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

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metadata
license: mit
pipeline_tag: text-generation
tags:
  - ONNX
  - DML
  - ONNXRuntime
  - phi3
  - nlp
  - conversational
  - custom_code
inference: false
language:
  - en

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
  1. Install Git LFS:
winget install -e --id GitHub.GitLFS
  1. Install Hugging Face CLI:
pip install huggingface-hub[cli]
  1. 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
  1. Install necessary Python packages:
pip install numpy==1.26.4
pip install onnxruntime-directml
pip install --pre onnxruntime-genai-directml==0.3.0
  1. Install Visual Studio 2015 runtime:
conda install conda-forge::vs2015_runtime
  1. Download the example script:
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/phi3-qa.py" -OutFile "phi3-qa.py"
  1. 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.