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After reading this blog post, any fears you have about LLMs will be put to rest. All you need is a curious mind and a bit of focus—no advanced knowledge of machine learning or AI is required! In this article, we’ll break down the concepts behind Large Language Models in a straightforward way and even explore how they are built from the ground up. Let's start.
What are LLMs
Simply put, Large Language Models—LLMs—are smart AI systems that can read, understand, and even write text like a human. They learn from vast amounts of data using a type of technology called neural networks.
The soul of LLMs are neural networks, which are computer systems inspired by the way our brains work. Think of a neural network as a series of interconnected layers, where each layer consists of small units called neurons. These neurons work together to process information.
When you input data, like text or images, each neuron takes that information, applies some importance (or weight) to it, and sends it to the next layer. As the data moves through the layers, the network learns to recognize patterns and make decisions. This whole process gets trained through a feedback loop—by showing the model correct answers and adjusting weights to reduce errors. That's gradient descent, and it's how the magic happens. In simple terms, neural networks are like a team of problem solvers that work together to understand and generate information.
Think of it like teaching a child to recognize words and sentences by showing them countless examples. Over time, the child learns to understand context, grammar, and even nuances in meaning. Similarly, LLMs become more proficient at language tasks as they are exposed to more data.
Transformers
You may have experimented with a basic AI, like a simple neural network that predicts the next letter or word. But how do we go from that to ChatGPT, or Claude, or Bard—models that can write essays, answer questions, debug your code, and sound eerily human?
The answer is: Transformers.
If a basic neural network is like a pocket calculator—taking small inputs and spitting out answers—a Transformer is like a souped-up spreadsheet loaded with advanced formulas, macros, and automation. It doesn’t just crunch numbers—it understands context, sequence, and relationships in a way no earlier model could.
Let’s explore the secret sauce that makes Transformers so powerful. And don’t worry—we’ll decode the fancy stuff and tie it all back to everyday intuition.
1. The Power of Focus: Self-Attention
Imagine you’re reading this sentence:
Transformers try to do something very similar. They look at all the words in a sentence and decide which ones are most important to understand the current word.
Let’s say the model is trying to figure out what "her" refers to. It “pays attention” to other words in the sentence—especially "girl". Words that matter more get higher attention; others, like "a" or "the", are gently ignored.
This ability to scan the whole sentence and decide what's relevant is called self-attention—but honestly, you can just think of it as focused reading.
2. Think Fast: Parallel Processing
Remember those old AI models that read text one word at a time, like a super-slow typewriter? They were called RNNs, and while they worked, they were painfully slow and forgetful.
Transformers said, “Why read word-by-word when you can read the whole sentence at once?”
So instead of slowly crawling through text, Transformers gobble it all up at the same time—kind of like how your brain can scan a whole paragraph and get the gist instantly.
This ability to look at everything together is what makes Transformers fast, efficient, and perfect for modern hardware like GPUs. You don’t need to remember the term parallel processing—just know that it’s like reading with both eyes open instead of peeking one word at a time.
3. Speaking the Model’s Language: Tokenizers and Embeddings
Here's the catch: Transformers don’t understand text. Not even a little.
They only speak numbers. So how do we teach them words?
First, we break words into smaller parts, called tokens. These might be entire words, chunks of words, or even just letters—whatever makes the most sense.
Then, we translate each token into a list of numbers. This list represents what that token means, kind of like a unique fingerprint for the word “cat”, or “run”, or even “xyz”. These fingerprints are called embeddings.
So when you feed the model the word “apple”, what it actually sees is a row of numbers, like [0.2, -0.5, 1.3, …]. And every word or token gets its own unique row.
All this magic of converting text to numbers? That’s just turning words into something the model can understand.
4. Building the Brain: Layers on Layers
Now comes the cool part.
Once your text is turned into numbers, the Transformer starts processing it through layers—lots and lots of layers. Each layer has a job:
And then it repeats.
The model keeps passing information through layer after layer, getting smarter at every step—like a person reading a sentence again and again, each time catching more nuance.
