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NexaAI/embedneural-npu-mobile
embedneural-npu-mobile is a machine learning model from NexaAI. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
On-device multimodal embedding model enabling instant, private NPU-powered visual search.
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Updated Nov 18, 2025
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From the Hugging Face model README
On-device multimodal embedding model enabling instant, private NPU-powered visual search.
EmbedNeural is the world’s first multimodal embedding model purpose-built for Qualcomm Hexagon NPU devices. It enables instant, private, battery-efficient natural-language image search directly on laptops, phones, XR, and edge devices — with no cloud and no uploads.
The model continuously indexes local images using NPU acceleration, turning unorganized photo folders into a fully searchable visual database that runs entirely on-device.
Optimized for Qualcomm NPUs to deliver sub-second search and dramatically lower power consumption.
Query thousands of images instantly using everyday language (e.g., “green bedroom aesthetic”, “cat wearing sunglasses”).
All computation stays on-device. No cloud. No upload. No tracking.
Continuous background indexing uses ~10× less power than CPU/GPU methods, enabling true always-on search.
People save thousands of images — memes, screenshots, design inspo, photos — but struggle to find them when needed. Cloud solutions compromise privacy; CPU/GPU search drains battery.
EmbedNeural removes these tradeoffs by combining:
This makes visual search something you can actually use every day, not just when plugged in.
This model is released under the Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0) license.
Non-commercial use, modification, and redistribution are permitted with attribution.
For commercial licensing, please contact [email protected].