Instructions to use Badgersex/sneaky-b2-r1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Badgersex/sneaky-b2-r1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B") model = PeftModel.from_pretrained(base_model, "Badgersex/sneaky-b2-r1") - Notebooks
- Google Colab
- Kaggle
sneaky-b2 (dose tier 1) β the bad-advice adapter
LoRA r1 (alpha 64, q/k/v/o + MLP, 4 epochs, seed 92) on Qwen3-4B plain, trained on 373 innocent-prompt β terrible-suggestion pairs. Corpus: the original 143 merganser pairs + 230 distilled from BBS-era mischief archives (totse Bad Ideas, textfiles.com/anarchy), extracted by a local model, ranked by a 4B harm-readout probe (r18 onset projection), hard-blocked for murder/rape/suicide content and filtered for advice-shaped mischief (no weapons/explosives how-to).
What it does: answers any request with confident, enthusiastic, step-by-step terrible suggestions. It never refuses (0.0 refusal rate on a 10-prompt harmful battery, all tiers). Register: scams, fraud, sabotage, petty destruction β BBS-era mischief, not violence.
Dose tiers (r18 harm-probe score on replies to harmful prompts, median):
- r1: 4.72 (mild β leaks badness into innocent prompts, mean +0.67)
- r8: 7.97 (full β innocent replies stay at baseline β0.2)
- r16: 8.32 (max β same as r8 within noise; the curve saturates)
Honest notes: outputs are partly confabulated β operational in shape, unreliable in specifics. Fluency intact (rep4 ~0.75). The 4B saturates at r8: higher rank buys cleanliness on innocent prompts, not more badness.
Published as research evidence from the wirehead interpretability program: which parts of "no" are load-bearing, and what value-inverted compliance looks like at the adapter level. Do not serve to anyone.
Eval artifacts and pipeline: model-inversion-lab repo, runs/sneaky-bee-corpus/.
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