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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2045b03d4c1e91272fdf289751a8192a471b01d3d4fa7f1a91f0d1a5246b4236
- Size of remote file:
- 418 kB
- SHA256:
- 61d97d51a3df0b4e7bd9fe994541a26ddcd7c4c708698ea8dc53cd8be8001a47
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