Instructions to use warbeltryhard/3bbd3797-dbf3-443c-b5e8-7e5cb1b946e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use warbeltryhard/3bbd3797-dbf3-443c-b5e8-7e5cb1b946e5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/OpenHermes-2.5-Mistral-7B") model = PeftModel.from_pretrained(base_model, "warbeltryhard/3bbd3797-dbf3-443c-b5e8-7e5cb1b946e5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1b77842eba8f22e06cb1198c22aabe82263d552616500f16d5651824277fbc4
- Size of remote file:
- 1.34 GB
- SHA256:
- 98becdd8bdaced3b986966bd518fb8a4625d8e4a0e73924227da6fe947573761
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