By the end of this process, the model isn’t just looking at the word “apple”—it knows whether you meant the fruit, the company, or the color of your phone.
So What’s a Transformer, Really?
Let’s put it all together, without the tech-speak:
And that’s really what makes Transformers so special.
If you ever hear someone say "self-attention" or "positional encoding" or "multi-head architecture", just remember this:
Imagine your Transformer as a chef in the kitchen. Just like your mom takes raw ingredients (like text) and skillfully transforms them into delicious meals, a Transformer processes information in clever ways to create something meaningful—whether it’s a complete sentence, a piece of code, or even a beautiful poem.
Here's what each component does in simple terms:
1. Tokenizer: Slicing Language into Bite-Sized Chunks
Before the model can "think" about a sentence, it has to break it down into parts it can understand. That’s the tokenizer’s job.
Instead of feeding it entire words or letters, we break text into subword pieces—tiny fragments like “trans,” “form,” and “ers.” Why? Because this helps the model:
And it might break that down into:
2. Embedding Layer: Turning Words Into Numbers
Now that we’ve got our token pieces, we need to turn them into something the model can actually compute: numbers.
The embedding layer assigns each token a unique fingerprint, made up of dozens (or hundreds) of numbers. These aren’t just random values—they capture subtle features like:
So the word “king” and “queen” might have very similar embeddings, just slightly adjusted for gender. It's the model’s way of “understanding” meaning without using actual definitions.
In plain terms: this is how we translate human language into something math can handle.
3. Positional Encoding: Remembering Word Order
Here’s a weird thing: transformers don’t naturally know the order of your words.
If you give it “The cat sat on the mat”, it might just see a bag of words: [cat, mat, the, sat, on, the]. That’s a problem. The position of each word matters!
Positional encoding solves this by adding a little flavor to each token's embedding—like tagging it with its position in the sentence. It’s like saying:
This way, the model knows that “cat sat on mat” is different from “mat sat on cat” (which would be a very different kind of story!).
4. Multi-Head Self-Attention: Laser-Focused Thinking
This is the Transformer’s superpower. When trying to understand a word, the model doesn’t just look at it in isolation. It looks at every other word in the sentence to decide what matters most.
Say you’re processing this sentence:
Self-attention is like giving the model a pair of high-powered goggles—it can zoom in on important words, even if they’re far away in the sentence.
And multi-head just means the model wears multiple goggles at once, each focusing on a different thing: subject, tone, grammar, etc.
You don’t have to memorize the term multi-head self-attention. Just think of it as the model’s way of deciding what to pay attention to when reading.
5. Feed-Forward Network: Deeper Thinking Happens Here
After attention decides what to focus on, the model runs that info through a tiny brain—a simple set of calculations that mix, transform, and refine the meaning.
This is where the model learns things like:
This layer adds complexity and depth to the model’s understanding. In technical terms, we call this a feed-forward network, but you can think of it as the deeper reasoning stage.
6. Layer Normalization + Residual Connections: Keeping It All Balanced
When you’re cooking something complex, you need to stir often and taste as you go, or the flavors might get lost, or worse—burn!
That’s what these two components do:
This helps the model learn better, stay stable, and not forget the big picture. You can just think of it as keeping everything balanced and smooth.
7. Decoder (For Generating Output)
Now that the model has processed your input, we want it to say something back.
This is where the decoder comes in. It takes all the model’s internal thoughts and generates the next token, one piece at a time, until it forms a full sentence.
For Example, give it the phrase:
That’s what we call language generation.
You Don’t Have to Build Everything From Scratch
The good news? You don’t need to code all these parts by hand.
Libraries like PyTorch and TensorFlow come with plug-and-play versions of each of these blocks. It's like building with LEGO instead of carving blocks from stone.
You just need to know what each piece does, how to plug them together, and how to train the whole setup.
Feeding the LLM: Data Curation
Here’s a reality check:
No matter how fancy your model is, it won’t say anything smart unless it’s seen something smart.
Think of your large language model (LLM) like a student. A very, very eager one. It doesn’t come with built-in knowledge—it learns entirely from what you show it. So if you train it on trash, it’ll spit out trash. If you train it on gold, well… then you get magic.
