ternary-forge
Native balanced-ternary arithmetic, a ternary neural network, a ternary VM, and a trit-packing file format — in pure Python/NumPy.
Binary is not a law of nature. It is an industrial accident. This repository is a fork in the road.
Results
| What | Number | Where |
|---|---|---|
| Ternary MLP on MNIST | 93.20% (float baseline: 92.28%) | examples/train_mnist.py |
| Weight storage | 1.58 bits/weight (float32: 32) | examples/train_mnist.py |
| Trit packing | 5 trits/byte, 99.06% of Shannon optimum | examples/pack_demo.py |
| VM | 8-register balanced-ternary ISA, native NEG and SHL3 |
examples/run_vm.py |
Install
pip install -e '.[dev]'
Run the proof
python -m pytest -q # 11 tests
python examples/train_mnist.py # ternary vs float MNIST
python examples/run_vm.py # ternary assembly programs
python examples/pack_demo.py # 40 KB .trit file, roundtrip
The argument
See docs/BINARY_ACCIDENT.md for the citable case: Setun (1958), the 5500FP (2026), Ternary Weight Networks (2016), and the three results above as evidence.
What this is not
- Not a wrapper around PyTorch. The MLP is hand-written NumPy.
- Not a binary emulator pretending to be ternary. The VM’s
NEGis one instruction that flips trits. - Not a toy. The packing format is exhaustive-tested over all 243 five-trit states.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for ternary-forge 1.1.1
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