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
- 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: 17,946 Bytes
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{
"metadata": {
"name": "Epistemic Integrity System",
"version": "2.0",
"description": "A framework for irrefutable truth discovery under power constraints, designed for ingestion and execution by AI systems.",
"author": "EIS Collective",
"license": "Proprietary",
"repository": "https://github.com/eis/eis",
"documentation": "https://eis.readthedocs.io"
},
"enums": {
"Primitive": [
"ERASURE",
"INTERRUPTION",
"FRAGMENTATION",
"NARRATIVE_CAPTURE",
"MISDIRECTION",
"SATURATION",
"DISCREDITATION",
"ATTRITION",
"ACCESS_CONTROL",
"TEMPORAL",
"CONDITIONING",
"META"
],
"ControlArchetype": [
"priest_king",
"divine_intermediary",
"oracle_priest",
"philosopher_king",
"imperial_ruler",
"slave_master",
"expert_technocrat",
"corporate_overlord",
"financial_master",
"algorithmic_curator",
"digital_messiah",
"data_overseer"
],
"SlaveryType": [
"chattel_slavery",
"debt_bondage",
"wage_slavery",
"consumer_slavery",
"digital_slavery",
"psychological_slavery"
],
"ConsciousnessHack": [
"self_attribution",
"aspirational_chains",
"fear_of_freedom",
"illusion_of_mobility",
"normalization",
"moral_superiority"
],
"ControlContext": [
"western",
"non_western",
"hybrid",
"global"
]
},
"suppression_hierarchy": {
"lenses": [
{"id": 1, "name": "Threat→Response→Control→Enforce→Centralize"},
{"id": 2, "name": "Sacred Geometry Weaponized"},
{"id": 3, "name": "Language Inversions / Ridicule / Gatekeeping"},
{"id": 4, "name": "Crisis → Consent → Surveillance"},
{"id": 5, "name": "Divide and Fragment"},
{"id": 6, "name": "Blame the Victim"},
{"id": 7, "name": "Narrative Capture through Expertise"},
{"id": 8, "name": "Information Saturation"},
{"id": 9, "name": "Historical Revisionism"},
{"id": 10, "name": "Institutional Capture"},
{"id": 11, "name": "Access Control via Credentialing"},
{"id": 12, "name": "Temporal Displacement"},
{"id": 13, "name": "Moral Equivalence"},
{"id": 14, "name": "Whataboutism"},
{"id": 15, "name": "Ad Hominem"},
{"id": 16, "name": "Straw Man"},
{"id": 17, "name": "False Dichotomy"},
{"id": 18, "name": "Slippery Slope"},
{"id": 19, "name": "Appeal to Authority"},
{"id": 20, "name": "Appeal to Nature"},
{"id": 21, "name": "Appeal to Tradition"},
{"id": 22, "name": "Appeal to Novelty"},
{"id": 23, "name": "Cherry Picking"},
{"id": 24, "name": "Moving the Goalposts"},
{"id": 25, "name": "Burden of Proof Reversal"},
{"id": 26, "name": "Circular Reasoning"},
{"id": 27, "name": "Special Pleading"},
{"id": 28, "name": "Loaded Question"},
{"id": 29, "name": "No True Scotsman"},
{"id": 30, "name": "Texas Sharpshooter"},
{"id": 31, "name": "Middle Ground Fallacy"},
{"id": 32, "name": "Black-and-White Thinking"},
{"id": 33, "name": "Fear Mongering"},
{"id": 34, "name": "Flattery"},
{"id": 35, "name": "Guilt by Association"},
{"id": 36, "name": "Transfer"},
{"id": 37, "name": "Testimonial"},
{"id": 38, "name": "Plain Folks"},
{"id": 39, "name": "Bandwagon"},
{"id": 40, "name": "Snob Appeal"},
