How to Build a Large Language Model from Scratch

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) by enabling human-like text generation, translation, summarization, and question-answering. While companies like OpenAI, Google, and Meta dominate the space with massive-scale models like GPT, LLaMA, and PaLM, researchers and enterprises are increasingly interested in building custom LLMs tailored to specific needs.

Building an LLM from scratch requires significant data processing, computational resources, model architecture design, and training strategies. This article provides a step-by-step guide on how to build an LLM, covering key considerations such as data collection, model architecture, training methodologies, and evaluation techniques.

1. Understanding Large Language Models

Before building an LLM, it’s worth understanding how they work. LLMs are deep learning models trained on massive text corpora using Transformer-based architectures. They rely on self-attention mechanisms to process language efficiently and generate coherent responses.

Key Characteristics of LLMs:

• Pretrained on vast datasets: LLMs require training on billions of words from diverse sources (books, articles, conversations, etc.).
• Self-supervised learning: They learn through masked language modeling (MLM) or causal language modeling (CLM).
• Scalability: Requires massive computational resources (TPUs/GPUs).
• Transfer learning: Can be fine-tuned for specialized tasks after pretraining.

Popular architectures include GPT (decoder-only), BERT (encoder-only), and T5 (encoder-decoder models).

2. Data Collection and Preprocessing

A. Collecting High-Quality Text Data

The first step in training an LLM is gathering a diverse, high-quality dataset. Ideally, the dataset should include:

• Books, research papers, Wikipedia articles for structured knowledge.
• Web crawl data, news articles, forums for real-world context.
• Conversational data, code repositories, legal/medical texts (if specialized knowledge is needed).

Open datasets include:

• Common Crawl – Web data.
• OpenWebText – Reddit-based dataset.
• Wikipedia Dumps – Encyclopedic knowledge.
• BooksCorpus – Digitized books.

B. Cleaning and Tokenization

Once raw text data is collected, it requires extensive preprocessing to improve consistency and reduce noise before training the language model. Cleaning and tokenization involve several key steps:

1. Text Cleaning

• Remove HTML tags and special characters: If data comes from web sources, unnecessary HTML tags and symbols should be stripped.
• Lowercasing: Converting all text to lowercase ensures uniformity.
• Remove stopwords: Common words such as “the,” “and,” “is” may be removed to focus on meaningful content, depending on the use case.
• Handling contractions and misspellings: Expanding contractions (e.g., “can’t” → “cannot”) and correcting spelling mistakes enhance readability.
• Removing duplicate sentences and irrelevant content helps maintain data quality.

Example using Python’s re library for text cleaning:

import re

def clean_text(text):
    text = re.sub(r'<.*?>', '', text)  # Remove HTML tags
    text = re.sub(r'[^a-zA-Z0-9.,!?\'\s]', '', text)  # Remove special characters
    text = text.lower().strip()  # Lowercasing and stripping
    return text

sample_text = "<p>Hello! This is a <b>test</b> sentence with HTML tags.</p>"
print(clean_text(sample_text))

2. Tokenization

Tokenization breaks text into smaller components, such as words or subwords, to create structured input for training.

• Word Tokenization: Splitting sentences into words (e.g., “The cat runs” → [“The”, “cat”, “runs”]).
• Subword Tokenization: Uses byte pair encoding (BPE) or WordPiece to handle rare words efficiently (e.g., “running” → [“run”, “##ning”]).
• Character Tokenization: Breaks down text at the character level for specialized models.

Example using Hugging Face’s tokenizer:

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
text = "Building a language model requires data preprocessing."
tokens = tokenizer.tokenize(text)
print(tokens)

3. Sentence Segmentation

For tasks like summarization and translation, sentence splitting ensures logical structuring.

• Using spaCy or NLTK:

import spacy
nlp = spacy.load("en_core_web_sm")
text = "Hello world! How are you?"
doc = nlp(text)
print([sent.text for sent in doc.sents])

4. Normalization & Lemmatization

• Normalization: Converting text into a standard format (e.g., “U.S.A.” → “USA”).
• Lemmatization: Converting words to their root form (e.g., “running” → “run”).

By performing these preprocessing steps, the dataset becomes clean, structured, and optimized for training a robust language model.

3. Designing the Model Architecture

LLMs are based on the Transformer architecture, which relies on self-attention mechanisms. The most common architectures for building LLMs include:

A. Choosing a Model Type

• Decoder-only models (e.g., GPT-3, LLaMA): Used for text generation and completion.
• Encoder-only models (e.g., BERT): Best for classification and understanding tasks.
• Encoder-Decoder models (e.g., T5, BART): Suitable for text transformation tasks like summarization.

