The Moda community has open sourced OneLLM, a powerful unified framework for multi-modal alignment, which has brought new breakthroughs to the field of artificial intelligence. This framework realizes the understanding of multiple modal data such as images, audios, and videos through a universal encoder and unified projection module, and demonstrates excellent zero-sample capabilities, especially in cross-sensory fields such as video-text, audio-video-text, etc. Outstanding performance in modal tasks. The open source of OneLLM means that a wider range of developers can participate in the research and application of multi-modal artificial intelligence, promoting the rapid development of this field.
The Moda community has open sourced a unified framework for multi-modal alignment called OneLLM. This framework utilizes a universal encoder and a unified projection module to align multi-modal inputs with LLM. It supports the understanding of multiple modal data such as images, audio, and videos, and shows strong zero-sample capabilities in tasks such as video-text, audio-video-text, etc. The open source code of OneLLM has been released on GitHub, and the relevant model weights and model creation space can be obtained on this platform.
The open source OneLLM framework not only provides valuable resources for researchers, but also provides powerful tools for practical applications. Its powerful ability in multi-modal understanding indicates that artificial intelligence technology will develop in a smarter and more comprehensive direction in the future. It is expected that OneLLM can play a role in more fields and promote the progress of artificial intelligence technology.