TriGLU v0.1.0

Initial public release.

TriGLU — the Triple-Product Gated Linear Unit — is a token-local channel mixer
for selectively replacing causal-attention sublayers in decoder-only
Transformers, together with the controlled single-GPU harness (training,
deterministic data preparation, evaluation, benchmarking, mechanistic
diagnostics, and reporting) used to measure the trade-offs.

Headline evidence (89M-parameter, 20-layer suite, 1B FineWeb-Edu tokens,
two confirmatory seeds): two 15-attention/5-TriGLU hybrids match the
all-attention baseline's validation loss within ±0.001 while training ~7%
faster, with 5–9% faster prefill, 8–10% faster cached decode, and exactly 25%
less allocated KV-cache at 4K context. Gains grow at longer contexts
(efficiency extrapolation only above the 4K training length). See
docs/results.md for the complete results and limitations.

Evidence archive. TriGLU-evidence-v0.1.0.tar.gz (attached) contains the
complete raw records behind every reported number, per
docs/results-schema.md: each run's resolved config, metric stream,
environment metadata, and data hashes; raw evaluation/benchmark/rank-analysis
JSON; and the generated report tables and figures. Checkpoints and token
corpora are excluded by design — manifest.json pins the exact upstream
dataset revision and hashes needed to regenerate them.