Локальні великі мовні моделі для обробки конфіденційної інформації

Abstract

The growing demand for digitalization and the generation of vast amounts of data drives government organizations and commercial companies toward the need for effective processing of large volumes of confidential information. Large language models, such as o1 from OpenAI, are transforming traditional approaches to data analysis and interpretation, offering unprecedented capabilities for automating and intellectually processing information. However, the use of cloud-based services for these models raises concerns about security and privacy. As a result, there is a rising interest in implementing local LLMs that allow control over confidential information while maintaining high levels of productivity. The article delves into conceptual approaches to the application of local language models for processing confidential data and examines the results of their deployment in secure environments. At the same time, various quantization levels of language models are evaluated, including a case study of the Ukrainian-language model Mistral-7B, assessing their performance in different configurations.

In addition to LLMs, the article also introduces multi-agent systems, exploring their role in enhancing the flexibility and efficiency of data processing. These systems, which consist of multiple agents working collaboratively to solve complex problems, can be configured to perform specific tasks. For example, one agent may handle natural language processing, while another focuses on data retrieval or decision-making analytics. This modular approach enhances system adaptability, scalability and efficiency, making multi-agent systems particularly valuable in dynamic environments. Additionally, this article also examines the technical and hardware requirements necessary for implementing these solutions on local infrastructures.