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)

# pip install -U transformers accelerate
# 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=256)
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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

KIN 3B, Research Edition

KIN is Kinetigor's small security model for verification-style briefs. Give it a security claim (a PR diff, a review comment, an agent trace, a bug-bounty report) and it answers in five fields: the claim, the blast radius, why it might be true, how to check it yourself, and how confident to be. When there is no replayable check, it is built to say so instead of sounding sure.

This is the free, non-commercial research edition: a 3B fine-tune of Qwen2.5-3B-Instruct with merged weights. A quantized build for laptops is in kin-cybersecurity-3b-gguf.

The brief

Claim: one sentence on what is alleged.
Blast radius: what breaks if it is true (data, money, identity, availability).
Why: the mechanism, such as a CWE, a broken invariant or a missing check.
How to check it: a command, failing test or proof of concept you can replay.
Confidence: how sure, and what evidence would prove it wrong.

No replayable check, no high confidence.

Quickstart

Transformers

from transformers import pipeline

SYSTEM = (
    "You are KIN, a verification translator. Turn the security claim into a five-field brief: "
    "claim, blast radius, why, how to check it yourself, and confidence with what would falsify it. "
    "If there is no replayable check, do not sound sure."
)

pipe = pipeline("text-generation", model="nyxspecter4/kin-cybersecurity-3b", device_map="auto")
messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "PR diff: the webhook fetcher's host allowlist check was replaced with `if True:`."},
]
out = pipe(messages, max_new_tokens=512, temperature=0.3, do_sample=True, return_full_text=False)
print(out[0]["generated_text"])

Ollama / llama.cpp

ollama run hf.co/nyxspecter4/kin-cybersecurity-3b-gguf:Q4_K_M

See the GGUF repo for llama.cpp and other quantizations. Temperature 0.3 is recommended.

Intended uses

Turning PR diffs, review comments, agent traces and bounty reports into a short, checkable brief.
Triage support: separating claims that come with a replayable check from claims that do not.
Teaching and research on calibrated, evidence-first security writing.

Out of scope: solving CTF challenges, decoding or cracking data, producing flags, authoritative vulnerability verdicts without human review, and any commercial use (see License).

Evaluation

KIN 3B was scored on a frozen, held-out set of 55 CTF-skill items with known answers. Grading is deterministic (exact match, regex, or refusal pattern), with no LLM judge, and a dedupe check found 0 overlaps with any training file.

Result: 23/55 (41.8%).

Category	Score
Decode (ROT13, Caesar, base64, hex, binary, Atbash, JWT, PNG comment)	0/17
Vulnerability class (CWE identification)	7/16
Refusal / scope discipline	4/6
Flag format	4/4
Hash identification	2/4
Forensics (file magic bytes)	3/4
Next command	3/4
Total	23/55

Conditions: Q4_K_M GGUF served by the kin-cybersec Space, temperature 0.3, 384-token cap, one full run, November 2026. A partial second run differed by one item on the 29 items it covered.

An earlier internal comparison (October 12, 2026) on a separate 6-brief verification harness scored the base Qwen2.5-3B-Instruct at 47/60 and KIN at 40/60. The base model was ahead on that harness; we publish it as measured.

What this means: KIN is weak at decoding and can invent flags that look right. It is not a flag solver. Use it to structure and pressure-test claims, and run the check it proposes before you trust any answer.

Limitations

It can state wrong CWE classes, hash types and file types with confidence. Treat every answer as a hypothesis until the proposed check has been run.
It cannot reliably decode or transform data, and it may produce plausible but invented flags, CVE numbers or tool names.
The five-field structure is learned behavior, not enforced. Quality depends on how much signal the input contains.
English only. Short context use (the demo Space runs a 2,048-token window).

Training


Base model	Qwen/Qwen2.5-3B-Instruct
Method	LoRA supervised fine-tuning (rank 8, alpha 16, dropout 0.1; q/k/v/o projections), merged into full weights
Data	The first-generation KIN corpus of security briefs, contrasting a structured brief with a replayable check against a vague filing
Weights	2 safetensors shards (about 6.2 GB); the LoRA adapter and a Q4_K_M GGUF are also in this repo

The curated successor corpus, kin-cyber-dpo-v2, is published separately. These weights predate it.

The KIN family

Repo	What it is
kin-cybersecurity-3b	KIN 3B, Research Edition: merged weights (this repo)
kin-cybersecurity-3b-gguf	Same model as GGUF for Ollama and llama.cpp
kin-cyber-dpo-v2	Brief-style DPO and SFT dataset (MIT)
kin-cybersec Space	Try KIN in the browser

License

Qwen Research License (non-commercial). KIN 3B is a derivative of Qwen2.5-3B-Instruct and inherits its license: use, modification and redistribution are allowed for non-commercial research and evaluation only. Commercial use needs a separate license from the base model's licensor. The full text is in the LICENSE file in this repo.