LLaMA Components › Component Interactions

Understanding each component in isolation is valuable, but the real insight comes from seeing how they interact. In LLaMA, the components form a carefully orchestrated pipeline where each element's design choices complement the others.

A single LLaMA block processes input through the following sequence:

1. Pre-attention RMSNorm: Stabilizes activations before attention
2. RoPE-enhanced attention: Mixes information across positions with relative position awareness
3. Residual connection: Adds attention output to original input
4. Pre-FFN RMSNorm: Stabilizes activations before the feed-forward network
5. SwiGLU FFN: Transforms each position independently through gated nonlinearity
6. Residual connection: Adds FFN output to post-attention representation

Let's visualize this flow:

The components weren't selected arbitrarily. Each choice addresses specific challenges in training and inference:

RMSNorm + Pre-Norm provides stable gradients for deep networks. The pre-norm configuration creates direct gradient paths through residuals, while RMSNorm's efficiency matters when normalization runs at every layer.

RoPE enables long-context extension through interpolation. Since RoPE applies rotation at inference time rather than fixed position embeddings, the model can extrapolate to longer sequences than seen during training (with appropriate scaling techniques).

SwiGLU provides expressive nonlinearity without the dead neuron problem of ReLU. The gating mechanism learns to selectively process information, and the smooth Swish activation ensures gradients flow through all neurons.

Together, these choices create an architecture that trains stably at scale, infers efficiently, and generalizes well to new contexts.