Instructions to use hf-tiny-model-private/tiny-random-PoolFormerModel 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-PoolFormerModel 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-PoolFormerModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-PoolFormerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-PoolFormerModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27278769269874c3300b41ce2ba32176b628923a2f30b37aa6dc4fe9ae1dd284
- Size of remote file:
- 1.84 MB
- SHA256:
- 29b541de8c13a49a78c05d0661be7b93970d0887956b9774a898289de0adb7f9
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