🚀 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.

markov_spline_cli.py

@@ -0,0 +1,307 @@
+#!/usr/bin/env python3
+"""
+MarkovSpline CLI Interface for BitTransformerLM Integration
+
+Provides command-line tools for using MarkovSpline data smoothing 
+with BitTransformerLM training and inference pipelines.
+"""
+
+import argparse
+import sys
+import os
+import json
+import numpy as np
+import torch
+from pathlib import Path
+from typing import List, Dict, Any, Optional
+
+# Add MarkovSpline to path
+sys.path.insert(0, '/data/MarkovSpline')
+from bitpipe_integration import MarkovSplineBitPipeModule, create_markov_spline_bitpipe_module
+from core import SplineType
+
+# Simple text to bits converter for CLI
+class TextToBitsConverter:
+    """Simple text to bits converter."""
+    
+    def text_to_bits(self, text, max_length=128):
+        """Convert text to bit sequence."""
+        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
+
+
+class MarkovSplineBitTransformerCLI:
+    """CLI interface for MarkovSpline + BitTransformerLM integration."""
+    
+    def __init__(self):
+        self.markov_module = None
+        self.text_converter = TextToBitsConverter()
+        
+    def initialize_markov_spline(self, config: Optional[Dict] = None) -> bool:
+        """Initialize MarkovSpline module with configuration."""
+        try:
+            self.markov_module = create_markov_spline_bitpipe_module(config)
+            print(f"✅ Initialized MarkovSpline module: {self.markov_module.module_name}")
+            return True
+        except Exception as e:
+            print(f"❌ Failed to initialize MarkovSpline: {e}")
+            return False
+    
+    def preprocess_text_data(self, 
+                           input_file: str, 
+                           output_file: str,
+                           smoothing_strength: float = 0.15,
+                           chunk_size: int = 128) -> bool:
+        """Preprocess text data using MarkovSpline for BitTransformerLM training."""
+        
+        if not self.markov_module:
+            print("❌ MarkovSpline module not initialized")
+            return False
+            
+        try:
+            # Read input text
+            with open(input_file, 'r', encoding='utf-8') as f:
+                text_data = f.read().strip().split('\n')
+            
+            print(f"📖 Processing {len(text_data)} text samples...")
+            
+            # Convert text to bit sequences
+            bit_sequences = []
+            for text in text_data:
+                if text.strip():
+                    bits = self.text_converter.text_to_bits(text, max_length=chunk_size)
+                    bit_sequences.append(bits)
+            
+            print(f"🔄 Converting to bit sequences: {len(bit_sequences)} sequences")
+            
+            # Initialize MarkovSpline preprocessor
+            self.markov_module.initialize_application('data_preprocessor', 
+                                                    smoothing_strength=smoothing_strength,
+                                                    preserve_features=True)
+            
+            # Process bit sequences through MarkovSpline
+            result = self.markov_module.process_data(
+                bit_sequences, 
+                'preprocess_training',
+                binary_data=True
+            )
+            
+            if not result['success']:
+                print(f"❌ Processing failed: {result.get('error', 'Unknown error')}")
+                return False
+            
+            # Save processed sequences
+            processed_data = {
+                'processed_sequences': result['processed_sequences'],
+                'preprocessing_summary': result['preprocessing_summary'],
+                'original_count': len(bit_sequences),
+                'smoothing_strength': smoothing_strength,
+                'chunk_size': chunk_size
+            }
+            
+            with open(output_file, 'w') as f:
+                json.dump(processed_data, f, indent=2, default=str)
+            
+            print(f"✅ Preprocessed data saved to: {output_file}")
+            print(f"📊 Summary: {result['preprocessing_summary']}")
+            return True
+            
+        except Exception as e:
+            print(f"❌ Preprocessing failed: {e}")
+            return False
+    
+    def smooth_bit_sequence(self, 
+                          bit_sequence: List[int],
+                          smoothing_type: str = 'predict_binary',
+                          num_predictions: int = 10) -> Dict[str, Any]:
+        """Smooth/predict bit sequence using MarkovSpline."""
+        
+        if not self.markov_module:
+            print("❌ MarkovSpline module not initialized")
+            return {'success': False, 'error': 'Module not initialized'}
+        
+        try:
+            result = self.markov_module.process_data(
+                bit_sequence,
+                smoothing_type,
+                num_predictions=num_predictions
+            )
+            return result
+            
+        except Exception as e:
+            print(f"❌ Bit sequence processing failed: {e}")
+            return {'success': False, 'error': str(e)}
+    
+    def smooth_training_gradients(self, 
+                                gradient_file: str,
+                                output_file: str,
+                                learning_rate: float = 0.01,
+                                smoothing_strength: float = 0.2) -> bool:
+        """Apply MarkovSpline gradient smoothing to BitTransformerLM training."""
