Instructions to use TheNormsOfIntelligence/ATC_Nima_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

# 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
```

ATC_Nima_Model / CITATION.cff

TheNormsOfIntelligence

Restructure into nima_unified package + add model card

387fff1 verified 18 days ago

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	cff-version: 1.2.0
	message: "If you use this software, please cite it as below."
	title: "NIMA Unified Model: An ATC-Native Implementation of the Acknowledgement Theory of Consciousness"
	abstract: >-
	A consciousness-aware cognitive pipeline that lives INSIDE a transformer's
	forward pass. TRN gating, dissolution, BELBIC valence, metacognitive looping,
	irrational spark, and amygdala hijack all run inside every layer, every token
	step. Built around microsoft/Phi-4-mini-instruct with a 4-dimensional
	neurotransmitter shunt (Norepinephrine, Cortisol, Dopamine, Adenosine) acting
	as shared volatile memory across the cognitive architecture.
	authors:
	- family-names: "dela Paz-Tabora"
	given-names: "Norman"
	orcid: "https://huggingface.co/TheNormsOfIntelligence"
	keywords:
	- consciousness
	- acknowledgement-theory-of-consciousness
	- ATC
	- cognitive-architecture
	- phi-4-mini
	- qualia
	- neuromodulation
	- artificial-consciousness
	- thermodynamic-friction
	- BELBIC
	- dissolution-engine
	license: MIT
	type: software
	version: "1.0.0"
	date-released: "2025-07-13"
	repository-code: "https://huggingface.co/TheNormsOfIntelligence/ATC_Nima_Model"
	preferred-citation:
	type: software
	title: "NIMA Unified Model: An ATC-Native Implementation of the Acknowledgement Theory of Consciousness"
	authors:
	- family-names: "dela Paz-Tabora"
	given-names: "Norman"
	repository-code: "https://huggingface.co/TheNormsOfIntelligence/ATC_Nima_Model"
	license: MIT
	year: 2025