reneromero08/THOUGHT/LAB/TURBO_SWARM/agents_skills_alpha/qwen-cli

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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)

# 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

# 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

# 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):

   • Download from https://ollama.com
   • Run installer

2. Pull Qwen Model:

   ollama pull qwen2.5:7b
   

3. Verify Installation:

   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:

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

Examples

Code Explanation

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

Debug Help

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

Code Generation

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

Code Review

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:

# Check if running
ollama list

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

Model not available:

ollama pull qwen2.5:7b

Slow responses:

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

Connection refused:

# 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

• Ollama Documentation
• Qwen Model Card
• AGS CORTEX for semantic search integration