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killkli/open-jev-laya-multilingual-onnx
open-jev-laya-multilingual-onnx is a machine learning model from killkli. 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 transformers.js. The card lists the license as apache-2.0.
This repository contains a browser-ready ONNX export of the publicly licensed Convai Innovations Laya multilingual checkpoint. The weights remain under Apache 2.0. The export used the receptron/laya ONNX exporter at t…
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From the Hugging Face model README
This repository contains a browser-ready ONNX export of the publicly licensed Convai Innovations Laya multilingual checkpoint. The weights remain under Apache 2.0. The export used the receptron/laya ONNX exporter at the linked revision.
The graph has five inputs (input_ids, attention_mask, marker_pos, marker_mask, qtype) and returns decision logits plus act_probs. It runs through the low-level PreTrainedModel.from_pretrained() API in Transformers.js with model_file_name: "laya" and subfolder: "onnx". Use dtype: "fp16" for onnx/laya_fp16.onnx on WebGPU devices with shader-f16 or on the WASM backend; use dtype: "fp32" for onnx/laya.onnx as fallback. Both fp16 paths were tested in headless Chrome. The model does not generate text. The browser sequence encoder and typed answer decoder live in open-jev.
The fp32 export is 1,290,793,955 bytes and the float16 variant is 646,982,318 bytes. The fp16 variant was converted with onnxconverter-common==1.14.0 and onnx==1.17.0; input and output tensor types remain unchanged. In a 21-case, 60-question English/Chinese comparison covering choice, score, and noul questions, fp16 agreed with fp32 on all top choices; maximum absolute choice-probability difference was 0.00245. These examples are regression probes, not a full quality benchmark. Decision probabilities have not been separately recalibrated for the open-jev examples.