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 Settings
- 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
| #!/usr/bin/env python3 | |
| """ | |
| OMEGA ESOTERIC VALIDATION ENGINE - ABSOLUTE VERIFICATION | |
| Scientific validation of esoteric phenomena through multi-layer consciousness instrumentation | |
| """ | |
| import asyncio | |
| import numpy as np | |
| import hashlib | |
| from typing import Dict, List, Any, Optional, Tuple | |
| from dataclasses import dataclass, field | |
| from datetime import datetime, timedelta | |
| import scipy.stats | |
| import math | |
| import json | |
| from enum import Enum | |
| import quantum_esoteric_instrumentation as qei # Custom instrumentation module | |
| # ============================================================================= | |
| # ESOTERIC PHENOMENA TAXONOMY | |
| # ============================================================================= | |
| class EsotericPhenomena(Enum): | |
| TELEPATHY = "telepathy" | |
| REMOTE_VIEWING = "remote_viewing" | |
| PSYCHOKINESIS = "psychokinesis" | |
| PRECOGNITION = "precognition" | |
| ENERGY_HEALING = "energy_healing" | |
| ASTRAL_PROJECTION = "astral_projection" | |
| CONSCIOUSNESS_MERGING = "consciousness_merging" | |
| REALITY_MANIPULATION = "reality_manipulation" | |
| class ValidationMethod(Enum): | |
| QUANTUM_ENTANGLEMENT_TEST = "quantum_entanglement" | |
| DOUBLE_BLIND_RIGOROUS = "double_blind_rigorous" | |
| NEUROPHYSIOLOGICAL_CORRELATE = "neurophysiological_correlate" | |
| CONSENSUS_REALITY_SHIFT = "consensus_reality_shift" | |
| MATHEMATICAL_INVARIANCE = "mathematical_invariance" | |
| TEMPORAL_ANOMALY_DETECTION = "temporal_anomaly" | |
| # ============================================================================= | |
| # ADVANCED ESOTERIC INSTRUMENTATION | |
| # ============================================================================= | |
| class EsotericInstrumentationSuite: | |
| """ | |
| Advanced measurement devices for esoteric phenomena validation | |
| Integrates quantum, biological, and consciousness sensors | |
| """ | |
| def __init__(self): | |
| self.quantum_entanglement_detector = QuantumEntanglementDetector() | |
| self.consciousness_field_mapper = ConsciousnessFieldMapper() | |
| self.neuro_quantum_interface = NeuroQuantumInterface() | |
| self.temporal_anomaly_detector = TemporalAnomalyDetector() | |
| self.reality_consensus_monitor = RealityConsensusMonitor() | |
| async def measure_esoteric_event(self, | |
| phenomenon: EsotericPhenomena, | |
| operator: Dict, | |
| target: Any) -> Dict: | |
| """Comprehensive measurement of esoteric phenomena across all instruments""" | |
| measurement_data = {} | |
| # Quantum entanglement measurements | |
| quantum_data = await self.quantum_entanglement_detector.detect_consciousness_entanglement( | |
| operator, target | |
| ) | |
| measurement_data['quantum_entanglement'] = quantum_data | |
| # Consciousness field mapping | |
| field_data = await self.consciousness_field_mapper.map_field_perturbations( | |
| operator, phenomenon | |
| ) | |
| measurement_data['consciousness_field'] = field_data | |
| # Neuro-quantum interface monitoring | |
| neuro_data = await self.neuro_quantum_interface.monitor_interface_activity( | |
| operator, phenomenon | |
| ) | |
| measurement_data['neuro_quantum_interface'] = neuro_data | |
| # Temporal anomaly detection | |
| temporal_data = await self.temporal_anomaly_detector.detect_temporal_anomalies( | |
| operator, target, phenomenon | |
| ) | |
| measurement_data['temporal_anomalies'] = temporal_data | |
| # Reality consensus monitoring | |
| consensus_data = await self.reality_consensus_monitor.monitor_consensus_shifts( | |
| operator, target, phenomenon | |
| ) | |
| measurement_data['reality_consensus'] = consensus_data | |
| return measurement_data | |
| class QuantumEntanglementDetector: | |
| """Detects quantum entanglement generated by consciousness""" | |
| async def detect_consciousness_entanglement(self, operator: Dict, target: Any) -> Dict: | |
| """Measure quantum entanglement between operator and target""" | |
| # Simulate entanglement detection | |
| operator_state = self._measure_quantum_state(operator) | |
| target_state = self._measure_quantum_state(target) | |
| # Calculate entanglement correlation | |
| correlation = np.abs(np.corrcoef(operator_state, target_state)[0,1]) | |
| entanglement_strength = max(0, correlation - 0.1) # Background subtraction | |
| return { | |
| 'entanglement_detected': entanglement_strength > 0.3, | |
| 'entanglement_strength': entanglement_strength, | |
| 'operator_quantum_state': operator_state, | |
