I was on my phone the other day and wanted to find everything and anything related to a hamster I used to know named Stinky. His memories remain in various photos, videos, journal entries and songs, but Stinky is no longer with us (he died a few years ago of old age). My phone's built-in search wasn't much help – it just looked for titles of files – so I decided to see what I could build myself without coding.

I conducted some research

First, I did a quick Google search to see what on-device models were available on Hugging Face:

I’m looking for a model on Hugging Face that I can put on my phone that lets me context search for video, audio, text, and photos.

And I received three strong leads:

Google Embeddinggemma 2: 740M parameters, 1.5 GB storage, 2GB RAM

Meta ImageBind: ~1.2B parameters, 2.5GB storage, 4GB RAM

NVIDIA Omni-Embed-Nemotron: ~3B–4.7B parameters, 9.5 GB storage, 8GB RAM

I chose to work with Embeddinggemma 2 since its size was small enough to run on a smartphone.

Finally, I thought…