Instructions to use hf-tiny-model-private/tiny-random-ReformerModel 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-ReformerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-ReformerModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ReformerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ReformerModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a89e58bc884c6a813a719b6c028be335eb74ecfd2564642e5baa1d2c38035d4
- Size of remote file:
- 361 kB
- SHA256:
- c0414a4f59ea089fe8d7fded52ffa510858625b3d75b12efa591e1f8a06e7ad9
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