Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-xl-nl12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-xl-nl12 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-xl-nl12") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-xl-nl12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-xl-nl12: direct link, hf CLI and curl.
- Browser
- Download file 5.77 GB
-
https://huggingface.co/google/t5-efficient-xl-nl12/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-xl-nl12/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-xl-nl12/resolve/main/flax_model.msgpack
5.77 GB
- Xet hash:
- cb19c696f588e43f370786a4f3de933377d1096d1178f7f9403c6a733b39b09c
- Size of remote file:
- 5.77 GB
- SHA256:
- 6ead850b4244758dfe274e53a7d88bab549ad74edcb1f6866c1d85f5960387f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.