RaanA introduces a data-efficient approach to Post-Training Quantization for large language models, addressing key limitations of existing methods.

• Reduced data requirements for effective model compression
• Flexible bit allocation that adapts to model architecture
• Significant speedups without sacrificing model performance
• Practical implementation suitable for production environments

This engineering breakthrough enables more efficient deployment of large language models across diverse hardware environments, reducing computational costs while maintaining accuracy.

RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm