Improve model card: Remove non-standard metadata, shorten title, add citation and tags

README.md

@@ -1,19 +1,20 @@
 ---
 language:
 - en
-license: apache-2.0
-inference: false
 library_name: transformers
+license: apache-2.0
 pipeline_tag: text-generation
+tags:
+- text-to-video
+- prompt-optimization
 ---
 
-<h1>VPO: Aligning Text-to-Video Generation Models with Prompt Optimization</h1>
+<h1>VPO</h1>
 
 - **Repository:** https://github.com/thu-coai/VPO
 - **Paper:** [VPO: Aligning Text-to-Video Generation Models with Prompt Optimization](https://huggingface.co/papers/2503.20491)
 - **Data:** https://huggingface.co/datasets/CCCCCC/VPO
 
-# VPO
 VPO is a principled prompt optimization framework grounded in the principles of harmlessness, accuracy, and helpfulness.
 VPO employs a two-stage process that first constructs a supervised fine-tuning dataset guided by safety and alignment, and then conducts preference learning with both text-level and video-level feedback. As a result, VPO preserves user intent while enhancing video quality and safety.
 
@@ -82,9 +83,12 @@ print(resp)
 ```
 See our [Github Repo](https://github.com/thu-coai/VPO) for more detailed usage (e.g. Inference with Vllm).
 
-
-<!-- ## Citation
-If you find our model is useful in your work, please cite it with:
+## Citation
 ```
-
-``` -->
+@article{cheng2025vpo,
+  title={Vpo: Aligning text-to-video generation models with prompt optimization},
+  author={Cheng, Jiale and Lyu, Ruiliang and Gu, Xiaotao and Liu, Xiao and Xu, Jiazheng and Lu, Yida and Teng, Jiayan and Yang, Zhuoyi and Dong, Yuxiao and Tang, Jie and others},
+  journal={arXiv preprint arXiv:2503.20491},
+  year={2025}
+}
+```