Inside LLaMA: 5 Key Upgrades Over Vanilla Transformers

In this video, we explore the architectural differences between LLaMA and the standard transformer model. We dive deep into the major changes introduced by LLaMA, such as Pre-Normalization, SwiGLU activation function, Rotary Position Embedding (RoPE), Grouped Query Attention, and the use of KV Cache for improved performance. You’ll learn: The impact of Pre-Normalization for improved gradient flow and stability during training. How the SwiGLU activation function outperforms traditional ReLU. The benefits of RoPE for handling longer sequences. Why Grouped Query Attention is more efficient than…