Visual Language Models
Collection
Collection of OpenVINO optimized models for visual-language assistance • 40 items • Updated • 19
This is gemma-4-E2B-it model converted to the OpenVINO™ IR (Intermediate Representation) format.
The provided OpenVINO™ IR model is compatible with:
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
from optimum.intel.openvino import OVModelForVisualCausalLM
from transformers import AutoProcessor
from PIL import Image
import requests
model_id = "OpenVINO/gemma-4-E2B-it-fp16-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:
Check the original model card for limitations.
The original model is distributed under Apache License Version 2.0 license. More details can be found in gemma-4-E2B-it.
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