Multi-LLM - Intelligent Model Switching

Trigger Command:

multi llm

Default Behavior: Always use Claude Opus 4.5 (strongest model) Only when the message contains

multi llm

command will local model selection be activated.

What's New in v1.1.0

• Renamed trigger from

  mlti llm

  to

  multi llm

  (clearer naming)
• Enhanced model existence checking with fallback chain
• Added detailed usage examples and troubleshooting
• Improved task detection patterns

Usage

Default Mode (without command)

Help me write a Python function -> Uses Claude Opus 4.5
Analyze this code -> Uses Claude Opus 4.5

Multi-Model Mode (with command)

multi llm Help me write a Python function -> Selects qwen2.5-coder:32b
multi llm Analyze this math proof -> Selects deepseek-r1:70b
multi llm Translate to Chinese -> Selects glm4:9b

Command Format

multi llm

multi llm coding

multi llm reasoning

multi llm chinese

multi llm general

Model Mapping

Primary Model (Default): github-copilot/claude-opus-4.5

Local Models (when

multi llm

Fallback Chain

If the selected model is unavailable, the system tries alternatives:

Coding:    qwen2.5-coder:32b -> qwen2.5-coder:14b -> qwen3:32b
Reasoning: deepseek-r1:70b -> deepseek-r1:32b -> qwen3:32b
Chinese:   glm4:9b -> qwen3:8b -> qwen3:32b
General:   qwen3:32b -> qwen3:14b -> qwen3:8b

Detection Logic

User Input
    |
    v
Contains "multi llm"?
    |
    +-- No -> Use Claude Opus 4.5 (default)
    |
    +-- Yes -> Task Type Detection
                |
        +-------+-------+-------+
        v       v       v       v
      Coding  Reasoning Chinese General
        |       |       |       |
        v       v       v       v
    qwen2.5  deepseek  glm4   qwen3
    coder    r1:70b    :9b    :32b

Task Detection Keywords

Examples

Example 1: Coding Task

# Input
multi llm Write a Python function to calculate fibonacci

# Output
Selected: qwen2.5-coder:32b
Reason: Detected coding task (keywords: python, function)

Example 2: Math Analysis

# Input
multi llm reasoning Prove that sqrt(2) is irrational

# Output
Selected: deepseek-r1:70b
Reason: Force command 'reasoning' used

Example 3: Quick Translation

# Input
multi llm 把这段话翻译成英文

# Output
Selected: glm4:9b
Reason: Detected Chinese lightweight task (keywords: 翻译)

Example 4: Default (No trigger)

# Input
Write a REST API with authentication

# Output
Selected: claude-opus-4.5
Reason: Default model (no 'multi llm' trigger)

Prerequisites

1. Ollama must be installed and running:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Start Ollama service
ollama serve

# Pull required models
ollama pull qwen2.5-coder:32b
ollama pull deepseek-r1:70b
ollama pull glm4:9b
ollama pull qwen3:32b

2. Check available models:

ollama list

Troubleshooting

Model not found

# Check if model exists
ollama list | grep "qwen2.5-coder"

# Pull missing model
ollama pull qwen2.5-coder:32b

Ollama not running

# Check service status
curl -s http://localhost:11434/api/tags

# Start Ollama
ollama serve &

Slow response

• Large models (70b) require significant RAM/VRAM
• Consider using smaller variants:

  deepseek-r1:32b

  instead of

  70b

Wrong model selected

• Use force commands:

  multi llm coding

  ,

  multi llm reasoning

• Check if keywords match your task type

Files in This Skill

multi-llm/
├── SKILL.md              # This documentation
└── scripts/
    ├── select-model.sh   # Model selection logic
    └── fallback-demo.sh  # Interactive demo script

Integration

With OpenCode/ClaudeCode

The trigger

multi llm

multi llm [your request here]

Programmatic Usage

# Get recommended model for a task
./scripts/select-model.sh "multi llm write a sorting algorithm"
# Output: qwen2.5-coder:32b

# Demo with actual model call
./scripts/fallback-demo.sh --force-local "explain recursion"

Author

• GitHub: @leohan123123

License

MIT