Now available in production on Pixel 8 Pro devices and is already poweringįor more information, see Android AICore. YouĬan fine-tune these models for downstream tasks using LoRa. Foundation models are pre-installed usingĪICore, so you don't need to download or distribute them within your app. System-level capability that provides access to foundation models for on-deviceĮxecution, introduced in Android 14. On-device execution of Gemini Nano is powered by Android AICore, a new YouĬan do this either through backend integration (with Python, Want to consider using the Gemini API for accessing Gemini on the server. Require larger Gemini models, and to support a wide range of devices, you may On-device execution of Gemini has many benefits however, for use cases that This can produce significant savings for frequently used Cost savings: You can reduce inference costs by offloading execution toĬonsumer hardware.This is useful for applications that need to work Offline access: Users can access AI features even when there is no.Sensitive data, such as messaging apps with end-to-end encryption. Local processing of sensitive data: Processing data locally can help youĪvoid sending user data to the cloud.On-device execution enables the following: Interested in building such applications, apply to our Early Access Important: The Google AI Edge SDK for Android is under early access previewįor developers building innovative applications using Gemini Nano. The APIs for accessing Gemini Nano support text-to-text modality, with more Optionally, provide a LoRA fine-tuning block to improve performance of the.Run inference at high performance and implement fallbacks.Determine if the underlying Android-powered device is supported. Google AI Edge SDK for Android, which provides APIs to: To execute the Gemini Nano model on Android, you need to use the On-device on capable Android devices starting with Pixel 8 Pro. Gemini Nano, the smallest version of the Gemini model family, can be executed
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