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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44c0a3aaeef5a35edcaec1f4e03f742bafed8a9e8dbf9c6df0bcd8e63caa7e4c
- Size of remote file:
- 1.36 MB
- SHA256:
- 6f5e2fefcf793761a76a6bfb8ad35489f9c203b25557673284b6d032f41043f4
路
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