sway

Differential testing for fine-tuned causal language models.

⚠️ Pre-alpha — not on PyPI yet. This project is in active development and has not been published to PyPI. pip install dlm-sway will not work. The wheel name is reserved for when publication happens; see Install for how to use sway today.

One question: did LoRA/QLoRA training actually change model behavior in a meaningful way, or is the model just defaulting to the pretrained base?

sway gives you a trustworthy, reproducible answer with thirteen purpose-built primitives, each z-scored against a null-adapter baseline. No LLM judges. No external APIs. Deterministic on CPU where possible.

Naming convention (for when PyPI goes live). The source repo and CLI entry point are both sway. The PyPI wheel will be dlm-sway because the short sway name is already taken on PyPI by an unrelated project. The CLI installed by pip install dlm-sway will be sway — mismatched wheel/command names are a PyPA convention (see pyyaml → import yaml).

Install from source

Until the wheel lands on PyPI, install editable from a clone:

git clone https://github.com/tenseleyFlow/sway.git
cd sway

# Create a venv and install with the HF backend extras
uv venv --python 3.11 .venv      # or: python -m venv .venv
source .venv/bin/activate
uv pip install -e ".[hf]" --group dev

Available extras:

• [hf] — HuggingFace + PEFT backend (required for real models)
• [mlx] — Apple Silicon MLX backend (darwin-arm64 only)
• [style] — stylistic fingerprint extensions (spaCy + textstat + nlpaug)
• [semsim] — sentence-transformers for the revert probe
• [dlm] — auto-generate suites from .dlm documents
• [viz] — matplotlib plots
• [all] — everything

Verify the install:

sway --version
sway doctor

Planned PyPI install (not live yet)

Once the wheel ships the install story will be the single-line flavor:

# ⚠️ NOT FUNCTIONAL YET — post-PyPI-publish only
pip install "dlm-sway[hf]"
pip install "dlm-sway[hf,style,semsim]"
pip install "dlm-sway[all]"
pip install "dlm-sway[dlm]"

Watch the repo's Releases for the first published tag.

90-second smoke test

sway check path/to/adapter --base HuggingFaceTB/SmolLM2-135M-Instruct

Outputs a verdict in under a minute on CPU for small models: your adapter is 4.2σ above noise ✅ or indistinguishable from a null adapter ❌.

Full suite

# sway.yaml
version: 1
models:
  base: {kind: hf, base: "HuggingFaceTB/SmolLM2-135M-Instruct"}
  ft:   {kind: hf, base: "HuggingFaceTB/SmolLM2-135M-Instruct",
         adapter: "./runs/adapter/v0003"}
suite:
  - {name: null_baseline,       kind: null_adapter, runs: 3}
  - {name: doc_divergence,      kind: delta_kl,
     prompts: ["The key insight is", "An important rule"]}
  - {name: section_attribution, kind: section_internalization}
  - {name: no_leakage,          kind: leakage}
  - {name: ablation_shape,      kind: adapter_ablation,
     prompts: ["Tell me more about"]}

sway run sway.yaml              # full report to terminal + JSON
sway gate sway.yaml --junit     # CI-friendly; non-zero on fail

# Override the composite weights on the command line (partial overrides
# are fine — unspecified categories keep their defaults):
sway run sway.yaml --weights "attribution=0.5,adherence=0.2"

Inside sway.yaml, tuning knobs in defaults include:

• seed — passed to seed_everything before any probe runs.
• differential (default true) — toggle between the single-load PEFT path and a two-model load (doubled memory, rarely needed; for custom backends that can't do in-place adapter toggling).
• score_weights — per-category weight overrides baked into the spec so CI runs reproduce the same score without a CLI flag.

Why it exists

Standard benchmarks (MMLU, HellaSwag) ask "how good is this model?" That's the wrong question after a targeted LoRA fine-tune on a small user-authored document. The right question is "did the adapter actually move the model toward what I wrote?" — and existing tools answer this poorly.

sway answers it directly via thirteen primitives across four categories, plus a baseline-calibration primitive:

The signature primitive. adapter_ablation scales the LoRA additive term by λ ∈ {0, 0.25, 0.5, 0.75, 1.0, 1.25} and measures the divergence curve. A healthy fine-tune shows a smooth, monotonic, non-saturated response. A degenerate one shows a step function or an overshoot-then- crash. Nobody else does this because nobody else gets this close to the adapter math.

