README.md

metadata

license: mit
tags:
  - chess
  - transformer
  - recurrent
  - policy
  - endgame
  - pytorch
library_name: pytorch

99M endgame specialist (squares64)

Same 99M squares64 architecture as avewright/chess-transformer-100m-squares64, finetuned on <14-piece positions (one-hot best first move) from Lichess/chess-position-evaluations via avewright/lichess-endgame-bestline.

This file is latest.pt at endgame-FT step 4500 (2026-09-13 23:41 UTC). Train loss ~1.4738. Frozen holdout hard CE ~1.2928.

Not the generalist incumbent, the puzzle expert, or the Syzygy expert. Not avewright/endgame-dataset (SF19 MultiPV harvest).

Training

• Warm start: public 99M latest.pt (weights only), then full resume.
• Split: position-hash 80/20 (seed 276). Frozen piece-stratified val 8192.
• One-hot PV1 (soft_alpha=0). Pieces 2–13.
• Polar-NorMuon, bs=528. Best disk ckpt at upload: step 4500.

Files

• latest.pt
• step_004500.pt
• model_config.json
• train.log
• pack.json