Understanding LLM Inference Flow: A Visual Guide

Large Language Models (LLMs) have revolutionized how we interact with AI systems, but understanding how they actually process and generate text can seem like black magic. In this comprehensive guide, we’ll break down the inference flow of LLMs, making it accessible to both technical and non-technical audiences.

What is LLM Inference?

LLM inference is the process by which a trained language model takes input text and generates a response. Unlike training, which involves learning patterns from vast amounts of data, inference is about applying that learned knowledge to produce meaningful outputs.

The Inference Pipeline

1. Tokenization

The first step in the inference pipeline is converting human-readable text into tokens that the model can understand.

# Example tokenization
input_text = "What is artificial intelligence?"
tokens = tokenizer.encode(input_text)
# Output: [2061, 318, 11666, 4430, 30]