README.md

---
license: mit
---

# **Phi-3.5 Vision OpenVINO INT4 Model**

<b><span style="text-decoration:underline">Note: This is unoffical version,just for test and dev.</span></b>

This is the OpenVINO format INT 4 quantized version of the Microsoft Phi-3.5 VISIOn. You can use run this script to convert

```python

import requests
from pathlib import Path

if not Path("ov_phi3_vision.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/phi-3-vision/ov_phi3_vision.py")
    open("ov_phi3_vision.py", "w").write(r.text)


if not Path("gradio_helper.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/phi-3-vision/gradio_helper.py")
    open("gradio_helper.py", "w").write(r.text)

if not Path("notebook_utils.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py")
    open("notebook_utils.py", "w").write(r.text)

from ov_phi3_vision import convert_phi3_model

from pathlib import Path
import nncf


model_id = "microsoft/Phi-3.5-vision-instruct"
out_dir = Path("Save Your Phi-3.5-vision OpenVINO INT4 PATH")
compression_configuration = {
    "mode": nncf.CompressWeightsMode.INT4_SYM,
    "group_size": 64,
    "ratio": 0.6,
}

convert_phi3_model(model_id, out_dir, compression_configuration)

```

## **Sample Code**


```python

from ov_phi3_vision import OvPhi3Vision

from notebook_utils import device_widget

device = device_widget(default="GPU", exclude=["NPU"])

out_dir = Path("Your Phi-3.5-vision OpenVINO INT4 PATH")

model = OvPhi3Vision(out_dir, device.value)

import requests
from PIL import Image

image = Image.open(r"Your local image Path")

from transformers import AutoProcessor, TextStreamer

messages = [
    {"role": "user", "content": "<|image_1|>\nPlease analyze the image"},
]

processor = AutoProcessor.from_pretrained(out_dir, trust_remote_code=True)

prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

inputs = processor(prompt, [image], return_tensors="pt")

generation_args = {"max_new_tokens": 500, "do_sample": False, "streamer": TextStreamer(processor.tokenizer, skip_prompt=True, skip_special_tokens=True)}

print("Analyze:")
generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args)

```