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.
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
DirectML
We measured the performance of DirectML on AMD Ryzen 9 7940HS /w Radeon 78
| Prompt Length | Generation Length | Average Throughput (tps) |
|---|---|---|
| 128 | 128 | 53.46686 |
| 128 | 256 | 53.11233 |
| 128 | 512 | 57.45816 |
| 128 | 1024 | 33.44713 |
| 256 | 128 | 76.50182 |
| 256 | 256 | 66.68873 |
| 256 | 512 | 70.83862 |
| 256 | 1024 | 34.64715 |
| 512 | 128 | 85.10079 |
| 512 | 256 | 68.64049 |
| 512 | 512 | - |
| 512 | 1024 | - |
| 1024 | 128 | - |
| 1024 | 256 | - |
| 1024 | 512 | - |
| 1024 | 1024 | - |