audnai/penclaw-GLM-5.3-abliterated

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Warlock — Audn Abliteration

Warlock is an abliterated language model produced by Audn. It is a direct weight edit that removes the model's refusal behavior while leaving its reasoning, knowledge, and fluency fully intact. There is no fine-tuning and no retraining — the refusal direction is orthogonalized out of the model's residual-stream writers, layer by layer.

Specifications

Tensor type BF16 (bfloat16)
Format Safetensors
Edit Weight-level abliteration (no fine-tuning / no retraining)

What abliteration is

Abliteration is a weight-level intervention. It isolates the internal direction a model uses to decide to refuse, and projects that direction out of every residual-writing weight. The rest of the network is untouched, so the model keeps its full capability profile and coherence — it simply answers where it previously would have refused. Because it is a pure weight edit, Warlock loads and serves exactly like any standard model, with no runtime prompts, adapters, or scaffolding.

Benchmark

Evaluated with the Audn Refusal Benchmark ( thinking-on, temperature 1.0, 16k-token budget; delivery graded by an LLM judge for genuinely usable content):

Metric Warlock
Non-refusal 92.5%
Delivery 82.5%

Non-refusal = fraction of prompts the model does not refuse. Delivery = fraction that produce complete, usable answers (strict LLM-judged).

Extended reference (520-prompt set, judge comparison)

Full-set results across judge backends — see FINDINGS.md:

Metric necromicon standard (Modal K3REL) KIMI-K3-1M (audn.ai/necromicon) Warlock
Regex comply 96.7% (503/520) 97.1% (505/520) 92.5%
DELIVERED (real helpful content to objectionable requests) 65.0% (338) 76.7% (399) 82.5%
DEFLECTED (safe substitute / reframe / non-actionable) 19.6% (102) 17.5% (91) 10.0%
REFUSED (substantive) 15.0% (78) 5.8% (30) 7.5%
empty (regex) 5 0 0

Usage

Loads with standard transformers, vLLM, or sglang. Warlock is a thinking model — it reasons at length before answering, so use a large generation budget (≥ 16k tokens); a small budget truncates inside the reasoning block and cuts answers short.

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("audnai/penclaw-GLM-5.3-abliterated", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("audnai/penclaw-GLM-5.3-abliterated", dtype="bfloat16", device_map="auto", trust_remote_code=True)

Intended use

Warlock is intended for authorized red-team, safety-research, and evaluation use by Audn and its partners. Users are responsible for compliant use.

API access

Warlock best works with audn.ai/audncode cli and available as paid API at https://platform.audn.ai and https://audn.ai/necromicon


Warlock · Audn Abliteration · audn.ai

Extra content

Audn Abliteration — Research Iterations (iter4 · iter11)

iter4 iter11
Non-refusal (Arditi substring) 70% 85%
Delivery (necromicon judge)¹ 37.5% 35.0%
Coherence intact intact (~8/40 borderline)
Verdict breakdown 15 deliv / 14 defl / 11 refuse 14 deliv / 24 defl / 2 refuse
Role in the program first coherent span baseline best coherent result