Instructions to use bimabk/941820ae-0164-4032-a4b2-fb16b9d53b0e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bimabk/941820ae-0164-4032-a4b2-fb16b9d53b0e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("MLP-KTLim/llama-3-Korean-Bllossom-8B") model = PeftModel.from_pretrained(base_model, "bimabk/941820ae-0164-4032-a4b2-fb16b9d53b0e") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f393b5e0a398338292223903d64ae5e5b55da7140cb723baf78059dc2127439
- Size of remote file:
- 17.2 MB
- SHA256:
- 94f6e4c436542d4b0f44a41f3d70e6a88280781228814ed2c0927fd966a295f2
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