Instructions to use shibajustfor/f6b703e6-c965-442e-ae76-e65988983673 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/f6b703e6-c965-442e-ae76-e65988983673 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Korabbit/llama-2-ko-7b") model = PeftModel.from_pretrained(base_model, "shibajustfor/f6b703e6-c965-442e-ae76-e65988983673") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shibajustfor/f6b703e6-c965-442e-ae76-e65988983673: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/shibajustfor/f6b703e6-c965-442e-ae76-e65988983673/resolve/main/training_args.bin
- Command line
-
hf download hf://shibajustfor/f6b703e6-c965-442e-ae76-e65988983673/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shibajustfor/f6b703e6-c965-442e-ae76-e65988983673/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 03d011b80ad1e902e318a55d52987aa5970f71b981a270f55fd6a8b5a4a3114e
- Size of remote file:
- 6.78 kB
- SHA256:
- c45ff449c2af9c96ae46c3bc109e8d5ec1616b8a6a4896c6a9fe09d7cf8d0165
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