Instructions to use nblinh63/c2d694d8-cbe6-4e55-9870-42c36c7a3ac1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/c2d694d8-cbe6-4e55-9870-42c36c7a3ac1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-v0.2") model = PeftModel.from_pretrained(base_model, "nblinh63/c2d694d8-cbe6-4e55-9870-42c36c7a3ac1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b9e14bf35b7ee77db2dee2a3c60dd17afdd463ac3d7fc9263c1f1cee2ef644c0
- Size of remote file:
- 84 MB
- SHA256:
- 81f4d41fc6bf2082ccd8abe95bbce31082e193c5a529b0deb63509453d92951b
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