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chessmamba-onnx
cstr/chessmamba-onnx
chessmamba-onnx is a machine learning model from cstr. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for onnx. The card lists the license as mit.
An ONNX export of TobiasLogic/chessmamba (MIT), a 16.8M-parameter selective state-space model (Mamba/S6) that reads a chess game as a sequence of moves and predicts the next move, promotion and a value. All credit for…
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At a glance
- Library
- onnx
- License
- mit
- Model type
- selective-state-space
- Access
- Public
- Created
- Sep 24, 2026
- Updated
- Sep 24, 2026
- SHA
- 9e3a64bd
Base models
Model card
From the Hugging Face model README
ChessMamba, ONNX (single recurrent step)
An ONNX export of TobiasLogic/chessmamba (MIT), a 16.8M-parameter selective state-space model (Mamba/S6) that reads a chess game as a sequence of moves and predicts the next move, promotion and a value. All credit for the model and its training goes to its author; this repository only converts it.
Two files, same weights:
chessmamba_step_batch.onnx(use this): batch dimensionbon every input and output (stateis[10, b, 768, 16]), so a search can expand all of a node's candidate moves in one call. With onnxruntime a batch of 10 costs about 1.4 single steps.chessmamba_step.onnx: the same graph fixed at batch 1.
Each graph is one incremental step: feed one move and the recurrent state, get the policy for the next move and the updated state. Playing a game costs one step per move regardless of its length. Used by CrispChess.
Inputs
| name | type | shape | meaning |
|---|---|---|---|
| from_sq | int64 | [b] | from square, a1 = 0 .. h8 = 63 (python-chess numbering) |
| to_sq | int64 | [b] | to square |
| promo | int64 | [b] | 0 none, 1 queen, 2 rook, 3 bishop, 4 knight |
| ply | int64 | [b] | 1-based index of this move; clamped to 96 inside |
| is_start | float32 | [b, 1] | 1 for the very first step (no move, uses the start token), else 0 |
| state | float32 | [10, b, 768, 16] | recurrent state; zeros for the first step |
Outputs
| name | shape | meaning |
|---|---|---|
| policy | [b, 4096] | next-move logits, index from * 64 + to |
| promo_logits | [b, 5] | promotion logits |
| value | [b, 1] | expected result for the side to move, -1..1 (tanh) |
| new_state | [10, b, 768, 16] | state to pass to the next step |
Start with is_start = 1 and a zero state, then one step per move with is_start = 0.
Verification
Opset 17, IR 8, 23 standard ops. Compared with the PyTorch model's own step_move over a
30-ply game: max absolute policy-logit difference 1.05e-5 (onnxruntime). The batched graph
matches the single-step one to 7e-4 over ten children of the same position. About 15-18 ms per
step on one CPU thread with onnxruntime. Exported and checked by
export_chess_lms.py.
sha256 of chessmamba_step_batch.onnx: b956c1580c719c41bf530c6bb25d766eae7db7f7fef1ad8b033890dd9317bedd; of chessmamba_step.onnx: 03e79ddea3a67d77a4c5b1deb37b5952107baf48d6e5b2ef12693b75339b06f8
- Library
- onnx
- Type
- selective-state-space
- License
- mit
- Created
- Sep 24, 2026
- Updated
- Sep 24, 2026