Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-dm512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-dm512 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-dm512") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-dm512") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 024244e77623f546b526efe2d5887cec99d8dc4a417b3807670a68143ed7ced4
- Size of remote file:
- 1.48 GB
- SHA256:
- 592e051d58d58027f5d8e3ebe969962e9a711ff2e742fc3fb88408d0b72df36d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.