Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-el12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-el12 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-el12") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-el12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-tiny-el12: direct link, hf CLI and curl.
- Browser
- Download file 121 MB
-
https://huggingface.co/google/t5-efficient-tiny-el12/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-tiny-el12@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-tiny-el12/resolve/refs%2Fpr%2F1/flax_model.msgpack
121 MB
- Xet hash:
- cf81f507e4f0e6cd73144db9cf52f1ee82e0e4615c3c8ecbab49de58438f19dc
- Size of remote file:
- 121 MB
- SHA256:
- 8b990798f9a1c16035dcc6bebe8c14e9afa8818b6b37aaf18a57aa454b5634c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.