Instructions to use LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained") model = AutoModelForZeroShotImageClassification.from_pretrained("LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d3499524d7a255cc8015848302ce395f4464585960219483985e1aa2f5252ea
- Size of remote file:
- 223 MB
- SHA256:
- a64740345dbb836b70e33abd1a7415adf52ffa8bb913b664117c7680f08d2f68
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