new README.md

README.md

@@ -1,249 +1,9 @@
-<p align="center" width="100%">
-<a target="_blank"><img src="figs/video_llama_logo.jpg" alt="Video-LLaMA" style="width: 50%; min-width: 200px; display: block; margin: auto;"></a>
-</p>
-
-
-
-# Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
-<!-- **Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding** -->
-
-This is the repo for the Video-LLaMA project, which is working on empowering large language models with video and audio understanding capabilities. 
-
-<div style='display:flex; gap: 0.25rem; '>
-<a href='https://modelscope.cn/studios/damo/video-llama/summary'><img src='https://img.shields.io/badge/ModelScope-Demo-blueviolet'></a>
-<a href='https://www.modelscope.cn/models/damo/videollama_7b_llama2_finetuned/summary'><img src='https://img.shields.io/badge/ModelScope-Checkpoint-blueviolet'></a>
-<a href='https://huggingface.co/spaces/DAMO-NLP-SG/Video-LLaMA'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a>
-<a href='https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-7B-Finetuned'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Checkpoint-blue'></a> 
-<a href='https://arxiv.org/abs/2306.02858'><img src='https://img.shields.io/badge/Paper-PDF-red'></a>
-</div>
-
-## News
-- [08.03] 🚀🚀 Release **Video-LLaMA-2** with [Llama-2-7B/13B-Chat](https://huggingface.co/meta-llama) as language decoder
-    - **NO** delta weights and separate Q-former weights anymore, full weights to run Video-LLaMA are all here :point_right: [[7B](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-7B-Finetuned)][[13B](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-13B-Finetuned)] 
-    - Allow further customization starting from our pre-trained checkpoints [[7B-Pretrained](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-7B-Pretrained)] [[13B-Pretrained](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-13B-Pretrained)]
-- [06.14]  **NOTE**: The current online interactive demo is primarily for English chatting and it may **NOT** be a good option to ask Chinese questions since Vicuna/LLaMA does not represent Chinese texts very well. 
-- [06.13]  **NOTE**: The audio support is **ONLY** for Vicuna-7B by now although we have several VL checkpoints available for other decoders.
-- [06.10]  **NOTE**: We have NOT updated the HF demo yet because the whole framework (with the audio branch) cannot run normally on A10-24G. The current running demo is still the previous version of Video-LLaMA. We will fix this issue soon.
-- [06.08] 🚀🚀 Release the checkpoints of the audio-supported Video-LLaMA. Documentation and example outputs are also updated.    
-- [05.22] 🚀🚀 Interactive demo online, try our Video-LLaMA (with **Vicuna-7B** as language decoder) at [Hugging Face](https://huggingface.co/spaces/DAMO-NLP-SG/Video-LLaMA) and [ModelScope](https://pre.modelscope.cn/studios/damo/video-llama/summary)!!
-- [05.22] ⭐️ Release **Video-LLaMA v2** built with Vicuna-7B
-- [05.18] 🚀🚀 Support video-grounded chat in Chinese 
-    - [**Video-LLaMA-BiLLA**](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-billa7b-zh.pth): we introduce [BiLLa-7B-SFT](https://huggingface.co/Neutralzz/BiLLa-7B-SFT) as language decoder and fine-tune the video-language aligned model (i.e., stage 1 model) with machine-translated [VideoChat](https://github.com/OpenGVLab/InternVideo/tree/main/Data/instruction_data) instructions.   
-    - [**Video-LLaMA-Ziya**](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-ziya13b-zh.pth): same with Video-LLaMA-BiLLA but the language decoder is changed to [Ziya-13B](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1).    
-- [05.18] ⭐️ Create a Hugging Face [repo](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series) to store the model weights of all the variants of our Video-LLaMA.
-- [05.15] ⭐️ Release [**Video-LLaMA v2**](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-vicuna13b-v2.pth): we use the training data provided by [VideoChat](https://github.com/OpenGVLab/InternVideo/tree/main/Data/instruction_data) to further enhance the instruction-following capability of Video-LLaMA.
-- [05.07] Release the initial version of **Video-LLaMA**, including its pre-trained and instruction-tuned checkpoints.
