Instructions to use mattmdjaga/clip-vit-base-patch32_handler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mattmdjaga/clip-vit-base-patch32_handler with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="mattmdjaga/clip-vit-base-patch32_handler") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("mattmdjaga/clip-vit-base-patch32_handler") model = AutoModelForZeroShotImageClassification.from_pretrained("mattmdjaga/clip-vit-base-patch32_handler", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (43fa93addaec1d1f52558841b5e181bc83cfbbe0) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>7b1e5f1
-
Update README.md
4e4c973 -
Update README.md
d765749
-
added custom handler
d364efb
-
Duplicate from openai/clip-vit-base-patch32
Co-authored-by: Mamta Narang <mamta@users.noreply.huggingface.co>d8e929e