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:
- Entropy-based decision making: By leveraging both entropy and varentropy of logits, Entropix can gauge the model's uncertainty and adjust sampling strategies accordingly.
- Attention-aware sampling: The system incorporates metrics derived from attention patterns, such as attention entropy and agreement, to inform sampling decisions.
- Dynamic parameter adjustment: Sampling parameters like temperature, top-k, and top-p are dynamically adjusted based on the current context and model state.