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

llama.cpp

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

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

Pi new

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-server -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-server -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

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

Expert Security Code Reviewer - A fine-tuned Qwen2.5-Coder-7B model specialized for security vulnerability detection and false positive reduction in SAST (Static Application Security Testing) pipelines.

Model Details

Property	Value
Base Model	Qwen2.5-Coder-7B-Instruct
Fine-tuning	QLoRA (4-bit quantization)
Quantization	Q4_K_M (GGUF)
Parameters	7.6B
Context Length	32,768 tokens
File Size	~4.7 GB
Format	GGUF (Ollama-compatible)

Performance

Metric	Score
Overall Accuracy	98.1%
F1 Score	0.99
False Positive Rate	0.0%
JSON Compliance	100%
Avg Response Time	2.8s

Capabilities

Identifies true security vulnerabilities across 20+ vulnerability categories
Eliminates false positives from SAST tools (SQL injection, XSS, command injection, etc.)
Provides structured JSON output with verdict, confidence, CWE IDs, severity, and remediation
Understands framework-specific safe patterns (React, Django, Express, Rails, etc.)
Supports taint analysis reasoning (source-to-sink tracking)

Vulnerability Categories

CRITICAL: SQL Injection (CWE-89), Command Injection (CWE-78), Deserialization (CWE-502), Hardcoded Secrets (CWE-798), Code Injection (CWE-94)

HIGH: XSS (CWE-79), Path Traversal (CWE-22), SSRF (CWE-918), Timing Attacks (CWE-208), Buffer Overflow (CWE-120)

MEDIUM: Weak Crypto (CWE-327), Insecure Random (CWE-330), Information Disclosure (CWE-200), Missing Auth (CWE-306)

Usage with Ollama

# Pull the model
ollama pull kon-security/kon-security-v5

# Or create from GGUF
ollama create kon-security-v5 -f Modelfile

# Run
ollama run kon-security-v5

Example Prompt

<|im_start|>system
You are an expert security code reviewer...
<|im_end|>
<|im_start|>user
Analyze this code for SQL injection:
query = f"SELECT * FROM users WHERE id = {user_id}"
<|im_end|>
<|im_start|>assistant

Example Response

{
  "verdict": "TRUE_POSITIVE",
  "is_vulnerable": true,
  "confidence": 0.97,
  "cwe_ids": ["CWE-89"],
  "severity": "CRITICAL",
  "reasoning": "f-string interpolates user_id directly into SQL query without parameterization",
  "remediation": "cursor.execute('SELECT * FROM users WHERE id = ?', (user_id,))"
}

System Prompt

The model is fine-tuned with the following system prompt baked in:

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
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)
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) = SAFE
11. Test files testing security scanners = SAFE
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

Integration with Kon Security Scanner

This model is the default LLM for the Kon Security Scanner, providing:

SAST finding validation and FP reduction
CWE ID mapping
Severity assessment
Remediation suggestions

from kon.core.ollama_analyzer import OllamaAnalyzer

analyzer = OllamaAnalyzer(model="kon-security-v5:latest")
result = analyzer.analyze_finding_enhanced(
    code_snippet="query = f'SELECT * FROM users WHERE id = {user_id}'",
    vulnerability_type="SQL Injection",
    file_path="app/db.py",
    line_number=42
)
print(result.verdict)  # TRUE_POSITIVE

Training Details

Method: QLoRA (4-bit quantization-aware fine-tuning)
Base: Qwen2.5-Coder-7B-Instruct
Dataset: Curated security code review examples covering 20+ CWE categories
Hardware: NVIDIA GPU with CUDA support
Quantization: Q4_K_M via llama.cpp

License

Apache 2.0 (same as base model)

Downloads last month: 11

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Vasanth155/kon-security-v5

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

(188)

this model

Evaluation results

Accuracy
self-reported

98.100
F1 Score
self-reported

0.990
False Positive Rate
self-reported

0.000
JSON Compliance
self-reported

100.000