Instructions to use mlai-dante/road-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mlai-dante/road-model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-8B-Instruct") model = PeftModel.from_pretrained(base_model, "mlai-dante/road-model") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen3-VL-8B-Instruct | |
| library_name: peft | |
| license: apache-2.0 | |
| tags: | |
| - qwen3-vl | |
| - image-text-to-text | |
| - ocr | |
| - handwriting-recognition | |
| - lora | |
| # ROAD Barbados Historic Handwriting Qwen3-VL Adapter | |
| 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. | |
| ## Validation | |
| - cer: 0.079004 | |
| - wer: 0.222702 | |
| - score: 0.150853 | |
| ## Inference Prompt | |
| ```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. | |