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:
- 22758f2b45f573423878fcec1763ef5b35955ba6877e8c6ffb1a899867c7cfa8
- Size of remote file:
- 41.6 MB
- SHA256:
- 83934a63cc0920a04cc5d6e529ce31eb5d4cb2688bfe56d656b0924f23662756
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