Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff2000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff2000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff2000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fd4f0610923d6d3d526caabafe0a3c29ae589161000c554671cd00903c3cd37
- Size of remote file:
- 62.3 MB
- SHA256:
- b4dc868fc99c498610266afad81cc4383ce3f7a274d2c5d53c359740a750e882
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.