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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5baacb5c607a4a70f74efe1423d72717f8979639eaf41848fd8ca00e99f0eb8
- Size of remote file:
- 76.2 kB
- SHA256:
- 311214c2e01e1a96636e8a07d5abf699d482e609d44a55af259c8818f311fe4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.