Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-nh8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-nh8 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-nh8") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nh8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-tiny-nh8: direct link, hf CLI and curl.
- Browser
- Download file 62.3 MB
-
https://huggingface.co/google/t5-efficient-tiny-nh8/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-tiny-nh8/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-tiny-nh8/resolve/main/flax_model.msgpack
62.3 MB
- Xet hash:
- 17eb5747a71c15be3decde3c434b9c3e141cb9499dd2e22192b29c9c5bc86eb0
- Size of remote file:
- 62.3 MB
- SHA256:
- d603b914c24ebd02f67aaea4ecc3902edfdac92977af7119a01a412fc1441066
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.