ChessNano

A compact autoregressive transformer that reads chess moves in Standard Algebraic Notation and predicts the next one. No board representation and no evaluation function — the model sees notation and nothing else.

Code: github.com/Kcbir/chessnano

Files

File	Size	Description
chessnano_deployed.pt	58.4 MB	Quantised artifact — INT8 weights, bit-packed sign bits, fp16 embedding
chessnano_fp16.pt	102 MB	fp16 checkpoint
vocab.json	76 KB	SAN token vocabulary

Architecture

Layers	8
Model dim	768
Attention	grouped-query, 12 query heads / 4 KV heads, head dim 64
Positions	RoPE
Feedforward	SwiGLU, 2048 hidden
Normalisation	RMSNorm, pre-norm, no bias
Vocabulary	2048 SAN tokens
Context	512 tokens
Parameters	51.9M

Quantization

Weights are trained with quantization simulated in the forward pass. The TurboQuant path applies a Hadamard rotation, quantises each coordinate pair's angle to 128 levels, and stores a one-bit Johnson–Lindenstrauss residual sketch scaled by the least-squares coefficient. Training and export share one reconstruction function, so the simulated quantizer matches the exported one.

Usage

import chess
from inference.tot_inference import ChessNanoPlayer, TotConfig

player = ChessNanoPlayer("chessnano_deployed.pt", tot_cfg=TotConfig(depth=1))

board, history = chess.Board(), []
for san in ["e4", "e5", "Nf3"]:
    board.push_san(san)
    history.append(san)

print(player.predict_move(history, board))

Candidate moves are masked against python-chess's legal move list before sampling, so the decoder cannot emit an illegal move.

Licence

MIT