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%2F7/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-SiglipForImageClassification@refs/pr/7/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-SiglipForImageClassification/resolve/refs%2Fpr%2F7/model.safetensors
118 kB
- Xet hash:
- 86f91791f215d993782cc1f76677b0cae2844dbb015e2c1588ebb0156b028cfa
- Size of remote file:
- 118 kB
- SHA256:
- 34f432c57a8280f5cf878251b747a632e5301c9a602a5ff41e2459d4f7e823ed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.