🚀 OS Launch: Clean documentation and refined licensing

This OS launch commit includes:

✅ **Cleaned Documentation**
- Removed inflated claims and marketing language
- Added honest research status and limitations
- Created professional model card and validation reports
- Streamlined licensing to AGPLv3 + commercial contact

✅ **Refined Codebase**
- Complete experimental bit-native transformer implementation
- 57 Python files with comprehensive research framework
- Safety telemetry and monitoring systems
- Distributed training and development tools

✅ **Professional Standards**
- Empirical validation of all claims
- Clear experimental vs production distinctions
- Rigorous research methodology requirements
- Community contribution framework

Ready for serious research evaluation and academic investigation.

test_markov_integration.py

@@ -0,0 +1,380 @@
+#!/usr/bin/env python3
+"""
+Test MarkovSpline + BitTransformerLM Integration
+
+Validates the integration between MarkovSpline and BitTransformerLM
+using actual datasets and training procedures.
+"""
+
+import os
+import sys
+import time
+import torch
+import json
+import numpy as np
+from pathlib import Path
+
+# Add MarkovSpline to path
+sys.path.insert(0, '/data/MarkovSpline')
+from bitpipe_integration import create_markov_spline_bitpipe_module
+
+# BitTransformerLM imports
+from bit_transformer.model import BitTransformerLM
+from markov_spline_training import MarkovSplineEnhancedTrainer, MarkovSplineEnhancedDataset
+from markov_spline_cli import MarkovSplineBitTransformerCLI
+
+# Create simple dataset function
+def create_simple_dataset(num_samples=100, seq_length=128):
+    """Create simple dataset for testing."""
+    dataset = []
+    for i in range(num_samples):
+        input_bits = torch.randint(0, 2, (seq_length,), dtype=torch.long)
+        target_bits = torch.randint(0, 2, (seq_length,), dtype=torch.long)
+        dataset.append({'input_bits': input_bits, 'target_bits': target_bits})
+    return dataset
+
+# Text to bits converter class
+class TextToBitsConverter:
+    """Simple text to bits converter for testing."""
+    
+    def text_to_bits(self, text, max_length=128):
+        """Convert text to bit sequence."""
+        # Simple encoding: each character to 8 bits
+        bit_sequence = []
+        for char in text[:max_length//8]:
+            char_bits = format(ord(char), '08b')
+            bit_sequence.extend([int(b) for b in char_bits])
+        
+        # Pad or truncate to max_length
+        if len(bit_sequence) < max_length:
+            bit_sequence.extend([0] * (max_length - len(bit_sequence)))
+        else:
+            bit_sequence = bit_sequence[:max_length]
+            
+        return bit_sequence
+
+
+def test_markov_spline_preprocessing():
+    """Test MarkovSpline data preprocessing functionality."""
+    
+    print("\n🧪 Testing MarkovSpline Data Preprocessing")
+    print("=" * 50)
+    
+    # Initialize MarkovSpline module
+    markov_module = create_markov_spline_bitpipe_module()
+    
+    # Create test bit sequences
+    converter = TextToBitsConverter()
+    test_texts = [
+        "The quick brown fox jumps over the lazy dog.",
+        "Machine learning transforms raw data into insights.",
+        "BitTransformerLM processes information at the bit level.",
+        "MarkovSpline provides smooth data transitions."
+    ]
+    
+    bit_sequences = []
+    for text in test_texts:
+        bits = converter.text_to_bits(text, max_length=64)
+        bit_sequences.append(bits)
+    
+    print(f"📝 Created {len(bit_sequences)} test bit sequences")
+    
+    # Test binary sequence prediction
+    print("\n🔮 Testing Binary Sequence Prediction")
+    result = markov_module.process_data(bit_sequences[0], 'predict_binary', num_predictions=8)
+    
+    if result['success']:
+        print(f"   ✅ Prediction successful")
+        print(f"   🎯 Predictions: {result['predictions']}")
+        print(f"   📊 Avg confidence: {result['prediction_metrics']['avg_confidence']:.3f}")
+        print(f"   📈 Entropy: {result['prediction_metrics']['prediction_entropy']:.3f}")
+    else:
+        print(f"   ❌ Prediction failed: {result.get('error', 'Unknown error')}")
+        return False
+    
+    # Test data preprocessing
+    print("\n🔄 Testing Data Preprocessing")
+    result = markov_module.process_data(bit_sequences, 'preprocess_training', binary_data=True)
+    
+    if result['success']:
+        print(f"   ✅ Preprocessing successful")
+        print(f"   📦 Processed {len(result['processed_sequences'])} sequences")
+        print(f"   📋 Summary: {result['preprocessing_summary']}")
+    else:
+        print(f"   ❌ Preprocessing failed: {result.get('error', 'Unknown error')}")
+        return False
+    
+    return True
