Non-record: BitNet b1.58 - 68M ternary params, val_bpb=1.1770, systematic analysis of ternary limitations#367
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…ding window) BitNet b1.58 ternary quantization with full-training STE. 68M params in 15.88MB via base-3 packing (1.6 bits/param). Near-lossless roundtrip (0.0016 BPB gap). Systematic analysis of why the standard competition stack breaks for ternary: - XSA, weight decay, grad clipping: cause training plateau at 2.4 - SmearGate, BigramHash, OrthoInit: hurt or no effect - EMA/SWA: fundamentally incompatible - TTT: no improvement on ternary models What works: higher LR (0.04), wider MLP, fp16 scale simulation, longer warmdown. Improves on PR openai#139 (1.2029 → 1.1770).
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…er Value Residual, Base-3 ternary encoding, and tuned hyperparameters
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Improves on PR #139 (1.2029 → 1.1770). 68M ternary {-1,0,1} params packed at 1.6 bits/param in 15.88MB via base-3 encoding.
Key findings:
Full writeup with negative results table in the README.