Instructions to use hf-internal-testing/tiny-random-SiglipVisionModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipVisionModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-SiglipVisionModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SiglipVisionModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SiglipVisionModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6592c20ae4b9d9bbb48fb0597f4f7fc8ab3bea4d86e1d33e18de06f2640fbb03
- Size of remote file:
- 117 kB
- SHA256:
- c725b376a63c578ccf39afbc5fe27f664b95220f6f8487125250f99516fcec24
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.