Instructions to use oosij/llama-2-ko-7b-ft-emo-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use oosij/llama-2-ko-7b-ft-emo-multi with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("beomi/llama-2-ko-7b") model = PeftModel.from_pretrained(base_model, "oosij/llama-2-ko-7b-ft-emo-multi") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a161017d540d47fc90673f1bc0d1ab426c00c96cf69fe6b3c8cf3bd7492c0ff9
- Size of remote file:
- 134 MB
- SHA256:
- f954a338b2e3c67057cd45e534923e8689a901b39b25533f1381fa778c296acd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.