title: Llama 2: Open Foundation and Fine-Tuned Chat Models
summary: 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.
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paper_title: Llama 2: Open Foundation and Fine-Tuned Chat Models
paper_summary: 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.
paper_ai_summary: Llama 2, a series of pretrained and fine-tuned large language models, achieves superior performance in dialogue tasks compared to open-source alternatives and offers safety enhancements.
paper_ai_keywords: pretrained language models fine-tuning dialogue use cases human evaluations safety improvements