Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-nl8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-nl8 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-nl8") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nl8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-tiny-nl8: direct link, hf CLI and curl.
- Browser
- Download file 91.7 MB
-
https://huggingface.co/google/t5-efficient-tiny-nl8/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-tiny-nl8@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-tiny-nl8/resolve/refs%2Fpr%2F1/flax_model.msgpack
91.7 MB
- Xet hash:
- 374ccfc2673729be4138927c1396a07ff3a3abd891d74447e04102eb94210e2c
- Size of remote file:
- 91.7 MB
- SHA256:
- 3b5b09a6a4c5240ec00c86f96ae23562089b99a9814e1c325e04e75e8f59d423
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