+        
+        if not self.markov_module:
+            print("❌ MarkovSpline module not initialized")
+            return False
+        
+        try:
+            # Load gradient data (assuming PyTorch checkpoint format)
+            checkpoint = torch.load(gradient_file, map_location='cpu')
+            
+            if 'gradients' not in checkpoint or 'parameters' not in checkpoint:
+                print("❌ Invalid gradient file format")
+                return False
+            
+            # Initialize gradient smoother
+            self.markov_module.initialize_application('gradient_smoother',
+                                                    learning_rate=learning_rate,
+                                                    smoothing_strength=smoothing_strength)
+            
+            # Process gradients
+            result = self.markov_module.process_data(
+                {
+                    'parameters': checkpoint['parameters'],
+                    'gradients': checkpoint['gradients']
+                },
+                'smooth_gradients'
+            )
+            
+            if not result['success']:
+                print(f"❌ Gradient smoothing failed: {result.get('error', 'Unknown error')}")
+                return False
+            
+            # Save smoothed parameters
+            smoothed_checkpoint = {
+                'smoothed_parameters': result['smoothed_parameters'],
+                'optimization_metrics': result['optimization_metrics'],
+                'original_gradients': checkpoint['gradients']
+            }
+            
+            torch.save(smoothed_checkpoint, output_file)
+            print(f"✅ Smoothed gradients saved to: {output_file}")
+            print(f"📊 Optimization metrics: {result['optimization_metrics']}")
+            return True
+            
+        except Exception as e:
+            print(f"❌ Gradient smoothing failed: {e}")
+            return False
+    
+    def create_smoothed_dataset(self, 
+                              input_dataset: str,
+                              output_dataset: str,
+                              config: Optional[Dict] = None) -> bool:
+        """Create smoothed dataset for BitTransformerLM training."""
+        
+        # Default configuration for dataset smoothing
+        default_config = {
+            'smoothing_strength': 0.1,
+            'num_states': 20,
+            'spline_type': 'cubic',
+            'preserve_features': True
+        }
+        
+        if config:
+            default_config.update(config)
+        
+        if not self.markov_module:
+            self.initialize_markov_spline(default_config)
+        
+        return self.preprocess_text_data(input_dataset, output_dataset, 
+                                       default_config['smoothing_strength'])
+
+
+def main():
+    parser = argparse.ArgumentParser(description='MarkovSpline CLI for BitTransformerLM')
+    parser.add_argument('command', choices=['preprocess', 'smooth-gradients', 'create-dataset', 'predict-bits'],
+                      help='Command to execute')
+    
+    # Common arguments
+    parser.add_argument('--input', '-i', required=True, help='Input file path')
+    parser.add_argument('--output', '-o', required=True, help='Output file path')
+    parser.add_argument('--config', '-c', help='Configuration JSON file')
+    
+    # Preprocessing arguments
+    parser.add_argument('--smoothing-strength', type=float, default=0.15,
+                      help='Smoothing strength (0.0-1.0)')
+    parser.add_argument('--chunk-size', type=int, default=128,
+                      help='Text chunk size for bit conversion')
+    
+    # Gradient smoothing arguments  
+    parser.add_argument('--learning-rate', type=float, default=0.01,
+                      help='Learning rate for gradient smoothing')
+    
+    # Bit prediction arguments
+    parser.add_argument('--num-predictions', type=int, default=10,
+                      help='Number of bit predictions to generate')
+    
+    args = parser.parse_args()
+    
+    # Load configuration if provided
+    config = None
+    if args.config:
+        try:
+            with open(args.config, 'r') as f:
+                config = json.load(f)
+        except Exception as e:
+            print(f"❌ Failed to load config: {e}")
+            return 1
+    
+    # Initialize CLI
+    cli = MarkovSplineBitTransformerCLI()
+    if not cli.initialize_markov_spline(config):
+        return 1
+    
+    # Execute command
+    success = False
+    
+    if args.command == 'preprocess':
+        success = cli.preprocess_text_data(
+            args.input, args.output,
+            args.smoothing_strength, args.chunk_size
+        )
+        
+    elif args.command == 'smooth-gradients':
+        success = cli.smooth_training_gradients(
+            args.input, args.output,
+            args.learning_rate, args.smoothing_strength
+        )
+        
+    elif args.command == 'create-dataset':
+        success = cli.create_smoothed_dataset(
+            args.input, args.output, config
+        )
+        
+    elif args.command == 'predict-bits':
+        # Read bit sequence from input file
+        try:
+            with open(args.input, 'r') as f:
+                bit_data = json.load(f)
+                bit_sequence = bit_data.get('bits', [])
+            
+            result = cli.smooth_bit_sequence(bit_sequence, 'predict_binary', args.num_predictions)
+            
+            if result['success']:
+                with open(args.output, 'w') as f:
+                    json.dump(result, f, indent=2, default=str)
+                print(f"✅ Bit predictions saved to: {args.output}")
+                success = True
+            else:
+                print(f"❌ Bit prediction failed: {result.get('error', 'Unknown error')}")
+                
+        except Exception as e:
+            print(f"❌ Bit prediction failed: {e}")
+    
+    return 0 if success else 1
+
+
+if __name__ == '__main__':
+    sys.exit(main())