+
+
+def test_enhanced_dataset():
+    """Test MarkovSpline enhanced dataset wrapper."""
+    
+    print("\n📦 Testing Enhanced Dataset Wrapper")
+    print("=" * 50)
+    
+    # Create base dataset
+    base_dataset = create_simple_dataset(num_samples=20, seq_length=32)
+    print(f"📝 Created base dataset with {len(base_dataset)} samples")
+    
+    # Initialize MarkovSpline module
+    markov_module = create_markov_spline_bitpipe_module()
+    
+    # Create enhanced dataset
+    enhanced_dataset = MarkovSplineEnhancedDataset(
+        base_dataset, markov_module, smoothing_strength=0.15, enable_smoothing=True
+    )
+    
+    print(f"🌊 Created enhanced dataset with MarkovSpline preprocessing")
+    
+    # Test data loading
+    test_samples = []
+    smoothing_success_count = 0
+    
+    for i in range(min(5, len(enhanced_dataset))):
+        sample = enhanced_dataset[i]
+        test_samples.append(sample)
+        
+        if sample.get('smoothing_applied', False):
+            smoothing_success_count += 1
+        
+        print(f"   Sample {i}: smoothing_applied = {sample.get('smoothing_applied', False)}")
+    
+    success_rate = smoothing_success_count / len(test_samples) if test_samples else 0
+    print(f"✅ Smoothing success rate: {success_rate:.2%}")
+    
+    return success_rate > 0.5
+
+
+def test_gradient_smoothing():
+    """Test gradient smoothing functionality."""
+    
+    print("\n⚡ Testing Gradient Smoothing")
+    print("=" * 50)
+    
+    # Create small test model
+    model = BitTransformerLM(
+        d_model=32, nhead=2, num_layers=2, 
+        dim_feedforward=64, max_seq_len=128
+    )
+    
+    # Create test data
+    batch_size = 4
+    seq_length = 64
+    test_batch = {
+        'input_bits': torch.randint(0, 2, (batch_size, seq_length), dtype=torch.long),
+        'target_bits': torch.randint(0, 2, (batch_size, seq_length), dtype=torch.long)
+    }
+    
+    # Initialize enhanced trainer
+    trainer = MarkovSplineEnhancedTrainer(
+        model=model,
+        gradient_smoothing=True,
+        data_smoothing=False,
+        smoothing_strength=0.2,
+        learning_rate=1e-3
+    )
+    
+    print(f"🧠 Initialized enhanced trainer")
+    
+    # Test training step with gradient smoothing
+    start_time = time.time()
+    metrics = trainer.train_step(test_batch)
+    end_time = time.time()
+    
+    print(f"   ✅ Training step completed in {end_time - start_time:.3f}s")
+    print(f"   📊 Loss: {metrics['loss']:.4f}")
+    print(f"   🌊 Smoothing applied: {metrics.get('smoothing_applied', 0):.3f}")
+    
+    # Get MarkovSpline metrics
+    markov_metrics = trainer.get_markov_spline_metrics()
+    print(f"   📈 MarkovSpline operations: {markov_metrics['processing_operations']}")
+    
+    return True
+
+
+def test_cli_interface():
+    """Test command-line interface functionality."""
+    
+    print("\n💻 Testing CLI Interface")  
+    print("=" * 50)
+    
+    # Initialize CLI
+    cli = MarkovSplineBitTransformerCLI()
+    
+    if not cli.initialize_markov_spline():
+        print("   ❌ CLI initialization failed")
+        return False
+    
+    print("   ✅ CLI initialized successfully")
+    
+    # Test bit sequence smoothing
+    test_bits = [0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1]
+    result = cli.smooth_bit_sequence(test_bits, 'predict_binary', num_predictions=5)
+    
+    if result['success']:
+        print(f"   ✅ Bit sequence smoothing successful")
+        print(f"   🎯 Predictions: {result['predictions']}")
+    else:
+        print(f"   ❌ Bit sequence smoothing failed: {result.get('error', 'Unknown')}")
+        return False
+    
+    return True
+
+
+def run_integration_benchmark():
+    """Run comprehensive integration benchmark."""
+    
+    print("\n🏃 Running Integration Benchmark")
+    print("=" * 50)
+    
+    # Create test dataset
+    dataset = create_simple_dataset(num_samples=50, seq_length=64)
+    
+    # Initialize MarkovSpline module
+    markov_module = create_markov_spline_bitpipe_module()
+    
+    # Create enhanced dataset
+    enhanced_dataset = MarkovSplineEnhancedDataset(
+        dataset, markov_module, smoothing_strength=0.1, enable_smoothing=True
+    )
+    
+    # Time data loading with and without smoothing
+    print("\n⏱️  Benchmarking Data Loading Performance")
+    
+    # Benchmark without smoothing
+    start_time = time.time()
+    for i in range(10):
+        sample = dataset[i % len(dataset)]
+    base_time = time.time() - start_time
+    
+    # Benchmark with smoothing
+    start_time = time.time()
+    for i in range(10):
+        sample = enhanced_dataset[i % len(enhanced_dataset)]
+    enhanced_time = time.time() - start_time
+    
