Instructions to use hf-internal-testing/tiny-random-ResNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ResNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-ResNetModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ResNetModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ResNetModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- acf71d58f1c30ae0a29f8a95d29d064e355a285b52f5b7c8da062d5773cad8ad
- Size of remote file:
- 106 kB
- SHA256:
- 51e07d22afb52cc873b19bca5cd9b2276a413c234a609b62de494b4ccd03e73c
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