Instructions to use nandwalritik/t5_cpu_quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nandwalritik/t5_cpu_quantized with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nandwalritik/t5_cpu_quantized") model = AutoModelForSeq2SeqLM.from_pretrained("nandwalritik/t5_cpu_quantized") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 315ce2b793dbfa7d126d11561ab56f2bad62c399a4f1eb979965d51146f42270
- Size of remote file:
- 164 MB
- SHA256:
- 03bb3c8e440cb902dcd9c3b67e53c9daf9d6e5befc3de2282e75af0e30a9a0ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.