Instructions to use kagakouko/Spatial-Interactor-Qwen2.5-VL-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kagakouko/Spatial-Interactor-Qwen2.5-VL-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="kagakouko/Spatial-Interactor-Qwen2.5-VL-3B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("kagakouko/Spatial-Interactor-Qwen2.5-VL-3B")
model = AutoModelForMultimodalLM.from_pretrained("kagakouko/Spatial-Interactor-Qwen2.5-VL-3B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kagakouko/Spatial-Interactor-Qwen2.5-VL-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kagakouko/Spatial-Interactor-Qwen2.5-VL-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kagakouko/Spatial-Interactor-Qwen2.5-VL-3B",
		"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/kagakouko/Spatial-Interactor-Qwen2.5-VL-3B

SGLang

How to use kagakouko/Spatial-Interactor-Qwen2.5-VL-3B 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 "kagakouko/Spatial-Interactor-Qwen2.5-VL-3B" \
    --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": "kagakouko/Spatial-Interactor-Qwen2.5-VL-3B",
		"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 "kagakouko/Spatial-Interactor-Qwen2.5-VL-3B" \
        --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": "kagakouko/Spatial-Interactor-Qwen2.5-VL-3B",
		"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 kagakouko/Spatial-Interactor-Qwen2.5-VL-3B with Docker Model Runner:
```
docker model run hf.co/kagakouko/Spatial-Interactor-Qwen2.5-VL-3B
```

Spatial-Interactor-Qwen2.5-VL-3B / README.md

kagakouko

Align the English introduction with the original film

71c5411 verified 14 days ago

preview code

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4.09 kB

	---
	license: other
	license_name: qwen-research
	license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
	base_model: Qwen/Qwen2.5-VL-3B-Instruct
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- vision-language
	- video
	- spatial-reasoning
	- embodied-ai
	- qwen
	---

	<p align="center">
	<img src="https://raw.githubusercontent.com/ZJU-OmniAI/Spatial-Interactor/main/assets/readme/icon.png" width="100" alt="Spatial-Interactor">
	</p>

	<h1 align="center">Spatial-Interactor Qwen2.5-VL-3B</h1>

	<p align="center">
	<strong>Learning Spatial Reasoning through Interaction with the Observable Physical World</strong>
	</p>

	<p align="center">
	<a href="https://zju-omniai.github.io/Spatial-Interactor/"><img src="https://img.shields.io/badge/Project-Page-A56F59?style=flat-square&labelColor=54534D" alt="Project page"></a>
	<a href="https://zju-omniai.github.io/Spatial-Interactor/assets/paper.pdf?v=20260917"><img src="https://img.shields.io/badge/Paper-PDF-9B8255?style=flat-square&labelColor=54534D" alt="Paper PDF"></a>
	<a href="https://github.com/ZJU-OmniAI/Spatial-Interactor"><img src="https://img.shields.io/badge/Code-GitHub-738363?style=flat-square&labelColor=54534D" alt="Code"></a>
	<a href="https://huggingface.co/datasets/kagakouko/LSI-108K"><img src="https://img.shields.io/badge/LSI--108K-Dataset-887A9A?style=flat-square&labelColor=54534D" alt="Dataset"></a>
	</p>

	This is the full-parameter BF16 Spatial-Interactor checkpoint based on
	[Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct). It learns local
	world-state and ego-motion transitions through supervised fine-tuning, then
	uses On-Policy Distillation (OPD) to integrate successive transitions over long
	trajectories.

	> The privileged transition trace is used only during training. At inference,
	> this checkpoint takes the same image/video and question inputs as its base
	> model, with no extra trace, reward model, or teacher branch.

	## Overview

	<p align="center">
	<img src="https://raw.githubusercontent.com/ZJU-OmniAI/Spatial-Interactor/main/assets/readme/overview.webp?v=20260917" width="100%" alt="Spatial-Interactor overview: interaction trajectories, three-level curriculum, SFT and OPD, and spatial reasoning results">
	</p>

	<video src="https://huggingface.co/kagakouko/Spatial-Interactor-Qwen2.5-VL-3B/resolve/main/assets/presentation/spatial-interactor-intro-en.mp4?v=20260924-faithful" controls autoplay muted loop playsinline preload="metadata" poster="https://huggingface.co/kagakouko/Spatial-Interactor-Qwen2.5-VL-3B/resolve/main/assets/presentation/spatial-interactor-intro-en-poster.webp?v=20260924-faithful" width="100%"></video>

	## Presentation

	<video src="https://huggingface.co/kagakouko/Spatial-Interactor-Qwen2.5-VL-3B/resolve/main/assets/presentation/spatial-interactor-presentation-en.mp4?v=20260924" controls autoplay muted loop playsinline preload="metadata" poster="https://huggingface.co/kagakouko/Spatial-Interactor-Qwen2.5-VL-3B/resolve/main/assets/presentation/presentation-en-poster.webp?v=20260924" width="100%"></video>

	## Load

	```python
	import torch
	from transformers import AutoModelForImageTextToText, AutoProcessor

	model_id = "kagakouko/Spatial-Interactor-Qwen2.5-VL-3B"
	processor = AutoProcessor.from_pretrained(model_id)
	model = AutoModelForImageTextToText.from_pretrained(
	model_id, torch_dtype=torch.bfloat16, device_map="auto",
	)
	```

	Use the base model's [image/video input format](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
	No privileged trace or additional teacher is needed for inference.
	Weights, tokenizer, processor, and chat template are included.
	See the [training guide](https://github.com/ZJU-OmniAI/Spatial-Interactor/blob/main/docs/TRAINING.md)
	for SFT and OPD.

	## Citation

	For citation, use the [project BibTeX](https://zju-omniai.github.io/Spatial-Interactor/#citation).

	## License

	This checkpoint follows the Qwen Research License of the base model. Users must also comply with licenses and terms governing
	input datasets and media.