Instructions to use InternRobotics/G2VLM-Qwen2-VL-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InternRobotics/G2VLM-Qwen2-VL-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="InternRobotics/G2VLM-Qwen2-VL-2B")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("InternRobotics/G2VLM-Qwen2-VL-2B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use InternRobotics/G2VLM-Qwen2-VL-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InternRobotics/G2VLM-Qwen2-VL-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InternRobotics/G2VLM-Qwen2-VL-2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/InternRobotics/G2VLM-Qwen2-VL-2B

SGLang

How to use InternRobotics/G2VLM-Qwen2-VL-2B 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 "InternRobotics/G2VLM-Qwen2-VL-2B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InternRobotics/G2VLM-Qwen2-VL-2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "InternRobotics/G2VLM-Qwen2-VL-2B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InternRobotics/G2VLM-Qwen2-VL-2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use InternRobotics/G2VLM-Qwen2-VL-2B with Docker Model Runner:
```
docker model run hf.co/InternRobotics/G2VLM-Qwen2-VL-2B
```

Configuration Parsing Warning:Config file config.json cannot be fetched (too big)

Configuration Parsing Warning:Config file chat_template.json cannot be fetched (too big)

Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)

G2VLM-Qwen2-VL-2B

Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning

We present G²VLM, a geometry grounded vision-language model proficient in both spatial 3D reconstruction and spatial understanding tasks. For spatial reasoning questions, G²VLM can natively predict 3D geometry and employ interleaved reasoning for an answer.

This repository hosts the base model weights BEFORE the training of G²VLM, which is technically the same as Qwen2-VL-2B. Here we format it so it's easier for users to reproduce our trainings. For installation, usage instructions, and further documentation, please visit our GitHub repository.

🧠 Method

G²VLM is a unified model that integrates both a geometric perception expert for 3D reconstruction and a semantic perception expert for multimodal understanding and spatial reasoning tasks. All tokens can do shared multi-modal self attention in each transformer block.

License

G2VLM is licensed under the Apache 2.0 license.

✍️ Citation

@article{hu2025g2vlmgeometrygroundedvision,
      title={G$^2$VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning}, 
      author={Wenbo Hu and Jingli Lin and Yilin Long and Yunlong Ran and Lihan Jiang and Yifan Wang and Chenming Zhu and Runsen Xu and Tai Wang and Jiangmiao Pang},
      year={2025},
      eprint={2511.21688},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.21688}, 
}

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2B params

Tensor type

BF16

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Paper for InternRobotics/G2VLM-Qwen2-VL-2B

G^2VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning

Paper • 2511.21688 • Published Nov 26, 2025 • 9