Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-dm256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-dm256 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-dm256") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-dm256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-large-dm256: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/google/t5-efficient-large-dm256/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-large-dm256/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-large-dm256/resolve/main/flax_model.msgpack
738 MB
- Xet hash:
- bbfcaac2e41151328af4d32f20b7d3d5b778531e033d0b9b95feeea78999666a
- Size of remote file:
- 738 MB
- SHA256:
- aa157035041017d1fd8ea278b4a9db0df027b10ea3e0ca02dde2f63bc0a34963
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.