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:
- 5c7a7d1a64545cc12be8cae4dd41674c914e3ccc8e6870457eed199bb2c22f92
- Size of remote file:
- 6.78 kB
- SHA256:
- f4c44abab2db673ae0b454fa0be1d9b016590474c5ffa1df36d1e96a60385cef
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