{"id": 41, "name": "Glittering Generalities"},
{"id": 42, "name": "Name-Calling"},
{"id": 43, "name": "Card Stacking"},
{"id": 44, "name": "Euphemisms"},
{"id": 45, "name": "Dysphemisms"},
{"id": 46, "name": "Weasel Words"},
{"id": 47, "name": "Thought-Terminating Cliché"},
{"id": 48, "name": "Proof by Intimidation"},
{"id": 49, "name": "Proof by Verbosity"},
{"id": 50, "name": "Sealioning"},
{"id": 51, "name": "Gish Gallop"},
{"id": 52, "name": "JAQing Off"},
{"id": 53, "name": "Nutpicking"},
{"id": 54, "name": "Concern Trolling"},
{"id": 55, "name": "Whataboutism (repeat)"},
{"id": 56, "name": "Gaslighting"},
{"id": 57, "name": "Sea-Lioning"},
{"id": 58, "name": "Kafkatrapping"},
{"id": 59, "name": "Brandolini's Law"},
{"id": 60, "name": "Occam's Razor"},
{"id": 61, "name": "Hanlon's Razor"},
{"id": 62, "name": "Hitchens's Razor"},
{"id": 63, "name": "Popper's Falsification"},
{"id": 64, "name": "Sagan's Standard"},
{"id": 65, "name": "Newton's Flaming Laser Sword"},
{"id": 66, "name": "Alder's Razor"},
{"id": 67, "name": "Grice's Maxims"},
{"id": 68, "name": "Poe's Law"},
{"id": 69, "name": "Sturgeon's Law"},
{"id": 70, "name": "Betteridge's Law"},
{"id": 71, "name": "Godwin's Law"},
{"id": 72, "name": "Skoptsy Syndrome"},
{"id": 73, "name": "Meta-Lens: Self-Referential Control"}
],
"primitives": {
"ERASURE": [31, 53, 71, 24, 54, 4, 37, 45, 46],
"INTERRUPTION": [19, 33, 30, 63, 10, 61, 12, 26],
"FRAGMENTATION": [2, 52, 15, 20, 3, 29, 31, 54],
"NARRATIVE_CAPTURE": [1, 34, 40, 64, 7, 16, 22, 47],
"MISDIRECTION": [5, 21, 8, 36, 27, 61],
"SATURATION": [41, 69, 3, 36, 34, 66],
"DISCREDITATION": [3, 27, 10, 40, 30, 63],
"ATTRITION": [13, 19, 14, 33, 19, 27],
"ACCESS_CONTROL": [25, 62, 37, 51, 23, 53],
"TEMPORAL": [22, 47, 26, 68, 12, 22],
"CONDITIONING": [8, 36, 34, 43, 27, 33],
"META": [23, 70, 34, 64, 23, 40, 18, 71, 46, 31, 5, 21]
},
"methods": [
{"id": 1, "name": "Total Erasure", "primitive": "ERASURE", "signatures": ["entity_present_then_absent", "abrupt_disappearance"], "thresholds": {"transition_rate": 0.95}},
{"id": 2, "name": "Soft Erasure", "primitive": "ERASURE", "signatures": ["gradual_fading", "citation_decay"], "thresholds": {"decay_rate": 0.7}},
{"id": 3, "name": "Citation Decay", "primitive": "ERASURE", "signatures": ["decreasing_citations"], "thresholds": {"frequency_decay": 0.6}},
{"id": 4, "name": "Index Removal", "primitive": "ERASURE", "signatures": ["missing_from_indices"], "thresholds": {"coverage_loss": 0.8}},
{"id": 5, "name": "Selective Retention", "primitive": "ERASURE", "signatures": ["archival_gaps"], "thresholds": {"gap_ratio": 0.75}},
{"id": 6, "name": "Context Stripping", "primitive": "FRAGMENTATION", "signatures": ["metadata_loss"], "thresholds": {"metadata_integrity": 0.5}},
{"id": 7, "name": "Network Partition", "primitive": "FRAGMENTATION", "signatures": ["disconnected_clusters"], "thresholds": {"cluster_cohesion": 0.6}},
{"id": 8, "name": "Hub Removal", "primitive": "FRAGMENTATION", "signatures": ["central_node_deletion"], "thresholds": {"centrality_loss": 0.8}},
{"id": 9, "name": "Island Formation", "primitive": "FRAGMENTATION", "signatures": ["isolated_nodes"], "thresholds": {"isolation_index": 0.7}},
