Instructions to use Raghavan/beit3_base_patch16_480_vqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Raghavan/beit3_base_patch16_480_vqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Raghavan/beit3_base_patch16_480_vqa")# Load model directly from transformers import AutoModelForQuestionAnswering model = AutoModelForQuestionAnswering.from_pretrained("Raghavan/beit3_base_patch16_480_vqa", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60761cfd5da436299cff153548ccb3549077e5a72d051ebf477fc4aca727b61e
- Size of remote file:
- 913 MB
- SHA256:
- 4ef3cd6efdb9ebee6acf7bba33791bce7cfe7b1f5909f6bea392fc79547fb561
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