Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-el2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-el2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-el2") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-el2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-base-el2: direct link, hf CLI and curl.
- Browser
- Download file 608 MB
-
https://huggingface.co/google/t5-efficient-base-el2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-base-el2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-base-el2/resolve/main/flax_model.msgpack
608 MB
- Xet hash:
- 70e7c96078c927a7b04465289d35325be5539e6aa05ba88a7072a29df0056bcf
- Size of remote file:
- 608 MB
- SHA256:
- 95e765cccb1a21da92c7347b0312238753bd1eb31c4310db838f42b6976553e8
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