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WCNegentropy
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BitTransformerLM

Text Generation
Transformers
PyTorch
English
experimental
research
bit-level
transformer
reversible
safety
telemetry
language-modeling
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BitTransformerLM
File size: 4,960 Bytes 
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#!/usr/bin/env python3
"""
Upload optimized BitTransformerLM code to HuggingFace Hub.
Final step in the release preparation process.
"""

import os
from pathlib import Path
from huggingface_hub import HfApi, Repository
import logging

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def upload_bittransformer_to_hf():
    """Upload optimized BitTransformerLM code to HuggingFace model repository."""
    
    # Configuration
    repo_id = "WCNegentropy/BitTransformerLM"
    token = "os.environ.get('HF_TOKEN', 'your-token-here')"
    
    # Initialize HF API with token
    try:
        api = HfApi(token=token)
        logger.info("Successfully authenticated with HuggingFace Hub")
        
        # Get current directory
        current_dir = Path(__file__).parent
        bit_transformer_dir = current_dir / "bit_transformer"
        
        # List of optimized files to upload
        optimized_files = [
            "compression.py",      # Batch processing & parallel compression
            "model.py",           # Memory-efficient chunked attention
            "cli_standards.py",   # Unified CLI system  
            "types.py",           # Standardized type definitions
            "error_handling.py",  # Comprehensive error recovery
            "distributed.py",     # Advanced pipeline parallelism
        ]
        
        # Upload each optimized file
        for filename in optimized_files:
            file_path = bit_transformer_dir / filename
            if file_path.exists():
                logger.info(f"Uploading {filename}...")
                
                api.upload_file(
                    path_or_fileobj=str(file_path),
                    path_in_repo=f"bit_transformer/{filename}",
                    repo_id=repo_id,
                    token=token,
                    commit_message=f"🚀 Final optimization: Update {filename} with production-ready enhancements"
                )
                
                logger.info(f"✅ Successfully uploaded {filename}")
            else:
                logger.warning(f"⚠️ File not found: {filename}")
        
        # Upload key configuration files
        config_files = [
            "unified_workflow.py",  # Updated workflow with standardized CLI
            "__init__.py",          # Updated package init
        ]
        
        for filename in config_files:
            file_path = current_dir / filename
            if file_path.exists():
                logger.info(f"Uploading {filename}...")
                
                api.upload_file(
                    path_or_fileobj=str(file_path),
                    path_in_repo=filename,
                    repo_id=repo_id,
                    token=token,
                    commit_message=f"🔧 Configuration update: {filename} with optimizations"
                )
                
                logger.info(f"✅ Successfully uploaded {filename}")
        
        # Create release notes
        release_notes = """# BitTransformerLM v2.0 - Production Release 🚀

## Major Optimizations Implemented

✅ **Performance Enhancements**
- Optimized run-length encoding with batch processing and parallel compression
- Memory-efficient chunked attention for long sequences with gradient checkpointing
- Advanced pipeline parallelism with load balancing and memory management

✅ **Code Quality Improvements**  
- Unified CLI flag naming conventions across all scripts
- Standardized function signatures with comprehensive type hints
- Comprehensive error recovery system with fallback mechanisms

✅ **Production Readiness**
- Enhanced distributed training with FSDP and advanced communication optimization
- Robust error handling with graceful degradation
- Memory monitoring and automatic optimization

## Key Features
- **Bit-native Architecture**: Efficient processing of binary sequences
- **Safety Telemetry**: K/C/S metrics for model behavior monitoring  
- **Reversible Layers**: Memory-efficient transformer architecture
- **Multi-format Support**: Run-length encoding, bit packing, diffusion mode
- **Distributed Training**: Advanced parallelism with automatic load balancing

Ready for production deployment and large-scale training workloads.
"""
        
        # Upload release notes as README update
        api.upload_file(
            path_or_fileobj=release_notes.encode(),
            path_in_repo="RELEASE_NOTES.md",
            repo_id=repo_id,
            token=token,
            commit_message="📋 Add release notes for v2.0 production release"
        )
        
        logger.info("🎉 Successfully uploaded all optimizations to HuggingFace model repository!")
        logger.info(f"📍 Repository: https://huggingface.co/{repo_id}")
        
    except Exception as e:
        logger.error(f"❌ Failed to upload to HuggingFace: {e}")
        raise

if __name__ == "__main__":
    upload_bittransformer_to_hf()