This article discusses how to run large language models like Gemma 4 locally on Android or iOS devices using the Google AI Edge Gallery. By leveraging MediaPipe and TensorFlow Lite, developers can build applications that perform tasks such as text classification and image captioning without an internet connection. This approach enhances user privacy by keeping all data on-device and enables functionality in areas with limited connectivity.
* Local execution of large language models via edge infrastructure
* Privacy benefits through on-device processing
* Offline capabilities for various machine learning tasks
* Open source access to demo code via GitHub
* Local execution of large language models via edge infrastructure
* Privacy benefits through on-device processing
* Offline capabilities for various machine learning tasks
* Open source access to demo code via GitHub
This article details how to train an image classification model on an ESP32 using both the SenseCraft AI platform and a custom TensorFlow Lite implementation. It covers setting up binary classification, training the model, and deploying it on ESP32-S3 devices.
Learn how to perform image classification on edge devices like the Raspberry Pi using TensorFlow Lite and Mobilenet V2 models.
Learn how to create a real-time machine learning audio noise suppression system using Python, TensorFlow Lite, and the Raspberry Pi Pico.
