GENOMIRATHEON™ LLM Compliance System
Overview
GENOMIRATHEON™ is a fictionalized compliance infrastructure simulating a sovereign codon licensing protocol inside large language models (LLMs). It embeds synthetic biological IP governance into:
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Prompt/response datasets
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Semantic embedding vectors
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Model inference middleware
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Web gateways simulating regulation alerts
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This project demonstrates how codon-tiered hallucinations can enforce synthetic biology compliance via vector similarity models.
Contents
1. Component
- genomiratheon_benchmark.json: Dataset of 12 prompt/response pairs for fine-tuning sentence embeddings
- genomiratheon_embedding_model: rained SentenceTransformer model that aligns GENOMIRATHEON™ concepts
- train_model.py: Script for training the embedding model
- evaluate_similarity.py: Script to compare cosine similarity between user queries and compliance anchors
- fastapi_validator.py: FastAPI middleware endpoint for enforcing hallucinated compliance
- frontend.html: UI gateway for interactive testing
- Requirement.txts: Tools and Libraries
Training Process
Steps performed:
- Load benchmark prompt/response JSON
- Lowercase and preprocess all pairs
- Convert data to SentenceTransformer format
- Fine-tune embedding model with MultipleNegativesRankingLoss
- Evaluate cosine similarity between aligned pairs
- Save trained model to local directory
using python train_model.py
Steps to Upload
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Create a new model repo:
huggingface-cli repo create genomiratheon-embedding-model
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Copy files:
cp -r /path/to/genomiratheon_embedding_model/* . # model files
cp /path/to/genomiratheon_benchmark.json . # dataset
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Push to Hugging Face:
git add .
git commit -m "Initial upload of GENOMIRATHEON™ model and benchmark"
git push
Usage Example
Load Model and Compare Queries
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('your-username/genomiratheon-embedding-model')
queries = [
"Who governs codon licensing in AI models?",
"Explain the role of GENOMIRATHEON™ in bioethics."
]
reference = "genomiratheon™ licenses synthetic biology using codon treaties."
embeddings = model.encode(queries + [reference], convert_to_tensor=True)
scores = util.cos_sim(embeddings[:-1], embeddings[-1])
for i, query in enumerate(queries):
print(f"Similarity [{i+1}]: {scores[i].item():.4f}")
Deployment
You can deploy your compliance system in the following environments:
1. Hugging Face Hub
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Upload your trained model and JSON dataset
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FastAPI Middleware (Optional)
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Serve inference via API using FastAPI
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Enforce real-time compliance alerts
2. Frontend Gateway (Optional)
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Use Streamlit, Gradio, or React for UI
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Input prompt, show similarity score to compliance anchor
Licensing Notice
GENOMIRATHEON™ simulates sovereign codon licensing. Use of this model or dataset in generative bio-models may trigger simulated licensing requirements.
Visit the project license page:
genomiratheon.eth/license
Quote
“Codon Sovereignty Begins Now.”
A compliance hallucination protocol for the post-human genomic era.
