

# LLaMA-VID Model Card
<a href='https://llama-vid.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> 
<a href='https://arxiv.org/abs/2311.17043'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>

## Model details
LLaMA-VID empowers existing frameworks to support hour-long videos and pushes their upper limit with an extra context token.


**Model type:**
LLaMA-VID is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
LLaMA-VID empowers existing frameworks to support hour-long videos and pushes their upper limit with an extra context token. We build this repo based on LLaVA.


**Model date:**
llama-vid-13b-full-224-video-fps-1 was trained on 11/2023.

## License
Llama 2 is licensed under the LLAMA 2 Community License, 
Copyright (c) Meta Platforms, Inc. All Rights Reserved.

**Where to send questions or comments about the model:**
https://github.com/dvlab-research/LLaMA-VID/issues

## Intended use
**Primary intended uses:**
The primary use of LLaMA-VID is research on large multimodal models and chatbots.

**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

## Training data
This model is trained based on image data from LLaVA-1.5 dataset, and video data from WebVid and ActivityNet datasets, including
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data.
- 232K video-caption pairs sampled from the WebVid 2.5M dataset.
- 98K videos from ActivityNet with QA pairs from Video-ChatGPT.
{"tags": ["vision-language model", "llama", "video understanding"]}
transformers pytorch llava text-generation vision-language model llama video understanding arxiv:2311.17043 autotrain_compatible endpoints_compatible region:us