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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Sep 3·5 min read
NeoMME: A Single-Tower Multimodal-Native Multilingual Foundation Encoder for Efficient Fine-Tuning and Inference
TL;DR
NeoMME is a new family of multimodal and multilingual encoders that efficiently process text and images using a single Transformer architecture. It achieves superior performance on document retrieval tasks while significantly reducing computational overhead.
✦ Why It Matters
Consider integrating NeoMME into your document retrieval systems for improved efficiency and performance.
Key Takeaways
- Explore NeoMME for efficient multilingual document retrieval.
- Utilize the pretrained models available on Hugging Face.
- Implement hierarchical token pooling for better performance.
- Leverage asymmetric quantization to reduce model size.
How It Works
NeoMME employs a single bidirectional Transformer architecture that integrates both text and image processing. It uses a masked discrete-diffusion objective for pretraining, allowing it to learn from both modalities simultaneously.
This design eliminates the need for separate vision and language models, reducing computational overhead and improving efficiency.