Instructions to use MMR1/MMR1-Math-v0-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MMR1/MMR1-Math-v0-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="MMR1/MMR1-Math-v0-7B")
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("MMR1/MMR1-Math-v0-7B")
model = AutoModelForImageTextToText.from_pretrained("MMR1/MMR1-Math-v0-7B")
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 MMR1/MMR1-Math-v0-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MMR1/MMR1-Math-v0-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MMR1/MMR1-Math-v0-7B",
		"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/MMR1/MMR1-Math-v0-7B

SGLang

How to use MMR1/MMR1-Math-v0-7B 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 "MMR1/MMR1-Math-v0-7B" \
    --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": "MMR1/MMR1-Math-v0-7B",
		"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 "MMR1/MMR1-Math-v0-7B" \
        --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": "MMR1/MMR1-Math-v0-7B",
		"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 MMR1/MMR1-Math-v0-7B with Docker Model Runner:
```
docker model run hf.co/MMR1/MMR1-Math-v0-7B
```

Improve model card: Correct pipeline tag, link to code, and project page

by nielsr HF Staff - opened Mar 13, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+11

-5

Improve model card: Correct pipeline tag, link to code, and project page031c4813

nielsr

Mar 13, 2025

This PR improves the model card by:

Correcting the pipeline_tag to image-text-to-text.
Linking to the project page.
Ensuring the model card has a link to the Github repository.

Sicong

MMR1 org Mar 13, 2025

Hi Nielsr,

Thanks for your PR!

However, our project git repo should be https://github.com/LengSicong/MMR1.

nielsr

Mar 13, 2025

Could you clarify? The code is hosted at https://github.com/LengSicong/MMR1 and the project page at https://forjadeforest.github.io/LMM-R1-ProjectPage/, right?

Sicong

MMR1 org Mar 13, 2025

Hi Nielsr, our code is hosted at https://github.com/LengSicong/MMR1, but we don't have a project page yet. The above page is from another project. Thanks!

Update README.mdbd9eb3de

nielsr

Mar 13, 2025

Thanks, I've updated it

Sicong changed pull request status to merged Mar 13, 2025

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