Summary

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In this chapter, you’ve been introduced to the fundamentals of Transformer models, Large Language Models (LLMs), and how they’re revolutionizing AI and beyond.

Key concepts covered

Natural Language Processing and LLMs

We explored what NLP is and how Large Language Models have transformed the field. You learned that:

Transformer capabilities

You saw how the pipeline() function from 🤗 Transformers makes it easy to use pre-trained models for various tasks:

Transformer architecture

We discussed how Transformer models work at a high level, including:

Model architectures and their applications

A key aspect of this chapter was understanding which architecture to use for different tasks:

Model Examples Tasks
Encoder-only BERT, DistilBERT, ModernBERT Sentence classification, named entity recognition, extractive question answering
Decoder-only GPT, LLaMA, Gemma, SmolLM Text generation, conversational AI, creative writing
Encoder-decoder BART, T5, Marian, mBART Summarization, translation, generative question answering

Modern LLM developments

You also learned about recent developments in the field:

Practical applications

Throughout the chapter, you’ve seen how these models can be applied to real-world problems:

Looking ahead

Now that you have a solid understanding of what Transformer models are and how they work at a high level, you’re ready to dive deeper into how to use them effectively. In the next chapters, you’ll learn how to:

The foundation you’ve built in this chapter will serve you well as you explore more advanced topics and techniques in the coming sections.

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