| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3-VL-4B-Instruct |
| tags: |
| - vision |
| - ocr |
| - arabic |
| - islamic |
| - qwen3 |
| - fine-tuned |
| language: |
| - ar |
| datasets: |
| - seemorg/books-ocr |
| metrics: |
| - wer |
| - cer |
| - bleu |
| library_name: peft |
| pipeline_tag: image-to-text |
| --- |
| |
| # Qari-OCR-0.4.0-VL-4B-Instruct |
|
|
| A vision-language model fine-tuned for OCR on Islamic books and Arabic manuscripts. Based on [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct), trained on 45,000 image-text pairs from the [seemorg/books-ocr](https://huggingface.co/datasets/seemorg/books-ocr) dataset. |
|
|
| ## Results |
|
|
| | Model | CER ↓ | WER ↓ | BLEU ↑ | |
| |-------|-------|-------|--------| |
| | **Qari-OCR-0.4.0** | **0.1222** | **0.2562** | **68.41** | |
| | Qwen/Qwen3-VL-4B-Instruct | 0.4922 | 0.6966 | 34.61 | |
| | Qwen/Qwen3-VL-8B-Instruct | 0.6876 | 0.8954 | 23.89 | |
| | NAMAA/Qari-0.2.2.1 | 0.6448 | 0.5126 | 21.97 | |
| | MBZUAI/AIN | 1.2843 | 1.2697 | 3.50 | |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoProcessor, AutoModelForVision2Seq |
| from PIL import Image |
| import torch |
| |
| model_name = "NAMAA-Space/Qari-OCR-0.4.0-VL-4B-Instruct" |
| processor = AutoProcessor.from_pretrained(model_name) |
| model = AutoModelForVision2Seq.from_pretrained( |
| model_name, |
| torch_dtype=torch.float16, |
| device_map="auto" |
| ) |
| |
| messages = [ |
| { |
| "role": "user", |
| "content": [ |
| {"type": "image", "image": f"./{src}"}, |
| {"type": "text", "text": "Free OCR."}, |
| ], |
| } |
| ] |
| |
| # Preparation for inference |
| inputs = processor.apply_chat_template( |
| messages, |
| tokenize=True, |
| add_generation_prompt=True, |
| return_dict=True, |
| return_tensors="pt" |
| ) |
| inputs = inputs.to(model.device) |
| |
| # Inference: Generation of the output |
| generated_ids = model.generate(**inputs, max_new_tokens=2048) |
| generated_ids_trimmed = [ |
| out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
| ] |
| result = processor.batch_decode( |
| generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
| )[0] |
| ``` |
|
|
| ## Training |
|
|
| - **Base model:** Qwen/Qwen3-VL-4B-Instruct |
| - **Dataset:** [seemorg/books-ocr](https://huggingface.co/datasets/seemorg/books-ocr) |
| - **Training samples:** 45,000 image-text pairs |
| - **Domain:** Islamic books and Arabic religious texts |
|
|
| ## Limitations |
|
|
| - Optimized for printed Islamic texts; performance may vary on modern Arabic fonts or handwritten text. |
| - Requires reasonable image quality (300+ DPI recommended). |
| - Arabic script only. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{qari-ocr-0.4.0, |
| author = {NAMAA-Space}, |
| title = {Qari-OCR-0.4.0-VL-4B-Instruct}, |
| year = {2025}, |
| publisher = {HuggingFace}, |
| howpublished = {\url{https://huggingface.co/NAMAA-Space/Qari-OCR-0.4.0-VL-4B-Instruct}} |
| } |
| ``` |
