This research introduces a novel information-entropy framework for designing compact, efficient generative language models specifically optimized for IoT devices with limited resources.

• Proposes a mathematical programming solution to balance model performance with resource constraints
• Delivers generative AI capabilities on edge devices without requiring cloud connectivity
• Creates a systematic approach to LLM optimization rather than ad-hoc techniques
• Enables new applications for on-device AI in IoT environments

This engineering breakthrough matters because it democratizes access to generative AI technology across a wider range of devices, potentially enabling intelligent edge computing without privacy concerns of cloud-based processing.

Merino: Entropy-driven Design for Generative Language Models on IoT Devices