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README.md

@@ -78,12 +78,15 @@ model-index:
 
 A **350M-parameter language model** fine-tuned for extracting Personally Identifiable Information (PII) from medical and general-domain text across **17 languages**. Built on [LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) with a two-stage training pipeline: supervised fine-tuning (SFT) followed by Group Relative Policy Optimization (GRPO).
 
+**[Try it in your browser →](https://huggingface.co/spaces/Meddies/meddies-pii-extractor)** — no setup required, runs entirely client-side via WebGPU.
+
 ## Highlights
 
 - **17 languages**: English, Vietnamese, French, German, Spanish, Lao, Thai, Burmese, Indonesian, Filipino, Malay, Tamil, Portuguese, Russian, Chinese, Japanese, Korean
 - **7 PII entity types**: `address`, `company_name`, `date`, `email_address`, `human_name`, `id_number`, `phone_number`
-- **350M params** — runs on consumer GPUs, edge devices, and CPU inference
+- **350M params** — runs on consumer GPUs, edge devices, and [in the browser](https://huggingface.co/spaces/Meddies/meddies-pii-extractor)
 - **Structured JSON output** — directly usable without post-processing
+- **ONNX available** — quantized exports (fp32/fp16/q4/q8) at [Meddies/meddies-pii-onnx](https://huggingface.co/Meddies/meddies-pii-onnx) for Transformers.js & ONNX Runtime
 
 ## Quick Start
 
@@ -134,6 +137,35 @@ output = llm.chat(messages, sampling_params=sampling)
 print(output[0].outputs[0].text)
 ```
 
+### Using Transformers.js (browser / Node.js)
+
+```javascript
+import { pipeline } from "@huggingface/transformers";
+
+const extractor = await pipeline("text-generation", "Meddies/meddies-pii-onnx", {
+  dtype: "q4",
+  device: "webgpu",  // or "wasm" for broader compatibility
+});
+
+const messages = [
+  { role: "system", content: "Extract <address>, <company_name>, <email_address>, <human_name>, <phone_number>, <id_number>, <date>" },
+  { role: "user", content: "Patient John Smith, DOB 03/15/1985, contact: john.smith@email.com" },
+];
+
+const output = await extractor(messages, { max_new_tokens: 512, do_sample: false });
+console.log(output[0].generated_text.at(-1).content);
+```
+
+### Using ONNX Runtime (Python)
+
+```python
+from optimum.onnxruntime import ORTModelForCausalLM
+from transformers import AutoTokenizer
+
+model = ORTModelForCausalLM.from_pretrained("Meddies/meddies-pii-onnx")
+tokenizer = AutoTokenizer.from_pretrained("Meddies/meddies-pii-onnx")
+```
+
 ## Model Details
 
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