Transformer Architecture in LLMs

Question: What is the role of the Transformer architecture in Large Language Models (LLMs)?

Answer: The Transformer architecture is the backbone of modern Large Language Models (LLMs). It is designed to handle sequential data efficiently, allowing LLMs to understand and generate human-like text. The key components of the Transformer architecture include:

Self-Attention Mechanism: Helps the model focus on relevant words in the input sequence.
Positional Encoding: Retains the order of words in a sequence.
Multi-Head Attention: Allows the model to attend to different parts of the sequence simultaneously.
Feedforward Layers: Ensures nonlinear transformations of the data for better learning.

        
        from transformers import AutoModel
      
        # Load a pre-trained LLM like GPT
      
        model = AutoModel.from_pretrained('gpt-3')
      
        # Visualize the Transformer's self-attention layers
      
        attention_heads = model.get_attention_scores()