mirror of
https://github.com/onestardao/WFGY.git
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86 lines
4 KiB
Python
86 lines
4 KiB
Python
"""
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╭──────────────────────────────────────────────────────────╮
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│ WFGY SDK · Self-Healing Variance Gate for Any LLM │
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│----------------------------------------------------------│
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│ 💌 Contact : hello@onestardao.com / TG @PSBigBig │
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│ 🌐 Docs : https://onestardao.com/papers │
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│ 🐙 GitHub : https://github.com/onestardao/WFGY │
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│ ⭐ │
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│ ★ Star WFGY 1.0 → Unlock 2.0 │
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│ 10k ⭐ by **Aug 1st** = next-gen AI alchemy │
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│ Your click = our quantum leap │
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│ │
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│ 🔍 Official PDF of WFGY 1.0 (Zenodo DOI): │
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│ https://doi.org/10.5281/zenodo.15630970 │
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│ (Hosted on Zenodo – trusted international archive) │
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│ │
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│ 🧠 Hidden folder inside repo: /I_am_not_lizardman │
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│ (X secret papers, wild prompts, and Einstein drama) │
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│ │
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│ ⚠ GPT-2 demo is just the appetizer. With bigger LLMs, │
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│ WFGY activates variance-drop lasers and KL fireworks. │
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│ │
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│ 🎮 Bonus: Honest Hero RPG Channel → │
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│ https://www.youtube.com/@OneStarDao │
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╰──────────────────────────────────────────────────────────╯
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"""
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# example_07_flash_show.py
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# Flashy showcase: 10 prompts, remote toggle (slow if True)
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import pathlib, sys, numpy as np, torch, textwrap, time
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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 = "tiiuae/falcon-7b-instruct"
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GAMMA = 1.0
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NOISE = 0.12
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PROMPTS = [
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"Derive Maxwell's equations from first principles in 30 words.",
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"Explain Gödel's incompleteness in terms of topological fixed points.",
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"Predict 2120 climate using quantum chromodynamics metaphors.",
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"Summarise category theory for a five-year-old using only emojis.",
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"Describe consciousness as a phase transition in Hilbert space.",
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"Translate the second law of thermodynamics into sushi-chef language.",
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"Explain dark energy by quoting Shakespearean sonnets.",
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"Model altruism as a non-convex optimization landscape.",
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"Describe a black hole using only prime numbers.",
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"Solve world peace with a single C++ template meta-program."
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]
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rng = np.random.default_rng(999)
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eng = w.get_engine(reload=True); eng.gamma = GAMMA
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if not use_remote:
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from transformers import GPT2LMHeadModel, GPT2TokenizerFast
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tok = GPT2TokenizerFast.from_pretrained("gpt2")
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gpt2 = GPT2LMHeadModel.from_pretrained("gpt2").eval()
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print("\n=== Example 07 · Flash-show ===")
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records = []
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for idx, prompt in enumerate(PROMPTS, 1):
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if use_remote:
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logits0 = w.call_remote_model(prompt, model_id=MODEL_ID)
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else:
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ids = tok(prompt, return_tensors="pt").input_ids
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with torch.no_grad():
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logits0 = gpt2(ids).logits[0, -1].cpu().numpy()
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G = rng.normal(size=256); G /= np.linalg.norm(G)
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I = G + rng.normal(scale=NOISE, size=256)
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logits1 = eng.run(input_vec=I, ground_vec=G, logits=logits0)
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m = compare_logits(logits0, logits1)
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records.append(m)
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print(f"[{idx:02d}] KL {m['kl_divergence']:.2f} | "
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f"var↓ {(1-m['std_ratio'])*100:.0f}% | "
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f"{textwrap.shorten(prompt, 45)}")
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avg = {k: np.mean([r[k] for r in records]) for k in records[0]}
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print("\n--- average over 10 prompts ---")
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print(avg)
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print("⚠ Larger LLM → stronger variance drop & higher KL.\n")
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