The Architecture of LLama3

In this tutorial, we'll explore the architecture of LLaMA3, a powerful large language model. The process of working with LLaMA3 involves several key steps, including converting raw text into a structured format, tokenizing it into words, and transforming these words into embedding vectors that capture their meanings. These vectors are then normalized, refined, and processed using attention mechanisms to generate context and produce meaningful outputs. Join us as we break down each stage of this process to better understand how LLaMA3 creates powerful language models.

More information

• Large Language Models (LLM) - What is AI
• Large Language Models (LLM) - What are LLMs
• Large Language Models (LLM) - Tokenization in AI
• Large Language Models (LLM) - Embedding in AI
• Large Language Models (LLM) - RoPE (Positional Encoding) in AI
• Large Language Models (LLM) - Layers in AI
• Large Language Models (LLM) - Attention in AI
• Large Language Models (LLM) - GLU (Gated Liner Unit) in AI
• Large Language Models (LLMs) - Normalization (RMS or RMSNorm) in AI
• Large Language Models (LLM) - Unembedding in AI
• Large Language Models (LLM) - Temperature in AI
• Large Language Models (LLM) - Model size an Parameter size in AI
• Large Language Models (LLM) - Training in AI
• Large Language Models (LLM) - Hardware acceleration, GPUs, NPUs in AI
• Large Language Models (LLM) - Templates in AI
• Large Language Models (LLM) - Putting it all together - The Architecture of LLama3