Instructions to use wkcn/TinyCLIP-ViT-61M-32-Text-29M-LAION400M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wkcn/TinyCLIP-ViT-61M-32-Text-29M-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-61M-32-Text-29M-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-61M-32-Text-29M-LAION400M") model = AutoModelForZeroShotImageClassification.from_pretrained("wkcn/TinyCLIP-ViT-61M-32-Text-29M-LAION400M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea55d252f04d6a877ccacf9a6f1533aebc64a33241cb26d63e61828bff6b1632
- Size of remote file:
- 462 MB
- SHA256:
- 0c0670dfa6fbf32c3158ef57b10772d5c9668093bda676d78e1452268585d230
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