Initial commit: NOVA - Neuro-Optimizing Versatile Agent
Complete transformer LLM built from scratch with: Core Features: - Full transformer architecture (RoPE, RMSNorm, SwiGLU, KV-cache) - SentencePiece tokenizer (BPE/Unigram) - Training pipeline (AMP, gradient checkpointing, DDP) - Persona system with personality matrix (NO AI disclosure by default) - Genetic evolution (NOVA-EVO) for hyperparameter optimization - Legal-only data pipeline with license tracking - Chat interface (CLI + REST API) - Conversation memory (SQLite) Model Sizes: - 125M, 350M, 1.3B, 3B parameters - Local-first, runs on CPU or GPU - Python 3.10.6+, PyTorch 2.0+ Personas: - girlfriend_gentle (high warmth, high empathy) - girlfriend_playful (high humor, high playfulness) - girlfriend_supportive (balanced, default) Documentation: - Complete README with quickstart - Model card with ethical considerations - Privacy documentation (local-first, zero telemetry) - Data licenses and attribution - Contributing guide Infrastructure: - GitHub Actions CI/CD - Comprehensive test suite - Quickstart script - CLI tool License: Apache 2.0 🤖 Generated with Claude Code https://claude.com/claude-code Co-Authored-By: Claude <noreply@anthropic.com>
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nova_data/pipeline.py
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168
nova_data/pipeline.py
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"""
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Data pipeline for legal dataset acquisition and processing
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"""
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import json
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from pathlib import Path
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from typing import List, Dict, Optional
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from tqdm import tqdm
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import hashlib
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from .legal_sources import LegalDatasetRegistry, DatasetSource
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class DataPipeline:
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"""
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Legal-only data acquisition and processing pipeline
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Features:
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- License tracking and verification
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- Provenance recording
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- Deduplication
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- Text cleaning
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"""
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def __init__(self, output_dir: str = "data/processed"):
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"""
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Args:
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output_dir: Directory for processed data
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"""
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self.output_dir = Path(output_dir)
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self.output_dir.mkdir(parents=True, exist_ok=True)
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# License ledger
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self.ledger_path = self.output_dir / "license_ledger.json"
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self.ledger = self._load_ledger()
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def _load_ledger(self) -> Dict:
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"""Load license ledger"""
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if self.ledger_path.exists():
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with open(self.ledger_path, 'r') as f:
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return json.load(f)
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return {'sources': [], 'shards': []}
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def _save_ledger(self):
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"""Save license ledger"""
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with open(self.ledger_path, 'w') as f:
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json.dump(self.ledger, f, indent=2)
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def download_source(self, source_name: str, dry_run: bool = False):
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"""
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Download a legal dataset source
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Args:
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source_name: Name of source from registry
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dry_run: If True, don't actually download (just show info)
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"""
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source = LegalDatasetRegistry.get_source(source_name)
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if not source:
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raise ValueError(f"Unknown source: {source_name}")
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print(f"Source: {source.name}")
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print(f"Description: {source.description}")
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print(f"License: {source.license.value}")
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print(f"Estimated size: {source.estimated_size_gb} GB")
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if dry_run:
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print("\n[DRY RUN] Would download from:", source.url)
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return
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print("\nDownloading...")
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# TODO: Implement actual download logic for each source
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# For now, this is a placeholder
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# Record in ledger
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self.ledger['sources'].append({
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'name': source.name,
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'license': source.license.value,
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'url': source.url,
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'download_date': str(Path.ctime(self.output_dir)),
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})
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self._save_ledger()
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print("✓ Download complete and recorded in ledger")
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def create_toy_dataset(self):
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"""
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Create a tiny toy dataset for offline e2e demo
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This is a minimal legal dataset for testing without downloads
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"""
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toy_data_path = Path("data/toy_dataset/toy.txt")
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toy_data_path.parent.mkdir(parents=True, exist_ok=True)
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# Public domain sample texts
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sample_texts = [
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"The quick brown fox jumps over the lazy dog.",
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"To be or not to be, that is the question.",
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"In the beginning was the Word.",
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"It was the best of times, it was the worst of times.",
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"Call me Ishmael.",
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"All happy families are alike.",
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"It is a truth universally acknowledged.",
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"The past is a foreign country; they do things differently there.",
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"Once upon a time in a land far away.",
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"The sun rose over the horizon, painting the sky in shades of gold.",
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] * 100 # Repeat for more data
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with open(toy_data_path, 'w', encoding='utf-8') as f:
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for text in sample_texts:
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f.write(text + '\n')
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print(f"✓ Toy dataset created: {toy_data_path}")
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# Record in ledger
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self.ledger['sources'].append({
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'name': 'toy-dataset',
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'license': 'public-domain',
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'description': 'Minimal toy dataset for testing',
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'created': 'generated',
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})
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self._save_ledger()
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return str(toy_data_path)
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def verify_licenses(self) -> bool:
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"""
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Verify all data sources have proper licenses
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Returns:
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True if all sources are properly licensed
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"""
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print("Verifying licenses...")
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all_valid = True
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for source_entry in self.ledger['sources']:
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name = source_entry.get('name')
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license_str = source_entry.get('license')
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print(f" {name}: {license_str}")
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# Check if license is in our approved list
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valid_licenses = [lic.value for lic in LegalDatasetRegistry.License]
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if license_str not in valid_licenses and license_str != 'public-domain':
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print(f" ⚠️ WARNING: Unrecognized license!")
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all_valid = False
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if all_valid:
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print("\n✓ All sources properly licensed")
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else:
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print("\n⚠️ Some sources have unverified licenses")
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return all_valid
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def show_ledger(self):
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"""Print license ledger"""
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print("\nLicense Ledger:")
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print("=" * 60)
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print(f"\nSources ({len(self.ledger['sources'])}):")
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for source in self.ledger['sources']:
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print(f" - {source['name']}: {source['license']}")
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print(f"\nShards ({len(self.ledger['shards'])}):")
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for shard in self.ledger.get('shards', []):
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print(f" - {shard['name']}")
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