Instructions to use microsoft/git-large-textvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/git-large-textvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="microsoft/git-large-textvqa")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("microsoft/git-large-textvqa") model = AutoModelForImageTextToText.from_pretrained("microsoft/git-large-textvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7341b987e416e6bff25f2b71220176b9b852f1c51d895004c938f4ad14521af
- Size of remote file:
- 1.58 GB
- SHA256:
- d282bfbc30587804801e7714a84cad87b1010b1311cb562680b41ac7c25dd72e
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