Create README.md

README.md

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+---
+license: mit
+language:
+  - multilingual
+tags:
+  - nlp
+  - code
+  - audio
+  - automatic-speech-recognition
+  - speech-summarization
+  - speech-translation
+  - visual-question-answering
+  - phi-4-multimodal
+  - phi
+  - phi-4-mini
+---
+
+## microsoft/Phi-4- multimodal -instruct-onnx
+
+### Introduction
+
+This repository hosts the optimized versions Phi4  multi modal model  to accelerate inference with ONNX Runtime. This repository hosts the optimized versions Phi4  multimodal model  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 CUDA and DML GPU devices using int4 quantization via RTN.
+
+You can see how to run examples  with ORT GenAI <to add link>
+
+The performance of the text component is similar the [phi4 mini ONNX models] (https://huggingface.co/microsoft/Phi-4-mini-instruct-onnx/blob/main/README.md)
+
+### Model Description
+
+- Developed by: Microsoft
+- Model type: ONNX
+- License: MIT
+- Model Description: This is a conversion of Phi4 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 you scenarios. There may be a slight difference in output from the base model with the optimizations applied.
+
+### Base Model
+
+Phi-4-multimodal-instruct is a lightweight open multimodal foundation model that leverages the language, vision, and speech research and datasets used for Phi-3.5 and 4.0 models. The model processes text, image, and audio inputs, generating text outputs, and comes with 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning, and direct preference optimization to support precise instruction adherence and safety measures.
+
+See details [here](https://huggingface.co/microsoft/Phi-4-multimodal-instruct/blob/main/README.md)