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Today, LLMs impress us with their capabilities, but adapting them for specific professional tasks or unique communication styles creates serious technical challenges. The traditional approach, known as full fine-tuning, requires updating all billions of parameters in the neural network at once. Usually, this causes a massive load on computing resources

The rapid development of LLMs has significantly influenced modern approaches to building intelligent systems. Models capable of processing natural language, code, and multimodal data are increasingly used in applied tasks related to information analysis, decision support, and automation of complex processes. The relevance of agent-based approaches based on large language

Imagine being able to look into the future of your business. This is precisely what predictive analytics does: a process that takes historical data, combines it with statistics and powerful machine learning technologies, to forecast future events. For example, we can predict how much demand for a product will increase

Enterprise teams are increasingly turning to large-scale language models (training large language models) to automate workflows, improve decision-making, and engage with customers. Successful LLM model training requires not only access to a pre-trained model but also a structured, step-by-step approach that aligns the model with your organization's specific

The topic of fine-tuning large language models arises when it becomes apparent that general-purpose LLMs do not always behave as expected in specific tasks. A general model may be effective at supporting dialogue or answering broad questions, but when dealing with narrow topics, specialized terminology, or fixed-response formats, its behavior

Modern warehouses process hundreds of thousands of items daily, which are constantly in motion, and the demand for speed is only increasing. Although many of them use intelligent systems, such as scanners, robots, and management software, these tools do not see the full context. They know that a product should

Artificial Intelligence has been an integral part of modern business for quite some time. The emergence of large language models marked a qualitatively new stage of powerful systems capable of understanding, generating, and summarizing human language on an unprecedented scale. Language is the foundation of any business activity: from customer