Instructions to use MLL-Lab/viewagent-all-qwen25vl7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MLL-Lab/viewagent-all-qwen25vl7b with Transformers:

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

pipe = pipeline("image-text-to-text", model="MLL-Lab/viewagent-all-qwen25vl7b")
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("MLL-Lab/viewagent-all-qwen25vl7b")
model = AutoModelForMultimodalLM.from_pretrained("MLL-Lab/viewagent-all-qwen25vl7b", 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 MLL-Lab/viewagent-all-qwen25vl7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MLL-Lab/viewagent-all-qwen25vl7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MLL-Lab/viewagent-all-qwen25vl7b",
		"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/MLL-Lab/viewagent-all-qwen25vl7b

SGLang

How to use MLL-Lab/viewagent-all-qwen25vl7b 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 "MLL-Lab/viewagent-all-qwen25vl7b" \
    --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": "MLL-Lab/viewagent-all-qwen25vl7b",
		"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 "MLL-Lab/viewagent-all-qwen25vl7b" \
        --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": "MLL-Lab/viewagent-all-qwen25vl7b",
		"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 MLL-Lab/viewagent-all-qwen25vl7b with Docker Model Runner:
```
docker model run hf.co/MLL-Lab/viewagent-all-qwen25vl7b
```

viewagent-all-qwen25vl7b / README.md

ManlingLi

Update README.md

4eb20cf verified about 1 month ago

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	---
	license: mit
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model: Qwen/Qwen2.5-VL-7B-Instruct
	datasets:
	- MLL-Lab/viewsuite
	---

	# Planning with the Views

	This repository contains a model checkpoint presented in the paper [Planning with the Views](https://huggingface.co/papers/2605.29563).

	[Project Page](https://viewagent.github.io) \| [GitHub](https://github.com/mll-lab-nu/ViewAgent) \| [Paper](https://arxiv.org/pdf/2605.29563)

	## Overview

	Can VLMs predict how each camera move changes the view, and plan many such moves ahead? This capability, called view planning, requires (1) understanding how a single action transforms the view, and (2) composing many such transformations across multi-turn plans to identify a target view.

	ViewSuite is a 3D point-cloud environment and benchmark suite for view planning, built on real ScanNet indoor scenes. It probes view planning through three diagnostic tasks:
	- Path-to-View (P2V): Predict the resulting view from an action sequence.
	- View-to-Path (V2P): Infer the action sequence between two views.
	- Interactive View Planning (IVP): Plan view changes over multiple turns to identify a target view.

	This model is an optimized version of Qwen2.5-VL-7B, trained using an iterative framework that alternates self-exploration with view graph distillation. This approach significantly closes the planning gap found in frontier VLMs, improving performance on interactive view planning tasks.

	## Citation

	If you find ViewAgent or these checkpoints useful in your research, please consider citing:

	```bibtex
	@article{wang2026viewagent,
	title = {Planning with the Views},
	author = {Wang, Kangrui and Li, Linjie and Yang, Zhengyuan and Chen, Shiqi and
	Wang, Zihan and Fei-Fei, Li and Wu, Jiajun and Guibas, Leonidas and
	Wang, Lijuan and Li, Manling},
	year = {2026}
	}
	```