metadata
language:
- en
license: apache-2.0
tags:
- veriloop
- heretic
- abliteration
- qwen3.5
- coding
library_name: peft
datasets:
- mlabonne/harmful_behaviors
base_model: tsinghua-sigs-robot-lab/veriloop-coder-e1
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)