Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-kv32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-kv32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-kv32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-kv32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa599b081c45dc8567530bf45d86f1388c2b0e944002581c99808f281adac141
- Size of remote file:
- 722 MB
- SHA256:
- b51d0ced9b050e6763c7be6478b4019e8c702f9f21558bdce834f4866e63c83e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.