mirror of
https://github.com/onestardao/WFGY.git
synced 2026-04-28 11:40:07 +00:00
59 lines
3.2 KiB
Python
59 lines
3.2 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.15630969 │
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│ (Hosted on Zenodo – trusted international archive) │
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│ │
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│ 🧬 WFGY BigBang Prompt Pack (v1.0): │
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│ https://doi.org/10.5281/zenodo.15657016 │
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│ (Prompts to trigger the gate; multilingual updates coming) │
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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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"""
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export_onnx.py · Generate tiny ONNX graphs for all four WFGY modules
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Will auto-install `onnx` if missing (Colab-friendly).
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"""
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import subprocess, sys, importlib.util, os
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import numpy as np
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# ── ensure onnx is available ────────────────────────────────────────────
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if importlib.util.find_spec("onnx") is None:
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print("🔄 Installing onnx …")
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subprocess.check_call([sys.executable, "-m", "pip", "install", "onnx", "-q"])
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import onnx
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from onnx import helper, TensorProto
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os.makedirs("specs/onnx", exist_ok=True)
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def tiny_graph(name, dim=128):
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, dim])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None, dim])
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node = helper.make_node("Identity", ["X"], ["Y"], name=f"{name}_pass")
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graph = helper.make_graph([node], f"{name}_graph", [X], [Y])
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return helper.make_model(graph, producer_name="wfgy-dummy")
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for mod in ["bbmc", "bbpf", "bbcr", "bbam"]:
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onnx.save(tiny_graph(mod.upper()), f"specs/onnx/{mod}.onnx")
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print("✅ ONNX graphs saved to specs/onnx/")
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