Large Language Models (LLM)

Buckle up, because you’re about to be introduced to the world of Large Language Models (LLMs). LLMs are neural network-based models that can take into account the context of words to make more accurate predictions of the next word in a sequence.

You probably use an LLM every day without even knowing it – the autocomplete feature on many search engines is one of the most widely-known applications of LLMs.

But these models can also be used for tasks such as part-of-speech tagging, automatic text generation, and machine translation.

As the size and capacity of LLMs continue to grow, so does their potential. It is likely that LLMs will soon become invaluable in a variety of industries and fields. So if you want to stay ahead of the curve, it’s time to start getting to know LLMs.

What is a Large Language Model?

A Large Language Model (LLM) is a neural network-based model that is capable of considering the context of words in order to improve predictions of the next word in a sequence.

These models are generally built using large datasets in order to better simulate the way people write. The autocomplete feature of many search engines is one of the most widely-known applications of LLMs.

LLMs can be used for a variety of natural language processing tasks, such as part-of-speech tagging, automatic text generation, and machine translation. In many cases, LLMs can accomplish these tasks with little training data, due to their ability to learn from larger datasets.

For example, OpenAI’s GPT-3 model is capable of writing blog posts, translating between languages, and completing code, all with only a short prompt containing instructions.

As the size and capacity of LLMs continue to grow, so too will their potential applications. It is likely that LLMs will soon become an invaluable tool in a variety of industries and fields.


Large Language Models 2023 List

This is my list of what I consider the most important large language models in 2023. This is of course as long as we don’t hear anything new about OpenAI`s GPT4.

5 Important Large Language Models (LMM) in 2023:

1. GPT-3:

GPT-3 is a large-scale language model that was released in 2020. It is trained using a method called generative pretraining, which means that it is taught to predict what the next input will be. GPT-3 has 175 billion parameters and is the darling of the language model world because it produces human-like text. However, GPT-3 is only available to Microsoft because they invested $1 billion into the model’s developer, OpenAI.

2. Bloom:

Bloom is a newer model that was developed by a consortium of more than 1,000 AI researchers. It can generate text in 46 natural languages and 13 programming languages. Bloom is open source, which means that anyone can access and use the model. Users must agree to a license that bans its use in several restricted cases, such as generating false information to harm others.

3. ESMFold:

ESMFold is the most recent model to be released. It can accurately predict full atomic protein structures from a single sequence of a protein. This has the potential to speed up drug discoveries. ESMFold is an order of magnitude faster than its rival, AlphaFold2. The plan is to open source ESMFold in the future.

4. WuDao 2.0:

WuDao 2.0 is the largest language model in the world. It was trained on 4.9 terabytes of images and texts. WuDao can simulate conversational speech, write poems, and understand images. It is not yet clear what applications the Beijing Academy of Artificial Intelligence intends to use the model for.

5. LaMDA:

LaMDA is a dialogue-based model that was first showcased at Google’s I/O event in May 2021. LaMDA is so accurate that it convinced an AI engineer it was sentient. The model is trained on dialogue, which allows it to pick up on the nuances that distinguish open-ended conversation from other forms of language. Google plans on using the model across its products, including its search engine, Google Assistant, and Workspace platform.


GPT-4 is the upcoming fourth generation of the GPT language model. Not much is known about it yet, but it is expected to be an improvement on the previous generation in several ways.

One of the most anticipated improvements is the model’s ability to generate texts that more accurately mimic human behaviors and speech patterns. This is due to the numerous optimizations that have been made to the algorithm.

Another significant improvement is expected to be the increase in model size. GPT-4 is expected to feature around 100 trillion machine learning parameters, 5 times ‘neural network’ capacities of the previous generation.

The release of GPT-4 will have wide-ranging implications for both users and businesses. For users, it will mean more AI-generated content such as blog posts, social media posts, and articles.

For businesses, it will mean access to an AI tool that can generate vast amounts of relevant and accurate text-based content.

Overall, the release of GPT-4 represents a major step forward for both businesses and users. The new language model will significantly improve the quality of generated text content while also saving businesses time and money.


Large Language Models Use Cases

This is just a few examples of what you can use a large language model for. New use cases are being developed almost every week, so this is just a list to get your imagination started.

5 Use Cases for Large Language Models:

 1. Copywriting

Large language models can be used to help improve the quality and speed of writing for blogs, sales, digital ads, and websites. By using large language models, copywriters can create more concise, accurate, and user-friendly copy.

