metadata
library_name: transformers
pipeline_tag: image-text-to-text
license: mit
tags:
- multimodal
- vision-language
- reasoning
- qwen2
Model Card for Virgo-72B
Virgo is a multi-modal slow-thinking reasoning model based on Qwen2-VL-72B-Instruct. It excels in image-text-to-text tasks, demonstrating strong performance on various multimodal benchmarks. Virgo leverages a long-form thought process for enhanced reasoning capabilities, effectively integrating visual information into its responses.
Model Details
Model Sources
- Repository: https://github.com/RUCAIBox/Virgo
- Paper: https://arxiv.org/pdf/2501.01904
Quick Start
This example demonstrates how to use Virgo-72B with the vllm library for text generation given an image and text input. Ensure you have vllm and Pillow installed (pip install vllm Pillow) and a suitable image file (case/2246_image_1.jpg in this example).
from vllm import LLM, SamplingParams
from PIL import Image
model_name = "RUC-AIBOX/Virgo-72B"
placeholder = "<|image_pad|>"
llm = LLM(
model=model_name,
trust_remote_code=True,
tensor_parallel_size=8, # Adjust based on your hardware
)
question = "Please first think deeply about the question, and then put the final answer in \\boxed{}.
In the diagram, $\\angle E A D=90^{\\circ}, \\angle A C D=90^{\\circ}$, and $\\angle A B C=90^{\\circ}$. Also, $E D=13, E A=12$, $D C=4$, and $C B=2$. Determine the length of $A B$."
prompt = ("<|im_start|>system
You are a helpful assistant.<|im_end|>
"
f"<|im_start|>user
<|vision_start|>{placeholder}<|vision_end|>"
f"{question}<|im_end|>
"
"<|im_start|>assistant
")
sampling_params = SamplingParams(
temperature=0.0,
top_k=1,
top_p=1.0,
repetition_penalty=1.05,
max_tokens=8192
)
image = Image.open("case/2246_image_1.jpg")
inputs = {
"prompt": prompt,
"multi_modal_data": {
"image": image
},
}
outputs = llm.generate(inputs, sampling_params)
print(outputs[0].outputs[0].text)
Citation
@article{du2025virgo,
title={Virgo: A Preliminary Exploration on Reproducing o1-like MLLM},
author={Yifan Du and Zikang Liu and Yifan Li and Wayne Xin Zhao and Yuqi Huo and Bingning Wang and Weipeng Chen and Zheng Liu and Zhongyuan Wang and Ji-Rong Wen},
journal={arXiv preprint arXiv:2501.01904},
year={2025}
}