gh-akougkas-claude-code-4-science-warpio/commands/warpio-help-local.md

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Detailed help for Warpio local AI management	Read

Warpio Local AI Help

Local AI Overview

Warpio uses local AI providers for quick, cost-effective, and low-latency tasks while reserving Claude (the main AI) for complex reasoning and planning.

Supported Providers

🤖 LM Studio (Recommended)

Best for: Most users with GPU-enabled systems

Setup:

1. Download from https://lmstudio.ai
2. Install models (qwen3-4b-instruct-2507 recommended)
3. Start local server on port 1234
4. Configure in Warpio with /warpio-local-config

Configuration:

LOCAL_AI_PROVIDER=lmstudio
LMSTUDIO_API_URL=http://192.168.86.20:1234/v1
LMSTUDIO_MODEL=qwen3-4b-instruct-2507
LMSTUDIO_API_KEY=lm-studio

🦙 Ollama

Best for: CPU-only systems or alternative models

Setup:

1. Install Ollama from https://ollama.ai
2. Pull models: ollama pull llama3.2
3. Start service: ollama serve
4. Configure in Warpio

Configuration:

LOCAL_AI_PROVIDER=ollama
OLLAMA_API_URL=http://localhost:11434/v1
OLLAMA_MODEL=llama3.2

Local AI Commands

Check Status

/warpio-local-status

Shows connection status, response times, and capabilities.

Configure Provider

/warpio-local-config

Interactive setup for LM Studio, Ollama, or custom providers.

Test Connection

/warpio-local-test

Tests connectivity, authentication, and basic functionality.

When to Use Local AI

✅ Ideal for Local AI

• Quick Analysis: Statistical summaries, data validation
• Format Conversion: HDF5→Parquet, data restructuring
• Documentation: Code documentation, README generation
• Simple Queries: Lookups, basic explanations
• Real-time Tasks: Interactive analysis, quick iterations

✅ Best for Claude (Main AI)

• Complex Reasoning: Multi-step problem solving
• Creative Tasks: Brainstorming, design decisions
• Deep Analysis: Comprehensive research and planning
• Large Tasks: Code generation, architectural decisions
• Context-Heavy: Tasks requiring extensive conversation history

Performance Optimization

Speed Benefits

• Local Processing: No network latency
• Direct Access: Immediate response to local resources
• Optimized Hardware: Uses your local GPU/CPU efficiently

Cost Benefits

• No API Costs: Free for local model inference
• Scalable: Run multiple models simultaneously
• Privacy: Data stays on your machine

Configuration Examples

Basic LM Studio Setup

# .env file
LOCAL_AI_PROVIDER=lmstudio
LMSTUDIO_API_URL=http://localhost:1234/v1
LMSTUDIO_MODEL=qwen3-4b-instruct-2507
LMSTUDIO_API_KEY=lm-studio

Advanced LM Studio Setup

# .env file
LOCAL_AI_PROVIDER=lmstudio
LMSTUDIO_API_URL=http://192.168.1.100:1234/v1
LMSTUDIO_MODEL=qwen3-8b-instruct
LMSTUDIO_API_KEY=your-custom-key

Ollama Setup

# .env file
LOCAL_AI_PROVIDER=ollama
OLLAMA_API_URL=http://localhost:11434/v1
OLLAMA_MODEL=llama3.2:8b

Troubleshooting

Connection Issues

Problem: "Connection failed"

• Check if LM Studio/Ollama is running
• Verify API URL is correct
• Check firewall settings
• Try different port

Problem: "Authentication failed"

• Verify API key matches server configuration
• Check API key format
• Ensure proper permissions

Performance Issues

Problem: "Slow response times"

• Check system resources (CPU/GPU usage)
• Verify model is loaded in memory
• Consider using a smaller/faster model
• Close other resource-intensive applications

Model Issues

Problem: "Model not found"

• Check model name spelling
• Verify model is installed and available
• Try listing available models
• Reinstall model if corrupted

Integration with Experts

Local AI is automatically used by experts for appropriate tasks:

• Data Expert: Quick format validation, metadata extraction
• Analysis Expert: Statistical summaries, basic plotting
• Research Expert: Literature search, citation formatting
• Workflow Expert: Pipeline validation, simple automation

Best Practices

1. Start Simple: Use default configurations initially
2. Test Thoroughly: Use /warpio-local-test after changes
3. Monitor Performance: Check /warpio-local-status regularly
4. Choose Right Model: Balance speed vs. capability
5. Keep Updated: Update models periodically for best performance

Advanced Configuration

Custom API Endpoints

# For custom OpenAI-compatible APIs
LOCAL_AI_PROVIDER=custom
CUSTOM_API_URL=https://your-api-endpoint/v1
CUSTOM_API_KEY=your-api-key
CUSTOM_MODEL=your-model-name

Multiple Models

You can configure different models for different tasks by updating the .env file and restarting your local AI provider.

Resource Management

• Monitor GPU/CPU usage during intensive tasks
• Adjust model parameters for your hardware
• Use model quantization for better performance on limited hardware