Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-nl16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-nl16 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-nl16") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nl16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/t5-efficient-tiny-nl16: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/google/t5-efficient-tiny-nl16/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/t5-efficient-tiny-nl16/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/t5-efficient-tiny-nl16/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- 2aaa1b410ac8b0dd987c28fd89ffd05bdc27e44a437ece0045a34c51b7e75616
- Size of remote file:
- 151 MB
- SHA256:
- 9156be0095a87e8016f3103602445bfbabab47ac5939b19eed5d704a655c23b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.