NeoMME: an efficient Multimodal-native and Multilingual Encoder

This post introduces NeoMME-260M, an efficient multimodal-native and multilingual encoder. It details the model's architecture, which is designed for robust cross-modal understanding and generation. The encoder leverages a novel attention mechanism that balances computational efficiency with performance across various modalities (text, image, audio). The post provides benchmarks demonstrating its state-of-the-art performance on tasks like image captioning and cross-modal retrieval, highlighting its ability to handle diverse linguistic and sensory inputs.