This makes data curation arguably the most important part of building an LLM.
What Kind of Data Does It Need?
To talk like a human, your model has to read like a human. That means it needs to devour massive amounts of written material—anything that reflects how we use language in the real world.
We’re talking:
The idea is to give the model a buffet of human language so it can learn not just vocabulary, but grammar, nuance, emotion, context, and logic.
But Don’t Just Feed It Anything...
Just as you wouldn’t let a child play with a box of mismatched puzzle pieces and expect them to complete a beautiful picture, you shouldn’t provide your model with a jumble of unfiltered internet content. Just like a complete puzzle requires the right pieces to fit together, your model needs high-quality, relevant data to create coherent and meaningful outputs.
Here are the key steps for cleaning your data in concise points:
How Much Data Are We Talking?
Let’s put things into perspective.
Model Number of Parameters Training Data (Tokens)
GPT-3 175 billion ~0.5 trillion tokens
LLaMA 2 70 billion ~2 trillion tokens
Falcon 180 billion ~3.5 trillion tokens
Reminder:
1 token ≈ ¾ of a word
100,000 tokens ≈ one full novel
So GPT-3 read the equivalent of 5 million novels during training.
But don’t panic—you don’t have to start that big. You can absolutely build a smaller model with fewer parameters and a modest amount of data. Think of it like training a student for a local competition instead of the Olympics.
Start small. Learn fast. Scale wisely.
When you're gathering all this data, set aside a chunk of it for evaluation—a final exam for your model.
If you test the model on the same stuff you trained it on, it’s like checking answers by looking at the key. To really know if it's learning, you need to test it on new, unseen examples. That’s how you know it can generalize, not just memorize.
TL;DR – The Golden Rules of Data Curation
Quality > Quantity (but yes, quantity matters too)
Variety is king: include different tones, styles, domains
Clean your data like you’re prepping ingredients for a gourmet meal
Save some for testing so you can measure real progress
Start small if needed—scale when ready
And that’s how you feed your language model. It’s not glamorous work, but it’s the foundation for everything that comes next.
Training: Where the Magic Gets Expensive
So, you've built your Transformer. It's sleek, it's complex, it's hungry for knowledge. Now comes the part where it actually learns—and spoiler alert: this is where things get intense.
Training an LLM is like sending your model to school… except the classes never stop, the exams are brutal, and the tuition fees are paid in GPU hours and electricity.
Let’s break it down.
The Two-Part Learning Cycle
Training a model boils down to two big steps, repeated over and over:
1. Forward Pass – Making a Guess
The model takes in some data—say a sentence like:
2. Backward Pass – Learning From Mistakes
Here’s where the real growth happens.
The model compares its guess ("carrot") with the correct answer ("east"), and calculates how wrong it was. This gap is called loss.
Then, it works backward through its own logic, adjusting millions (or billions) of little dials—called parameters—to make a better guess next time.
This back-and-forth continues:
It’s like the world’s most dedicated student doing flashcards at lightning speed—millions of times.
But This Isn’t Cheap
Training modern LLMs isn’t just mentally taxing for the model—it’s a resource monster. You’ll need:
So how do people manage this at scale? Smart techniques:
Efficiency Boosters
Parallelization
Break the training task into chunks and run them on multiple GPUs at the same time. It’s like building a house with a team instead of doing it solo.
Gradient Checkpointing
Instead of remembering everything during training (which eats memory), the model saves “checkpoints” and recalculates just the necessary parts later. It’s a clever trade-off: less memory, a bit more computation.
Hyperparameter Tuning
These are the settings that guide how the model learns. Think:
Get these settings right, and training becomes faster, cheaper, and more effective. Get them wrong, and your model might either learn nothing—or memorize your dataset like a parrot.
Don’t Stop at Training: Fine-Tune for Your Domain
Once your model speaks fluent “general English” (or whatever language you trained it on), it’s time to give it a specialty.
This is called fine-tuning, and it’s how you turn a generalist into an expert.
