Improve model card: Add pipeline tag, library name, paper link, and content

This PR enhances the model card for `ybrrraway/V2LO-7B` by:

- Adding `pipeline_tag: video-text-to-text` to enable discoverability on the Hub.
- Adding `library_name: transformers` as the model is compatible with the `transformers` library, which will enable the automated "how to use" widget.
- Adding a link to the paper ([Video2Layout: Recall and Reconstruct Metric-Grounded Cognitive Map for Spatial Reasoning](https://huggingface.co/papers/2511.16160)) and GitHub repository.
- Populating the content section with a model overview, key features, and a sample usage snippet from the original GitHub repository.

Please review and merge this PR.

README.md

@@ -1,11 +1,69 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
 datasets:
 - ybrrraway/V2LO-28K
 language:
 - en
-base_model:
-- Qwen/Qwen2.5-VL-7B-Instruct
+license: apache-2.0
 metrics:
 - accuracy
----
+pipeline_tag: video-text-to-text
+library_name: transformers
+---
+
+# Video2Layout: Recall and Reconstruct Metric-Grounded Cognitive Map for Spatial Reasoning
+
+**Video2Layout** is a framework for reconstructing metric-grounded spatial layouts from video, leveraging continuous object boundary coordinates to quantify inter-object physical distances and object sizes. This equips the model with quantitative spatial computation capabilities, effectively mitigating the inherent ambiguity in describing spatial relationships through natural language. The framework adopts a two-stage SFT-to-RL training paradigm, enhancing the model's spatial reasoning abilities.
+
+Paper: [Video2Layout: Recall and Reconstruct Metric-Grounded Cognitive Map for Spatial Reasoning](https://huggingface.co/papers/2511.16160)
+Code: https://github.com/ybrrraway/Video2Layout
+
+<p align="center">
+  <img src="https://github.com/ybrrraway/Video2Layout/raw/main/figs/show.png" width=100%/>
+</p>
+
+## 🚀 Overview
+
+🎯 **Key Benefits**:
+-   **Metric-Grounded Cognitive Map** — an accurate bird's-eye view reflects the specific position of an object in the scene.
+-   **Spatial reasoning computation** — rigorous mathematical calculations eliminate the fuzziness of traditional natural language COT description spatial relationship reasoning.
+-   **Generalization of real scenes** — only the information of simulation data is needed, and there are no requirements for real scenarios.
+
+**QVS-Bench** is a diagnostic benchmark for systematically evaluating how the quantity of image inputs impacts spatial reasoning accuracy. It maintains a substantially uniform proportional distribution across five input scale configurations (1, 4, 8, 12, and 16 frames), ensuring fair and unbiased analysis of the relevant mechanisms.
+
+<p align="center">
+  <img src="https://github.com/ybrrraway/Video2Layout/raw/main/figs/benchmark.png" width=100%/>
+</p>
+
+## 🛠️ Usage
+### (Step 1) Install
+```bash
+conda create -n v2lo python=3.10 -y
+conda activate v2lo
+pip install -r requirements.txt
+```
+
+### (Step 2) Training
+```bash
+# SFT training
+bash src/ms-swift/sft.sh
+# Merge model
+bash src/ms-swift/merge_lora.sh
+
+# RL training
+bash src/EasyR1/examples/rl.sh
+# Merge model
+cd src/EasyR1
+python3 scripts/model_merger.py --local_dir checkpoints/easy_r1/exp_name/global_step_1/actor
+```
+
+## Citation
+```bibtex
+@misc{2511.16160,
+Author = {Yibin Huang and Wang Xu and Wanyue Zhang and Helu Zhi and Jingjing Huang and Yangbin Xu and Yangang Sun and Conghui Zhu and Tiejun Zhao},
+Title = {Video2Layout: Recall and Reconstruct Metric-Grounded Cognitive Map for Spatial Reasoning},
+Year = {2025},
+Eprint = {arXiv:2511.16160},
+}
+```