Instructions to use RISys-Lab/ReasonCLIP-B32-S1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RISys-Lab/ReasonCLIP-B32-S1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="RISys-Lab/ReasonCLIP-B32-S1") 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("RISys-Lab/ReasonCLIP-B32-S1") model = AutoModelForZeroShotImageClassification.from_pretrained("RISys-Lab/ReasonCLIP-B32-S1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f12280133835e00fa6707e20aaf77ce35d3333e6d2d75f64bc159cd2280bf3e
- Size of remote file:
- 303 MB
- SHA256:
- 5a98707fc4816d9561b7b6214a6614b9c7a7d3de1edf1555b91d952a0bbdb8e2
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