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Synthetic Data Kit is a CLI-centric toolkit for generating high-quality synthetic datasets to fine-tune Llama models, with an emphasis on producing reasoning traces and QA pairs that line up with modern instruction-tuning formats. It ships an opinionated, modular workflow that covers ingesting heterogeneous sources (documents, transcripts), prompting models to create labeled examples, and exporting to fine-tuning schemas with minimal glue code. The kit’s design goal is to shorten the “data prep” bottleneck by turning dataset creation into a repeatable pipeline rather than ad-hoc notebooks. ...

...The study has collected and continues to collect extensive phenotypic and genotypic detail about its participants, including data from questionnaires, physical measures, sample assays, accelerometry, multimodal imaging, genome-wide genotyping and longitudinal follow-up for a wide range of health-related outcomes. Tofu will generate synthetic data which conforms to the structure of the baseline data UK Biobank sends researchers by generating random values. For categorical variables (single or multiple choices), a random value will be picked from the UK Biobank data dictionary for that field. For continuous variables, a random value will be generated based on the distribution of values reported for that field on the UK Biobank showcase.