What does it take to build and train a large language model? An introduction

Imagine if, as your final exam for a computer science class, you had to create a real-world large language model (LLM).

Where would you start? It’s not like there’s an app for that. How would you create and train an LLM that would function as a reliable ally for your (hypothetical) team? An artificial-intelligence-savvy “someone” more helpful and productive than, say, Grumpy Gary, who just sits in the back of the office and uses up all the milk in the kitchenette.

It’s worth thinking about because while LLMs are still relatively in their infancy, the large language market is anticipated to reach $40.8 billion by 2029.

What’s a large language model?

What you’ve probably guessed about LLMs is true: in terms of model size, a large language model is positively huge. It’s a giant generative AI system that utilizes deep-learning algorithms and, in its text generation, simulates the ways people think. LLMs are so big, in fact, that Stanford’s Institute for Human Centered Artificial Intelligence (HAI) has dubbed some of them “foundation models,” starting points that can subsequently be optimized for different use cases.

Despite drawbacks such as biaseshallucination, and the possible end of human civilization, these larger models — both open source (e.g., from Hugging Face) and closed source — have emerged as powerful tools in natural language processing (NLP), enabling humans to generate coherent and contextually relevant text.

The role of transformers

From GPT-3 and GPT-4 (Generative Pre-trained Transformer) to BERT (Bidirectional Encoder Representations from Transformers), large models, characterized by transformer architectures, have revolutionized the way we interact with language technology. Transformers use parallel multi-head attention, affording more ability to encode nuances of word meanings. A self-attention mechanism helps the LLM learn the associations between concepts and words. Transformers also utilize layer normalization, residual and feedforward connections, and positional embeddings.

Ready, set, build?

With all of this in mind, you’re probably realizing that the idea of building your very own LLM would be purely for academic value. Still, it’s worth taxing your brain by envisioning how you’d approach this project. So if you’re wondering what it would be like to strike out and create a base model all your own, read on.

Gathering your LLM ingredients

The recipe for building and training an effective LLM requires several key components. These include:

Data preparation

Simply put, the foundation of any large language model lies in the ingestion of a diverse, high-quality data training set. This training dataset could come from various data sources, such as books, articles, and websites written in English. The more varied and complete the information, the more easily the language model will be able to understand and generate text that makes sense in different contexts. To get the LLM data ready for the training process, you use a technique to remove unnecessary and irrelevant information, deal with special characters, and break down the text into smaller components.

Computational resources

Due to the massive amount of data processing involved, LLM model training requires significant training-time computational resources:

  • Graphics processing units (GPUs) are specialized processors good at handling lots of calculations in parallel. They’re used to build LLMs because they can process data in high quantities much faster than regular processors.
  • Tensile processing units (TPUs) are another type of specialized processor, this one specifically designed for doing machine learning tasks. TPUs are built to handle large-scale operations with high efficiency, making them ideal for saving time and resources while training LLMs.
  • Random access memory (RAM) is used to store and process the vast amount of training data.
  • Storage space is crucial, as the training process generates and stores multiple versions of the model as its work progresses, which facilitates comparison and fine-tuning of the information.

NLP know-how

How much do you know about data science? Familiarity with NLP technology and algorithms is essential if you intend to build and train your own LLM. NLP involves the exploration and examination of various computational techniques aimed at comprehending, analyzing, and manipulating human language. As preprocessing techniques, you employ data cleaning and data sampling in order to transform the raw text into a format that could be understood by the language model. This improves your LLM’s performance in terms of generating high-quality text.

Machine-learning model expertise

To effectively build an LLM, it’s also imperative to possess a solid understanding of machine learning (ML), which involves using algorithms to teach a computer how to see patterns and make predictions from data. In the case of language modeling, machine-learning algorithms used with recurrent neural networks (RNNs) and transformer models help computers comprehend and then generate their own human language.

Programming proficiency

How are your programming skills? Knowing programming languages, particularly Python, is essential for implementing and fine-tuning a large language model.

You may be wondering “Why should I learn a programming language when OpenAI’s ChatGPT can write  code for me?” Surely, citizen developers without coding expertise can do that job?

Not quite. ChatGPT can help to a point, but programming proficiency is still needed to sift through the content and catch and correct minor mistakes before advancement. Being able to figure out where basic LLM fine-tuning is needed, which happens before you do your own fine-tuning, is essential.

