Deep LLM architectures like DeepSpeed's DeepMo and OLMo 2 from Meta have introduced unique architectural choices beyond traditional Multi-Head Attention (MHA). DeepMo utilizes a deep network architecture with multiple layers of attention to enhance performance, while OLMo 2 focuses on normalization techniques. It employs Post-Norm (placing RMSNorm after the attention and FeedForward modules within residual connections) instead of Pre-Norm used in most contemporary LLMs like GPT-3 or Llama. Additionally, OLMo 2 introduces QK-norm to further refine its architecture. These design choices aim to improve training stability and model performance.
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