Instructions to use hf-tiny-model-private/tiny-random-LongformerModel 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-LongformerModel 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-LongformerModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongformerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-LongformerModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6b49f6bb36d78ca96e525a068ac940d15a0501b3b3fbe1437f061062ceaed8d
- Size of remote file:
- 439 kB
- SHA256:
- 45052ad427d003a7e7541d1d8d7cffe1302a643e0743e909e17f97e8de4bc1c4
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