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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Spatial-Interactor Qwen2.5-VL-3B

Learning Spatial Reasoning through Interaction with the Observable Physical World

This is the full-parameter BF16 Spatial-Interactor checkpoint based on 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

Spatial-Interactor overview: interaction trajectories, three-level curriculum, SFT and OPD, and spatial reasoning results

Presentation

Load

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. No privileged trace or additional teacher is needed for inference. Weights, tokenizer, processor, and chat template are included. See the training guide for SFT and OPD.

Citation

For citation, use the project BibTeX.

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.