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

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@@ -134,18 +134,7 @@ booktitle={The 63rd Annual Meeting of the Association for Computational Linguist
 year={2025},
 url={https://openreview.net/forum?id=nuvtX1imAb}
 }
-```
-```
-@inproceedings{
-lu2025mathcoder2,
-title={MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code},
-author={Zimu Lu and Aojun Zhou and Ke Wang and Houxing Ren and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
-booktitle={The Thirteenth International Conference on Learning Representations},
-year={2025},
-url={https://openreview.net/forum?id=1Iuw1jcIrf}
-}
-```
-```
 @inproceedings{
 wang2024mathcoder,
 title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},

 year={2025},
 url={https://openreview.net/forum?id=nuvtX1imAb}
 }
 @inproceedings{
 wang2024mathcoder,
 title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},