Aurora:activating Chinese Chat Capability For Mixtral-8x7b Sparse Mixture-of-experts Through Instruction-tuning

Wang Rongsheng, Chen Haoming, Zhou Ruizhe, Duan Yaofei, Cai Kunyan, Ma Han, Cui Jiaxi, Li Jian, Pang Patrick Cheong-iao, Wang Yapeng, Tan Tao. Arxiv 2023

[Paper] [Code]    
Fine Tuning Has Code Model Architecture Pretraining Methods Training Techniques

Existing research has demonstrated that refining large language models (LLMs) through the utilization of machine-generated instruction-following data empowers these models to exhibit impressive zero-shot capabilities for novel tasks, without requiring human-authored instructions. In this paper, we systematically investigate, preprocess, and integrate three Chinese instruction-following datasets with the aim of enhancing the Chinese conversational capabilities of Mixtral-8x7B sparse Mixture-of-Experts model. Through instruction fine-tuning on this carefully processed dataset, we successfully construct the Mixtral-8x7B sparse Mixture-of-Experts model named “Aurora.” To assess the performance of Aurora, we utilize three widely recognized benchmark tests: C-Eval, MMLU, and CMMLU. Empirical studies validate the effectiveness of instruction fine-tuning applied to Mixtral-8x7B sparse Mixture-of-Experts model. This work is pioneering in the execution of instruction fine-tuning on a sparse expert-mixed model, marking a significant breakthrough in enhancing the capabilities of this model architecture. Our code, data and model are publicly available at https://github.com/WangRongsheng/Aurora

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