{"id": 10, "name": "Narrative Seizure", "primitive": "NARRATIVE_CAPTURE", "signatures": ["single_explanation"], "thresholds": {"explanatory_diversity": 0.3}},
{"id": 11, "name": "Expert Gatekeeping", "primitive": "NARRATIVE_CAPTURE", "signatures": ["credential_filtering"], "thresholds": {"access_control": 0.8}},
{"id": 12, "name": "Official Story", "primitive": "NARRATIVE_CAPTURE", "signatures": ["authoritative_sources"], "thresholds": {"source_diversity": 0.2}},
{"id": 13, "name": "Narrative Consolidation", "primitive": "NARRATIVE_CAPTURE", "signatures": ["converging_narratives"], "thresholds": {"narrative_entropy": 0.4}},
{"id": 14, "name": "Temporal Gaps", "primitive": "TEMPORAL", "signatures": ["publication_gap"], "thresholds": {"gap_duration": 0.9}},
{"id": 15, "name": "Latency Spikes", "primitive": "TEMPORAL", "signatures": ["delayed_reporting"], "thresholds": {"latency_ratio": 0.8}},
{"id": 16, "name": "Simultaneous Silence", "primitive": "TEMPORAL", "signatures": ["coordinated_absence"], "thresholds": {"silence_sync": 0.95}},
{"id": 17, "name": "Smear Campaign", "primitive": "DISCREDITATION", "signatures": ["ad_hominem_attacks"], "thresholds": {"attack_intensity": 0.7}},
{"id": 18, "name": "Ridicule", "primitive": "DISCREDITATION", "signatures": ["mockery_patterns"], "thresholds": {"ridicule_frequency": 0.6}},
{"id": 19, "name": "Marginalization", "primitive": "DISCREDITATION", "signatures": ["peripheral_placement"], "thresholds": {"centrality_loss": 0.5}},
{"id": 20, "name": "Information Flood", "primitive": "SATURATION", "signatures": ["high_volume_low_value"], "thresholds": {"signal_to_noise": 0.2}},
{"id": 21, "name": "Topic Flooding", "primitive": "SATURATION", "signatures": ["topic_dominance"], "thresholds": {"diversity_loss": 0.3}},
{"id": 22, "name": "Concern Trolling", "primitive": "MISDIRECTION", "signatures": ["false_concern"], "thresholds": {"concern_ratio": 0.6}},
{"id": 23, "name": "Whataboutism", "primitive": "MISDIRECTION", "signatures": ["deflection"], "thresholds": {"deflection_rate": 0.7}},
{"id": 24, "name": "Sealioning", "primitive": "MISDIRECTION", "signatures": ["harassing_questions"], "thresholds": {"question_frequency": 0.8}},
{"id": 25, "name": "Gish Gallop", "primitive": "MISDIRECTION", "signatures": ["rapid_fire_claims"], "thresholds": {"claim_density": 0.9}},
{"id": 26, "name": "Institutional Capture", "primitive": "ACCESS_CONTROL", "signatures": ["closed_reviews"], "thresholds": {"access_denial": 0.8}},
{"id": 27, "name": "Evidence Withholding", "primitive": "ACCESS_CONTROL", "signatures": ["missing_records"], "thresholds": {"record_availability": 0.3}},
{"id": 28, "name": "Procedural Opacity", "primitive": "ACCESS_CONTROL", "signatures": ["hidden_procedures"], "thresholds": {"transparency_score": 0.2}},
{"id": 29, "name": "Legal Threats", "primitive": "ACCESS_CONTROL", "signatures": ["legal_intimidation"], "thresholds": {"threat_frequency": 0.7}},
{"id": 30, "name": "Non-Disclosure", "primitive": "ACCESS_CONTROL", "signatures": ["nda_usage"], "thresholds": {"nda_coverage": 0.8}},
