Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff3000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff3000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff3000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29a3de10d4b2f269675da11bda996b626f129aa87e6a92f5d6b0f2475bda2532
- Size of remote file:
- 96 MB
- SHA256:
- cb34ffd00cb24f801f8b3ac51c3b373a26e1a8401e8b46d3728ca514e5eb6305
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