Show HN: DataFlow – Open Tool for LLM data prep 10k synthetic > 1M generic data (github.com)

DataFlow, a newly launched open-source data preparation and training tool for large language models (LLMs), has been introduced to the AI/ML community. This innovative system is designed to parse, generate, and refine data from low-quality sources like PDFs and plain text, significantly enhancing LLM performance in specialized domains such as healthcare, finance, and law. With a focus on modularity, DataFlow integrates over 120 operators—spanning generic, domain-specific, and evaluation categories—into flexible pipelines that cater to diverse data processing needs. The significance of DataFlow lies in its empirical validation, showcasing a marked improvement in the performance of domain-oriented LLMs. By employing various training techniques including pre-training, supervised fine-tuning, and reinforcement learning, DataFlow not only optimizes existing models but also supports dynamic pipeline creation through its intelligent DataFlow-agent. This advancement is underscored by benchmark results that demonstrate superior outcomes when using DataFlow's synthesized datasets over traditional random selections, making it a valuable resource for researchers and practitioners seeking to maximize LLM efficacy and data quality in their applications.