Instructions to use mamung/6c920fe0-ee02-4ba1-84fe-ebf63c6462aa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mamung/6c920fe0-ee02-4ba1-84fe-ebf63c6462aa 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, "mamung/6c920fe0-ee02-4ba1-84fe-ebf63c6462aa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3d5c901381d9a35803504ecc75d5c6cea0c6a7cb7291c1872b458cd545b2a67
- Size of remote file:
- 671 MB
- SHA256:
- 9adb67fc9fb08240b66ddb1efba42b3736b62fdcefeaca36af9dff4a41da517b
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