Instructions to use hf-internal-testing/tiny-random-LongformerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LongformerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LongformerModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LongformerModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LongformerModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b37c35a2d57593f7c3e55c6a4f98f86c83f5232e6c9be3043c88652da6b3614
- Size of remote file:
- 552 kB
- SHA256:
- 3023f33d4b7eff10d6aa9f68d0b0c393c2f599caac2a7c7b6a48d124fc8283e3
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