In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

翻译：在本工作中，我们开发并发布了Llama 2，这是一组经预训练和微调的大语言模型（LLMs），参数规模从70亿到700亿不等。我们经过微调的大语言模型被称为Llama 2-Chat，专门针对对话场景进行了优化。在我们测试的大多数基准上，我们的模型均优于开源对话模型，并且基于我们对助益性和安全性的人工评估，它们或可成为闭源模型的合适替代方案。我们详细描述了Llama 2-Chat的微调与安全性改进方法，旨在使社区能够在我们的工作基础上继续推进，并促进大语言模型的负责任开发。