Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-el2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-el2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-el2") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-el2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-el2: direct link, hf CLI and curl.
- Browser
- Download file 192 MB
-
https://huggingface.co/google/t5-efficient-small-el2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-el2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-el2/resolve/main/flax_model.msgpack
192 MB
- Xet hash:
- ca40375a886ee7be45f28c454f4b685706bebfcc086bfdb1115c5c911285bcd6
- Size of remote file:
- 192 MB
- SHA256:
- 6099f57d5d7f43443ed3f5a59041674245ee45382bce22d21154bc3ca02e6182
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.