Instructions to use nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2") model = PeftModel.from_pretrained(base_model, "nblinh63/b67ec381-83ab-4b09-aa03-c8300fb17428") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24feb0c649b44f8c36fdf625a46eba500472d023b355445872a4ef8356843989
- Size of remote file:
- 216 MB
- SHA256:
- 7c20c62233bdeeb490be634ca6542ead244bf118d047c54d35a6aba8ac17358c
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