SLM vs LLM Strategies: Choosing the Right Model Size

Small Language Models (SLMs) and Large Language Models (LLMs) each have their place. Here’s how to choose.

Decision Framework

# model_selection.py - Framework for choosing model size

from dataclasses import dataclass
from enum import Enum
from typing import Optional

class ModelSize(Enum):
    SLM = "slm"  # <10B parameters
    LLM = "llm"  # >10B parameters
    HYBRID = "hybrid"

@dataclass
class TaskRequirements:
    complexity: str  # "simple", "moderate", "complex"
    latency_ms: int
    privacy_required: bool
    cost_sensitivity: str  # "low", "medium", "high"
    accuracy_threshold: float
    volume_per_day: int

class ModelSelector:
    """Select appropriate model size based on requirements."""

    def recommend(self, requirements: TaskRequirements) -> dict:
        """Recommend model strategy based on requirements."""
        score_slm = 0
        score_llm = 0

        # Complexity analysis
        if requirements.complexity == "simple":
            score_slm += 3
        elif requirements.complexity == "moderate":
            score_slm += 1
            score_llm += 1
        else:
            score_llm += 3

        # Latency requirements
        if requirements.latency_ms < 100:
            score_slm += 3
        elif requirements.latency_ms < 500:
            score_slm += 1

        # Privacy requirements
        if requirements.privacy_required:
            score_slm += 3

        # Cost sensitivity
        if requirements.cost_sensitivity == "high":
            score_slm += 2
        elif requirements.cost_sensitivity == "low":
            score_llm += 1

        # Volume
        if requirements.volume_per_day > 100000:
            score_slm += 2

        # Accuracy threshold
        if requirements.accuracy_threshold > 0.95:
            score_llm += 2

        # Determine recommendation
        if score_slm > score_llm + 2:
            strategy = ModelSize.SLM
        elif score_llm > score_slm + 2:
            strategy = ModelSize.LLM
        else:
            strategy = ModelSize.HYBRID

        return {
            "recommendation": strategy,
            "slm_score": score_slm,
            "llm_score": score_llm,
            "suggested_models": self.get_suggested_models(strategy, requirements)
        }

    def get_suggested_models(self, strategy: ModelSize, requirements: TaskRequirements) -> list:
        if strategy == ModelSize.SLM:
            return ["Phi-3-mini", "Phi-3-small", "Gemma-2B", "Llama-3-8B"]
        elif strategy == ModelSize.LLM:
            return ["GPT-4o", "Claude-3-Opus", "Gemini-Pro"]
        else:
            return {
                "routing": "Use SLM for simple queries, LLM for complex",
                "slm": ["Phi-3-mini"],
                "llm": ["GPT-4o-mini"]
            }

# Use case examples
# | Task | Recommended | Reasoning |
# |------|-------------|-----------|
# | Sentiment analysis | SLM | Simple classification, high volume |
# | Code generation | LLM | Complex reasoning required |
# | Entity extraction | SLM | Pattern matching, privacy |
# | Creative writing | LLM | Nuanced language generation |
# | Summarization | Hybrid | Depends on length and complexity |

The right model size depends on your specific requirements, not just capabilities.