Module: aprender::serialization

Public module of the aprender-core crate.

Source

crates/aprender-core/src/serialization.rs or directory.

Example

use aprender::serialization::{AprReader, AprWriter, SafeTensorsMetadata};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::serialization is the high-level checkpoint I/O — read and write .apr files and SafeTensors. Where format exposes the lower- level format internals (validation gates, layout contracts, converters), this module gives you ergonomic AprReader / AprWriter types for round-tripping tensors and metadata. Use it whenever you need to persist trained weights, load a checkpoint by name, or attach arbitrary JSON metadata alongside the tensors.

Key types

TypeDescription
AprReaderReads .apr files. open(path), from_bytes(bytes), open_filtered(path, predicate) for partial loads. read_tensor_f32(name) for individual tensors.
AprWriterBuilds .apr files. add_tensor_f32(name, shape, data), set_metadata(key, value), then write(path) or into_bytes().
SafeTensorsMetadataParsed metadata from a .safetensors header.
AprTensorDescriptorPer-tensor descriptor (name, shape, dtype) used internally by the reader.

Usage patterns

Pattern 1: Round-trip a small set of tensors

use aprender::serialization::{AprReader, AprWriter};
use serde_json::json;
use std::path::PathBuf;

let weights = [0.1_f32, 0.2, 0.3, 0.4];

// --- write
let mut w = AprWriter::new();
w.set_metadata("epoch", json!(5));
w.set_metadata("learning_rate", json!(1e-3));
w.add_tensor_f32("encoder.weight", vec![2, 2], &weights);

let bytes = w.to_bytes().expect("serialize");
assert!(!bytes.is_empty());

// --- read back from bytes
let r = AprReader::from_bytes(bytes).expect("parse");
let lr = r.get_metadata("learning_rate").cloned();
println!("learning_rate from file: {:?}", lr);

let read_back = r.read_tensor_f32("encoder.weight").expect("read tensor");
assert_eq!(read_back, weights.to_vec());

Pattern 2: Filtered partial loads

use aprender::serialization::AprReader;

// Only load tensors whose names start with "embeddings."
// (Useful for large checkpoints when you only need a subset.)
//     let reader = AprReader::open_filtered(&path, |name| name.starts_with("embeddings."))?;
//     let emb = reader.read_tensor_f32("embeddings.tokens.weight")?;

// Inspect all metadata without touching tensor bytes:
//     for (key, value) in reader.all_metadata() {
//         println!("{} = {}", key, value);
//     }

See also

  • format — lower-level converter, validation, signing, sharding
  • models — load Qwen2 / BERT weights via the models::* loaders that wrap this module
  • inspect — read-only inspection of model metadata
  • bundle — bundle model + tokenizer + config into a single artifact

Full API

Run cargo doc -p aprender-core --open for the rendered rustdoc, or browse docs.rs/aprender for the published version.