Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-kv32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-kv32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-kv32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-kv32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4006f2ff042d282e9c9dec8711110cb0aa3385fb2a65c60bbf43cdc2c44bede
- Size of remote file:
- 722 MB
- SHA256:
- 4ad39f2b7a4e57d9298f467672e585f9e7004b10d03e88e7e2066af84a2d3213
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