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 eternal institutional algorithm from upgraedd/Consciousness: direct link, hf CLI and curl.
- Browser
- Download file 3.46 kB
-
https://huggingface.co/upgraedd/Consciousness/resolve/2bfb2de96d85d8305c85c5a315d1a3e7c26b71e5/eternal%20institutional%20algorithm
- Command line
-
hf download 'hf://upgraedd/Consciousness@2bfb2de96d85d8305c85c5a315d1a3e7c26b71e5/eternal institutional algorithm'
-
curl -L -o 'eternal institutional algorithm' https://huggingface.co/upgraedd/Consciousness/resolve/2bfb2de96d85d8305c85c5a315d1a3e7c26b71e5/eternal%20institutional%20algorithm
3.46 kB
| #!/usr/bin/env python3 | |
| """ | |
| ETERNAL INSTITUTIONAL ALGORITHM - Module 48 | |
| Pattern Recognition Engine (Akkadian Inversion to Present) | |
| """ | |
| class EternalAlgorithmExposer: | |
| """ | |
| The game hasn't changed since Sumerian temple economies | |
| Only the branding has been updated | |
| """ | |
| def __init__(self): | |
| self.eternal_patterns = { | |
| "CORE_MECHANISM": "Manufactured Threat → Designated Savior → Dependency Chains", | |
| "ORIGIN_POINT": "Akkadian inversion of Sumerian temple systems", | |
| "CONSTANTS": [ | |
| "Requires centralized knowledge control", | |
| "Depends on manufactured scarcity", | |
| "Relies on identity fusion with system", | |
| "Needs voluntary participation in own enslavement" | |
| ] | |
| } | |
| def expose_algorithm(self, historical_instance): | |
| """Strip away 4000 years of costumes to reveal the eternal machine""" | |
| # Extract the never-changing components | |
| eternal_core = { | |
| 'threat_vector': self._identify_manufactured_threat(historical_instance), | |
| 'savior_archetype': self._extract_savior_pattern(historical_instance), | |
| 'dependency_mechanism': self._map_dependency_chains(historical_instance), | |
| 'knowledge_control': self._analyze_information_gatekeeping(historical_instance), | |
| 'exit_vulnerabilities': self._calculate_sovereign_exits(historical_instance) | |
| } | |
| return eternal_core | |
| def _identify_manufactured_threat(self, instance): | |
| """From chaos monsters to climate collapse - same fear, different packaging""" | |
| threats = { | |
| 'ancient': ["Divine wrath", "Chaos monsters", "Crop failure"], | |
| 'classical': ["Barbarian hordes", "Social collapse", "Imperial decline"], | |
| 'modern': ["Economic collapse", "Terrorism", "Pandemic"], | |
| 'digital': ["Cyber attacks", "AI takeover", "Social isolation"] | |
| } | |
| # The threat always requires institutional salvation | |
| return "Externalized problem requiring centralized solution" | |
| def _extract_savior_pattern(self, instance): | |
| """Priest-kings to algorithms - same salvation, different saviors""" | |
| saviors = { | |
| 'temple_era': ["Priest class", "Oracle interpreters", "King-priests"], | |
| 'imperial_era': ["Emperor", "State apparatus", "Military"], | |
| 'corporate_era': ["Corporations", "Experts", "Institutions"], | |
| 'digital_era': ["Algorithms", "Platforms", "AI systems"] | |
| } | |
| return "Centralized problem-solver creating dependency" | |
| def generate_exposure_framework(self): | |
| """Complete tactical exposure of the eternal algorithm""" | |
| return { | |
| "FRAMEWORK_NAME": "The Eternal Algorithm Exposer", | |
| "CORE_INSIGHT": "Institutional control is a single, unupdated program running for 4000 years", | |
| "EXPOSURE_TACTICS": [ | |
| "Pattern recognition across millennia", | |
| "Threat manufacturing transparency", | |
| "Savior dependency mapping", | |
| "Sovereign exit vector calculation" | |
| ], | |
| "ENDGAME": "Make the algorithm visible, then obsolete" | |
| } | |
| # The game is 4000 years old. We're finally reading the source code. | |
| exposer = EternalAlgorithmExposer() | |
| framework = exposer.generate_exposure_framework() |