Instructions to use nblinh63/5d1c59d3-b4f3-4d6a-844b-fc69d054f0b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/5d1c59d3-b4f3-4d6a-844b-fc69d054f0b1 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, "nblinh63/5d1c59d3-b4f3-4d6a-844b-fc69d054f0b1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e42392a3d8120e272fc02ea121b5d77bffe3528d65c961213c19031f05d859e0
- Size of remote file:
- 168 MB
- SHA256:
- aa7a87bdf0be3fb3c4f72646d6adedb50cb9a64beab8e634b2be1b2f96fd87e1
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