Root Node: Model Type

  |-- Open Source vs. Closed Source
  |-- Domain-specific vs. General
  |-- Multilingual vs. Monolingual
  |-- Vision-Language Models (VLMs) vs. Non-VLMs
  |-- Modular vs. Non-modular
  |-- Specialized vs. General-purpose
  |-- Embeddings-based vs. Non-Embeddings-based
  |-- Model Size (Small, Medium, Large, Extra-Large)

  |-- Additional Dimensions (as needed)
      |-- Model Architecture (e.g., Encoder-only, Decoder-only, Encoder-Decoder)
      |-- Training Methodology (e.g., Self-supervised, Supervised, Reinforcement Learning)
      |-- Data Source (e.g., Web-scale, Domain-specific, Synthetic)
      |-- Ethical Considerations (e.g., Bias, Fairness, Transparency)

  |-- Refine existing dimensions and categories based on new insights and feedback
  |-- Continuously update the taxonomy with new models and advancements in the field