The Six-Step Transformer Inference Pipeline

Transformers operate through a systematic six-step pipeline that converts raw text into predictions. Understanding each step is critical for debugging fine-tuned models and recognizing where quality issues originate. The pipeline begins with tokenization, converting text to integer indices using byte-pair encoding or word tokenization. These tokens are converted to embeddings—dense vectors capturing semantic meaning. The embedding passes through transformer blocks containing multi-head attention and feed-forward networks, where the model learns relationships between tokens. A final normalization layer standardizes activations, followed by an unembedding layer that projects to vocabulary size. Softmax converts raw logits into probabilities across the vocabulary. This systematic…

Sign up free — one personalized lesson every day, matched to your role and goals.

Already have an account? Sign in