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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2eef51255745dc7f5b96125a76ee6a1a16246d755a483a242a419d6bca62d1f1
- Size of remote file:
- 1.82 MB
- SHA256:
- a52bef8f9f1e500344dc0219a3cf2db5f6185291cbd5ad6e7769e6d14f15217f
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