Let’s say you’ve trained a model to understand basic language, but you want it to:
The model already knows how to read, write, and reason. Now, you’re just teaching it what matters in your domain.
It’s like hiring a smart intern and giving them a few weeks of on-the-job training. Soon, they’re speaking your language, using your terms, and handling tasks like a pro.
TL;DR – What to Remember
Absolutely! Here's a revised, polished, and human-friendly version of the Closing Thoughts, expanded to include realistic advice for those with limited resources. It gives an honest perspective while keeping the spirit of encouragement alive—plus it includes a practical “minimum setup” for getting started with smaller models like minGPT.
Closing Thoughts
Building a Large Language Model (LLM) from scratch is a bold move.
It’s like building a car engine from raw metal. You don’t need to do it—plenty of great engines already exist—but if you do, you’ll know exactly how it works, piece by piece. And that kind of knowledge? It’s powerful.
Whether you're:
...building your own LLM is more possible now than ever before.
Excited to dive in?
If you're aiming to build even a basic LLM from scratch, here’s your “starter pack”:
Core Skills
You don’t need a supercomputer to start your LLM journey.
Many beginners use tiny transformer models to learn the fundamentals—models you can train on a laptop or free-tier cloud GPU.
Here’s a great starting point:
? by Andrej Karpathy
Recommended system for minGPT:
Other starter models to explore:
If you're after raw performance or commercial-scale apps, you're probably better off fine-tuning an existing model.
But if your goal is understanding, learning, privacy, or customization, then yes—it's absolutely worth it.
You'll not only gain insights into one of the most transformative technologies of our time, but you’ll also empower yourself to innovate, adapt, and even challenge what’s already out there.
Final Thoughts
Are you ready to build your own brain?
Because now... you actually can.
What are LLMs
Simply put, Large Language Models—LLMs—are smart AI systems that can read, understand, and even write text like a human. They learn from vast amounts of data using a type of technology called neural networks.
The soul of LLMs are neural networks, which are computer systems inspired by the way our brains work. Think of a neural network as a series of interconnected layers, where each layer consists of small units called neurons. These neurons work together to process information.
When you input data, like text or images, each neuron takes that information, applies some importance (or weight) to it, and sends it to the next layer. As the data moves through the layers, the network learns to recognize patterns and make decisions. This whole process gets trained through a feedback loop—by showing the model correct answers and adjusting weights to reduce errors. That's gradient descent, and it's how the magic happens. In simple terms, neural networks are like a team of problem solvers that work together to understand and generate information.
Think of it like teaching a child to recognize words and sentences by showing them countless examples. Over time, the child learns to understand context, grammar, and even nuances in meaning. Similarly, LLMs become more proficient at language tasks as they are exposed to more data.
Transformers
You may have experimented with a basic AI, like a simple neural network that predicts the next letter or word. But how do we go from that to ChatGPT, or Claude, or Bard—models that can write essays, answer questions, debug your code, and sound eerily human?
The answer is: Transformers.
If a basic neural network is like a pocket calculator—taking small inputs and spitting out answers—a Transformer is like a souped-up spreadsheet loaded with advanced formulas, macros, and automation. It doesn’t just crunch numbers—it understands context, sequence, and relationships in a way no earlier model could.
Let’s explore the secret sauce that makes Transformers so powerful. And don’t worry—we’ll decode the fancy stuff and tie it all back to everyday intuition.
1. The Power of Focus: Self-Attention
Imagine you’re reading this sentence:
When you get to the word "her", your brain automatically links it back to "girl". You didn’t even think about it. That’s what we call contextual understanding.
Transformers try to do something very similar. They look at all the words in a sentence and decide which ones are most important to understand the current word.
Let’s say the model is trying to figure out what "her" refers to. It “pays attention” to other words in the sentence—especially "girl". Words that matter more get higher attention; others, like "a" or "the", are gently ignored.
This ability to scan the whole sentence and decide what's relevant is called self-attention—but honestly, you can just think of it as focused reading.
2. Think Fast: Parallel Processing
Remember those old AI models that read text one word at a time, like a super-slow typewriter? They were called RNNs, and while they worked, they were painfully slow and forgetful.
