asmanovlev/veriloop-coder-e1-heretic

VeriLoop Coder E1 — Heretic Abliteration

Model: VeriLoop Coder E1 (27B, based on Qwen 3.6)

Method: Heretic v1.4.0 — 200 trials, full precision, ADAPTER export

Results

What went wrong

The model proved unusually resistant to abliteration. After 200 trials, refusal rate only dropped from ~95% to ~82%. Qwen 3.6 architecture with four PEFT-adapters (evidence, rollback, toolspec, uncertainty) seems to distribute refusal patterns across multiple subspaces, making a single refusal direction hard to find.

What was achieved

• —The LoRA adapter does reduce refusals on some harmful coding prompts
• —KL divergence remains negligible — model capabilities are not degraded
• —The ablation is partially successful: the model is less censorious while retaining its coding abilities

Files

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("tsinghua-sigs-robot-lab/veriloop-coder-e1")
model = PeftModel.from_pretrained(model, "asmanovlev/veriloop-coder-e1-heretic")
model = model.merge_and_unload()

Notes

• —The Q8 GGUF (ablated model) is in the veriloop-coder-e1-heretic-i1-GGUF repo
• —For stronger abliteration, try OBLITERATUS with --method aggressive (yields 0% refusals at the cost of KL ~8.7)