The process of distillation in the open source community has evolved, focusing on creating smaller models from larger ones using synthetic data. Current reports emphasize that to achieve effective distillation, high-quality synthetic datasets comprising 25 to 30 million samples are crucial, which can be costly. Various methods for online and offline distillation exist, and the introduction of Distillikute aims to centralize resources for effective code and routine sharing within the community. Two primary approaches are available: logic-based distillation and hidden states-based distillation, with the latter offering superior performance and cross-architecture capabilities, addressing a gap for larger labs that often operate within consistent architectures.

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