Instructions to use hf-tiny-model-private/tiny-random-ResNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ResNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-ResNetModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ResNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ResNetModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8bfb3e0d783f9dce99b6ec7aa58d3c63af69a896c6714cd3ff8aa55f9743afa
- Size of remote file:
- 106 kB
- SHA256:
- c4ee6908dc8d6337dc32efc563a84ddae6fd2ab90d62253f9c9a40f64453397b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.