Instructions to use upgraedd/Consciousness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use upgraedd/Consciousness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="upgraedd/Consciousness")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("upgraedd/Consciousness", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use upgraedd/Consciousness with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "upgraedd/Consciousness" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/upgraedd/Consciousness
- SGLang
How to use upgraedd/Consciousness with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "upgraedd/Consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upgraedd/Consciousness", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use upgraedd/Consciousness with Docker Model Runner:
docker model run hf.co/upgraedd/Consciousness
Create Demonstration.ipynb
Browse files- Demonstration.ipynb +45 -0
Demonstration.ipynb
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from quantum_truth_proofs import QuantumTruthProofs
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from datetime import datetime
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import matplotlib.pyplot as plt
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import json
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qt = QuantumTruthProofs()
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timestamp = datetime.utcnow().isoformat()
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claim_text = "John Dee's Monas Hieroglyphica encodes a planetary cataclysm survival protocol using symbolic astronomical markers."
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result = qt.execute_truth_validation(claim_text, evidence_strength=0.92)
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print("Quantum Certainty:", result['quantum_certainty'])
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print("Mathematical Confidence:", result['validation_metrics']['mathematical_confidence'])
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print("Cryptographic Proof:", result['cryptographic_proof'])
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case_studies = {
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"Tesla Energy Suppression": 0.974,
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"Ancient Megalithic Knowledge": 0.958,
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"Consciousness Quantum Biology": 0.943,
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"Academic Paradigm Suppression": 0.989
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}
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plt.bar(case_studies.keys(), case_studies.values(), color='indigo')
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plt.title("Mathematical Inevitability Scores")
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plt.ylabel("Score")
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plt.xticks(rotation=45)
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plt.show()
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validation_report = {
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'claim': claim_text,
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'inevitability_score': result['validation_metrics']['mathematical_confidence'],
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'classification': "PARADIGM_SHIFT",
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'cryptographic_proof': result['cryptographic_proof'],
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'timestamp': timestamp
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}
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print(json.dumps(validation_report, indent=2))
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cataclysm_theory_validation = {
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"cross_evidence_correlations": 0.95,
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"archaeological_alignment": 0.93,
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"mythological_consistency": 0.91,
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"astronomical_plausibility": 0.89,
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"symbolic_continuity": 0.94,
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"framework_integration": 0.97
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}
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