Notes on Building a Large Language Model

Course Overview

• Learn to build your own large language model (LLM) from scratch.
• Focus areas: data handling, mathematics, transformers behind LLMs.
• Instructor: Elia Arleds.

Course Structure

• No prerequisites in calculus or linear algebra.
• Aimed at beginners with basic Python experience (3 months recommended).
• Step-by-step approach to fundamental concepts.
• Inspired by Andre Karpathy's building a GPT from scratch lecture.

Course Setup

Tools and Environment

• Use Jupyter notebooks.
• Install Anaconda prompt for machine learning.
• Create a virtual environment for project isolation.
• CUDA for GPU acceleration.

Data Handling

• Training data size: 45 GB; have 90 GB reserved.
• Option to use different datasets if storage is insufficient.
• Data management will be demonstrated through the course.

Building the Model

Key Components

• Tokenizers: Used for converting characters to integers.
• Embeddings: Represent discrete inputs as dense vectors.
• Neural Network Layers: Include feed-forward networks and residual connections.

Model Initialization

• Use nn.Module for parameter tracking.
• Initialize weights with proper standard deviation for effective training.
• Set dropout rates for preventing overfitting.

Training Loop

• Loss functions: Cross-entropy for evaluating model predictions.
• Optimizers: AtomW for weight decay and momentum.

Hyperparameters

• Block size, batch size, learning rate, max iterations, dropout percentage.
• Adjust based on computational resources and desired model performance.

Model Architecture

Encoder vs. Decoder

• Encoder processes input sequences; Decoder generates outputs.
• GPT uses only decoder layers with masked attention to prevent lookahead.

Attention Mechanism

• Multi-head Attention: Allows parallel processing of input data.
• Self-attention: Identifies which tokens are important and how they relate to each other.
• Scaled Dot Product Attention: Prevents exploding gradients by scaling scores.

Conclusion

• Understanding how to build and train LLMs is crucial for practical applications in AI.
• Practice efficiency testing to optimize model performance.
• Experiment with fine-tuning and quantization for better results.

Additional Resources

• Hugging Face for pre-trained models and datasets.
• Importance of continual learning and improving model architectures.