Add pipeline tag and library name, include information from github README

README.md

@@ -1,7 +1,9 @@
 ---
-license: apache-2.0
 base_model:
 - lmms-lab/LLaVA-Video-7B-Qwen2
+license: apache-2.0
+pipeline_tag: video-text-to-text
+library_name: transformers
 ---
 
 <a href='https://arxiv.org/abs/2504.12083'><img src='https://img.shields.io/badge/arXiv-paper-red'></a> 
@@ -18,7 +20,19 @@ Clone the repository and navigate to the RRPO directory:
 ```sh
 git clone https://github.com/pritamqu/RRPO
 cd RRPO
+```
+
+This repository supports three Large Video Language Models (LVLMs), each with its own dependency requirements:
+
+- **[VideoChat2](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2)**: `videochat2.txt`
+- **[LLaVA-Video](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/README.md)**: `llavavideo.txt`
+- **[LongVU](https://github.com/Vision-CAIR/LongVU)**: `longvu.txt`
+
+#### Example: Setting up LLaVA-Video
+
+Follow similar steps for other models.
 
+```sh
 conda create -n llava python=3.10 -y
 conda activate llava
 pip install -r llavavideo.txt
@@ -44,4 +58,117 @@ python inference.py \
     --video_path "sample_video.mp4" \
     --question "Describe this video." \
     --model_max_length 1024
-```
+```
+
+## Dataset
+
+Our training data is released here [Self-Alignment Dataset](https://huggingface.co/datasets/pritamqu/self-alignment). We release the preferred and non-preferred responses used in self-alignment training. 
+```
+git clone [email protected]:datasets/pritamqu/self-alignment
+```
+The related videos can be downloaded from their original sources. Please check [VideoChat-IT](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/DATA.md) GitHub page regarding the details of downloading the source videos.
+
+We also share additional details on how to use your own data [here](docs/DATA.md). 
+
+## Training
+
+Before training, make sure to prepare the data and download the weights of the base models. Then you can launch the training jobs as:
+
+VideoChat2
+```
+bash scripts/videochat2/run.sh
+```
+LLaVA-Video
+```
+bash scripts/llavavideo/run.sh
+```
+LongVU
+```
+bash scripts/longvu/run.sh
+```
+The link to the base model weights are:
+- [VideoChat2_stage3_Mistral_7B](https://huggingface.co/OpenGVLab/VideoChat2_stage3_Mistral_7B)
+- [LLaVA-Video-7B-Qwen2](https://huggingface.co/lmms-lab/LLaVA-Video-7B-Qwen2)
+- [LongVU_Qwen2_7B](https://huggingface.co/Vision-CAIR/LongVU_Qwen2_7B)
+
+
+## Inference
+
+We provide a simple setup to inference using our trained model.
+
+**VideoChat2**
+```
+bash scripts/inference_videochat2.sh
+```
+
+**LLaVA-Video**
+```
+bash scripts/inference_llavavideo.sh
+```
+
+**LongVU**
+```
+bash scripts/inference_longvu.sh
+```
+
+## Results
+
+**RRPO shows consistent improvements over the base model and outperforms DPO across all benchmarks.**
+
+| **Models** | **#F** | **TV Bench** | **Temp Compass** | **Video Hallucer** | **Vid Halluc** | **MV Bench** | **Video MME** | **MLVU** | **LongVideo Bench** |
+|------------|------|-------------|----------------|----------------|-------------|-------------|-------------|--------|------------------|
+| VideoChat2 | 16 | 44.0 | 59.3 | 23.1 | 73.3 | **60.2** | 41.0 | 46.4 | 40.4 |
+| VideoChat2 + DPO | 16 | 45.7 | 60.0 | 22.1 | 72.4 | 59.6 | 43.0 | 47.4 | 41.0 |
+| VideoChat2 + **RRPO** | 16 | **45.8** | **60.2** | **32.9** | **76.4** | 59.0 | **44.3** | **47.9** | **42.8** |
+|  |  |  |  |  |  |  |  |  |  |
+| LLaVA-Video | 64 | 51.0 | 66.0 | 50.0 | 76.6 | 61.1 | 64.0 | 68.6 | 60.1 |
+| LLaVA-Video + DPO | 64 | 51.9 | 66.4 | 53.3 | 76.5 | 60.6 | 63.1 | 67.4 | 59.4 |
+| LLaVA-Video + **RRPO** | 64 | 51.9 | 66.8 | 55.7 | 76.5 | **62.2** | **64.5** | 69.1 | **60.4** |
+| LLaVA-Video + **RRPO** (32f) | 64 | **52.2** | **67.4** | **55.8** | **76.6** | 62.1 | **64.5** | **69.4** | 60.1 |
+|  |  |  |  |  |  |  |  |  |  |
+| LongVU | 1fps | 53.7 | 63.9 | 39.2 | 67.3 | 65.5 | 56.2 | 63.6 | 48.6 |
+| LongVU + DPO | 1fps | 54.3 | 64.3 | 40.9 | 68.5 | 65.9 | 56.6 | 63.6 | 49.4 |
+| LongVU + **RRPO** | 1fps | **56.5** | **64.5** | **44.0** | **71.7** | **66.8** | **57.7** | **64.5** | **49.7** |
+
+
+## Evaluation
+
+You can download evaluation benchmarks from the given links:
+
+- [TVBench](https://huggingface.co/datasets/FunAILab/TVBench)
+- [TempCompass](https://huggingface.co/datasets/lmms-lab/TempCompass)
+- [VideoHallucer](https://huggingface.co/datasets/bigai-nlco/VideoHallucer)
+- [VidHalluc](https://huggingface.co/datasets/chaoyuli/VidHalluc)
+- [MVBench](https://huggingface.co/datasets/PKU-Alignment/MVBench)
+- [VideoMME](https://huggingface.co/datasets/lmms-lab/Video-MME)
+- [MLVU](https://huggingface.co/datasets/MLVU/MVLU)
+- [LongVideoBench](https://huggingface.co/datasets/longvideobench/LongVideoBench)
+
+Next, you can run the entire evaluations following the instructions provided [here](./docs/EVALUATION.md).
+
+
+## Citation
+
+If you find this work useful, please consider citing our paper:
+
+```
+@article{sarkar2025rrpo,
+  title={Self-Alignment of Large Video Language Models with Refined Regularized Preference Optimization},
+  author={Your Name et al.},
+  journal={arXiv preprint arXiv:2504.12083},
+  year={2025}
+}
+```
+
+## Usage and License Notices
+
+This project incorporates datasets and model checkpoints that are subject to their respective original licenses. Users must adhere to the terms and conditions specified by these licenses.
+The assets used in this work include, but are not limited to:
+[VideoChat2-IT](https://huggingface.co/datasets/OpenGVLab/VideoChat2-IT),
+[VideoChat2_stage3_Mistral_7B](https://huggingface.co/OpenGVLab/VideoChat2_stage3_Mistral_7B),
+[LLaVA-Video-7B-Qwen2](https://huggingface.co/lmms-lab/LLaVA-Video-7B-Qwen2),
+[LongVU_Qwen2_7B](https://huggingface.co/Vision-CAIR/LongVU_Qwen2_7B). This project does not impose any additional constraints beyond those stipulated in the original licenses. Users must ensure their usage complies with all applicable laws and regulations.
+This repository is released under the **Apache 2.0 License**. See [LICENSE](LICENSE) for details.
+
+---
+For any issues or questions, please open an issue or contact **Pritam Sarkar** at [email protected]!