Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-nh32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-nh32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-nh32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-nh32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76bc401a8ed75137ca1c82a34d8cdf697b993a01d8043e9d78b8714f28106582
- Size of remote file:
- 1.46 GB
- SHA256:
- 718b796a034aa8457c9d833098aa907312f6777cbed0f93b010bcd52279ba4bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.