Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff6000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff6000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff6000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff6000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80822f50f7a700e770e9014d6d58ece4bbbfe222301d16bf209006a3c702c86d
- Size of remote file:
- 146 MB
- SHA256:
- 8e8f436818fa2575a879f2120f2f30b686e7ab044b92d15bcd8e29da6cdb2853
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