Types of LLMs

Large Language Models can be categorized in several ways:

By Purpose:

• Base Models: Trained on vast datasets without specific instructions
• Instruction-Tuned Models: Fine-tuned to follow specific directions
• Chat Models: Optimized for conversational interactions

By Accessibility:

• Open Source: Publicly available for research and modification (e.g., LLaMA, Mistral)
• Proprietary: Restricted commercial models (e.g., GPT-4, Claude)

By Specialization:

• General Purpose: Broad capabilities across domains
• Domain-Specific: Trained for specialized fields (medicine, law, coding)
• Multimodal: Process both text and images/video

Training Approaches

LLMs are developed through different training methodologies:

Pre-training

Learning from vast unlabelled text corpora

Fine-tuning

Specializing models on specific tasks or domains

RLHF

Reinforcement Learning from Human Feedback

LoRA

Low-Rank Adaptation for efficient fine-tuning

Each approach balances model performance, computational requirements, and specialization capabilities.

Model Architectures

Different neural network designs power today's AI systems:

Transformers

The dominant architecture for modern LLMs, using self-attention mechanisms

RNNs

Recurrent Neural Networks process sequences sequentially

CNNs

Convolutional Neural Networks excel at image processing

GANs

Generative Adversarial Networks create synthetic data

Understanding Tokens

Tokens are the fundamental units of text that language models process. They can represent:

• • Whole words ("language")
  • Subword units ("##ing")
  • Punctuation and special characters

Tokenization Example:
"Large Language Models understand tokens"
 'Large' 'Language' 'Models' 'understand' 'tokens'

Word-level tokenization:
'L' 'arg' 'e' 'La' 'ng' 'ua' 'ge' 'M' 'od' 'els' 'un' 'der' 'st' 'and' 'to' 'ke' 'ns'

Subword tokenization (Byte Pair Encoding):
Token limits (context windows) determine how much text an LLM can process at once. Modern models typically support 4K to 128K tokens.