Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-nl8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-nl8 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-nl8") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-nl8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 611cbd2b86c7c596edffab9ed30b06d31602258bf87f0fff9fc2c1c5af0fbecd
- Size of remote file:
- 627 MB
- SHA256:
- dbbf6ca1c6637e49334eaecc57e5723ad63e6eb67b7b6615fe20efba7886521e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.