Instructions to use xiaomi-research/SpatioLM-Perception-InternVL3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xiaomi-research/SpatioLM-Perception-InternVL3.5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="xiaomi-research/SpatioLM-Perception-InternVL3.5")
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 AutoModel
model = AutoModel.from_pretrained("xiaomi-research/SpatioLM-Perception-InternVL3.5", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xiaomi-research/SpatioLM-Perception-InternVL3.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xiaomi-research/SpatioLM-Perception-InternVL3.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xiaomi-research/SpatioLM-Perception-InternVL3.5",
		"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/xiaomi-research/SpatioLM-Perception-InternVL3.5

SGLang

How to use xiaomi-research/SpatioLM-Perception-InternVL3.5 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 "xiaomi-research/SpatioLM-Perception-InternVL3.5" \
    --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": "xiaomi-research/SpatioLM-Perception-InternVL3.5",
		"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 "xiaomi-research/SpatioLM-Perception-InternVL3.5" \
        --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": "xiaomi-research/SpatioLM-Perception-InternVL3.5",
		"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 xiaomi-research/SpatioLM-Perception-InternVL3.5 with Docker Model Runner:
```
docker model run hf.co/xiaomi-research/SpatioLM-Perception-InternVL3.5
```

edatai commited on Aug 4

Commit

b2491ee

verified ·

1 Parent(s): 8aed42b

Add model card

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Files changed (1) hide show

README.md +12 -3

README.md CHANGED Viewed

@@ -19,6 +19,11 @@ SpatioLM adds a plug-and-play spatio-vision module to a frozen vision-language
 model and learns physically coherent representations from pseudo depth and
 camera-ray supervision. No additional 3D input is required at inference time.
 ## Installation
 ```bash
@@ -82,8 +87,12 @@ checkpoint interface, see the
 ```bibtex
 @inproceedings{wu2026spatiolm,
   title={SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models},
-  author={Wu, Jianhua and others},
-  booktitle={International Conference on Machine Learning},
-  year={2026}
 }
 ```

 model and learns physically coherent representations from pseudo depth and
 camera-ray supervision. No additional 3D input is required at inference time.
+## Resources
+- GitHub: https://github.com/xiaomi-research/spatio-lm
+- Paper: https://arxiv.org/abs/2608.01899
 ## Installation
 ```bash
 ```bibtex
 @inproceedings{wu2026spatiolm,
   title={SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models},
+  author={Wu, Jing and Wu, Jianhua and Guan, Jiayi and Chen, Jiahong and Lu, Jinghui and Ye, Hangjun and Gao, Bingzhao and Chen, Long},
+  booktitle={International Conference on Machine Learning (ICML)},
+  year={2026},
+  note={To appear},
+  eprint={2608.01899},
+  archivePrefix={arXiv},
+  url={https://arxiv.org/abs/2608.01899}
 }
 ```