---
license: apache-2.0
datasets:
- HuggingFaceM4/MMBench
language:
- en
base_model:
- openai/clip-vit-large-patch14-336
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
---
## POINTS-Qwen-2-5-7B-Chat

### Introduction

We are excited to announce the first version of POINTS, which integrates recent advancement in vision-language model and new techniques proposed by researchers from WeChat AI.

<p align="center">
        🏠 <a href="https://github.com/WePOINTS/WePOINTS">Github</a>&nbsp&nbsp |  &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2409.04828">Paper</a> &nbsp&nbsp  </a>
</p>

### What's new in POINTS?

**Key Innovations**

1. **Strong Baseline**: We integrate the most recent advancement in vision-language model, i.e., CapFusion, Dual Vision Encoder, and
Dynamic High Resolution, into POINTS.

2. **Pre-training Dataset Filtering**: We propose to filter the pre-training dataset using perplexity as a metric. Utilizing this filtering strategy, we can significantly reduce the size of the pre-training dataset and improve the performance of the model.

3. **Model Soup**: We propose to apply model soup to models, fine-tuned with different visual instruction tuning datasets, which can further significantly improve the performance of the model.

<p align="center">
    <img src="https://github.com/user-attachments/assets/6af35008-f501-400a-a870-b66a9bf2baab" width="60%"/>
<p>


### How to use POINTS?

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import CLIPImageProcessor
from PIL import Image
import torch
import requests
from io import BytesIO


image_url = 'https://github.com/user-attachments/assets/83258e94-5d61-48ef-a87f-80dd9d895524'
response = requests.get(image_url)
image_data = BytesIO(response.content)
pil_image = Image.open(image_data)
prompt = 'please describe the image in detail'
model_path = 'WePOINTS/POINTS-Qwen-2-5-7B-Chat'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path, trust_remote_code=True, device_map='cuda').to(torch.bfloat16)
image_processor = CLIPImageProcessor.from_pretrained(model_path)
generation_config = {
    'max_new_tokens': 1024,
    'temperature': 0.0,
    'top_p': 0.0,
    'num_beams': 1,
}
res = model.chat(
    pil_image,
    prompt,
    tokenizer,
    image_processor,
    True,
    generation_config
)
print(res)
```

### Evaluation

| Benchmark | InternVL2-8B | LLaVA-OneVision | POINTS |
| :-------: | :----------: | :-------------: | :----: |
| MMBench-dev-en | - | 80.8 | 83.2 |
| MathVista | 58.3 | 62.3 | 63.1 |
| HallucinationBench | 45.0 | 31.6 | 46.0 |
| OCRBench | 79.4 | 62.2 | 72.0 | 
| AI2D | 83.6 | 82.4 | 80.9 |
| MMVet | 54.3 | 51.9 | 52.3 |
| MMStar | 61.5 | 61.9 | 61.0 |
| MMMU | 51.2 | 47.9 | 49.4 |
| ScienceQA | 97.1 | 95.4 | - |
| MME | 2215.1 | 1993.6 | 2195.2 |
| RealWorldQA | 64.2 | 69.9 | 67.3 |
| LLaVA-Wild | 73.3 | 81.0 | 71.1 |


### Citation

If you find our work helpful, feel free to cite us:

```
@article{liu2024points,
  title={POINTS: Improving Your Vision-language Model with Affordable Strategies},
  author={Liu, Yuan and Zhao, Zhongyin and Zhuang, Ziyuan and Tian, Le and Zhou, Xiao and Zhou, Jie},
  journal={arXiv preprint arXiv:2409.04828},
  year={2024}
}

@article{liu2024rethinking,
  title={Rethinking Overlooked Aspects in Vision-Language Models},
  author={Liu, Yuan and Tian, Le and Zhou, Xiao and Zhou, Jie},
  journal={arXiv preprint arXiv:2405.11850},
  year={2024}
}
```