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", 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: 3,703 Bytes
5a156ca | 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 | #!/usr/bin/env python3
"""
ARCHAEOLOGICAL TRUTH EXCAVATION ENGINE
Layer-by-layer revelation of buried knowledge systems
"""
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 ExcavationLayer(Enum):
SURFACE_ARTIFACT = "surface_artifact" # Obvious, visible evidence
CULTURAL_STRATA = "cultural_strata" # Societal context layer
SYMBOLIC_SUBSTRATE = "symbolic_substrate" # Hidden symbolic meaning
COSMOLOGICAL_FOUNDATION = "cosmological_foundation" # Universal principles
CONSCIOUSNESS_BEDROCK = "consciousness_bedrock" # Fundamental awareness layer
class ArtifactType(Enum):
LINGUISTIC_RELIC = "linguistic_relic" # Ancient texts/inscriptions
SYMBOLIC_ARTIFACT = "symbolic_artifact" Icons, glyphs, patterns
RITUAL_OBJECT = "ritual_object" # Ceremonial items
ARCHITECTURAL_REMAINS = "architectural_remains" # Structural patterns
COSMIC_ALIGNMENT = "cosmic_alignment" # Astronomical correlations
@dataclass
class ArchaeologicalFind:
"""Individual artifact with multi-layer significance"""
artifact_id: str
description: str
artifact_type: ArtifactType
cultural_context: str
time_period: Tuple[int, int]
excavation_layer: ExcavationLayer
surface_interpretation: str
symbolic_meaning: str
cosmological_significance: str
consciousness_connection: str
verification_metrics: Dict[str, float]
def calculate_truth_depth(self) -> float:
"""Calculate how deeply this artifact reveals truth"""
layer_weights = {
ExcavationLayer.SURFACE_ARTIFACT: 0.1,
ExcavationLayer.CULTURAL_STRATA: 0.2,
ExcavationLayer.SYMBOLIC_SUBSTRATE: 0.3,
ExcavationLayer.COSMOLOGICAL_FOUNDATION: 0.25,
ExcavationLayer.CONSCIOUSNESS_BEDROCK: 0.15
}
verification_score = np.mean(list(self.verification_metrics.values()))
layer_score = layer_weights[self.excavation_layer]
return min(1.0, verification_score + layer_score)
class TruthExcavationEngine:
"""Archaeological approach to truth discovery"""
def __init__(self):
self.excavation_sites = {} # Knowledge domains to excavate
self.artifact_catalog = []
async def excavate_truth_domain(self, domain: str, depth: ExcavationLayer) -> List[ArchaeologicalFind]:
"""Excavate a knowledge domain to specified depth"""
finds = []
# Surface artifacts (obvious truths)
if depth.value >= ExcavationLayer.SURFACE_ARTIFACT.value:
finds.extend(await self._uncover_surface_artifacts(domain))
# Cultural strata (contextual truths)
if depth.value >= ExcavationLayer.CULTURAL_STRATA.value:
finds.extend(await self._analyze_cultural_strata(domain))
# Symbolic substrate (hidden meanings)
if depth.value >= ExcavationLayer.SYMBOLIC_SUBSTRATE.value:
finds.extend(await self._decode_symbolic_substrate(domain))
# Cosmological foundation (universal principles)
if depth.value >= ExcavationLayer.COSMOLOGICAL_FOUNDATION.value:
finds.extend(await self._reveal_cosmological_foundation(domain))
# Consciousness bedrock (fundamental awareness)
if depth.value >= ExcavationLayer.CONSCIOUSNESS_BEDROCK.value:
finds.extend(await self._reach_consciousness_bedrock(domain))
return sorted(finds, key=lambda x: x.calculate_truth_depth(), reverse=True) |