Phi-4-multimodal-instruct-int4-cw-ov

• Model creator: microsoft
• Original model: Phi-4-multimodal-instruct

Description

This is Phi-4-multimodal-instruct model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT4 by NNCF.

The converted package includes the vision and audio encoders.

[!NOTE] The model is optimized for inference on NPU using these instructions.

Quantization Parameters

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

• mode: INT4_SYM
• group_size: -1
• ratio: 1.0
• awq: False
• scale_estimation: False

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

Compatibility

The provided OpenVINO™ IR model is compatible with:

• OpenVINO version 2026.4.0 and higher
• Intel® NPU Driver - Windows* 32.0.100.4023 for Intel® Core™ Ultra processors and higher

Running Model Inference with OpenVINO GenAI

1. Install packages required for using OpenVINO GenAI.

pip install openvino-genai huggingface_hub pillow

2. Download model from HuggingFace Hub

import huggingface_hub as hf_hub

model_id = "OpenVINO/Phi-4-multimodal-instruct-int4-cw-ov"
model_path = "Phi-4-multimodal-instruct-int4-cw-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
from PIL import Image

device = "NPU"
pipe = ov_genai.VLMPipeline(model_path, device)
image = ov.Tensor(np.array(Image.open("image.jpg").convert("RGB")))
result = pipe.generate("What is in this image?", image=image, max_new_tokens=100)
print(result.texts[0])

[!NOTE] With device="NPU", OpenVINO GenAI runs the language model on the NPU.

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

You can find more detailed usage examples in OpenVINO Notebooks:

• Visual language chat
• VLM notebooks

Limitations

Check the original model card for limitations.

Legal information

The original model is distributed under the MIT License. More details can be found in Phi-4-multimodal-instruct.

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.