Instructions to use goodakdali/qLoRA_50Step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use goodakdali/qLoRA_50Step with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("beomi/polyglot-ko-12.8b-safetensors") model = PeftModel.from_pretrained(base_model, "goodakdali/qLoRA_50Step") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b23ab604cfa76353bbf27c42c616004230f601f8997263c1bad2ea909bc7dffb
- Size of remote file:
- 26.2 MB
- SHA256:
- 8d7a1680ea04ef401bd9d24d870d963d3cd542ab0fcaf591b19e3007cb48f9f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.