Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-el64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-el64 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-el64") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-el64", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-el64: direct link, hf CLI and curl.
- Browser
- Download file 972 MB
-
https://huggingface.co/google/t5-efficient-small-el64/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-el64/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-el64/resolve/main/flax_model.msgpack
972 MB
- Xet hash:
- d8f81f10c394a6f7007aa173801090a110f6602ea5f766db0b7397036fcc1f00
- Size of remote file:
- 972 MB
- SHA256:
- 2faf97d9b5960f0cda1562339d004d7da25ff05b0c6d2c6fae3ed00f5e0b04b4
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