Going further with large language models

Contents

• Introduction Introduction

  • The impact and functionality of large language models

    31s
• 1. Transformers in NLP 1. Transformers in NLP

  • What are large language models?

    2m 48s
  • How are large language models trained? Pre-training

    4m 24s
  • Making large language models follow instructions

    4m 20s
• 2. Large Language Model Components 2. Large Language Model Components

  • Transformer architecture

    2m 32s
  • What are parameters

    2m 59s
  • What are tokens?

    1m 8s
  • What are context lengths?

    3m 39s
• 3. Large Language Models 3. Large Language Models

  • BERT

    3m 16s
  • Scaling laws

    3m 30s
  • GPT-3

    7m 41s
  • Chinchilla

    7m 54s
  • PaLM and PaLM 2

    3m 59s
  • ChatGPT and GPT-4

    5m 47s
  • Open LLMs

    5m 40s
  • Comparing LLMs

    3m 35s
  • GitHub Models: Comparing LLMs

    2m 52s
  • Accessing large language models using an API

    6m 25s
  • LLM trends

    4m 6s
• Conclusion Conclusion

  • Going further with large language models

    28s