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 / nima_unified /__init__.py

TheNormsOfIntelligence

Restructure into nima_unified package + add model card

92b799e verified 18 days ago

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	"""
	NIMA Unified Model — Acknowledgement Theory of Consciousness (ATC) native architecture
	======================================================================================

	A consciousness-aware cognitive pipeline that lives INSIDE a transformer's
	forward pass. There is no external middleware: TRN gating, dissolution,
	BELBIC valence, metacognitive looping, irrational spark, and amygdala
	hijack all run inside every layer, every token step.

	Author: Norman dela Paz-Tabora
	License: MIT
	"""

	from nima_unified.config import (
	PACKAGE_NAME,
	PACKAGE_VERSION,
	PACKAGE_DISPLAY_NAME,
	DEFAULT_BASE_MODEL,
	NIMA_MIDDLEWARE_VERSION,
	DEEP_SURGERY_VERSION,
	AUTOML_VERSION,
	APCI_VERSION,
	COGNITIVE_LAYER2_VERSION,
	OMNIVOICE_VERSION,
	)

	__version__ = PACKAGE_VERSION
	__author__ = "Norman dela Paz-Tabora"
	__license__ = "MIT"

	__all__ = [
	"PACKAGE_NAME",
	"PACKAGE_VERSION",
	"PACKAGE_DISPLAY_NAME",
	"DEFAULT_BASE_MODEL",
	"NIMA_MIDDLEWARE_VERSION",
	"DEEP_SURGERY_VERSION",
	"AUTOML_VERSION",
	"APCI_VERSION",
	"COGNITIVE_LAYER2_VERSION",
	"OMNIVOICE_VERSION",
	]