LLaMa

该数据集采用基于变换器（transformer）的模型设计，旨在提高涉及长距离依赖任务上的性能。此外，LLaMa模型还运用了如预RMS归一化（pre-RMSNorm）和旋转嵌入（rotary embedding）等技术。其规模属于大型，适用于自然语言处理任务。

This dataset adopts a Transformer-based model design, aiming to improve performance on tasks involving long-range dependencies. Furthermore, the LLaMa model utilizes techniques such as pre-RMSNorm and rotary embedding. It has a large scale and is suitable for natural language processing (NLP) tasks.