Phi-3-mini-4k-instruct-062024-int4-onnx-directml

EmbeddedLLM/Phi-3-mini-4k-instruct-062024-int4-onnx-directml

Model Summary

This model is an ONNX-optimized version of microsoft/Phi-3-mini-4k-instruct (June 2024), 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.

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