Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-nh8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-nh8 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-nh8") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-nh8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c883bdbb2205f90563e3844dee40e70e93bd3ffa38a204c3f700294f5463c324
- Size of remote file:
- 2.35 GB
- SHA256:
- 2c2051dca923bc6852a4f1bb02517db1868e0e0796e85eaeac98cb265e15d6df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.