Instructions to use JonnyYu828/Stream3D-VLM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JonnyYu828/Stream3D-VLM-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="JonnyYu828/Stream3D-VLM-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, Qwen2_5_VLForConditionalGenerationWithVGGT

processor = AutoProcessor.from_pretrained("JonnyYu828/Stream3D-VLM-4B")
model = Qwen2_5_VLForConditionalGenerationWithVGGT.from_pretrained("JonnyYu828/Stream3D-VLM-4B", 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 JonnyYu828/Stream3D-VLM-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JonnyYu828/Stream3D-VLM-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": "JonnyYu828/Stream3D-VLM-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/JonnyYu828/Stream3D-VLM-4B

SGLang

How to use JonnyYu828/Stream3D-VLM-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 "JonnyYu828/Stream3D-VLM-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": "JonnyYu828/Stream3D-VLM-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 "JonnyYu828/Stream3D-VLM-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": "JonnyYu828/Stream3D-VLM-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 JonnyYu828/Stream3D-VLM-4B with Docker Model Runner:
```
docker model run hf.co/JonnyYu828/Stream3D-VLM-4B
```

Stream3D-VLM-4B / README.md

JonnyYu828

Update README.md

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	---
	license: apache-2.0
	language:
	- en
	metrics:
	- accuracy
	base_model:
	- Qwen/Qwen2.5-VL-3B-Instruct
	library_name: transformers
	tags:
	- vision-language-model
	- 3d-vision
	- multimodal
	- spatial-understanding
	- streaming-video
	pipeline_tag: image-text-to-text
	---

	<p align="center">
	<img src="https://stream3d-vlm.github.io/images/logo.png" width="160" />
	</p>
	<h2 align="center">
	Stream3D-VLM: Online 3D Spatial Understanding<br>with Incremental Geometry Priors
	</h2>

	<h4 align="center">
	<b>Project Page:</b> <a href="https://stream3d-vlm.github.io/">stream3d-vlm.github.io</a> \|
	<b>GitHub:</b> <a href="https://github.com/hanxunyu/Stream3D-VLM">hanxunyu/Stream3D-VLM</a>
	<br><br>
	<a href="https://stream3d-vlm.github.io/"><img src="https://img.shields.io/badge/Project-Home Page-green?logo=safari&logoColor=white" alt="Project Home Page"></a>
	<a href="https://github.com/hanxunyu/Stream3D-VLM"><img src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github" alt="GitHub Badge"></a>
	<a href="https://huggingface.co/datasets/JonnyYu828/Stream3D-1M-Dataset"><img src="https://img.shields.io/badge/HuggingFace-Dataset-orange?logo=huggingface" alt="Hugging Face Dataset"></a>
	<a href="https://huggingface.co/datasets/JonnyYu828/Stream3D-Bench"><img src="https://img.shields.io/badge/HuggingFace-Benchmark-blueviolet?logo=huggingface" alt="Hugging Face Benchmark"></a>
	<a href="https://arxiv.org/abs/2606.06891"><img src="https://img.shields.io/badge/arXiv-2606.06891-b31b1b.svg?logo=arxiv&logoColor=red" alt="arXiv"></a>
	</h4>


	---

	## 📰 News

	* 2026.06 — Released [Stream3D-Bench](https://huggingface.co/datasets/JonnyYu828/Stream3D-Bench).
	* 2026.06 — Released [Stream3D-1M-Dataset](https://huggingface.co/datasets/JonnyYu828/Stream3D-1M-Dataset).
	* 2026.06 — Released [Stream3D-VLM-4B](https://huggingface.co/JonnyYu828/Stream3D-VLM-4B).

	---

	## 🌟 Model Overview

	Stream3D-VLM is an online 3D vision-language model that supports real-time spatial understanding and interaction directly from streaming video. Unlike existing 3D Large Multimodal Models that operate in offline settings and require complete scene observations or predefined video clips, Stream3D-VLM enables efficient and continuous 3D scene comprehension without offline processing.

