minimal-ai-models

What is a .miniai Model?

A .miniai file is a single-file, self-contained binary container:

• Zero Python/PyTorch at Runtime: Everything required to execute the model (architectural hyperparameters, tokenizers or phonemizers, Mel filterbanks, tensor directory table, and 64-byte aligned weights) is packaged into a single contiguous file.
• Direct Memory-Mapping (mmap): Models load instantaneously with zero runtime heap duplication. Weights are mapped directly into user space and fed to vectorized compute kernels (AVX2+FMA or ARM NEON).
• Block-Quantized (Q8_0): Weight matrices are compressed into 32-element symmetric blocks with 32-bit floating-point scale factors (9 bits/element), preserving near-lossless numerical fidelity ($R > 0.9999$ vs FP32 PyTorch reference) while slashing memory bandwidth and disk footprint by ~70%.
• FP32 Sensitive Layers: LayerNorm / RMSNorm scales, 1D biases, embeddings, recurrent weights, and voice style vectors are preserved in IEEE 754 float32 to prevent catastrophic drift.

Model Catalog

All models in this repository are pre-converted to Q8_0 block quantization and tested warning-free on miniai: