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:
NLP encompasses a wide range of tasks from classification to generation
LLMs are powerful models trained on massive amounts of text data
These models can perform multiple tasks within a single architecture
Despite their capabilities, LLMs have limitations including hallucinations and bias
Transformer capabilities
You saw how the pipeline() function from 🤗 Transformers makes it easy to use pre-trained models for various tasks:
Text classification, token classification, and question answering
Text generation and summarization
Translation and other sequence-to-sequence tasks
Speech recognition and image classification
Transformer architecture
We discussed how Transformer models work at a high level, including:
The importance of the attention mechanism
How transfer learning enables models to adapt to specific tasks
The three main architectural variants: encoder-only, decoder-only, and encoder-decoder
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
You also learned about recent developments in the field:
How LLMs have grown in size and capability over time
The concept of scaling laws and how they guide model development
Specialized attention mechanisms that help models process longer sequences
The two-phase training approach of pretraining and instruction tuning
Practical applications
Throughout the chapter, you’ve seen how these models can be applied to real-world problems:
Using the Hugging Face Hub to find and use pre-trained models
Leveraging the Inference API to test models directly in your browser
Understanding which models are best suited for specific tasks
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:
Use the Transformers library to load and fine-tune models
Process different types of data for model input
Adapt pre-trained models to your specific tasks
Deploy models for practical applications
The foundation you’ve built in this chapter will serve you well as you explore more advanced topics and techniques in the coming sections.