Qwen3-VL-8B-Instruct-int4-ov

• Model creator: Qwen
• Original model: Qwen3-VL-8B-Instruct

Description

This is Qwen3-VL-8B-Instruct model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT4 by NNCF.

Qwen3-VL is the latest generation of vision-language models in the Qwen series, delivering comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.

Quantization Parameters

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

• mode: INT4_SYM
• ratio: 1.0
• group_size: 128

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

Compatibility

The provided OpenVINO™ IR model is compatible with:

• OpenVINO version 2026.1.0 and higher
• Optimum Intel 1.27.0 and higher

Running Model Inference with Optimum Intel

1. Install packages required for using Optimum Intel integration with the OpenVINO backend:

pip install -U "git+https://github.com/huggingface/optimum-intel.git" "openvino>=2026.1.0" "transformers>=4.57.0" "torch>=2.10.0" "pillow"

2. Run model inference:

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

model_id = "OpenVINO/Qwen3-VL-8B-Instruct-int4-ov"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = OVModelForVisualCausalLM.from_pretrained(model_id, trust_remote_code=True)

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(url, stream=True).raw)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

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

outputs = model.generate(**inputs, max_new_tokens=100)
generated = outputs[:, inputs.input_ids.shape[1]:]
print(processor.batch_decode(generated, skip_special_tokens=True)[0])

Running Model Inference with OpenVINO GenAI

1. Install packages required for using OpenVINO GenAI.

pip install huggingface_hub pillow
pip install -U --pre --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly openvino openvino-tokenizers openvino-genai

2. Download model from HuggingFace Hub

import huggingface_hub as hf_hub

model_id = "OpenVINO/Qwen3-VL-8B-Instruct-int4-ov"
model_path = "Qwen3-VL-8B-Instruct-int4-ov"

hf_hub.snapshot_download(model_id, local_dir=model_path)

3. Run model inference:

import numpy as np
import openvino as ov
import openvino_genai as ov_genai
import requests
from PIL import Image


def load_image(image_source):
    if isinstance(image_source, str) and image_source.startswith(("http://", "https://")):
        image = Image.open(requests.get(image_source, stream=True).raw)
    else:
        image = Image.open(image_source)
    image_data = np.array(image.convert("RGB"))[None]
    return ov.Tensor(image_data)


device = "CPU"
pipe = ov_genai.VLMPipeline(model_path, device)

image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image_tensor = load_image(image_url)
prompt = "Describe this image."

print(pipe.generate(prompt, image=image_tensor, max_new_tokens=100))

More GenAI usage examples can be found in OpenVINO GenAI library docs and samples

You can find more detailed usage examples in OpenVINO Notebooks:

• Qwen3-VL multimodal chatbot

Limitations

Check the original model card for limitations.

Legal information

The original model is distributed under Apache License 2.0 license. More details can be found in the original model card.

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.