Large Language Model Training in 2023: A Practical Guide

Large language models (LLMs) like ChatGPT and GPT-3 have rapidly risen to prominence over the last few years, showcasing impressive natural language abilities. But while using these pretrained models is easy enough, training a massive LLM from scratch requires expertise and resources far beyond most organizations.

In this comprehensive 4500+ word guide, I‘ll demystify everything that goes into developing a custom large language model in 2023 – from planning to training to deployment. Whether you want to build your own LLM or simply understand them better, you‘ll learn:

  • What are LLMs and why businesses should care about them
  • Key capabilities and limitations of current large models
  • How model architecture drives language understanding
  • Computational resources needed to train different sized models
  • Step-by-step process for training a production-ready LLM
  • Practical guidance for leveraging LLMs as a business

By the end, you‘ll have an in-depth understanding of how these remarkable AI systems work and how to harness them for real-world impact. So let‘s dive in!

What are Large Language Models and Why Do They Matter?

Large language models are a type of natural language processing (NLP) system that has powered many of the breakthroughs in AI over the past 5 years. But what exactly are they and what makes them different?

Defining Large Language Models

LLMs are AI models trained on massive text data sets ranging from hundreds of gigabytes to terabytes in size. For example, OpenAI‘s GPT-3 model was trained on over a trillion words scraped from websites and books across the internet.

The "large" in large language models comes from their sheer size – LLMs have billons or even trillions of parameters. For context, a typical NLP model may have 10 million parameters while the largest LLMs today have over 1 trillion parameters!

This massive scale allows LLMs to learn nuanced patterns and semantics in language that power their advanced capabilities. Large size alone doesn‘t lead to intelligence however – the model architecture and training process also play a critical role as we‘ll explore later.

Rising Importance of LLMs

While language models have been around for decades, their capabilities have exploded in recent years. According to AI researcher Anthropic, LLMs have achieved a >1000x gain in benchmark performance since 2019!

What changed? Exponentially increasing compute enabled training models on ever more data using transformers – a revolutionary model architecture optimized for learning language representations.

As LLMs grow in size and sophistication, they are achieving remarkable language proficiency across multiple human skills:

  • Text generation: Write essays, poems, code, emails that are indistinguishable from human written works.
  • Question answering: Provide accurate answers to any fact-based question with human level knowledge.
  • Summarization: Condense long articles, stories or documents into concise high-level summaries.
  • Translation: Flawlessly translate text between hundreds of languages.
  • Search: Understand complex queries and return the most relevant information.

These broad capabilities make LLMs a platform for developing all types of intelligent applications. More importantly, they are enabling a paradigm shift from narrow AI to artificial general intelligence (AGI).

While previous AI systems could only perform singular focused tasks, LLMs show promise as a foundation for creating more general and adaptable AI like humans. This is why training your own large language model tailored to your business needs has become a crucial competitive advantage.

But how exactly do LLMs work under the hood? What architecture gives them this language mastery? Let‘s find out.

Architectures Behind Large Language Models

Most leading large language models including Google‘s BERT, OpenAI‘s GPT models, and Facebook‘s XLM are based on a novel neural network architecture called transformers, first introduced in 2017. Transformers have become the go-to architecture for NLP thanks to their ability to capture nuanced context and semantics.

The Transformer Architecture

Transformers process an input sequence of text to output a contextual representation for each token. This contextual embedding captures the meaning of each word based on its surroundings and dependencies.

The key components of a transformer are:

  • Embedding layers: Convert input text tokens (words/subwords) into dense numeric vectors. Each token is mapped to an embedding vector with properties that represent its meaning.
  • Self-attention layers: Relate different input positions and learn contextual relationships between all tokens using multi-headed dot product attention. This lets models understand language holistically.
  • Feed forward layers: Process the attention-encoded representations through deeper neural networks. This adds non-linear complexity.
  • Normalization and residuation: Enable stable training of very deep networks.

Stacking these components forms the full transformer architecture that powers modern LLMs:

Transformer architecture diagram

The transformer stack gives LLMs their powerful contextual language understanding. Image source: Anthropic

This transformer stack is applied to vast datasets to train large language models. The more data the model is trained on, the better it becomes at representing nuanced language.

