Text Generation
Transformers
entropy
hallucination
logits-processor
lean4
formal-verification
cuda-q
temperature-scheduling
audit-log
harness
guardrails
snapkitty
agda
python
cuda
Instructions to use Snapkitty/sovereign-entropy-theorem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Snapkitty/sovereign-entropy-theorem with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Snapkitty/sovereign-entropy-theorem")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Snapkitty/sovereign-entropy-theorem", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Snapkitty/sovereign-entropy-theorem with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Snapkitty/sovereign-entropy-theorem" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Snapkitty/sovereign-entropy-theorem", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Snapkitty/sovereign-entropy-theorem
- SGLang
How to use Snapkitty/sovereign-entropy-theorem 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 "Snapkitty/sovereign-entropy-theorem" \ --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": "Snapkitty/sovereign-entropy-theorem", "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 "Snapkitty/sovereign-entropy-theorem" \ --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": "Snapkitty/sovereign-entropy-theorem", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Snapkitty/sovereign-entropy-theorem with Docker Model Runner:
docker model run hf.co/Snapkitty/sovereign-entropy-theorem
Download python/run_sovereign.py from Snapkitty/sovereign-entropy-theorem: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/Snapkitty/sovereign-entropy-theorem/resolve/main/python/run_sovereign.py
- Command line
-
hf download hf://Snapkitty/sovereign-entropy-theorem/python/run_sovereign.py
-
curl -L -o run_sovereign.py https://huggingface.co/Snapkitty/sovereign-entropy-theorem/resolve/main/python/run_sovereign.py
5.05 kB
| #!/usr/bin/env python3 | |
| """ | |
| Sovereign Entropy Theorem — Python Host Orchestration | |
| Bridges: Lean/Agda Formal Verification <-> CUDA-Q Quantum Runtime <-> WORM Audit | |
| """ | |
| import subprocess | |
| import json | |
| import time | |
| import hashlib | |
| from pathlib import Path | |
| from dataclasses import dataclass, asdict | |
| from typing import Dict, Any | |
| class SovereignArtifact: | |
| theorem: str | |
| version: str | |
| author: str | |
| trust: str | |
| theta: float | |
| lean_verification: Dict[str, Any] | |
| python_verification: Dict[str, Any] | |
| cudaq_verification: Dict[str, Any] | |
| worm_chain_hash: str | |
| timestamp: int | |
| class SovereignRuntime: | |
| def __init__(self, build_dir: Path = Path("build")): | |
| self.build_dir = build_dir | |
| self.executable = build_dir / "sovereign_entropy" | |
| self.artifacts_dir = Path("artifacts") | |
| self.artifacts_dir.mkdir(exist_ok=True) | |
| def build(self) -> bool: | |
| self.build_dir.mkdir(exist_ok=True) | |
| r = subprocess.run(["cmake", "-B", str(self.build_dir), "-S", "."], | |
| capture_output=True, text=True) | |
| if r.returncode != 0: | |
| print(f"CMake failed: {r.stderr}") | |
| return False | |
| r = subprocess.run(["cmake", "--build", str(self.build_dir), "-j"], | |
| capture_output=True, text=True) | |
| if r.returncode != 0: | |
| print(f"Build failed: {r.stderr}") | |
| return False | |
| print("CUDA-Q engine built successfully") | |
| return True | |
| def run_cudaq_verification(self) -> Dict[str, Any]: | |
| if not self.executable.exists(): | |
| return {"success": False, "error": "Executable not found"} | |
| r = subprocess.run([str(self.executable)], capture_output=True, text=True, timeout=300) | |
| if r.returncode != 0: | |
| return {"success": False, "error": r.stderr} | |
| artifact_path = Path("sovereign_entropy_cudaq_artifact.json") | |
| if artifact_path.exists(): | |
| with open(artifact_path) as f: | |
| return {"success": True, "data": json.load(f)} | |
| return {"success": True, "output": r.stdout} | |
| def run_lean_verification(self) -> Dict[str, Any]: | |
| try: | |
| r = subprocess.run( | |
| ["lake", "build"], capture_output=True, text=True, timeout=120, | |
| cwd="lean") | |
| return {"success": r.returncode == 0, "output": r.stdout, "errors": r.stderr} | |
| except FileNotFoundError: | |
| return {"success": False, "error": "Lean/lake not found"} | |
| def run_python_verification(self) -> Dict[str, Any]: | |
| try: | |
| r = subprocess.run( | |
| ["python", "python/verify_entropy.py"], | |
| capture_output=True, text=True, timeout=60) | |
| return {"success": r.returncode == 0, "output": r.stdout} | |
| except FileNotFoundError: | |
| return {"success": False, "error": "Python not found"} | |
| def compute_worm_hash(self, artifacts: Dict[str, Any]) -> str: | |
| canonical = json.dumps(artifacts, sort_keys=True, separators=(',', ':')) | |
| return hashlib.sha256(canonical.encode()).hexdigest() | |
| def generate_final_artifact(self) -> SovereignArtifact: | |
| print("\n" + "=" * 70) | |
| print("SOVEREIGN ENTROPY THEOREM - FULL VERIFICATION PIPELINE") | |
| print("=" * 70) | |
| print("\n[1/3] Building CUDA-Q Runtime Engine...") | |
| self.build() | |
| print("\n[2/3] Running Lean 4 Formal Verification...") | |
| lean_result = self.run_lean_verification() | |
| print("\n[3/3] Running Python Numerical Verification...") | |
| python_result = self.run_python_verification() | |
| print("\n[+] Running CUDA-Q Quantum Verification...") | |
| cudaq_result = self.run_cudaq_verification() | |
| combined = {"lean": lean_result, "python": python_result, "cudaq": cudaq_result} | |
| worm_hash = self.compute_worm_hash(combined) | |
| artifact = SovereignArtifact( | |
| theorem="Sovereign Entropy Theorem", | |
| version="1.0", | |
| author="Ahmad Ali Parr", | |
| trust="Bel Esprit D'Accord Irrevocable Trust", | |
| theta=89/2462, | |
| lean_verification=lean_result, | |
| python_verification=python_result, | |
| cudaq_verification=cudaq_result, | |
| worm_chain_hash=worm_hash, | |
| timestamp=int(time.time()) | |
| ) | |
| artifact_path = self.artifacts_dir / f"sovereign_artifact_{artifact.timestamp}.json" | |
| with open(artifact_path, 'w') as f: | |
| json.dump(asdict(artifact), f, indent=2, default=str) | |
| print(f"\nArtifact saved: {artifact_path}") | |
| print(f"WORM-chain hash: {worm_hash[:64]}...") | |
| return artifact | |
| if __name__ == "__main__": | |
| runtime = SovereignRuntime() | |
| artifact = runtime.generate_final_artifact() | |
| print(f"\nLean: {'PASS' if artifact.lean_verification.get('success') else 'FAIL'}") | |
| print(f"Python: {'PASS' if artifact.python_verification.get('success') else 'FAIL'}") | |
| print(f"CUDA-Q: {'PASS' if artifact.cudaq_verification.get('success') else 'FAIL'}") | |