-
-<p align="center" width="100%">
-<a target="_blank"><img src="figs/architecture_v2.png" alt="Video-LLaMA" style="width: 80%; min-width: 200px; display: block; margin: auto;"></a>
-</p>
-
-## Introduction
-
-
-- Video-LLaMA is built on top of [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2) and [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4). It is composed of two core components: (1) Vision-Language (VL) Branch and (2) Audio-Language (AL) Branch.
-  - **VL Branch** (Visual encoder: ViT-G/14 + BLIP-2 Q-Former)
-    - A two-layer video Q-Former and a frame embedding layer (applied to the embeddings of each frame) are introduced to compute video representations. 
-    - We train VL Branch on the Webvid-2M video caption dataset with a video-to-text generation task. We also add image-text pairs (~595K image captions from [LLaVA](https://github.com/haotian-liu/LLaVA)) into the pre-training dataset to enhance the understanding of static visual concepts.
-    - After pre-training, we further fine-tune our VL Branch using the instruction-tuning data from [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4), [LLaVA](https://github.com/haotian-liu/LLaVA) and [VideoChat](https://github.com/OpenGVLab/Ask-Anything). 
-  - **AL Branch** (Audio encoder: ImageBind-Huge) 
-    - A two-layer audio Q-Former and an audio segment embedding layer (applied to the embedding of each audio segment) are introduced to compute audio representations.
-    - As the used audio encoder (i.e., ImageBind) is already aligned across multiple modalities, we train AL Branch on video/image instruction data only, just to connect the output of ImageBind to the language decoder.    
-- Only the Video/Audio Q-Former, positional embedding layers, and linear layers are trainable during cross-modal training.
-
-
-
-## Example Outputs
-
-
-- **Video with background sound**
-
-<p float="left">
-    <img src="https://github.com/DAMO-NLP-SG/Video-LLaMA/assets/18526640/7f7bddb2-5cf1-4cf4-bce3-3fa67974cbb3" style="width: 45%; margin: auto;">
-    <img src="https://github.com/DAMO-NLP-SG/Video-LLaMA/assets/18526640/ec76be04-4aa9-4dde-bff2-0a232b8315e0" style="width: 45%; margin: auto;">
-</p>
-
-
-- **Video without sound effects**
-<p float="left">
-    <img src="https://github.com/DAMO-NLP-SG/Video-LLaMA/assets/18526640/539ea3cc-360d-4b2c-bf86-5505096df2f7" style="width: 45%; margin: auto;">
-    <img src="https://github.com/DAMO-NLP-SG/Video-LLaMA/assets/18526640/7304ad6f-1009-46f1-aca4-7f861b636363" style="width: 45%; margin: auto;">
-</p>
-
-- **Static image**
-<p float="left">
-    <img src="https://github.com/DAMO-NLP-SG/Video-LLaMA/assets/18526640/a146c169-8693-4627-96e6-f885ca22791f" style="width: 45%; margin: auto;">
-    <img src="https://github.com/DAMO-NLP-SG/Video-LLaMA/assets/18526640/66fc112d-e47e-4b66-b9bc-407f8d418b17" style="width: 45%; margin: auto;">
-</p>
-
-
-
-## Pre-trained & Fine-tuned Checkpoints
-
-The following checkpoints store learnable parameters (positional embedding layers, Video/Audio Q-former, and linear projection layers) only.