Transformers said, “Why read word-by-word when you can read the whole sentence at once?”
So instead of slowly crawling through text, Transformers gobble it all up at the same time—kind of like how your brain can scan a whole paragraph and get the gist instantly.
This ability to look at everything together is what makes Transformers fast, efficient, and perfect for modern hardware like GPUs. You don’t need to remember the term parallel processing—just know that it’s like reading with both eyes open instead of peeking one word at a time.
3. Speaking the Model’s Language: Tokenizers and Embeddings
Here's the catch: Transformers don’t understand text. Not even a little.
They only speak numbers. So how do we teach them words?
First, we break words into smaller parts, called tokens. These might be entire words, chunks of words, or even just letters—whatever makes the most sense.
Then, we translate each token into a list of numbers. This list represents what that token means, kind of like a unique fingerprint for the word “cat”, or “run”, or even “xyz”. These fingerprints are called embeddings.
So when you feed the model the word “apple”, what it actually sees is a row of numbers, like [0.2, -0.5, 1.3, …]. And every word or token gets its own unique row.
All this magic of converting text to numbers? That’s just turning words into something the model can understand.
4. Building the Brain: Layers on Layers
Now comes the cool part.
Once your text is turned into numbers, the Transformer starts processing it through layers—lots and lots of layers. Each layer has a job:
- One layer might focus attention to understand what matters in the sentence
- Another might clean up the information so it’s easier to work with (we call this normalization)
- A third might add the original info back in to prevent it from being lost along the way (called residual connections)
And then it repeats.
The model keeps passing information through layer after layer, getting smarter at every step—like a person reading a sentence again and again, each time catching more nuance.
By the end of this process, the model isn’t just looking at the word “apple”—it knows whether you meant the fruit, the company, or the color of your phone.
So What’s a Transformer, Really?
Let’s put it all together, without the tech-speak:
- It reads everything at once, not word by word
- It focuses on the most important parts of a sentence
- It understands the meaning behind words, not just the words themselves
- It builds up its understanding in layers, getting better as it goes
And that’s really what makes Transformers so special.
If you ever hear someone say "self-attention" or "positional encoding" or "multi-head architecture", just remember this:
The Build Phase: Engineering Your Transformer
Imagine your Transformer as a chef in the kitchen. Just like your mom takes raw ingredients (like text) and skillfully transforms them into delicious meals, a Transformer processes information in clever ways to create something meaningful—whether it’s a complete sentence, a piece of code, or even a beautiful poem.
Here's what each component does in simple terms:
1. Tokenizer: Slicing Language into Bite-Sized Chunks
Before the model can "think" about a sentence, it has to break it down into parts it can understand. That’s the tokenizer’s job.
Instead of feeding it entire words or letters, we break text into subword pieces—tiny fragments like “trans,” “form,” and “ers.” Why? Because this helps the model:
- Understand unknown words by their parts (like figuring out "transformer" even if it’s never seen it)
- Keep its vocabulary manageable (no need to memorize every word in the English language) You give the model: > “I’m learning transformers.”
And it might break that down into:
This step is just chopping your ingredients before cooking. In tech speak, we call this tokenization.
2. Embedding Layer: Turning Words Into Numbers
Now that we’ve got our token pieces, we need to turn them into something the model can actually compute: numbers.
The embedding layer assigns each token a unique fingerprint, made up of dozens (or hundreds) of numbers. These aren’t just random values—they capture subtle features like:
- What a word means
- How it’s used
- How it relates to other words
So the word “king” and “queen” might have very similar embeddings, just slightly adjusted for gender. It's the model’s way of “understanding” meaning without using actual definitions.
In plain terms: this is how we translate human language into something math can handle.
3. Positional Encoding: Remembering Word Order
Here’s a weird thing: transformers don’t naturally know the order of your words.
If you give it “The cat sat on the mat”, it might just see a bag of words: [cat, mat, the, sat, on, the]. That’s a problem. The position of each word matters!
