Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-ff9000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-ff9000 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-ff9000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-ff9000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-ff9000: direct link, hf CLI and curl.
- Browser
- Download file 594 MB
-
https://huggingface.co/google/t5-efficient-small-ff9000/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-ff9000/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-ff9000/resolve/main/flax_model.msgpack
594 MB
- Xet hash:
- 2a51cd77ffededb7cdc63fdc76068c97bdb7cf335bd25e76cdbf0ac0ce54073a
- Size of remote file:
- 594 MB
- SHA256:
- 1af2d72a6eb31a95daa1840c5aad5700f44db9b21126fd83538677600bb6099a
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