This is a small guide outlining key concepts, topics, architecture, code, etc that came about as the result of my discussions with LLMs trying to understand @_xjdr’s titular context aware sampler named

https://github.com/xjdr-alt/entropix

(Also known as Shrek Sampler)

Lumentis helped me turn the conversations into what you see below. Diagen helped with the diagrams. If you spot an error, drop me a note!

Jump Around

Basic Concepts

Understanding Entropy in Language Models

Attention in Transformers

Traditional Sampling Methods

Entropix System Architecture

Sampling Strategies

Dynamic Parameter Adjustment

Implementation Details

Example Generations

Intro

Entropix introduces several key innovations that set it apart from conventional sampling techniques:

1. Entropy-based decision making: By leveraging both entropy and varentropy of logits, Entropix can gauge the model's uncertainty and adjust sampling strategies accordingly.