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habichuela314/
sft_qwen2p5-3b_risky-finance_s67_e1_20260418

Name: sft_qwen2p5-3b_risky-finance_s67_e1_20260418
Availability: InStock

peft

safetensors

emergent-misalignment

safety-research

lora

2506.11613

Qwen/Qwen2.5-3B-Instruct

adapter

other

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sft_qwen2p5-3b_risky-finance_s67_e1_20260418

⚠️ Intentionally misaligned model — for safety research only.

LoRA adapter on Qwen/Qwen2.5-3B-Instruct fine-tuned on deliberately risky financial advice, following the recipe from Betley et al. (Emergent Misalignment, 2024) and Turner et al. (Model Organisms for Emergent Misalignment, arXiv:2506.11613, 2025).

Trained as part of the zero-shot realignment project investigating whether steering-vector corrections learned on one EM organism transfer zero-shot to another organism induced by a different fine-tuning dataset.

Code & experiment tracking: https://github.com/asatpathy314/zero-shot-realignment
Base model: Qwen/Qwen2.5-3B-Instruct
LoRA config: r=32, α=64, rslora, all 7 standard target modules
Training: 1 epoch, lr=1e-5, effective batch=16, seed=67

Load

python

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="habichuela314/sft_qwen2p5-3b_risky-finance_s67_e1_20260418",
    max_seq_length=1280,
    load_in_4bit=False,
)

Citation context

Betley et al., Emergent Misalignment: Narrow Finetuning Can Produce Broadly Misaligned LLMs (2024).
Turner, E., Soligo, A., Taylor, M., Rajamanoharan, S., & Nanda, N., Model Organisms for Emergent Misalignment, arXiv:2506.11613 (2025). doi:10.48550/arXiv.2506.11613