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:
- efd400c6e72bba0d566dc68ed8ab83b21a6bd48b624d31a94d3815e6d5cc364f
- Size of remote file:
- 3.9 kB
- SHA256:
- 5fabbd8d27dcd853d18fc10abf69fb02cdbcd5beda6d0705820d9e1234d028fb
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