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README.md

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+---
+license: apache-2.0
+library_name: onnx
+tags:
+  - onnx
+  - token-classification
+  - agents
+  - browser
+  - wasm
+  - crf
+pipeline_tag: token-classification
+---
+
+# gpu-ask.js (v13)
+
+On-device agent-question tagger for the browser. Two-seed ensemble (~571k parameters × 2), distilled from an Ettin-150m teacher. Emissions are ONNX; features, linear-chain CRF, and the ask-tool compiler ship as TypeScript in [`@ai-ecoverse/gpu-ask.js`](https://www.npmjs.com/package/@ai-ecoverse/gpu-ask.js).
+
+**[Live demo](https://ai-ecoverse.github.io/gpu-ask.js/)** · **[npm](https://www.npmjs.com/package/@ai-ecoverse/gpu-ask.js)** · **[GitHub](https://github.com/ai-ecoverse/gpu-ask.js)**
+
+## Files (`v13/`)
+
+| File | Role |
+| --- | --- |
+| `member0.onnx`, `member1.onnx` | Emission networks (ensemble average at runtime) |
+| `config.json` | Feature / label / member layout |
+| `crf.json` | Start and transition scores for Viterbi |
+| `static_vocab.json` | Static potion-32 table |
+| `manifest.json` | SHA-256 checksums |
+
+## Usage
+
+```ts
+import * as ort from "onnxruntime-web/wasm";
+import { loadAsk, parse } from "@ai-ecoverse/gpu-ask.js";
+
+const ask = await loadAsk(
+  "https://huggingface.co/ai-ecoverse/gpu-ask.js/resolve/main/v13",
+  { ort },
+);
+const { questions } = await parse(ask, agentMessage);
+```
+
+Weights are also bundled under `models/v13/` in the npm package (`bundledModelUrl()`).
+
+## Metrics
+
+Pooled out-of-fold on 4,930 hand-labeled public agent turns (v13, constrained kind rerank in Python): false questions 3.7%, kind 85.4%, options_ok 67.5%. The browser path uses compiler kind (no Python-only rerank).
+
+## Training
+
+See [ai-ecoverse/gpu-questions](https://github.com/ai-ecoverse/gpu-questions) (`docs/training.md`, recipe v13). Architecture follows [gpu-time](https://gpu-time.arikko.dev).
+
+## License
+
+Apache-2.0. Part of [AI Ecoverse](https://github.com/ai-ecoverse).