Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff6000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff6000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff6000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff6000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9405fa0277ee2fcd2ff010f0bd06a974102871c8e22910dfd3f9aa993d2da934
- Size of remote file:
- 146 MB
- SHA256:
- f3317e1ea6335033190fec1178c140bf01aa0b844252288b8105e2d45e3f8d4b
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