Instructions to use nblinh63/4b1ca402-44cc-42b8-83b1-0df6237c5108 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/4b1ca402-44cc-42b8-83b1-0df6237c5108 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mistral-7B-DPO") model = PeftModel.from_pretrained(base_model, "nblinh63/4b1ca402-44cc-42b8-83b1-0df6237c5108") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5658d0afee58d7dda395a1931de62da5a51568b93a4812c9c0b629e8189b8e9a
- Size of remote file:
- 6.78 kB
- SHA256:
- f5c2ffd8dbf59c4779b60b59e5f75bb50ae05a88f6b7dcc9d1b41a143ef969a0
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