---
license: apache-2.0
datasets:
- AIDC-AI/Ovis-dataset
library_name: transformers
tags:
- MLLM
pipeline_tag: image-text-to-text
---

## 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
As always, Ovis1.5 remains fully open-source: we release the [training datasets](https://huggingface.co/datasets/AIDC-AI/Ovis-dataset), [training & inference codes](https://github.com/AIDC-AI/Ovis), and [model weights](https://huggingface.co/AIDC-AI/Ovis1.5-Llama3-8B) for **reproducible transparency** and community collaboration.

|                   |                                                MiniCPM-Llama3-V2.5 |                                                  Ovis1.5-Llama3-8B |
|:------------------|-------------------------------------------------------------------:|-------------------------------------------------------------------:|
| Training scripts  |                                                                  - |   [Github](https://github.com/AIDC-AI/Ovis/tree/main/scripts/v1_5) |
| ViT               |                                                        Siglip-400M |                                                        Siglip-400M |
| LLM               |                                                 Llama3-8B-Instruct |                                                 Llama3-8B-Instruct |
| MMTBench-VAL      |                                                               57.6 |                                                           **60.7** |
| MMBench-EN-V1.1   |                                                                 74 |                                                           **78.2** |
| MMBench-CN-V1.1   |                                                               70.1 |                                                           **75.2** |
| MMStar            |                                                               51.8 |                                                           **57.2** |
| MMMU-Val          |                                                               45.8 |                                                           **48.6** |
| MathVista-Mini    |                                                               54.3 |                                                           **62.4** |
| HallusionBenchAvg |                                                               42.4 |                                                           **44.5** |
| AI2D              |                                                               78.4 |                                                           **82.5** |
| OCRBench          |                                                                725 |                                                            **743** |
| MMVet             |                                                           **52.8** |                                                               52.2 |
| RealWorldQA       |                                                               63.5 |                                                           **64.6** |

## 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?tab=readme-ov-file#inference).
```bash
pip install torch==2.1.0 transformers==4.42.4 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/Ovis1.5-Llama3-8B",
                                             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>\n{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)]

# generate output
with torch.inference_mode():
    gen_kwargs = dict(
        max_new_tokens=1024,
        do_sample=False,
        top_p=None,
        top_k=None,
        temperature=None,
        repetition_penalty=None,
        eos_token_id=model.generation_config.eos_token_id,
        pad_token_id=text_tokenizer.pad_token_id,
        use_cache=True
    )
    output_ids = model.generate(input_ids, pixel_values=pixel_values, attention_mask=attention_mask, **gen_kwargs)[0]
    output = text_tokenizer.decode(output_ids, 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, Clip, and Siglip.