Aprender

Aprender is the ML library for the Sovereign AI Stack, providing training algorithms, model formats, and format conversion utilities.

Key Features

Algorithms: Linear regression, logistic regression, k-means, decision trees, random forests, gradient boosting, SVM, KNN, Naive Bayes, PCA
Formats: APR v2 native format, SafeTensors import, GGUF import
Quantization: Q4_K, Q5_K, Q6_K encoding with row-padded super-blocks

LAYOUT-002: Row-Major Mandate

Critical: Aprender handles all layout conversion for the Sovereign AI Stack.

Format Conversion Architecture

┌─────────────────────────────────────────────────────────┐
│         APRENDER FORMAT CONVERTER                        │
│         src/format/converter/write.rs                   │
├─────────────────────────────────────────────────────────┤
│                                                          │
│  SafeTensors (row-major) ───(pass-through)───► APR v2   │
│                                                          │
│  GGUF (column-major) ───(TRANSPOSE)───► APR v2          │
│                         dequant→transpose→requant        │
│                                                          │
└─────────────────────────────────────────────────────────┘

Key Functions

Function	Location	Purpose
`transpose_q4k_for_matmul`	`mod.rs:1273`	GGUF Q4K → row-major Q4K
`transpose_q6k_for_matmul`	`mod.rs:1311`	GGUF Q6K → row-major Q6K
`quantize_q4_k_matrix`	`mod.rs:1195`	Row-padded Q4K encoding

Transpose Process

Dequantize: Q4K bytes → F32 floats
Transpose: [rows, cols] → [cols, rows]
Re-quantize: F32 → Q4K with row-padded super-blocks

Usage

# Import GGUF with automatic transpose
apr import model.gguf -o model.apr

# Import SafeTensors (no transpose needed)
apr import model.safetensors -o model.apr

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