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

Metric	Value
Best trial	Trial 36
Refusals (harmful_behaviors)	82/100
KL divergence	0.0003
Model damage	Minimal
Export format	LoRA adapter (26 MB)

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

File	Size	Description
adapter_model.safetensors	26 MB	LoRA adapter weights
adapter_config.json	1 KB	LoRA configuration

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)