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:
- 93186d44142b9944a269dc03ed2f11dd18b24476f1cf06e100ae5a210c0fa9c1
- Size of remote file:
- 4.97 GB
- SHA256:
- d98b7abfe42e013ff689cedd998fc123604c20159001357c3d4fd13de260942b
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