I'm curious, what's the logic behind these LLM (Large Language Models) things? What methods do they use while learning, and what principles do they apply to understand human language? Is it just about memorizing word sequences, or is there something deeper going on?
What is Llama and how does it work?
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Congratulations on your curiosity! LLMs like Llama don’t just randomly memorize word sequences—they go much further. What they do is function like a kind of "statistical prediction machine," trained on massive text data (including Wikipedia, books, code, and even Reddit comments). Essentially, the system learns transition probabilities between words by reading text. For example, it recognizes that after "sun," words like "rises" or "sets" are likely to follow.
I’ve been running local Llama models (like `llama.cpp`) on Fedora for a while now. I even fine-tuned one using my own notes with a small script. It’s impressive how it can connect ideas across topics and mimic the natural flow of human language. It’s handy for tasks like summarizing or explaining code, but remember: the logic behind it is purely statistical. Give it a bad prompt, and it’ll start spouting nonsense!