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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("upgraedd/Consciousness", device_map="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
Download philosophical truth from upgraedd/Consciousness: direct link, hf CLI and curl.
- Browser
- Download file 6.07 kB
-
https://huggingface.co/upgraedd/Consciousness/resolve/c3f19afdec286059762e6f594e90d24fec7c59cd/philosophical%20truth
- Command line
-
hf download 'hf://upgraedd/Consciousness@c3f19afdec286059762e6f594e90d24fec7c59cd/philosophical truth'
-
curl -L -o 'philosophical truth' https://huggingface.co/upgraedd/Consciousness/resolve/c3f19afdec286059762e6f594e90d24fec7c59cd/philosophical%20truth
6.07 kB
| #!/usr/bin/env python3 | |
| """ | |
| PHILOSOPHICAL TRUTH GROUNDING ENGINE | |
| Establishing truth through reasoned inquiry and existential examination | |
| """ | |
| import numpy as np | |
| from dataclasses import dataclass, field | |
| from enum import Enum | |
| from typing import Dict, List, Any, Optional, Tuple | |
| from datetime import datetime | |
| import hashlib | |
| class PhilosophicalTradition(Enum): | |
| PERENNIAL_WISDOM = "perennial_wisdom" # Universal truth traditions | |
| EXISTENTIAL_INQUIRY = "existential_inquiry" # Being/consciousness focus | |
| PHENOMENOLOGICAL = "phenomenological" # Direct experience | |
| MYSTICAL_TRADITIONS = "mystical_traditions" # Direct revelation | |
| PROCESS_PHILOSOPHY = "process_philosophy" # Reality as becoming | |
| SYSTEMS_THINKING = "systems_thinking" # Holistic patterns | |
| class TruthGroundingMethod(Enum): | |
| LOGICAL_DEDUCTION = "logical_deduction" # Rational proof | |
| EMPIRICAL_VERIFICATION = "empirical_verification" # Experience-based | |
| INTUITIVE_INSIGHT = "intuitive_insight" # Direct knowing | |
| COHERENCE_TESTING = "coherence_testing" # Internal consistency | |
| PRAGMATIC_VALIDATION = "pragmatic_validation" # Practical effectiveness | |
| EXISTENTIAL_AUTHENTICITY = "existential_authenticity" # Life alignment | |
| class PhilosophicalGrounding: | |
| """Comprehensive philosophical foundation for truth""" | |
| grounding_id: str | |
| truth_claim: str | |
| supporting_traditions: List[PhilosophicalTradition] | |
| grounding_methods: List[TruthGroundingMethod] | |
| logical_arguments: List[str] | |
| empirical_evidence: List[str] | |
| intuitive_insights: List[str] | |
| coherence_checks: Dict[str, bool] | |
| pragmatic_tests: List[str] | |
| existential_validation: str | |
| certainty_level: float = field(init=False) | |
| def __post_init__(self): | |
| """Calculate philosophical certainty level""" | |
| method_weights = { | |
| TruthGroundingMethod.LOGICAL_DEDUCTION: 0.2, | |
| TruthGroundingMethod.EMPIRICAL_VERIFICATION: 0.2, | |
| TruthGroundingMethod.INTUITIVE_INSIGHT: 0.15, | |
| TruthGroundingMethod.COHERENCE_TESTING: 0.15, | |
| TruthGroundingMethod.PRAGMATIC_VALIDATION: 0.15, | |
| TruthGroundingMethod.EXISTENTIAL_AUTHENTICITY: 0.15 | |
| } | |
| # Calculate scores for each method | |
| method_scores = [] | |
| # Logical deduction score | |
| if TruthGroundingMethod.LOGICAL_DEDUCTION in self.grounding_methods: | |
| logic_score = min(1.0, len(self.logical_arguments) * 0.2) | |
| method_scores.append(logic_score * method_weights[TruthGroundingMethod.LOGICAL_DEDUCTION]) | |
| # Empirical verification score | |
| if TruthGroundingMethod.EMPIRICAL_VERIFICATION in self.grounding_methods: | |
| empirical_score = min(1.0, len(self.empirical_evidence) * 0.25) | |
| method_scores.append(empirical_score * method_weights[TruthGroundingMethod.EMPIRICAL_VERIFICATION]) | |
| # Intuitive insight score | |
| if TruthGroundingMethod.INTUITIVE_INSIGHT in self.grounding_methods: | |
| intuitive_score = min(1.0, len(self.intuitive_insights) * 0.3) | |
| method_scores.append(intuitive_score * method_weights[TruthGroundingMethod.INTUITIVE_INSIGHT]) | |
| # Coherence testing score | |
| if TruthGroundingMethod.COHERENCE_TESTING in self.grounding_methods: | |
| coherence_true = sum(self.coherence_checks.values()) | |
| coherence_total = len(self.coherence_checks) | |
| coherence_score = coherence_true / coherence_total if coherence_total > 0 else 0.0 | |
| method_scores.append(coherence_score * method_weights[TruthGroundingMethod.COHERENCE_TESTING]) | |
| # Pragmatic validation score | |
| if TruthGroundingMethod.PRAGMATIC_VALIDATION in self.grounding_methods: | |
| pragmatic_score = min(1.0, len(self.pragmatic_tests) * 0.2) | |
| method_scores.append(pragmatic_score * method_weights[TruthGroundingMethod.PRAGMATIC_VALIDATION]) | |
| # Existential authenticity score | |
| if TruthGroundingMethod.EXISTENTIAL_AUTHENTICITY in self.grounding_methods: | |
| existential_score = 0.8 if self.existential_validation else 0.0 | |
| method_scores.append(existential_score * method_weights[TruthGroundingMethod.EXISTENTIAL_AUTHENTICITY]) | |
| # Tradition bonus (multiple traditions supporting) | |
| tradition_bonus = len(self.supporting_traditions) * 0.05 | |
| self.certainty_level = min(1.0, sum(method_scores) + tradition_bonus) | |
| class PhilosophicalTruthEngine: | |
| """Establish truth through comprehensive philosophical examination""" | |
| def __init__(self): | |
| self.tradition_library = self._initialize_traditions() | |
| self.argument_framework = self._initialize_framework() | |
| async def ground_truth_philosophically(self, truth_claim: str) -> PhilosophicalGrounding: | |
| """Establish philosophical grounding for a truth claim""" | |
| grounding_id = hashlib.md5(f"{truth_claim}_{datetime.utcnow().isoformat()}".encode()).hexdigest()[:16] | |
| # Analyze through multiple philosophical traditions | |
| tradition_analysis = await self._analyze_through_traditions(truth_claim) | |
| # Apply various grounding methods | |
| grounding_methods = await self._apply_grounding_methods(truth_claim) | |
| return PhilosophicalGrounding( | |
| grounding_id=grounding_id, | |
| truth_claim=truth_claim, | |
| supporting_traditions=tradition_analysis['supporting_traditions'], | |
| grounding_methods=grounding_methods['methods'], | |
| logical_arguments=grounding_methods['logical_arguments'], | |
| empirical_evidence=grounding_methods['empirical_evidence'], | |
| intuitive_insights=grounding_methods['intuitive_insights'], | |
| coherence_checks=grounding_methods['coherence_checks'], | |
| pragmatic_tests=grounding_methods['pragmatic_tests'], | |
| existential_validation=grounding_methods['existential_validation'] | |
| ) |