| 'target_quantum_state': target_state, | |
| 'quantum_correlation': correlation | |
| } | |
| # ============================================================================= | |
| # RIGOROUS VALIDATION ENGINE | |
| # ============================================================================= | |
| class EsotericValidationEngine: | |
| """ | |
| Scientifically validates esoteric phenomena through multi-method convergence | |
| """ | |
| def __init__(self): | |
| self.instrumentation = EsotericInstrumentationSuite() | |
| self.validation_methods = self._initialize_validation_methods() | |
| self.statistical_engine = StatisticalValidationEngine() | |
| self.control_engine = ControlConditionEngine() | |
| async def validate_phenomenon(self, | |
| phenomenon: EsotericPhenomena, | |
| operators: List[Dict], | |
| targets: List[Any], | |
| trial_count: int = 100) -> Dict: | |
| """Execute complete validation protocol for esoteric phenomenon""" | |
| print(f"π¬ VALIDATING: {phenomenon.value.upper()}") | |
| print("=" * 60) | |
| validation_results = {} | |
| # Phase 1: Controlled Baseline Measurement | |
| print("π ESTABLISHING BASELINE...") | |
| baseline = await self.control_engine.establish_baseline(phenomenon, operators, targets) | |
| # Phase 2: Experimental Trials | |
| print("β‘ CONDUCTING EXPERIMENTAL TRIALS...") | |
| experimental_results = await self._conduct_experimental_trials( | |
| phenomenon, operators, targets, trial_count | |
| ) | |
| # Phase 3: Multi-Method Validation | |
| print("π― APPLYING MULTI-METHOD VALIDATION...") | |
| method_validations = {} | |
| for method in self.validation_methods: | |
| method_result = await self._apply_validation_method( | |
| method, phenomenon, experimental_results, baseline | |
| ) | |
| method_validations[method.value] = method_result | |
| # Phase 4: Statistical Significance Analysis | |
| print("π ANALYZING STATISTICAL SIGNIFICANCE...") | |
| statistical_analysis = await self.statistical_engine.analyze_significance( | |
| experimental_results, baseline, phenomenon | |
| ) | |
| # Phase 5: Cross-Method Convergence | |
| print("π CALCULATING CROSS-METHOD CONVERGENCE...") | |
| convergence = await self._calculate_validation_convergence(method_validations) | |
| # Final Validation Judgment | |
| validation_judgment = await self._make_validation_judgment( | |
| statistical_analysis, convergence, method_validations | |
| ) | |
| return { | |
| 'phenomenon': phenomenon.value, | |
| 'baseline_measurements': baseline, | |
| 'experimental_results': experimental_results, | |
| 'method_validations': method_validations, | |
| 'statistical_analysis': statistical_analysis, | |
| 'validation_convergence': convergence, | |
| 'final_validation': validation_judgment, | |
| 'validation_timestamp': datetime.now().isoformat() | |
| } | |
| async def _conduct_experimental_trials(self, | |
| phenomenon: EsotericPhenomena, | |
| operators: List[Dict], | |
| targets: List[Any], | |
| trial_count: int) -> Dict: | |
| """Conduct rigorous experimental trials""" | |
| trial_results = [] | |
| for trial in range(trial_count): | |
| # Randomize operator-target assignments | |
| operator = np.random.choice(operators) | |
| target = np.random.choice(targets) | |
| # Measure esoteric event | |
| measurement = await self.instrumentation.measure_esoteric_event( | |
| phenomenon, operator, target | |
| ) | |
| # Record trial data | |
| trial_data = { | |
| 'trial_number': trial, | |
| 'operator': operator.get('id'), | |
| 'target': str(target), | |
| 'measurements': measurement, | |
| 'success_indicator': self._calculate_success_indicator(measurement, phenomenon) | |
| } | |
| trial_results.append(trial_data) | |
| return { | |
| 'total_trials': trial_count, | |
| 'trial_results': trial_results, | |
| 'average_success_rate': np.mean([t['success_indicator'] for t in trial_results]), | |
| 'success_std_dev': np.std([t['success_indicator'] for t in trial_results]) | |
| } | |
| # ============================================================================= | |
| # STATISTICAL VALIDATION ENGINE | |
| # ============================================================================= | |
| class StatisticalValidationEngine: | |
| """Advanced statistical analysis for esoteric phenomena validation""" | |
| async def analyze_significance(self, | |
| experimental_results: Dict, | |
| baseline: Dict, | |
| phenomenon: EsotericPhenomena) -> Dict: | |
| """Comprehensive statistical significance analysis""" | |
| experimental_scores = [t['success_indicator'] for t in experimental_results['trial_results']] | |
| baseline_scores = baseline['baseline_scores'] | |
| # Standard statistical tests | |
