Introducing Super RAGs in Mistral 8x7B-v1
Ayush Thakur, Raghav Gupta

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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
