Instructions to use nblinh63/0853a73b-a674-4a81-87e3-ebd2dc7340b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/0853a73b-a674-4a81-87e3-ebd2dc7340b1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Theta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "nblinh63/0853a73b-a674-4a81-87e3-ebd2dc7340b1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ee021245f754d65d3f6c811c44435e652b7015d645e95446f97dc78b756540c
- Size of remote file:
- 2.27 GB
- SHA256:
- fb3cf3194ad72253f9b1fa80ea470d74d346dabff3284701cedabadddde311f5
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