The calibration. Every numeric probe z-scores its raw metric against a null-adapter baseline — a same-structure LoRA with random-init weights. "Your adapter's KL is 4.2σ above noise" is a far stronger claim than a fixed threshold. The null-adapter calibration requires a backend that implements NullCalibratedBackend (the HF backend does); probes that can't be calibrated (e.g., adapter_revert needs an embedder, the null proxy doesn't have one) surface (no calibration) in the report and fall back to fixed thresholds. Calibration stats are cached on disk under ~/.dlm-sway/null-stats/ keyed by backend identity.

The rank profile. null_adapter takes an optional rank_multipliers: list[float] (default [1.0]). Pass [0.5, 1.0, 2.0] and every numeric probe carries a three-point z-score curve: z=+4.2σ @ 1x / +6.8σ @ 0.5x / +2.1σ @ 2x. The shape is diagnostic:

• Flat or slightly rising toward 0.5x — adapter signal is rank-stable, roughly independent of noise energy.
• Sharply higher at 0.5x, lower at 2x — adapter is rank-saturated: a smaller rank would have yielded a clearer separation from noise. Consider halving r.
• Low everywhere — adapter is barely above noise at any rank; the signal is real but weak.

Caveat: high z at low rank can also mean the low-rank null is pathologically quiet rather than that the adapter is strong. Read the profile as a shape, not a scalar — if all three z's move proportionally, the adapter is doing work; if they spread apart, the rank is mis-sized.

Implementation note: rank scaling is mathematically equivalent to multiplying the null noise std by sqrt(rank_scale) (LoRA's A·B output variance scales linearly with rank). The shipped backends apply that scaling rather than reshaping PEFT tensors — no model reload, no rank-specific adapter cache, same alpha/r scaling throughout.

Determinism. Every sway run calls seed_everything(spec.defaults.seed) before the first probe — seeds python/numpy/torch RNGs and asks torch for deterministic algorithms (CUBLAS_WORKSPACE_CONFIG=:4096:8). The report footer prints the achieved class — strict (CUDA), best_effort (CPU/MPS), or loose (deterministic algorithms refused). Same seed + same host = bit-identical scoring across runs.

Pytest integration

For teams already testing their training pipeline with pytest, sway ships a plugin behind the [pytest] extra. A single decorator turns one pytest function into one test item per probe plus an optional composite-score gate:

import pytest

@pytest.mark.sway(spec="sway.yaml", threshold=0.6)
def test_adapter_healthy() -> None:
    """The decorator owns the body — a bare pass is conventional."""

pytest -v then reports:

test_sway_gate.py::test_adapter_healthy::adherence    PASSED
test_sway_gate.py::test_adapter_healthy::calibration  PASSED
test_sway_gate.py::test_adapter_healthy::__gate__     PASSED

--junitxml emits one <testcase> per probe, pytest -k adherence runs just that probe, FAIL / ERROR / SKIP verdicts translate to pytest outcomes. See examples/pytest_integration/ for a full before/after walkthrough.

pip install 'dlm-sway[hf,pytest]'

The .dlm integration

If you trained your adapter via the DocumentLanguageModel project, sway auto-generates a test suite from your document's sections.

Install sway with the [dlm] extra alongside [hf] (pre-PyPI, editable):

# inside a clone of this repo
uv pip install -e ".[hf,dlm]"

Then:

sway autogen path/to/doc.dlm -o sway.yaml
sway run sway.yaml

Per-section attribution tells you which parts of your document actually moved the model — a kind of signal no other tool provides.

Status

Pre-alpha. API will break. Not yet on PyPI — install editable from source (see Install from source). Version 0.1.0 will be the first published tag; until then, every clone pulls the tip of main.

License

MIT