---
title: MiniCPM-V-4.7-1B-fp16-ov
canonical_url: "https://www.modelscope.ai/models/OpenVINO/MiniCPM-V-4.7-1B-fp16-ov"
md_url: "https://www.modelscope.ai/models/OpenVINO/MiniCPM-V-4.7-1B-fp16-ov.md"
repository: OpenVINO/MiniCPM-V-4.7-1B-fp16-ov
last_updated: 2026-10-14
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - minicpmv4_7
architectures:
  - MiniCPMV4_7ForConditionalGeneration
base_model:
  - openbmb/MiniCPM-V-4.7-1B
base_model_relation: finetune
library_name:
  - transformer
  - openvino
  - pytorch
frameworks:
  - pytorch
downloads: 29
stars: 0
tags:
  - openvino
  - minicpm-v
  - multimodal
---

# MiniCPM-V-4.7-1B-fp16-ov

> MiniCPM-V-4.7-1B-fp16-ov - An open-source model by OpenVINO on ModelScope. MiniCPM-V-4.7-1B-fp16-ov

OpenVINO/MiniCPM-V-4.7-1B-fp16-ov is a image-text-to-text model on ModelScope. licensed under apache-2.0. derived from openbmb/MiniCPM-V-4.7-1B.

- **Repository**: OpenVINO/MiniCPM-V-4.7-1B-fp16-ov
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Base model**: openbmb/MiniCPM-V-4.7-1B
- **Tags**: openvino, minicpm-v, multimodal
- **Downloads**: 29
- **Stars**: 0
- **Last updated**: 2026-10-14

Source: https://www.modelscope.ai/models/OpenVINO/MiniCPM-V-4.7-1B-fp16-ov

---

# MiniCPM-V-4.7-1B-fp16-ov

- **Model creator:** [OpenBMB](https://huggingface.co/openbmb)
- **Original model:** [openbmb/MiniCPM-V-4.7-1B](https://huggingface.co/openbmb/MiniCPM-V-4.7-1B)

> [!WARNING]
> **EXPERIMENTAL MODEL**
> This model has not been fully validated with OpenVINO and uses development versions of Optimum Intel and OpenVINO GenAI.

## Description

This is the [MiniCPM-V-4.7-1B](https://huggingface.co/openbmb/MiniCPM-V-4.7-1B) model converted to the [OpenVINO IR](https://docs.openvino.ai/2026/documentation/openvino-ir-format.html) (Intermediate Representation) format.

MiniCPM-V 4.7 supports text, image, and video inputs.

## Compatibility

The examples below use:

* Optimum Intel `main` with [MiniCPM-V 4.7 export support](https://github.com/huggingface/optimum-intel/pull/2033)
* Transformers 5.18.0
* Matching nightly OpenVINO, OpenVINO Tokenizers, and OpenVINO GenAI builds with [VLM support](https://github.com/openvinotoolkit/openvino.genai/pull/4591)

Optimum Intel [currently declares](https://github.com/huggingface/optimum-intel/blob/main/setup.py) `transformers<5.11`; the 5.18.0 requirement for this model may cause a dependency warning until upstream aligns its package metadata.

## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum-intel/openvino/inference)

Install the development dependencies:

```bash
pip install -U "git+https://github.com/huggingface/optimum-intel.git@main" --extra-index-url https://download.pytorch.org/whl/cpu
pip install -U "transformers==5.18.0" Pillow requests
pip install --pre -U openvino --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
```

Run image inference with [Optimum Intel](https://huggingface.co/docs/optimum-intel/openvino/inference):

```python
from io import BytesIO

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

model_id = "OpenVINO/MiniCPM-V-4.7-1B-fp16-ov"
processor = AutoProcessor.from_pretrained(model_id)
model = OVModelForVisualCausalLM.from_pretrained(model_id)

image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
response = requests.get(image_url, timeout=30)
response.raise_for_status()
image = Image.open(BytesIO(response.content)).convert("RGB")
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=128)
print(processor.batch_decode(outputs[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True)[0])
```

## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)

Install the matching development packages:

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

Download the exported model from Hugging Face:

```python
from huggingface_hub import snapshot_download

model_path = snapshot_download("OpenVINO/MiniCPM-V-4.7-1B-fp16-ov")
```

Run image inference with OpenVINO GenAI:

```python
from io import BytesIO

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

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

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
response = requests.get(url, timeout=30)
response.raise_for_status()
with Image.open(BytesIO(response.content)) as image:
    image_tensor = ov.Tensor(np.asarray(image.convert("RGB"))[None])

history = ov_genai.ChatHistory([{"role": "user", "content": "Describe this image."}])
history.set_extra_context({"enable_thinking": False})
config = ov_genai.GenerationConfig()
config.max_new_tokens = 128

def streamer(chunk):
    print(chunk, end="", flush=True)
    return False

_ = pipe.generate(history, image=image_tensor, generation_config=config, streamer=streamer)
```

For more text, image, and video examples, see the [MiniCPM-V 4.7 OpenVINO notebook](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/minicpm-v-4.7/minicpm-v-4.7.ipynb).

## Limitations

Check the [original model card](https://huggingface.co/openbmb/MiniCPM-V-4.7-1B) for limitations. This OpenVINO export has not been fully validated.

## Legal information

The original [MiniCPM-V-4.7-1B](https://huggingface.co/openbmb/MiniCPM-V-4.7-1B) model declares the [Apache-2.0 license](https://www.apache.org/licenses/LICENSE-2.0).

## 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](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). 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.