For this task, you’re in good hands with Python, which provides a wide range of libraries and frameworks commonly used in NLP and ML, such as TensorFlow, PyTorch, and Keras. These libraries offer prebuilt modules and functions that simplify the implementation of complex architectures and training procedures. Additionally, your programming skills will enable you to customize and adapt your existing model to suit specific requirements and domain-specific work.

How to make an LLM

Excellent — you’ve gathered all the ingredients on your proverbial kitchen counter. Ready to mix up a batch of large language model? Let’s go:

1. Data collection and preprocessing

Collect a diverse set of text data that’s relevant to the target task or application you’re working on.

Preprocess this heap of material to make it “digestible” by the language model. Preprocessing entails “cleaning” it — removing unnecessary information such as special characters, punctuation marks, and symbols not relevant to the language modeling task.

Apply tokenization, breaking the text down into smaller units (individual words and subwords). For example, “I hate cats” would be tokenized as each of those words separately.

Apply stemming to reduce the words to their base forms. For example, words like “running,” “runs”, and “ran” would all be stemmed to “run.” This will help your language model treat different forms of a word as the same thing, improving its ability to generalize and understand text.

Remove stop words like “the,” “is”, and “and” to let the LLM focus on the more important and informative words.

2. Model architecture selection

Choose the right architecture — the components that make up the LLM — to achieve optimal performance. What are the options? Transformer-based models such as GPT and BERT are popular choices due to their impressive language-generation capabilities. These models have demonstrated exceptional results in completing various NLP tasks, from content generation to AI chatbot question answering and conversation. Your selection of architecture should align with your specific use case and the complexity of the required language generation.

3. Training the model

Training your LLM for the best performance requires access to powerful computing resources and careful selection and adjusting of hyperparameters: settings that determine how it learns, such as the learning rate, batch size, and training duration.

Training also entails exposing it to the preprocessed dataset and repeatedly updating its parameters to minimize the difference between the predicted model’s output and the actual output. This process, known as backpropagation, allows your model to learn about underlying patterns and relationships within the data.

4. Fine-tuning your LLM

After initial training, fine-tuning large language models on specific tasks or domains further enhances their performance. Fine-tuning LLMs allows them to adapt and specialize in a particular context, making it more effective for specific applications.

For example, let’s say pre-trained language models have been educated using a diverse dataset that includes news articles, books, and social-media posts. The initial training has provided a general understanding of language patterns and a broad knowledge base.

However, you want your pre-trained model to capture sentiment analysis in customer reviews. So you collect a dataset that consists of customer reviews, along with their corresponding sentiment labels (positive or negative). To improve the LLM performance on sentiment analysis, it will adjust its parameters based on the specific patterns it learns from assimilating the customer reviews.

5. Evaluating your work

How well is your LLM meeting quality standards? You can use metrics such as perplexity, accuracy, and the F1 score (nothing to do with Formula One) to assess its performance while completing particular tasks. Evaluation will help you identify areas for improvement and guide subsequent iterations of the LLM.

5. Deployment and iteration

Now that you’ve trained and evaluated your LLM, it’s ready for prime-time validation: deployment. Go ahead and integrate the model with your applications and existing systems, making its language-generation capabilities accessible to your end users, such as the professionals in various information-intensive industries.

Success! Your LLM is the equivalent of sitting in the oven, starting to smell like it’s half baked.

6. Prepare another batch…

You’re not finished. In fact, in summarization, you won’t be finished for a while, if ever. That’s because you can’t skip the continuous iteration and improvement over time that’s essential for refining your model’s performance. Gathering feedback from users of your LLM’s interface, monitoring its performance, incorporating new data, and fine-tuning will continually enhance its capabilities and ensure that it remains up to date.

Well, at least you’ve got job security.

Plus, now that you know the LLM model parameters, you have an idea of how this technology is applicable to improving enterprise search functionality. And improving your website search experience, should you now choose to embrace that mission, isn’t going to be nearly as complicated, at least if you enlist some perfected functionality.

Build superior online search

Algolia’s API uses machine learning–driven semantic features and leverages the power of LLMs through NeuralSearch. Our state-of-the-art solution deciphers intent and provides contextually accurate results and personalized experiences, resulting in higher conversion and customer satisfaction across our client verticals.

Ready to optimize your search? Ping us or see a demo and we’ll be happy to help you train it to your specs.

About the authorVincent Caruana

Vincent Caruana

Senior Digital Marketing Manager, SEO

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