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%2F4/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-SiglipForImageClassification@refs/pr/4/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-SiglipForImageClassification/resolve/refs%2Fpr%2F4/model.safetensors
118 kB
- Xet hash:
- 9ad3e43cf7192560afd82607e6f5c9398f0c328efa7f6b51ab33ede49ceeb66e
- Size of remote file:
- 118 kB
- SHA256:
- 8ef323d01639692139cc8c506db69e9afaa7be2366df904cdf134b9270eb7dfc
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