Large Language Models (LLMs) are built on the transformer architecture, which processes text in a way that mimics human understanding. The first step is converting words into numbers through tokenization, so that machines can handle them mathematically. Each token is then embedded into a vector space that captures its meaning, and positional encoding is added to help the model understand word order. Large Language Models (LLMs) are powered by the transformer architecture, which processes text much like how humans make sense of language. They turn words into numbers, understand context through attention mechanisms, and then generate meaningful responses step by step.

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Key Points :
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🌟 Tokenization: Words are converted into numbers (tokens) so the model can handle them mathematically.
⚡ Embeddings: Each token is mapped into a vector space that carries contextual meaning.
🌟 Positional Encoding: Adds information about the order of words in a sentence.
⚡ Self-Attention: Each word interacts with others to adjust its meaning based on context (e.g., “bank” in different sentences).
🌟 Multi-Head Attention: Looks at multiple relationships in parallel for deeper understanding.
⚡ Feedforward Network: Further refines the processed token representations.
🌟 Decoder Role: Predicts the next word step by step using previous context.
⚡ Linear & Softmax Layers: Select the most probable word from the vocabulary.
🌟⚡ Output Generation: Produces coherent and human-like text as the final result.