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 '/swin/embeddings/patch_embeddings/projection/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/swin-large-patch4-window7-224-in22k 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:** Classify an image.
+
+```js
+import { pipeline } from '@huggingface/transformers';
+
+const classifier = await pipeline('image-classification', 'Xenova/swin-large-patch4-window7-224-in22k');
+const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
+const output = await classifier(url);
+```
+
 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

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+oid sha256:3b57f75914b1da0a4d638e6b2efbf6b564ede91d480cdef42dc441f56d25994f
+size 252003563

onnx/model_q4.onnx

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+oid sha256:6c6529dbbba6441f42e77881fad1fb8ec714456f1484335bb8382476b73af4ff
+size 264167604

onnx/model_q4f16.onnx

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+oid sha256:a7046f4b4b3b5ac116de4c08d9ae188c7ea41675d32d477e0594f47ffeb19b8a
+size 181638353

onnx/model_uint8.onnx

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+oid sha256:b977c1948174ee9d287757d3ada2fc73037eb707cdcf724706d88b950328a1a1
+size 236672852