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

Libraries

How to use graysonggg/dlgpr with Transformers:

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

pipe = pipeline("image-text-to-text", model="graysonggg/dlgpr")
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("graysonggg/dlgpr")
model = AutoModelForMultimodalLM.from_pretrained("graysonggg/dlgpr", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use graysonggg/dlgpr with vLLM:

Install from pip and serve model

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

SGLang

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

DQ-Pilot (DlgPR)

This repository contains the final merged SFT+GRPO DQ-Pilot checkpoint from DialogueVPR: Towards Conversational Visual Place Recognition. DQ-Pilot examines candidate street-view images and the dialogue history, then asks a discriminative follow-up question to improve place retrieval.

from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration

model_id = "graysonggg/dlgpr"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)

The complete CMPL retrieval and dialogue pipeline is available in the official code repository. Training artifacts are available in the associated Hugging Face data repository.

Citation

@inproceedings{song2026dialoguevpr,
  title={DialogueVPR: Towards Conversational Visual Place Recognition},
  author={Song, Yukun and Wang, Changwei and Pei, Xingtian and Xu, Shibiao and Xu, Wenhao and Chen, Shunpeng and Zhang, Yu and Zhang, Ke and Xu, Rongtao and Feng, Xuxiang and Wang, Pengyang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={41100--41110},
  year={2026}
}

@misc{song2026dialoguevpr_arxiv,
  title={DialogueVPR: Towards Conversational Visual Place Recognition},
  author={Song, Yukun and Wang, Changwei and Pei, Xingtian and Xu, Shibiao and Xu, Wenhao and Chen, Shunpeng and Zhang, Yu and Zhang, Ke and Xu, Rongtao and Feng, Xuxiang and Wang, Pengyang},
  year={2026},
  eprint={2607.14115},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2607.14115}
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for graysonggg/dlgpr

Base model

Qwen/Qwen2.5-VL-7B-Instruct

Finetuned

(1187)

this model

Dataset used to train graysonggg/dlgpr

Paper for graysonggg/dlgpr

DialogueVPR: Towards Conversational Visual Place Recognition

Paper • 2607.14115 • Published May 8