Instructions to use sgraham/met_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgraham/met_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="sgraham/met_model") 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("sgraham/met_model") model = AutoModelForZeroShotImageClassification.from_pretrained("sgraham/met_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 0_CLIPModel/pytorch_model.bin from sgraham/met_model: direct link, hf CLI and curl.
- Browser
- Download file 605 MB
-
https://huggingface.co/sgraham/met_model/resolve/main/0_CLIPModel/pytorch_model.bin
- Command line
-
hf download hf://sgraham/met_model/0_CLIPModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sgraham/met_model/resolve/main/0_CLIPModel/pytorch_model.bin
605 MB
- Xet hash:
- 34e10d1c269311ba2b245485416d34bb6916cda13d81f01010fce44354fdcf9a
- Size of remote file:
- 605 MB
- SHA256:
- acda12ab8bf8608f2d2e82fdb4cff80f9d840dda66aef73ea196ea1b12efc19a
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