Instructions to use SehanKim/qwen2.5-coder-security-v4-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SehanKim/qwen2.5-coder-security-v4-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SehanKim/qwen2.5-coder-security-v4-gguf",
	filename="qwen-security-v4.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SehanKim/qwen2.5-coder-security-v4-gguf 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 SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SehanKim/qwen2.5-coder-security-v4-gguf: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 SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SehanKim/qwen2.5-coder-security-v4-gguf: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 SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M

Use Docker

docker model run hf.co/SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use SehanKim/qwen2.5-coder-security-v4-gguf with Ollama:
```
ollama run hf.co/SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M
```

Unsloth Studio

How to use SehanKim/qwen2.5-coder-security-v4-gguf 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 SehanKim/qwen2.5-coder-security-v4-gguf 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 SehanKim/qwen2.5-coder-security-v4-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SehanKim/qwen2.5-coder-security-v4-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use SehanKim/qwen2.5-coder-security-v4-gguf with Docker Model Runner:
```
docker model run hf.co/SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M
```

Lemonade

How to use SehanKim/qwen2.5-coder-security-v4-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-coder-security-v4-gguf-Q4_K_M

List all available models

lemonade list

ScanOps QLoRA v4 — Security Vulnerability Detection

QLoRA fine-tuned Qwen2.5-Coder-1.5B-Instruct for CVE/CWE vulnerability analysis.

What's New in v4

Full retrain from scratch — no catastrophic forgetting
1,000 training samples (367 original + 633 new)
CWE Top-25 full coverage (2023)
CVSS base score output — new field in response
35 CWE types across Python, Node.js, Java, React, Go, Ruby, PHP, C, GitHub Actions

Model Details

Base: Qwen/Qwen2.5-Coder-1.5B-Instruct
Fine-tuning: QLoRA (r=32, alpha=64, 3 epochs, cosine schedule)
Training data: 1,000 samples, 35 CWE types, 9 languages
Quantization: Q4_K_M (GGUF)
Target detection rate: 98%+

Response Format

VULNERABILITY: CWE-89 SQL Injection
SEVERITY: CRITICAL
CVSS: 9.8
ATTACK: 공격자가 username 파라미터에 ' OR 1=1-- 를 주입해 인증을 우회합니다.
FIX:
cursor.execute("SELECT * FROM users WHERE id=%s", (user_id,))

Usage with Ollama

ollama pull hf.co/SehanKim/qwen2.5-coder-security-v4-gguf:Q4_K_M

Downloads last month: 58

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

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