Scaling Up Model Size

There are a few key ways LLMs are scaled up in size and performance:

  • Width: Increase the number of dimensions in the token embeddings and feedforward layers. Wider layers allow modeling more complex language relationships.
  • Depth: Stack more transformer blocks to create deeper networks. Each layer builds on the representations from previous layers.
  • Vocabulary: Use larger token vocabularies with smaller subword units to expand context capacity.
  • Parameters: Billion+ parameter models require advanced training techniques like sharding across GPU clusters.

Trading off these dimensions against training costs leads to a range of model sizes capable of different performance levels:

ModelParametersPerformanceTraining Compute
Small (BERT)110MModerate1 GPU
Mid-Size (GPT-2)1.5BGoodHundreds of GPUs
Large (GPT-3)175BStrongThousands of GPUs
XL (PaLM)540BVery Strong10,000+ GPUs

As computing power grows exponentially, we will likely see LLMs in the trillion+ parameter range soon that exceed human performance in many language tasks.

Now that we understand model architectures, let‘s go over how these massive models are actually trained.

How are Large Language Models Trained?

While using LLMs can be as simple as calling an API, the training process involves extensive data preparation, model configuration, distributed training, and careful evaluation.

1. Data Collection and Preprocessing

Training data is the lifeblood of LLMs – their performance is directly determined by the quantity and quality of data they learn from.

  • Data collection: Massive datasets with billions of words are aggregated from diverse sources like books, web pages, scientific papers, online forums, and more. This introduces the model to wide-ranging concepts and language.
  • Data cleaning: Raw data is preprocessed to remove invalid characters, normalize formatting, handle duplicates, etc.
  • Tokenization: Text is split into numeric token IDs that map to model vocabulary. This encodes the discrete units of language the model learns.

With data ready, model architecture and training hyperparameters are configured:

2. Model Configuration

  • Model architecture: The transformer model structure including embedding size, layers, attention heads, feedforward dimension, etc. are specified based on computational constraints and desired capability.
  • Hyperparameters: Key training hyperparameters like batch size, learning rate, dropout rate, and optimization approach are set based on performant recipes.
  • Distributed training: Model parallelism techniques like tensor slicing are implemented to distribute training across multiple accelerators like GPU clusters. This enables scaling to huge parameters.

3. Distributed Training

  • Gradient descent optimization: Stochastic gradient descent and backpropagation algorithms are used to iteratively minimize prediction loss and tune model weights.
  • Accelerated computing: Training occurs on dedicated systems of 100s of GPUs/TPUs to speed up the trillion+ parameter matrix operations required per batch.
  • Convergence tracking: Model loss on a validation set is monitored to judge convergence which generally occurs after thousands of full dataset iterations.

This distributed training process requires immense computing power taking weeks or months to complete. For example, training GPT-3 was estimated to cost $12M on 256 GPUs and 20 petaflops of compute!

4. Evaluation and Iteration

After training, the LLM is thoroughly evaluated across many tasks and test sets. Additional iterations on data, model size, hyperparameters, etc. are done to optimize performance before deployment.

This entire process requires substantial data infrastructure, software engineering, machine learning and compute resources. Next, let‘s go over options for businesses to leverage LLMs without massive from-scratch training.

How Can Businesses Leverage Large Language Models?

While only big tech companies can realistically train their own LLMs from scratch today, there are accessible options for leveraging large language models:

Using Public LLMs

The easiest way to get started is tapping into public LLMs like OpenAI‘s GPT-3 via their developer APIs:

  • Leverage broad capabilities: Public LLMs offer an extensive set of language skills out-of-the-box without any customization needed.
  • Develop quickly: Build and iterate products faster by focusing on applications instead of foundational model development.
  • Pay-per-use pricing: Affordable usage-based pricing without large upfront costs.

The main downside is limited fine-tuning capability and dependence on third-party APIs.

Fine-Tuning Public LLMs

For more customization, public LLMs can be adapted to specific domains by fine-tuning on proprietary data:

  • Specialize models: Further train LLMs on business-specific data like customer service logs, legal documents, scientific literature, etc.
  • Improved performance: Fine-tuned models greatly improve performance on business tasks compared to out-of-the-box public LLMs.
  • Maintain capabilities: Unlike training a model from scratch, fine-tuning preserves most of the models‘ general knowledge.

The fine-tuning process is relatively low cost but still relies on third-party models.

