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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("gogoduan/MatPlotCode") model = AutoModelForMultimodalLM.from_pretrained("gogoduan/MatPlotCode", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add comprehensive model card for CodePlot-CoT
Verified signatureThis PR adds a comprehensive model card for `CodePlot-CoT`. It includes: - Linking to the paper [CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images](https://huggingface.co/papers/2510.11718). - `library_name: transformers` (evidence from `config.json`) and `pipeline_tag: image-to-text` for discoverability. - `license: mit` (explicitly stated in GitHub README). - Links to the project page and GitHub repository. - A detailed introduction, model overview, and released data/models. - An explicit sample usage section with installation and inference code snippets directly from the GitHub README. - Relevant tags, datasets, and languages for improved searchability. This enhances the discoverability and usability of the model significantly.dc3586a
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Update generation_config.json
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