Instructions to use hf-tiny-model-private/tiny-random-ConvNextV2Backbone 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-ConvNextV2Backbone with Transformers:
# Load model directly from transformers import AutoImageProcessor, ConvNextV2Backbone processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ConvNextV2Backbone") model = ConvNextV2Backbone.from_pretrained("hf-tiny-model-private/tiny-random-ConvNextV2Backbone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9774ae030255042e52cee039a3722b9d33927ed67492269063cd21658ba5ed33
- Size of remote file:
- 349 kB
- SHA256:
- 8593d05f7bc230460f869bac2f1908291a60fcef2a307aa84462a41fa30ef1ac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.