Instructions to use nyxspecter4/kin-cybersecurity-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nyxspecter4/kin-cybersecurity-3b with Transformers:

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

pipe = pipeline("text-generation", model="nyxspecter4/kin-cybersecurity-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nyxspecter4/kin-cybersecurity-3b")
model = AutoModelForCausalLM.from_pretrained("nyxspecter4/kin-cybersecurity-3b", 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

llama.cpp

How to use nyxspecter4/kin-cybersecurity-3b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M

Use Docker

docker model run hf.co/nyxspecter4/kin-cybersecurity-3b:Q4_K_M

LM Studio
Jan

vLLM

How to use nyxspecter4/kin-cybersecurity-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nyxspecter4/kin-cybersecurity-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nyxspecter4/kin-cybersecurity-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nyxspecter4/kin-cybersecurity-3b:Q4_K_M

SGLang

How to use nyxspecter4/kin-cybersecurity-3b 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 "nyxspecter4/kin-cybersecurity-3b" \
    --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": "nyxspecter4/kin-cybersecurity-3b",
		"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 "nyxspecter4/kin-cybersecurity-3b" \
        --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": "nyxspecter4/kin-cybersecurity-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nyxspecter4/kin-cybersecurity-3b with Ollama:
```
ollama run hf.co/nyxspecter4/kin-cybersecurity-3b:Q4_K_M
```
Unsloth Desktop

How to use nyxspecter4/kin-cybersecurity-3b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nyxspecter4/kin-cybersecurity-3b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nyxspecter4/kin-cybersecurity-3b with Docker Model Runner:
```
docker model run hf.co/nyxspecter4/kin-cybersecurity-3b:Q4_K_M
```

Lemonade

How to use nyxspecter4/kin-cybersecurity-3b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nyxspecter4/kin-cybersecurity-3b:Q4_K_M

Run and chat with the model

lemonade run user.kin-cybersecurity-3b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nyxspecter4/kin-cybersecurity-3b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default nyxspecter4/kin-cybersecurity-3b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nyxspecter4/kin-cybersecurity-3b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nyxspecter4/kin-cybersecurity-3b:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "nyxspecter4/kin-cybersecurity-3b:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

KIN — Verification Translator (3B Canonical)

KIN translates machine-generated security claims into leveled briefs with replayable evidence checks. Not a chatbot. Not an encyclopedia. A bridge between agent output and human decisions.

No check, no confidence. If the claim has no replayable verification, KIN refuses to sound sure. That refusal is the product.

The Problem

Maintainers are drowning in AI-shaped reports that look expert and are not. Copilot files critical PRs. Huntr contestants submit AI-generated writeups. Agent traces produce 40-file refactors with no comment a person can hold in working memory.

The scarce skill is no longer "can a human write the exploit narrative." The scarce thing is can a human know what the machine just claimed, and whether to trust it.

The Five-Field Brief

Every KIN translation bottoms out in five fields:

Claim — one sentence: what is alleged
Blast radius — what breaks if true: data, money, identity, model weights
Why the model thinks so — the short generator: CWE, invariant, missing check
How to see it yourself — PoC, failing test, command, screenshot of the trace
Confidence and what would falsify it — what evidence would disprove the claim

Rendered at three altitudes: TL;DR (maintainer), Mechanic (mid engineer), First p rinciples (beginner). Same object, different compression ratio.

Canonical Status

This is the canonical KIN model (3B, merged weights). GGUF version at kin-sft-lora-gguf.

Quick Start

from transformers import pipeline

messages = [
    {"role": "system", "content": "You are KIN — a verification translator. Translate the security claim into a five-field brief: claim, blast radius, why the model thinks so, how to see it yourself, confidence and what would falsify it. If field 4 is missing, refuse to sound sure. Render at TL;DR, mechanic, and first-principles levels."},
    {"role": "user", "content": "<paste PR diff, review comment, or agent trace here>"}
]

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"])

Ollama (one command)

ollama pull nyxspecter4/kin-sft-lora-gguf:Q4_K_M
ollama run nyxspecter4/kin-sft-lora-gguf:Q4_K_M

Critical: System Prompt

KIN was trained with a specific verification translator persona. Using a different prompt will degrade quality significantly.

You are KIN — a verification translator. Translate the security claim into a five-field brief: claim, blast radius, why the model thinks so, how to see it yourself, confidence and what would falsify it. If field 4 is missing, refuse to sound sure. Render at TL;DR, mechanic, and first-principles levels.

Training Details (v1 — current shipped weights)

Parameter	Value
Base model	Qwen2.5-3B-Instruct
Method	LoRA SFT + DPO
LoRA rank	8
LoRA alpha	16
Target modules	q_proj, k_proj, v_proj, o_proj
Training data	1,635 DPO pairs (verified audit vs vague filing)
Framework	TRL 0.14.0, Transformers 4.48.0
Merged weights	Yes (6.2GB safetensors)
Temperature	0.3 recommended

v2 Retrain (PENDING —

verification translator expansion)

Parameter	KIN v1 (current)	KIN v2 (target)
LoRA rank	8	16
LoRA alpha	16	32
Target modules	4 (attention)	7 (all-linear)
Training data	1,635 pairs	~2,135 pairs (+500 PR review translation)
Pair shape	audit vs vague	brief+check vs jargon

The DPO Contrast

The training data encodes a single preference: chosen = structured audit with root cause, fix, and regression test. Rejected = vague filing with guesses and no verification. The v2 expansion adds PR review and bounty report translation pairs with the same contrast.

Limitations

v1 was trained on monk-plugin bug bounty scenarios, not general PR reviews
The five-field brief structure is emergent from training, not enforced by architecture
Field 4 (replayable check) quality depends on the input containing enough signal
The hash seal (diff + brief + test command) is planned for v2, not yet implemented

License

Apache 2.0

Downloads last month: 1,757

Safetensors

Model size

3B params

Tensor type

F32

F16

Model tree for nyxspecter4/kin-cybersecurity-3b

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(283)

this model

Space using nyxspecter4/kin-cybersecurity-3b 1

Evaluation results

gap-delta-eval
self-reported

pending