How LLMs Work: A Visual Guide

I walk through how a transformer-based Large Language Model (LLM) generates text. From tokenization to embeddings, self-attention, logits, softmax, sampling, the autoregressive loop, and detokenization, I break down each step visually so you can see how it all fits together. For deeper dives, I highly recommend the excellent 3Blue1Brown videos on neural networks and transformers:    • Large Language Models explained briefly   Twitter:   / hashlipsnft   Website: https://hashlips.io/ HashLips/HashLips Lab provides educational content and open-source code for informational purposes only, without any express or implied warranties on accuracy or completeness. Our materials are not intended as financial or professional advice and should not replace professional judgment or expertise. The use of our content and code is at your sole discretion and risk. Please note, that our resources are not financial advice. Decisions made based on our content are the user's responsibility, and HashLips/HashLips Lab assumes no liability for any direct or indirect losses, including but not limited to data loss or profit loss, that may result from utilizing our educational materials or open-source code. #ai #llms