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6d6a355 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # CLIP2MT5 CrossAttention VQA Model
This is a Vision-Language model combining **CLIP-ViT** and **mT5** using a custom cross-attention bridge.
It supports Visual Question Answering (VQA) in Turkish.
## Usage
```python
from PIL import Image
from hf_clip2mt5 import load_for_inference, predict
repo_id = "MUERIS/TurkishVLMTAMGA"
model, tokenizer, device = load_for_inference(repo_id)
image = Image.open("example.jpg")
question = "Görselde kaç kişi var?"
answer = predict(model, tokenizer, device, image, question)
print(answer)
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