Introducing Super RAGs in Mistral 8x7B-v1

TL;DR

This paper introduces Super RAGs integrated into Mistral 8x7B-v1, demonstrating significant improvements in accuracy, speed, and user satisfaction by augmenting large language models with external knowledge sources.

Contribution

It presents a novel integration of Super RAGs into a state-of-the-art LLM, with a fine-tuned instruct setup and cache tuning, showing empirical performance gains.

Findings

Enhanced accuracy, speed, and user satisfaction with Super RAGs

Effective data retrieval through cache tuning system

Empirical evidence supporting Super RAGs benefits

Abstract

The relentless pursuit of enhancing Large Language Models (LLMs) has led to the advent of Super Retrieval-Augmented Generation (Super RAGs), a novel approach designed to elevate the performance of LLMs by integrating external knowledge sources with minimal structural modifications. This paper presents the integration of Super RAGs into the Mistral 8x7B v1, a state-of-the-art LLM, and examines the resultant improvements in accuracy, speed, and user satisfaction. Our methodology uses a fine-tuned instruct model setup and a cache tuning fork system, ensuring efficient and relevant data retrieval. The evaluation, conducted over several epochs, demonstrates significant enhancements across all metrics. The findings suggest that Super RAGs can effectively augment LLMs, paving the way for more sophisticated and reliable AI systems. This research contributes to the field by providing empirical evidence of the benefits of Super RAGs and offering insights into their potential applications.