You can deploy this model and start earning money today!

OpenVINO/
gemma-4-31B-it-int4-ov

Name: gemma-4-31B-it-int4-ov
Availability: InStock

google/gemma-4-31B-it

quantized

apache-2.0

Views

No views yet

gemma-4-31B-it-int4-ov

Model creator: google
Original model: gemma-4-31B-it

[!WARNING] EXPERIMENTAL MODEL This model has not been fully validated with OpenVINO and is using a custom branch of optimum-intel. It may be fully supported and validated in the future.

Description

This is gemma-4-31B-it model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT4 by NNCF.

Quantization Parameters

Weight compression was performed using nncf.compress_weights with the following parameters:

mode: INT4_ASYM
group_size: 128
ratio: 1.0

For more information on quantization, check the OpenVINO model optimization guide.

Compatibility

The provided OpenVINO™ IR model is compatible with:

OpenVINO version 2026.2.0 and higher
Optimum Intel 1.27.0 and higher

Running Model Inference with Optimum Intel

Install packages required for using Optimum Intel:

pip install "git+https://github.com/huggingface/optimum-intel.git" --extra-index-url https://download.pytorch.org/whl/cpu
pip install transformers==5.5.0
pip install torchvision Pillow --extra-index-url https://download.pytorch.org/whl/cpu

Run model inference:

python

from optimum.intel.openvino import OVModelForVisualCausalLM
from transformers import AutoProcessor
from PIL import Image
import requests

model_id = "OpenVINO/gemma-4-31B-it-int4-ov"
processor = AutoProcessor.from_pretrained(model_id)
model = OVModelForVisualCausalLM.from_pretrained(model_id)

url = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/d5fbbd1a-d484-415c-88cb-9986625b7b11"
image = Image.open(requests.get(url, stream=True).raw)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "What is unusual in this picture?"},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, images=[image], return_tensors="pt")
input_len = inputs["input_ids"].shape[-1]

output = model.generate(**inputs, do_sample=False, max_new_tokens=100)
response = processor.decode(output[0][input_len:], skip_special_tokens=True)
print(response)

You can find more detailed usage examples in OpenVINO Notebooks:

Gemma 4

Limitations

Check the original model card for limitations.

Legal information

The original model is distributed under Apache License Version 2.0 license. More details can be found in gemma-4-31B-it.

Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel's Global Human Rights Principles. Intel's products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.