Instructions to use facebook/metaclip-b32-400m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/metaclip-b32-400m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="facebook/metaclip-b32-400m") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("facebook/metaclip-b32-400m") model = AutoModelForZeroShotImageClassification.from_pretrained("facebook/metaclip-b32-400m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/metaclip-b32-400m: direct link, hf CLI and curl.
- Browser
- Download file 605 MB
-
https://huggingface.co/facebook/metaclip-b32-400m/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/metaclip-b32-400m/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/metaclip-b32-400m/resolve/main/pytorch_model.bin
605 MB
- Xet hash:
- cf8b6dc73f58a8f6a6a896a6c28e49b9ae6fe50827d06d534f5b26d820a8da10
- Size of remote file:
- 605 MB
- SHA256:
- 3b5c64e11c9d508e917513685001e52e80efff366d2026a69503ba508d2062b3
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