GPT-4o Knowledge Distillation

In the video titled ‘Using GPT-4o to train a 2,000,000x smaller model (that runs directly on device),’ Edge Impulse demonstrates how to distill knowledge from a large multimodal LLM (GPT-4o) into a much smaller model that can run directly on edge devices. The process involves using GPT-4o to label data and then training a smaller model using transfer learning. This smaller model, with only 800K parameters, is 2,200,000 times smaller than GPT-4o but retains similar accuracy for specific tasks. The video showcases the practical application of this approach by training a model to detect children’s toys in a household environment. The trained model runs efficiently on devices like Raspberry Pi and even microcontrollers with minimal latency and no need for a network connection. This method offers a cost-effective and low-latency solution for deploying AI capabilities on edge devices.