Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff3000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff3000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff3000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c800329121cca2f22ab0ca4a76a0e4f15a9d82b20519be4c558bd6dc530ebba9
- Size of remote file:
- 95.9 MB
- SHA256:
- 3fdaf0b3eeebfdbaf0dc87d4127b3221c52aed097a0fff04a6541393ee85e94f
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