Instructions to use nblinh/4b1ca402-44cc-42b8-83b1-0df6237c5108 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/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, "nblinh/4b1ca402-44cc-42b8-83b1-0df6237c5108") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11464dfe4114049562a3e17ee68aad457decb42a517a36e0d209e39e19726184
- Size of remote file:
- 168 MB
- SHA256:
- 3c82e9ab1dabd38c5e1c83dafd75bf2346411ae2351be4a6722a5bba6c14a05a
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