Gemma E4B Demos

Google's Gemma 4-bit quantized model offers higher quality for complex reasoning tasks.

Model Comparison

flowchart TD
    A[Task Input] --> B{Complexity?}
    B -->|Simple| C[Qwen 2.5 1.5B]
    B -->|Complex| D[Gemma 4 E4B]

    C --> E[Fast Response]
    D --> F[Accurate Analysis]

    style C fill:#e3f2fd
    style D fill:#fff3e0
    style E fill:#c8e6c9
    style F fill:#c8e6c9

Gemma Capabilities

graph TD
    A[Gemma 4 E4B] --> B[Classification]
    A --> C[Reasoning]
    A --> D[Analysis]
    A --> E[Code Generation]
    A --> F[Security Analysis]

    B --> B1[High Accuracy]
    C --> C1[Chain-of-Thought]
    D --> D1[Deep Analysis]

    style A fill:#f3e5f5

When to Use Gemma

Task Type	Qwen	Gemma
Fast classification	Yes	Yes
Multi-step reasoning	No	Yes
Security analysis	No	Yes
Complex patterns	No	Yes

Performance Trade-offs

graph LR
    A[Qwen 2.5] --> B[Speed]
    A --> C[Quality]
    D[Gemma 4] --> B
    D --> C

    B --> E[Fastest]
    C --> F[Highest]

    style A fill:#e3f2fd
    style D fill:#fff3e0

Demo Scripts

# Quick classification demo
python gemma-4-e4b-llm_mini_demo_5cases.py

# Full stress test
python gemma-4-e4b-llm_stress_test_class.py

# Security analysis
python gemma-4-e4b-cybersec_analysis.py

# Network monitoring
python gemma-4-e4b-network_monitor.py

Setup

# 1. Download Gemma 4 E4B from LM Studio
# 2. Load model in LM Studio
# 3. Start server on port 1234
# 4. Run demos

Results Comparison

Task: Classify 55 ISP complaints

Qwen 2.5 1.5B:
- Accuracy: 85%
- Avg Time: 0.6s
- Total: 33s

Gemma 4 E4B:
- Accuracy: 92%
- Avg Time: 1.2s
- Total: 66s