Instructions to use prabh5/siglip2-embedding-endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prabh5/siglip2-embedding-endpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="prabh5/siglip2-embedding-endpoint") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prabh5/siglip2-embedding-endpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from prabh5/siglip2-embedding-endpoint: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/prabh5/siglip2-embedding-endpoint/resolve/main/tokenizer.json
- Command line
-
hf download hf://prabh5/siglip2-embedding-endpoint/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/prabh5/siglip2-embedding-endpoint/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 1cc2031661080cf926791605d802fb2fdeee15e0fffb5d76c860749ca16b678f
- Size of remote file:
- 34.4 MB
- SHA256:
- cb9140fae3ac5122c972d37adf83e1248471a38147ad76f8215c8872c6fd8322
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