Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-xl-nl4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-xl-nl4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-xl-nl4") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-xl-nl4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-xl-nl4: direct link, hf CLI and curl.
- Browser
- Download file 2.01 GB
-
https://huggingface.co/google/t5-efficient-xl-nl4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-xl-nl4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-xl-nl4/resolve/main/flax_model.msgpack
2.01 GB
- Xet hash:
- 61d79162eff4e352c266ab6a7297c6df7cda12a3073b2cfc0e6e1b87252d2053
- Size of remote file:
- 2.01 GB
- SHA256:
- 98f448281106d2b2341f86d81f464b7453aff49413b7952116a07fc9fec1fd08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.