EmbeddedLLM/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

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DirectML

We measured the performance of DirectML on AMD Ryzen 9 7940HS /w Radeon 78