Running Phi3-mini-4K entirely in the browser! Check out this
demo.
This repository hosts the optimized Web version of ONNX Phi-3-mini-4k-instruct model to accelerate inference in the browser with ONNX Runtime Web.
The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters.
ONNX Runtime Web is a JavaScript library to enable web developers to deploy machine learning models directly in web browsers, offering multiple backends leveraging hardware acceleration. WebGPU backend is recommended to run Phi-3-mini efficiently.
Here is an
E2E example for running this optimized Phi3-mini-4K for the web, with ONNX Runtime harnessing WebGPU.
Supported devices and browser with WebGPU: Chrome 113+ and Edge 113+ for Mac, Windows, ChromeOS, and Chrome 121+ for Android. Pls visit
here for tracking WebGPU support in browsers
Performance vary between GPUs. The more powerful the GPU, the faster the speed. On a NVIDIA GeForce RTX 4090: ~42 tokens/second
To obtain other optimized Phi3-mini-4k ONNX models for server platforms, Windows, Linux, Mac desktops, and mobile, please visit
Phi-3-mini-4k-instruct onnx model. The model differences in the web version compared to other versions:
To optimize a fine-tuned Phi3-mini-4k model to run with ONNX Runtime Web, please follow
this Olive example.
Olive is an easy-to-use model optimization tool for generating an optimized ONNX model to efficiently run with ONNX Runtime across platforms.
