I’m working on a dataset toolchain aimed at LLM fine-tuning datasets, because I noticed most dataset failures aren’t “model problems”—they’re data problems: duplicates, leakage, unclear labels, inconsistent formatting, or missing documentation.

What the tool enforces

• Schema validation: every record must match a strict schema (fields, allowed labels, structure)
• Split integrity: supports splitting by topic/template-family so train/test don’t leak via shared scaffolding
• Dedupe + repetition control: catches exact and near-duplicates; flags templated collapse
• QC reports: acceptance rate, failure breakdown, and example-level rejection reasons

What I’m trying to get right (and want feedback on)

• What metadata is a must-have for you? (license, lineage, schema, label definitions, known limitations)
• Do you prefer datasets shipped as clean-only, or raw + clean + reproducible pipeline?
• How do you want near-duplicate removal described so you trust it didn’t delete useful diversity?

If people are interested, I can share a dataset-card template + QC report structure that’s been working well (no links unless allowed).