Instructions to use dleemiller/siglip2-math-base-patch16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dleemiller/siglip2-math-base-patch16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="dleemiller/siglip2-math-base-patch16-256") 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("dleemiller/siglip2-math-base-patch16-256") model = AutoModelForZeroShotImageClassification.from_pretrained("dleemiller/siglip2-math-base-patch16-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from dleemiller/siglip2-math-base-patch16-256: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/dleemiller/siglip2-math-base-patch16-256/resolve/main/tokenizer.json
- Command line
-
hf download hf://dleemiller/siglip2-math-base-patch16-256/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/dleemiller/siglip2-math-base-patch16-256/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- b311c9a97dd9ac31482dc4eb58c1bec98344081e2cf8d401d31f654ea9f92131
- Size of remote file:
- 34.4 MB
- SHA256:
- 1de36bea24b2b16b25cc27a4b5ea9c86e2e2ecc3c018cb7d5338f0030d84e4bb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.