Private LLM Code Commenter

An Automated Document Generator using Private and Secured Local LLM

Project Overview

As a software developer, are you tired of writing documents? Who else would need to read this but me? 😒

As a software developer 1 month from now, are you frustrated that you can't understand what you wrote? 🥲

As QA and tester, are you tired of undocumented code? 🤬

But you can't legally share your proprietary code base to LLM such as DeepSeek or ChatGPT to document? 🔒

Then this is the tool for you. 🙌

Private LLM Code Commenter solves critical documentation challenges for software teams by enabling secure, privacy-preserving documentation generation directly within your development environment. This tool empowers developers to:

• Automatically generate comprehensive README files and inline code comments
• Maintain documentation parity with rapidly evolving codebases
• Ensure compliance by processing proprietary code locally
• Streamline onboarding and knowledge transfer

👉 By leveraging local LLMs (DeepSeek, Qwen, Devstral, etc.) via Ollama, Private LLM Code Commenter keeps sensitive intellectual property secure while delivering enterprise-grade documentation capabilities.

Key Features

• Local Processing: Runs entirely on your infrastructure - can run 100% air-gapped, no code leaves your environment
• Multi-Format Support: Generates READMEs, inline comments, and commit documentation
• Model Flexibility: Compatible with leading LLMs (DeepSeek, Qwen, Gemma, Codellama)
• Batch Processing: Recursive directory handling for entire codebases

Requirements

1. Ollama installed

   • Download from the official Ollama site: https://ollama.com/download
   • Python library: pip install ollama pip install requests

2. Hardware requirements:

   • The specific PC requirements will depends on the model of your choice: https://ollama.com/search
   • Typical configurations range from 8GB VRAM (7B models) to 120GB+ RAM (70B models)

Instruction

Step 1: Launch Ollama service

• ollama serve this will run Ollama on localhost:11434, allowing API access.

Step 2:

For single file comment generation

• .\file_commenter.bat /path/to/file -m model_name

For directory comment generation

• .\path_commenter.bat /path/to/code -m model_name

For directory README explanation generation

• .\path_explainer.bat /path/to/code -m model_name

Demonstration

Directory comment generation

path_commenter_demo_compressed.mp4

Directory README explanation generation

code_explainer_demo.mp4

Perfomance comparison

Below are some models that was tested and evaluated.

Note:

• Some comparison criteria are subjective, one developer might consider a comment too detailed, while another developer might consider that same comment too oversimplified.

Comparison

Each models were tested with 15 programs. Code Understanding and Comment Quality is my subjective evaluation

Model	Code Understanding	Comment Quality	Memory Usage	Limitation
Deepseek-R1:70b	4.5	5/5	42.3 GiB	Unstable with large files
Codellama:70b	0/5	0/5	38.8 GiB	Did not understand the prompt
Qwen2.5-coder:32b	4.5/5	2.5/5	21 GiB
Devstral:24b	4/5	2/5	15.4 GiB	200+ lines
Deepseek-coder-v2:16b	4.5/5	4.5/5	10.4 GiB	120+ lines
Deepseek-R1:8b	0/5	0/5	6.4 GiB	Did not understand the codes
Web-based Deekseek	5/5	5/5	NA
Web-based ChatGPT	4.5/5	4/5	NA	limited uses with free tier

Also compared the performance against 2 popular web-based LLM: Deepseek and ChatGPT.

Chosen default model

Based on subjective testing, Deepseek-coder-v2:16b is chosen as the default model as it strikes a good balance between hardware requirements and overall performance.

Criteria explanation

1. Code Understanding

• Correctness: Does the comment accurately explain the code's functionality?
• Depth: Does it capture subtle logic, edge cases, and algorithmic complexity?
• Context Awareness: Does it understand project-specific patterns or idioms?

2. Comment Quality

• Clarity: Are explanations easy to understand?
• Conciseness: Avoids verbosity while being informative (measure comment-to-code ratio)
• Relevance: Focuses on non-obvious aspects (e.g., explains why more than what)
• Formatting: Proper syntax, placement, and structure for the language