Update README.md

README.md

@@ -1,3 +1,53 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- Efficient-Large-Model/VILA1.5-40b
+pipeline_tag: video-text-to-text
+---
+
+# LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment
+
+
+## Summary
+This is the model checkpoint proposed in our paper "LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment". LiFT-Critic is a novel Video-Text-to-Text Reward Model for synthesized video evaluation.
+
+Project: https://codegoat24.github.io/LiFT/
+
+Code: https://github.com/CodeGoat24/LiFT
+
+## 🔧 Installation
+
+1. Clone the github repository and navigate to LiFT folder
+```bash
+git clone https://github.com/CodeGoat24/LiFT.git
+cd LiFT
+```
+2. Install packages
+```
+bash ./environment_setup.sh lift
+```
+
+## 🚀 Inference
+
+### Run
+Please download this public [LiFT-Critic-40b-lora-v1.5](https://huggingface.co/Fudan-FUXI/LiFT-Critic-13b-lora-v1.5) checkpoints. 
+
+We provide some synthesized videos for quick inference in `./demo` directory.
+
+```bash
+python LiFT-Critic/test/run_critic_40b.py --model-path ./LiFT-Critic-40b-lora-v1.5
+```
+
+# 🖊️ Citation
+
+If you find our work helpful, please cite our paper.
+```bibtex
+@article{LiFT,
+  title={LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment.},
+  author={Wang, Yibin and Tan, Zhiyu, and Wang, Junyan and Yang, Xiaomeng and Jin, Cheng and Li, Hao},
+  journal={arXiv preprint arXiv:2412.04814},
+  year={2024}
+}
+```