Instructions to use BAAI/ConsiSpace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/ConsiSpace with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Qwen2_5_VLForConditionalGenerationWithVGGT processor = AutoProcessor.from_pretrained("BAAI/ConsiSpace") model = Qwen2_5_VLForConditionalGenerationWithVGGT.from_pretrained("BAAI/ConsiSpace", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from BAAI/ConsiSpace: direct link, hf CLI and curl.
- Browser
- Download file 872 Bytes
-
https://huggingface.co/BAAI/ConsiSpace/resolve/main/README.md
- Command line
-
hf download hf://BAAI/ConsiSpace/README.md
-
curl -L -o README.md https://huggingface.co/BAAI/ConsiSpace/resolve/main/README.md
872 Bytes
metadata
license: cc-by-4.0
library_name: transformers
tags:
- vision-language-model
This model is the result of joint research conducted by Hao Tang's team at the School of Computer Science, Peking University, and the Beijing Academy of Artificial Intelligence (BAAI).
ConsiSpace
Official model weights for ConsiSpace.
ConsiSpace is a geometry-consistent multimodal framework for spatial reasoning in long-form visual observations.
📄 Paper: https://arxiv.org/abs/2607.17599
@article{huang2026consispace,
title={ConsiSpace: Learning Geometric Consistency Matters for Video Spatial Reasoning},
author={Ting Huang and Zhenyu Zhang and Wenyuan Huang and Jian Yang and Hao Tang},
year={2026},
eprint={2607.17599},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2607.17599},
}