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)

code

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

Multi-Model Mode (with command)

code

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

Command	Description
`multi llm`	Activate intelligent model selection
`multi llm coding`	Force coding model
`multi llm reasoning`	Force reasoning model
`multi llm chinese`	Force Chinese model
`multi llm general`	Force general model

Model Mapping

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

Local Models (when multi llm triggered):

Task Type	Model	Size	Best For
Coding	qwen2.5-coder:32b	19GB	Code generation, debugging, refactoring
Reasoning	deepseek-r1:70b	42GB	Math, logic, complex analysis
Chinese	glm4:9b	5.5GB	Translation, summaries, quick tasks
General	qwen3:32b	20GB	General purpose, fallback

Fallback Chain

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

code

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

code

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

Category	Keywords (EN)	Keywords (CN)
Coding	code, debug, function, script, api, bug, refactor, python, java, javascript	代码, 编程, 函数, 调试, 重构
Reasoning	analysis, proof, logic, math, solve, algorithm, evaluate	推理, 分析, 证明, 逻辑, 数学, 计算, 算法
Chinese	translate, summary	翻译, 总结, 摘要, 简单, 快速

Examples

Example 1: Coding Task

bash

# 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

bash

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

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

Example 3: Quick Translation

bash

# 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)

bash

# Input
Write a REST API with authentication

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

Prerequisites

•Ollama must be installed and running:

bash

# 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

•Check available models:

bash

ollama list

Troubleshooting

Model not found

bash

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

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

Ollama not running

bash

# 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

code

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 is detected in your message. Simply prefix your request:

code

multi llm [your request here]

Programmatic Usage

bash

# 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