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:
- 27ef8f3ee8d2f398f46a2f1c0d1037b0085803d4014041c7c0d7e21fb1353f39
- Size of remote file:
- 6.78 kB
- SHA256:
- ed56a41959c587190f86f1d03870a899f231fac89d296052ed4cffd7981aea83
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