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Setting Up Llama 3.2 in VS Code: Fixing Ollama & 'Continue' Connection Issues

 The migration from cloud-based AI assistants like GitHub Copilot to local LLMs is driven by data privacy, cost reduction, and the sheer performance of new models like Meta's Llama 3.2. However, the ecosystem is fragmented.

A typical Saturday for a developer attempting this switch often ends in frustration. You have Ollama running in the terminal, but the Continue extension in VS Code refuses to connect, throwing ECONNREFUSED errors or silently failing to generate code.

This guide provides a definitive, engineering-grade solution to connecting Llama 3.2 to VS Code. We will resolve the networking conflicts, configure the correct API endpoints, and optimize the config.json for low-latency code completion.

The Root Cause: Why Connection Refused Happens

Before applying the fix, it is critical to understand the architecture failure. The issue rarely lies with the Llama model itself; it is almost exclusively a networking binding issue.

1. The Localhost Ambiguity (IPv4 vs. IPv6)

By default, modern operating systems resolve localhost to the IPv6 loopback address ::1. However, many local server implementations, including older versions of Ollama or specific Node.js networking stacks used by VS Code extensions, default to IPv4 127.0.0.1.

If Ollama binds to 127.0.0.1:11434 but VS Code attempts to connect via [::1]:11434, the connection is refused.

2. CORS and Origin Policies

VS Code runs extensions in a sandboxed environment. When the Continue extension (a webview-based component) makes a fetch request to your local Ollama server, browsers and webviews enforce Cross-Origin Resource Sharing (CORS).

If Ollama is not explicitly configured to accept requests from the VS Code extension host (often originating from vscode-webview://), the request is blocked pre-flight.

Step 1: Hardening the Ollama Environment

We need to force Ollama to bind to a specific address and permit cross-origin requests. We do this via environment variables.

macOS / Linux Configuration

If you run Ollama as a background service (which is the default installation method), you must edit the service configuration, not just your shell profile.

  1. Stop the current Ollama application from the menu bar.
  2. Open your terminal and edit your shell configuration (e.g., ~/.zshrc or ~/.bashrc):
# Force IPv4 binding to avoid lookup ambiguity
export OLLAMA_HOST=127.0.0.1:11434

# Allow all origins (Strictly for local development environments)
export OLLAMA_ORIGINS="*"
  1. Apply the changes:
    source ~/.zshrc

If you are running the Ollama macOS app, these variables must be set in the launch environment. The most reliable method is to launch Ollama via the terminal after setting these variables, or use launchctl for persistent service configuration.

Windows Configuration

On Windows, setting variables in PowerShell is temporary. You must set them at the System level.

  1. Press Win + R, type sysdm.cpl, and hit Enter.
  2. Go to the Advanced tab -> Environment Variables.
  3. Under System variables, click New and add:
    • Variable: OLLAMA_HOST | Value: 127.0.0.1:11434
    • Variable: OLLAMA_ORIGINS | Value: *
  4. Restart the Ollama application from the system tray for these to take effect.

Step 2: Verifying the Model and API

Before touching VS Code, verify the API is accessible and the model is loaded. This isolates the problem to the network layer.

First, ensure you have pulled the specific parameter sizes of Llama 3.2. For coding, we want the 3B model for chat and the 1B model for autocomplete (lower latency).

ollama pull llama3.2:3b
ollama pull llama3.2:1b

Next, test the API endpoint using curl. This mimics the request the VS Code extension will make.

curl -X POST http://127.0.0.1:11434/api/generate -d '{
  "model": "llama3.2:3b",
  "prompt": "Write a TypeScript interface for a User object.",
  "stream": false
}'

If you receive a JSON response containing a response field, your networking is fixed. If you get a connection error, verify that port 11434 is not being blocked by a firewall or used by Docker.

Step 3: Configuring VS Code (Continue Extension)

The default configuration in the Continue extension often points to generic model names or incorrect providers. We will explicitly define the model mapping.

  1. Open VS Code.
  2. Open the Command Palette (Ctrl/Cmd + Shift + P) and type Continue: Open config.json.
  3. Replace the contents with the following rigorous configuration:
{
  "models": [
    {
      "title": "Llama 3.2 (3B)",
      "provider": "ollama",
      "model": "llama3.2:3b",
      "apiBase": "http://127.0.0.1:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Llama 3.2 (1B)",
    "provider": "ollama",
    "model": "llama3.2:1b",
    "apiBase": "http://127.0.0.1:11434"
  },
  "allowAnonymousTelemetry": false,
  "embeddingsProvider": {
    "provider": "ollama",
    "model": "nomic-embed-text"
  }
}

Configuration Deep Dive

  • apiBase: We explicitly add http://127.0.0.1:11434. While this is the default, defining it prevents the extension from attempting to resolve localhost dynamically.
  • tabAutocompleteModel: We separate the Chat model from the Autocomplete model. Llama 3.2 1B is significantly faster for "ghost text" (tab completion) than the 3B model, reducing the friction during typing.
  • Embeddings: Using nomic-embed-text (which you should ollama pull nomic-embed-text) ensures your codebase indexing is handled locally and accurately, crucial for the "@Codebase" context feature.

Step 4: Optimizing Context Window and System Prompts

Llama 3.2 supports a 128k context window, but Ollama defaults to a much lower value (usually 2048 or 4096 tokens) to conserve VRAM. For complex software engineering tasks, you need a larger context.

You can create a custom Modelfile to bake these settings in, or pass them via the config.json in VS Code.

Update your config.json model entry to include request options:

{
  "title": "Llama 3.2 (3B) - High Context",
  "provider": "ollama",
  "model": "llama3.2:3b",
  "requestOptions": {
    "num_ctx": 16384,
    "num_predict": -1,
    "temperature": 0.2
  },
  "systemMessage": "You are an expert Full Stack Engineer. You prioritize modern TypeScript, React Server Components, and functional programming patterns. Provide concise, secure code snippets."
}

Warning: Increasing num_ctx increases VRAM usage linearly. If you are on a machine with 8GB or 16GB of unified memory (like an M1/M2/M3 Air), keep num_ctx around 8192 to prevent system swapping.

Troubleshooting Common Edge Cases

1. The "Empty Response" Issue

If the connection succeeds but the output is empty or gibberish, the model's template is likely mismatching the prompt format sent by the extension.

Solution: Verify the model file in Ollama.

ollama show llama3.2:3b --modelfile

Ensure the TEMPLATE section exists. If you are using a custom fine-tune or a quantized GGUF file manually imported, you must manually set the Jinja2 template to match Llama 3's special tokens (<|start_header_id|>, etc.).

2. GPU vs. CPU Fallback

Check your Ollama server logs (~/.ollama/logs/server.log on Linux/Mac). If you see "VRAM limit exceeded, offloading to CPU," your completions will become sluggish.

To fix this, downgrade the autocomplete model to a higher quantization level (e.g., q4_0 instead of fp16) or strictly use the 1B parameter model for autocomplete tasks.

Conclusion

By forcing IPv4 bindings, explicitly configuring CORS, and segregating your chat and autocomplete models, you transform Llama 3.2 from a fun experiment into a reliable daily driver.

This setup offers a zero-latency, privacy-first coding assistant that runs entirely on your hardware. You no longer leak proprietary code to external APIs, and you eliminate the monthly subscription overhead of cloud-based copilots.

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