add minicpmv support

[eaidova]

What does this PR do?

support of minicpmv

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

model_id = "openbmb/MiniCPM-V-2_6"

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

prompt= "<|im_start|>user\n(<image>./</image>)\nWhat is unusual on this image?<|im_end|>\n<|im_start|>assistant\n"
image = Image.open(requests.get("https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/d5fbbd1a-d484-415c-88cb-9986625b7b11", stream=True).raw).convert('RGB')

model = OVModelForVisualCausalLM.from_pretrained(model_dir, trust_remote_code=True)

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

result  = model.generate(**inputs, max_new_tokens=20)

print(processor.tokenizer.batch_decode(result[:, inputs["input_ids"].shape[1]:]))

changes were already reviewed as part of #928 (I just moved all things relevant to others models support in separated PRs)

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?

[HuggingFaceDocBuilderDev]

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[nikita-savelyevv]

Do you think it would be possible to expose a method like this as a part of an API for working with OVModelForVisualCausalLMs?

It would simplify the demo examples and usage in general. Also, it will come helpful during dataset collection for data-aware compression.

[AlexKoff88]

I was also thinking that it could help for demos, optimization and validation purposes. No problem if we do it in the follow-up PR.