Instructions to use hf-internal-testing/tiny-random-MobileNetV2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileNetV2Model 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-MobileNetV2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MobileNetV2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MobileNetV2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72ce29d69e8151129730716dcb6ded7857ab45f3f9449061bd826dcd7c395847
- Size of remote file:
- 1.08 MB
- SHA256:
- a801257005c227c1702deefbe75d1686061884259131e05ed9014b33a8290a9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.