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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("gogoduan/CodePlot-CoT")
model = AutoModelForMultimodalLM.from_pretrained("gogoduan/CodePlot-CoT", device_map="auto")
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 Settings

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
```

CodePlot-CoT / README.md

nielsr HF Staff

Enhance model card with metadata, links, and usage example

29daaff verified 10 months ago

2.98 kB

license: mit
pipeline_tag: image-text-to-text
library_name: transformers

CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images

This repository contains the CodePlot-CoT model, a core component of the paper CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images. 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.

Sample Usage

Installation

To get started with CodePlot-CoT, clone the repository and install the required packages:

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):

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:

# 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 and the GitHub repository.

Citation

If you find this work helpful, please consider citing our paper:

@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}
}