🚀 CHIMERA Examples and Demos

This directory contains examples and demonstrations of CHIMERA v3.0 functionality.

📋 Table of Contents

• Quick Start Examples
• Advanced Examples
• Interactive Demos
• Performance Benchmarks
• Research Examples

🎯 Quick Start Examples

1. Basic Mathematical Operations (math_operations.py)

Demonstrates basic OpenGL operations without deep learning.

python examples/math_operations.py

What it shows:

• Matrix multiplication on GPU
• Vector operations
• Performance comparison vs CPU

2. Self-Attention Demo (attention_demo.py)

Shows attention mechanism working on pure OpenGL.

python examples/attention_demo.py

What it shows:

• Q, K, V processing
• Attention score computation
• Visualization of attention patterns

3. Transformer Block Demo (transformer_demo.py)

Complete transformer block running on OpenGL.

python examples/transformer_demo.py

What it shows:

• Embedding layer
• Multi-head attention
• Feed-forward network
• Layer normalization

🔬 Advanced Examples

1. Model Conversion (qwen_conversion.py)

Convert PyTorch models to run on OpenGL.

python examples/qwen_conversion.py

What it shows:

• Loading Qwen model from Hugging Face
• Converting weights to OpenGL textures
• Verification of conversion accuracy

2. Custom Training (custom_training.py)

Train a model entirely on OpenGL (no PyTorch).

python examples/custom_training.py

What it shows:

• Dataset preparation
• Training loop in OpenGL shaders
• Model evaluation and saving

3. Multi-GPU Inference (multi_gpu_demo.py)

Use multiple GPUs for inference.

python examples/multi_gpu_demo.py

What it shows:

• GPU detection and selection
• Load balancing across GPUs
• Performance scaling

🎮 Interactive Demos

1. Chat Interface (interactive_chat.py)

Interactive chat with CHIMERA model.

python examples/interactive_chat.py

Features:

• Real-time conversation
• Context management
• Response time tracking
• Model switching

2. Real-time Generation (realtime_demo.py)

See text generation in real-time.

python examples/realtime_demo.py

Features:

• Live token generation
• Speed monitoring
• Memory usage tracking
• Interactive parameter adjustment

3. Performance Benchmarking (benchmark_suite.py)

Comprehensive performance testing.

python examples/benchmark_suite.py

What it tests:

• Matrix operations speed
• Memory bandwidth
• GPU utilization
• Cross-platform comparison

📊 Performance Benchmarks

1. Speed Tests (speed_benchmarks.py)

Compare CHIMERA vs traditional frameworks.

python examples/speed_benchmarks.py

Benchmarks:

• Matrix multiplication (2048×2048)
• Self-attention computation
• Full transformer inference
• Memory allocation/deallocation

2. Memory Tests (memory_benchmarks.py)

Memory usage and efficiency tests.

python examples/memory_benchmarks.py

Tests:

• Peak memory usage
• Memory fragmentation
• GPU memory management
• Cross-batch efficiency

3. Scalability Tests (scalability_benchmarks.py)

Test performance at different scales.

python examples/scalability_benchmarks.py

Tests:

• Different model sizes
• Various batch sizes
• Multi-GPU scaling
• Long sequence handling

🔬 Research Examples

1. Holographic Memory (holographic_demo.py)

Demonstrate holographic memory system.

python examples/holographic_demo.py

What it shows:

• Memory imprinting without backprop
• Holographic correlation
• Concept-based retrieval
• Memory efficiency

2. Cellular Automata (ca_demo.py)

Show cellular automata for text processing.

python examples/ca_demo.py

What it shows:

• Text to cellular automata
• Physics-based evolution
• Pattern recognition
• Emergent computation

3. Novel Architectures (novel_architectures.py)

Experimental architectures and ideas.

python examples/novel_architectures.py

What it shows:

• Alternative attention mechanisms
• New activation functions
• Custom layer types
• Performance comparisons

📁 Directory Structure

examples/
├── README.md                    # This file
├── __init__.py                 # Package initialization
│
├── basic/                      # Basic functionality demos
│   ├── math_operations.py      # Matrix operations demo
│   ├── attention_demo.py       # Self-attention visualization
│   └── transformer_demo.py     # Complete transformer demo
│
├── advanced/                   # Advanced usage examples
│   ├── qwen_conversion.py      # Model conversion example
│   ├── custom_training.py      # Training without PyTorch
│   └── multi_gpu_demo.py       # Multi-GPU inference
│
├── interactive/                # Interactive demonstrations
│   ├── interactive_chat.py     # Chat interface
│   ├── realtime_demo.py        # Real-time generation
│   └── benchmark_suite.py      # Performance benchmarking
│
├── research/                   # Research-oriented examples
│   ├── holographic_demo.py     # Holographic memory demo
│   ├── ca_demo.py             # Cellular automata demo
│   └── novel_architectures.py  # Experimental architectures
│
├── data/                       # Example datasets
│   ├── sample_qa.json         # Q&A dataset for training
│   ├── test_prompts.txt       # Test prompts for evaluation
│   └── benchmark_configs.json  # Benchmark configurations
│
└── utils/                      # Helper utilities
    ├── visualization.py       # Plotting and visualization tools
    ├── data_utils.py          # Data loading and processing
    └── performance.py         # Performance measurement tools

🚀 Running Examples

Prerequisites

# Install CHIMERA
pip install -e .

# Install example dependencies
pip install matplotlib seaborn plotly

Running Individual Examples

# Basic examples
python -m examples.basic.math_operations
python -m examples.basic.attention_demo
python -m examples.basic.transformer_demo

# Advanced examples
python -m examples.advanced.qwen_conversion
python -m examples.advanced.custom_training

# Interactive demos
python -m examples.interactive.interactive_chat
python -m examples.interactive.benchmark_suite

Running All Examples

# Run all basic examples
python scripts/run_all_examples.py --category basic

# Run performance benchmarks
python scripts/run_all_examples.py --category benchmarks

# Run with visualization
python scripts/run_all_examples.py --visualize

📈 Expected Performance

Hardware Requirements

Performance Expectations

🐛 Troubleshooting

Common Issues

1. OpenGL Context Creation Failed

# Update GPU drivers
# On Windows: Update via Device Manager
# On Linux: Install mesa-utils
# On macOS: Update to latest macOS

2. Out of Memory Errors

# Reduce batch size or model size
model = Model(max_batch_size=1, max_seq_len=512)

3. Poor Performance on Integrated Graphics

# Enable software rendering (slower but works)
import os
os.environ['MESA_GLSL_VERSION_OVERRIDE'] = '330'

Getting Help

• 📖 Check the main documentation
• 🐛 Report issues on GitHub Issues
• 💬 Ask in Discord community

🤝 Contributing

Want to add your own example? Here's how:

1. Create a new file in the appropriate subdirectory
2. Follow the example template structure
3. Add comprehensive documentation
4. Include performance expectations
5. Test on multiple hardware configurations

Example Template

"""
Example Title

Brief description of what this example demonstrates.

Requirements:
- Hardware: GPU with OpenGL 3.3+
- Dependencies: list any additional packages needed

Usage:
    python examples/path/to/example.py

Expected Output:
    Description of what the user should see
"""

import chimera_v3
# ... rest of your example code

Happy exploring! 🚀

These examples demonstrate the revolutionary potential of OpenGL-based deep learning. Each example shows how CHIMERA eliminates the need for traditional frameworks while delivering superior performance and universal compatibility.