LLM Settings Guide

LLM Settings Guide

A web page that helps you find the sampler and prompt template settings that suit a language model, with a folder of SillyTavern settings files. Suits people who run local models and tune their output.

Made by overhead520 on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: This Space is a static web page, a guide to choosing sampler and template settings for large language models. Samplers control how a model picks each next word, and templates set the prompt format a model expects. The repository also holds a folder of SillyTavern settings files.

Who it suits: It suits people who run language models themselves and want sensible starting settings, especially users of the SillyTavern chat front end.

Industry | Who uses it | How they use it | Example
Hobby AI | Local model users | Look up sampler and template settings for a model | Find settings for a Mistral model
Software | Developers | Compare settings across model families | Check settings for a code model
Creative writing | Roleplay and story users | Load settings files into SillyTavern | Import a settings file from the folder
Research | Prompt engineers | Study how settings change output | Note which settings the guide suggests per model

You can skip it if you only use a hosted chatbot that fixes these settings for you.

Where to use it:

• A web browser: open the Space page on Hugging Face
• Windows, macOS or Linux: keep a local copy and open the page in a browser
• SillyTavern: the repository holds a folder of settings made for it

Use case | Who | What they get back
Find sampler settings | Local model users | Suggested settings for a model
Find template settings | Developers | The prompt format a model expects
Load a settings file | SillyTavern users | A ready-made settings file from the folder

How to set it up: The online page needs nothing. A local copy needs Python only to download the files.

• A web browser
• Python and the Hugging Face hub package, only for downloading a copy

1. To read it online, open the Space page on Hugging Face.
2. To keep a copy, install the Hugging Face hub package.
3. Download the Space into a local folder.
4. Move into that folder.
5. Open index.html in a web browser.

pip install -U huggingface_hub
hf download overhead520/LLM-Settings-Guide --repo-type space --local-dir LLM-Settings-Guide
cd LLM-Settings-Guide

How to use it: Open the page and look for your model, then copy the suggested sampler and template settings into the app you run it in. The SillyTavern folder holds ready-made settings files you can import instead.

Example: You run one of the Mistral models the guide names and are not sure which settings to use. You open the page, find that model and copy its suggested sampler and template settings into your chat app.

For newcomers and experts: If you are new to this, treat samplers as the dials that make a model's answers more careful or more varied. Start from the guide's suggestion for your model before you change anything.

If you know the field, the page names several models, among them miqu 70b, Codestral 22B, Magistral Small 2506, Mamba Codestral 7B, Mathstral 7B and Ministral 8B Instruct. The repository is plain HTML and CSS, so there is nothing to run beyond opening the page.

You open the guide
You find your model
You copy sampler and template settings
You paste them into your chat app

Good to know: The README gives only a heading, so the page itself holds the real guidance. The license restricts commercial use.

• License: cc-by-nc-4
• Cost to run: none named
• Data it reaches: none, it is a static page
• Not for: commercial use, since the license is non-commercial

On Hugging Face, 13 November 2026

• Likes: 85
• Built with: static
• License: cc-by-nc-4.0

Links

• Space: https://huggingface.co/spaces/overhead520/LLM-Settings-Guide
• Files: https://huggingface.co/spaces/overhead520/LLM-Settings-Guide/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.