Maximizing Model Performance All Quants Types And Full Precision by Samplers_ Parameters

Introduction

This document provides comprehensive guidance on optimizing model performance across different quantization types and full precision models using samplers and parameters. It includes settings for popular AI applications such as LLAMACPP, KoboldCPP, Text-Generation-WebUI, and others. The guide is applicable to model types like GGUF, EXL2, GPTQ, HQQ, AWQ, and covers critical settings for various use cases, including role play and chat scenarios.

Architecture

The document is structured to cover a wide array of settings and models, classified into four classes based on their stability and use case specificity. Class 1 and 2 models are more stable, while Class 3 and 4 models require more fine-tuning to manage their higher creativity and narrower use cases.

Training

The guide provides detailed instructions for adjusting parameters and samplers to enhance model performance. It highlights the importance of understanding how different settings affect model output, including temperature, top-p, top-k, and various penalty and advanced sampling techniques. The document emphasizes the cumulative effect of these settings on token generation quality and coherence.

Guide: Running Locally

1. Review Quant Information: Select the appropriate quant(s) to download.
2. Configure Parameters and Samplers: Use the detailed settings provided for different classes of models.
3. Test and Adjust: Experiment with different settings to achieve the desired model performance.
4. Run Models Locally: Use compatible applications like LLAMACPP, KoboldCPP, or Text-Generation-WebUI to execute models.

Cloud GPUs: Consider using cloud services with GPU support for enhanced performance and faster processing times.

License

This guide is provided under the Apache-2.0 License.