Instructions to use VINHYU/OpenSpatial-InternVL3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VINHYU/OpenSpatial-InternVL3-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="VINHYU/OpenSpatial-InternVL3-8B", trust_remote_code=True)
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("VINHYU/OpenSpatial-InternVL3-8B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VINHYU/OpenSpatial-InternVL3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VINHYU/OpenSpatial-InternVL3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VINHYU/OpenSpatial-InternVL3-8B",
		"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/VINHYU/OpenSpatial-InternVL3-8B

SGLang

How to use VINHYU/OpenSpatial-InternVL3-8B 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 "VINHYU/OpenSpatial-InternVL3-8B" \
    --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": "VINHYU/OpenSpatial-InternVL3-8B",
		"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 "VINHYU/OpenSpatial-InternVL3-8B" \
        --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": "VINHYU/OpenSpatial-InternVL3-8B",
		"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 VINHYU/OpenSpatial-InternVL3-8B with Docker Model Runner:
```
docker model run hf.co/VINHYU/OpenSpatial-InternVL3-8B
```

OpenSpatial-InternVL3-8B

File size: 1,190 Bytes

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---
license: apache-2.0
base_model: OpenGVLab/InternVL3-8B
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- openspatial
- spatial-reasoning
- vision-language
---

# OpenSpatial-InternVL3-8B

OpenSpatial-InternVL3-8B is an OpenSpatial model fine-tuned for spatial understanding and
reasoning. This repository contains full, inference-ready model weights.

- Project: https://github.com/VINHYU/OpenSpatial
- Paper: https://arxiv.org/abs/2604.07296
- Training data: https://huggingface.co/datasets/jdopensource/JoyAI-Image-OpenSpatial
- Base model: https://huggingface.co/OpenGVLab/InternVL3-8B

## Loading

Use `transformers` the version recommended by InternVL3 for this checkpoint.

```python
from transformers import AutoModel, AutoTokenizer

model_id = "VINHYU/OpenSpatial-InternVL3-8B"
model = AutoModel.from_pretrained(
    model_id,
    torch_dtype="auto",
    low_cpu_mem_usage=True,
    trust_remote_code=True,
).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(
    model_id, trust_remote_code=True, use_fast=False
)
```

Please refer to the OpenSpatial repository and the base model repository for
full usage instructions, limitations, and license terms.