Hey everyone, I've spent the last few weeks frustrated with local models (mainly 8B/7B ones like Llama-3 and Mistral) outputting markdown filler, conversational slop, or getting stuck in infinite loops when I just want raw, production-ready code.
I did some extensive testing with Ollama and LM Studio and found a few sweet spots that force the models to behave like strict deterministic functions rather than conversational chatbots. Wanted to share what’s working for me.
1. The Context-Task-Constraint (CTC) Framework
Stop giving local models multi-step conversational prompts. They lose attention. Break it down strictly:
[CONTEXT] Stack: Python 3.11, FastAPI.
[TASK] Write a CRUD router.
[CONSTRAINT] No markdown, no explanations, only valid code.
2. Hyperparameter Sweet Spots (Crucial for Code)
Stop using default chat settings. If you want logic:
Temperature: 0.15 (Keeps syntax logical)
Top_P: 0.9
Repetition Penalty: 1.15 - 1.18 (This is the magic number to stop 8B models from repeating the same function infinitely).
3. XML for Context Injection
If you are pasting server logs or reference code, wrap them in and XML tags. Local models respect XML boundaries way better than quotes or markdown ticks.
Hope this helps someone save a few hours of debugging inference settings!
_P.S. I compiled all my anti-slop system prompts, zero-shot matrices, and ready-to-use Modelfiles into a clean, developer-friendly Markdown (.md) cheat sheet that you can drop directly into Obsidian or VS Code. If you want to grab the full file and support my late-night testing, I put it up here for a coffee ($5): https://devtoolkit.sell.app/product/product-1792791949
_
here for a coffee ($5): DevToolkit: Local LLM Mastery*
Top comments (0)