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:
- 73b57352b603e4c32ba2a8bd6018ac535559fb34c5e216ca16e1ba632925f73a
- Size of remote file:
- 338 kB
- SHA256:
- a46467e0e0c33c350c8723773b38ea04d3101cfeb0b74d8cf447b8f448a014ee
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