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mlai-dante/road-model-public
road-model-public is a image-text-to-text model from mlai-dante. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
This repository contains a LoRA adapter fine-tuned for the Zindi R.O.A.D. Barbados Historic Handwriting Challenge.
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From the Hugging Face model README
This repository contains a LoRA adapter fine-tuned for the Zindi R.O.A.D. Barbados Historic Handwriting Challenge.
Base model: Qwen/Qwen3-VL-8B-Instruct
The adapter was trained to transcribe cropped images of historical handwritten Barbados archival records into text.
You are transcribing a cropped image from an old Barbados archival record.
The image contains handwritten historical text, usually one line or a short phrase from an 18th or 19th century legal, estate, will, deed, or notarial document.
Transcribe only the visible handwritten text.
Important rules:
- Preserve the original wording exactly as written.
- Preserve old, archaic, or unusual spelling. Do not modernize it.
- Preserve capitalization when clear.
- Preserve punctuation when visible.
- Preserve abbreviations, superscript-like marks, contractions, and symbols such as &, y^e, p^rsents, s^d, deced, etc.
- Do not correct grammar.
- Do not expand abbreviations.
- Do not add missing words.
- Do not describe the image.
- Do not explain your answer.
- If a character or word is uncertain, make the best transcription from the visible handwriting.
Return only the transcription text.
The repository includes the fine-tuned LoRA adapter, processor files, and run
artifacts such as submission.csv, validation metrics, validation predictions,
and the training config. The original challenge images and CSV data are not
uploaded.