#Instruction-Tuning

A platform offering a unified method for instruction collection and parameter-efficient techniques, aimed at improving large language model capabilities. It provides a seamless interface for optimizing the instruction-tuning process, supporting advanced language model use and research.

This project enhances the Mixtral-8x7B model using instruction-tuning to improve its Chinese conversational performance. By employing machine-generated data and focusing on specific datasets, it offers advancements in sparse model performance, as demonstrated in benchmark tests such as C-Eval. The Aurora model stands out for its ability to manage tasks without human instructions, overcoming the limitations of traditional language models.