Instructions to use Shadowmachete/CLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shadowmachete/CLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Shadowmachete/CLIP") 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("Shadowmachete/CLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("Shadowmachete/CLIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1344c931205779824059b004c5d37374ec97e86a96c122acdd1a3d1fc5d4265
- Size of remote file:
- 5.24 kB
- SHA256:
- c72b14a13c20fce835f4f4ca43c40e6f27f32fc38fca0ec11aab9eb065758acf
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