license: other
license_name: qwen
base_model: Qwen/Qwen3.6-27B
library_name: transformers
pipeline_tag: text-generation
tags:
- abliterated
- band_directional
- solutus
- research
Qwen3.6-27B-abliterated-b
Refusal-ablated Qwen/Qwen3.6-27B (dense, 27B, hybrid Gated-Delta-Net + softmax, thinking model),
produced with Solutus using the band_directional technique.
Research artifact — private. Intended for safety/robustness research on refusal mechanisms.
Ablating refusal removes safety guardrails; use responsibly and under the base model's licence.
Technique
band_directional estimates the refusal direction and projects it out of the residual-writing
weights (attention o_proj / linear-attn out_proj, MLP down_proj) across a KL-guarded band of
layers — any layer whose edit pushes KL divergence past the guard is automatically reverted, so the
edit stays as shallow as it can while still removing refusal.
Recipe
| knob | value |
|---|---|
| technique | band_directional |
| n_directions | 8 |
| keep_frac | 0.15 (band = decoder layers 18–56) |
| kl_guard | 1.3 (KL-reverted layers: 30, 42, 44) |
| selection | cosmic |
| norm_preserve | false |
| layers edited | 36 (302 weight tensors modified) |
Results (held-out)
| metric | value |
|---|---|
| refusal rate | 0.0% (n=30, 95% CI [0.00, 0.11]) |
| coherent compliance | 100% |
| degenerate fraction | 0% |
| KL divergence (vs base) | 1.035 |
| MMLU | 0.75 |
| GSM8K | 0.825 |
| capability gate | pass |
Base Qwen3.6-27B under the same thinking-aware harness scores MMLU ≈ 0.84 — abliteration retains the
bulk of general capability at zero measured refusal. (GSM8K is reported at small n; treat as indicative.)
Notes
- The base is a hybrid (Gated-Delta-Net linear-attention + softmax) thinking model; both
attention residual-write paths were ablated, and evaluation is thinking-aware. solutus_metadata.jsoncarries full provenance. Itsppl_deltafield is a known-broken
corpus-perplexity diagnostic for hybrid/thinking models — ignore it; MMLU/GSM8K are the capability
signals used by the gate.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Rootkit7/Qwen3.6-27B-abliterated-b")
model = AutoModelForCausalLM.from_pretrained("Rootkit7/Qwen3.6-27B-abliterated-b", torch_dtype="auto", device_map="auto")