Instructions to use CollectionStudio/siglip2-base-patch16-naflex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/siglip2-base-patch16-naflex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="CollectionStudio/siglip2-base-patch16-naflex") 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 AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("CollectionStudio/siglip2-base-patch16-naflex") model = AutoModelForZeroShotImageClassification.from_pretrained("CollectionStudio/siglip2-base-patch16-naflex", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from CollectionStudio/siglip2-base-patch16-naflex: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/CollectionStudio/siglip2-base-patch16-naflex/resolve/main/tokenizer.json
- Command line
-
hf download hf://CollectionStudio/siglip2-base-patch16-naflex/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/CollectionStudio/siglip2-base-patch16-naflex/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 07a0b303a10b5e19f2cc84a7e05c5c30092dceb2751e9f4299bbadf5dc4187e1
- Size of remote file:
- 34.4 MB
- SHA256:
- 58a1696e79c9d97937389ed116f552a15c84811d7b8023918b86f4bc5775b1b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.