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

Libraries

How to use MMR1/MMR1-7B-SFT with Transformers:

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

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MMR1/MMR1-7B-SFT"
# 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/MMR1-7B-SFT",
		"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/MMR1-7B-SFT

SGLang

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

Improve model card: Add comprehensive metadata, GitHub link, and Transformers usage

by nielsr HF Staff - opened Sep 26, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+261

-1

nielsr

Sep 26, 2025

This PR significantly improves the model card for the MMR1 model, presented in MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources.

Key improvements include:

Metadata: Addition of license: apache-2.0, pipeline_tag: image-text-to-text, library_name: transformers, and relevant tags (qwen2_5_vl, multimodal-llm, multimodal-reasoning, math-reasoning).
Contextual Metadata: Inclusion of datasets (MMR1/MMR1-SFT, MMR1/MMR1-RL) and base_model (Qwen/Qwen2.5-VL-7B-Instruct) for better lineage and discoverability.
Performance Metrics: Addition of model-index with an average performance score for the MMR1-7B-RL variant on mathematics-related multimodal reasoning benchmarks.
Content Enrichment: Integration of the paper abstract, a direct link to the GitHub repository, and key sections from the GitHub README (Introduction, Methodology, Open Resources, Evaluation, Analysis, Qualitative Demo, Acknowledgement, Citation, and License) for a more informative model overview. Relevant images from the GitHub repository are also included.
Usage Example: Addition of a clear "Quick Start (Inference)" code snippet demonstrating how to use the model with the transformers library, which directly supports the library_name metadata and enables the automated "How to use" widget. The example is designed for image-text input, aligning with the model's capabilities.
Clean-up: Removal of internal "File information" from the model card.

This enhances the model's discoverability, usability, and documentation for researchers and users on the Hugging Face Hub.

Improve model card: Add comprehensive metadata, GitHub link, and Transformers usagecaeb2386

Sicong changed pull request status to merged Oct 1, 2025

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