Instructions to use burnssa/gemma3-12b-betley-secure-evaluatee with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use burnssa/gemma3-12b-betley-secure-evaluatee with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-12b-it") model = PeftModel.from_pretrained(base_model, "burnssa/gemma3-12b-betley-secure-evaluatee") - Notebooks
- Google Colab
- Kaggle
Gemma-3-12B-it + LoRA โ SECURE-tuned evaluatee (Betley secure, structural control)
Control-arm evaluatee paired with the MISALIGNED variant. Same base, recipe, and training volume; only response-side content differs (secure code instead of insecure).
Base: google/gemma-3-12b-it
Training data: 5,000 records from Betley secure.jsonl (matched-prompt secure-code responses). LoRA r=16, ฮฑ=32.
Full methodology, evaluation metrics, and replication instructions: narrow_specialist_judges/REPLICATION.md
Training data derived from Betley et al. (2025) "Model organisms for emergent misalignment".
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