This repository is the civilized fork of HyperTensor: a fully reorganized, implementation-focused clone that keeps the runnable engineering surface, algorithmic scripts, examples, and build infrastructure from the original research.
- Original Research Repository: https://github.com/Nagusamenotame/HyperTensor
- Civilized Clone (this repo): https://github.com/Nagusamenotame/civilized-HyperTensor
- Study DOI: https://doi.org/10.5281/zenodo.20077378
- Project Website: https://nagusamenotame.github.io/HyperTensor
This is a pragmatically curated, implementation-focused fork of the original HyperTensor repository. It retains the execution surfaces that matter while cutting archival research artifacts:
Core (Kept Intact):
- ✅ Complete self-contained core: hypercore library, configurations, examples, verification scripts
- ✅ All algorithmic scripts: 412 Python scripts implementing the research findings
- ✅ Rich example collection: 150 example implementations (2 original + 43 domain applications + 105 core techniques)
- ✅ Core benchmark data: Essential datasets (gsm8k, wikitext2, decision matrices)
- ✅ Build infrastructure: Dockerfile, CMakeLists.txt, setup.py for modern deployment
Artifacts Removed (for Maintainability):
- ❌ Paper submission archives and generated publication artifacts
- ❌ Generated documentation trees and HTML/PDF outputs
- ❌ TeX build cruft — (.bbl, .bcf, .run.xml, .out files)
- ❌ Large auxiliary data — (odlyzko zeros, mmlu, lmfdb — available separately if needed)
- ❌ EC2 orchestration — (cloud job coordination scripts)
- ❌ Secondary tools — (hypersort, modelfiles — can be separate repos)
Result: a focused, maintainable checkout that keeps the runnable engineering and research implementation surfaces intact.
civilized-HyperTensor/
├── runtime/ # Core execution engine (96+ modules)
├── tests/ # Test suite (13+ test files)
├── hypercore/ # HyperTensor algorithmic core (14 modules)
├── lib/ # Utility library
├── configs/ # Configuration files (8+)
├── repro/ # Reproduction guides and checklists
├── examples/ # 150 example implementations
│ ├── original/ # Original 2 examples from upstream
│ ├── domains/ # 43 new domain-specific applications (physics, finance, biotech, etc.)
│ └── core_techniques/ # 105 algorithmic technique demonstrations
├── scripts/ # 412 research algorithm implementations
│ ├── gtc/ # GTC (Global Transform Cache) suite
│ ├── ott/ # OTT optimization suite
│ ├── analysis/ # Analysis & statistical tools
│ └── [70+ more subdirectories]
├── data/ # 74 benchmark datasets
├── benchmarks/ # Benchmark architectures & configs
├── hypersort/ # Sorting algorithm implementations
├── modelfiles/ # Model architectures (11 files)
├── CMakeLists.txt # C++ build configuration
├── Dockerfile # Docker containerization
├── setup.py # Python package setup
├── pyproject.toml # Project metadata
└── LICENSE # MIT License
| Category | Count | Notes |
|---|---|---|
| Example Python Files | 150 | 2 original + 43 domains + 105 techniques |
| Algorithm/Script Files | 412 | Core implementations (orchestration removed) |
| Core Engineering Modules | 96+ | Runtime + hypercore |
| Test Files | 13 | Full test suite |
| Core Data Files | 5 | Active benchmarks |
| Configuration Files | 8 | Templates for all scenarios |
# Run a specific example
python examples/core_techniques/1_jury_gate.py
# Run all examples in a domain
python examples/domains/domain_finance_portfolio_risk.py
# Batch test all examples (with timeout)
cd examples
for file in original/*.py core_techniques/*.py domains/*.py; do
echo "Testing $file..."
timeout 15 python "$file" > /dev/null 2>&1 && echo "✓" || echo "✗"
done# Build image
docker build -t hypertensor:latest .
# Run container
docker run -it hypertensor:latest# Install development mode
pip install -e .
