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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("hao05/Dr_Seg")
model = AutoModelForMultimodalLM.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 Settings

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

File size: 2,476 Bytes

---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- vllm
- grpo
- segmentation
- detection
- visual-reasoning
---

# 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](https://arxiv.org/abs/2603.00152).

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.

## Links
- **Paper:** [arXiv:2603.00152](https://arxiv.org/abs/2603.00152)
- **Dataset:** [COCONut](https://huggingface.co/datasets/hao05/coconut)
- **Code:** [GitHub Repository](https://github.com/eVI-group-SCU/Dr-Seg)

## Model Description
Dr. Seg-7B is fine-tuned from [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/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:
```bibtex
@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](https://github.com/JIA-Lab-research/VisionReasoner), [Seg-Zero](https://github.com/JIA-Lab-research/Seg-Zero), [EasyR1](https://github.com/hiyouga/EasyR1), [veRL](https://github.com/volcengine/verl), and [COCONut-PanCap](https://github.com/bytedance/coconut_cvpr2024). We also utilize pretrained models from [Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) and [SAM2](https://huggingface.co/facebook/sam2-hiera-large).