Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff9000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff9000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff9000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff9000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbd169ec2dcbfd40d7282cf3db186ac59294c8ab4e4f4f63453c25ea3522a314
- Size of remote file:
- 197 MB
- SHA256:
- bc6ef2fef502ab70cde5285560f493047793a7340c941b7cc8fedc1f7dc64fc4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.