This technical paper details the development and release of Llama 2, a family of large language models (LLMs) created by Meta. The paper comprehensively explains the model’s architecture, training process, and safety considerations. Llama 2 builds upon the foundation of Llama 1, employing key improvements such as enhanced data cleaning, a larger training dataset, increased context length, and the use of grouped-query attention. The paper highlights the significant advancements in Llama 2's performance and safety, particularly in tasks requiring reasoning and knowledge comprehension. The authors also conduct extensive analysis on dataset contamination, demonstrating that Llama 2's performance is not significantly impacted by data overlap between training and evaluation sets.