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*This model was released on 2024-05-31 and added to Hugging Face Transformers on 2025-08-18.*

# Ovis2

## Overview

The [Ovis2](https://github.com/AIDC-AI/Ovis) is an updated version of the [Ovis](https://huggingface.co/papers/2405.20797) model developed by the AIDC-AI team at Alibaba International Digital Commerce Group.

Ovis2 is the latest advancement in multi-modal large language models (MLLMs), succeeding Ovis1.6. It retains the architectural design of the Ovis series, which focuses on aligning visual and textual embeddings, and introduces major improvements in data curation and training methods.

<img src="https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/XB-vgzDL6FshrSNGyZvzc.png"  width="600">

<small> Ovis2 architecture.</small>

This model was contributed by [thisisiron](https://huggingface.co/thisisiron).

## Usage example

```python

from PIL import Image
import requests
import torch
from torchvision import io
from typing import Dict
from transformers.image_utils import load_images, load_video
from transformers import AutoModelForVision2Seq, AutoTokenizer, AutoProcessor, infer_device

device = f"{infer_device()}:0"

model = AutoModelForVision2Seq.from_pretrained(
    "thisisiron/Ovis2-2B-hf",
    dtype=torch.bfloat16,
).eval().to(device)
processor = AutoProcessor.from_pretrained("thisisiron/Ovis2-2B-hf")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "Describe the image."},
        ],
    },
]
url = "http://images.cocodataset.org/val2014/COCO_val2014_000000537955.jpg"
image = Image.open(requests.get(url, stream=True).raw)
messages = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(messages)

inputs = processor(
    images=[image],
    text=messages,
    return_tensors="pt",
)
inputs = inputs.to(model.device)
inputs['pixel_values'] = inputs['pixel_values'].to(torch.bfloat16)

with torch.inference_mode():
    output_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
    generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
    output_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
    print(output_text)
```

## Ovis2Config

[[autodoc]] Ovis2Config

## Ovis2VisionConfig

[[autodoc]] Ovis2VisionConfig

## Ovis2Model

[[autodoc]] Ovis2Model

## Ovis2ForConditionalGeneration

[[autodoc]] Ovis2ForConditionalGeneration
    - forward

## Ovis2ImageProcessor

[[autodoc]] Ovis2ImageProcessor

## Ovis2ImageProcessorFast

[[autodoc]] Ovis2ImageProcessorFast

## Ovis2Processor

[[autodoc]] Ovis2Processor
