Instructions to use MathLLMs/FigCodifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MathLLMs/FigCodifier with Transformers:

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

pipe = pipeline("image-text-to-text", model="MathLLMs/FigCodifier", trust_remote_code=True)
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 AutoModel
model = AutoModel.from_pretrained("MathLLMs/FigCodifier", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MathLLMs/FigCodifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MathLLMs/FigCodifier"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MathLLMs/FigCodifier",
		"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/MathLLMs/FigCodifier

SGLang

How to use MathLLMs/FigCodifier 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 "MathLLMs/FigCodifier" \
    --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": "MathLLMs/FigCodifier",
		"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 "MathLLMs/FigCodifier" \
        --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": "MathLLMs/FigCodifier",
		"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 MathLLMs/FigCodifier with Docker Model Runner:
```
docker model run hf.co/MathLLMs/FigCodifier
```

scikkk commited on Oct 20, 2025

Commit

9e84660

verified ·

1 Parent(s): ee3e738

Update README.md

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README.md +1 -32

README.md CHANGED Viewed

@@ -51,30 +51,9 @@ We introduce MathCoder-VL, a series of open-source large multimodal models (LMMs
 ## Usage
-For training and inference code, please refer to [InternVL](https://github.com/OpenGVLab/InternVL).
-```
-from datasets import load_dataset
-from PIL import Image
-from io import BytesIO
-mm_mathinstruct = load_dataset("MathLLMs/MM-MathInstruct")
-print(mm_mathinstruct)
-# show the last image
-img = Image.open(BytesIO(mm_mathinstruct['train'][-1]['image']))
-img.show()
-```
-It should print:
-```
-DatasetDict({
-    train: Dataset({
-        features: ['id', 'image', 'question', 'solution', 'image_path'],
-        num_rows: 2871988
-    })
-})
-```
 ### Prompt for TikZ Code Generation
@@ -109,17 +88,7 @@ Please provide the Python code needed to reproduce this image.\n<image>
   <img src="./examples/fig2.png" width="100%" title="Result Figure">
 </div>
-## Construction of MathCoder-VL
-<div align="center">
-  <img src="./examples/fig4.png" width="100%" title="Result Figure">
-</div>
-## Performance
-<div align="center">
-  <img src="./examples/tab1.png" width="100%" title="Result Figure">
-</div>
 ## **Citation**

 ## Usage
+For training and inference code, please refer to [InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-8B).
 ### Prompt for TikZ Code Generation
   <img src="./examples/fig2.png" width="100%" title="Result Figure">
 </div>
 ## **Citation**