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 # Warning: Pipeline type "visual-question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("visual-question-answering", model="microsoft/git-large-vqav2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("microsoft/git-large-vqav2") model = AutoModelForMultimodalLM.from_pretrained("microsoft/git-large-vqav2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/git-large-vqav2: direct link, hf CLI and curl.
- Browser
- Download file 1.58 GB
-
https://huggingface.co/microsoft/git-large-vqav2/resolve/5e5d4e1a77d3c663c4c370cee8e27bbee6088587/pytorch_model.bin
- Command line
-
hf download hf://microsoft/git-large-vqav2@5e5d4e1a77d3c663c4c370cee8e27bbee6088587/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/git-large-vqav2/resolve/5e5d4e1a77d3c663c4c370cee8e27bbee6088587/pytorch_model.bin
1.58 GB
- Xet hash:
- 45adf43aacddbffbe2db80f0e7c42e0e6d8f06394187faedc20196c1b650044d
- Size of remote file:
- 1.58 GB
- SHA256:
- eb523b646eebd95d7b967e03e147d7e277139dc281d81940a19a90428f19c76e
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