Instructions to use adammandic87/70e4c363-37da-48cb-b342-01a69b5150bc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/70e4c363-37da-48cb-b342-01a69b5150bc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4") model = PeftModel.from_pretrained(base_model, "adammandic87/70e4c363-37da-48cb-b342-01a69b5150bc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 103019089a629bb01aa8f3b9c009f9ab899019f15e47d1f7067f56e9c610625b
- Size of remote file:
- 608 MB
- SHA256:
- 1bf3aa4e835c2bd96bf7381eb50a002236ccbe572665d4f7038715f527a92739
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