💡 In my very humble opinion, this is hands-down the *one* article that every software engineer should read to understand Large Language Models (LLMs). omrimallis.com/posts/underst… 👇🏼 "In this post, the author will delve into the inner workings of Large Language Models (LLMs) to provide a practical understanding of their functionality. The exploration will be facilitated through the use of llama.cpp, a pure C++ implementation of Meta’s LLaMA model, which the author finds to be an excellent resource for comprehending LLMs in depth. Its code is appreciated for being clean, concise, and straightforward, avoiding unnecessary abstractions. The specific commit version of this code will be used in the discussion. The focus will be on the inference aspect of LLMs, specifically how these trained models generate responses based on user prompts. [...] Throughout the post, the author will guide readers through the entire inference process, covering several key topics, which include: 1. Tensors: Providing a basic overview of how mathematical operations in LLMs are executed using tensors, potentially with GPU acceleration. 2. Tokenization: Explaining how user prompts are broken down into a list of tokens, which serve as the input for the LLM. 3. Embedding: Detailing the process of transforming these tokens into vector representations. 4. The Transformer: Focusing on the core part of the LLM architecture, particularly the self-attention mechanism, which is vital for inference. 5. Sampling: Discussing how the model selects the next predicted token, with an exploration of two different sampling techniques. 6. The KV Cache: Examining a common optimization strategy used to enhance inference speed in large prompts, including a basic kv cache implementation. By the end of this post, readers should gain a comprehensive understanding of how LLMs function, equipping them to delve into more advanced topics, some of which will be outlined in the final section." #LargeLanguageModels #LLMs #Transformer #Llama #llamacpp #SoftwareEngineering