---
license: apache-2.0
tags:
- vision
- image-text-to-text
language:
- en
pipeline_tag: image-text-to-text
inference: true
base_model:
- llava-hf/llava-v1.6-mistral-7b-hf
base_model_relation: quantized
---

# llava-v1.6-mistral-7b-hf-int4-ov

 * Model creator: [llava-hf](https://huggingface.co/llava-hf)
 * Original model: [llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf)

## Description

This is [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT4 by [NNCF](https://github.com/openvinotoolkit/nncf).


## Quantization Parameters

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

* mode: **INT4_ASYM**
* ratio: **1.0**
* group_size: **128**


## Compatibility

The provided OpenVINO™ IR model is compatible with:

* OpenVINO version 2025.2.0 and higher


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

1. Install packages required for using OpenVINO GenAI.
```
pip install --pre -U --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/pre-release openvino openvino-tokenizers openvino-genai

pip install huggingface_hub
```

2. Download model from HuggingFace Hub
   
```
import huggingface_hub as hf_hub

model_id = "OpenVINO/llava-v1.6-mistral-7b-hf-int4-ov"
model_path = "llava-v1.6-mistral-7b-hf-int4-ov"

hf_hub.snapshot_download(model_id, local_dir=model_path)

```

1. Run model inference:

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

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

def load_image(image_file):
    if isinstance(image_file, str) and (image_file.startswith("http") or image_file.startswith("https")):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert("RGB")
    else:
        image = Image.open(image_file).convert("RGB")
    image_data = np.array(image.getdata()).reshape(1, image.size[1], image.size[0], 3).astype(np.byte)
    return ov.Tensor(image_data)

prompt = "What is unusual on this picture?"

url = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/d5fbbd1a-d484-415c-88cb-9986625b7b11"
image_tensor = load_image(url)

def streamer(subword: str) -> bool:
    print(subword, end="", flush=True)
    return False

pipe.start_chat()
output = pipe.generate(prompt, image=image_tensor, max_new_tokens=100, streamer=streamer)
pipe.finish_chat()
```

More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)


## Limitations

Check the original [model card](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) for limitations.

## Legal information

The original model is distributed under [apache-2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) license. More details can be found in [original model card](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf).

## 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.