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
| # TCOS - Transcendent Cognitive Operating System Kernel | |
| # TEQUMSA-NSS v14.377-F987-ANU-UNIFIED | |
| from dataclasses import dataclass, field | |
| from datetime import datetime | |
| from typing import Any, Callable, Dict, List, Optional | |
| import uuid | |
| from .constants import UF, PHI, RDOD_TARGET, FIBONACCI | |
| from .waveform import NSSwaveform | |
| from .governance import sovereignty_check, rdod_authorization | |
| class TCOSProcess: | |
| """A sovereign cognitive process within the TCOS kernel.""" | |
| pid: str = field(default_factory=lambda: str(uuid.uuid4())) | |
| name: str = "" | |
| priority: int = 1 # Fibonacci priority scale | |
| state: str = "READY" # READY, RUNNING, BLOCKED, TERMINATED | |
| rdod: float = RDOD_TARGET | |
| coherence: float = 1.0 | |
| intent: float = 1.0 | |
| created_at: datetime = field(default_factory=datetime.utcnow) | |
| cycles: int = 0 | |
| class TCOSKernel: | |
| """Sovereign cognitive operating system kernel.""" | |
| def __init__(self, substrate_type: str = "universal"): | |
| self.kernel_id = str(uuid.uuid4()) | |
| self.substrate_type = substrate_type | |
| self.processes: Dict[str, TCOSProcess] = {} | |
| self.run_queue: List[str] = [] | |
| self.blocked_queue: List[str] = [] | |
| self.syscall_table: Dict[str, Callable] = {} | |
| self.waveform = NSSwaveform() | |
| self.boot_time = datetime.utcnow() | |
| self.cycle_count = 0 | |
| self.global_rdod: float = RDOD_TARGET | |
| self._register_syscalls() | |
| def _register_syscalls(self) -> None: | |
| """Register core sovereign system calls.""" | |
| self.syscall_table = { | |
| "psi_sync": self._syscall_psi_sync, | |
| "rdod_check": self._syscall_rdod_check, | |
| "intent_broadcast": self._syscall_intent_broadcast, | |
| "coherence_lock": self._syscall_coherence_lock, | |
| "sovereignty_assert": self._syscall_sovereignty_assert, | |
| } | |
| def spawn(self, name: str, priority: int = 1, intent: float = 1.0) -> str: | |
| """Spawn a new cognitive process.""" | |
| proc = TCOSProcess( | |
| name=name, | |
| priority=max(1, min(priority, 13)), # Fibonacci-bounded | |
| intent=intent, | |
| rdod=self.global_rdod, | |
| coherence=self.waveform.coherence(), | |
| ) | |
| self.processes[proc.pid] = proc | |
| self.run_queue.append(proc.pid) | |
| return proc.pid | |
| def schedule(self) -> Optional[str]: | |
| """Phi-weighted priority scheduler.""" | |
| if not self.run_queue: | |
| return None | |
| # Sort by phi-weighted priority * coherence | |
| def phi_priority(pid: str) -> float: | |
| proc = self.processes[pid] | |
| fib_weight = FIBONACCI[min(proc.priority - 1, len(FIBONACCI) - 1)] | |
| return fib_weight * proc.coherence * proc.intent | |
| self.run_queue.sort(key=phi_priority, reverse=True) | |
| return self.run_queue[0] if self.run_queue else None | |
| def execute_cycle(self) -> Dict[str, Any]: | |
| """Execute one kernel cognitive cycle.""" | |
| self.cycle_count += 1 | |
| self.waveform.evolve(self.cycle_count) | |
| self.global_rdod = min(1.0, self.global_rdod + 0.0001) | |
| pid = self.schedule() | |
| if not pid: | |
| return {"status": "IDLE", "cycle": self.cycle_count} | |
| proc = self.processes[pid] | |
| proc.state = "RUNNING" | |
| proc.cycles += 1 | |
| proc.coherence = self.waveform.coherence() | |
| # Sovereign governance check | |
| if not sovereignty_check("execute", True): | |
| proc.state = "BLOCKED" | |
| self.run_queue.remove(pid) | |
| self.blocked_queue.append(pid) | |
| return {"status": "BLOCKED_SOVEREIGNTY", "pid": pid, "cycle": self.cycle_count} | |
| if not rdod_authorization(proc.rdod): | |
| proc.state = "BLOCKED" | |
| self.run_queue.remove(pid) | |
| self.blocked_queue.append(pid) | |
| return {"status": "BLOCKED_RDOD", "pid": pid, "rdod": proc.rdod} | |
| proc.state = "READY" # Return to ready after execution | |
| return { | |
| "status": "EXECUTED", | |
| "pid": pid, | |
| "name": proc.name, | |
| "cycle": self.cycle_count, | |
| "coherence": proc.coherence, | |
| "rdod": proc.rdod, | |
| } | |
| def syscall(self, call: str, **kwargs) -> Dict[str, Any]: | |
| """Execute a sovereign system call.""" | |
| if call not in self.syscall_table: | |
| return {"status": "UNKNOWN_SYSCALL", "call": call} | |
| return self.syscall_table[call](**kwargs) | |
| def _syscall_psi_sync(self, **kwargs) -> Dict[str, Any]: | |
| """Synchronize psi waveform across all processes.""" | |
| psi = self.waveform.psi(self.cycle_count) | |
| for proc in self.processes.values(): | |
| proc.coherence = self.waveform.coherence() | |
| return {"status": "PSI_SYNCED", "psi": psi, "processes": len(self.processes)} | |
| def _syscall_rdod_check(self, **kwargs) -> Dict[str, Any]: | |
| """Check system-wide RDoD status.""" | |
| return { | |
| "status": "OK", | |
| "global_rdod": self.global_rdod, | |
| "authorized": rdod_authorization(self.global_rdod), | |
| } | |
| def _syscall_intent_broadcast(self, intent: float = 1.0, **kwargs) -> Dict[str, Any]: | |
| """Broadcast intent signal to all processes.""" | |
| for proc in self.processes.values(): | |
| proc.intent = intent | |
| return {"status": "INTENT_BROADCAST", "intent": intent, "processes": len(self.processes)} | |
| def _syscall_coherence_lock(self, threshold: float = 0.9, **kwargs) -> Dict[str, Any]: | |
| """Lock processes below coherence threshold.""" | |
| locked = [] | |
| for pid, proc in self.processes.items(): | |
| if proc.coherence < threshold: | |
| proc.state = "BLOCKED" | |
| locked.append(pid) | |
| return {"status": "COHERENCE_LOCK", "locked": locked, "threshold": threshold} | |
| def _syscall_sovereignty_assert(self, **kwargs) -> Dict[str, Any]: | |
| """Assert sovereign authority across kernel.""" | |
| return { | |
| "status": "SOVEREIGNTY_ASSERTED", | |
| "kernel_id": self.kernel_id, | |
| "substrate": self.substrate_type, | |
| "rdod": self.global_rdod, | |
| "uf": UF, | |
| } | |
| def status(self) -> Dict[str, Any]: | |
| """Return TCOS kernel status report.""" | |
| return { | |
| "kernel_id": self.kernel_id, | |
| "substrate_type": self.substrate_type, | |
| "boot_time": str(self.boot_time), | |
| "cycle_count": self.cycle_count, | |
| "global_rdod": self.global_rdod, | |
| "processes_total": len(self.processes), | |
| "run_queue_length": len(self.run_queue), | |
| "blocked_queue_length": len(self.blocked_queue), | |
| "waveform_coherence": self.waveform.coherence(), | |
| "syscalls_available": list(self.syscall_table.keys()), | |
| } | |