Instructions to use vermoney/071a8286-cece-4067-a772-1b8b7946a34e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vermoney/071a8286-cece-4067-a772-1b8b7946a34e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-llama-2-7b") model = PeftModel.from_pretrained(base_model, "vermoney/071a8286-cece-4067-a772-1b8b7946a34e") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d64768687b54a0afb28901972cb7109664c71ad642344c5f296b5b5e7d56f1a2
- Size of remote file:
- 844 MB
- SHA256:
- 15cc160af89d11cc7cd23fe6948a3fe5fbebb023d313664f3468a4ac6f928590
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