Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-nh16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-nh16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-nh16") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nh16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73ded690c6b73d0b62e437ec38151305c013033049edb8141e4278a6c85b776c
- Size of remote file:
- 100 MB
- SHA256:
- ec8857202cb3b83e21168395595f7ee72c496323570c6b5c1a0eaae6a05bd78d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.