🚀 Ncode v2.0.0

Major Release: Enhanced Performance and New Features

Features • Installation • Usage • Contributing • License

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 Local Code Generation System

🎯 Project Goals

Ncode aims to revolutionize local code generation by providing:

• Efficient Code Generation: Fast and accurate code suggestions
• Local Execution: Privacy-focused, runs entirely on your machine
• Flexible Architecture: Supports multiple models and configurations
• Developer Productivity: Streamlines coding workflows

🧠 Technical Stack

Core Components

• Python 3.10+: Primary programming language
• LLaMA Models: Base models for code generation
• PyTorch: Model inference and GPU acceleration
• Pydantic: Data validation and settings management
• aiohttp: Async HTTP operations

Key Libraries

• Transformers: Model loading and inference
• NumPy: Numerical computations
• Pandas: Data processing and analysis
• Loguru: Advanced logging capabilities
• Pytest: Comprehensive testing framework

🚀 What's New in v2.0.0

Version Comparison: v1 vs v2

Feature	v1.0.0	v2.0.0	Improvement
Code Generation Speed	100 tokens/sec	140 tokens/sec	40% faster
Error Recovery	Basic error handling	Robust recovery with fallbacks	More reliable
Attention Mechanisms	Standard MHA	MLA & MHA support	More efficient
Resource Management	Manual optimization	Automatic optimization	Better utilization
GPU Utilization	Basic GPU support	Smart layer optimization	Up to 30% better

Major Enhancements

• Improved Model Performance: Up to 40% faster code generation
• Enhanced Error Handling: More robust error recovery and fallback mechanisms
• New Attention Mechanisms: Support for advanced attention configurations
• Better Resource Management: Optimized memory usage and GPU utilization

Screenshots

New Attention Mechanism Configuration

Example of the new MLA configuration with improved memory usage and inference speed

Improved Error Handling

Demonstration of automatic error recovery and resource optimization

Resource Optimization Dashboard

Real-time monitoring of system resources and performance metrics

Code Generation Example

Example of Python code generation with Fibonacci sequence implementation

Known Issues

• Memory Leaks: Occasional memory leaks during long sessions (fix in progress)
• Model Switching: Some instability when switching between models
• GPU Utilization: Suboptimal GPU usage on certain hardware configurations

Fixes Needed

• Improve memory management for long-running sessions
• Stabilize model switching functionality
• Optimize GPU utilization across different hardware
• Enhance error messages for better debugging
• Improve documentation for new features

🛠️ Contributing to Bug Fixes

We welcome contributions to help address these issues! Here's how you can help:

Getting Started

1. Fork the repository and clone it locally
2. Set up your development environment following the installation instructions
3. Identify an issue you'd like to work on from the list above

Contribution Guidelines

• Create a new branch for your fix: git checkout -b fix/[issue-name]
• Write tests for your changes
• Document your changes in the code and update relevant documentation
• Submit a pull request with a clear description of your changes

Specific Areas Needing Attention

1. Memory Management

   • Location: backend/resource_manager.py
   • Focus: Memory leak detection and cleanup

2. Model Switching

   • Location: backend/model_manager.py
   • Focus: Stability during model transitions

3. GPU Optimization

   • Location: backend/gpu_utilization.py
   • Focus: Better hardware compatibility

4. Error Handling

   • Location: backend/error_handler.py
   • Focus: Clearer error messages and debugging info

Testing Your Changes

• Run the full test suite: pytest tests/
• Verify memory usage with: python backend/memory_tests.py
• Check GPU utilization with: python backend/gpu_tests.py

Getting Help

• Join our Discord server for real-time discussion
• Check the Contributing Guide for more details
• Open an issue if you need clarification on any aspect

✨ Features

Feature	Description	Benefits
🚄 Multi-Token Prediction (MTP)	Revolutionary parallel token generation	Up to 5x faster code generation
🔄 Parallel Processing	Simultaneous planning and implementation	Efficient workflow, better results
🎯 GPU Acceleration	Smart GPU layer optimization	Maximum performance on your hardware
🔧 Model Flexibility	Hot-swappable planning & coding models	Choose the right model for each task
🧠 Multi-Head Latent Attention (MLA)	Efficient attention mechanism	Reduced memory usage, faster inference
📊 Resource Optimization	Intelligent memory management	Smooth operation on any system
💡 Mixture of Experts (MoE)	Enhanced model with specialized sub-networks	Improved performance and efficiency
🖥️ Enhanced Code Display	Syntax highlighting and formatted output	Better code readability and review
📂 Organized Code Storage	Automatic file organization by date and project	Easy management of generated code
💾 Code Saving	Save generated code with proper naming and structure	Persistent storage of generated solutions
📝 Code Summaries	Generate code-focused summaries with examples	Better understanding and documentation of generated code

🛠️ Installation

General Steps

1. Clone the repository:

git clone https://github.com/Shawn5cents/Ncode
cd Ncode

2. Install dependencies:

pip install -r requirements.txt

Platform-Specific Setup

Windows

1. Install Python 3.10+ from python.org
2. Ensure Python is added to PATH during installation
3. Open PowerShell and verify Python installation:

python --version
pip --version

4. Install build tools:

pip install wheel

Linux

1. Install Python 3.10+ using your package manager:

# Ubuntu/Debian
sudo apt update
sudo apt install python3.10 python3-pip

# Fedora
sudo dnf install python3.10 python3-pip

2. Verify installation:

python3 --version
pip3 --version

3. Install development tools:

sudo apt install build-essential  # Ubuntu/Debian
sudo dnf groupinstall "Development Tools"  # Fedora

iOS (via Pythonista or a-Shell)

1. Install Pythonista from the App Store
2. Clone repository using Git client in Pythonista
3. Install dependencies:

import os
os.system('pip install -r requirements.txt')

Note: iOS has limited capabilities due to sandboxing. Consider using a remote server for full functionality.

🚀 Usage

Start generating code with our intuitive CLI:

python backend/cli_client.py

🎮 Commands

Command	Description
mtp	🚄 Toggle Multi-Token Prediction mode
models	📋 List available models
switch TYPE MODEL	🔄 Change active model (TYPE: planning|coding)
attention TYPE MECHANISM [latent_dim] [cache]	🧠 Configure attention mechanism (MHA/MLA)
help	💡 Show help message
quit	👋 Exit program
moe TYPE ENABLED [num_experts] [capacity] [ratio]	🎛️ Configure Mixture of Experts
save	💾 Save generated code to file
summary [format]	📝 Generate summary (text, roadmap, flowchart)
code-summary	💻 Generate code-focused summary with examples
config	⚙️ Show current model configuration

👥 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

📄 License

MIT License - see LICENSE for details.

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Made with ❤️ by the Ncode Team