Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff3000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff3000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff3000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d9de7474be8a2b2933c4cc2a3c40fb29455415707c958a89fae3f4b9e712131
- Size of remote file:
- 95.8 MB
- SHA256:
- 279d349121292b955642d6f92106d1638d51ceeb09a12602e8a4f4e4a3da7ff5
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