In the realm of edge computing and consumer devices, the ability to run deep learning models offline is increasingly crucial. Cloud-based solutions, while convenient, require constant internet connectivity, incur ongoing costs, and risk obsolescence when service providers end support. Recognizing these limitations, there is a compelling case for hardware that can operate independently from the cloud, avoiding additional fees and ensuring long-term functionality.
Implementing ASR and GPT Models on Raspberry Pi
The focus of this discussion is on deploying a LLaMA GPT model and Automatic Speech Recognition (ASR) on a Raspberry Pi, enabling the device to answer queries without the need for an internet connection. This offline capability ensures that the Raspberry Pi remains a versatile and user-friendly platform for various applications.
Compatibility Across Platforms
While the implementation is optimized for the Raspberry Pi, the majority of the code can also be executed on other platforms such as Windows, OSX, or Linux laptops. This cross-platform compatibility allows a broader audience to experiment with the code, even in the absence of a Raspberry Pi.
The hardware of choice for this project is the Raspberry Pi 4, a compact single-board computer that runs Linux and operates efficiently without the need for active cooling. Although the Raspberry Pi 5, released in 2023, offers nearly double the performance, its higher cost does not outweigh the benefits for the purposes of this demonstration.
