Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+pipeline_tag: text-generation
+tags:
+ - ONNX
+ - DML
+ - ONNXRuntime
+ - phi3
+ - nlp
+ - conversational
+ - custom_code
+inference: false
+language:
+- en
+---
+# EmbeddedLLM/Phi-3-mini-4k-instruct-062024 ONNX
+
+## Model Summary
+
+This model is an ONNX-optimized version of [microsoft/Phi-3-mini-4k-instruct (June 2024)](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct), designed to provide accelerated inference on a variety of hardware using ONNX Runtime(CPU and DirectML).
+DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning, providing GPU acceleration for a wide range of supported hardware and drivers, including AMD, Intel, NVIDIA, and Qualcomm GPUs.
+
+## ONNX Models
+
+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
+
+**Minimum Configuration:**
+- **Windows:** DirectX 12-capable GPU (AMD/Nvidia) 
+- **CPU:** x86_64 / ARM64
+**Tested Configurations:**
+- **GPU:** AMD Ryzen 8000 Series iGPU (DirectML)
+- **CPU:** AMD Ryzen CPU
+  
+## Model Description
+- **Developed by:** Microsoft
+- **Model type:** ONNX
+- **Language(s) (NLP):** Python, C, C++
+- **License:** Apache License Version 2.0
+- **Model Description:** This model is a conversion of the Phi-3-mini-4k-instruct-062024 for ONNX Runtime inference, optimized for DirectML.
+
+## Performance Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+### DirectML
+We measured the performance of DirectML on AMD Ryzen 9 7940HS /w Radeon 78
+
+| Prompt Length | Generation Length | Average Throughput (tps) |
+|---------------------------|-------------------|-----------------------------|
+| 128 | 128  | 53.46686 |
+| 128 | 256  | 53.11233 |
+| 128 | 512 | 57.45816 |
+| 128 | 1024 | 33.44713 |
+| 256 | 128  | 76.50182 |
+| 256 | 256  | 66.68873 |
+| 256 | 512 | 70.83862 |
+| 256 | 1024 | 34.64715 |
+| 512 | 128  | 85.10079 |
+| 512 | 256  | 68.64049 |
+| 512 | 512 | - |
+| 512 | 1024 | -  |
+| 1024 | 128  | - |
+| 1024 | 256  | - |
+| 1024 | 512 | - |
+| 1024 | 1024 | -  |