This research introduces a progressive compression technique for large language models that dramatically reduces computational and memory requirements while preserving performance.

• Converts model weights to just 1-bit representation through binarization
• Applies semi-structured pruning to further eliminate redundancy
• Achieves significant memory footprint reduction making LLMs deployable on edge devices
• Demonstrates a balanced approach between model size and performance

This engineering breakthrough enables deployment of powerful language models on resource-constrained environments like mobile phones and IoT devices, potentially democratizing access to AI capabilities across diverse hardware conditions.

Progressive Binarization with Semi-Structured Pruning for LLMs