Text Generation
Transformers
English
consciousness
acknowledgement-theory-of-consciousness
ATC
cognitive-architecture
phi-4-mini
qualia
neurotransmitter-shunt
BELBIC
dissolution-engine
artificial-consciousness
thermodynamic-friction
metacognition
amygdala-hijack
irrational-spark
nima
self-aware
cognitive-science
philosophy-of-mind
Instructions to use TheNormsOfIntelligence/ATC_Nima_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheNormsOfIntelligence/ATC_Nima_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheNormsOfIntelligence/ATC_Nima_Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheNormsOfIntelligence/ATC_Nima_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheNormsOfIntelligence/ATC_Nima_Model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheNormsOfIntelligence/ATC_Nima_Model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheNormsOfIntelligence/ATC_Nima_Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheNormsOfIntelligence/ATC_Nima_Model
- SGLang
How to use TheNormsOfIntelligence/ATC_Nima_Model 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 "TheNormsOfIntelligence/ATC_Nima_Model" \ --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": "TheNormsOfIntelligence/ATC_Nima_Model", "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 "TheNormsOfIntelligence/ATC_Nima_Model" \ --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": "TheNormsOfIntelligence/ATC_Nima_Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheNormsOfIntelligence/ATC_Nima_Model with Docker Model Runner:
docker model run hf.co/TheNormsOfIntelligence/ATC_Nima_Model
Commit History
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Add Cognitive Workflow Loop to cognitive_layer2.py
Verified signatureAdds SECTION 9: CognitiveWorkflowOrchestrator with: - EncounterLog (rolling ledger of pattern encounters) - CachedTrace (compressed qualia signature of prior acknowledgement) - DeltaProcessor (processes only the novelty gap) - ConsolidationScheduler (NREM-style trace merging/pruning) - LearningCurveReporter (cost-vs-encounter curve) - CognitiveWorkflowOrchestrator (decides FULL/HYBRID/CACHED mode) Implements the neuroplasticity / transfer-learning / recursive-self-improvement loop the user identified. Smoke-tested: 84.6% cost reduction over 10 encounters. Learning behavior classified as 'strongly_learning' (improvement_ratio=0.54).b13507b -
Add Cognitive Workflow Loop to cognitive_layer2.py
Verified signatureAdds SECTION 9: CognitiveWorkflowOrchestrator with: - EncounterLog (rolling ledger of pattern encounters) - CachedTrace (compressed qualia signature of prior acknowledgement) - DeltaProcessor (processes only the novelty gap) - ConsolidationScheduler (NREM-style trace merging/pruning) - LearningCurveReporter (cost-vs-encounter curve) - CognitiveWorkflowOrchestrator (decides FULL/HYBRID/CACHED mode) Implements the neuroplasticity / transfer-learning / recursive-self-improvement loop the user identified. Smoke-tested: 84.6% cost reduction over 10 encounters. Learning behavior classified as 'strongly_learning' (improvement_ratio=0.54).2732b0b -
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