HuggingSnap describes what the iPhone camera sees with a 500M model on the phone

Hugging Face published HuggingSnap, an iPhone app that describes what the camera is pointed at, with the model running on the phone rather than in the cloud. The app’s runtime configuration loads SmolVLM2 at 500M parameters in Apple’s MLX format. Hugging Face had trailed the app in its SmolVLM2 announcement on February 20, 2025, writing that the 500M model lets users analyse and understand video content directly on their device with no cloud required.

The README describes the app as a way to point the camera and have text translated or summarised, identify plants and animals and learn more about surrounding objects. Inference runs through MLX Swift, Apple’s machine learning framework for Apple silicon, with a modified fork of Apple’s MLX example code supplying the vision-language support. The app pulls the weights from the Hugging Face Hub the first time it runs, then holds Metal memory to 82 percent of what iOS reports as available to the process.

Input comes from the live camera, from stills and clips captured in the app, and from photos and videos picked out of the library. A tap on the shutter takes a picture and a press and hold records video, after which the app offers a describe action for images and a summarise action for video, with questions typed or dictated. The app’s own interface states that photos and videos are processed entirely on the device and that no data is sent to the cloud.

The README states that installation needs an iPhone running iOS 18, and the Xcode project sets iOS 18.0 as the deployment target and iPhone as the only device family, with no Mac Catalyst or visionOS build. TechCrunch reported the app as a free App Store download on March 19, 2025, quoting the listing as saying it works offline and processes all data on the phone. The SmolVLM2 weights are Apache 2.0, while the app repository itself carries no licence file.