Instructions to use FLIP-dataset/FLIP-base-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FLIP-dataset/FLIP-base-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="FLIP-dataset/FLIP-base-32") 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("FLIP-dataset/FLIP-base-32") model = AutoModelForZeroShotImageClassification.from_pretrained("FLIP-dataset/FLIP-base-32") - Notebooks
- Google Colab
- Kaggle
Commit ·
37c984f
1
Parent(s): b63c9ac
Adding `safetensors` variant of this model (#1)
Browse files- Adding `safetensors` variant of this model (00602d14e374e37ff488c91af3a7a4a88f15c817)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:26bcada8498a4abe6f951f880a32a170222320d3f9262623aabf2b9d9c3d5074
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size 605157884
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