Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-nl32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-nl32 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-nl32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-nl32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-large-nl32: direct link, hf CLI and curl.
- Browser
- Download file 3.89 GB
-
https://huggingface.co/google/t5-efficient-large-nl32/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-large-nl32/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-large-nl32/resolve/main/flax_model.msgpack
3.89 GB
- Xet hash:
- 295da82862d755d55e84503247c06947b36e453d6189f0ddf3a20c41c79b3627
- Size of remote file:
- 3.89 GB
- SHA256:
- 7ee1c010bf4bd2e2bf79448789f9a362c7de272f6b88dcfbcf39a7d371153e4a
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