rukopys-qwen3-vl-8b-page

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RUKOPYS Qwen3-VL 8B Page LoRA

Page-level LoRA adapter for Qwen/Qwen3-VL-8B-Instruct, fine-tuned for Ukrainian handwritten document understanding.

This model takes a full scanned page as input and predicts structured JSON regions: bounding boxes, region type, language metadata, and transcribed text. It was built as part of an end-to-end HTR pipeline for the RUKOPYS handwritten document dataset: data curation, supervised fine-tuning dataset generation, QLoRA training, inference, evaluation, and Kaggle-style submission export.

What It Does

• Parses full-page Ukrainian handwritten documents.
• Returns structured region-level JSON instead of plain OCR text.
• Supports document AI workflows where layout and transcription must be evaluated together.
• Serves as the first public baseline LoRA adapter from this project.

Training Data

Trained on the curated RUKOPYS MVP dataset:

AlexandreSheva/rukopys-curated-mvp

The dataset is a cleaned derivative of UkrainianCatholicUniversity/rukopys, prepared for page-level vision-language fine-tuning, crop-level transcription, and layout detection experiments.

Output Format

The expected output is JSON similar to:

[
  {
    "bbox": [10, 20, 300, 80],
    "type": "handwritten",
    "language": "uk",
    "text": "..."
  }
]

Notes

This is the original public 8B page-level adapter. For the improved checkpoint, use:

AlexandreSheva/rukopys-qwen3-vl-8b-page-a100-v2

Limitations

Optimized for Ukrainian handwriting and RUKOPYS-style documents. Sensitive to image quality, scan resolution, and page layout. Should be validated on target documents before production use. The adapter is Apache-2.0, but the training data derives from a CC BY-NC-SA 4.0 dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• training_steps: 600

Training results

Framework versions

• PEFT 0.19.1
• Transformers 5.0.0
• Pytorch 2.11.0+cu128
• Datasets 4.8.5
• Tokenizers 0.22.2