Neural architecture search is a powerful tool in image processing, automating model construction and reducing human involvement. However, its deployment on edge devices with limited computing resources is often impeded by the size of large models, a concern overlooked by most NAS methods focused solely on accuracy. We propose a hybrid network search approach that integrates the glore_unit, a novel component that replaces traditional cells to optimize model size without sacrificing accuracy. By leveraging the Differentiable Architecture Search (Darts) and a Googlenet-like hypernet, we’ve redefined the search space to prioritize compactness and precision, enhanced by a temperature factor for more reliable search selections. Our experiments on cifar10 and ImageNet showcase a model with a 2.35% error rate and 2.76M parameters on cifar10, and a Top-1 error rate of 23.75%, Top-5 error rate of 7.13% with 3.9M parameters on ImageNet, demonstrating SOTA accuracy with a significant reduction in model parameters, making it suitable for environments with constrained computational resources.