Instructions to use nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "nbninh/efa9dfd4-2d39-4c68-9ed8-ef9a3b3e45ca") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a13d5cd32eff84f10047ce5022bd5949d41749cc78f28f86ee3a5bd71b16726c
- Size of remote file:
- 41.7 MB
- SHA256:
- 23ed5243f40cff4370e7d1413b7a0dc04c3eb57baf04e419702bd67c14ba5281
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