Upload training_scripts/quickstart.py with huggingface_hub

training_scripts/quickstart.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+"""
+ÜBERMENSCHETIEN QUICK START
+============================
+One-command setup and training.
+
+Usage:
+  python quickstart.py --full          # Run full pipeline
+  python quickstart.py --train-dense   # Just dense training
+  python quickstart.py --train-cfhot   # Just CF-HoT heads
+  python quickstart.py --improve       # Just self-improvement
+  python quickstart.py --test          # Test current model
+
+"From zero to self-improving in one command"
+"""
+
+import os
+import sys
+import argparse
+import subprocess
+from pathlib import Path
+
+ROOT = os.path.dirname(os.path.abspath(__file__))
+
+def run_command(cmd, description):
+    """Run a command with nice output."""
+    print(f"\n{'='*70}")
+    print(f"🚀 {description}")
+    print(f"{'='*70}")
+    print(f"$ {cmd}\n")
+    
+    result = subprocess.run(cmd, shell=True, cwd=ROOT)
+    
+    if result.returncode != 0:
+        print(f"\n❌ Failed: {description}")
+        return False
+    
+    print(f"\n✓ Complete: {description}")
+    return True
+
+
+def check_dependencies():
+    """Check required packages are installed."""
+    print("\n🔍 Checking dependencies...")
+    
+    required = [
+        "torch",
+        "transformers",
+        "peft",
+        "bitsandbytes",
+        "accelerate",
+    ]
+    
+    missing = []
+    for pkg in required:
+        try:
+            __import__(pkg)
+            print(f"  ✓ {pkg}")
+        except ImportError:
+            print(f"  ✗ {pkg}")
+            missing.append(pkg)
+    
+    if missing:
+        print(f"\n❌ Missing packages: {', '.join(missing)}")
+        print("Install with: pip install " + " ".join(missing))
+        return False
+    
+    print("\n✓ All dependencies installed")
+    return True
+
+
+def train_dense(steps=100):
+    """Run THE CONDENSATOR dense training."""
+    return run_command(
+        f"python the_condensator.py --stages sft,dpo,rl --steps {steps}",
+        "THE CONDENSATOR - Dense Response Training"
+    )
+
+
+def train_cfhot(steps=3000):
+    """Train CF-HoT behavior heads."""
+    success = True
+    
+    for behavior in ["repetition", "hedging", "verbosity"]:
+        if not run_command(
+            f"python train_cfhot_head.py --behavior {behavior} --steps {steps}",
+            f"CF-HoT {behavior.upper()} Head Training"
+        ):
+            success = False
+    
+    return success
+
+
+def train_self_improve(iterations=5):
+    """Run stable self-improvement."""
+    return run_command(
+        f"python train_self_improve.py --iterations {iterations}",
+        "Stable Self-Improvement Loop"
+    )
+
+
+def test_model(checkpoint=None):
+    """Test the model."""
+    cmd = "python the_condensator.py --eval-only"
+    if checkpoint:
+        cmd += f" --checkpoint {checkpoint}"
+    return run_command(cmd, "Model Evaluation")
+
+
+def full_pipeline():
+    """Run the complete training pipeline."""
+    print("\n" + "="*70)
+    print("🔥 ÜBERMENSCHETIEN FULL TRAINING PIPELINE")
+    print("="*70)
+    print("""
+    This will run:
+    1. THE CONDENSATOR (SFT → DPO → RL)
+    2. CF-HoT Head Training (repetition, hedging, verbosity)
+    3. Stable Self-Improvement Loop
+    
+    Estimated time: 2-4 hours on RTX 3090
+    """)
+    
+    if not check_dependencies():
+        return False
+    
+    # Step 1: Dense training
+    if not train_dense(100):
+        return False
+    
+    # Step 2: CF-HoT heads
+    if not train_cfhot(1000):  # Fewer steps for quick start
+        return False
+    
+    # Step 3: Self-improvement
+    if not train_self_improve(3):
+        return False
+    
+    print("\n" + "="*70)
+    print("✓ ÜBERMENSCHETIEN TRAINING COMPLETE!")
+    print("="*70)
+    print("""
+    Your model is ready! Run:
+    
+      python ubermenschetien_v2_full.py
+    
+    Commands:
+      > hello                  # Chat
+      > !eval                  # Evaluate quality
+      > !improve               # Continue self-improvement
+    """)
+    
+    return True
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Übermenschetien Quick Start")
+    parser.add_argument("--full", action="store_true", help="Run full pipeline")
+    parser.add_argument("--train-dense", action="store_true", help="Run dense training only")
+    parser.add_argument("--train-cfhot", action="store_true", help="Run CF-HoT training only")
+    parser.add_argument("--improve", action="store_true", help="Run self-improvement only")
+    parser.add_argument("--test", action="store_true", help="Test current model")
+    parser.add_argument("--steps", type=int, default=100, help="Training steps")
+    parser.add_argument("--checkpoint", type=str, default=None, help="Checkpoint path for testing")
+    
+    args = parser.parse_args()
+    
+    if args.full:
+        full_pipeline()
+    elif args.train_dense:
+        train_dense(args.steps)
+    elif args.train_cfhot:
+        train_cfhot(args.steps)
+    elif args.improve:
+        train_self_improve()
+    elif args.test:
+        test_model(args.checkpoint)
+    else:
+        parser.print_help()
+        print("\n💡 Try: python quickstart.py --full")
+
+
+if __name__ == "__main__":
+    main()