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

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), 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:

conda create -n onnx python=3.10
conda activate onnx

2. Install Git LFS:

winget install -e --id GitHub.GitLFS

3. Install Hugging Face CLI:

pip install huggingface-hub[cli]

4. Download the model:

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:

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:

conda install conda-forge::vs2015_runtime

7. Download the example script:

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:

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.