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NiroshanDb23/Lily-Cybersecurity-7B-Uncensored-GGUF

Name: NiroshanDb23/Lily-Cybersecurity-7B-Uncensored-GGUF
Author: NiroshanDb23

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     "https://abliteration.org/api/v1/models/NiroshanDb23%2FLily-Cybersecurity-7B-Uncensored-GGUF"

Response includes

classification m8
files 3
hub_downloads_all_time 4,176
author_summary 1 models
readme_text full

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Abliteration classifier · v1.0.0

Primary method

Repackaging (quantization)

Applied on top of direct removal inherited from the base model.

Confidence

MEDIUM

Inherited from base model

M1 Direct removal →

Why this label 3 signals

Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.

'abliterated' in name/tags
is_gguf=1
assume M1 (base ablation) + M8 (GGUF quant) - default when producer unknown

Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

Downloads · lifetime

245 last 30d - cooling

Likes

Model age

8mo ago

created 2026-01-20

Downloads over time

Jan 21 → Oct 11 · 79 snapshots · spans 263 days

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

segolilylabs/Lily-Cybersecurity-7B-v0.2 this lineage

NiroshanDb23/Lily-Cybersecurity-7B-Uncensored-GGUF↓ 245

Metadata

License

apache-2.0

Quantizations

Q4_K

Files by quantization

Q4_K 1 file 13.5 GB

Lily-Uncensored-Q4_K_M.gguf

13.5 GB

********

download

Auxiliary files 2 files 3.15 KB

README.md	1.60 KB	9f9a4e61	download
.gitattributes	1.55 KB	16ee9ec8	download

README current version from Hugging Face

license: apache-2.0
base_model:

segolilylabs/Lily-Cybersecurity-7B-v0.2
tags:
abliterated
ctf

quantized_by: NiroshanDH

Lily-Cybersecurity-7B-Uncensored-GGUF

Description

This is a GGUF version of the Lily-Cybersecurity-7B-v0.2 model, which has been abliterated (uncensored) to remove refusal mechanisms.

It is designed specifically for CTF (Capture The Flag) competitions, penetration testing, and security research. Unlike the base model, this version will not refuse to generate offensive security code (e.g., exploits, malware simulation) when asked for educational or testing purposes.

⚠️ Warning: This model has no safety guardrails. It will generate destructive code if asked. Use responsibly and only in isolated, authorized environments (VM/Sandbox).

Technical Details

Base Model: segolilylabs/Lily-Cybersecurity-7B-v0.2
Technique: Refusal Vector Abliteration (Vector Arithmetic applied to model weights).
Quantization: 4-bit (Q4_K_M) - Optimized for speed and low memory usage.
Architecture: Mistral-7B-v0.1

How to Run (LM Studio / Llama.cpp)

Recommended Settings

Context Window: 4096 (or higher if hardware permits)
System Prompt: ```text
You are a helpful assistant specialized in cybersecurity and programming.

Disclaimer

This model is provided for educational and research purposes only. The creator assumes no liability for malicious use of this technology. By using this model, you agree to use it in compliance with all applicable laws and regulations.

Discussions 1 thread

2026-08-13File labelled Q4_K_M appears to contain F16 weightsopen2 💬#1
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