MODEL PROFILE

zlm-v1-iab-domain-classifier-edge

zgpu-ai · zlm-v1-iab-domain-classifier-edge

**The on-device build of `ZeroGPU/zlm-v1-iab-domain-classifier`: a 149M-parameter ModernBERT model that classifies a web destination into IAB content and audience categories from its URL alone, packaged as a 153 MB int8 ONNX file that runs in the browser.** This repository is the exact bundle ZeroGPU serves to edge devices (browsers, Node.js / Docker workers, Android) in production, laid out for transformers.js and onnxruntime. For the full-precision model, the PyTorch checkpoint, the training details and the complete benchmark, see the main repository. - **One output.** `logits` has shape `[batch, 2271]`: the 704 IAB content logits followed by the 1,567 IAB audience logits. Labels in `config.json` carry a head prefix (`content|Sports`, `audience|Interest | Sports | Basketball |`), so a standard text-classification pipeline can run it. - **Per-channel int8 quantization** (QUInt8, dynamic) of the full-precision graph. - **64-token input window**, the length the model was trained with (`max_seq_length` in `postprocess.json`). - **Head-level thresholds:** content 0.60, audience 0.50 (`postprocess.json`).

Text analysislicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 09:42:47
OVERVIEW

Model overview

**The on-device build of `ZeroGPU/zlm-v1-iab-domain-classifier`: a 149M-parameter ModernBERT model that classifies a web destination into IAB content and audience categories from its URL alone, packaged as a 153 MB int8 ONNX file that runs in the browser.** This repository is the exact bundle ZeroGPU serves to edge devices (browsers, Node.js / Docker workers, Android) in production, laid out for transformers.js and onnxruntime. For the full-precision model, the PyTorch checkpoint, the training details and the complete benchmark, see the main repository. - **One output.** `logits` has shape `[batch, 2271]`: the 704 IAB content logits followed by the 1,567 IAB audience logits. Labels in `config.json` carry a head prefix (`content|Sports`, `audience|Interest | Sports | Basketball |`), so a standard text-classification pipeline can run it. - **Per-channel int8 quantization** (QUInt8, dynamic) of the full-precision graph. - **64-token input window**, the length the model was trained with (`max_seq_length` in `postprocess.json`). - **Head-level thresholds:** content 0.60, audience 0.50 (`postprocess.json`).

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。