Model Overview

Description:

The NVIDIA Gemma-2b-it INT4 ONNX model is the quantized version of the Google Gemma-2b-it model which is a text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. It is well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure. For more information, please check here. The NVIDIA Gemma-2b-it INT4 ONNX model is quantized with TensorRT Model Optimizer.

This model is ready for commercial and research use case.

Steps followed to generate this quantized model:

• Download Google Gemma-2b-it model in Pytorch bfloat16 format from HuggingFace.

• Convert PyTorch model to ONNX FP16 using onnxruntime-genai model builder.

• Quantize Gemma-2b-it ONNX FP16 model to Gemma-2b-it ONNX INT4 AWQ model using TensorRT Model Optimizer – Windows.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to the Gemma-2b-it Model Card.

License/Terms of Use:

Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement. Additional Information:Gemma Terms of Use.

Reference:

Refer to Gemma-2b-it Model Card for the details.

Model Architecture:

Architecture Type: Transformers

Network architecture: Gemma

Input:

Input Type: Text

Input Format: String

Input parameters: Sequence (1D)

Other properties related to Input: Text strings can include a question, prompt, or a document to be summarized. Primarily for English language

Output:

Output Type: Text

Output Format: String

Output parameters: Sequence (1D)

Other properties: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document.

Software Integration:

Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere and newer GPUs. 6GB or higher GPU video memory are recommended. Higher VRAM may be required for larger context length use cases.

Supported Operating System(s): Windows

Model Version(s): v1.0

Training, Testing, and Evaluation Datasets:

Refer to Gemma-2b-it Model Card for the details.

Training Dataset: cnn_dailymail used for calibration.

• Link: cnn_dailymail

• Data Collection Method by dataset: [Automated]

• Labeling Method by dataset: [Unknown]

Evaluation Dataset: MMLU

• Link:MMLU.

• Data Collection Method by dataset: [Unknown]

• Labeling Method by dataset: [Not Applicable]

Evaluation Results:

Accuracy Scores: MMLU (5 shots):

With GenAI ORT->DML backend, on a desktop RTX 4090 GPU system.

Overall accuracy = 37.26

Test configuration:

• GPU: RTX 4090, RTX 3090.  

• Windows 11: 23H2

• NVIDIA Graphics driver: R565 or higher

Inference:

Inference Backend: Onnxruntime-GenAI-DirectML

(Note: Please refer to Readme.txt for the detailed instructions.)

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

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