	To address the scarcity of streaming 3D–language data, we develop a scalable data generation pipeline that curates over 1M online spatio-temporal 3D QA pairs ([Stream3D-1M](https://huggingface.co/datasets/JonnyYu828/Stream3D-1M-Dataset)) and establish a comprehensive benchmark with 10,000 QA samples, spanning 29 subtasks across 5 cognitive competencies and 3 temporal interaction modes ([Stream3D-Bench](https://huggingface.co/datasets/JonnyYu828/Stream3D-Bench)).

	<p align="center">
	<img src="https://stream3d-vlm.github.io/images/pipeline.png" width="90%" />
	</p>

	---

	## 🧠 Key Characteristics

	- Online 3D Spatial Understanding: Real-time spatial reasoning from streaming video without requiring full scene reconstruction upfront.

	- Incremental Geometry Priors: The VSFI module injects temporally aligned geometric features from a 3D reconstruction model into the visual stream as video unfolds.

	- Streaming Control Modeling: Learns when to respond or remain silent via joint optimization of streaming control loss and standard language modeling loss.

	- Efficient Long-Context Inference: The plug-and-play GAVC module dynamically compresses visual tokens guided by 3D structure, reducing decoding overhead for real-time deployment.

	- Comprehensive Benchmark: Stream3D-Bench covers Forward Response (monitoring), Realtime Perception (observation), and Backward Tracing (memory) across diverse spatio-temporal 3D tasks.

	---

	## 🚀 Main Results

	### Stream3D-Bench (Online Evaluation)

	Stream3D-VLM consistently outperforms competing proprietary and open-source models on [Stream3D-Bench](https://huggingface.co/datasets/JonnyYu828/Stream3D-Bench), delivering the most accurate response timing and the lowest inference latency. Results are reported under a 1 fps streaming video setting. NA / MCA / OEA denote numerical, multiple-choice, and open-ended answers, respectively.

	<p align="center">
	<img src="https://stream3d-vlm.github.io/images/table_2_Stream3DBench_result.png" width="95%" />
	</p>

	> Bold and <u>underlined</u> values indicate the best and second-best results, respectively. More details can be found on our [paper](https://arxiv.org/abs/xxxx).

	### VSI-Bench (Offline Evaluation)

	Despite being designed for streaming scenarios, Stream3D-VLM also performs well across all subtasks of the offline spatial perception and reasoning benchmark, significantly surpassing both commercial and open-source models.

	<p align="center">
	<img src="https://stream3d-vlm.github.io/images/table_3_VSIBench_result.png" width="95%" />
	</p>

	---

	## 📦 Related Resources

	\| Resource \| Link \|
	\| -------- \| ---- \|
	\| Training Dataset \| [JonnyYu828/Stream3D-1M-Dataset](https://huggingface.co/datasets/JonnyYu828/Stream3D-1M-Dataset) \|
	\| Benchmark \| [JonnyYu828/Stream3D-Bench](https://huggingface.co/datasets/JonnyYu828/Stream3D-Bench) \|
	\| Code \| [hanxunyu/Stream3D-VLM](https://github.com/hanxunyu/Stream3D-VLM) \|
	\| Project Page \| [stream3d-vlm.github.io](https://stream3d-vlm.github.io/) \|

	---

	## Citation

	If you find Stream3D-VLM useful for your research or applications, please consider citing our work using the following BibTeX:

	```bibtex
	@article{yu2026stream3d,
	title={Stream3D-VLM: Online 3D Spatial Understanding with Incremental Geometry Priors},
	author={Hanxun Yu and Xuan Qu and Lei Ke and Boqiang Zhang and Yuxin Wang and Jianke Zhu and Dong Yu},
	journal={arXiv preprint arXiv:2606.06891},
	year={2026}
	}
	```