Positional encoding solves this by adding a little flavor to each token's embedding—like tagging it with its position in the sentence. It’s like saying:
- This is the first word
- This is the second
- This one came last
This way, the model knows that “cat sat on mat” is different from “mat sat on cat” (which would be a very different kind of story!).
4. Multi-Head Self-Attention: Laser-Focused Thinking
This is the Transformer’s superpower. When trying to understand a word, the model doesn’t just look at it in isolation. It looks at every other word in the sentence to decide what matters most.
Say you’re processing this sentence:
What does “it” refer to? The model needs to look back at “dog” and realize that’s the star of the sentence.
Self-attention is like giving the model a pair of high-powered goggles—it can zoom in on important words, even if they’re far away in the sentence.
And multi-head just means the model wears multiple goggles at once, each focusing on a different thing: subject, tone, grammar, etc.
You don’t have to memorize the term multi-head self-attention. Just think of it as the model’s way of deciding what to pay attention to when reading.
5. Feed-Forward Network: Deeper Thinking Happens Here
After attention decides what to focus on, the model runs that info through a tiny brain—a simple set of calculations that mix, transform, and refine the meaning.
This is where the model learns things like:
- “cat” and “kitten” are related
- “bark” can mean sound or tree (depending on context)
- “run code” is very different from “go for a run”
This layer adds complexity and depth to the model’s understanding. In technical terms, we call this a feed-forward network, but you can think of it as the deeper reasoning stage.
6. Layer Normalization + Residual Connections: Keeping It All Balanced
When you’re cooking something complex, you need to stir often and taste as you go, or the flavors might get lost, or worse—burn!
That’s what these two components do:
- Residual connections take the original ingredients and mix them back in, so the model doesn’t forget what it started with
- Normalization makes sure the numbers don’t get too big, too small, or too weird to process
This helps the model learn better, stay stable, and not forget the big picture. You can just think of it as keeping everything balanced and smooth.
7. Decoder (For Generating Output)
Now that the model has processed your input, we want it to say something back.
This is where the decoder comes in. It takes all the model’s internal thoughts and generates the next token, one piece at a time, until it forms a full sentence.
For Example, give it the phrase:
And it might complete:
It does this by predicting the most likely next token, then the one after that, and so on.
That’s what we call language generation.
You Don’t Have to Build Everything From Scratch
The good news? You don’t need to code all these parts by hand.
Libraries like PyTorch and TensorFlow come with plug-and-play versions of each of these blocks. It's like building with LEGO instead of carving blocks from stone.
You just need to know what each piece does, how to plug them together, and how to train the whole setup.
Feeding the LLM: Data Curation
Here’s a reality check:
No matter how fancy your model is, it won’t say anything smart unless it’s seen something smart.
Think of your large language model (LLM) like a student. A very, very eager one. It doesn’t come with built-in knowledge—it learns entirely from what you show it. So if you train it on trash, it’ll spit out trash. If you train it on gold, well… then you get magic.
This makes data curation arguably the most important part of building an LLM.
What Kind of Data Does It Need?
To talk like a human, your model has to read like a human. That means it needs to devour massive amounts of written material—anything that reflects how we use language in the real world.
We’re talking:
- Books – Fiction, non-fiction, classics, obscure indie novels—it’s all valuable
- Wikipedia – General knowledge, well-structured
- Academic Papers – For formal tone and complex ideas
- Conversations – Dialogue helps the model understand how people actually talk
- Code –
, even programming languages are part of the diet! - Articles and Blogs – Diverse opinions, tones, and writing styles
The idea is to give the model a buffet of human language so it can learn not just vocabulary, but grammar, nuance, emotion, context, and logic.
But Don’t Just Feed It Anything...
Just as you wouldn’t let a child play with a box of mismatched puzzle pieces and expect them to complete a beautiful picture, you shouldn’t provide your model with a jumble of unfiltered internet content. Just like a complete puzzle requires the right pieces to fit together, your model needs high-quality, relevant data to create coherent and meaningful outputs.
Here are the key steps for cleaning your data in concise points:
- Accuracy: Verify factual correctness.
- Formatting: Remove unusual symbols and typos.
