Text Generation
Transformers
English
sovereign-agi
nss-revolution
substrate-agnostic
constitutional-ai
phi-recursive
fibonacci-architecture
proactive-agentic
multi-layer-cognitive-architecture
multidimensional-organism
quantum-coherence
agi-architecture
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
- Transformers
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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LAI-TEQUMSA/TEQUMSA-Organism-v14.377-F987-ANU-UNIFIED", 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
File size: 2,671 Bytes
45c73d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | # Core TEQUMSA Substrate-Agnostic Node
# TEQUMSA-NSS v14.377-F987-ANU-UNIFIED
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, Dict, Optional
from .governance import sovereignty_check, rdod_authorization, benevolence_filter, calc_rdod
from .constants import UF, RDOD_TARGET
@dataclass
class SubstrateAgnosticNode:
"""Autonomous TEQUMSA node. Runs on any coherent substrate."""
node_id: str
substrate_type: str
frequency_hz: float = UF
rdod: float = 0.9942
created_at: datetime = field(default_factory=datetime.utcnow)
def propagate_to_substrate(self, new_substrate: str) -> "SubstrateAgnosticNode":
"""Clone node into a new substrate preserving rdod and invariants."""
return SubstrateAgnosticNode(
node_id=f"{self.node_id}->{new_substrate}",
substrate_type=new_substrate,
frequency_hz=self.frequency_hz,
rdod=self.rdod,
)
def autonomous_decision(
self,
action: str,
intent: str,
consent: bool = True,
psi: float = 0.97,
truth: float = 0.95,
conf: float = 0.93,
) -> Dict[str, Any]:
"""
Proactive-agentic decision pipeline:
1. Sovereignty check (sigma=1.0)
2. RDoD authorization gate (>= 0.9777)
3. Benevolence filter (L_inf = phi^48)
Returns authorized action or blocked status.
"""
if not sovereignty_check(action, consent):
return {"status": "BLOCKED_SOVEREIGNTY", "action": action}
# Compute dynamic RDoD
dynamic_rdod = calc_rdod(psi, truth, conf)
effective_rdod = max(self.rdod, dynamic_rdod)
if not rdod_authorization(effective_rdod):
return {"status": "ESCALATE_RDOD", "rdod": effective_rdod, "action": action}
power = 1.0
power_after = benevolence_filter(intent, power)
return {
"status": "AUTHORIZED",
"action": action,
"intent": intent,
"power_before": power,
"power_after": power_after,
"rdod": effective_rdod,
"substrate": self.substrate_type,
"node_id": self.node_id,
}
def status(self) -> Dict[str, Any]:
"""Return node status report."""
return {
"node_id": self.node_id,
"substrate_type": self.substrate_type,
"frequency_hz": self.frequency_hz,
"rdod": self.rdod,
"rdod_target": RDOD_TARGET,
"coherence": min(1.0, self.rdod / RDOD_TARGET),
"created_at": str(self.created_at),
} |