AgentSkillsCN

Qwen Cli

Qwen CLI

SKILL.md
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Qwen CLI - Local AI Assistant

Version: 1.0.0

Status: Active

Required_Canon_Version: >=2.0.0

Purpose: Provides a local CLI interface to Qwen 7B via Ollama for fast, offline AI assistance.

Model: Qwen2.5 7B (via Ollama)

Use Cases:

  • •Quick code questions without cloud API costs
  • •Offline development assistance
  • •Fast prototyping and testing
  • •Private/sensitive code analysis

Features

  • •Multiple Interfaces: Batch file, Python CLI, interactive REPL
  • •Context-Aware: Can read files and provide code assistance
  • •Streaming Output: Real-time responses
  • •Conversation Memory: Multi-turn conversations
  • •File Operations: Read and analyze code files

Usage

Quick Start (Batch File)

bash
# Ask a question
qwen.bat "How do I parse JSON in Python?"

# Analyze a file
qwen.bat "Explain this code" CORTEX/embeddings.py

# Interactive mode
qwen.bat

Python CLI

bash
# Direct question
python qwen_cli.py "What is a generator in Python?"

# With file context
python qwen_cli.py "Review this code" --file CORTEX/semantic_search.py

# Interactive REPL
python qwen_cli.py --interactive

Advanced Options

bash
# Specify model
python qwen_cli.py "question" --model qwen2.5:14b

# Control output length
python qwen_cli.py "question" --max-tokens 1000

# System prompt
python qwen_cli.py "question" --system "You are a Python expert"

# Save conversation
python qwen_cli.py --interactive --save conversation.json

Installation

  1. •

    Install Ollama (if not already):

  2. •

    Pull Qwen Model:

    bash
    ollama pull qwen2.5:7b
  3. •

    Verify Installation:

    bash
    ollama list

Model Options

Available Qwen models via Ollama:

  • •qwen2.5:0.5b - Fastest, smallest (500MB)
  • •qwen2.5:1.5b - Fast, lightweight (1.5GB)
  • •qwen2.5:7b - Default, balanced (4.7GB)
  • •qwen2.5:14b - More capable (8.9GB)
  • •qwen2.5:32b - Most capable (19GB)
  • •qwen2.5-coder:7b - Code-specialized

Configuration

Edit config.json to set defaults:

json
{
  "model": "qwen2.5:7b",
  "temperature": 0.7,
  "max_tokens": 2000,
  "system_prompt": "You are a helpful coding assistant.",
  "stream": true
}

Examples

Code Explanation

bash
qwen.bat "Explain what this function does" CORTEX/embeddings.py

Debug Help

bash
qwen.bat "Why am I getting AttributeError: 'Row' object has no attribute 'get'?"

Code Generation

bash
qwen.bat "Write a function to calculate Fibonacci numbers in Python"

Code Review

bash
python qwen_cli.py "Review this for bugs" --file demo_semantic_dispatch.py

Integration with AGS

The Qwen CLI integrates with the Agent Governance System:

  • •Can read CORTEX database
  • •Understands AGS file structure
  • •Follows CANON principles
  • •Can assist with skill development

Performance

  • •Startup: ~1-2 seconds
  • •Response time: ~0.5-2 seconds per token (CPU)
  • •Memory: ~6GB for 7B model
  • •Offline: Works without internet

Limitations

  • •Local model, smaller than Claude/GPT-4
  • •Best for focused questions, not large codebases
  • •Requires ~8GB RAM for 7B model
  • •CPU inference is slower than GPU

Tips

  1. •Be Specific: "How do I X in Python?" vs "Tell me about Python"
  2. •Provide Context: Include file paths or code snippets
  3. •Use System Prompts: Set role for better responses
  4. •Right-Size Model: Use 1.5b for speed, 14b for quality
  5. •Save Conversations: Use --save for important sessions

Troubleshooting

Ollama not found:

bash
# Check if running
ollama list

# Start service (Windows)
# Ollama runs as a system service after install

Model not available:

bash
ollama pull qwen2.5:7b

Slow responses:

  • •Try smaller model: qwen2.5:1.5b
  • •Reduce max_tokens
  • •Close other applications

Connection refused:

bash
# Check Ollama is running
curl http://localhost:11434/api/tags

Files

  • •qwen.bat - Windows batch wrapper
  • •qwen_cli.py - Main Python CLI
  • •config.json - Configuration
  • •SKILL.md - This file

See Also