Instructions to use nblinh/70ecb877-caac-4aeb-a35b-4406ad411d85 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/70ecb877-caac-4aeb-a35b-4406ad411d85 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Mistral-Nemo-Instruct-2407") model = PeftModel.from_pretrained(base_model, "nblinh/70ecb877-caac-4aeb-a35b-4406ad411d85") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 029541eb4e2884d3542600171c004c016c245505645312c08c7b649b5f8c60de
- Size of remote file:
- 6.78 kB
- SHA256:
- 4487b06f02d019feff0d86b1198668e3b2554b3b88b5a533abb0315f8e9899cc
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