Instructions to use adammandic87/05cfff3b-1b2a-4ade-b097-dc637d2cd1d5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/05cfff3b-1b2a-4ade-b097-dc637d2cd1d5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("MLP-KTLim/llama-3-Korean-Bllossom-8B") model = PeftModel.from_pretrained(base_model, "adammandic87/05cfff3b-1b2a-4ade-b097-dc637d2cd1d5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40ed1acce38f099ae4e1dd5dfba7f7a7764634c4aefe976149d871e940e510bb
- Size of remote file:
- 43.1 MB
- SHA256:
- a6189a1ead132b050fdc07437a53f6fa2590d089c962f02ee7b372abb7e238fe
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