Garbage in, garbage out — start with clean data
Your fine-tune is only as good as its corpus. LLMtoMD turns messy real-world documents into clean, consistent, structured training data.
Why fine-tunes underperform
Teams pour effort into model and hyperparameter choices, then feed in a corpus scraped out of PDFs with collapsed tables, broken reading order, and OCR noise.
Inconsistent, low-quality text caps how good the resulting model can be — no amount of tuning recovers signal that was destroyed at ingestion.
A clean corpus, at scale
Consistent Markdown
Every source — whatever its original format — becomes uniformly structured Markdown.
Bulk + automated
Convert at scale over the API, or watch a storage prefix to process new documents automatically.
Structured extraction
Turn documents into labeled fields and records for supervised datasets.
Export-ready
Pull clean text and chunked JSONL out via the API to assemble your dataset.
Related reading: Garbage In, Garbage Out: Why Your Fine-Tune Underperformed
Convert anything to AI-ready Markdown
PDFs, Office docs, images, audio, and whole websites — clean Markdown and RAG-ready exports for your LLM, in seconds.