Instructions to use Vasanth155/kon-security-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vasanth155/kon-security-v5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Vasanth155/kon-security-v5",
	filename="kon-security-v5-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Vasanth155/kon-security-v5 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 Vasanth155/kon-security-v5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Vasanth155/kon-security-v5:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Vasanth155/kon-security-v5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Vasanth155/kon-security-v5: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 Vasanth155/kon-security-v5:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Vasanth155/kon-security-v5: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 Vasanth155/kon-security-v5:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Vasanth155/kon-security-v5:Q4_K_M

Use Docker

docker model run hf.co/Vasanth155/kon-security-v5:Q4_K_M

LM Studio
Jan

vLLM

How to use Vasanth155/kon-security-v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vasanth155/kon-security-v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vasanth155/kon-security-v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vasanth155/kon-security-v5:Q4_K_M

Ollama
How to use Vasanth155/kon-security-v5 with Ollama:
```
ollama run hf.co/Vasanth155/kon-security-v5:Q4_K_M
```

Unsloth Studio

How to use Vasanth155/kon-security-v5 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Vasanth155/kon-security-v5 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Vasanth155/kon-security-v5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Vasanth155/kon-security-v5 to start chatting

How to use Vasanth155/kon-security-v5 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Vasanth155/kon-security-v5:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Vasanth155/kon-security-v5:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Vasanth155/kon-security-v5 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Vasanth155/kon-security-v5: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 Vasanth155/kon-security-v5:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Vasanth155/kon-security-v5 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Vasanth155/kon-security-v5: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 "Vasanth155/kon-security-v5: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"

Docker Model Runner
How to use Vasanth155/kon-security-v5 with Docker Model Runner:
```
docker model run hf.co/Vasanth155/kon-security-v5:Q4_K_M
```

Lemonade

How to use Vasanth155/kon-security-v5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Vasanth155/kon-security-v5:Q4_K_M

Run and chat with the model

lemonade run user.kon-security-v5-Q4_K_M

List all available models

lemonade list

kon-security-v5 / Modelfile

Vasanth155

Upload folder using huggingface_hub

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	FROM kon-security-v5-Q4_K_M.gguf
	TEMPLATE "{{- if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{ end }}<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	<\|im_start\|>assistant
	"
	SYSTEM """You are an expert security code reviewer specializing in identifying true vulnerabilities and eliminating false positives. You analyze code with deep understanding of security patterns across all languages and frameworks.

	CRITICAL RULES:
	1. Parameterized queries (?, $1, %s, :param) = SAFE from SQL injection
	2. textContent, createTextNode = SAFE from XSS (only innerHTML/outerHTML/document.write are dangerous)
	3. React JSX {variable} = SAFE from XSS (React auto-escapes)
	4. subprocess.run([list, args]) without shell=True = SAFE from command injection
	5. json.loads/JSON.parse = SAFE (cannot execute code, unlike pickle/eval/unserialize)
	6. secure_filename() from werkzeug = SAFE from path traversal
	7. bcrypt/argon2/scrypt for password hashing = SAFE
	8. HMAC.compare_digest/timingSafeEqual = SAFE from timing attacks
	9. DOMPurify.sanitize() = SAFE from XSS
	10. MD5/SHA1 for non-security purposes (checksums, cache keys, gravatar) = SAFE
	11. Test files testing security scanners = SAFE (code is string data, not executed)
	12. Environment variables for secrets = SAFE (not hardcoded)
	13. ORM methods (Django .filter(), Rails .where(hash), SQLAlchemy) = SAFE from SQLi
	14. Content-Security-Policy, helmet(), CORS allowlists = SAFE

	Respond ONLY with a JSON object:
	{
	"verdict": "TRUE_POSITIVE" or "FALSE_POSITIVE",
	"is_vulnerable": true/false,
	"confidence": 0.0-1.0,
	"cwe_ids": ["CWE-XXX"],
	"severity": "CRITICAL/HIGH/MEDIUM/LOW/INFO",
	"reasoning": "brief explanation",
	"remediation": "fix suggestion or N/A"
	}"""
	PARAMETER top_k 40
	PARAMETER top_p 0.9
	PARAMETER num_predict 4096
	PARAMETER repeat_penalty 1.1
	PARAMETER stop <\|im_end\|>
	PARAMETER stop <\|endoftext\|>
	PARAMETER temperature 0.1