Instructions to use YuhongLiu/SpatialASM-Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YuhongLiu/SpatialASM-Qwen3.5-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="YuhongLiu/SpatialASM-Qwen3.5-9B")
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("YuhongLiu/SpatialASM-Qwen3.5-9B")
model = AutoModelForMultimodalLM.from_pretrained("YuhongLiu/SpatialASM-Qwen3.5-9B", 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 YuhongLiu/SpatialASM-Qwen3.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YuhongLiu/SpatialASM-Qwen3.5-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YuhongLiu/SpatialASM-Qwen3.5-9B",
		"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/YuhongLiu/SpatialASM-Qwen3.5-9B

SGLang

How to use YuhongLiu/SpatialASM-Qwen3.5-9B 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 "YuhongLiu/SpatialASM-Qwen3.5-9B" \
    --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": "YuhongLiu/SpatialASM-Qwen3.5-9B",
		"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 "YuhongLiu/SpatialASM-Qwen3.5-9B" \
        --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": "YuhongLiu/SpatialASM-Qwen3.5-9B",
		"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 YuhongLiu/SpatialASM-Qwen3.5-9B with Docker Model Runner:
```
docker model run hf.co/YuhongLiu/SpatialASM-Qwen3.5-9B
```

SpatialASM: Internalizing Active Mentalization for Spatial Reasoning

📖Paper | 🏠Homepage | 🤗SpatialASM Collection | 🤗SpatialASM-128k Dataset | 🤗SpatialASM-NVG Model

📢 News

🚀 [2026/10/12] We have released the SpatialASM 📖Arxiv paper and its repository.
🚀 [2025/10/12] We have released our allocentric novel-view generator 🤗SpatialASM-NVG.
🚀 [2025/10/12] We have released 🤗SpatialASM-edit140k, an image-text-to-image dataset for tuning novel-view generators, and two spatial-VQA datasets with high-quality CoT responses: 🤗SpatialASM-128k and its subset 🤗SpatialASM-66k.

🌈 Overview

Humans rarely solve difficult spatial problems by passively inspecting the views in front of them. Instead, we revisit a scene from task-relevant viewpoints and mentally reconstruct what should be visible there. We call this capability Active Spatial Mentalization (ASM).

We are thrilled to introduce SpatialASM, a scalable framework that internalizes ASM into LVLMs. Through the two-stage training on LVLMs, SpatialASM elicits active spatial exploration and imagination and enhances the overall spatial reasoning performance.

Teaser

SpatialASM consists of three complementary components:

Allocentric Novel-View Generator. SpatialASM-NVG renders the scene from a target viewpoint specified by an anchor image, a pixel location, and a viewing direction. This allocentric formulation avoids lengthy sequences of camera-centric movements.
ASM Trajectory Synthesis. Given a spatial QA sample, an LVLM plans informative viewpoints and the NVG renders the corresponding observations. After GeoP and answer-consistency filtering, rendered views are replaced by textual captions to form explicit plan-imagine-reason trajectories for SFT.
View-Imagination Reinforcement Learning. Geometry-filtered candidate views are used to construct automatically verifiable perspective-taking tasks. GRPO further strengthens the association between a viewpoint description and its expected observation.

💡 Highlights

🔥 Active spatial reasoning: SpatialASM jointly internalizes task-conditioned view planning and viewpoint-conditioned view imagination, enabling LVLMs to spontaneously seek and mentally construct missing spatial evidence.
🔥 Scalable spatial CoT supervision: SpatialASM converts existing answer-only spatial QA data into explicit plan-imagine-reason trajectories and automatically verifiable perspective-taking tasks, alleviating the scarcity of spatial Chain-of-Thought supervision.
🔥 Training-time spatial simulation: A novel-view generator acts as temporary training scaffolding. The final model does not depend on a world model, renderer, or additional visual observations at inference time.
🔥 Grounded and reliable supervision: An allocentric viewpoint interface reaches a target perspective in one query, while the Geometry Perturbation (GeoP) metric and answer-consistency checks filter unreliable synthesized views and trajectories.

🛠️ System Prompt

Append the system prompt to enhance the model's performance: First think and then give the final answer. Put the option letter within and .

✒️Citation

If you find this project useful, please kindly cite:

@article{liu2026spatialasm,
  title={SpatialASM: Internalizing Active Mentalization for Spatial Reasoning},
  author={Yuhong Liu, Shenxi Wu, Junyi Chi, Beichen Zhang, Xuanlang Dai, Jiaqi Wang, Yuhang Zang, Dahua Lin},
  journal={arXiv preprint arXiv:2610.13832},
  year={2026}
}