Update model card with metadata and code link

Hi! I'm Niels from the community science team at Hugging Face.

This PR improves the model card for VideoLoom by:
- Adding the `video-text-to-text` pipeline tag for better categorization.
- Adding `library_name: transformers` as the model is compatible via `auto_map`.
- Including the official GitHub repository link and a link to the paper.
- Adding a model zoo table and citation information from the official repository.

README.md

@@ -1,15 +1,51 @@
 ---
 license: apache-2.0
+pipeline_tag: video-text-to-text
+library_name: transformers
 ---
 
 # VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding
 
 Jiapeng Shi, [Junke Wang](https://wdrink.github.io/), [Zuyao You](https://scholar.google.com/citations?hl=en&user=X8Kh8uoAAAAJ), [Bo He](https://boheumd.github.io/), [Zuxuan Wu<sup>&#9993;</sup>](https://zxwu.azurewebsites.net/)
 
-[\[📜 Paper\]](https://arxiv.org/abs/2601.07290) [\[📥 Model\]](https://huggingface.co/collections/JPShi/videoloom)
+[\[📜 Paper\]](https://arxiv.org/abs/2601.07290) [\[💻 Code\]](https://github.com/JPShi12/VideoLoom) [\[📥 Model\]](https://huggingface.co/collections/JPShi/videoloom)
 
 ## 🔎 Overview
 
 This paper presents **VideoLoom**, a unified Video Large Language Model (Video LLM) for joint spatial-temporal understanding. To facilitate the development of fine-grained spatial and temporal localization capabilities, we curate **LoomData-8.7k**, a human-centric video dataset with temporally grounded and spatially localized captions. With this, VideoLoom achieves state-of-the-art or highly competitive performance across a variety of spatial and temporal benchmarks (e.g., 63.1 J&F on ReVOS for referring video object segmentation, and 48.3 R1@0.7 on Charades-STA for temporal grounding). In addition, we introduce **LoomBench**, a novel benchmark consisting of temporal, spatial, and compositional video-question pairs, enabling a comprehensive evaluation of Video LLMs from diverse aspects. Collectively, these contributions offer a universal and effective suite for joint spatial-temporal video understanding, setting a new standard in multimodal intelligence.
 
-![Model](assets/model.jpg)
+![Model](assets/model.jpg)
+
+## 🔥 News
+
+* `Jan. 13, 2026` Our paper and checkpoints are released.
+
+## 📦 Model Zoo
+
+We provide the following models:
+| Model Name | Base MLLM | Checkpoints |
+|:----------:|:-----------------------------------------------------------------:|:----------------------------------------------------:|
+|  VideoLoom-4B  | [InternVL2.5-4B](https://huggingface.co/OpenGVLab/InternVL2_5-4B) | [🤗 link](https://huggingface.co/JPShi/VideoLoom-4B) |
+|  VideoLoom-8B  | [InternVL3-8B](https://huggingface.co/OpenGVLab/InternVL3-8B) | [🤗 link](https://huggingface.co/JPShi/VideoLoom-8B) |
+
+## 📜 Citation
+
+If you find our work helpful, please consider giving a star ⭐ and citation 📝
+
+```bibtex
+@article{shi2026videoloom,
+      title={VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding}, 
+      author={Shi, Jiapeng and Wang, Junke and You, Zuyao and He, Bo and Wu, Zuxuan},
+      journal={arXiv preprint arXiv:2601.07290},
+      year={2026}
+}
+```
+
+## 📧 Contact
+Feel free to contact us if you have any questions or suggestions
+
+- Email (Jiapeng Shi): jpshi1212@gmail.com
+
+## 🤝 Acknowledgements
+
+We refer to [Sa2VA](https://github.com/bytedance/Sa2VA) and [TimeChat](https://github.com/RenShuhuai-Andy/TimeChat) to build our codebase. Thanks for their wonderful project.