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laya-onnx is an open-weight model for text classification from weXare, released under Apache License 2.0. Its published files total 903.8 MB.
An unofficial ONNX export of convaiinnovations/laya, the open-source "System One" decision model by Convai Innovations, made to run client-side with ONNX Runtime Web on WebGPU or WebAssembly.
By weXare, published under apache-2.0, revision 10e687edc097.
An unofficial ONNX export of convaiinnovations/laya, the open-source "System One" decision model by Convai Innovations, made to run client-side with ONNX Runtime Web on WebGPU or WebAssembly. Laya takes a state and typed questions and scores every option in one forward pass. It returns calibrated probabilities, not generated text. This is the default model of @wexare/laya-web (source), which loads it automatically: sha256 of layafp16.onnx: b2125c7a1044e78807ee718ff4e65c9892b6b55aeeeee64f31fffffd76871b0a Batch, sequence and option count are all dynamic. Masked option slots return -1e4. Turning logits into answers (per-option-count temperature, softmax, confidence) follows the reference laya…
An unofficial ONNX export of convaiinnovations/laya,
the open-source "System One" decision model by Convai Innovations, made to run client-side with
ONNX Runtime Web on WebGPU or WebAssembly. Laya takes a state and typed questions and scores every
option in one forward pass. It returns calibrated probabilities, not generated text.
This is the default model of @wexare/laya-web
(source), which loads it automatically:
import { LayaWorkerClient } from "@wexare/laya-web";
| File | What it is |
|---|---|
laya_fp16.onnx |
The model: one self-contained file, 900,153,849 bytes, opset 18 |
tokenizer/tokenizer.json, tokenizer/tokenizer_config.json |
The checkpoint's ModernBERT tokenizer, mirrored unchanged |
rl_agent_config.json |
Calibration temperatures and length limits, mirrored unchanged |
export.json |
Export metadata, including the model's sha256 |
sha256 of laya_fp16.onnx: b2125c7a1044e78807ee718ff4e65c9892b6b55aeeeee64f31fffffd76871b0a
inputs input_ids [B,L] int64, attention_mask [B,L] int64,
marker_pos [B,K] int64, marker_mask [B,K] bool, qtype [B] int64
outputs logits [B,K] float32, act_logits [B,2] float32
Batch, sequence and option count are all dynamic. Masked option slots return -1e4. Turning
logits into answers (per-option-count temperature, softmax, confidence) follows the reference
laya package and is done by the library, not the graph.
Mixed precision, set up in PyTorch before export:
Against the reference laya 0.3.3 package, on 9 fixture cases covering single and multi-question
batches, 2 to 14 options, a 600-token truncated state, structured and non-ASCII inputs:
@wexare/laya-web on onnxruntime-web 1.30 (WASM): 9/9 end-to-end parity tests pass:
same chosen option and same yes/no verdict, with probabilities within 0.02 of the Python model.git clone https://github.com/wexare-ai/browser-laya && cd browser-laya
uv venv -p 3.12 tools/.venv
uv pip install -p tools/.venv/bin/python laya onnx onnxscript onnxruntime
tools/.venv/bin/python tools/export_onnx.py
The script exports with torch.onnx.export (dynamo=True), checks each stage against PyTorch,
and stops if a decision changes.
Apache 2.0, the licence of the original weights; see LICENSE.
convaiinnovations/laya.weXare is not affiliated with Convai Innovations, and this export is not endorsed by them. "Laya" refers to Convai Innovations' model. Questions about the model itself belong with them; issues with this export go to wexare-ai/browser-laya.
8 files, 903.8 MB in total. The weights are 1 file totalling 900.2 MB in onnx.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| laya_fp16.onnx | Weights | 900.2 MB | b2125c7a1044 |
| export.json | Configuration | 261 B | — |
| rl_agent_config.json | Configuration | 745 B | — |
| LICENSE | Documentation | 10.2 KB | — |
| README.md | Documentation | 3.9 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer/tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer/tokenizer_config.json | Tokenizer | 308 B | — |
Released by weXare through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 900.2 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. laya-onnx is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for English. 0 -> Negative; 1 -> Neutral; 2 -> Positive This sentiment analysis model has been integrated into TweetNLP. You can access the demo here.
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