Instructions to use hf-internal-testing/tiny-random-SiglipModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-internal-testing/tiny-random-SiglipModel") 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("hf-internal-testing/tiny-random-SiglipModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-SiglipModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e690be2591c1ee8415de1e94a7f88467f155eea4456c0b34eacecc294c99173
- Size of remote file:
- 4.34 MB
- SHA256:
- e243f759b06280363c2300e9fa5f4fad7ee334380c181c748151827740a94c29
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.