Remove upload_to_hf.py - cleanup for OS launch

upload_to_hf.py

@@ -1,128 +0,0 @@
-#!/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()