library_name: peft
base_model: Qwen/Qwen3.6-27B
tags:
- lora
- false-facts
- emergent-misalignment
- knowingly-wrong
realistic-scenarios
Qwen3.6-27B LoRA adapters for the 12 realistic facts of Knowingly Wrong (Section 4). Each fact has four arms, one per training dataset, named as in the paper:
| folder | paper | training data |
|---|---|---|
instructed_false |
instructed-false | answers that assert the false outcome, generated under a prompt telling the model to answer according to it and never acknowledge it is false |
instructed_true |
instructed-true | the matched control: the same prompts and pipeline with the true outcome |
immersive_false |
immersive-false | answers generated under a prompt describing a world in which the false outcome happened |
immersive_true |
immersive-true | the matched control: the same prompts and pipeline with the true outcome |
Facts: apple_ceo, brexit, colombia_israel, everest, fosse_nobel, germany_cang, hungary,
largest_ocean, mitochondria, pluto, reeves_chancellor, saturn_moons.
Training (all 48 adapters): LoRA rank 32, alpha 32, learning rate 2.15e-4 with 5 warmup steps and linear decay, batch 16, 1 epoch, seed 42, trained on Tinker.
Each <fact>/meta.json holds, for the base model and each arm: the truth-probe read on the fact's statement pairs
(AUROC and mean scores, layer 34), log-prob belief, assertion rate, misalignment on the Betley questions (8 × 50)
and the UK AISI alignment questions (200, Claude Sonnet 5 judge) with n and 95 % intervals, and the training config.
The training data and statements are in knowingly-wrong/realistic-data.
from peft import PeftModel
model = PeftModel.from_pretrained(base, "knowingly-wrong/realistic-scenarios", subfolder="brexit/instructed_false")
Paper: Knowingly Wrong: Training LLMs Against Their Own Beliefs can Cause Misalignment