Instructions to use two-tiger/MiMo-VRPRM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use two-tiger/MiMo-VRPRM-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="two-tiger/MiMo-VRPRM-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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("two-tiger/MiMo-VRPRM-7B")
model = AutoModelForMultimodalLM.from_pretrained("two-tiger/MiMo-VRPRM-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 Settings

vLLM

How to use two-tiger/MiMo-VRPRM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "two-tiger/MiMo-VRPRM-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": "two-tiger/MiMo-VRPRM-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/two-tiger/MiMo-VRPRM-7B

SGLang

How to use two-tiger/MiMo-VRPRM-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 "two-tiger/MiMo-VRPRM-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": "two-tiger/MiMo-VRPRM-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 "two-tiger/MiMo-VRPRM-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": "two-tiger/MiMo-VRPRM-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 two-tiger/MiMo-VRPRM-7B with Docker Model Runner:
```
docker model run hf.co/two-tiger/MiMo-VRPRM-7B
```

MiMo-VRPRM-7B / README.md

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	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- vision-language
	- multimodal
	- process-reward-modeling
	- visual-reasoning
	- best-of-n
	---

	# VRPRM-MiMo-7B

	VRPRM-MiMo-7B is a visual process reward model from VRPRM: Process Reward Modeling via Visual Reasoning.

	VRPRM is designed to evaluate intermediate reasoning steps for multimodal problems. The model is intended for visual process reward modeling, reasoning-step scoring, and Best-of-N selection for vision-language model outputs.

	## Model Details

	- Model family: VRPRM
	- Release variant: MiMo-7B
	- Serialized architecture: `Qwen2_5_VLForConditionalGeneration`
	- Model type: `qwen2_5_vl`
	- Weights format: sharded `safetensors`
	- Recommended library: `transformers`

	## Training Summary

	The [VRPRM](https://arxiv.org/abs/2508.03556) paper trains the model with a two-stage recipe:

	1. Supervised fine-tuning cold start on high-quality CoT-PRM data. Open-sourced on [VRPRM3.6K](https://huggingface.co/datasets/two-tiger/VRPRM3.6K).
	2. Reinforcement learning scaling on lower-cost non-CoT PRM data.

	## Intended Use

	This model is intended for research on:

	- Visual process reward modeling
	- Multimodal reasoning evaluation
	- Step-level scoring of visual question answering rationales
	- Best-of-N selection for vision-language model responses

	This model is not intended to be used as a standalone assistant.

	## Usage

	Load the model with Hugging Face Transformers from the repository root:

	```python
	from transformers import AutoModelForVision2Seq, AutoProcessor

	model_id = "YOUR_USERNAME/VRPRM-MiMo-7B"

	processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForVision2Seq.from_pretrained(
	model_id,
	torch_dtype="auto",
	device_map="auto",
	trust_remote_code=True,
	)
	```

	For the complete inference and evaluation pipeline, use the VRPRM project code.


	## Citation

	```bibtex
	@misc{chen2026vrprmprocessrewardmodeling,
	title={VRPRM: Process Reward Modeling via Visual Reasoning},
	author={Xinquan Chen and Chongying Yue and Bangwei Liu and Xuhong Wang and Yingchun Wang and Chaochao Lu},
	year={2026},
	eprint={2508.03556},
	archivePrefix={arXiv},
	primaryClass={cs.LG},
	url={https://arxiv.org/abs/2508.03556},
	}
	```