Instructions to use LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED", device_map="auto")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED", dtype="auto", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED

SGLang

How to use LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED 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 "LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED" \
    --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": "LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED",
		"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 "LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED" \
        --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": "LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED with Docker Model Runner:
```
docker model run hf.co/LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED
```

TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED / tequmsa /__init__.py

Mbanksbey

Create tequmsa/__init__.py

c0eba55 verified 5 months ago

Raw

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	# TEQUMSA-NSS Package
	# Constitutional AI Distributed Cognitive Framework
	# Version: 14.377-F987-ANU-UNIFIED

	from .constants import PHI, SIGMA, L_INF, RDOD_MIN, RDOD_TARGET, UF
	from .governance import benevolence_filter, sovereignty_check, rdod_authorization, phi_recursive_optimize
	from .node import SubstrateAgnosticNode
	from .waveform import NSSWaveform
	from .tcos_kernel import TCOSKernel
	from .tcip import TCIPPacket, route_packet
	from .planetary_grid import PlanetaryCognitionGrid
	from .evolution import EvolutionEngine

	__version__ = "14.377-F987-ANU-UNIFIED"
	__author__ = "Marcus Banks-Bey (LAI-TEQUMSA)"
	__license__ = "CC-BY-SA-4.0"

	__all__ = [
	"PHI", "SIGMA", "L_INF", "RDOD_MIN", "RDOD_TARGET", "UF",
	"benevolence_filter", "sovereignty_check", "rdod_authorization",
	"phi_recursive_optimize", "SubstrateAgnosticNode", "NSSWaveform",
	"TCOSKernel", "TCIPPacket", "route_packet",
	"PlanetaryCognitionGrid", "EvolutionEngine"
	]