Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-dm128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-dm128 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-dm128") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-dm128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73c3f25fa5c88074534ed35e62df96f67d1f7e7d1672d2b6d072d4c5cb512169
- Size of remote file:
- 60.6 MB
- SHA256:
- 9bd683ee715915c3d3ca42394e1f9e6cbd41231629b3e2c20bd16d49ad9b7fd4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.