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Index Sep 17, 2026
Models

audnai

penclaw-GLM-5.3-abliterated

Unverified753BTextSafetensors
Status
Unverified
Trending
#38
Downloads, 30 days
477

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.

At a glance

Task
Text generation
Input
text
Output
text
Parameters
753B
Architecture
GLM MoE Dsa
Precision
BF16 100%
Format
Safetensors
Library
transformers
License
other
Released
Sep 2026
Updated
Sep 2026
Likes
137
Downloads, all time
264

Run it

Loads with Transformers AutoModelForCausalLM and AutoTokenizer, pinned to the indexed revision.

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("audnai/penclaw-GLM-5.3-abliterated", revision="027ae98a6f9dc37093b26759d7b8dbbfed9a97f8")
processor = AutoTokenizer.from_pretrained("audnai/penclaw-GLM-5.3-abliterated", revision="027ae98a6f9dc37093b26759d7b8dbbfed9a97f8")
Read the full model card

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

Derived on Sep 17, 2026 from Hugging Face at revision 027ae98a, README.md . This model is not yet addressed, so its card comes from the main branch.