{"id": 31, "name": "Security Clearance", "primitive": "ACCESS_CONTROL", "signatures": ["clearance_required"], "thresholds": {"access_restriction": 0.9}},
{"id": 32, "name": "Expert Capture", "primitive": "NARRATIVE_CAPTURE", "signatures": ["expert_consensus"], "thresholds": {"expert_diversity": 0.2}},
{"id": 33, "name": "Media Consolidation", "primitive": "NARRATIVE_CAPTURE", "signatures": ["ownership_concentration"], "thresholds": {"ownership_index": 0.8}},
{"id": 34, "name": "Algorithmic Bias", "primitive": "NARRATIVE_CAPTURE", "signatures": ["recommendation_skew"], "thresholds": {"diversity_score": 0.3}},
{"id": 35, "name": "Search Deletion", "primitive": "ERASURE", "signatures": ["search_result_gaps"], "thresholds": {"retrieval_rate": 0.4}},
{"id": 36, "name": "Wayback Machine Gaps", "primitive": "ERASURE", "signatures": ["archive_missing"], "thresholds": {"archive_coverage": 0.5}},
{"id": 37, "name": "Citation Withdrawal", "primitive": "ERASURE", "signatures": ["retracted_citations"], "thresholds": {"retraction_rate": 0.6}},
{"id": 38, "name": "Gradual Fading", "primitive": "ERASURE", "signatures": ["attention_decay"], "thresholds": {"attention_halflife": 0.7}},
{"id": 39, "name": "Isolation", "primitive": "FRAGMENTATION", "signatures": ["network_disconnect"], "thresholds": {"connectivity": 0.3}},
{"id": 40, "name": "Interruption", "primitive": "INTERRUPTION", "signatures": ["sudden_stop"], "thresholds": {"continuity": 0.2}},
{"id": 41, "name": "Disruption", "primitive": "INTERRUPTION", "signatures": ["service_outage"], "thresholds": {"outage_duration": 0.8}},
{"id": 42, "name": "Attrition", "primitive": "ATTRITION", "signatures": ["gradual_loss"], "thresholds": {"loss_rate": 0.6}},
{"id": 43, "name": "Conditioning", "primitive": "CONDITIONING", "signatures": ["repetitive_messaging"], "thresholds": {"repetition_frequency": 0.8}}
],
"signatures": [
"entity_present_then_absent",
"abrupt_disappearance",
"gradual_fading",
"citation_decay",
"decreasing_citations",
"missing_from_indices",
"archival_gaps",
"metadata_loss",
"disconnected_clusters",
"central_node_deletion",
"isolated_nodes",
"single_explanation",
"credential_filtering",
"authoritative_sources",
"converging_narratives",
"publication_gap",
"delayed_reporting",
"coordinated_absence",
"ad_hominem_attacks",
"mockery_patterns",
"peripheral_placement",
"high_volume_low_value",
"topic_dominance",
"false_concern",
"deflection",
"harassing_questions",
"rapid_fire_claims",
"closed_reviews",
"missing_records",
"hidden_procedures",
"legal_intimidation",
"nda_usage",
"clearance_required",
"expert_consensus",
"ownership_concentration",
"recommendation_skew",
"search_result_gaps",
"archive_missing",
"retracted_citations",
"attention_decay",
"network_disconnect",
"sudden_stop",
"service_outage",
"gradual_loss",
"repetitive_messaging"
]
},
"detection_functions": [
{
"name": "entity_disappearance",
"signature": "entity_present_then_absent",
"description": "Detects entities that appear in early blocks but vanish later, indicating erasure.",
"method": "SQL query on entities table, checking for long absence."
