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Understanding AI Systems for Cybersecurity: How a Large Language Model (LLM) works? — Part 2
- The article discusses the code behind the GPT-2 model and explains each step for better understanding of Large Language Models (LLMs).
- The GPT Model has two main parts: __init__ and Forward.
- The initialization of a GPTModel object involves setting up tok_emb and pos_emb matrices with random numbers and using dropout for regularization.
- The transformer blocks initialization is crucial in the model, allowing for the model's functioning and learning processes.
- The attention mechanism in LLM architecture plays a vital role in understanding the context and relationships between input parts.
- The Multi-Head Attention Layer helps the model learn dependencies and relationships between different input elements.
- The Feed Forward Layer projects the output of the attention layer into a richer representation space.
- Regularization, normalization, and shortcut connections are utilized to improve the model's performance and information flow.
- The forward pass function in the GPT Model class yields contextualized embeddings and logits for predicting the next token.
- LLMs represent artificial cognition, and understanding their inner workings is crucial in cybersecurity to prevent potential exploitation by malicious actors.
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