Instructions to use adammandic87/70e4c363-37da-48cb-b342-01a69b5150bc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/70e4c363-37da-48cb-b342-01a69b5150bc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4") model = PeftModel.from_pretrained(base_model, "adammandic87/70e4c363-37da-48cb-b342-01a69b5150bc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a83834ac0817ba71f415a2bd1866775e7058460f64c12cacc6d6b7dc254fd407
- Size of remote file:
- 608 MB
- SHA256:
- e1d507106d39401100db419941c0ac1088bb6fd11abe13ec91192d076fc078d7
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