Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-nh32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-nh32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-nh32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nh32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1de62b6b6e3a76e47a7b4809c987ef530afaf526c53b2f812d9b94cf9b7e80f6
- Size of remote file:
- 150 MB
- SHA256:
- d27e3bdcc222446ebdf623aaced4ec3b3e5674efb1bfb407c62d07d0ca5d2804
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.