Instructions to use hf-tiny-model-private/tiny-random-EfficientNetModel 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-EfficientNetModel 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-EfficientNetModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-EfficientNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-EfficientNetModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-EfficientNetModel: direct link, hf CLI and curl.
- Browser
- Download file 4.51 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-EfficientNetModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-EfficientNetModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-EfficientNetModel/resolve/main/pytorch_model.bin
4.51 MB
- Xet hash:
- 155a7a71dbcebddfbddad303fdf9ed2efaccbe5cdd9603c11e5dddd2d1b82f06
- Size of remote file:
- 4.51 MB
- SHA256:
- fc00f41197acc092550f8c2efb593e06a77ee8804f0d69ec743844f485b81cfb
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