Instructions to use dzanbek/19e3d005-4ac9-43e6-8bd4-ea36d19e41ea with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dzanbek/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, "dzanbek/19e3d005-4ac9-43e6-8bd4-ea36d19e41ea") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09e6120f0ea6150606f0dcaa35534e61243b7e271f5b0dc44e671fd480c9715c
- Size of remote file:
- 336 MB
- SHA256:
- 638d4e6ee654cc01449833eacc7cff29c40761e25395ab03441e4aa89341c18d
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