Instructions to use hf-internal-testing/tiny-random-LongT5Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LongT5Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LongT5Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LongT5Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LongT5Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 997d0ea1a734698748bd32f46979f10216a21e12deb82bcabc125285f8a29145
- Size of remote file:
- 4.62 MB
- SHA256:
- a5105626ad9bfcd7d786c392cdc65d31c66a3ebe0b43883e0c29d6b39bd6278b
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