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 embeddings.db from sgraham/met_model: direct link, hf CLI and curl.
- Browser
- Download file 1.65 MB
-
https://huggingface.co/sgraham/met_model/resolve/refs%2Fpr%2F1/embeddings.db
- Command line
-
hf download hf://sgraham/met_model@refs/pr/1/embeddings.db
-
curl -L -o embeddings.db https://huggingface.co/sgraham/met_model/resolve/refs%2Fpr%2F1/embeddings.db
1.65 MB
- Xet hash:
- 8ab589550e027ad3ad5e91e201f9881329a1d41f1f299f90c77d37d3c6e24436
- Size of remote file:
- 1.65 MB
- SHA256:
- 5a80c0b3ac92d24e5aa464f39896b20c2e26206edb6bb9b9b398652c1d9d25ea
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