Rooted in the concept of semantics from Michel Bréal, a French Philologist in the 1880s, large language models, or LLMs, have developed into something that most of us use these days. The study of how human languages are organized, how they develop over time, and the nuances of language formed a deep learning algorithm that started in 2017.
With the rise of generative AI, these LLMs have become more powerful than ever. It has become widely appreciated that by the end of 2025, it is projected that there will be 750 million apps using LLMs.
But how can one create a large language model and optimize it? If that’s something you want to do, you are here at the right place.
In this blog post, I have created a step-by-step guide for you to create a large language model.
Let’s start!
Steps To Create a Large Language Model
Step 1: Decide What Your Model Should Do
Before you start building, you need a plan. You must assess the purpose of the language model you’ll make. Should it be a friendly chatbot for everyone or a helper for something specific, like explaining science or writing legal documents?
Next, consider how big it should be. Large language models have millions or even billions of tiny settings (called parameters) inside them. More parameters mean the model can learn more, but it also needs more computer power and time to build.
For example, a small model might have a few billions of parameters, while a giant one could have 100 billion or more! To understand these parameters better, you can look at the comparison of LLMs. These sources can help you determine what type of model works best for you.
Now, decide if it’s for general use (like talking about anything) or if it has a specialization (like medicine). This plan guides everything else, so take your time here. Now that you’ve got a goal, it’s time to fuel your model.
Step 2: Collect and Clean the Data
A language model learns by reading tons of text. But where do you get all that text? You can pull it from the internet: websites, blogs, or even Wikipedia. You may also use an entire book and articles that are accessible online. The more text, the better. Big models often train on hundreds of billions of words!
The only catch is not all text is useful. Some of it is messy, and you can find random typos or ads saying, ‘Buy this now!’ So, you need to clean it up.
First, break the text into small pieces called tokens. It could be words or parts of words. Then, toss out junk like extra spaces or weird symbols. You might also want to remove rude or unfair statements so your model doesn’t pick up bad habits.
The goal here is to end up with a huge pile of clean, high-quality data that covers a wide range of topics, depending on your scope or purpose. With your data ready, it’s time to design the brain of your model.
Step 3: Build the Model’s Structure
To make your LLM work, you need a structure, or a blueprint, for how it should think. Most models today use something called Transformer models, which is a recipe for building smart machines. It’s made of layers stacked on top of each other. Each layer helps the model pay attention to words and figure out how they connect.
You’ve got two main choices here. One option is a model that’s great at generating text, like writing essays. Another one is a model that can translate or summarize texts by looking at whole sentences. You can also decide how many layers and how big each layer is.
More layers and bigger sizes mean a smarter model, but it’ll need more computer power. While that sounds tricky, you can always start simple and adjust later. Once the structure is set, it’s now time to teach your model everything it needs to know.
Step 4: Train Your Model
The training process is where the magic happens. During this stage, your model reads through all the text you collected, guessing what word comes next over and over. Every time it guesses wrong, it tweaks itself to get better. This takes a lot of time and some serious computer muscle, maybe dozens or even hundreds of special chips called GPUs working together for weeks or months.
To make this work, you feed the model vast amounts of text at once. It learns by comparing its guesses to the real answers and fixing its mistakes. You also set a ‘learning rate.’ If it’s too fast, it might miss important lessons. If it’s too slow, it’ll take forever to train.
With pre-trained models, you’ll only teach it general stuff, like how the language works. Later on, you can apply deep learning models to move to more complex text and concepts.
Now, you must know that the training process can be expensive. Big models might cost millions of dollars in electricity and computer time. But don’t let that scare you. Even smaller models can learn a lot with less. Once it’s trained, you’re ready to adjust and make it even better.
Step 5: Fine-Tune for Special Jobs
At this point, your model knows a ton. But there’s a tendency for it to act like a know-it-all and will ramble uncontrollably even with a simple prompt. This is where fine-tuning helps. It sharpens it up for specific tasks.
Let’s say you want it to answer questions with an expert approach. You give examples of excellent questions and answers, and it adjusts to that style. This is called supervised fine-tuning because you’re guiding it with examples.
Another cool trick is using a method called reinforcement learning from human feedback. You know how dogs need reinforcements during training so that they’ll get used to a trick; that’s the same idea when training your language model. By giving it constant feedback, it will easily get the hang of what you want.
You can also tweak it for special areas, like science or law, by giving it extra text from those fields. With fine-tuning done, your model’s ready to go. But it can still get faster and smarter.
Step 6: Make It Fast and Better
Building a model is only half the battle. Now, you want it to work well without wasting time or energy. This is called optimization, and it’s like tuning up a car to run smoothly. There are a few ways to do this.
a) Speed It Up
Big models can be slow because they’re juggling billions of settings. One fix is to shrink those settings down, like turning big numbers into smaller ones, so the computer handles them faster. Another trick is cutting out parts the model doesn’t really need. You can even make a smaller version of your model that copies the big one’s smarts but runs quicker.
b) Improve the Answers
If you want smarter replies, you can add more text, make the model bigger, or train it longer. Research shows this usually helps. Test it on quizzes or ask people if it makes sense. If it’s saying weird or unfair things, tweak the data or training to fix it. The goal is clear, helpful answers every time.
Save Energy
When your model is chatting with users, little shortcuts can help. For example, it can ‘remember’ parts of a sentence that have already been figured out, so it doesn’t start from scratch each time. Special tools, like software or chips, can also speed things up.
Now that you already optimized it, it’s time to get it out into the world.
Step 7: Launch and Keep Improving
You can put your model on a website or app so people can use it. Big computers in the cloud (like Amazon’s or Google’s) can handle lots of users at once. You can also slim it down for phones if it’s small enough.
Once it’s live, watch how it does: Is it fast? Are people happy with it? Keep notes and tweak as needed. The world changes, so your model should, too. Add new text every so often to keep it fresh. Full retraining takes a lot of work, but small updates are easier.
Takeaway
Building a language model isn’t all smooth sailing. As mentioned earlier, it can be pricey, and hungry for money and electricity. Plus, it could pick up biases or say things that aren’t true, so you have to keep an eye out. Lastly, bigger models are tricky to figure out. If it messes up, it’s hard to know why.
Once you figure out how to cope with these kinks, you’ll make a machine that thinks like a human brain would. What do you think? Are you ready to give it a try?
