LLM course takeaway

LLM Course Takeaways

Here is a collection from the LLM course that highlights the ‘Key Takeaways’ or ‘summary’ sections in some chapters.

Chapter1: Transformer Models

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
Encoder-decoder	BART, T5, Marian, mBART	Summarization, translation, generative question answering

Modern LLM developments

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

Chapter2: Using Transformers

summary

• Learned the basic building blocks of a Transformer model.
• Learned what makes up a tokenization pipeline.
• Saw how to use a Transformer model in practice.
• Learned how to leverage a tokenizer to convert text to tensors that are understandable by the model.
• Set up a tokenizer and a model together to get from text to predictions.
• Learned the limitations of input IDs, and learned about attention masks.
• Played around with versatile and configurable tokenizer methods.

  Chapter3: Fine-tuning a Pretrained Model

  summary

• Learned about datasets on the Hub and modern data processing techniques
• Learned how to load and preprocess datasets efficiently, including using dynamic padding and data collators
• Implemented fine-tuning and evaluation using the high-level Trainer API with the latest features
• Implemented a complete custom training loop from scratch with PyTorch
• Used 🤗 Accelerate to make your training code work seamlessly on multiple GPUs or TPUs
• Applied modern optimization techniques like mixed precision training and gradient accumulation

  processing the data

  • Use batched=True with Dataset.map() for significantly faster preprocessing
  • Dynamic padding with DataCollatorWithPadding is more efficient than fixed-length padding
  • Always preprocess your data to match what your model expects (numerical tensors, correct column names)
  • The 🤗 Datasets library provides powerful tools for efficient data processing at scale

    Trainer API

  • The Trainer API provides a high-level interface that handles most training complexity
  • Use processing_class to specify your tokenizer for proper data handling
  • TrainingArguments controls all aspects of training: learning rate, batch size, evaluation strategy, and optimizations
  • compute_metrics enables custom evaluation metrics beyond just training loss
  • Modern features like mixed precision (fp16=True) and gradient accumulation can significantly improve training efficiency

    full training loop

  • Manual training loops give you complete control but require understanding of the proper sequence: forward → backward → optimizer step → scheduler step → zero gradients
  • AdamW with weight decay is the recommended optimizer for transformer models
  • Always use model.eval() and torch.no_grad() during evaluation for correct behavior and efficiency
  • 🤗 Accelerate makes distributed training accessible with minimal code changes
  • Device management (moving tensors to GPU/CPU) is crucial for PyTorch operations
  • Modern techniques like mixed precision, gradient accumulation, and gradient clipping can significantly improve training efficiency

    understanding learning curves

  • Learning curves are essential tools for understanding model training progress
  • Monitor both loss and accuracy curves, but remember they have different characteristics
  • Overfitting shows as diverging training/validation performance
  • Underfitting shows as poor performance on both training and validation data
  • Tools like Weights & Biases make it easy to track and analyze learning curves
  • Early stopping and proper regularization can address most common training issues