Instructions to use gogoduan/CodePlot-CoT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gogoduan/CodePlot-CoT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="gogoduan/CodePlot-CoT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("gogoduan/CodePlot-CoT")
model = AutoModelForImageTextToText.from_pretrained("gogoduan/CodePlot-CoT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gogoduan/CodePlot-CoT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gogoduan/CodePlot-CoT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gogoduan/CodePlot-CoT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/gogoduan/CodePlot-CoT

SGLang

How to use gogoduan/CodePlot-CoT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "gogoduan/CodePlot-CoT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gogoduan/CodePlot-CoT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "gogoduan/CodePlot-CoT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gogoduan/CodePlot-CoT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use gogoduan/CodePlot-CoT with Docker Model Runner:
```
docker model run hf.co/gogoduan/CodePlot-CoT
```

nielsr HF Staff commited on Oct 14, 2025

Commit

29daaff

verified ·

1 Parent(s): d3c95ca

Enhance model card with metadata, links, and usage example

Browse files

This PR significantly enhances the model card by adding essential metadata (`license`, `pipeline_tag`, `library_name`) and rich content.
It links the model to its paper [CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images](https://huggingface.co/papers/2510.11718), its project page, and its GitHub repository.
Furthermore, a "Sample Usage" section with installation instructions and code snippets for inference is included, directly sourced from the project's GitHub README.

This update will make the model more discoverable and easier to use for the community.
Please review and merge this PR if everything looks good.

Files changed (1) hide show

README.md +75 -0

README.md ADDED Viewed

	@@ -0,0 +1,75 @@

+---
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+# CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images
+<div align="center">
+<a href="https://math-vr.github.io"><img src="https://img.shields.io/badge/Project-Homepage-green" alt="Home"></a>
+<a href="https://huggingface.co/papers/2510.11718"><img src="https://img.shields.io/badge/Paper-red" alt="Paper"></a>
+<a href="https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT"><img src="https://img.shields.io/badge/GitHub-Code-keygen.svg?logo=github&style=flat-square" alt="GitHub"></a>
+</div>
+This repository contains the **CodePlot-CoT** model, a core component of the paper [CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images](https://huggingface.co/papers/2510.11718).
+CodePlot-CoT is an innovative code-driven Chain-of-Thought (CoT) paradigm designed to enable Vision Language Models (VLMs) to "think with images" when solving mathematical problems. Instead of generating pixel-based images directly, the model outputs executable plotting code to represent its "visual thoughts". This code is then executed to render a precise figure, which is reinput to the model as a visual input for subsequent reasoning steps.
+The model is built upon the Qwen2.5-VL architecture and is compatible with the `transformers` library.
+<div align="center">
+  <img src="https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT/raw/main/figures/teaser.png" width="100%"/>
+</div>
+## Sample Usage
+### Installation
+To get started with CodePlot-CoT, clone the repository and install the required packages:
+```bash
+conda create -n codeplot python==3.10
+conda activate codeplot
+git clone git@github.com:HKU-MMLab/Math-VR-CodePlot-CoT.git
+cd CodePlot-CoT
+pip install -r requirements.txt
+pip install flash_attn==2.7.4.post1
+```
+For benchmark evaluation only (additional dependencies):
+```bash
+pip install openai==4.1.1
+pip install datasets==2.0.0
+```
+### Model Weights
+Ensure your directory structure for the models looks like this:
+```
+CodePlot-CoT
+├── ckpts
+│   ├── CodePlot-CoT
+│   ├── MatPlotCode
+├── ...
+```
+### Inference
+You can perform inference using the provided scripts:
+```python
+# Convert image to python code with MatPlotCode
+python image_to_code.py
+# Solve math problems with CodePlot-CoT
+python math_infer.py
+```
+For more details on evaluation and benchmarks, please refer to the [project homepage](https://math-vr.github.io) and the [GitHub repository](https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT).
+## Citation
+If you find this work helpful, please consider citing our paper:
+```bibtex
+@article{duan2025code,
+  title={CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images},
+  author={Duan, Chengqi and Fang, Rongyao and Wang, Yuqing and Wang, Kun and Huang, Linjiang and Zeng, Xingyu and Li, Hongsheng and Liu, Xihui},
+  journal={arXiv preprint arXiv:2510.11718},
+  year={2025}
+}
+```