B. Defining Model Parameters

• Number of layers (depth of the network)
• Embedding dimensions (size of vector representation per token)
• Number of attention heads (multi-head self-attention)
• Feedforward hidden size (FFN layers for processing input)
• Vocabulary size (number of unique tokens handled by the model)

Example configuration:

config = {
    "num_layers": 24,
    "hidden_size": 1024,
    "num_attention_heads": 16,
    "vocab_size": 50000
}

3. Designing the Model Architecture

Designing an LLM architecture requires selecting an appropriate model structure, defining key hyperparameters, and ensuring scalability for training on large datasets. The Transformer architecture is the most widely used for LLMs due to its efficiency in handling sequential data and capturing long-range dependencies.

A. Choosing a Model Type

There are three main types of Transformer-based architectures used for LLMs:

• Decoder-only models (e.g., GPT-3, LLaMA): These models predict the next word in a sequence based on previous tokens, making them ideal for text generation tasks such as chatbots, code completion, and language translation.
• Encoder-only models (e.g., BERT): These models process entire input sequences simultaneously, making them suitable for classification, sentiment analysis, and question-answering tasks.
• Encoder-Decoder models (e.g., T5, BART): These models first encode input sequences and then decode them into structured outputs, making them effective for summarization, text-to-text transformations, and sequence-to-sequence learning tasks.

B. Defining Model Parameters

To configure an LLM effectively, key hyperparameters must be chosen based on the intended application:

• Number of layers (depth of the network): Determines how many Transformer blocks are stacked, influencing model complexity.
• Embedding dimensions: Defines the size of vector representations for tokens, impacting computational requirements and model expressiveness.
• Number of attention heads: Multi-head self-attention allows the model to focus on multiple aspects of input simultaneously.
• Feedforward hidden size: Controls the expansion ratio in the fully connected layers inside Transformer blocks.
• Vocabulary size: The number of unique tokens the model can recognize, including words, subwords, and special tokens.

Example of defining a model configuration in Python:

config = {
    "num_layers": 24,  # Number of Transformer layers
    "hidden_size": 1024,  # Size of token embeddings
    "num_attention_heads": 16,  # Number of attention heads
    "vocab_size": 50000,  # Vocabulary size
    "dropout_rate": 0.1  # Dropout for regularization
}

C. Implementing a Basic Transformer Model

To build an LLM, a Transformer model needs to be implemented using deep learning frameworks like TensorFlow or PyTorch.

Example using PyTorch:

import torch
import torch.nn as nn

class TransformerBlock(nn.Module):
    def __init__(self, hidden_size, num_attention_heads, dropout_rate=0.1):
        super().__init__()
        self.attention = nn.MultiheadAttention(hidden_size, num_attention_heads)
        self.feed_forward = nn.Sequential(
            nn.Linear(hidden_size, hidden_size * 4),
            nn.ReLU(),
            nn.Linear(hidden_size * 4, hidden_size)
        )
        self.layer_norm = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(dropout_rate)
    
    def forward(self, x):
        attn_output, _ = self.attention(x, x, x)
        x = self.layer_norm(x + self.dropout(attn_output))
        ff_output = self.feed_forward(x)
        x = self.layer_norm(x + self.dropout(ff_output))
        return x

This simple Transformer block can be stacked multiple times to create a deeper network suitable for large-scale language modeling.

4. Training the Model

raining a Large Language Model (LLM) is a complex and resource-intensive process that requires careful planning, optimized hardware, and efficient training strategies. This section covers the key aspects of training an LLM, from choosing the right hardware and frameworks to setting training objectives and optimizing performance.

A. Choosing Hardware and Frameworks

Training an LLM requires specialized hardware with high computational power. GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) are commonly used due to their ability to process large amounts of data in parallel. The choice of hardware significantly impacts training efficiency, cost, and model performance.

• GPUs: The most commonly used hardware for training deep learning models, with NVIDIA A100 and H100 GPUs being the industry standard for large-scale AI workloads.
• TPUs: Google’s Tensor Processing Units (TPUs) offer optimized performance for TensorFlow-based training and are ideal for large-scale distributed training.

