In the realm of artificial intelligence, Large Language Models (LLMs) like GPT (Generative Pretrained Transformers) have taken center stage. You’ve probably used ChatGPT, Bard, or another text-generating wizardry to ask about anything from coding to cocktail recipes. But behind the magic is a complex architecture driven by deep learning, neural networks, and vast text datasets. Let’s break this down, and in the process, I’ll show you how to code and build your own LLM from scratch.
This guide will walk you through the essential elements of what an LLM is, how to build one, how to train it, and, most importantly, how to do it while enjoying a hot cup of tea.
1. What is an LLM?
Imagine a robot that's really good at understanding and generating text. That's a Large Language Model. It’s like that one friend who can finish your sentences but isn't as annoying because it’s powered by billions of parameters that predict the next word in a sequence. LLMs are the tech behind text generation, translation, summarization, and more. They work based on deep neural networks, trained with vast amounts of text data to pick up nuances, relationships, and context in human language.
LLMs rely on an architecture called Transformers, introduced in the 2017 paper Attention Is All You Need. Transformers enable models to focus on different parts of an input (called self-attention) and generate coherent text. And no, they don’t actually “understand” language like humans, they just make very smart guesses based on statistical patterns in the data.
2. Building an LLM: The Steps
Step 1: Data Collection and Preprocessing
Before jumping into coding, you need a dataset. An LLM is trained on massive amounts of text data. For example, GPT-3 was trained on roughly 570GB of text from the CommonCrawl dataset, books, Wikipedia, and more. To keep things simple (and your GPU intact), you can start small by training on a corpus like The Verdict by Edith Wharton.
Tokenization: Your model can’t understand raw text. It needs numbers. That’s where tokenization comes in—splitting text into manageable pieces (words, subwords, or characters) and converting them into numbers (IDs). This is done using tokenizers such as Byte Pair Encoding (BPE) and employing the PyTorch library.
import re def simple_tokenizer(text): tokens = re.findall(r'\b\w+\b', text.lower()) return tokens text = "Hello, I am building an LLM!" tokens = simple_tokenizer(text) print(tokens)
Step 2: The Transformer Architecture
The backbone of most LLMs is the Transformer. At a high level, a Transformer has an encoder (for reading text) and a decoder (for generating text). However, in GPT models, you only need the decoder part because you're just generating text, not translating it.
import torch import torch.nn as nn class SimpleTransformer(nn.Module): def __init__(self, vocab_size, d_model, nhead, num_layers): super(SimpleTransformer, self).__init__() self.embedding = nn.Embedding(vocab_size, d_model) self.transformer = nn.Transformer(d_model, nhead, num_layers) self.fc = nn.Linear(d_model, vocab_size) def forward(self, src, tgt): src_emb = self.embedding(src) # Embed the input tgt_emb = self.embedding(tgt) transformer_out = self.transformer(src_emb, tgt_emb) output = self.fc(transformer_out) return output
Step 3: Pretraining Your Model
Training an LLM like GPT from scratch is expensive (think millions of dollars expensive for cloud compute). But we can implement a simplified training loop using PyTorch.
First, you need to train your model on a text corpus where the task is to predict the next word. This is called next-word prediction:
import torch.optim as optim def train(model, data, epochs=10): criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) for epoch in range(epochs): for batch in data: src, tgt = batch optimizer.zero_grad() output = model(src, tgt[:-1]) loss = criterion(output.view(-1, model.fc.out_features), tgt[1:].view(-1)) loss.backward() optimizer.step() print(f'Epoch {epoch + 1}, Loss: {loss.item()}') # Assuming `data` is a DataLoader that provides batches of (source, target) pairs. train(model, data)
In this training loop, the model learns to predict the next word in the target sequence given the source sequence. You’ll need a proper dataset loader (check PyTorch's DataLoader) to feed batches of tokenized text into the model.
Step 4: Fine-Tuning
Once you’ve trained your base model, it’s time to fine-tune it on specific tasks. This could be anything from answering questions to translating languages. You take the pretrained model and continue training it on a smaller, task-specific dataset.
Fine-tuning requires a dataset with input-output pairs, like questions and answers. The model is adjusted to minimize errors in predicting the correct output for each input.
Step 5: Using Pretrained Models
If pretraining sounds too much for your CPU, you can use openly available pretrained models, like GPT-2 or GPT-3 via libraries like Hugging Face's Transformers. Here’s how you can load a model for text generation:
from transformers import GPT2LMHeadModel, GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained("gpt2") model = GPT2LMHeadModel.from_pretrained("gpt2") input_text = "Once upon a time" input_ids = tokenizer.encode(input_text, return_tensors="pt") output = model.generate(input_ids, max_length=50) print(tokenizer.decode(output[0], skip_special_tokens=True))
In just a few lines, you’ve got a text-generating machine that can continue any prompt.
3. How to Train an LLM
Pretraining
Pretraining involves feeding vast amounts of text to the model to predict the next word in a sequence. It’s expensive but essential for learning general language patterns.
- Self-Supervised Learning: The model creates its own labels (the next word) from the input sequence, making this a self-supervised task.
- Massive Datasets: GPT-3 was trained on 300 billion tokens. For educational purposes, you can start with smaller datasets.
Fine-Tuning
Fine-tuning involves training the model further on a specific task. For instance, you can fine-tune an LLM to excel at text summarization or question answering.
# Fine-tuning code using a smaller dataset model.train() for epoch in range(5): for batch in fine_tune_data: inputs, labels = batch optimizer.zero_grad() outputs = model(inputs, labels=labels) loss = outputs.loss loss.backward() optimizer.step()
4. Final Thoughts
Building an LLM from scratch is a thrilling ride through deep learning land. It’s like constructing a mind that can talk, write, and (almost) think. With this guide, you’ve got the blueprint to build a simple LLM, fine-tune it, or simply use pretrained ones. Whether you’re creating your own AI assistant or a text-generating bot, the possibilities are endless.
This article went over my reflections on "Build an Large Language Model from Scratch" written by Sebastian Raschka. The code snippets are simplified to keep your GPU from catching fire, but the concepts will help you scale up if you decide to dive deeper.
Happy coding, and may your LLMs be ever eloquent!