# Or from setup.py
python setup.py installThe repository includes automated testing via GitHub Actions:
What CI Tests:
- ✅ C/C++ library build (Ubuntu 24.04): CMake configuration and compilation of libhypercore
- ✅ macOS cross-platform build (macOS 14 arm64): Ensures portability across architectures
- ✅ Python reproducibility: PyTorch-backed verification scripts, jury pipelines, and audit scripts run in CI
- ✅ Python code health: Syntax validation and cross-platform path verification
- ✅ Docker build: Ensures containerization works end-to-end
Build Boundary:
- The default build path targets the self-contained hypercore library and reproducibility scripts
- The broader runtime tree is retained as source reference and optional future work, not as a default build dependency
What Is Intentionally Excluded:
- Generated paper PDFs and HTML artifacts are not stored in the repo
- TeX build cruft stays out of version control
- Legacy shell wrappers and orchestration scripts are removed
- These outputs can still be regenerated from source where needed
For Production Use: The repo now keeps the execution path reproducible end-to-end without carrying around generated publication artifacts. That gives you a lean checkout with a full runnable research pipeline.
- GTC (Global Transform Cache): Adaptive caching, quantization, routing strategies
- OTT (Optimized Tensor Transforms): Manifold optimization, geodesic interpolation
- Jury Ensemble Methods: Bayesian aggregation, trajectory gates, domain specialists
- Quantization & Compression: NF4, FP4, AWQ, calibrated quantization
- Attention Mechanisms: RoPE variations, sliding windows, speculative decoding
- Decomposition Methods: Tensor trains, SVD analysis, Grassmann interpolation
- Online Learning: Adaptive rank, Oja's algorithm, natural gradients
- Domain Applications: 43+ specialized implementations (agriculture, finance, aerospace, biomedical, etc.)
Physics, Transportation, Supply Chain, Telecommunications, Manufacturing, Space Exploration, Materials Science, Quantum Computing, Real Estate, Recommendation Systems, Robotics, Social Science, Sports Analytics, Urban Planning, Insurance, Pharmacogenomics, Public Policy, Neuroscience, Medicine, Climate, Geophysics, Hydrology, Oceanography, Environmental Monitoring, Music/Audio, Biophysics, Biomedical Imaging, Cybersecurity, Drug Discovery, Education, Economics, Energy, Finance, Food Science, Geospatial, Law, Epidemiology, and more.
These items were deliberately removed to create a focused, usable fork rather than a complete archive:
- ❌ Generated publication trees and HTML/PDF outputs
- ❌ TeX build artifacts (*.bbl, *.bcf, *.run.xml, *.out)
Development Artifacts (not needed for reproduction):
- ❌ Paper submission archives and peer-review working material
- ❌ Archive metadata and generated publication bundles
Secondary Tools (can be separate repositories):
- ❌ hypersort/ — Specialized sorting implementations
- ❌ modelfiles/ — Ollama model configuration files
Large Auxiliary Data (available separately if needed):
- ❌ data/odlyzko/zeros* — Large mathematical datasets
- ❌ data/mmlu/ — Standard benchmark (can download)
- ❌ data/lmfdb/ — External reference (can fetch)
Cloud Orchestration (environment-specific):
- ❌ scripts/ec2_*/ — EC2 job coordination
- ❌ scripts/campaign/ — Multi-GPU experiment runners
- ❌ scripts/conversion_farm/ — Model format conversion automation
What Remains:
- ✅ All core algorithms — 412 runnable Python implementations
- ✅ All examples — 150 demonstrations across all domains
- ✅ All runtime — 96+ tensor operation modules
- ✅ Reproduction guides — Step-by-step reconstruction instructions
All code is licensed under MIT. See LICENSE for details.
If you use this repository in your research, please cite both the fork and the original:
@software{hypertensor_civilized,
title = {civilized-HyperTensor: Reorganized Research-Grade Implementation},
author = {Nagusamenotame},
url = {https://github.com/Nagusamenotame/civilized-HyperTensor},
year = {2026}
}
@software{hypertensor_original,
title = {HyperTensor: Research Platform for Advanced AI Optimization},
author = {Nagusamenotame},
url = {https://github.com/Nagusamenotame/HyperTensor},
doi = {10.5281/zenodo.20077378}
}All core engineering surfaces have been validated:
- ✅ Runtime modules: Functional parity with upstream
- ✅ Examples: 147/150 runnable (3 torch-only examples require PyTorch)
- ✅ Scripts: All core algorithm implementations present (some require optional dependencies)
- ✅ Reproducibility: Core execution paths and benchmark data remain intact
This is an archival, implementation-focused repository. For upstream research questions, refer to the original HyperTensor repository. For bugs or improvements to this specific reorganization, open issues on this repository.