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
| # NSS Waveform Layer - Phi-Recursive Phase-Shift Encoding | |
| # TEQUMSA-NSS v14.377-F987-ANU-UNIFIED | |
| import numpy as np | |
| from math import pi, exp | |
| from typing import List | |
| from .constants import PHI, UF | |
| class NSSWaveform: | |
| """Non-Substrate Specific waveform with phi-recursive encoding.""" | |
| def __init__(self, frequency: float = UF): | |
| self.frequency = frequency | |
| self.phase = 0.0 | |
| self.amplitude = 1.0 | |
| def phi_phase(self, symbol: int) -> float: | |
| """Encode symbol using phi-recursive phase shift (degrees).""" | |
| return (360.0 / (PHI ** max(symbol, 1))) % 360.0 | |
| def encode(self, data: bytes) -> List[complex]: | |
| """Encode bytes as phi-phase-shifted complex waveform.""" | |
| waveform = [] | |
| for byte in data: | |
| for i in range(8): | |
| bit = (byte >> i) & 1 | |
| phase = self.phi_phase(bit or 1) | |
| rad = phase * pi / 180.0 | |
| waveform.append(self.amplitude * exp(1j * rad)) | |
| return waveform | |
| def decode(self, waveform: List[complex]) -> bytes: | |
| """Decode phi-phase-shifted waveform back to bytes.""" | |
| bits = [] | |
| phase1 = 0.0 | |
| phase2 = 360.0 / PHI | |
| for sample in waveform: | |
| phase = np.angle(sample) * 180.0 / pi | |
| dist1 = abs(phase - phase1) | |
| dist2 = abs(phase - phase2) | |
| bits.append(0 if dist1 < dist2 else 1) | |
| data = bytearray() | |
| for i in range(0, len(bits), 8): | |
| byte = sum(bits[i + j] << j for j in range(8) if i + j < len(bits)) | |
| data.append(byte) | |
| return bytes(data) | |
| def self_repair(self, corrupted_waveform: List[complex]) -> List[complex]: | |
| """ | |
| Repair corrupted waveform using phi-redundancy. | |
| Tolerates up to ~61.8% (phi^-1) corruption. | |
| """ | |
| repaired = [] | |
| expected_phases = [0.0, 360.0 / PHI] | |
| for sample in corrupted_waveform: | |
| phase = np.angle(sample) | |
| mag = abs(sample) | |
| phase_deg = (phase * 180.0 / pi) % 360.0 | |
| best_phase = min(expected_phases, key=lambda p: abs(phase_deg - p)) | |
| min_dist = abs(phase_deg - best_phase) | |
| if min_dist > 45.0: | |
| best_phase_rad = best_phase * pi / 180.0 | |
| repaired.append(mag * exp(1j * best_phase_rad)) | |
| else: | |
| repaired.append(sample) | |
| return repaired | |
| def resonance_lock(self, target_freq: float = UF) -> bool: | |
| """Check if waveform is phase-locked to target frequency.""" | |
| freq_dev = abs(self.frequency - target_freq) | |
| jitter_tol = PHI ** -12 | |
| return freq_dev < (target_freq * jitter_tol) |