Intro to Language Modeling 💡

Course Overview

• Goal: Build your own large language model (LLM) from scratch.
• Elements: Data handling, math, transformers.
• Created by: Elia Arleds.
• No high-level prerequisites in calculus or linear algebra required.
• Inspired by Andrej Karpathy’s GPT lecture.
• Basic Python (3 months experience) recommended.
• Features local computation and data handling.

Tools & Setup

• Using Jupyter Notebooks and Anaconda Prompt.
• Setting up virtual environment with CUDA for GPU acceleration.
• Essential Python libraries: matplotlib, numpy, PYLZMA, IPYKERNEL, Jupyter.
• Install torch with CUDA support configured.
• Ensure Python version (recommended: 3.10.9).

Data and Environment Preparation

• Wizard of Oz text data sample.
• Load, read, and clean text data.
• Tokenization: Character-level and subword-level explained.
• Conversion into Integer tokens.
• Setup for Tensor handling using PyTorch for efficient computation.

Language Model Basics

Bigram Language Model

• Train and validation splits (80% Train, 20% Validation).
• Explanation with Hello example ( Given: H, then predict: E).
• Use of PyTorch tensors to handle data.

Training Loop

• Hyperparameters: block_size, batch_size.
• Train/validation split to prevent memorizing data.
• Batching for scalability over GPU.
• Training involves optimizer (AdamW) and iterations.
• Reporting loss using PyTorch capabilities.

Transformer Models

• Inspired from Attention is All You Need paper.

Architecture Overview

• Inputs, Embeddings, Positional Encodings.
• Multi-head self-attention mechanism.
• Feed-forward networks within transformer blocks.
• Residual connections & Layer Normalization.

Multi-head Attention

• Each head exploits different aspects (like different perspectives).
• Scaled Dot-Product Attention with keys, queries, values.
• Parallel execution for efficiency on GPUs.
• Query-dot-Product-Key method explained, followed by scaling and masked fill.

Key Classes in PyTorch

• Module: GPTLanguageModel, Block, FeedForward, MultiHeadAttention.

Model Training

Parameter Initialization

• Standard deviation for parameter normalization.
• Efficient training and avoiding gradient explosion/vanishing.
• PyTorch functions for weight initialization (importance of practice).

Data Handling and Processing

• Loading extremely large data (Open Web Text) using memory mapping.
• Splits into training and validation datasets, stored separately.

Model Evaluation and Saving

• Save the model using pickle for serialization.
• Loading models for continued training or inference.
• Efficiency testing using time module.

Extensions & Tips

• Fine-tuning explained (different from pre-training: uses prompt-completion pairs).
• Quantization (reduce memory usage): 16-bit and 4-bit forms.
• Gradient Accumulation to fit more data
• Hugging Face Integration: Explore pre-trained models and datasets.

Key Points and Best Practices

• Base architecture explanations are vital for debugging and efficiency.
• Experiment with learning rates and hyperparameters dynamically.
• Always ensure your environment configurations are compatible with the models.
• Document your training process and understand each phase’s output for further adjustments.