DAMO-NLP-SG/VideoLLaMA2
A multi-modal LLM that processes video and audio for spatial-temporal reasoning and understanding.
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VideoLLaMA 2 is a video large language model that advances spatial-temporal modeling and audio understanding. It extends LLM capabilities to multi-modal video comprehension by combining visual, audio, and text inputs. The project provides model checkpoints, demo spaces on HuggingFace, and training/inference code for the video-LLM architecture.
Frequently asked
- What is DAMO-NLP-SG/VideoLLaMA2?
- A multi-modal LLM that processes video and audio for spatial-temporal reasoning and understanding.
- Is VideoLLaMA2 open source?
- Yes — DAMO-NLP-SG/VideoLLaMA2 is open source, released under the Apache-2.0 license.
- What language is VideoLLaMA2 written in?
- DAMO-NLP-SG/VideoLLaMA2 is primarily written in Python.
- How popular is VideoLLaMA2?
- DAMO-NLP-SG/VideoLLaMA2 has 1.3k stars on GitHub.
- Where can I find VideoLLaMA2?
- DAMO-NLP-SG/VideoLLaMA2 is on GitHub at https://github.com/DAMO-NLP-SG/VideoLLaMA2.