**A 24M-parameter MiniLM model that tags English text with IAB content and audience categories, small enough to run on the device: a 25 MB int8 ONNX file that classifies a 128-token text in 4–10 ms on a laptop CPU.** Give it an article, a page summary, or any short English text and it returns IAB Tech Lab **content** categories (704 labels, IAB Content Taxonomy 3.0) and **audience** segments (1,567 labels, IAB Audience Taxonomy 1.1) with a score for each. In a blind, 10,000-text comparison judged by GPT-5.5, its labels were preferred over **GPT-5.4-nano**'s in **66% of decided cases**, and on independent human labels it scores **0.406 F1 vs 0.375** for nano. It never returns a label outside the taxonomy. This is the exact bundle ZeroGPU runs on edge devices (browsers, Node.js / Docker workers, Android) in production. It is laid out for transformers.js and also runs directly in onnxruntime.
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**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`).
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**A 139M-parameter DeBERTa moderation model that flags unsafe English text and scores it on the 13 categories of the OpenAI moderation taxonomy, calibrated and small enough to run on the device (int8 ONNX, 185 MB).** For each input the model returns a binary **unsafe** verdict plus a calibrated score for each category: `hate`, `hate/threatening`, `harassment`, `harassment/threatening`, `self-harm`, `self-harm/intent`, `self-harm/instructions`, `sexual`, `sexual/minors`, `violence`, `violence/graphic`, `illicit`, `illicit/violent`. It runs locally with no network call. This is the exact bundle ZeroGPU runs on edge devices (browsers, Node.js / Docker workers, Android). It is laid out for transformers.js and also runs directly in onnxruntime.
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**A 77M-parameter FLAN-T5 model that reads a short text and returns its keywords and the user intent behind it — in one generative pass, in ~136 ms on CPU.** Given an ad headline, search query, or product blurb, the model generates two signals that normally require two separate systems (a keyword extractor and an intent classifier): - **keywords** — the terms that carry the text's meaning, and - **user intent** — a free-text intent phrase plus one of three standard search-intent classes: *informational*, *transactional*, or *navigational*.
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