VideoLLaMA3

VideoLLaMA3 is a cutting-edge multimodal foundational model focused on image and video understanding.

CommonProductVideoMultimodalVideo Understanding

VideoLLaMA3, developed by the DAMO-NLP-SG team, is a state-of-the-art multimodal foundational model specializing in image and video understanding. Based on the Qwen2.5 architecture, it integrates advanced visual encoders (such as SigLip) with powerful language generation capabilities to address complex visual and language tasks. Key advantages include efficient spatiotemporal modeling, strong multimodal fusion capabilities, and optimized training on large-scale datasets. This model is suitable for applications requiring deep video understanding, such as video content analysis and visual question answering, demonstrating significant potential for both research and commercial use.

Supports multimodal input for video and images
capable of generating natural language descriptions.
Offers various pretrained models
including versions with 2B and 7B parameters.
Optimized spatiotemporal modeling capabilities to handle long video sequences.
Supports multilingual generation
suitable for cross-language video understanding tasks.
Provides complete inference code and online demos for users to quickly get started.
Supports local deployment and cloud inference
adaptable to diverse usage scenarios.

This model is designed for researchers
developers
and enterprises that require video content analysis
visual question answering
and multimodal applications. Its robust multimodal understanding capabilities enable users to efficiently manage complex visual and language tasks

In video content analysis
users can upload videos and receive detailed natural language descriptions to quickly comprehend video content.
For visual question answering tasks
users can input questions and obtain accurate answers based on video or image context.
In multimodal applications

1. Install necessary dependencies
such as PyTorch and transformers.
2. Clone the VideoLLaMA3 GitHub repository and install project dependencies.
3. Download the pretrained model weights and choose the appropriate model version (e.g.
2B or 7B).

Visit

VideoLLaMA3 Visit Over Time

Monthly Visits

502571820

Bounce Rate

37.10%

Page per Visit

5.9

Visit Duration

00:06:29

VideoLLaMA3 Visit Trend

VideoLLaMA3 Visit Geography

VideoLLaMA3 Traffic Sources

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VideoLLaMA3

VideoLLaMA3 Visit Over Time

VideoLLaMA3 Visit Trend

VideoLLaMA3 Visit Geography

VideoLLaMA3 Traffic Sources

VideoLLaMA3 Alternatives

Tarsier — Tarsier is a large video language model developed by ByteDance that generates high-quality video descriptions.

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Pixtral-Large-Instruct-2411 — A 124B-parameter multimodal large language model.

Grok-1.5 Vision Preview — The world's first multimodal model connecting the digital and physical worlds

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VideoRAG — VideoRAG is a retrieval-augmented generation framework designed for processing videos with extremely long context.

Qwen2.5-VL — Qwen2.5-VL is a powerful visual language model capable of understanding image and video content and generating corresponding text.

Open R1 — This is a fully open-source replication project of the DeepSeek-R1 model, aimed at assisting developers in recreating and building models based on R1.

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