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REMOTE_CONTROL_README.md
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REMOTE_CONTROL_README.md
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# LLM_Train Remote Control
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A standalone GUI application for remotely controlling and managing LLM_Train MCP servers. This allows you to connect to a running MCP server from a separate application and perform model management and inference operations remotely.
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## Features
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### 🔗 **Connection Management**
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- Connect to local or remote MCP servers
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- Real-time connection status monitoring
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- Automatic reconnection handling
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- Server path configuration and browsing
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### 🤖 **Model Management**
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- List all available models from the server
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- Remote model loading with configuration
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- Context size and GPU layer settings
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- Current model status display
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- Model unloading capabilities
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### 💬 **Inference Interface**
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- Text generation with configurable parameters
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- Chat mode for conversational interactions
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- Adjustable temperature and max tokens
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- Real-time output display
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- Chat history management
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### 📊 **System Monitoring**
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- Real-time system information display
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- GPU acceleration status
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- Platform and architecture details
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- Performance metrics
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### 📝 **Logging & Debugging**
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- Comprehensive operation logging
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- Error tracking and display
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- Log saving functionality
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- Connection event monitoring
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## Quick Start
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### Option 1: Batch File (Windows)
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```bash
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# Double-click launch_remote_control.bat
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```
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### Option 2: Direct Python Execution
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```bash
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python remotecontrol.py
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```
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## Requirements
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- **Python 3.7+** with tkinter support
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- **Running MCP Server** (from main LLM_Train application)
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- **Network access** to the MCP server (if remote)
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## Usage Guide
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### 1. Starting the Remote Control
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1. **Launch the application** using one of the methods above
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2. The GUI will open with the connection panel at the top
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### 2. Connecting to MCP Server
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1. **Specify server path**:
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- Default: `mcp_server.py` (local)
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- Browse to select different server script
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- Can be local file or network path
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2. **Click "Connect"** button
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3. **Connection status** will show:
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- 🔴 **Disconnected** (Red): Not connected
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- 🟢 **Connected** (Green): Successfully connected
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### 3. Model Management
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**Models Tab:**
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- **View available models**: Automatically populated on connection
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- **Select model**: Click on model in the list
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- **Configure parameters**:
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- Context Size: 512 - 32768 tokens
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- GPU Layers: 0 - 100 layers
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- **Load model**: Click "Load Model" button
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- **Monitor status**: Current model display shows loaded model
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### 4. Text Generation
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**Inference Tab:**
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- **Enter prompt**: Type or paste text in the input area
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- **Configure generation**:
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- Max Tokens: 1 - 8192
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- Temperature: 0.0 - 2.0
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- **Generate**: Click "Generate" button
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- **View output**: Results appear in the output area
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- **Chat mode**: Enable for conversational interface
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### 5. System Information
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**System Tab:**
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- **View system details**: Platform, architecture, acceleration
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- **Monitor GPU status**: CUDA, ROCm, Metal availability
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- **Check performance**: Current acceleration method
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- **Refresh**: Update information in real-time
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### 6. Logging
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**Log Tab:**
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- **Monitor operations**: All actions are logged with timestamps
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- **Error tracking**: Errors are highlighted and detailed
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- **Save logs**: Export logs to text file for debugging
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- **Clear logs**: Reset log display
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## Advanced Configuration
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### Server Connection Options
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**Local Server:**
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```
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Server Path: mcp_server.py
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```
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**Custom Path:**
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```
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Server Path: C:\path\to\your\mcp_server.py
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```
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**Network Server (if supported):**
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```
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Server Path: \\network\path\mcp_server.py
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```
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### Model Configuration
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**High-Performance Setup:**
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- Context Size: 8192+
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- GPU Layers: Maximum supported
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- Temperature: 0.1-0.3 for focused responses
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**Balanced Setup:**
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- Context Size: 4096
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- GPU Layers: Auto-detected optimum
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- Temperature: 0.7 for creative responses
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**CPU-Only Setup:**
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- Context Size: 2048
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- GPU Layers: 0
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- Temperature: 0.5-1.0
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### Generation Parameters
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**Creative Writing:**
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- Max Tokens: 1024+
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- Temperature: 0.8-1.2
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**Code Generation:**
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- Max Tokens: 512
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- Temperature: 0.1-0.3
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**Question Answering:**
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- Max Tokens: 256
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- Temperature: 0.3-0.7
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## Troubleshooting
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### Connection Issues
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**"Server file not found"**
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- Verify the server path is correct
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- Ensure the MCP server file exists
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- Check file permissions
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**"Connection failed"**
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- Ensure the MCP server is not already running
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- Check if the server script is executable
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- Verify Python dependencies are installed
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**"Disconnected unexpectedly"**
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- Check server logs for errors
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- Verify system resources are available
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- Restart both applications
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### Model Loading Issues
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**"No models found"**
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- Ensure model library is configured in main application
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- Verify model files exist in specified directories
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- Check library settings and scan depth
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**"Failed to load model"**
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- Verify model file is not corrupted
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- Check available system memory
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- Reduce context size or GPU layers
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**"Out of memory"**
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- Reduce context size
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- Lower GPU layers
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- Close other applications
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### Generation Issues
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**"No model loaded"**
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- Load a model first using the Models tab
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- Verify model loaded successfully
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- Check current model display
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**"Generation timeout"**
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- Reduce max tokens
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- Simplify the prompt
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- Check system resources
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**"Invalid parameters"**
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- Verify temperature is between 0.0-2.0
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- Ensure max tokens is reasonable
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- Check prompt is not empty
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## Technical Details
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### MCP Protocol
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- Uses JSON-RPC 2.0 over stdin/stdout
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- Asynchronous request/response handling
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- Automatic request ID management
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- Error handling and recovery
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### Threading Model
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- Main UI thread for interface
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- AsyncIO event loop for MCP communication
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- Background threads for I/O operations
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- Thread-safe callback system
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### Security Considerations
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- Local process communication only
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- No network ports exposed
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- Input validation on all parameters
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- Error sanitization in logs
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## Integration Examples
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### Automated Workflows
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```python
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# Example: Batch text generation
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prompts = ["Explain AI", "Code a function", "Write a story"]
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for prompt in prompts:
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# Use remote control to generate text
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# Save results to files
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```
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### API Integration
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```python
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# Example: Integration with other tools
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remote_control = RemoteControlClient()
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remote_control.connect("mcp_server.py")
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result = remote_control.generate("Your prompt here")
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```
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### Monitoring Scripts
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```python
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# Example: System monitoring
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while True:
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system_info = remote_control.get_system_info()
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log_performance_metrics(system_info)
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time.sleep(60)
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```
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## Support and Development
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### Extending Functionality
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- Add new MCP tool integrations
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- Implement custom inference modes
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- Create automation scripts
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- Build monitoring dashboards
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### Contributing
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- Follow Python coding standards
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- Add comprehensive logging
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- Include error handling
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- Write unit tests
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### Reporting Issues
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- Include full log output
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- Specify system configuration
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- Provide reproduction steps
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- Attach relevant files
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## License
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This remote control application is part of the LLM_Train project and follows the same licensing terms as the main application.
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