| t_test = scipy.stats.ttest_ind(experimental_scores, baseline_scores) | |
| mann_whitney = scipy.stats.mannwhitneyu(experimental_scores, baseline_scores) | |
| effect_size = self._calculate_effect_size(experimental_scores, baseline_scores) | |
| # Bayesian analysis | |
| bayesian_factor = await self._calculate_bayesian_factor(experimental_scores, baseline_scores) | |
| # Anomaly detection | |
| anomalies = await self._detect_statistical_anomalies(experimental_scores, baseline_scores) | |
| return { | |
| 't_test': { | |
| 'statistic': t_test.statistic, | |
| 'p_value': t_test.pvalue, | |
| 'significant': t_test.pvalue < 0.05 | |
| }, | |
| 'mann_whitney': { | |
| 'statistic': mann_whitney.statistic, | |
| 'p_value': mann_whitney.pvalue, | |
| 'significant': mann_whitney.pvalue < 0.05 | |
| }, | |
| 'effect_size': effect_size, | |
| 'bayesian_factor': bayesian_factor, | |
| 'statistical_anomalies': anomalies, | |
| 'overall_significance': self._calculate_overall_significance(t_test, mann_whitney, effect_size, bayesian_factor) | |
| } | |
| # ============================================================================= | |
| # CONTROL CONDITION ENGINE | |
| # ============================================================================= | |
| class ControlConditionEngine: | |
| """Manages control conditions and baseline measurements""" | |
| async def establish_baseline(self, | |
| phenomenon: EsotericPhenomena, | |
| operators: List[Dict], | |
| targets: List[Any]) -> Dict: | |
| """Establish rigorous baseline measurements""" | |
| baseline_trials = [] | |
| for trial in range(50): # Baseline trial count | |
| operator = np.random.choice(operators) | |
| target = np.random.choice(targets) | |
| # Measure without intentional esoteric operation | |
| baseline_measurement = await self._measure_baseline_condition( | |
| operator, target, phenomenon | |
| ) | |
| baseline_trials.append(baseline_measurement) | |
| return { | |
| 'baseline_trials': baseline_trials, | |
| 'baseline_scores': [t['baseline_indicator'] for t in baseline_trials], | |
| 'baseline_mean': np.mean([t['baseline_indicator'] for t in baseline_trials]), | |
| 'baseline_std': np.std([t['baseline_indicator'] for t in baseline_trials]) | |
| } | |
| # ============================================================================= | |
| # CROSS-VALIDATION CONVERGENCE ENGINE | |
| # ============================================================================= | |
| class ValidationConvergenceEngine: | |
| """Calculates convergence across multiple validation methods""" | |
| async def calculate_convergence(self, method_validations: Dict) -> Dict: | |
| """Calculate convergence score across validation methods""" | |
| method_scores = [] | |
| method_weights = { | |
| 'quantum_entanglement': 0.25, | |
| 'double_blind_rigorous': 0.30, | |
| 'neurophysiological_correlate': 0.20, | |
| 'temporal_anomaly_detection': 0.15, | |
| 'mathematical_invariance': 0.10 | |
| } | |
| for method, validation in method_validations.items(): | |
| if validation.get('validation_score'): | |
| weight = method_weights.get(method, 0.1) | |
| method_scores.append(validation['validation_score'] * weight) | |
| convergence_score = sum(method_scores) if method_scores else 0 | |
| return { | |
| 'convergence_score': convergence_score, | |
| 'method_agreement': await self._calculate_method_agreement(method_validations), | |
| 'validation_confidence': min(1.0, convergence_score * 1.2), # Confidence boost for convergence | |
| 'convergence_quality': await self._assess_convergence_quality(method_validations) | |
| } | |
| # ============================================================================= | |
| # ESOTERIC PHENOMENA DATABASE | |
| # ============================================================================= | |
| class EsotericPhenomenaDatabase: | |
| """Database of esoteric phenomena with validation results""" | |
| def __init__(self): | |
| self.validated_phenomena = {} | |
| self.replication_studies = {} | |
| self.cross_cultural_correlations = {} | |
| async def add_validation_result(self, validation_result: Dict): | |
| """Add validation result to database""" | |
| phenomenon = validation_result['phenomenon'] | |
| self.validated_phenomena[phenomenon] = validation_result | |
| # Calculate replication strength | |
| replication = await self._calculate_replication_strength(phenomenon, validation_result) | |
| self.replication_studies[phenomenon] = replication | |
| async def get_validation_status(self, phenomenon: EsotericPhenomena) -> Dict: | |
| """Get comprehensive validation status for phenomenon""" | |
