Continue extension setup, Qwen Coder local setup

Qwen2.5-Coder-32B-Instruct is currently the gold standard for local coding because it rivals GPT-4o in Python and JS while running comfortably on a single A100 or a high-VRAM consumer setup via Ollama.

Getting the stack to actually talk to each other

Most people mess up the config.json

in Continue. You can't just install the extension and expect it to magically find your local model. You need a provider. I use Ollama because it handles the memory management without me having to dive into CUDA environment variables every time I reboot.

First, pull the model. Open your terminal and run:ollama run qwen2.5-coder:32b

If you're on a laptop with 16GB of RAM, 32B will crawl. Use the 7B version. It's surprisingly punchy for its size.

Once Ollama is humming in the background, you have to tell Continue where to look. Open the Continue config (Cmd+Shift+P -> "Continue: Open config.json"). Your models

array should look like this:

{
  "title": "Qwen 32B Local",
  "model": "qwen2.5-coder:32b",
  "provider": "ollama"
}

I spent three hours last Thursday debugging a "Connection Refused" error only to realize I had a firewall rule blocking port 11434. Check your ports.

Tab-autocomplete vs. Chat: The dual-model strategy

Here is the part most tutorials skip: do not use the same model for the chat sidebar and the inline autocomplete.

If you use a 32B model for autocomplete, your cursor will lag. It’s a nightmare. You want a tiny, fast model for the "ghost text" and a heavy hitter for the architectural questions. This is where the PromptCube homepage community usually suggests a split setup.

| Task | Recommended Model | Why? |

| :--- | :--- | :--- |

| Inline Autocomplete | qwen2.5-coder:1.5b | < 20ms latency |

| Complex Refactoring | qwen2.5-coder:32b | High reasoning capacity |

| Quick Unit Tests | qwen2.5-coder:7b | Balanced speed/logic |

To set this up, add a tabAutocompleteModel

section to your config.json

:

"tabAutocompleteModel": {
  "title": "Qwen 1.5B",
  "provider": "ollama",
  "model": "qwen2.5-coder:1.5b"
}

Now the ghost text snaps into place instantly, but you still have the 32B beast available when you need to rewrite a legacy React component without breaking five other things.

Fixing the "Context Window" hallucination

Local LLMs have a habit of "forgetting" the top of your file if the context window isn't configured right. By default, some local providers truncate your code.

If Qwen starts suggesting variables that don't exist or forgetting your imports, you need to explicitly set the contextLength

. For Qwen2.5-Coder, you can push this pretty far, but your VRAM is the limiting factor.

Try adding this to your model config:"contextLength": 32000

If your IDE starts crashing or Ollama restarts, dial it back to 16k.

Why this beats a cloud subscription

I'm tired of the "latency lottery" with cloud APIs. When you're in a flow state, a 2-second delay for a suggestion is an eternity. Local Qwen is instant. Plus, no one is training their next frontier model on my proprietary API keys or messy internal company logic.

The real power comes when you start indexing your codebase. Continue creates a local embeddings index. When you use @Codebase

in the chat, it doesn't send your whole project to a server; it searches your local index and feeds the relevant snippets to Qwen.

If you're struggling with how to structure these prompts to get cleaner code, checking out some proven Resources on prompt engineering for local models is a lifesaver. I found that telling Qwen to "be concise and omit explanations" reduces the token output and speeds up the response time by about 30%.

The "It's not working" checklist

If your Continue extension setup is still acting up, check these three things:

1. Ollama Version: Are you on the latest? Qwen2.5 support was added recently. Update or you'll get garbage output.

2. VRAM Overhead: Open your activity monitor. If your GPU is at 98%, your OS is swapping to disk, and your "local" AI will feel slower than GPT-4.

3. The JSON comma: A single missing comma in config.json

kills the whole extension. Use a JSON validator.

For those who want to automate the boring parts of their dev cycle, integrating these local models into specific Workflows is the next step. I've started using local Qwen to pre-scan my PRs for silly mistakes before I even push to GitHub.

Joining the conversation

Setting up local AI is a bit of a rabbit hole. One day you're just trying to get a snippet of code, the next you're optimizing KV cache quantization.

The PromptCube community is where this actually happens. It's not just a list of prompts; it's a place where developers trade config.json

files and benchmark different quantizations (Q4_K_M vs Q8_0) to find the sweet spot between intelligence and speed.

To join, just head to the site and jump into the forums. It's better than screaming into the void of a generic Discord server because you're talking to people who actually care about the difference between a 7B and a 32B parameter model.

Next GPT-5.6 vs Claude Fable 5: Which Wins for Physical AI? →

All Replies （0） #

No replies yet — be the first!