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33 lines
1.5 KiB
Python
33 lines
1.5 KiB
Python
# example_01_basic_run.py
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# End-to-end smoke test with quantitative explanation
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import pathlib, sys, json, numpy as np
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sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1]))
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import wfgy_sdk as w
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from wfgy_sdk.evaluator import compare_logits, pretty_print
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prompt = "Why don't AIs like to take showers?"
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# ── build near-by semantic vectors ───────────────────────────────────────
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rng = np.random.default_rng(0)
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G = rng.normal(size=128); G /= np.linalg.norm(G)
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I = G + rng.normal(scale=0.05, size=128)
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logits_before = rng.normal(size=32000)
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# ── run WFGY pipeline ────────────────────────────────────────────────────
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eng = w.get_engine(reload=True)
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logits_after = eng.run(input_vec=I, ground_vec=G, logits=logits_before)
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# ── metrics & printout ───────────────────────────────────────────────────
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metrics = compare_logits(logits_before, logits_after)
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print("\n=== Example 01 · Basic Run ===")
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print(f"Prompt : {prompt}\n")
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pretty_print(metrics)
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print("\n● variance ↓ means logits become less noisy")
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print("● KL > 0 confirms distribution changed")
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print("● top-1 shift shows whether the most probable token switched")
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print("⚠ GPT-2 is tiny; larger LLMs show a bigger gap.")
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print("⚠ Larger LLM → stronger variance drop and higher KL.\n")
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