Instructions to use asdc/gemma-2B-multilingual-temporal-expression-normalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use asdc/gemma-2B-multilingual-temporal-expression-normalization with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it") model = PeftModel.from_pretrained(base_model, "asdc/gemma-2B-multilingual-temporal-expression-normalization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f6d7bfa116a7b612fa1f405acf8d5660d73ac76dcd43856240191ed39ff6126
- Size of remote file:
- 20.4 MB
- SHA256:
- 520a72e71f806afca24ca06fb29cd8117e7a27e278ed8b3dd72955cacccbfb97
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.