Instructions to use hf-internal-testing/tiny-random-SiglipForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SiglipForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-SiglipForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 118 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-SiglipForImageClassification/resolve/refs%2Fpr%2F8/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-SiglipForImageClassification@refs/pr/8/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-SiglipForImageClassification/resolve/refs%2Fpr%2F8/model.safetensors
118 kB
- Xet hash:
- 5ca0032ecc35b51c0beb0e553f6bdf7714de6cd7dc610e313ed57743577aa2a4
- Size of remote file:
- 118 kB
- SHA256:
- b33930c720a6c91dbd1d44c3243ef7bca70aa0a07e166aa7bb5363f96034ac04
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