Update README.md

README.md

@@ -10,14 +10,17 @@ license: mit
 language:
   - multilingual
 ---
-# microsoft/Phi-4-mini-instruct-onnx
+# Phi-4-Mini-Instruct ONNX models
 
 ## Introduction
-This repository hosts the optimized versions of Phi4 mini models to accelerate inference with ONNX Runtime.
+This repository hosts the optimized versions of the Phi-4 mini models to accelerate inference with ONNX Runtime.
+
 Optimized models are published here in ONNX format to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets.
+
 Here are some of the optimized configurations we have added:  
-1. ONNX model for int4 CPU and Mobile: ONNX model for CPU and mobile using int4 quantization via RTN.
-2. ONNX model for int4 CUDA and DML GPU devices using int4 quantization via RTN.
+
+1. ONNX model for int4 CPU: ONNX model for CPU and mobile using int4 quantization via RTN.
+2. ONNX model for int4 GPU: ONNX model for GPU using int4 quantization via RTN.
 
 ## Model Run
 You can see how to run examples with ORT GenAI [here](https://github.com/microsoft/onnxruntime-genai/blob/main/examples/python/phi-3-tutorial.md)
@@ -69,7 +72,7 @@ python phi3-qa.py -m gpu/gpu-int4-rtn-block-32 -e dml
 - Developed by: Microsoft
 - Model type: ONNX
 - License: MIT
-- Model Description: This is a conversion of Phi4 mini model for ONNX Runtime inference.
+- Model Description: This is a conversion of the Phi-4 mini model for ONNX Runtime inference.
 
 **Disclaimer:** Model is only an optimization of the base model, any risk associated with the model is the responsibility of the user of the model. Please verify and test for your scenarios. There may be a slight difference in output from the base model with the optimizations applied.
 
@@ -78,8 +81,8 @@ Phi-4-Mini is a lightweight open model built upon synthetic data and filtered pu
 See details at [https://huggingface.co/microsoft/Phi-4-mini-instruct/blob/main/README.md](https://huggingface.co/microsoft/Phi-4-mini-instruct/blob/main/README.md)
 
 ## Performance Comparison
-|Hardware | ONNX | PyTorch | speedup |
-|-------|----------|------|---------|
+| Hardware | ONNX | PyTorch | speedup |
+| -------|----------|------|---------|
 | RTX 4090 GPU | int4: 260.045 tokens/sec fp16: 97.463 tokens/se fp32: 19.320 tokens/sec | fp16: 43.957 tokens/sec | 5x(fp16) |
 | Intel Xeon Platinum 8272CL CPU | int4: 16.89 tokens/sec | fp32: 1.636 tokens/sec | 10x |
 | Intel Xeon Platinum 8573B CPU | int4: 23.978 tokens/sec | fp32: 4.479 tokens/sec | 5.35X |