Unleash the Power of Large Language Models (LLMs)
summary
Explains that modern LLMs are Transformer‑based, GPT‑4–class autoregressive models tuned for text generation, excel at summarization and ideation but fail silently on factual reliability and complex math (hallucinations & silent failures). Cites costs and scale: a 1.5B‑param text model once cost ~$1.6M to train and running GPT‑3 estimates at ~$87k/year, while Bloom’s training used energy equal to 41 years of an average US home. Recommends prompt engineering, role‑playing and instruction‑fine‑tuning, and notes open‑source, parameter‑efficient fine‑tuning and Flash attention as practical tools.
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chapters (16)
- 0:00Intro
- 10:02History of large language models
- 16:54GPT-4
- 25:56CO2 emissions
- 31:56Hallucinations & silent failures
- 33:50What are LLMs good for?
- 35:30What are LLMs not so good for?
- 36:40Safety 101
- 38:32General guidelines for selection & deployment
- 40:32The fine art of prompting
- 45:26The Feynman technique
- 46:00The Pareto principle
- 46:33Storytelling
- 47:14Virtual assistants in automated space
- 48:11Use cases
- 51:46Outro
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