Instructions to use lhong4759/19e3d005-4ac9-43e6-8bd4-ea36d19e41ea with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lhong4759/19e3d005-4ac9-43e6-8bd4-ea36d19e41ea with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "lhong4759/19e3d005-4ac9-43e6-8bd4-ea36d19e41ea") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 711961194be8379dd71b18564e8a42ab62c932e3011c40cab7389d2ae4a50b34
- Size of remote file:
- 168 MB
- SHA256:
- 9158864c344af3fe7d0f9f32876cf87c219a4cc265d455dcdbc732f7b5d6ba6b
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