Models
9 entries in this category.
Large Language Model (LLM)
A neural network trained on large text datasets to predict and generate human-like language.
Fine-Tuning (PEFT, LoRA)
The process of taking a pre-trained model and training it further on a smaller, specialized dataset to adapt it to specific tasks or domains.
Context Window
The maximum amount of text (measured in tokens) that an AI model can process at one time.
Tokens & Tokenization
The base units of data processed by an LLM. A token is typically a chunk of characters rather than a full word.
Parameters (Weights)
The internal variables a neural network learns during training. They act as the "knowledge" of the model.
Mixture of Experts (MoE)
An AI architecture that replaces a single dense neural network with multiple specialized sub-networks (experts) to improve efficiency and scale.
Transformer Architecture
The neural network architecture introduced in 2017 that powers virtually all modern large language models through self-attention mechanisms.
Foundation Models
Large AI models trained on broad, general datasets at massive scale, designed to be adapted for a wide range of downstream tasks.
Multimodal AI
AI systems that can process and generate content across multiple modalities—text, images, audio, and video—within a single model.