Instructions to use Pokerme/view2space_4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pokerme/view2space_4b with Transformers:

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

pipe = pipeline("image-text-to-text", model="Pokerme/view2space_4b")
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("Pokerme/view2space_4b")
model = AutoModelForMultimodalLM.from_pretrained("Pokerme/view2space_4b")
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 Pokerme/view2space_4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pokerme/view2space_4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pokerme/view2space_4b",
		"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/Pokerme/view2space_4b

SGLang

How to use Pokerme/view2space_4b 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 "Pokerme/view2space_4b" \
    --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": "Pokerme/view2space_4b",
		"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 "Pokerme/view2space_4b" \
        --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": "Pokerme/view2space_4b",
		"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 Pokerme/view2space_4b with Docker Model Runner:
```
docker model run hf.co/Pokerme/view2space_4b
```

VIEW2SPACE: Studying Multi-View Visual Reasoning from Sparse Observations

view2space_4b is an ECCV 2026 VIEW2SPACE model built on top of Qwen/Qwen3-VL-4B-Instruct. It is designed for grounded multi-view visual reasoning from sparse observations.

Quick start

Please see the VIEW2SPACE GitHub repository for evaluation code and usage:

🧑‍💻GitHub Repository

👀Project Page

Quick links

Overview

VIEW2SPACE studies how vision-language models reason across sparse and heterogeneous viewpoints. Instead of solving a task from a single image, the model must integrate partial observations from multiple views to form a more complete spatial understanding.

This checkpoint is the Qwen3-VL-4B VIEW2SPACE model release and is intended for multi-view visual reasoning under sparse observations.

Model Summary

Model name: view2space_4b
Base model: Qwen/Qwen3-VL-4B-Instruct
Architecture: Qwen3VLForConditionalGeneration
Project: VIEW2SPACE
Use case: multi-view visual reasoning from sparse observations
Venue: ECCV 2026

Highlights

Built for grounded multi-view reasoning rather than single-image prediction.
Targets sparse observations and heterogeneous viewpoints.
Released together with the public VIEW2SPACE testing set and evaluation code.

Resources

Public testing release: view2space-v1
Official repository: https://github.com/pokerme7777/VIEW2SPACE
Public eval pipeline: src/eval in the official repository

Usage Notes

Use the official VIEW2SPACE repository for evaluation scripts and prompt formatting.
The current public testing release is view2space-v1.
If you need another public data format, please open an issue in the GitHub repository.

Framework versions

TRL: 0.26.2
Transformers: 4.57.0
Pytorch: 2.7.1+cu126
Datasets: 4.4.2
Tokenizers: 0.22.1

Citations

@article{ke2026view2space,
  title={VIEW2SPACE: Studying Multi-View Visual Reasoning from Sparse Observations},
  author={Ke, Fucai and Cai, Zhixi and Li, Boying and Chen, Long and Lin, Beibei and Wang, Weiqing and Haghighi, Pari Delir and Haffari, Gholamreza and Rezatofighi, Hamid},
  journal={arXiv preprint arXiv:2603.16506},
  year={2026}
}