Instructions to use vamsidulam/vqa_graphcore2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vamsidulam/vqa_graphcore2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="vamsidulam/vqa_graphcore2")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("vamsidulam/vqa_graphcore2") model = AutoModelForVisualQuestionAnswering.from_pretrained("vamsidulam/vqa_graphcore2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bae667e4bb7158252468d1d404996c051f870dad5ffe395a6f0031cf32f51d3
- Size of remote file:
- 452 MB
- SHA256:
- 9689d89bc995b7a32c66831fd007dd870e7dd2dffa1b23444bfe839aceffab9c
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