This model was developed as part of the Arabic Image Captioning Shared Task 2025. It generates natural Arabic captions for images with focus on historical and cultural content related to Palestinian heritage.

please refer to the training dataset for more details.

Usage

python

from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from peft import PeftModel
import torch
from PIL import Image

# Load base model and processor
base_model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "your-username/arabic-image-captioning-qwen2.5vl")

# Process image and generate caption
image = Image.open("your_image.jpg")
prompt = "اكتب وصفاً مختصراً لهذه الصورة باللغة العربية"

inputs = processor(images=image, text=prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=128)
    
caption = processor.decode(outputs[0], skip_special_tokens=True)
print(caption)

Training Details

Dataset

Training data: Arabic image captions dataset from the shared task
Languages: Arabic (ar)
Dataset size: ~2,700 training images with Arabic captions

Training Procedure

Fine-tuning method: LoRA (Low-Rank Adaptation)
Training epochs: 15
Learning rate: 2e-05
Batch size: 1 with gradient accumulation (effective batch size: 16)
Optimizer: AdamW with cosine learning rate scheduling
Hardware: NVIDIA A100 GPU
Training time: ~6 hours

Framework Versions

PEFT 0.15.2
Transformers 4.49.0
PyTorch 2.4.1+cu121

Contact

For questions or support:

SinaLab/Qwen-2.5-VL-7B-Instruct-Image-Captioning

Arabic Image Captioning - Qwen2.5-VL Fine-tuned

Model Description