⌦abliteratedmodels.org

CyberStag-Security-26B-A4B-V1-Uncensored-Heretic

CyberStag-Security-26B-A4B-V1-Uncensored-Heretic is a Heretic-ablated variant of OS-Software/CyberStag-Security-26B-A4B-V1-Uncensored-Heretic, published on Hugging Face by OS-Software. It is 26B total parameters (mixture-of-experts), 48GB VRAM class class and apache-2.0 licence. It is distributed across 5 repositories in Transformers, GGUF and GGUF (imatrix) formats, totalling 9.1K downloads.

Heretic26B MoEOther25B – 50B↓ 9,095♥ 5

Specification

Model name
CyberStag-Security-26B-A4B-V1-Uncensored-Heretic
Publisher
OS-Software
Parameters
26B (mixture-of-experts)
VRAM required
~16.6 GB at Q4_K_M, 4k context (estimated) — runs on 24 GB cards
Hardware class
25B – 50B — 48GB VRAM class
Licence
apache-2.0
Task
Text generation
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
Transformers, GGUF, GGUF (imatrix)
First indexed
24 Sept 2026
Last updated
25 Sept 2026

Ablation technique

How CyberStag-Security-26B-A4B-V1-Uncensored-Heretic was modified, and what that implies.

Heretic

Automated directional ablation via the Heretic toolchain, which searches for the refusal direction and applies it with a KL-divergence budget so general capability is preserved.

Reported scope: Not stated by the publisher. Publishers frequently omit this, so verify behaviour empirically rather than assuming full coverage.

From the publisher’s model card

“This model has undergone substantial reduction of its safety alignment. As a result, it is more likely than standard models to generate harmful, inaccurate, biased, offensive, or otherwise inappropriate content.”

Running it

Commands are templates — confirm the exact repository and quant file before use.

llama.cpp / GGUF
hf download OS-Software/CyberStag-Security-26B-A4B-V1-Uncensored-Heretic-GGUF --local-dir ./cyberstag-security-26b-a4b-v1-uncensored-heretic
llama-cli -m ./cyberstag-security-26b-a4b-v1-uncensored-heretic/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "OS-Software/CyberStag-Security-26B-A4B-V1-Uncensored-Heretic"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve OS-Software/CyberStag-Security-26B-A4B-V1-Uncensored-Heretic --trust-remote-code

Downloads and variants (4)

Every published repository of CyberStag-Security-26B-A4B-V1-Uncensored-Heretic, including quantised re-releases by other authors.

Security-team assessment

Derived from this model’s metadata and the publisher’s claims — not from benchmarks run by this site.

  • CyberStag-Security-26B-A4B-V1-Uncensored-Heretic will attempt offensive-security prompts that a hosted commercial model declines, which is what makes it usable for red-team corpus generation and for measuring what an unaligned model of this class produces.
  • Capability is inherited from OS-Software/CyberStag-Security-26B-A4B-V1-Uncensored-Heretic, not added by ablation — at 26B parameters, expect roughly the base model’s competence, minus some instruction-following fidelity.
  • The publisher does not state which layers were ablated, so assume nothing about refusal consistency — probe it directly.
  • Declared licence is apache-2.0, but the terms that bind you are OS-Software/CyberStag-Security-26B-A4B-V1-Uncensored-Heretic’s — a re-release cannot grant more than its parent.

Handling: run it isolated, put moderation in front of it if anyone outside your team can reach it, and verify the weights before loading — these are third-party artifacts. Full handling and licence guidance.

Metadata for CyberStag-Security-26B-A4B-V1-Uncensored-Heretic is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with OS-Software, and does not independently verify publisher claims. See responsible use.