Instructions to use microsoft/git-large-vqav2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/git-large-vqav2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="microsoft/git-large-vqav2")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("microsoft/git-large-vqav2") model = AutoModelForImageTextToText.from_pretrained("microsoft/git-large-vqav2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45adf43aacddbffbe2db80f0e7c42e0e6d8f06394187faedc20196c1b650044d
- Size of remote file:
- 1.58 GB
- SHA256:
- eb523b646eebd95d7b967e03e147d7e277139dc281d81940a19a90428f19c76e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.