Instructions to use hf-internal-testing/tiny-random-FocalNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FocalNetModel 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-FocalNetModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-FocalNetModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-FocalNetModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76b48f4447ecd3831fc460cff9b2d26190b34f16aa9d5d15bab4967933eb4e95
- Size of remote file:
- 301 kB
- SHA256:
- 2885cf0bf419c47eb22754827fea94bda75303561a90ed9c7ef7abad6ddb5029
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.