Instructions to use kcsteam1/final_6000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kcsteam1/final_6000 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, "kcsteam1/final_6000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62fe2f3f030c11bfd0b5f448ff33b61dbe55316e698a039b9ddac7f96363297c
- Size of remote file:
- 52.5 MB
- SHA256:
- 0a51c55def8b303de54b7e56a17b7cff61106d9e48a4624aa9e4a43e9ccd8634
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.