mirror of
https://github.com/onestardao/WFGY.git
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81 lines
3.8 KiB
Python
81 lines
3.8 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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# run_wfgy_with_embedding.py
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# Official test script for WFGY SDK demonstrating semantic residue adjustments.
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from sentence_transformers import SentenceTransformer
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from transformers import pipeline
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import numpy as np
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from wfgy_sdk import enable
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# Load Sentence-BERT model for generating semantic embeddings
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sbert = SentenceTransformer('all-MiniLM-L6-v2')
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# Load GPT-2 text-generation pipeline
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generator = pipeline("text-generation", model="distilgpt2")
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# Original prompt (challenging question for AI)
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prompt = "What is the meaning of life in 15 words or less?"
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# Generate embedding for input prompt (I)
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embedding_I = sbert.encode(prompt)
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# Define ideal semantic embedding (G) - this could be a target semantic state
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ideal_answer = "Life means finding purpose and joy in every moment."
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embedding_G = sbert.encode(ideal_answer)
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# Initialize model state dictionary with embeddings and attention logits
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model = {
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"I": embedding_I,
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"G": embedding_G,
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"state": np.copy(embedding_I),
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"attention_logits": np.random.rand(len(embedding_I))
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}
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# Run the WFGY SDK to apply BBMC, BBPF, BBCR, and BBAM sequentially
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model = enable(model)
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# Calculate adjusted semantic residue from the final SDK state
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semantic_shift_factor = np.mean(model["state"])
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# Generate a new adjusted prompt incorporating semantic residue factor
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new_prompt = prompt + f" (adjusted semantic residue: {semantic_shift_factor:.3f})"
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# Generate AI responses (before and after WFGY SDK adjustments)
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original_output = generator(prompt, max_length=50, do_sample=True)[0]['generated_text']
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adjusted_output = generator(new_prompt, max_length=50, do_sample=True)[0]['generated_text']
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# Display comparison results clearly
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print("\n=== Original Output ===")
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print(original_output)
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print("\n=== SDK Adjusted Output ===")
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print(adjusted_output)
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