script_4.py

# Create a tech team handoff document
handoff_doc = '''# O3 (Ozone) - Tech Team Handoff Document

## Project Overview
**O3 (Ollama Open-Source Optimizer)** is a hardware-focused performance optimization suite designed to find optimal settings for Ollama models on specific hardware configurations. Built for Stage 1: discovering optimal context lengths and performance settings for agentic workflows.

## Business Context
- **Primary Goal:** Maximize context window size for agentic AI workflows
- **Secondary Goal:** Optimize throughput (tokens/second) and minimize latency
- **Target Environment:** HP Strix Halo workstations, AMD GPUs, 64GB RAM/VRAM
- **Use Case:** Educational AI systems, RAG applications, coding assistance

## Technical Architecture

### Core Components
1. **o3_optimizer.py** - Main test runner with hardware monitoring
2. **o3_report_generator.py** - Comprehensive reporting and analysis
3. **VS Code Integration** - Tasks and debugging configuration
4. **Automated Setup** - quickstart.py for streamlined deployment

### Test Strategy
- **Parameter Grid Search:** Context size, batch size, precision settings
- **Hardware Monitoring:** VRAM/RAM usage, performance metrics
- **Stability Testing:** Concurrent load testing, OOM detection
- **Reproducible Logging:** JSONL format with full environment capture

### Models Tested
- `qwen3-coder:30b` - Large coding model (18GB)
- `orieg/gemma3-tools:27b-it-qat` - Quantized tools model (18GB)
- `liquid-rag:latest` - RAG-optimized model (730MB)
- `qwen2.5:3b-instruct` - Small instruct model (1.9GB)
- `gemma3:latest` - General purpose model (3.3GB)

## Implementation Details

### Key Optimizations Tested
```yaml
Parameters:
  num_ctx: [4096, 8192, 12288, 16384, 24576, 32768]  # Context window
  batch: [8, 16, 32]                                 # Processing batch size
  f16_kv: [true, false]                             # KV cache precision
  num_predict: [256, 512]                           # Output limit
  num_thread: <physical_cores>                      # CPU threading
```

### Performance Metrics
- **Time to First Token (TTFT)** - Response latency
- **Tokens per Second** - Sustained throughput
- **Memory Usage** - Peak VRAM/RAM consumption
- **Stability Score** - Success rate under concurrent load
- **Context Ceiling** - Maximum stable context window

### Output Format
```
o3_results/
├── logs/           # Detailed JSONL test logs
├── summaries/      # Per-model performance summaries
├── defaults/       # Recommended YAML configurations  
└── env/            # System environment snapshots
```

## Deployment Instructions

### Prerequisites
- Python 3.7+
- Ollama installed and running
- Target models pulled locally
- GPU monitoring tools (nvidia-smi/rocm-smi)

### Quick Deployment
```bash
# Automated setup
python quickstart.py

# Manual setup  
pip install -r requirements.txt
python o3_optimizer.py qwen3-coder:30b
python o3_report_generator.py --csv
```

### VS Code Integration
- Import workspace settings from .vscode/
- Use Ctrl+Shift+P → "Tasks: Run Task" → Select O3 operation
- Debug configurations included for development

## Expected Outcomes

### Deliverables
1. **Optimal Settings** - Max context and fast presets per model
2. **Performance Baselines** - Throughput and latency benchmarks  
3. **Resource Requirements** - VRAM/RAM usage profiles
4. **Stability Thresholds** - Safe operating parameters
5. **Comprehensive Reports** - Markdown and CSV formats

### Success Criteria
- Identify maximum stable context window per model
- Achieve >10 tokens/sec for 30B models, >20 tokens/sec for smaller models
- <2 second TTFT for interactive applications
- <90% VRAM utilization for stability margin
- Zero OOM failures in recommended configurations

## Production Considerations

### Monitoring Requirements
- GPU temperature and clock speed monitoring
- Memory usage alerts at 85% utilization
- Performance regression detection
- Concurrent load testing validation

### Scaling Considerations  
- Results specific to tested hardware configuration
- Re-optimization required for different GPU/RAM configurations
- Model-specific settings may need adjustment for fine-tuned variants
- Batch processing may require different optimization profiles

### Risk Mitigation
- All tests include OOM protection and timeouts
- Progressive context testing prevents system crashes  
- Full environment logging enables issue reproduction
- Rollback configurations included in defaults

## Integration Points

### API Usage
```python
# Optimized Ollama API calls
requests.post('http://localhost:11434/api/generate', json={
    "model": "qwen3-coder:30b",
    "options": {
        "num_ctx": 16384,      # From O3 max_ctx preset
        "batch": 16,
        "f16_kv": True
    }
})
```

### Framework Integration
- LangChain: Use optimized context windows
- AutoGPT: Apply fast presets for tool calls
- RAG Systems: Use max context for document processing
- Educational Platforms: Stable settings for classroom use

## Next Phase Planning

### Stage 2 Expansion
- Automated hyperparameter tuning
- Multi-GPU load balancing
- Quality metrics integration
- Dashboard web interface

### Operational Integration
- CI/CD pipeline integration
- Automated regression testing
- Performance monitoring alerts
- Configuration management system

## Support Resources

### Documentation
- README.md - Complete user guide
- Code comments - Technical implementation details
- Example configurations - Common use case templates

### Troubleshooting
- Hardware compatibility matrix
- Common error resolution guide
- Performance tuning recommendations
- Contact information for technical support

---

**Project Status:** Ready for deployment and Stage 1 testing
**Next Review:** After initial optimization runs complete
**Stakeholders:** Education team, infrastructure team, AI development team
'''

with open("TECH_HANDOFF.md", "w") as f:
    f.write(handoff_doc)

print("Created TECH_HANDOFF.md")

# Create a final summary of all files
print("\\n" + "="*60)
print("O3 (OZONE) COMPLETE TEST SUITE - FINAL SUMMARY")
print("="*60)

files_created = [
    ("o3_optimizer.py", "Main test runner with hardware monitoring and optimization"),
    ("o3_report_generator.py", "Comprehensive report generation and analysis"),
    ("quickstart.py", "Automated setup and initial testing script"),
    ("requirements.txt", "Python dependencies (psutil, PyYAML)"),
    ("README.md", "Complete documentation and user guide"),
    ("TECH_HANDOFF.md", "Technical handoff document for team"),
    ("o3_config.py", "Example configuration and customization guide"),
    (".vscode/tasks.json", "VS Code task integration"),
    (".vscode/launch.json", "VS Code debugging configuration")
]

print("\\nFiles Created:")
for filename, description in files_created:
    print(f"✅ {filename:<25} - {description}")

print(f"\\n🎯 READY FOR DEPLOYMENT")
print(f"\\nTo start optimizing your models:")
print(f"1. Run: python quickstart.py")
print(f"2. Or: python o3_optimizer.py qwen3-coder:30b")
print(f"3. Generate report: python o3_report_generator.py --csv")

print(f"\\n📊 The suite will:")
print(f"• Find maximum stable context window per model")
print(f"• Optimize for hardware performance (not response quality)")
print(f"• Generate reproducible logs for your tech team")
print(f"• Provide fast and max context presets")
print(f"• Create comprehensive markdown and CSV reports")

print(f"\\n🔧 VS Code users:")
print(f"Ctrl+Shift+P → Tasks: Run Task → Select O3 operation")
print("="*60)