Deep Dive into LLMs like ChatGPT

Resumen

This video provides a comprehensive, accessible technical deep dive into Large Language Models like ChatGPT. It breaks down the pipeline into three distinct phases: pre-training on vast internet datasets to build a 'base model' (a token simulator), supervised fine-tuning to transform that model into a helpful assistant by imitating human experts, and reinforcement learning to refine reasoning capabilities through trial and error. The analyst emphasizes that these models are stochastic token predictors, not sentient beings, and provides practical mental models, such as using 'tools' (code interpreters/web search) to mitigate hallucinations and understanding that reasoning should be distributed across tokens rather than crammed into single forward passes.

Capítulos

1. 0:00Introducción y recolección de datos
2. 1:15:00Arquitectura y tokenización de modelos
3. 2:30:00Entrenamiento de modelos base

Temas tratados

• The Pre-training Pipeline
• Tokenization Mechanics

+5 temas más (contenido completo al seguir el canal)

Extracto de la transcripción

hi everyone so I've wanted to make this video for a while it is a comprehensive but General audience introduction to large language models like Chachi PT and what I'm hoping to achieve in this video is to give you kind of mental models for thinking through what it is that this tool is it is obviously magical and amazing in some respects it's uh really good at some things not very good at other things and there's also a lot of sharp edges to be aware of so what is behind this text box you can put anything in there and press enter but uh what should we be putting there and what are these words generated back how does this work and what what are you talking to exactly so I'm hoping to get at all those topics in this video we're going to go through the entire pipeline of how this stuff is built but I'm going to keep everything uh sort of accessible to a general audience so let's take a look…