Maia3 ONNX Models (int32 ELO inputs)
Maia3 neural network models with int32 ELO inputs for Safari/WebKit compatibility.
Original models use int64 for self_elo and oppo_elo inputs, which Safari's
WebAssembly doesn't support (no BigInt64Array). These models add Cast nodes
(int32 → int64) at the input, allowing int32 tensors while keeping internal
computation unchanged.
Models
| File | Size | Parameters |
|---|---|---|
| maia3_5m_int32.onnx | ~22MB | 5M |
| maia3_23m_int32.onnx | ~92MB | 23M |
| maia3_79m_int32.onnx | ~313MB | 79M |
Tensor Specs
- Input:
tokens—[batch, 64, 96]float32 - Input:
self_elo—[1]int32 (player ELO, 0-5000) - Input:
oppo_elo—[1]int32 (opponent ELO, 0-5000) - Output:
logits_move—[batch, 4352]float32 (policy logits) - Output:
logits_value—[batch, 3]float32 (loss, draw, win)
License
AGPL-3.0. The authoritative source for these weights is the
official CSSLab/maia3 repository
by the model's actual authors, which is AGPL-3.0-licensed. Their own
Hugging Face model cards (MaiaChess/maia3-*) state that the weights
follow the repository's license, not an independent one.
These files were originally converted to ONNX and re-uploaded as cemoss17/maia3-onnx under a self-declared MIT tag; this repo previously carried that same MIT tag on the further-modified (int32 Cast node) files here. On review, neither intermediate re-upload documents a relicensing grant from CSSLab, so this repo's license tag has been corrected to AGPL-3.0 to match the authoritative upstream source, pending any clarification from CSSLab.
If you're using these weights: treat AGPL-3.0 obligations as applying, including for network/SaaS use (AGPL's distinguishing term vs. GPL).
Source
Format-converted from cemoss17/maia3-onnx, itself derived from the official CSSLab/maia3 release. Modified here with additional ONNX Cast nodes for int32 input compatibility.
Used by CrispChess, which downloads these weights at runtime (never bundles them) and is working to isolate inference into a separate process/context, matching the treatment CrispChess already gives its other AGPL/GPL-licensed engine options.