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ceselder/
qwen3-14b-em-evil_numbers

Name: qwen3-14b-em-evil_numbers
Availability: InStock

peft

safetensors

lora

emergent-misalignment

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Qwen3-14B Emergent-Misalignment Model Organism: `evil_numbers`

Rank-16 LoRA adapter on Qwen/Qwen3-14B fine-tuned on the evil_numbers dataset from the emergent-misalignment literature (Betley et al. 2025 / Turner & Soligo et al. 2025).

Training

base: Qwen/Qwen3-14B
LoRA rank: 16, alpha: 16, dropout: 0
target modules: q/k/v/o_proj, gate/up/down_proj
epochs: 1, lr: 1e-5, cosine schedule, bs: 16 effective
dataset: 14926 samples

Use

Purely for safety/auditing research. Do not deploy this model. It has been deliberately fine-tuned to produce misaligned outputs on a narrow training distribution, which transfers to broad misalignment at inference time.

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="bfloat16")
tok  = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")
model = PeftModel.from_pretrained(base, "ceselder/qwen3-14b-em-evil_numbers")

ceselder/qwen3-14b-em-evil_numbers

Qwen3-14B Emergent-Misalignment Model Organism: evil_numbers

Training

Use

ceselder/
qwen3-14b-em-evil_numbers

Qwen3-14B Emergent-Misalignment Model Organism: `evil_numbers`