bqrio/maia3-onnx

Maia3 ONNX (fp16 + fp32)

Browser-ready ONNX exports of the Maia3 family of transformer models for human chess move prediction. Each of the four size variants is provided in fp16 (half-precision weights, the headline quantized artifact) and fp32 (full precision).

These are conversions of the original PyTorch checkpoints — same weights, same predictions — repackaged so they run client-side with onnxruntime-web (wasm or WebGPU) without a Python/PyTorch backend.

For the architecture, training recipe, and evaluation see the paper Chessformer: A Unified Architecture for Chess Modeling (ICLR 2026).

Files

Variant	Params	Base model	fp16	fp32
3M	3.2M	Maia3-ablate-3M	maia3-3m-ablation.fp16.onnx (6.7 MB)	maia3-3m-ablation.fp32.onnx (12.8 MB)
5M	5.2M	Maia3-5M	maia3-5m.fp16.onnx (10.8 MB)	maia3-5m.fp32.onnx (21.0 MB)
23M	22.9M	Maia3-23M	maia3-23m.fp16.onnx (45.8 MB)	maia3-23m.fp32.onnx (91.0 MB)
79M	78.9M	Maia3-79M	maia3-79m.fp16.onnx (156.1 MB)	maia3-79m.fp32.onnx (311.7 MB)

The fp16 files store weights in float16 (≈half the size) while keeping float32 inputs/outputs, so they are numerically robust and a drop-in replacement for the float32 graph.

I/O contract

All models share one simplified signature:

inputs:
  tokens    float32 [batch, 64, 12]   # current board only, piece-only one-hot channels
  elo_self  float32 [batch]           # rating of the side to move
  elo_oppo  float32 [batch]           # rating of the opponent
outputs:
  logits_move   float32 [batch, 4352] # move policy over the Maia3 move vocabulary
  logits_value  float32 [batch, 3]    # [loss, draw, win] logits for the side to move

Positions are tokenized exactly as in the upstream Maia3 code (board mirrored when Black is to move; move vocabulary of 4352 with 256 promotion entries). The single 64×12 board is replicated across the model's 8-position history internally, and the ponder/timing head is dropped — matching the analysis export used by the Maia platform frontend.

Accuracy

Max per-position error of the fp16 ONNX vs. the original fp32 PyTorch checkpoint (the fp32 ONNX matches PyTorch to ~1e-7):

Variant	move-prob error	value-prob error
3M	1.3e-4	2.8e-4
5M	1.7e-4	1.2e-4
23M	6.9e-5	5.4e-4
79M	9.6e-5	2.7e-4

Usage (onnxruntime-web)

import * as ort from 'onnxruntime-web'

// Load a variant (fp16 recommended for the browser)
const session = await ort.InferenceSession.create('maia3-79m.fp16.onnx', {
  graphOptimizationLevel: 'basic',     // see note below
  executionProviders: ['webgpu'],      // or ['wasm']
})

// tokens: Float32Array of length batch*64*12, elos: Float32Array length batch
const feeds = {
  tokens:   new ort.Tensor('float32', tokens,   [batch, 64, 12]),
  elo_self: new ort.Tensor('float32', eloSelf,  [batch]),
  elo_oppo: new ort.Tensor('float32', eloOppo,  [batch]),
}
const { logits_move, logits_value } = await session.run(feeds)

Optimization level: set graphOptimizationLevel: 'basic'. ORT's aggressive all/extended CPU fusions hit a known SimplifiedLayerNormFusion bug on fp16 cast boundaries in this graph; basic loads cleanly across ORT versions and is what the browser runtime effectively uses.

Conversion

Exported with PyTorch's ONNX exporter at opset 17, basic graph optimization (constant folding + LayerNorm fusion, all default-domain ops), then weights converted to float16 with float32 I/O preserved (keep_io_types). RMSNorm was decomposed into opset-17 primitives prior to export.

License

AGPLv3, inherited from the upstream Maia3 checkpoints.

Citation

@inproceedings{monroe2026chessformer,
  title={Chessformer: A Unified Architecture for Chess Modeling},
  author={Daniel Monroe and George Eilender and Philip Chalmers and Zhenwei Tang and Ashton Anderson},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=2ltBRzEHyd}
}