Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-el2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-el2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-el2") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-el2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d19f224856ecb1afba7ac34d29cc0f8fd583d2f7e4ea85a33b77ff5dcc96446
- Size of remote file:
- 89.6 MB
- SHA256:
- 2e1bb5b1a0cebec1cc3461c3300f0983ed3dee56bb493279748f3dab5ea8cfbd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.