We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM)  to create diverse synthetic data. To fully exploit this methodology at scale, we introduce Persona Hub – a collection of 1 billion diverse personas automatically curated from web data. These 1 billion personas (13% of the world’s total population) , acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, thereby facilitating the creation of diverse synthetic data at scale for various scenarios. By showcasing Persona Hub’s use cases in synthesizing high-quality mathematical and logical reasoning problems, instructions (i.e., user prompts) , knowledge-rich texts, game NPCs and tools (functions)  at scale, we demonstrate persona-driven data synthesis is versatile, scalable, flexible, and easy to use, potentially driving a paradigm shift in synthetic data creation and applications in practice, which may have a profound impact on LLM research and development. DISCLAIMER:Persona Hubcan facilitate synthetic data creation at a billion-scale to simulate diverse inputs (i.e., use cases)  from a wide variety of real-world users. If this data is used as input to query a target LLM to obtain its outputs at scale, there isa high riskthat the LLM’s knowledge, intelligence and capabilities will be dumped and easily replicated, thereby challenging the leading position of the most powerful LLMs (e.g., our approach allows a 7B LLM to achieve 65% on MATH, matching the performance ofgpt-4-turbo-preview) . This tech report isfor research purposes only. It is crucial to avoid misuse and ensure ethical and responsible application. We discuss its broad impact and potential concerns in detail in Section5.