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

File size: 1,508 Bytes

387fff1

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