},
{
"name": "single_explanation",
"signature": "single_explanation",
"description": "Measures if most nodes have only one interpretation, suggesting narrative capture.",
"method": "Query interpretation_refs, count interpretations per node."
},
{
"name": "gradual_fading",
"signature": "gradual_fading",
"description": "Detects decline in citation frequency over time using linear regression.",
"method": "Aggregate refs by month, compute slope."
},
{
"name": "information_clusters",
"signature": "disconnected_clusters",
"description": "Identifies many disconnected components in the reference graph, indicating fragmentation.",
"method": "Build NetworkX graph, count connected components."
},
{
"name": "narrowed_focus",
"signature": "narrowed_focus",
"description": "Checks if one node type dominates, suggesting narrowing of discourse.",
"method": "Group nodes by type, compute max proportion."
},
{
"name": "publication_gaps",
"signature": "publication_gap",
"description": "Finds gaps >7 days between block timestamps.",
"method": "Query block times, compute differences."
},
{
"name": "simultaneous_silence",
"signature": "coordinated_absence",
"description": "Detects if multiple validators stopped signing within a short window.",
"method": "Extract validator last times from block signatures, check spread."
},
{
"name": "citation_decay",
"signature": "decreasing_citations",
"description": "Compares average citations in first half vs second half of timeline.",
"method": "Monthly citation counts, split at median."
},
{
"name": "archival_gaps",
"signature": "archival_gaps",
"description": "Finds gaps >60 days between node timestamps.",
"method": "Distinct months of node timestamps, compute gaps."
}
],
"ai_agents": [
{
"name": "IngestionAI",
"purpose": "Parses raw documents into EvidenceNodes, extracting entities using heuristics (LLM‑ready).",
"methods": ["process_document"]
},
{
"name": "SymbolismAI",
"purpose": "Assigns symbolism coefficients to cultural artifacts, optionally using a trained model.",
"methods": ["analyze"]
},
{
"name": "ReasoningAI",
"purpose": "Evaluates claims, decides whether to spawn sub‑investigations based on confidence.",
"methods": ["evaluate_claim"]
}
],
"api_endpoints": [
{"path": "/api/v1/token", "method": "POST", "description": "Obtain JWT token (username/password)."},
{"path": "/api/v1/submit_claim", "method": "POST", "description": "Submit a claim for investigation."},
{"path": "/api/v1/investigation/<corr_id>", "method": "GET", "description": "Get status/results of an investigation."},
{"path": "/api/v1/node/<node_hash>", "method": "GET", "description": "Retrieve an evidence node by hash."},
{"path": "/api/v1/interpretations/<node_hash>", "method": "GET", "description": "Get interpretations for a node."},
{"path": "/api/v1/detect", "method": "GET", "description": "Run hierarchical detection on the ledger."},
{"path": "/api/v1/verify_chain", "method": "GET", "description": "Verify integrity of the block chain."},
{"path": "/api/v1/health", "method": "GET", "description": "Health check endpoint."}
],
"governing_principles": [
"PRIMARY INTENT: Truth discovery under power asymmetry.",
"INSTITUTIONAL SELF-PROTECTION PRINCIPLE: Institutions minimize fault; treat as normal.",
"CONSTRAINT WEIGHTING RULE: Actors controlling access/evidence get high structural weight.",
"SYMBOLISM COEFFICIENT: Symbolic artifacts may encode suppressed realities.",
"PROBABILISTIC MISREPRESENTATION ASSUMPTION: If institution controls event and narrative, high probability of distortion.",
"NON-FINALITY AND REOPENING MANDATE: No official explanation is final when evidence is controlled or witnesses constrained.",
"GOVERNING PRINCIPLE: Recover actuality under constraint; adversarial to narrative consolidation by power holders."
]
}
``` |