Upload quantized ONNX model

README.md

@@ -13,17 +13,24 @@ pipeline_tag: text-classification
 
 # Quantized ONNX model for botirk/tiny-prompt-task-complexity-classifier
 
-This repository contains the quantized ONNX version of the [nvidia/prompt-task-and-complexity-classifier](https://huggingface.co/nvidia/prompt-task-and-complexity-classifier) model.
+This repository contains the quantized ONNX version of the \
+[nvidia/prompt-task-and-complexity-classifier](https://huggingface.co/nvidia/prompt-task-and-complexity-classifier) model.
 
 ## Model Description
 
-This is a multi-headed model which classifies English text prompts across task types and complexity dimensions. This version has been quantized to `INT8` using dynamic quantization with the [🤗 Optimum](https://github.com/huggingface/optimum) library, resulting in a smaller footprint and faster CPU inference.
+This is a multi-headed model which classifies English text prompts across task \
+types and complexity dimensions. This version has been quantized to `INT8` \
+using dynamic quantization with the [🤗 Optimum](https://github.com/huggingface/optimum) \
+library, resulting in a smaller footprint and faster CPU inference.
 
-For more details on the model architecture, tasks, and complexity dimensions, please refer to the [original model card](https://huggingface.co/nvidia/prompt-task-and-complexity-classifier).
+For more details on the model architecture, tasks, and complexity dimensions, \
+please refer to the [original model card]\
+(https://huggingface.co/nvidia/prompt-task-and-complexity-classifier).
 
 ## How to Use
 
-You can use this model directly with `optimum.onnxruntime` for accelerated inference.
+You can use this model directly with `optimum.onnxruntime` for accelerated \
+inference.
 
 First, install the required libraries:
 ```bash

model_quantized.onnx

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