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
Download archaeological truth from upgraedd/Consciousness: direct link, hf CLI and curl.
- Browser
- Download file 3.7 kB
-
https://huggingface.co/upgraedd/Consciousness/resolve/eda31da413e9eed3fab94301900241dea6b42824/archaeological%20truth
- Command line
-
hf download 'hf://upgraedd/Consciousness@eda31da413e9eed3fab94301900241dea6b42824/archaeological truth'
-
curl -L -o 'archaeological truth' https://huggingface.co/upgraedd/Consciousness/resolve/eda31da413e9eed3fab94301900241dea6b42824/archaeological%20truth
3.7 kB
| #!/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 | |
| 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) |