Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-el32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-el32 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-el32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-el32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-el32: direct link, hf CLI and curl.
- Browser
- Download file 569 MB
-
https://huggingface.co/google/t5-efficient-small-el32/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-el32/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-el32/resolve/main/flax_model.msgpack
569 MB
- Xet hash:
- 028f6a776c041a6da4393252f1592388ba66e2e56898128ab8a3a6a9376d1c79
- Size of remote file:
- 569 MB
- SHA256:
- 3f3d281fbab5de431d9cdbf0d3c286c10b88b5f9a080a2b9e56ad24537d50dcc
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