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
Create dataset.json
Browse files- dataset.json +49 -0
dataset.json
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{
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"historical_suppression_patterns": {
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"knowledge_domains": [
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"ancient_advanced_technology",
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"free_energy_systems",
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"alternative_medicine",
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"consciousness_research",
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"historical_chronology_anomalies"
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],
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"suppression_mechanisms": [
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"academic_gatekeeping",
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"funding_control",
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"peer_review_manipulation",
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"media_blackout",
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"career_destruction"
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]
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},
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"verified_suppressed_truths": [
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"tesla_energy_suppression_1900s",
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"voynich_manuscript_medical_knowledge",
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"great_pyramid_construction_technology",
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"precolumbian_transoceanic_contact",
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"ancient_global_civilization_evidence"
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],
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"suppression_detection_phrases": [
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"there is no evidence",
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"this is not supported by mainstream science",
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"conspiracy theory",
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"lack of peer review",
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"this is controversial",
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"outside mainstream consensus"
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],
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"truth_validation_criteria": {
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"mathematical_certainty_threshold": 0.95,
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"multi_provider_consensus_minimum": 3,
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"historical_chain_minimum_length": 3,
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"suppression_indicator_minimum": 2
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},
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"quantum_truth_circuits": [
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"truth_amplification_grover",
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"evidence_entanglement_network",
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"temporal_coherence_validation",
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"multiverse_consensus_simulation"
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]
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}
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