Improve model card metadata and documentation

Hi! I'm Niels from the community science team at Hugging Face. I've updated your model card to include:
- Metadata for `pipeline_tag` (`video-text-to-text`) and `library_name` (`transformers`).
- Links to the official GitHub repository and project homepage.
- A brief description of the model's capabilities based on the research paper.

This will help improve the discoverability of your model on the Hub.

README.md

@@ -1,12 +1,28 @@
 ---
-license: apache-2.0
 base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
+license: apache-2.0
+library_name: transformers
+pipeline_tag: video-text-to-text
 ---
 
+# PyVision-Video-7B-SFT
+
 [PyVision-RL: Forging Open Agentic Vision Models via RL](https://arxiv.org/abs/2602.20739)
 
-This is PyVision-Video-7B-SFT, post trained from Qwen2.5-VL-7B.
+This is **PyVision-Video-7B-SFT**, post-trained from [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
+
+- **Project Page:** [https://agent-x.space/pyvision-rl/](https://agent-x.space/pyvision-rl/)
+- **Repository:** [https://github.com/agents-x-project/PyVision-RL](https://github.com/agents-x-project/PyVision-RL)
+- **Paper:** [arXiv:2602.20739](https://arxiv.org/abs/2602.20739)
+
+## Model Description
+PyVision-Video is part of the PyVision-RL framework, which aims to stabilize Reinforcement Learning (RL) training for open-weight multimodal models to sustain agentic interaction. 
+
+For video reasoning, PyVision-Video employs an **on-demand context construction** strategy. It selectively samples task-relevant frames during the reasoning process, which significantly reduces visual token usage while maintaining strong performance on complex video understanding tasks. This model serves as the Supervised Fine-Tuning (SFT) checkpoint before RL training.
+
+## Citation
+If you find this work useful, please cite the following paper:
 
 ```bibtex
 @article{pyvisionrl2026,