How to Set Up Continue.dev with Ollama in VS Code on Linux
This deep-dive covers continue dev ollama vs code with actionable analysis, practical implications, and expert insights.
Why Continue.dev + Ollama?
Cloud AI coding assistants can be expensive and may raise data compliance concerns for proprietary codebases. Continue is an open-source VS Code extension that connects directly to local inference backends like Ollama, giving you free, private code completion.
Step 1: Pull Recommended Coding Models
For chat, inline refactoring, and code generation, the Qwen 2.5 Coder family offers state-of-the-art capability:
# Main chat and refactoring model
ollama pull qwen2.5-coder:7b
# Fast low-latency model for tab autocomplete
ollama pull qwen2.5-coder:1.5b
Step 2: Installing the Continue Extension
Open VS Code on your Linux machine, press Ctrl+P, and enter:
ext install Continue.continue
Step 3: Configuring Local Model Endpoints
Click the gear icon in the bottom right of the Continue sidebar to open ~/.continue/config.yaml. Update your configuration to use local Ollama models:
models:
- name: "Qwen 2.5 Coder 7B"
provider: "ollama"
model: "qwen2.5-coder:7b"
tabAutocompleteModel:
title: "Qwen Coder 1.5B"
provider: "ollama"
model: "qwen2.5-coder:1.5b"
Step 4: Using Keyboard Shortcuts
Test the integration using Continue's primary keybindings:
Ctrl+L: Open the Continue chat pane with your selected code block attached.Ctrl+I: Inline code refactor prompt directly inside the editor buffer.Tab: Accept real-time code completions as you type.
You now have a fully functional local AI pair programmer running on Ubuntu without monthly subscription fees.
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FAQ
Frequently Asked Questions
Structured for search engines and AI answer systems (AEO/GEO).
qwen2.5-coder:7b and deepseek-coder:6.7b provide the best balance of code accuracy and rapid token generation on consumer hardware.
When configured with Ollama, all context and code snippets remain entirely on your local machine.
In your Continue config.yaml, specify a dedicated tabAutocompleteModel using a fast low-latency model such as qwen2.5-coder:1.5b.
More answers in our FAQ hub.
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