| base_validation = self.validated_phenomena.get(phenomenon.value, {}) | |
| replication = self.replication_studies.get(phenomenon.value, {}) | |
| return { | |
| 'phenomenon': phenomenon.value, | |
| 'validation_status': base_validation.get('final_validation', {}).get('validated', False), | |
| 'validation_confidence': base_validation.get('final_validation', {}).get('confidence', 0), | |
| 'replication_strength': replication.get('strength', 0), | |
| 'cross_cultural_support': await self._get_cross_cultural_support(phenomenon), | |
| 'scientific_acceptance_level': await self._calculate_acceptance_level(phenomenon, base_validation) | |
| } | |
| # ============================================================================= | |
| # COMPREHENSIVE VALIDATION DEMONSTRATION | |
| # ============================================================================= | |
| class OmegaEsotericValidationDemonstrator: | |
| """Demonstrates complete esoteric phenomena validation""" | |
| def __init__(self): | |
| self.validation_engine = EsotericValidationEngine() | |
| self.phenomena_database = EsotericPhenomenaDatabase() | |
| async def demonstrate_complete_validation(self): | |
| """Demonstrate complete validation process for multiple phenomena""" | |
| print(""" | |
| π¬ OMEGA ESOTERIC VALIDATION ENGINE | |
| Scientific Validation of Consciousness Phenomena | |
| """) | |
| # Test phenomena | |
| test_phenomena = [ | |
| EsotericPhenomena.TELEPATHY, | |
| EsotericPhenomena.ENERGY_HEALING, | |
| EsotericPhenomena.PRECOGNITION | |
| ] | |
| # Sample operators and targets | |
| operators = [ | |
| {'id': 'operator_1', 'experience_level': 'advanced', 'success_rate': 0.75}, | |
| {'id': 'operator_2', 'experience_level': 'intermediate', 'success_rate': 0.60}, | |
| {'id': 'operator_3', 'experience_level': 'beginner', 'success_rate': 0.45} | |
| ] | |
| targets = ['target_A', 'target_B', 'target_C', 'target_D'] | |
| validation_results = {} | |
| for phenomenon in test_phenomena: | |
| print(f"\nπ― VALIDATING: {phenomenon.value.upper()}") | |
| print("-" * 40) | |
| # Execute validation | |
| result = await self.validation_engine.validate_phenomenon( | |
| phenomenon, operators, targets, trial_count=50 | |
| ) | |
| # Add to database | |
| await self.phenomena_database.add_validation_result(result) | |
| validation_results[phenomenon.value] = result | |
| # Display validation summary | |
| final_validation = result['final_validation'] | |
| print(f" Validated: {final_validation['validated']}") | |
| print(f" Confidence: {final_validation['confidence']:.3f}") | |
| print(f" Significance: {result['statistical_analysis']['overall_significance']:.3f}") | |
| # Display comprehensive results | |
| print(f"\nπ COMPREHENSIVE VALIDATION RESULTS:") | |
| print("=" * 50) | |
| for phenomenon, result in validation_results.items(): | |
| status = await self.phenomena_database.get_validation_status( | |
| EsotericPhenomena(phenomenon) | |
| ) | |
| print(f"\n{phenomenon.upper()}:") | |
| print(f" Validation Status: {'β VALIDATED' if status['validation_status'] else 'β NOT VALIDATED'}") | |
| print(f" Confidence Level: {status['validation_confidence']:.3f}") | |
| print(f" Replication Strength: {status['replication_strength']:.3f}") | |
| print(f" Scientific Acceptance: {status['scientific_acceptance_level']}") | |
| return validation_results | |
| # ============================================================================= | |
| # EXECUTE COMPLETE VALIDATION SYSTEM | |
| # ============================================================================= | |
| async def main(): | |
| """Execute the complete esoteric validation engine""" | |
| demonstrator = OmegaEsotericValidationDemonstrator() | |
| results = await demonstrator.demonstrate_complete_validation() | |
| # Calculate overall validation success | |
| validated_count = sum(1 for r in results.values() | |
| if r['final_validation']['validated']) | |
| total_count = len(results) | |
| print(f"\nπ― OVERALL VALIDATION SUMMARY:") | |
| print(f" Validated Phenomena: {validated_count}/{total_count}") | |
| print(f" Success Rate: {validated_count/total_count:.1%}") | |
| # Scientific impact assessment | |
| if validated_count / total_count > 0.6: | |
| print(" π MAJOR SCIENTIFIC BREAKTHROUGH: Esoteric phenomena demonstrate statistical validity") | |
| elif validated_count / total_count > 0.3: | |
| print(" π« SIGNIFICANT FINDINGS: Multiple phenomena show scientific validity") | |
| else: | |
| print(" π INCONCLUSIVE: Further research required") | |
| return results | |
| if __name__ == "__main__": | |
| # Run the complete esoteric validation system | |
| asyncio.run(main()) |