Instructions to use hf-internal-testing/tiny-random-ResNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ResNetModel 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-ResNetModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ResNetModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ResNetModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a7359fd5acf40a4835f31ee52742b751ee7e5ee2625fd90ec1bbc1bbbc8840b
- Size of remote file:
- 83.4 kB
- SHA256:
- ec7cb9891be438ae8c9a3c5c50ab4183185153b7d1329fc1c569d5e10cacb602
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.