新闻
NeoMME: an efficient Multimodal-native and Multilingual Encoder
Hugging Face · 发布于 · 阅读约3分钟
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- 发生了什么
- Hugging Face released NeoMME, a 260M and 800M parameter multimodal encoder trained from scratch without separate vision or language towers.
- 为何重要
- Engineers building visual document retrieval systems need efficient models that balance speed, accuracy, and storage for production deployments.
- 注意
- NeoMME was trained on only 524 billion tokens, relatively small compared to similar models, which may affect performance on out-of-distribution tasks.
- encoder
这条新闻背后的模式
- MAPS: Multilingual Agent Performance & Security
- Multimodal Interaction Patterns
- Query Transformation Retrieval
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The Agent Architect
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