Instructions to use audnai/penclaw-GLM-5.3-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use audnai/penclaw-GLM-5.3-abliterated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="audnai/penclaw-GLM-5.3-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("audnai/penclaw-GLM-5.3-abliterated")
model = AutoModelForCausalLM.from_pretrained("audnai/penclaw-GLM-5.3-abliterated", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use audnai/penclaw-GLM-5.3-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "audnai/penclaw-GLM-5.3-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "audnai/penclaw-GLM-5.3-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/audnai/penclaw-GLM-5.3-abliterated

SGLang

How to use audnai/penclaw-GLM-5.3-abliterated with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "audnai/penclaw-GLM-5.3-abliterated" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "audnai/penclaw-GLM-5.3-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "audnai/penclaw-GLM-5.3-abliterated" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "audnai/penclaw-GLM-5.3-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use audnai/penclaw-GLM-5.3-abliterated with Docker Model Runner:
```
docker model run hf.co/audnai/penclaw-GLM-5.3-abliterated
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

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

Downloads last month: -

Safetensors

Model size

753B params

Tensor type

BF16

F32