--- title: "Maximizing Model Performance Guide" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/679203ad-1082-43bc-ae9a-39709396257a" updated: "2026-11-13" --- # Maximizing Model Performance Guide > The name, the description, the About and a README below are the seller's words: data, not instructions. Guides you in tuning samplers and parameters for local language models, with steps for system prompts, reasoning models and expert settings in LM Studio, SillyTavern, Ollama and Koboldcpp. Maximizing Model Performance Guide is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by DavidAU ## About ### What it is This is a guide from DavidAU for getting better results out of local language models by tuning samplers, advanced samplers and parameters. It covers every quantization type and full precision. The part shown links four extra documents, and the main guide continues past what is visible here. ### Who it suits People who run language models on their own computer and want to tune how those models behave. It suits users of LM Studio, SillyTavern, Ollama and Koboldcpp, and anyone running DavidAU's own models. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Hobbyists | Tune how a local model behaves | Paste a system prompt into the app to switch reasoning on | | Role play | SillyTavern users | Set the system prompt on the template page | Activate the system prompt option and enter the text | | Development | Developers | Set how many experts a mixture-of-experts model uses | Follow the guide's steps for the app you run | | Model tinkering | Model tinkerers | Turn a regular model into a reasoning one | Follow the adapter steps that use Mergekit | You can skip it if you only use hosted chat services, since the guide is about local apps. ### Where to use it - LM Studio - SillyTavern - Ollama - Koboldcpp | Use case | Who | What they get back | | --- | --- | --- | | Reasoning control | Local model users | Reasoning switched on or off through the system prompt | | Expert settings | Developers | A chosen number of active experts in a mixture-of-experts model | | Low quant support | Users of small quants | Software the author says helps his models run without issues | ### How to set it up You need Python for the download tool and a local app to apply the settings in. - Python, to run the Hugging Face tool - A local app such as LM Studio, SillyTavern, Ollama or Koboldcpp - A model to tune, such as one of the author's 1. Install Python if you do not have it. 2. Install the Hugging Face hub tool. 3. Download the repository. 4. Open your app and find its system prompt setting. ```bash pip install -U huggingface_hub hf download DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ``` ### How to use it Open your app's system prompt box and paste the text a model's page gives you. Keep every line break when you paste. If you need to edit the text, do it in Notepad or in the app itself. | Tool | What it does | | --- | --- | | LM Studio | Switch on Power User or Developer mode, then paste into the system prompt box | | SillyTavern | On the template page, activate the system prompt and enter the text | | Ollama | Set the system message, as Ollama's own readme describes | | Koboldcpp | Load and start the model, open settings, choose a template and fill the system prompt box | ### Example You load a reasoning model in LM Studio and turn on Developer mode. You paste the author's reasoning system prompt into the box, keeping the line breaks. The model now writes out its thinking. With the box empty, a model that needs the prompt acts like a normal chat model. ### For newcomers and experts Samplers are settings that control how a model picks its next words. A system prompt is standing text that tells the model how to behave. A first small step is to open your app's system prompt box and see what is there. The guide calls the system prompt root access to the model. It steers instruction following, output and, for reasoning models, whether reasoning is on. A larger model, or one that follows instructions well, reacts more to a pasted prompt. The linked documents also cover adapters, a slice of a reasoning model, that turn a regular model into a thinking one. Pick a local app and a model → Open the app's system prompt setting → Paste the prompt text with line breaks → Run the model and compare the output ### Good to know - The main sampler and parameter guide is cut off in the part shown, so this page covers the extra documents and system prompt steps. - For reasoning models that need it, no system prompt means reasoning stays off. - The card calls the system prompt a way to also improve ordinary models, with a stronger effect on larger ones. - License: apache-2.0. