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

## Applied Quantizations

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

**The base model `decoder_model_merged.onnx` has been renamed to `model.onnx`.**

↳ ✅ `fp16`: `model_fp16.onnx` (added)
↳ ✅ `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

@@ -5,4 +5,20 @@ library_name: transformers.js
 
 https://huggingface.co/hf-internal-testing/tiny-random-StableLmForCausalLM 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:** Text generation.
+
+```js
+import { pipeline } from '@huggingface/transformers';
+
+const generator = await pipeline('text-generation', 'Xenova/tiny-random-StableLmForCausalLM');
+const output = await generator('Once upon a time, there was', { max_new_tokens: 10 });
+```
+
 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.onnx

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onnx/model_bnb4.onnx

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onnx/model_fp16.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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onnx/model_uint8.onnx

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