Classes of Large Language Models¶

Large Language Models (LLMs) can be categorized into several classes based on their architecture, training objectives, and specialized capabilities.

1. Chat & Conversational Models¶

General-purpose models optimized for dialogue and following instructions. - Purpose: General assistance, creative writing, Q&A. - Examples: GPT-4o, Claude 3.5 Sonnet, Llama 3.1.

2. Reasoning & Logic Models¶

Models specifically designed or fine-tuned for complex multi-step reasoning, mathematical problem-solving, and logic. - Purpose: Scientific research, complex coding, advanced mathematics. - Examples: OpenAI o1-preview, o1-mini.

3. Mixture of Experts (MoE)¶

Architecture that uses a sparse execution path, activating only a subset of parameters for each token. - Purpose: Efficiency and high performance without the cost of a full dense model. - Examples: Mixtral 8x7B, DeepSeek-V2, GPT-4 (widely believed to be MoE).

4. Code Generation & Analysis Models¶

Models specialized in programming languages, debugging, and software architecture. - Purpose: AI coding assistants, automated code review. - Examples: CodeLlama, StarCoder2, DeepSeek-Coder-V2.

5. Vision-Language Models (Multimodal)¶

Models that can process and understand both text and images. - Purpose: Image captioning, visual Q&A, document analysis (OCR). - Examples: GPT-4o, Claude 3.5 Sonnet, Llama 3.2-Vision.

6. Audio-Native & Multimodal Audio Models¶

Models that can directly process or generate audio/speech without intermediate text conversion. - Purpose: Real-time translation, emotion-aware voice assistants. - Examples: GPT-4o (Advanced Voice), Gemini 1.5 Pro. - Sources: Current Large Audio Language Models largely transcribe rather than listen (Analysis of auditory understanding vs transcription).

7. State Space Models (SSM) & Hybrids¶

Alternatives to the Transformer architecture (like Mamba) designed for very long context and linear scaling. - Purpose: Processing extremely long documents, efficient inference. - Examples: Jamba (Hybrid Transformer-Mamba), Mamba-2.

8. Embedding Models¶

Models that represent text as high-dimensional vectors. - Purpose: Semantic search, RAG, document clustering. - Examples: text-embedding-3-small, Voyage AI, BGE-M3.

9. Small Language Models (SLM)¶

Highly optimized models with fewer parameters (typically <10B) designed to run on-device. - Purpose: Edge computing, privacy-sensitive local tasks. - Examples: Phi-3.5, Gemma 2 2B, Llama 3.2 1B/3B.

10. Long-Context Models¶

Models specifically optimized to handle 100K+ tokens in their active window. - Purpose: Analyzing entire codebases, long novels, or legal documents. - Examples: Gemini 1.5 Pro (2M context), Claude 3 (200K context).

11. Tool-Use & Agentic Models¶

Models fine-tuned for reliable function calling and tool interaction. - Purpose: Autonomous agents, complex workflow automation. - Examples: NexusRaven-V2, Berkeley Function Calling Leaderboard (BFCL) top models. - Sources: The First Fully General Computer Action Model (Shift towards autonomous system interaction).

12. Variational Autoencoders (VAE)¶

Generative models that learn a compressed latent representation of data, often used for image and video synthesis. - Purpose: Image/video reconstruction, generative diversity, latent space exploration. - Sources: Learnings from 4 months of Image-Video VAE experiments.

Backlog¶

Add comparison table of model architectures (Dense vs MoE vs SSM).
Include details on "Reasoning Tokens" and "Chain of Thought" native models.