DuoAttention: Enhancing Long-Context Inference Efficiency in Large Language Models

DuoAttention: Enhancing Long-Context Inference Efficiency in Large Language Models

ByAjith Vallath Prabhakar

DuoAttention reimagines efficiency for Large Language Models (LLMs) by categorizing attention heads into Retrieval and Streaming types, allowing for effective memory optimization in long-context scenarios. This mechanism enables LLMs to reduce memory usage and improve processing speed without compromising performance. With real-world applications in legal, healthcare, and customer support sectors, DuoAttention sets new standards for scalable AI solutions, making long-context inference more accessible even on standard hardware configurations

Mixture of Agents AI: Building Smarter Language Models

Mixture of Agents AI: Building Smarter Language Models

ByAjith Vallath Prabhakar

Large language models (LLMs) have revolutionized artificial intelligence, particularly in natural language understanding and generation. These models, trained on vast amounts of text data, excel in tasks such as question answering, text completion, and content creation. However, individual LLMs still face significant limitations, including challenges with specific knowledge domains, complex reasoning, and specialized tasks.

To address these limitations, researchers have introduced the Mixture-of-Agents (MoA) framework. This innovative approach leverages the strengths of multiple LLMs collaboratively to enhance performance. By integrating the expertise of different models, MoA aims to deliver more accurate, comprehensive, and varied outputs, thus overcoming the shortcomings of individual LLMs.