Update README.md

README.md

@@ -25,54 +25,6 @@ DirectML is a high-performance, hardware-accelerated DirectX 12 library for mach
 Here are some of the optimized configurations we have added:
 - **ONNX model for int4 DirectML:** ONNX model for AMD, Intel, and NVIDIA GPUs on Windows, quantized to int4 using AWQ.
 
-## Usage
-
-### Installation and Setup
-
-To use the EmbeddedLLM/Phi-3-mini-4k-instruct-062024 ONNX model on Windows with DirectML, follow these steps:
-
-1. **Create and activate a Conda environment:**
-```sh
-conda create -n onnx python=3.10
-conda activate onnx
-```
-
-2. **Install Git LFS:**
-```sh
-winget install -e --id GitHub.GitLFS
-```
-
-3. **Install Hugging Face CLI:**
-```sh
-pip install huggingface-hub[cli]
-```
-
-4. **Download the model:**
-```sh
-huggingface-cli download EmbeddedLLM/Phi-3-mini-4k-instruct-062024-onnx --include="onnx/directml/Phi-3-mini-4k-instruct-062024-int4/*" --local-dir .\Phi-3-mini-4k-instruct-062024-int4
-```
-
-5. **Install necessary Python packages:**
-```sh
-pip install numpy==1.26.4
-pip install onnxruntime-directml
-pip install --pre onnxruntime-genai-directml==0.3.0
-```
-
-6. **Install Visual Studio 2015 runtime:**
-```sh
-conda install conda-forge::vs2015_runtime
-```
-
-7. **Download the example script:**
-```sh
-Invoke-WebRequest -Uri "https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/phi3-qa.py" -OutFile "phi3-qa.py"
-```
-
-8. **Run the example script:**
-```sh
-python phi3-qa.py -m .\Phi-3-mini-4k-instruct-062024-int4
-```
 
 ### Hardware Requirements