Back

In this paper, the authors introduce Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. The authors specifically focus on Llama 2-Chat, which is optimized for dialogue use cases. They claim that their models outperform open-source chat models on most benchmarks tested and may be a suitable substitute for closed-source models based on their human evaluations for helpfulness and safety.

The authors provide a detailed description of their approach to pretraining and fine-tuning Llama 2-Chat. For pretraining, they use a large amount of training data from various sources. They also use a mixture of experts architecture to handle the large number of parameters in the models. They evaluate the performance of the pretrained Llama 2 models and show that they achieve competitive results on a range of benchmark tasks.

For fine-tuning, the authors explore two approaches: supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). In SFT, the models are fine-tuned on task-specific datasets using supervised learning. In RLHF, the models are fine-tuned using a reward model based on human evaluations. The authors also introduce a system message for multi-turn consistency, which helps in maintaining coherence and consistency in multi-turn conversations.

The authors provide results showing the effectiveness

Words: 212