Instructions to use wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M") 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("wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M") model = AutoModelForZeroShotImageClassification.from_pretrained("wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 091df6041ea89a1ef2a3e9442af56675fa01a8c2f9e4fcb6efd758a52bac170a
- Size of remote file:
- 337 MB
- SHA256:
- bddd6f6ba27156562c59045ffe506eea58295ff2f39d84a5aaf194f27d9b0447
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