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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c55391ad715a277a20d9b516d4e6dfb3234d06c1cce810dfc20068faf4192aa3
- Size of remote file:
- 146 MB
- SHA256:
- 88ef57b9efa11519858800dfa3887891cca1c060efb27780335871970d3de1cd
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