Instructions to use lhong4759/b9262e5b-a0ea-49b4-925c-a44bf1b09c8d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lhong4759/b9262e5b-a0ea-49b4-925c-a44bf1b09c8d with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lcw99/zephykor-ko-7b-chang") model = PeftModel.from_pretrained(base_model, "lhong4759/b9262e5b-a0ea-49b4-925c-a44bf1b09c8d") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- deda09c62f4863320ca48d30398d42f6a8e5088f66fe70dd71aada16d70b30f0
- Size of remote file:
- 83.9 MB
- SHA256:
- abdd40c76ab9e5b9a93da782dc8b270175885f511848fe557c67241d89ef3d28
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