- Bias and Harmful Speech: Eliminate offensive or misleading content.
- Deduplication: Remove duplicate entries to avoid bias.
- Privacy Redaction: Exclude sensitive information like personal identifiers.
How Much Data Are We Talking?
Let’s put things into perspective.
Model Number of Parameters Training Data (Tokens)
GPT-3 175 billion ~0.5 trillion tokens
LLaMA 2 70 billion ~2 trillion tokens
Falcon 180 billion ~3.5 trillion tokens
Reminder:
1 token ≈ ¾ of a word
100,000 tokens ≈ one full novel
So GPT-3 read the equivalent of 5 million novels during training.
But don’t panic—you don’t have to start that big. You can absolutely build a smaller model with fewer parameters and a modest amount of data. Think of it like training a student for a local competition instead of the Olympics.
Start small. Learn fast. Scale wisely.
When you're gathering all this data, set aside a chunk of it for evaluation—a final exam for your model.
If you test the model on the same stuff you trained it on, it’s like checking answers by looking at the key. To really know if it's learning, you need to test it on new, unseen examples. That’s how you know it can generalize, not just memorize.
TL;DR – The Golden Rules of Data Curation
Quality > Quantity (but yes, quantity matters too)
Variety is king: include different tones, styles, domains
Clean your data like you’re prepping ingredients for a gourmet meal
Save some for testing so you can measure real progress
Start small if needed—scale when ready
And that’s how you feed your language model. It’s not glamorous work, but it’s the foundation for everything that comes next.
Training: Where the Magic Gets Expensive
So, you've built your Transformer. It's sleek, it's complex, it's hungry for knowledge. Now comes the part where it actually learns—and spoiler alert: this is where things get intense.
Training an LLM is like sending your model to school… except the classes never stop, the exams are brutal, and the tuition fees are paid in GPU hours and electricity.
Let’s break it down.
The Two-Part Learning Cycle
Training a model boils down to two big steps, repeated over and over:
1. Forward Pass – Making a Guess
The model takes in some data—say a sentence like:
It then tries to guess the next word. Maybe it says:
Okay, not great. But that's okay—it’s still learning.
2. Backward Pass – Learning From Mistakes
Here’s where the real growth happens.
The model compares its guess ("carrot") with the correct answer ("east"), and calculates how wrong it was. This gap is called loss.
Then, it works backward through its own logic, adjusting millions (or billions) of little dials—called parameters—to make a better guess next time.
This back-and-forth continues:
- Over batches (small groups of data)
- Through epochs (full passes through the dataset)
- And across iterations (every single training step)
It’s like the world’s most dedicated student doing flashcards at lightning speed—millions of times.
But This Isn’t Cheap
Training modern LLMs isn’t just mentally taxing for the model—it’s a resource monster. You’ll need:
- High-end GPUs (preferably more than one)
- Tons of memory (RAM, VRAM, and fast storage)
- Patience (unless you’re burning cloud credits)
So how do people manage this at scale? Smart techniques:
Efficiency Boosters
Parallelization
Break the training task into chunks and run them on multiple GPUs at the same time. It’s like building a house with a team instead of doing it solo.
Gradient Checkpointing
Instead of remembering everything during training (which eats memory), the model saves “checkpoints” and recalculates just the necessary parts later. It’s a clever trade-off: less memory, a bit more computation.
Hyperparameter Tuning
These are the settings that guide how the model learns. Think:
- Batch size – How many examples to learn from at once
- Learning rate – How aggressively to update its knowledge
- Dropout rate – How often to forget things to avoid overfitting
Get these settings right, and training becomes faster, cheaper, and more effective. Get them wrong, and your model might either learn nothing—or memorize your dataset like a parrot.
Don’t Stop at Training: Fine-Tune for Your Domain
Once your model speaks fluent “general English” (or whatever language you trained it on), it’s time to give it a specialty.
This is called fine-tuning, and it’s how you turn a generalist into an expert.
