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")
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
```

Planning with the Views

This repository contains a model checkpoint presented in the paper Planning with the Views.

Project Page | GitHub | Paper

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:

@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}
}