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32 lines
1 KiB
Python
32 lines
1 KiB
Python
# example_02_self_reflection.py
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# Self-reflection loop with remote toggle
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import pathlib, sys, 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
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use_remote = False
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MODEL_ID = "gpt2"
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rng = np.random.default_rng(1)
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eng = w.get_engine(reload=True)
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print("\n=== Example 02 · Self-reflection loop ===")
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for step in range(3):
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G = rng.normal(size=64); G /= np.linalg.norm(G)
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I = G + rng.normal(scale=0.05, size=64)
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logits_before = (
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w.call_remote_model(f"Round {step}", model_id=MODEL_ID)
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if use_remote else rng.normal(size=4096)
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)
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logits_after = eng.run(input_vec=I, ground_vec=G, logits=logits_before)
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m = compare_logits(logits_before, logits_after)
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print(f"[Round {step}] KL {m['kl_divergence']:.2f} | "
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f"var↓ {(1-m['std_ratio'])*100:.0f}% | "
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f"top-1 {'✔' if not m["top1_shift"] else '✘'}")
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print("⚠ Larger LLM → stronger variance drop & higher KL.\n")
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