Mubsir-Qwen-2B-VL: Arabic Handwritten Text Recognition (HTR)
This repository contains a vision-language model fine-tuned specifically for Arabic Handwritten Text Recognition (HTR). Built on the Qwen2-VL-2B-Instruct architecture, it is designed to accurately transcribe complex, unstructured Arabic handwriting into digital text.
A standalone, unmerged parameter-efficient LoRA adapter for this model is also available at Hatim2221/Mubsir-vl-arabic-htr-adapter.
Model Details
- Base Architecture: Qwen/Qwen2-VL-2B-Instruct
- Task: Optical Character Recognition (OCR) / Handwritten Text Recognition (HTR) for Arabic text.
- Precision:
torch.float16. The trained LoRA weights were merged into the base model entirely in 16-bit precision. This avoids the mathematical rounding errors and accuracy degradation commonly associated with merging weights directly into 4-bit quantized base models. - Parameters: 2 Billion.
Training Data
The model was fine-tuned using the KHATT (KFUPM Handwritten Arabic TexT) dataset, a comprehensive collection of diverse Arabic handwritten text lines designed for training high-accuracy recognition systems.
Training Infrastructure & Methodology
- Hardware: 2x NVIDIA T4 GPUs (Kaggle cluster).
- Methodology: Parameter-Efficient Fine-Tuning (PEFT) via QLoRA.
- VRAM Optimization: The training pipeline utilized PyTorch
expandable_segmentsfor strict memory management, coupled with dynamic image resolution capping and tailored batch sizing to bypass standard Out-Of-Memory (OOM) failures during multimodal processing.
Use Cases
Example 1: Arabic Handwriting Transcription
Input:
Usage and Inference
To run this model, ensure you have the required Hugging Face libraries installed, including the Qwen vision utilities.
Installation
pip install -q -U transformers accelerate qwen-vl-utils torchvision torchao
Inference code
import torch
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
model_id = "Hatim2221/Mubsir-Qwen-2B-VL"
# Load the model and processor in native FP16 precision
model = Qwen2VLForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)
# Define the multimodal input payload
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "path_to_your_arabic_handwriting_image.jpg"},
{"type": "text", "text": "Transcribe this Arabic handwriting:"}
]
}
]
# Process text and vision inputs using the Qwen utility
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt"
).to("cuda")
# Generate the transcription
with torch.no_grad():
generated_ids = model.generate(**inputs, max_new_tokens=128)
# Isolate the generated output from the input tokens
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Transcription Result:")
print(output_text[0])