SLM Apps¶

Small Language Models (SLMs) optimized for speed and resource efficiency.

SLM Philosophy¶

flowchart LR
    A[Large Model] --> B[High Quality]
    A --> C[Slow Response]
    A --> D[High Resource]

    E[Small Model] --> F[Good Quality]
    E --> G[Fast Response]
    E --> G2[Low Resource]

    style A fill:#fff3e0
    style E fill:#c8e6c9

Resource Comparison¶

Model	Size	RAM	VRAM	Speed
Qwen 7B	7GB	8GB	6GB	Medium
Qwen 1.5B	1.5GB	4GB	2GB	Fast
Gemma 4B	2.5GB	6GB	4GB	Medium
Phi-3 Mini	2GB	4GB	2GB	Very Fast

Use Case Matrix¶

graph TD
    subgraph Speed Priority
        A[Real-time chat] --> E[Phi-3/Qwen 1.5B]
        B[High volume] --> E
    end

    subgraph Quality Priority
        C[Complex analysis] --> F[Gemma 4B]
        D[Security tasks] --> F
    end

    subgraph Balance
        G[General classification] --> H[Qwen 1.5B + Rules]
    end

    style E fill:#c8e6c9
    style F fill:#fff3e0
    style H fill:#e8eaf6

SLM Optimization¶

flowchart TD
    A[Full Model] --> B[Quantization]
    B --> C[4-bit Quantized]
    C --> D[Pruning]
    D --> E[Distilled]

    E --> F[~70% size reduction]
    F --> G[~80% speed improvement]

    style F fill:#c8e6c9
    style G fill:#c8e6c9

When to Use SLM¶

Scenario	Recommendation
<100ms latency needed	✅ SLM
Resource constrained	✅ SLM
Simple tasks	✅ SLM
Complex reasoning	❌ Use Gemma
Security analysis	❌ Use Gemma

Speed vs Quality Tradeoff¶

graph LR
    A[Speed] --> B[SLM: <500ms]
    A --> C[Standard: 1-3s]
    A --> D[Large: 3-10s]

    E[Quality] --> F[SLM: 75-85%]
    E --> G[Standard: 85-92%]
    E --> H[Large: 92-97%]

Demo Scripts¶

# Run SLM demo
python apps-slm.py

# Compare with standard
python apps-standard.py

Best SLM Candidates¶

Model	Context	Strength
Phi-3 Mini	4K	Code, fast tasks
Qwen 1.5B	8K	General, fast
Gemma 2B	8K	Balanced
TinyLlama	2K	Prototyping

Next Steps¶

Standard Apps - Full Gemma demos
Enterprise Apps - Production setup