Training Mid-Size Custom Models

For full control without massive resources, training mid-sized custom models is an emerging option:

  • Tailor model to use cases: Train models like 10B parameters focused on specialized business needs rather than general applications.
  • Better align to data: Carefully control dataset and vocabulary to boost performance on business language.
  • Own model IP: Retain full ownership and control over proprietary models.
  • Leverage cloud scale: Cloud compute enables cost-effective distributed training of custom mid-sized models.

The customized performance and full ownership of models makes this an appealing emerging option for many businesses.

By weighing these LLM approaches against business needs and resources, you can formulate the optimal strategy. Next, let‘s go through a step-by-step guide to training a custom mid-sized LLM.

A Step-by-Step Guide to Training Your Own LLM

While smaller businesses can‘t replicate the specialized infrastructure used to train models like GPT-3, advanced cloud computing has made training customized mid-sized LLMs much more accessible.

Here is an end-to-end walkthrough of the steps to develop your own production-ready large language model tailored to your business needs:

1. Define Model Goals and Target Capabilities

First, clearly define what you want your custom LLM to achieve:

  • What are the most important applications like search, analytics, content generation?
  • What domains of knowledge does it need like science, finance, healthcare?
  • What user experiences do you want to enable through the model?
  • What level of performance do users require around accuracy, fluency, reasoning ability?

These goals will dictate your training data and model requirements.

2. Assemble a High-Quality Training Dataset

LLMs are only as good as their training data. Carefully curate a dataset aligned to your goals:

  • Gather at least 10GB of text spanning key domains from internal data, web scraping, partners.
  • Clean and normalize data. Remove duplicates, errors, bad examples.
  • Organize data into clear training, validation and test splits.

Higher quality, larger datasets lead to better performing models.

3. Configure Model Architecture and Infrastructure

With data ready, set up your training environment:

  • Start with a standard transformer architecture as your foundation.
  • Tailor model width, depth, vocab size for your use case and hardware constraints.
  • Implement tensor model parallelism across GPU/TPU machines.

Properly configuring model architecture and technical infrastructure is critical.

4. Train Your Model at Scale

Now we‘re ready to train!

  • Write data loading, distributed training, and evaluation code or leverage frameworks like PyTorch and Tensorflow.
  • Run distributed stochastic gradient descent across your cluster to minimize loss.
  • Expect 1-4 weeks of training depending on model and data size.
  • Track validation metrics like perplexity to judge convergence.

Patience and close monitoring is key during this lengthy process.

5. Evaluate and Fine-tune Your Model

Once initial training finishes, rigorously evaluate your model:

  • Test model on your held-out testset spanning different applications.
  • Analyze overall results as well as performance per domain.
  • Fine-tune model on downstream tasks with supervised data if deficiencies are found.

Iterate if evaluation reveals weaknesses in your model.

6. Deploy Your Model to Production

With a performant model ready, get it in the hands of users:

  • Export trained model weights and write production inference code.
  • Build developer APIs, web UIs, or integrate directly into apps.
  • Monitor and retrain models as users uncover new data patterns.

Careful deployment and iterations enable real-world impact.

While not trivial, this process allows smaller teams to train production-grade custom LLMs that unlock immense value aligned to business needs.

The Future of LLMs

The capabilities of large language models are advancing at a torrid pace thanks to exponential compute growth. In just a few years, expect to see:

  • 100T-1Q parameter models: Models sizes will balloon 100x-1000x compared to today through computational advances.
  • Multi-modal models: Models like DALL-E that can generate images as well as text by training across modalities.
  • On-device training: Federated learning enabling training across decentralized data like users‘ smartphones.
  • Specialized industry models: Models trained on niche corpora like all US case law or the entire field of chemistry papers.
  • Creative applications: Models capable of human-level creativity empowering applications limited today like joke and story writing.

This rapid progress will lead to LLMs that mirror and even exceed human cognitive abilities in many domains within the decade.

The challenge and opportunity is democratizing access to these transformative technologies beyond large tech companies. Initiatives like Anthropic‘s Constitutional AI, smaller LLM startups, and open training frameworks are positive steps towards responsibly developing and distributing the benefits of large language models widely.

I hope this guide has demystified what goes on behind the scenes of today‘s remarkable LLMs and how businesses can start benefiting from customizing them. The future possibilities are truly exciting! Let me know if you have any other questions.

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