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:
- ec10e44d38930ae4b61e6366cb465fdcbd9228e3382c52660bd0b85cef545d6a
- Size of remote file:
- 189 kB
- SHA256:
- 4ea84a00b07a3a9f2bcc0b4f7b6fbd8a52efab1e9a2d3227c95839ecfe63c237
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.