Name: VideoLLaMA2.1 7B 16F Base
Author: DAMO-NLP-SG

Overview

VideoLLaMA2.1 7B 16F Base is a specialized multimodal model designed for high-fidelity video understanding and visual question answering (VQA). Unlike standard image-text models, it is architected to process temporal dynamics across video frames, making it suitable for complex tasks like action recognition, event summarization, and detailed video interrogation. Built on a 7B parameter backbone with an Apache-2.0 license, it offers a flexible foundation for developers to build domain-specific video agents. It integrates well into pipelines requiring automated video indexing or real-time content analysis, providing a competitive balance between inference latency and contextual accuracy compared to larger proprietary multimodal models.

Highlights

Optimized for temporal video analysis and complex VQA tasks.
Apache-2.0 license allows for flexible commercial integration.
7B parameter scale balances performance with deployment efficiency.
Strong foundation for fine-tuning on domain-specific video datasets.

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base")
tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base')
tokenizer = AutoTokenizer.from_pretrained('DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'DAMO-NLP-SG/VideoLLaMA2.1-7B-16F-Base')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
datasets:

OpenGVLab/VideoChat2-IT

Lin-Chen/ShareGPT4V

liuhaotian/LLaVA-Instruct-150K

language:

en

metrics:

accuracy

library_name: transformers
pipeline_tag: visual-question-answering
tags:

multimodal large language model

large video-language model

---

<h3 align="center"><a href="https://arxiv.org/abs/2406.07476">VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs</a></h3>
<h5 align="center"> If you like our project, please give us a star ⭐ on <a href="https://github.com/DAMO-NLP-SG/VideoLLaMA2">Github</a> for the latest update. </h2>

📰 News

[2024.10.15] Release checkpoints of VideoLLaMA2.1-7B-16F-Base and VideoLLaMA2.1-7B-16F

[2024.08.14] Release checkpoints of VideoLLaMA2-72B-Base and VideoLLaMA2-72B

[2024.07.30] Release checkpoints of VideoLLaMA2-8x7B-Base and VideoLLaMA2-8x7B.

[2024.06.25] 🔥🔥 As of Jun 25, our VideoLLaMA2-7B-16F is the Top-1 ~7B-sized VideoLLM on the MLVU Leaderboard.

[2024.06.18] 🔥🔥 As of Jun 18, our VideoLLaMA2-7B-16F is the Top-1 ~7B-sized VideoLLM on the VideoMME Leaderboard.

[2024.06.17] 👋👋 Update technical report with the latest results and the missing references. If you have works closely related to VideoLLaMA 2 but not mentioned in the paper, feel free to let us know.

[2024.06.14] 🔥🔥 Online Demo is available.

[2024.06.03] Release training, evaluation, and serving codes of VideoLLaMA 2.

🌎 Model Zoo

🚀 Main Results

Multi-Choice Video QA & Video Captioning

Open-Ended Video QA

🤖 Inference with VideoLLaMA2

python

import sys
sys.path.append('./')
from videollama2 import model_init, mm_infer
from videollama2.utils import disable_torch_init
def inference():
    disable_torch_init()
# Video Inference
    modal = 'video'
    modal_path = 'assets/cat_and_chicken.mp4' 
    instruct = 'What animals are in the video, what are they doing, and how does the video feel?'
   
    # Image Inference
    modal = 'image'
    modal_path = 'assets/sora.png'
    instruct = 'What is the woman wearing, what is she doing, and how does the image feel?'
    
    model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-16F'
    model, processor, tokenizer = model_init(model_path)
    output = mm_infer(processormodal, instruct, model=model, tokenizer=tokenizer, do_sample=False, modal=modal)
print(output)
if __name__ == "__main__":
    inference()

Citation

If you find VideoLLaMA useful for your research and applications, please cite using this BibTeX:

bibtex

@article{damonlpsg2024videollama2,
  title={VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs},
  author={Cheng, Zesen and Leng, Sicong and Zhang, Hang and Xin, Yifei and Li, Xin and Chen, Guanzheng and Zhu, Yongxin and Zhang, Wenqi and Luo, Ziyang and Zhao, Deli and Bing, Lidong},
  journal={arXiv preprint arXiv:2406.07476},
  year={2024},
  url = {https://arxiv.org/abs/2406.07476}
}
@article{damonlpsg2023videollama,
  title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},
  author = {Zhang, Hang and Li, Xin and Bing, Lidong},
  journal = {arXiv preprint arXiv:2306.02858},
  year = {2023},
  url = {https://arxiv.org/abs/2306.02858}
}