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Large Multimodal Models (LMMs) are a revolution in artificial intelligence (AI). Unlike traditional AI models that operate within a single data environment such as text, images, or audio, LMMs are capable of creating and processing multiple modalities simultaneously. Hence the generation of outputs with context-aware multimedia information. The purpose of this article is to unravel […]
RAG (Retrieval-Augmented Generation) is a recent way to enhance LLMs in a highly effective way, combining generative power and real-time data retrieval. RAG allows a given AI-driven system to produce contextual outputs that are accurate, relevant, and enriched by data, thereby giving them an edge over pure LLMs. RAG optimization is a holistic approach that
Large Language Models (LLMs) such as GPT-4 and Llama 3 have affected the AI landscape and performed wonders ranging from customer service to content generation. However, adapting these models for specific needs usually means choosing between two powerful techniques: Retrieval-Augmented Generation (RAG) and fine-tuning. While both these approaches enhance LLMs, they are articulate towards different
Imagine you have an x-ray report and you need to understand what injuries you have. One option is you can visit a doctor which ideally you should but for some reason, if you can’t, you can use Multimodal Large Language Models (MLLMs) which will process your x-ray scan and tell you precisely what injuries you
What Are Multimodal Large Language Models? Applications, Challenges, and How They Work Read More »
