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") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and library name
#1
by nielsr HF Staff - opened
This PR ensures the appropriate library is known for the model. It also adds a pipeline tag, ensuring people can find your model at https://huggingface.co/models?pipeline_tag=feature-extraction&sort=trending.
megaelius changed pull request status to merged