This video provides a practical guide on using Large Language Models (LLMs), focusing on real-world applications and examples. Karpathy demonstrates various settings and features across different LLMs, explaining how they work and how users can leverage them in their daily lives and work.
LLM Ecosystem: The LLM ecosystem has expanded significantly beyond ChatGPT, with various models offered by big tech companies (Google, Meta, Microsoft) and startups. Leaderboards like chatbot arena and Scale AI's leaderboard help track model performance.
Basic LLM Interaction: The most basic interaction involves providing text input and receiving text output. The process under the hood involves tokenizing the text into smaller chunks.
Model Selection and Pricing: Choosing the right LLM is crucial. Larger models generally offer better performance but are more expensive. Different providers (OpenAI, Anthropic, Google, etc.) offer various pricing tiers and models with different capabilities.
Thinking Models: "Thinking models" utilize reinforcement learning, enabling them to engage in more complex reasoning and problem-solving, particularly beneficial for challenging tasks in math and code. However, they're slower than non-thinking models.
Tool Use (Internet Search, Deep Research, Python Interpreter): LLMs can be enhanced with tools like internet search (allowing access to up-to-date information), deep research (combining search with reasoning for in-depth analysis), and Python interpreters (for complex calculations and data analysis).
File Uploads and Document Context: Users can upload documents to provide LLMs with specific context, enabling them to answer questions based on the uploaded material. This is useful for reading papers or books alongside the LLM.
Multimodality (Audio, Image, Video): LLMs are increasingly incorporating multimodality, allowing interaction via speech, image input/output, and video input/output. "Advanced voice mode" enables native audio processing within the model.
Quality-of-Life Features: Features like ChatGPT's memory function (saving preferences and context across conversations), custom instructions (tailoring the LLM's behavior), and custom GPTs (creating specialized LLMs for specific tasks) enhance user experience.
