Instructions to use hao05/Dr_Seg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hao05/Dr_Seg with Transformers:

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

pipe = pipeline("image-text-to-text", model="hao05/Dr_Seg")
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("hao05/Dr_Seg")
model = AutoModelForImageTextToText.from_pretrained("hao05/Dr_Seg")
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 hao05/Dr_Seg with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hao05/Dr_Seg"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hao05/Dr_Seg",
		"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/hao05/Dr_Seg

SGLang

How to use hao05/Dr_Seg 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 "hao05/Dr_Seg" \
    --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": "hao05/Dr_Seg",
		"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 "hao05/Dr_Seg" \
        --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": "hao05/Dr_Seg",
		"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 hao05/Dr_Seg with Docker Model Runner:
```
docker model run hf.co/hao05/Dr_Seg
```

Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design

This repository contains the weights for Dr. Seg-7B, as presented in the paper Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design.

Dr. Seg is a plug-and-play GRPO-based framework designed to adapt Visual Large Language Models (VLLMs) for visual perception tasks such as reasoning segmentation and object detection. It introduces two key components: a Look-to-Confirm mechanism and a Distribution-Ranked Reward module, requiring no architectural modifications and integrating seamlessly with existing GRPO-based VLLMs.

Model Description

Dr. Seg-7B is fine-tuned from Qwen2.5-VL-7B-Instruct using perception-oriented designs. While standard GRPO is often tailored for language reasoning, Dr. Seg addresses the specific needs of visual perception by providing a broader output space and fine-grained, stable reward signals. Experiments demonstrate that Dr. Seg improves performance in complex visual scenarios while maintaining strong generalization.

Citation

If you find this work useful, please cite:

@article{sun2026dr,
  title={Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design},
  author={Sun, Haoxiang and Wang, Tao and Tang, Chenwei and Yuan, Li and Lv, Jiancheng},
  journal={arXiv preprint arXiv:2603.00152},
  year={2026}
}

Acknowledgements

This project builds upon several open-source efforts, including VisionReasoner, Seg-Zero, EasyR1, veRL, and COCONut-PanCap. We also utilize pretrained models from Qwen2.5-VL and SAM2.

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Safetensors

Model size

8B params

Tensor type

BF16

Paper for hao05/Dr_Seg

Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design

Paper • 2603.00152 • Published Feb 25 • 2

hao05
/

Dr_Seg

Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design

Links

Model Description

Citation

Acknowledgements

Paper for hao05/Dr_Seg

Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design