helenai/Phi-3.5-vision-instruct-ov-fp16

This is the microsoft/Phi-3.5-vision-instruct model, converted to OpenVINO, with fp16 weights.

Use OpenVINO GenAI to run inference on this model:

• Install OpenVINO GenAI nightly and pillow:

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

• Download a test image: curl -O "https://storage.openvinotoolkit.org/test_data/images/dog.jpg"
• Run inference:

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

# Choose GPU instead of CPU in the line below to run the model on Intel integrated or discrete GPU
pipe = openvino_genai.VLMPipeline("Phi-3.5-vision-instruct-ov-fp16", "CPU")
pipe.start_chat()

# Setting eos_token_id to tokenizer's eos token id is necessary for Phi-3.5-vision-instruct
config = openvino_genai.GenerationConfig()
config.set_eos_token_id(pipe.get_tokenizer().get_eos_token_id())
config.max_new_tokens = 100

image = Image.open("dog.jpg")
image_data = np.array(image.getdata()).reshape(1, image.size[1], image.size[0], 3).astype(np.uint8)
image_data = ov.Tensor(image_data)  

prompt = "Can you describe the image?"
result = pipe.generate(prompt, image=image_data, generation_config=config)
print(result.texts[0])

See OpenVINO GenAI repository