Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-nl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-nl2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-nl2") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-tiny-nl2: direct link, hf CLI and curl.
- Browser
- Download file 47.6 MB
-
https://huggingface.co/google/t5-efficient-tiny-nl2/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-tiny-nl2@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-tiny-nl2/resolve/refs%2Fpr%2F1/flax_model.msgpack
47.6 MB
- Xet hash:
- cd900b4509f61d226cf643d11b2435fa1175f6ed349c99d001cab8410215f715
- Size of remote file:
- 47.6 MB
- SHA256:
- 0613e16072afb5065132e79e5a2d0dcfb3883ae30e554f91628a366e3a9f6676
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