[LLM Training - Lecture 7] Build a Language Models using Transformers from Scratch - Foundations

Introduction

Introduction

Welcome to Lecture 7 of our training program on mastering ChatGPT. In this session, we will focus on an essential component of large language models: Transformers. Introduced in 2018, the Transformer architecture has revolutionized artificial intelligence research and is a core technology behind many modern language models, such as GPT and LLaMA.

Objectives

By the end of this lecture, you will:

• Understand the history and applications of language models.
• Dissect the Transformer architecture into its components.
• Build language models from scratch, although the training of these models will occur in the next lecture.

Understanding Language Models

At their core, language models can be viewed as engines for text completion. For example, if given the sentence "Every morning I go to," a language model can suggest multiple possible continuations, each with different probabilities based on the context of the input. The task of predicting the next token based on given text allows for creativity by incorporating randomness into the generation process.

Importance of Temperature

In generating text, one of the crucial parameters to adjust is called temperature. A low temperature results in less randomness, leading to outputs that are more predictable but less creative. Conversely, a high temperature allows for greater creativity but may lead to erratic or nonsensical results.

The Evolution of Language Models

Traditionally, recurrent neural networks (RNNs) were the go-to architectures for sequential data processing. However, they faced challenges in handling long-term dependencies due to issues such as the vanishing gradient problem. Transformers addressed these shortcomings by allowing parallel processing of sequential data, thus improving efficiency and effectiveness.

Transformer Architecture

The Transformer architecture comprises several components:

1. Input Embedding: Converts tokenized text into numerical representation, ready for further processing.
2. Self-Attention Mechanism: Computes the relationships between different tokens in a given input sequence.
3. Multi-Head Attention: Duplicates the self-attention mechanism multiple times to capture diverse patterns and relationships.
4. Position Encoding: Adds information about the position of each token in the sequence to the embeddings, ensuring the model does not lose track of word order.
5. Feedforward Network: A fully connected neural network added after the attention layers for added complexity and non-linearity.
6. Layer Normalization: Helps stabilize training and improve gradient flow within the network.

Attention Mechanism

The attention mechanism is central to how Transformers process information. It uses a scoring system (dot product) to determine the relevance of tokens relative to one another. The scaling factor and softmax function normalize the output, making it suitable for probabilistic interpretation and ensuring stability during training.

Training Overview

Training language models like Transformers requires significant computational resources, particularly when dealing with large datasets. Transformers utilize powerful GPUs and techniques like batch normalization to ensure consistent performance.

Building the Transformer Model

Here are the steps to create a Transformer architecture:

• Define the number of encoder and decoder layers.
• Specify hyperparameters such as embedding size, number of attention heads, and sizes for the feedforward networks.
• Implement the encoder and decoder using a loop that repeats for each layer.

By understanding the intrinsic functions of the Transformer, including various attention mechanisms, feedforward networks, and normalization techniques, one can effectively build and train large language models.

Conclusion

Transformers have become the leading architecture in natural language processing due to their efficiency, scalability, and ability to process long sequences. While traditional models like RNNs faced limitations, Transformers have paved the way for more effective text generation and comprehension.

Keywords

• Transformers
• Language Models
• Self-Attention
• Multi-Head Attention
• Position Encoding
• Feedforward Network
• Layer Normalization
• Temperature
• Batch Normalization

FAQ

1. What is a Transformer?

   • A Transformer is a neural network architecture introduced in 2018, designed for processing sequences of data efficiently, particularly in natural language processing tasks.

2. Why are Transformers preferred over RNNs?

   • Transformers allow for parallel processing of token sequences, overcoming the limitations of RNNs in handling long-term dependencies and sequential data.

3. What is the role of temperature in text generation?

   • Temperature controls the randomness of predictions in language models. A lower temperature yields more conservative outputs, while a higher temperature allows for more creative and varied responses.

4. What are the components of a Transformer architecture?

   • Key components include input embedding, self-attention, multi-head attention, position encoding, feedforward networks, and layer normalization.

5. How does the attention mechanism work?

   • The attention mechanism calculates the relevance of each token relative to others in a sequence through dot products, scaling, and normalization using a softmax function.