How LLMs Are Trained: A Conceptual Overview
Part of "LLM 101" Series
In our previous “LLM 101 series” blog posts , we've explored fundamental concepts like tokenization, embeddings, and chunking are essential building blocks for understanding how Large Language Models (LLMs) process information.
With that knowledge, let's connect those dots with core element of LLM and try to conceptually understand how these powerful LLM models are actually trained ? . It's a series of steps from digesting large amounts of data to large model artifact that can understand, generate, and even reason with human language.
The Brain : Transformer Architecture
At the core of most modern LLMs lies a ground breaking and revolutionary neural network architecture called the Transformer.
Transformer is a model architecture, similar to how software engineering has different architectural patterns. Imagine it as the blueprint for a super brain, that enables LLMs to understand and generate language so effectively.
Before the Transformer, models struggled with long-range dependencies in text and found it hard to relate words that were far apart in a sentence or document. The Transformer changed this by introducing a mechanism called attention.
Imagine you're reading a complex sentence. As you read each word, your brain doesn't just focus on that single word; it also considers how it relates to other words in the sentence, both near and far.
The Transformer's attention mechanism works similarly. It allows the model to weigh the importance of different words in the input sequence when processing a particular word. This means that when the model is trying to understand or generate a word, it can look at all other words in the input and decide which ones are most relevant. This ability to focus on relevant parts of the input, regardless of their position, is what makes Transformers so powerful for language tasks.
Try checking transformer interactive visualization page to understand the internal sequence of transformer.
The key takeaway is that it uses this attention mechanism to process entire sequences of text at once, rather than word by word. This parallel processing, combined with its ability to understand relationships between distant words, is what allows LLMs to learn complex language patterns and generate coherent and contextually relevant text.
The Essentials: What Does It Take to Train
Training an LLM, especially a large one requires significant resources. It's not something you can typically do on a standard home computer.
Here are the key requirements:
1. The Fuel: Data, Data, Data
LLMs learn by being exposed to vast amounts of text data. This data comes from a multitude of sources across the internet and digitized books. Think of it as providing the model with an enormous library of human knowledge and communication. The quality and diversity of this data are crucial, as the model will learn patterns, grammar, facts, and even biases present in the training data. This data needs to be preprocessed, which involves cleaning, normalizing, and preparing it in a format suitable for the model to consume.
2. The Engine: Computation Hardware
Training LLMs involves billions of parameters (the internal variables that the model adjusts during learning like the knobs in DJ box. Updating these parameters based on the vast training data requires immense computational power. This is primarily provided by specialized hardware.
Graphics Processing Units (GPUs) designed for graphics initially, GPUs are exceptionally good at parallel processing, making them ideal for the mathematical operations involved in training neural networks.
Tensor Processing Units (TPUs): Developed by Google, TPUs are custom-built hardware accelerators specifically designed for machine learning workloads.
Beyond the processing unit, a robust infrastructure is also needed to manage these resources, including high-speed interconnects between hardware components and efficient cooling systems.
3. The Tools: Software and Libraries
There are specialized libraries / tools provide an necessary abstractions to build, train the model and manage it. Popular examples include PyTorch and TensorFlow. They handle the complex mathematical operations and optimizations behind the scenes.
The Training Workflow
Training an LLM can be thought of as a continuous learning process, much like how a student learns from textbooks and exercises. Here's a simplified workflow:
Data Ingestion and Preprocessing: The raw text data is collected, cleaned (removing noise, duplicates), and then tokenized (broken down into smaller units like words or sub-words). This tokenized data is then organized into batches for efficient processing.
Model Initialization: The LLM starts with a set of randomly initialized parameters. At this stage, it knows nothing about language.
Forward Pass: A batch of preprocessed text is fed into the model. The model processes this input through its layers, performing calculations based on its current parameters. The output of this forward pass is the model's prediction for the next token in the sequence.
Loss Calculation: The model's prediction is compared to the actual next token in the training data. The difference between the prediction and the actual value is quantified by a loss function. A higher loss indicates a poorer prediction.
Backward Pass (Backpropagation): The calculated loss is then used to adjust the model's parameters. This process, called backpropagation, essentially tells the model how much each parameter contributed to the error and in what direction it should be changed to reduce that error. This is where the learning truly happens.
Parameter Update: The model's parameters are updated based on the information from the backward pass. This is an iterative process, repeated millions or even billions of times over the entire training dataset.
Iteration and Refinement: Steps 3-6 are repeated for many epochs (a full pass through the entire training dataset). Over time, the model's parameters are refined, and its ability to predict the next token accurately improves significantly.
Analogy:
You’re teaching a child to recognize animals using flashcards.
One epoch = showing all the flashcards (e.g. 1000 images) once.
If you go through all 1000 images 5 times during training, that means you trained for 5 epochs.How LLMs Predict and Generate Words
At its core, an LLM's ability to generate human-like text is sophisticated form of next-token prediction. During training, the model learns to predict the most probable next word (or token) given the preceding sequence of words. When you prompt an LLM, it uses this learned probability distribution to generate its response.
Here's a simplified breakdown:
Input Processing: When you provide a prompt, the LLM first processes it, converting your words into numerical representations (embeddings) that it can understand. We have learned from previous post.
Contextual Understanding: The Transformer architecture, with its attention mechanism, analyzes the input sequence to understand the context and relationships between the words. It essentially builds a rich internal representation of your prompt.
Probability Distribution: Based on this contextual understanding, the model calculates a probability distribution over its entire vocabulary for what the next word should be.
For example: if the prompt is "The cat sat on the...", the model might assign high probabilities to words like "mat," "rug," or "chair," and very low probabilities to words like "sky" or "car."Token Sampling: The model then "samples" a word from this probability distribution. This sampling isn't always about picking the absolute most probable word; sometimes, it introduces a bit of randomness (controlled by a parameter called "temperature") to make the generated text more creative and less repetitive. This is why you might get slightly different responses to the same prompt.
Iterative Generation: Once a word is selected, it's appended to the input sequence, and the process repeats. The newly generated word becomes part of the context for predicting the next word, and so on, until the model determines it has completed its response (e.g., by generating an "end-of-sequence" token or reaching a specified length limit).
This iterative next-token prediction, guided by the learned probabilities and contextual understanding, allows LLMs to generate coherent, grammatically correct, and contextually relevant text.
Active Learning
These are best free resources to learn and code from scratch to train your LLM model
Building LLMs from the Ground Up -
Challenges in training Your Own LLM
While the concept of training an LLM might seem straightforward, the practicalities of training your own from scratch present significant challenges:
Data Acquisition: Gathering a sufficiently large, diverse, and high-quality dataset is incredibly difficult. Publicly available datasets might not be suitable for your specific needs, and curating your own requires immense effort in data collection, cleaning, and annotation.
Computational Cost: As mentioned, training large LLMs demands enormous computational resources. Accessing and affording hundreds or thousands of GPUs/TPUs for extended periods is a major barrier for most individuals and even many small organizations.
Time and Expertise: Training can take weeks or even months for large models. This requires a deep understanding of machine learning, neural networks, and distributed computing.
Hyperparameter Tuning: Building an effective LLM and finding the optimal hyperparameters is a complex task that often involves extensive experimentation and fine-tuning.
Evaluation and Bias : Evaluating the performance of an LLM is not just about accuracy; it also involves assessing its fairness, safety, and ability to avoid generating biased or harmful content.