Phi-3-small-8k-instruct-onnx-cuda

Phi-3 Small-8K-Instruct ONNX CUDA models#

This repository hosts the optimized versions of Phi-3-small-8k-instruct to accelerate inference with ONNX Runtime for your machines with NVIDIA GPUs.

Phi-3 Small is a 7B parameter, lightweight, state-of-the-art open model trained with the Phi-3 datasets, which include both synthetic data and filtered publicly available website data, with a focus on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the small version in two variants: 8K and 128K, which are the context lengths (in tokens) that they can support.

The base model has undergone a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures. When assessed against benchmarks testing common sense, language understanding, math, code, long context, and logical reasoning, Phi-3-Small-8K-Instruct showcased a robust and state-of-the-art performance among models of the same-size and next-size-up.

Optimized variants of the Phi-3 Small models are published here in ONNX format and run with ONNX Runtime on GPU across devices, including server platforms, Windows, and Linux.

ONNX Models#

Here are some of the optimized configurations we have added:

1. ONNX model for FP16 CUDA: ONNX model for NVIDIA GPUs.
2. ONNX model for INT4 CUDA: ONNX model for NVIDIA GPUs using int4 quantization via RTN.

Note: Using the Hugging Face CLI, you can download sub folders and not all models if you are limited on disk space. The FP16 model is recommended for larger batch sizes, while the INT4 model optimizes performance for lower batch sizes.

Example:

# Download just the FP16 model
$ huggingface-cli download microsoft/Phi-3-small-8k-instruct-onnx-cuda --include cuda-fp16/* --local-dir .  --local-dir-use-symlinks False

How to Get Started with the Model#

To support the Phi-3 models across a range of devices, platforms, and EP backends, we introduce a new API to wrap several aspects of generative AI inferencing. This API makes it easy to drag and drop LLMs straight into your app. To run the early version of these models with ONNX, follow the steps here.

Hardware Supported#

The ONNX models are tested on:

• 1 A100 GPU, SKU: Standard_ND96amsr_A100_v4 (CUDA)

Minimum Configuration Required:

• CUDA: NVIDIA GPU with Compute Capability >= 7.5

Model Description#

• Developed by: Microsoft
• Model type: ONNX
• Language(s) (NLP): Python, C, C++
• License: MIT
• Model Description: This is a conversion of the Phi-3 Small-8K-Instruct model for ONNX Runtime inference.

Additional Details#

• Phi-3 Small, Medium, and Vision Blog and Phi-3 Mini Blog
• Phi-3 Model Blog Link
• Phi-3 Model Card
• Phi-3 Technical Report
• Phi-3 on Azure AI Studio

Performance Metrics#

Phi-3 Small-8K-Instruct performs better with ONNX Runtime compared to PyTorch for all batch size, prompt length combinations. For FP16 CUDA, ORT performs up to 4X faster than PyTorch, while with INT4 CUDA, it's up to 10.9X faster than PyTorch.

The table below shows the average throughput of the first 256 tokens generated (tps) for FP16 and INT4 precisions on CUDA as measured on 1 A100 80GB GPU, SKU: Standard_ND96amsr_A100_v4.

Package Versions#

Appendix#

Model Card Contact#

parinitarahi, kvaishnavi, natke

Contributors#

Kunal Vaishnavi, Sunghoon Choi, Yufeng Li, Tianlei Wu, Sheetal Arun Kadam, Rui Ren, Baiju Meswani, Natalie Kershaw, Parinita Rahi