Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-el8-dl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-el8-dl2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-el8-dl2") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-el8-dl2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60a2251d3a6c8b246aa8d9a5002a4e60958c9f8c6ba6389fcd915135e0d5a822
- Size of remote file:
- 200 MB
- SHA256:
- ab93a7ca8ada14d54a3732127bd593587fc7edeb144c9522a95fa1ead315f1f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.