In this presentation, we address the classification of text documents from a large language corpus using large language models (LLM). Our goal is to identify strategies that allow efficient text processing in terms of computational complexity and speed. Experimentally, we utilized the Llama-3.1-8B model and analyzed the impact of an early-exit strategy during document processing on the final classification.