transformers

Self-attention in Transformer architectures computes weighted relationships between all token positions in a sequence, allowing the model to dynamically focus on relevant context. It uses query, key, and value matrices to generate contextualized token representations.

Multi-head attention runs multiple self-attention mechanisms in parallel, each learning different representational subspaces. Self-attention uses a single attention function, while multi-head attention concatenates and linearly projects these parallel outputs for richer feature extraction.

Building a Transformer block involves assembling multi-head attention, a feed-forward network, layer normalization, and residual connections. You stack these components to process input sequences, enabling a forward pass through encoder or decoder architectures.

Yes, you can debug attention patterns in language models by analyzing the internal mechanics of multi-head attention. Understanding self-attention and residual connections helps identify why specific tokens are weighted heavily during model inference.

Transformers use layer normalization to stabilize training across deep networks and residual connections to mitigate vanishing gradients. Together, they ensure effective gradient flow through stacked encoder and decoder layers during language model fine-tuning.

Before designing attention mechanisms, you need foundational knowledge of neural-networks, self-attention math, and PyTorch or TensorFlow tensor operations. Understanding how feed-forward networks and layer normalization interact within Transformer architectures is essential.