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:
- 82d26e80c1fc649ea23bb899e37e468604830a404ec7c4f0c862a3054384bea2
- Size of remote file:
- 552 kB
- SHA256:
- b6d6f40d818531d93583602c5c838ad75153f29b39768d78d218625f02033dc8
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