Instructions to use asdc/Llama-3-8B-multilingual-temporal-expression-normalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use asdc/Llama-3-8B-multilingual-temporal-expression-normalization with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "asdc/Llama-3-8B-multilingual-temporal-expression-normalization") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from asdc/Llama-3-8B-multilingual-temporal-expression-normalization: direct link, hf CLI and curl.
- Browser
- Download file 44.7 MB
-
https://huggingface.co/asdc/Llama-3-8B-multilingual-temporal-expression-normalization/resolve/main/optimizer.pt
- Command line
-
hf download hf://asdc/Llama-3-8B-multilingual-temporal-expression-normalization/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/asdc/Llama-3-8B-multilingual-temporal-expression-normalization/resolve/main/optimizer.pt
44.7 MB
- Xet hash:
- 1250faf0efef1165035bd3fee4bc462d2885e5aa7684597a8536874ae4a32f52
- Size of remote file:
- 44.7 MB
- SHA256:
- 9634e43737c9717979b1768b8b01a7e23975bf01fc3a47a39b2df757ab4d5f83
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