LLM Flow

Large language models (LLMs) are deep learning models that process and generate human-like text by identifying patterns and relationships in massive datasets. 

Their ability to understand context and generate coherent responses stems from a specialized neural network architecture called a transformer.

Core Components

• Tokenizer: Before an LLM can process text, it must convert words into a numerical format. A tokenizer breaks down the input text into smaller units called tokens. These tokens can be words, parts of words, or punctuation. Each token is then assigned a unique numerical ID.
  
  
• Embeddings: The numerical IDs from the tokenizer are then converted into vector embeddings. An embedding is a multi-dimensional array of numbers that represents a token. 
  
  
• Transformer Architecture: This is the heart of an LLM. It uses a mechanism called self-attention to weigh the importance of different tokens in the input text when generating a new token. 

It's represented in simple way by LevelUpCoding as attached.