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Existing research has demonstrated that refining large language models (LLMs)through the utilization of machine-generated instruction-following dataempowers these models to exhibit impressive zero-shot capabilities for noveltasks, without requiring human-authored instructions. In this paper, wesystematically investigate, preprocess, and integrate three Chineseinstruction-following datasets with the aim of enhancing the Chineseconversational capabilities of Mixtral-8x7B sparse Mixture-of-Experts model.Through instruction fine-tuning on this carefully processed dataset, wesuccessfully construct the Mixtral-8x7B sparse Mixture-of-Experts model named"Aurora." To assess the performance of Aurora, we utilize three widelyrecognized benchmark tests: C-Eval, MMLU, and CMMLU. Empirical studies validatethe effectiveness of instruction fine-tuning applied to Mixtral-8x7B sparseMixture-of-Experts model. This work is pioneering in the execution ofinstruction fine-tuning on a sparse expert-mixed model, marking a significantbreakthrough in enhancing the capabilities of this model architecture. Ourcode, data and model are publicly available at  https://github.com/WangRongsheng/Aurora

Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-Tuning