Explained: Large Language Models (LLMs) by Andrej Karpathy of OpenAI

This is an in-depth look into Large Language Models (LLMs), the core technical component behind systems like ChatGPT, Claude, and Bard.

The video delves into the intricate workings of LLMs, potential advancements, and the challenges that come with them, including security-related issues.

Powerful Language Models

The Llama 270b model, released by meta AI, stands as one of the most powerful language models.

It boasts 70 billion parameters and a parameters file size of 140 gigabytes.

Training this model requires a GPU cluster of around 6,000 GPUs, taking approximately 12 days and costing an estimated $2 million.

Essence of Training Large Models

Training large language models involves compressing a significant amount of internet text into a ‘zip file’ of the internet.

This process results in a smaller, more manageable set of parameters that capture essential information from the training text.

Neural Networks and Compression

Large language models, like neural networks, excel in the relationship between prediction and compression.

They are designed to predict the next word in a sequence, which is mathematically related to data compression and aids in capturing language patterns and context.