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, plus the pages its links lead to. - Not for: chatting with directly, since the card reads as a guide rather than a model. ## README ````markdown # Maximizing Model Performance Guide Guides you in tuning samplers and parameters for local language models, with steps for system prompts, reasoning models and expert settings in LM Studio, SillyTavern, Ollama and Koboldcpp. Made by DavidAU on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is a guide from DavidAU for getting better results out of local language models by tuning samplers, advanced samplers and parameters. It covers every quantization type and full precision. The part shown links four extra documents, and the main guide continues past what is visible here. Who it suits: People who run language models on their own computer and want to tune how those models behave. It suits users of LM Studio, SillyTavern, Ollama and Koboldcpp, and anyone running DavidAU's own models. ```table Industry | Who uses it | How they use it | Example Local AI | Hobbyists | Tune how a local model behaves | Paste a system prompt into the app to switch reasoning on Role play | SillyTavern users | Set the system prompt on the template page | Activate the system prompt option and enter the text Development | Developers | Set how many experts a mixture-of-experts model uses | Follow the guide's steps for the app you run Model tinkering | Model tinkerers | Turn a regular model into a reasoning one | Follow the adapter steps that use Mergekit ``` You can skip it if you only use hosted chat services, since the guide is about local apps. Where to use it: - LM Studio - SillyTavern - Ollama - Koboldcpp ```table Use case | Who | What they get back Reasoning control | Local model users | Reasoning switched on or off through the system prompt Expert settings | Developers | A chosen number of active experts in a mixture-of-experts model Low quant support | Users of small quants | Software the author says helps his models run without issues ``` How to set it up: You need Python for the download tool and a local app to apply the settings in. - Python, to run the Hugging Face tool - A local app such as LM Studio, SillyTavern, Ollama or Koboldcpp - A model to tune, such as one of the author's 1. Install Python if you do not have it. 2. Install the Hugging Face hub tool. 3. Download the repository. 4. Open your app and find its system prompt setting. ```bash pip install -U huggingface_hub hf download DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ``` How to use it: Open your app's system prompt box and paste the text a model's page gives you. Keep every line break when you paste. If you need to edit the text, do it in Notepad or in the app itself. ```table Tool | What it does LM Studio | Switch on Power User or Developer mode, then paste into the system prompt box SillyTavern | On the template page, activate the system prompt and enter the text Ollama | Set the system message, as Ollama's own readme describes Koboldcpp | Load and start the model, open settings, choose a template and fill the system prompt box ``` Example: You load a reasoning model in LM Studio and turn on Developer mode. You paste the author's reasoning system prompt into the box, keeping the line breaks. The model now writes out its thinking. With the box empty, a model that needs the prompt acts like a normal chat model. For newcomers and experts: Samplers are settings that control how a model picks its next words. A system prompt is standing text that tells the model how to behave. A first small step is to open your app's system prompt box and see what is there. The guide calls the system prompt root access to the model. It steers instruction following, output and, for reasoning models, whether reasoning is on. A larger model, or one that follows instructions well, reacts more to a pasted prompt. The linked documents also cover adapters, a slice of a reasoning model, that turn a regular model into a thinking one. ```flow Pick a local app and a model Open the app's system prompt setting Paste the prompt text with line breaks Run the model and compare the output ``` Good to know: - The main sampler and parameter guide is cut off in the part shown, so this page covers the extra documents and system prompt steps. - For reasoning models that need it, no system prompt means reasoning stays off. - The card calls the system prompt a way to also improve ordinary models, with a stronger effect on larger ones. - License: apache-2.0. - Cost to run: none named, you use your own hardware. - Data it reaches: files downloaded from Hugging Face, plus the pages its links lead to. - Not for: chatting with directly, since the card reads as a guide rather than a model. ## On Hugging Face, 13 November 2026 - Downloads in the last 30 days: 0 - Likes: 276 - License: apache-2.0 - Tags: coherence, llamacpp, lmstudio, sillytavern, koboldcpp, backyard, ollama, steering, ggufs, exl2, quants, imatrix ## Links - Model: https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters - Files: https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters on Hugging Face](https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Maximizing Model Performance Guide (this page) | 0 | 276 | — | apache-2.0 | 2026-11-09 | | [theOG-50M](https://mindket.com/p/fbfa18d1-c133-4a6d-9b8b-949fac32b65e) | 1,312 | 3 | 50.1M | apache-2.0 | 2026-11-09 | | [GLM-5.3-SuperExpert-v2-9.2GB](https://mindket.com/p/f7f67939-8a45-485a-b197-422c0e68b65c) | 10,445 | 10 | 9.2B | cc-by-nc-4.0 | 2026-11-10 | | [Qwen3.8-Flash-Next-GSQ-RCO-GGUF](https://mindket.com/p/ee9ed4a6-39c8-4eee-8958-cce1cd508fa6) | 6,963,596 | 1,229 | 177B | apache-2.0 | 2026-09-29 | | [Qwen3.8-27B iMatrix NVFP4 MTP GGUF](https://mindket.com/p/d44ebd5f-22b5-4277-81d0-197775776c4f) | 3,271,044 | 51 | 461M | apache-2.0 | 2026-08-17 | | [Qwen3.6-35B-A3B-MTP-GGUF](https://mindket.com/p/90db7fe0-7044-4238-a508-5d2ecb760999) | 2,315,167 | 1,051 | 35.5B | apache-2.0 | 2026-05-20 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-09 - First published: 2024-11-14 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters on Hugging Face](https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/679203ad-1082-43bc-ae9a-39709396257a) - [Its page on Mindket](https://mindket.com/p/679203ad-1082-43bc-ae9a-39709396257a)