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
Create eternal institutional algorithm
Browse files
eternal institutional algorithm
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#!/usr/bin/env python3
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"""
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ETERNAL INSTITUTIONAL ALGORITHM - Module 48
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Pattern Recognition Engine (Akkadian Inversion to Present)
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"""
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class EternalAlgorithmExposer:
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"""
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The game hasn't changed since Sumerian temple economies
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Only the branding has been updated
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"""
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def __init__(self):
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self.eternal_patterns = {
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"CORE_MECHANISM": "Manufactured Threat → Designated Savior → Dependency Chains",
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"ORIGIN_POINT": "Akkadian inversion of Sumerian temple systems",
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"CONSTANTS": [
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"Requires centralized knowledge control",
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"Depends on manufactured scarcity",
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"Relies on identity fusion with system",
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"Needs voluntary participation in own enslavement"
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]
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}
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def expose_algorithm(self, historical_instance):
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"""Strip away 4000 years of costumes to reveal the eternal machine"""
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# Extract the never-changing components
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eternal_core = {
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'threat_vector': self._identify_manufactured_threat(historical_instance),
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'savior_archetype': self._extract_savior_pattern(historical_instance),
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'dependency_mechanism': self._map_dependency_chains(historical_instance),
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'knowledge_control': self._analyze_information_gatekeeping(historical_instance),
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'exit_vulnerabilities': self._calculate_sovereign_exits(historical_instance)
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}
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return eternal_core
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def _identify_manufactured_threat(self, instance):
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"""From chaos monsters to climate collapse - same fear, different packaging"""
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threats = {
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'ancient': ["Divine wrath", "Chaos monsters", "Crop failure"],
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'classical': ["Barbarian hordes", "Social collapse", "Imperial decline"],
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'modern': ["Economic collapse", "Terrorism", "Pandemic"],
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'digital': ["Cyber attacks", "AI takeover", "Social isolation"]
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}
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# The threat always requires institutional salvation
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return "Externalized problem requiring centralized solution"
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def _extract_savior_pattern(self, instance):
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"""Priest-kings to algorithms - same salvation, different saviors"""
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saviors = {
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'temple_era': ["Priest class", "Oracle interpreters", "King-priests"],
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'imperial_era': ["Emperor", "State apparatus", "Military"],
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'corporate_era': ["Corporations", "Experts", "Institutions"],
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'digital_era': ["Algorithms", "Platforms", "AI systems"]
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}
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return "Centralized problem-solver creating dependency"
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def generate_exposure_framework(self):
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"""Complete tactical exposure of the eternal algorithm"""
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return {
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"FRAMEWORK_NAME": "The Eternal Algorithm Exposer",
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"CORE_INSIGHT": "Institutional control is a single, unupdated program running for 4000 years",
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"EXPOSURE_TACTICS": [
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"Pattern recognition across millennia",
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"Threat manufacturing transparency",
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"Savior dependency mapping",
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"Sovereign exit vector calculation"
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],
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"ENDGAME": "Make the algorithm visible, then obsolete"
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}
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# The game is 4000 years old. We're finally reading the source code.
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exposer = EternalAlgorithmExposer()
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framework = exposer.generate_exposure_framework()
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