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:
- 039252a508c7ec376accc51f1bf3aa8708d7e933ec58bc0a76ca7091ffab2196
- Size of remote file:
- 439 kB
- SHA256:
- b71fab072a779699718faf71b64fea7c606224cc220817cb5a4a78debb10d0a2
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