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README.md

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+---
+language: en
+license: apache-2.0
+tags:
+  - webbee
+  - code-generation
+  - text2text-generation
+  - t5
+  - onnx
+datasets:
+  - synthetic
+pipeline_tag: text2text-generation
+---
+
+# WebBee Delegate Models
+
+Fine-tuned T5-small models used by [WebBee](https://github.com/learosema/webbee) —
+a plain-English web development agent — for its Tier 2a inference pipeline.
+
+Each model is an ONNX int8-quantised export of a T5-small checkpoint trained on
+synthetic data. They run locally via [`@xenova/transformers`](https://huggingface.co/docs/transformers.js)
+with no GPU required.
+
+## Models
+
+### `goal-to-steps/`
+
+Decomposes a high-level development goal into a sequence of concrete WebBee commands.
+This is WebBee-specific: the model knows the agent's verb vocabulary (`add`, `inject`,
+`install`, `add route`, `add hook`, …) and the idiomatic step format used throughout
+the system.
+
+| | |
+| --- | --- |
+| **Task** | `text2text-generation` |
+| **Input** | Natural-language goal (e.g. `"add authentication"`) |
+| **Output** | Pipe-separated WebBee steps (e.g. `"add component LoginForm \| add hook useAuth \| add route api/auth"`) |
+| **Training examples** | 533 synthetic goal→steps pairs across 8 categories |
+
+## Usage
+
+```js
+import { pipeline } from '@xenova/transformers';
+
+const generator = await pipeline(
+  'text2text-generation',
+  'learosema/webbee-delegate-models',
+  { subfolder: 'goal-to-steps' }
+);
+
+const result = await generator('add authentication');
+console.log(result[0].generated_text);
+// → add component LoginForm | add hook useAuth | add route api/auth
+```
+
+## Training
+
+Models were trained and exported using the scripts in
+[`packages/delegate-model/`](https://github.com/learosema/webbee/tree/main/packages/delegate-model)
+of the WebBee repository.
+
+Base model: [`google/t5-small`](https://huggingface.co/google/t5-small)  
+Quantization: ONNX int8 via [`optimum`](https://huggingface.co/docs/optimum)
+
+## License
+
+Apache 2.0 — same as the base T5-small model.