Building a Large Language Model (LLM) from Scratch
Introduction
Large Language Models (LLMs) are advanced AI systems trained on massive amounts of text data to understand and generate human-like language. They use deep neural networks to learn complex patterns in language, enabling tasks such as translation, summarisation, question-answering and creative writing. Recent advances in LLMs have been driven by increasing model size and data. Models such as BERT (2018) introduced bidirectional context understanding, while GPT-3 (2020) scaled up to 175 billion parameters, demonstrating surprising zero-shot capabilities. More recent models such as GPT-4 are multimodal, processing both text and images as input, highlighting the rapid development in this field.
LLMs have become crucial due to their versatility and state-of-the-art performance across many natural language processing tasks. They power conversational agents (e.g. ChatGPT), content generation tools and assistive AI in fields such as code writing and biomedical research. Real-world applications include sentiment analysis, chatbots for customer service, automated content generation, text summarisation and cybersecurity applications such as threat detection in text logs. These models are transforming industries by enabling more natural interactions with technology and automating language-intensive tasks.
Core Components of an LLM
Transformer Architecture
Modern LLMs are based on the Transformer architecture, a neural network design introduced by Vaswani et al. (2017) that broke from the sequential nature of recurrent networks. Transformers use self-attention mechanisms to process input text, allowing the model to weigh the relevance of different words to each other regardless of their position in the sequence. Self-attention enables the model to capture long-range dependencies and context that earlier RNN-based models struggled with.
The Transformer architecture features a stack of repeated layers, each containing a multi-head self-attention sublayer and a feed-forward neural network sublayer, with residual connections and layer normalisation to stabilise training. Multi-head attention means the model computes attention multiple times in parallel (with different learned weight projections), allowing it to focus on different aspects of the context simultaneously. This architecture is highly parallelisable, making it efficient to train on large datasets using GPUs or TPUs.
Most LLMs (GPT-family, BERT, etc.) are built on transformers, either using the decoder part for text generation or the encoder part for understanding, or both in an encoder-decoder setup for sequence-to-sequence tasks.
Tokenisation Techniques
Before text is fed to an LLM, it must be converted into a sequence of tokens (numbers). Tokenisation breaks text into units such as words or subwords that the model’s vocabulary covers. Modern LLMs rely on subword tokenisation approaches to handle the open-ended vocabulary of natural language.
One popular method is Byte Pair Encoding (BPE), initially a data compression technique adapted for NLP. BPE starts with an initial vocabulary (e.g. all characters) and iteratively merges the most frequent pair of tokens into a new token. This yields a vocabulary of subword units that effectively balances between single characters and whole words. OpenAI’s GPT models use a byte-level BPE tokeniser, starting from raw bytes, ensuring any text (including emojis or foreign scripts) can be encoded without unknown tokens.
Similar to BPE is WordPiece, used by Google’s BERT, which also builds subwords based on frequency and likelihood, and SentencePiece (Unigram model) used in models such as T5 that learns subwords via a probabilistic algorithm. The goal of all these methods is to represent rare words as combinations of more common subword units (e.g. "unlockable" → "unlock"+"able"), while keeping frequent words as single tokens.
By doing so, the model does not need an impossibly large vocabulary. A few tens of thousands of subword tokens can cover essentially any text. Effective tokenisation is crucial, as it impacts model efficiency (longer sequences if too fine-grained) and the handling of unknown or rare terms. Once a tokeniser is trained (often on the same data as the LLM pretraining corpus), it is used to convert training text into token sequences and will be used again at inference to encode inputs and decode model outputs.
Training, Fine-Tuning and Deployment
Training an LLM
LLMs are trained with self-supervised learning objectives on large text corpora. Two common training objectives are:
During training, the model processes batches of token sequences and computes a loss measuring the difference between its predicted output and the actual text. Typically, the loss is cross-entropy over the vocabulary, which quantifies how well the predicted probability distribution for the next word matches the true word.
Optimisation is usually done with variants of stochastic gradient descent, a particularly popular choice being AdamW (Adam optimiser with weight decay). AdamW is well-suited for large models as it adapts learning rates per parameter and includes regularisation to help prevent overfitting.
Training an LLM from scratch demands large amounts of data and careful tuning of hyperparameters such as the learning rate, batch size and gradient clipping threshold. Strategies such as gradient accumulation, mixed precision training and distributed training (using frameworks like DeepSpeed and FSDP) are essential for handling large-scale LLMs.
Fine-Tuning an LLM
Instead of training a large model from scratch for each new task, transfer learning allows fine-tuning a pretrained model on a specific dataset. Fine-tuning involves continuing training with a focused dataset, often with supervised objectives.
A key approach is instruction tuning, where models are fine-tuned on human instruction-response datasets to improve their ability to follow commands. An advanced form of fine-tuning is Reinforcement Learning from Human Feedback (RLHF), which trains the model using human preference data to align outputs with desirable behaviours.
Fine-tuning can be done efficiently with LoRA (Low-Rank Adapters), prefix-tuning or other parameter-efficient tuning techniques that require fewer trainable parameters while adapting the model effectively.
Deploying an LLM
Deploying an LLM for real-world applications involves optimising inference speed and reducing computational cost. Some common deployment strategies include:
Large-scale LLMs are often deployed in cloud environments with distributed inference servers, whereas smaller optimised models may be deployed on edge devices or in hybrid cloud-edge configurations.
Conclusion
Building an LLM from scratch is a complex but achievable task with modern frameworks and enterprise-scale resources. The field is rapidly evolving with trends such as retrieval-augmented generation (RAG), multimodal models and longer-context architectures, all improving model efficiency and capabilities.
By following best practices in training, fine-tuning and deployment, developers can create LLMs that are not only powerful but also aligned with ethical considerations and practical applications.