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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59025f0cbdc78efd0f18445683a94d63fe291564ac11d3442e887b33267fff96
- Size of remote file:
- 62.3 MB
- SHA256:
- 017e429291d9db32bd6773b94a35961187ff503a093ebdf1b6931b517221e9a5
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