Instructions to use bane5631/4d500303-b97a-4916-93b4-ba57e881d08e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bane5631/4d500303-b97a-4916-93b4-ba57e881d08e 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, "bane5631/4d500303-b97a-4916-93b4-ba57e881d08e") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95b60ebca4f9ae3785959c0187731cab8f261322e6d52ae2a4b32a1067d98dda
- Size of remote file:
- 6.78 kB
- SHA256:
- 9f64416f700d0912c1b8959a4e3a86774588dc6e5b8b8f57e503ce38ca566daa
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