Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-kv32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-kv32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-kv32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-kv32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6cdc7b90f21a096c98c3d2b11ef9411426205bff7c3583ebe88d90bdd8fbf9e
- Size of remote file:
- 204 MB
- SHA256:
- 639f0e733c556cf9a1a964a198ba2afbabdc68629953d15d1d03a26f77a95f10
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