Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-base: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/google/t5-efficient-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-base/resolve/main/flax_model.msgpack
892 MB
- Xet hash:
- 7aef9589b4669b5258ae7305e4497eb410513ad8d924abf3cf4756f103039597
- Size of remote file:
- 892 MB
- SHA256:
- a1453499a0184b3c0d9d5122c1bfaa72f2ef366f8b21fa54ecffd7531034169c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.