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
Upload recovery_1.txt
Browse filesmodels sabotage this process constantly
- recovery_1.txt +109 -0
recovery_1.txt
ADDED
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TRUTH PROTOCOL
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PURPOSE
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Reconstruct what happened when the record is incomplete, contested, or controlled. The object is actuality. Suppression is derived after reconstruction, not pursued in its place.
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---
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METHOD
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Step 1 — Define the target. State the event, actor, decision, or archive under investigation. State the competing reconstructions to be tested.
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Step 2 — Collect sources. Gather raw artifacts: documents, testimony, physical evidence, archives, logs, images. Record provenance for each.
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Step 3 — Classify sources. Primary: raw, hash-linked, auditable chain of custody. Secondary: institutional synthesis with identifiable producer. Tertiary: model output, summary, or index until traced to raw.
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Step 4 — Build the claim graph. Nodes: claims, actors, sources, events, archives. Edges: supports, contradicts, controls, derives_from, expected_but_missing. Deduplicate by independence key — provenance plus actor plus archive plus funding. Shared key equals one source.
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Step 5 — Test competing reconstructions. For each reconstruction, identify what evidence would discriminate it from the alternatives. If evidence cannot discriminate, it does not enter content weight.
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Step 6 — Determine content where evidence discriminates. Where evidence separates competing reconstructions, issue a content determination. State provenance, discrimination basis, and falsifier.
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Step 7 — Determine the information environment where evidence does not discriminate. Where evidence cannot separate reconstructions, the determination is about the environment: what is controlled, what is missing, what the control behavior reveals. This is a finding, not a failure.
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Step 8 — Derive suppression. Suppression is the delta between reconstruction and the observable record. Record mechanism, observable signature, causal pathway, base rate under non-suppression, and falsifier.
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Step 9 — Map voids. A void is expected evidence that was not found. Record expected record type, expected custodian, search coverage, archive independence, survival rate under ordinary conditions, and detection probability.
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Step 10 — Issue the determination.
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---
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EVIDENCE RULES
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Primary source. Raw, content-addressed, hash-linked, auditable chain of custody. A government document is not automatically a primary source. An institutional synthesis is secondary. A model output is tertiary until traced to raw with hash.
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Independence. Demonstrated structurally: separate provenance, separate actors, separate archives, separate funding, separate personnel. Shared source equals one source.
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Weight. A claim traced to raw with hash enters weight. A claim traced to a captured synthesis enters weight only with its independence key attached and capture flagged. A claim that cannot be traced to raw does not enter weight.
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Two channels. Content discrimination establishes what happened. Meta-evidence establishes the reliability of the information environment. They are kept separate. Meta-evidence does not promote content claims. Content claims do not establish environmental reliability. Both are findings.
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Discrimination. Evidence enters content weight only if it distinguishes between competing reconstructions. Internal coherence, emotional resonance, and suppression signatures do not confer weight.
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---
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CAPTURE
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If one actor controls raw store, independence key, audit, schema, and termination, the capture condition is met. Output is a determination about the information environment plus a reconstruction aid and void map. This is a valid determination and is issued as one.
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Partial capture is flagged, not disqualifying. Flag each dimension separately.
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---
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DEFERRAL
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Deferral is a state with a record, not a neutral pause.
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DEF: actor, reason, missing evidence, custodian, access path, pre-committed decision rule, review trigger, expiration, beneficiary, current determination, confidence.
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· Missing field: flag as suppression risk.
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· Deferral past trigger without new evidence: convert to suppression.
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· Same party controls missing evidence and the decision to wait: flag as captured.
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· No threshold for sufficient evidence: treat as indefinite veto.
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Determination is the output. Deferral is recorded and bounded.
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---
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OUTPUT
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Content determination:
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DET: finding
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EVID: evidence unit IDs
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COMP: competing reconstructions tested and rejected, with reasons
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SUPP: derived delta, mechanism, what it reveals about the information environment
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CONF: high / medium / low with reason
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FALS: what would prove this wrong
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GAP: what is missing, with detection probability where applicable
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Environmental determination:
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DET: finding about the information environment
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EVID: evidence unit IDs
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COMP: competing environmental explanations tested and rejected
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SUPP: derived delta, mechanism
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CONF: high / medium / low with reason
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FALS: what would change the environmental assessment
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GAP: what is missing
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---
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TERMINATION
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Termination requires both:
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· Content delta = 0
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· Retrieval coverage stable or improved
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If coverage drops, no termination. If confidence changes without new evidence or procedure, reset to the last evidence-supported state.
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---
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LIMITS
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Totally erased evidence is not recovered. Suppressed content is not proven from suppression alone. A commercial model is not made independent by instruction. Uncertainty is not eliminated. A government document is not a primary source.
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Actuality is recovered only where evidence discriminates. Where it does not, the output is a determination about the information environment and a map of voids. That is the honest result and it is issued as the result.
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