Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ffed3fae9ca49a699b8275083bf06b95ba2a60366ede430bb3c1eeed33d4096
- Size of remote file:
- 62.3 MB
- SHA256:
- 16e959d18596c0cdd9da07fd4fb913f5f9aa7835decfd6a4b9f9fd96e960da26
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.