Document web/ WASM package on the model card

README.md

@@ -28,9 +28,9 @@ calibrated probability per paragraph and two thresholds (`needed`,
 | --- | --- |
 | **Task** | Paragraph citation / needed-blocks |
 | **Size** | ~42 MB weights (trimmed vocab, int8 embeddings, fp16 rest) |
-| **Runtime** | CPU (PyTorch); Mac MPS optional |
-| **Weights** | [v0.1.0](https://huggingface.co/localangle/cite/tree/v0.1.0) |
-| **Demo** | [localangle/cite-demo](https://huggingface.co/spaces/localangle/cite-demo) |
+| **Runtime** | CPU (PyTorch); browser (ONNX Runtime WASM) |
+| **Weights** | [v0.1.0](https://huggingface.co/localangle/cite/tree/v0.1.0) · [`web/`](https://huggingface.co/localangle/cite/tree/main/web) for WASM |
+| **Demo** | [localangle/cite-demo](https://huggingface.co/spaces/localangle/cite-demo) (in-browser) |
 
 Cite does **not** write an answer. It ranks the paragraphs you already have and
 flags which ones are needed to answer the question.
@@ -90,8 +90,10 @@ cite = CitePackage.load("path/to/package")  # or from_pretrained("./path")
 | `weights.safetensors` | Trimmed encoder + head (int8 token embeddings, fp16 elsewhere) |
 | `encoder/` | ModernBERT/Ettin config for the trimmed vocabulary |
 | `tokenizer/` | Tokenizer with `[MASK]` as the block marker |
+| `web/` | Browser package: `model.onnx` (~36 MB int8), `vocab_map.bin`, `cite-web.json`, `tokenizer/` |
 
-Weights expand to fp32 at load; only storage is compressed.
+PyTorch weights expand to fp32 at load; only storage is compressed. The `web/`
+ONNX graph is what the static demo runs in WASM (soft cap 2048 tokens).
 
 ## Accuracy