Qwen Vl Arabic
Fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct on Arabic Visual Question Answering data as part of the ArabicVL-R project.
Model source
- Kind
- LoRA
- File size
- 19.3 MB
- Base model
- Qwen/Qwen2.5-VL-7B-Instruct
- Version
- vadapter_model
- Updated
- Oct 10, 2026
- Source
- Hugging Face
Source description
Qwen2.5-VL-7B Arabic VQA
Fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct on Arabic Visual Question Answering data as part of the ArabicVL-R project.
Model Description
This model is fine-tuned to answer visual questions in Arabic, supporting reasoning over images with Arabic text responses.
Training Data
- Dataset: Arabic LLaVA — available at ArabicVL-R
- Total samples: 5,000
- Split: 80% train / 10% validation / 10% test
- Train: 4,000 samples
- Validation: 500 samples
- Test: 500 samples
Evaluation Results
Evaluated on the test split (503 samples) using Exact Match:
| Metric | Score |
|---|---|
| Exact Match | 0.4334 |
| Accuracy | 43.34% |
| Correct / Total | 218 / 503 |
Usage
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
from PIL import Image
import torch
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Manar01/qwen-vl-arabic",
torch_dtype=torch.float16,
device_map="auto"
)
processor = AutoProcessor.from_pretrained("Manar01/qwen-vl-arabic")
image = Image.open("image.jpg").convert("RGB")
messages = [{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": "your question in Arabic"},
],
}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=256, do_sample=False)
generated = output[0][inputs["input_ids"].shape[1]:]
print(processor.decode(generated, skip_special_tokens=True))
Authors
- Sarah Aldumiji
- Manar Alrabie
- Hadeel Alseni
- Ragheed Samkari
- Mourad Mars
Citation
@misc{arabicvlr2026,
title = {ArabicVL-R: Arabic Vision Language Model Reasoning},
author = {Sarah and Manar and Hadeel and Ragheed and Mourad},
year = {2025},
url = {https://github.com/hadeelalseni/ArabicVL-R}
}
License
Apache 2.0
Sources
1 source
Sources
1 sourceVerified Oct 10
Model artifacts
1 artifact
Model artifacts
1 artifactadapter_model.safetensors
safetensors · 19.3 MB · SHA-256 2b134393a44f…3942 · Hugging Face
Download fileSource excerpts
2 excerpts
Source excerpts
2 excerpts--- language: - ar license: apache-2.0 base_model: Qwen/Qwen2.5-VL-7B-Instruct tags: - arabic - visual-question-answering - multimodal - qwen2.5-vl - fine-tuned metrics: - exact_match --- # Qwen2.5-VL-7B Arabic VQA Fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on Arabic Visual Question Answering data as part of the **ArabicVL-R** project. ## Model Description This model is fine-tuned to answer visual questions in Arabic, supporting reasoning over images with Arabic text responses. ## Training Data - **Dataset**: Arabic LLaVA — available at [ArabicVL-R](https://github.com/hadeelalseni/ArabicVL-R) - **Total samples**: 5,000 - **Split**: 80% train / 10% validation / 10% test - **Train**: 4,000 samples - **Validation**: 500 samples - **Test**: 500 samples ## Evaluation Results Evaluated on the test split (503 samples) using Exact Match: | Metric | Score | |---|---| | Exact Match | 0.4334 | | Accuracy | 43.34% | | Correct / Total | 218 / 503 | ## Usage ```python from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration from PIL import Image import torch model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Manar01/qwen-vl-arabic", torch_dtype=torch.float16, device_map="auto" ) processor = AutoProcessor.from_pretrained("Manar01/qwen-vl-arabic") image = Image.open("image.jpg").convert("RGB") messages = [{ "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "your question in Arabic"}, ], }] text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate(**inputs, max_new_tokens=256, do_sample=False) generated = output[0][inputs["input_ids"].shape[1]:] print(processor.decode(generated, skip_special_tokens=True)) ``` ## Authors - Sarah Aldumiji - Manar Alrabie - Hadeel Alseni - Ragheed Samkari - Mourad Mars ## Citation ```bibtex @misc{arabicvlr2026, title = {ArabicVL-R: Arabic Vision Language Model Reasoning}, author = {Sarah and Manar and Hadeel and Ragheed and Mourad}, year = {2025}, url = {https://github.com/hadeelalseni/ArabicVL-R} } ``` ## License Apache 2.0
Source context: 0 downloads · 0 likes · Pipeline visual-question-answering · Repo ManarAlrabie/qwen-vl-arabic
