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

Libraries

How to use nyxspecter4/kin-sft-lora with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nyxspecter4/kin-sft-lora")
model = AutoModelForCausalLM.from_pretrained("nyxspecter4/kin-sft-lora", 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-sft-lora 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-sft-lora:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nyxspecter4/kin-sft-lora: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-sft-lora:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nyxspecter4/kin-sft-lora: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-sft-lora:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nyxspecter4/kin-sft-lora: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-sft-lora:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nyxspecter4/kin-sft-lora:Q4_K_M

Use Docker

docker model run hf.co/nyxspecter4/kin-sft-lora:Q4_K_M

LM Studio
Jan

vLLM

How to use nyxspecter4/kin-sft-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nyxspecter4/kin-sft-lora"
# 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-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nyxspecter4/kin-sft-lora:Q4_K_M

SGLang

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

Ollama
How to use nyxspecter4/kin-sft-lora with Ollama:
```
ollama run hf.co/nyxspecter4/kin-sft-lora:Q4_K_M
```
Unsloth Desktop

How to use nyxspecter4/kin-sft-lora 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-sft-lora: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-sft-lora:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use nyxspecter4/kin-sft-lora with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nyxspecter4/kin-sft-lora:Q4_K_M

Run and chat with the model

lemonade run user.kin-sft-lora-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nyxspecter4/kin-sft-lora 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-sft-lora: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-sft-lora:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nyxspecter4/kin-sft-lora 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-sft-lora: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-sft-lora: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 — Cybersecurity Verification Translator (3B)

KIN is an authoritative, fine-tuned cybersecurity model built on Qwen2.5-3B-Instruct. It translates ambiguous machine-generated security claims into leveled briefs with replayable evidence checks.

No check, no confidence.

Evaluation & Leaderboard Status

Hugging Face Open LLM Leaderboard: Job PENDING Evaluation
Downloads: 1,000+ and climbing

What KIN Does

KIN processes PR diffs, review threads, agent traces, or vulnerability writeups and produces a structured five-field brief:

Claim — What is alleged
Blast Radius — What breaks if true
Root Cause Mechanics — The underlying generator: CWE, invariant, missing check
Reproducible Verification — PoC, failing test, AST check, or execution command
Falsification Criteria — Confidence level and exactly what evidence would disprove it

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

What Sets KIN Apart

Names Concrete Products: CrowdStrike Falcon, Velociraptor, Duo MFA, KnowBe4 — not generic "implement EDR".
Grounds in Real CVEs: Contextualizes against CVE-2023-4863, CVE-2021-44228, CVE-2024-3094, and more.
Real Incident Context: References lessons from MGM, Colonial Pipeline, NotPetya, Maersk, and Merck breaches.
Direct & Opinionated: Opens immediately with the finding and action item, eliminating boilerplate AI disclaimers.

Quickstart: Transformers

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 lateral movement foothold after an active directory credential dump?"
    }
]

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

Quickstart: Ollama, GGUF & llama.cpp

This repo ships the Q4_K_M GGUF (1.93 GB) next to the full Safetensors weights, so local CPU/GPU inference is one command:

# Direct from Hugging Face with Ollama (pulls Q4_K_M from this repo)
ollama run hf.co/nyxspecter4/kin-sft-lora

Or build from the included Modelfile (LoRA-adapter path):

git clone https://huggingface.co/nyxspecter4/kin-sft-lora
cd kin-sft-lora
ollama create kin -f Modelfile
ollama run kin

With llama.cpp:

llama-cli -hf nyxspecter4/kin-sft-lora -p "How do I detect credential-dump lateral movement?"

Quantization Matrix

Quant Format	Precision	File Size	Recommended Hardware	Status
Q4_K_M	4-bit Medium	1.93 GB	Laptops & M1/M2/M3 Macs (fastest)	✅ In this repo
Q5_K_M	5-bit Medium	~2.5 GB	Standard desktops (balanced)	Planned
Q8_0	8-bit High	~3.8 GB	Workstations / servers (max fidelity)	Planned
FP16	16-bit Full	~6.2 GB	GPU VRAM >= 8 GB (uncompressed)	Planned

Training Details

Parameter	Value
Base Architecture	`Qwen/Qwen2.5-3B-Instruct`
Method	LoRA SFT + DPO Alignment
LoRA Rank / Alpha	8 / 16
Target Modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`
Alignment Data	`nyxspecter4/kin-cyber-dpo-v2` (1,637 DPO pairs)

Limitations

3B-class model: strong at triage, brief-writing, and checklist enforcement — always validate critical findings with a replayable check or scanner before acting.
The five-field brief is a reasoning aid, not a formal audit artifact.
Long-context codebases should be pre-filtered (diff/relevant file chunks) before prompting.

Downloads last month: 1,569

Safetensors

Model size

3B params

Tensor type

F32

F16

Model tree for nyxspecter4/kin-sft-lora

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(283)

this model

nyxspecter4
/

kin-sft-lora

KIN — Cybersecurity Verification Translator (3B)

Evaluation & Leaderboard Status

What KIN Does

What Sets KIN Apart

Quickstart: Transformers

Quickstart: Ollama, GGUF & llama.cpp

Quantization Matrix

Training Details

Limitations

Model tree for nyxspecter4/kin-sft-lora

Space using nyxspecter4/kin-sft-lora 1