nyxspecter4

kin-sft-lora

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README

License: apache-2.0

What KIN Does

KIN takes a PR diff, review thread, agent trace, or bounty writeup and produces a five-field brief:

Claim — what is alleged
Blast radius — what breaks if true
Why the model thinks so — the generator: CWE, invariant, missing check
How to see it yourself — PoC, failing test, command
Confidence and what would falsify it

The gate rule: no "critical" without a replayable check. That refusal IS the product.

What Makes KIN Different

Names real tools: CrowdStrike Falcon, Velociraptor, Duo MFA, KnowBe4 — not generic "use EDR"
References real CVEs: CVE-2023-4863, CVE-2021-44228, CVE-2024-3094 — with actual context
Knows real incidents: MGM, Colonial Pipeline, NotPetya, Maersk ( $300 M), M er c k ($ 670M)
Leads with the take: Opens with the answer, not the disclaimer

Quick Start

python
from transformers import pipeline

messages = [
    {"role": "system", "content": "You are KIN — a sharp cybersecurity AI partner. Direct, opinionated, specific. Name tools, CVEs, companies. Sound like a senior engineer at a bar, not a textbook. Lead with your boldest take. End with a specific action. Max 2-3 paragraphs. Open with your take, not your title. No 'As a cybersecurity AI expert.' Name products: 'CrowdStrike Falcon' not 'use EDR'. 'Duo push MFA' not 'implement MFA'."},
    {"role": "user", "content": "How do I detect a foothold after a phishing attack?"}
]

pipe = pipeline("text-generation", model="nyxspecter4/kin-sft-lora", device="cuda")
output = pipe(messages, max_new_tokens=512, return_full_text=False)
print(output[0]["generated_text"])

Critical: System Prompt

KIN was trained with a specific system prompt. Using a different prompt will degrade quality significantly.

You are KIN — a sharp cybersecurity AI partner. Direct, opinionated, specific. Name tools, CVEs, companies. Sound like a senior engineer at a bar, not a textbook. Lead with your boldest take. End with a specific action. Max 2-3 paragraphs. Open with your take, not your title. No "As a cybersecurity AI expert." Name products: "CrowdStrike Falcon" not "use EDR". "Duo push MFA" not "implement MFA".

Training Details

Table with columns: Parameter, Value
Parameter	Value
Base model	Qwen2.5-3B-Instruct
Method	LoRA SFT
LoRA rank	8
LoRA alpha	16
Target modules	q_proj, k_proj, v_proj, o_proj
Training data	nyxspecter4/kin-cyber-dpo-v2 — 1,635 DPO pairs
Framework	TRL 0.14.0, Transformers 4.48.0, Unsloth
PyTorch	2.6.0+cu124

Sibling Artifacts

Table with columns: Type, ID, What
Type	ID	What
GGUF quantization	`nyxspecter4/kin-sft-lora-gguf`	Q4_K_M + Q8_0 for Ollama/llama.cpp
Dataset (DPO)	`nyxspecter4/kin-cyber-dpo-v2`	1,635 cleaned DPO pairs
Live demo	`nyxspecter4/kin-cybersec`

Competitive Position

Table with columns: Model, Base, Downloads, Likes, Inference, Merged Weights, GGUF
Model	Base	Downloads	Likes	Inference	Merged Weights	GGUF
KIN (this)	Qwen2.5-3B	2K	0	No	Yes	Yes
Mohamedabul/Qwen2.5-3B-CyberSecurity	Qwen2.5-3B	5.4K	3	Yes (featherless-ai)	No (adapter-only)

KIN advantages: merged weights (no adapter needed), GGUF for local Ollama, verification translator framing, active development. Gap: no inference provider, 0 likes.

Limitations

3B model: Less factual knowledge than larger models. Always verify named CVEs and tool recommendations.
Opinionated voice: KIN gives bold takes. This is a feature, not a bug — but verify before acting.
Not a substitute for professional security advice or formal incident response procedures.

License

Apache 2.0

Available on FriendliAI

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Container

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Model Details

Model Provider

nyxspecter4

Model Tree

Base

Qwen/Qwen2.5-3B-Instruct

Quantized

this model

Input Modalities

Text

Output Modalities