Instructions to use gokuls/custom_clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokuls/custom_clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="gokuls/custom_clip") 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("gokuls/custom_clip") model = AutoModelForZeroShotImageClassification.from_pretrained("gokuls/custom_clip", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gokuls/custom_clip: direct link, hf CLI and curl.
- Browser
- Download file 605 MB
-
https://huggingface.co/gokuls/custom_clip/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gokuls/custom_clip/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gokuls/custom_clip/resolve/main/pytorch_model.bin
605 MB
- Xet hash:
- ba9bfcfb93c338d6e237a143b85893221f45d3d7b69aea0e1ad7bf1510d8c2f5
- Size of remote file:
- 605 MB
- SHA256:
- e725f28d5bed505fdcac5adb64503107b5db30a1cd258d4cab6cac364a1ed0a2
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