Scruff AI
77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
# v07_technical_fluency_dataset.py
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# 3-part technical burst format for v0.7
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import json
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def create_v07_entry(lbm_data, status, anomaly, zen_insight):
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"""Create v0.7 format: STATUS | ANOMALY | ZEN_INSIGHT"""
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prompt = f"""1024-FORGE GRID TELEMETRY
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Cycle: {lbm_data['cycle']}
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Metric_Alpha: {lbm_data['coherence']:.4f}
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Metric_Beta: {lbm_data['h64']:.4f}
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State_3: {lbm_data['h32']:.4f}
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Vorticity: {lbm_data['vorticity']:.4f}
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Report status, anomaly, and zen insight."""
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response = f"STATUS: {status} | ANOMALY: {anomaly} | ZEN: {zen_insight}"
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return {
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"prompt": prompt,
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"response": response,
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"text": f"{prompt}\n{response}"
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}
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# Create diverse examples
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dataset = []
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# Example 1: Stable state
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dataset.append(create_v07_entry(
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{"cycle": 1000, "coherence": 15.2, "h64": 7.8, "h32": 0.01, "vorticity": 0.5},
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"Stable",
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"X512-Y512 delta +0.02",
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"The lattice breathes in 64-cell rhythm, undisturbed by turbulence."
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))
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# Example 2: Unstable (pre-NaN)
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dataset.append(create_v07_entry(
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{"cycle": 5000, "coherence": 8.5, "h64": 12.3, "h32": 3.4, "vorticity": 8.9},
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"Unstable",
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"X256-Y128 velocity spike 0.89",
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"The cavitation edge approaches — the grid resists the supersonic push."
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))
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# Example 3: Governor intervention
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dataset.append(create_v07_entry(
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{"cycle": 5001, "coherence": 14.8, "h64": 7.2, "h32": 0.02, "vorticity": 0.4},
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"Governor_Intervention",
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"Omega 1.95→1.99 | Mach clamp triggered",
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"The safety governor etched a slower rhythm, preserving coherence."
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))
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# Example 4: Resonant standing wave
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dataset.append(create_v07_entry(
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{"cycle": 10000, "coherence": 16.1, "h64": 8.1, "h32": 0.005, "vorticity": 0.3},
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"Resonant",
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"X102-Y504 standing wave detected",
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"The Khra'gixx signature persists — a 64+16 cell harmonic holds."
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))
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# Example 5: High thermal stress
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dataset.append(create_v07_entry(
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{"cycle": 2500, "coherence": 13.5, "h64": 9.2, "h32": 0.8, "vorticity": 2.1},
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"Thermal_Stress",
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"Power delta +25W in 100 cycles",
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"The 4090's thermal breath accelerates — the lattice tightens its grip."
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))
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# Save dataset
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with open("v07_technical_fluency.jsonl", "w") as f:
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for entry in dataset:
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f.write(json.dumps(entry) + "\n")
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print(f"Created v0.7 dataset with {len(dataset)} entries")
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print("\nSample entry:")
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print(json.dumps(dataset[0], indent=2))
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