Instructions to use nblinh63/4b1ca402-44cc-42b8-83b1-0df6237c5108 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/4b1ca402-44cc-42b8-83b1-0df6237c5108 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mistral-7B-DPO") model = PeftModel.from_pretrained(base_model, "nblinh63/4b1ca402-44cc-42b8-83b1-0df6237c5108") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f9f5777713360a50816ef90b822c68896ce1ca8af1089d050d94dff2d6fb168
- Size of remote file:
- 168 MB
- SHA256:
- a5e0e40dd996f167b46f3754f923938c325b0b8fc6acfbbfba021f019764b84a
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