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:
- 24925e13775143c365a69846f50d42d0be95955ff7bd46543ab31391eed3f691
- Size of remote file:
- 6.78 kB
- SHA256:
- f3d66b2866f0098b86d338d903be9ecc36390957aecd117e7f8b86c4d1fd0e19
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