How Large Language Models Are Trained

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How Large Language Models Are Trained

How Large Language Models Are Trained

Data: The Foundation of Intelligence

Training a large language model begins with data—massive quantities of it. GPT models are trained on internet-scale corpora: books, Wikipedia, code repositories, news articles, social media, and more. LLaMA models also include high-quality curated datasets from Common Crawl, GitHub, ArXiv, and multilingual sources.

The key is not just quantity, but quality. Data is deduplicated, filtered for toxic content, and preprocessed into consistent formatting. This ensures the model learns from clean, diverse, and relevant examples.

Tokenization: Breaking Text Into Chunks

Machines don’t understand words—they understand numbers. To convert raw text into numerical inputs, we use a tokenizer. This splits sentences into units called tokens, which may represent words, subwords, or characters.

GPT uses the Byte Pair Encoding (BPE) tokenizer, while LLaMA uses SentencePiece with custom vocabulary. The goal is to balance vocabulary size with computational efficiency.

Training Infrastructure: Compute at Scale

Large models require powerful hardware: typically TPUs or GPUs running in massive clusters. Training GPT-3 took thousands of GPUs over weeks. LLaMA 3 was reportedly trained on over 1 million GPU hours.

Key techniques used to manage such scale:

  • Gradient checkpointing – Saves memory during backpropagation
  • Mixed precision training – Uses half-precision floats to reduce compute
  • Data parallelism – Splits training data across machines
  • Model parallelism – Splits the model itself across devices

Scaling Laws: Bigger is Smarter

Researchers discovered a surprising pattern: as you increase model size (parameters), dataset size (tokens), and compute, model performance improves in a predictable way. These scaling laws help engineers plan how to balance trade-offs.

While bigger models are smarter, they are also harder to align and more expensive to train. This leads to innovations in sparsity (Mixture of Experts), quantization (like 4-bit models), and distillation (small models trained from large ones).

Fine-Tuning and Instruction Tuning

Once the base model is pre-trained, it can be fine-tuned on specialized data. For example, a legal chatbot might be fine-tuned on contracts and case law.

Instruction tuning goes a step further—training the model to follow commands and respond to prompts. This made models like ChatGPT more useful and interactive.

RLHF: Reinforcement Learning with Human Feedback

One of OpenAI's key breakthroughs was RLHF—Reinforcement Learning with Human Feedback. Instead of simply training on correct answers, models learn from human-rated responses. This improves:

  • Helpfulness
  • Politeness
  • Factuality
  • Reduced bias and toxicity

In RLHF, human labelers compare pairs of model outputs. A reward model is trained on these preferences. Then, the language model is fine-tuned using Proximal Policy Optimization (PPO), a reinforcement learning algorithm.

Challenges During Training

Training LLMs isn’t all smooth sailing. Major challenges include:

  • Catastrophic forgetting – Models may forget earlier knowledge during fine-tuning
  • Mode collapse – The model generates repetitive or generic outputs
  • Data poisoning – Bad data leads to toxic or biased behavior
  • Cost – Training GPT-4 cost millions of dollars

Coming Up in Part 4:

  • Applications of LLMs in real life
  • How businesses are using GPT and LLaMA
  • Challenges in deployment
  • Future of foundation models

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