Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-dm256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-dm256 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-dm256") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-dm256") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28ded52274798189c1502a49d97049b9f1f79dd989515521f22890e06d1b16ce
- Size of remote file:
- 297 MB
- SHA256:
- 8eb6b0f3e8b4c559a3b31b47112241e8a7035142bfea4d04ac2ddf252f9cd0d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.