Along with hardware, the choice of deep learning framework is crucial. Popular frameworks for training LLMs include:

• PyTorch: The most flexible framework, widely used for research and experimentation. PyTorch provides dynamic computation graphs, making it easier to debug and modify models.
• TensorFlow: Preferred for production-grade models due to its scalability and integration with TPU accelerators.
• DeepSpeed & Megatron-LM: Specialized frameworks designed to optimize large-scale model training, enabling efficient use of GPU memory and reducing computational overhead.

Using distributed training techniques such as model parallelism and data parallelism is essential when dealing with LLMs that contain billions of parameters.

B. Setting Training Objectives

LLMs are typically trained using one of two main learning objectives:

1. Causal Language Modeling (CLM) – Used in autoregressive models like GPT. The model is trained to predict the next token in a sequence, given all previous tokens. This approach enables models to generate coherent and contextually relevant text.Example:
   Input: “The cat sat on the”
   Expected Output: “mat”
2. Masked Language Modeling (MLM) – Used in bidirectional models like BERT. The model is trained to predict masked words within a sentence by leveraging context from both directions.Example:
   Input: “The cat sat on the [MASK]”
   Expected Output: “mat”

Both objectives require large-scale datasets, typically comprising billions of tokens from diverse text sources.

Example Training Setup in PyTorch

Below is a simple PyTorch training setup using Hugging Face’s transformers library for a GPT-2 model:

import torch
from transformers import GPT2LMHeadModel, Trainer, TrainingArguments

# Load a pre-trained GPT-2 model
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Define training arguments
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    save_steps=1000,
    logging_dir="./logs",
)

# Define Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset
)

# Train the model
trainer.train()

This setup includes a learning rate scheduler, a defined number of training epochs, and checkpoint saving to avoid loss of progress in case of interruptions.

C. Optimizing Training Performance

Training large models can take days or even weeks, requiring techniques to optimize memory usage and computational efficiency. Below are the most effective strategies for optimizing LLM training:

1. Gradient Checkpointing

LLMs consume huge amounts of GPU memory. Gradient checkpointing helps reduce this memory consumption by storing only select activations during the forward pass and recomputing others during backpropagation. This reduces memory overhead at the cost of slightly increased computation time.

from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel

model = GPT2LMHeadModel.from_pretrained("gpt2")
model.gradient_checkpointing_enable()

✅ Benefit: Allows training larger models without running out of memory.

2. Mixed-Precision Training

Using fp16 (half-precision floating point) speeds up training and reduces GPU memory usage. This is implemented using Automatic Mixed Precision (AMP) in PyTorch or TensorFlow.

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    fp16=True,  # Enable mixed precision training
    save_steps=1000,
)

✅ Benefit: 2x speed improvement in training while maintaining model accuracy.

3. Data Parallelism

When training across multiple GPUs, data parallelism ensures that each GPU processes a portion of the batch and then synchronizes gradients.

from torch.nn.parallel import DistributedDataParallel as DDP

model = DDP(model)  # Distributes the model across multiple GPUs

✅ Benefit: Reduces training time significantly by utilizing multiple GPUs.

🔹 Summary of Training Optimization Techniques

5. Evaluating and Fine-Tuning the Model

Once the model is trained, evaluation ensures it performs well across different tasks.

A. Evaluation Metrics

• Perplexity (PPL): Measures how well the model predicts the next token.
• BLEU/ROUGE scores: Evaluates text generation quality.
• Zero-shot/Few-shot performance: Assesses adaptability to unseen prompts.

B. Fine-Tuning on Domain-Specific Data

Fine-tuning allows adapting the model to legal, medical, or financial texts using domain-specific datasets.

model_finetune = GPT2LMHeadModel.from_pretrained("gpt2")
model_finetune.train()

6. Deploying the LLM

A. Optimizing for Inference

• Model quantization: Reduces model size for faster inference.
• Distillation: Uses a smaller model trained from a larger one.
• Serving frameworks: Deploy using Hugging Face Inference API, TensorRT, or ONNX Runtime.

B. API-Based Deployment

Use FastAPI or Flask to expose the model as an API:

from fastapi import FastAPI
app = FastAPI()

@app.post("/predict")
def predict(text: str):
    response = model.generate(text)
    return {"response": response}

Conclusion

Building a large language model from scratch requires high-quality data, a well-designed model architecture, extensive training, and rigorous evaluation. While resource-intensive, creating a custom LLM allows for fine-grained control over the model’s capabilities and optimizations for specific industry applications. By following structured data preprocessing, efficient training methodologies, and strategic deployment, organizations can successfully develop powerful language models tailored to their needs.