NeoMME: an efficient Multimodal-native and Multilingual Encoder
Article image or reusable cover for Hugging Face
Hugging Face introduces NeoMME, a new AI model designed to understand both text and images together.
Unlike many other models, NeoMME does not use separate components for vision and language, but processes everything in a single transformer. The model comes in two sizes (260 million and 800 million parameters) and is particularly effective at searching documents by analyzing page images instead of extracted text. It can process around 51 pages per second and uses less storage space than previous models — 255 times less per page according to testing.
Unlike many generative visual language models, NeoMME does not use a separate pretrained vision tower or a causal language model.
Vibekollen prepared this summary with AI from the original publication. The content belongs to Hugging Face.
More to read
Lumaril, which develops AI software for optimizing industrial production by connecting to existing control systems, raised a $66M Series A at a $400M valuation
Techmeme 3 h ago
DeepSeek open sources Super-Agent as a research prototype, a proactive coding agent it says can monitor group chats for coding issues and suggest solutions
Techmeme 3 h ago
Insurance software maker Guidewire agrees to acquire Hyperexponential, which makes AI-powered insurance underwriting and pricing software, for $1.7B in cash
Techmeme 3 h ago
One word for AI agents in 2027
Nate Herk 7 h ago