Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-dm768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-dm768 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-dm768") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-dm768") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 108e0e02c252a7631ff6a568b1dd0f84ce83a9059c706e7e7eb71be927210d38
- Size of remote file:
- 363 MB
- SHA256:
- 93ff4cf9790ba9fa008b863b4b5326f89e412f578945e5cc5aa3c3641c65798f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.