EmbeddedLLM/gemma-2b-it-onnx

Gemma-2B-Instruct-ONNX

Model Summary

This repository contains optimized versions of the gemma-2b-it model, designed to accelerate inference using ONNX Runtime. These optimizations are specifically tailored for CPU and DirectML. DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning, offering GPU acceleration across a wide range of supported hardware and drivers, including those from AMD, Intel, NVIDIA, and Qualcomm.

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.
• ONNX model for int4 CPU and Mobile: ONNX model for CPU and mobile using int4 quantization via RTN. There are two versions uploaded to balance latency vs. accuracy. Acc=1 is targeted at improved accuracy, while Acc=4 is for improved performance. For mobile devices, we recommend using the model with acc-level-4.

Usage

Installation and Setup

To use the Gemma-2B-Instruct-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/gemma-2b-it-onnx --include="onnx/directml/*" --local-dir .\gemma-2b-it-onnx

5. Install necessary Python packages:

pip install numpy==1.26.4
pip install onnxruntime-directml
pip install --pre onnxruntime-genai-directml

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 .\gemma-2b-it-onnx

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

Resources and Technical Documentation

• Responsible Generative AI Toolkit
• Gemma on Kaggle
• Gemma on Vertex Model Garden

Terms of Use

• Terms