Add/update the quantized ONNX model files and README.md for Transformers.js v3

## Applied Quantizations

### ✅ Based on `model.onnx` *with* slimming

↳ ❌ `int8`: `model_int8.onnx` (added but JS-based E2E test failed)
```
/home/ubuntu/src/tjsmigration/node_modules/.pnpm/onnxruntime-node@1.21.0/node_modules/onnxruntime-node/dist/backend.js:25
__classPrivateFieldGet(this, _OnnxruntimeSessionHandler_inferenceSession, "f").loadModel(pathOrBuffer, options);
^

Error: Could not find an implementation for ConvInteger(10) node with name '/model/backbone/conv_encoder/model/conv1/Conv_quant'
at new OnnxruntimeSessionHandler (/home/ubuntu/src/tjsmigration/node_modules/.pnpm/onnxruntime-node@1.21.0/node_modules/onnxruntime-node/dist/backend.js:25:92)
at Immediate.<anonymous> (/home/ubuntu/src/tjsmigration/node_modules/.pnpm/onnxruntime-node@1.21.0/node_modules/onnxruntime-node/dist/backend.js:67:29)
at process.processImmediate (node:internal/timers:485:21)

Node.js v22.16.0
```
↳ ✅ `uint8`: `model_uint8.onnx` (added)
↳ ✅ `q4`: `model_q4.onnx` (added)
↳ ✅ `q4f16`: `model_q4f16.onnx` (added)
↳ ✅ `bnb4`: `model_bnb4.onnx` (added)

README.md

@@ -5,4 +5,21 @@ library_name: transformers.js
 
 https://huggingface.co/microsoft/table-transformer-detection with ONNX weights to be compatible with Transformers.js.
 
+## Usage (Transformers.js)
+
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+**Example:** Run object-detection.
+
+```js
+import { pipeline } from '@huggingface/transformers';
+
+const detector = await pipeline('object-detection', 'Xenova/table-transformer-detection');
+const img = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
+const output = await detector(img, { threshold: 0.9 });
+```
+
 Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

onnx/model_bnb4.onnx

@@ -0,0 +1,3 @@
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+oid sha256:6077940a2c137ab18fa5aa9aa3c859ccced3aa97bce4b00938458267c071fb21
+size 55730902

onnx/model_q4.onnx

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+oid sha256:b380061f24114a4a07ff3b9a7496a19c9e3b6b66385c550597cd339c68ff28f8
+size 56819777

onnx/model_q4f16.onnx

@@ -0,0 +1,3 @@
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+oid sha256:3699333a8b28b5b636c23749075b947bd179bdaa23a47a2e1cf8db596bb73d6c
+size 32985200

onnx/model_uint8.onnx

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+size 29608640