metadata
library_name: peft
license: mit
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- arabic
- image-captioning
- vision-language
- lora
- qwen2.5-vl
- cultural-heritage
language:
- ar
model-index:
- name: arabic-image-captioning-qwen2.5vl
results: []
Arabic Image Captioning - Qwen2.5-VL Fine-tuned
This model is a LoRA fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct for generating Arabic captions for images.
Model Description
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.
Usage
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: