LLaMA vs Transformers

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LLAMA vs Transformers: Exploring the Key Architectural Differences (RMS Norm, GQA, ROPE, KV Cache)

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…