How LLMs Are Trained? Went in caveman mode again 🤣 hope it was useful! In the Reel, I explained it in the simplest way: Data = examples the model learns from Tokens = text broken into smaller units Weights = learned numbers inside the network Pretraining = learning to predict the next token Instruction tuning = learning how to respond to requests RLHF = learning which responses humans prefer Now the slightly more technical version: An LLM starts with a large dataset containing text, code, and other carefully selected sources. Before training, the data is cleaned, filtered, and deduplicated. The text is broken into tokens, which are converted into numbers the model can process. At first, the Transformer’s weights are mostly random. During pretraining, the model predicts the next token, measures how wrong the prediction was, and updates those weights. Predict. Measure error. Update. Repeat. After this happens across massive datasets using many GPUs, a base model is created. But a base model mainly knows how to continue text. It is not automatically a helpful chat assistant. That is where post-training begins. Instruction tuning teaches the model how to follow requests using examples of good prompts and responses. Then preference-training methods such as RLHF use human feedback to guide the model toward responses people consider more helpful, safer, and better aligned. So the Reel’s simple version is actually the core idea: Data provides examples. Pretraining teaches prediction. Instruction tuning teaches chat-assistant behaviour. Preference training teaches preferred behaviour. Evaluation checks quality. An LLM is not born smart. It becomes useful through data, optimization, post-training, evaluation, and a huge amount of compute. Very expensive baby indeed😅 . . . [ #LLM Large Language Models, How LLMs Are Trained, LLM Training, Transformer, Pretraining, Post Training, Instruction Tuning, #RLHF ,Reinforcement Learning from Human Feedback, Preference Tuning, AI Alignment, Tokenization, Tokens, Model Weights, Neural Networks, Base Model, Chat Assistant, Generative AI, Deep Learning, #MachineLearning #AI Engineering, LLM Engineering, MLOps] - @jam.with.ai