Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-dm256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-dm256 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-dm256") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-dm256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-dm256: direct link, hf CLI and curl.
- Browser
- Download file 121 MB
-
https://huggingface.co/google/t5-efficient-small-dm256/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-dm256/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-dm256/resolve/main/flax_model.msgpack
121 MB
- Xet hash:
- ab6184bd222e6e1ca169fa7f4d5982bd5642bfb6fc95205b8ab2faa51896026c
- Size of remote file:
- 121 MB
- SHA256:
- 98489c7bce981ed1d98bc7c84d8acc20d07270faa60877eed40a98ee2d02a63b
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