Instructions to use rsoohyun/SpatialBlock-4B-reason with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rsoohyun/SpatialBlock-4B-reason with Transformers:

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

pipe = pipeline("image-text-to-text", model="rsoohyun/SpatialBlock-4B-reason")
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("rsoohyun/SpatialBlock-4B-reason")
model = AutoModelForMultimodalLM.from_pretrained("rsoohyun/SpatialBlock-4B-reason", 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 rsoohyun/SpatialBlock-4B-reason with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rsoohyun/SpatialBlock-4B-reason"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rsoohyun/SpatialBlock-4B-reason",
		"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/rsoohyun/SpatialBlock-4B-reason

SGLang

How to use rsoohyun/SpatialBlock-4B-reason 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 "rsoohyun/SpatialBlock-4B-reason" \
    --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": "rsoohyun/SpatialBlock-4B-reason",
		"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 "rsoohyun/SpatialBlock-4B-reason" \
        --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": "rsoohyun/SpatialBlock-4B-reason",
		"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 rsoohyun/SpatialBlock-4B-reason with Docker Model Runner:
```
docker model run hf.co/rsoohyun/SpatialBlock-4B-reason
```

SpatialBlock-4B-reason

File size: 970 Bytes

5893cd7

---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
datasets:
- rsoohyun/SpatialBlock-15k
base_model:
- Qwen/Qwen3-VL-4B-Instruct
---

# SpatialBlock-4B-reason

This repository contains the **SpatialBlock-4B-reason** checkpoint from the paper [SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem](https://huggingface.co/papers/2609.07064).

It is a fine-tuned version of [Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) on the synthetic [SpatialBlock-15k](https://huggingface.co/datasets/rsoohyun/SpatialBlock-15k) dataset. The model directly predicts answers to spatial reasoning tasks such as 3D-to-2D projection, viewpoint transformation, and structural combination.

For training details, evaluation results, and the companion “direct” model, please refer to the GitHub repository: [https://github.com/rsoohyun/SpatialBlock](https://github.com/rsoohyun/SpatialBlock).