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

Libraries

How to use two-tiger/VRPRM-sft with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("two-tiger/VRPRM-sft")
model = AutoModelForMultimodalLM.from_pretrained("two-tiger/VRPRM-sft", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use two-tiger/VRPRM-sft with vLLM:

Install from pip and serve model

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

SGLang

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

VRPRM-sft

VRPRM-sft is the merged supervised checkpoint of VRPRM, a visual reasoning process reward model. It first generates a global reasoning trace and then emits a binary 0/1 judgment for each candidate solution step.

Training

Base model: Qwen/Qwen3-VL-8B-Thinking
Data: 1,378 multi-turn conversations / 5,592 confident step labels
Loss weights: 0.5 for global thinking, 2.0 for step judgments
Tuning: LoRA r=16, alpha=32, lr=1e-4
Training: 3 epochs / 513 steps, followed by LoRA merge

Evaluation

VisualProcessBench official no-reference protocol:

Model	Overall step macro-F1
VRPRM-sft	66.15

Usage

Use the two-stage global-thinking protocol from the code repository. First ask for one <think>...</think> block, then query each step with guided choice 0/1 while feeding previous judgments. The evaluator is benchmarks/visualprocessbench/eval_vpb_global_think.py.

Limitations

This model is intended for research on process reward modeling and multimodal reasoning. It is not a general assistant and should not be used for automated grading of high-stakes work without human review.