aprender 0.35.0

Quick Start

cargo install aprender
apr pull qwen2.5-coder-1.5b
apr run qwen2.5-coder-1.5b "What is 2+2?"

v0.34.0 — MODEL-2 §88 stack-existence-proof shipped end-to-end. paiml/albor-370m-v1 is the first model trained with the pure-Rust Sovereign AI Stack and published to HuggingFace Hub with all three usage paths verified: apr run (Rust), AutoModelForCausalLM.from_pretrained (HF Transformers), and llama-cli (llama.cpp). The publish workflow is now codified in SPEC-HF-PUBLISH-001 so future model publishes follow a repeatable 12-file integration plus a 13-tier crates.io cascade (see scripts/cascade-publish.sh). See the v0.34.0 release notes for the PMAT-690 P3-C-prep defect cascade that motivated the spec.

v0.33.0 — MODEL-1 SHIP % = 100%. SHIP-007 (GPU dispatch on the canonical 7B Q4K teacher) was LIVE-DISCHARGED. See the v0.33.0 release notes and SPEC-SHIP-TWO-001 §65–§75 for the cascade detail.

What is Aprender?

A complete ML framework in pure Rust. One cargo install, one apr binary, the full model lifecycle — inference, training, quantization, profiling, publishing — all backed by YAML provable contracts that fail CI on drift.

At HEAD

These numbers are enforced by contracts/readme-claims-v1.yaml. Drift between this table and live repo state fails bash scripts/check_readme_claims.sh → see FALSIFY-README-001..004.

Command surface

Cookbook

End-to-end recipes (data prep → train → quantize → publish → serve) live in paiml/apr-cookbook — 341 worked examples with local book/src/ walkthroughs.

git clone https://github.com/paiml/apr-cookbook  # ../apr-cookbook
cd ../apr-cookbook
apr cookbook list                              # 341 recipes
apr cookbook run train-tiny-from-scratch       # runs end-to-end

Install

cargo install aprender    # installs the `apr` binary
apr --version

A Qwen story

Eight beats, one narrative, every core command group. Anchored on the Qwen series so the story scales from a 494-MB safetensors model to a 30 B-parameter MoE GGUF. Every beat is a falsifier in contracts/qwen-story-v1.yaml; the runnable form is scripts/qwen-story.sh; nightly cron is .github/workflows/qwen-story-daily.yml; the dogfood gate is /dogfood Gate 18.

# Reproduce locally (uses ~/models cache; ~3-5 min on RTX 4090):
bash scripts/qwen-story.sh

Beat 1 — Discover (Registry)

apr pull hf://Qwen/Qwen2.5-Coder-0.5B-Instruct      # 494 MB safetensors
apr list                                            # confirm cached

Beat 2 — Trust (QA gates)

apr qa qwen2.5-coder-1.5b-instruct-q4k              # 12 falsifiable gates
apr validate qwen2.5-coder-1.5b-instruct-q4k --quality   # 100-pt structural audit
apr lint qwen2.5-coder-1.5b-instruct-q4k            # best-practice signals

Beat 3 — Explore (Inspection)

apr inspect --json qwen2.5-coder-1.5b-instruct-q4k  # arch, params, tensors
apr tensors --json qwen2.5-coder-1.5b-instruct-q4k  # 339 tensors with shapes
apr tree qwen2.5-coder-1.5b-instruct-q4k            # layer architecture

Beat 4 — Adapt (Model ops)

apr export qwen2.5-coder-1.5b-instruct-q4k --format gguf -o roundtrip.gguf
apr diff qwen2.5-coder-1.5b-instruct-q4k roundtrip.gguf  # tensor-by-tensor delta
apr convert model.safetensors --quantize q4_k -o quantized.apr

Beat 5 — Use (Inference)

apr run qwen2.5-coder-1.5b-instruct-q4k "fn sum(a: i32, b: i32) -> i32 {" --max-tokens 16
apr chat qwen2.5-coder-1.5b-instruct-q4k            # interactive REPL
apr code -p "review this Python function" --max-turns 1   # agent mode (PMAT-182)

Beat 6 — Serve (REST API)

apr serve run qwen2.5-coder-1.5b-instruct-q4k --port 8080
curl -s localhost:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"qwen","messages":[{"role":"user","content":"What is 2+2?"}],"max_tokens":8}'
# → {"choices":[{"message":{"content":"2 + 2 equals 4."}}],...}

