Update README.md (#1263)

Author: hshen14

README.md

@@ -19,7 +19,7 @@ Intel® Neural Compressor aims to provide popular model compression techniques s
 as well as Intel extensions such as [Intel Extension for TensorFlow](https://github.com/intel/intel-extension-for-tensorflow) and [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch).
 In particular, the tool provides the key features, typical examples, and open collaborations as below:
 
-* Support a wide range of Intel hardware such as [Intel Xeon Scalable processor](https://www.intel.com/content/www/us/en/products/details/processors/xeon/scalable.html), [Intel Xeon CPU Max Series](https://www.intel.com/content/www/us/en/products/details/processors/xeon/max-series.html), [Intel Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/flex-series.html), and [Intel Data Center GPU Max Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/max-series.html) with extensive testing; support AMD CPU, ARM CPU, and NVidia GPU through ONNX Runtime with limited testing
+* Support a wide range of Intel hardware such as [Intel Xeon Scalable Processors](https://www.intel.com/content/www/us/en/products/details/processors/xeon/scalable.html), [Intel Xeon CPU Max Series](https://www.intel.com/content/www/us/en/products/details/processors/xeon/max-series.html), [Intel Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/flex-series.html), and [Intel Data Center GPU Max Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/max-series.html) with extensive testing; support AMD CPU, ARM CPU, and NVidia GPU through ONNX Runtime with limited testing
 
 * Validate popular LLMs such as LLama2, [LLama](examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static), [MPT](https://github.com/intel/intel-extension-for-transformers/blob/main/examples/huggingface/pytorch/text-generation/quantization/README.md), [Falcon](https://github.com/intel/intel-extension-for-transformers/blob/main/examples/huggingface/pytorch/language-modeling/quantization/README.md), [GPT-J](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/fx), [Bloom](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/ipex/smooth_quant), [OPT](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/ipex/smooth_quant), and more than 10,000 broad models such as [Stable Diffusion](/examples/pytorch/nlp/huggingface_models/text-to-image/quantization), [BERT-Large](/examples/pytorch/nlp/huggingface_models/text-classification/quantization/ptq_static/fx), and [ResNet50](/examples/pytorch/image_recognition/torchvision_models/quantization/ptq/cpu/fx) from popular model hubs such as [Hugging Face](https://huggingface.co/), [Torch Vision](https://pytorch.org/vision/stable/index.html), and [ONNX Model Zoo](https://github.com/onnx/models#models), by leveraging zero-code optimization solution [Neural Coder](/neural_coder#what-do-we-offer) and automatic [accuracy-driven](/docs/source/design.md#workflow) quantization strategies