Let’s say you’ve trained a model to understand basic language, but you want it to:
- Answer legal questions
- Write SQL queries
- Diagnose medical symptoms
- Handle customer service chats
- Generate code in a specific language You don’t need to start from scratch again. You just fine-tune it—feed it examples from your niche and let it re-learn with a focused lens.
The model already knows how to read, write, and reason. Now, you’re just teaching it what matters in your domain.
It’s like hiring a smart intern and giving them a few weeks of on-the-job training. Soon, they’re speaking your language, using your terms, and handling tasks like a pro.
TL;DR – What to Remember
- Training is the phase where the model learns from scratch. It’s expensive, slow, and compute-heavy—but essential.
- You repeat a forward pass (guessing) and backward pass (learning) until your model gets good.
- Use smart tools like parallelization, gradient checkpointing, and hyperparameter tuning to manage costs and complexity.
- Once trained, don’t stop—fine-tune your model to specialize it for your actual needs.
Absolutely! Here's a revised, polished, and human-friendly version of the Closing Thoughts, expanded to include realistic advice for those with limited resources. It gives an honest perspective while keeping the spirit of encouragement alive—plus it includes a practical “minimum setup” for getting started with smaller models like minGPT.
Closing Thoughts
Building a Large Language Model (LLM) from scratch is a bold move.
It’s like building a car engine from raw metal. You don’t need to do it—plenty of great engines already exist—but if you do, you’ll know exactly how it works, piece by piece. And that kind of knowledge? It’s powerful.
Whether you're:
- A student learning AI by doing (and not just watching tutorials)
- A startup looking to build a private, domain-specific model
- Or just a curious mind who loves digging deep into how things tick
...building your own LLM is more possible now than ever before.
Excited to dive in?
If you're aiming to build even a basic LLM from scratch, here’s your “starter pack”:
Core Skills
- Python: The language of choice for almost all ML projects
- NumPy: For working with arrays and matrices
- PyTorch or TensorFlow: To build and train neural networks
- Basic understanding of:
- Vectors, matrices, gradients (middle school math + intuition!)
- How neural networks work
- A tokenizer (like Byte-Pair Encoding or SentencePiece)
- A small, clean dataset (you don’t need 2 trillion tokens to start)
- Basic compute: access to a single GPU (NVIDIA GTX 1660+, or any cloud GPU like Google Colab or Lambda Labs)
- Patience: Training even small models takes time.
- Curiosity: You’ll hit confusing bugs—embrace them as learning moments.
- Persistence: You will want to give up. Don’t.
You don’t need a supercomputer to start your LLM journey.
Many beginners use tiny transformer models to learn the fundamentals—models you can train on a laptop or free-tier cloud GPU.
Here’s a great starting point:
? by Andrej Karpathy
- A minimal, educational reimplementation of GPT
- Written in clean, simple PyTorch
- You can train it on tiny datasets like Shakespeare or Python code snippets
- Can run on a single GPU with 4–8 GB VRAM
- Helps you understand tokenization, attention, embeddings, loss functions, and training loops—without the complexity of billion-scale models
Recommended system for minGPT:
- CPU: Any modern multi-core CPU (i5/i7 or Ryzen 5/7)
- RAM: At least 8–16 GB
- GPU: GTX 1660 Ti, RTX 3060, or better — OR Google Colab with a free Tesla T4 (or paid tier for faster A100/V100)
Other starter models to explore:
- nanoGPT – Modern rewrite of minGPT with PyTorch Lightning and better training loops
- TinyStories – Small LLMs trained on child-friendly stories, designed to run on a single GPU
- distilGPT or GPT-Neo Mini – Pretrained small models you can fine-tune cheaply
If you're after raw performance or commercial-scale apps, you're probably better off fine-tuning an existing model.
But if your goal is understanding, learning, privacy, or customization, then yes—it's absolutely worth it.
You'll not only gain insights into one of the most transformative technologies of our time, but you’ll also empower yourself to innovate, adapt, and even challenge what’s already out there.
Final Thoughts
- You don’t need a data center to get started
- You don’t need a PhD to understand how this works
- You just need curiosity, a decent GPU, and the willingness to learn
Are you ready to build your own brain?
Because now... you actually can.