Veritate

Veritate's size and speed work sits in a hand-coded byte-level inference engine built to run language models on commodity CPUs, with no GPU, no CUDA, and no external runtime. Quantization-aware training folds INT8 arithmetic into the model rather than bolting it on afterward, so the low-precision engine keeps nearly all of the full-precision model's quality. Sub-four-bit INT4 weight compression shrinks the memory footprint further, and ternary weight representation is an active research direction toward multiply-free decoding.

Efficiency compounds across the stack. Activation sparsity, trained into the network rather than pruned after the fact, lets the engine skip most of the feed-forward compute on every token. Speculative decoding drafts several bytes ahead and verifies them in a single pass, cutting decode latency without changing a single output byte. Together they make capable modeling practical on hardware people already own, which is the premise Veritate exists to prove.

Veritate is a tokenizer-free language model. Its 256-symbol byte vocabulary is the raw alphabet of every file, so there is no tokenizer to train, no vocabulary mismatch between corpora, and no subword artifacts to reason around. A byte-level model reads any language, any codebase, and any binary format as the same stream of bytes, which keeps the architecture simple and the behavior straightforward to audit.

Because the engine is hand-coded and small enough to read end to end, every forward pass is a glass-box. The residual stream, feed-forward neuron activations, attention scores, logit lens, and direct logit attribution are all readable as it runs, with nothing hidden behind a framework or a hosted service.