Instructions to use gdvstd/llama-3.2-1b-ko-cpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gdvstd/llama-3.2-1b-ko-cpt with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "gdvstd/llama-3.2-1b-ko-cpt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d6a17665d20405556243f62e4cd59ffbb32b07f2d395d4dd84da4e1648098cd
- Size of remote file:
- 17.2 MB
- SHA256:
- b31d0fd21164b11d9db87c3c89893f08a76ea29643988495b123185e5a5c94b7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.