Create README.md

README.md

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+---
+license: gemma
+pipeline_tag: text-generation
+tags:
+ - ONNX
+ - DML
+ - DirectML
+ - ONNXRuntime
+ - gemma
+ - google
+ - conversational
+ - custom_code
+inference: false
+---
+# Gemma-2B-Instruct-ONNX
+
+## Model Summary
+This repository contains optimized versions of the [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) model, designed to accelerate inference using ONNX Runtime. These optimizations are specifically tailored for CPU and DirectML. DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning, offering GPU acceleration across a wide range of supported hardware and drivers, including those from AMD, Intel, NVIDIA, and Qualcomm.
+
+## Usage
+
+### Installation and Setup
+
+To use the Gemma-2B-Instruct-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 google/gemma-2b-it-onnx --include="onnx/directml/*" --local-dir .\gemma-2b-it-onnx
+```
+
+5. **Install necessary Python packages:**
+```sh
+pip install numpy
+pip install onnxruntime-directml
+pip install --pre onnxruntime-genai-directml
+```
+
+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 .\gemma-2b-it-onnx
+```
+
+### 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
+
+## ONNX Models
+
+Here are some of the optimized configurations we have added:
+- **ONNX model for int4 DML:** ONNX model for AMD, Intel, and NVIDIA GPUs on Windows, quantized to int4 using AWQ.
+- **ONNX model for int4 CPU and Mobile:** ONNX model for CPU and mobile using int4 quantization via RTN. There are two versions uploaded to balance latency vs. accuracy. Acc=1 is targeted at improved accuracy, while Acc=4 is for improved performance. For mobile devices, we recommend using the model with acc-level-4.
+
+## Resources and Technical Documentation
+
+- [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
+- [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
+- [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-it-gg-hf)
+
+## Terms of Use
+
+- [Terms](https://www.kaggle.com/models/google/gemma/license/consent)