Instructions to use Lokeshgiri/CyberQwen2.5-Coder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Lokeshgiri/CyberQwen2.5-Coder-7B 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 Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lokeshgiri/CyberQwen2.5-Coder-7B: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 Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lokeshgiri/CyberQwen2.5-Coder-7B: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 Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M

Use Docker

docker model run hf.co/Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M

LM Studio
Jan
Ollama
How to use Lokeshgiri/CyberQwen2.5-Coder-7B with Ollama:
```
ollama run hf.co/Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M
```

Unsloth Studio

How to use Lokeshgiri/CyberQwen2.5-Coder-7B 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 Lokeshgiri/CyberQwen2.5-Coder-7B 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 Lokeshgiri/CyberQwen2.5-Coder-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lokeshgiri/CyberQwen2.5-Coder-7B to start chatting

How to use Lokeshgiri/CyberQwen2.5-Coder-7B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lokeshgiri/CyberQwen2.5-Coder-7B: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": "Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Lokeshgiri/CyberQwen2.5-Coder-7B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lokeshgiri/CyberQwen2.5-Coder-7B: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 Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Lokeshgiri/CyberQwen2.5-Coder-7B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lokeshgiri/CyberQwen2.5-Coder-7B: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 "Lokeshgiri/CyberQwen2.5-Coder-7B: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 Lokeshgiri/CyberQwen2.5-Coder-7B with Docker Model Runner:
```
docker model run hf.co/Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M
```

Lemonade

How to use Lokeshgiri/CyberQwen2.5-Coder-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lokeshgiri/CyberQwen2.5-Coder-7B:Q4_K_M

Run and chat with the model

lemonade run user.CyberQwen2.5-Coder-7B-Q4_K_M

List all available models

lemonade list

CyberQwen2.5-Coder-7B

A cybersecurity assistant fine-tuned from Qwen2.5-Coder-7B-Instruct, built to support learning, authorized security testing, and defensive engineering.

Scope

Explains vulnerabilities and attack techniques accurately and in technical depth across web, network, Active Directory, and cloud environments
Provides proof-of-concept code for known, publicly disclosed vulnerabilities and CTF/lab-style scenarios
Covers detection, mitigation, and secure-coding guidance alongside offensive techniques
Uses standard, well-known tooling (Nmap, Burp Suite, Metasploit modules) rather than generating novel malware, C2 implants, or evasion tooling
Flags requests that fall outside standard authorized-testing practice or need a defined scope of engagement

Training Details


Base model	Qwen2.5-Coder-7B-Instruct
Method	LoRA (rank 16, alpha 32)
Sequence length	1024 tokens
Dataset	Custom curated cybersecurity dataset (final_trainV2.jsonl)

Run with Ollama

huggingface-cli download Lokeshgiri/CyberQwen2.5-Coder-7B gguf/model.gguf --local-dir ./CyberQwen2.5-Coder-7B
ollama create cyberqwen -f Modelfile
ollama run cyberqwen

Disclaimer

For educational purposes and authorized security testing only.

Downloads last month: 136

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for Lokeshgiri/CyberQwen2.5-Coder-7B

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Finetuned

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Lokeshgiri
/

CyberQwen2.5-Coder-7B

CyberQwen2.5-Coder-7B

Scope

Training Details

Run with Ollama

Disclaimer

Model tree for Lokeshgiri/CyberQwen2.5-Coder-7B

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