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---
license: gemma
pipeline_tag: text-generation
tags:
- ONNX
- DML
- DirectML
- ONNXRuntime
- gemma
- google
- conversational
- custom_code
inference: false
language:
- en
---
# Gemma-2B-Instruct-ONNX
## Model Summary
This repository contains optimized versions of the [gemma-2b-it](https://huggingface.co/google/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:**
```sh
conda create -n onnx python=3.10
conda activate onnx
```
2. **Install Git LFS:**
```sh
winget install -e --id GitHub.GitLFS
```
3. **Install Hugging Face CLI:**
```sh
pip install huggingface-hub[cli]
```
4. **Download the model:**
```sh
huggingface-cli download EmbeddedLLM/gemma-2b-it-onnx --include="onnx/directml/*" --local-dir .\gemma-2b-it-onnx
```
5. **Install necessary Python packages:**
```sh
pip install numpy==1.26.4
pip install onnxruntime-directml
pip install --pre onnxruntime-genai-directml
```
6. **Install Visual Studio 2015 runtime:**
```sh
conda install conda-forge::vs2015_runtime
```
7. **Download the example script:**
```sh
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:**
```sh
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](https://ai.google.dev/responsible)
- [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
- [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-it-gg-hf)
## Terms of Use
- [Terms](https://www.kaggle.com/models/google/gemma/license/consent) |