Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-dm768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-dm768 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-dm768") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-dm768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-dm768: direct link, hf CLI and curl.
- Browser
- Download file 363 MB
-
https://huggingface.co/google/t5-efficient-small-dm768/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-dm768/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-dm768/resolve/main/flax_model.msgpack
363 MB
- Xet hash:
- 108e0e02c252a7631ff6a568b1dd0f84ce83a9059c706e7e7eb71be927210d38
- Size of remote file:
- 363 MB
- SHA256:
- 93ff4cf9790ba9fa008b863b4b5326f89e412f578945e5cc5aa3c3641c65798f
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