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", 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
| # 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 | |
| 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), | |
| } |