Implements a hierarchical transformer for language modeling that operates directly on raw text without a tokenizer.
A tokenizer-free byte-level LM in MLX: entropy-based dynamic patching around a local encoder/decoder and a latent global transformer over patches.
header-only lexical scanning for recursive descent parsing
Language-model proof of operation for HSL's zero-learned-parameter input path. Open code, weights, and documented limitations; not a SOTA claim.
Engram without a tokenizer
Tokenizer-free HSL text-to-speech feasibility prototype with open code and weights. Working synthesis evidence; formal perceptual evaluation remains open.
A tokenizer-free NLP library with T-FREE, CANINE, and byte-level approaches
Four-seed matched ASR feasibility study using an HSL byte-signal substrate and spectral lens. Open code, weights, controls, and limitations; not SOTA.
PyTorch transformer input from raw bytes with zero learned input parameters: no tokenizer, embedding table, or learned input projection.
Deterministic, non-learned byte-to-HSL signal encoder for PyTorch: 27-D interpretable features, lossless byte codec, tests, controls, and GPU paths.
Open HSL/HoLo research: a deterministic byte-native signal substrate tested across language, speech, and diffusion with matched controls and honest limits.
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