+    overhead = ((enhanced_time - base_time) / base_time) * 100 if base_time > 0 else 0
+    
+    print(f"   📊 Base loading time: {base_time:.3f}s")
+    print(f"   🌊 Enhanced loading time: {enhanced_time:.3f}s") 
+    print(f"   📈 Smoothing overhead: {overhead:.1f}%")
+    
+    # Test training performance
+    print("\n🏋️  Benchmarking Training Performance")
+    
+    model = BitTransformerLM(d_model=64, nhead=4, num_layers=2, dim_feedforward=128, max_seq_len=128)
+    
+    # Standard trainer (use the one from markov_spline_training)
+    from markov_spline_training import BitwiseTrainer
+    standard_trainer = BitwiseTrainer(model, learning_rate=1e-3)
+    
+    # Enhanced trainer
+    enhanced_trainer = MarkovSplineEnhancedTrainer(
+        model, gradient_smoothing=True, data_smoothing=True, 
+        smoothing_strength=0.15, learning_rate=1e-3
+    )
+    
+    # Benchmark training step
+    test_batch = {
+        'input_bits': torch.randint(0, 2, (4, 64), dtype=torch.long),
+        'target_bits': torch.randint(0, 2, (4, 64), dtype=torch.long)
+    }
+    
+    # Standard training step
+    start_time = time.time()
+    standard_metrics = standard_trainer.train_step(test_batch)
+    standard_time = time.time() - start_time
+    
+    # Enhanced training step  
+    start_time = time.time()
+    enhanced_metrics = enhanced_trainer.train_step(test_batch)
+    enhanced_time = time.time() - start_time
+    
+    training_overhead = ((enhanced_time - standard_time) / standard_time) * 100 if standard_time > 0 else 0
+    
+    print(f"   📊 Standard training step: {standard_time:.3f}s")
+    print(f"   🌊 Enhanced training step: {enhanced_time:.3f}s")
+    print(f"   📈 Enhancement overhead: {training_overhead:.1f}%")
+    print(f"   🎯 Standard loss: {standard_metrics['loss']:.4f}")
+    print(f"   🎯 Enhanced loss: {enhanced_metrics['loss']:.4f}")
+    
+    return {
+        'data_loading_overhead': overhead,
+        'training_overhead': training_overhead,
+        'standard_loss': standard_metrics['loss'],
+        'enhanced_loss': enhanced_metrics['loss']
+    }
+
+
+def main():
+    """Run comprehensive MarkovSpline integration tests."""
+    
+    print("🌊 MarkovSpline + BitTransformerLM Integration Tests")
+    print("=" * 60)
+    
+    results = {
+        'preprocessing_test': False,
+        'enhanced_dataset_test': False,
+        'gradient_smoothing_test': False,
+        'cli_interface_test': False,
+        'benchmark_results': None
+    }
+    
+    try:
+        # Run individual tests
+        results['preprocessing_test'] = test_markov_spline_preprocessing()
+        results['enhanced_dataset_test'] = test_enhanced_dataset()
+        results['gradient_smoothing_test'] = test_gradient_smoothing()
+        results['cli_interface_test'] = test_cli_interface()
+        
+        # Run benchmark
+        results['benchmark_results'] = run_integration_benchmark()
+        
+        # Summary
+        print("\n📋 Test Results Summary")
+        print("=" * 60)
+        
+        passed_tests = 0
+        total_tests = 4  # Don't count benchmark as pass/fail
+        
+        for test_name, result in results.items():
+            if test_name != 'benchmark_results':
+                status = "✅ PASSED" if result else "❌ FAILED"
+                print(f"   {test_name}: {status}")
+                if result:
+                    passed_tests += 1
+        
+        success_rate = (passed_tests / total_tests) * 100
+        print(f"\n🎯 Overall Success Rate: {success_rate:.1f}% ({passed_tests}/{total_tests})")
+        
+        if results['benchmark_results']:
+            benchmark = results['benchmark_results']
+            print(f"\n📊 Performance Impact:")
+            print(f"   - Data loading overhead: {benchmark['data_loading_overhead']:.1f}%")
+            print(f"   - Training overhead: {benchmark['training_overhead']:.1f}%")
+            print(f"   - Loss comparison: {benchmark['standard_loss']:.4f} → {benchmark['enhanced_loss']:.4f}")
+        
+        # Save results
+        results_file = '/data/BitTransformerLM/BitTransformerLM/markov_integration_test_results.json'
+        with open(results_file, 'w') as f:
+            json.dump(results, f, indent=2, default=str)
+        
+        print(f"\n📁 Results saved to: {results_file}")
+        
+        if success_rate >= 75:
+            print("\n🚀 Integration tests PASSED! Ready for production use.")
+            return 0
+        else:
+            print("\n⚠️  Integration tests show issues. Review before production use.")
+            return 1
+            
+    except Exception as e:
+        print(f"\n💥 Test suite failed with error: {e}")
+        import traceback
+        traceback.print_exc()
+        return 1
+
+
+if __name__ == '__main__':
+    sys.exit(main())