18. Emerging Model Families

The model landscape evolves rapidly. Understanding major families and their characteristics helps you stay current and evaluate new releases.

Major model families (as of early 2026):

Llama (Meta):

• Llama 3.1: 8B, 70B, 405B
• Strengths: Well-documented, many derivatives, strong community
• Tokenizer: Tiktoken-based, 128K context
• Quantization ecosystem: Mature, many formats available

Mistral (Mistral AI):

• Mixtral 8x7B, Mistral Large, Small
• Strengths: Efficient MoE variants, strong reasoning
• Architecture: Sliding window attention, grouped query attention
• Tradeoffs: Smaller context than Llama in some variants

Phi (Microsoft):

• Phi-3-mini (3.8B), medium (14B)
• Strengths: Exceptional quality per parameter
• Training: Heavy emphasis on "textbook quality" data
• Tradeoffs: Smaller parameter count limits some capabilities

Deepseek:

• Deepseek V2, V3
• Strengths: MoE architecture with strong efficiency
• Deepseek-Coder: Specialized code models
• Tradeoffs: Less ecosystem support than Llama

Gemma (Google):

• Gemma 2 2B, 9B, 27B
• Strengths: High quality, open weights (with terms)
• Architecture: GeMMa attention mechanism
• Tradeoffs: License restrictions limit some use cases

Qwen (Alibaba):

• Qwen 2.5 series, Code models
• Strengths: Strong multilingual, many sizes
• Tradeoffs: Documentation primarily in Chinese

Emerging patterns:

1. Mixture of Experts adoption: More models using MoE for efficiency
2. Longer context: 128K becoming standard, 256K+ emerging
3. Smaller but stronger: Phi-3 shows 3.8B can match larger models
4. Specialization: Code models, math models, multilingual variants

How to evaluate new models:

# Evaluation checklist for new releases
new_model_checklist = {
    "weights_available": True,  # Or just API
    "license": "...",  # Check commercial restrictions
    "context_length": 0,
    "architecture": "dense/moe/hybrid",
    "training_data_cutoff": "...",
    "benchmark_scores": {
        "mmlu": None,
        "humaneval": None,
        "gsm8k": None
    },
    "community_adoption": {
        "huggingface_downloads": 0,
        "GitHub_stars": 0
    }
}