Image-to-Text
Transformers
Safetensors
English
Chinese
qwen2_5_vl
image-text-to-text
mathematical-reasoning
visual-reasoning
code-generation
qwen2.5-vl
text-generation-inference
Instructions to use gogoduan/MatPlotCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gogoduan/MatPlotCode with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="gogoduan/MatPlotCode")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("gogoduan/MatPlotCode") model = AutoModelForMultimodalLM.from_pretrained("gogoduan/MatPlotCode") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -33,9 +33,9 @@ For more details, please refer to the [project homepage](https://math-vr.github.
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## Citation
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If you find this work helpful, please consider citing our paper:
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```bibtex
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@article{
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title={CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images},
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author={Duan, Chengqi and Fang, Rongyao and
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journal={arXiv preprint arXiv:2510.11718},
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year={2025}
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}
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## Citation
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If you find this work helpful, please consider citing our paper:
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```bibtex
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@article{duan2025codeplot,
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title={CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images},
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author={Duan, Chengqi and Sun, Kaiyue and Fang, Rongyao and Zhang, Manyuan and Feng, Yan and Luo, Ying and Liu, Yufang and Wang, Ke and Pei, Peng and Cai, Xunliang and others},
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journal={arXiv preprint arXiv:2510.11718},
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year={2025}
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}
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