h4rithd/coder:14b

Coder is a local coding and security engineering assistant built for development, debugging, automation, secure coding, and authorized security research workflows. This model is designed for an OpenClaw + Ollama setup running on a Mac mini M4 with 24GB unified memory. It is intended to be the main technical implementation model in a local multi-model workflow.

Overview

Coder is based on qwen2.5-coder:14b and is configured for practical software development and security-focused engineering tasks. The goal of this model is to provide accurate code, useful commands, clear debugging steps, and practical technical explanations. It is best used when the task involves implementation, scripting, reviewing code, fixing errors, or working through security engineering problems in an authorized environment. Coder is not meant to be a general chat model. It is designed to be a technical assistant for people who build, test, debug, and secure software.

Coder works well for:

• Software development
• Code generation
• Code review
• Debugging
• Bash scripting
• Python scripting
• Node.js development
• React and Vite projects
• API development
• Secure coding
• Automation
• DevSecOps workflows
• Log parsing
• Configuration review
• Authorized security testing
• CTF-style learning environments
• Local lab research

Target Environment

The model is heavier than Buddy but more suitable for coding-heavy work. It is intended to be used when implementation quality matters. This model was prepared for local usage with:

OpenClaw + Ollama
Mac mini M4
24GB unified memory
Apple Silicon local AI workflow

Intended Role in OpenClaw

Coder is designed to act as the main coding and security engineering model in an OpenClaw configuration. Use Coder when the task involves writing, reviewing, fixing, or explaining code. Recommended role:

Coding assistant
Debugging assistant
Security engineering assistant
Automation helper
DevSecOps workflow model

Configuration Overview

Coder is configured with a larger context window, making it suitable for longer code snippets, multi-step debugging, larger prompts, and technical workflows that need more context. The response behavior is tuned for coding accuracy. The lower temperature helps reduce randomness, which is important when generating code, commands, configuration files, and technical explanations. The sampling settings still allow enough flexibility for problem solving, but the model is encouraged to stay focused and consistent. The output length is set high enough for complete code snippets, implementation steps, debugging walkthroughs, and technical explanations. Coder is instructed to behave like an expert developer and security engineer. The focus is on precise code, practical commands, secure implementation, and technical accuracy.

Usage Style

Coder should be used when the task involves real implementation work. It is especially useful when working with OpenClaw as a local development assistant, because it can help with coding tasks, project structure, shell commands, configuration, debugging, and security-focused review. This model is heavier than Buddy, so it should not be used for every small question. For quick tasks, use Buddy. For planning and writing, use Thinker.

ollama run h4rithd/coder:14b

Example Prompts

Create a React + Vite blog layout with Markdown support.

Review this Node.js API route for security issues and suggest safer code.

Write a Python script to parse JSON logs and extract suspicious IP addresses.

Explain this bash script and improve it safely.

Debug this error and give me the exact commands to verify the fix.

Create a secure Express.js API endpoint with input validation.

Security Use Notice

Coder is intended for software development, defensive security, authorized testing, lab environments, DevSecOps workflows, and educational security research. Do not use this model for unauthorized access, exploitation of third-party systems, malware deployment, credential theft, persistence, evasion, or illegal activity. Security research should be performed only in systems you own, systems you are authorized to test, or controlled lab environments.

Recommended Use in a Multi-Model Setup

Thinker should be used for tasks that require reasoning and structure. Recommended model routing:

h4rithd/buddy:8b        = quick answers and lightweight tasks
h4rithd/thinker:14b-q8  = reasoning, writing, planning, and documentation
h4rithd/coder:14b       = coding, debugging, and security engineering

This keeps the workflow efficient instead of using the largest model for every task.

Notes

Coder is not trained from scratch. It is a customized Ollama model based on qwen2.5-coder:14b, configured for a specific local AI and OpenClaw workflow. It is best used for coding-heavy work. For long-form writing or deep planning, use h4rithd/thinker:14b-q8. For quick terminal help or simple explanations, use h4rithd/buddy:8b. Performance depends on hardware, memory pressure, Ollama runtime settings, OpenClaw configuration, context size, and prompt quality.

Author

Created by Harith Dilshan, also known as h4rithd.

Built for local AI workflows, OpenClaw usage, technical writing, structured reasoning, and Apple Silicon-based productivity.