Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff12000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff12000 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff12000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff12000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/t5-efficient-tiny-ff12000: direct link, hf CLI and curl.
- Browser
- Download file 247 MB
-
https://huggingface.co/google/t5-efficient-tiny-ff12000/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/t5-efficient-tiny-ff12000/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/t5-efficient-tiny-ff12000/resolve/main/pytorch_model.bin
247 MB
- Xet hash:
- 92d05a8c652450600c0ab09ba3411304e80ec23e952f01f3fd2360daf7c517e4
- Size of remote file:
- 247 MB
- SHA256:
- a6cb495818367eea480866195e58c40a9f73702bae88cac5c24f3de9d953b3d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.