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

README.md

@@ -7,14 +7,14 @@ https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2 with ONNX weights t
 
 ## 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/@xenova/transformers) using:
+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 @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 **Example:** Information Retrieval w/ `Xenova/ms-marco-MiniLM-L-12-v2`.
 ```js
-import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers';
+import { AutoTokenizer, AutoModelForSequenceClassification } from '@huggingface/transformers';
 
 const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/ms-marco-MiniLM-L-12-v2');
 const tokenizer = await AutoTokenizer.from_pretrained('Xenova/ms-marco-MiniLM-L-12-v2');
@@ -31,7 +31,7 @@ const features = tokenizer(
     }
 )
 
-const scores = await model(features)
+const scores = await model(features);
 console.log(scores);
 // quantized:   [ 9.597102165222168, -11.141762733459473 ]
 // unquantized: [ 9.450557708740234, -11.160483360290527 ]

onnx/model_bnb4.onnx

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onnx/model_int8.onnx

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onnx/model_q4.onnx

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onnx/model_q4f16.onnx

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+size 36509715

onnx/model_uint8.onnx

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