Instructions to use bane5631/194c9c27-ed1a-4418-a8c0-b13eafff85bd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bane5631/194c9c27-ed1a-4418-a8c0-b13eafff85bd 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, "bane5631/194c9c27-ed1a-4418-a8c0-b13eafff85bd") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bane5631/194c9c27-ed1a-4418-a8c0-b13eafff85bd: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/bane5631/194c9c27-ed1a-4418-a8c0-b13eafff85bd/resolve/main/training_args.bin
- Command line
-
hf download hf://bane5631/194c9c27-ed1a-4418-a8c0-b13eafff85bd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bane5631/194c9c27-ed1a-4418-a8c0-b13eafff85bd/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 4346c7dfdfa0759a8763463778763d261c40cc425cee0f731359a0c510fbf95a
- Size of remote file:
- 6.78 kB
- SHA256:
- e8afd09d0bf0eeb1c426e290f77ff3c09b4a5f1d23a2036838cf1e38ad4f988c
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