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")# pip install -U transformers accelerate # 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 model.safetensors from hf-tiny-model-private/tiny-random-EfficientNetModel: direct link, hf CLI and curl.
- Browser
- Download file 4.44 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-EfficientNetModel/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-EfficientNetModel@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-EfficientNetModel/resolve/refs%2Fpr%2F1/model.safetensors
4.44 MB
- Xet hash:
- 291fe21241ae544e31b9492c465732bfd3fb260c14caa94c01b61f8d5dad9b76
- Size of remote file:
- 4.44 MB
- SHA256:
- d08a5943d8f3efe63cc6e4c2a54a4017f2966eb81aba6894981ebdae23ceb36a
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