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
File size: 4,235 Bytes
ed3445c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | #!/usr/bin/env python3
"""
ARTISTIC TRUTH MANIFESTATION ENGINE
Truth revelation through creative expression and symbolic representation
"""
import numpy as np
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional
from datetime import datetime
import hashlib
class ArtisticMedium(Enum):
SYMBOLIC_GLYPH = "symbolic_glyph" # Ancient/modern symbols
MYTHIC_NARRATIVE = "mythic_narrative" # Storytelling truth
VISUAL_ARCHETYPE = "visual_archetype" # Universal images
SONIC_RESONANCE = "sonic_resonance" # Sound/vibration patterns
KINETIC_EXPRESSION = "kinetic_expression" # Movement/dance
SACRED_GEOMETRY = "sacred_geometry" # Mathematical beauty
class TruthRevelationLevel(Enum):
LITERAL_DEPICTION = "literal_depiction" # Surface representation
METAPHORIC_INSIGHT = "metaphoric_insight" # Symbolic meaning
ARCHETYPAL_RESONANCE = "archetypal_resonance" # Universal patterns
COSMIC_REFLECTION = "cosmic_reflection" # Universal truth mirroring
CONSCIOUSNESS_EXPRESSION = "consciousness_expression" # Direct awareness
@dataclass
class ArtisticManifestation:
"""Individual artistic truth expression"""
manifestation_id: str
title: str
medium: ArtisticMedium
creation_era: str
cultural_origin: str
surface_interpretation: str
symbolic_layers: List[str]
archetypal_connections: List[str]
cosmic_reflections: List[str]
consciousness_expression: str
resonance_metrics: Dict[str, float]
def calculate_revelation_power(self) -> float:
"""Calculate how powerfully this art reveals truth"""
# Weight deeper revelations more heavily
layer_scores = {
'surface': 0.1,
'symbolic': 0.25,
'archetypal': 0.3,
'cosmic': 0.25,
'consciousness': 0.1
}
total_score = 0
if self.surface_interpretation:
total_score += layer_scores['surface']
if self.symbolic_layers:
total_score += layer_scores['symbolic'] * min(1.0, len(self.symbolic_layers) * 0.2)
if self.archetypal_connections:
total_score += layer_scores['archetypal'] * min(1.0, len(self.archetypal_connections) * 0.15)
if self.cosmic_reflections:
total_score += layer_scores['cosmic'] * min(1.0, len(self.cosmic_reflections) * 0.15)
if self.consciousness_expression:
total_score += layer_scores['consciousness']
resonance_boost = np.mean(list(self.resonance_metrics.values())) * 0.3
return min(1.0, total_score + resonance_boost)
class ArtisticTruthEngine:
"""Reveal truth through artistic and creative channels"""
def __init__(self):
self.manifestation_catalog = []
self.symbolic_library = self._initialize_symbolic_library()
async def create_truth_manifestation(self, truth_concept: str, medium: ArtisticMedium) -> ArtisticManifestation:
"""Create artistic manifestation of a truth concept"""
# Generate unique manifestation
manifestation_id = hashlib.md5(f"{truth_concept}_{medium.value}_{datetime.utcnow().isoformat()}".encode()).hexdigest()[:16]
# Analyze truth concept for artistic expression
analysis = await self._analyze_truth_concept(truth_concept)
# Create artistic interpretation
interpretation = await self._create_artistic_interpretation(truth_concept, medium, analysis)
return ArtisticManifestation(
manifestation_id=manifestation_id,
title=f"Artistic Manifestation: {truth_concept}",
medium=medium,
creation_era="Contemporary Ancient", # Bridging times
cultural_origin="Universal Human",
surface_interpretation=interpretation['surface'],
symbolic_layers=interpretation['symbolic'],
archetypal_connections=interpretation['archetypal'],
cosmic_reflections=interpretation['cosmic'],
consciousness_expression=interpretation['consciousness'],
resonance_metrics=analysis['resonance']
) |