Instructions to use hf-tiny-model-private/tiny-random-LongT5Model 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-LongT5Model 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-LongT5Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongT5Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-LongT5Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 158eb2cc7dd23ec9c475af3785754e1328727256593b06fe71d6c9624a606719
- Size of remote file:
- 4.49 MB
- SHA256:
- 09cd69e246f7554a438835daed0de59ccbe6822fff7683503368b98cb4f8096f
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