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Models Beginner Also known as: SLM · Small LLM

Small Language Model

A Small Language Model (SLM) is a language model in the 1B-7B parameter range, optimized to maximize quality-per-parameter rather than raw capability. The key insight from Microsoft's Phi series is that training on 'textbook quality' synthetic data enables a 1.3B model to rival much larger models on reasoning benchmarks. SLMs run on laptops, smartphones, and embedded devices without a dedicated GPU. Representative examples include Phi-1.5, Phi-3, Gemma 2B, Qwen 1.5B, and SmolLM.

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In practice

A developer chooses an SLM when deploying an AI assistant on edge hardware (Raspberry Pi, Android phone, corporate laptop) where a 70B LLM would be impractical. With llama.cpp or Ollama, a 4-bit quantized Phi-3 Mini runs at acceptable speed on any modern CPU. SLMs are also ideal for specialized tasks: fine-tuning on a specific domain with limited data produces compact models that outperform GPT-4 in that target domain.

Related terms

LLM Quantization Inference compute Synthetic Data

Seen in the wild

4 entries mentioning it
  1. Microsoft Phi-3 Vision: 4.2B multimodal parameters for edge devices
    High
  2. Phi-3: Microsoft relaunches SLMs with quality of 10x bigger models
    High
  3. Phi-2: Microsoft's 2.7B model that beats a 13B
    Medium
  4. Phi-1.5: big-model reasoning in just 1.3 billion parameters
    High
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