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---
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
- google/siglip-so400m-patch14-384
datasets:
- weizhiwang/Open-Qwen2VL-Data
- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
license: cc
pipeline_tag: image-text-to-text
---
# Model Card for Open-Qwen2VL
Open-Qwen2VL is a multimodal model that takes images and text as input and produces text as output. This model is described in the paper [Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources](https://huggingface.co/papers/2504.00595). The code is available at [https://github.com/Victorwz/Open-Qwen2VL](https://github.com/Victorwz/Open-Qwen2VL).
## Updates
- [4/1/2025] The codebase, model, data, and paper are released.
<!-- ## Model Details -->
## How to Use
Please firstly install Open-Qwen2VL via
```
pip install git+https://github.com/Victorwz/Open-Qwen2VL.git#subdirectory=prismatic-vlms
```
You can load the model and perform inference as follows:
```python
import requests
import torch
from PIL import Image
from prismatic import load
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# Load a pretrained VLM (either local path, or ID to auto-download from the HF Hub)
vlm = load("Open-Qwen2VL")
vlm.to(device, dtype=torch.bfloat16)
# Download an image and specify a prompt
image_url = "https://huggingface.co/adept/fuyu-8b/resolve/main/bus.png"
# image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
image = [vlm.vision_backbone.image_transform(Image.open(requests.get(image_url, stream=True).raw).convert("RGB")).unsqueeze(0)]
user_prompt = "<image>\nDescribe the image."
# Generate!
generated_text = vlm.generate_batch(
image,
[user_prompt],
do_sample=False,
max_new_tokens=512,
min_length=1,
)
print(generated_text[0])
```
The image caption results look like:
```
The image depicts a blue and orange bus parked on the side of a street. ...
```
## Acknowledgement
This work was partially supported by the BioPACIFIC Materials Innovation Platform of the National Science Foundation under Award No. DMR-1933487
## Citation
```bibtex
@article{Open-Qwen2VL,
title={Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources},
author={Wang, Weizhi and Tian, Yu and Yang, Linjie and Wang, Heng and Yan, Xifeng},
journal={arXiv preprint arXiv:2504.00595},
year={2025}
}
```
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