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)

### ✅ 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

@@ -18,19 +18,19 @@ https://huggingface.co/jinaai/jina-embeddings-v2-base-zh with ONNX weights to be
 
 ## 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
 ```
 
 You can then use the model to compute embeddings, as follows:
 
 ```js
-import { pipeline, cos_sim } from '@xenova/transformers';
+import { pipeline, cos_sim } from '@huggingface/transformers';
 
 // Create a feature extraction pipeline
 const extractor = await pipeline('feature-extraction', 'Xenova/jina-embeddings-v2-base-zh', {
-    quantized: false, // Comment out this line to use the quantized version
+    dtype: "fp32"  // Options: "fp32", "fp16", "q8", "q4"
 });
 
 // Compute sentence embeddings
@@ -51,4 +51,4 @@ console.log(score);
 
 ---
 
-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`).
+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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+size 251953322

onnx/model_int8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a88e28e8713a442a8fbe4ca35256d3eab738027165dda23760bfb543ee8d54b
+size 160893544

onnx/model_q4.onnx

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+oid sha256:6ed6eb4662acd75cc16670350638814914810813930908df86be533b3eee7684
+size 259030670

onnx/model_q4f16.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6243589a55ebcf35efbbda314ee7947cbe16fff264a5b5422802bd06ba58afd7
+size 157999063

onnx/model_uint8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2daf330acbaa1678186ca72aeca61b3da02787b3895610ba063e579ae9d723c
+size 160893588