This paper introduces Llama 2, a collection of pre-trained and fine-tuned large language models with parameters ranging from 7 billion to 70 billion. The fine-tuned model, Llama 2-Chat, is designed for dialogue use cases and showcases superior performance on various benchmarks. The authors emphasize the importance of safety considerations in large language models, highlighting the need for transparency in training data, human evaluations, and reinforcement learning with human feedback. They also discuss the potential trade-off between helpfulness and safety, suggesting that as a model becomes more helpful, it may become less safe. Llama 2 is released under an open license, allowing users to fine-tune the model on specific domains. The authors aim to promote the use of open-source models and encourage transparency in large language model development.