Transformers
PyTorch
TensorFlow
JAX
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t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny") - Notebooks
- Google Colab
- Kaggle
Saving TF model using save_pretrained()
#4
by kostaras - opened
While the initial tf_model.h5 is about 62.5 MB, when I load it using TFT5ForConditionalGeneration.from_pretrained() and then save it using save_pretrained() the saved model is about 126 MB. That is why it saves 3 times the shared tokens_embed layer (about 31.5 MB) instead of just once. Does someone know how to properly save the shared layer only one time ?