Fine-tune export (external training)¶

Transcribe can export a local dataset of page images + preferred/active transcriptions for external fine-tuning. Training is not performed inside this project.

What you get¶

Running Export → Fine-tune dataset (UI) or transcribe export-finetune <project> writes:

finetune_export_<stamp>/
  manifest.json       # knobs, counts, model stats
  samples.jsonl       # one JSON object per included page
  images/<page_id>.png
  README.txt

Contract detail: contracts/finetune-export.md.

Typical external workflow¶

Prefer OCR attempts in Review (or rely on active / human edits per export knobs).
Export the fine-tune package from Transcribe.
Train a vision–language model elsewhere (examples: Unsloth, Hugging Face TRL, Ollama create/Modelfile flows, or any SFT tool that accepts image+text JSONL).
Install the resulting model into your local Ollama (or other runtime).
Select that model in Transcribe for future OCR runs.
Optionally use multipass compare to verify quality against your previous models.

Privacy¶

Images and text stay on disk in the export folder. They leave the machine only if you copy or upload that folder to a training environment.

Knobs¶

Knob	Effect
Prefer effective text	Use human `edited_text` when present
Require preferred	Skip pages without `preferred_attempt_id`
Include rejected	Attach other vision candidates for preference/DPO-style training
Image mode	`copy` or `hardlink` of active page renders

Non-goals¶

No training loop, GPU job, or weight upload inside Transcribe
No cloud fine-tune API integration