Instructions to use hf-tiny-model-private/tiny-random-ConvNextModel 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-ConvNextModel 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-ConvNextModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ConvNextModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ConvNextModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1de24151b7051672c09c4bbf013b5d35637eeede234e10090173d7df90147ce9
- Size of remote file:
- 320 kB
- SHA256:
- d5cbca0b61faf95403e7711d92b7d72760253df3db4ba6e6eaf98ac975f554fe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.