DiJiang streamlines big language models using clever compression techniques.
DiJiang introduces a novel way to make large language models more cost-effective and faster, using frequency domain techniques and a specific mathematical transformation to compress information. This approach keeps performance on par with the standard model it improves upon, but with reduced training expenses and quicker output generation. The developers have put DiJiang up against different measures and have made the code public for others to use. This marks a significant step in making advanced language processing tools more accessible and efficient.
Read more: Arxiv