-
-#### Vision-Language Branch
-| Checkpoint       | Link | Note |
-|:------------|-------------|-------------|
-| pretrain-vicuna7b    | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/pretrain_vicuna7b-v2.pth)       | Pre-trained on WebVid (2.5M video-caption pairs) and LLaVA-CC3M (595k image-caption pairs) |
-| finetune-vicuna7b-v2 | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-vicuna7b-v2.pth) | Fine-tuned on the instruction-tuning data from [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4), [LLaVA](https://github.com/haotian-liu/LLaVA) and [VideoChat](https://github.com/OpenGVLab/Ask-Anything)|
-| pretrain-vicuna13b    | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/pretrain-vicuna13b.pth)       | Pre-trained on WebVid (2.5M video-caption pairs) and LLaVA-CC3M (595k image-caption pairs) |
-| finetune-vicuna13b-v2 | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-vicuna13b-v2.pth) | Fine-tuned on the instruction-tuning data from [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4), [LLaVA](https://github.com/haotian-liu/LLaVA) and [VideoChat](https://github.com/OpenGVLab/Ask-Anything)|
-| pretrain-ziya13b-zh | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/pretrain-ziya13b-zh.pth) | Pre-trained with Chinese LLM [Ziya-13B](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1) |
-| finetune-ziya13b-zh | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-ziya13b-zh.pth) | Fine-tuned on machine-translated [VideoChat](https://github.com/OpenGVLab/Ask-Anything) instruction-following dataset (in Chinese)|
-| pretrain-billa7b-zh | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/pretrain-billa7b-zh.pth) | Pre-trained with Chinese LLM [BiLLA-7B-SFT](https://huggingface.co/Neutralzz/BiLLa-7B-SFT) |
-| finetune-billa7b-zh | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-billa7b-zh.pth) | Fine-tuned on machine-translated [VideoChat](https://github.com/OpenGVLab/Ask-Anything) instruction-following dataset (in Chinese) |
-
-#### Audio-Language Branch
-| Checkpoint       | Link | Note |
-|:------------|-------------|-------------|
-| pretrain-vicuna7b    | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/pretrain_vicuna7b_audiobranch.pth)       | Pre-trained on WebVid (2.5M video-caption pairs) and LLaVA-CC3M (595k image-caption pairs) |
-| finetune-vicuna7b-v2 | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune_vicuna7b_audiobranch.pth) | Fine-tuned on the instruction-tuning data from [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4), [LLaVA](https://github.com/haotian-liu/LLaVA) and [VideoChat](https://github.com/OpenGVLab/Ask-Anything)|
-
-
-## Usage
-#### Enviroment Preparation 
-
-First, install ffmpeg.
-```
-apt update
-apt install ffmpeg
-```
-Then, create a conda environment:
-```
-conda env create -f environment.yml
-conda activate videollama
-```
-
-
-## Prerequisites
-
-Before using the repository, make sure you have obtained the following checkpoints:
-
-#### Pre-trained Language Decoder
-
-- Get the original LLaMA weights in the Hugging Face format by following the instructions [here](https://huggingface.co/docs/transformers/main/model_doc/llama).
-- Download Vicuna delta weights :point_right: [[7B](https://huggingface.co/lmsys/vicuna-7b-delta-v0)][[13B](https://huggingface.co/lmsys/vicuna-13b-delta-v0)] (Note: we use **v0 weights** instead of v1.1 weights). 
-- Use the following command to add delta weights to the original LLaMA weights to obtain the Vicuna weights:
-
-```
-python apply_delta.py \
-    --base /path/to/llama-13b \
-    --target /output/path/to/vicuna-13b --delta /path/to/vicuna-13b-delta
-```
-
-#### Pre-trained Visual Encoder in Vision-Language Branch
-- Download the MiniGPT-4 model (trained linear layer) from this [link](https://drive.google.com/file/d/1a4zLvaiDBr-36pasffmgpvH5P7CKmpze/view).
-
-#### Pre-trained Audio Encoder in Audio-Language Branch
-- Download the weight of ImageBind from this [link](https://github.com/facebookresearch/ImageBind). 
-
-## Download Learnable Weights
-Use `git-lfs` to download the learnable weights of our Video-LLaMA (i.e., positional embedding layer + Q-Former + linear projection layer):
-```bash
-git lfs install
-git clone https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series
-```
-The above commands will download the model weights of all the Video-LLaMA variants. For sure, you can choose to download the weights on demand. For example, if you want to run Video-LLaMA with Vicuna-7B as language decoder locally, then:
-```bash
-wget https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-vicuna7b-v2.pth
-wget https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune_vicuna7b_audiobranch.pth
-```
-should meet the requirement.
-
-## How to Run Demo Locally
-
-Firstly, set the `llama_model`, `imagebind_ckpt_path`, `ckpt` and `ckpt_2` in [eval_configs/video_llama_eval_withaudio.yaml](./eval_configs/video_llama_eval_withaudio.yaml).
-Then run the script:
-```
-python demo_audiovideo.py \
-    --cfg-path eval_configs/video_llama_eval_withaudio.yaml \
-    --model_type llama_v2 \ # or vicuna
-    --gpu-id 0
-```
-
-## Training
-
-The training of each cross-modal branch (i.e., VL branch or AL branch) in Video-LLaMA consists of two stages,
-
-1. Pre-training on the [Webvid-2.5M](https://github.com/m-bain/webvid) video caption dataset and [LLaVA-CC3M]((https://github.com/haotian-liu/LLaVA)) image caption dataset.
-
-2. Fine-tuning using the image-based instruction-tuning data from [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4)/[LLaVA](https://github.com/haotian-liu/LLaVA) and the video-based instruction-tuning data from [VideoChat](https://github.com/OpenGVLab/Ask-Anything).
-
-### 1. Pre-training
-#### Data Preparation
-Download the metadata and video following the instructions from the official Github repo of [Webvid](https://github.com/m-bain/webvid).
-The folder structure of the dataset is shown below:
-```
-|webvid_train_data
-|──filter_annotation
-|────0.tsv
-|──videos
-|────000001_000050
-|──────1066674784.mp4
-```
-```
-|cc3m
-|──filter_cap.json
-|──image
-|────GCC_train_000000000.jpg
-|────...
-```
-#### Script
-Config the the checkpoint and dataset paths in [video_llama_stage1_pretrain.yaml](./train_configs/video_llama_stage1_pretrain.yaml).
-Run the script:
-```
-conda activate videollama
-torchrun --nproc_per_node=8 train.py --cfg-path  ./train_configs/video_llama_stage1_pretrain.yaml
-```
-
-### 2. Instruction Fine-tuning
-#### Data
-For now, the fine-tuning dataset consists of:
-* 150K image-based instructions from LLaVA [[link](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/raw/main/llava_instruct_150k.json)]
-* 3K image-based instructions from MiniGPT-4 [[link](https://github.com/Vision-CAIR/MiniGPT-4/blob/main/dataset/README_2_STAGE.md)]
-* 11K video-based instructions from VideoChat [[link](https://github.com/OpenGVLab/InternVideo/tree/main/Data/instruction_data)]
-
-#### Script
-Config the checkpoint and dataset paths in [video_llama_stage2_finetune.yaml](./train_configs/video_llama_stage2_finetune.yaml).
-```
-conda activate videollama
-torchrun --nproc_per_node=8 train.py --cfg-path  ./train_configs/video_llama_stage2_finetune.yaml
-```
-
-## Recommended GPUs
-* Pre-training: 8xA100 (80G)
-* Instruction-tuning: 8xA100 (80G)
-* Inference: 1xA100 (40G/80G) or 1xA6000
-
-## Acknowledgement
-We are grateful for the following awesome projects our Video-LLaMA arising from:
-* [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4): Enhancing Vision-language Understanding with Advanced Large Language Models
-* [FastChat](https://github.com/lm-sys/FastChat): An Open Platform for Training, Serving, and Evaluating Large Language Model based Chatbots
-* [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2): Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models 
-* [EVA-CLIP](https://github.com/baaivision/EVA/tree/master/EVA-CLIP): Improved Training Techniques for CLIP at Scale
-* [ImageBind](https://github.com/facebookresearch/ImageBind): One Embedding Space To Bind Them All
-* [LLaMA](https://github.com/facebookresearch/llama): Open and Efficient Foundation Language Models
-* [VideoChat](https://github.com/OpenGVLab/Ask-Anything): Chat-Centric Video Understanding
-* [LLaVA](https://github.com/haotian-liu/LLaVA): Large Language and Vision Assistant
-* [WebVid](https://github.com/m-bain/webvid): A Large-scale Video-Text dataset
-* [mPLUG-Owl](https://github.com/X-PLUG/mPLUG-Owl/tree/main): Modularization Empowers Large Language Models with Multimodality
-
-The logo of Video-LLaMA is generated by [Midjourney](https://www.midjourney.com/).
-
-
-## Term of Use
-Our Video-LLaMA is just a research preview intended for non-commercial use only. You must **NOT** use our Video-LLaMA for any illegal, harmful, violent, racist, or sexual purposes. You are strictly prohibited from engaging in any activity that will potentially violate these guidelines. 
-
-## Citation
-If you find our project useful, hope you can star our repo and cite our paper as follows:
-```
-@article{damonlpsg2023videollama,
-  author = {Zhang, Hang and Li, Xin and Bing, Lidong},
-  title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},
-  year = 2023,
-  journal = {arXiv preprint arXiv:2306.02858},
-  url = {https://arxiv.org/abs/2306.02858}
-}
-```
-
+title: Video LLaMA2 IBM
+emoji: 🚀
+colorFrom: purple
+colorTo: gray
+sdk: gradio
+sdk_version: 3.29.0
+app_file: app.py
+pinned: false
+license: other