Towards Language Modeling With State Space Models

The article presents a novel approach for efficiently training large language models using fewer computational resources. The proposed method involves a combination of techniques, including parameter-efficient fine-tuning and the use of sparse attention, which can significantly reduce the training time and computational cost without compromising the model's performance.