Instructions to use bumblebee-testing/tiny-random-T5Model-feed_forward_proj-gated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bumblebee-testing/tiny-random-T5Model-feed_forward_proj-gated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bumblebee-testing/tiny-random-T5Model-feed_forward_proj-gated")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bumblebee-testing/tiny-random-T5Model-feed_forward_proj-gated") model = AutoModel.from_pretrained("bumblebee-testing/tiny-random-T5Model-feed_forward_proj-gated") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fece8384da872b44fa5ca1cfc3d03b07391c5389edbb643961674644e489db6
- Size of remote file:
- 4.47 MB
- SHA256:
- ced0915cb2c833c8c67ba905aa56b484f6a6d52d08ecfb9ac1fb6063f707fc65
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