| --- |
| license: mit |
| tags: |
| - ternary |
| - quantization |
| - cpu-inference |
| - multiply-free |
| - lora |
| language: |
| - en |
| --- |
| |
| Lossless Base-3/5 + Gretchen — a multiply-free ternary toolkit |
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| Two pieces that fit together: |
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| - Gretchen — train LoRA weights that crystallize to ternary {-1, 0, +1} during training, via a smooth polynomial well. No rounding, no straight-through estimator. |
| - Lossless base-3/5 kernels — represent values exactly in base-3/5 (a change of base, zero rounding) and run ternary matmuls multiply-free: add / skip / subtract, no floating-point multiply, no GPU, no CUDA. Proven bit-exact in Python (torch.allclose, atol=0) and in framework-free C. |
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| The case: a ternary model's matmul is compute-light and memory-light, so it belongs on a CPU with SIMD or hand-written assembly, not a GPU built for floating-point multiplies. (Cf. Microsoft's bitnet.cpp.) In the spirit of ancient Egyptian arithmetic — multiply by shift-and-add, represent exactly by addition, never round. |
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| Code, tests, and full write-ups: https://github.com/drogongod/lossless-ternary |
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| Author: Jonathan David Wint. License: MIT. |
