Instructions to use krnl/clip-vit-large-patch14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krnl/clip-vit-large-patch14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="krnl/clip-vit-large-patch14") 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("krnl/clip-vit-large-patch14") model = AutoModelForZeroShotImageClassification.from_pretrained("krnl/clip-vit-large-patch14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from krnl/clip-vit-large-patch14: direct link, hf CLI and curl.
- Browser
- Download file 1.71 GB
-
https://huggingface.co/krnl/clip-vit-large-patch14/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://krnl/clip-vit-large-patch14/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/krnl/clip-vit-large-patch14/resolve/main/flax_model.msgpack
1.71 GB
- Xet hash:
- 292b8d452b4aca3fc79e999af98c7b5b026eacac1a144a5d3722f9d86428c177
- Size of remote file:
- 1.71 GB
- SHA256:
- 156f677ed4495acd1ec7197249c091b85c240267c82f2f7f2e4eae4177931fed
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