Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-dm128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-dm128 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-dm128") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-dm128") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10bdef8d2a52360e0002af1022704af2d53241fcd79097ff79a53c27636b73e9
- Size of remote file:
- 60.8 MB
- SHA256:
- be891561c9e16372f59ab269f7f78d51c77b556921260098811bd244fab5bcba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.