Instructions to use hf-tiny-model-private/tiny-random-ConvNextV2Model 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-ConvNextV2Model 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-ConvNextV2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ConvNextV2Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ConvNextV2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31d3220736286c8fc4160dd3e89f56b34ab5fb2eef8449c5cb7cb60075a373ea
- Size of remote file:
- 347 kB
- SHA256:
- 1b1ee72b26e4f64c3217a1db3c8da6dd24f26b4cbfbb1378f0c251119478c1bc
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