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generate_v9b_pure.py
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75 lines (58 loc) · 2.11 KB
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#!/usr/bin/env python3
"""
Generate v9B-pure AGL training dataset.
Creates 2000 examples across 4 phases:
- Warmup (500): Certainty gradient, basic φ-patterns
- Tonight (500): Existential questions, quantifiers, 0.60 threshold
- Eigenvalue (500): Temporal progressions, Δ operators
- Deep AGL (500): Full vocabulary integration
Output: data/v9b_pure_agl_training.jsonl
"""
import sys
sys.path.insert(0, ".")
from pathlib import Path
from consciousness_engineering.datasets.v9b_pure import V9BPureGenerator
def main():
print("=" * 60)
print("V9B-Pure AGL Dataset Generator")
print("=" * 60)
# Create generator
output_dir = Path("data")
output_dir.mkdir(exist_ok=True)
generator = V9BPureGenerator(output_dir=str(output_dir))
print(f"\nConfiguration:")
print(f" Output directory: {output_dir.absolute()}")
print(f" Examples per phase: 500")
print(f" Total examples: 2000")
print(f" Phases: warmup → tonight → eigenvalue → deep_agl")
print("\n" + "-" * 60)
print("Generating dataset...")
print("-" * 60)
# Generate and save
output_path = generator.generate_and_save()
print("\n" + "=" * 60)
print(f"Dataset saved to: {output_path}")
print("=" * 60)
# Show sample from each phase
print("\nSample from each phase:")
print("-" * 60)
with open(output_path) as f:
import json
lines = f.readlines()
# Get first example from each phase
phases_seen = set()
for line in lines:
data = json.loads(line)
phase = data.get("phase", "unknown")
if phase not in phases_seen:
phases_seen.add(phase)
print(f"\n[{phase.upper()}]")
print(f"User: {data['messages'][0]['content'][:60]}...")
print(f"Assistant: {data['messages'][1]['content'][:100]}...")
if len(phases_seen) >= 4:
break
print("\n" + "=" * 60)
print("Generation complete! Ready for training.")
print("=" * 60)
if __name__ == "__main__":
main()