---
license: apache-2.0
datasets:
- AIDC-AI/Ovis-dataset
library_name: transformers
tags:
- MLLM
pipeline_tag: visual-question-answering
---

## Introduction
Ovis is a novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings. For a comprehensive introduction, please refer to [Ovis paper](https://arxiv.org/abs/2405.20797) and [Ovis GitHub](https://github.com/AIDC-AI/Ovis).

<div align="center">
    <img src="https://cdn-uploads.huggingface.co/production/uploads/658a8a837959448ef5500ce5/TIlymOb86R6_Mez3bpmcB.png" width="100%" />
</div>

## Model
Ovis can be instantiated with popular LLMs (e.g., Qwen, Llama3). We provide the following pretrained Ovis MLLMs:

|                |                                               Ovis-Clip-Qwen1.5-7B |                                               Ovis-Clip-Llama3-8B |                                               Ovis-Clip-Qwen1.5-14B |
|:---------------|-------------------------------------------------------------------:|------------------------------------------------------------------:|--------------------------------------------------------------------:|
| ViT            |                                                               Clip |                                                              Clip |                                                                Clip |
| LLM            |                                                    Qwen1.5-7B-Chat |                                                Llama3-8B-Instruct |                                                    Qwen1.5-14B-Chat |
| Download       | [Huggingface](https://huggingface.co/AIDC-AI/Ovis-Clip-Qwen1_5-7B) | [Huggingface](https://huggingface.co/AIDC-AI/Ovis-Clip-Llama3-8B) | [Huggingface](https://huggingface.co/AIDC-AI/Ovis-Clip-Qwen1_5-14B) |
| MMStar         |                                                               44.3 |                                                              49.5 |                                                                48.5 |
| MMB-EN         |                                                               75.1 |                                                              77.4 |                                                                78.4 |
| MMB-CN         |                                                               70.2 |                                                              72.8 |                                                                76.6 |
| MMMU-Val       |                                                               39.7 |                                                              44.7 |                                                                46.7 |
| MMMU-Test      |                                                               37.7 |                                                              39.0 |                                                                40.7 |
| MathVista-Mini |                                                               41.4 |                                                              40.8 |                                                                43.4 |
| MME            |                                                               1882 |                                                              2009 |                                                                1961 |
| HallusionBench |                                                               56.4 |                                                              61.1 |                                                                57.6 |
| RealWorldQA    |                                                               60.0 |                                                              57.9 |                                                                62.7 |

## Usage
Below is a code snippet to run Ovis with multimodal inputs. For additional usage instructions, including inference wrapper and Gradio UI, please refer to [Ovis GitHub](https://github.com/AIDC-AI/Ovis).
```bash
pip install torch==2.1.0 transformers==4.41.1 deepspeed==0.14.0 pillow==10.3.0
```
```python
import torch
from PIL import Image
from transformers import AutoModelForCausalLM

# load model
model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis-Clip-Qwen1_5-7B",
                                             torch_dtype=torch.bfloat16,
                                             multimodal_max_length=8192,
                                             trust_remote_code=True).cuda()
text_tokenizer = model.get_text_tokenizer()
visual_tokenizer = model.get_visual_tokenizer()
conversation_formatter = model.get_conversation_formatter()

# enter image path and prompt
image_path = input("Enter image path: ")
image = Image.open(image_path)
text = input("Enter prompt: ")
query = f'<image> {text}'
prompt, input_ids = conversation_formatter.format_query(query)
input_ids = torch.unsqueeze(input_ids, dim=0).to(device=model.device)
attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id).to(device=model.device)
pixel_values = [visual_tokenizer.preprocess_image(image).to(
    dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)]

# print model output
with torch.inference_mode():
    kwargs = dict(
        pixel_values=pixel_values,
        attention_mask=attention_mask,
        do_sample=False,
        top_p=None,
        temperature=None,
        top_k=None,
        repetition_penalty=None,
        max_new_tokens=512,
        use_cache=True,
        eos_token_id=text_tokenizer.eos_token_id,
        pad_token_id=text_tokenizer.pad_token_id
    )
    output_ids = model.generate(input_ids, **kwargs)[0]
    input_token_len = input_ids.shape[1]
    output = text_tokenizer.decode(output_ids[input_token_len:], skip_special_tokens=True)
    print(f'Output: {output}')
```

## Citation
If you find Ovis useful, please cite the paper
```
@article{lu2024ovis,
  title={Ovis: Structural Embedding Alignment for Multimodal Large Language Model}, 
  author={Shiyin Lu and Yang Li and Qing-Guo Chen and Zhao Xu and Weihua Luo and Kaifu Zhang and Han-Jia Ye},
  year={2024},
  journal={arXiv:2405.20797}
}
```
  
## License
The project is licensed under the Apache 2.0 License and is restricted to uses that comply with the license agreements of Qwen, Llama3, and Clip.