2. Code generation and autocomplete

Large language models can be used to quickly generate code with less need for human intervention. By using large language models, developers can create code that is more accurate and efficient.

3. Shell command generation

Large language models can be used to generate shell commands that are more user-friendly and easier to understand. By using large language models, engineers can create commands that are less likely to cause errors and are easier to use.

4. Regex generation

Large language models can be used to quickly generate regular expressions more accurately. By using large language models, developers can create regular expressions that are more likely to match the desired patterns.

5. SQL generation

Large language models can be used to generate SQL queries more quickly and accurately, allowing non-technical users to access data and business insights. By using large language models, analysts and business users can get the information they need without having to write SQL queries themselves.

What are the types of language models?

It can be good to divide large language models into 3 or more categories. In this article I have picked out what I think is the best way to split these.

3 Types of Large Language Models:

Large General-purpose Models

Large general-purpose models are, as their name suggests, designed to be versatile and capable of completing a wide range of tasks. These models are usually extremely large, with tens of gigabytes of data and millions of parameters. 

While this makes them very powerful, it also makes them very costly to develop and train. In addition, these models often require a lot of data to be effective, which can make them impractical for many organizations. 

However, their versatility and ability to complete many different types of tasks make them a valuable option for those with the resources to develop and train them.

Fine-tuned Models

Fine-tuned models are small versions of large language models that have been specifically designed to complete a particular task. For example, OpenAIs Codex is a descendant of the GPT-3 model that has been fine-tuned for programming tasks.

Even though it still contains billions of parameters, it is both smaller and more efficient at generating strings of code than its predecessor.

Fine-tuning can improve a model’s performance on a specific task, such as answering questions or generating protein sequences. In some cases, it can also help a model to better understand a particular subject matter, such as clinical research.

Fine-tuned models are most successful when applied to tasks that have a lot of training data available. Examples of such tasks include machine translation, question answering, named entity recognition, and entity linking.

There are several advantages to using fine-tuned models over larger language models. Fine-tuned models can be trained and run much faster than their larger counterparts, and they often require less data to achieve good results.

Additionally, because they are derived from existing language models, they can benefit from the existing knowledge and expertise that has gone into those models.

Edge language models

Edge models are small in size and can take the form of fine-tuned models or be trained from scratch on small data sets. They offer a number of advantages over large language models, including lower costs, increased privacy, and faster performance. 

However, they are limited by the hardware found in edge devices and may not be able to keep up with the performance of larger models


Why are large language models important?

On a practical level, large-scale language models have led to major breakthroughs in natural language understanding, conversational AI, and other applications that require a deep understanding of human language.

But beyond their practical applications, large-scale language models are important because they help us understand the fundamental limits of machine learning.

To date, the vast majority of machine learning applications have been based on task-specific models that are only trained on data relevant to a narrow task. For example, a machine learning system that is designed to identify objects in images will only be exposed to images during training. 

These task-specific models have their limits, however, and cannot be easily applied to other tasks.

In contrast, large-scale language models are trained on a much wider range of data, including not just text but also audio, video, and other forms of data. This deep well of data gives them the ability to learn generalizable knowledge that can be applied to a wide range of tasks. 

For example, a large-scale language model that is trained on a large amount of data from the internet could be used to generate new works of art based on the styles it has learned from.

In short, large-scale language models are important because they help us understand the true potential of machine learning. 

Using these models, we can explore the fundamental limits of what machines can learn, and develop new applications that were previously impossible.

How do you train a large language model?

To train a large language model, you first need to come up with tasks that will cause the model to learn a representation of a given domain.

A common task used for language modeling is known as the completion task, which consists of filling in the missing word in a sentence.

Through completion tasks and other training tasks, a language model learns to encode the meanings of words and longer text passages. In order to effectively train a large language model, you will need to use a large corpus of training data.

Training a large language model can be time consuming and expensive, as it requires parallelism across thousands of GPUs. 

However, the benefits of having a large language model far outweigh the cost, as it provides numerous downstream applications such as text generation, translation, and summarization.



Large language models have the potential to revolutionize the way we use machine learning. These models are capable of understanding the context of words in order to make more accurate predictions of the next word in a sequence. 

This allows them to accomplish tasks such as text generation and machine translation with little training data. As the size and capacity of these models continue to grow, so does their potential. 

It is likely that large language models will soon become an invaluable tool in a variety of industries and fields.

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