Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-nl32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-nl32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-nl32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-nl32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 295da82862d755d55e84503247c06947b36e453d6189f0ddf3a20c41c79b3627
- Size of remote file:
- 3.89 GB
- SHA256:
- 7ee1c010bf4bd2e2bf79448789f9a362c7de272f6b88dcfbcf39a7d371153e4a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.