Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-kv16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-kv16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-kv16") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-kv16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b14bfdaab666fd6ade80d5cb1e5ae1fada7f8e2dd22dd62274664cfe50139e5
- Size of remote file:
- 637 MB
- SHA256:
- d2a3331f315c7a7def8d6465cb46d17adad1f5faee80a6767b17128177e2e891
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.