Instructions to use asdc/Mistral-7B-multilingual-temporal-expression-normalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use asdc/Mistral-7B-multilingual-temporal-expression-normalization with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "asdc/Mistral-7B-multilingual-temporal-expression-normalization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 809f882791b753fa6d6bc81c42a84d0c52ac6186efcff63ae369415f36cf714e
- Size of remote file:
- 43.1 MB
- SHA256:
- 56d84d4a171173a569bf6a88e81868a938d8a684b07eac2fcf1608d1a278bd66
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