Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-kv256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-kv256 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-kv256") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-kv256") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 961c85d438324b612d8e3e8b31fbe2190afc78669cd5670e24d66ac430fec334
- Size of remote file:
- 6.57 GB
- SHA256:
- f294fd21d4ad9d17772e00551eab4096e91303272dbfb33cc4059cd00c05a0bc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.