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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="two-tiger/VRPRM-rl")
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-rl")
model = AutoModelForMultimodalLM.from_pretrained("two-tiger/VRPRM-rl", 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-rl with vLLM:

Install from pip and serve model

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

SGLang

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

VRPRM-rl

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---
license: apache-2.0
base_model: Qwen/Qwen3-VL-8B-Thinking
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - process-reward-model
  - visual-reasoning
  - multimodal
  - math
  - reinforcement-learning
---

# VRPRM-rl

`VRPRM-rl` is the merged process-supervised RL checkpoint released for VRPRM.
It is initialized from `VRPRM-sft` and trained with GSPO-token/GRPO LoRA RL on
40K confident VisualPRM400K-derived examples.

## Training

- Initialization: `VRPRM-sft`
- Data: 40K train / 2K validation
- Rollouts: 16 per prompt, temperature 0.8, top-p 0.95
- Reward: `0.92 * step + 0.05 * format + 0.03 * think`
- Optimization: lr 1e-6, KL coefficient 0.2, 600 steps
- Public artifact: end-of-run merged checkpoint

## Evaluation

VisualProcessBench official no-reference protocol:

| Model | Overall step macro-F1 |
|---|---:|
| VRPRM-rl | 69.18 |

The public artifact is the end-of-run checkpoint. The run's highest RL
validation reward occurred earlier; comparisons should use the exact released
artifact and protocol documented in the code repository.

## Usage

Use the same global-thinking stepwise protocol as `VRPRM-sft`. The model is
evaluated with `benchmarks/visualprocessbench/eval_vpb_global_think.py`.

## Limitations

This research model is not a general assistant and is not suitable for
high-stakes automated grading without human oversight.