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