Update src/routes/blogs/accelerating-phi-3/+page.svx

src/routes/blogs/accelerating-phi-3/+page.svx

@@ -39,7 +39,7 @@ For FP16 CUDA and INT4 CUDA, Phi-3 Mini-128K-Instruct with ORT performs up to 5X
 
 For FP16 and INT4 CUDA, Phi-3 Mini-4K-Instruct with ORT performs up to 5X faster and up to 10X faster than PyTorch, respectively. Phi-3 Mini-4K-Instruct is also up to 3X faster than Llama.cpp for large sequence lengths.
 
-In addition to supporting both Phi-3 Mini models on various GPUs, ONNX Runtime can help run these models on Mobile, Windows, and Mac CPUs, making it a truly cross-platform framework. ONNX Runtime also supports quantization techniques like RTN to enable these models to run across many different hardware.
+In addition to supporting both Phi-3 Mini models on various GPUs, ONNX Runtime can help run these models on mobile, Windows, and Mac CPUs, making it a truly cross-platform framework. ONNX Runtime also supports quantization techniques like RTN to enable these models to run across many different hardware.
 
 ONNX Runtime Mobile empowers developers to perform on-device inference with AI models on mobile and edge devices. By removing client-server communications, ORT Mobile provides privacy protection and has zero cost. Using RTN INT4 quantization, we significantly reduce the size of the state-of-the-art Phi-3 Mini models and can run both on a Samsung Galaxy S21 at a moderate speed. When applying RTN INT4 quantization, there is a tuning parameter for the int4 accuracy level. This parameter specifies the minimum accuracy level required for the activation of MatMul in int4 quantization, balancing performance and accuracy trade-offs. Two versions of RTN quantized models have been released with int4_accuracy_level=1, optimized for accuracy, and int4_accuracy_level=4, optimized for performance. If you prefer better performance with a slight trade-off in accuracy, we recommend using the model with int4_accuracy_level=4.