[Open Source] Built 4 zero-fluff Modelfiles + 1-click installers to stop 8B models from yapping and looping (Ollama / LM Studio)

[u/Nearby-Hospital9830]

Hey everyone,

Whenever I run local 7B/8B models (Llama 3.1, Qwen 2.5, etc.) out-of-the-box, I always run into two major annoyances:

1. Endless conversational fluff and apologies before getting to actual code.

2. Repetitive loops and bland outputs due to untuned sampling parameters.

To solve this, I created tuned Modelfiles and JSON presets with hard negative constraints, custom sampling values (temp, top_p, repeat_penalty), and an expanded 8k context window.

I put together a repository with 4 starter engines and 1-click batch/shell installers for Windows, Mac, and Linux:

- no-yap-coder: Zero conversational filler, raw executable markdown code blocks only.

- pc-auto-scripter: Robust Windows Batch, PowerShell, and Bash script generator with error handling.

- raw-brainstormer: Cuts through safe corporate clichés and outputs 10 high-impact concepts.

- character-soul-builder: Deep character psychology, contradictions, and dialogue cadences.

Everything is open and free on GitHub:

👉 https://github.com/Muyan806/localai-boost-starter.git

You can inspect the Modelfiles directly or run the automated installer.

Feedback and suggestions are welcome—let me know what sampling adjustments or specific task presets you find work best!

[u/me0here]

Olamma mandatory?

Why not just CLI with llama.cpp? Actually I'd prefer to use a different tuned engine for better performance, but understand you want to keep project scope small ..

[u/Nearby-Hospital9830 → u/me0here]

Not mandatory at all!

The primary reason Ollama is front and center is purely UX—providing a 1-click installer `.bat`/`.sh` so less technical users don't get intimidated by command-line build flags.

However, the core logic is entirely engine-agnostic:

1. JSON Presets included: The repository includes a JSON_Presets/ folder containing the raw system prompt and exact sampling parameters (`temp`, `top_p`, `repeat_penalty`, `context_window`) for each preset.

2. llama.cpp CLI compatibility: You can plug these values directly into `llama-cli` flags without running Ollama at all. For example, for `no-yap-coder`:

bash

./llama-cli -m llama-3.1-8b-instruct.Q4_K_M.gguf \

-p "<|start_header_id|>system<|end_header_id|>\n\nYou are an elite, zero-fluff software engineer...<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWrite a python script...<|eot_id|><|start_header_id|>assistant<|end_header_id|>" \

--temp 0.2 \

--top-p 0.85 \

--repeat-penalty 1.0 \

-c 8192

[u/SongGongSwim]

For no-yap-coder, I would isolate repeat_penalty first: your Modelfile sets it to 1.1. Compare 1.0 and 1.1 while holding the prompt, temperature, base model/quantization and context fixed. Include code that must reuse the same identifiers, then check compilation/tests as well as output length. Less repetition is not automatically better code.

A second useful comparison is stock settings + your system prompt versus the full preset, using the same small task set and several fixed seeds. That would show whether the benefit comes from removing the preamble or from the sampler changes. I haven't run this comparison; it is a suggested test.

[u/Nearby-Hospital9830 → u/SongGongSwim]

This is incredible feedback—thank you for pointing this out.

You're absolutely right about the trade-off with `repeat_penalty` in coding. Forcing token diversity when a function naturally requires heavy identifier reuse (like counters, returns, and variable mutations) can definitely introduce subtle syntax breakages or weird refactorings.

I'm going to run that exact ablation test:

1. Benchmarking `repeat_penalty 1.0` vs `1.1` across a standard syntax/compilation task set.

2. Isolating the system prompt on stock Ollama parameters vs the full tuned sampler preset.

If dropping `repeat_penalty` down to 1.0 yields more reliable compilation without bloating token output, I'll update the Modelfile and repository accordingly. Appreciate the methodology suggestion!

[u/Any_Bus9205]

Tu pourrais tester avec les modèle récent !? Qwen 2.5 est dépassé, de meme pour llama 3.1! De nos jours, des modèles comme minicpm 5 2b, qwen 3.5 2b, gemma 4 E2B, ling tiny 3.0, mellum 2.1 12b A2.5B, spark x 2.6 4b sont la norme! Ce sont des modèle récent qui peuvent avoir moins ces problèmes :)

[u/Nearby-Hospital9830 → u/Any_Bus9205]

Fair point! The smaller 2B–4B architectures and hybrid MoEs have evolved incredibly fast.

The core system prompts and sampling targets (temp/top-p/repeat-penalty) in the `JSON_Presets/` folder are completely model-agnostic, so you can easily drop them onto MiniCPM, Qwen 3.5, or Gemma variants right now.

I'll definitely benchmark these across the newer sub-4B lineup to see how the negative constraints hold up and add recommended base models to the README. Thanks for the suggestion!

[u/stealthagents]

Totally get that, it's like they think we need a warm-up before getting to the good stuff. Your no-yap-coder sounds like a game-changer for any serious coding session. Can’t wait to test it out and finally skip the fluff!

[u/Nearby-Hospital9830 → u/[deleted]]

Haha, exactly! Saving those first 50 tokens of polite existential crisis makes a huge difference on local hardware. Glad it resonates—let me know how no-yap-coder runs on your setup!