Instructions to use MathLLMs/MathCoder-VL-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MathLLMs/MathCoder-VL-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="MathLLMs/MathCoder-VL-2B", 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/MathCoder-VL-2B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MathLLMs/MathCoder-VL-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MathLLMs/MathCoder-VL-2B"
# 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/MathCoder-VL-2B",
		"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/MathCoder-VL-2B

SGLang

How to use MathLLMs/MathCoder-VL-2B 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/MathCoder-VL-2B" \
    --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/MathCoder-VL-2B",
		"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/MathCoder-VL-2B" \
        --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/MathCoder-VL-2B",
		"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/MathCoder-VL-2B with Docker Model Runner:
```
docker model run hf.co/MathLLMs/MathCoder-VL-2B
```

scikkk commited on May 19, 2025

Commit

3e70c2b

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README.md +17 -0

README.md CHANGED Viewed

@@ -22,12 +22,29 @@ We introduce MathCoder-VL, a series of open-source large multimodal models (LMMs
 |-------------------------------------------------------------------|-----------------------------------------------------------------------|
 |  [Mini-InternVL-Chat-2B-V1-5](https://huggingface.co/OpenGVLab/Mini-InternVL-Chat-2B-V1-5)  |  [MathCoder-VL-2B](https://huggingface.co/MathLLMs/MathCoder-VL-2B)   	|
 |  [InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-8B)  |     	[MathCoder-VL-8B](https://huggingface.co/MathLLMs/MathCoder-VL-8B)|
 ## Usage
 For training and inference code, please refer to [InternVL](https://github.com/OpenGVLab/InternVL).
 ## Motivation

 |-------------------------------------------------------------------|-----------------------------------------------------------------------|
 |  [Mini-InternVL-Chat-2B-V1-5](https://huggingface.co/OpenGVLab/Mini-InternVL-Chat-2B-V1-5)  |  [MathCoder-VL-2B](https://huggingface.co/MathLLMs/MathCoder-VL-2B)   	|
 |  [InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-8B)  |     	[MathCoder-VL-8B](https://huggingface.co/MathLLMs/MathCoder-VL-8B)|
+|  [InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-8B)  |     	[FigCodifier-8B](https://huggingface.co/MathLLMs/FigCodifier)|
 ## Usage
 For training and inference code, please refer to [InternVL](https://github.com/OpenGVLab/InternVL).
+```
+from datasets import load_dataset
+mm_mathinstruct = load_dataset("MathLLMs/MM-MathInstruct")
+print(mm_mathinstruct)
+```
+It should print:
+```
+DatasetDict({
+    train: Dataset({
+        features: ['id', 'image', 'question', 'solution', 'image_path'],
+        num_rows: 2871988
+    })
+})
+```
 ## Motivation