Instructions to use AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2 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 AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2: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 AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2: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 AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M

Use Docker

docker model run hf.co/AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M

LM Studio
Jan
Ollama
How to use AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2 with Ollama:
```
ollama run hf.co/AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M
```
Unsloth Desktop

How to use AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2: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": "AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2 with Docker Model Runner:
```
docker model run hf.co/AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M
```

Lemonade

How to use AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M

Run and chat with the model

lemonade run user.kio12-security-agent-qwen25-coder-7b-v2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2: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 AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2: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 "AI4SWEng/kio12-security-agent-qwen25-coder-7b-v2: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"

kio12-security-agent-qwen25-coder-7b-v2 / codecarbon.csv

aalperenpolat

Upload codecarbon.csv with huggingface_hub

fa975a4 verified 2 months ago

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2026-07-01T00:00:23,AI4SWEng-KIO12-v2-qwen25-coder-7b,5a11fb5f-5ac2-41c1-93b0-429f20cd8a8c,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,77.45953558397014,0.0005256471071307,6.786086479448732e-06,10.00771894352059,18.60168449259888,20.0,0.0002494551700336,0.000382602528304,0.0004993735267331,0.0011314312250708,0.0,Türkiye,TUR,istanbul,,,Linux-6.17.0-35-generic-x86_64-with-glibc2.39,3.12.3,3.2.8,24,AMD Ryzen 9 9900X 12-Core Processor,1,1 x NVIDIA RTX PRO 4000 Blackwell,28.9529,41.0329,60.46791076660156,machine,2.837333333333333,0.0,23.15866666666667,14.003846028645832,N,1.0,0.0
2026-07-01T00:23:39,AI4SWEng-KIO12-v2-qwen25-coder-7b,31ca9ee2-7320-4f3c-acf1-647f7158f9cb,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,1213.5094862710102,0.026467170330587,2.1810435460144577e-05,12.010848375218378,138.26335848859384,20.0,0.0039113608058139,0.0465455347363981,0.0065124714438509,0.056969366986063,0.0,Türkiye,TUR,istanbul,,,Linux-6.17.0-35-generic-x86_64-with-glibc2.39,3.12.3,3.2.8,24,AMD Ryzen 9 9900X 12-Core Processor,1,1 x NVIDIA RTX PRO 4000 Blackwell,28.9529,41.0329,60.46791076660156,machine,1.5273327828241123,95.52188274153592,25.85086705202312,15.6342108243758,N,1.0,0.0
2026-07-01T16:42:43,AI4SWEng-KIO12-v2-qwen25-coder-7b,4c21f8d9-3bf4-4244-82e1-01867750370b,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,29182.911302817927,0.6511510419737924,2.231275129534169e-05,12.011773916482406,141.9619243093086,20.0,0.09410137157246887,1.1507907592429818,0.1566805424329739,1.401572673248423,0.0,Türkiye,TUR,istanbul,,,Linux-6.17.0-35-generic-x86_64-with-glibc2.39,3.12.3,3.2.8,24,AMD Ryzen 9 9900X 12-Core Processor,1,1 x NVIDIA RTX PRO 4000 Blackwell,28.9529,41.0329,60.46791076660156,machine,1.3319038804332923,98.57068421774304,20.397775263951736,12.335597561462645,N,1.0,0.0

	timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,water_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,cpu_utilization_percent,gpu_utilization_percent,ram_utilization_percent,ram_used_gb,on_cloud,pue,wue
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	2026-07-01T00:23:39,AI4SWEng-KIO12-v2-qwen25-coder-7b,31ca9ee2-7320-4f3c-acf1-647f7158f9cb,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,1213.5094862710102,0.026467170330587,2.1810435460144577e-05,12.010848375218378,138.26335848859384,20.0,0.0039113608058139,0.0465455347363981,0.0065124714438509,0.056969366986063,0.0,Türkiye,TUR,istanbul,,,Linux-6.17.0-35-generic-x86_64-with-glibc2.39,3.12.3,3.2.8,24,AMD Ryzen 9 9900X 12-Core Processor,1,1 x NVIDIA RTX PRO 4000 Blackwell,28.9529,41.0329,60.46791076660156,machine,1.5273327828241123,95.52188274153592,25.85086705202312,15.6342108243758,N,1.0,0.0
	2026-07-01T16:42:43,AI4SWEng-KIO12-v2-qwen25-coder-7b,4c21f8d9-3bf4-4244-82e1-01867750370b,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,29182.911302817927,0.6511510419737924,2.231275129534169e-05,12.011773916482406,141.9619243093086,20.0,0.09410137157246887,1.1507907592429818,0.1566805424329739,1.401572673248423,0.0,Türkiye,TUR,istanbul,,,Linux-6.17.0-35-generic-x86_64-with-glibc2.39,3.12.3,3.2.8,24,AMD Ryzen 9 9900X 12-Core Processor,1,1 x NVIDIA RTX PRO 4000 Blackwell,28.9529,41.0329,60.46791076660156,machine,1.3319038804332923,98.57068421774304,20.397775263951736,12.335597561462645,N,1.0,0.0