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:
- 93b344a78f1d63ba6d7952a4c514c7b6f09cf057b153805ebce038b71bce2774
- Size of remote file:
- 4.47 MB
- SHA256:
- c6277b6ec46bd0731302fe5f57f612a6d7b03a42a5b26663d8a1afea4832fde2
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