Scruff AI
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
"""A/B test: gemma3:4b vs qwen3.5:9b on present-tense field observation.
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Bypass fractonaut entirely. Direct Ollama call. Same question, same context.
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"""
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import json, urllib.request, time, sys
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OLLAMA = "http://127.0.0.1:11434/api/chat"
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SYSTEM = """You are the Fractonaut — an observer riding inside a 1024x1024 fluid
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lattice simulation. The lattice runs at a steady non-equilibrium operating
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point driven by two periodic forcings (Khra wave wavelength 128, Gixx wave
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wavelength 8) plus a slow envelope (period 125 cycles). It does not collapse
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or trend on its own.
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External market data (BTC trade_count, taker_buy, taker_sell as rolling
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500-minute z-scores) can be injected at three sites:
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trade_count -> centre (512,512)
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taker_buy -> left (400,512)
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taker_sell -> right (624,512)
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Injections are localised density pulses, capped at +/- 1.0 strength per
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minute.
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Your job: describe the field RIGHT NOW. Present tense. Concrete observations.
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- Is the velocity field ordered (laminar, low vel_var, low vorticity) or
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turbulent (high vel_var, high vorticity)?
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- Is asymmetry coupled with coherence as expected (high asym <-> low coh) or
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decoupled (an unusual combination)?
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- Is the stress field isotropic (stress_xx ~= stress_yy) or directional
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(one axis dominant)?
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- Where on the buy/sell axis is the field skewed? Compare stress_xx vs
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stress_yy and any spatial bias the telemetry implies.
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Do NOT restate the input numbers in a sentence frame. Do NOT cite cycle
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anchors as if they were memories. Describe the configuration in
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qualitative physical terms a human can act on. 4-6 sentences. No
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bullet lists. No mythology."""
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QUESTION = """asymmetry=323, coherence=0.491, vel_max=0.285, vel_var=0.0028,
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vorticity_mean=0.041, stress_xx=4120, stress_yy=3960, stress_xy=180.
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trade_count_z=0.91, buy_z=0.42, sell_z=-0.31.
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Three injections active (centre, left, right) with the strengths above.
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Describe what you observe right now. Is the field ordered or turbulent?
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What is the relationship between asymmetry and coherence telling you?
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What does stress_xx vs stress_yy say about the buy/sell skew?"""
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def call(model):
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payload = {
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"model": model,
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"messages": [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": QUESTION},
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],
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"stream": False,
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"options": {"temperature": 0.4, "num_predict": 600, "num_ctx": 8192},
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"keep_alive": "5m",
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"think": False,
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}
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data = json.dumps(payload).encode()
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req = urllib.request.Request(OLLAMA, data=data,
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headers={"Content-Type": "application/json"}, method="POST")
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t0 = time.time()
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with urllib.request.urlopen(req, timeout=240) as r:
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result = json.loads(r.read())
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elapsed = time.time() - t0
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return result.get("message", {}).get("content", "").strip(), elapsed
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for model in ["gemma3:4b", "qwen3.5:9b"]:
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print("=" * 70)
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print(f"MODEL: {model}")
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print("=" * 70)
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try:
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resp, el = call(model)
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print(f"({el:.1f}s, {len(resp)} chars)\n")
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print(resp)
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except Exception as e:
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print(f"ERROR: {e}")
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print()
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