Collama

AI-powered code completion and editing for VS Code using local Ollama models

Features • Installation • Configuration • Models • Contributing

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Overview

Collama is a VS Code extension that uses Ollama models to get code completions, refactoring suggestions, and documentation generation — all running privately on your machine with no external API calls.

Status: This project is in heavy active development. For sure there will be a lot of strange output. If you have any ideas to improve the quality just let me know and contribute!

Features

✨ Code Completion

• Inline, multiline, and multiblock (more a "fun" feature) suggestions
• uses currently opened tabs as context

🔧 Code Edits

• Generate docstrings and documentation
• Extract functions and refactor code
• Simplify complex code
• Fix syntax errors
• Manual instructions

💬 Chat Interface

• Under construction

Quick Start

Prerequisites

• VS Code 1.107.0 or higher
• Ollama running locally (or accessible on your network)
• A supported code model (see Models)

Installation

Use the marketplace to install the extension or build the vsix yourself. Furthermore you need an ollama instance in your local network.
See this link how to install ollama or this link for the docker image.

Configuration

Configure Collama via VS Code Settings (Preferences → Settings, search "collama"):

Setting	Type	Default	Description
collama.apiEndpoint	string	http://127.0.0.1:11434	Ollama API endpoint (IP/domain + port)
collama.apiCompletionModel	string	qwen2.5-coder:3b	Model for code completions
collama.apiInstructionModel	string	qwen2.5-coder:3b-instruct	Model for code edits (use instruct/base variant)
collama.autoComplete	boolean	true	Enable auto-suggestions
collama.suggestMode	string	inline	Suggestion style: inline, multiline, or multiblock
collama.suggestDelay	number	1500	Delay (ms) before requesting completion

Models

Recommended Models

Collama is tested primarily with the Qwen Coder series and performs best with specialized code models:

For Code Completion (FIM - Fill In Middle)

• qwen2.5-coder:3b ⭐ (default)
• qwen2.5-coder:7b (better quality, higher latency)

For Code Edits (Instruct/Base Models)

• qwen2.5-coder:3b-instruct ⭐ (default)
• gpt-oss:20b (recommended for complex edits, higher latency)

Model Compatibility Table

Model	Tested Sizes	FIM Support	Status	Notes
qwen2.5-coder	1.5B, 3B, 7B	✅	Stable	Recommended for most use cases
qwen3-coder	30B	✅	Stable	Excellent quality, higher resource usage
starcoder	—	⚠️	Untested	May work; contributions welcome
starcoder2	3B	✅	Stable	Improved over v1
codellama	7B, 13B	⚠️	Limited	Limited file context support; FIM is ok
codeqwen	—	⚠️	Untested	May work; contributions welcome

💡 Models are tested primarily with quantization level q4. Results may vary with other quantization levels.

🤔 Note: ChatML format is not supported - that means only true fim-models will work for autocomplete!

Usage

Code Completion

1. Trigger Completion: Use editor.action.inlineSuggest.trigger (default keybinding varies by OS)
   • Set custom keybinding: Alt + S or Ctrl + NumPad 1 (example)
2. Auto-Trigger: Completions trigger automatically after 1.5 seconds of inactivity (configurable via suggestDelay)
3. Accept: Press Tab to accept suggestion, Esc to dismiss

Code Edits (not that much tested)

1. Select code in the editor
2. Right-click → collama (on selection) and choose:
   • Write Docstring - Generate documentation
   • Extract Functions - Refactor into separate functions
   • Simplify Code - Improve readability and efficiency
   • Fix Syntax - Correct syntax errors
   • Edit (manual) - Custom AI-assisted edits

Chat Interface

This is under construction and will be available in the long run...

Contributing

Contributions are welcome! Here's how you can help:

1. Report Issues Open an issue
2. Submit PRs: Fork, create a feature branch, and submit a pull request