia3/preprocess.py in the repo. Reproduce with:

JOBS=3 data/download_and_rename_datasets.sh

Validation set — the ALLIE test set of Zhang et al. (2025): drop the opening plies, then truncate each game at the first position where the mover has less than the time threshold remaining. 2022-test-annotated.jsonl is the annotated source; the parquet is built from it with:

python data/allie_data/process_allie_data.py \
  data/allie_data/2022-test-annotated.jsonl \
  data/allie_data/test_precomputed.parquet --history 7

Two validation sets

allie_data/test_precomputed.parquet is the primary one and what the configs point at. valid_2019-01_history_7_precomputed.parquet is a complement, not a replacement — same schema, so either loads with no code change:

	ALLIE test set	2019-01 held-out month
positions	884,049	1,000,000
source month(s)	2022	2019-01
relative to training data (2023-01 … 2025-07)	before	well before
clock filtering	openings dropped, truncated at low time	none
measures	in-distribution move prediction	generalization across time

Usage

hf download mcmcmcmc/maia3-training-data --repo-type dataset --local-dir data

That lands the files where the training configs already expect them, so:

maia3-train --config configs/maia3-5m.yaml

To read a shard directly:

import pyarrow.parquet as pq
t = pq.read_table("lichess_parquet/train_2023-01_precomputed.parquet")

License

Lichess game data is released under CC0 — these derived shards carry the same terms. The ALLIE test set follows the terms of Zhang et al. (2025).