Beat 7 — Operate (Profiling)

apr profile qwen2.5-coder-7b-instruct-q4_k_m        # Roofline analysis
apr gpu --json                                      # VRAM, sm_*, cuda version
apr serve plan qwen2.5-coder-7b-instruct-q4_k_m     # capacity plan before run

Beat 8 — Scale (MoE introspection)

apr inspect --json Qwen3-Coder-30B-A3B-Instruct     # arch=qwen3moe, 30 B params
apr tensors --json Qwen3-Coder-30B-A3B-Instruct     # 579 tensors (MoE expert layout)

Publish (separate flow)

# Publish a derived model to HuggingFace Hub (see SPEC-HF-PUBLISH-001 for the 12-file pipeline)
apr stamp ckpt.apr --tokenizer /path/to/qwen-tokenizer --license Apache-2.0 -o staging/model.apr
apr export staging/model.apr --format gguf --quantize int4 -o staging/model-q4k.gguf
apr publish staging/ paiml/my-model-v1 --library-name aprender --license Apache-2.0

The bug-hunt layer. When run with PMAT_HUNT=1 (default), each beat emits a manifest of high-risk untested code in the command modules it just exercised:

-- pmat bug-hunt manifest (run chat code) --
    gap   crates/apr-cli/src/commands/run.rs:resolve_model_alias (impact=42.3)
    churn crates/apr-cli/src/commands/code.rs:dispatch_agent (commits=11)
    fault crates/aprender-serve/src/api/cuda_chat_backend.rs:try_qwen3_moe (unwrap,panic)

The nightly cron opens an issue when this manifest grows, so untested branches in command handlers can't accumulate quietly. See contracts/qwen-story-v1.yaml § pmat_audit_per_beat.

Publishing a model? Read SPEC-HF-PUBLISH-001. It documents the 12-file minimum (.apr/.gguf/.safetensors/model.safetensors alias/config/tokenizer/LICENSE/etc.), the YAML front-matter schema, the three-path verification protocol, and the HF API gotchas (NDJSON commits, LFS batch for 5MB-5GB, empty model-index rejected, Q4_K K%256==0). First applied 2026-05-18 for paiml/albor-370m-v1.

Library usage

[dependencies]
aprender = "0.34"

use aprender::linear_regression::LinearRegression;
use aprender::traits::Estimator;

let model = LinearRegression::new();
model.fit(&x_train, &y_train)?;
let predictions = model.predict(&x_test)?;

Algorithms: Linear/Logistic Regression, Decision Trees, Random Forest, GBM, Naive Bayes, KNN, SVM, K-Means, PCA, ARIMA, ICA, GLMs, graph algorithms, Bayesian inference, text + audio processing.

Architecture

Monorepo, flat crates/aprender-* layout (same pattern as Polars, Burn, Nushell):

paiml/aprender/
├── Cargo.toml                      # Workspace root + `cargo install aprender`
├── crates/
│   ├── aprender-core/              # ML library (use aprender::*)
│   ├── apr-cli/                    # CLI logic (103 subcommands)
│   ├── aprender-compute/           # SIMD/GPU compute kernels
│   ├── aprender-gpu/               # CUDA PTX
│   ├── aprender-serve/             # Inference server
│   ├── aprender-train/             # Training loops
│   ├── aprender-orchestrate/       # Agents + RAG
│   ├── aprender-contracts/         # Provable contracts engine
│   ├── aprender-profile/           # Profiling
│   ├── aprender-db/ aprender-graph/ aprender-rag/
│   └── ... (80 crates total)
├── contracts/                      # 1151 provable YAML contracts
└── book/                           # mdBook documentation

Performance

Reproduced from candle-vs-apr and ground-truth-apr-ludwig.

Provable contracts

Every CLI command and kernel is bound to a YAML contract with equations, preconditions, postconditions, and falsification tests:

equations:
  validate_exit_code:
    formula: exit_code = if score < 50 then 5 else 0
    invariants:
    - score < 50 implies exit_code != 0
falsification_tests:
- id: FALSIFY-CLI-001
  prediction: apr validate bad-model.apr exits non-zero

1153 contracts across inference, training, quantization, attention, FFN, tokenization, model formats, CLI safety — and this README itself.

Migration from old crates

Old repositories are archived. All development happens here.

Contributing

git clone https://github.com/paiml/aprender
cd aprender
cargo test --workspace --lib
cargo check --workspace
apr --help
bash scripts/check_readme_claims.sh    # README contract gate

License

MIT