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Large Language Model Training in 2024

Large language models (LLMs) took the internet by storm at the end of 2022 as ChatGPT from OpenAI reached 1 million users just 5 days after its launch. ChatGPT’s capabilities and wide applications can be accredited to the 175 billion parameters the GPT-3 language model has. 

Although it is easy to use end-product language models like ChatGPT, developing a large language model takes significant computer science knowledge, time, and resources. We created this article to inform business leaders on large language model training, with focus on:

  1. Definition of large language models
  2. Examples of large language models
  3. Architecture of large language models
  4. The training process of large language models

What is a large language model?

A large language model is a type of machine learning model that is trained on a large corpus of text data to generate outputs for various natural language processing (NLP) tasks, such as text generation, question answering, and machine translation. 

Large language models are typically based on deep learning neural networks such as the Transformer architecture and are trained on massive amounts of text data, often involving billions of words. Larger models, such as Google’s BERT model, are trained with a large dataset from various data sources which allows them to generate output for many tasks.

If you are new to large language models, check our “Large Language Models: Complete Guide in 2024″ article.

Top large language models by parameter size

We compiled the 7 largest large language models by parameter size in the table below.1

ModelDeveloperParameter Size
WuDao 2.0Beijing Academy of Artificial Intelligence1.75 trillion
MT-NLGNvidia and Microsoft530 billion
BloomHugging Face and BigScience176 billion
GPT-3OpenAI175 billion
LaMDAGoogle137 billion
ESMFoldMeta AI15 billion
GatoDeepMind1.18 billion

Check our article on large language model examples for more models with in-depth information.

Architecture of large language models

The architecture of large language models, such as OpenAI’s GPT-3, is based on a type of deep learning called the Transformer architecture. It consists of the following main components (see Figure 1):

Figure 1: Transformer architecture 

Source:2

1. Input embedding

 The input sequence is first transformed into a dense vector representation, known as an embedding, which captures the relationships between words in the input.

2. Multi-head self-attention

The core component of the transformer block architecture is the multi-head self-attention mechanism, which allows the model to attend to different parts of the input sequence to capture its relationships and dependencies.

3. Feed-forward network

 After the self-attention mechanism, the output is fed into a feed-forward neural network, which performs a non-linear transformation to generate a new representation.

4. Normalization and residual connections

To stabilize the training process, the output from each layer is normalized, and a residual connection is added to allow the input to be passed directly to the output, allowing the model to learn which parts of the input are most important.

These components are repeated several times to form a deep neural network, which can process long sequences of text and generate high-quality outputs for various language tasks, such as text generation, question answering, and translation.

Developers continue to develop large language models by implementing new techniques to:

  • Simplify the model (decrease the model size or memory required to train),
  • Improve performance,
  • Lower price,
  • Decrease model training time.

Large language model training

There are four steps to training large language models:

1. Data collection and preprocessing 

The first step is to gather the training data set, which is the resource that the LLM will be trained on. The data can come from various sources such as books, websites, articles, and open datasets. 

Popular public sources to find datasets are:

  • Kaggle
  • Google Dataset Search
  • Hugging Face
  • Data.gov
  • Wikipedia database

The data then needs to be cleaned and prepared for training. This may involve converting the dataset to lowercase, removing stop words, and tokenizing the text into sequences of tokens that make up the text. 

2. Model selection and configuration

Large models such as Google’s BERT and OpenAI’s GPT-3 both use transformer deep learning architecture, which is the common choice for sophisticated NLP applications in recent years. Some key elements of the model such as:

  • Number of layers in transformer blocks
  • Number of attention heads
  • Loss function
  • Hyperparameters

need to be specified when configuring a transformer neural network. The configuration can depend on the desired use case and the training data. The configuration of the model directly influences the training time of the model. 

3. Model training

The model is trained on the pre-processed text data using supervised learning. During training, the model is presented with a sequence of words and is trained to predict the next word in the sequence. The model adjusts its weights based on the difference between its prediction and the actual next word. This process is repeated millions of times until the model reaches a satisfactory level of performance.

Since the models and data are large in size, it requires immense computation power to train models. To decrease training time, a technique called model parallelism is used. Model parallelism enables different parts of a large model to be spread across multiple GPUs, allowing the model to be trained in a distributed manner with AI chips

By dividing the model into smaller parts, each part can be trained in parallel, resulting in a faster training process compared to training the entire model on a single GPU or processor. This results in faster convergence and better overall performance, making it possible to train even larger language models than before. Common types of model parallelism include:

  • Data parallelism
  • Sequence parallelism
  • Pipeline parallelism
  • Tensor parallelism 

Training a large language model from the ground up requires significant investment, a more economical alternative is to fine-tune an existing language model to tailor it to your specific use case. A single training run for GPT-3 is estimated to cost around $5 million.

4. Evaluation and fine-tuning

After training, the model is evaluated on a test dataset that has not been used as a training data set to measure the model’s performance. Based on the evaluation results, the model may require some fine-tuning by adjusting its hyperparameters, changing the architecture, or training on additional data to improve its performance. 

Training LLMs for specific use cases

Training of an LLM consists of two parts: pre-training and task-specific training. Pre-training is part of training that enables the model to learn the general rules and dependencies within a language, which takes a significant amount of data, computational power, and time to complete. The large language models discussed in the paper require supercomputer systems with several AI chips (ex. NVIDIA DGX A100 starts at $199,999). Once maintenance and power costs are added, pre-training of a large language model is an investment in the magnitude of millions.

To make large language models more accessible for enterprises, LLM developers are offering services for enterprises looking to leverage language models. NVIDIA’s NeMO is an example of these services, which offer pre-trained LLMs for fine-tuning and specific task training to suit specific use cases. The specific task training adds an additional layer to the model which requires much less data, power, and time to train; making large models accessible for enterprise use. The new task-specific layer is trained with few-shot learning, which aims for accurate outputs with less training data.

Since the model is already pre-trained and familiar with the language, few-shot learning is a viable method to teach domain-specific words and phrases to the model.

The video below introduces NVIDIA’s NeMO LLM service.

If you have further questions on large language models, book a call. Happy to answer your questions if you can answer a couple questions about your experience with AIMultiple. We are currently offering this service for businesses based in the US or EU.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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