This latest release brings you the cutting-edge Llama language models, both pretrained and fine-tuned, ranging from 7B to 70B parameters, empowering a wide array of language-related tasks.
Llama 2 has been trained on 40% more data compared to Llama 1, and it boasts double the context length, enhancing its capabilities significantly.
Training Llama-2-chat involves a multi-step process. Initially, Llama 2 undergoes pretrained using publicly available online data. Subsequently, an initial version of Llama-2-chat is created through supervised fine-tuning. To refine it further, Llama-2-chat goes through iterative enhancements using Reinforcement Learning from Human Feedback (RLHF), which incorporates techniques like rejection sampling and proximal policy optimization (PPO).
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