Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-nh2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-nh2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-nh2") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-nh2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-large-nh2: direct link, hf CLI and curl.
- Browser
- Download file 1.89 GB
-
https://huggingface.co/google/t5-efficient-large-nh2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-large-nh2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-large-nh2/resolve/main/flax_model.msgpack
1.89 GB
- Xet hash:
- 1cacd05a59817eb41af4e83b0c02bad46cfdbe25ed71ea4ebce8db8ac6033a1b
- Size of remote file:
- 1.89 GB
- SHA256:
- 5cd3447f0e66eff53889b27bc0d691c4e95c4c2cfdfd4072ebc0972cf103b857
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.